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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1626244510.1371/journal.pbio.0030383Research ArticleBioinformatics/Computational BiologyCell BiologyMolecular Biology/Structural BiologyIn VitroMechanism of Filament Nucleation and Branch Stability Revealed by the Structure of the Arp2/3 Complex at Actin Branch Junctions Molecular Organization of Arp2/3 Branch JunctionEgile Coumaran
1
Rouiller Isabelle
2
Xu Xiao-Ping
2
Volkmann Niels
2
Li Rong [email protected]
1
3
Hanein Dorit [email protected]
2
1Department of Cell Biology, Harvard Medical School, Boston, Massachusetts, United States of America,2Cell Adhesion Program, Burnham Institute for Medical Research, La Jolla, California, United States of America,3The Stowers Institute for Medical Research, Kansas City, Missouri, United States of AmericaBourne Henry Academic EditorUCSF Medical CenterUnited States of America11 2005 8 11 2005 8 11 2005 3 11 e38323 6 2005 12 9 2005 Copyright: © 2005 Egile et al.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Arp2/3: Structural Insights into a Primary Engine of Cell Motility
Actin branch junctions are conserved cytoskeletal elements critical for the generation of protrusive force during actin polymerization-driven cellular motility. Assembly of actin branch junctions requires the Arp2/3 complex, upon activation, to initiate a new actin (daughter) filament branch from the side of an existing (mother) filament, leading to the formation of a dendritic actin network with the fast growing (barbed) ends facing the direction of movement. Using genetic labeling and electron microscopy, we have determined the structural organization of actin branch junctions assembled in vitro with 1-nm precision. We show here that the activators of the Arp2/3 complex, except cortactin, dissociate after branch formation. The Arp2/3 complex associates with the mother filament through a comprehensive network of interactions, with the long axis of the complex aligned nearly perpendicular to the mother filament. The actin-related proteins, Arp2 and Arp3, are positioned with their barbed ends facing the direction of daughter filament growth. This subunit map brings direct structural insights into the mechanism of assembly and mechanical stability of actin branch junctions.
Genetic labeling and electron microscopy were used to examine actin branch junctions assembled in vitro. The subunit map obtained offers insights into the assembly of these conserved cytoskeletal elements.
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Introduction
The Arp2/3 complex is a key cytoskeletal regulator of actin polymerization [1]. The complex promotes the assembly of dendritic actin networks that drive cell locomotion, phagocytosis, and intracellular motility of lipid vesicles, organelles, and invasive pathogens [2]. Conserved among eukaryotes, this seven-subunit, 220-kDa complex consists of two actin-related proteins, Arp2 and Arp3, and five additional subunits named ARPC1 through ARPC5. The isolated complex has a low nucleation activity, but upon binding to nucleation promoting factors (NPFs), ATP, and preexisting (mother) actin filaments, the Arp2/3 complex promotes the formation of a branched actin structure where the complex itself is situated at the branch junction [3,4].
Despite intensive study, the mechanistic details of the branch junction formation are still poorly understood, partly because of the lack of high-resolution information about the structure of the activated conformation of the complex at the branch junction. Two speculative models have been proposed for subunit organization of the Arp2/3 complex at these branch junctions. Information used for the modeling included sequence conservation among species, available biochemical and structural information, and, most important, the hypothesis that Arp2 and Arp3 assume an actin filament dimer-like configuration that templates the initiation of the daughter filament in the barbed end direction [5,6]. Another conceptually different model, derived primarily from kinetic analysis, suggested that the Arp2/3 complex induces branching and elongation at the barbed end of growing filaments with Arp2 and Arp3 being incorporated in two different actin filaments [7]. However, no direct structural data were available to support any of the proposed nucleation models.
We provide here the first structural analysis to our knowledge of the Arp2/3 subunit organization at the branch junction at molecular resolution using genetic labeling, electron microscopy, and computational analysis. We show that various NPFs, except cortactin, dissociate from the complex after branch formation and that all of the Arp2/3 subunits are in a position to contact the mother filament. In contrast to the previous attempts to model the orientation of Arp2/3 within the actin branch, we have not assumed that Arp2 and Arp3 are orientated toward the daughter filament. Thus, our unbiased subunit localization provides direct evidence that Arp2 and Arp3 are positioned with their barbed ends facing the direction of daughter filament growth.
Results/Discussion
A direct observation of the complex within the branch junction at molecular resolution is required to better understand the mechanism of branched actin nucleation by the Arp2/3 complex. The general strategy that we have taken to achieve this goal was to assemble actin branches in vitro using a complex with one of the subunits carrying a label that can be detected by electron microscopy. The location of the label (and the corresponding subunit) in the image plane can be determined by difference mapping between the two-dimensional (2D) projection maps of branches assembled with labeled and unlabeled complexes.
The WASp-Family NPFs, but Not Cortactin, Detach from the Arp2/3 Complex after Branch Formation
We first employed this strategy to compare branch junctions formed in the presence of different NPFs of increasing molecular weight. Difference mapping would allow detection of the additional density contributed by the larger NPF, allowing localization of the NPF in the branch junction. We assembled actin branches with Saccharomyces cerevisiae Arp2/3 complex or Acanthamoeba complex in the presence of WASp-family NPFs of various sizes that contained the Arp2/3-activating region WA (WASp homology 2 and acidic motifs) (Figure S1). These were N-WASp WA (~12 kDa), glutathione-S-transferase (GST)–N-WASp WA (~40 kDa), WAVE1/Scar WA (~12 kDa), maltose binding protein (MBP)–tagged WAVE1/Scar WA (~55 kDa), full-length N-WASp (GST-N-WASp bound to its activator GST-Nck and forming a complex of ~153 kDa), and a non-WASp activator, cortactin. Projection images of the branches were boxed, aligned, and averaged to yield two-dimensional (2D) projection maps of the branch junction structure with a resolution of approximately 2.2 nm (Figure 1). The resolution was estimated based on the Fourier ring correlation criteria with a cut-off value of 0.5. Interestingly, no statistically significant differences (at 99.5% confidence level, p = 0.005) were observed between the density maps of branches assembled with various WA proteins (12 to 153 kDa) (Figure 1A-1F), whereas a clearly visible, statistically significant difference was observed with GST-cortactin (90 kDa) (Figure 1G-1I). The ability to detect the activator was verified by difference maps using free activated complexes selected from the same electron microscope grids from which the branches were selected (X.-P. X., D. H., and N. V., unpublished data).
Figure 1 Visualization of NPFs in the Actin Branch Junction by Difference Mapping
(A-F) WASp protein dissociate from the actin branch. 2D average projection obtained (A) using the unlabeled yeast Arp2/3 complex and N-WASp WA, and (B) using the full-length GST-N-WASp complexed with its activator GST-Nck. (C) Difference map between (A) and (B). 2D average projection obtained using (D) the amoeba Arp2/3 complex and Scar WA, and (E) MBP-Scar1WA. (F) Difference map between (D) and (E).
(G-I) Cortactin is present in the actin branch. (G) 2D average projection obtained using GST-cortactin and the amoeba Arp2/3 complex. (H) Difference map between (G) and (D). (I) The peak in the difference map shown in yellow superimposed with the projection map.
Bar = 10 nm.
The additional density attributed to cortactin was located on the obtuse side of the branch next to the main bridge of density on the daughter filament side (Figure 1I). Cortactin enhances the persistence of lamellipodia protrusion during cell motility [8] and probably promotes this effect by stabilizing Arp2/3 branches induced by WAVE2/Scar2 [9]. Thus, the localization of cortactin at the branch junction provides a mechanism for stabilizing either the Arp-mother or the Arp-daughter interaction. We favor a stabilization of the Arp-mother interaction, as this would explain the nucleation promoting effect of cortactin on the Arp2/3 complex. However, the relatively weak signal observed with GST-cortactin construct precludes determination of the molecular nature of cortactin interactions with the mother or the daughter filament. Our localization positioned the construct density at a site consistent with the idea that cortactin might bind to the Arp3 subunit. The absence of WASp-family NPFs at the branch junction, as revealed by the difference maps, is consistent with the observation that N-WASp/WASp-coated beads undergo motility by cycles of binding, activation, and release of the Arp2/3 complex [10,11].
Localization of Arp2, Arp3, Arc40/ARPC1, and Arc18/ARPC3 Subunits at Actin Branch Junction
To locate Arp2/3 complex subunits in the branch junction by difference mapping, we took a genetic approach to introduce a label to individual subunits of the yeast Arp2/3 complex. Yeast genes encoding Arp2, Arp3, Arc40/ARPC1, and Arc18/ARPC3 subunits were tagged with green fluorescent protein (GFP) or yellow fluorescent protein (YFP) coding sequence at their genomic loci through homologous recombination. The C-terminus of each labeled subunit was separated from the label by an eight-amino-acid linker. The advantages of this strategy over the more traditional gold-labeling methods are that our strategy allows highly efficient labeling (~100%) of each subunit and convenient assessment of the functionality of the labeled complex. All four GFP/YFP-tagged strains grew normally at room temperature (not shown) and 30 °C compared to the unlabeled (control) strain (Figure 2A). The GFP label also contained a (His)10 tag at the C-terminus, allowing purification of the labeled complex by Ni-NTA affinity (Figure 2B). The unlabeled control Arp2/3 complex was also isolated by Ni-NTA affinity. The nucleation activities of these complexes were tested using the pyrene-actin polymerization assay in the presence of GST-N-WASp WA. The labeled complexes exhibited the same level of nucleation activity as the unlabeled complex (Figure 2C). Actin branches were assembled in the presence of the unlabeled complex or each of the labeled Arp2/3 complexes. Projection maps of the branch junction structures at a resolution of approximately 2.2 nm were generated (Figure 3). Difference maps between branches obtained with the labeled complexes and the unlabeled complex were calculated (Figure 3B and 3C). For cross-validation, each dataset was analyzed independently by two different operators using two different image analysis protocols (Figure S2). All difference maps contain peaks in the branch junction that are statistically significant at a confidence level of 99.5% (p = 0.005) using Student's t-test. The sizes of the peaks are consistent with the presence of an additional protein of the size of a GFP or YFP monomer (~30 kDa).
Figure 2 Characterization of GFP- or YFP-Labeled Yeast Arp2/3 Complexes
(A) Serial dilutions of yeast strain cultures expressing Arp3-GFP, Arp2-GFP, Arc40-YFP, and Arc18-GFP Arp2/3-labeled complexes, as well as the unlabeled control complex, were grown on YPD plates at 30 °C.
(B) Purified yeast Arp2/3 complexes visualized by SDS-PAGE and Coomassie blue staining; unlabeled (control), and GFP- or YFP-labeled Arp3, Arp2, Arc40, or Arc18 complexes. The labeled subunits are marked by arrowheads. The Arc40 subunit in the labeled Arc40/ARPC1-YFP complex ran as 30-kDa and high-molecular-weight species (previously confirmed by immunoblotting and peptide sequencing), owing to an unusual electrophoretic mobility [16]. The Arp3 subunit of the unlabeled complex is denoted by an asterisk, and the Arp3 subunit of labeled Arc40/ARPC1-YFP complex is denoted by two asterisks.
(C) Pyrenyl-actin polymerization kinetics obtained with actin alone (black), control complex (light blue), Arp3-GFP complex (red), Arp2-GFP complex (purple), Arc40/ARPC1-YFP complex (green), and Arc18/ARPC3-GFP complex (dark blue).
Figure 3 Localization of the Labels Attached to Arp2, Arp3, Arc40/ARPC1, and Arc18/ARPC3 at the Actin Branch Junction
Color codes used: Arp2 (pink), Arp3 (orange), Arc40/ARPC1 (green), and Arc18/ARPC3 (red).
(A) 2D average projection maps of the branches obtained with Arp2-GFP (row 1), Arp3-GFP (row 2), Arc40/ARPC1-YFP (row 3), and Arc18/ARPC3-GFP (row 4).
(B) Difference maps calculated between maps obtained with labeled and unlabeled complexes.
(C) Difference maps superimposed with the projection maps. The position of the difference peaks was cross-validated (see Materials and Methods).
(D) The average projection map obtained with the unlabeled complex.
(E) The main difference peaks are superimposed with the unlabeled projection map.
(F and G) Circles of 3.9-nm radius centered on the difference peaks indicate the possible locations of the C-termini of each labeled subunit. The GFP/YFP label was attached to the C-terminus of the relevant subunit with an eight-amino-acid flexible linker that in fully extended conformation can reach a length of up to approximately 3.2 nm. The distance of the N-terminus of GFP or YFP from the center of mass of its beta-barrel (14 × 8 × 8 nm) is approximately 2.5 nm. The centers of the peaks determined from the difference maps probably coincide with the center of mass.
Bar = 10 nm.
In the 2D projection of actin branch junctions, the Arp2/3 complex forms three bridges of density between the mother and daughter filament: a strong bridge of density on the side of the acute angle, a weak bridge of density on the side of the obtuse angle, and a medium bridge of density in the middle (see Figure 1A) [3]. The difference maps between the projection densities obtained from the labeled complexes and the control complex showed that the YFP attached to Arc40/ARPC1 was present on the main bridge close to the mother filament, the GFP attached to Arp3 was on the same side but further away from the mother filament, and the GFP attached to Arc18/ARPC3 was located on the weak bridge close to the mother filament. The GFP attached to Arp2 generated two statistically significant peaks in the difference maps of Arp2-GFP: one located on the obtuse angle of the branch, and the other on the acute side. The two peaks correspond to two alternative stable positions of the GFP, because the population of the Arp2-GFP branches can be sorted into two clusters that each show only one peak (Figure S3). Both GFP positions are compatible with the same C-terminus location owing to the length of the flexible linker (Figure 3F).
Determination of the Orientation of the Arp2/3 Complex at the Branch Junction by Computational Modeling
For all difference maps, the peaks correspond to a projection onto the image plane (XY) of the respective center-of-mass position of the label. Despite the lack of information on the out-of-plane (Z) coordinates, we can use the XY coordinates of the centers of mass as efficient constraints for the three-dimensional (3D) orientation of the complex, because the XY projection of the C-terminus of each labeled subunit must fall within a distance defined by the length of the covalently attached linker and the topology of the label (Figure 3E-3G). In the branch junction, all of the individual positions must be satisfied simultaneously. For example, the C-terminus of Arp2 needs to be in a position that allows attachment to GFP at both positions detected in the difference maps. This restricts the possible XY projection of the Arp2 C-terminus to the small area where the distance to both peaks is below the cut-off distance (i.e., the common area of the two circles in Figure 3F). Addition of constraints for the other subunits further reduces the number of compatible orientations. A global orientation search with the crystal structure of the inactive Arp2/3 complex [12] was carried out to map all orientations that are compatible with the given constraints. The results revealed only a single cluster of orientations that satisfied the label constraints (Figure 4) with an estimated precision of approximately 1 nm. In all permissible orientations, domains I and III of both Arp2 and Arp3, corresponding to the fast growing (barbed) end in an actin filament, are facing away from the mother filament toward the daughter filament.
Figure 4 Structure Models of the Arp2/3 Complex at Actin Branch Junction
Color codes used: Arp2 (light pink), Arp3 (orange), Arc40/ARPC1 (green), Arc35/ARPC2 (cyan), Arc19/ARPC4 (blue), Arc18/ARPC3 (dark pink), and Arc15/ARPC5 (yellow). Gray arrows indicate the mother and daughter filaments.
(A) Orientation of the Arp2/3 complex relative to the mother and daughter filaments as determined using the labeling constraints.
(B) Model rotated vertically anticlockwise by 90° from view in (A) and tilted so that the mother filament coincides with the vertical axis. The gray arrow is positioned to pass through the center of the complex.
(C) Model rotated vertically by 180° from the view in (A).
(D) Label positions and their corresponding C-termini localization. GFP or YFP, shown as ribbon diagram with the same color coding as in Figure 3, were superimposed on the respective difference peak (Figure 3) with their orientation matching the peak shape.
(E) Model superimposed on the projection density map (white corresponds to high density).
(F) Model and ribbon diagram of a daughter filament (white) as it would grow after small relative rotations of Arp2 and Arp3 (see text) superimposed on the projection density map.
(G and H) Model proposed by Beltzner and Pollard [6] (G) and by Aguda et al. [5] (H) shown for comparison. Note that in (G), the daughter filament will be oriented out of the paper plane toward the reader.
(I)Arp2/3 crystal structure in the same orientation as originally presented in Robinson et al. [12].
The relative orientations of Arp2 and Arp3 would need to change from that in the inactive structure in order to provide a suitable template for the growth of the daughter filament. The exact nature of these changes are unknown, but the amplitudes of the changes detected so far [13] are small enough to argue against massive subunit rearrangements such as dissociation of Arp2 and rebinding to Arp3 in a long-pitch filament conformation. The preservation of overall topology of the complex is supported by the fact that all of the constraints obtained in this study for the positions of the labels can be satisfied without the need to introduce changes in the relative orientation of the subunits in the inactive complex. Consistent with a conformational change upon activation, the relative orientation of both Arp2 and Arp3 would need to be altered in our model to provide an exact match of the daughter filament with the direction of its projection density. This conformation could be achieved by an approximately 15° rotation of Arp3 around its short axis and an approximately 15° rotation of Arp2 around an axis parallel to its short axis passing through domain I of Arp3, accompanied by a slight adjustment of the overall complex orientation (<5°). Even though this rearrangement corresponds to a substantial conformational change in the Arp2/3 complex, these rotations would be fully compatible with the constraints imposed by the labels, leading to displacements of the labeled C-termini projections by less than the estimated precision (<1 nm). The resulting daughter filament would not only grow parallel to the XY plane and coincide with the direction of the daughter filament in the projection maps but also fit the shape of the projection density remarkably well (Figure 4E and 4F). Domains II and IV of Arp3 (orange in Figure 4) are well positioned to make direct contact with the mother filament. The other Arp2/3 subunits are also in a position to contact the mother filament, with Arp2 (pink) being the furthest away from the mother filament (Figure 4A-4D). The fact that Arp3 is close to the mother filament was not apparent from the projection images alone [3] and could not be inferred from other available data. The data from subunit labeling, in conjunction with the crystal structure of the isolated complex, allowed a much more detailed and accurate assignment of the densities than previously possible and indicate that the previous assignment was one unit off (i.e., the previous Arp3 position corresponds to Arp2 and Arp2 to the first actin monomer in the daughter filament).
Conclusions
The data presented here support the model that Arp2 and Arp3 adopt an actin short-pitch dimer-like configuration that templates the initiation of the daughter filament in the barbed end direction. The data are incompatible with the proposed incorporation of Arp2 and Arp3 into two different actin filaments at the branch junction [7]. The two available hypothetical structural models of the branch junction [5,6] (illustrated in Figure 4G and 4H) relied on the assumption that the barbed ends of Arp2 and Arp3 face the daughter filament to orient the complex within the branch junction. In contrast, the labeling-based model presented here did not use this assumption as a constraint, and therefore our results lend unbiased evidence to the proposed mechanism where Arp2 and Arp3 serve as a template dimer for the barbed-end-directed growth of the daughter filament. Additionally, our localization data are incompatible with the positions of the C-termini of the subunits proposed in both these previous models (compare Figure 4A with 4G and 4H). These models suggested that the longest axis of the complex, comprising ARPC1, −5, −4, and −2 (Arc40, −15, −19, and −35 in the yeast Arp2/3 complex), contacts the side of the mother filament (Figure 4G and 4H). Our model deviates from these models by an anticlockwise rotation of approximately 100° around the axis of the daughter filament, resulting in an alignment of the longest axis almost perpendicular rather than parallel to the mother filament (Figure 4A). This geometry could allow comprehensive interactions between the axis formed by ARPC2/4 (with possible contribution from ARPC5) and a groove of the mother filament, with Arp3 and ARPC3 on one side and ARPC1 on the other side to provide stabilizing interactions that would prevent the complex from rocking horizontally as well as vertically against the mother filament.
In summary, our model provides the structural basis for the mechanical stability of branch junction that is important for effective force generation upon filament elongation at the barbed ends. It is fully consistent with the available biochemical data and the growth direction of the daughter filament and directly supports the template-dimer model of Arp2/3-mediated actin nucleation. The subunit map established in this analysis thus provides a new structural framework for further understanding the spatial and temporal control of branch nucleation and turnover in the generation of an advancing dendritic network that drives protrusive cellular movement.
Materials and Methods
Plasmids, genetic manipulations, and yeast strains
Yeast strains expressing C-terminal GFP- or YFP-labeled Arp2/3 complex subunits were generated by homologous recombination by the integration of a cassette containing a linker (GDGAGLIN), the yEGFP (or yECitrine) coding sequence, and a polyhistidine (His)10 tag at the 3′ end of each open reading frame. The cassette was generated using pCE36, a derivative of pKT128 [14]. Strains used in this study are listed in Table 1.
Table 1 Yeast Strains Used in This Study
Proteins
Actin was purified from rabbit muscle and isolated as Ca2+-ATP-G-actin in G buffer (5 mM Tris-Cl [pH 7.8], 0.1 mM CaCl2, 0.2 mM ATP, and 1 mM DTT) according to Pardee and Spudich [15] and pyrenyl labeled. The yeast Arc40/ARPC1-YFP Arp2/3 complex was isolated from a strain expressing an Arp3-CaMBM-tev-ProtA subunit (RLY1945) as previously described [16]. The unlabeled control complex (Arp3MH, which has a [Myc]5His6 tag on Arp3 [16]) as well as the Arp3-, Arp2-, and Arc18/ARPC3-GFP-His10-labeled complexes were isolated as follows. Yeast cells were grown to mid log phase in YPD medium (OD600 2−4) washed in U buffer (50 mM HEPES [pH 7.5], 100 mM KCl, 3 mM MgCl2, and 1 mM EGTA) and stored at −80 °C until use. A 50- to 100-g cell pellet was resuspended in five volumes of cold U buffer supplemented with 0.5% Triton X-100, 0.2 mM ATP, 1 mM DTT, and protease inhibitor mix (0.5 μg/ml antipain, leupeptin, pepstatin A, chymostatin, and aprotinin, and 1 mM PMSF) and passed through a microfludizer (Microfluidics, Newton, Massachusetts, United States) until 70% lysis was obtained. The cell extracts were cleared by centrifugation at 100,000 × g for 1 h and filtered through a 0.45-μm filter. A 60% ammonium sulfate precipitation of cell extracts was performed, and the pellet was dialyzed into NaP buffer (100 mM phosphate [pH 7.8], 100 mM KCl, and 20 mM imidazole). This fraction was cleared by centrifugation and incubated with Ni-NTA agarose beads (Qiagen, Valencia, California, United States). Beads were washed with NaP buffer, NaP buffer plus 0.5 M KCl, and NaP buffer plus 0.5% Triton X-100, and the complex was eluted with 250 mM imidazole. The complex was further purified through a HiTrapS column (Amersham Biosciences, Little Chalfont, United Kingdom) in 50 mM MES (pH 6.5), 25 mM NaCl; a UnoQ1 column (Bio-Rad, Hercules, California, United States) in U buffer; and a Superose 12 gel filtration column (Amersham Biosciences) in U buffer on a BioLogic chromatography system (Bio-Rad). Acanthamoeba Arp2/3 complex was purified by poly(L)-proline [18] and gel filtration chromatography as described [19]. Purified complexes were immediately used to assemble actin branches or stored in U buffer supplemented with 0.2 M sucrose, flash frozen in liquid nitrogen, and stored at −80 °C. Bovine GST-N-WASp WA, bovine GST-N-WASp, murine GST-cortactin, and murine GST-Nck were purified as previously described [20,21]. MBP-Scar1 WA was generated by fusing Scar1 S495–C559 to MBP followed by a C-terminal His6 (E. Kim and D. H., unpublished data).
Pyrene-actin polymerization assays
Pyrene-actin polymerization assays were performed at Harvard Medical School and repeated at the Burnham Institute. Typically, G-actin was clarified at 436,000 × g for 30 min. Reactions were performed by mixing 2 μM Mg2+-ATP-G-actin (10% pyrene labeled) with Arp2/3 complex and the appropriate NPF, and actin polymerization was initiated in 1× KMEI buffer (50 mM KCl, 2 mM MgCl2, 1 mM EGTA, 0.2 mM DTT, 0.1 mM ATP, 0.02% azide, and 2 mM imidazole [pH 7.0]). Polymerization was followed using a fluorescence spectrophotometer (Cary Eclipse Varian at Harvard Medical School and an MOS-250 spectrofluorometer [BioLogic, Claix, France] equipped with BioKine 32 software at the Burnham Institute), using 365 nm as the excitation wavelength and 407 nm as the emission wavelength. All of the GFP-labeled complexes were used at 50 to 200 nM concentration with 100 to 200 nM GST-N-WASp WA. When the reaction reached the plateau, 2 μM phalloidin was added to stabilize the branches, and the reaction was diluted as required in F buffer (5 mM Tris-Cl [pH 7.8], 50 mM KCl, 1 mM MgCl2, 0.1 mM CaCl2, 0.2 mM ATP, and 1 mM DTT). The slight reduction in the polymerization rate with Arc18/ARPC3-GFP complex was due to frozen storage of the complex. This reduction was not observed with fresh complex (data not shown), which was used to obtain the actin branches studied using EM. For the NPF detection experiments, we used the following conditions. Yeast actin branches were assembled with 2 μM actin with 25–50 nM yeast Arp2/3 complex activated by either 250 nM N-WASp WA, 250 nM GST-N-WASp WA, or 125 nM GST-N-WASp/250 nM GST-Nck. Amoeba actin branches were assembled with 2 μM actin with 100 nM amoeba Arp2/3 complex activated by either 200 nM Scar WA, 200 nM MBP-Scar WA, or 200 nM GST-cortactin. No actin polymerization was observed when 2 μM G-actin is incubated with 25–50 nM yeast or 100 nM amoeba complexes (data not shown). Activation of the amoeba Arp2/3 complex with GST-cortactin was significantly weaker than the activation observed with Scar WA or MBP-Scar WA.
Electron microscopy
Freshly purified Arp2/3 complexes were used to assemble actin filament branches, which were applied to glow-discharged EM carbon-coated grids and stained with 2% uranyl acetate. Images were recorded under low-dose conditions at a magnification of 42,000 and at a defocus of 1.8 μm using a Tecnai 12 G2 microscope (FEI, Hillsboro, Oregon, United States) equipped with a Lab6 filament at 120 kV and a 1,024 × 1,024 MSC 600HP (model 794; Gatan, Pleasanton, California, United States). The pixel size was 0.57 nm. Branches were selected and boxed using EMAN [22]. Image analysis was performed independently by two different experimentalists (I. R. and X.-P. X.), using two different image analysis packages: Spider [23] and EMAN [22]. Results were compared only at the end of the analysis.
Image processing and cross-validation
For alignment using Spider, selected branches were aligned with a reference-based alignment procedure using standard alignment protocols implemented in Spider [23]. The initial reference was a well-stained branch chosen from the dataset. After alignment, branches were inspected visually, outliers (branches that obviously were not aligned) were discarded, and aligned branches were averaged. This new average was used for another round of alignment. This process was repeated until no more changes were observed (typically three or four times). For several datasets (Arp2-GFP, full-length N-WASp, and cortactin), three different initial references (two different branches and the average obtained with the control) were used. Comparison of the different final averages for individual datasets showed that they were practically identical (and the difference map between the average and the control maps showed the same difference peaks), i.e., the final average was not biased by the choice of the initial reference. The other datasets were aligned to one or two initial references and the results were cross-validated with the results from EMAN (see below). Of 109 branches, 103 were included in the unlabeled yeast Arp2/3complex with GST-N-WASp WA averaging, 169 (of 170) in the unlabeled yeast Arp2/3 with N-WASp WA averaging, 111 (of 121) in the unlabeled yeast Arp2/3 complex with GST-N-WASp and GST-Nck averaging, 204 (of 211) in the Arc40/ARPC1-YFP averaging, 173 (of 188) in the Arp2-GFP averaging, 158 (of 163) in the Arp3-GFP averaging, 162 (of 165) in the Arc18/ARPC3-GFP averaging, 205 (of 211) in the amoeba Arp2/3 complex with Scar WA averaging, 135 (of 147) in the amoeba Arp2/3 complex with MBP-Scar WA averaging, and 250 (of 284) in the amoeba Arp2/3 complex with GST-cortactin averaging.
For alignment using EMAN, for the dataset of the unlabeled yeast Arp2/3 branches in the presence of N-WASp WA and amoeba Arp2/3 complex in the presence of Scar WA, initial references with good quality (straight and with high contrast) were picked from the respective dataset. Projection maps were generated using the correlation-based iterative alignment algorithm and outlier screening implemented in EMAN [22]. To further reduce model bias, the procedure was repeated for nine different references each. The final projection maps were generated by aligning and averaging the respective nine maps. For all other datasets, the final projection map of either the amoeba complex in the presence of Scar WA (for amoeba-based samples) or yeast complex in the presence of N-WASp WA (for yeast-based samples) was used as the initial reference. The individual branches used for the analysis (final projection maps versus the total boxed branches) are for Arp2-GFP (84/98), Arp3-GFP (110/159), Arc18/ARPC3-GFP (109/154), Arc40/ARPC1-YFP (134/178), unlabeled Arp2/3 complex with N-WASp WA (105/157), unlabeled Arp2/3 complex with GST-N-WASp WA (76/101), unlabeled yeast Arp2/3 complex with full-length GST-N-WASp with GST-Nck (60/82), amoeba Arp2/3 complex with Scar WA (146/200), amoeba Arp2/3 complex with MBP-Scar WA (94/132), and amoeba Arp2/3 complex with GST-cortactin (187/254).
Averaging and significance testing
The aligned images selected for averaging (separately for the Spider and EMAN sets) were normalized and averaged using routines from CoAn [24]. CoAn was also used to calculate the difference maps and the standardized variance maps that are suitable for input to Student's t-test procedures [25]. All tests were performed at a confidence level of 99.5%. All peaks presented were statistically significant and virtually at the same location in the two independent image analyses.
Fitting of constraints and precision estimate
In order to computationally fit the constraints obtained by labeling, we adapted routines from the CoAn package [24] that were previously used in the context of density fitting and subsequent evaluation of 3D real-space constraints derived from mutagenesis and biochemistry experiments [26]. The routines, which perform a global scan of the orientations, were modified to handle 2D constraints. After applying a rotation to the crystal structure of the inactive Arp2/3 complex, the positions of the four C-termini were projected onto the XY plane. Then, a translational least-squares fit between the projected C-termini and the respective constraints (in-plane positions of the labels, one constraint each for Arc40/ARPC1, Arc18/ARPC3, and Arp3 and two for Arp2) was performed. Next, the distances between the C-termini projections and the respective constraints were tested using a predetermined cut-off value. If the distance was below this value, the orientation was kept for further processing. A complete global scan using a 10° increment with this configuration completes within 3 min on an Athlon Opteron dual processor box. An advantage of a global scan versus the more traditional least-squares fitting is that all solutions that satisfy the constraints are mapped and can be used for solution set analysis similar to that used for density-based docking [27].
To determine an estimate for the uncertainty of the orientation in three dimensions, we used the following procedure. The length of the linker and the 3D structures of GFP and YFP determine that the (projected) distance between the respective C-terminus and the difference peak (assumed to represent the center of the GFP/YFP) can be anywhere between 0 and 6 nm. A priori, we do not know which value to choose, but we can use the following argument to find the most appropriate cut-off. The set of 3D orientations that satisfy the constraints at a certain cut-off value can be used to calculate a central orientation (centroid) that minimizes the average root-mean-square deviation to all other members of the solution set. If the cut-off value is too small, the constraints are too tight and the centroid will be biased toward the tightest constraint. If the cut-off value is too large, the centroid will not change, but the solution set will be too large and give unrealistically large estimates of precision. Thus, the most appropriate cut-off distance is the smallest value that still gives the same centroid orientation as larger values. The solution set from this value can be used to get an estimate of the precision for the orientation determination by calculating the average root-mean-square deviation in the set.
Using this procedure with test cut-off values between 1 and 6 nm, we found that the most appropriate cut-off value is 3.9 nm. The centroid orientation (which is the one presented in Figure 4) has an average in-plane distance between the C-termini and the respective peaks of 2.43 nm (Arp3: 2.59 nm; Arp2: 2.98 and 3.48 nm; Arc40/ARPC1: 1.30 nm; Arc18/ARPC3: 1.78 nm; see also Figure 3). The precision of the 3D orientation was estimated from the solution set as 0.99 nm.
Molecular graphics
In Figure 4, the low-resolution representations of the Arp2/3 complex were generated from the crystal structure [12]. Coordinates for domains I and II of Arp2 are not available owing to disorder in the crystal structure. We substituted these two domains by subdomains 1 and 2 of an actin monomer [28] after least-squares fitting of subdomains 3 and 4 of actin to domains III and IV of Arp2. Representation of atomic models and densities was done using Pymol (http://www.pymol.org).
Supporting Information
Figure S1 Activity Curves of the Arp2/3 Complexes with Different NPFs
Pyrene-actin polymerization curves obtained with 2 μM actin (pink curves) and 2 μM actin (blue curves) and (A) 25 nM yeast Arp2/3 complex, 250 nM N-WASp WA; (B) 50 nM yeast Arp2/3, 125 nM GST-N-WASp, 250 nM GST-Nck; (C) 50 nM yeast Arp2/3, 250 nM GST-N-WASp WA; (D) 100 nM amoeba Arp2/3, 200 nM Scar WA; (E) 100 nM amoeba Arp2/3, 200 nM MBP-Scar WA; and (F) 200 nM amoeba Arp2/3, 200 nM GST-cortactin. No actin polymerization was observed when 2 μM G-actin was incubated with 25 nM yeast or 100 nM amoeba complexes (data not shown). The time scale of each experiment varied because these were performed with different actin preparations on different days. Activation of the amoeba Arp2/3 complex with GST-cortactin was significantly weaker than the activation observed with Scar WA or MBP-Scar WA.
(7.3 MB TIF).
Click here for additional data file.
Figure S2 Cross-Validation of the Position of the Difference Peaks Obtained between Projection Maps Calculated with Labeled and Unlabeled Complexes
(A-C) Difference maps obtained using the Spider package (see Materials and Methods). (A) 2D projection maps of the branches obtained with unlabeled and labeled yeast Arp2/3 complexes (rows 1–4). (B) Difference maps calculated between maps obtained with labeled and unlabeled complexes. (C) Difference maps (difference peaks shown in yellow) superposed with the projection maps.
(D-F) Difference maps obtained using the EMAN package. (D) 2D projection maps of the branches. (E) Difference maps between maps obtained with labeled and unlabeled yeast Arp2/3 complexes. (F) Projection maps superimposed with the difference maps (difference peaks shown in blue).
(G) Comparison of the results obtained by Spider and EMAN. The difference peaks of the original experiment, shown in yellow, and the cross-validation experiment, shown in blue, are superimposed with the overlapping area shown in green.
Bar = 10 nm.
(1.8 MB TIF).
Click here for additional data file.
Figure S3 Sorting the Population of the Arp2-GFP Branches
The Arp2-GFP branches were sorted based on the density values in the two areas (pink peaks shown in Figure 3C and 3F). For each peak, the density within the peak area was measured for every aligned branch image. This resulted in distinct bimodal distributions with one peak at high values (peak present) and another at low values (peak absent). The bimodal character of the distribution indicates that we indeed have a systematic difference; otherwise, a single Gaussian distribution would occur. The averages were then calculated from the subpopulation with high values only.
(A-C) Difference map obtained from all Arp2-GFP branches.
(D–I) 2D average map averages from the data sorted based on higher density in the lower peak (middle row D-F), and in the upper peak (bottom row G-I) are shown.
(J) 2D average map average from the unlabeled yeast Arp2/3 complex.
Columns 2 and 3 are the difference maps between the three Arp2-GFP branch averages and the control average at two different contour levels. The branch numbers used for the averages are 84 in (A), 31 in (D), and 42 in (G) for Arp2-GFP branches and 146 for the control in (J). Bar = 10 nm.
(9.4 MB TIF).
Click here for additional data file.
Accession Numbers
The GenBank (http://www.ncbi.nlm.nih.gov/Genbank) accession numbers for proteins discussed in this paper are S. cerevisiae Arc15/ARPC5 (NP_012202), Arc18/APRC3 (NP_013474), Arc19/ARPC4 (NP_012912), Arc35/ARPC2 (NP_014433), Arc40/ARPC1 (NP_009793), Arp2 (NP_010255), and Arp3 (NP_012599), bovine N-WASp (NP_776644), human WAVE1/Scar1 (Q92558), and murine cortactin (Q60598).
We thank members of the Hanein Lab (D. Kaiser and E. Kim) and M. J. Dayel and R. D. Mullins (UCSF) for providing reagents, S. Calapiz for invaluable technical assistance, and T. Pollard, K. Amman, and D. Ryan for critical reading of the manuscript. This work was supported by National Institutes of Health (NIH) Cell Migration Consortium grant (U54 GM646346) and grant P01 GM66311 to DH and NV, a Human Frontier Science Program Postdoctoral Fellowship (LT0029/2000-M) to CE, and NIH grant P01 GM66311 to RL.
Competing interests. The authors have declared that no competing interests exist.
Author contributions. CE, IR, NV, RL, and DH conceived and designed the experiments. CE and IR performed the experiments. CE, IR, XPX, NV, RL, and DH analyzed the data. CE, IR, XPX, NV, RL, and DH wrote the paper.
Citation: Egile C, Rouiller I, Xu XP, Volkmann N, Li R, et al. (2005) Mechanism of filament nucleation and branch stability revealed by the structure of the Arp2/3 complex at actin branch junctions. PLoS Biol 3(11): e383.
Abbreviations
2Dtwo-dimensional
3Dthree-dimensional
Arpactin-related protein
GFPgreen fluorescent protein
GSTglutathione-S-transferase
MPBmaltose binding protein
NPFnucleation promoting factor
WAWASp homology 2 and acidic motifs
YFPyellow fluorescent protein
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Weaver A Karginov AV Kinley AW Weed SA Li Y Cortactin promotes and stabilizes Arp2/3-induced actin filament network formation Curr Biol 2001 11 370 374 11267876
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Otterbein LR Graceffa P Dominguez R The crystal structure of uncomplexed actin in the ADP state Science 2001 293 708 711 11474115
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 1626244610.1371/journal.pbio.0030393Research ArticleCancer BiologyCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyBiochemistryMus (Mouse)MammalsDynamic Acetylation of All Lysine 4–Methylated Histone H3 in the Mouse Nucleus: Analysis at c-fos and c-jun
Dynamic Modification of K4-Methylated H3Hazzalin Catherine A
1
Mahadevan Louis C [email protected]
1
1Nuclear Signalling Laboratory, Department of Biochemistry, University of Oxford, Oxford, United KingdomBecker Peter Academic EditorAdolf Butenandt InstituteGermany12 2005 8 11 2005 8 11 2005 3 12 e3937 4 2005 16 9 2005 Copyright: © 2005 Hazzalin and Mahadevan.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
For Some Genes, Acetylation/Deacetylation Cycling Is the Real Turn-On
A major focus of current research into gene induction relates to chromatin and nucleosomal regulation, especially the significance of multiple histone modifications such as phosphorylation, acetylation, and methylation during this process. We have discovered a novel physiological characteristic of all lysine 4 (K4)–methylated histone H3 in the mouse nucleus, distinguishing it from lysine 9–methylated H3. K4-methylated histone H3 is subject to continuous dynamic turnover of acetylation, whereas lysine 9–methylated H3 is not. We have previously reported dynamic histone H3 phosphorylation and acetylation as a key characteristic of the inducible proto-oncogenes c-fos and c-jun. We show here that dynamically acetylated histone H3 at these genes is also K4-methylated. Although all three modifications are proven to co-exist on the same nucleosome at these genes, phosphorylation and acetylation appear transiently during gene induction, whereas K4 methylation remains detectable throughout this process. Finally, we address the functional significance of the turnover of histone acetylation on the process of gene induction. We find that inhibition of turnover, despite causing enhanced histone acetylation at these genes, produces immediate inhibition of gene induction. These data show that all K4-methylated histone H3 is subject to the continuous action of HATs and HDACs, and indicates that at c-fos and c-jun, contrary to the predominant model, turnover and not stably enhanced acetylation is relevant for efficient gene induction.
Continuous turnover rather than stable acetylation of histone H3 (methylated at Lysine 4) is necessary for the induction of certain genes including c-fos and c-jun.
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Introduction
Histone modifications have been co-located to specific genes by chromatin immunoprecipitation (ChIP) assays or by immunocytochemistry, and flowing from that, their functions in processes involving these genes, such as epigenetic cellular memory, silencing, and transcriptional regulation, have been implied (reviewed in [1,2]). However, the extraordinary biochemical susceptibility of histone tails carrying one modification to further modification has received little attention. The first clear example of such biochemical compartmentalisation in the mouse nucleus was the observation that all histone H3 phosphorylated at serine 10 (S10) becomes immediately and very highly acetylated upon treatment with histone deacetylase (HDAC) inhibitors sodium butyrate [3] or Trichostatin A (TSA) [4]. This was revealed by analysis of the modification state of 32P-radiolabelled H3 on acid-urea gels, in which each additional acetylation or phosphorylation event causes an incremental shift, giving rise to a “ladder” of increasingly modified H3 bands (see Figure 1). Two aspects of this observation deserve emphasis. First, the majority of Coomassie-stainable H3 is resistant to TSA treatment, remaining in lower rungs of the H3 ladder on these gels. Second, by contrast, phosphorylated H3 responds not only quantitatively and especially sensitively to such treatment, but rises to occupy the highest possible rungs of the H3 ladder, indicating that on phosphorylated H3, most, if not all, available lysines in the H3 tail become acetylated. This shows that in mouse nuclei, blockade of HDACs results in histone acetyltransferases (HATs) extensively modifying all available lysines on a tiny fraction of phosphorylated H3 tails rather than random lysine residues on all tails throughout the nucleus.
Figure 1 Acetylation and Methylation of Histone H3 TSA- and TPA-Treated Cells
(A) Quiescent C3H 10T½ cells were treated with increasing concentrations of TSA (1, 10, or 500 ng/ml; 15 min to 4 h). “C” indicates control (unstimulated).
(B) Quiescent C3H 10T½ cells were untreated (−) or pre-treated with increasing concentrations of TSA (1, 10, or 500 ng/ml; 15 min). Cells were left unstimulated (C) or stimulated with TPA (15 to 60 min).
(C) Quiescent C3H 10T½ cells were treated with TSA (10 or 500 ng/ml; 5 min to 4 h).
Acid-soluble proteins were extracted and separated on acid-urea gels. Western blots were carried out with anti-acetyl-H3 ([A], panel i; [B], panel ii; [C], panel v), anti-phospho-H3 ([B], panel i), anti-phosphoacetyl-H3 ([B], panel iii), anti-monomethyl-K4 H3 ([C], panel i), anti-dimethyl-K4 H3 ([C], panel ii), anti-trimethyl-K4 H3 ([C], panel iii), or anti-dimethyl-K9 H3 ([C], panel iv) antibodies. An equivalent gel was stained with Coomassie to control for protein loading ([A], panel ii; [B], panel iv; [C], panel vi). Positions of histone isoforms are shown on the right of each panel, with zero being unmodified histone H3.
The availability of modification-specific antibodies for histones H3 and H4 allowed use of ChIP assays to identify specific genes that showed the TSA-responsive trait of continuous dynamic acetylation. Since c-fos and c-jun nucleosomes carried phosphoacetylated histone H3 upon gene activation [4], these genes were tested and shown to become hyperacetylated upon TSA treatment [5]. These studies showed also that c-fos and c-jun nucleosomes became hyperacetylated even when cells were not stimulated, when these genes were inactive and not therefore carrying any phosphorylated H3. This implied that HATs and HDACs are constitutively targeted to these genes, causing continuous turnover of acetylation in unstimulated cells. Further, TSA sensitivity of phosphorylated H3 might simply be a reflection of the fact that phosphorylation is also targeted to these same cycling nucleosomes upon stimulation of these cells.
In this paper, we first extend characterisation of dynamic acetylation in the mouse nucleus by analysis of H3 methylation. Histone H3 can be methylated at lysine 4 (K4) and/or lysine 9 (K9), the former being generally associated with active or poised genes [6–8] and the latter with repressed genes [9,10], although it is now emerging that both modifications can co-exist on the same genes ([11]; reviewed in [2]). We show that all K4-methylated H3 is also subject to dynamic acetylation, whereas K9-methylated H3 is not, a clear and unambiguous physiological difference between these two modifications. Further, we show that c-fos and c-jun nucleosomes are highly methylated at K4 irrespective of whether these genes are induced or not. Upon gene activation, acetylation and phosphorylation co-exist transiently on K4-methylated histone H3 tails at c-fos and c-jun. Secondly, we have analysed the consequence of HDAC blockade and hyperacetylation of these nucleosomes on the process of c-fos and c-jun induction. Contrary to expectation, this analysis showed that elevated acetylation does not correspond to enhanced expression of these genes, but in fact, inhibits their induction. This argues against the predominant model, whereby enhanced histone acetylation at these genes correlates with transcription, and suggests instead that it is turnover of histone acetylation that is relevant.
Results
Dynamic Acetylation of K4-Methylated Histone H3 in the Mouse Nucleus
Titration experiments were first carried out to biochemically define TSA-hypersensitive chromatin (Figure 1A). Histones from cells treated with TSA at 1, 10, and 500 ng/ml over a 4-h time course were analysed on acid-urea gels on which each additional acetyl modification causes an incremental shift of H3 up the ladder. As reported previously [4,5], the Coomassie-stained gel (Figure 1A, panel ii) showed very little effect on bulk H3 even at high concentrations and long TSA treatments. However, Western blotting with anti-acetyl-H3 antibody (Figure 1A, panel i) clearly showed increased acetyl-H3 at higher positions on the H3 ladder above the major stainable H3 bands, indicating that a minute fraction of H3 becomes fully modified. Compared to control cells, this increase in intensity and appearance of higher acetylated H3 bands could be seen at 10 ng/ml TSA and 15–30 min time points.
Due to occlusion by phosphate on S10, this anti-acetyl-H3 antibody only recognises H3 that is not phosphorylated (for data, see Figure 1 of [5]; A. L. Clayton, L. C. M., unpublished data). We also investigated early TSA-sensitive events (Figure 1B) using our (acetyl-K9/phospho-S10) phosphoacetyl antibody (characterised in [4]). At concentrations as low as 1 ng/ml TSA and at the earliest time point tested, there was extensive appearance of phosphoacetyl-H3 at the highest positions on the H3 ladder (Figure 1B, panel iii, lane 7). This confirms previous studies discussed above that all phosphorylated H3 is hypersensitive to TSA-induced acetylation, with a fraction rapidly appearing in fully acetylated form, despite bulk Coomassie-stained H3 being largely unaffected (Figure 1B, panel iv). For all further work, we used 10 ng/ml TSA at short time courses as definitive of TSA hypersensitivity, although effects can clearly be seen at 1 ng/ml.
In this study, we analysed other H3 modifications for the TSA-hypersensitive trait and found that H3 methylated at K4 falls in this category (Figure 1C). Note that methylation does not cause any shift of histone H3 on acid-urea gels. TSA treatment at 10–500 ng/ml resulted in rapid acetylation of K4 that was mono- (Figure 1C, panel i), di- (panel ii), and tri-methylated (panel iii), as evidenced by progress up the H3 ladders. This effect was particularly clear with trimethylated K4 (trimethyl-K4) H3, where appearance of higher bands corresponded with complete loss of lower bands of the ladder (Figure 1C, panel iii). By contrast, H3 dimethylated at K9 was resistant to TSA. Although a small effect could be seen at higher concentrations and later time points (Figure 1C, panel iv), this largely correlated with the slight effect of TSA on bulk H3, as seen in Coomassie-staining H3 bands (panel vi).
Over many similar experiments, we conclude that in the mouse nucleus, a defining characteristic of virtually all histone H3 methylated on K4, particularly trimethyl-K4 H3, is its TSA hypersensitivity. This indicates that most or all of histone H3 methylated at K4 in the mouse nucleus is targeted for continuous acetylation and deacetylation, such that inhibition of the latter by TSA produces immediate and extensive acetylation, exactly as described for phospho-S10 H3. Although previous analyses have localised K4- and K9-methylated H3 at different genes, alluding to their differing functions, this is the first description to our knowledge of a clear physiological distinction between histone H3 methylated at these two residues.
TSA Hypersensitivity Is Targeted to Nucleosomes at c-fos and c-jun
Previous studies have indicated that histones at c-fos and c-jun chromatin are rapidly acetylated in the presence of high concentrations of TSA [5]. We next used ChIP assays to ask if TSA hypersensitivity could be observed at specific regions of c-fos and c-jun, shown schematically in Figure 2A. Cells treated with TSA (10 ng/ml) for 15 min to 4 h were assayed by ChIP with anti-acetyl-H3 antibody. PCR was used to probe immunoprecipitated DNA for specific regions of c-fos (Figure 2B, panels i–v) and c-jun (Figure 2C, panels i–v). TSA treatment (10 ng/ml) clearly produced targeted acetylation at these genes that was detectable, and in many cases maximal, at the earliest time point tested (15 min), and then dropped off to almost basal levels by 4 h (Figure 2B–2D). For both genes, TSA-enhanced acetylation was region-specific. For c-fos, +132 and +414, representing the first exon and intron, respectively, showed the clearest enhancement, whereas regions that flank this, i.e., the promoter, −519, and second exon, +1056, showed a weaker response. No increase in acetylation was detectable further downstream at +2622 (Figure 2B). For c-jun, apart from the region around the transcription start site (−110), which showed a poor response as reported previously [5], and +2904 at the 3′ end of the gene, the promoter (−732) and other coding regions (+774 and +1608) showed very clear responses to TSA (Figure 2C). Note that Bound/Input values for ChIP assays plotted here (Figure 2D) are a measure of total quantitative enhancement at each position. However, because of lower background acetyl-H3 signal at some positions in control cells, the fold-stimulation values between control and stimulated cells (Figure S1) appear greatest where this background is lowest.
Figure 2 Effect of TSA Treatment on Acetylation of Nucleosomes Associated with c-fos and c-jun Genes
(A) Schematic diagram representing relative positions of regions of c-fos and c-jun amplified by primer pairs used in the PCR step of ChIP assays. Exons are indicated by boxes, open for untranslated regions and filled for coding regions. Polyadenylation sites (pA) are indicated.
(B and C) Cross-linked chromatin fragments were prepared from quiescent C3H 10T½ cells treated with TSA (10 ng/ml; 15 min to 4 h). “C” indicates control (unstimulated). Specific DNAs were immunoprecipitated with anti-acetyl-H3 antibodies. Recovered DNAs from antibody-bound fractions (AcH3 IP) as well as total input DNA (Input) from released chromatin used for ChIP were analysed for the presence of c-fos (B) and c-jun (C) gene sequences. Controls for PCR included a DNA minus (−DNA) reaction and 5- to 20-ng loadings of input DNA to ensure all amplifications were within the linear range. PCR reactions were carried out in triplicate and gels quantified by phosphorimaging. Representative gels are shown.
(D) Data are expressed graphically as average Bound/Input (± standard deviation). Note that to correct for the dilution factor applied to anti-acetyl-H3 immunoprecipitated DNA prior to PCR analyses, values obtained from quantification of PCR gels were multiplied by three.
Because TSA does not induce these genes (see below), these effects are unrelated to transcription. Furthermore, there was a clear correlation between pre-existing levels of acetylation at each region in control cells and its sensitivity to TSA treatment. Regions with no pre-existing acetylation (fos +2622) showed no TSA enhancement, whereas those with low (fos −519 and +1056, and jun −110 and +2904) and higher (fos +132 and +414, and jun −732, +774, and +1608) pre-existing levels showed TSA enhancement to intermediate and high levels, respectively. The basal level was presumably a reflection of continuous turnover at these positions. Finally, whenever detected, TSA-enhanced acetylation was close to maximal at early time points (15–30 min), dropping off to return to almost basal levels by 4 h (Figure 2D), in striking contrast to acetylation in the nucleus as a whole (see Figure 1A, panel i, lanes 7–11), which rises very strongly throughout this period. Localised reversal of acetylation is therefore highly gene-specific and must be mediated either by TSA-insensitive HDACs or possibly by a histone H3 replacement mechanism.
K4 Trimethylation of Histone H3 at c-fos and c-jun
From data in Figure 1C, TSA hypersensitivity of c-fos and c-jun nucleosomes implies that they may also carry trimethyl-K4 H3, verifiable by ChIP assays (Figure 3). Preliminary experiments showed that trimethyl-K4 H3 antibody recovered much more of these chromatin fragments than di- or monomethyl antibodies (data not shown). Direct comparison of regions of c-fos (Figure 3A) and c-jun (Figure 3B) recovered by anti-trimethyl-K4 and anti-acetyl antibodies showed remarkably that the distribution and relative levels of trimethyl-K4 and acetylated H3 parallel each other across these genes. Acetylation levels increased in response to TSA treatment, whereas trimethyl-K4 levels remained unaffected (Figure 3A and 3B, compare lanes 9–11 and 5–7). At c-fos, both modifications were high at +132 and +414 and intermediate at −519 and +1056, but both were undetectable at +2622 (Figure 3A). Similarly, for c-jun, both modifications were detectable at −732, +774, and +1608, but both fell to lower levels at −110 and +2904 (Figure 3B).
Figure 3 Histone H3 K4 Trimethylation and TSA Hypersensitivity at c-fos and c-jun
(A and B) Cross-linked chromatin fragments were prepared from quiescent C3H 10T½ cells treated with TSA (10 ng/ml; 15 to 30 min) or stimulated with TPA (T; 30 min). “C” indicates control (unstimulated). Chromatin fragments were immunoprecipitated with anti-trimethyl-K4 H3 (3K4 IP) or anti-acetyl-H3 (AcH3 IP) antibodies.
(C) Cross-linked chromatin fragments were prepared from quiescent C3H 10T½ cells untreated (−) or pre-treated with TSA (10 ng/ml; 15 min). Cells were left unstimulated (C) or stimulated with TPA (T; 30 min). Chromatin fragments were immunoprecipitated with anti-trimethyl-K4 H3 (3K4 IP) or anti-phosphoacetyl-H3 (PAcH3 IP) antibodies.
DNA recovered from antibody-bound fractions as well as total input DNA (Input) from released chromatin used for ChIP was analysed for the presence of c-fos ([A]; [C], panels i and ii) and c-jun ([B]; [C], panels iii and iv) gene sequences. DNA immunoprecipitated with anti-acetyl-H3 antibody was diluted one in three, anti-trimethyl-K4 H3 was diluted one in 20, and anti-phosphoacetyl-H3 antibody was not diluted before PCR analyses.
We also performed ChIP assays to examine histone H3 acetylation and phosphoacetylation at c-fos and c-jun in 12-O-tetradecanoyl phorbol-13-acetate (TPA)–stimulated cells. Like many other stimuli [5], TPA produced acetylation (Figure 3A and 3B, lanes 9 and 12) and phosphoacetylation at these genes, with higher levels of the latter detectable in TSA-treated cells (Figure 3C, lanes 9–12; data not shown). Unlike acetylation and phosphorylation, which are rapidly induced upon stimulation and gene induction, K4 H3 methylation remained detectable throughout this time course with all stimuli tested (Figure 3; data not shown). At all regions where it was detected, a clear K4 H3 methylation signal was seen in unstimulated cells, and upon TPA treatment alone or in combination with TSA (Figure 3C, lanes 5–8; data not shown). This indicates that K4 H3 methylation occurs at these genes in quiescent cells and upon stimulation, raising the possibility that it functions as a marker to target these regions for further modification.
K4 Methylation of Histone H3 and TSA Hypersensitivity at Constitutively Active and Silent Genes
We have now analysed many inducible genes (fosB, junB, junD, and nur77; data not shown) and found all have regions of K4 methylation and TSA hypersensitivity, the two traits being extremely well correlated. However, the literature shows that K4 methylation of histone H3 occurs at many active genes, whereas many repressed silent genes are not methylated at K4. We compared K4 trimethylation and TSA hypersensitivity at a constitutively expressed gene and a silent gene, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) and β-globin, respectively, shown schematically in Figure 4A. The constitutively expressed GAPDH gene showed clear K4 methylation of histone H3 at −615 in the promoter and also in coding regions of the gene, with lower levels at position +2095. All regions analysed were subject to TSA-hypersensitive acetylation, maintaining the correlation between H3 K4 methylation and TSA hypersensitivity (Figure 4B, panels i and ii; data not shown). Despite these similarities, there was a clear difference: TPA stimulation, which activates c-fos and c-jun transcription but not GAPDH, enhanced acetylation at specific regions of c-fos and c-jun (Figure 3A and 3B, lanes 9 and 12), but had no effect at any position of GAPDH tested (Figure 4B, panels i and ii, lanes 9 and 12; data not shown). By contrast, the silent β-globin gene showed neither H3 K4 methylation nor TSA hypersensitivity nor any stimulus-dependent acetylation at any position analysed, including −840 and +261 (Figure 4B, panels iii and iv; data not shown).
Figure 4 Histone H3 K4 Trimethylation and Acetylation at Other Genes
(A) Schematic diagram representing relative positions of regions of GAPDH and β-globin amplified by primer pairs used in the PCR step of ChIP assays. Exons are indicated by boxes, open for untranslated regions and filled for coding regions. Polyadenylation sites (pA) are indicated.
(B) Cross-linked chromatin fragments were prepared from quiescent C3H 10T½ cells treated with TSA (10 ng/ml; 15 to 30 min) or stimulated with TPA (T; 30 min). “C” indicates control (unstimulated). Specific DNAs were immunoprecipitated with anti-trimethyl-K4 H3 (3K4 IP) or anti-acetyl-H3 (AcH3 IP) antibodies. Recovered DNAs from antibody-bound fractions as well as total input DNA (Input) from released chromatin used for ChIP were analysed for the presence of GAPDH ([B], panels i and ii) and β-globin ([B], panels iii and iv) gene sequences. DNA immunoprecipitated with anti-acetyl-H3 antibody was diluted one in three and anti-trimethyl-K4 H3 was diluted one in 20 before PCR analyses.
These studies revealed three characteristic types of chromatin modification response in these cells. Inducible genes showed regions of H3 K4 methylation that were TSA-hypersensitive, and in addition, stimulus-dependent gene induction led to H3 acetylation and phosphoacetylation. The constitutively active GAPDH gene showed H3 K4 methylation and TSA hypersensitivity, but as might be expected, no stimulus-dependent acetylation. And finally, the silent β-globin gene showed none of these three characteristics. An important observation that emerges here is the correlation between K4 methylation and TSA hypersensitivity, which is as yet unbroken (see Discussion).
Trimethyl-K4, Acetyl-K9, and Phospho-S10 Histone H3 Occur on the Same Nucleosome at c-fos and c-jun
Western blotting of H3 ladders on acid-urea gels (see Figure 1) showed that phospho-S10 H3 or trimethyl-K4 H3 shifted upwards upon TSA treatment, proving that acetyl groups must co-exist on the same H3 tails carrying these modifications. Further, the specificity of our phosphoacetyl antibody proves that acetyl-K9 and phospho-S10 must co-exist on the same H3 tail in stimulated cells. However, this leaves unanswered the question of whether trimethyl-K4 and phospho-S10 are themselves on the same nucleosome, or if they represent two separate populations of H3 that are independently TSA-hypersensitive.
In the absence of antibodies that recognise the triple modification, two approaches involving sequential immunoprecipitation (IP) were used to ask if all three modifications occur on the same nucleosomes at c-fos and c-jun. First, we asked if an antibody against one H3 modification (Figure 5, “1st IP”) would also recover H3 with a second modification, depletion of the co-immunoprecipitated epitope in the unbound fraction being verifiable by re-IP with a second antibody (Figure 5, “2nd IP Unbound”). Conversely, antibody-bound chromatin from a first IP was subject to a second round of IP with a different modification-specific antibody (Figure 5, “2nd IP Bound”). Positions along c-fos and c-jun analysed here correspond to peaks of modification seen in previous figures.
Figure 5 Three Histone H3 Modifications: Methylation, Acetylation, and Phosphorylation Are Targeted to the Same Nucleosome
(A) Cross-linked chromatin fragments were prepared from quiescent C3H 10T½ cells treated with TSA (10 ng/ml; 15 to 30 min) or stimulated with TPA (T; 30 min). “C” indicates control (unstimulated). Specific DNAs were immunoprecipitated directly (1st IP; upper line) with anti-acetyl-H3 (AcH3) or anti-trimethyl-K4 H3 (3K4) before a second IP with anti-acetyl-H3 (2nd IP; lower line) to analyse chromatin in the anti-trimethyl-K4 H3 unbound or bound fraction (Unbound AcH3 and Bound AcH3, respectively). DNA immunoprecipitated with anti-acetyl-H3 antibody (1st or 2nd IP) was diluted one in three before PCR analyses.
(B) Cross-linked chromatin fragments were prepared from quiescent C3H 10T½ cells pre-treated with TSA (10 ng/ml; 15 min). Cells were unstimulated (C) or stimulated with TPA (T; 30 min). Specific DNAs were immunoprecipitated directly (1st IP; upper line) with anti-phosphoacetyl-H3 (PAcH3) or anti-trimethyl-K4 H3 (3K4) before a second IP (2nd IP; lower line) to analyse chromatin in the unbound or bound fraction with anti-phosphoacetyl-H3 (Unbound PAcH3), or with anti-trimethyl-K4 H3 antibodies (Bound 3K4). DNA immunoprecipitated with anti-phosphoacetyl-H3 (1st or 2nd IP) was not diluted before PCR analyses. Recovered DNAs as well as total input DNA (Input) from released chromatin used for ChIP were analysed for the presence of c-fos ([A], panels i and ii; [B], panels i and ii) and c-jun ([A], panels iii and iv; [B], panels iii and iv) gene sequences.
To investigate co-existence of trimethyl-K4 on acetylated H3 tails at c-fos and c-jun, chromatin from control and TSA-treated cells were first immunoprecipitated with trimethyl-K4 antibodies (Figure 5A). The unbound material was then immunoprecipitated with acetyl-H3 antibodies, which showed a marked reduction in c-fos and c-jun fragments (Figure 5A, lanes 9–12) compared to similar anti-acetyl-H3 IPs from total chromatin (lanes 5–8). This indicates that trimethyl-K4 H3 antibodies had sequestered acetylated H3 in the first round of ChIP. To prove this beyond doubt, anti-trimethyl-K4-immunoprecipitated material was subject to a second round of IP using anti-acetyl-H3 antibody (Figure 5A, lanes 13 and 14). This confirmed that anti-trimethyl-K4 antibody did indeed recover acetylated nucleosomes at these positions on c-fos and c-jun, accounting for its loss from the unbound fraction. This verifies Western blotting evidence that both modifications are on the same tails and proves the case at c-fos and c-jun.
Finally, to prove all three modifications occur on the same nucleosomes at c-fos and c-jun, unbound and bound fractions from control and TPA-stimulated cells, both pre-treated with TSA, were analysed using anti-phosphoacetyl and anti-trimethyl-K4 antibodies (Figure 5B). Cross-linked chromatin was subject to a first IP with anti-trimethyl-K4, and the unbound fraction was analysed in a second IP using anti-phosphoacetyl-H3. Compared to a direct IP using anti-phosphoacetyl antibody, analysis of the unbound fraction after IP with anti-trimethyl-K4 clearly showed depletion of phosphoacetyl epitope (Figure 5B, compare lanes 3 and 4 with lanes 5 and 6). Anti-phosphoacetyl antibody bound material was then subjected to a second IP with anti-trimethyl-K4 antibody (Figure 5B, lanes 9 and 10). This showed that anti-trimethyl-K4 is able to recover chromatin first immunoprecipitated with anti-phosphoacetyl-H3 antibody, indicating that all three modifications are on the same nucleosomes. Taken together, these studies prove that all three modifications, S10 phosphorylation, K9 acetylation, and K4 trimethylation, can certainly co-exist on the same nucleosome, possibly on the same H3 tail, at transcriptionally active c-fos and c-jun.
Sequential IP experiments, together with Western blotting data, prove conclusively that all three modifications can co-exist on the same nucleosome. It is not claimed here that all nucleosomes at these positions along c-fos and c-jun carry all three modifications, but rather that a sub-fraction of chromatin fragments must carry trimethyl-K4, acetyl-K9, and phospho-S10 to give a triply modified nucleosome. This follows from the previously established highly dynamic nature of acetylation and phosphorylation at these positions [5]. The fraction that is triply modified represents a “snapshot” of a dynamic process frozen at a particular moment by the cross-linking protocol used.
Effect of TSA Pre-Treatment on c-fos and c-jun Induction
We next investigated the functional significance of turnover of histone acetylation observed above for transcriptional activation of c-fos and c-jun. A major theme in the literature, dating from the original discovery of histone acetylation, suggests that enhanced acetylation correlates with more relaxed chromatin and greater transcriptional activity. If true, the expectation is that TSA treatment would lead to enhanced induction of these genes. If, however, it is the dynamic turnover of acetyl groups observed at these nucleosomes that is critical, then inhibition of turnover with TSA would be expected to lead to inhibited gene induction.
In preliminary studies, we found that TSA itself did not induce these genes, but its effect on c-fos or c-jun induction by other agents was dependent on the duration of TSA pre-treatment. The phorbol ester TPA produces rapid, transient activation of c-fos and c-jun in C3H 10T½ cells (Figure 6A; [12]) and Swiss 3T3 cells (Figure S2). In both cell types, short pre-treatment with TSA (15 min; Figure 6A, lanes 6–10; Figure S2, lanes 4–6) inhibited TPA-stimulated induction of both genes. By contrast, longer pre-treatment (4 h; Figure 6A, lanes 11–15; Figure S2, lanes 7–9) produced gene-specific effects, enhancing c-fos, but continuing to inhibit c-jun induction. We examined the effect of short and long TSA pre-treatment on c-fos and c-jun induction in response to several other stimuli: EGF, bFGF, serum, and anisomycin (Figure 6B). The inhibitory effect of short (15 min) TSA pre-treatment on induction of these genes was very consistent irrespective of the stimulus. By contrast, its effect after 4 h was both gene- and stimulus-specific (Figure 6B).
Figure 6 Effect of TSA Pre-Treatment on c-fos and c-jun Induction
(A) Quiescent C3H 10T½ cells were untreated (−) or pre-treated with TSA (500 ng/ml) for 15 min (TSA 15′) or 4 h (TSA 4h). Cells were left unstimulated (C) or stimulated with TPA for 15 to 60 min, and RNA was analysed by Northern blot.
(B) Quiescent C3H 10T½ cells were untreated, or pre-treated with TSA (500 ng/ml [TPA- and EGF-stimulated cells] or 10 ng/ml [bFGF-, serum-, sAn-stimulated cells]) for 15 min or 4 h. Cells were left unstimulated, or stimulated with TPA (T), EGF (E), bFGF (bF), serum (S), or sAn for 30 or 60 min.
(C) Quiescent C3H 10T½ cells were untreated (−) or pre-treated with TSA (500 ng/ml) for 15 min (TSA 15′), 30 min (TSA 30′), 1 h (TSA 1h), 2 h (TSA 2h), 3 h (TSA 3h), or 4 h (TSA 4h). Cells were left unstimulated (C) or stimulated with TPA for 30 or 60 min.
Northern blots in (B) and (C) were quantified by phosphorimaging, corrected for variations in loading using GAPDH, and expressed graphically. The normal response at 30 min (c-fos)/60 min (c-jun) was set to 0%, and inhibition/enhancement in response to TSA pre-treatment expressed as a percentage change relative to this value.
Finally, we performed detailed time-course analyses (15 min to 4 h) of TSA pre-treatment to determine precisely when the changeover from inhibition to enhancement of c-fos induction occurs (Figure 6C). TPA-stimulated induction of both genes was inhibited by 15 min to 1 h of TSA pre-treatment (Figure 6C, lanes 4–12). The switch from inhibition to enhancement of c-fos occurred between 1 and 2 h of TSA pre-treatment (Figure 6C, panel i). Beyond 2 h, TSA clearly enhanced TPA-stimulated c-fos expression (Figure 6C, lanes 13–20). By contrast, there was no switch to enhancement with longer TSA treatment for c-jun, and similar levels of inhibition were seen over the entire time course (Figure 6C, panel ii). Thus, in the case of c-fos, but not c-jun, a switch from inhibition to enhancement occurred between 1 and 2 h of TSA pre-treatment. Note that these data confirm that TSA itself does not induce c-fos and c-jun in these cells (Figure 6C, lanes 4, 7, 10, 13, and 16; Figure 6A, lanes 6 and 11).
In further experiments, we made additional advances on these observations. First, the early inhibitory effect on gene induction was extremely TSA-sensitive and could be observed at a concentration of 1 ng/ml TSA (Figure S3A). Second, inhibition was extremely rapid, and could be observed even if TSA and stimulus were added at the same time or indeed for up to 5 min after stimulation of these cells (Figure S3B), but interestingly, not 10 min after stimulation. This could possibly be due to the very rapid peak of transcription after stimulation, or to the possibility that once transcriptional induction is fully initiated, TSA can no longer affect the process. Third, we found no effect on mRNA degradation rates under these conditions (Figure S4), indicating that inhibition occurred at the transcriptional level, borne out by preliminary ChIP assays using RNA polymerase II antibodies (data not shown). Finally, we found that the later effects of TSA (i.e., beyond 2 h), which varied depending on the gene and stimulus studied (Figure 6B), were secondary effects dependent on fresh translation (Figure S5), whereas the immediate TSA effects were primary and independent of translation. It is not possible to understand the later effects of TSA without knowledge of why exactly translation is required, but it is clear that they do not correlate with enhanced histone acetylation at these genes.
Inhibition of c-fos and c-jun Induction Is a Specific Consequence of HDAC Inhibition by TSA
To confirm that the effects of TSA were directly due to inhibition of HDACs and not due to non-specific effects of these compounds, two approaches were taken: first, to verify that TSA does not affect intracellular signalling (Figure 7), and, second, to ask if other HDAC inhibitors produce the same effects (Figures 8A, 8B, and S6). The c-fos and c-jun genes are critically dependent on MAP kinase cascades and transcription factor phosphorylation for their activation. TSA alone at 10 or 500 ng/ml did not activate any of these MAP kinase cascades (see Figure 7A, lanes 2–13). By contrast, TPA activated ERKs and sub-inhibitory anisomycin (sAn) activated JNKs and p38 (Figure 7A, lanes 14–16). Further, TSA at 10 or 500 ng/ml had no effect on TPA-induced activation of the ERKs (Figure 7B), indicating that it does not affect any of the several signalling steps upstream of ERKs. More critically, we analysed TPA-stimulated phosphorylation of transcription factors ATF-2 and CREB downstream of these cascades and also found no effect of TSA (Figure 7C). Thus, the inhibitory effect of TSA is unlikely to arise from any interference with signalling to transcription factors.
Figure 7 Effect of TSA Pre-Treatment on TPA-Stimulated MAP Kinase Activation and Transcription Factor Phosphorylation
(A) Quiescent C3H 10T½ cells were treated with TSA (10 or 500 ng/ml; 5 min to 4 h). Positive controls for MAP kinase activation included ERK1/2 (TPA [T]; 10 min), JNK/SAPKs, and p38 (sAn; 30 to 60 min). “C” indicates control (unstimulated). Cell extracts were analysed by Western blotting with anti-ERK1/2, anti-phospho-p38, and anti–ACTIVE JNK antibodies. The mobility of ERKs is retarded on activation. Activation of p38 and JNK/SAPK results in phosphorylation. Note that anti–ACTIVE JNK also recognises activated ERK1/2 (lane 14).
(B) Quiescent C3H 10T½ cells were untreated (−) or pre-treated with TSA (10 or 500 ng/ml; 15 min). Cells were then left unstimulated (C) or stimulated with TPA for 5 to 30 min. Cell extracts were analysed by Western blotting with anti-ERK1/2 antibody.
(C) Quiescent C3H 10T½ cells were untreated (−) or pre-treated with TSA (500 ng/ml; 15 min). Cells were stimulated with TPA for 15 to 30 min. Cell extracts were analysed by Western blotting with anti-ATF-2 and anti-phospho-CREB antibodies. Phosphorylation of ATF-2 results in retarded mobility.
Figure 8 Specificity of Inhibition and Association of HDACs with Regions of c-fos and c-jun
(A) Quiescent C3H 10T½ cells were treated with TSA (10 ng/ml) and HDAC inhibitor API or TPX for 15 min to 4 h. “C” indicates control (unstimulated). Acid-soluble proteins were extracted and separated on acid-urea gels. Western blots were carried out with anti-acetyl-H3 antibodies (panel i). A representative gel was stained with Coomassie to indicate protein loading (panel ii). Positions of histone isoforms are shown on the right of each panel, with zero being unmodified histone H3.
(B) Quiescent C3H 10T½ cells were untreated (−) or pre-treated with TSA (10 ng/ml) and HDAC inhibitor API or TPX for 15 min. Cells were then left unstimulated (C) or stimulated with TPA for 30 or 60 min. RNA was analysed by Northern blot.
(C) Cross-linked chromatin fragments were prepared from untreated quiescent C3H 10T½ cells. Specific DNAs were immunoprecipitated with anti-HDAC1, anti-HDAC3, anti-HDAC4, or anti-HDAC6 antibodies. Recovered DNAs from antibody-bound fractions as well as total input DNA (Input) from released chromatin used for ChIP were analysed for the presence of c-fos (panels i and ii) and c-jun (panels iii and iv) gene sequences.
As a second test that inhibition of gene induction is a direct and specific result of HDAC inhibition, we tested three different HDAC inhibitors that are structurally related to TSA (MS-275, CBHA, and M344; Figure S6) and two other inhibitors, apicidin (API) and trapoxin (TPX), that are structurally unrelated to TSA and would not be expected to have the same side effects as TSA (Figure 8A and 8B). Preliminary studies determined the concentration of each inhibitor that increased histone H3 acetylation to a level similar to that produced by TSA (data not shown). In every case, at these concentrations, there was a clear correlation between the ability of each HDAC inhibitor to enhance histone acetylation and its ability to inhibit c-fos and c-jun induction. Treatment with TSA-related inhibitors CBHA and M344 (Figure S6A, panel i, lanes 9–16) and API and TPX (Figure 8A, panel i, lanes 5–8 and 14–17) produced a clear increase in H3 acetylation comparable to that seen in TSA-treated cells, and strongly inhibited TPA-stimulated c-fos and c-jun induction (Figure S6B, lanes 10–15; Figure 8B, lanes 4–6 and 10–12). By contrast, treatment with MS-275, which produced weak acetylation of histone H3 (Figure S6A, panel i, lanes 5–8) poorly inhibited TPA-stimulated c-fos induction, and did not inhibit c-jun (Figure S6B, lanes 7–9).
Differential Association of Specific HDACs with c-fos and c-jun
The above data allow a hypothesis that HDACs are specifically associated not just with upstream promoters and enhancer elements, but also with the coding regions of these genes even when quiescent, thereby explaining turnover of acetylation at these positions. To test this hypothesis, we used antibodies against specific HDACs (HDAC1 and HDAC3–HDAC7) in ChIP assays using quiescent cells, analysing both promoter and coding regions of these genes. Using these antibodies, the clearest and most reproducible associations with these genes were obtained with anti-HDAC1, −3, −4, and −6 antibodies (Figure 8C; data not shown). HDAC6 was found to be associated with the coding region and promoter of both c-fos and c-jun (Figure 8C, lane 9). We also observed coding-region-specific association of HDAC4 for both c-fos and c-jun, but not at the promoters of these genes (Figure 8C, lane 8; data not shown). HDAC1 and HDAC3 were only seen at the c-fos coding region and were not detected at c-jun (Figure 8C, lanes 2 and 5). This shows that several HDACs can be found specifically associated with particular regions of c-fos and c-jun, which can account for the continuous turnover of histone acetylation that we report here. Due to the lower recovery of chromatin fragments using these HDAC antibodies, we may only have been able to detect the most abundant HDACs present at specific regions and cannot rule out the presence of other HDACs at these regions.
Discussion
Turnover of acetyl groups on histone tails [13] has been a contentious issue since the discovery of acetylation (reviewed in [14]). Metabolic labelling of bulk histones suggests at least two populations with very fast (half-life of 1–5 min) and moderately fast (half-life of 30–60 min) turnover rates (reviewed in [14]). ChIP-based analyses of yeast promoters reveal localised transient histone acetylation associated with remodelling [15], as well as rapid targeted reversal of acetylation after removal of either acetylase (deacetylation within 1.5 min) or deacetylase (acetylation restored in 5–8 min) from a promoter [16]. This model of dynamic rather than stable modification in yeast is supported by the association of the HDAC Hos2 with coding regions of active genes [17]. Evidence of very fast turnover of acetyl groups on a sub-fraction of histones in mammalian cells came from metabolic-labelling studies of phosphorylated H3, which rapidly shifts to the highest acetylated forms upon HDAC inhibition [3].
Detailed analysis of the effects of TSA and other HDAC inhibitors on histone H3 acetylation and c-fos and c-jun induction here has produced two surprising results. First, these analyses show very highly targeted TSA hypersensitivity, uniquely targeted to all phospho-S10 and methyl-K4, but not dimethyl-K9, H3 in the mouse nucleus. These multiply modified TSA-hypersensitive nucleosomes have been localised to specific regions on c-fos and c-jun, indicating that these genes are subject to continuous acetylation and deacetylation irrespective of transcription. Second, inhibition of deacetylases at these genes rapidly enhances histone acetylation but inhibits transcription; contrary to the predominant view that increased histone acetylation is characteristic of enhanced transcription. Our data suggest an alternative model, whereby turnover of acetyl groups on K4- methylated histone H3 tails is both characteristic of the poised c-fos and c-jun genes, and is required for their efficient induction.
Differential Sensitivity of Nuclear Events to HDAC Inhibition
Classification of TSA sensitivity in the mouse nucleus would have TSA-insensitive genes such as β-globin (see Figure 4) at one extreme, followed by a broad swathe of TSA-responsive events, including many in the literature for which high levels of TSA and long treatments are required (see Table S1). Within this second category, data presented here raise the serious complication that some TSA-responsive phenomena, e.g., enhanced c-fos induction after 4 h of TSA pre-treatment (see Figure 6A), are secondary events requiring fresh translation (see Figure S5), unrelated to the state of acetylation at the gene, which by 4 h has subsided (see Figure 2). At the other extreme, TSA-hypersensitive genes can be defined as those at which the TSA effect is virtually instantaneous, requiring low concentrations of TSA and detectable by immediate localised histone acetylation. This provides both a molecular model of localised cycling acetylation at these genes, as well as an experimental definition for their detection: brief treatment with TSA followed by ChIP using acetyl-specific antibodies recovers chromatin containing these genes. Here, to our knowledge for the first time, we have demonstrated TSA hypersensitivity directed towards all K4-methylated H3 in the mouse nucleus but not K9-methylated H3. Furthermore, K4-methylated TSA-hypersensitive histone H3 occurs at the poised c-fos and c-jun genes irrespective of transcription, as well as at the continuously transcribed GAPDH (polymerase II) and 18S rRNA (polymerase I) genes, but not at a gene transcribed by RNA polymerase III (tRNA) or silenced in these cells (β-globin; see Figures 3 and 4; data not shown). This raises the possibility, discussed further below, that K4 methylation acts as a prior mark that renders histone H3 particularly susceptible to HATs and/or TSA-sensitive HDACs. In yeast, support for this model comes from the recent observation that SAGA and SLIK histone acetyltransferase-containing complexes also contain the chromodomain-containing protein Chd1 that binds to K4-methylated histone H3 [18].
Owing to differential sensitivity of individual HDACs to inhibition with TSA ([19]; reviewed in [20]), effects seen at the lowest concentrations used here are likely mediated by HDACs particularly sensitive to TSA, with other HDACs becoming involved at higher concentrations. Furthermore, we do not exclude the possibility that acetylation of non-histone proteins may also contribute to the inhibition of c-fos and c-jun seen here. We failed to detect such non-histone acetylation events using various anti-acetyllysine antibodies, but this may be because of limitations of reagents or sensitivity of techniques used. However, the highly targeted enhancement of histone acetylation specifically at c-fos and c-jun offers an obvious and local cause for their inhibited induction. Until any non-histone acetylation events can be demonstrated at these genes, the simplest interpretation is that the proven histone acetylation shown here is responsible for these observations.
Deployment of Histone-Modifying Enzymes to Produce Dynamic Acetylation of K4-Methylated Histone H3
TSA-hypersensitive chromatin and K4-methylated H3 must be under continuous opposed action of HATs and HDACs implying micro-compartmentalisation of HAT and/or HDAC function in mammalian nuclei. An H3 K4 methyltransferase must also be directed to these same nucleosomes, but because K4 methylation at these genes appears stable in all the experiments described here and over longer time courses through the cell cycle (data not shown), it is not clear when K4 methylation is deposited, an issue currently under investigation. These two targeted modifications, methylation and the turnover of acetylation, are constitutive in quiescent cells and differ from the third modification, phosphorylation, in not requiring activation of signalling cascades or gene induction.
Localised dynamic turnover of acetylation may be explained by physical distribution of HATs and HDACs in three conceivable ways: (1) HATs are tightly restricted while HDACs act in a diffuse global way, (2) HDACs are tightly restricted while HATs act in a diffuse global way, or (3) both enzymes are tightly co-localised to these genes. It is unlikely that HATs act in a diffuse global mode, as there is much evidence that protein–protein interactions with transcription factors target HATs to genes. Local recruitment of HATs such as CREB-binding protein (CBP), p300, and PCAF via interactions with CREB, SRF, and Elk-1 at regulatory elements upstream of c-fos [21,22], and with c-Jun, ATF-2, and MEF upstream of c-jun, have been described [23,24]. Association of pre-assembled Elk-1/CBP complexes with gene promoters such as c-fos in the absence of transcriptional activation has been demonstrated, but CBP activation is reported to require MAP kinase signalling and Elk-1 phosphorylation [21,25]. Our data show that HATs act continuously, irrespective of cell stimulation or transcription at these genes. Furthermore, TSA-induced acetylation occurs not just at promoter nucleosomes but also more distantly in the body of the gene, as far as ten nucleosomes away for c-jun and four nucleosomes away for c-fos, but not across the entire transcribed region in either case (see Figure 2). Although there are HATs in RNA pol II holoenzyme and elongation complexes [26], turnover of acetylation occurs independently of transcription and, furthermore, also occurs in upstream non-transcribed regions.
Although targeted HATs alone might explain TSA hypersensitivity, it is likely that HDACs are also targeted to these genes. This has been studied in Saccharomyces cerevisiae by deleting specific HATs and HDACs and using ChIP assays to analyse effects on histone acetylation either at specific loci or globally using microarrays [27–29]. These results show that both HATs and HDACs are targeted in yeast, and further, that there is “division of labour” among HDACs [28], e.g., HDAC Sir2p at telomeres, Rpd3p at centromeres, Hos1p and Hos3p at ribosomal DNA, and Hos2p at ribosomal protein genes. For c-fos in mammalian cells, mSin3A-HDAC co-repressor complex is reported to be recruited via interactions with Elk-1 [30] but this requires ERK activation and is proposed to mediate shut-off after induction.
Co-location of K4 methylation with H3 acetylation might be implied from the association of both with active transcription (reviewed in [1,2]). Some of the most interesting recent work co-locates H3 acetylation directly with K4 methylation at the same regions of the genome and on the same H3 tail, or places the relevant enzymes in the same complexes. Liang et al. [31], using an adaptation of ChIP assays, found H3 acetylation and K4 methylation around the transcription start sites of several active genes in human bladder cancer cells. H3 has been found on active genes in Drosophila euchromatin hyperacetylated and hypermethylated on K4 and K27 [32]. Bradbury and colleagues [33,34], using mass spectrometry, showed that H3 tails that are K4-methylated are also likely to be hyperacetylated, an important study that, unlike the majority of ChIP assays that address localisation to specific regions on a gene, definitively places multiple modifications on the same H3 molecule. Relating to enzyme complexes, the tethering of an HDAC Sin3 to a Trithorax-related K4 methyltransferase (Set1/Ash2) is reported to be mediated by host cell factor-1 (HCF-1) in human cells [35]. Finally, the TAC1 chromatin-modifying complex in Drosophila contains the K4 methyltransferase Trithorax and the HAT CBP ([36]; reviewed in [37]).
Gene Induction and Histone Modifications in the Mouse Nucleus
A major advantage of our mouse model system is that c-fos and c-jun are poised to transcribe and can be readily induced. Using this system, we find no strict correlation between the K4 methylation state and transcriptional activity. Spatially, K4 methylation spans the start site and extends in both directions at active or poised genes; upstream regions that are not transcribed are K4-methylated, whereas regions furthest downstream that can be transcribed are not K4-methylated. Furthermore, K4 methylation is present whether the gene is constitutively active (GAPDH), poised to transcribe (c-fos and c-jun in control cells), or induced to transcribed (in stimulated cells), but is absent in a constitutively silent gene (β-globin). This removes any strict link between K4 methylation and the progress of RNA polymerase II across a gene in mouse cells. Instead, what is established are “islands” of K4 methylation spanning the start sites, whose key characteristic is the continuous turnover of acetylation by the action of HATs and HDACs. Induction of c-fos and c-jun causes transient MAP-kinase-mediated phosphorylation at S10 on these H3 tails as well as a shift in the HAT/HDAC equilibrium to favour acetylation. Importantly, blocking turnover of acetylation results in inhibited c-fos and c-jun induction. These two changes are transient whereas pre-existing K4 methylation at these positions remains detectable for many hours afterwards. Temporarily, therefore, a proportion of nucleosomes at these positions on c-fos and c-jun can be formaldehyde-fixed in a state where they simultaneously carry methyl-K4, acetyl-K9, and phospho-S10 on the same H3 tail. One of the key advances of this work is that it removes comprehensively the notion that “active” genes in the mouse nucleus differ in histone modification from “inactive” genes in a stable and definable way, but highlights instead the highly dynamic nature of these modifications and regional variations across genes. This study, in common with a recent high-resolution histone modification mapping study in yeast [38], challenges the idea of a “histone code” differentiating active from inactive genes.
Materials and Methods
Cell culture and stimulation
C3H 10T½ and Swiss 3T3 mouse fibroblasts were grown in DMEM with 10% FCS. Confluent cultures were quiesced by incubation for 16–18 h in DMEM containing 0.5% FCS. Pre-treatment refers to addition of TSA at the times indicated prior to stimulation and left on throughout the period of stimulation until harvest. Cells were pre-treated with histone deacetylase inhibitor TSA (1–500 ng/ml [3.3 nM–1.65 μM]; Sigma, St. Louis, Missouri, United States), API (0.5 μM [39]; Alexis Biochemicals, Montreal, Quebec, Canada), or TPX (500 ng/ml [40]; kindly provided by B. M. Turner, University of Birmingham) and then stimulated with TPA (100 nM; Sigma), EGF (50 ng/ml; Promega, Fitchburg, Wisconsin, United States), bFGF (20 ng/ml; Roche, Basel, Switzerland), 15% FCS (Invitrogen, Carlsbad, California, United States), or sAn (25 ng/ml; Sigma).
Northern blot analysis of RNA
Gene expression was analysed by Northern blotting as described previously [41]. Blots were sequentially hybridised to c-fos and c-jun probes and then to the loading control GAPDH. Northern blots were visualised by autoradiography and quantified using a PhosphorImager (Molecular Dynamics, Sunnyvale, California, United States).
Analysis of proteins by Western blotting
Extraction of histones, acid-urea polyacrylamide gel electrophoresis, and Western blotting were carried out as described previously [4,42]. Antibodies were used at the following dilutions: anti-phospho-H3 and anti-phosphoacetyl-H3, 1:1,000; anti-acetyl-H3, 1:2,000; anti-monomethyl-K4 H3, 1:500; anti-dimethyl-K4 H3, 1:2,500; anti-trimethyl-K4 H3, 1:2,000; and anti-dimethyl-K9 H3, 1:2,000. Protein extraction and gel electrophoresis for analysis of MAP kinase activation was carried out as described previously [12,43]. Antibody specificity is described in Protocol S1.
Chromatin immunoprecipitation
ChIP was performed using formaldehyde cross-linked chromatin as described previously [4] with modifications [5]. ChIPs were carried out using 5 μg of anti-acetyl-H3 antibody/500 μl of chromatin, 25 μg of anti-phosphoacetyl-H3/500 μl of chromatin, and 5 μl of anti-trimethyl-K4 H3/200 μl of chromatin. ChIP analyses using HDAC antibodies were performed using dimethyl adipimidate (Pierce Biotechnology, Rockford, Illinois, United States) and formaldehyde cross-linked chromatin as described in Protocol S1, and 10 μl of anti-HDAC1, −3, −4, or −6/500 μl of chromatin. To analyse unbound chromatin after primary ChIP, unbound supernatant chromatin was removed and retained after incubation with protein A–Sepharose beads (Sigma), and a different second antibody used in ChIP. Sequential ChIPs were performed to analyse histone H3 tails carrying multiple modifications. After primary ChIP, antibody-bound chromatin isolated by binding to protein A–Sepharose was washed and eluted as described previously [4]. Eluted chromatin was diluted to 0.1% SDS, adjusted to RIPA buffer and pre-cleared by incubation with protein A–Sepharose beads for 2 h at 4 °C with rotation. After pre-clearing, the supernatant was concentrated using Vivaspin concentrators (50,000 MWCO; Vivascience, Hannover, Germany). A second ChIP was then carried out using a different antibody. The specificity of antibodies used in ChIPs is detailed in Protocol S1. Quantitative radiolabelled PCR products resolved on 6% polyacrylamide-TAE gels were visualised by autoradiography and quantified by phosphorimaging [4]. Input DNA was diluted one in 60 to approximately 1 ng/μl, and 5 μl was used per PCR reaction. To ensure signals were within the linear range, DNA recovered using anti-acetyl-H3 was diluted one in three and anti-trimethyl-K4 H3 one in 20, and anti-phosphoacetyl-H3 was used undiluted for PCR analyses [5]. DNA recovered using HDAC antibodies was also used undiluted for PCR analyses. Primer names relate to the 5′ position of the forward primer relative to the transcription start site. Details of regions amplified by primers used in this study and sequences of primers are provided in Protocol S1. Similar results were obtained in at least two separate experiments. Quantified PCR data, from PCRs performed in triplicate, are presented as Bound/Input, which corrects for variations in input loading and gives an indication of the relative abundance of immunoprecipitated DNA sequences.
Supporting Information
Figure S1 Increased Acetylation of Nucleosomes Associated with c-fos and c-jun Genes on TSA Treatment
Chromatin fragments were immunoprecipitated with anti-acetyl-H3 antibodies (see Figure 2). Recovered DNAs from antibody-bound fractions (AcH3 IP) as well as total input DNA (Input) from released chromatin used for ChIP were analysed for the presence of c-fos (A) and c-jun (B) gene sequences. Quantified PCR data, from PCRs performed in triplicate, are presented here as fold-induction. Note that in contrast to presenting data as Bound/Input (see Figure 2D), which is a measure of the relative abundance of immunoprecipitated DNA sequences, fold-induction shows changes in the level of acetylated H3 on TSA treatment at specific regions of c-fos and c-jun. Fold-induction appears highest at regions displaying lower basal levels of acetylation. This can be misleading as there can be much greater amounts of acetylation at other regions, as shown by the Bound/Input graphs in Figure 2.
(40 KB PDF).
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Figure S2 Effect of TSA Pre-Treatment on c-fos and c-jun Induction in Swiss 3T3 Cells
Quiescent Swiss 3T3 cells were untreated (−) or pre-treated with TSA (500 ng/ml) for 15 min (TSA 15′) or 4 h (TSA 4h). Cells were left unstimulated (C), or stimulated with TPA for 30 or 60 min, and RNA was analysed by Northern blot.
(52 KB PDF).
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Figure S3 TPA-Stimulated c-fos and c-jun Induction Is Hypersensitive to TSA Pre-Treatment
(A) Quiescent C3H 10T½ cells were untreated (−) or pre-treated with increasing concentrations of TSA (0.01, 0.1, 1, 10, 100, or 500 ng/ml) for 15 min. Cells were left unstimulated (C) or stimulated with TPA for 30 or 60 min.
(B) Quiescent C3H 10T½ cells were untreated (−) or pre-treated with TSA (10 ng/ml) for 15 min (−15′), 5 min (−5′), or 0 min (0′), and then stimulated with TPA for 30 or 60 min; or cells were first stimulated with TPA, and then TSA (10 ng/ml) was added after 2.5 min (+2.5′), 5 min (+5′), 10 min (+10′), or 15 min (+15′); cells were then harvested after 30 or 60 min of TPA treatment. RNA was analysed by Northern blot.
(59 KB PDF).
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Figure S4 Effect of TSA Pre-Treatment on c-fos and c-jun mRNA Stability
Quiescent C3H 10T½ cells were untreated (−) or pre-treated with TSA (500 ng/ml) for 15 min (TSA 15′). Cells were stimulated with TPA (+) for 30 min before the addition of DRB to block transcription. Cells were harvested from 0 to 30 min after DRB addition. As a control for transcriptional inhibition, DRB treatment for 5 min prior to TPA stimulation (30 min) was included. “C” indicates control (unstimulated). Northern blots were quantified by phosphorimaging, corrected for variations in loading using GAPDH, and expressed graphically (panels i and ii). The response at TPA 30 min/DRB 0 min was set to 100%, and the rate of mRNA decay expressed relative to this value.
(52 KB PDF).
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Figure S5 Effect of the Translational Inhibitor Emetine on TSA-Mediated Changes in TPA-Stimulated c-fos and c-jun Induction
Quiescent C3H 10T½ cells were untreated, or pre-treated with TSA alone (TSA; 500 ng/ml), emetine alone (Em), or emetine plus TSA (Em + TSA) for 15 min or 4 h. Cells were left unstimulated, or stimulated with TPA for 30 or 60 min. Northern blots were quantified by phosphorimaging, corrected for variations in loading using GAPDH, and expressed graphically. Values for c-fos and c-jun mRNA from control untreated cells were negligibly low. The normal TPA response at 30 min (c-fos)/60 min (c-jun) was set to 0%, and inhibition/enhancement in response to TSA/emetine pre-treatment expressed as a percentage change relative to this value.
(35 KB PDF).
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Figure S6 Effect of Different HDAC Inhibitors on Histone H3 Acetylation and c-fos and c-jun Induction
(A) Quiescent C3H 10T½ cells were treated with TSA (10 ng/ml) and HDAC inhibitor MS-275, CBHA, or M344, for 15 min to 4 h. “C” indicates control (unstimulated). Acid-soluble proteins were extracted and separated on acid-urea gels. Western blots were carried out with anti-acetyl-H3 antibodies (panel i). A representative gel was stained with Coomassie to indicate protein loading (panel ii). Positions of histone isoforms are shown on the right of each panel, with zero being unmodified histone H3.
(B) Quiescent C3H 10T½ cells were untreated (−) or pre-treated with TSA (10 ng/ml) and HDAC inhibitor MS-275, CBHA, or M344, for 15 min. Cells were then left unstimulated (C) or stimulated with TPA for 30 or 60 min. RNA was analysed by Northern blot.
(64 KB PDF).
Click here for additional data file.
Protocol S1 Additional Experimental Procedures
(59 KB DOC).
Click here for additional data file.
Table S1 Examples of Different TSA Concentrations and Treatment Times
(50 KB DOC).
Click here for additional data file.
We thank all members of the Nuclear Signalling Laboratory, especially Drs. Alison Clayton and Stuart Thomson, for their invaluable help and criticisms of this work, and Dr. Bryan Turner (Birmingham University) for provision of HDAC inhibitors. This work was funded by a Wellcome Trust Programme Grant (ie. refering to the Wellcome Trust Programme Grant (065373/Z/01/Z)).
Competing interests. The authors have declared that no competing interests exist.
Author contributions. CAH and LCM conceived and designed the experiments. CAH performed the experiments. CAH and LCM analyzed the data. CAH contributed reagents/materials/analysis tools. CAH and LCM wrote the paper.
Citation: Hazzalin CA, Mahadevan LC (2005) Dynamic acetylation of all lysine 4–methylated histone H3 in the mouse nucleus: Analysis at c-fos and c-jun. PLoS Biol 3(12): e393.
Abbreviations
APIapicidin
ChIPchromatin immunoprecipitation
CBPCREB-binding protein
GAPDH
glyceraldehyde-3-phosphate dehydrogenase
HAThistone acetyltransferase
HDAChistone deacetylase
IPimmunoprecipitation
K4lysine 4
K9lysine 9
S10serine 10
sAnsub-inhibitory anisomycin
trimethyl-K4trimethylated K4
TPA12-O-tetradecanoyl phorbol-13-acetate
TPXtrapoxin
TSATrichostatin A
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Li QJ Yang SH Maeda Y Sladek FM Sharrocks AD MAP kinase phosphorylation-dependent activation of Elk-1 leads to activation of the co-activator p300 EMBO J 2003 22 281 291 12514134
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030411SynopsisBioinformatics/Computational BiologyCell BiologyMolecular Biology/Structural BiologyIn VitroArp2/3: Structural Insights into a Primary Engine of Cell Motility Synopsis11 2005 8 11 2005 8 11 2005 3 11 e411Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Mechanism of Filament Nucleation and Branch Stability Revealed by the Structure of the Arp2/3 Complex at Actin Branch Junctions
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The capacity for directed motion arises from core cellular processes shared by organisms from protozoa to primates, indicating an ancient origin over a billion years ago. Amoeba foraging for food, pathogenic microbes invading host tissue, host immune cells ingesting those pathogens—all depend on the same core motility components. Actin, the most abundant protein in eukaryotic cells (cells with nuclei), lies at the heart of the motility machinery. Individual actin proteins assemble into polymers and organize into long filaments that form intricate networks under the direction of a seven-subunit protein complex called the actin-related protein (Arp) 2/3 complex. As actin polymerization occurs, newly formed daughter filaments grow out at an angle from mother filaments, much like branches sprout from a tree trunk. The place where daughter filaments bud from a mother filament (branching nucleation) is called the branch junction.
Branch formation requires Arp2/3 activation, which occurs when the Arp2/3 complex binds to nucleation promoting factors (which includes a family of proteins called WASp), molecules of adenosine triphosphate (ATP)—a molecule that fuels most energy-requiring processes—and already-existing mother filaments. The details of branch junction formation have remained obscure, though nucleation models have proposed that Arp2 and Arp3 assemble around the mother filament and form a template for actin subunits to nucleate from. While the atomic structure of the inactive Arp2/3 complex (previously determined) shows that the two actin-related proteins resemble actin enough to form a template for nucleation, the spatial relationship between the proteins in the inactive complex does not match that in actin filaments.
Current models postulate that Arp2/3 complex activation triggers a change in spatial relationship so that Arp2 and Arp3 resemble actin monomers in a filament; but without evidence on the structure of the Arp2/3 subunits at the branch junctions, these models have to assume that this is how nucleation occurs. In a new study, Coumaran Egile et al. combine electron microscopy, genetic labeling, and computational analysis to resolve the structure of the Arp2/3 complex at the branch junction to a resolution of one nanometer (that's one billionth of a meter), and demonstrate that the Arp2/3 template assumption is correct.
To study the mechanics of branched actin nucleation by the Arp2/3 complex, Egile et al. assembled actin branches in test tubes and observed the action at the molecular level. To do this, the researchers tagged the different Arp2/3 subunits with labels that can be detected with electron microscopy, allowing them to determine the location of the proteins. Using this approach, they introduced different nucleation promotion factors—WASp proteins as well as cortactin (an Arp2/3 activator that is found near the inner cell membrane)—and compared the resulting branch junction formations. Only cortactin was found at the branch junction, supporting the model that WASp activators transiently bind, activate, and release Arp2/3 after branch formation. Cortactin, on the other hand, may stay behind to help stabilize the interaction between Arp2/3 and the mother filament.
The actin-related protein (Arp) 2/3 complex provides a template for new actin filaments to branch off from a mother filament
After genetically engineering yeast to express fluorescently labeled versions of the different subunits, Egile et al. observed the complexes' nucleation activities and located their position in the branch junction. The likely orientation of the subunits at the branch junction was determined with computational modeling. Given the position of the subunits and the number of possible combinations at this site, the authors used the crystal structure of the inactive Arp2/3 complex to map all the possible orientations. Only one cluster of orientations satisfied the constraints: Arp2 and Arp3, associated with the fast-growing end in an actin filament, facing away from the mother filament and toward the daughter filament.
Though Arp2 and Arp3 orientations would have to change upon activation to support daughter filament growth, the authors argue that the change would not disrupt the overall architecture of the complex. Rotating the subunits to reflect their activated conformations places Arp3 next to the mother filament and Arp2 farthest away. In this orientation, the longest axis of the complex aligns perpendicular to the mother filament (in all previous models, they align parallel), an arrangement that could provide stability at the branch junction, by taking advantage of protein interactions on either side of the mother filament.
Altogether, these results provide conclusive evidence for the starting assumption of a nucleation model in which Arp2 and Arp3 form a template at the branch junction that triggers daughter filament growth. And with the help of the subunit map presented here, researchers can further dissect the molecular mechanisms of actin branch nucleation and elucidate the dynamics of cellular motion. —Liza Gross
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030419SynopsisBiochemistryIn VitroSelenium Speeds Reactions Synopsis12 2005 8 11 2005 8 11 2005 3 12 e419Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Different Catalytic Mechanisms in Mammalian Selenocysteine- and Cysteine-containing Methionine-R-Sulfoxide Reductases
Selenoproteins-Tracing the Role of a Trace Element in Protein Function
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At the heart of every reaction of every cell lies an enzyme, a protein catalyst. At its active site—a special pocket on its surface—it binds reactants (substrates) and rearranges their chemical bonds, before releasing them as useful products. Rearranging some bonds may require help from certain chemical elements that are present in trace amounts. Many enzymes place these elements at the center of their active sites to do the most critical job.
Selenium is one such element. In large quantities, selenium is toxic, but, in trace amounts, it is absolutely essential for life in many organisms, including humans. Selenium is present in proteins in the form of selenocysteine, a rare amino acid that helps promote antioxidant reactions. These selenocysteine-containing proteins are called selenoproteins. One important selenoprotein is the enzyme methionine-R-sulfoxide reductase (MsrB) 1, whose job is to repair proteins injured by oxidative damage, caused by sunlight, toxic chemicals, or a variety of other insults.
In mammals, there are two other forms of MsrB, which also can efficiently perform this task, but use the abundant amino acid cysteine instead of selenocysteine. So why do cells go to the trouble and metabolic expense of acquiring selenium from the environment? In this issue, Hwa-Young Kim and Vadim Gladyshev explore the details of active-site chemistry of these three related enzymes, and show that the selenoprotein form employs a different catalytic mechanism.
The authors began by identifying three key amino acids in the active site of the cysteine-containing forms, which did not occur in the selenoprotein MsrB1. When any of these amino acids were mutated, the activity of the cysteine-containing enzymes was greatly diminished. This result indicates that these amino acids likely play a role at the active site, a supposition supported by previous work on related enzymes in bacteria.
These surface models of mouse MsrB2 and human MsrB3 show the catalytic Cys95 residues in red and the Asn97 residues in blue
Kim and Gladyshev next systematically mutated MsrB1 to include one, two, or all three of these amino acids, and discovered that inclusion of one or any combination of them diminished activity of the selenocysteine-containing enzyme. This suggested that while these amino acids support the mechanism of the cysteine-containing forms, they interfere with the mechanism of the selenoprotein. Not surprisingly, when the selenium was removed from MsrB1, the enzyme was significantly impaired. But when the three amino acids were added to this crippled enzyme, they restored some of the diminished activity, probably by carrying out the same mechanism they do in the cysteine-containing enzymes.
The authors then inserted a selenium atom into each of the cysteine-containing enzymes, in the same spot in the active site where it sits in MsrB1. They found that the initial activity of each enzyme was increased over 100-fold, indicating the inherent capacity of selenium to promote catalytic activity. These souped-up enzymes were unable to complete the reaction, however, because they lacked other features of MsrB1's active site. Further scrutiny of the enzymes revealed these critical features, and inserting them allowed the artificial selenoproteins to carry out the entire reaction.
The authors suggest the explanation for these findings relates to a difference in the catalytic mechanism of selenocysteine- and cysteine-containing enzymes. The substrate for both enzyme types, methionine-R-sulfoxide, is found within oxidized proteins. The job of both enzymes is to reduce this compound back to the amino acid methionine. Both do so by accepting an oxygen atom from the sulfoxide.
In the presence of selenium, the oxygen temporarily binds to the selenium. The selenium's electrons then shift to bond with a sulfur on a neighboring cysteine amino acid, kicking out the oxygen as part of a water molecule. Finally, the selenium-sulfur bond is broken and the enzyme is restored to its original state by the intervention of thioredoxin, a ubiquitous cell molecule whose job is to undo just such temporary linkages in a wide variety of enzymes.
Without selenium, the oxygen binds directly to sulfur, and thioredoxin intervenes to form the water and restore the sulfur. This reaction occurs in fewer steps, but is slower. The authors propose that the evolution of selenium-containing MsrB1 from cysteine-containing forms was likely favored by the higher rate of reaction it offered, although this trend is likely limited by the requirement for changes in other portions of the enzyme to accommodate the trace element. The authors suggest that selenium provides inherent catalytic advantages to certain types of enzymatic reactions, even though utilization of these advantages is sometimes tricky. If so, manipulation of related enzymes by insertion of selenium may increase their catalytic efficiency, perhaps much above that designed by nature. This may offer advantages for some biotechnology and biomedical applications that depend on antioxidants. —Richard Robinson
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PLoS BiolPLoS BiolpbioplosbiolPLoS Biology1544-91731545-7885Public Library of Science San Francisco, USA 10.1371/journal.pbio.0030431SynopsisCancer BiologyCell BiologyGenetics/Genomics/Gene TherapyMolecular Biology/Structural BiologyBiochemistryMus (Mouse)MammalsFor Some Genes, Acetylation/Deacetylation Cycling Is the Real Turn-On Synopsis12 2005 8 11 2005 8 11 2005 3 12 e431Copyright: © 2005 Public Library of Science.2005This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
Dynamic Acetylation of All Lysine 4-Methylated Histone H3 in the Mouse Nucleus: Analysis at c-fos and c-jun
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The human genome contains some 20,000–25,000 protein-coding genes, but at any given moment, only a small fraction of them is actively transcribed. The DNA that constitutes a gene is wound around multiple nucleosomes, barrel-shaped protein clusters that serve to organize and protect the DNA. When nucleosomes are packed tightly together, the transcription machinery can't easily reach the gene's promoter segment, where it must bind to begin the transcription process; thus, the gene remains silent. Each nucleosome is a bundle of proteins called histones, with ends (tails) that extend from the nucleosome and are accessible for regulatory modification. Modifications can serve two functions. They may regulate how tightly or loosely nucleosomes pack together, or alternatively, they may function as recognition motifs, allowing other regulatory proteins to be recruited to these nucleosomes. In recent years, therefore, histone modifications have come to be appreciated as a major route for controlling gene expression.
One such modification is the addition of a two-carbon acetyl group to histone H3. Histone acetylation has been widely believed to enhance gene expression. But in this issue, Catherine Hazzalin and Louis Mahadevan show that, for at least some genes, dynamic turnover of acetyl groups on the histone, rather than stable acetylation, is the key to turning on the gene.
Acetyl groups are added to histone H3 by acetyltransferase enzymes, and removed by deacetylase enzymes. The authors showed that when they added a deacetylase inhibitor to cultures of mouse cells, the acetylation level of H3 increased, as they expected. But unexpectedly, they found two modes of sensitivity to deacetylase inhibitors. The majority of H3 was largely insensitive to the presence of the inhibitor. In contrast, the minute fraction of H3 already modified by methyl groups at the fourth amino acid in the tail was immediately and very highly sensitive to deacetylase inhibitors, and picked up new acetyl groups rapidly. Methyl modification at position 4 has previously been associated with increased gene activity.
Acetylation was rapid in genes with multiple methyl groups at position 4, such as c-fos and c-jun. In contrast, the deacetylase inhibitor did not increase acetylation in β-globin, which lacks histone H3 methylated at position 4. For those genes in which acetylation increased, not every nucleosome across the entire gene was equally acetylated, and the pattern of increase appeared to be gene specific. Both c-fos and c-jun had increased histone H3 acetylation at sites adjacent to the gene's promoter and across the start-of-transcription site, but other regions of each gene were affected differentially between the two.
The authors also showed that three different modifications—methylation at position 4, acetylation at position 9, and addition of a phosphate at position 10—can all occur on the same histone H3 molecule. This tight cluster of modifications is likely to induce significant structural changes in this portion of the molecule, setting the stage for further effects associated with increased gene expression. Another recent paper from this group and others describes the function of some of these modifications at these genes, which is to transiently recruit the phosphate-binding adapter protein 14-3-3 to these nucleosomes.
Finally, the authors asked whether the increase in acetylation brought on by deacetylase inhibition led to an increase in gene activity, in keeping with the prevailing model of gene regulation. The transcription of c-fos and c-jun can be stimulated by the addition of a chemical inducer. But when cells received the deacetylase inhibitor before, or even up to ten minutes after, inducer treatment, both genes were inhibited. This was due to a direct inhibition of the transcription process, and not from effects on cell signaling or other secondary pathways. Thus, it is turnover, or cycling, of acetylation and deacetylation that is needed to increase expression of these genes.
The mechanism by which continuous acetylation/deacetylation cycling promotes gene expression remains unknown, but these findings add to the complex picture of gene regulation that has emerged since the discovery of histone modification. The hypothesis that there might be a “histone code”—a predictable pattern of modifications invariably associated with increased gene activity—appears to be a simplification, and one that does not, at least in some cases, correspond to the actual dynamic system the cell uses to regulate its genes. —Richard Robinson
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7784ehp0113-00092016079059Commentaries & ReviewsCommunity-Initiated Breast Cancer and Environment Studies and the Precautionary Principle Brody Julia Green 1Tickner Joel 2Rudel Ruthann A. 11 Silent Spring Institute, Newton, Massachusetts, USA2 Department of Community Health and Sustainability, University of Massachusetts, Lowell, Massachusetts, USAAddress correspondence to J.G. Brody, Silent Spring Institute, 29 Crafts St., Newton, MA 02458 USA. Telephone: (617) 332-4288. Fax: (617) 332-4284. E-mail:
[email protected] authors declare they have no competing financial interests.
8 2005 31 3 2005 113 8 920 925 23 11 2004 31 3 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The precautionary principle implies the need for research paradigms that contribute to “strength of the evidence” assessments of the plausibility of health effects when scientific uncertainty is likely to persist and prevention is the underlying goal. Previous discussions of science that inform precautionary decision making are augmented by examining three activist-initiated breast cancer and environment studies—the Long Island, New York, and Cape Cod, Massachusetts, studies and the National Institute of Environmental Health Sciences breast cancer and environment centers. These studies show how the choice of research questions affects the potential of results to inform action. They illustrate a spectrum of public involvement, population- and individual-level epidemiologic study designs, and the crucial importance of developing and applying new exposure assessment methods. The exposure studies are key because they are critical in assessing plausibility (without exposure to a causal agent, there is no health effect), are prerequisite to health studies, and identify preventable exposures that could be reduced by precautionary policies, even in the absence of strong evidence of harm. The breast cancer studies have contributed to environmental and biological sampling programs for endocrine-disrupting compounds in drinking water and household air and dust and the application of geographic information systems for surveillance and historical exposure assessment. They leave unanswered questions about when to invest in large epidemiologic studies, when negative results are sufficient, and how to pursue ambiguous positive results in further research and policy.
breast cancercommunity-based participatory researchendocrine-disrupting compoundsenvironmental exposure assessmentgeographic information systemprecautionary principlepublic involvement
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More than 200,000 new cases of invasive breast cancer and 55,000 cases of in situ disease are diagnosed annually in the United States (American Cancer Society 2003), and U.S. women’s lifetime risk of breast cancer has doubled from about 1 in 14 in the 1960s to 1 in 7 today, or 1 in 6 including in situ disease (Ries et al. 2004). Incidence continues to rise incrementally in the United States (Ries et al. 2004), and it is increasing more rapidly in developing nations (Parkin et al. 2001). High incidence makes breast cancer an urgent public health priority, and because an increased risk over just one generation must reflect modifiable change rather than inherited genes, incidence patterns also signal that breast cancer is a realistic target for prevention. Further evidence comes from a study of women with high-risk BRCA1 and BRCA2 genetic variations that showed that 24% of women born before 1940 were diagnosed with breast cancer by age 50, compared with 67% of women born later, indicating that modifiable factors affect even women at high genetic risk (King et al. 2003).
Factors affecting estrogen and progesterone are among the best-established risk factors for the disease. These include age at menarche and menopause, parity, age at first full-term pregnancy, weight gain after menopause, hormone replacement therapy, lack of physical activity, and alcohol use (Bernstein 2002). These effects, although relatively weak, consistently appear in many epidemiologic studies, leading to high confidence in their roles as risk factors. A much more limited inquiry into chemical exposures as breast cancer risk factors provides a new hypothesis for study: Laboratory animal and cell studies support the hypothesis that animal mammary carcinogens and chemicals that mimic estrogen or otherwise disrupt hormones may increase breast cancer risk, just as endogenous and pharmaceutical hormones do (Brody and Rudel 2003; Davis et al. 1993; Wolff et al. 1996). Exposures to mammary carcinogens and endocrine-disrupting compounds (EDCs) are common from sources such as gasoline, pesticides, detergents, plastics, home furnishings, personal care products, and air and water pollution (Brody and Rudel 2003; Rudel et al. 2003).
In the early 1990s, a number of breast cancer activist organizations began pursuing research into these environmental pollutants as possible avenues to breast cancer prevention (Brown et al., in press; McCormick et al. 2003). They won Congressional legislation mandating the Long Island Breast Cancer Study Project (LIBCSP) (U.S. Congress 1993), founded Silent Spring Institute as an independent organization dedicated to breast cancer and environment research (Brody et al. 1996), and later initiated a multicenter National Institute of Environmental Health Sciences (NIEHS) program of research into environmental factors in the course of puberty in girls (NIEHS 2003).
The design of epidemiologic studies to address activist concerns is problematic, however, particularly because the exposure assessments themselves pose challenges (Brody and Rudel 2003). Self-reports, the basis for exposure classification in studies of most known breast cancer risk factors, are at best a weak method for assessing exposure to many pollutants. Randomized clinical trials, the source of most knowledge about the effects of exogenous chemicals on breast cancer, effectively measure pharmaceutical exposures but are not an ethical option for exposures from pollution, work-places, or consumer products. Biomarkers of exposure and sampling methods for environmental media, such as air, water, and food, have been developed for relatively few of the many chemicals hypothesized to affect breast cancer. They are expensive to use in studies large enough to detect risks of the magnitude (probably < 2-fold) we would expect for EDCs based on the relative risks for known hormonal risk factors. Also, they are difficult to apply across the life span, a problem because higher breast cancer risk is associated with hormonal exposures beginning in utero (e.g., twinning and maternal diethylstilbestrol use) and extending to within 5 years of diagnosis (e.g., pregnancy and hormone replacement therapy). Finally, strategies for aggregating effects of mixtures have yet to be developed.
Given these challenges, as activist-generated breast cancer research unfolded, tensions emerged from the mismatch between what investigators can achieve through prevailing epidemiologic research paradigms and what activists had hoped to accomplish in time to help their daughters. Lessons from these conflicting perspectives carry many parallels with, and can inform, other health issues for which relevant exposures are similarly difficult to assess and where disease has multifactorial causation (e.g., asthma and learning disabilities). In this commentary, we seek to draw out these lessons by discussing the relationship between activist goals and scientific methods in reference to the precautionary principle, because it provides a framework for generalizing from the breast cancer studies to other public health issues where scientific uncertainty is likely to persist. We focus particularly on the Cape Cod Breast Cancer and Environment Study, because two of us have been involved for nearly a decade in its development and implementation (J.G.B. as principal investigator and R.A.R. as co-investigator for toxicology and environmental science). We also comment on the Long Island study and the NIEHS breast cancer and environment research centers program. The Bay Area Breast Cancer and the Environment Study Group and research in Marin County, California, which offers another early example, is now affiliated with the NIEHS centers program.
Precaution as a Guide to Environmental Health Research Design
The precautionary principle calls for preventive action in the face of uncertain but suggestive evidence of risk, especially when safer alternatives exist. The 1998 Wingspread Statement on the Precautionary Principle (Raffensperger and Tickner 1999) identifies four central components of precautionary policies: a) taking preventive action in the face of uncertainty, b) placing responsibility on those who create risks to study and prevent them, c) considering alternatives to potentially harmful activities, and d) increasing public participation and transparency in decision making. In contrast, current U.S. chemical regulations require substantial evidence of harm before regulatory action is taken, regardless of the availability of alternatives.
Previous discussions have outlined how the precautionary principle calls for changes in research process, questions, interpretations, and policy applications (Kriebel et al. 2001; Stirling and Gee 2002; Tickner 2002, 2003). By approaching public health policy with a greater willingness to act in the face of uncertainty, the precautionary principle expands the scope of relevant science and increases the utility of evidence about hypothesized harms even when that evidence is far from definitive. It calls for assessment of the “strength of evidence” that accrues from a broadly defined toolbox of methods that includes typical hypothesis-testing epidemiologic designs and extends to hypothesis-generating epidemiology, toxicology, exposure assessment, risk assessment, wildlife studies, and human case reports. Precautionary science seeks integrative methods to deal with chemical mixtures and multiple health effects from the same exposure. It implies an iterative process of research and policy making with an explicit role for judgment, which in turn argues for democratization.
Many breast cancer activist organizations, including groups involved in the Long Island and Cape Cod studies, have explicitly endorsed the precautionary principle {examples come from New York (Huntington Breast Cancer Action Coalition 2003), California (Breast Cancer Action 2004), Massachusetts [Massachusetts Breast Cancer Coalition (MBCC) 2004], and Oregon (Crumpacker 2002)}. The history of community-initiated breast cancer studies reflects the influence of the activists’ precautionary thinking on expanding the scope of research and strategies for public involvement.
Public Participation in Decision Making
Increased public participation and transparency in decision making are a logical starting point for applying the precautionary principle to breast cancer studies because democratizing scientific research opens the door for activists’ priorities to influence study design. Breast cancer activists, following the example of AIDS activists, have become leaders in helping to drive research agendas by catalyzing federal and state legislation and appropriations and participating in research design (Brown et al. 2000; McCormick et al. 2003, 2004). In 1993 and 1994, Long Island and Massachusetts activists initiated unprecedented public roles in research by seeking empowerment in study design and implementation. Frustrated that decades of the war on cancer had not addressed their questions about environmental factors and prevention, both groups circumvented traditional federal grant making and sought help through elected officials. Long Island activists generated the first large-scale breast cancer and environment research through a congressional mandate, and the Cape Cod study, funded by the state legislature, pioneered activist governance in research.
Long Island study.
The LIBCSP was the first of the activist-generated studies to become nationally visible. Mandated by Congress in 1993, it grew to encompass > 10 studies totaling > $30 million. Beyond winning funds, the Long Island activists specified in the Congressional mandate several aspects of the research design, including a case–control study using biologic markers and the development of a geographic information system (GIS) (U.S. Congress 1993). The grants were then awarded to academic scientists, however, and activists sometimes felt shut out of the process (McCormick et al. 2003). For example, conflict emerged about the list of environmental pollutants under study, with activists advocating for a more extensive set of target compounds (Balaban B, personal communication). The academics, motivated partly by the limited availability of biomarkers for historical exposure, chose to study organochlorines that had been banned in the United States, generating data that would not directly inform current environmental health policy. In addition, hopes that the GIS would allow activists to extend community-based mapping efforts were dashed by delays and limits on public access to many types of data.
Cape Cod study.
The Cape Cod Breast Cancer and Environment Study also began from a legislative mandate (General Court of the Commonwealth of Massachusetts 1994), although at the state rather than federal level. In response to Massachusetts Department of Public Health (MDPH) data showing elevated incidence on Cape Cod (Brody et al. 1996), MBCC founded Silent Spring Institute in 1994 to bid for and win a $1 million annual state appropriation for breast cancer and environment research. The institute’s researchers in epidemiology, toxicology, and environmental science now collaborate with co-investigators from Boston, Brown, Harvard, and Tufts universities and elsewhere.
The founders’ vision transcended “science as usual” and gave activists governance roles on the scientific team. As a nonprofit organization, the institute has a public-interest board of directors (including three directors chosen as MBCC representatives) with the authority to hire and fire the study’s principal investigator. The board’s authority is tempered by grant requirements for funder approval before key personnel can be replaced, however, and the Silent Spring Institute board developed additional mechanisms to ensure that it exercises its authority responsibly. The board convened a science advisory committee of outside experts, frequently sent a representative to co-investigator meetings, gave added weight to input from board members trained in biology and medicine, and supported publication of research results in peer-reviewed scientific journals even when MDPH disallowed use of state funds for this purpose. This activist-governed research model is particularly notable at a time when government increasingly relies on industry science in regulatory decisions and academic science is growing more dependent on industry funding (Krimsky 2003). Further, breast cancer activists often cite their hope of putting themselves out of business by finding scientific answers to “stop the epidemic,” so their governance role may help check any possible bias stemming from researchers’ interests in perpetuating their own work.
NIEHS Centers.
The NIEHS Centers program began as an initiative of the National Breast Cancer Coalition, which, along with NIEHS, convened a series of invitational brainstorming sessions for researchers and activists. These sessions, coupled with public meetings, shaped the request for applications (RFA). The RFA specified a multidisciplinary approach, including laboratory and epidemiologic components, and required ongoing public involvement (NIEHS/National Cancer Institute 2002).
Among these three examples of activist-initiated research, the Cape Cod and NIEHS models have extended the democratization of science in ways that can offer models for the development of new norms for environmental health research: public empowerment that goes beyond mere involvement on advisory boards, a shift away from purely investigator-defined research to joint activist–scientist definition of research problems, and integration across disciplines and across institutions. The Cape Cod study is perhaps unique even in the history of community-based participatory research in that activists govern the research team.
Research Questions and Study Design
If democratization in science makes a difference, we would expect to see activist-initiated studies that differ in design from the typical investigator-initiated studies funded by the National Cancer Institute, Department of Defense, and major foundations. Consistent with breast cancer activists’ support for the pre-cautionary principle, we expect study designs that will inform preventive public health policies in the face of uncertainty [the first principle of the Wingspread Statement on the Precautionary Principle (Raffensperger and Tickner 1999)]. The research that serves this goal includes assessments of such factors as “upstream” health outcomes (e.g., precursors of disease), multiple sources of uncertainty in measurements and models, effects on sensitive individuals, the nature and effects of high exposures, exposure pathways, cumulative and interactive effects of multiple exposures, population as well as individual effects, and the environmental justice implications of the distribution of health risks across exposure levels and across populations (Kriebel et al. 2001; Stirling and Gee 2002; Tickner 2002, 2003). We add to this list of relevant research activities the development and application of animal and cell models that can inform understanding of natural systems and the plausibility of effects in humans (Brody and Rudel 2003). If EDCs make breast cancer cells grow in the laboratory, for example, they may also affect breast cancer in women. Animal and cell studies are particularly valuable when human studies are technically or ethically difficult to undertake.
Long Island study.
The Breast Cancer and the Environment on Long Island case–control study, the centerpiece of the LIBCSP, applied a typical hypothesis-testing framework to investigate whether an association exists between breast cancer risk and organochlorine compounds [dichlorodiphenyltrichloroethane (DDT)/dichlorodiphenyldichloroethylene (DDE), chlordane, dieldrin, and polychlorinated biphenyl], which are EDCs, and poly-cyclic aromatic hydrocarbons (PAHs), which are mammary carcinogens (Gammon et al. 2002a, 2002b). From the perspective of a pre-cautionary science model, the choice of exposures for study is mixed. The organochlorine compounds are banned in the United States, so findings are not directly actionable, but if the study had shown an effect, it would have strengthened the existing evidence from studies of pharmaceutical estrogens that exogenous hormones contribute to breast cancer, adding support for precautionary action regarding other EDCs. PAHs—the source of ubiquitous and avoidable exposure from grilled and smoked foods, tobacco smoke, and air pollution from vehicle exhaust and other fossil fuel burning—have clear action implications.
Aside from the choice of target compounds, case–control studies can serve public health decision making by generating an estimate of relative risk and its confidence interval. However, we consider this a high-risk strategy in both the Long Island and Cape Cod studies from a precautionary perspective, because of the considerable expense coupled with the likelihood of generating inconclusive negative findings, which are common in case–control studies of hard-to-assess exposures to pollutants in the general population. Several factors favored the potential in the LIBCSP to produce persuasive evidence that organochlorines increase breast cancer risk: the biologically plausible hypothesis that EDCs affect breast cancer, several earlier studies showing an association between breast cancer and serum organochlorines, a large sample size (providing good statistical power to detect an effect), rapid case ascertainment (so serum measures could not be affected by breast cancer treatment), extensive interviews about established and hypothesized breast cancer risk factors (to control for confounding and investigate effect modification), and individual-level biologic markers of exposure. On the other hand, results that failed to show an association could contribute little, because study design limitations mean we cannot conclude from null results that no association exists. For example, no one in the study can reasonably be considered unexposed, raising questions about whether there is adequate exposure variability to detect effects. In addition, the one-time exposure measures do not accord well with the evidence that timing in the life cycle is important in breast cancer etiology. Specifically, serum measures taken near the time of diagnosis may not represent early life exposures or even total lifetime exposure, because recent levels are influenced by variables related to mobilization and excretion, such as weight gain/loss and lactation, and by intake of breakdown products in food that have different toxicologic properties from the parent compound (e.g., DDE, which is ingested in meat and dairy, is less estrogenic than the parent compound DDT) (Brody and Rudel 2003; Snedeker 2001). Results did not show an association between recent serum measures and breast cancer (Gammon et al. 2002b).
The Long Island study reported 50% higher breast cancer risk among women with the highest levels of DNA damage from selected PAHs, statistically significant at the traditional p < 0.05 level, but with no linear dose response (Gammon et al. 2002a). It now falls to the public and policy makers to evaluate whether this result supports precautionary steps to reduce exposure, particularly in light of other evidence of health damage from PAHs and available alternatives to reduce exposure. This decision is hindered, however, because the biologic exposure measure does not reveal the exposure source where policies might be designed to intervene. The DNA adduct measure was poorly correlated with self-reported dietary and tobacco sources, leaving us to speculate that air pollutants may be an important source. It is also useful to consider the policy implications if air pollutants are an important source. The study’s effect size—50% higher breast cancer risk with high PAH DNA damage—is sometimes considered small in epidemiology but is nevertheless larger than the estimated 30% reduction in mortality associated with regular mammogram screening (Nystrom et al. 2002; Olsen and Gotzsche 2001). Epidemiologists have good reason to be cautious about a relatively modest risk increase observed in a single study with a poorly understood exposure measure. Given the potentially enormous public health implications, however, we believe a substantial investment in follow-up is appropriate.
Follow-up research currently under way is investigating possible interactions between exposure and genetic susceptibility. This approach is consistent with the precautionary principle call to study vulnerable populations, and it may yield additional information of value for prevention.
Cape Cod study.
In the Cape Cod study, the activists’ request for state funds for an unusual 3-year scoping and planning process helped define the research questions. During this phase, the study team formed a public advisory committee and a scientific advisory committee, established a field office on Cape Cod, and conducted focus groups that included physicians, nurses, women with breast cancer, and long-time residents. We reviewed scientific literature, analyzed existing Cape Cod environmental and epidemiologic data, conducted pilot environmental studies, and developed new methodologies suited to the nascent research questions.
This process provided an opportunity for the convergence of public and scientific priorities. Usually, study questions and protocols are defined by researchers (in investigator-initiated programs) or by funding agencies (in RFAs). Thus, the development of the research ideas, goals, and methods precedes formal funding of the study, making it more difficult for scientists and the community members to debate together the research agenda at this crucial design stage.
The Cape Cod study team reviewed nine issue areas—ranging from local food distribution systems to military facilities—as candidates for study and set priorities based on three criteria: scientific literature showing a plausible link to breast cancer; evidence of exposure on Cape Cod, particularly distinctive exposure; and community concern. Scientific evidence included laboratory studies of animal models and cellular mechanisms and epidemiologic studies. These criteria and types of evidence provide widely applicable guidelines for selecting research questions under the precautionary principle, because they emphasize assessing plausibility in situations in which proof is unlikely to be achievable. Including community concern as a decision-making criterion helps avoid studies that, although elegantly designed, do not answer relevant questions, a pitfall sometimes referred to as a type III error (Tickner 2003).
The scoping process also incorporated surveillance and ecologic epidemiology to refine the definition of the problem and generate hypotheses. This process illustrates how a precautionary approach can generate evidence that appropriately reduces public concern in some areas and focuses attention on more promising hypotheses. Using GIS technology, we integrated breast cancer and environmental data and searched for geographic and temporal patterns. We geocoded home addresses from the Massachusetts Cancer Registry of about 2,600 Cape Cod women diagnosed between 1982 and 1994 and used U.S. Census data and population growth models to estimate age-adjusted standardized incidence ratios annually by census block group (Silent Spring Institute 1997).
Results showed consistently higher incidence rates on Cape Cod than in the rest of the state; rates of “early” stage 1 diagnosis and mammography could not account for the higher incidence rates (Silent Spring Institute 1997, 2004). Mapping revealed that exposure of residences to electromagnetic fields (EMFs) from power lines was uncommon and regional high incidence was not localized around the military reservation or nuclear power plant. These population-level analyses confirmed suspicions that elevated breast cancer risk on Cape Cod was significant and long-standing; refocused public attention away from the military reservation, nuclear plant, and power line EMFs as the cause; and developed the GIS that would later be used for individual-level exposure assessments.
Phase 1 also included an innovative field study of EDCs in Cape Cod wastewater, groundwater, and drinking water to assess the plausibility of exposure from drinking water wells affected by septic systems. This aspect of the study had several characteristics designed to meet community precautionary goals. It was small in scope, with 12 groundwater and wastewater samples and 28 drinking water samples designed to assess plausibility rather than to establish representative results. It cast a broad net by testing for 29 target compounds; was integrative in that it used an in vitro bioassay of estrogen-sensitive cells—the E-Screen bioassay—to assess total estrogenicity (Soto et al. 1995); and used low detection limits, often below regulatory thresholds. The study contributed to a new field of inquiry by reporting the first measurements of estrogenic activity in groundwater, supplementing previous research on surface water (Silent Spring Institute 1997). And the study had local as well as national significance because land use and wastewater management policies to protect drinking water are under active discussion on Cape Cod. Results showed high levels of estrogenic alkylphenols in wastewater and groundwater and low levels in a small number of private wells, documenting an exposure pathway through drinking water (Rudel et al. 1998).
During phase 1, the study team updated community members and local officials through quarterly meetings of the public advisory committee, legislative briefings, and “poster sessions” where scientists and community members could interact informally to respond to community concerns. At the close of phase 1, the scientific team prepared technical and lay documentation and atlases of health and environmental data (Silent Spring Institute 2004). The drinking-water quality data page in the atlas has become the second most visited page in the Silent Spring Institute website, which hosts 400,000 visits per year. Based on the phase 1 assessment, the study team recommended further investigation of EDCs, particularly from wide-area pesticide use and wastewater-contaminated drinking water.
The second phase began in 1997 with a new competitive bidding process in which MDPH specified a cohort or case–control study (MDPH 1997), although the 3-year time frame argued against a cohort study. Silent Spring Institute won funding for a case–control epidemiologic study, which ultimately included 2,100 Cape Cod women and an environmental sampling study of 89 EDCs in air, dust, and women’s urine from 120 homes. Negotiation of the final study protocol revealed contrasting perspectives between the activist-scientist team and MDPH. For example, MDPH required that the proposed research questions be recast as statements of null hypotheses, a more yes-or-no approach than the study team thought best fit the state of the science. The state also declined to fund research in a comparison geographic area off Cape Cod—a decision, perhaps motivated mostly by cost concerns, that fundamentally precluded answering the public’s original question: Why is breast cancer incidence higher on Cape Cod? Other proposed elements that were not funded included soil sampling to validate the GIS-based pesticide exposure estimates (Brody et al. 2002) and additional testing of groundwater and drinking water to follow up on phase 1 findings of high concentrations of EDCs in groundwater, a research area with potentially far-reaching and expensive public health policy implications.
Nevertheless, the study retained many elements of a scientific approach focused on pre-cautionary strength-of-evidence goals. The study’s scientific publications have addressed seven core research questions, more than half of which focus on exposure assessment: a) What is the history of exposure to wastewater contaminants (particularly EDCs) in public and private drinking water (Swartz et al. 2003)? b) What is the history of exposure to pesticides from wide-area application (Brody et al. 2002)? c) What EDCs are women exposed to at home (Rudel et al. 2003)? d) How do EDCs from septic systems travel in groundwater, which supplies drinking water (Rudel et al. 1998)? e) After controlling for established risk factors, is living longer on Cape Cod associated with breast cancer risk (McKelvey et al. 2003)? f ) Is exposure to pesticides from wide-area application associated with breast cancer risk (Brody et al. 2004)? g) Is exposure to drinking water contaminants associated with breast cancer risk (Brody JG, Aschengrau A, McKelvey W, et al., unpublished observations)?
The exposure questions are key because they are critical in assessing plausibility (without exposure to a causal agent, there is no health effect), they are prerequisite to health studies, and they identify preventable exposures that could be reduced by precautionary policies, even in the absence of strong evidence of harm.
Ideally, a breast cancer study would estimate exposures years before diagnosis and at particular times in the life cycle. Retrospective self-reporting can offer this standard for exposures that women themselves can identify and are likely to report without bias, such as the year and their age at the births of their children, which reveals that pregnancy within 5 years of diagnosis and older age at the birth of a first child both increase breast cancer risk (Bernstein 2002). To approach this goal for environmental exposures that women cannot report, Silent Spring Institute developed GIS methods to map pesticide drift and drinking water contamination from historical records (Brody et al. 2002; Swartz et al. 2003) and incorporated these assessments with interview data (Brody et al. 2004). We also estimated the consequences of uncertainty in the exposure assessment by using sensitivity analyses. Missing environmental data and a lack of precision in address histories form the primary limitations in GIS exposure assessments, so future studies could be strengthened by the systematic geographic tracking of environmental data and the ascertainment of address histories at the time of reportable diagnoses, such as cancer (Hurley et al. 2003; Wakefield 2000).
Although the GIS exposure assessment is valuable for developing new methods for public health studies, its application in the Cape Cod study shares with the Long Island study the risk of generating findings that are difficult to interpret because of uncertainties in the exposure assessment. Indeed, the results have been ambiguous. We found no consistent association between pesticides and breast cancer and weak evidence of associations with certain types of pesticide use (Brody et al. 2004). After controlling for established breast cancer risk factors, however, we did find that living longer on Cape Cod is significantly associated with higher breast cancer risk (McKelvey et al. 2003). This “black box epidemiology” (Greenland et al. 2004) result provides convincing evidence that an additional regional risk factor remains to be discovered but offers no further guidance on where to look.
Parallel to the drinking water sampling in phase 1, phase 2 included monitoring of EDC exposures in homes with these goals: identify common exposures, including mixtures, for toxicologic and epidemiologic study and regulation; identify the products or practices that lead to common exposures; identify factors that contribute to high-end exposures; test methods to reduce contaminant levels by changing product use and other practices; and develop methods of exposure assessment for future health studies.
The household exposure study has not been linked to health outcomes in the epidemiologic study because of low statistical power for that purpose, and information on the health significance of these exposures is not available. This strategy of broadly studying exposure without an identified health outcome is atypical in public health studies—perhaps because health officials are uncomfortable dealing with the uncertain action implications of reporting on exposure without an established tie to health—but it has received strong scientific and public interest (e.g., Betts 2003; Cone 2003). This approach produced the first reported indoor concentrations for 30 pollutants and data directly relevant to public health debates, such as the use of polybrominated diphenyl ethers as flame retardants.
State funding for environmental sampling in the Cape Cod study resulted from advocacy by MBCC, and breast cancer activist organizations also have provided financial support for the work. Recently, the household exposure study has become a point of connection between breast cancer advocacy and other health-affected groups. For example, the study team is currently collaborating with Brown University researchers and Communities for a Better Environment (Oakland, CA), a community-based environmental justice organization, to apply the methods in a low-income, ethnic-minority fenceline community, where the immediate focus will be on whether exposure data can be useful in evaluating emissions limits, flare rules, and emergency procedures.
NIEHS Centers.
Still in a relatively early stage of development, the NIEHS Breast Cancer Centers and the Environment Research Centers were initiated with several important elements consistent with the precautionary principle. First, the RFA specified girls’ development through puberty as the health outcome, which represents a breakthrough in moving “upstream” in breast cancer research. Early age at puberty is a well-established risk factor for breast cancer, and age at puberty is falling, particularly among African-American girls, a group at greater risk than whites for breast cancer mortality, though not incidence (Bernstein et al. 2003; Krieger 2002). In addition, researchers hypothesize that rapid breast cell proliferation during adolescence may make this a critical exposure period. Thus, research questions about adolescence resonate with the precautionary principle because they address vulnerable populations, allow investigation of subtle and complex phenomena, and contribute to the understanding of the natural development process.
By including a laboratory research component as well, the centers elucidate biologic mechanisms, an important element in assessing plausibility, and develop tools for screening and testing chemicals for possible regulation. The laboratory component also facilitates research on a longer list of chemicals than the epidemiologic study. The RFA did not, however, specifically call for an investment in exposure assessment, although the lack of such methods is a significant barrier to studying EDCs (Rudel et al. 1998, 2001). The epidemiologic study will evaluate the EDCs bisphenol A, dioxin, and di(2-ethylhexyl) phthalate, as well as individual factors such as diet and body size. The study will bank biologic specimens, an increasingly common practice, so that researchers can “try again” as science advances, a strategy that may improve the payback on investments in large epidemiologic studies.
The centers’ steering committee, composed of scientist and advocate representatives, integrates epidemiologic and laboratory work, scientist and activist perspectives, and the interests of the different centers. This management approach represents innovation in both science and public involvement. The centers program recently held its first scientist–advocate conference (Russo 2004), at which both scientists and breast cancer activists were session chairs and presenters.
Conclusion
As the continuing increase in breast cancer incidence sparked activist demands for prevention-oriented research, laboratory evidence that many common pollutants are mammary carcinogens and/or EDCs provided new hypotheses about environmental factors. But the challenges in assessing relevant exposures to pollutants in a breast cancer study meant a mismatch between activist goals and the scientific methods typically used in investigator-initiated epidemiologic studies. By examining recent research—the Long Island and Cape Cod breast cancer and environment studies and the new NIEHS Centers—we can draw lessons for many public health problems for which scientific uncertainty is likely to persist.
Each of these studies contributes novel public involvement methods and increases transparency in public health science, providing new models for community-based participatory research. Activists used legislation and appropriations processes to direct scientific inquiry and, in Massachusetts, founded the Silent Spring Institute as a scientific team with activist participation in governance. The 3-year scoping process in the Cape Cod study provided an opportunity to review scientific plausibility of multiple hypotheses, allowing activist and scientist perspectives to converge.
Far from hindering science, the involvement of breast cancer activists has helped drive scientific innovation, particularly in the development and application of exposure assessment methods. Environmental and biologic sampling methods can identify common mixtures for further study and inform precautionary exposure reduction. GIS methods can assess historical exposures that women cannot report. The suggestive positive result for PAHs in the Long Island study provides the impetus for policies to reduce ubiquitous PAH exposure. At the same time, however, unresolved weaknesses in exposure assessment methodologies have hindered the success of epidemiologic components of the research programs, because they mean that negative results are insufficient to conclude that no relationship exists.
Breast cancer activists were among the first and most powerful health-affected groups to make environmental research and prevention a priority. The resulting studies provide paradigmatic models for public health science for diseases whose links to environmental factors are difficult to prove. They argue for greater emphasis on exposure studies before undertaking health studies and on laboratory research on questions that do not lend themselves to human research. Yet they leave unanswered questions about when to invest in traditional epidemiologic studies, when negative results are sufficient, and how to pursue ambiguous positive results in further research and policy.
Preparation of this article was supported by a grant from the V. Kann Rasmussen Foundation.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7814ehp0113-00100116079070ResearchBenchmark Calculations for Perchlorate from Three Human Cohorts Crump Kenny S. 1Gibbs John P. 21 Environ Health Sciences Institute, Ruston, Louisiana, USA2 Health Management Division, Kerr-McGee Shared Services LLC, Oklahoma City, Oklahoma, USAAddress correspondence to K.S. Crump, Environ Health Sciences Institute, 602 East Georgia Ave., Ruston, LA 71270 USA. Telephone: (318) 251-6985. Fax: (318) 255-2040. E-mail:
[email protected] 2005 20 4 2005 113 8 1001 1008 2 12 2004 20 4 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The presence of low concentrations of perchlorate in some drinking water sources has led to concern regarding potential effects on the thyroid. In a recently published report, the National Academy of Sciences indicated that the perchlorate dose required to cause hypothyroidism in adults would probably be > 0.40 mg/kg-day for months or longer. In this study, we calculated benchmark doses for perchlorate from thyroid-stimulating hormone (TSH) and free thyroxine (T4) serum indicators from two occupational cohorts with long-term exposure to perchlorate, and from a clinical study of volunteers exposed to perchlorate for 2 weeks. The benchmark dose for a particular serum indicator was defined as the dose predicted to cause an additional 5 or 10% of persons to have a serum measurement outside of the normal range. Using the data from the clinical study, we estimated the half-life of perchlorate in serum at 7.5 hr and the volume of distribution at 0.34 L/kg. Using these estimates and measurements of perchlorate in serum or urine, doses in the occupational cohorts were estimated and used in benchmark calculations. Because none of the three studies found a significant effect of perchlorate on TSH or free T4, all of the benchmark dose estimates were indistinguishable from infinity. The lower 95% statistical confidence limits on benchmark doses estimated from a combined analysis of the two occupational studies ranged from 0.21 to 0.56 mg/kg-day for free T4 index and from 0.36 to 0.92 mg/kg-day for TSH. Corresponding estimates from the short-term clinical study were within these ranges.
benchmark doseperchloratereference dosethyroidthyroid-stimulating hormonethyroxine
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Perchlorate is one of several monovalent anions that have been shown to competitively inhibit thyroidal iodide uptake (Wyngaarden et al. 1952). At sufficiently high doses, perchlorate can block iodine uptake and thus inhibit thyroid function. This characteristic of perchlorate has been used in the treatment of Graves disease, a condition characterized by hyperthyroidism (Crooks and Wayne 1960; Godley and Stanbury 1954). The specific iodide transporter protein at which perchlorate competitively inhibits iodine uptake has been characterized (Dai et al. 1996).
The discovery of the widespread presence of low concentrations of perchlorate in drinking water in the southwestern United States (Urbansky 2002) has led to concern regarding the possibility that environmental perchlorate may induce a relative iodine deficiency and thereby decrease thyroid hormone production. If this were to happen during pregnancy among women with lower iodine intake, adverse neurodevelopmental outcomes in the fetus could result.
In response to these concerns, the U.S. Environmental Protection Agency (EPA) has undertaken to determine a reference dose (RfD) for perchlorate in drinking water. In 2003, the agency asked the National Academy of Sciences (NAS) to review the science supporting potential toxicity of perchlorate. An NAS committee recently reported their findings regarding the health implications of perchlorate ingestion (NAS 2005). In their report, the committee stated:
“Inhibition of iodide uptake by the thyroid clearly is not an adverse effect.”
“To cause declines in thyroid hormone production that would have adverse health effects, iodide uptake would most likely have to be reduced by at least 75% for months or longer.”
“The committee does not agree that transient changes in serum thyroid hormone or TSH [thyroid-stimulating hormone] concentrations are adverse health effects; they are simply biochemical changes that might precede adverse effects.”
“The committee concludes that the first adverse effect in the continuum would be hypothyroidism. Any effects that follow and result from hypothyroidism clearly would be adverse.”
The committee also concluded that “on the basis of the studies of long-term treatment of hyperthyroidism in which patients continued to be given perchlorate after their hyperthyroidism resolved . . . the perchlorate dose required to cause hypothyroidism in adults would probably be more than 0.40 mg/kg per day,” thus establishing a no observed adverse effect level (NOAEL) (NAS 2005).
The NAS further recommended an RfD of 0.0007 mg/kg-day based on a no observed effect level (NOEL) for iodine uptake inhibition provided by Greer et al. (2002) and a 10-fold uncertainty factor. They stated that this “value is supported by other clinical studies, occupational and environmental epidemiologic studies, and studies of long-term perchlorate administration to patients with hyperthyroidism.”
The U.S. EPA formally adopted the RfD of 0.0007 mg/kg-day recommended by the NAS committee. The agency defines an RfD as “an estimate (with uncertainty spanning perhaps an order of magnitude) of a daily oral exposure to the human population (including sensitive subpopulations) that is likely to be without deleterious noncancer effects during a lifetime” (U.S. EPA 2002). Given a body weight and a daily drinking water consumption, an RfD can be converted to an equivalent concentration in drinking water.
Traditionally, an RfD has been calculated by dividing a NOAEL by various safety factors. More recently a benchmark calculation has been used instead of a NOAEL in the determination of an RfD. A benchmark dose (BMD) is a dose corresponding to a prescribed increase in an adverse response (Budtz-Jørgensen et al. 2001; Crump 1984, 1995, 2002a, 2002b; Gaylor and Slikker 1990; Kodell et al. 1995; West and Kodell 1993). A statistical lower bound on the BMD (BMDL) replaces a NOAEL in determining an RfD (U.S. EPA 2000a, 2000b). A BMDL has several potential advantages over a NOAEL, including better reflection of the power of a study to detect effects, better use of dose response information from a study, and less dependence on the spacing of doses in a study. A BMDL can be calculated from data in which adverse responses occur at every dose, such that no NOAEL can be defined, and also from negative data in which there is no clear evidence of a dose-related effect (Crump et al. 2000).
Although an RfD can be calculated from either animal or human data, it is generally agreed that use of human data is appropriate whenever suitable human data are available. The NAS found that in the case of perchlorate, the animal data were not conclusive and that the human data are more than sufficient for risk assessment. They based their RfD on the NOEL for inhibition of radioactive iodine uptake (RAIU) from Greer et al. (2002), who studied thyroid function in volunteers given perchlorate in drinking water for 14 consecutive days. Two other studies of thyroid effects in humans exposed occupationally to perchlorate have been conducted that contain data suitable for supporting a benchmark analysis. Lamm et al. (1999) studied thyroid function of long-term ammonium perchlorate workers at a facility in Utah. Braverman et al. (2005) conducted a similar study of long-term workers from the same Utah facility. The workers typically worked three 12-hr shifts followed by 3 days off. Each of these studies evaluated various measures of thyroid function and either assigned perchlorate doses or collected serum or urine samples that could be used to quantify perchlorate exposure.
Not all of the different thyroidal end points that were evaluated in these studies are suitable to develop a BMDL. The various thyroidal measurements made included RAIU, thyroid volume, thyroglobulin (Tg), tri-iodothyronine (T3), total thyroxine (T4), free T4 (FT4), free T4 index (FTI), and TSH. T3 and T4 are the active thyroid hormones, and they can be either bound to serum proteins or free. Increases in Tg and thyroid volume are generally considered adaptive responses. FTI and FT4 are comparable quantitative measurements of non-protein-bound T4 in the serum.
The adverse outcome of most concern is a delay in T4-dependent neuronal cell migration during fetal development that occurs near the late first trimester and early second trimester (Lavado-Autric et al. 2003). At this critical early stage of pregnancy, the fetal thyroid is not yet functional, and the only source of fetal thyroid hormone is T4 that crosses the placenta from the maternal circulation. With maternal hypothyroidism, maternal T4 decreases significantly, resulting in a corresponding decrease of fetal thyroid hormone levels and also triggering an increase in maternal TSH.
Recent epidemiologic studies in Europe and the United States have concluded that maternal hypothyroidism during pregnancy, even when mild and considered subclinical, and especially when occurring during early gestation, may be associated with an impairment of normal brain development and intelligence in offspring (Haddow et al. 1999; Pop et al. 1999). Maternal FT4 and TSH are the critical thyroidal parameters that endocrinologists are focused on as measurable thyroidal end points of concern, although there remains uncertainty over which of these is most important.
Thus, to cause an adverse neurodevelopmental effect in the fetus, perchlorate would have to be present at levels sufficient to cause hypothyroidism and thus cause a measurable decrease in maternal T4 or increase in maternal TSH. In the present report, we calculated BMD using FT4 and TSH measurements in conjunction with calculated or measured perchlorate doses from these three studies. These calculations are compared with the 0.4 mg/kg-day NOAEL estimate from the NAS.
Materials and Methods
Human Studies
Study of ingestion of perchlorate by volunteers.
Greer et al. (2002) gave perchlorate in drinking water to 37 male and female volunteers for 14 consecutive days. This study is likely too short in duration to find significant changes in FT4 and TSH, based on the NAS (NAS 2005) statement that “iodide uptake would most likely have to be reduced by at least 75% for months or longer.” Nevertheless, it may be useful to calculate a BMDL for short-term exposure.
In the main study, four subjects of each sex received a perchlorate dose of 0.02, 0.1, or 0.5 mg/kg-day. Subsequently, six women and one man received a dose of 0.007 mg/kg-day and one additional subject of each sex received a dose of 0.02, 0.1, or 0.5 mg/kg-day (the uptake study). All participants were instructed to drink one-fourth of a mixture containing a body-weight-adjusted daily dose at 0800, 1200, 1600, and 2000 hr on each scheduled perchlorate ingestion day and to record the time and volume of each ingestion.
Greer et al. (2002) collected blood samples during the main study at the screening visit held 6 days before the beginning of exposure, on the day before the beginning of exposure (0800 hr), exposure day 1 (1200 and 1600 hr), exposure day 2 (0800, 1200, and 1700 hr), exposure day 3 (0800 hr), exposure day 4 (0800 and 1200 hr), exposure day 8 (0800 hr), exposure day 14 (0800, 1200, and 1700 hr), postexposure day 1 (0900, 1200, and 1700 hr), postexposure day 2 (0800 and 1700 hr), post-exposure day 3 (0800 and 1700 hr), postexposure day 4 (0800 hr), and postexposure day 14 (0800 hr). Serum perchlorate was measured in most of these 23 sampling times, and critical thyroid function measurements (FT4 and TSH) were obtained at 16 of these times.
In the uptake study, serum perchlorate was not measured, and thyroid function measurements were made only from blood samples collected at the screening visit and on exposure day 14 (0800 hr). The precise time of each sample collection was recorded in both studies. Also, RAIU measurements and urine samples were collected at various times in both studies.
Greer et al. (2002) found iodine uptake to be inhibited at the three highest doses but found no association between dose and indicators of thyroid function, other than a slight reduction in TSH levels in morning blood draws during perchlorate exposure within the highest exposure group (0.5 mg/kg-day), with recovery during 15 days of postexposure. This downward trend is opposite the effect expected if it were due to perchlorate, although TSH can be decreased in regions with iodine deficiency.
Study of ammonium perchlorate workers: I.
Lamm et al. (1999) conducted a cross-sectional study of workers engaged in the production of either ammonium perchlorate (n = 37) or sodium azide (control workers, n = 21) in the same industrial complex. The workers worked 12-hr shifts on 3 consecutive days followed by 3 consecutive days off. They rotated shifts from days to nights monthly. Forty percent of the perchlorate production workers and 50% of the control workers had been employed for > 5 years.
Lamm et al. (1999) measured urinary perchlorate in each worker at the beginning and at the end of one shift. Postshift blood samples were obtained from all but one worker. The critical thyroid parameters measured were serum FTI and TSH. A perchlorate elimination half-life of 8 hr was estimated by monitoring urinary perchlorate in two of the more highly exposed workers for 3 subsequent days in which there was no known perchlorate exposure. The absorbed perchlorate dose (milligrams per shift) of each worker was then estimated by applying a first-order elimination model to the pre- and postshift measurements of perchlorate per gram of creatinine, assuming a creatinine excretion rate of 1 mg/min and a perchlorate elimination half-life of 8 hr.
Workers were grouped into four exposure categories with mean absorbed perchlorate dosages of 1, 4, 11, and 34 mg of perchlorate per day. No differences in thyroid function parameters were found between these groups.
Study of ammonium perchlorate workers: II.
Braverman et al. (2005) conducted a cross-sectional study of 29 workers employed in the same ammonium perchlorate production facility that was studied earlier by Lamm et al. (1999). Twelve community controls were also studied. The minimum duration of employment in ammonium perchlorate manufacture among the workers was 1.7 years, and the median was 5.9 years. This study was conducted approximately 6 years after the earlier study, and several workers were included in both study cohorts.
The workers worked the same shift pattern (12 hr on, 12 hr off for 3 consecutive days, followed by 3 days off) as in the Lamm et al. (1999) study. The study period for each worker encompassed work shifts on 3 consecutive nights, Tuesday through Thursday. Among the employees with the highest perchlorate exposure, RAIU was decreased 75% or more after 3 consecutive 12-hr shifts.
Urine and blood samples were collected from workers on Tuesday morning before their first night shift and at the beginning and end of the Thursday night shift. Community controls had their blood and urine sampled once, on Tuesday morning. Tuesday and Friday morning serum samples were analyzed for TSH, FTI, and perchlorate. Thursday evening serum samples were analyzed for perchlorate only. Creatinine and perchlorate were measured in all urine samples.
Serum FTI was slightly but significantly increased in the workers on Friday morning compared with Tuesday morning, and TSH was lower on Tuesday morning in perchlorate workers than in community controls. Both of these responses are opposite what would be expected from perchlorate exposure. Otherwise, there were no statistically significant differences in FTI or TSH in perchlorate workers between results on Tuesday and Friday mornings, or between perchlorate workers and community controls on Tuesday morning.
Methods
Determination of serum half-life and volume of distribution.
To estimate perchlorate exposures in the Braverman et al. (2005) occupational study, we need estimates of the half-life, t1/2, and volume of distribution, Vd, of per-chlorate. These parameters were estimated using the Greer et al. (2002) study of perchlorate uptake in volunteers.
The elimination of perchlorate from serum was assumed to be a first-order process with time constant β = ln(2)/t1/2. The movement of perchlorate from the gut into the systemic circulation was described by active transport with time constant α. If at time t0 an amount D (milligram per kilogram body weight) of perchlorate is ingested, the resulting serum concentration of perchlorate at time t, C(t) (milligram per liter), satisfies the differential equation
Solving, the contribution to serum by perchlorate ingested at time t0 is
By summing the contributions from each ingestion time, t0, the total predicted serum concentration at each serum sample time, t, in the Greer et al. (2002) study was calculated.
To estimate α, β, and Vd, the model defined by Equation 2 was fit to the Greer et al. (2002) serum perchlorate data assuming that the serum measurements (either the serum perchlorate concentrations or their log-transforms) can be modeled as Ei,j + τi + ɛij, where Ei,j is the measurement predicted by the above model (or its log-transform) for the ith subject at the jth measurement time, and τi and ɛij (where i and j index subject and measurement time, respectively) are independent and normally distributed with zero mean, τi having standard deviation, στ, that represents interindividual variability, and ɛij having standard deviation, σɛ, that represents measurement error and temporal variability. It follows that the variance of a serum measurement is στ2 + σɛ2, and the correlation between two measurements is στ2/(στ2 + σɛ2) if the measurements are from the same individual, and zero otherwise.
Estimation of absorbed doses in Braverman et al.
We used the values for serum half-life, t1/2, and volume of distribution, Vd, estimated from the Greer et al. (2002) study to estimate the perchlorate exposure for each worker in the Braverman et al. (2005) occupational study. We obtained two sets of estimates, one based on serum concentrations and one based on urine concentrations. Retaining the assumption that the elimination of perchlorate from serum is a first-order process, and assuming that inhaled perchlorate taken up by the lungs immediately enters the systemic circulation, the concentration (mg/L) in serum at the end of a 12-hr shift is
where C0 is the serum concentration (mg/L) at the beginning of the shift, r is the uptake rate of perchlorate (milligrams per hour, assumed constant throughout a shift for a given worker), and w is body weight (kilograms). Summing terms of this form for each of the first two shifts yielded an expression for the predicted concentration at the beginning of the third shift that was set equal to a worker’s measured serum concentration and solved for the uptake rate, r, for the first two shifts. Using this value of r, the concentration predicted at the beginning of the third shift was used as C0 in Equation 3, and the resulting predicted concentration at the end of the third shift was set equal to the measured serum level to determine a worker’s uptake rate, r, associated with the third shift.
We applied a conceptually similar approach to the perchlorate urinary concentrations (milligrams per gram creatinine) measured at the beginning and end of the third shift to estimate the uptake rate for the third shift based on the urine data. This analysis assumed the serum half-life, t1/2, estimated from the Greer et al. (2002) study and a constant creatinine elimination rate of 1 mg/min for all workers. This approach requires knowledge of the time since the most recent bladder void, which was not recorded. Different values of this time ranging from zero to 12 hr were considered.
Data used for benchmark calculations.
BMD calculations for the Lamm et al. (1999) study used postshift serum TSH and FTI measurements from both control and perchlorate workers. Calculations for the Braverman et al. (2005) study used TSH and FTI measurements collected from perchlorate workers and community controls on Tuesday morning, just before the Tuesday–Thursday night shifts. In both cases, the dose used in the calculation was a worker’s estimated average daily dose (milligrams), calculated as one-half of his estimated shift dose (reflecting the 3-day-on/3-day-off pattern of work).
BMD calculations for the Greer et al. (2002) study used TSH and FT4 data obtained from serum collected at the screening visit, on the day before the beginning of exposure, and on the last day of exposure (exposure day 14). To reflect to the fullest any effect of perchlorate exposure, measurements from serum collected after exposure began but before the last day of exposure were not used. The perchlorate dose (milligrams) assumed for serum collected on exposure day 14 was the assigned daily dose (0.007, 0.02, 0.1, or 0.5 mg/kg), multiplied by a subject’s body weight.
Statistical analysis.
In modeling the FTI and TSH data from Lamm et al. and Braverman et al. cohorts, it was assumed that a thyroid measurement, or its log-transform, was normally distributed with mean
and constant SD, σ, where dose is the daily perchlorate dose in milligrams.
When applied to the Greer et al. (2002) FT4 and TSH data, we expanded the model defined by Equation 4 in two ways. First, an error structure was assumed like that applied to the Greer et al. (2002) serum perchlorate data (described above) that accounted for repeated measurements made in the same individual. Second, to account for potential daily variation in FT4 and TSH, the parameter α was allowed to assume different values depending on the time of day a sample was collected (before 1000 hr, between 1000 hr and 1400 hr, and after 1400 hr).
Unless otherwise specified, we estimated model parameters using maximum likelihood, hypothesis tests were likelihood ratio tests, and we computed 90% confidence intervals (90% CIs) by the profile likelihood method (Cox and Hinkley 1974; Crump 2002b; Venzon and Moolgavkar 1988). Residuals from model fits to the untransformed serum measurements were tested by the Shapiro-Wilk test for conformity with a normal distribution (Shapiro and Wilk 1965). If normality was rejected (p < 0.05), the analysis was repeated using the log-transform of the serum measurements. If the data were still non-normal, any outliers present were omitted (an outlier defined as a measurement whose residual was more than three times the SE) and the data retested for normality. The reported analysis was the one in which the residuals conformed most closely to normality. Because the potential adverse effect of perchlorate is an increase in TSH or a decrease in T4, the maximum likelihood estimate of β was not allowed to be negative when calculating BMDLs for TSH and not allowed to be positive when calculating BMDLs for FTI or FT4. To avoid biologically implausible dose–response curve shapes, the shape parameter, K, was not allowed to be < 1.0 (Crump 2002a) and, for computational reasons, was not allowed to exceed 10. Because there was no significant effect of dose on any thyroid parameter, K was allowed to differ from 1.0 only in BMD calculations. All calculations were performed in Excel (version 2002 SP3; Microsoft Corporation, Redmond, WA). Excel Visual Basic macros were used to calculate likelihoods, and parameters were estimated by maximizing the likelihood using the Excel optimizer routine.
Benchmark analysis.
A BMD is a dose corresponding to a specified change in response. For binary data coded as presence or absence of disease, the response is usually defined as the probability of disease. For continuous data, such as serum thyroid measurements, there is less agreement regarding how the BMD should be defined.
In the present analysis, the BMD is defined as the change in the mean serum measurement equal to a factor, Q, times the SD of the measurements. It follows from this definition and Equation 4 that BMD = (Q × σ/β)1/K. BMD calculations are presented using three different values for Q: 1.0, 0.82, and 0.52. In its benchmark technical guidance document, the U.S. EPA recommends that this method with Q = 1 (termed the “SD approach”) always be among the methods applied (U.S. EPA 2000a). The other two values, Q = 0.82 and Q = 0.52, are derived from the equivalent “hybrid” approach for defining a BMD, as explained below.
In the “hybrid” approach for defining a BMD from a continuous response, the proportion P(0) of unexposed individuals whose response is considered adverse is first specified, and the BMD is defined as the dose that increases the probability of an adverse response by a specified amount termed the benchmark risk (BMR) (Budtz-Jørgensen et al. 2001; Crump 1995; Crump et al. 2000; Gaylor and Slikker 1990; Kodell et al. 1995; West and Kodell 1993). This approach is conceptually similar to that generally applied to binary responses, and consequently its use provides comparability between BMR calculated from continuous and binary data. It can be shown that the hybrid approach, when implemented using Equation 4, is equivalent to the method described above in terms of Q, with
where N–1 is the inverse of the standard normal distribution (Budtz-Jørgensen et al. 2001; Crump 2002a).
The convention in evaluating serum thyroid clinical measurements is that the normal range includes the measurements of 95% of a normal (unexposed) population, and that abnormal values include 2.5% of the lowest measurements and 2.5% of the highest measurements. Because an adverse effect of perchlorate would be expected to result in decreased FTI or FT4 and increased TSH, P(0) was set to 0.025. Two values of BMR were used with the hybrid method: 0.05 and 0.1. BMR = 0.1 has often been used by the U.S. EPA when calculating a BMD from binary data, and BMR = 0.05 represents a more conservative choice. From Equation 5, when combined with the choice P(0) = 0.025, BMR = 0.1 is equivalent to Q = 0.82 and BMR = 0.05 is equivalent to Q = 0.52.
Ideally, the BMD calculation should reflect interindividual variation but not measurement error. In most data sets, it is not possible to separate contributions to variation from these two sources. However, in the Greer et al. (2002) study it is possible to separately estimate interindividual variation because repeated measurements were collected from each individual. Consequently, in benchmark calculations based on the Greer et al. data, the standard deviation for interindividual variability, στ, was used in place of the total standard deviation, σ = (στ2 + σɛ2)1/2.
Each of the data sets from which BMD calculations were made was negative; that is, there was no statistically significant evidence of a relationship between perchlorate dose and thyroid function. Even if there is apparently no effect of dose, a BMDL calculated from such negative data still represents a valid lower bound on the dose corresponding to any (undetected) effect of perchlorate that may have been present. There are a number of examples in the literature where a BMD analysis has been applied to data in which no adverse effects were detected (Clewell et al. 2000, 2003; Crump et al. 2000; NAS 2000).
Because the Lamm et al. (1999) and Braverman et al. (2005) studies were conducted in the same facility and made comparable measurements of thyroid function and perchlorate dose, we conducted a combined benchmark analysis of these two data sets. In this analysis the basic model (Equation 4) was expanded to account for potential differences between the two studies in laboratory techniques for measuring FTI and TSH, and for potential drift in normal thyroid function values. In the expanded model, the responses in one study were assumed to differ from the other, on average, by a fixed factor. This was accomplished by retaining Equation 4 to model the Lamm et al. data, but in modeling the Braverman et al. data multiplying both the mean (Equation 4) and the standard deviation, σ, by the same free parameter. The BMD as defined herein is functionally independent of this parameter.
The BMDL was calculated as a 95% statistical lower bound on the BMD in all instances.
Results
Serum half-life and volume of distribution.
In fitting the perchlorate uptake and elimination model (Equation 1) to the Greer et al. (2002) serum perchlorate data, we included only data from subjects in the main study exposed to the two highest doses (0.5 and 0.1 mg/kg/day). Serum perchlorate was not measured in the uptake study, and perchlorate was below the level of detection in most (58 of 62) of the serum measurements in subjects exposed to 0.02 mg/kg-day. Ninety-one additional samples in which no perchlorate was detected were also omitted. All but three of these were collected after dosing had ceased. One of these three appears to be an error, and the remaining two were collected from a single individual in the 0.1 mg/kg-day exposure group during the first day of exposure. After these exclusions, there remained 238 serum perchlorate measurements from the Greer et al. study available for analysis.
The log-transformed serum concentrations were described much better by a normal distribution than by the untransformed concentrations. After elimination of four outliers, the residuals from fitting the log-transform of the model (Equation 1) to the log-transforms of the concentrations were marginally well described by a normal distribution (p = 0.04). Figure 1 shows, as an example, the serum concentration data for a subject in the 0.5 mg/kg-day dose group compared with the concentration profile predicted by the best-fitting model. The concentration spikes resulting from each of the four ingested doses on each of the 14 dosing days are visible in this graph. The peak serum concentration is predicted to have occurred at about 2200 hr each evening, a couple of hours after the last scheduled ingested dose for the day, and several hours after any scheduled serum measurement.
Based on this analysis, the half-life of perchlorate in serum was estimated as t1/2 = 7.5 hr (90% CI, 7.2–7.8) and the volume of distribution as Vd = 0.34 L/kg (90% CI, 0.31–0.39). The estimated between-subject and within-subject SD were στ = 0.24 and σɛ = 0.25. Estimates of t1/2 and Vd changed only slightly (by at most 8%) when outliers were not removed or when a normal distribution was assumed.
Using the best estimates from Greer et al. (2002) for the elimination half-life (t1/2 = 7.5 hr) and volume of distribution (Vd = 0.34 L/kg) for perchlorate, and Equation 3, we estimated the average per-shift dose (milligrams per shift) of each worker in the Braverman et al. (2005) study from the serum perchlorate measurements. Separate estimates were obtained for the first two shifts combined and for the third shift, and a weighted average of these two estimates was used as the average dose per shift for a worker.
Figure 2 compares the estimated per-shift dose for the first two shifts with the estimated dose for the third shift. Although there is a wide range in individual doses, from 0.41 mg/shift to 392 mg/shift, the difference in individual shift doses between the first two shifts and the third shift are relatively small in most cases, which supports the assertion that workers tended to work in similar jobs on different shifts.
Figure 3 compares estimates of dose during the third shift computed using urine data with those computed using serum data. The estimates based on the urine data assumed that the data are from a 4-hr void. By comparison, if a theoretical “instantaneous” void was assumed, these estimates were about 10% smaller, and if a 12-hr void was assumed, they were about 50% larger. Because the dose estimates based on the urine data were only for the third shift, and because of the uncertainty in both the void time for the urine samples and the rate of creatinine elimination, the average dose per shift over the three shifts obtained from the serum data were used in the benchmark analysis.
Benchmark results.
In the Lamm et al. (1999) study, the residuals of the FTI were adequately described by a normal distribution (p = 0.38), and after eliminating one TSH measurement that was four times larger than that of any other worker, the residuals from the log-transformed TSH measurements were also normal (p = 0.12). In Braverman et al. (2005), both the residuals of the FTI measurements and the log-transformed TSH measurements were adequately described by a normal distribution (p = 0.54 and 0.16, respectively). Consequently, BMD analyses of FTI from these studies used the untransformed measurements, whereas analyses of TSH used the log-transformed measurements.
The model fit to the combined Lamm et al. (1999) and Braverman et al. (2005) data sets contained four estimated parameters, compared with the total of six that were estimated from the separate fits to the two data sets. By adding the log-likelihoods from the individual fits, we determined that the combined individual fits were not significantly better than the fit of the single model to the combined data (p = 0.52 and 0.98, 2 df, for FTI and TSH, respectively). The multiplicative factors needed to adjust the Braverman et al. results to conform to the Lamm et al. results were 0.38 for FTI values and 0.85 for the logarithms of TSH.
The estimated average daily exposure of one worker from the Braverman et al. cohort was 84 mg/day, which was more than twice that of any other worker. Although this worker’s FTI and TSH values were not exceptional, the BMDLs were highly dependent on this single data point. Consequently, BMDLs were calculated both with this influential point included and with it omitted.
Figure 4 shows plots of the FTI measurements from Lamm et al. along with the adjusted FTI measurements from Braverman et al. Figure 5 shows comparable plots for the logarithm of TSH. These figures also show the maximum likelihood model fits that define the BMD (termed “BMD curve” in the figures) and the model curves that define the BMDL for the case Q = 1, both using all data and with the influential point omitted. The calculation of the BMDLs for Q = 1 are illustrated graphically.
Table 1 contains the BMD and BMDL estimates obtained from the combined data from the two occupational studies using all three values of Q, both including and omitting the influential data point. Also shown are the values of the shape parameter, K, corresponding to each BMDL calculation. The BMD estimates for TSH were infinite, because in both cases the maximum likelihood estimate of the parameter β was zero. Nevertheless, the corresponding BMDLs were all finite. The six calculations of BMDL based on all the data ranged from 24 to 83 mg/day or, by dividing by the average body weight in the study of 90 kg, from 0.27 to 0.92 mg/kg-day, and those with the influential point eliminated ranged from 16 to 39 mg/day, or from 0.18 to 0.43 mg/kg-day.
Turning now to the Greer et al. (2002) study, after elimination of two outliers, residuals from fitting the benchmark model to the remaining 134 FT4 measurements from this study were adequately described by a normal distribution (p = 0.11). Likewise, after elimination of two outliers, residuals from fitting the model to the log-transforms of the remaining 137 TSH measurements were normally distributed (p = 0.49). Time of day was a significant predictor of TSH (p = 0.0002), but not of FT4 (p = 0.24), with TSH measurements made before 1000 hr being larger than those made later in the day, but with no difference between midday and afternoon measurements. Consequently BMD analyses for FT4 did not allow for differences in time of day, whereas those for TSH permitted morning measurements to be different from those made later in the day. TSH was significantly decreased in serum collected at the conclusion of the 14-day exposure period (p = 0.002), whereas the expected effect of perchlorate would be to increase TSH.
Table 2 summarizes the benchmark calculations made from the Greer et al. (2002) short-term study. The BMD analyses for TSH assumed that an increase in TSH was adverse, despite the fact that a significant decrease was observed. The BMDLs from this study are all in a relatively narrow range, between 49 and 60 mg/day or, after dividing by the average body weight in the study of 76.4 kg, between 0.64 and 0.79 mg/kg-day, which is within the range of the BMDLs obtained from the occupational studies (Table 1).
Discussion
The Lamm et al. (1999) and Braverman et al. (2005) studies were conducted 6 years apart in the same plant on overlapping cohorts. The same types of serum measurements were collected in the two studies. A single model that accounted for potential differences in serum thyroid measurement techniques, and for potential drift in normal thyroid function values, fit the combined data statistically as well as the separate models fit the individual data sets. The BMDLs from this combined analysis ranged from 0.18 to 0.56 mg/kg-day for FTI and from 0.36 to 0.92 mg/kg-day for TSH. The BMDLs from the Greer et al. (2002) study were within the range of those from the two occupational studies.
None of the three studies found evidence of an effect of perchlorate on thyroid function. Nevertheless, BMDLs calculated from such negative results represent valid statistical lower bounds on the dose that accounts for a potential, but unobserved, effect of perchlorate. However, BMDLs such as these based on negative data could possibly be highly conservative (Clewell et al. 2000; Crump et al. 2000).
Forty percent of the perchlorate workers in the Lamm et al. cohort had worked at the facility for > 5 years, and 50% of the workers in the Braverman et al. cohort had worked for at least 5.9 years. Based on jobs assigned to workers during the previous year and estimated perchlorate exposures in each job, Braverman et al. (2005) estimated a median shift dose over the previous year of 0.33 mg/kg. Therefore, the BMDLs obtained from the combined analysis of the two occupational studies pertain to exposures extending to 5 years or more in duration. Overall, the BMDLs calculated from the occupational studies are in excellent agreement with the NOAEL of 0.4 mg/kg-day obtained by the NAS based on clinical studies (NAS 2005).
In addition to BMDLs from the combined occupational cohorts, BMDLs were also computed from the two studies individually (results not shown). As expected, BMDLs from the combined data were larger than the smallest BMDLs from the individual data sets and tended to be either intermediate between those calculated from the individual data sets, or somewhat larger than the larger of the two BMDLs obtained from the individual data sets. Given that the two occupational studies had similar designs, that they were conducted in the same facility, and that the data were compatible with a single model, we believe the combined analysis makes the most appropriate use of the data from these studies.
The BMD calculations for the Braverman et al. (2005) cohort were based on serum obtained from perchlorate workers on Tuesday morning when they had been without perchlorate exposure for > 2 days. It could be hypothesized that this lack of recent exposure had allowed recovery time that obscured some perchlorate effect. To evaluate this possibility, BMDLs for Q = 1 based on the complete data set were recalculated after replacing the workers’ Tuesday morning serum values with their Friday morning values. In both cases, the estimated effect was opposite that expected from exposure to perchlorate, and the two BMDLs were within 4% (FTI) and 50% larger (TSH) than the corresponding BMDLs based on the Tuesday morning data (Table 1). Thus, there was no indication in the data that lack of very recent exposure had allowed time for recovery.
The perchlorate doses used for the Lamm et al. study (1999) were those estimated in the original study, which used a perchlorate half-life of 8 hr compared with the 7.5 hr used for the Braverman et al. study, based on the Greer et al. study. This difference is expected to make no more than a 7% difference in the computed doses. Also, the formula used by Lamm et al. to calculate the shift doses effectively assumed an instantaneous void. More realistic assumptions would result in slightly larger estimated doses and corresponding larger BMD estimates. For example, if a 4-hr void is assumed, it is estimated that the doses would increase by approximately 10%.
Merrill et al. (2003, 2004) used data from the Greer et al. (2002) study in developing a physiologically based pharmacokinetic model for the kinetics and distribution of both iodine and perchlorate, and for the inhibition of thyroid uptake of radiolabeled iodide by perchlorate. Although the two figures are not entirely comparable, the time course of serum perchlorate in the Greer et al. study as predicted by the Merrill et al. model (Merrill et al. 2004; their Figure 6) appears to be very similar to that shown in Figure 1.
There have been three other BMD analyses for perchlorate based on human data, all of which used the Greer et al. (2002) study of perchlorate consumption by volunteers. Two of these analyses, performed by the California Environmental Protection Agency’s Office of Environmental Health Hazard Assessment (CAL/OEHHA 2004) and by the U.S. EPA (2003), modeled the ratio of the RAIU after perchlorate exposure to the baseline RAIU value, and defined the BMD as the dose corresponding to a 5% change in this ratio. The OEHHA calculated a BMDL of 0.0037 mg/kg-day, which in a 70-kg individual is equivalent to 0.26 mg/day. The U.S. EPA calculated a large number of BMDLs but emphasized some that correspond to a range of about 0.09–0.7 mg/day. These values are much smaller than the BMDLs obtained in the present analysis either from the Greer et al. study (Table 2) or from the occupational studies (Table 1). The main reason for this difference is that an RfD has traditionally been based on an adverse response, and the present analysis did not consider reduction in uptake of labeled iodine, without accompanying changes in critical thyroid parameters, to be adverse.
A third BMD analysis based on the Greer et al. study (2002) was conducted for the National Aeronautics and Space Administration by ICF Consulting (2004). BMDLs were calculated from data on FT4, T4, and T3. The thyroidal measurements for an individual at each of three measurement times (morning, midday, and afternoon) on day 14 minus the average of his or her measurements on two preexposure days and one postexposure day was modeled. For each of these nine cases, BMDLs were calculated corresponding to 5, 10, or 20% change in response. This resulted in 27 BMDLs that, after multiplying the average body weight in this study of 76.4 kg, range from 7 to 89 mg/day. The nine BMDLs calculated for FT4 correspond to a range of 8–76 mg/day, which contains the range of BMDLs for FT4 obtained in the present analysis both from the Greer et al. study (Table 2) and from the occupational studies (Table 1). The differences between the ICF results and those from the present analysis for FT4 are likely due largely to the decisions by ICF to segment the data by time of day (vs. our approach of using all the data and controlling for time of day) and to exclude the data from the uptake study. As a result, the BMDLs obtained by ICF (2004) were based on at most 22 serum values collected during exposure, whereas BMDLs for FT4 obtained in the present analysis (Table 2) were based on 75 such values. The ICF analysis also employed a different definition of the BMD than was used in the present analysis and employed a linear model as opposed to the nonlinear model used in the present analysis.
A more flexible dose–response model (e.g., a nonlinear model that contains a linear model as a particular case) will always result in a BMDL at least as small as that resulting from a less flexible model (e.g., a linear one). Consequently, the nonlinear model employed herein, with shape parameter K = 1, will never result in a larger BMDL than will use of a linear model (K = 1). Allowing the dose response to be highly nonlinear can also prevent the BMDL from appreciably exceeding the doses in a negative study. For example, the BMDLs calculated herein from the Greer et al. study range from 49 to 60 mg/day, whereas the largest exposure in this study was 50 mg/day. Notice also that, for the occupational studies, the values of the shape parameter, K, estimated in conjunction with the BMDL (Table 1) increase with increasing BMDL or, equivalently, as the BMDLs get closer to the highest dose in the study.
This work was funded by the Perchlorate Study Group.
In addition to receiving support from the Perchlorate Study Group for the research reported herein, K.C. has received support from the Perchlorate Study Group and from Kerr McGee for other research regarding perchlorate. J.G. is the medical director for Kerr McGee, which previously manufactured perchlorate.
Figure 1 Serum perchlorate measurements for a subject from Greer et al. (2002) dosed at 0.5 mg/kg/day, versus expected concentrations.
Figure 2 Comparison of shift doses in Braverman et al. (2005): average dose in first two shifts compared with third-shift dose.
Figure 3 Comparison of shift doses in Braverman et al. (2005): third-shift doses estimated from urine or serum data.
Figure 4 FTI data from Lamm et al. (1999) and scaled FTI data from Braverman et al. (2005) versus estimated daily perchlorate dose, with graphical indication of the BMDL calculation for Q = 1 (BMD defined as change in mean response equal to SD).
Figure 5 Log-transformed TSH data from Lamm et al. (1999) and scaled FTI data from Braverman et al. (2005) versus estimated daily perchlorate dose, with graphical indication of the BMDL calculation for Q = 1 (BMD defined as change in mean response equal to SD).
Table 1 BMD estimates (mg/day) and corresponding BMDLs for perchlorate obtained from two occupational studies (Braverman et al. 2005; Lamm et al. 1999).
Thyroid indicator P (0) BMR Q BMD BMDL BMDL Ka
All data
FTI 0.025 0.05 0.52 67 24 1.0
0.025 0.1 0.81 98 38 1.0
0.025 1 118 50 1.2
TSH 0.025 0.05 0.52 INF 57 1.3
0.025 0.1 0.81 INF 76 2.1
1 INF 83 6.0
Eliminating influential point
FTI 0.025 0.05 0.52 34 19 1.1
0.025 0.1 0.81 42 27 1.5
1 47 30 1.8
TSH 0.025 0.05 0.52 INF 32 1.9
0.025 0.1 0.81 INF 38 4.5
1 INF 39 6.9
Abbreviations: INF, infinite; Q, factor.
a Value of shape parameter, K, estimated in BMDL calculation.
Table 2 BMD estimates (mg/day) and corresponding BMDLs for perchlorate obtained from the Greer et al. (2002) study of perchlorate ingestion by volunteers.
Thyroid indicator P (0) BMR Q BMD BMDL BMDL Ka
FT4 0.025 0.05 0.52 INF 49 10
0.025 0.1 0.81 INF 52 10
1 INF 53 10
TSH 0.025 0.05 0.52 INF 56 10
0.025 0.1 0.81 INF 59 10
1 INF 60 10
Q, factor.
a Value of shape parameter, K, estimated in BMDL calculation.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7712ehp0113-00101516079072ResearchComparison of Blood and Brain Mercury Levels in Infant Monkeys Exposed to Methylmercury or Vaccines Containing Thimerosal Burbacher Thomas M. 123Shen Danny D. 4Liberato Noelle 123Grant Kimberly S. 123Cernichiari Elsa 5Clarkson Thomas 51 Department of Environmental and Occupational Health Sciences, School of Public Health and Community Medicine,2 Washington National Primate Research Center,3 Center on Human Development and Disability, and4 Departments of Pharmacy and Pharmaceutics, School of Pharmacy, University of Washington, Seattle, Washington, USA5 Department of Environmental Medicine, University of Rochester School of Medicine, Rochester, New York, USAAddress correspondence to T.M. Burbacher, Department of Environmental and Occupational Health Sciences, 1705 NE Pacific St., Health Sciences Building (F555), School of Public Health and Community Medicine, University of Washington, Seattle, WA 98195 USA. Telephone: (206) 685-1862. Fax: (206) 685-4696. E-mail:
[email protected] authors declare they have no competing financial interests.
8 2005 21 4 2005 113 8 1015 1021 2 11 2004 20 4 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Thimerosal is a preservative that has been used in manufacturing vaccines since the 1930s. Reports have indicated that infants can receive ethylmercury (in the form of thimerosal) at or above the U.S. Environmental Protection Agency guidelines for methylmercury exposure, depending on the exact vaccinations, schedule, and size of the infant. In this study we compared the systemic disposition and brain distribution of total and inorganic mercury in infant monkeys after thimerosal exposure with those exposed to MeHg. Monkeys were exposed to MeHg (via oral gavage) or vaccines containing thimerosal (via intramuscular injection) at birth and 1, 2, and 3 weeks of age. Total blood Hg levels were determined 2, 4, and 7 days after each exposure. Total and inorganic brain Hg levels were assessed 2, 4, 7, or 28 days after the last exposure. The initial and terminal half-life of Hg in blood after thimerosal exposure was 2.1 and 8.6 days, respectively, which are significantly shorter than the elimination half-life of Hg after MeHg exposure at 21.5 days. Brain concentrations of total Hg were significantly lower by approximately 3-fold for the thimerosal-exposed monkeys when compared with the MeHg infants, whereas the average brain-to-blood concentration ratio was slightly higher for the thimerosal-exposed monkeys (3.5 ± 0.5 vs. 2.5 ± 0.3). A higher percentage of the total Hg in the brain was in the form of inorganic Hg for the thimerosal-exposed monkeys (34% vs. 7%). The results indicate that MeHg is not a suitable reference for risk assessment from exposure to thimerosal-derived Hg. Knowledge of the toxicokinetics and developmental toxicity of thimerosal is needed to afford a meaningful assessment of the developmental effects of thimerosal-containing vaccines.
brain and blood distributionelimination half-lifeethylmercuryinfant nonhuman primatesmethylmercurythimerosal
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Public perception of the safety and efficacy of childhood vaccines has a direct impact on immunization rates (Biroscak et al. 2003; Thomas et al. 2004). The current debate linking the use of thimerosal in vaccines to autism and other developmental disorders [Institute of Medicine (IOM) 2001, 2004] has led many families to question whether the potential risks associated with early childhood immunizations may outweigh the benefits (Blaxill et al. 2004; SafeMinds 2005). Thimerosal is an effective preservative that has been used in the manufacturing of vaccines since the 1930s. Thimerosal consists of 49.6% mercury by weight and breaks down in the body to ethyl-mercury and thiosalicylate (Tan and Parkin 2000). Recent reports have indicated that some infants can receive ethylmercury (in the form of thimerosal) at or above the U.S. Environmental Protection Agency (EPA) guidelines for methylmercury exposure (U.S. EPA 2005), depending on the exact vaccinations, schedule, and size of the infant (Ball et al. 2001). Clements et al. (2000) calculated that children receive 187.5 μg of ethylmercury from thimerosal-containing vaccines given over the first 14 weeks of life. According to the authors, this amount approaches or, in some cases, exceeds the U.S. EPA guidelines for MeHg exposure during pregnancy (0.1 μg/kg/day). Other estimates (Halsey 1999) have indicated that the schedule could provide repeated doses of ethylmercury from approximately 5 to 20 μg/kg over the first 6 months of life. Studies in preterm infants indicate that blood levels of Hg after just one vaccination (hepatitis B) increase by > 10-fold to levels above the U.S. EPA guidelines (Stajich et al. 2000).
The U.S. EPA guidelines for MeHg (U.S. EPA 2005) are based on several decades of studies of humans and animal models of developmental toxicity (Burbacher et al. 1990a; National Research Council 2000). Because few data exist for ethylmercury, the use of the MeHg guidelines would seem appropriate if the two compounds have similar toxicokinetic profiles and neurodevelopmental effects. The results from the few studies that have provided a direct comparison of these two compounds have been reviewed recently by Magos (2003), who concluded that a) Hg clears from the body faster after the administration of ethylmercury than after the administration of MeHg; b) the brain-to-blood Hg concentration ratio established for MeHg will overestimate Hg in the brain after exposure to ethylmercury; and c) because ethylmercury decomposes faster than MeHg, the risk of brain damage is less for ethylmercury than for MeHg. These conclusions are based on only a few studies, none of which included measurements of both blood and brain Hg levels in infant subjects.
We initiated the present study in order to directly compare the blood and brain levels of Hg in infant nonhuman primates exposed orally to MeHg or via intramuscular (im) injections of vaccines containing thimerosal. Nonhuman primates have been used extensively in previous studies of MeHg toxicokinetics and developmental neurotoxicity (Burbacher et al. 1986, 1990b; Gunderson et al. 1986, 1988; Rice and Gilbert 1982, 1990, 1995; Stinson et al. 1989; Vahter et al. 1994, 1995). The routes of administration (oral for MeHg and im injection for thimerosal-containing vaccines) were chosen to mimic the two routes of Hg exposure for humans. The dosages and schedule of administration of Hg were chosen to be comparable with the current immunization schedule for human newborns, taking into consideration the faster growth (~ 4 to 1) of the macaque infant (Gunderson and Sackett 1984). The results of the present study provide important new information regarding the comparative toxicokinetics of these two compounds in newborns and infants.
Materials and Methods
Subjects.
Forty-one infant Macaca fascicularis born at the Washington National Primate Research Center’s Infant Primate Research Laboratory were used in the study. The birth weights of the infant monkeys were within the normal range for this species; the average birth weight was 341 g (range, 255–420 g). Infants were weighed daily throughout the study, and any clinical problems were recorded.
Mercury dosing schedule.
The Hg dosing schedule is shown in Table 1. Infants were assigned to one of three exposure groups at birth. Seventeen infant monkeys assigned to the thimerosal group were given the typical schedule of vaccines for human infants (Table 1). Thimerosal (Omicron Quimica S.A., Barcelona, Spain), dissolved in saline, was mixed with thimerosal-free vaccines to yield a final concentration of 4, 8, or 20 μg/mL Hg, depending on the vaccine and the age of the infant. The total dose of Hg administered via the vaccines was 20 μg/kg on day 0 and at 7, 14, and 21 days of age. A dose of 20 μg/kg was chosen based on the range of estimated doses received by human infants receiving vaccines during the first 6 months of life.
Seventeen infant monkeys assigned to the MeHg group were given MeHg hydroxide (MeHgOH, 97% pure; Alfa Aesar, Johnson Matthey Co., Ward Hill, MA) dissolved in water to a concentration of 20 μg Hg/mL. MeHg was administered to infant monkeys via oral gavage at a dose of 20 μg/kg on their day of birth (day 0) and at 7, 14, and 21 days of age.
Seven infant monkeys were assigned to a control group. These monkeys did not receive any gavages or im injections. Infants were assigned to the three groups on a semirandom basis, in order to balance sex ratios and average birth weights across groups.
Blood draw schedule.
Blood was drawn from the saphenous vein of all infant monkeys at birth (before any Hg exposure). Blood was also drawn 2, 4, and 7 days after the initial Hg exposure (day 0) and after subsequent exposures on days 7 and 14. Depending on the sacrifice group, blood was drawn up to 28 days after the final exposure on day 21 to further characterize the washout kinetics of Hg (Table 1).
Sacrifice schedule.
Infants were sacrificed 2, 4, 7, or 28 days after their last Hg exposure on day 21 (Table 1). Infants were sedated with an im injection of ketamine (10 mg/kg) and atropine (0.4 mg/kg) and then given an intravenous overdose of Nembutal (20 mg/kg; Abbott Labs, North Chicago, IL). Autopsy personnel from the primate center drew blood and removed the brain and other organs for analysis. The autopsy typically lasted approximately 1 hr.
The numbers of monkeys at each sacrifice day for both the MeHg and thimerosal groups were as follows: day 23 (2 days after most recent dose), n = 4; day 25 (4 days after most recent dose), n = 4; day 28 (7 days after most recent dose), n = 5; and day 49 (28 days after most recent dose), n = 4. The seven control monkeys were assigned sacrifice days as follows: day 23, n = 3; day 25, n = 1; day 28, n = 2; and day 49, n = 1 (Table 1). Monkeys were assigned to sacrifice groups at birth on a semirandom basis that balanced sex ratios and average birth weights across groups.
Blood and brain Hg measurements.
Blood samples were prepared for Hg analysis by diluting them with an equal volume of 1% wt/vol NaCl solution. Aliquots were removed for Hg determination without digestion. One drop of antifoam reagent was added to the aliquot at the time of the analysis.
Half brain samples were fixed in formaldehyde before analysis. Samples of the fixative were analyzed to check for Hg content. The tissue was removed from the jar and blotted dry. A homogenate of the brain in 1% NaCl was prepared using a Polytron homogenyzer PT 10-35 (Brinkmann Instruments, Westbury, NY) while keeping the sample in an ice slurry. An aliquot of the homogenate was digested with 1 mL 1% wt/vol cysteine and 2 mL 45% NaOH by heating at 95°C for 10–15 min. Digest was allowed to cool and then diluted to volume by addition of 7 mL 1% wt/vol NaCl. The digests were kept in an ice slurry until analysis. Aliquots were removed for Hg determination. One drop of antifoam was added to the aliquot at the time of the analysis.
Total Hg concentrations in blood and total and inorganic Hg concentrations in brain were measured using a procedure adapted from Greenwood et al. (1977). The method determines total Hg and its inorganic fraction (Magos and Clarkson 1972). Cadmium chloride in the presence of stannous chloride at high pH breaks the Hg–carbon bond with the subsequent reduction of Hg2+ to Hg0; the latter is then measured by cold vapor atomic absorption at 254 nm with a Hg monitor (Laboratory Data Control, model 1235; Thermo Separation Products, Waltham, MA). Inorganic Hg is determined by the addition of SnCl2 in the absence of cadmium chloride. Concentration of organic Hg was calculated from the difference between the measured total and inorganic Hg concentrations. The original concentration of SnCl2 used for the Magos method (Magos and Clarkson 1972) was modified to prevent the decomposition of the ethylmercury during assay (Magos et al. 1985). To measure Hg in aqueous solution of thimerosal, the amount of SnCl2 was reduced from 100 μg to 50 μg/aliquot analyzed. For tissue homogenate samples, 500 μg SnCl2 was added to each aliquot. All reagents used for preparation and analysis of the samples were of analytical grade.
Quality control was assured by analysis of reference samples before each assay run. Fisher Mercury Reference Standard Solution (SM114-100, certified 1,000 ppm ± 1%; Fisher Scientific, Hampton, NH) was used as a stock solution. Working standards of 30 and 10 ng Hg/mL were made daily from appropriate dilutions of the stock solution. In addition, the following certified reference materials were analyzed daily before analysis of the samples: trace elements in whole blood (Seronorm Trace Elements, Certified Reference Material 201605, 6.8–8.5 μg/L; Accurate Chemical & Scientific Corp., Westburg, NY), and trace elements in human hair (Certified Reference Material 397, 12 μg/g ± 0.5; Commission of the European Communities, Geel, Belgium). The detection limit of the instrument was estimated to be 0.75 ng Hg per aliquot used for analysis.
Data analysis.
The mean total blood Hg concentration data from both the oral MeHg and im thimerosal groups (n = 17 in each) were analyzed using the compartmental module of the pharmacokinetic modeling software SAAM II (SAAM Institute, Seattle, WA).
The accumulation and washout of total blood Hg concentration–time data from the MeHg monkeys were well described by a one-compartment model featuring a first-order absorption process. Regression fit of the data to the model yielded estimates of the absorption rate constant (ka), elimination rate constant (K), and an apparent volume of distribution (V/F; F is the implicit bioavailability term). Half-lives (T1/2) corresponding to each of the rate constants were calculated by dividing ln 2 by the rate constant estimate. Blood clearance (Cl/F ) was derived from the product of K and V/F.
A one-compartment model failed to provide a satisfactory fit of the mean total blood Hg concentration–time data from the thimerosal monkeys. The model overpredicted the blood concentration during accumulation; at the same time, it underpredicted the blood concentration during washout rate (i.e., overpredicted washout rate). Further examination of a scatter plot of the individual monkey data suggested a biphasic pattern in the washout of Hg from the blood after the last dose. Accordingly, we attempted a regression fit of the mean total blood Hg concentration data with a two-compartment model. This yielded a much better visual fit of the data, with minimal change in the objective function and Akaike information criterion. The two-compartment parameter estimates from the regression analysis included the absorption rate constant (ka), rate constants for Hg transfer from the central to the peripheral compartment (k12) and the return from the peripheral to the central compartment (k21), the elimination rate constant from the central compartment (k10), and the apparent volume of the central compartment (Vc/F ). From these primary parameters, we further estimated the apparent distribution volume at steady state (Vss/F ) and the peripheral volume referenced to blood concentration (i.e., Vp = Vss – Vc). The initial and terminal rate constants and half-lives (T1/2,αand T1/2,β) for the biexponential decline of total blood Hg concentration were estimated by standard formulas (Gibaldi and Perrier 1982). Blood clearance was computed by the product of Vc and k10. For both the MeHg and thimerosal model fits, a fractional SD of 0.1 was used as the weighting scheme.
The washout half-life of total and organic Hg in the brain of both the oral MeHg and im thimerosal groups was estimated by regression fit to a monoexponential model using WinNonlin software (Pharsight Corp., Mountain View, CA). One of the day 28 brain samples from the MeHg exposure group had a spuriously high total Hg concentration, that is, a concentration of 151 ng/g, which is more than 50% higher than the other samples obtained on day 28 (71–90 ng/mL) and higher than those observed at the earliest sacrifice time at day 2 (75–129 ng/g). The unreasonably high concentration is most likely due to contamination of the sample. Therefore, data from this brain and its corresponding blood were excluded from the regression analysis. The average brain-to-blood concentration ratio was also calculated using data from the earliest sacrifice duration (2 days). Because of different washout half-lives in blood and the brain, brain-to-blood concentration ratio is expected to vary with the duration of washout. Samples at day 2 offered the best measure of the extent of uptake of Hg species into the brain that are least confounded by differences in their clearance rate.
Between-group statistical comparisons of the rate of washout of total Hg in blood, as well as total and organic concentrations in the brain, were accomplished through multiple regression analysis as implemented in the PROC GLM subroutine in SAS (version 9.1; SAS Institute, Cary, NC). PROC GLM performs multiple regression within the framework of general linear models and can accommodate missing data or sparse sampling and confounding from correlations between repeated measures. Hence, it is able to provide tests of hypotheses for the effects of time and group using blood and brain data obtained from sacrifice of individual animals at varying times during washout. Log-transformed blood or brain Hg concentrations in animals from both the MeHg and thimerosal groups were entered as the dependent variable. The independent variables consisted of sampling time, group (MeHg = 0, thimerosal = 1), and a time-by-group interaction. Once the overall significance of the regression model was verified, the significant sources of variation (i.e., time, group, and time by group) were identified. A difference in the rate of washout of Hg in blood or brain between groups was indicated by a significant regression coefficient for time-by-group interaction. If there was no evidence for interaction, a significant decline in blood or brain Hg concentration over time for each group was assessed by the t-statistic associated with the estimated regression coefficient for time.
The following statistical comparisons of the washout rate of Hg were also undertaken: total Hg in blood versus total Hg in the brain, total Hg in blood versus organic Hg in the brain, and total Hg versus organic Hg concentration in the brain. The difference between the pair of log-transformed Hg concentrations for each animal sacrificed at the various times was calculated. Individual difference values in both groups were then entered as the dependent variable in the regression model. The independent variables were time, group, and time-by-group interaction. A significant regression coefficient for the time variable indicates that the paired-log concentration difference (or the concentration ratio) varied with time; that is, the two concentration measures (e.g., blood and brain) did not decline in parallel with time.
Results
Growth and health status.
The weights of infant monkeys during the study are shown in Figure 1. We found no significant differences in the weight gain across the three groups (p > 0.10, all comparisons); the average weight gain during the first 23 days of life was 135 g. The brain weights at sacrifice and brain-to-body weight ratios are shown in Table 2; we found no significant differences in brain weights or brain-to-body weight ratios across the three groups (p > 0.10, all comparisons). Also, no serious medical complications were observed in any of the monkeys.
Oral MeHg kinetics.
The total blood Hg concentrations at 2 days (observed peak) after the first dose ranged from 8 to 18 ng/mL across the monkeys, that is, a 2-fold variation. Progressive accumulation of total blood Hg was observed over the three subsequent doses of MeHg, such that the peak total blood Hg concentrations after the fourth dose were about 3-fold higher (30–46 ng/mL). The interanimal variation in blood Hg concentrations remained at about 2-fold during accumulation. Blood Hg persisted through the entire period of washout and was readily measurable in all four monkeys in the 28 day sacrifice group (16–21 ng/mL). This is consistent with previous reports of an elimination T1/2 > 20 days for MeHg in adult M. fascicularis (Stinson et al. 1989; Vahter et al. 1994, 1995) and explains the minimal decline (< 20%) in blood Hg concentrations during the weekly intervals between MeHg doses.
The time course of total blood Hg was fitted to a one-compartment model. Figure 2 shows the excellent regression fit of the mean blood concentration–time data. Table 3 presents parameter estimates from the one-compartment model fit of the mean blood Hg concentration–time data. The distribution volume of total Hg after MeHg administration is estimated to be 1.7 L/kg, or about 20 times the blood volume (~ 8%). This means that only 1/20th of the body burden of Hg is confined to the vascular space. This is consistent with the known extensive extravascular distribution of Hg after MeHg exposure in primates and agrees with previous estimates of Hg distribution volume in adult M. fascicularis (Stinson et al. 1989). The elimination T1/2 of total blood Hg is 21.5 days, which agrees with reported estimates in adult M. fascicularis (Stinson et al. 1989; Vahter et al. 1994, 1995). The blood clearance is estimated at 46.1 mL/day/kg, well within the range of clearance values observed earlier in adult M. fascicularis (Stinson et al. 1989). It appears that the systemic disposition kinetics of MeHg are the same between infant and adult M. fascicularis, that is, no change during development.
A plot of the blood and brain total Hg concentration data from the monkeys sacrificed at various times during the washout period is shown in Figure 3. There was a significant decrease in total Hg from the blood during the washout period (p < 0.01). The apparent T1/2 for total Hg in blood is 19.1 ± 5.1 days (± SE of regression estimate). The decrease in total Hg in the brain over time was marginally significant (p < 0.07), with an apparent T1/2 of 59.5 ± 24.1 days. The T1/2 for total Hg in brain was significantly longer than the T1/2 for total Hg in blood (p = 0.05) for the MeHg-exposed monkeys. The T1/2 for total Hg in brain is also longer than the previously reported washout T1/2 from the brain for adult M. fascicularis (37 days; Vahter et al. 1994, 1995). It should be noted that the relatively high SE of the half-life estimates for the brain reflects the large interanimal variation in Hg concentrations at each sampling time, limited number of data points, and the short duration of sacrifice relative to the washout half-life. The concentration of total Hg in the brain is 1.7- to 3-fold higher than in the blood (mean ± SE of 2.5 ± 0.3) 2 days after the last MeHg dose. This brain-to-blood concentration ratio increased as the duration between the last dose and the sacrifice lengthened. The ratio ranged from 3.9 to 7.4 at 28 days after the last exposure. The time dependence for the brain-to-blood ratio (p = 0.06) is primarily due to the difference in the washout T1/2 between total Hg in the blood and brain. The average brain-to-blood ratio for these infant monkeys at day 2 after the last MeHg dose (2.5 ± 0.3) is slightly lower than previously reported values (3–5) for adult macaque and squirrel monkeys over various durations of washout (Berlin et al. 1975; Stinson et al. 1989; Vahter et al. 1994). Although the cited differences in brain uptake and clearance of MeHg between adult and infant monkeys may be attributed to the effects of postnatal brain growth and development, it may also be related to variation in exposure regimen between studies.
A plot of the organic and inorganic Hg concentrations in the brain of MeHg-exposed monkeys sacrificed at various times during the washout period is shown in Figure 4. The decrease in organic Hg in the brain over time was not statistically significant (p = 0.17). The apparent T1/2 for the washout of organic Hg from the brain was 58.4 ± 25.0 days, close to the T1/2 for total Hg. The concentration of inorganic Hg in the brain samples was below the quantifiable limit of the assay (7 ng/mL) in 8 of 17 MeHg-exposed monkeys. The average concentration of inorganic Hg for those monkeys with values above the detection limit (n = 10) did not change significantly over 28 days of washout and was approximately 7–8 ng/mL (Figure 4). Inorganic Hg represented only 6–10% of total Hg in the brain. These values are consistent with previously reported data in adult M. fascicularis (Vahter et al. 1994, 1995).
Intramuscular thimerosal kinetics.
The initial total Hg concentrations in the day 2 blood samples, which ranged from 6 to 14 ng/mL, are comparable with the concentrations observed in the oral MeHg group. These blood levels are also similar to those reported in preterm human infants receiving 12.5 μg Hg from a hepatitis B vaccine (Stajich et al. 2000). Blood Hg concentrations declined relatively rapidly (by > 50%) between doses. As a result, there was minimal accumulation in blood Hg concentrations during weekly dosing. Also, blood Hg concentrations dropped below the detection limit of the assay in some animals by day 10 after the last vaccine injection.
The time course of total blood Hg concentrations was best described by a two-compartment model; that is, the disposition kinetics are biphasic, with a rapid initial phase followed by a slower terminal phase of clearance. Table 4 presents the parameter estimates derived from the two-compartment model analysis. A comparison of the model prediction and the observed blood concentration data is shown in Figure 5. The model predicted some accumulation in peak blood Hg concentrations and minimal accumulation in trough concentrations. Because blood concentration data were not available before day 2, the predicted peak concentrations are extrapolations and should be viewed with caution. The initial volume of distribution in the central compartment was 1.7 L/kg, which is comparable with the overall distribution volume for oral MeHg. The initial and terminal blood half-lives were 2.1 and 8.6 days, respectively. Mercury derived from thimerosal is eliminated much more rapidly than MeHg. The steady-state volume of distribution (i.e., Vss or the fully equilibrated volume) was estimated to be 2.5 L/kg, which is 50% larger than the initial distribution volume (i.e., Vc). Hence, the effective peripheral compartment volume at steady state is about 0.8 L/kg. Alternately, this means that, at steady state, partitioning of the body burden of Hg between the tissue regions associated with the central and peripheral compartments is about 2:1. The blood clearance of total Hg was estimated to be 248 mL/day/kg, which is 5.4-fold higher than the estimate for oral MeHg.
Figure 6 presents a scatter plot of the blood and brain total Hg concentration data for monkeys sacrificed at various times during the washout. There was a significant decrease in total Hg concentration in the blood during the washout period (p < 0.01). The apparent T1/2 for total Hg in blood is 6.9 ± 1.7 days. There was also a significant decrease in total Hg concentration in the brain over time (p < 0.01), with an apparent T1/2 of 24.2 ± 7.4 days. The T1/2 for total Hg in brain was significantly longer than the T1/2 for total Hg in blood (p < 0.01) for the thimerosal-exposed monkeys. In addition, the T1/2 for total Hg in blood and brain for these monkeys (6.9 ± 1.7 days and 24.2 ± 7.4 days, respectively) are significantly shorter (p < 0.01) than the T1/2 for total Hg in blood and brain for the MeHg monkeys (19.1 ± 5.1 days and 59.5 ± 24.1 days). The concentration of total Hg in the brain of the thimerosal-exposed monkeys is 2.6- to 4.6-fold higher than in the blood (mean ± SE, 3.5 ± 0.5) at 2 days after the last injection. Again, this ratio increased as the sacrifice was performed at longer durations from the last dose, primarily due to the difference in the half-lives of total Hg in the blood and brain.
A plot of the organic and inorganic Hg concentrations in the brain of thimerosal-exposed infant monkeys sacrificed at various times during the washout period is shown in Figure 7. There was a significant decrease in organic Hg in the brain over the washout period (p < 0.01). The apparent T1/2 for the washout of organic Hg from the brain was 14.2 ± 5.2 days, which is significantly shorter than the T1/2 for total Hg in brain (p < 0.01). The inorganic form of Hg was readily measurable in the brain of the thimerosal-exposed monkeys. The average concentration of inorganic Hg did not change across the 28 days of washout and was approximately 16 ng/mL (Figure 7). This level of inorganic Hg represented 21–86% of the total Hg in the brain (mean ± SE, 70 ± 4%), depending on the sacrifice time. These values are considerably higher than the inorganic fraction observed in the brain of MeHg monkeys (6–10%).
Discussion
There are notable similarities and differences in the kinetics of Hg after oral administration of MeHg and im injection of thimerosal in vaccines. The absorption rate and initial distribution volume of total Hg appear to be similar between im thimerosal and oral MeHg. This means approximately equal peak total blood Hg levels after a single exposure to either MeHg or thimerosal or after episodic exposures that are apart by longer than four elimination half-lives (i.e., > 80 days for MeHg or > 28 days for thimerosal). Studies in preterm and term human infants have reported similar results (Stajich et al. 2000). Infants receiving 12.5 μg Hg from a single hepatitis B vaccine had blood Hg levels at 48–72 hr, consistent with what would be anticipated after an equivalent dose of MeHg.
Although the initial distribution volume of total Hg is similar for the two groups, a biphasic exponential decline in total blood Hg is observed only after im injections of thimerosal. This suggests continual distribution into and localization in tissue sites over time. It is relevant to note that the kidney-to-blood concentration gradient of total Hg is much higher in the thimerosal monkeys than in the MeHg monkeys (mean ± SE, 95.1 ± 10 vs. 5.8 ± 0.6). The second slower phase of washout could also represent the gradual biotransformation of ethylmercury (the presumed principal organic form of Hg after thimerosal administration) to Hg-containing metabolites that have a different tissue distribution or are more slowly eliminated. Further investigations of the disposition fate of thimerosal-derived Hg should address these issues.
Total Hg derived from im thimerosal is cleared from the infant M. fascicularis much more quickly than MeHg. The washout T1/2 of total blood Hg after im injections of thimerosal in vaccines is much shorter than the T1/2 of MeHg (6.9 vs. 19.1 days). These results support the earlier conclusion of Magos (2003) that Hg is cleared from the body faster after the administration of ethylmercury than after the administration of MeHg. More interestingly, the washout blood Hg T1/2 in the thimerosal-exposed infant macaques (7 days) is remarkably similar to the blood Hg T1/2 reported for human infants injected with thimerosal-containing vaccines reported by Pichichero et al. (2002).
An important consequence of the difference in blood half-lives is the remarkable accumulation of blood Hg during repeated exposure to MeHg. Although the initial blood Hg concentration (at 2 days after the first dose) did not differ between the MeHg and thimerosal groups, the peak blood Hg concentration in the MeHg-exposed monkeys rose to a level nearly three times higher than in the thimerosal monkeys after the fourth dose. Furthermore, the blood clearance of total Hg is 5.4-fold higher after im thimerosal than after oral MeHg exposure. The results indicate that for an equivalent level of chronic exposure, the area under the curve of total blood Hg concentrations in human infants receiving repeated im injections of thimerosal-containing vaccines will be significantly lower than that in those exposed chronically to MeHg via the oral route.
A much lower brain concentration of total Hg was observed in the thimerosal monkeys compared with the MeHg monkeys, that is, a 3- to 4-fold difference for an equivalent exposure of Hg. Moreover, total Hg is cleared much more rapidly from the brain after thimerosal than after MeHg exposure (24 vs. 60 days). It appears that the difference in brain Hg exposure between thimerosal and MeHg is largely driven by their differences in systemic disposition kinetics (i.e., the blood level). The average brain-to-blood partitioning ratio of total Hg in the thimerosal group was slightly higher than that in the MeHg group (3.5 ± 0.5 vs. 2.5 ± 0.3, t-test, p = 0.11). Thus, the brain-to-blood Hg concentration ratio established for MeHg will underestimate the amount of Hg in the brain after exposure to thimerosal.
The large difference in the blood Hg half-life compared with the brain half-life for the thimerosal-exposed monkeys (6.9 days vs. 24 days) indicates that blood Hg may not be a good indicator of risk of adverse effects on the brain, particularly under conditions of rapidly changing blood levels such as those observed after vaccinations. The blood concentrations of the thimerosal-exposed monkeys in the present study are within the range of those reported for human infants after vaccination (Stajich et al. 2000). Data from the present study support the prediction that, although little accumulation of Hg in the blood occurs over time with repeated vaccinations, accumulation of Hg in the brain of infants will occur. Thus, conclusion regarding the safety of thimerosal drawn from blood Hg clearance data in human infants receiving vaccines may not be valid, given the significantly slower half-life of Hg in the brain as observed in the infant macaques.
There was a much higher proportion of inorganic Hg in the brain of thimerosal monkeys than in the brains of MeHg monkeys (up to 71% vs. 10%). Absolute inorganic Hg concentrations in the brains of the thimerosal-exposed monkeys were approximately twice that of the MeHg monkeys. Interestingly, the inorganic fraction in the kidneys of the same cohort of monkeys was also significantly higher after im thimerosal than after oral MeHg exposure (0.71 ± 0.04 vs. 0.40 ± 0.03). This suggests that the dealkylation of ethylmercury is much more extensive than that of MeHg.
Previous reports have indicated that the dealkylation of Hg is a detoxification process that helps to protect the central nervous system (Magos 2003; Magos et al. 1985). These reports are largely based on histology and histochemistry studies of adult rodents exposed to Hg for a short period of time. The results of these studies indicated that damage to the cerebellum was observed only in MeHg-treated animals that had much lower levels of inorganic Hg in the brain than animals comparably treated with ethylmercury. Moreover, the results did not indicate the presence of inorganic Hg deposits in the area where the cerebellar damage was localized (granular layer).
In contrast, previous studies of adult M. fascicularis monkeys exposed chronically to MeHg have indicated that demethylation of Hg occurs in the brain over a long period of time after MeHg exposure and that this is not a detoxification process (Charleston et al. 1994, 1995, 1996; Vahter et al. 1994, 1995). Results from these studies indicated higher inorganic Hg concentrations in the brain 6 months after MeHg exposure had ended, whereas organic Hg had cleared from the brain. The estimated half-life of organic Hg in the brain of these adult monkeys was consistent across various brain regions at approximately 37 days (similar to the brain half-life in the present infant monkeys). The estimated half-life of inorganic Hg in the brain in the same adult cohort varied greatly across some regions of the brain, from 227 days to 540 days. In other regions, the concentrations of inorganic Hg remained the same (thalamus) or doubled (pituitary) 6 months after exposure to MeHg had ended (Vahter et al. 1994, 1995). Stereologic and autometallographic studies on the brains of these adult monkeys indicated that the persistence of inorganic Hg in the brain was associated with a significant increase in the number of microglia in the brain, whereas the number of astrocytes declined. Notably, these effects were observed 6 months after exposure to MeHg ended, when inorganic Hg concentrations were at their highest levels, or in animals solely exposed to inorganic Hg (Charleston et al. 1994, 1995, 1996). The effects in the adult macaques were associated with brain inorganic Hg levels approximately five times higher than those observed in the present group of infant macaques. The longer-term effects (> 6 months) of inorganic Hg in the brain have not been examined. In addition, whether similar effects are observed at lower levels in the developing brain is not known. It is important to note that “an active neuroinflammatory process” has been demonstrated in brains of autistic patients, including a marked activation of microglia (Vargas et al. 2005).
The American Academy of Pediatrics and the U.S. Public Health Service (1999) published a joint statement that urged “all government agencies to work rapidly toward reducing children’s exposure to mercury from all sources.” The statement recommended that thimerosal be removed from vaccines as soon as possible as part of this overall process. Between 1999 and 2001, vaccines currently recommended for children ≤ 6 years of age were made available in thimerosal-free formulations in the United States (Centers for Disease Control and Prevention 2001). Exposures to thimerosal through pediatric vaccines, however, still occur in other countries where multiple-dose vials are used to maintain childhood immunization programs and the control of preventable disease (Knezevic et al. 2004).
Recent publications have proposed a direct link between the use of thimerosal-containing vaccines and the significant rise in the number of children being diagnosed with autism, a serious and prevalent developmental disorder (for review, see IOM 2001). Results from an initial IOM review of the safety of vaccines found that there was not sufficient evidence to render an opinion on the relationship between ethylmercury exposure and developmental disorders in children (IOM 2001). The IOM review did, however, note the possibility of such a relationship and recommended further studies be conducted. A recently published second review (IOM 2004) appears to have abandoned the earlier recommendation as well as backed away from the American Academy of Pediatrics goal. This approach is difficult to understand, given our current limited knowledge of the toxicokinetics and developmental neurotoxicity of thimerosal, a compound that has been (and will continue to be) injected in millions of newborns and infants.
The key findings of the present study are the differences in the disposition kinetics and demethylation rates of thimerosal and MeHg. Consequently, MeHg is not a suitable reference for risk assessment from exposure to thimerosal-derived Hg. Knowledge of the biotransformation of thimerosal, the chemical identity of the Hg-containing species in the blood and brain, and the neurotoxic potential of intact thimerosal and its various biotransformation products, including ethylmercury, is urgently needed to afford a meaningful interpretation of the potential developmental effects of immunization with thimerosal-containing vaccines in newborns and infants. This information is critical if we are to respond to public concerns regarding the safety of childhood immunizations.
Correction
In the original manuscript published online, there were two errors that have been corrected here. First, in the Abstract, the standard errors for the average brain-to-blood concentration ratios were incorrect for thimerosal and MeHg-exposed monkeys. Second, in the last paragraph of “Data analysis,” the statement about the two concentration measures has been corrected to “the two concentration measures (e.g., blood and brain) did not decline in parallel with time.”
We thank the staff of the Infant Primate Research Laboratory for their cooperation during this study and D. Blough for his assistance with statistical analyses. We also thank J. Treanor from the University of Rochester for supplying the vaccines used in the study.
This project was supported by funds from the National Institutes of Health, grants RO1ES03745, P51HD02274, P51RR00166, P30ES07033, and NO1-A1-25460.
Figure 1 Weight gain of infant monkeys during study. Error bars indicate SE.
Figure 2 Comparison of predicted and observed mean blood total Hg concentrations during and after four weekly oral doses (20 μg/kg) of MeHg. Error bars indicate SD.
Figure 3 A semilogarithmic plot of washout of total Hg in blood and the brain after four weekly oral doses (20 μg/kg) of MeHg. The data were collected from groups of infant monkeys sacrificed 2, 4, 7, and 28 days after the last dose. The lines represent nonlinear regression fit of the data to a monoexponential model; the regression estimate (± SE) of T1/2 is T1/2 = 19.1 ± 5.1 days (r = 0.81) for blood and T1/2 = 59.5 ± 24.1 days (r = 0.59) for brain.
Figure 4 A semilogarithmic plot of the washout of organic and inorganic Hg in the brain after four weekly oral doses (20 μg/kg) of MeHg. The data were collected from groups of infant monkeys sacrificed at 2, 4, 7, and 28 days after the last dose. The lines represent nonlinear regression fit of the data to a monoexponential model. The regression estimate (± SE) for organic Hg is T1/2 = 58.4 ± 25.0 days (r = 0.57). The half-life of inorganic Hg is too long (> 120 days) to be accurately estimated from the present data (i.e., r is not significantly different from 0).
Figure 5 Comparison of predicted and observed mean blood total Hg concentration during and after four weekly im injections of vaccine containing thimerosal (20 μg/kg Hg). Error bars indicate SD.
Figure 6 A semilogarithmic plot of washout of total Hg in blood and the brain after four weekly im injections of vaccine thimerosal (20 μg/kg Hg). The data were collected from groups of infant monkeys sacrificed at 2, 4, 7, 10, 17, and 21 days after the last dose. The lines represent nonlinear regression fit of the data to a monoexponential model. The regression estimate (± SE) of T1/2 is 24.2 ± 7.4 days (r = 0.74) for brain and 6.9 ± 1.7 days (r = 0.82) for blood.
Figure 7 A semilogarithmic plot of washout of organic and inorganic Hg in the brain after four weekly im injection of vaccines containing thimerosal (20 μg/kg Hg). The data were collected from groups of infant monkeys sacrificed at 2, 4, 7, and 28 days after the last dose. The lines represent nonlinear regression fit of the data to a monoexponential model. The regression estimate (± SE) of T1/2 for organic Hg is T1/2 = 14.2 ± 5.2 days (r = 0.76). The half-life of inorganic Hg is too long (> 120 days) to be accurately estimated from the present data (i.e., r is not significantly different from 0).
Table 1 Study design and schedule.
Age (days)
0 (birth) 2 4 7 9 11 14 16 18 21 23 25 28 31 35 38 42 45 49
MeHg group (oral dose, μg/kg) 20 20 20 20
OPV (0) OPV (0)
Thimerosal group [ethylmercury dose, in im vaccine (μg/kg)] OPV (0) HB (4) OPV (0) HB (4)
HB (20) DTP (8) DTP (10) DTP (8)
Hib (8) Hib (10) Hib (8)
Blood draws (days after most recent dose) 0 2 4 7 2 4 7 2 4 7 2 4 7 10 14 17 21 24 28
Sacrifice day (days after last dose) 2 4 7 28
Abbreviations: DTP, diphtheria/tetanus/pertussis vaccine; HB, hepatitis B vaccine; HIB, haemophilus influenzae type b vaccine; OPV, oral polio vaccine.
Table 2 Mean ± SE body and brain weight and brain-to-body weight ratio at sacrifice.
Exposure group Body weight (g) Brain weight (g) Brain:body weight ratio
Controls (n = 9) 509.3 ± 52.0 52.1 ± 2.5 0.107 ± 0.009
MeHg exposed (n = 17) 499.1 ± 17.5 51.1 ± 1.1 0.103 ± 0.003
Thimerosal exposed (n = 17) 529.1 ± 25.4 52.7 ± 1.2 0.102 ± 0.003
Table 3 Parameter estimates derived from a one-compartment analysis of the mean blood total Hg concentration for the oral MeHg group (n = 17).
Model parameters Mean ± SD
V/F (L/kg) 1.67 ± 0.07
ka (day−1) 2.07 ± 1.04
K (day−1) 0.0276 ± 0.0024
T1/2 (days) 21.5
Cl/F (mL/day/kg) 46.1
Table 4 Parameter estimates derived from a two-compartment analysis of the mean blood total Hg concentration for the im thimerosal group (n = 17).
Model parameters Mean ± SD
ka (day−1) 3.24 ± 3.00
k12 (day−1) 0.081 ± 0.076
k21 (day−1) 0.177 ± 0.138
k10 (day−1) 0.148 ± 0.024
T1/2,α (day) 2.13
T1/2,β (day) 8.62
Vc/F (L/kg) 1.68 ± 0.30
Vss/F (L/kg) 2.45
Vp (L/kg) 0.77
Cl/F (mL/day/kg) 248
==== Refs
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Vahter M Mottet NK Friberg L Lind B Shen D Burbacher T 1994 Speciation of mercury in the primate blood and brain following long-term exposure to methylmercury Toxicol Appl Pharmacol 124 221 229 8122267
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7735ehp0113-00102216079073ResearchLong-Term Effects of Neonatal Exposure to Hydroxylated Polychlorinated Biphenyls in the BALB/cCrgl Mouse Martinez Jeanelle M. 1Stephens L. Clifton 2Jones Lovell A. 11 Department of Gynecologic Oncology and 2 Department of Veterinary Medicine and Surgery, University of Texas, M.D. Anderson Cancer Center, Houston, Texas, USAAddress correspondence to J.M. Martinez, National Institute of Environmental Health Sciences, P.O. Box 12233, MD C4-05, Research Triangle Park, NC 27709 USA. Telephone: (919) 541-3466. Fax: (919) 541-4702. E-mail:
[email protected] authors declare they have no competing financial interests.
8 2005 20 4 2005 113 8 1022 1026 8 11 2004 20 4 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The neonatal mouse model has been a valuable tool in determining the long-term effects of early exposure to estrogenic agents in mammals. Using this model, we compared the effects of 2′,4′,6′-trichloro-4-biphenylol (OH-PCB-30) and 2′,3′,4′,5′-tetrachloro-4-biphenylol (OH-PCB-61) as prototype estrogenic hydroxylated PCBs (OH-PCBs) because they are reported to exhibit relatively high estrogenic activity both in vivo and in vitro. The purpose of this study was to examine the relationship between estrogenicity and carcinogenicity of OH-PCB congeners. The OH-PCBs were tested individually and in combination to determine whether effects of combined OH-PCBs differed from those of these OH-PCBs alone. We evaluated the long-term effects of neonatal exposure to OH-PCBs with treatment doses that were based on the reported binding affinity of specific OH-PCB congeners to estrogen receptor α. BALB/cCrgl female mice were treated within 16 hr after birth by subcutaneous injections every 24 hr, for 5 days. The mice treated with OH-PCB-30 (200 μg/day) or 17β-estradiol (5 μg/day) showed similar increased incidences of cervicovaginal (CV) tract carcinomas (43% and 47%, respectively). In addition, when mice were treated with OH-PCBs as a mixture, a change in the type of CV tract tumor was observed, shifting from predominantly squamous cell carcinomas to adenosquamous cell carcinoma. From our results, we conclude that the individual OH-PCBs tested were estrogenic and tumorigenic in mice when exposed during development of the reproductive tract. These data support the hypothesis that mixtures may act differently and unexpectedly than do individual compounds.
BALB/cCrgl mouseestrogenicityfemale reproductionhydroxylated polychlorinated biphenylsOH-PCBstumorigenicity
==== Body
Evidence that estrogen acts as a gynecologic carcinogen comes from cases of adenocarcinoma and nonneoplastic abnormalities of the genital tract in females who had been exposed to diethylstilbestrol (DES) in utero (Herbst et al. 1971; Robboy et al. 1977). The subsequent cases of cancer and other gynecologic abnormalities in females exposed to DES in utero helped to establish the paradigm that a developing fetus is sensitive to compounds tolerated by adults. This paradigm led researchers to reexamine the potential effects of endocrine-disrupting chemicals in human and wildlife species (Gray 1998; Santodonato 1997; Semenza et al. 1997; Zou and Fingerman 1997).
In mice, neonatal exposure to potent natural and synthetic estrogens results in the development of cervicovaginal (CV) tumors, some of which resemble tumors in human females exposed to DES in utero (Bern et al. 1975; Bern and Talamantes 1981). Most significantly, these tumors in the mouse model, like those in women transplacentally exposed to DES, are dependent on the dose and time of exposure to the estrogen. Correlation of estrogenicity of DES with carcinogenicity has been demonstrated in the mouse uterus but requires an endogenous source of estrogen for both tumor initiation and progression (Newbold et al. 1990). 17α-Estradiol is a natural estrogen that binds weakly to the estrogen receptor (ER). In mice, exposure to 17α-estradiol during a critical period of reproductive tract development leads to subsequent gynecologic malignancies (Hajek et al. 1997). These studies exemplify that various abnormalities in long-term studies are dependent on when mammals are exposed to a natural or synthetic estrogen.
Although there are many known estrogenic chemicals, we were interested specifically in estrogenic hydroxylated polychlorinated biphenyls (OH-PCBs) because the role they play in breast cancer is controversial and uncertain (Adami et al. 1995; Aschengrau et al. 1998; Krieger et al. 1994). PCBs belong to a class of organochlorine synthetic chemicals that have up to 209 congeners or configurations possible, depending on the number and location of chlorines on the molecule. These PCBs vary in the number of chlorine atoms present, which ranges from 1 to 10, as well as their position on the two benzene rings. The relative toxicity of PCBs depends upon chemical characteristics such as chlorination, hydrophobicity, and planarity (Brouwer et al. 1999). The biologic activity of PCBs is generally classified as dioxin-like or nondioxin-like depending on their mechanism of action. Dioxin-like compounds assume a coplanar configuration with chlorine atoms on the meta or para benzene position and have a high binding affinity to the aryl hydrocarbon receptor (AhR). Through activation of the AhR, they elicit dioxin-like biochemical and toxic responses. Nondioxin-like chemicals assume a noncoplanar configuration with chlorine atoms on the ortho benzene position and bind with variable affinities to steroid hormone receptors. Certain PCBs found in the environment have been shown to be are estrogenic; for example, Hansen et al. (1995) demonstrated that landfill-associated extracts containing PCBs are uterotropic in prepubertal rats. PCB congeners that are capable of binding to the ER can induce the following estrogen-related effects in rodents: increased uterine wet weight, increased glycogen content, prolonged estrous cycle, and proto-oncogene expression (Ecobichion and MacKenzie 1974; Gellert 1978; Korach et al. 1988). 4-OH-PCBs are the major metabolites of PCBs. They are found in human and wildlife blood and appear to persist and bioaccumulate (Bergman et al. 1994; Hovander et al. 2002; Li et al. 2003). 4-OH-PCBs are formed by an arene oxide intermediate catalyzed by phase I cytochrome P450 enzymes. However, the toxicologic impact of the OH-PCBs and their adverse effect in humans are not well characterized. The placental transfer of OH-PCBs has been recently established (Soechitram et al. 2004), suggesting that these PCB metabolites could have adverse effects during developmental exposure. OH-PCBs have been shown to be antiestrogenic and estrogenic and to bind to the ER and to the thyroid hormone receptor, and they are, in general, endocrine-disrupting chemicals (Arulmozhiraja et al. 2005; Connor et al. 1997; Kitamura et al. 2005; Korach et al. 1988).
The goal of this study was to determine if neonatal exposure to the estrogenic chemicals 2′,4′,6′-trichloro-4-biphenylol (OH-PCB-30) and 2′,3′,4′,5′-tetrachloro-4-biphenylol (OH-PCB-61) results in carcinogenicity. The positions of the chlorines for these two PCBs are indicated in Figure 1. The OH-PCBs are the 4-hydroxylated metabolites of parent PCB-30 and PCB-61. We chose these PCB congeners because they have known estrogenic activity and their binding affinity to the ER is reported in the literature (Table 1). Investigations of early-life-stage exposure to polychlorinated biphenyls (PCBs) are warranted because these organochlorine chemicals and their metabolites readily cross the placenta to the fetus in both humans and rodents and are transferred through breast milk to the newborn (Ando 1978; Ando et al. 1985, 1986). There is a growing database on developmental effects for endocrine-disrupting chemicals with multiple end points, including cancer. In this study, we examined the neonatal effects of OH-PCBs. Although the specific OH-PCBs investigated in this study may not occur in the environment, they are sound prototypes for estrogenic OH-PCBs that bind to ER-αand elicit estrogen-mediated responses.
Materials and Methods
Chemicals.
All chemicals were of the highest grade available. 17β-Estradiol (E2) was purchased from Sigma Chemical Co. (St. Louis, MO). Both OH-PCB-30 and OH-PCB-61 were generously provided by S. Safe (Texas A&M University, College Station, TX). These OH-PCBs were synthesized and purity confirmed as described previously (Safe et al. 1995). For this study, E2 and the OH-PCBs were dissolved in 1 mL 100% ethanol and warmed to dissolve the chemical. Sesame oil was added to obtain the desired concentrations for 20-μL subcutaneous injections. Ethanol was then evaporated using nitrogen gas while keeping the solution warm to prevent recrystallization. OH-PCB doses used in this study are based on their reported respective binding affinity to ER-α. E2 (5 μg/day) was used as a predictive dose because the frequency of CV tumors in BALB/cCrgl mice neonatally exposed to E2 is approximately 50% (Jones and Bern 1979). To test for interactive effects, doses were selected using the high dose of OH-PCB-30 as a basis of comparison because it has a higher binding affinity to ER-α.
Animals.
Mice were handled according to the Guide for the Care and Use of Laboratory Animals (Institute of Laboratory Animal Resources 1985), and the Institutional Animal Care and Use Committee approved all procedures performed on animals. Adult mice were fed Purina Rodent Chow 5001 (Alies Seed, Houston, TX). Pregnant female BALB/cCrgl mice were purchased from Harlan Sprague Dawley (Indianapolis, IN). The inbred BALB/cCrgl strain was used because it has a low mammary tumor incidence and its response to E2 treatment during neonatal development is well documented. Beginning within 16 hr after birth, female pups were pooled from several litters and distributed four or five pups per mother per cage. Each cage was then given five daily subcutaneous injections with 20 μL sesame oil alone, 5 μg E2, 2.5 μg E2 plus 100 μg OH-PCB-30, 20 μg OH-PCB-30, 200 μg OH-PCB-30, 40 μg OH-PCB-61, 400 μg OH-PCB-61, 10 μg OH-PCB-30 plus 10 μg OH-PCB-61, or 100 μg OH-PCB-30 plus 100 μg OH-PCB-61 (Table 2). Animals were weaned 21 days of age. Mice were examined daily for premature vaginal opening for the first 35 days of life and checked monthly with blunt forceps to detect concretions (calcium carbonate deposits in the vagina that are a result of malformation of the urogenital tract in developmentally estrogenized animals). When concretions were found, they were removed. All mice that survived to 20 months of age were sacrificed by CO2 fixation. Tissues were dissected and fixed in 10% buffered formalin for at least 24 hr before being embedded in paraffin. Paraffin-embedded blocks were serially sectioned and stained with hematoxylin and eosin (H&E).
Statistical analyses.
We used one-way analysis of variance to assess differences in body weight, uterine weight, and vaginal opening. Pairwise comparisons of each experimental group versus sesame oil control were made by Tukey-HSD (honest significant difference) tests. Survival comparisons were made by Wilcoxon rank sum tests. The proportions of animals with malignant tumors were compared by Fisher exact tests. Animals that died before the appearance of the first tumor were excluded from the analysis.
Results
Gross observations.
A biologic index of sexual maturity can be visually assessed by day of vaginal opening (DVO). The DVO was significantly shorter in mice given E2 alone, E2 plus OH-PCB-30 (200 μg), OH-PCB-61 (40 and 400 μg), and the mixture OH-PCB-30/61 (100/100 μg; Table 2). There was a dose-dependent effect with the higher dose yielding the shortest DVO. The lower doses of OH-PCBs had a DVO similar to that in control mice. Body weight was significantly decreased in mice given 5 μg E2. Mortality was increased in mice given OH-PCB at high doses (p < 0.05; Table 2).
Tumor incidence.
Tumor incidences are summarized in Table 3. The only tumor seen in control mice was one malignant lymphoma. The incidence of malignant tumors was significantly greater in all groups exposed to E2 and/or PCB than in controls. Among mice given E2 alone, incidence of CV tract carcinomas was 43% (16 of 37), and for any tumor, 49% (18 of 37; some mice had more than one type of tumor detected). We detected other tumors that are commonly observed in mice neonatally treated with E2, including cholangiocarcinoma of the gallbladder and granulosa cell tumor. The E2-treated mice also had one incidence of bronchoalveolar adenoma of the lung. Among mice given E2 plus OH-PCB-30, incidences of CV tract carcinomas (47%; 9 of 19) and granulosa cell tumors (15%; 3 of 19) were significantly increased. In addition, there was one reticulum cell sarcoma detected in mice treated with E2 plus OH-PCB-30.
In mice given the high dose of OH-PCB-30 (200 μg/pup/day), the incidences of CV tract carcinomas and granulosa cell tumors were 45% (10 of 22) and 14% (3 of 22), respectively; one mouse was found with cholangio-carcinoma. Incidences of tumors in the low-dose OH-PCB-30 mice (20 μg/pup/day) were as follows: CV tract carcinomas, 6% (2 of 33); mammary gland adenocarcinoma, 15% (5 of 33); and bronchoalveolar adenoma/ carcinoma, 9% (3 of 33).
In mice neonatally treated with 400 μg OH-PCB-61, we found a 20% incidence of CV tract carcinomas (5 of 24), 4% incidence of mammary gland adenocarcinoma (1 of 24); and an 8% incidence of hemangiosarcoma (2 of 24). From all of the treatment groups, we observed one animal with hepato-cellular carcinoma—a mouse treated with 400 μg OH-PCB-61. In mice treated with the low dose of OH-PCB-61 (40 μg), tumor incidences were as follows: CV tract carcinomas, 13% (4 of 30); granulosa cell tumor, 10% (3 of 30); mammary gland tumors, 13% (4 of 30); and hemangiosarcomas, 7% (1 of 30). In mice given 200 μg of the mixture (OH-PCB-30/61), the incidences of neoplasms detected were as follows: CV tract carcinomas, 38% (8 of 21); granulosa cell tumor, 10% (2 of 21); malignant lymphomas, 10% (2 of 21); and bronchoalveolar carcinoma, 5% (1 of 21). Incidence rates for mice treated with 20 μg OH-PCB-30/61 were 8% (3 of 36) for CV tract carcinomas, 3% (1 of 36) for granulosa cell tumor, and 8% (3 of 36) for mammary gland carcinomas.
Interactive effects of chemical mixtures.
The two types of tumors detected in groups administered estrogenic compounds alone and in combination were compared by Fisher exact tests (Table 4). We observed no detectable differences in the overall incidence of CV tract tumors. However, there was a difference in the relative distributions of tumor types. In 8% (3 of 37) of animals treated with E2 and in 14% (3 of 22) of animals treated with 200 μg OH-PCB-30, we observed a significant difference between the combined incidences of CV tract adenosquamous cell carcinoma compared with that of animals treated with E2/OH-PCB-30 (32%; 6 of 19), as determined using Fisher exact tests. Although it was not statistically significant, there appeared to be a trend for an increased incidence of CV tract development of adenosquamous cell carcinoma versus squamous cell carcinoma when comparing the combined incidence of OH-PCB-30 (14%; 3 of 22) and OH-PCB-61 (8%; 2 of 20) to that of OH-PCB-30/61 (24%; 5 of 21).
Discussion
In this study, we used the DES neonatal mouse model to evaluate the tumorigenic effects of estrogenic OH-PCBs. The results show that the production of CV tract tumors occurred to a similar degree between 5 μg E2 (43%; 16 of 37) and 200 μg OH-PCB-30 (47%; 9 of 19). A rather large number of different tumors were detected in this study, but the tumors with the highest frequency were the CV tract tumors (Table 3). These CV tract tumors were induced by neonatal OH-PCB treatment. A limitation of this study was the number of doses used, but there appeared to be a pattern of increased CV-tract tumors with the higher doses. These data strongly support the theory that relatively weak estrogens can induce tumors in mice when exposure occurs during a critical period of development (Hajek et al. 1997).
The neonatal mouse model has been extensively studied for more than four decades and has proven extremely valuable in assessing human in utero exposure to DES. The defined period for causation of genital tract tumors by natural (17α-estradiol and E2) and synthetic (e.g., DES) estrogens occurs during the development of the reproductive tract in both humans and rodents (Hajek et al. 1997). The use of the neonatal mouse model was necessary because, unlike findings in adult-treated rodents (Liehr et al. 1986), an apparent correlation between estrogenicity and carcinogenicity exists in neonatally treated rodents (Newbold et al. 1990, 1997). In addition, species-specific E2-mediated tumor induction occurs in different strains of mice. For example, outbred female CD-1 mice are susceptible to uterine tumors, and inbred BALB/cCrgl mice are hormonally susceptible to CV tract tumors (Jones and Bern 1977). E2-mediated tumor induction is also age dependent and dose related and, most important, occurs in a tissue-dependent manner (Newbold et al. 1990).
Our experiments were aimed at determining a relationship between estrogenicity and carcinogenicity for estrogenic PCBs. The first indication of the estrogenicity of E2 and/or OH-PCBs in the present study was premature vaginal opening (Table 2). OH-PCBs tested alone or in combination facilitated premature vaginal opening in a time frame similar to that of E2. Both OH-PCB-30 and OH-PCB-61 have also tested positive for in vivo estrogenicity in juvenile fish and mice (Carlson and Williams 2001; Korach et al. 1988). Like other studies testing interactions, we only found additive effects from the combined chemicals (Carlson and Williams 2001; Ramamoorthy et al. 1997). We found that the highest mortality rates were seen in mice treated with high doses of OH-PCBs, indicating that neonatal exposure to PCBs has a chronic toxic effect because the lethality occurred close to 12 months. Some of the chronic carcinogenic effects attributed to OH-PCB exposure in this study were similar to those known for E2, but others, such as tumor formation in organs other than the CV tract, were not. Thus, the tumors seen in E2-treated mice reflect the species-specific E2-mediated tumor susceptibility of BALB/cCrgl mice. In contrast to findings in the literature that mixtures of PCBs promote hepatocellular carcinoma (Dutch Expert Committee 1995; Mayes et al. 1998; Sleight 1985), a variety of malignant tumors were identified in the OH-PCB–treated mice, but only one mouse developed a hepatocellular carcinoma; thus, the mechanisms are likely to be very different.
The incidence of mammary gland carcinomas was significantly increased to 13% (4 of 30) in mice treated with 40 μg OH-PCB-61. Mammary gland tumors were also detected in mice treated with E2 (3%; 1 of 37), 400 μg OH-PCB-61 (13%; 4 of 30), 20 μg OH-PCB-30 (15%; 5 of 33), and 20 μg OH-PCB-30/61 (8%; 3 of 36). Although several published studies support the idea that developmental exposure to PCBs may lead to an increase in breast cancer (Birnbaum and Fenton 2003; Desaulniers et al. 2001; Mayes et al. 1998), the results from the present study are striking in that we detected an increased number of mammary tumors. Historically, BALB/cCrgl mice do not develop mammary gland tumors (Dunn and Green 1963; Mori et al. 1976). We did not find a clear dose-dependent increase in mammary gland tumor responses because there were fewer mammary gland tumors detected in the high-dose OH-PCB-61 mice than in the low-dose OH-PCB-61 mice. Also, we detected no mammary gland tumors in the high dose OH-PCB-30 mice, but 5 were found in the low-dose OH-PCB-30 mice. This effect is probably due to the increased mortality in high-dose groups (Table 2). Unfortunately, no dissections or histologic analysis occurred if animals died on weekends or at night. In addition, the mammary glands were not dissected out from control animals, and the only reason mammary gland tumors were detected at all is because they were visibly obvious.
Effects on mammary growth, lobuloalveolar development, and hyperplastic alveolar nodules as well as dysplasias have been detected (Jones and Bern 1977, 1979) in virgin female BALB/cCrgl mice neonatally treated with estrogen. Mammary tumors have been found in transplantation studies (Medina 1976) where hyperplastic alveolar nodules from 7,12-dimethylbenz[a]anthracene-treated mice were placed into the mammary fat pad of virgin BALB/cCrgl mice. The average time for development (6 of 6; 100%) of tumors was 6 months. It has been postulated that the mouse mammary tumor virus (MMTV) is essential for the development of mammary gland tumors. This theory is strongly supported by findings that hormonally neonatally treated mice that have MMTV develop mammary gland tumors (Jones and Bern 1979). It was unfortunate that the mammary gland was not chosen as a target organ, but we did not expect to find mammary gland tumors in treated inbred mice that lack MMTV. The induction of mammary gland tumors by neonatal OH-PCB may be due to the combination of its overall carcinogenicity with its estrogenicity. Future studies using this animal model are necessary to determine the mechanism of action. In humans, the association of PCBs with breast cancer has not been determined. Although exposure to elevated levels of PCBs is still a potential factor in breast cancer (Laden et al. 2002; Wolff and Toniolo 1995), a correlation has not been established (Brown 1987; Higginson 1985; Krieger et al. 1994; Laden et al. 2001).
There are two significant results of this study: the demonstration that OH-PCB congeners are carcinogenic, and that the type of CV tract tumors observed in response to treatment with a mixture was significantly different than from those found after individual OH-PCBs treatment. For both mixture groups (E2/OH-PCB-30, and OH-PCB-30/61), we found a lower incidence of CV tract squamous cell carcinomas and elevated incidence of CV tract adenosquamous cell carcinoma. Thus, a shift from squamous to adenosquamous was observed in mice treated with mixtures. This is a very interesting result because it illustrates clearly that the toxic response to mixtures may be different from the toxic response of the individual components of the mixture. Gynecologic epithelial tumors are generally grouped into these two major categories based on whether they are derived from Mullerian epithelium (adenocarcinoma) or squamous epithelium (squamous cell carcinoma) of the urogenital sinus. The adenosquamous carcinoma of the CV tract may be similar to the adenosquamous carcinoma of the lung, which is an example of a heterogeneous tumor (Kanazawa et al. 2000). Adenosquamous carcinomas of the lung and CV tract are similar in clinical outcome: the prognosis is poorer than for patients with either squamous carcinomas or adenocarcinomas (Farley et al. 2003; Hofmann et al. 1994).
The present study supports the hypothesis that neonatal exposure to estrogenic OH-PCBs mimics the ability of E2 to induce CV tract tumors in the BALB/cCrgl mouse. For example, there was an increase in CV tumors induced by higher doses of OH-PCB-30 compared with lower doses. In addition, similar molecular and morphologic effects were true to a lesser extent for PCB-61. The dose of OH-PCB-61 was twice that of OH-PCB-30; therefore, a similar incidence of CV tract tumors was expected based on receptor binding affinities. Instead, there was less than half as many: incidence rates for CV tract tumors were 21% (5 of 24) versus 46% (10 of 22) for the high doses of OH-PCB-61 and OH-PCB-30, respectively. This may be a result of toxicity as indicated by higher mortality (Table 2).
Assessing the long-term effects of PCBs is important because the general population is exposed to these chemicals at all stages of human development. In a series of reports, researchers from the Netherlands associated prenatal exposure to PCBs with biologic effects (Huisman et al. 1995; Patandin et al. 1999a, 1999b). Similarly, perinatal exposure to PCBs is linked to a variety of immunologic, neural, and endocrine effects and potentially linked with biologic effects on growth, sexual development, and long-term reproductive health (Weisglas-Kuperus 1998). Perinatal exposure to PCBs has been associated with smaller head circumference and lower birth weight (Fein et al. 1984; Taylor et al. 1989). One study also reported a decrease in penis size in boys born to mothers exposed to PCBs, but this finding may be difficult to interpret because the maternal exposure was to a mixture of PCBs most likely contaminated with similar organochlorines, that is the polychlorinated dibenzo-p-dioxins/dibenzofurans (Guo et al. 1995). These studies emphasize the need for testing individual compounds and as compounds in mixtures.
Conclusion
OH-PCBs induced predominantly mammary gland and CV tract tumors in mice that were exposed during a critical period of development. OH-PCBs induced tumors in other organs, suggesting that the carcinogenic effect is not restricted to estrogen-sensitive organs. These findings suggest that other organs should be examined in future epidemiologic studies with OH-PCBs. Finally, we believe this report is the first to show that a chemical mixture shifts the tumor type from squamous to adenosquamous, suggesting that exposure to a mixture may result in the formation of a more aggressive tumor type.
We thank J.K. Haseman and G.E. Kissling for their help in all of the statistical analysis. We thank G. Boorman for his confirmation of tumor pathology and R. Newbold and T. Eling for their review of the manuscript.
This research was funded by the M.D. Anderson Cancer Center; the Center of BioEnvironmental Research, Tulane/Xavier University; and grant 16652 from the National Institute of General Medical Sciences, National Institutes of Health.
Figure 1 Chemical structures for OH-PCB-30 (A) and OH-PCB-61 (B).
Table 1 Chemical nomenclature, abbreviations, and ER-αbinding.
Chemical name Abbreviation C50a Observed log IC50b
17β -Estradiol E2 1 0.837
2′,4′,6′-Trichlorobiphenyl PCB-30 — 6.77
2′,4′,6′-Trichloro-4-biphenylol OH-PCB-30 42 2.84
2′,3′,4′,5′-Tetrachlorobiphenyl PCB-61 — NDc
2′,3′,4′,5′-Tetrachloro-4-biphenylol OH-PCB-61 95 2.15
a The molar equivalent required to occupy 50% of the mouse uterine ER-αbinding site (Korach et al. 1988).
b The concentration of competitor predicted to cause a 50% reduction in specific binding of radiolabeled 17β-estradiol to calf uterine ER.
c Not detected (ND) at doses tested (Kramer and Giesy 1999).
Table 2 Gross observations from neonatally treated BALB/c mice at 20 months of age.
Neonatal treatment (μg/pup/day) DVO (mean ± SE) Body weight (g; mean ± SE) Mortalitya (%) No.b
Oil 23.8 ± 0.6 25.0 ± 0.37 9 35
E2 (5) 10.5 ± 0.4* 23.0 ± 0.43* 16 43
E2 (2.5) plus OH-PCB-30 (100) 10.9 ± 0.4* 24.8 ± 0.50 21 24
OH-PCB-30 (200) 11.1 ± 0.2* 24.6 ± 0.40 31** 32
OH-PCB-30 (20) 24.8 ± 0.4 24.8 ± 0.51 21 39
OH-PCB-61 (400) 12.4 ± 0.4* 24.7 ± 0.40 33** 33
OH-PCB-61 (40) 17.7 ± 0.8* 25.0 ± 0.44 19 31
OH-PCB-30/61 (100 + 100)c 12.1 ± 0.4* 25.7 ± 0.62 30** 27
OH-PCB-30/61 (10 + 10)c 22.4 ± 0.6 25.3 ± 0.33 18 40
DVO, day of vaginal opening. Pups were treated as described in “Materials and Methods.”
a Percentage of animals that died before the end of the study.
b Number of animals used for study.
c Equal concentrations of OH-PCB-30 and OH-PCB-61 were used as a mixture.
*p < 0.05 versus sesame oil control (Tukey-HSD test).
**p < 0.05 versus sesame oil control (Wilcoxon rank sum test).
Table 3 Summary of specific tumor incidence in BALB/c mice treated neonatally and sacrificed at 20 months of age.
Incidence of tumor type
Neonatal treatment (μg/pup/day) ML H BA C CV OG MG Ota TNTb No.c
Oil 1 0 0 0 0 0 0 0 1 33
E2 (5) 0 0 1 2 16** 1 1 0 18** 37
E2 (2.5)/OH-PCB-30 (100) 0 0 0 0 9** 3* 0 1 11* 19
OH-PCB-30 (200) 0 0 0 1 10** 3 0 0 12* 22
OH-PCB-30 (20) 0 0 3 0 2 0 5 0 9* 33
OH-PCB-61 (400) 0 2 0 0 5* 0 1 2 11* 24
OH-PCB-61 (40) 2 1 0 0 4* 3 4* 2 15* 30
OH-PCB-30/61 (100 + 100)d 2 0 1 0 8* 2 0 2 13* 21
OH-PCB-30/61 (10 + 10)d 0 0 0 0 3 1 3 1 8* 36
Abbreviations: BA, bronchoalveolar; C, cholangiocarcinoma of the gallbladder; CV, cervicovaginal tract carcinoma; H, hemangiosarcoma; OG, ovarian granulosa cell tumor; MG, mammary gland carcinoma; ML, malignant lymphoma; OG, ovarian granulosa cell tumor; Ot, other types of tumors not listed; TNT, total number of tumors found in that treatment group. Pups were treated as described in “Materials and Methods.”
a Tumor type occurred in no more than one animal per group.
b Some mice had more than one type of tumor.
c Number of mice diagnosed by H&E staining.
d Equal concentrations of OH-PCB-30 and OH-PCB-61 were used as a mixture.
*p < 0.05 versus sesame oil control (Fisher exact test).
**p < 0.01 versus sesame oil control (Fisher exact test).
Table 4 Interactive effects on frequency of carcinoma types in the CV tract.
Percent frequency
Neonatal treatment (μg/pup/day) Total incidencea Squamous Adenosquamous
E2 (5) 16/37b 41 (15/37) 8 (3/37)
OH-PCB-30 (200) 10/22b 36 (8/22) 14 (3/22)
OH-PCB-61 (400) 5/24 13 (3/24) 8 (2/24)
E2 (2.5)/OH-PCB-30 (100) 9/19 16 (3/19) 32 (6/19)*
OH-PCB-30/61 (100 + 100)c 8/21 14 (3/21) 24 (5/21)
Pups were treated as described in “Materials and Methods.”
a Total incidence is the number of CV tract tumors per total number of mice treated.
b Some mice had more than one type of CV tract tumor.
c Equal concentrations of OH-PCB-30 and OH-PCB-61 were used as a mixture.
*p < 0.05 versus a combination of E2 (5) and OH-PCB-30 (200), Fisher exact test.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7785ehp0113-00105216079078ResearchEnvironmental MedicineAcute Blood Pressure Responses in Healthy Adults During Controlled Air Pollution Exposures Urch Bruce 12Silverman Frances 123Corey Paul 12Brook Jeffrey R. 1245Lukic Karl Z. 1Rajagopalan Sanjay 6Brook Robert D. 71 Gage Occupational and Environmental Health Unit, St. Michael’s Hospital, Toronto, Ontario, Canada2 Department of Public Health Sciences,3 Department of Medicine, and4 Department of Chemical Engineering, University of Toronto, Toronto, Ontario, Canada5 Air Quality Research Branch, Meteorological Service of Canada, Environment Canada, Toronto, Ontario, Canada6 Mount Sinai School of Medicine, New York, New York, USA7 Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USAAddress correspondence to B. Urch, Gage Occupational and Environmental Health Unit, 223 College St., Toronto, Ontario, Canada M5T 1R4. Telephone: (416) 978-5886. Fax: (416) 978-2608. E-mail:
[email protected] authors declare they have no competing financial interests.
8 2005 19 5 2005 113 8 1052 1055 23 11 2004 20 4 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Exposure to air pollution has been shown to cause arterial vasoconstriction and alter autonomic balance. Because these biologic responses may influence systemic hemodynamics, we investigated the effect of air pollution on blood pressure (BP). Responses during 2-hr exposures to concentrated ambient fine particles (particulate matter < 2.5 μm in aerodynamic diameter; PM2.5) plus ozone (CAP+O3) were compared with those of particle-free air (PFA) in 23 normotensive, non-smoking healthy adults. Mean concentrations of PM2.5 were 147 ± 27 versus 2 ± 2 μg/m3, respectively, and those of O3 were 121 ± 3 versus 8 ± 5 ppb, respectively (p < 0.0001 for both). A significant increase in diastolic BP (DBP) was observed at 2 hr of CAP+O3 [median change, 6 mm Hg (9.3%); binomial 95% confidence interval (CI), 0 to 11; p = 0.013, Wilcoxon signed rank test] above the 0-hr value. This increase was significantly different (p = 0.017, unadjusted for basal BP) from the small 2-hr change during PFA (median change, 1 mm Hg; 95% CI, −2 to 4; p = 0.24). This prompted further investigation of the CAP+O3 response, which showed a strong association between the 2-hr change in DBP (and mean arterial pressure) and the concentration of the organic carbon fraction of PM2.5 (r = 0.53, p < 0.01; r = 0.56, p < 0.01, respectively) but not with total PM2.5 mass (r ≤ 0.25, p ≥ 0.27). These findings suggest that exposure to environmentally relevant concentrations of PM2.5 and O3 rapidly increases DBP. The magnitude of BP change is associated with the PM2.5 carbon content. Exposure to vehicular traffic may provide a common link between our observations and previous studies in which traffic exposure was identified as a potential risk factor for cardiovascular disease.
air pollutionblood pressurehypertensionozoneparticulate matterPM2.5
==== Body
Exposure to fine particulate air pollution [aerodynamic diameter < 2.5 μm (PM2.5)] is associated with increased cardiopulmonary mortality (Pope et al. 2002; Samet et al. 2000). Coronary ischemic events, occurring as rapidly as 1–2 hr after exposure, account for a major portion of this heightened mortality (Peters et al. 2001, 2004; Pope et al. 2004). In addition, an enhanced risk for acute cerebrovascular strokes has been linked to particulate air pollution (Hong et al. 2002; Tsai et al. 2003).
Several biologic mechanisms have been demonstrated that may in part explain these findings (Brook et al. 2003), including acute arterial vasoconstriction after exposure to concentrated ambient fine particles (CAP) with added ozone (Brook et al. 2002). In the latter study (Brook et al. 2002), subjects exposed to CAP with a higher organic carbon content had greater vasoconstriction than did those subjects exposed to CAP with less organic carbon content (Urch et al. 2004). Considering that PM2.5 has also been shown to alter autonomic balance (Devlin et al. 2003; Gold et al. 2000; Magari et al. 2001), it is reasonable to hypothesize that air pollution exposure can meaningfully affect blood pressure (BP). Using a randomized, sham [particle-free air (PFA) without added O3] controlled study, we investigated the effect of short-term inhalation of CAP with added O3 (CAP+O3) on BP and heart rate (HR) measured during exposure.
Materials and Methods
Study participants.
BP and HR were measured in healthy individuals at 30-min intervals during 2-hr controlled exposures carried out at the Gage Occupational and Environmental Health Unit. Measurements were included from two randomized CAP exposure studies (studies A and B) with identical exposure protocols but different cardiorespiratory outcome measures. We report data for the exposures that were common to both studies, 150 μg/m3 CAP+O3 and PFA. Twenty-one subjects were included from study A. We have previously reported BP data from this study (Brook et al. 2002), measured immediately before and 10–20 min after CAP+O3 exposure, which showed no significant changes in systolic BP (SBP) or diastolic BP (DBP). Two subjects were also included from study B (which included both respiratory and vascular measures in individuals exposed to CAP±O3) who had complete data for both exposures. Therefore, 23 individuals (21 from study A and 2 from study B) had both treatments. Exposures were a minimum of 1 day apart, and the median interval between exposures was 1 week. Both studies had prior approval from the human subjects research ethics review committees of the University of Toronto and St. Michael’s Hospital; all participants gave written informed consent before enrollment. Healthy 18- to 50-year-old non-smokers were enrolled who met the following criteria: no cardiovascular disease or diabetes, normotensive (100/50 < BP < 140/90 mm Hg), not using lipid-lowering medications or inhaled/oral corticosteroids, and free of upper respiratory tract infections for at least 3 weeks before exposure testing.
Exposure protocol.
To reduce the impact of diurnal variation, subjects arrived at the lab around 0900 hr for each exposure. Each subject underwent a vascular and respiratory work-up of approximately 1 hr followed immediately by a 2-hr exposure to either CAP+O3 or PFA using a facemask delivery. During exposure, subjects were asked to list any symptoms that they had, and immediately afterward, they were asked if they thought that they were exposed to a pollutant. Subjects were blind to the two exposure treatments. CAP exposures were produced with a high-flow virtual impactor system, using fine PM (PM2.5) drawn from outside the laboratory; O3 produced by an arc generator was added upstream of the particle concentrator. During PFA exposures, a HEPA filter was inserted inline downstream of the particle concentrator. The human exposure facility and exposure characterization have been described in detail elsewhere (Petrovic et al. 2000; Urch et al. 2004).
BP and HR measures.
BP and HR are highly variable, and even minor alterations in temperature and physical activity can substantially alter readings (Parati et al. 2003). In order to minimize effects not mediated by air pollution, BP and HR were measured throughout the actual exposure period while subjects were resting quietly and at constant temperature (~ 23°C). Participants were provided with an automated oscillometric BP monitor (Oscar-1; SunTech Medical Instruments, Inc., Raleigh, NC, USA) immediately before entering the exposure enclosure, with the arm-cuff secured on their left upper arm. In addition, a PC-based real-time 12-lead electrocardiogram (ECG; PC-ECG 1200; Norav Medical Ltd., Kiryat Bialik, Israel) was connected. After sitting inside the exposure enclosure, the door was sealed and concentrator pumps turned on. When resting quietly, subjects were asked to place their left forearm on their left leg and then turn on the BP monitor with their right hand (0 hr baseline exposure measure). BP measures were repeated at 30-min intervals during exposure, with the final BP measurement made at 2 hr, immediately before the end of the exposure. HR was determined at 30-min intervals by the 12-lead ECG. The technician recording the BP and HR was blinded to the exposure treatments (CAP+O3 vs. PFA).
Statistical methods.
Intraexposure comparisons of BP (SBP, DBP, mean) and HR outcome variables were made using a 0-hr value as the baseline reference to assess the change over the course of the 2-hr exposure. The respective exposure values at 2 hr were calculated as a linear change (Δ = 2 hr – 0 hr) for each participant. The nonparametric Wilcoxon signed rank test was used to compare the difference in the 0- to 2-hr change between the two exposure treatments and was confirmed using percentage change, as well as the slopes of the individual straight lines fitted over the five 30-min measurements. Further support was provided by Student’s t-test of the linear and percentage changes and by a repeated-measures analysis of the linear trend over time. Linear regression analysis was also used to estimate the slope of the relationship between the 2-hr change in BP (SBP, DBP, mean) and the concentration of individual PM constituents (organic and elemental carbon, inorganic ions, and trace elements) as well as the total mass concentration. PM constituent measurements have previously been described (Urch et al. 2004). An unpaired t-test was used for interexposure comparisons of the mean PM2.5, O3, and environmental values. The Wilcoxon signed rank test was used for inter-exposure comparison of the number of symptoms reported during exposure. All analyses were performed using SAS for Windows (release 8.02; SAS Institute Inc., Cary, NC, USA).
Results
The participants ranged in age from 19 to 50 years, with a mean (± SD) age of 32 ± 10 years, and included 13 males and 10 females (Table 1).
The mean exposure concentration (± SD) of the PM2.5 total mass was 147 ± 27 with a range of 102–214 μg/m3 (Table 2). The mean O3 concentration was less variable, with a mean (± SD) of 121 ± 3 and range of 115–128 ppb. As expected, the PM2.5 total mass and O3 concentrations were significantly different between exposure treatments (p < 0.0001 for both). The exposure concentrations of the other copollutants measured, which included nitrogen oxides, sulfur dioxide, and carbon monoxide, were low and less than measured ambient levels. There were no significant treatment differences for any of these copollutants, for temperature, or for relative humidity.
Symptoms reported by subjects during exposures were few, if any (mean number of symptoms reported ± SD was 1.0 ± 1.9 for CAP+O3 vs. 0.5 ± 1.2 for PFA; p = 0.039). For example, only 4 of 23 subjects (17%) reported smelling an odor during CAP+O3 exposure, compared with 1 during PFA, which is not surprising because the O3 generation began at the exposure start (time 0 hr) and progressively increased over the first 10 min, then remained stable at 120 ppb over the duration of the exposure. Subjects were not able to identify the pollutant (CAP+O3) exposure with any precision, because only 52% (12 of 23) responded “yes” immediately afterward to the question, “Do you think you were exposed to a pollutant?” After PFA exposure, 30% (7 of 23) responded “yes” they were exposed to a pollutant, including 4 of the 12 who said “yes” after CAP+O3.
Table 3 shows the median DBP and SBP values over the course of CAP+O3 and PFA exposures. The last column shows the 0- to 2-hr linear change. DBP showed a progressive trend to increase over time during CAP+O3 exposure, from a 0-hr baseline, compared with a flat response during PFA. A significant 2-hr increase of 6 mm Hg (binomial 95% CI, 0–11; p = 0.013), or 9.3% above the 0-hr value, was observed during CAP+O3 exposure. This increase was significantly different (p = 0.017, unadjusted for basal BP) from the small, nonsignificant, 1 mm Hg (95% CI, −2 to 4) change observed during PFA. Analyses using percent change and slope as end points, as well as parametric analyses, supported these findings. There were no significant changes of SBP, mean arterial pressure (MAP; 2/3 DBP + 1/3 SBP), or HR during either exposure.
The observed changes in DBP prompted us to look more closely at which constituents of the CAP may be responsible. Figure 1 suggests a nonlinear relationship between the individual 2-hr linear changes in DBP during CAP+O3 exposure and the estimated exposure concentration of organic carbon. Indeed, the strongest association was found with the loge (ln) concentration of organic carbon (r = 0.53, p = 0.009), much stronger, for example, than with PM2.5 total mass (r = 0.25, p = 0.27). For these individuals, the mean total carbon (organic + elemental carbon) fraction of the PM2.5 total mass was 28.4 ± 13.3 μg/m3 (range, 11.4–56.5), of which the majority, 25.0 ± 11.6 μg/m3, was organic carbon. There was also a significant correlation for the 2-hr change in MAP and the ln concentration of organic carbon (r = 0.56, p = 0.006) but, again, much weaker with total mass (r = 0.21, p = 0.35). A weaker correlation was shown between SBP and ln organic carbon (r = 0.45, p = 0.031) but not with total mass (r = 0.05, p = 0.83).
Discussion
For individuals exposed to CAP+O3 for 2 hr, we observed a significant increase of 6 mm Hg (9.3%) in DBP. A particularly interesting finding is the strong association between the size of the 2-hr DBP change (and MAP) and the carbon content of the fine particles. This result provides additional validity and biologic plausibility to the observed blood pressure increase. The levels of both pollutants in this study, although high, were at environmentally relevant concentrations [U.S. Environmental Protection Agency (EPA) 2002]. These findings shed further light upon the biologic mechanisms that link air pollution exposure to enhanced cardiovascular risk.
The significant correlation between the organic carbon fraction of the PM2.5 and BP change suggests that PM composition is an important factor in cardiovascular health effects and that the carbonaceous fraction in particular warrants further study. It is also possible that carbon is only a measurement proxy for the pollutant actually responsible. A source apportionment study of local ambient PM2.5, over the same time period, has shown that 40–50% of the organic carbon was motor vehicle related—through either combustion or road dust (Lee et al. 2003). Exposure to urban traffic has been identified as a potential risk factor for cardiovascular disease (Peters and Pope 2002). Of particular note, a recent study reported an association between exposure to urban traffic and the onset of myocardial infarction, as soon as 1 hr afterward (Peters et al. 2004). Although we observed no association between other pollutants (e.g., O3) and the change in BP, further studies will be required to clarify the specific pollutant(s) responsible. In particular, studies will need to be performed using concentrated particles with and without added O3 to determine if there are important additive or synergistic interactions.
Of substantial interest is that three epidemiologic studies have previously demonstrated associations between air pollution and elevated BP (Ibald-Mulli et al. 2001; Linn et al. 1999; Zanobetti et al. 2004). However, the findings of our study, using a controlled experimental design, greatly strengthen the evidence that air pollution actually plays a causal role in elevating BP.
Several published controlled human exposure studies have not reported a significant increase in DBP after exposures to fine CAP (Gong et al. 2003), fine CAP+O3 (Brook et al. 2002), diesel exhaust particles (Nightingale et al. 2000), or O3 (Gong et al. 1998). Gong et al. (1998) did, however, report a significant increase in the rate pressure product (SBP × HR) and HR after O3 exposure compared with filtered air, although the dose of O3 the subjects were exposed to (300 ppb for 3 hr with intermittent exercise, 30–40 L/min) was at least 12 times higher than in our study (120 ppb for 2 hr without exercise). It is probable that other experimental differences at least partially explain the disparity between the present and previous reports. For example, as far as we are aware, the hemodynamic responses (in the aforementioned four controlled human exposure studies) have previously been reported only before and several minutes after exposures. The accurate determination of any pollutant-induced alteration in BP was likely hindered by temperature and pressure changes, physical exertion, and the probable time delay (several minutes) during patient transport from the exposure facility to the site of physiologic measurements. Our present study design minimized some of these confounding variables, by performing repeated measurements of BP during the actual exposure period using an automated oscillometric device while resting stationary in the enclosure.
At present, the precise pathophysiologic mechanisms of the BP increase remain speculative. It is possible that, through activation of lung macrophages or alveolar cells after cellular uptake and/or via interaction with the cell membranes, inhaled pollutants (particles/O3) could promote systemic oxidative stress (Donaldson et al. 2003; Kelly 2003). Induction of systemic oxidative stress (Gurgueira et al. 2002; Sorensen et al. 2003) and a pro-inflammatory response (Salvi et al. 1999; Tan et al. 2000) may alter the bioavailability of nitric oxide within the vasculature and/or by increasing several vasoconstrictive factors, such as endothelin (Taniyama and Griendling 2003; Vincent et al. 2001) or asymmetric dimethyl-arginine (Cooke 2000; Dvonch et al. 2004). Indeed, enhanced oxidative stress and systemic inflammation are known to play a key role in the pathophysiology of many cardiovascular diseases, in particular, hypertension (Oparil et al. 2003). An imbalance of the autonomic nervous system favoring an increase in sympathetic drive (Devlin et al. 2003; Gold et al. 2000; Magari et al. 2001) may also contribute to this hypertensive response.
The risks for myocardial infarctions (Peters et al. 2001, 2004), as well as strokes (Hong et al. 2002; Tsai et al. 2003), increase in response to acute elevations in air pollution. These observations suggest that a factor common to the etiology of both adverse outcomes, such as an elevated BP, may play a significant pathophysiologic role. It is well established that relatively small sustained increases in DBP even within the normotensive range, as observed in this study (median change of 6 mm Hg), increase the long-term risk for coronary events and strokes by approximately 30% and 40%, respectively (Lewington et al. 2002; Vasan et al. 2001). However, the cardiovascular risk imposed by the brief vasopressor response observed in this study is less certain. Nevertheless, two hypothetical scenarios may be envisioned. Exposure to high ambient concentrations of air pollutants may initiate a rapid hypertensive response, thus promoting acute cardiovascular events in susceptible individuals. In conjunction, if this vasopressor response continues unabated, gradients in personal exposure to air pollution could contribute to long-term differences in interindividual BP levels. Continued exposure to air pollution could thereby increase the risk for developing chronically elevated BP and possibly overt hypertension.
Conclusions
We observed a rapid and a statistically significant increase of diastolic BP among individuals exposed to ambient fine particles and O3. Lending biologic plausibility to this observation is that the size of the 2-hr change in blood pressure is associated with the carbon content of the fine particles. Although this may help explain the mechanisms whereby air pollution increases cardiovascular risk, the clinical significance of this finding, the responsible pollutants, biologic mechanisms, and the duration of the response require further investigation. Exposure to vehicular traffic in urban centers may provide a common link between our findings and previous studies in which this exposure source was identified as a potential risk factor for cardiovascular disease.
We thank all the staff members at the Gage Occupational and Environmental Health Unit who contributed to this study.
This study was funded by contributions from Natural Resources Canada; Health Canada through the Toxic Substances Research Initiative; Air Quality Health Effects Research Section, Government of Canada; and the Ontario Thoracic Society.
Figure 1 Change in DBP at 2-hr exposure to approximately 150 μg/m3 of CAP+O3 versus the estimated exposure mass concentration of the organic carbon fraction of CAP (shown as 2-hr – 0-hr linear change). The solid line indicates the regression line. y = 10.8 ln(x) – 28.8; r = 0.53; p = 0.009; n = 23.
Table 1 Participant characteristics (n = 23; 13 male and 10 female).
Characteristic Mean ± SD
Age (years) 32 ± 10
Height (cm) 173 ± 8
Weight (kg) 72 ± 11
SBP (mm Hg) 117 ± 10
DBP (mm Hg) 77 ± 9
HR (beats/min) 70 ± 11
Table 2 Particle, O3, and environmental exposure measures (mean ± SD; n = 23).
Measure CAP+O3 PFA
PM2.5 (μg/m3) 147 ± 27* 2 ± 2
O3 (ppb) 121 ± 3* 8 ± 5
NOx (ppb) 55 ± 18 51 ± 23
SO2 (ppb) 3 ± 2 4 ± 5
CO (ppm) 0.6 ± 0.2 0.5 ± 0.2
Temperature (°C) 23.0 ± 1.3 23.3 ± 1.3
Relative humidity (%) 49 ± 8 48 ± 9
NOx, nitrogen oxides.
* p < 0.0001, unpaired t-test for CAP+O3 versus PFA.
Table 3 Median (binomial 95% CI) DBP and SBP (mm Hg) over 2-hr exposures (n = 23).
Exposure time
0 hr 0.5 hr 1 hr 1.5 hr 2 hr 2 hr Δa
CAP+O3
DBP 69 (65–75) 73 (68–79) 75 (72–76) 75 (70–76) 78 (71–82) 6 (0–11)*#
SBP 118 (112–127) 117 (111–126) 119 (111–126) 118 (112–124) 121 (113–124) −1 (–5–4)
PFA
DBP 74 (69–78) 73 (71–76) 72 (68–78) 76 (70–81) 73 (70–76) 1 (−2–4)
SBP 117 (113–124) 115 (107–121) 118 (110–123) 120 (114–131) 121 (112–123) 0 (−2–5)
a 2-hr - 0-hr linear change.
* p = 0.013 for CAP+O3 DBP 2 hr Δ, Wilcoxon signed rank test.
# p = 0.017 for CAP+O3 DBP 2 hr Δ versus PFA DBP 2 hr Δ, Wilcoxon signed rank test.
==== Refs
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Environ Health Perspect. 2005 Aug 19; 113(8):1052-1055
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7617ehp0113-00108316079083ResearchMini-MonographBiologic Monitoring of Exposure to Environmental Chemicals throughout the Life Stages: Requirements and Issues for Consideration for the National Children’s Study Barr Dana B. Wang Richard Y. Needham Larry L. National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USAAddress correspondence to D.B. Barr, CDC, 4770 Buford Highway, Mailstop F17, Atlanta, GA 30341 USA. Telephone: (770) 488-7886. Fax: (770) 488-0142. E-mail:
[email protected] authors declare they have no competing financial interests.
8 2005 12 5 2005 113 8 1083 1091 20 9 2004 31 3 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Biomonitoring of exposure is a useful tool for assessing environmental exposures. The matrices available for analyses include blood, urine, breast milk, adipose tissue, and saliva, among others. The sampling can be staged to represent the particular time period of concern: preconceptionally from both parents, from a pregnant woman during each of the three trimesters, during and immediately after childbirth, from the mother postnatally, and from the child as it develops to 21 years of age. The appropriate sample for biomonitoring will depend upon matrix availability, the time period of concern for a particular exposure or health effect, and the different classes of environmental chemicals to be monitored. This article describes the matrices available for biomonitoring during the life stages being evaluated in the National Children’s Study; the best biologic matrices for exposure assessment for each individual chemical class, including consideration of alternative matrices; the analytical methods used for analysis, including quality control procedures and less costly alternatives; the costs of analysis; optimal storage conditions; and chemical and matrix stability during long-term storage.
adductsadipose tissuebioaccumulativebiomonitoringbloodbreast milkmatrixmetaboliteNational Children’s Studynonpersistentpersistenttoxicanturine
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After an individual’s exposure to a given chemical, a proportion of the chemical may be absorbed into the bloodstream, distributed among the bodily tissues, metabolized, and/or excreted. These four complex steps [i.e., absorption, distribution, metabolism, and excretion (ADME)] make up the pharmaco-kinetic process of a chemical (reviewed in Rozman and Klaasen 2001). In order to assess human exposure to a given chemical, biologic measurements of the chemical can be made after the absorption step or during each of the subsequent steps of ADME. Biomonitoring of exposure involves the measurement of the concentration of a chemical in a given biologic matrix during or after ADME, and its concentration level depends on the amount of the chemical that has been absorbed into the body, the pharmacokinetics (ADME) of the chemical, and the exposure scenario, including the time sequence of exposure and time since last exposure (Sexton et al. 1995). Biomonitoring data are independent of the pathway of exposure (Pirkle et al. 1995). Ideally, in order to link the dose with adverse health outcomes, measurements of the biologically effective dose, the dose at the target site that causes an adverse health effect, are preferred (Pirkle et al. 1995). However, often the target organ is not known and, even if known, frequently is not available for sampling. In these situations, we measure the level of the chemical in another biologic sample to gauge the internal dose.
For the National Children’s Study (NCS 2005), the biologic sample can be taken preconceptionally from both parents; from a pregnant woman during each of the three trimesters, during and immediately after childbirth; from the mother postnatally; and from the child as it develops up to 21 years of age. The appropriate sample for monitoring will depend upon matrix availability and the different classes of environmental chemicals to be monitored. Under the auspices of the Chemical Exposures Workgroup of the NCS, we developed this article as part of a larger white paper (NCS 2004) to provide guidance on which biologic samples may be most useful for characterizing exposures of interest in the NCS. Although this guidance may be applicable to other exposure studies, it was developed with the life stages of interest to the NCS and with the recognition that the specimens available for testing may be limited in volume or quantity (Needham et al. 2004, 2005). Unless otherwise stated, we refer to measurements made on biologic samples from the parents or the child but not from the fetus. Further, we focus primarily on chemical measurements made in a biologic matrix that is taken from the participant, a commonly used strategy in human exposure assessment. Although newer methodologies such as imaging techniques and “omics” technology are becoming more readily available (Chaussabel 2004; de Hoog and Mann 2004; Dettmer and Hammock 2004; Kiechle et al. 2004; Olden 2004; Wetmore and Merrick 2004), they are not included in this article.
The General Behavior of a Chemical in the Body
Absorption of a chemical into the body occurs when the chemical enters the bloodstream by passing through absorption membrane barriers after contact of the chemical with an outer boundary (i.e., skin, nostrils, mouth, or eyes). Without absorption, there can be no direct internal toxic effect even if the chemical is toxic, although effects are possible at the absorption barrier (e.g., skin irritation, eye lens irritation). Once the chemical has been absorbed into the bloodstream, it undergoes distribution to the primary deposition sites. Distribution is crucial to toxicity because if the chemical is never distributed to the target site, the toxic effect may be negligible. The concentration of the chemical in the storage depot is in equilibrium with the concentration in the blood, thus the chemical is slowly released from the storage depot as it is eliminated from the blood to maintain the equilibrium. Low concentrations may reach the target organ.
Metabolism takes place primarily in the liver. The overall purpose of metabolism is to make the chemical less toxic and more hydrophilic. Phase 1 metabolism of the chemical typically involves inserting or substituting a functional group to make the chemical more water soluble. Phase 2 metabolism usually chemically links the chemical to a glucuronide or sulfate group, which increases the water solubility and facilitates elimination of the chemical in the urine. However, metabolism does not always render a chemical less toxic.
Metabolized chemicals may be more hydrophilic and can be excreted in urine or may be passed into the feces. If the chemical is not absorbed, it can go straight into the feces. Lipophilic compounds, in particular, are eliminated primarily in the feces. Volatile organic compounds (VOCs) can be excreted through the alveoli or in the expired air through exhalation. Chemicals can also be deposited in certain secretory structures and be excreted as tears, saliva, sweat, or milk in lactating women.
In addition to the internal movement of chemicals in the body, a pregnant woman can distribute the chemicals via the bloodstream through the placenta and into the fetal blood supply. Biomonitoring matrices unique to the fetus include amniotic fluid and meconium. In addition, cord blood, the placenta, and the umbilical cord can be collected at birth.
Behavior of Specific Chemical Classes in the Body
Persistent organic chemicals.
Persistent organic pollutants or chemicals (POPs) include polychlorinated dibenzo-p-dioxins, polychlorinated biphenyls (PCBs), and organochlorine insecticides (Needham et al. 2005; United Nations Environment Program 2001). Polycyclic aromatic hydrocarbons (PAHs) are also often included in this class because they persist in the environment; however, because PAHs behave more like nonpersistent chemicals in the body, we have chosen to exclude them from POPs (Needham et al. 2005). The primary route of exposure to POPs is ingestion. POPs are readily absorbed into the blood supply by passive diffusion. Their blood level initially decays relatively rapidly, representing the alpha decay period (Flesch-Janys et al. 1996). During the alpha decay, the POP is distributed into the fatty portions of tissues and, in lactating women, in breast milk. The concentration of the POP in the fatty portions of tissues is in equilibrium with the concentration in the lipid portion of blood. The fat content of blood serum is 0.5–0.6%, milk is approximately 4% lipid, and adipose tissue may be as much as 95% lipid. Thus, although the equilibrium concentrations of the chemical in the blood and fatty tissues may differ over orders of magnitude, they may be very similar when matrices are adjusted for lipid content.
In pregnant women, the POP may also distribute in the fetal compartment; therefore, other matrices such as cord blood or serum may be used for POP measurements. However, the lipid content of cord blood is lower than that of an adult’s blood, so the sensitivity of the analytical measurement may play a key role in obtaining a valid measurement in cord blood. Other fetal matrices, such as meconium, have not been fully explored for their potential in assessing POP exposures in the fetus. Maternal blood or adipose tissue taken before or during pregnancy and maternal blood, milk, or adipose tissue taken soon after parturition (if breast-feeding or can be taken later if not breast-feeding) are considered the best matrices for estimating fetal exposures to POPs.
Because metabolism and excretion of POPs are very slow, they have a long half-life in the body, usually along the order of years (Michalek et al. 1996; Phillips et al. 1989b). However, because the lipophilic POPs accumulate in the breast milk of lactating women and the milk is removed from the woman’s body, the half-life of POPs in lactating women is about 6 months (LaKind et al. 2000).
Nonpersistent organic chemicals.
Nonpersistent organic chemicals, such as current-use pesticides, phthalates, and VOCs (Needham et al. 2005), can be much more challenging to measure. Their primary routes of exposure for the general population, depending on the scenario, are generally ingestion or inhalation. These chemicals are rapidly metabolized, and their metabolites are eliminated in urine (Figure 1). The deposition matrices are minor matrices for monitoring because only small amounts of the chemical are deposited in the body. The major matrices for assessing exposure are excreta. Blood has also been used as a matrix for biomonitoring. Nonpersistent chemicals tend to have very short half-lives in blood (Figure 1), and the concentrations are usually about three orders of magnitude lower than urinary metabolite levels (Barr et al. 1999). Thus, if blood is used as a matrix, the sensitivity of the analytical method and the matrix volume available for analysis may become important. Blood can also be a valuable matrix for measuring biomolecular adducts such as hemoglobin, albumin, or DNA adducts, such as DNA–PAH adducts.
Saliva has also been explored as a matrix for measuring selected nonpersistent chemicals such as atrazine (Lu et al. 1998). The existing data indicate that saliva levels can be considerably lower than blood levels of a nonpersistent chemical depending upon the degree of protein binding that may occur; thus, a very sensitive analytical technique is required. Further research on additional chemicals and the relation of these measurements to more commonly used approaches is required before this can routinely be used for analysis.
To evaluate fetal exposures, maternal samples collected throughout pregnancy may be used. However, because these chemicals are, by definition, nonpersistent, urine or blood measurements made at a single point in time during pregnancy will address only the exposures that may have occurred in the previous few days unless the exposure is continuous (e.g., pervasive air levels of a chemical resulting from smokers in the home) or continual (e.g., eating the same foods daily with measurable levels of pesticides) (Figure 2). To circumvent this problem, multiple biologic samples can be taken every few days during pregnancy; however, this can be costly and logistically difficult to collect and store and may present an undue burden on the participant. An alternative may be to collect multiple samples over particularly vulnerable stages of the pregnancy, if such stages can be appropriately identified. Another potential approach is to measure nonpersistent chemicals in fetal matrices such as cord blood or meconium.
Bioaccumulative metals.
Bioaccumulative metals persist in the environment and bio-accumulate in humans. This group of chemicals includes some forms of mercury, lead, and cadmium (Needham et al. 2005). For example, lead is readily absorbed, particularly in children, with distribution from the blood to its storage depots—bone and teeth (Aufderheide and Wittmers 1992). Both metabolism and excretion are slow, so monitoring lead levels is more straightforward. The best matrices to use are blood, bone, and teeth. For general population exposures to mercury, methyl mercury is the form of highest concern. Blood, hair, and nails are viable matrices for measuring methyl mercury levels.
Nonbioaccumulative metals.
Non-bioaccumulative metals are readily absorbed into the body, and although some proportion may distribute to various tissues, most will pass through the body rapidly. These metals are typically measured in urine (Horng et al. 1999). However, to gain a longer term dosimeter for exposure, arsenic can also be measured in hair (Wilhelm and Idel 1996) and nails (Lin et al. 1998).
Criteria pollutants and bioallergens.
In general, biomonitoring has a limited role in the measurement of criteria pollutants [e.g., carbon monoxide (CO), oxides of nitrogen, ozone] and bioallergens (e.g., pollen, endotoxins) (Needham et al. 2005). Exposure to CO can be assessed by measuring the carboxyhemoglobin adduct (Shenoi et al. 1998; Smith et al. 1998) or expired CO (Lapostolle et al. 2001; Paredi et al. 2002) in blood and breath, respectively. The adduct measurements provide a longer-term dosimeter for the exposure than breath measurements because hemoglobin has a lifetime of about 4 months.
Bioallergen response can be measured by IgE in maternal, cord blood, or child blood (Carrer and Moscato 2004; Goodman and Leach 2004; Lee et al. 2004). In addition, certain endotoxins or metabolites may be measured in blood or urine samples (Makarananda et al. 1998; Malir et al. 2004). Typically, the endotoxin measurements would reflect a more recent exposure, similar to nonpersistent chemical exposures.
Assessing Exposure throughout the Life Cycle
Biomonitoring measurements have been used for many years to assess exposures in adults (Ashley et al. 1994; Blount et al. 2000; CDC 2003; Pirkle et al. 1995) and, to some extent, in adolescents and children (Adgate et al. 2001; Fenske et al. 2000). Biomonitoring of fetuses, infants, and small children has been performed much less frequently, if at all. Various biologic matrices have been used or considered for assessing environmental exposures throughout the life cycle (Table 1). The mother or pregnant woman has generally been used as a surrogate to evaluate fetal exposures. However, for many chemicals, their ability to transfer from the mother to the fetus is not known and the relationship between maternal and fetal chemical levels has not been defined. Another potential option to evaluate fetal exposures is the use of meconium as a matrix of measurement because it begins accumulating in the bowels of the infant during the second trimester (Bearer 2003; Bearer et al. 2003; Ostrea et al. 1994). However, meconium use has many limitations. Meconium measurements are still in their infancy of development, and to date, no reliable way to relate these measurements to measurements in more commonly used matrices (e.g., urine, blood) exists. In addition no information is gleaned from exposures that occurred in the first trimester. However, many meconium measures have been shown to correlate well with reported maternal exposures to tobacco (Ostrea et al. 1994), drugs of abuse (Ostrea 1999), and alcohol consumption (Bearer et al. 2003), and this matrix shows promise for other chemical exposures of concern (Whyatt and Barr 2001).
The period from birth through 1 year of age is also very important (Needham and Sexton 2000; Needham et al. 2004, 2005). During this time the infants may be breast-feeding, so they may be exposed to chemicals via breast milk. In addition their micro-environments are often close to the floor and substantially different from an older child or adults. At this age probably only urinary chemical measurements and breast milk measurements can be made. Urine volume will likely be limited, usually 10 mL or less.
Once children start school, another environment with potential chemical contamination is included in the exposure scenario; however, biologic sample collections become easier. At this stage in life some blood can be collected, but it is often limited to a small amount. Urine and saliva samples also can be readily collected. As children approach adolescence and adulthood, more biologic samples and/or a greater quantity of a matrix can be collected. At this life stage perhaps up to 100 mL of blood can be collected for various measurements, and urine is typically plentiful.
Biologic Matrices for Exposure Assessment
The two primary matrices used to assess human exposure to chemicals are urine and blood (serum, plasma, blood cells, etc.) (Barr et al. 1999; Needham and Sexton 2000; Pirkle et al. 1995).
Blood.
Many persistent and nonpersistent chemicals can be measured in blood (Angerer 1988; Angerer and Horsch 1992; Barr et al. 2002; Leng et al. 1997). Although the amount of blood is similar in all adults, the chemical composition of blood, such as lipid content, varies between individuals and within an individual, especially postprandial (Phillips et al. 1989a). Blood concentrations of lipophilic chemicals are routinely normalized using blood lipid concentrations to allow a direct comparison of their concentrations within and among individuals, regardless of the time of day the blood was collected. However, other chemicals that can be measured in blood may not vary based upon the blood lipid content. For example, fluorinated chemicals in blood are not dependent upon the lipid content; instead, they bind to blood albumin (Jones et al. 2003). Therefore, these measurements should not be adjusted based upon the blood lipid content; however, other adjustments, such as for albumin content, may be required if deemed appropriate.
Measuring a chemical in blood is inherently advantageous (Barr et al. 1999). Because we know how much blood is in the body, we can calculate the body burden more accurately than if we measure the chemical or its metabolite in urine. However, blood collection is invasive, which may severely limit the ability to collect it from infants and small children. In addition nonpersistent chemicals are usually found in very low concentrations in blood (Barr et al. 1999, 2002). Also, if testing is not performed soon after sample collection, which will likely be the case in the NCS, long-term storage of blood may be problematic, depending upon what form of blood is being stored. Storage conditions and stability of various matrices and chemicals are shown in Table 2.
Urine.
One major advantage of using urine in biomonitoring is the ease of its collection for spot urine samples (Barr et al. 1999; Needham et al. 2004); however, the collection of 24-hr urine voids can be very cumbersome and result in nonadherence (Kissel et al. 2005). Therefore, spot urine samples, whether first-morning voids or “convenience” samplings, are most generally used for biomonitoring purposes. The major disadvantages of spot urine samples include the variability of the volume of urine and the concentrations of endogenous and exogenous chemicals from void to void (Barr et al. 1999; Kissel et al. 2005). The issue on how best to adjust the urinary concentrations of environmental chemicals in a manner analogous to the adjustment of the concentrations of lipophilic chemicals in blood is a subject of continued research. Adjustment using urinary creatinine concentrations [i.e., dividing the analyte concentration by the creatinine concentration (in grams creatinine per liter urine)] is the most routinely used method for correcting for dilution. Analyte results are then reported as weight of analyte per gram of creatinine (e.g., micrograms analyte per gram creatinine). This may work well when comparing analyte levels in a single individual because the intraindividual variation in creatinine excretion is relatively low; however, for diverse populations the interindividual variation is extremely high (Barr et al. 2005).
Breast milk and adipose tissue.
Many of the chemicals measured in blood have been found in breast milk (LaKind et al. 2001) and adipose tissue (Patterson et al. 1987). Breast milk measurements are unique in that they not only provide data on ingestion exposures for the infant but also are indicators of maternal exposures. Breast milk and adipose tissue are lipid-rich matrices, more so than blood, so similar lipid adjustments are required for reporting concentrations of lipophilic analytes. In general these lipophilic analytes partition among the lipid stores in blood, breast milk, and adipose tissue on nearly a 1:1:1 basis (Patterson et al. 1987). More laboratory work needs to be done on the partitioning of less bioaccumulative analytes in these matrices.
Alternative matrices.
Chemicals have been successfully measured in alternative matrices such as saliva (Bernert et al. 2000; Lu et al. 1998), meconium (Bearer et al. 1999, 2003; Ostrea 1999; Ostrea et al. 1994; Whyatt and Barr 2001), amniotic fluid (Bradman et al. 2003; Foster et al. 2002), and breath (Pellizzari et al. 1992). Because many of these matrices are not commonly analyzed, the resulting chemical concentration data are more difficult to relate to measurements made in the more commonly used matrices such as urine, blood, or breast milk and, consequently, may be more difficult to relate to exposure. However, because many of these matrices are available and could provide potentially useful information, they should not be discounted. Instead, preliminary studies evaluating the partitioning of chemicals in the various matrices should be conducted that will allow for comparison of data among matrices.
Measurement method specificity and sensitivity requirements.
Specificity—how specific an analysis method is for a particular exposure—and sensitivity—the ability to measure the chemical at the desired level—are critical parameters for analysis methods, and both must be considered when deciding which matrix to measure. The half-life of a chemical may affect the sensitivity requirement; however, because persistent chemicals have long half-lives, it is not nearly as important as it is for nonpersistent chemicals, which metabolize rapidly. For instance, in adult men, 2,3,7,8-tetrachlorodibenzo-p-dioxin has a half-life of about 7.6 years (Pirkle et al. 1989). Therefore, to assess exposure over a period of time, for example, 9 months, the sample could be collected at any time period within the 9 months or even afterward, and the biologic measurement information would still be useful for accurate exposure classification (e.g., exposure quartiles—people whose exposure is high, medium, low, or none). When measuring exposure to persistent chemicals by analyzing adipose tissue, it makes little difference which portion of the body the sample is taken from; however, because blood is easy to collect and readily available, blood is an ideal medium in which to measure persistent chemicals. In lactating women, milk is also frequently used.
Nonpersistent chemicals have half-lives of hours or minutes; therefore, the postexposure fate of a nonpersistent chemical is dramatically different (Figure 1) (Needham and Sexton 2000). After each exposure the concentration of the chemical in blood declines rapidly. The window of opportunity for measuring nonpersistent chemicals in blood is narrow and requires the use of a very sensitive technique. By measuring these chemicals in blood as the intact, or parent, chemical, we gain information on the exact chemical to which one was exposed. For example, if someone was exposed to chlorpyrifos, we can measure chlorpyrifos in the blood rather than its metabolite, which is formed from more than one parent chemical and is also the same chemical as environmentally degraded chlorpyrifos. In addition to blood, certain nonpersistent chemicals such as cotinine have been measured in saliva because cotinine is in equilibrium in blood and saliva.
In urine we generally measure metabolites of the chemical that may lack the desired specificity for analysis; however, measurements in urine allow a much wider window of opportunity in which to take the sample. Generally, we assess exposure to nonpersistent chemicals by measuring their metabolites in urine, even though this method may not have the specificity of the blood measurement.
When chronic exposure to a nonpersistent chemical occurs, the exposure is continually replenishing the chemical in the blood and urinary elimination may reach a steady state. Therefore, urine becomes a better matrix for measurement because we integrate exposure over a longer period.
Biomolecular adducts.
Persistent and nonpersistent chemicals can also react with bio-molecules such as DNA, hemoglobin, or fatty acids to form biomolecular adducts (Angerer et al. 1998; Schettgen et al. 2002). By measuring these adducts, we are able to increase the amount of time after exposure that we can measure a nonpersistent chemical because the amount of time the adduct remains in the body is largely dependent upon the lifetime of the biomolecule itself (Needham and Sexton 2000). For example, the average life span of a red blood cell is about 120 days. If a chemical formed an adduct with hemoglobin on the day a red blood cell was created, the adduct should remain in the body about 4 months, allowing a much longer time after exposure to collect the sample. Other adducts are formed with DNA, albumin, and other prominent proteins. Because adducts are not formed from every chemical molecule to which one is exposed, adduct measurements must be very sensitive, and usually a large amount of matrix is required. In addition the measurements are usually cumbersome and time-consuming, so the analytical throughput is very low and the cost is very high.
When measuring persistent chemicals, we do not gain much advantage by measuring them as adducts. Blood is still the matrix of choice because the concentration is higher in blood, and we have a wide window of opportunity (Barr and Needham 2002). To form an adduct, the chemical must have an electrophilic site to which a nucleophile on the biomolecule (usually sulfur or nitrogen) can covalently bind.
Sampling time frame.
For persistent organic chemicals, the time frame for sampling is reasonably straightforward. In general, a blood sample can be taken any time, up to several years after exposure, has occurred and the exposure can still be accurately identified; however, the investigator will not have any information about when the exposure occurred. For example, if a PCB concentration of 1,000 ng/g lipid was measured in a blood sample, it is not known if a recent exposure to this amount of PCB occurred or whether a larger exposure occurred many years ago and, although a portion of the PCB has been eliminated from the body over time, this amount is still circulating in the bloodstream. By coupling questionnaire data with these biologic measurements, investigators may gain information on the timing of the exposure (e.g., breast-feeding, subsistence food consumption).
The sampling time frame for nonpersistent chemicals is not straightforward. Because these chemicals have short biologic half-lives, the samples, whether blood or urine, must be collected soon after the exposure in order to appropriately assess the exposure. If the primary exposure medium is the air and the exposure is continuous, a first-morning-void urine sample is probably the best biologic sample for measuring the exposure. However, if the exposure is from a source related to personal grooming (e.g., VOCs from showers or phthalates from personal care products), a first-morning-void urine sample or an early morning blood sample (before showering) would likely miss the exposure from the next day. Rather, a late morning or early afternoon sample would more accurately characterize the daily exposure to these chemicals. Similarly, samples designed to evaluate dietary exposures, such as pesticides, should be collected several hours after mealtimes so that these exposures can be identified.
In general, sample collection for nonpersistent chemical measurements should reflect the residence time of the chemical in each individual matrix. The half-lives of nonpersistent chemicals in blood are typically much less than in urine samples; thus, blood samples may need to be collected within minutes or hours after the exposure, whereas urine samples may be collected several hours or in some instances days after the exposure. Saliva samples will typically mimic blood, whereas meconium samples may provide a longer window for capturing the exposure. Measurements of biomolecular adducts need to consider the lifetime of the biomolecule, rather than the lifetime of the chemical, in the particular matrix; however, more adduct will likely be present immediately after exposure than several weeks after exposure.
Collecting samples from infants and children.
Difficulty is often encountered when collecting urine samples from infants and children who are not toilet trained. The traditional approach is similar to that in a clinical setting, using an infant urine collection bag. This technique is rather straightforward; however, it is usually bothersome to the child and often requires that the child be given liquids to encourage urination within a given time frame. Encouraging urination with drinks will usually dilute the urine and make the analytical measurement more difficult. Other approaches for urine collection, primarily from cloth diapers or cotton inserts, have also been investigated (Calafat et al. 2004; Hu et al. 2000). Another approach of ongoing investigation is the collection of the target analytes directly from the coagulated gel matrix of disposable diapers (Hu et al. 2004). If proven viable for isolating a broad array of target analytes, this method of collection would be most attractive because it is the least burdensome on the participant and the most logistically practical.
Temporal variability in urine and blood samples.
The variability of nonpersistent target analyte levels in samples collected from an individual over time is of concern, whether the sample is biologic or environmental. Temporal variability can include the variation of a given chemical in multiple samples collected on a single day or can include variation among days, months, or seasons. For chronic exposures to nonpersistent chemicals, the exposure is repeated; thus, the amount in a given sample would likely represent the average exposure. However, for episodic exposures, the variability is often greater. For urine matrix, a 24-hr urine sample is preferred; however, this can be burdensome on the participant and often logistically difficult. If a 24-hr sample cannot be obtained, a first-morning void is often preferred because the urine is more concentrated and the collection represents a longer window of accumulation (usually > 8 hr). However, a first-morning collection may not be ideal for certain exposures because the timing for capturing the exposure is “off.” For evaluation of daily, monthly, and/or seasonal variations of analyte in urine, sequential samples are often taken days and weeks apart to evaluate how the intraindividual variation over time compares with the interindividual variation and whether an accurate classification of exposure is possible. These studies are important in interpreting the biomonitoring data and should be considered, at some level, in the NCS. These data will help to determine whether multiple samples should be taken and at what intervals. In most instances sampling for nonpersistent chemicals will require multiple samples taken at regular intervals.
Methodology
Organic chemicals.
Most methods for measuring organic chemicals in biologic matrices use a sample preparation step to isolate the target chemical(s) from the matrix, an analytical technique with a detection system, data processing, and quality assurance (QA) processes (Needham et al. 2005).
The sample preparation steps are usually the most common source of analytic error, whether systematic or random (Barr et al. 1999). If the chemical is inherently incompatible with the analytic system that follows, a chemical derivatization or reduction procedure may also be required. The addition of steps into the sample preparation procedure usually increases the overall imprecision of the method.
Common analytical techniques for separation of individual chemicals include gas chromatography, high-performance liquid chromatography, or capillary electrophoresis that are coupled in-line to a detection system. Common detection systems include mass spectrometry (MS), electron capture, flame photometric, nitrogen phosphorus, fluorescence, and ultraviolet (UV) absorbance detection. Of the detection systems, mass spectrometers provide the most specificity, and UV absorbance detection usually provides the least (Barr et al. 1999). Most MS-based methods have limits of detection (LODs) in the range of picograms to nanograms per gram of matrix, typically adequate enough to detect levels in the general population when 1–10 g of matrix is used (Table 3). The analytical imprecision usually ranges from 10 to 20%.
Other analytic techniques that are often employed with organic chemicals are immunoassays and bioassays (Biagini et al. 1995; Brady et al. 1989). For these techniques a sample preparation step to isolate the chemical from the matrix may or may not be used. Many are commercially available for selected chemicals. However, their development for a new chemical is a lengthy process that typically requires the generation and isolation of antibodies, then the development of the assay itself. Usually UV, fluorescence, or radioactivity detection is used for the assays. They may be very specific for a given chemical, or they may have a great deal of cross-reactivity that may limit their utility. Their LODs can vary widely; however, many have adequate sensitivity for measuring levels in the general population.
Because organic chemicals are measured using expensive instrumentation and require highly trained analysts, these measurements are usually costly. The most selective and sensitive methods are usually the most complex and can range in cost from $100 to $1500 per sample analyzed (Table 3). However, many of the analyses are multianalyte panels, so the cost per analyte per sample is much more reasonable. Generally, immunoassays are less specific and less complex; therefore, their cost is usually less than $50 per test. However, usually only one chemical can be measured per test, and new chemicals cannot be easily incorporated into the method.
Inorganic chemicals.
The sample preparation process for inorganic chemicals is typically much simpler than for organic chemicals. In some instances the sample matrix just needs to be diluted with water before analysis. However, special precautions must be taken to avoid contamination, both preanalytically and in the analytic system. For example, prescreened collection materials should be used for sample collection, all analytic supplies should be appropriately free of the target chemicals, and special clean rooms may be required for analysis.
Inorganic chemicals are usually measured using atomic absorption spectrometry (AAS) or inductively coupled plasma (ICP) MS. In some instances a dynamic collision cell may also be used to eliminate potentially interfering salts from the system. When various forms of inorganic chemicals are speciated, such as for arsenic or mercury, the AAS or ICP–MS will be preceded in-line by some chromatographic unit. For lead screening an efficient portable lead analyzer can be used for in-field measurements.
Similar to organic chemicals because expensive instrumentation is used, the analyses are usually costly, ranging from $50 for single chemicals to $250 for multichemical panels (Table 3). The LODs are comparable with those of organic chemicals and are suitable for general population studies. Because the handling of the sample is usually minimal, the precision is usually better, within 5–10%.
Quality assurance and quality control.
A vital component of all biomonitoring methodology is a sound quality assurance/quality control (QA/QC) program. QA/QC programs typically require strict adherence to protocols and multiple testing procedures that easily allow the detection of systematic failures in the methodology. The requirements for QA/QC are described in detail in Needham et al. (2005).
Conclusions
As a part of the NCS, many researchers will be competing for the matrices available for biologic measurements. We should refine existing methodology to include as many chemicals as possible using as little blood or urine as possible. In addition we should investigate ways to use more readily available, less invasive matrices. We must consider all matrices and analytes that integrate exposure over longer periods in order to maximize the exposure information gained on an individual using the matrices available during a particular life stage.
Another consideration is the quality and cost of analyses. We should evaluate low-cost techniques such as immunoassays for some applications. In addition to requiring smaller volumes of samples, these analyses are often less expensive and require less training to effectively perform the analyses. Before using these less costly techniques, they should be compared with more commonly used techniques to confirm that quality exposure assessment information—as rated by the method sensitivity, accuracy, specificity, and precision—can be obtained and that the resulting data will be comparable with data existing in the literature.
Generally, persistent organic chemicals are measured more readily in blood-based matrices or other lipid-rich matrices. Maternal measurements serve as good surrogates for fetal exposures and even early childhood exposures if levels are not reduced by breast-feeding. Assessments of exposure to nonpersistent chemicals are the most challenging, but they can be measured in multiple matrix types. Urine is the most commonly used matrix for measurement of these chemicals, but interpretation of the information obtained is often complicated by coexposures, urine dilution, specificity issues, and the temporality of the measurement. To date, no ideal way exists to interpret many of these measurements without the use of additional measures, for example, repeat measurements or environmental measurements. Measurements of metals have been performed in many matrices over the years and are, in general, well understood. Biomonitoring will likely have limited utility in the assessment of exposure to criteria pollutants and bioallergens.
This article is part of the mini-monograph “Assessing Exposures to Environmental Agents during the National Children’s Study.”
We thank the members of the Chemical Exposures Workgroup of the National Children’s Study for their invaluable input and critical review of the content of this manuscript, especially G. Akland, K. Thomas, and H. Özkaynak.
Figure 1 Hypothetical postexposure fate of a nonpersistent toxicant in blood and urine. Reproduced from Needham and Sexton (2000) with permission of Nature Publishing.
Figure 2 Hypothetical postexposure fate from chronic exposure to a nonpersistent toxicant in blood and urine.
Table 1 Importance of various biologic matrices for measuring exposure during the different life stages.
Fetal period (trimester)
Age (years)
Matrices Adult pre-conception 1st 2nd 3rd 0–1 2–3 4–11
POPs
Blood (whole) 1 NA NA NA 1 1 1
Blood (serum) 1 NA NA NA 1 1 1
Blood (plasma) 1 NA NA NA 1 1 1
Urine 3 NA NA NA 3 3 3
Saliva 3 NA NA NA NA 3 3
Hair 3 NA NA NA 3 3 3
Nails 3 NA NA NA 3 3 3
Adipose tissue 1 NA NA NA NA NA NA
Feces 3 NA NA NA 3 3 3
Semen 3 NA NA NA NA NA NA
Breath 3 NA NA NA NA 3 3
Teeth NA NA NA NA NA NA 3
Cord blood 1 1 1 1 3 3 3
Meconium 3 2 2 2 3 3 3
Milk (maternal) 1 1 1 1 1 3 3
Blood (maternal) 1 1 1 1 1 3 3
Urine (maternal) 3 3 3 3 3 3 3
Hair (maternal) 3 3 3 3 3 3 3
Adipose tissue (maternal) 1 1 1 1 1 3 3
Nonpersistent organic chemicals
Blood (whole) 1 NA NA NA 1 1 1
Blood (serum) 1 NA NA NA 1 1 1
Blood (plasma) 1 NA NA NA 1 1 1
Urine 1 NA NA NA 1 1 1
Saliva 2 NA NA NA NA 2 2
Hair 3 NA NA NA 3 3 3
Nails 3 NA NA NA 3 3 3
Adipose tissue 3 NA NA NA NA NA NA
Feces 3 NA NA NA 3 3 3
Semen 3 NA NA NA NA NA NA
Breath 3 NA NA NA NA 3 3
Teeth 3 NA NA NA NA NA 3
Cord blood 3 3 3 1 3 3 3
Meconium 3 3 2 2 3 3 3
Milk (maternal) 3 3 3 3 2 3 3
Blood (maternal) 3 1 1 1 3 3 3
Urine (maternal) 3 1 1 1 3 3 3
Hair (maternal) 3 3 3 3 3 3 3
Adipose tissue (maternal) 3 3 3 3 3 3 3
VOCs
Blood (whole) 1 NA NA NA 1 1 1
Blood (serum) 3 NA NA NA 3 3 3
Blood (plasma) 3 NA NA NA 3 3 3
Urine 2 NA NA NA 2 2 2
Saliva 3 NA NA NA NA 3 3
Hair 3 NA NA NA 3 3 3
Nails 3 NA NA NA 3 3 3
Adipose tissue 2 NA NA NA NA NA NA
Feces 3 NA NA NA 3 3 3
Semen 3 NA NA NA NA NA NA
Breath 1 NA NA NA NA 1 1
Teeth 3 NA NA NA NA NA 3
Cord blood 3 3 3 1 3 3 3
Meconium 3 3 3 3 3 3 3
Milk (maternal) 3 3 3 3 2 3 3
Blood (maternal) 3 1 1 1 3 3 3
Urine (maternal) 3 3 3 3 3 3 3
Hair (maternal) 3 3 3 3 3 3 3
Adipose tissue (maternal) 3 3 3 3 3 3 3
Bioaccumulative inorganic chemicals
Blood (whole) 1 NA NA NA 1 1 1
Blood (serum) 3 NA NA NA 3 3 3
Blood (plasma) 3 NA NA NA 3 3 3
Urine 2 NA NA NA 2 2 2
Saliva 3 NA NA NA NA 3 3
Hair 2 NA NA NA 2 2 2
Nails 2 NA NA NA 2 2 2
Adipose tissue 3 NA NA NA NA NA NA
Feces 3 NA NA NA 3 3 3
Semen 3 NA NA NA NA NA NA
Breath 3 NA NA NA NA 3 3
Teeth 3 NA NA NA NA NA 2
Cord blood 2 2 2 1 3 3 3
Meconium 3 2 2 2 3 3 3
Milk (maternal) 3 3 3 3 3 3 3
Blood (maternal) 1 1 1 1 3 3 3
Urine (maternal) 3 2 2 2 3 3 3
Hair (maternal) 2 2 2 2 3 3 3
Adipose tissue (maternal 3 3 3 3 3 3 3
Nonbioaccumulative inorganic chemicals
Blood (whole) 3 NA NA NA 3 3 3
Blood (serum) 3 NA NA NA 3 3 3
Blood (plasma) 3 NA NA NA 3 3 3
Urine 1 NA NA NA 1 1 1
Saliva 3 NA NA NA NA 3 3
Hair 2 NA NA NA 2 2 2
Nails 2 NA NA NA 2 2 2
Adipose tissue 3 NA NA NA NA NA NA
Feces 3 NA NA NA 3 3 3
Semen 3 NA NA NA NA NA NA
Breath 3 NA NA NA NA 3 3
Teeth 3 NA NA NA NA NA 3
Cord blood 3 3 3 3 3 3 3
Meconium 3 3 3 3 3 3 3
Milk (maternal) 3 3 3 3 3 3 3
Blood (maternal) 3 3 3 3 3 3 3
Urine (maternal) 3 1 1 1 3 3 3
Hair (maternal) 2 2 2 2 3 3 3
Adipose tissue (maternal) 3 3 3 3 3 3 3
Criteria pollutants (CO only)
Blood (whole) 1 NA NA NA 1 1 1
Blood (serum) 3 NA NA NA 3 3 3
Blood (plasma) 3 NA NA NA 3 3 3
Urine 3 NA NA NA 3 3 3
Saliva 3 NA NA NA NA 3 3
Hair 3 NA NA NA 3 3 3
Nails 3 NA NA NA 3 3 3
Adipose tissue 3 NA NA NA NA NA NA
Feces 3 NA NA NA 3 3 3
Semen 3 NA NA NA NA NA NA
Breath 1 NA NA NA NA 1 1
Teeth 3 NA NA NA NA NA 3
Cord blood 3 3 3 1 3 3 3
Meconium 3 3 3 3 3 3 3
Milk (maternal) 3 3 3 3 3 3 3
Blood (maternal) 3 1 1 1 3 3 3
Urine (maternal) 3 3 3 3 3 3 3
Hair (maternal) 3 3 3 3 3 3 3
Adipose tissue (maternal) 3 3 3 3 3 3 3
Bioallergens
Blood (whole) 1 NA NA NA 1 1 1
Blood (serum) 1 NA NA NA 1 1 1
Blood (plasma) 1 NA NA NA 1 1 1
Urine 2 NA NA NA 2 2 2
Saliva 3 NA NA NA NA 3 3
Hair 3 NA NA NA 3 3 3
Nails 3 NA NA NA 3 3 3
Adipose tissue 3 NA NA NA NA NA NA
Feces 3 NA NA NA 3 3 3
Semen 3 NA NA NA NA NA NA
Breath 3 NA NA NA NA 3 3
Teeth 3 NA NA NA NA NA 3
Cord blood 3 1 1 1 3 3 3
Meconium 3 3 3 3 3 3 3
Milk (maternal) 3 3 3 3 3 3 3
Blood (maternal) 3 1 1 1 3 3 3
Urine (maternal) 3 2 2 2 3 3 3
Hair (maternal) 3 3 3 3 3 3 3
Adipose tissue (maternal) 3 3 3 3 3 3 3
Amount of matrix reasonably obtainable at each life stagea
Blood (whole) 100 0 0 0 9 22 38
Blood (serum) 40 0 0 0 3.6 8.8 15.2
Blood (plasma) 40 0 0 0 3.6 8.8 15.2
Urine > 100 0 0 0 1–10 10–20 30–50
Saliva 2 0 0 0 0 1–2 1–2
Hair 0.5–4 g 0 0 0 < 0.5 g 0.5–2 g 0.5–4 g
Nails * 0 0 0 * * *
Adipose tissue 10 g 0 0 0 0 0 0
Feces 10 g 0 0 0 3 g 5 g 1 0g
Semen 2 0 0 0 0 0 0
Breath * 0 0 0 * * *
Teeth 0 0 0 0 0 0 6–10
Cord blood 30–60 30–60 30–60 30–60 NA NA NA
Meconium 2 g 2 g 2 g 2 g NA NA NA
Milk (maternal) > 100 > 100 > 100 > 100 > 100 NA NA
Blood (maternal) 100 100 100 100 100 100 100
Urine (maternal) > 100 > 100 > 100 > 100 > 100 > 100 > 100
Hair (maternal) * * * * * * *
Adipose tissue (maternal) 10 g 10 g 10 g 10 g 0 0 0
Abbreviations: 1, important matrix for most chemicals in category; 2, important matrix for one or two chemicals in category; 3, not an important matrix for assessing exposure for chemicals in the category; NA, matrix not viable for life stage because it cannot be feasibly collected or the chemical cannot typically be measured in the matrix or does not represent exposures in a given life stage;
*, unknown amount. Note that matrices available for assessment in 12- to 21-year-olds are similar to those for adults.
a All units are in milliliters unless otherwise stated.
Table 2 Storage requirements and characteristics for biologic matrices and chemical classes.
Chemical class Chemicals Storage temperature Matrix Matrix stability Chemical stability Container Preservative requirements
POPs All –70ºC Milk Years Years Polypropylene, no glass or Teflon NA
All –70ºC Serum/plasma Years Years Polypropylene, no glass or Teflon NA
All –70ºC Adipose tissue Years Years Polypropylene, no glass or Teflon NA
Nonpersistent organic compounds All –70ºC Urine Years Years Polypropylene or glass NA
Phthalates –70ºC Serum/plasma Years Years Polypropylene or glass 125 μmol H3PO4/mL matrix
Pesticides –70ºC Serum/plasma ~ 5 years Up to 1 year (less for many of the reactive pesticides) Polypropylene or glass None
Others –70ºC Serum/plasma Years Years Polypropylene or glass NA
VOCs 4°C Whole blood 10 weeks > 10 weeks Heat and vacuum-purged glass gray-top Vacutainer; restore sterility NaF/potassium oxalate
Bioaccumulative metals 4°C Whole blood Indefinitely Indefinitely Purple-top liquid EDTA Vacutainer; second or third draw NA
Nonbioaccumulative metals –20ºC Urine Indefinitely Indefinitely Prescreened For Hg, Triton X-100, sulfamic acid
Room temperature Hair Indefinitely Indefinitely Zipper bag NA
Table 3 Characteristics of analytical methods for measuring chemical classes in biologic matrices.
Chemical class Most typical matrices Methodology used Detection limits (per gram) Relative SD (%) Throughput (samples/day) Volume for analysis Costa
POPs Blood (serum or plasma) GC-HRMS fg–pg 15–25 20 2–30 mL H
Milk GC-HRMS fg–pg 15–25 20 2–30 mL H
Adipose tissue GC-HRMS fg–pg 15–25 10 1–2 g H
Nonpersistent organic chemicals Blood (serum or plasma) GC-HRMS; HPLC-MS/MS pg–ng 10–20 30 2–10 mL H
Urine GC-MS/MS; HPLC-MS/MS; immunoassay pg–ng 10–15 50 1–4 mL L–M
Saliva GC-HRMS; GC-MS/MS; HPLC-MS/MS pg–ng 10–15 30 1–4 mL H
Milk GC-HRMS; GC-MS/MS; HPLC-MS/MS pg–ng 10–15 40 1–10 mL H
VOCs Blood (whole) GC-MSD; GC-HRMS pg 10–20 10–20 5–10 mL M
Breath GC-MSD ng 10–20 20 10–20 mL M
Bioaccumulative metals Blood (whole) ICP-MS ng 10–15 40 1–2 mL L–M
Hair ICP-MS ng 10–15 40 M
Nonbioaccumulative metals Blood (whole) ICP-MS ng 10–15 40 1–2 mL L–M
Urine ICP-MS ng 10–15 40 1–5 mL L–M
Hair ICP-MS ng 10–15 40 M
Abbreviations: GC, gas chromatography; HPLC, high-performance liquid chromatography; HRMS, high-resolution mass spectrometry; MS/MS, tandem mass spectrometry; MSD, mass selective detector.
a L, low cost: < $100; M, medium cost: $100–500; H, high cost: > $500.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00519EnvironewsForumEHPnet: WHO/AFRO Division of Healthy Environments and Sustainable Development Dooley Erin E. 8 2005 113 8 A519 A519 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The people of Africa are besieged by a wide range of diseases that are hard to eradicate because of widespread lack of sanitation and medical facilities. A number of factors—including poverty, lack of technology, undeveloped infrastructure, and political conflict—mean that the vast mineral, water, and forest resources of the continent for the most part are not sustainably managed, leaving ecosystems degraded, biodiversity severely affected, and human health at risk. The World Health Organization Regional Office for Africa (WHO/AFRO) has implemented a Division of Healthy Environments and Sustainable Development to identify, control, and prevent environmental conditions that adversely impact human health in the context of sustainable development. The division has set up a website at http://www.afro.who.int/des/index.html to educate the public about its activities.
The homepage features overviews of the division’s 12 focus areas: coordination of macroeconomics and health; environmental health policy; environment and promotion of health; environmental risk assessment; food safety; health action in times of crisis; health in sustainable development; long-term health; occupational health; poverty and ill health; protection of the human environment; and water, sanitation, and health. Each area has its own subpage with links to relevant publications and other related resources.
Work in the environmental health policy arena concentrates on assisting countries in developing and implementing environmental health policies, and in building and strengthening nations’ capacity to offer sound environmental health services. Falling under the umbrella of environmental risk assessment are initiatives to improve and promote drinking water quality, chemical safety, environmental health impact assessments and mapping, sustainable management of biomedical wastes, and radiation safety.
One of the more in-depth sections of the website covers food safety. Contained here are fact sheets for health care workers on topics such as genetically modified foods, mycotoxins, informal food trading, groups at high risk for foodborne illness, and hand-washing to prevent disease, among others. This section also includes profiles of 28 African countries with statistics on population, food production and consumption, food-related legislation, and other related topics. Other links go to the WHO’s main food safety pages and a photo gallery depicting the many problems encountered by Africans in obtaining safe food.
The occupational health section provides a link to the WHO/International Labour Organization Joint Effort on Occupational Health and Safety in Africa site, available in English, French, Portuguese, and Arabic. This effort aims to bring occupational safety and health professionals from throughout Africa together in a collaborative network. Also available is contact information for the two occupational health training centers on the continent and links to WHO publications on the subject. The protection of human environment section has profiles for the 46 African countries under the jurisdiction of the WHO/AFRO. The profiles list details about the environmental health laws in each country, and in some instances about plans for developing and implementing new policies.
The water, sanitation, and health section has information on the Africa 2000 Initiative. Launched in 1994 by the health ministries of the 46 WHO/AFRO nations, this initiative seeks to expand water and sanitation services throughout Africa. This section also has information on the Participatory Hygiene and Sanitation Transformation Initiative, a program developed by the WHO and other partners to promote community management of water and sanitation resources.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0508b16079049PerspectivesCorrespondenceArsenic on Children’s Hands: Le et al. Respond Le X. Chris Kwon Elena Zhang Hongquan Wang Zhongwen Jhangri Gian S. Lu Xiufen Li Xing-Fang University of Alberta Edmonton, Alberta, Canada, E-mail:
[email protected] Nelson Capital Health Edmonton, Alberta, CanadaGabos Stephan Alberta Health and Wellness Edmonton, Alberta, CanadaThe authors declare they have no competing financial interests.
8 2005 113 8 A508 A509 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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We appreciate the comments of Zagury and Pouschat and their support of our overall conclusions presented in our article (Kwon et al. 2004). In response to their thoughtful comments, we would like to offer the following clarifications.
In the introduction of our article (Kwon et al. 2004), we cited Balasoiu et al. (2001), Zagury et al. (2003), and others (Stilwell and Gorny 1997; Townsend et al. 2003), who examined arsenic in soil and sand samples from the field or from the laboratory. These references provide the readers with useful background information on the sources and levels of potential arsenic exposure. Examining the distribution, partitioning, and concentration of arsenic in the environmental media (e.g., soil, sand, water, and wood surface) appeared to be the primary objectives of these studies. Arsenic levels had not been directly measured on the hands of children after contact with either chromated copper arsenate (CCA)-treated wood or soil in playgrounds until our study (Kwon et al. 2004).
Because the primary objective of our study was to determine the amount of arsenic on the hands of children after playing in playgrounds, we did not focus on the characterization of arsenic in the soil. Although we determined the levels of arsenic in the composite soil samples from the playgrounds, a detailed characterization of the spatial distribution of arsenic was outside the scope of our study. We agree that the concentration of arsenic in the soil samples varies greatly with the sampling protocols and the location of the samples with respect to the CCA-treated wood structures (Chirenje et al. 2003; Stilwell and Gorny 1997; Zagury et al. 2003; Ursitti et al. 2004). Our composite soil samples could not provide any information on the spatial distribution of arsenic concentration in soil samples collected from the playgrounds. These composite samples were obtained from areas under decks and away from any wood structures. We did not collect soil/sand samples from areas immediately adjacent to the CCA-treated wood. Further studies to understand the distribution of arsenic in playgrounds would benefit from extensive collection and analysis of soil samples from different locations in the playgrounds.
We clearly stated that “children playing in playgrounds constructed with CCA-treated wood have approximately five times more arsenic on their hands than do those playing in playgrounds that do not have CCA-treated wood structures.” We also feel that “it is important to point out to the general public that arsenic is naturally present in the soil and sand regardless whether the playgrounds contain CCA-treated wood structures.” During our study, we found that many of the parents of the participating children did not know that arsenic was naturally present in the environment, albeit with varying concentrations. They thought that if there was any arsenic, it must have been added to the environment by someone. Conversely, if there was no added “synthetic” arsenic, they did not consider arsenic as a potential health concern. This attitude toward toxic substances (natural versus synthetic) can be counterproductive in the effort to achieve the goal of protecting public health. Properly informing the public that arsenic is naturally present in the soil helps people to understand that it is important for children to wash their hands after playing, regardless of whether the playgrounds contain CCA-treated wood structures. The hand–mouth activities of young children can result in the ingestion of arsenic that may be adsorbed on their hands. Children should wash their hands after playing to reduce their potential exposure to arsenic.
We agree with Zagury and Pouschat that “potential ingestion of arsenic from soil under CCA-treated structures should not be neglected.” All efforts need to be made to minimize children’s exposure to the toxic species of arsenic.
==== Refs
References
Balasoiu CF Zagury GJ Deschênes L 2001 Partitioning and speciation of chromium, copper, and arsenic in CCA-contaminated soils: influence of soil composition Sci Total Environ 280 239 255 11763270
Chirenje T Ma LQ Clark C Reeves M 2003 Cu, Cr and As distribution in soils adjacent to pressure-treated decks, fences and poles Environ Pollut 124 407 417 12758021
Kwon E Zhang H Wang Z Lu X Jhangri GS Fok N 2004 Arsenic on the hands of children after playing in playgrounds Environ Health Perspect 112 1375 1380 15471728
Stilwell DE Gorny KD 1997 Contamination of soil with copper, chromium, arsenic under decks built from pressure treated wood Bull Environ Contam Toxicol 58 22 29 8952921
Townsend T Solo-Gabriele H Tolaymat T Stook K Hosein N 2003 Chromium, copper, and arsenic concentrations in soil underneath CCA-treated wood structures Soil Sediment Contam 12 779 798
Ursitti F Vanderlinden L Watson R Campbell M 2004 Assessing and managing exposure from arsenic in CCA-treated wood play structures Can J Public Health 95 429 433 15622791
Zagury G Samson R Deshênes L 2003 Occurrence of metals in soil and groundwater near chromated copper arsenate-treated utility poles J Environ Qual 32 507 514 12708674
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0518aEnvironewsForumFood Safety: A Tea-Time Mystery Szpir Michael 8 2005 113 8 A518 A518 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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When a 52-year-old Missouri woman approached physicians in 1998 complaining of stiffness and pain in her spine, the symptoms were at first attributed to “disc disease.” But a series of laboratory tests showed that the woman had abnormally thick, dense bones and strikingly high levels of fluoride in her urine—hallmarks of skeletal fluorosis, a disease that has been diagnosed only a handful of times in the United States.
The only way to develop skeletal fluorosis is to ingest or inhale too much fluoride. The woman’s drinking water had only about 2.8 parts per million (ppm) fluoride, well below the Environmental Protection Agency (EPA) limit of 4.0 ppm. Other sources of fluoride were also eliminated: She didn’t swallow her toothpaste, she didn’t work with pesticides, and she didn’t live near a mine. So where was she getting all the fluoride?
Then the woman revealed she had drunk up to two gallons of extra-strength instant tea every day of her adult life. Physician Michael Whyte of Washington University School of Medicine and his colleagues decided to measure the fluoride levels in her tea preparation.
They found that, counting the fluoride in her water, the woman was ingesting 37–74 milligrams of fluoride per day. EPA studies suggest that severe skeletal fluorosis could occur over the course of 20 years from a continuous exposure of 20 milligrams of fluoride per day.
Whyte and colleagues then tested 10 instant teas available in grocery stores. They found average fluoride concentrations of 1.0–6.5 ppm in regular-strength tea made with fluoride-free water, with several brands exceeding the Food and Drug Administration limit of 1.4–2.4 ppm for bottled beverages. Their study appears in the January 2005 issue of The American Journal of Medicine.
Whyte believes that individuals who drink large volumes of instant tea for a prolonged period may be putting themselves at risk for skeletal fluorosis. But Joe Simrany, president of The Tea Association of the USA, believes that the Missouri incident was highly unusual. “It had less to do with tea than it had to do with excessive behavior,” he says.
So should the average tea drinker be concerned? “It may be that certain brands ought to cut down the amount of fluoride in their tea or add a warning label to their product,” says Michael Kleerekoper, director of research for bone and mineral metabolism at Wayne State University, “but it would be a real mistake to throw out the baby with the bathwater.” He adds, “I drink tea—it’s wonderful on a hot summer’s afternoon.”
Whyte, who also hasn’t stopped drinking tea, says, “Our research is a call for better understanding of fluoride levels in various teas.” He is now investigating the fluoride levels of bottled tea preparations.
Meanwhile, the woman in Missouri has stopped drinking tea, and her pains have abated. She has since switched to lemonade.
Tea total.
Some instant teas may exceed safe levels of fluoride, suggesting a little refreshment goes a long way.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0518bEnvironewsForumDiet and Nutrition: Olestra’s Second Wind Potera Carol 8 2005 113 8 A518 A518 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Olestra, the nonabsorbable fat substitute, has had a rocky past. Originally explored as a cholesterol-lowering drug, olestra was approved in 1996 for use in fat-free snack foods with the proviso that these snacks carry a warning about possible cramping and loose stools. The Food and Drug Administration dropped this warning in 2003 after determining that initial reports of such effects did not hold up in postmarketing studies. Now olestra may be set to take on a new role: as a way to rid the body of toxicants such as dioxin and polychlorinated biphenyls (PCBs).
“It sounds like a snake oil pitch,” admits chemist Ronald Jandacek, an adjunct professor at the University of Cincinnati College of Medicine who once worked for olestra developer Procter & Gamble. Jandacek and his colleagues fed mice the radioactively tagged toxicant hexachlorobenzene (HCB) and tracked its levels in the brain and liver during a weight-loss-and-regain diet cycle, which parallels the “yo-yo diet” pattern many Americans follow.
In the first weight loss, HCB increased threefold in the brain, fell with weight regain, and increased with the second weight loss. In the liver, HCB acted differently, increasing with weight regain. When the researchers added olestra, fecal excretion of the toxicant soared 30 times, and its accumulation in the brain fell by half. The study details appear in the February 2005 issue of the American Journal of Physiology—Gastrointestinal and Liver Physiology.
Jandacek and colleagues have also completed a preliminary study looking at excretion of HCB in mice during normal food intake and fasting. Olestra appears to enhance the rate of excretion during both, with excretion during the fasting period slightly higher than during the fed period.
“Olestra may be a logical means for biological remediation to remove toxicants,” says Bernard Hennig, a professor of nutrition and food science at the University of Kentucky, adding, “[this work] needs to be confirmed in humans.” Jandacek hopes to eventually feed olestra chips to people living in an area with known organochlorine contamination and monitor toxicant excretion.
In a few case reports, feeding olestra chips to human victims of dioxin poisoning has already been shown to reverse effects. A case report in the June 2005 issue of the Journal of Nutritional Biochemistry describes a patient exposed to high levels of Aroclor at work. Under the supervision of researchers at the University of Western Australia, Perth, the patient ate 16 grams of olestra chips daily for two years. His adipose Aroclor levels dropped from 3,200 parts per million to 56, and his physical symptoms disappeared.
The Center for Science in the Public Interest, which opposed the removal of olestra warning labels, is cautious about recommending olestra for toxicant removal. “More power to them if it works as a medicine,” says executive director Michael Jacobsen. He warns, however, that olestra blocks the absorption of cancer-fighting carotenoids such as beta carotene and lycopene, and advises people to replenish these nutrients by eating carotenoid-rich foods like carrots and tomatoes at different times than olestra chips.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0558a16079057AnnouncementsBook ReviewStreet Science: Community Knowledge and Environmental Health Justice Sze Julie Julie Sze is an assistant professor in American Studies at the University of California, Davis. Her research examines race and urban environmentalism, community-based planning and environmental health research. Her forthcoming book from MIT Press looks at environmental justice activism in New York City, asthma politics, and changes in garbage and energy resulting from privatization and deregulation.8 2005 113 8 A558 A558 Corburn Jason . .
Street Science: Community Knowledge and Environmental Health Justice .
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2005 . . MIT Press . : Cambridge, MA . . ISBN: ISBN: 0-262-53272-7. . $242005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Jason Corburn’s Street Science: Community Knowledge and Environmental Health Justice is an important addition to the literature on the science and politics of environmental health decision making. In clear prose, Corburn provides a “descriptive, analytic, and prescriptive understanding of local environmental-health knowledge” through what he calls “street science” (p. 217). Street science is a framework that joins local insights with professional scientific techniques, with concurrent goals: to improve scientific inquiry and environmental health policy and decision making.
At the heart of Street Science are four case studies from Greenpoint/ Williamsburg, in New York City, where diverse racial and ethnic, low-income populations practice what Corburn calls “science on the streets of Brooklyn.” These studies were centered on complex environmental health issues: subsistence fishing risks, asthma, childhood lead poisoning, and small sources of air pollution. Some of the larger issues addressed through these particular studies include the limits of traditional risk assessment and the politics of mapping health and environment risk. Through these studies, Corburn provides a theoretical model for understanding key characteristics of what he calls “local knowledge,” its paradoxes, and contributions to environmental health policy. Street science, at its best, identifies hazards and highlights research questions that professionals may ignore, provides hard-to-gather exposure data, involves difficult-to-reach populations, and expands possibilities for interventions, resulting in “improved science and democracy.” One of the strengths of this book is that it succeeds where most studies of local knowledge fail, “scaling up” and providing generalizations about the nature of local knowledge, how it is acquired, the typical problems that occur when local and scientific knowledge conflict and why.
Drawing from social science, particularly science and technology studies, Corburn explicitly calls for environmental and public health researchers, policy makers, and urban planners to become “reflective practitioners.” At the same time, he is careful to reject the idea that street science is a panacea. It does not devalue, but rather revalues science. He is not calling for a populism where the “community” replaces “experts,” but for a better understanding of how knowledge “co-produced” among local and professional constituencies can lead to better health, science, and politics.
The greatest strength of the book is in the details about the particular interventions that street science made in these four examples. One of the stronger cases was in the story about subsistence fishing. Local residents added to a U.S. Environmental Protection Agency (EPA) Air Toxics Modeling and Cumulative Exposure project by contributing local knowledge to the dietary exposure assessment. The U.S. EPA had no idea that local residents consumed contaminated fish from the East River, but as a result of community challenges to the U.S. EPA’s risk assessment models, the agency was able to conduct angler surveys and to more accurately represent the real-life exposures that local residents faced. Local knowledge was culturally sensitive, linked with the environmental justice movement, successfully used intermediaries, and was low-cost enough to be incorporated successfully into the U.S. EPA’s practices. Corburn does not claim that each example of street science is successful or equivalent with one another. But even these failures and limits are instructive. For policy makers and health researchers who face hostile communities, his accounts of conflictive public meetings in Greenpoint/ Williamsburg offer a good guide to “what goes wrong and why.”
Agencies such as the National Institute of Environmental Health Sciences are increasingly recognizing community-based research and environmental justice concerns [exemplified, for example, by “Advancing Environmental Justice through Community-Based Participatory Research,” Environ Health Perspect 110(suppl 2)]. At the same time, more focus and funding is being channeled into investigating and eliminating health disparities. Corburn’s Street Science is an essential and critical investigation into the science and politics of local knowledge and environmental health justice at this crucial juncture.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0558b16079057AnnouncementsNew BooksNew Books 8 2005 113 8 A558 A558 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Are Chemical Journals Too Expensive and Inaccessible?: A Workshop Summary to the Chemical Sciences Roundtable
Ned D. Heindel, Tina M. Masciangioli, Eva von Schaper, eds.
Washington, DC:National Academies Press, 2005. 50 pp. ISBN: 0-309-09590-5, $18
Data Analysis and Visualization in Genomics and Proteomics
Francisco Azuaje, Joaquin Dopazo, eds.
Hoboken, NJ:John Wiley & Sons, Inc., 2005. 284 pp. ISBN: 0-470-09439-7, $150
Does the Built Environment Influence Physical Activity? Examining the Evidence—Special Report 282
Committee on Physical Activity, Health, Transportation, and Land Use
Washington, DC:National Academies Press, 2005. 268 pp. electronic report available via PDF, free
Encyclopedic Reference of Genomics and Proteomics in Molecular Medicine
Detlev Ganten, Klaus Ruckpaul, eds.
New York:Springer-Verlag, 2005. 1,500 pp. ISBN: 3-540-44244-8, $499
Energy and Environment
Richard Loulou, Jean-Philippe Waaub, Georges Zaccour, eds.
New York:Springer-Verlag, 2005. 282 pp. ISBN: 0-387-25351-3, $79.95
Estimating the Contributions of Lifestyle-Related Factors to Preventable Death: A Workshop Summary
Planning Committee on Estimating the Contributions of Lifestyle-Related Factors to Preventable Death
Washington, DC:National Academies Press, 2005. 80 pp. ISBN: 0-309-09690-1, $18
From Resource Scarcity to Ecological Security: Exploring New Limits to Growth
Dennis Pirages, Ken Cousins, eds.
Cambridge, MA:MIT Press, 2005. 280 pp. ISBN: 0-262-16231-8, $60
Guide to Analysis of DNA Microarray Data, 2nd ed.
Steen Knudsen
Hoboken, NJ:John Wiley & Sons, Inc., 2005. 160 pp. ISBN: 0-471-65604-6, $39.95
Health Effects of Transport-Related Air Pollution
Michal Krzyzanowski, Birgit Kuna-Dibbert, and Jürgen Schneider, eds.
Geneva:World Health Organization, 2005. 206 pp. ISBN: 92-890-1373-7, $54
Implications of Genomics for Public Health: Workshop Summary
Lyla Hernandez, ed.
Washington, DC:The National Academies Press, 2005. 98 pp. ISBN: 0309096073, $18
Introduction to Bioethics
John Bryant
Hoboken, NJ:John Wiley & Sons, Inc., 2005. 256 pp. ISBN: 0-470-02197-7, $125
Metabolome Analyses: Strategies for Systems Biology
Seetharaman Vaidyanathan, George G. Harrigan, Royston Goodacre, eds.
New York:Springer-Verlag, 2005. 383 pp. ISBN: 0-387-25239-8, $129
Modeling Biological Systems: Principles and Applications, 2nd ed.
James W. Haefner
New York:Springer-Verlag, 2005. 480 pp. ISBN: 0-387-25011-5, $79.95
Proteomics of Spermatogenesis
G.S.Gupta
New York:Springer-Verlag, 2005. 837 pp. ISBN: 0-387-25398-X, $179
RNA Silencing
Esra Galun
Hackensack, NJ:World Scientific Publishing Co., 2005. 468 pp. ISBN: 981-256-206-0, $78
Stem Cells: From Bench to Bedside
Ariff Bongso, Eng Hin Lee, eds.
Hackensack, NJ:World Scientific Publishing Co., 2005. 700 pp. ISBN: 981-256-126-9, $112
The Carcinogenic Effects of Polycyclic Aromatic Hydrocarbons
Andreas Luch, ed.
Hackensack, NJ:World Scientific Publishing Co., 2005. 740 pp. ISBN: 1-86094-417-5, $178
Viral Genome Packaging: Genetics, Structure, and Mechanism
C.E. Catalano, ed.
New York:Springer-Verlag, 2005. 164 pp. ISBN: 0-306-48227-4, $139
World Health Statistics 2005
World Health Organization
Geneva:World Health Organization, 2005. 95 pp. ISBN: 92-4-159326-1, $36
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8254ehp0113-00111716140613Commentaries & ReviewsThe NAS Perchlorate Review: Questions Remain about the Perchlorate RfD Ginsberg Gary 1Rice Deborah 21 Connecticut Department of Public Health, Hartford, Connecticut, USA2 Maine Bureau of Health, Augusta, Maine, USAAddress correspondence to G. Ginsberg, 410 Capitol St., Mail Stop 11CHA, Hartford, CT 06134 USA. Telephone: (860) 509-7750. Fax: (860) 509-7785. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 25 5 2005 113 9 1117 1119 28 4 2005 24 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Human exposure to perchlorate is commonplace because it is a contaminant of drinking water, certain foods, and breast milk. The U.S. Environmental Protection Agency (EPA) conducted a perchlorate risk assessment in 2002 that yielded a reference dose (RfD) based on both the animal and human toxicology data. This assessment has been superceded by a recent National Academy of Science (NAS) review that derived a perchlorate RfD that is 20-fold greater (less stringent) than that derived by the U.S. EPA in 2002. The NAS-derived RfD was put on the U.S. EPA’s Integrated Risk Information System (IRIS) database very quickly and with no further public review. In this commentary we raise concerns about the NAS approach to RfD development in three areas of toxicity assessment: the dose that the NAS described as a no observable adverse-effect level is actually associated with perchlorate-induced effects; consideration of uncertainties was insufficient; and the NAS considered the inhibition of iodine uptake to be a nonadverse effect. We conclude that risk assessors should carefully evaluate whether the IRIS RfD is the most appropriate value for assessing perchlorate risk.
NASneurodevelopmentalperchlorateRfDthyroid hormone
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Perchlorate is a widespread contaminant in drinking water, food, and breast milk [California Department of Health Services (CDHS) 2005; Food and Drug Administration 2004; Kirk et al. 2005]. The U.S. Environmental Protection Agency (EPA) developed a draft risk assessment for perchlorate in 2002 (U.S. EPA 2002a, 2003), but this was recently superceded by an analysis by the National Academy of Science (NAS 2005). The NAS derived a perchlorate reference dose (RfD) that is approximately 20-fold higher (less stringent) than the U.S. EPA draft RfD from 2002. Without any further deliberation or public review, the U.S. EPA has adopted the NAS value and placed it on its Integrated Risk Information System (IRIS) website (U.S. EPA 2005), a primary source of data for state risk assessors.
Given the disparity between the initial U.S. EPA analysis (U.S. EPA 2002a) and the 2005 NAS report, the perchlorate RfD posted on IRIS (U.S. EPA 2005) merits careful consideration before health officials embrace this less stringent value. Our current purpose is to highlight issues with the primary human studies used in the NAS perchlorate determination. However, it is also worth noting that the NAS discounted the studies in rats, arguing that rats are more sensitive to the effects of perchlorate than are humans. We believe that the rat studies provide important information, particularly with respect to thyroid suppression, that should be considered in concert with the human data as part of a comprehensive risk assessment.
We present the outstanding toxicology issues, particularly with respect to the human studies, when considering the public health implications of perchlorate in drinking water and the diet.
What Is the NOAEL?
A key step in deriving any RfD is finding a dose at which toxic effects can no longer be demonstrated—the no observable adverse effect level (NOAEL). This dose is believed to be below the threshold resulting in toxicity and so can be used to extrapolate a “safe” dose for the public, including sensitive subgroups. For perchlorate, there is disagreement over where this NOAEL exists. The critical study used by the NAS involved 14-day exposure of adult humans in which perchlorate induced a dose-dependent decline in iodine uptake into the thyroid (Greer et al. 2002). The U.S. EPA called the lowest dose in that study a LOAEL (lowest observable adverse effect level), whereas the NAS called it a NOAEL on the basis that, although there was a slight numerical difference from controls, it was not statistically significant. We should mention that the NAS actually used the term “NOEL” (no observed effect level), leaving out the “adverse” descriptor.
Examination of Figure 1 (from Greer et al. 2002) reveals that in fact the low-dose group did show clear evidence of a perchlorate effect based on data from individual subjects. Of the seven subjects in the low-dose group (Figure 1D), three showed no perchlorate effect on radioiodine uptake. This is seen as the essentially flat line from baseline value through 2 weeks of perchlorate exposure (exposure day 14; E14) and 2 weeks of perchlorate-free recovery (postexposure day 15; P15). However, four low-dose subjects evidenced the characteristic perchlorate effect observed at the higher doses (Figure 1A–C). Their baseline values decreased after perchlorate exposure and returned to baseline thereafter. The subjects who were resistant to the perchlorate effect, as shown in Figure 1D, had baseline values for radioiodine uptake that were low (< 15%), whereas the responders all had values > 15%. This trend can be seen in the higher dose groups as well (Figures 1B,C), in which the greatest perchlorate effects are in those whose baseline uptake is highest. Baseline uptake may be high in those with induced levels of iodide transporter in response to suboptimal iodide intake (Dohan et al. 2003). How this may affect sensitivity to perchlorate is unclear, although it is possible that the ability of the method to detect perchlorate-induced inhibition in iodide uptake may be enhanced when starting at a higher baseline. Whatever the explanation, the individual results of Greer et al. (2002) point to an effect in four of the seven individuals tested at the lowest dose, indicating that this dose is an effect level.
The NAS, however, relied on an average of the group’s response, and called the low dose a NOAEL rather than a LOAEL. It is true that the group mean for the low dose was not statistically different from the control value, but this was due to the high degree of variability (Figure 1D). In fact, one “nonresponder” in the low-dose group had iodide uptake that was 140% of control. This is an influential data point that tends to obscure uptake inhibition in other subjects when data are pooled.
It is important to evaluate variable data sets to determine whether subgroups can be identified that may have a different threshold for an effect than the main group. This is especially true when evaluating human data because of the greater intersubject variability that can be anticipated relative to inbred animals. The data of Greer et al. (2002) point toward a more sensitive subgroup, which in this case represents more than half of the test group but is not evident in the group mean analysis due to the variable nature of the response. All of this points to the fact that the small number of individuals tested at each of the dose groups [n = 7 at the low dose (Greer et al. 2002)] and the variability inherent in the data resulted in weak power to identify statistically significant effects, even where closer inspection shows that they were present (U.S. EPA 2002a).
The influence of this determination on the RfD is direct: if the lowest dose is a LOAEL, then one estimates the NOAEL via application of an uncertainty factor (typically 3- or 10-fold) or via benchmark dose analysis. Either of these procedures would result in an RfD below that derived by the NAS (2005) and more consistent with that initially drafted by the U.S. EPA (2002a)
How Much Uncertainty Is There in the Perchlorate Database?
Both the NAS (2005) and the U.S. EPA (2002a) applied a 10-fold intrahuman uncertainty factor, acknowledging that the critical study examined a small number of healthy, iodine-replete adults. Perchlorate effects on iodine uptake and thyroid hormone status may be more dramatic in pregnant women who are already somewhat iodine deficient, a high-risk scenario that is not uncommon (Azizi et al. 2003; Kibirige et al. 2004). Unlike the U.S. EPA’s 2002 assessment (U.S. EPA 2002a), the NAS’s assessment did not apply any other uncertainty factors. One dissenting member of the NAS committee thought that an additional uncertainty factor of 3-fold should be applied to account for additional issues.
We believe that a database uncertainty factor of 3- to 10-fold is warranted based upon key data gaps as follows.
Potential for greater toxicity to newborns from lactational exposure.
Perchlorate is actively transported into breast milk, where relatively high levels have been reported in the United States and Chile (Gibbs 2004; Kirk et al. 2005). However, there is very little information on perchlorate effects from lactational exposure. This is an important data gap because perchlorate not only concentrates in breast milk but also inhibits iodide uptake into this medium (Kirk et al. 2005). Therefore, the nursing infant may receive less iodide at the same time that it receives a dose of perchlorate that may inhibit iodide use.
A rat study by Argus Research Laboratories, Inc. (2001), which involved a combination of gestational and lactational exposure, found postnatal effects on thyroid hormone status at low doses. However, it is difficult to separate the effects of lactational and gestational exposure in this study, and all doses were associated with effects, so a NOAEL was not established. Regarding human lactational exposure, there is also very limited information. The Chilean study (Crump et al. 2000) did involve lactational exposure to breast milk and found no effects on newborn thyroid-stimulating hormone (TSH) levels in areas with high levels of perchlorate in drinking water. However, women in the Chilean high-perchlorate area did not have lower iodine content in breast milk relative to the control area despite relatively high levels of perchlorate in breast milk (Crump et al. 2000; Gibbs 2004). This is in contrast to recent results in the United States in which there was an inverse relationship between perchlorate and iodide levels in breast milk (Kirk et al. 2005). The difference in breast milk iodide results may be related to the high intake of iodide in the Chilean areas studied, considerably higher than that common in the United States (NAS 2005). The postnatal parameters evaluated in the Chilean study (Crump et al. 2000) are also very limited (serum TSH concentrations, evidence of goiter). The lack of useful lactational exposure studies is a critical data gap that adds uncertainty to perchlorate risk assessment.
Uncertain relationship between short-term and chronic perchlorate toxicity.
The key study is of 14 days’ duration (Greer et al. 2002). There is uncertainty that longer-term exposure may have a cumulative effect due to prolonged perturbation of iodine transport or increasing storage of perchlorate in the thyroid and other tissues. A study in rats suggests greater perchlorate toxicity to the thyroid from 90-day exposure than from 14-day exposure (Springborn Laboratories 1998). One longer-term study in humans was reported only as an abstract (Braverman et al. 2004). Three- or 6-month exposure to perchlorate caused no effects on thyroid hormone status or iodine uptake inhibition in small numbers of subjects (n ≤5/group). The dose levels should have been high enough to inhibit iodine uptake based on the 14-day studies of Greer et al. (2002). Although this might indicate an adaptive response to longer-term perchlorate exposure, this study (Braverman et al. 2004) lacked statistical analysis and has not been formally published or peer reviewed, and it is uncertain whether it had sufficient power to detect an effect.
What Is an Adverse Effect?
Part of the NAS rationale for not including any other uncertainty factors is that they consider the critical end point, perchlorate-induced iodine uptake inhibition in the thyroid, as a precursor effect, but not an adverse effect (NAS 2005). The NAS states that perchlorate’s toxic effects on brain development would not occur unless thyroid hormone levels available to the fetus or neonate are diminished. Inhibition of iodine uptake is a step in this pathway, but compensatory mechanisms may prevent an effect on circulating thyroid hormone levels. The NAS stated that one would need a 75% inhibition in iodine uptake for the perchlorate effect to be adverse. However, this statement was not supported with evidence or references, and it is unclear whether the NAS meant this to apply to all individuals or just healthy adults replete in iodine and thyroid hormone.
The issue of what constitutes an adverse effect has been debated in risk assessment circles from time to time. Adaptive responses such as induction of hepatic metabolizing enzymes in response to agents such as phenobarbital are generally considered to be non-adverse and are not used to formulate an RfD (Williams and Iatropoulos 2002). However, precursor effects that are part of the toxic pathway are of concern. The risk assessment process acknowledges that other exposures or disease states may also affect the mode of action of the chemical under consideration or may contribute to the adverse effect by some other means. Regarding perchlorate, inhibition of iodine uptake may be compounded by factors that exist in at least some pregnant women and neonates—reduced stores of iodine and thyroid hormone. Therefore, the perchlorate precursor effect seen in healthy adult subjects should be viewed as a critical effect that warrants prevention.
This strategy is consistent with RfD-setting policy at the U.S. EPA (2002b), which states
The critical effect is defined as the first adverse effect, or its known precursor, that occurs to the most sensitive species, as the dose rate of the agent increases.
RfDs have been set based on precursor biochemical changes such as plasma or red cell cholinesterase inhibition for various organo-phosphates [e.g., chlorpyrifos (U.S. EPA 2004a) and malathion (U.S. EPA 2004b)].
Where the distinction between precursor and adverse effect may become important regards how large a LOAEL-to-NOAEL factor should be used. For mild or precursor effects, one might choose to use a smaller LOAEL-to-NOAEL factor than if one were extrapolating from a clearly toxic effect. For this reason, the LOAEL-to-NOAEL factor for perchlorate based upon iodine uptake inhibition (Greer et al. 2002) may reasonably be set at 3-fold rather than 10-fold. However, the distinction between precursor and adverse effect does not normally affect the size of other uncertainty factors considered in RfD determination, such as database deficiencies. Therefore, we do not see the distinction that the NAS made between precursor and adverse effect as affecting the RfD-setting process except as a consideration in choosing a LOAEL-to-NOAEL uncertainty factor.
The factors described above lead us to conclude that the perchlorate RfD derived by the NAS and now on IRIS (U.S. EPA 2005) is higher than what is needed to protect public health with a reasonable margin of safety. We recommend that risk assessors carefully evaluate the IRIS RfD in terms of whether this RfD is based upon a valid NOAEL and whether sufficient uncertainty factors have been applied. The importance of reevaluating the perchlorate RfD is underscored by the issues we raise in this commentary, by the potential for widespread human exposure in utero and for nursing infants, and because there has been no opportunity for public comment on the U.S. EPA’s IRIS value (U.S. EPA 2005) once the NAS completed its review (NAS 2005).
Figure 1 Radioiodine (123I) uptake (RAIU) inhibition profiles at various perchlorate doses in human subjects over 2 weeks of exposure, followed by 2 weeks of recovery. (A) 0.5 mg/kg/day. (B) 0.1 mg/kg/day. (C) 0.02 mg/kg/day. (D) 0.007 mg/kg/day. BV, baseline value. Reprinted from Greer et al. (2002) with permission from Environmental Health Perspectives.
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References
Argus Research Laboratories, Inc 2001. Hormone, Thyroid and Neurohistological Effects of Oral (Drinking Water) Exposure to Ammonium Perchlorate in Pregnant and Lactating Rats and in Fetuses and Nursing Pups Exposed to Ammonium Perchlorate during Gestation or Via Maternal Milk. Protocol 1416-003. Horsham, PA:Argus Research Laboratories, Inc. Available: http://www.tera.org/perchlorate/1416-003.pdf [accessed 8 July 2005].
Azizi F Aminorroya A Hedayati M Rezvanian H Amini M Mirmiran P 2003 Urinary iodine excretion in pregnant women residing in areas with adequate iodine intake Public Health Nutr 6 95 98 12581471
Braverman LE He X Pino S Magnani B Firek A 2004 The effect of low dose perchlorate on thyroid function in normal volunteers [Abstract] Thyroid 14 691
CDHS 2005. Perchlorate in California Drinking Water: Overview and Links. Sacramento:California Department of Health Services. Available: http://www.dhs.ca.gov/ps/ddwem/chemicals/perchl/perchlindex.htm [accessed 15 April 2005].
Crump C Michaud P Tellez R Reyes C Gonzalez G Montgomery EL 2000 Does perchlorate in drinking water affect thyroid function in newborns or school-age children? J Occup Environ Med 42 603 612 10874653
Dohan O De La Vieja A Paroder V Riedel C Artani M Reed M 2003 The sodium/iodide symporter (NIS): characterization, regulation, and medical significance Endocr Rev 24 48 77 12588808
Food and Drug Administration 2004. Exploratory Data on Perchlorate in Food. Available: http://www.cfsan.fda.gov/~dms/clo4data.html [accessed 15 April 2005].
Gibbs JP 2004. Chronic Environmental Exposure to Perchlorate in Drinking Water and Thyroid Function during Pregnancy and the Neonatal Period. 8 August 2004 Update. Letter to Richard Johnston, Chair NAS Perchlorate Committee, from John P. Gibbs, Kerr-McGee Corp.
Greer MA Goodman G Pleus RC Greer SE 2002 Health effects assessment for environmental perchlorate contamination: the dose response for inhibition of thyroidal radioiodine uptake in humans Environ Health Perspect 110 927 937 12204829
Kibirige MS Hutchison S Owen CJ Delves HT 2004 Prevalence of maternal dietary iodine insufficiency in the north east of England: implications for the fetus Arch Dis Child Fetal Neonatal Ed 89 F436 F439 15321965
Kirk AB Martinelango PK Tian K Dutt A Smith EE Dasgupta PK 2005 Perchlorate and iodide in dairy and breast milk Environ Sci Technol 39 2011 2017 15871231
NAS (National Academy of Science) 2005. Health Implicatons of Perchlorate Ingestion. Washington, DC:National Academies Press.
Springborn Laboratories, Inc 1998. A 90-Day Drinking Water Toxicity Study on Rats with Ammonium Perchlorate. Study No. 3455.1. Spencerville, OH:Springborn Laboratories, Inc., Health and Environmental Sciences.
U.S. EPA 2002a. Perchlorate Environmental Contamination: Toxicological Review and Risk Characterization. External Review Draft. NCEA-1-0503. Washington, DC:U.S. Environmental Protection Agency, National Center for Environmental Assessment, Office of Research and Development.
U.S. EPA 2002b. A Review of the Reference Dose and Reference Concentration Processes. EPA/630/P-02/002F. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/iris/RFD_FINAL%5B1%5D.pdf [accessed 8 July 2005].
U.S. EPA 2003. Disposition of Comments and Recommendations for Revisions to “Perchlorate Environmental Contamination: Toxicological Review and Risk Characterization—External Review Draft.” Washington, DC:U.S. Environmental Protection Agency.
U.S. EPA (U.S. Environmental Protection Agency) 2004a. Chlorpyrifos (CASRN 2921-88-2). Available: http://www.epa.gov/iris/subst/0026.htm [accessed 8 July 2005].
U.S. EPA (U.S. Environmental Protection Agency) 2004b. Malathion (CASRN 121-75-5). Available: http://www.epa.gov/iris/subst/0248.htm [accessed 8 July 2005].
U.S. EPA (U.S. Environmental Protection Agency) 2005. Perchlorate and Perchlorate Salts. Available: http://www.epa.gov/iris/subst/1007.htm [accessed 8 July 2005].
Williams GM Iatropoulos MJ 2002 Alteration of liver cell function and proliferation: differentiation between adaptation and toxicity Toxicol Pathol 30 41 53 11890475
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Environ Health Perspect. 2005 Sep 25; 113(9):1117-1119
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7816ehp0113-00112016140614Commentaries & ReviewsMethylmercury Contamination of Laboratory Animal Diets Weiss Bernard Stern Sander Cernichiari Elsa Gelein Robert Department of Environmental Medicine, University of Rochester School of Medicine and Dentistry, Rochester, New York, USAAddress correspondence to B. Weiss, Department of Environmental Medicine, School of Medicine and Dentistry, Box EHSC, Room G-6820, 575 Elmwood Ave., Rochester, NY 14642 USA. Telephone: (585) 275-1736. Fax: (585) 256-2591. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 20 4 2005 113 9 1120 1122 2 12 2004 20 4 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In the midst of research focusing on the neurodevelopmental effects of mercury vapor in rats, we detected significant levels of mercury (30–60 ng/g) in the blood of nonexposed control subjects. We determined that the dominant form of the mercury was organic and that the standard laboratory chow we used in our vivarium was the source of the contamination. The dietary levels were deemed of potential biologic significance, even though they might have fallen below the limits of measurement specified by the supplier. All investigators employing animals in research must assess such potential contamination because dietary agents may alter a) conclusions based on intentionally administered doses, b) outcomes by interacting with other agents that are the primary focus of the research, and c) outcomes of research unrelated to the toxic effects of experimentally administered agents.
animal feedlaboratory dietsmethylmercuryrats
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Methylmercury is recognized as a potent poison, especially for its neurotoxic properties (Davidson et al. 2004). We report here that diets commonly employed in laboratory animal research may contain concentrations of organic mercury, methylmercury most likely, that are sufficient to directly affect the results. Our concerns are 2-fold. First, research focusing on methylmercury effects will include control data contaminated by nonzero exposure levels, and exposure concentrations for detected effects in “exposure groups” will differ from dose levels measured in the intentionally administered agent. Use of such data could compromise conditions for setting adequate exposure standards. Second, investigations not focusing on methylmercury directly, for example, studies of polychlorinated biphenyls (PCBs), which interact with methylmercury (Grandjean et al. 2003; Stewart et al. 2003), might inadvertently include control baselines determined partially by exposures to methylmercury. In such instances, treatment-group comparisons may be distorted by such effects. And, experiments directly aimed at combined PCB–methylmercury effects (e.g., Widholm et al. 2004) might produce confusing outcomes.
Methods and Results
The data described in this article are the byproducts of an investigation we undertook to study the developmental neurotoxicity of mercury vapor in rats. We did not a priori plan the diet assay protocols reported here, and although limited, the results of these evaluations have significance that must be considered in both evaluating past studies and designing future ones. Because surprisingly little is known about the developmental effects of metallic mercury despite its lengthy history in toxicology and its recognized potency as a neurotoxicant (Clarkson 2002), we had planned to examine this aspect of it.
In experiment 1, female Long-Evans rats (Charles River, Wilmington, MA) were bred 3 weeks after receipt from the supplier and then exposed via inhalation to mercury vapor concentrations of 0, 30, 100, or 300 μg/m3 during gestational days (GD)6–20. The 0-ppm control group was held in a separate mercury-free chamber during exposures. The mercury vapor concentration within a chamber was monitored continuously by a continuous mercury vapor analyzer dual-beam ultraviolet photometer in standard flow configuration (model 791.741; EPM Environmental Products Manufacturing, Dalerstraat, the Netherlands), which was capable of measuring concentrations from 2 to 1,999 μg/m3 in air. Mercury in the blood served as a biomarker of exposure. A cold vapor atomic absorption procedure (Lapham et al. 1995; Magos and Clarkson 1972) was used to assay blood samples from the pregnant dams on GD18 and from the pups on postnatal day (PND)4 and PND18.
Control dam (Figure 1) and pup (Table 1) samples showed unexpected, relatively high levels of mercury (particularly as organic mercury). By analyzing the samples for the presence of inorganic mercury specifically, we could estimate the amount of organic mercury (i.e., total – inorganic). As shown in Figure 1 and Table 1, the blood values were predominantly of the organic form.
When we first detected the high levels of mercury in our control subjects, we immediately sought to evaluate, on a probing basis, potential sources. Our sampling procedures were designed and employed to prevent and mitigate recognized potential sources of contamination, as we have done in the past. We did not detect mercury in either the control chamber or the room housing the chambers; in either the atmosphere in the vivarium room assigned to the animals in the experiment or the bedding in the animal cages; in the breath of the investigators who pipetted the blood during the tail-nick procedure used with the dams; or in the heparin that was used for the collection procedures. We did not believe our mercury assay procedures were at fault because they are continually evaluated as part of an international mercury quality control program administered by the Centre de Toxicologie du Québec (Institute National de Santéé Publique, Sainte-Foy, Québec, Canada), which has run the Interlaboratory Comparison Program since 1979.
Together, these results led us to suspect the diet as the source of contamination. Purina Laboratory Rodent Diet 5001 (Scott’s Distribution, Hudson, NH), which has widespread use, was fed to the rats in this research. Sample pellets from the batch in use at that time were ground or milled and then analyzed (we used more than one procedure to systematically replicate our observations and convince ourselves that we had not introduced confounds). For the second procedure, we used a ball mill with zirconium pellets. Between samples, both were washed, and then the jar and Zr pellets were baked at 150°C for several hours to ensure the absence of mercury. Then the samples were individually ground for at least 48 hr. These analyses, as shown by the examples in Table 2, verified that the elevated mercury levels in our control dams and pups were due to the contaminated diet and that they reflected organic mercury.
The Purina 5001 diet is an open diet; that is, its ingredients are subject to change, depending on the source of the raw materials. Fish meal is one of the ingredients, and it is possible that methylmercury present in tuna scraps, for example, may have been the source of the fish meal used in the batch provided by the vivarium. The supplier gives the limit of detection as 0.02 ppm (20 ng/g), so the problem apparently escaped detection. Even so, such levels are excessively high for experiments on mercury, especially those focusing on low-level dose–response outcomes. We were unprepared for the results in the present study because, in an earlier methylmercury study with mice (Stern et al. 2001) also fed the Purina 5001 diet, we detected no mercury in control dams or pups.
To preclude contamination in further experiments, we contacted BioServ (Frenchtown, NJ), a supplier of laboratory animal feed, which recommended the synthetic AIN-93G diet. The protein in this diet is casein. BioServ provided samples of whole pellets as well as the casein incorporated into the diet. Table 3 shows the results of our analysis of the ground pellets and, independently, of the casein. Although the pellets contained mercury, it was 100% inorganic. To determine its effects on blood levels, we fed three females the AIN-93G diet and three males the Purina 5001 diet. We detected no mercury in blood samples from the females fed the AIN-93G diet, but we did find it in the males fed the Purina 5001 diet (28.24, 22.84, and 16.08 ng/mL). (Only total mercury was measured because the focus was on comparing mercury levels in the AIN-93G diet-fed subjects with those fed Purina 5001.) Because inorganic mercury is poorly absorbed after ingestion, these findings are not surprising. These results also confirmed that the rats did not carry a significant mercury burden when they were received from the supplier.
More recently, in our ongoing attempts to find a suitable, mercury-free diet, we analyzed samples of the Teklad 2018 diet (Harlan Teklad, Madison, WI), which does not contain fish meal. We ground four pellets in a mortar to obtain a fine powder, which was then digested with sulfuric acid. No mercury was detected.
Discussion
Figure 1 shows why the possibility of methylmercury contamination in laboratory animal diets cannot be ignored. The levels in control dams were close to the 58 ng/g determined by the National Academy of Sciences committee on methylmercury, on the basis of developmental neurotoxicity, as the benchmark dose lower bound for cord blood in human populations (National Research Council 2000). Although not measured here, we would certainly expect fetal levels in our rats to be even higher (Watanabe et al. 1999), especially in brain, because levels in rodent neonates fall rapidly after birth (Newland and Reile 1999; Stern et al. 2001).
It is impossible to know how much of the published experimental data, as well as ongoing research, may be distorted by contaminated diets. Although the “certified” diets provided by manufacturers may prove useful to investigators, independent confirmation of ingredients should be encouraged. Biomarkers of exposure, that is, tissue indices, are the key to interpreting exposure data. Such direct measures in experimental subjects (including controls) provide assurances that the investigator’s protocols are properly conducted. We uncovered our problem only because we include blood and tissue assays in our standard operating procedures when conducting research with mercury. We strongly urge all researchers to do likewise. In a brief survey of recent literature, we have been surprised by how often researchers neglect to mention diet, or describe it in terms such as “standard rat chow.” Infrequently, the authors may provide the name of the supplier and the diet label, which should be the minimum information provided.
Although the results reported here stem from our research focusing on mercury, the issue of diet-based contamination certainly is not limited to one agent. For example, investigators who study endocrine disruptors have become concerned by the presence of agents in laboratory animal diets that may mimic estrogens (Boettger-Tong et al. 1998). Particularly in investigations of low-level, environmentally relevant exposures, diet is an unwelcome confounder.
We thank M. Balys and M. Langdon for technical assistance.
This work was supported by National Institute of Environmental Health Sciences Center Grant ES-01247 and research grant ES-08109.
Figure 1 Blood levels of total mercury, inorganic mercury, and percent inorganic mercury in control dams. The inorganic component is the product of the slow conversion of methylmercury, the source of the mercury, to the inorganic form (e.g., Rowland et al. 1984).
Table 1 Blood mercury in control pups from dams that had been fed the Purina 5001 diet (experiment 1).
Mercury
Litter ID Age Total (ng/mL) Inorganic (%)
1-001-1D1 PND4 15.5 ND
1-003-1D1 PND4 18.3 ND
1-009-1D1 PND4 11.1 ND
1-010-1D1 PND4 11.5 ND
1-011-1D1 PND4 14.6 ND
1-001-11 PND18 5.3 62
1-003-11 PND18 3.8 87
1-009-11 PND18 3.2 ND
1-010-11 PND18 3.3 ND
1-011-11 PND18 4.1 ND
ND, not detected (the detection limit in our laboratory is 0.75 ng Hg). Samples were pooled within litters to provide a volume adequate for the assays. By PND18, mercury levels had declined substantially (compare Newland and Reile 1999; Stern et al. 2001).
Table 2 Total mercury in rat chow samples.
Mercury
Pellet/method Total (ng/g) Inorganic (%)
Purina 5001
Ground 57.9 0
30.1 48
27.6 31
15.3
12.0
6.7
Homogenized 33.0
8.6
18.0
12.0
Harlan Teklad 2018 ND
ND
ND, not detected. The percentage of inorganic mercury, determined only for the first three ground pellet samples, indicated significant organic mercury contamination.
Table 3 Mercury content analysis of BioServ AIN-93G diet and casein.
Mercury
Sample Total (ng/g) Inorganic (%)a
AIN-93G
1 317.9
2 191.5
3 223.8
4 182.6
5 85.1
6 96.9 100
7 62.9 100
8 71.8 100
9 123.3 100
10 117.7 100
11 144.8 100
Milled sampleb 77.98
Milled sampleb 110.20
Ground sampleb 122.78
Ground sampleb 38.04
Milled samplec 139.35
Ground samplec 54.76
Mean 127.14
Casein
1 ND
2 ND
3 ND
4 ND
ND, not detected. Although variability in total mercury across samples was large, organic mercury was consistently absent.
a Determined for only samples 6–11.
b Samples were digested normally with sodium hydroxide and cysteine and then collected on silver traps to detect the presence of mercury.
c Samples were dissolved in 10% nitric acid and then collected on silver traps to detect the presence of mercury.
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References
Boettger-Tong H Murthy L Chiappetta C Kirkland JL Goodwin B Adlercreutz H 1998 A case of a laboratory animal feed with high estrogenic activity and its impact on in vivo responses to exogenously administered estrogens Environ Health Perspect 106 369 373 9637793
Clarkson TW 2002 The three modern faces of mercury Environ Health Perspect 110 suppl 1 11 23 11834460
Davidson PW Myers GJ Weiss B 2004 Mercury exposure and child development outcomes Pediatrics 113 4 suppl 1023 1029 15060195
Grandjean P Budtz-Jorgensen E Steuerwald U Heinzow B Needham LL Jorgensen PJ 2003 Attenuated growth of breast-fed children exposed to increased concentrations of methylmercury and polychlorinated biphenyls FASEB J 17 699 701 12586743
Lapham LW Cernichiari E Cox C Myers GJ Baggs RB Brewer R 1995 An analysis of autopsy brain tissue from infants prenatally exposed to methylmercury Neurotoxicology 16 689 704 8714873
Magos L Clarkson TW 1972 Atomic absorption determination of total, inorganic and organic mercury in blood J Assoc Anal Chem 55 966 971
National Research Council 2000. Committee on the Toxicological Effects of Methylmercury. Toxicological Effects of Methylmercury. Washington, DC:National Academy Press.
Newland MC Reile PA 1999 Blood and brain mercury levels after chronic gestational exposure to methylmercury in rats Toxicol Sci 50 106 116 10445759
Rowland IR Robinson RD Doherty RA 1984 Effects of diet on mercury metabolism and excretion in mice given methylmercury: role of gut flora Arch Environ Health 39 401 408 6524959
Stern S Cox C Cernichiari E Balys M Weiss B 2001 Perinatal and lifetime exposure to methylmercury in the mouse: blood and brain concentrations of mercury to 26 months of age Neurotoxicology 22 467 477 11579926
Stewart PW Reihman J Lonky EI Darvill TJ Pagano J 2003 Cognitive development in preschool children prenatally exposed to PCBs and MeHg Neurotoxicol Teratol 25 11 22 12633733
Watanabe C Yoshida K Kasanuma Y Kun Y Satoh H 1999 In utero methylmercury exposure differentially affects the activities of selenoenzymes in the fetal mouse brain Environ Res 800 208 214 10092441
Widholm JJ Villareal S Seegal RF Schantz SL 2004 Spatial alternation deficits following developmental exposure to Aroclor 1254 and/or methylmercury in rats Toxicol Sci 82 577 589 15456922
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Environ Health Perspect. 2005 Sep 20; 113(9):1120-1122
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7834ehp0113-00112316140615Commentaries & ReviewsArsenic: A Roadblock to Potential Animal Waste Management Solutions Nachman Keeve E. 1Graham Jay P. 2Price Lance B. 2Silbergeld Ellen K. 21 Department of Health Policy and Management, and2 Department of Environmental Health Sciences, Johns Hopkins University, Baltimore, Maryland, USAAddress correspondence to K. Nachman, 624 North Broadway, Hampton House, Room 513, Baltimore, MD 21205 USA. Telephone: (410) 614-2188. Fax: (410) 614-4535. E-mail:
[email protected] authors contributed equally to the content of this commentary.
The authors declare they have no competing financial interests.
9 2005 12 5 2005 113 9 1123 1124 7 12 2004 12 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The localization and intensification of the poultry industry over the past 50 years have incidentally created a largely ignored environmental management crisis. As a result of these changes in poultry production, concentrated animal feeding operations (CAFOs) produce far more waste than can be managed by land disposal within the regions where it is produced. As a result, alternative waste management practices are currently being implemented, including incineration and pelletization of waste. However, organic arsenicals used in poultry feed are converted to inorganic arsenicals in poultry waste, limiting the feasibility of waste management alternatives. The presence of inorganic arsenic in incinerator ash and pelletized waste sold as fertilizer creates opportunities for population exposures that did not previously exist. The removal of arsenic from animal feed is a critical step toward safe poultry waste management.
arsenicbiomass burningfertilizerincinerationpelletizationpoultry litterpoultry wastewaste managementwaste-to-energy
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The United States produces approximately 8.5 billion broiler chickens annually (U.S. Department of Agriculture 2004), providing an unprecedented range of relatively low cost meat products for consumers worldwide. These production figures have been achieved by extraordinary changes and intensification in poultry production methods that have incidentally created a largely ignored crisis in environmental management. Every chicken produces between 1.46 and 2.67 kg of waste in its life span (Miner et al. 2000; Sharpe et al. 2004), resulting in an annual total of between 12 and 23 billion kilograms. Current federal and state regulations permit largely unrestricted land disposal of animal house wastes, which include excreta, house litter, animal carcasses, and spilled food. This practice is no longer sustainable given the dramatic changes in poultry production in the United States over the past 50 years. Because the number of farms producing livestock and poultry has dropped more than 80%, despite increasing production (Miner et al. 2000), there is now a significant concentration of animals within a given farm [or concentrated animal feeding operation (CAFO)] as well as an increased localization of these CAFOs within relatively few regions of the United States. For example, nearly 7% of U.S. broiler production takes place on the Delmarva Peninsula (Delaware, Maryland, and Virginia), with nearly 600 million chickens producing approximately 1 billion kilograms of poultry waste annually. As a result, CAFOs produce far more waste than can be managed by land disposal within the regions where it is produced. Attention has been paid to the ecologic impacts of this land disposal. When rates of land application exceed soil uptake capacity, the resulting runoff contributes to surface water eutrophication and sudden toxic algal blooms in the Chesapeake Bay and elsewhere.
However, much less attention has been given to the potential risks related to poultry waste constituents, including pathogenic bacteria, antibiotic-resistant bacteria, and residues of the drugs added to poultry feeds. Arsenic in waste results from the use of arsenicals added to poultry feed for growth promotion and prevention of parasitic infections. The U.S. Geological Survey has calculated, based on arsenic concentrations measured in poultry waste, that between 250,000 and 350,000 kg arsenic is annually applied to land in the United States (Rutherford et al. 2003). Although roxarsone, the predominant arsenical added to poultry feed, is an organoarsenical, there is strong evidence that the drug is converted into inorganic arsenic within the chicken (Arai et al. 2003) and is also rapidly transformed into inorganic arsenic in wastes and soils (Garbarino et al. 2003). Elevations in soil arsenic levels have been reported in fields where poultry wastes have been applied (Gupta and Charles 1999). This form of arsenic is readily leachable and may therefore move into groundwater (Rutherford et al. 2003).
Management of the increasing volume of poultry wastes is now being recognized as a serious challenge (Ribaudo et al. 2003), and alternatives to land amendment are being proposed, and in some cases, actively implemented. Two of these proposed alternatives, use as fuel for biomass energy plants and pelletization, are currently in commercial operation and will be expanding. Because of this, there is real urgency for a thorough examination of these solutions.
Three biomass-fueled power plants owned by Energy Power Resources (EPR) are currently in operation in the United Kingdom, and several are planned for the United States. Existing incinerators burn 680 million kilograms of poultry litter each year, and ash from the incineration process is sold as fertilizer. Fibrophos, a subsidiary of EPR, reported sales of > 63,000 metric tons of incinerator ash fertilizer between 2004 and 2005 (EPR 2005). The other new method of disposal technology is to produce fertilizer pellets directly from the waste by drying and pelletizing it. This is currently being implemented in Delaware at a relatively low rate of 55 million kilograms pellets annually (Parker 2001). A partnership has been formed between a major poultry producer and Scotts (Maysville, OH), the nation’s leading source of consumer garden products (The Scotts Company 2005), so that these pellets will be used not only in crop production but also for golf courses, landscaping, and home gardening. The use of these pellets in such settings will create a variety of opportunities for human exposures to arsenic.
Arsenic is a roadblock to potential solutions to the animal waste management crisis. Although biosolid incineration can potentially reduce or eliminate harmful pathogens in wastes (including pathogens that are resistant to antibiotics) and pelletizing processes can also in theory reduce the microbiologic risks of CAFO wastes, neither of these technologies can destroy or detoxify arsenic. Moreover, there is reason to be concerned that these new solutions to an old problem may well increase human exposures to arsenic either through air emissions from waste-to-energy plants or through contamination of soils, water, and food crops through the use of arsenic-contaminated fertilizer products. It is well known that crops grown in arsenic-contaminated soils can accumulate arsenic (Warren et al. 2003). There have been no measurements of air concentrations of arsenic at or near poultry waste incinerators. Preliminary measurements of arsenic concentrations in pelletized waste sold as fertilizer showed levels between 18 and 22 mg/kg (Chesapeake Bay Foundation, personal communication), similar to those reported in unprocessed poultry waste (Jackson and Bertsch 2001).
Arsenic is recognized as a human carcinogen by the U.S. Environmental Protection Agency (EPA), National Research Council (NRC), International Agency for Research on Cancer, National Toxicology Program, and American Conference of Industrial Hygienists, and exposures have also been associated with increased risks of heart disease, diabetes, neurologic effects, and birth defects in humans. A comprehensive reassessment of health risks of arsenic performed by the NRC in 2001 (NRC 2001) formed the basis for a recent regulatory decision by the U.S. EPA to lower the maximum contaminant level for drinking water by 5-fold (U.S. EPA 2001). As noted by Arai et al. (2003), this action must raise concerns about land disposal of arsenic-laden poultry wastes because of the likelihood of ground-water contamination.
Clearly, actions are urgently needed to deal with the increasing burden of poultry wastes from CAFOs. Existing regulations for animal waste disposal are ill-equipped to address the variety of health threats presented by poultry waste; current policies are focused on nutrient content and, as a result, do not take into account the presence of pharmaceuticals, pathogens, and heavy metals in waste. Animal waste is currently not classified as hazardous waste by the U.S. EPA. If animal waste were classified as hazardous waste, it would be prohibited from land disposal based solely on its concentrations of leachable arsenic (Rutherford et al. 2003; U.S. EPA 2004). Given the problems associated with the hazardous constituents of poultry wastes, land disposal is not a viable option. Many of these problems have been addressed in the European Union, where arsenicals were withdrawn from the poultry production process in 1998. Economic analyses have demonstrated that removal of growth-promoting antimicrobials, such as arsenic, has come at no net cost for the poultry industry [World Health Organization (WHO) 2003]. The removal of arsenic from animal feed is a critical step toward safe poultry waste management. In addition, this step will enhance food safety by reducing concentrations of arsenic in poultry products, a potentially significant source of total arsenic exposure for Americans (Lasky et al. 2004; Silbergeld 2004).
Correction
In the manuscript originally published online, the reported sales of incinerator ash fertilizer by Fibrophos were given for 2002 and 2003; the sales have been updated here for 2004 and 2005.
This research was funded by the Johns Hopkins Center for a Livable Future.
==== Refs
References
Arai Y Lanzirotti A Sutton S Davis JA Sparks DL 2003 Arsenic speciation and reactivity in poultry litter Environ Sci Technol 37 4083 4090 14524439
EPR (Energy Power Resources) 2005. Fibrophos. Available: http://www.eprl.co.uk/assets/fibrophos/overview.html [accessed 11 July 2005].
Garbarino JR Bednar AJ Rutherford DW Beyer RS Wershaw RL 2003 Environmental fate of roxarsone in poultry litter. I. Degradation of roxarsone during composting Environ Sci Technol 37 1509 1514 12731831
Gupta G Charles S 1999 Trace elements in soils fertilized with poultry litter Poult Sci 78 1695 1698 10626643
Jackson BP Bertsch PM 2001 Determination of arsenic speciation in poultry wastes by IC-ICP-MS Environ Sci Technol 35 4868 4873 11775163
Lasky T Sun W Kadry A Hoffmann M 2004 Mean total arsenic concentrations in chicken 1989–2000 and estimated exposures for consumers of chicken Environ Health Perspect 112 18 21 14698925
Lichtenberg E Parker D Lynch L 2002. Economic Value of Poultry Litter Supplies in Alternative Uses. College Park, MD:Center for Agricultural and Natural Resource Policy.
Miner JR Humenik FJ Overcash MR 2000. Managing Livestock Wastes to Preserve Environmental Quality. Ames, IA:Iowa State University Press.
NRC (National Research Council) 2001. Arsenic in Drinking Water: 2001 Update. Washington, DC:National Academy Press.
Parker D 2001. Economic Situation and Prospects for Maryland Agriculture. Policy Report. College Park, MD:Center for Agricultural and Natural Resource Policy.
Ribaudo NR Gollehon NR Agapoff J 2003 Land application of manure by animal feeding operations: is more land needed? J Soil Water Conserv 58 30 38
Rutherford DW Bednar AJ Garbarino JR Needham R Staver KW Wershaw RL 2003 Environmental fate of roxarsone in poultry litter. Part II. Mobility of arsenic in soils amended with poultry litter Environ Sci Technol 37 1515 1520 12731832
Sharpe RR Schomberg HH Harper LA Endale DM Jenkins MB Franzluebbers AJ 2004 Ammonia volatilization from surface-applied poultry litter under conservation tillage management practices J Environ Qual 33 4 1183 1188 15254099
Silbergeld EK 2004 Arsenic in food [Letter] Environ Health Perspect 112 A338 A339 15121529
The Scotts Company 2005. What Organic Fertilizer Is Used in Your Miracle-Gro Organic Choice Products? Available: http://www.gardenadvice.com/index.cfm/event/Article.Detail/documentId/2adf31285e52651a08f7062830308eb6 [accessed 11 July 2005].
U.S. Department of Agriculture 2004. Poultry and Eggs: Background. Available: http://www.ers.usda.gov/Briefing/Poultry/background.htm [accessed 1 December 2005].
U.S. EPA (U.S. Environmental Protection Agency) 2001. Federal Register Notice January 22, 2001—National Primary Drinking Water Regulations; Arsenic and Clarifications to Compliance and New Source Contaminants Monitoring. Available: http://www.epa.gov/safewater/ars/arsenic_finalrule.html [accessed 19 November 2004].
U.S. EPA (U.S. Environmental Protection Agency) 2004. Land Disposal Restrictions, Subpart D: Universal Treatment Standards. Available: http://a257.g.akamaitech.net/7/257/2422/12feb20041500/edocket.access.gpo.gov/cfr_2004/julqtr/40cfr268.48.htm [accessed 11 July 2005].
Warren GP Alloway BJ Lepp NW Singh B Bochereau FJ Penny C 2003 Field trials to assess the uptake of arsenic by vegetables from contaminated soils and soil remediation with iron oxides Sci Total Environ 311 1-3 19 33 12826380
WHO 2003. Impacts of Antimicrobial Growth Promoter Termination in Denmark. Geneva:World Health Organization. Available: http://whqlibdoc.who.int/hq/2003/WHO_CDS_CPE_ZFK_2003.1.pdf [accessed 7 July 2005].
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7667ehp0113-00112516140616Commentaries & ReviewsAssessing Susceptibility from Early-Life Exposure to Carcinogens Barton Hugh A. 1Cogliano V. James 2*Flowers Lynn 2Valcovic Larry 2Setzer R. Woodrow 1Woodruff Tracey J. 31 Office of Research and Development, National Center for Computational Toxicology, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USA2 Office of Research and Development, National Center for Environmental Assessment, U.S. Environmental Protection Agency, Washington, DC, USA3 Office of Policy, Economics, and Innovation, U.S. Environmental Protection Agency, San Francisco, California, USAAddress correspondence to T.J. Woodruff, U.S. EPA, 75 Hawthorne St., PPA-1, San Francisco, CA 94105 USA. Telephone: (415) 947-4277. Fax: (415) 947-3519. E-mail:
[email protected]*Present address: International Agency for Research on Cancer, Lyon, France.
The authors declare they have no competing financial interests.
9 2005 7 4 2005 113 9 1125 1133 15 10 2004 7 4 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Cancer risk assessment methods currently assume that children and adults are equally susceptible to exposure to chemicals. We reviewed available scientific literature to determine whether this was scientifically supported. We identified more than 50 chemicals causing cancer after perinatal exposure. Human data are extremely limited, with radiation exposures showing increased early susceptibility at some tumor sites. Twenty-seven rodent studies for 18 chemicals had sufficient data after postnatal and adult exposures to quantitatively estimate potential increased susceptibility from early-life exposure, calculated as the ratio of juvenile to adult cancer potencies for three study types: acute dosing, repeated dosing, and lifetime dosing. Twelve of the chemicals act through a mutagenic mode of action. For these, the geometric mean ratio was 11 for lifetime exposures and 8.7 for repeat exposures, with a ratio of 10 for these studies combined. The geometric mean ratio for acute studies is 1.5, which was influenced by tissue-specific results [geometric mean ratios for kidney, leukemia, liver, lymph, mammary, nerve, reticular tissue, thymic lymphoma, and uterus/vagina > 1 (range, 1.6–8.1); forestomach, harderian gland, ovaries, and thyroid < 1 (range, 0.033–0.45)]. Chemicals causing cancer through other modes of action indicate some increased susceptibility from postnatal exposure (geometric mean ratio is 3.4 for lifetime exposure, 2.2 for repeat exposure). Early exposures to compounds with endocrine activity sometimes produce different tumors after exposures at different ages. These analyses suggest increased susceptibility to cancer from early-life exposure, particularly for chemicals acting through a mutagenic mode of action.
cancerchildrenearly-life exposureexposuremode of actionrisk assessmentsusceptible populations
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The cancer database used by the U.S. Environmental Protection Agency (EPA) and other agencies for risk assessment for exposure to carcinogens focuses on adults and adult exposures. Much cancer epidemiology comes from occupational studies and rodent cancer studies, which were designed to last approximately a lifetime (2 years) beginning after sexual maturity. Cancer risks from childhood exposures to chemicals are generally analyzed using methods based on exposure to adults, which assumes chemicals are equally potent for inducing risks of exposures in both early life and later life. Animal and human data suggest that further analysis is warranted to determine whether early-life exposure results in increased susceptibility to cancer compared with adult exposures (Anderson et al. 2000; National Research Council 1993). There is extensive literature demonstrating that exposures early in life (i.e., transplacental or in utero, early postnatal, and lactational) in animals can result in the development of cancer (reviewed in Anderson et al. 2000; Della Porta and Terracini 1969; Druckrey 1973; Rice 1979; Rice and Ward 1982; Toth 1968; Vesselinovitch 1983; Vesselinovitch et al. 1979a). However, except for data on radiation and prenatal exposure to diethylstilbesterol (DES), there are virtually no human data adequate for quantitative analysis.
Standard animal bioassays generally begin dosing after the animals are 6–8 weeks of age, when many organs and systems are relatively mature, although substantial growth in body size continues thereafter. Reviews comparing perinatal carcinogenesis bioassays with standard bioassays for a limited number of chemicals (McConnell 1992; U.S. EPA 1996) have concluded that the same tumor sites usually are observed after either perinatal or adult exposure, and that perinatal exposure in conjunction with adult exposure usually increases the incidence of tumors or reduces the latent period before tumors are observed.
There is limited evidence to inform the mode(s) of action leading to differences in tumor type and tumor incidence after early-life exposure compared with exposure later in life. Differences in the capacity to metabolize and clear chemicals at early ages can result in larger or smaller internal doses of the active agent(s), either increasing or decreasing risk (Ginsberg et al. 2002; Renwick 1998). Mechanistic data supporting early-life susceptibility to DNA damaging mutagenic chemicals include increased formation of DNA adducts after exposure to vinyl chloride (Laib et al. 1989; Morinello et al. 2002a, 2002b), increased induction of micronuclei in fetal tissues (Hayashi et al. 2000), and increased mutations in brains of transgenic mice exposed to ethylnitrosourea (ENU; Slikker et al. 2004). The neonatal mouse model for carcinogenesis, which uses two doses before weaning followed by observation of tumors at 1 year, shows carcinogenic responses for mutagenic carcinogens (Flammang et al. 1997; Fujii 1991; McClain et al. 2001).
Current understanding of biologic processes involved in carcinogenesis leads to a reasonable expectation that children are more susceptible to some carcinogenic agents than adults (Anderson et al. 2000; Birnbaum and Fenton 2003; Ginsberg 2003; Miller et al. 2002; Scheuplein et al. 2002). Several aspects potentially lead to increased childhood susceptibility. More frequent cell division during development can result in enhanced fixation of mutations because of the reduced time available for repair of DNA lesions, and clonal expansion of mutant cells gives a larger population of mutants (Slikker et al. 2004). Some components of the immune system are not fully functional during development (Holladay and Smialowicz 2000; Holsapple et al. 2003). Hormonal systems operate at different levels during various life stages (Anderson et al. 2000). Induction of developmental abnormalities may result in a predisposition to carcinogenic effects later in life (Birnbaum and Fenton 2003; Fenton and Davis 2002).
Finally, theoretical analyses suggest that differential susceptibility would depend, in part, on the mode of action [i.e., at what step in the cancer process(es) the chemical was acting] and that the lifetime average daily dose may underestimate or overestimate the cancer risk when exposures are time dependent (Goddard and Krewski 1995; Murdoch et al. 1992).
We reviewed the available scientific literature to determine the extent of potential increased susceptibility from early-life exposure. We evaluated potential susceptibility by individual study and tumor type rather than a combined analysis of all data by certain categories (e.g., gender) (Hattis et al. 2004). Further supporting evidence from ionizing radiation is given in the Appendix (http://ehp.niehs.nih.gov/docs/2005/7667/app.pdf). Although there is evidence showing that prenatal exposures can result in tumors later in life (Anderson et al. 2000; Birnbaum and Fenton 2003; Diwan et al. 1992; Hatch et al. 1998; Waalkes et al. 2003), this analysis focuses only on exposures in animals occurring postnatally up to approximately 5–8 weeks of age.
Materials and Methods
Procedures
Data sources for animal studies.
We identified initial studies for consideration through review articles and a search of the National Toxicology Program database (NTP 2003). A literature search was conducted using key words and MeSH headings from the PubMed database (PubMed 2004) from studies identified in the available reviews. The chemicals considered and then included for quantitative evaluation are listed in Table 1 and in Supplementary Table S1 (http://ehp.niehs.nih.gov/docs/2005/7667/sup.pdf).
We reviewed abstracts or papers to determine if a study provided information that could be used for quantitative analysis based on the following criteria:
Exposure groups at different postnatal ages in the same study or same laboratory, if not concurrent (to control a large number of potential cross-laboratory experimental variables including pathologic examinations)
Same strain/species (to eliminate strain-specific responses confounding age-dependent responses)
Approximately the same dose within the limits of diets, and drinking water intakes that obviously can vary with age (to eliminate dose-dependent responses confounding age-dependent responses).
Similar or identical period for tumor expression after exposures at different ages—variations of around 10–20% in time to sacrifice are acceptable arising from sacrifice at > 1 year for all groups exposed at different ages, where early-life exposure can occur up to about 7 weeks (to control for confounding differential periods for tumor expression with age-dependent changes in tumor incidences).
Postnatal exposure for juvenile rats and mice at ages younger than the standard 6–8 week start for bioassays; studies that have postnatal exposure were included even if they also involved prenatal exposure, but studies with only prenatal (in utero) exposures are not part of the present analysis.
“Adult” rats and mice exposure beginning at approximately 6–8 weeks of age, that is, comparable with the age at initiation of a standard cancer bioassay. Studies in other species were used as supporting evidence, because they are relatively rare and the determination of the appropriate comparison ages across species is not simple.
Number of affected animals and total number of animals examined must be available or reasonably reconstructed for control, young, and adult groups (i.e., studies reporting only percent response or not including a control group would be excluded unless a reasonable estimate of historical background for the strain was obtainable).
Supplementary Tables S2 and S3 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf) include information used for the calculations. Pertinent information on species, sex, dosing regimen, and tumor incidence is given.
The literature includes studies that can be divided roughly into three types of exposure scenarios (Figure 1): repeated exposures for the early postnatal to juvenile period compared with chronic later-life dosing; lifetime (i.e., combined perinatal and adult) exposure compared with chronic later-life dosing; and acute exposures such as a single intraperitoneal or subcutaneous injection for both early-life and later-life dosing.
Evaluating the mode of action of carcinogens.
Chemicals were classified into categories based on evaluating the mode of action using a weight-of-evidence approach. Determination of carcinogens that are operating by a mutagenic mode of action entails evaluation of short-term testing results for genetic end points, metabolic profiles, physicochemical properties, and structure–activity relationship (SAR) analyses in a weight-of-evidence approach (Dearfield et al. 1991; U.S. EPA 1986
U.S. EPA 1991; Waters et al. 1999), as has been done for several chemicals (e.g., Dearfield et al. 1999; McCarroll et al. 2002; U.S. EPA 2000). Key data for a mutagenic mode of action may be evidence that the carcinogen or a metabolite is DNA reactive and/or has the ability to bind to DNA. Also, mutagenic carcinogens usually produce positive effects in multiple test systems for different genetic end points, particularly gene mutations, and in tests performed in vivo that generally are supported by positive tests in vitro. Additionally, carcinogens may be identified as operating via a mutagenic mode of action if they have properties and SARs similar to those of established mutagenic carcinogens. Those with a mutagenic mode of action are identified in Table 1.
Quantitative Methods
To estimate the potential difference in susceptibility between early-life and adult exposure, we calculated the ratio of the estimated cancer potency from early-life exposure compared with the estimated cancer potency from adult exposure. The cancer potency was estimated from a one-hit model, or a restricted form of the Weibull model, which is commonly used to estimate cumulative incidence for tumor onset. The general form of the equation is as follows:
Juvenile and adult cancer potencies and the ratio of the two were calculated by fitting this model to the data for each age group. The model fit depended upon the design of the experiment that generated the data. Two designs need to be handled separately: repeat and acute exposure and lifetime exposures.
For the first case, the model equations are as follows:
where subscripts A and J refer to the adult and juvenile period, respectively; λ is the natural logarithm of the juvenile:adult cancer potency ratio; P0 is the fraction of control animals with the particular tumor type being modeled; Px is the fraction of animals exposed in age period x with the tumor; mA is the cancer potency, the rate of accumulation of “hits” per unit of time for adults; and δx is the duration or number of exposures during age period x. For a substantial number of data sets (acute exposures), δJ = δA = 1. We are interested in determining λ , which is the logarithm of the estimated ratio of juvenile to adult cancer potencies, a measure of potential susceptibility for early-life exposure.
For the lifetime exposures, the model equations need to take into account that exposures were initiated in the juvenile period continue through the adult period. The model equations for the fraction of animals exposed only as adults with tumors in this design is the same as in the first design, but the fraction of animals whose first exposure occurred in the juvenile period is
All symbols in Equation 2 have the same interpretation as their counterparts in Equation 1, but now δJ includes the duration of exposure during the juvenile period as well as the subsequent adult period.
Parameters mA and λ in these models were estimated using Bayesian methods ( e.g., see Carlin and Louis 2000), and all inferences about the ratios were based on the marginal posterior distribution of λ. The data for estimating each ratio were in the form of numbers of animals tested and number affected for each of control, juvenile-exposed, and adult-exposed animals, and duration of exposure for each of the juvenile-exposed and adult-exposed groups. A few data sets had separate control groups for the juvenile-exposed and adult-exposed groups, and Equations 1 and 2 were modified accordingly. The likelihood for the parameters mA and λ in the model was the product of three (or four, if there were two control groups) binomial probabilities: the number of animals with tumors in the adult control group, in the juvenile control group, in the juvenile-exposed group, and in the adult-exposed group. The prior for P0 (the fraction of control animals with a particular tumor) was right triangular, which assumes incidences should be relatively low. The effect of exposure in adults is quantified by the extra risk, Q, where the probability that an animal has a tumor is P0 + (1 – P0)Q. So, from Equation 1, Q was given a uniform prior on the interval (0,1), reflecting total ignorance about the extra risk of adult exposure. Finally, the prior for λ was Gaussian with mean 0 (corresponding to a median or geometric mean ratio of 1) and standard deviation of 3. The prior for the log ratio has some influence over the posterior estimates for the ratio of juvenile to adult potency. The magnitude of that influence depends on the amount of support in the data for different values of the log ratio. This potential influence is further discussed with illustrations in the Appendix (http://ehp.niehs.nih.gov/docs/2005/7667/app.pdf).
To examine the sensitivity of the estimates to choice of the prior, values were re-estimated using a prior with a larger standard deviation. This was initially chosen to be 9, but for some of the lifetime exposure and acute exposure studies, a standard deviation of 9 led to numerical problems integrating the posterior, so a prior with a standard deviation of 6 was used in these cases. Sensitivity to the choice of prior was evaluated by looking at changes of individual log ratios and their variances and through differences in geometric means.
The posterior distribution for the unknown parameters in these models is the product of the likelihood from the data and the priors (the “unnormalized” prior), divided by a normalization constant that is the integral of the unnormalized prior over the ranges of all the parameters. This normalization constant was computed using numerical integration, as were posterior means and variances and marginal posterior quantiles for the log ratio λ. All numerical computations were carried out in the R statistical programming language (version 1.8.1; R Development Core Team 2003).
This method produced a posterior mean of ratio of the early-life to adult cancer potency, which is an estimate of the potential susceptibility of early-life exposure to carcinogens. Ratios > 1 indicated greater susceptibility from early-life exposure. Ratios < 1 indicated less susceptibility from early-life exposure. Summaries of the individual ratios from each of the dose groups from the different experiments for different groupings were also calculated (e.g., for all acute mutagenic chemicals). The summary ratios were constructed from the individual ratios within a group by inverse variance-weighting the means of each ratio. The individual means were weighted by using reciprocals of posterior variances, so ratios with more variance were given less weight in the summary ratios. Exponentiating the resulting variance-weighted mean yielded variance-weighted geometric means of ratios.
Results
A review of the literature identified several hundred references reporting more than 50 chemicals able to cause cancer after perinatal exposure in animals [Supplementary Table S1 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)]. Often, these studies demonstrated carcinogenesis after perinatal exposure but did not directly compare exposures in adults. A large number of studies address in utero exposures only. Studies across laboratories often varied in their use of animal strains [e.g., for 3′-azido-3′-deoxythymidine (AZT) studies, Diwan et al. (1999) used CD-1 mice, whereas the National Toxicology Program (NTP 1999) used B6C3F1 mice] or had different periods of tumor follow-up (e.g., tamoxifen and uterine tumors in Carthew et al. 2000). Because of these factors, many of these chemicals in Supplementary Table S1 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf) were not evaluated quantitatively. Studies assessing radiation in animals exist, but lack of uniformity regarding radiation doses, gestational age at exposure, and the animal strains used make it difficult to make comparisons across studies (Preston et al. 2000).
Some of the studies that did not have sufficient information for quantitative evaluation provide important supporting information for early-life susceptibility. Increased multiplicity of colon tumors was observed after earlier versus later azoxymethane exposures (Paulsen et al. 2003). Shortened mammary tumor latency after estradiol exposure occurred for exposures between 8 and 18 weeks as opposed to exposures earlier or later in life (Yang et al. 2003), consistent with results for dimethylbenz[a]anthracene (DMBA; Meranze et al. 1969). Notable examples exist of developmental windows leading to cancer susceptibilities that were not observable in adults. Several potent estrogens, including DES, tamoxifen, and genistein, produce uterine tumors with early postnatal exposures of mice, although there also appear to be strain-dependent differences in the tumor sites in adult mice (Gass et al. 1964; Greenman et al. 1990; Newbold et al. 1990, 1997, 2001). Developmental susceptibilities are believed to play a key role in effects observed with saccharin (Cohen et al. 1995; Whysner and Williams 1996) and ascorbate (Cohen et al. 1998; NTP 1983), with bladder tumors arising only when early-life exposures occurred; studies with other food additives did not find cancers after either adult-only or combined early-life and adult exposures (U.S. EPA 1996). Finally, central nervous systems tumors appear highly dependent upon exposures to ENU and several other chemicals during appropriate developmental windows, particularly prenatally, as observed in several species including rat, mouse, and opossum (Jurgelski et al. 1979; Rice 1979; Rice and Ward 1982).
Quantitative Evaluation of the Database
Studies (or groups of studies from a single laboratory on a given chemical) that provided quantitative data were identified for 18 chemicals [Table 1; Supplementary Tables S2, S3 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)]. Nine chemicals involved repeated exposures during early postnatal and adult life stages, eight chemicals had lifetime exposure starting in the juvenile period and adult-only exposure, and eight chemicals used acute exposures (typically single doses) at different ages (Table 1). Some studies evaluated single tissues for tumors (e.g., only liver), whereas others evaluated multiple tissues. Mice, rats, or both species and sometimes multiple strains were tested.
Carcinogens with a Mutagenic Mode of Action
The most informative database on early–life-stage susceptibility exists for chemicals with a well accepted mutagenic mode of action and includes both acute-exposure and repeated-exposure studies involving periods of perinatal and/or chronic exposure.
Repeat and lifetime exposure studies of mutagenic chemicals.
Studies comparing repeated dosing for early postnatal, juvenile, adult time periods, or lifetime exposures exist for six mutagens [benzidine, diethylnitrosamine (DEN), 3-methylcholanthrene, safrole, urethane, and vinyl chloride; Tables 1, 2, 3; Supplementary Table S4 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)]. DEN and urethane also had acute-dosing studies. These chemicals all require metabolic activation to the active carcinogenic form.
For the repeated-dosing studies, the ratios of juvenile to adult cancer potency ranged from 0.12 to 111 with a geometric mean ratio of 11 (Tables 1, 2, 4; Figure 2). For the lifetime studies, the ratios ranged from 0.18 to 79 with a geometric mean of 8.7 [Tables 3, 4; Supplementary Table S5 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf); Figure 2]. The geometric mean combining the repeated and lifetime data for six chemicals was 10 (Table 4). Calculations based upon even broader, less informative prior distributions (e.g., SD = 6 or 9) gave higher estimates for the geometric means, 17 and 20, for repeated and lifetime studies, respectively, and wider ranges (0.0011–115.2 for repeated-dosing studies, 0.0014–157.78 for lifetime studies). The prior estimates appear to have a greater influence on the estimates of λ (natural log of the ratio of juvenile to adult cancer potency) from the lifetime study designs, reflecting the relative insensitivity to this parameter, as described further in the Appendix (http://ehp.niehs.nih.gov/docs/2005/7667/app.pdf). For benzidine and safrole, there was a notable sex difference, with high liver tumor incidence observed for early postnatal exposures of male, but not female, mice.
Acute-dosing studies of mutagenic chemicals.
Acute-dosing studies are available for eight mutagenic chemicals that were administered to mice or rats (benzo[a]pyrene, dibenzanthracene, DEN, DMBA, dimethylnitrosamine, ENU, N-methylnitrosourea [MNU], and urethane; Table 1). Except for ENU and MNU, these compounds require metabolic activation to their active carcinogenic forms.
Early acute exposures often resulted in higher potency, with increased early susceptibilities up to 178-fold (ratios of juvenile to adult potencies range from 0.011 to 178; geometric mean, 1.5) [Figure 2; Table 4; Supplementary Table S6 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)]. Use of a broader prior distribution for λ (natural log of the ratio of juvenile to adult cancer potency) had no effect on the overall geometric mean (1.5) because the data highly informed the posterior distributions, although the range of individual ratios changed (0.00008–2,055). In studies comparing exposures on specific postnatal days 1, 15, and 42, general age-dependent declines in susceptibility of tumor response were observed, for example, benzo[a]pyrene (liver tumors), DEN (liver tumors), ENU (liver and nervous system tumors), and urethane (liver and lung tumors). Although generally the ratios for day 1 and day 15 time points were higher than those for later time points, in several cases similar tumor incidence was observed at both of these early times [e.g., ENU-induced kidney tumors; Supplementary Table S6 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)].
Although the degree of susceptibility generally declines with age, there are exceptions, such as for pubertal periods of tissue development. Meranze et al. (1969) reported 8% mammary tumors after a single dose of DMBA at < 2 weeks of age, 56% if given once to animals between 5 and 8 weeks of age, and 15% when given once to 26-week-old rats. Thus, a ratio of 7.1 is obtained when comparing susceptibilities of 5- to 8-week-old and 26-week-old rats compared with a ratio of 0.2 when comparing the exposure at 2 weeks versus 26 weeks [Table 4; Supplementary Table S6 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)]. A similar effect was observed by Russo et al. (1979; Supplementary Table S3 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf). This observation corresponds well with pubertal development of the mammary tissue, with ovarian function commencing between 3 and 4 weeks [after the < 2-week time point in the Meranze et al. (1969) study], and mammary ductal growth and branching occurring such that it is approximately two-thirds complete by week 5, consistent with the 5- to 8-week sensitive period of Meranze et al. (Silberstein 2001).
Early-life susceptibility of different tissues varies substantially in the acute studies (Table 4). It should be noted that the target tissues and tissues evaluated vary with chemical, so the number of chemicals for which data are available varies for each tissue. Several tissues have geometric mean ratios > 1, including kidney, leukemia, liver, lymph, mammary, nerve, reticular tissue, thymic lymphoma, and uterus/vagina. Some of these, such as the nerve and mammary tumors, appear to have a very specific window of susceptibility, and the ratios were much higher if the exposure occurred during this window. Tissues with mean ratios < 1 include forestomach, harderian gland, ovaries, and thyroid. Lung has a geometric mean of 1. Many of the studies produced very high lung tumor responses regardless of age, so the results are difficult to interpret, as illustrated by the dose–response data with urethane in Rogers (1951), in which the increased early susceptibility is only apparent when the dose is low. The large numbers of studies with high lung tumor responses at all ages are a major contributor to the differences in the geometric means for the acute and repeated-dosing studies.
Carcinogens with modes of action other than mutagenicity.
Studies comparing tumors observed at the same sites after early postnatal and chronic adult exposures in a single protocol were available for six chemicals that do not act through a mutagenic mode of action [amitrole, dichlorodiphenyltrichloroethane (DDT), dieldrin, ethylene thiourea (ETU), diphenylhydantoin (DPH), polybrominated biphenyls (PBB); Table 5]. These chemicals cause tumors through several different, not necessarily well-defined, modes of action. For example, thyroid hormone disruption by ETU causes thyroid tumors; some PBBs act through the aryl hydrocarbon receptor, whereas other PBBs are phenobarbital-like pleiotrophic inducers of liver enzymes and liver tumors. Three of these studies evaluated only mouse liver tumors (amitrole, DDT, dieldrin), whereas the other three evaluated a large number of tissues in both mice and rats (ETU, DPH, PBB). No acute-dosing studies were identified for these agents; such protocols are generally considered largely nonresponsive for modes of action other than mutagenicity and potent estrogenicity (e.g., DES).
Discussion
The database overall is of modest size (particularly compared with the number of chemicals that have been studied in adult occupational epidemiologic studies or chronic bioassays). Information on different life-stage susceptibilities to cancer risks for humans exists for ionizing radiation [Appendix (http://ehp.niehs.nih.gov/docs/2005/7667/app.pdf)]. The effects on cancer induction by chemical mutagens at different life stages are derived from laboratory animal studies. Although the induction of cancer by ionizing radiation and chemical mutagens are not identical processes, both involve direct damage to DNA as critical causal steps in the process. In both cases, the impacts of early exposure can be greater than the impacts of later exposures. As indicated in Table 5 and Supplementary Tables S7–S10 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf), A-bomb survivors exhibit different life-stage dependencies at different tumor sites, although there are apparent differences at some sites. However, it is clear that the total radiation-related tumor incidence showed a general slow decline with age at exposure. The mutagenic chemical studies in rodents similarly support a general decline in cancer risk with age of exposure and similarly show differences for individual tumor sites. In general, the first 2 or 3 postnatal weeks in mice and rats appeared to be the most sensitive.
Analyses of the difference in cancer risk from exposures during different lifetime periods ideally need to address both the period of potential susceptibility and the magnitude of the susceptibility. Available studies used a variety of different study designs [Supplementary Tables S2, S3 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)], which can be valuable because they provide different information. However, variations in study design can result in a lack of comparability across chemicals and can limit information on the consistency of effects with different chemicals acting through different modes of action. The acute-dosing (largely single-dose) studies are valuable because they involve identical exposures with explicitly defined doses and time periods demonstrating that differential tumor incidences arise exclusively from age-dependent susceptibility.
The repeated-dosing studies with exposures during early postnatal or adult lifetime provide useful information on the relative impact of repeated exposures at different life stages and may be more likely to have exposure occur during a window of susceptibility, if there is one. One notable difference in study designs was that studies with repeated early postnatal exposures were included in the analysis even if they also involved earlier maternal and/or prenatal exposure, whereas studies addressing only prenatal exposure were not otherwise a part of this analysis. The impact of this is limited because it applies to the lifetime safrole study and the studies with PBB, DPH, and ETU. Another notable difference among studies involved the tissues that were evaluated for tumors: some studies focused on a single tissue, particularly liver, whereas others evaluated multiple tissues.
This analysis assumes that the doses animals received during the different periods of repeated dose studies were similar. This assumption is a limitation because these studies involved exposures via lactation, drinking water, diet, or inhalation, which potentially deliver different doses at different life stages. However, the range of the magnitudes of the tumor incidence ratios of juvenile to adult exposures is similar for the repeated-dosing studies for chemicals with a mutagenic mode of action [0.12–111; geometric mean, 11; Tables 2, 4; Supplementary Table S5 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf), lifetime-dosing studies [0.28–79; geometric mean, 8.7; Tables 3, 4; Supplementary Table S5 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)], and acute-dosing studies [0.01–178; geometric mean, 1.5; Table 4; Supplementary Tables S5, S6 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)], suggesting that differences in dosing are not the sole determinant of the increased incidence of early tumors.
Because these comparisons include chemicals with different tissue specificities, it is useful to consider the liver as a target organ affected by all these chemicals; in so doing, even greater consistency is observed. The range of the magnitudes of the liver tumor potency ratios of juvenile to adult exposures of mutagenic chemicals is similar for the repeated-dosing studies (geometric mean, 41.8; range, 0.12–111; Table 2), lifetime-dosing studies (geometric mean, 14.9; range, 0.47–79; Table 3), and acute-dosing studies (geometric mean, 8.1; range, 0.1–40; Table 4). In some cases, windows of susceptibility could occur prenatally. For example, it is plausible that the major window of susceptibility for lung is during in utero development, so sensitivity of the lung tissue would have been missed in this analysis (Miller 2004).
The acute studies exposures are largely by subcutaneous or intraperitoneal injection, which historically have not been considered relevant routes of environmental exposure for human cancer risk assessment by the U.S. EPA. For purposes of comparing age-dependent susceptibilities with tumor development, these data are highly relevant. The injection route typically alters the pharmacokinetic time courses of the parent compound and the metabolites compared with oral or other exposures because of altered kinetics of absorption and metabolism. However, for these compounds and the systemic organ effects observed, there are several pharmacokinetic reasons to believe that the age-dependent trends would be similar with other routes of exposure. These compounds are expected to be reasonably well absorbed orally, comparable with injection routes, and largely require metabolic activation, so partial or complete absence of first-pass metabolism in the injection studies would be similar to or underestimate metabolic activation compared with oral exposure. These studies provide the clearest demonstrations of periods of differential susceptibility because the exposure rate is constant at the different ages.
The information on life-stage susceptibility for chemicals inducing cancers through other than mutagenic modes of action is more varied, showing an increase in potency from perinatal exposure (e.g., PBBs induced liver tumors in female rats), no effect of combined perinatal and adult exposure (e.g., DPH liver tumors in rats), and different tumors from perinatal exposure versus adult exposure (e.g., DES, ascorbate). These variations are likely a result of the modes of action of these chemicals and the pharmacokinetic differences in doses during different periods of life.
An important factor that complicates the interpretation of the results for other modes of action is that these studies, except those with DDT and dieldrin, involved dietary feeding initially to the mother, which potentially could increase or decrease the dose received by the pups. Because of the maternal dosing during pregnancy and lactation, the extent to which offspring received similar doses during different early and adult life stages is particularly uncertain for DPH, ETU, and PBBs. Thus, these studies provide suggestive evidence that early life stages can be more sensitive to exposures to chemical causing cancer through a variety of modes of action other than mutagenicity. However, the studies with ETU indicate that this is not necessarily the case for all modes of action. No single-dose studies for chemicals with a nonmutagenic mode of action were evaluated that were directly comparable with the single-dose studies with mutagens.
There are important demonstrations of chemicals causing different tumor types with early–life-stage exposures compared with those for adults, for example, tamoxifen and DES (Carthew et al. 1996, 2000; Gass et al. 1964; Newbold et al. 1990, 1997). In addition, studies with in utero exposure to atrazine (Fenton and Davis 2002), DES, arsenic (Waalkes et al. 2003), and genistein (Newbold et al. 2001) indicate that early-life exposures to compounds can alter susceptibility of endocrine and reproductive organs. There is an actively growing database from which to consider issues of childhood exposure and cancer for compounds acting through the estrogen receptor or other mechanisms of endocrine disruption.
The ability to estimate with any accuracy the juvenile to adult cancer potency ratio depends on the experimental design used. The lifetime design has less ability to distinguish increased susceptibility from early-life exposure than the other study designs, as is more thoroughly explained with an example in the Appendix (http://ehp.niehs.nih.gov/docs/2005/7667/app.pdf).
The proper measure of relative potency of an exposure in the juvenile period relative to an exposure in the adult period is the ratio of doses in the two periods that give the same incidence of tumors. However, most of the data sets used in this report contained only one non-control dose, precluding the extensive dose–response modeling that would be required to estimate this ratio of doses. However, this analysis largely considered chemicals for which a mutagenic mode of action has been established and for which a linear, no-threshold dose–response function is assumed for the low-dose range being considered for risk assessment, and comparing potencies can be shown to be the same as comparing doses. This is illustrated in the Appendix (http://ehp.niehs.nih.gov/docs/2005/7667/app.pdf).
Conclusions
In summary, the existing animal database supports the conclusion that there can be greater susceptibility for the development of tumors as a result of exposures early in life to chemicals acting through a mutagenic mode of action. Thus, a risk assessment approach using estimates from chronic studies with appropriate modifications to address the impact of early–life-stage exposure appears feasible. The U.S. EPA has recently released guidelines for multiplying an extra factor to the cancer potency for chemicals with a mutagenic mode of action for exposures that occur during childhood. The proposed factors are 10 for exposures to children between 0 and 2 years of age, and 3 for exposures to children between 2 and 15 years of age. The factor of 10 is based on the data derived from this analysis, and the factor of 3 represents a decline in potency expected to occur as children mature (U.S. EPA 2005). For chemicals acting through a nonmutagenic mode of action, the available data suggest that a range of approaches needs to be developed over time for addressing cancer risk estimates from childhood exposures. Development of such approaches requires additional research to provide an expanded scientific basis for their support, including additional research and the possible development of new toxicity testing protocols that consider early–life-stage dosing.
Supplementary tables are available on the EHP website (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf). An Appendix is also available on the EHP website (http://ehp.niehs.nih.gov/docs/2005/7667/app.pdf).
We thank D. Bennett for work in leading the initial efforts for this work, B. Wood for support, and J. Preston and S. Fenton for helpful reviews of the document. Special thanks to R. Castorina, N. Choksi, R. Brown, E.P. Donovan, N. Bekarian, and B. Hurley for their efforts in compiling the underlying information.
This document does not constitute U.S. EPA policy. Mention of trade names or commercial products does not constitute endorsement or recommendation for use.
Figure 1 Schematic representation of several cancer study designs reported in the evaluated literature. The standard rodent bioassay begins after puberty, and exposures continue for about 2 years. Repeated-dosing studies typically dose during the postnatal period, with observations for tumors at approximately 2 years. Lifetime studies combine postnatal and adult exposures, sometimes beginning with in utero exposure. Acute studies (not shown) generally involve one or a few exposure during the in utero, preweaning, prepubertal, and adult periods. The adult tumors were often evaluated much earlier than 2 years
*Can also include prenatal exposure.
Figure 2 Posterior geometric means and 95% confidence intervals for the ratios of juvenile to adult cancer potency for carcinogens acting primarily through a mutagenic mode of action. (A) Repeated and lifetime exposure studies (geometric mean in black). (B) Acute exposure studies mutagens (geometric mean in white). The horizontal lines to the left and right of each geometric mean correspond to 95% confidence limits. The vertical solid line represents the geometric mean; the horizontal solid line represents the 95th percentile; the vertical dotted line is the geometric mean of the 95th percentile. The geometric mean for repeat and lifetime exposures is 10.4; for acute exposures the geometric mean value is 1.5.
Table 1 List of chemicals considered in the quantitative analysis for which there are both early-life and adult exposure reported in the same animal experiment.
Chemical References Study type Mutagenic mode of action
Amitrole Vesselinovitch 1983 Repeat dosing
Benzidine Vesselinovitch et al. 1975b Repeat dosing X
Benzo[a]pyrene Vesselinovitch et al. 1975a Acute exposure X
Dibenzanthracene Law 1940 Acute exposure X
Dichlorodiphenyltrichloroethane Vesselinovitch et al. 1979a Repeat dosing
Lifetime exposure
Dieldrin Vesselinovitch et al. 1979a Repeat dosing
Lifetime exposure
Diethylnitrosamine Peto et al. 1984 Lifetime exposure X
Vesselinovitch et al. 1984 Acute exposure
Dimethylbenz[a]anthracene Meranze et al. 1969 Acute exposure X
Pietra et al. 1961 Acute exposure
Walters 1966 Acute exposure
Dimethylnitrosamine Hard 1979 Acute exposure X
Diphenylhydantoin, 5,5- Chhabra et al. 1993b Repeat dosing
Lifetime exposure
Ethylnitrosourea Naito et al. 1981 Acute exposure X
Vesselinovitch et al. 1974 Acute exposure
Vesselinovitch 1983 Acute exposure
Ethylene thiourea Chhabra et al. 1992 Repeat dosing
Lifetime exposure
3-Methylcholanthrenea Klein 1959 Repeat dosing X
N-Methylnitrosourea Terracini and Testa 1970 Acute exposure
Terracini et al. 1976 Acute exposure X
Polybrominated biphenyls Chhabra et al. 1993a Repeat dosing
Lifetime exposure
Safrole Vesselinovitch et al. 1979a Repeat dosing
Lifetime exposure X
Urethane Chieco-Bianchi et al. 1963 Acute exposure X
Choudari Kommineni et al. 1970 Acute exposure
De Benedictis et al. 1962 Acute exposure
Fiore-Donati et al. 1962 Acute exposure
Klein 1966 Acute exposure
Lifetime exposure
Liebelt et al. 1964 Acute exposure
Rogers 1951 Acute exposure
Vinyl chloride Maltoni et al. 1984 Repeat dosing X
X, chemicals with a mutagenic mode of action. The chemicals listed here are from the list of more than 50 chemicals found to have carcinogenic effects from prenatal or postnatal exposures in animals [Supplementary Table S1 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf)].
a Formerly known as 20-methylcholanthrene.
Table 2 Ratio of early-life to adult cancer potencies for studies with repeat exposures of juvenile and adult animals to mutagenic chemicals.
Ratio of juvenile to adult cancer potency
Compound Species, strain Sex Dose Tumor Geometric mean 2.5% Median 97.5% Reference
Benzidine Mice (B6C3F1) Male Liver 111 64 110 198 Vesselinovitch et al. 1975b
Female Liver 0.16 0.004 0.22 1.1
3-Methylcholanthrenea Mice (albino) Male 0.25 mg/g Hepatoma 33 7.4 30 268 Klein 1959
Female 0.25 mg/g Hepatoma 7.7 1.1 7.1 85
Male 0.25 mg/g Forestomach 0.91 0.39 0.91 2.1
Female 0.25 mg/g Forestomach 1.5 0.58 1.5 4.2
Male 0.25 mg/g Skin 1.8 0.048 2.1 22
Female 0.25 mg/g Skin 1.5 0.023 1.8 21
Safrole Mice (B6C3F1) Male Liver 46 16 44 198 Vesselinovitch et al. 1979b
Female Liver 0.12 0.002 0.18 1.1
Vinyl chlorideb Rats (Sprague-Dawley) Male 10,000 ppm Liver angiosarcoma 7.4 0.035 11 62 Maltoni et al. 1984
Female 10,000 ppm Liver angiosarcoma 30 8.7 29 121
Male 10,000 ppm Zymbal gland 0.27 0.0022 0.4 5.4
Female 10,000 ppm Zymbal gland 0.15 0.0014 0.19 4.5
Male 10,000 ppm Leukemia 21 0.026 37 514
Female 10,000 ppm Leukemia 0.29 0.0019 0.35 17
Male 10,000 ppm Nephroblastomas 0.17 0.0015 0.21 6.2
Female 10,000 ppm Nephroblastomas 0.24 0.0017 0.29 11
Male 10,000 ppm Angiosarcomas other sites 0.25 0.0017 0.30 12
Female 10,000 ppm Angiosarcomas other sites 0.32 0.0019 0.38 20
Male 10,000 ppm Angiomas and fibromas other sites 1.4 0.0045 2.36 47
Female 10,000 ppm Angiomas and fibromas other sites 0.52 0.0024 0.63 41
Male 10,000 ppm Hepatoma 34 8.2 32 218
Female 10,000 ppm Hepatoma 55 8.4 53 513
Male 10,000 ppm Skin carcinomas 0.41 0.0024 0.56 15
Female 10,000 ppm Skin carcinomas 0.31 0.0019 0.37 19
Male 10,000 ppm Neuroblastoma 0.20 0.0016 0.24 8.5
Female 10,000 ppm Neuroblastoma 0.14 0.0014 0.18 4.4
a Formerly known as 20-methylcholanthrene.
b Results for 6,000 ppm are similar to those for 10,000 ppm and are given in Supplementary Table S4 (http://ehp.niehs.nih.gov/docs/2005/7667/supp.pdf).
Table 3 Ratio of early-life to adult cancer potencies for studies with lifetime exposures starting with juvenile and adult, for chemicals acting through a mutagenic mode of action.
Ratio of juvenile to adult cancer potency
Compound Species, strain Sex Dose Tumor Geometric mean 2.5% Median 97.5% References
Diethylnitrosamine Rats (Colworth) Multiple Liver 2.8 0.0093 5.6 23 Peto et al. 1984
Esophagus 0.18 0.0015 0.23 4.8
Safrole Mice (B6C3F1) Male Liver 46 3.7 50 253 Vesselinovitch et al. 1979b
Female Liver 1.9 0.007 4.0 23
Urethane Mice (B6AF1/J) Male 2.5 μg/g/bw Liver 79 0.36 102 1,064 Klein 1966
Female 2.5 μg/g/bw Liver 0.47 0.0022 0.55 43
bw, body weight.
Table 4 Summary of quantitative estimates of ratio of early-life to adult cancer potencies.
Dose Tissue No. of chemicals Geometric mean ratio Range of ratios No. of ratios
Chemicals with mutagenic mode of action
Repeated 4 10.5 0.12–111 45
Lifetime 3 8.7 0.18–79 6
Combined repeated and lifetime 6 10.4 0.12–111 51
Acute
Combined (all tissues) 8 1.5 0.01–178 268
Forestomach 3 0.076 0.01–1.9 32
Harderian 2 0.48 0.06–0.8 20
Kidney 2 1.6 0.17–7.1 18
Leukemia 1 5.9 5.1–6.7 2
Liver 5 8.1 0.10–40 70
Lung 7 1.1 0.04–178 77
Lymph 2 1.8 1.1–2.7 3
Mammary
Week 5 vs. week 26 1 7.1 NA 1
Week 2 vs. weeks 5–8 or 26 1 0.071 NA 2
Nerve 2 2.3 0.24–64 10
Nerve (day 1 comparison) 2 10 0.24–64 3
Ovarian 1 0.033 0.01–0.13 3
Reticular tissue 1 6.5 2.0–8.6 2
Thymic lymphoma 1 2.8 1.0–7.9 6
Thyroid 1 0.05 0.03–0.08 2
Uterine/vaginal 1 1.6 0.03–8.6 3
Day 1 (all tissues) 7 1.7 0.01–178 127
Day 15 (all tissues) 3 1.5 0.06–52 74
Chemicals with nonmutagenic mode of action
Repeated 6 2.2 0.06–13 22
Lifetime 5 3.4 0.15–36 38
NA, not applicable.
Table 5 Ratio of early-life to adult cancer potencies for studies with repeated exposures of juvenile and adult animals to nonmutagenic chemicals.
Ratio of juvenile to adult cancer potency
Compound Species, strain Sex Dose Tumor Geometric mean 2.5% Median 97.5% References
Amitrole Mice (B6C3F1) Male 500 Liver 13 5.1 14 30 Vesselinovitch 1983
Female 500 Liver 0.14 0.0013 0.18 3.9
DDT Mice (B6C3F1) Male 150 Liver 1.3 0.0044 2.5 25 Vesselinovitch et al. 1979a
Dieldrin Mice (B6C3F1) Male 10 Liver 0.75 0.0031 1.2 27 Vesselinovitch et al. 1979a
DPH Rats (F344/N) Male 630 Liver 0.40 0.0024 0.54 16 Chhabra et al. 1993b
Female 630 Liver 0.24 0.0017 0.29 12
Mice (B6C3F1) Male 210 Liver 1.5 0.0040 2.4 71
Female 210 Liver 1.3 0.0056 2.6 15
ETU Rats (F344/N) Male 90 Thyroid 0.37 0.0029 0.61 5.4 Chhabra et al. 1992
Female 90 Thyroid 0.23 0.0018 0.30 7.0
Mice (B6C3F1) Male 330 Liver 0.091 0.0011 0.12 1.9
Female 330 Liver 0.057 0.0010 0.081 0.65
Male 330 Thyroid 0.41 0.0022 0.52 25
Female 330 Thyroid 0.40 0.0024 0.55 16
Male 330 Pituitary 0.32 0.0019 0.38 22
Female 330 Pituitary 0.24 0.0018 0.32 6.9
PBB Rats (F344/N) Male 10 Liver 0.59 0.0041 1.1 6.6 Chhabra et al. 1993a
Female 10 Liver 0.063 0.0009 0.079 1.2
Male 10 Mononuclear cell leukemia 0.79 0.0035 1.4 18
Female 10 Mononuclear cell leukemia 0.21 0.0017 0.28 6.0
Mice (B6C3F1) Male 30 Liver 3.9 1.9 3.9 7.5
Female 30 Liver 1.0 0.37 1.05 2.1
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7832ehp0113-00113416140617ResearchAcquisition of Androgen Independence by Human Prostate Epithelial Cells during Arsenic-Induced Malignant Transformation Benbrahim-Tallaa Lamia 1Webber Mukta M. 23Waalkes Michael P. 11 Inorganic Carcinogenesis Section, Laboratory of Comparative Carcinogenesis, National Cancer Institute at the National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA2 Department of Medicine, and3 Department of Zoology, Michigan State University, East Lansing, Michigan, USAAddress correspondence to M.P. Waalkes, Inorganic Carcinogenesis Section, NCI at NIEHS, P.O. Box 12233, Mail Drop F0-09, 111 Alexander Dr., Research Triangle Park, NC 27709 USA. Telephone: (919) 541-2328. Fax: (919) 541-3970. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 5 5 2005 113 9 1134 1139 7 12 2004 5 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Lethal phenotypes of human prostate cancer are characterized by progression to androgen independence, although the mechanisms behind this progression remain unclear. Arsenic is a potential human prostate carcinogen that may affect tumor progression. In this study, we used a prostate cancer cell model in which an immortalized, nontumorigenic human prostate epithelial cell line (RWPE-1) had been malignantly transformed by chronic low-level arsenic to help determine whether arsenic affects prostate tumor progression. Control and CAsE-PE (chronic-arsenic–exposed human prostate epithelial) cells were continuously maintained in a complete medium [keratinocyte serum-free medium (K-SFM) with bovine pituitary extract and epidermal growth factor] or in a steroid-depleted medium (K-SFM alone). The arsenic-transformed cells showed a more rapid proliferation rate in complete medium than did control cells and also showed sustained proliferation in steroid-reduced medium. Although both control and CAsE-PE cells showed similar levels of androgen receptor (AR), androgens were less effective in stimulating cell proliferation and AR-related gene expression in CAsE-PE cells. For instance, dihydrotestosterone caused a 4.5-fold increase in prostate-specific antigen transcript in control cells but only a 1.5-fold increase in CAsE-PE cells. CAsE-PE cells also showed relatively low levels of growth stimulation by nonandrogen steroids, such as estradiol. Thus, arsenic-induced malignant transformation is associated with acquired androgen independence in human prostate cells. This acquired androgen independence was apparently not due to AR up-regulation, increased activity, or altered ligand specificity. The precise manner in which arsenic altered CAsE-PE growth and progression is undefined but may involve a bypass of AR involving direct stimulation of downstream signaling pathways.
androgen independentARarseniccancer progressionmalignant transformationprostate
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The carcinogenicity of arsenic in humans has been unambiguously demonstrated in a variety of epidemiologic studies (Pott et al. 2001). Inorganic arsenic exposure has been associated with cancers of the skin, lung, liver, kidney, and urinary bladder [National Toxicology Program (NTP) 2000]. Although arsenic carcinogenesis has many other targets, a significant association has also been observed between prostate cancer and chronic arsenic exposure (Chen and Wang 1990; Lewis et al. 1999). Arsenic can cause malignant transformation of human prostate epithelial cells in vitro, and these CAsE-PE (chronic-arsenic–exposed prostate epithelial) cells produce aggressive, carcinoma-like tumors when inoculated into nude mice (Achanzar et al. 2002). There is also evidence that arsenic can enhance tumor progression. For instance, oral exposure to arsenic in mice not only increases the incidence but also greatly increases the progression of skin cancers associated with ultraviolet irradiation (Rossman et al. 2004). Furthermore, transplacental exposure to arsenic is an effective carcinogen in mice, resulting in malignant tumors of the liver and lung (Waalkes et al. 2004). In this model system, arsenic also appears to act as a tumor progressor because it greatly increases malignant liver tumor multiplicity (Waalkes et al. 2004). Although arsenic exposure is associated with prostate cancer in humans (Chen and Wang, 1990; Lewis et al. 1999), the role of arsenic in prostate cancer progression is undefined.
Prostate cancer is the second leading cause of cancer death in American men (Crawford 2003). The normal prostate gland requires androgen for growth and maintenance of differentiated function and will undergo regression if androgen is withdrawn (Kyprianou and Isaacs 1988). Prostate cancer therapy often involves orchiectomy and pharmacologic intervention to diminishing availability of androgen at the androgen receptor (AR) within prostate cancer cells. However, prostate cancer cells often lose the need for androgen as a survival, growth, or differentiation factor and become androgen independent (Westin and Bergh 1998). Although poorly understood, this progression to androgen independence is clearly a critical step in the development of advanced prostate cancer (Suzuki et al. 2003). Androgen-independent prostate cancers are typically more advanced and difficult to treat, and acquisition of such independence has been called a “death sentence” for prostate cancer patients (Arnold and Isaacs 2002).
Altered AR levels or activity can be key elements in acquired androgen independence in prostate cancer. AR is a nuclear transcription factor that normally binds androgen to activate its signaling pathway. Prostate cancer cells can achieve functional AR signaling in the presence of greatly diminished androgens in a variety of ways (Deutsch et al. 2004). AR gene amplification and overexpression can make cells hypersensitive to low levels of androgen, and many prostate cancers show overexpression of AR (Taplin and Balk 2004; Visakorpi et al. 1995). In addition, AR mutations have been recognized that change the ligand specificity of AR such that it can be activated by nonandrogens and even antiandrogens (Deutsch et al. 2004; Tilley et al. 1996). Furthermore, ligand-independent activation of the AR pathway appears to occur in some instances, creating, in essence, a bypass of AR (Feldman and Feldman 2001; Gleave et al. 1999). For instance, certain growth factors, such as insulin-like growth factor-1, keratinocyte growth factor, and epidermal growth factor (EGF), as well as HER2/ neu, a member of the EGF-receptor family of receptor tyrosine kinase, can activate AR-dependent genes in absence of AR ligand (Culig et al. 1994; Yeh et al. 1999). Thus, evidence suggests that altered AR levels, activity, or function can play a major role in the development of androgen-refractory prostate cancer cells (Deutsch et al. 2004; Zegarra-Moro et al. 2002), although an AR bypass can also be important (Culig et al. 1994; Yeh et al. 1999). In men the primary circulating androgen is testosterone. In the prostate, testosterone is converted to the more potent androgen 5-α-dihydrotestosterone (DHT) by the enzyme 5α-reductase (5α-R) (Bonkhoff et al. 1996). DHT is 3–10 times more potent than testosterone in activating AR-regulated downstream events (Martin and Coffey 1998). There is evidence that a significant portion of human prostate cancers overexpress 5α-R type 1 (Thomas et al. 2003). Androgens may be converted to estrogens by the enzyme 5α-aromatase (5α-A) (Simpson et al. 1999). This aromatase is expressed in the human prostate, suggesting a local role for estrogen. Indeed, estrogen can elicit direct actions affecting the growth of prostate cells and can affect estrogen receptor (ER)-mediated gene transcription (Curtis et al. 1997; Robertson et al. 1996). Estrogens have been implicated in the promotion of aberrant prostate growth (Farnsworth 1999) and do not necessarily always work through indirect inhibition of androgen pathways (Harkonen and Makela 2004). In animal models, it has been well established that estrogen may play an important role in prostate carcinogenesis (Bosland 2000). As in other tissues, the effects of estrogen on the prostate are likely transduced primarily by ERs. Prostate cells can be a direct target of estrogen regulation, because they contain both ER-α and ER-β(Harkonen and Makela 2004). Recent evidence indicates that antiestrogens can perturb prostate cancer formation and progression and that this effect is at the level of the ER within prostate cells (Harkonen and Makela 2004; Raghow et al. 2002).
In the present study, we used a model system in which chronic arsenic exposure induced malignant transformation of the human prostate epithelial cell line RWPE-1 (Achanzar et al. 2002) in order to help define the role of arsenic in prostate cancer progression. These transformed CAsE-PE cells rapidly produce very aggressive prostate carcinoma-like tumors upon inoculation into nude mice that overexpress prostate-specific antigen (PSA) while maintaining epithelial characteristics (Achanzar et al. 2002). Specifically, we tested the hypothesis that arsenic may induce androgen-independent growth of human prostate epithelial cells. Our data show that there is loss of androgen dependence after chronic arsenic exposure and the simultaneous acquisition of an aggressive growth behavior. AR expression or ligand specificity played a minimal role in this arsenic-induced prostate cancer cell progression.
Materials and Methods
Chemicals and reagents.
We purchased sodium arsenite (NaAsO2; purity, 96.6%) from Sigma Chemical Co. (St. Louis, MO) and keratinocyte serum-free medium (K-SFM), EGF, bovine pituitary extract (BPE), 100× antibiotic-antimycotic mixture, and TRIzol reagent from Life Technologies, Inc. (Grand Island, NY). The mouse monoclonal anti-ER-α, the rabbit polyclonal anti-ER-β, and the mouse monoclonal antiactin were purchased from Oncogene Research Products (Cambridge, MA). We purchased the rabbit polyclonal anti-AR from Affinity BioReagents (Golden, CO); horse-radish peroxidase–conjugated secondary antibody from Amersham (Piscataway, NJ); and the Quick Start Bradford protein assay from Bio-Rad Laboratories (Hercules, CA).
Cells and cell culture.
Control (untransformed) RWPE-1 cells were originally derived from normal human prostate epithelial cells and are immortalized but nontumorigenic (Bello et al. 1997; Webber et al. 1997). Unless otherwise noted, cells were grown in K-SFM containing 50 μg/mL BPE and 5 ng/mL EGF, supplemented with 1% antibiotic/antimycotic mixture. K-SFM containing BPE and EGF is henceforth termed “complete medium.” Cultures were incubated at 37°C in a humidified atmosphere containing 5% CO2 and passaged weekly. Cells were exposed continuously to 5 μM arsenite (as NaAsO2). The arsenic-exposed cells were designated chronic-arsenic–exposed prostate epithelial (CAsE-PE) cells to distinguish them from the parental RWPE-1 control cells. Parallel cultures grown in arsenic-free medium provided passage-matched controls. After 29 weeks of exposure, CAsE-PE cells produced malignant tumors when inoculated into nude mice (Achanzar et al. 2002). To establish persistence of the observed changes, cells that had been treated for 30 weeks with arsenic were grown in arsenic-free medium for an additional 6 weeks. The phenotypic changes observed in CAsE-PE cells were stable during this period.
Cell growth rate and the effects of steroids.
To determine the rate of cellular growth, normal and transformed prostate epithelial cells were seeded at a density of 3.2 × 103 cells/cm2 in six-well culture plates, and cell proliferation was determined by cell counting as previously described (Igawa et al. 2002). After 3 days, one set of cells was harvested and counted at time 0 with a Z1 model Coulter counter (Coulter Corporation, Miami, FL). The remaining cells were provided with fresh complete medium, and total cell number was determined at various times thereafter. Fresh complete medium was added to the cells every 2 days. Steroid-reduced medium was K-SFM without BPE and EGF. The BPE is likely the major source of steroids in complete medium. To determine the effect of steroid depletion on cell proliferation, cells were exposed to steroid-depleted medium for 2 days, harvested, and counted at time 0. Additional cells were counted on days 3, 6, and 10. Fresh medium was added at each time point. To determine the effects of exogenous androgen or estradiol (E2) effects, cells were seeded at a density of 4 × 103 cells/cm2 and maintained in regular culture medium for 3 days. Cells were then fed with the steroid-reduced medium and cultured for an additional 48 hr before addition of DHT (0.1 μM) or E2 (1 μM; both from Sigma). In a separate series of experiments to test the effects of AR blockade on cell growth, control and CAsE-PE cells were grown in steroid-depleted media for 48 hr then fed fresh steroid-depleted media with or without the antiandrogen flutamide (5μg/mL; Sigma) in the absense or presence of DHT (0.1μM). Cell proliferation was then determined after an additional 4 days. Cells were harvested at various time periods after treatment, and cell numbers were determined.
RNA extraction and RT-PCR.
Total RNA was isolated using TRIzol reagent by manufacturer’s instructions. Reverse transcription–polymerase chain reaction (RT-PCR) was performed using a TITANIUM one-step RT-PCR kit (Clontech, San Jose, CA) and a GeneAmp PCR system 9700 (Applied Biosystems, Foster City, CA) according to the kit’s instructions. Amplification conditions were as follows: 60 min at 50°C and 5 min at 94°C followed by 35 cycles for 1 sec at 94°C, 1 sec at 55°C (ER-α), 58°C (ER-β), 50°C (5α-A and 5α-R), or 54°C (PSA), 1 min at 72°C; 1 μg total RNA was used in each amplification. Primers were designed for ER-α, ER-β, 5α-A, 5α-R, PSA, and β-actin and were synthesized by Invitrogen (Grand Island, NY) as follows: ER-α(5′-TACTGCATCAGATCCAAGGG-3′ and 5′-ATCAATGGTGCACTGGTTGG-3′), product size: 650 bp; ERβ(5′-TGAAAAGGAAGGTTAGTGGGAACC-3′ and 3′-TGGTCAGGGACATCATCATGG-5′), product size: 530 bp; 5α-A (5′-ATACCAGGTCCTGGCTACTG-3′ and 5′-TTGTTGTTAAATATGATGCC-3′), product size: 273 bp; 5α-R1 (5′-AGCAGATACTTGAGCCA-3′ and 5′-CCAAAATAGTTGGCTGC-3′), product size: 209 bp; 5α-R2 (5′-ACATTACTTCCACAGGACATTT-3′ and 5′-AGGAAATTGGCTCCAGA-3′), product size: 318 bp; PSA (5′-GAGGTCCACACACTGAAGTT-3′ and 5′-CCTCCTGAAGAATCGATTCCT-3′), product size: 214 bp; β-actin (5′-AGAGATGGCCACGGCTGCTT-3′ and 5′-ATTTGCGGTGGACGATGGAG-3′), product size: 460 bp. PCR products were electrophoresed on 1.7% agarose gels, and the gel image was captured and quantified with a Gel Doc 2000 System equipped with TDS Quantity One software (Bio-Rad). The level of β-actin was used to normalize results.
Western blot analysis.
Total proteins were isolated using M-PER reagent (Pierce, Rockford, IL) as directed by the manufacturer. Protein concentration was determined using the Bradford assay, and 20–40 μg of each sample was electrophoresed on NuPage 4–12% Bis-Tris gels (200V, 30 min) and transferred to nitrocellulose membranes according to the manufacturer’s directions (Invitrogen). Immunoblotting was performed using the ER-αantibody at a 1:100 dilution, horseradish peroxidase–conjugated anti-mouse secondary antibody at a 1:5,000 dilution, ER-βantibody at a 1:1,000 dilution, AR antibody at a 1:100 dilution, or horseradish peroxidase–conjugated anti-rabbit secondary antibody at a 1:5,000 dilution, and SuperSignal West Pico chemiluminescent substrate (Pierce). Signals were visualized by exposure to Hyperfilm (Amersham). Densitometric analysis was performed using Quantity One software (Bio-Rad). AR levels were assessed with and without treatment with the nonmetabolizable androgen mibolerone (5 nM, 6 days; Sigma).
Statistical analysis.
All data are represented as mean ± SE derived from three or more independent experiments. Statistical significance of the results was determined by the Student’s t-test or analysis of variance followed by Dunnett’s t-test as appropriate, with p ≤0.05 considered statistically significant.
Results
Impact of arsenic-induced malignant transformation on cellular proliferation.
Arsenic can induce malignant transformation of the human prostate epithelial cell line RWPE-1, such that the transformed CAsE-PE cell line produces aggressive tumors remarkably resembling prostate carcinoma upon inoculation into nude mice (Achanzar et al. 2002). Because androgen independence is often associated with advanced prostate cancers, we examined the growth of control and arsenic-transformed prostate epithelial cells in complete or steroid-reduced medium. In complete medium, the transformed CAsE-PE cells proliferated approximately twice as fast as control cells (Figure 1A), in keeping with their malignant behavior. In a steroid-reduced medium (K-SFM medium without steroid-containing BPE complement or EGF), the growth rate of both cell lines decreased (Figure 1B). However, CAsE-PE cells still had a much more rapid growth rate in steroid-depleted medium, with a doubling time approximately 2.5-fold higher than control cells. Thus, the transformed CAsE-PE cells showed a more rapid growth than did control cells, which was at least partially independent of exogenous steroids. This is consistent with androgen independence in CAsE-PE cells.
Among many possible mechanisms, there are four ways by which androgen independence is attained in prostate cancers through modification of the AR status or function: a) overexpression of functional AR, b) AR mutation resulting in hyper-responsiveness to androgens, c) activation by nonandrogens (loss of ligand specificity), or d) activation of ligand-independent AR signaling pathways (Deutsch et al. 2004). Thus, experiments were designed to test these possibilities.
AR expression, responsiveness, and activity.
To determine whether the androgen-independent growth in CAsE-PE cells was dictated by overexpression of AR, we conducted AR expression analysis. As shown in Figure 2, AR protein in both control and CAsE-PE cells was expressed at the same level. Thus, overexpression was clearly not required for the apparent steroid-independent growth in CAsE-PE cells. Other studies have shown that androgens can increase AR levels via up-regulation of AR (Yeap et al. 1999). To help test AR responsiveness in control and CAsE-PE cells, mibolerone, a nonmetabolizable androgen, was used to induce AR expression. Mibolerone produced a 2.6-fold increase in the AR protein level in control cells but increased AR in CAsE-PE cells to a significantly lesser extent (Figure 2).
DHT is known to stimulate gene expression and prostate cell growth through AR. When DHT was added to cells growing in reduced steroid medium, both control and CAsE-PE cells exhibited growth stimulation (Figure 3A). However, the growth of control cells was stimulated nearly 2-fold by DHT at optimal levels (0.1 μM), whereas arsenic-transformed CAsE-PE cells showed significantly less growth stimulation (Figure 3A). The time course for DHT stimulation of cellular growth of control and CAsE-PE cells clearly shows the diminished response in CAsE-PE cells (Figure 3B). The growth of control cells on day 10 was stimulated by DHT approximately 3.5-fold, whereas the growth of CAsE-PE cells was increased only about 2-fold compared with cells grown in steroid-depleted medium. Thus, arsenic-induced malignant transformation actually appears to confer a diminished responsiveness of AR.
To further assess the activity of AR in these cells, we examined androgen-induced gene expression through AR stimulation. In this case, we examined PSA expression, which is activated by androgens through AR. As is typical with prostate malignancies, CAsE-PE cells expressed significantly more PSA than did control cells (Figure 4). However, a marked 4.6-fold increase in cellular PSA occurred with DHT treatment in control cells, whereas levels increased only 23% in CAsE-PE cells. Indeed, DHT-induced increases in PSA were to a significantly lower maximal level in CAsE-PE cells compared with control cells (Figure 4). This indicates that stimulation of the AR pathway by androgen is less effective in production of AR-related products in arsenic-transformed cells. These data, together with mibolerone data, indicate that the AR in CAsE-PE cells is actually less responsive, and argue against an AR mutation that causes AR hypersensitivity to androgens in these cells.
Impact of antiandrogens on cell proliferation.
Because androgen-independent prostate cancers often become resistant to antiandrogens and AR mutations can result in stimulation by other steroids, including antiandrogens, we tested the effect of the antiandrogen flutamide on DHT-stimulated growth in control and CAsE-PE cells. DHT-stimulated growth was completely suppressed by flutamide in control cells (Figure 5). On the other hand, in CAsE-PE cells, the androgen-stimulated growth was blocked only partially by flutamide.
Collectively, CAsE-PE cells responded differently to DHT, flutamide, or mibolerone, all of which are thought to act through the AR. In light of the findings that AR levels are similar, the androgen-independent growth component of CAsE-PE cells does not appear to be due to overexpression of a functional AR, or through an AR modification that alters steroid sensitivity or selectivity.
Expression of androgen metabolism enzymes.
It is possible that an aspect of androgen independence in CAsE-PE cells could involve a more efficient conversion of testosterone to DHT by 5α-R. Thus, we evaluated the expression of 5α-R isoforms in control and CAsE-PE cells (Figure 6). Both control and arsenic-transformed CAsE-PE cells expressed 5α-R1 RNA with an elevated expression in CAsE-PE cells (~ 53%). 5α-R2 mRNA was not detectable in either control or CAsE-PE cells (data not shown). The expression of 5α-A, which produces E2 from testosterone, was also assessed in each cell line, and both cell lines showed a similar expression level.
Effect of E2 on cell proliferation.
In many instances of acquired androgen independence in prostate cancer, the AR is modified such that it becomes sensitive to a variety of steroids, including nonandrogens. To test this hypothesis, we also determined the cellular growth of control and CAsE-PE cells after exposure to various concentrations of E2. Both control and CAsE-PE cells exhibited optimal growth stimulation by E2 at a concentration of 1 μM (Figure 7A). However, the growth of control cells on day 7 was stimulated by 1.8-fold, whereas the growth of CAsE-PE cells was stimulated only about 1.2-fold. We subsequently determined the time course of cellular growth of control and CAsE-PE cells after exposure to 1 μM E2. As shown in Figure 7B, the growth of control cells on day 10 was stimulated by approximately 3-fold, whereas the growth of CAsE-PE cells was increased only about 1.3-fold. Growth of control cells is significantly stimulated by physiologic concentrations of E2; this growth increase appears to be comparable with that induced by DHT. In contrast, the E2 growth-stimulating effect in CAsE-PE cells is significantly less than that observed in control cells.
We also studied the expression of ERs. ER-αand ER-βtranscripts occurred in both control and CAsE-PE cells (Figure 8A). ER-α was down-regulated in CAsE-PE cells compared with control cells (~ 50%, p < 0.05). ER-βmRNA levels are distinctly lower in both cell lines compared with ER-α. Nevertheless, ER-βexpression was increased in CAsE-PE cells compared with control cells (1.6-fold, p = 0.05). ER-αand ER-βproteins were expressed in both control and CAsE-PE cells and were consistent with the data on mRNA (Figure 8B).
Discussion
The results demonstrate that inorganic arsenic can potentially affect prostate cancer progression. In this regard, a clear transition from the androgen-sensitive to androgen-independent state occurs during arsenic-induced malignant transformation of human prostate epithelial cells. The androgen response program is critical to the progression of human prostate cancer (Feldman and Feldman 2001). Prostate cancer initially requires androgen for growth and responds to hormone ablation therapies (Feldman and Feldman 2001). However, the disease often progresses to a state of reduced hormone dependence, which is commonly fatal (Arnold and Isaacs 2002). Several mechanisms may contribute to the progression of prostate cancer to an androgen-independent state (Grossmann et al. 2001). AR amplification is found in approximately 30% of clinically advanced prostate cancer cases (Koivisto et al. 1997; Visakorpi et al. 1995). Overexpression of transcriptional coactivators also accompanies progression in some cases and facilitates the activity of AR (Comuzzi et al. 2003). Mutations in the AR may allow it to respond to different steroids as well as antiandrogens (Taplin et al. 1999). However, arsenic-induced androgen independence in CAsE-PE cells is not associated with AR overexpression or altered AR ligand specificity, indicating that arsenic affects progression through a non-AR-dependent mechanism. In this regard, the growth factors and receptors associated with prostate cancer progression often regulate cell growth through stimulation of Ras signaling pathways (Weber and Gioeli 2004). Recent data from our laboratory indicate that wild-type k-ras activation is strongly correlated with arsenic-induced transformation in CAsE-PE cells (Benbrahim-Tallaa et al., in press). Chronic activation of ras by autocrine and paracrine growth factor stimulation is thought to be a common mechanism for prostate cancer progression, and attenuation of ras signaling can restore androgen sensitivity to hormone-refractory prostate cancer cells (Bakin et al. 2003a, 2003b). Because arsenic-induced androgen independence does not appear to involve AR overexpression or altered ligand specificity, a bypass of AR through chronic overexpression of Ras may well contribute to this progression. Further research on arsenic stimulation of this important growth signaling pathway is ongoing.
The role of ER in prostate cancer progression is not completely understood. In the present study, ER-αexpression was significantly reduced in arsenic-transformed CAsE-PE cells. ER-αexpression is often down-regulated in prostate cancer (Linja et al. 2003) and is associated with a poor prognosis because it reduces the effectiveness of endocrine therapy (Konishi et al. 1993). Thus, the reduced ER-αexpression in arsenic-transformed CAsE-PE cells may indicate a more advanced tumor cell, consistent with the production of invasive carcinoma when these cells are inoculated into nude mice (Achanzar et al. 2002). ER-βexpression may be reduced in primary prostate cancers, but its expression returns in metastases (Weihua et al. 2002). In fact, recent studies have shown that ER-βis the predominant ER subtype expressed in prostate cancer metastases (Lai et al. 2004; Leav et al. 2001). Therefore, the overexpression of ER-βin CAsE-PE cells may also suggest a more progressed state.
Both control and CAsE-PE cells expressed only the type 1 isoform of 5α-R in the present study. This is consistent with the androgen-independent prostate tumor cell lines DU-145 and PC3 (Delos et al. 1995; Negri-Cesi et al. 1999) and isolated human prostate cancer epithelial cells (Delos et al. 1995) where only 5α-R1 is detected. Generally speaking, 5α-R1 appears to predominate in cancerous prostate tissue (Occhiato et al. 2004) and is highly overexpressed in a subset of prostate cancers but not highly expressed in benign prostatic hyperplasia (Thomas et al. 2003). The overexpression of 5α-R1 in arsenic-transformed CAsE-PE cells indicates that it is possible that these cells could convert more testosterone to DHT. However, 5α-R1 overexpression does not appear to be involved in aberrant cell proliferation in DU-145 cells, because a specific 5α-R1 inhibitor (LY306089), which blocks DHT formation, has no effect on proliferation of DU-145 cells (Kaefer et al. 1996). Thus, 5α-R1 overexpression does not appear to account for the hyperproliferation observed for arsenic-transformed CAsE-PE cells.
The low levels of 5α-A transcript in control and CAsE-PE cells indicate that the intracellular production of estrogens is not a characteristic of these cells and limits the possibility that testosterone, through estrogen formation, might still indirectly be active in CAsE-PE cells. 5α-A is observed in normal and pathologic prostate specimens (Matzkin and Soloway 1992) and in prostate cancer cells (Block et al. 1996). However, the poor response to E2 and the very weak expression of aromatase in androgen-independent CAsE-PE cells indicates local aromatization of testosterone probably does not play a major role in arsenic-induced prostate cancer progression.
In summary, the present results clearly show that arsenic can precipitate events leading to rapid growth and greatly reduce androgen dependence during malignant transformation of human prostate epithelial cells. Arsenic-induced acquisition of androgen independence does not involve overexpression of AR or any apparent changes in AR ligand sensitivity. Changes in androgen metabolism, estrogen production, or ER levels and sensitivity also appear to have limited roles in this conversion. However, the fact that a common contaminant of the human environment can potentially affect prostate cancer progression provides strong incentive to further define the role of arsenic in prostate cancer progression.
We thank L. Keefer, J. Liu, and W. Qu for their critical review of the manuscript.
Figure 1 Growth rates of control (RWPE-1) and CAsE-PE cells in complete (A) and steroid-reduced (B) medium. Cells were seeded and maintained as described in “Materials and Methods.” The total cell number was counted on days 2, 4, and 6 for cells grown in complete culture medium (A) or on days 3, 6, and 10 in steroid-reduced medium (B). The data shown are the means of triplicate wells after normalization to day 0, indicating cell growth (n = 3); error bars represent SE.
*Significantly different from control at the same time point.
Figure 2 Basal and mibolerone-induced AR protein expression in control (RWPE-1) and CAsE-PE cells assessed by Western blot analysis. Cells were grown in complete medium, and mibolerone (5 nM) was added 6 days before assessment. Densitometric data normalized to β-actin are given as fold increase over control and are expressed as means (n = 3); error bars represent SE.
*Significantly different from untreated cell-line–matched cells. **Significantly different from control cells treated with mibolerone.
Figure 3 Effect of DHT on the growth of control and CAsE-PE cells. (A) Cells were plated in the presence of 0.1 μM DHT, harvested at 7 days, and counted; growth stimulation by DHT was normalized to the control cells (set as 1.0). (B) Time course of growth stimulation of normal and arsenic-transformed prostate epithelial cells by 0.1 μM DHT; the total cell numbers were counted on days 3, 6, and 10. Densitometric data are given as fold increase over control and are expressed as means (n = 3); error bars represent SE.
*Significantly different from untreated, cell-line–matched cells. **Significantly different from control cells treated with DHT.
Figure 4 Androgen effects on PSA expression of control (RWPE-1) and CAsE-PE cells. RNA was isolated and subjected to RT-PCR analysis using a set of primers designed to amplify PSA and β-actin gene products after DHT treatment. See “Material and Methods” for details.
*Significantly different from untreated control cells. **Significantly different from control cells treated with DHT.
Figure 5 The effect of flutamide on the growth of control (RWPE-1) and CAsE-PE cells. Control and CAsE-PE were exposed to flutamide in the presence or absence of DHT. Data are expressed as means (n = 3); error bars represent SE.
*Significantly different from untreated, cell-line–matched cells. **Significantly different from control cells treated with DHT or DHT plus flutamide.
Figure 6 Expression of 5α-R1 and 5α-A in control (RWPE-1) and CAsE-PE cells. RNA was isolated and subjected to RT-PCR analysis using a set of primers designed to amplify 5α-R, 5α-A, and β-actin genes products. (A) Representative blot. (B) Densitometric analysis normalized to β-actin. Data are expressed as means (n = 3); error bars represent SE.
*Significantly different from control cells.
Figure 7 Effect of E2 on the growth of control (RWPE-1) and CAsE-PE cells plated in the presence of 1 μM E2, harvested at 7 days, and counted. (A) Growth stimulation by E2 normalized to the control cells (set as 1.0). (B) Time course of growth stimulation of normal and arsenic-transformed prostate epithelial cells by 1 μM E2. Total cell numbers were counted on days 3, 6, and 10; the data shown are the means and SEs of triplicates. Similar results were found in two independent experiments. Densitometric data are given as fold increase over control and are expressed as means (n = 3); error bars represent SE.
*Significantly different from untreated cell-line–matched cells. **Significantly different from control cells treated with E2.
Figure 8 Expression of ERs in control (RWPE-1) and CAsE-PE cells. (A) RNA was isolated and subjected to RT-PCR analysis using a set of primers designed to amplify ER-α, ER-β, and β-actin gene products. (B) Proteins were isolated and separated and subjected to Western blot analysis monoclonal anti-ER-α, polyclonal anti-ER-β, and monoclonal antiactin. Densitometric data are normalized to β-actin and expressed as means (n = 3); error bars represent SE.
*Significantly different from control cells.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7822ehp0113-00114016140618ResearchFine Particulate Matter National Ambient Air Quality Standards: Public Health Impact on Populations in the Northeastern United States Johnson Philip R.S. Graham John J. Northeast States for Coordinated Air Use Management (NESCAUM), Boston, Massachusetts, USAAddress correspondence to P.R.S. Johnson, NESCAUM, 101 Merrimac St., 10th Floor, Boston, MA 02114 USA. Telephone: (617) 259-2075. Fax: (617) 742-9162. E-mail:
[email protected] authors are employed by NESCAUM, a clean air association of the Northeast states.
9 2005 10 5 2005 113 9 1140 1147 1 12 2004 10 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In this article we identify the magnitude of general and susceptible populations within the northeastern United States that would benefit from compliance with alternative U.S. Environmental Protection Agency (EPA) annual and 24-hr mass-based standards for particulate matter (PM) with an aerodynamic diameter ≤2.5 μm (PM2.5). Understanding the scale of susceptibility in relation to the stringency or protectiveness of PM standards is important to achieving the public health protection required by the Clean Air Act of 1970. Evaluative tools are therefore necessary to place into regulatory context available health and monitoring data appropriate to the current review of the PM National Ambient Air Quality Standards (NAAQS). Within the New England, New Jersey, and New York study area, 38% of the total population are < 18 or ≥65 years of age, 4–18% of adults have cardiopulmonary or diabetes health conditions, 12–15% of children have respiratory allergies or lifetime asthma, and 72% of all persons (across child, adult, and elderly age groups) live in densely populated urban areas with elevated PM2.5 concentrations likely creating heightened exposure scenarios. The analysis combined a number of data sets to show that compliance with a range of alternative annual and 24-hr PM2.5 standard groupings would affect a large fraction of the total population in the Northeast. This work finds that current PM2.5 standards in the eight-state study area affect only 16% of the general population, who live in counties that do not meet the existing annual/24-hr standard of 15/65 μg/m3. More protective PM2.5 standards recommended or enacted by California and Canada would protect 84–100% of the Northeast population. Standards falling within current ranges recommended by the U.S. EPA would protect 29–100% of the Northeast population. These considerations suggest that the size of general and susceptible populations affected by the stringency of alternative PM standards has broad implications for risk management and direct bearing on the U.S. EPA’s current NAAQS review and implementation.
air pollutionNational Ambient Air Quality Standardsnortheastern United Statesparticulate matterPM2.5populationspublic healthsensitivesusceptible
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Exposure to ambient fine particulate matter [particulate matter (PM) with an aerodynamic diameter ≤2.5 μm (PM2.5)] has been associated with a wide range of PM-related human health effects in general populations, including the aggravation of heart and lung disease and premature mortality (Brook et al. 2004; Holgate et al. 1999; Samet et al. 2000). The Clean Air Act of 1970 (CAA 1970) mandates the U.S. Environmental Protection Agency (EPA) to set health-based National Ambient Air Quality Standards (NAAQS) for certain pollutants known to be hazardous to human health, including PM. NAAQS provisions require the U.S. EPA to establish standards requisite to protect public health with an adequate margin of safety at a level that avoids unacceptable risks. Legislative history has interpreted the PM NAAQS margin of safety provision as requiring the protection of both general populations and sensitive subpopulations, or those subgroups potentially at increased risk for ambient particle health effects (National Air Quality Standards Act of 1970). Accordingly, the PM NAAQS—which are currently under review by the U.S. EPA—are intended to protect the health of the most sensitive members of society as well as the general population.
During the last decade, regulatory agencies have increasingly recognized that persons sensitive or susceptible to PM are more numerous and diverse than once thought. To achieve the public health protection called for by the CAA, the National Research Council (NRC) has recommended that subpopulations at increased risk from PM pollution should be identified and the nature and magnitude of their risk understood in the context of standard setting (NRC 2004). These groups comprise a large fraction of the U.S. population, including people with respiratory disease, heart disease, or diabetes; older people; young children; and populations experiencing heightened exposure levels (e.g., those engaged in outdoor work or exercise) [California Air Resources Board (CARB) 2002; U.S. EPA 2004a, 2004b].
Despite regulatory efforts over the past 40 years to improve air quality, the protection of public health with an adequate margin of safety is constrained by the inability of scientists to determine a safe level of exposure to PM2.5 below which populations are safe (Daniels et al. 2004; DiBattista and Brown 2003; Schwartz et al. 2002). The American Thoracic Society’s (ATS) statement on the nature of an adverse health effect of air pollution notes that although the NAAQS affords health protection to subgroups with increased susceptibility to air pollution using a margin of safety provision, this margin has not been quantified (ATS 2000). Given the likely heterogeneity of individual responses to air pollution, the severity of health effects experienced by a susceptible subgroup may be much greater than that experienced by the population at large (Zanobetti et al. 2000). Therefore, varying host susceptibility factors may hinder adequate protection of an entire population, even at low exposure levels [ATS 2000; Peters et al. 2004; World Health Organization (WHO) 2004].
Notwithstanding the limitations of current standard-setting methods, ambient air quality standards do ultimately determine the number of persons affected by air pollution (Deck et al. 2001). The more stringent the standard, the greater the emission reduction required and the more extensive the control strategies used to reduce PM concentrations. Reduction in ambient PM levels presumably reduces the public health toll exacted by PM pollution. However, given the current lack of an accepted threshold level for adverse health effects, any nonzero PM standard represents the air-pollution–related health burden that policy makers consider “acceptable” (Peters et al. 2004). This presents an important and challenging public health question because PM standards are the fulcrum on which society decides how many people will be at increased health risk to ambient PM. Furthermore, there may be variation in PM–health outcome associations for different subgroups and for different geographic regions, including the northeastern United States, which require consideration in the standard-setting process.
We assessed the extent to which compliance with various combinations of alternative PM2.5 standards would provide supplemental protection to general populations and susceptible subgroups in the northeastern United States. We first conducted a state-of-knowledge review of key regulatory and research organizations in the United States and Canada to determine which subgroups were considered to be at elevated risk to PM. We then integrated existing demographic and disease or health condition prevalence databases from the U.S. Census Bureau and Centers for Disease Control and Prevention (CDC) with various combinations of PM2.5 annual and 24-hr U.S. EPA design values generated from a network of air pollution monitoring sites across an eight-state Northeast study region. This analysis estimated the number of general population and susceptible subgroups in the northeastern United States that would benefit from compliance with alternative U.S. EPA annual and 24-hr mass-based PM2.5 standards. We believe the methodologic approach used provides an evaluative tool that may help decision makers place into regulatory context health data appropriate to the current review of the PM NAAQS. The analysis makes evident the public health implications of selecting among alternative PM2.5 standards with different degrees of health protection.
Materials and Methods
We identified subpopulations considered potentially at elevated risk for adverse health effects related to PM by reviewing recent health assessment reviews and research reports. These included the Canadian Council of Ministers of the Environment’s (CCME) human health effects of PM2.5 report in support of the Canada-wide standards (CCME 2004); the CARB’s staff report to consider amendments to the ambient air quality standards for PM and sulfates (CARB 2002); the U.S. EPA’s PM criteria document (U.S. EPA 2004b), PM staff paper (U.S. EPA 2005), and Particulate Matter Research Program progress report (U.S. EPA 2004a); and comments provided by the NRC’s fourth report on research priorities for airborne PM (NRC 2004). To the extent that the four organizations identified or commented on subgroups likely or possibly at increased risk to PM, we estimated the magnitude of these subgroups for an eight-state study area (Connecticut, Maine, Massachusetts, New Hampshire, New Jersey, New York, Rhode Island, and Vermont) where data were sufficient. Common subgroups identified included susceptibility by age group, preexisting disease or health condition, heightened exposure, and socioeconomic status. Sufficient demographic and health prevalence data allowed for the estimation of subgroup size using age group and preexisting disease or health condition indicators. To a lesser extent, heightened exposure subgroups were also estimated using population density data.
We calculated age subgroup sizes from the 2000 Census (U.S. Census Bureau 2000) and matched preexisting disease or health condition indicators to available prevalence rates generated by recently published CDC health surveys desegregated by either state or Northeast region. Adult (≥ 18 years) self-reported asthma rates (ever) were obtained from the 2002 Behavioral Risk Factor Surveillance System (BRFSS), which was state specific. Lifetime asthma was defined as an affirmative response to the question “Have you ever been told by a doctor (nurse or other health professional) that you have asthma?” (CDC 2002a). We calculated the mean lifetime asthma prevalence rate for the eight states in the study area from each state-level prevalence rate. Adult sinusitis rates (preceding 12 months) and chronic bronchitis rates were obtained from the 2000 U.S. Adult National Health Interview Survey (NHIS) for the northeastern United States. The NHIS defines the northeastern United States as the six New England states, plus New Jersey, New York, and Pennsylvania. Respondents were asked in separate questions whether they had been told by a doctor or other health professional in the past 12 months that they had sinusitis or bronchitis (CDC 2003a).
We acquired adult cardiac prevalence rates from the 2000 NHIS for the northeastern United States (CDC 2003a). In separate questions, respondents were asked if they had ever been told by a doctor or other health professional that they had hypertension (or high blood pressure), coronary heart disease, angina (or angina pectoris), heart attack (or myocardial infarction), or any other heart condition or disease not already mentioned. Persons had to have been told on two or more different visits that they had hypertension, or high blood pressure, to be classified as hypertensive. Heart disease was defined to include coronary heart disease, angina pectoris, heart attack, or any other heart condition or disease (CDC 2003a). We obtained adult diabetes prevalence rates (ever) from the 2001 BRFSS report, which was state specific. Diabetes was defined as an affirmative response to the question “Have you ever been told by a doctor that you have diabetes?” (CDC 2002b).
We acquired child (< 18 years) respiratory allergies (preceding 12 months) and asthma (ever) prevalence rates from the 2001 U.S. Children NHIS for the northeastern United States (CDC 2003b). Allergy rates were based on the following questions: “During the past 12 months, has [child’s name] had any of the following conditions? Hay fever? Any kind of respiratory allergy?” Asthma rates were based on the question “Has a doctor or other health professional ever told you that [child’s name] has asthma?” (CDC 2003b).
To integrate demographic and health prevalence databases with various combinations of PM2.5 annual and 24-hr U.S. EPA design values generated from a network of air pollution monitoring sites, federal reference method (FRM) PM2.5 air pollution data from 2000, 2001, and 2002 were obtained from the U.S. EPA’s air quality system in August 2003 for 127 FRM monitors in U.S. EPA Region 1 (six New England states) and Region 2 (New Jersey, New York), 65 FRM monitors outside these regions in bordering states (Delaware, Maryland, and Pennsylvania, as well as the District of Columbia), and three Interagency Monitoring of Protected Visual Environments (IMPROVE) sites in Regions 1 and 2 [U.S. EPA 2003a; Visibility Information Exchange Web System (VIEWS) 2003]. Within the 2000–2002 period, 192 PM monitoring sites had data in all 12 quarters. Data flagged with the forest fire exemption for 2002 were removed. More than 75% of the 192 sites had better than 50% data capture within each quarter. Data completeness affecting the remaining sites was primarily isolated to one quarter. For sites with collocated monitors, the primary monitor at a site was used to determine the PM2.5 concentration (27 pairs of 192 monitors). Although less than half of the primary monitors satisfied the 75% data completeness criteria, no substitution from collocated monitors was attempted.
To determine whether data completeness would affect the relationship between the annual and 24-hr standards at each site, the 81 sites meeting the U.S. EPA’s strict 75% completeness requirement for 12 consecutive quarters were compared with 111 sites that did not meet completeness requirements. Regression equations and slopes between the two monitoring data sets were statistically indistinguishable. The regression (where y is the level of the 24-hr standard and x is the level of the annual standard) for the subset of monitors with complete data was y = 1.86x + 10.43 (R
2 = 0.76). The regression for the subset of monitors with incomplete data was y = 1.82x + 10.90 (R2 = 0.78). One data point was excluded from the linear regression because of its undue influence by virtue of its extreme value pair. Inclusion of this point changed the regression to y = 2.00x + 8.79 (although this slope is also statistically equivalent to that of the incomplete data).
To estimate the number of persons living in counties not likely to meet different combinations of alternative annual and 24-hr PM2.5 standards, 3-year average annual and 24-hr design values were calculated for all counties (150) in the eight-state study area and integrated with Census county-level population data using ArcGIS software (version 8.2; ESRI, Redlands, CA). Design values for state data were generated in adherence with the U.S. EPA’s criteria for determination of design values (U.S. EPA 1997, 1999). Alternative standard combinations were put forward for annual standards ranging from 11 to 15 μg/m3 (1-μg/m3 intervals) and for 24-hr (98th percentile) standards ranging from 20 to 65 μg/m3 (5-μg/m3 intervals). These ranges were selected to encompass recent California, U.S. EPA, and CCME recommended PM2.5 ranges or selected standards.
Design values for the 70 counties with monitors were assigned from the highest monitored levels in each county for 2000–2002. Design values for 80 counties lacking monitors were generated by interpolating county-level monitored design value data from 104 monitors within the eight-state study region and 61 monitors outside the region for border counties. An interpolation scheme was employed using inverse distance-squared weighting for the six nearest monitors within a 111-km radius (corresponding to 1° latitude). Massachusetts and New Hampshire had very few sites with complete data for the 3-year period, requiring an approximation of design values for counties in those states. For the other counties in the eight-state study region, the annual design values used were generally within 0.2 μg/m3 of those reported by the U.S. EPA using customary guidelines for data substitution and completeness determinations (U.S. EPA 2003b).
We calculated the number of susceptible persons identified as potentially at elevated risk to PM living in counties with PM2.5 levels exceeding various annual/24-hr standard combinations for age subgroups and persons with preexisting health conditions using Census age demographic and BRFSS and NHIS health survey prevalence data (CDC 2002a, 2002b, 2003a, 2003b; U.S. Census Bureau 2000). Prevalence rates were multiplied by the number of persons in respective adult and child age groups estimated to be living in counties with PM2.5 levels exceeding PM2.5 standard combinations.
Differing forms of PM2.5 annual and 24-hr primary standards of selected U.S. and Canadian government agencies were normalized to facilitate general comparisons across agencies. This allows for the estimation of how other agency’s standard levels correspond to the U.S. EPA’s standard level. Relationships were generated using 2000–2002 data from 192 PM monitors located in the eight states and border states of the study region. To compare California’s 1-year not-to-be-exceeded (NTBE) target annual standard with the U.S. EPA’s 3-year mean annual standard, the relationship between the 3-year annual average and the individual annual averages from the 3 years was reviewed. The highest 3-year average annual value for which no individual year exceeded the California standard was 11.5 μg/m3. However, several sites showed a 3-year average lower than this where an individual year had exceeded 12 μg/m3. There were no annual excursions above the 12 μg/m3 level for a site when the 3-year annual average was < 11.0 μg/m3. These values (11.0–11.5 μg/m3) represent a reasonable range of equivalency between a 3-year annual average and a 1-year annual average NTBE standard form.
The relationship between California’s proposed 1-year NTBE target 24-hr standard and the U.S. EPA’s 3-year mean 98th percentile 24-hr standard was also derived from the 3-year data set (U.S. EPA 2003a; VIEWS 2003). Unlike the annual standard, California’s 24-hr standard is structured to allow the exclusion of one extreme day per year over 3 years. To account for these potential extreme day exclusions, the 24-hr values were ranked over 3 years and exclusions were permitted based on total available collected samples; for each 365 sample days, the highest concentration value was excluded. For most sites that sampled on a 1-in-3-day schedule, no exclusions were allowed. For 24-hr sampling sites, generally the top 2 concentration days were excluded, leaving the third highest day as the 24-hr standard level. Because the lowest maximum 24-hr value for any site was > 25 μg/m3, a conservative corresponding 98th percentile form value (18 μg/m3) was extrapolated from the linear regression between the maximum value at a site (after exclusion) over 3 years and the 3-year average 98th percentile value. A second approach relied on the regression relationship of the 3-year average of the year-specific maximum values and the 3-year average 98th percentile, yielding 20 μg/m3. This approach is roughly equivalent to excluding 1 extreme day over 3 years. These values were used to establish the tabulated 98th percentile range of 18–20 μg/m3 that corresponds to the 25-μg/m3 24-hr maximum.
Results
We conducted a review of recent PM reports from CARB, the U.S. EPA, CCME, and NRC to assess whether ambient PM is believed to have a disproportionate effect or increased risk on certain populations. This was accomplished by comparing how the various organizations conceived of sensitive populations and defined determinants of sensitivity among subgroups. Previous research on sensitivity or susceptibility has noted varying conceptual approaches to defining the terms and subgroups, given different interpretations of the state of knowledge (ATS 2000; ATS Committee 1996; Parkin and Balbus 2000; Pope 2000). The ATS has broadly defined “susceptibility” as including extrinsic factors, such as the profile of exposure to other pollutants, and intrinsic factors, such as genotype. As scientific advances more precisely identify those at risk within the distribution of the degree of susceptibility, it may become increasingly challenging to regulate outdoor air pollution to assure protection for all individuals against adverse health effects. Such effects may already or eventually include biomarker changes, health-related quality of life, physiologic impact, symptoms, clinical outcomes, and mortality (ATS 2000).
The U.S. EPA and NRC each provided definitions of susceptibility and construed the term differently. The U.S. EPA’s PM criteria document defined susceptibility as generally encompassing “innate or acquired factors that make individuals more likely to experience effects with exposure to pollutants” (U.S. EPA 2004b). Innate susceptibility can entail genetic or developmental factors, whereas acquired susceptibility may result from age, disease, or personal risk factors such as smoking, diet, or exercise. The U.S. EPA also referred to the concept of increased vulnerability to pollution-related effects, as distinct from susceptibility, because of factors including socioeconomic status or experiencing “particularly elevated exposure levels” (U.S. EPA 2004b). NRC’s Committee on Research Priorities for Airborne Particulate Matter was charged to gauge research progress on susceptible subpopulations by evaluating new evidence that has appeared since 1998. NRC commented on a broadening scope of health concerns, including an increasing number of adverse health outcomes associated with PM and related susceptible subpopulations. The committee referred to groups as “particularly susceptible” to the effects of air pollution based on one or more of the following factors: a) increased exposure due to longer-duration and/or higher-than-normal pollution concentrations, b) higher delivered dose due to physiologic factors, and c) a greater health response than the general population to a given dose of air pollution (NRC 2004).
Overall, the current list of subgroups for which PM likely or possibly has disproportionate health effects is reasonably congruent across the four organizations. Six categories or determinates of susceptibility were identified: age, preexisting disease, heightened exposure, genetic makeup, sex, and socioeconomic status. The level of scientific understanding associated with research findings for these categories was characterized by groups to which exposure to PM likely or possibly has disproportionate health effects and groups to which exposure to PM is of concern, but overall evidence is insufficient or limited.
Two categories listed as likely or possibly affected by PM were identified explicitly in all four reports. These categories comprised population subgroups defined by age (infants, children, and persons ≥ 65 years of age) and by preexisting disease (cardiopulmonary disease and diabetes). The category defined by heightened exposure levels (e.g., populations involved in outdoor exercise, outdoor work, and living near high PM sources) was either listed as likely or possibly affected by PM or was not considered explicitly.
The NRC and U.S. EPA identified population subgroups defined by heightened exposure levels as likely or possibly affected by PM in report sections devoted specifically to assessing susceptible or vulnerable subpopulations (NRC 2004; U.S. EPA 2004b). However, both the U.S. EPA and NRC offered different interpretations of whether these groups are “susceptible” or “vulnerable.” The NRC defined groups with heightened exposure status—such as proximity to source or outdoor exercise—as susceptible, whereas the U.S. EPA defined these groups as vulnerable. CARB and CCME reports recognized the potential impact of heightened exposures on subpopulations, but not within sections specifically devoted to susceptible or vulnerable populations (CARB 2002; CCME 2004). Heightened exposure as a determinate of increased risk was instead discussed in other sections (e.g., human exposure assessment) or by reference to scientific investigations in sections devoted to epidemiologic field studies.
The U.S. EPA characterized socioeconomic status as both likely and possibly having disproportionate health effects and of concern, but with insufficient or limited overall evidence (U.S. EPA 2004b). This divergence of outcomes relates to long-term epidemiologic studies that find PM–mortality risk may be greater for those with lower socioeconomic status, whereas time-series epidemiologic studies provide less evidence of effect modification for short-term exposure effects by socioeconomic status.
Finally, four categories were either not considered in all the research reports or, if listed, were believed to be of concern but with insufficient evidence. These subgroup categories were defined by age (fetus), genetic makeup, sex, and socioeconomic status (for time-series studies).
Based on the framework of susceptibility criteria established in the review, age, preexisting disease, heightened exposure, and socioeconomic categories were identified as likely or possibly at increased risk to PM. In the eight-state northeastern U.S. study area, data were analyzed to estimate the magnitude of susceptible groups in the age and preexisting disease categories, and to a lesser extent to estimate the heightened exposure category. Tables 1 and 2 illustrate that subgroups susceptible to PM represent a large fraction of the northeastern U.S. population. Table 1 shows the population age group distributions for the eight-state study region. The number and percentage of persons in age-related susceptible subgroups are indicated for < 3-year, 3- to 17-year, and ≥65-year age classes. Thirty-eight percent or 15.6 million persons of the region’s total population (41.3 million persons) were infants, children, or older adults.
Table 2 summarizes information on the prevalence of chronic cardiopulmonary conditions and diabetes in the northeastern U.S. population. The number of adults (≥ 18 years of age) and children (< 18 years of age) in the northeastern United States with cardiac and respiratory conditions and diabetes was estimated by compiling recent BRFSS and NHIS surveys on disease or health condition prevalence between 2000 and 2002 (CDC 2002a, 2002b, 2003a, 2003b). Adults with preexisting heart and lung conditions ranged from approximately 4 to 18% of the total northeastern adult population. For respiratory conditions, 15% have been told by a doctor or other health professional they have sinusitis (preceding 12 months), 13% asthma (ever), and 4% chronic bronchitis (preceding 12 months). For circulatory conditions, 10% of the adult population has received a diagnosis of heart disease (ever) and 18% hypertension (ever). The percentage of adults with hypertension was likely > 18% because persons may have a silent or undiagnosed condition. The CDC’s National Health and Nutrition Examination Survey found that measured hypertension (physical examination) in the United States among persons ≥ 20 years of age is 30% (National Center for Health Statistics 2003). Six percent of adults in the northeastern United States have ever been told by a doctor they have diabetes. Twelve percent of children have been diagnosed with respiratory allergies (preceding 12 months). Fifteen percent of children have been diagnosed with asthma at some point in their life. Comparing across age groups, cardiovascular conditions were more common among older age groups, whereas asthma prevalence was higher in children.
Given the need to identify the nature and magnitude of susceptible population risk in the context of standard setting (NRC 2004), compliance with various combinations of alternative PM standards could benefit general populations and especially benefit susceptible populations in the northeastern United States. Figures 1–4 reflect the benefits from improved air quality as a result of additional PM2.5 control strategies.
Figure 1 shows the percentage of the eight-state total population living in U.S. EPA Regions 1 and 2 counties with PM2.5 concentrations less or greater than various combinations of annual and 24-hr (98th percentile) alternative standards and levels for 2000–2002. The U.S. EPA’s current annual and 24-hr PM2.5 standards are 15 and 65 μg/m3 (98th percentile), respectively. As indicated in Figure 1, 16% of the region’s population currently lives in counties that do not meet the existing annual/24-hr standard of 15/65 μg/m3. Were the revised annual standard of 15 μg/m3 to remain unchanged, the percentage of the total population living in counties not meeting annual/24-hr standards would change only after the 24-hr standard is lowered to < 40 μg/m3. A 24-hr standard of 30 μg/m3 coupled with an annual standard of 12, 13, 14, or 15 μg/m3 would result in 84% of the population living in counties that would not meet the regulation. As depicted in Figure 1, compliance with alternative annual/24-hr standard setting in U.S. EPA Regions 1 and 2 would benefit populations if the annual standard moved to < 15 μg/m3 or the 24-hr standard moved to < 40 μg/m3. An annual standard of 12 μg/m3 would result in 68% of the population living in counties that would not meet the regulation, whereas a 24-hr standard of 20 μg/m3 would result in 100% of the population living in counties not meeting the regulation.
Figures 2–4 condense the analysis to combinations of an annual standard of 15 μg/m3 with alternative 24-hr standards ranging from 65 down to 20 μg/m3 (98th percentile). The condensed annual/24-hr range of alternatives captures the entire sphere of all annual 11–15 μg/m3/24-hr 20–65 μg/m3 ranges with respect to affected populations. As presented in Table 1, 38% of the eight-state region’s population is composed of infant, children, and older adult subgroups considered susceptible to PM. Figure 2 shows the percentage of these subgroups living in counties with PM2.5 concentrations less or greater than various combinations of annual and 24-hr (98th percentile) alternative standards and levels for 2000–2002. In Figure 2, the current annual/24-hr standard of 15/65 μg/m3 results in 15% of the region’s susceptible age groups living in counties with PM2.5 levels at or above the standard. Compliance with a revised annual/24-hr PM2.5 standard of 15/30 μg/m3 would especially benefit 84% of the region’s susceptible age groups with improved air quality.
Figures 3 and 4 show adult and children subgroups with preexisting health conditions considered to be determinates of susceptibility, by ages ≥ 18 years and < 18, respectively, as a percentage of the total population. These sub-groups live in counties with PM2.5 concentrations less or greater than various combinations of annual and 24-hr (98th percentile) alternative standards and levels for 2000–2002. In Figure 3, adult populations with preexisting health conditions contributing to susceptibility represent 0.6–3% of the total adult population living in counties with PM2.5 levels above the current annual/24-hr standard of 15/65 μg/m3. A revised annual/24-hr PM2.5 standard of 15/20 μg/m3 would especially benefit about 4–18% of the total population, or 100% of all adults in the northeastern region currently estimated to have these health conditions. In Figure 4, child populations with preexisting respiratory conditions represent 2–2.4% of the total children population living in counties with PM2.5 levels above the current annual/24-hr standard of 15/65 μg/m3. A revised annual/24-hr PM2.5 standard of 15/20 would especially benefit about 12–15% of the total population, or 100% of all children in the northeastern region currently estimated to have these health conditions.
In addition to age and preexisting disease or health condition indicators, heightened air pollution exposure status represents another category of susceptibility wherein populations are possibly or likely at increased risk to PM. Possible subpopulations affected include outdoor workers, children and adults physically active outdoors, and people living near high-intensity sources. Presently, there is no universal indicator used to quantify the number of persons that may be at risk because of heightened exposure status. Given that combustion-source particulate air pollution is common to many urban environments, these areas may function as examples of environments in which populations commonly experience heightened PM levels. Urban airsheds in the northeastern United States experience elevated 24-hr average and annual mean PM concentrations and are home to numerous intense sources [Cass et al. 1999; NARSTO (formerly North American Research Strategy for Tropospheric Ozone) 2004].
Using population density as an indicator of an urban-scale demographic, 2000 U.S. Census data are presented in Table 3. The northeastern region’s urban areas, defined as having census tract population densities greater than 1,000 persons/miles2, consisted of 6% of the total land mass and were home to about 30 million persons or 72% of the region’s total population of 41.3 million persons. The percentage of child, adult, and elderly age subgroups living in urban areas was nearly identical, ranging from 71 to 73% across groups, and comprised 27% of the region’s total population. The density of this eight-state region is among the highest in the nation, because five of eight states (New Jersey, Rhode Island, Massachusetts, Connecticut, New York) are among the six most densely populated states in the United States. Thus, most persons—across child, adult, and elderly age groups—in the northeastern United States live in densely populated urban areas that are also characterized by elevated PM levels and heightened exposure scenarios.
Discussion
This study draws attention to public health issues facing regulators charged to minimize the harmful impact of ambient PM2.5 on populations. Our analysis of northeastern U.S. monitoring and demographic data suggests the population size of susceptible groups—a key indicator of the potential impact of PM2.5 exposure on public health—is extensive. Although additional knowledge is needed about the biologic mechanisms and host characteristics involved in susceptibility, a variety of groups are likely more susceptible or vulnerable to PM. Within the eight-state study area, 38% of the total population are < 18 or ≥ 65 years of age, 4–18% of adults have cardiopulmonary or diabetes health conditions, 12–15% of children have respiratory allergies or lifetime asthma, and 72% of all persons (across child, adult, and elderly age groups) live in densely populated urban areas with elevated PM2.5 concentrations likely creating heightened exposure scenarios. In addition, current PM2.5 standards in the eight-state study area affect only 16% of the general population, who live in counties that do not meet the existing annual/24-hr standard of 15/65 μg/m3. A combination of more stringent annual/24-hr standards would result in a larger percentage of the population living in counties that would not meet the regulation; these populations would therefore benefit from greater emission reduction requirements and more extensive control strategies to reduce PM concentrations.
When taking into account susceptible subgroups, it is difficult to set standards consistent with the intent of the CAA—which stipulates that the U.S. EPA establish primary NAAQS at a level that protects sensitive populations—because of science’s inability to confirm the existence of a PM2.5 threshold level under which there are no health effects. In response, major regulatory organizations in the United States and Canada set enforceable or target standard levels to limit PM2.5 concentrations below those where epidemiologic evidence is most consistent and coherent. This approach recognizes both the strengths and the limitations of the full range of scientific and technical information on the health effects of PM, as well as associated uncertainties.
The interpretation of available data by different standard-setting bodies may reflect the varying levels of health protection required by the controlling statute and the level of public health protection commitment. Table 4 estimates the relationship among current or recently recommended California, Canada, and U.S. PM2.5 standards by normalizing differing annual and 24-hr forms. This facilitates a comparison of corresponding standard levels and forms that differ among the three agencies. Both Canada and the U.S. EPA currently use a 98th percentile 3-year average form for the 24-hr PM2.5 standard. Canada’s 24-hr standard of 30 μg/m3 would result in 84% of the eight-state Northeast study area population living in counties that would not meet the regulation. Although Canada does not have an annual standard, the U.S. EPA’s annual PM2.5 standard form is expressed as the annual arithmetic mean averaged over 3 years.
California’s proposed (later deferred) 24-hr and adopted annual standard form are based on year-to-year NTBE values, which include maximum monitoring values and are more stringent than 3-year and 98th percentile forms. Were California’s proposed 24-hr standard of 25 μg/m3 (NTBE) converted into a 98th percentile form, the standard would range from 18 to 20 μg/m3. This 24-hr standard would result in 100% of the eight-state Northeast study area population living in counties that would not meet the regulation. Were California’s adopted annual standard of 12 μg/m3 (NTBE) converted into the U.S. EPA’s form, the standard would range from 11 to 11.5 μg/m3. An annual standard of 11 μg/m3 would result in 88% of the eight-state Northeast study area population living in counties that would not meet the regulation.
Although differences in health-related PM air pollution standard setting are common across agencies (Benner 2004), PM2.5 exposure associations with adverse health effects may well extend to levels lower than the most stringent recommended target standards. Even if PM2.5 NAAQS attainment were reached, health risks within the U.S. population would not be totally eliminated. As demonstrated by this study, however, the stringency of PM2.5 standards can determine the magnitude of the PM2.5-related health burden that decision makers choose to place on the population. Within the framework of standard-setting logic, incrementally more stringent standards would offer the expectation of increased public health protection from PM2.5 exposures. Epidemiologic evidence shows that large-scale interventions and natural reductions in ambient PM have resulted in decreases in disease and death (Clancy et al. 2002; Laden et al. 2001; Pope 1991). This underscores the importance of setting appropriately stringent PM2.5 standards to trigger control measures intended to reduce ambient PM2.5.
A central limitation of the study was its inability to generate additive estimates of total susceptibility across the eight-state study region. The population as a whole is considered diverse in its susceptibility to inhaled pollutants, and persons may be represented in multiple categories of susceptibility. The range of sensitivity among persons is uncertain because variations in PM exposure, PM dose, and host-related factors can cause exposed people to be more susceptible.
The study could have benefited from more refined estimates of factors determining susceptibility in urban populations, including those experiencing heightened exposures such as outdoor worker, child, athlete, other exercising adult and child, and commuter subgroups. The study also did not account for other potential susceptibility indicators, such as socioeconomic status, which may influence exposure scenarios and health disparities, especially among urban populations (American Lung Association 2001). Moreover, a consideration of projected demographic shift and epidemiologic transitions likely would have augmented the import of study findings. For example, in the U.S. populations ≥65 years of age are projected to increase from 12.4% in 2000 to 19.6% in 2030, or from about 35 million to 71 million, respectively. Approximately 80% of all persons in this age cohort have at least one chronic condition, 50% have at least two, and overall chronic diseases such as diabetes and heart disease affect older adults disproportionately (Anderson and Smith 2003; Goulding et al. 2003).
In addition, the study did not quantify the potential for a varying profile of susceptibility to PM across spatial scales. The NHIS study findings were regional and included the eight-state study area and Pennsylvania (CDC 2003a, 2003b). The BRFSS asthma and diabetes surveys provided prevalence rates by state, but only for adults (CDC 2002a, 2002b). Regional and state resolution scales do not enable one to distinguish prevalence rates between, for example, urban and non-urban populations with respect to specific states or other geographic scales.
Concerning the integration of prevalence rate data with design value estimates, the uniform application of CDC prevalence rate data to populations living in counties not meeting alternative PM2.5 standards assumes that CDC data for the region are representative of those counties. With respect to the study’s use of monitoring data, the assessment followed U.S. EPA methods by assigning the highest annual or 24-hr design values as the design values for the entire county (U.S. EPA 1999). Likewise, for those counties without monitors, the highest annual or 24-hr interpolated levels were used from counties with monitors. This method could overestimate the number of persons exposed to PM2.5 concentrations at the county level. However, the study applied county-level population estimates to achieve greater resolution and accuracy. The U.S. EPA currently defines attainment/nonattainment areas by consolidated metropolitan statistical areas that aggregate counties (Holmstead 2003). Finally, application of a 3-year data set (2000–2002) incorporating a wide range of monitoring sites and concentration values allowed us to establish the relationship between various PM2.5 standard metrics. The inclusion of additional years to the analysis probably would not materially change this relationship unless factors driving PM concentrations across the northeastern region were suddenly to change. Since 2002, this has not happened.
The above limitations recommend more definitive data collection efforts, as future research using this study’s integrative analytical approach would benefit from improved knowledge about susceptible subpopulations and the use of highly spatially resolved monitoring data. This might be fostered by the U.S. EPA and U.S. Department of Health and Human Services cross-agency research platforms guiding future investigations, and further broadening of problem definitions in each organization. For example, the CDC and U.S. EPA might develop a common health survey framework to a) augment our understanding of specific subpopulations by exploring disease, vital, and behavioral variability among regions (or even states or metropolitan areas) across all age groups; b) provide information about urban-scale (and other scales, e.g., rural) health impacts—rather than gross national or regional-scale impacts; c) help explain putative heterogeneity of health effects in urban areas across U.S. regions as reported by epidemiologic studies; and d) gain insight into populations at high risk residing near source-dominated environments. These suggested approaches would provide policy makers with a greater understanding of how the U.S. EPA’s PM NAAQS recommendation will affect public health.
In conclusion, this study was conducted to assess the public health implications of the current PM NAAQS revision process. Using susceptibility criteria compiled from major regulatory and research reports, we found that a significant percentage of the eight-state region’s population is potentially susceptible to PM2.5, including 38% of the total population by age group and 4–18% of adults and 12–15% of children by preexisting health condition. More than 70% of the child, adult, and elderly population age groups in the study area live in urban areas that experience elevated PM2.5 concentrations and heightened exposure scenarios. This finding may be relevant to studies suggesting the potential for heterogeneity in U.S. city-specific excess risk estimates for acute health effects, including higher mortality coefficients in the Northeast (Dominici et al. 2002). We also devised an evaluative method that uniformly applied CDC prevalence rates for selected health conditions and Census age distributions to the number of persons living in areas with PM2.5 concentrations above annual/24-hr standard combinations. We found that currently only 16% of the eight-state region’s general population lives in counties that do not meet the annual/24-hr PM2.5 standards. However, a large fraction of the region’s total population would benefit and a large number of adult and children populations with chronic health conditions would especially benefit from compliance with PM2.5 levels less or greater than various combinations of annual and 24-hr average (98th percentile) concentrations currently under review by the U.S. EPA. More protective PM2.5 standards falling within ranges recommended by California and Canada would protect 84–100% of the general population.
We thank E. Savelli, D. Brown, K. Colburn, and A. Marin at NESCAUM. We also thank R. White, Johns Hopkins University, and three anonymous reviewers for their helpful comments.
Figure 1 Percentage of the northeastern population that would benefit from compliance with alternative annual/24-hr PM2.5 (98th percentile) standards.
Figure 2 Percentage of northeastern susceptible age subgroups that would especially benefit from compliance with alternative annual/24-hr PM2.5 (98th percentile) standards.
Figure 3 Percentage of all adults that would especially benefit (members of subgroups with preexisting health conditions) from compliance with alternative annual/24-hr PM2.5 (98th percentile) standards.
Figure 4 Percentage of all children that would especially benefit (members of subgroups with pre-existing health conditions) from compliance with alternative annual/24-hr PM2.5 (98th percentile) standards.
Table 1 Number and percentage of age subgroups living in the northeastern United States.
Age group (years) No. Percent
< 3 1,574,903 4
3–17 8,550,659 21
≥ 65 5,453,117 13
Total (< 18, ≥ 65) 15,578,679 38
18–64 25,734,645 62
Total (all ages) 41,313,324 100
Table 2 Prevalence and number of children and adults with specific preexisting disease conditions living in the northeastern United States.
Age group and health condition Prevalence rate (%) No.
< 18 years 10,125,562
Respiratory allergies (preceding 12 months) 12.2 1,235,319
Asthma (ever) 14.8 1,498,583
≥ 18 years 31,187,762
Sinusitis (preceding 12 months) 14.7 4,584,601
Asthma (ever) 12.8 3,992,034
Chronic bronchitis (preceding 12 months) 3.9 1,216,323
Hypertension (ever) 17.9 5,582,609
Heart disease (ever) 10.4 3,243,527
Diabetes (ever) 6.2 1,933,641
Table 3 Distribution of population age groups by nonurban and urban population density scales (persons/mi2 land area) in the northeastern United States.
0–1,000 (94% of total land mass)
> 1,000 (6% of total land mass)
Age (years) No. Percent total No. Percent total Percent age group
< 18 2,915,526 7 7,210,036 17 71
18–64 7,008,390 17 18,726,255 45 73
≥ 65 1,460,005 4 3,993,112 10 71
Total 11,383,921 28 29,929,403 72 72
Table 4 PM2.5 primary standards of selected government agencies.
California
U.S. EPA
2003, targeta 2002, deferredb Canada 2000, targetc 1997, final 2005, recommended ranged
24-hr standard
Level (μg/m3) NA 25 30 65 25–40
Form NTBE of 98th percentile 3-year average of 98th percentile 3-year average of 98th percentile 3-year average of 98th or 99th percentile
Normalized ~18–20 30 65 25–40
Annual standard
Level (μg/m3) 12 NA 15 12–15
Form NTBE 3-year average 3-year average
Normalized ~11–11.5 15 12–15
NA, not applicable.
a California’s new state standards amount to new clean air goals for the state and took effect in June 2003 (CARB 2002).
b California proposed a new 24-hr average standard for PM2.5 at 25 μg/m3, NTBE, in May 2002 but subsequently deferred a final decision (CARB 2002).
c Target implementation to be achieved by 2010 and ratified by ministers on June 2000.
d U.S. EPA (2005).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7774ehp0113-00114816140619ResearchUsing Moving Total Mortality Counts to Obtain Improved Estimates for the Effect of Air Pollution on Mortality Roberts Steven School of Finance and Applied Statistics, Faculty of Economics and Commerce, Australian National University, Canberra, AustraliaAddress correspondence to S. Roberts, School of Finance and Applied Statistics, Faculty of Economics and Commerce, Australian National University, Canberra ACT 0200, Australia. Telephone: 61-2-6125-3470. Fax: 61-2-6125-0087. E-mail:
[email protected] author declares he has no competing financial interests.
9 2005 10 5 2005 113 9 1148 1152 20 11 2004 10 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In many cities of the United States, measurements of ambient particulate matter air pollution (PM) are available only once every 6 days. Time-series studies conducted in these cities that investigate the relationship between mortality and PM are restricted to using a single day’s PM as the measure of PM exposure. This is undesirable because current evidence suggests that the effects of PM on mortality are spread over multiple days. And studies have shown that using a single day’s PM as the measure of PM exposure can result in estimates that have a large negative bias. In this article, I introduce a new model for estimating the mortality effects of PM when only every-sixth-day PM data are available. This new model uses information available in the daily mortality time series to infer otherwise lost information about the effect of PM on mortality over a period of more than a single day. This new model typically offers an increase in both statistical estimation precision and accuracy compared with existing models.
air pollutiondistributed lag modelmortalityparticulate mattertime series
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Numerous time-series studies have investigated the association between daily mortality and some measure of daily ambient particulate matter air pollution (PM) (Chock et al. 2000; Cifuentes et al. 2000; Goldberg et al. 2003; Ito et al. 1995; Kelsall et al. 1997; Klemm et al. 2000; Kwon et al. 2001; Moolgavkar 2000; Ostro et al. 1999; Roemer and van Wijnen 2001; Smith et al. 2000; Stieb et al. 2002; Styer et al. 1995). These studies typically fit a generalized additive model (Hastie and Tibshirani 1990) or generalized linear model (McCullagh and Nelder 1989) to concurrent time series of daily mortality, PM, and meteorologic covariates. The fitted models are then used to quantify the effect of PM on mortality. The general consensus from these studies is that a 2- or 3-day moving average of PM better describes the relationship between PM and mortality than does a single day’s PM (Schwartz 2000). In addition, some recent studies have suggested that distributed lag models (DLMs) that allow differential PM mortality effects spread over multiple days may be preferable to single-day or multiple-day moving average PM exposure measures (Schwartz 2000; Smith et al. 2000). The reason is that DLMs do not leave to chance the question of how the mortality effects of PM are distributed over time.
Historically, in the United States most monitors that measure PM operate on an every-sixth-day collection schedule (Ito et al. 1995). This is a consequence of the U.S. Environmental Protection Agency often requiring PM concentrations to be collected only every sixth day. For most of the 108 cities contained in the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) database (Peng et al. 2004), measurements of ambient PM < 10 μm in diameter (PM10) are available only once every sixth day. Consequently, in most large cities in the United States, time-series studies conducted to investigate the health effects of PM cannot use a moving average of PM or a DLM for PM. Instead, they must use a single day’s PM as the measure of PM exposure. An example of this is the recent 90-city NMMAPS analysis that was restricted to using either a lag 0, lag 1, or lag 2 PM concentration as the measure of PM exposure (Dominici et al. 2003).
The constraint of being able to use only a single day’s PM is problematic. Studies have shown that using a single day’s PM can result in a large underestimation of the relationship between PM and mortality (Roberts 2005; Schwartz 2000). The reason for this is that if the effects of PM on mortality last for > 1 day, a single-day PM exposure measure will detect the effect of PM only on 1 day’s mortality. Even worse, the wrong single-day PM exposure measure may be used. Several PM mortality time-series studies have demonstrated that the effects of PM on mortality last for multiple days (Schwartz 2000; Zanobetti et al. 2003). In addition, toxicologic evidence has shown that the morbidity effects of PM can persist for > 1 day (Clarke et al. 1999). It has been shown that DLMs avoid the problems of underestimation experienced by single-day PM exposure measures. For this reason, it has been suggested that DLMs should be the preferred measure of PM exposure if daily PM measurements are available (Roberts 2005; Schwartz 2000).
In this article, I introduce a model that typically improves both the accuracy and precision of the PM mortality effect estimates obtainable from time-series studies where PM measurements are available only every sixth day. This model uses the daily mortality time-series data to create a moving total mortality time series. The moving total mortality time series is then used in place of the current day’s mortality time series in the subsequent analysis. Simulation studies will show that for estimating the mortality effects of PM, this model offers a substantial decrease in estimation variance and typically a decrease in estimation bias compared with the standard method of using the current day’s mortality time series. With this new model, improved estimates of the effect of PM on mortality will be available for a large number of cities in the United States. This may in turn lead to a better understanding of the public health significance of PM exposure.
Materials and Methods
Materials
The data used in this article were obtained from the publicly available NMMAPS database (Peng et al. 2004). The data extracted consist of concurrent daily time series of mortality, weather, and PM for Cook County, Illinois, and Allegheny County, Pennsylvania, for 1987–2000. The Allegheny County data were subsequently truncated at the end of the year 1998 because PM measurements were unavailable from this time forward.
The mortality time-series data, aggregated at the level of county, are nonaccidental daily deaths of individuals ≥65 years of age. Deaths of nonresidents were excluded from the mortality counts. The weather time-series data are 24-hr averages of temperature and dew point temperature, computed from hourly observations. The measure of PM used was the ambient 24-hr concentration of PM10, measured in micrograms per cubic meter. PM10 is the most commonly used measure of PM in air pollution mortality time-series studies.
The Cook County PM time series of length 5,114 days had 251 days that were missing PM concentrations, and the Allegheny County PM time series of length 4,383 days had 24 days that were missing PM concentrations. The missing PM concentrations were imputed by taking the average of the previous and subsequent day’s PM concentration. If either the previous or subsequent day’s PM concentration was missing, the average was set equal to the nonmissing value. This method has previously been used to impute missing PM concentrations (Roberts 2004). The missing PM concentrations were imputed because a DLM of PM will be fit to the data, and missing values propagate by up to a factor of 5 when DLMs are used.
Methods
In many community time-series studies on the effect of PM on mortality, an additive Poisson log-linear model is fit to the time series of observed mortality. Under this model, the daily mortality counts are modeled as independent Poisson random variables with a time-varying mean μt on day t given by
Here, confounderst represents other time-varying variables that are related to daily mortality. PMt is the time series containing the PM exposure measure, and β is the effect of this PM exposure measure on mortality. Equation 1 will be referred to as the “standard model.”
Because of data limitations, the PM exposure measure used in the standard model is typically restricted to be a single day’s PM rather than a moving average of PM or a DLM of PM. In this article, I assume that we are in such a situation; that is, only every-sixth-day PM measurements are available. As discussed above, using a single day’s PM is undesirable because it can result in estimates that have a large negative bias. And even in the unlikely event that the effect of PM on mortality is concentrated on a single day, it is possible that the wrong single-day PM exposure measure will be used. These problems would be avoided if daily PM measurements were available, making it possible for a DLM of PM to be used.
Daily mortality counts are available for cities in the NMMAPS database regardless of the sampling frequency used for PM. The model I introduce takes advantage of this fact by using information available in the daily mortality data to extract information about the effect of PM on mortality over a period of more than a single day, information otherwise unavailable with every-sixth-day PM measurements. To do this, I replace the current day’s mortality count used in the standard model with a moving total mortality count. The moving total used is a forward-moving total, meaning that the current day’s mortality count is replaced by the sum of the current day’s mortality count, the next day’s mortality, and so on, for some specified number of days. I use the term “k day moving total” to mean the sum of today’s and the subsequent k – 1 days’ mortality counts. Under this model, the k-day moving total mortality counts are modeled as independent Poisson random variables with a time varying mean μt,k on day t given by
Here, confounderst has the same specification as in the standard model, and PMt is a single day’s PM, as is the case for the standard model. Simulation studies will show that the mortality effect estimates for PM obtained from Equation 2 are typically both more accurate and more precise compared with those obtained from the standard model. Equation 2 will be referred to as the “moving total model.”
A heuristic argument for why the moving total model may provide more accurate estimates of the mortality effect of PM compared with the standard model is now provided. If the mortality effect of PM lasts for more than a single day, a day of high PM will cause not only the current day’s mortality count to be elevated but also the mortality counts on subsequent days. By using a moving total mortality count, we are able to capture the increased mortality on subsequent days, information that is lost if only the current day’s mortality count is used. Obviously, if daily PM measurements are available, the best way to capture the effect of PM on mortality is through a DLM of PM. However, in the more common situation where PM measurements are available only every sixth day, using a moving total mortality count provides a “poor person’s” substitute for a DLM.
Implementing the moving total model is no harder than implementing the standard model. To fit the moving total model instead of using the current day’s mortality count (dt) as the response variable, as done in the standard model, a moving total mortality count (dt,k) is used instead. dt,k represents the k-day moving total mortality count for day t; that is, dt,k = dt + dt+1 + . . . + dt+k−1.
Simulation Study
The simulation study compares the statistical properties of the standard model for estimating the mortality effects of PM with those of the moving total model. In the simulations, the actual weather and PM data from Cook County are used. Although the weather and PM time series are actual, the corresponding mortality time series are generated using models that describe PM mortality effects.
Realistic mortality generation.
To conduct the simulations, we need a way to generate realistic mortality time series. I used a method previously shown to generate realistic mortality time series (Roberts 2005), which proceeds by estimating the effects of time, temperature, dew point temperature, and day of the week on mortality using the data from Cook County. This was done by fitting the following Poisson log-linear model similar to those used in previous NMMAPS analyses (Daniels et al. 2000) to the actual Cook County mortality and meteorologic time-series data:
Here the subscript t refers to the day of the study; μt is the mean number of deaths on day t; the Sti( ) are smooth functions of time, temperature, and dew point temperature with the indicated degrees of freedom (the smooth functions are represented using natural cubic splines); temp0 is the current day’s mean 24-hr temperature; temp1–3 is the average of the previous 3 days’ 24-hr mean temperatures; dew0 and dew1–3 are similarly defined for the 24-hr mean dew point temperature; DOWt is a set of indicator variables for the day of the week. All the models in this article were fit using the glm function in R (version 2.0.0; R Development Core Team 2005).
Once Equation 3 was fit, I extracted the estimated mean mortality counts, denotedμfit,t. The effects of PM on mortality were explicitly specified and incorporated in the generated mortality time series. I did this by generating mortality time series that were Poisson distributed with mean ψt on day t given by
Here PMt-i is the time series of lag i PM concentrations; θ is the total mortality effect of a unit increase in PM over time (1,000θ is approximately the percentage increase in mean daily mortality for each 10-μg/m3 increment in PM), and θαi is the mortality effect of a unit increase in PM at lag i. Equation 4 assumes the mortality effects of PM can last for a maximum of 6 days.
Mortality time series were generated using various specifications for the “true” effect of PM on mortality in Equation 4. Because previous studies have shown that PM lags of more than a few days have little correlation with daily mortality (Schwartz 2000), the specifications used span a suite of plausible lag structures for the effect of PM on mortality: no effect, PM has no effect on mortality; single-day effect, the effect of PM on mortality is concentrated on a single day [the single days considered were the current day’s PM (lag 0), the previous day’s PM (lag 1), or the 2 day’s previous PM (lag 2)]; moving average effect, the effect of PM on mortality depends on a moving average of PM [the moving averages considered were the average of the current and previous day’s PM (lag 0–1), and the average of the current and previous 2 days’ PM (lag 0–2)]; distributed lag effect, differential effects of PM on mortality over time were allowed. The distributed lag effects considered were as follows:
For the moving average and distributed lag effects, five θ values corresponding to 0.25, 0.5, 1, 2, and 4% increases in mortality for each 10-μg/m3 increment in PM were used. For the single-day effects, four θ values corresponding to 0.25, 0.5, 1, and 2% increases in mortality for each 10-μg/m3 increment in PM were used. These values of θ span a plausible range for the total effect of PM on mortality.
Fitting models to generated mortality.
For each specification of the “true” effect of PM on mortality and θ combination 400 mortality time series were generated using Equation 4. Because we are interested in the situation where PM measurements are available only every sixth day, after the mortality time series were generated, I extracted every sixth PM measurement from the PM time series of length 5,114 days that was used to generate mortality. These 852 every-sixth-day PM measurements, assumed to be the only PM measurements available, were then used in both the standard model and moving total model to estimate the effect of PM on mortality (θ). The confounderst term in both the standard and moving total models had the same specification as the confounder adjustment used in the mortality generating Equation 4. It is important to remember that in the NMMAPS database daily measurements are available for mortality, temperature, and dew point temperature irrespective of the sampling frequency used for PM.
The standard model was fit to each generated mortality time series using in turn the current day’s PM (standard model – lag 0), the previous day’s PM (standard model – lag 1), and the 2 day’s previous PM (standard model – lag 2) as the PM exposure measure (PMt in Equation 1). The moving total model was fit to each generated mortality time series using the current day’s PM as the PM exposure measure (PMt in Equation 2) and 2-, 3-, 4-, and 5-day moving total mortality counts (k = 2, 3, 4, 5 in Equation 2). Moving total mortality counts with k > 5 were not considered because the current evidence suggests that mortality counts more than a few days forward have little association with the current day’s PM concentration (Schwartz 2000). The standard and moving total models that are being fit to the generated mortality time series are identical except for the specification of the mortality response variable: For the standard models, a single day’s mortality count is used, whereas for the moving total models a moving total mortality count is used. This means that for both the standard and moving total models, the same every-sixth-day PM time series is used.
Results
Tables 1 and 2 contain the results of the simulations. Table 1 contains the results for mortality generated using the no effect, single-day effect, and moving average effect specifications for the “true” effect of PM on mortality. Table 2 contains the results for the distributed lag effect specifications for the “true” effect of PM on mortality. These tables contain the standard deviation and bias of the estimates of the effect of PM on mortality (θ) obtained from the three forms of the standard model and the moving total models with k = 2, 3, and 4. A moving total model with k = 5 was also investigated, but the results for this model were not reported because it performed poorly compared with the moving total models with k = 2, 3, and 4. The reason for this is discussed further below.
Tables 1 and 2 show that the moving total models always offer a substantial reduction in estimation variance compared with the standard models. The reduction in estimation variance increases as the number of days used in the moving total mortality count (k) increases. The reason for this is that as the number of days used in the moving total mortality count increases, the estimates for the effect of PM on mortality are based on successively more data. For a given model, the standard deviation remains constant across the simulations because the amount of data used remains constant. Tables 1 and 2 also show that the estimation bias is typically smaller for the moving total models than for the standard models. The smaller bias for the moving total models is a consequence of the moving total mortality counts allowing the moving total models to capture the effect of PM on more than a single day’s mortality. This is something that is not possible with the standard models. For a given model, the bias increases as the total PM effect (θ) increases because the absolute amount of information lost by not observing the daily PM time series increases as θ increases. These results show that the moving total model offers a way to estimate the effect of PM on mortality that is both more precise (smaller variance) and typically more accurate (smaller bias) than the standard model.
The results reported in Tables 1 and 2 suggest that a moving total model with k = 2 or k = 3 would be preferred to moving total models with k ≥4. The reason for this is that k = 2 or k = 3 offers a better compromise between bias and variance. That is, the increased variance of using a moving total model with k = 2 or k = 3, as opposed to k ≥4, is more than offset by a decrease in bias. This is supported by the fact that in the simulations the mean squared error (for brevity, values are not reported here) for the moving total models with k = 2 or k = 3 was typically smaller than the mean squared error for the moving total models with k = 4 or k = 5. The reason for the poorer performance of the moving total models with k ≥4 was that in the simulations, because of evidence from previous studies (Schwartz 2000), the effect of PM on mortality was mainly concentrated at lags of up to 2 days. This meant that the last 1 or 2 days of mortality included in the moving total mortality counts when k = 4 or k = 5, respectively, were typically not associated with the measure of PM used in the model. This resulted in a dampening of the estimated effect of PM on mortality for the moving total models with k = 4 or 5, and hence an increase in estimation bias compared with the moving total models with k = 2 or k = 3.
Application.
In this section I compare the results of applying the standard and moving total models to the actual Cook County and Allegheny County mortality time-series data. To do this, I first fit a DLM of PM to the mortality, meteorologic, and PM time-series data from both counties described in “Materials and Methods.” The DLM of PM contained PM concentrations lagged for 5 days, and the confounder adjustments used in this model were the same as those used in Equation 3. The effect of PM on mortality obtained from the DLM of PM was then used as a basis for judging the performance of the standard and moving total models. The rationale is that, in the ideal situation where daily PM data are available, a DLM of PM should be the method of choice for estimating the effect of PM on mortality (Roberts 2005; Schwartz 2000; Smith et al. 2000). Hence, in the situation where only every-sixth-day PM data are available, it is desirable that the method used to estimate the effect of PM on mortality return an estimate as close as possible to that obtainable in the ideal situation of daily PM data.
After fitting the DLM of PM, an every-sixth-day PM time series was obtained by extracting every-sixth-day PM concentration from the daily PM time series. With the every-sixth-day PM time series, I then estimated the effect of PM on mortality using both the standard and moving total models. The confounder adjustments used in the standard and moving total models were the same as those used in Equation 3. The estimates obtained from the standard and moving total models were then compared with the basis estimates obtained from the DLM of PM.
Table 3 contains the estimates obtained from fitting the DLM of PM, the standard models, and the moving total models to the data from both Cook County and Allegheny County. Using the estimates obtained from the DLM of PM as the basis for comparison, we can see that the moving total model with k = 2 provides the “best” estimates for the effect of PM on mortality. In both counties, this model produces an estimate that is closer to the basis value than the estimates obtained from the standard models. In addition, the estimate obtained from the moving total model with k = 2 has smaller variance than the estimates obtained from the standard models. These results reinforce the conclusions from the simulations that the moving total model offers a way to estimate the effect of PM on mortality that is both more precise and more accurate than the standard model. These results also show that the moving total model may provide a more robust estimate of the effect of PM on mortality than that obtained from the standard model. This is illustrated by the moving total models with k = 2, 3, and 4, avoiding the relatively poor estimates obtained from the standard model – lag 2 in Cook County and standard model – lag 1 in Allegheny County.
It is important to note the substantially smaller estimates obtained from the moving total models of Cook County data with k = 3 and k = 4 compared with that obtained with k = 2. The reason for this is that the large negative effect of PM on mortality observed for this data at a lag of 2 days (see standard model – lag 2) is incorporated into the estimates obtained from the moving total models with k = 3 and k = 4 but not the moving total model with k = 2.
Discussion
PM air pollution is an important determinant of community health, and numerous time-series studies in the United States have investigated the association between PM and mortality (Crosignani et al. 2002; Health Effects Institute 2001). One major limitation of these studies is that in most large cities PM measurements are available only every sixth day. Time-series studies conducted in these cities cannot investigate how the effects of PM on mortality are distributed over time; instead, they are forced to examine the mortality effects of PM on a single day only. However, because the current evidence suggests that the mortality effects of PM are spread over multiple days, examining the effect of PM on a single day results in important information about the effect of PM on mortality being lost and estimates that have a large negative bias (Roberts 2005; Schwartz 2000). The moving total model introduced in this article uses information available in the daily mortality time-series data to infer some of this lost information.
The results presented here show that, for estimating the total effect of PM on mortality, the moving total model produced estimates that were substantially more precise (smaller variance) compared with those obtained from the standard model. In addition, the moving total model typically produced estimates that were more accurate (smaller bias) compared with those obtained from the standard model. These results indicate that the moving total model should be used in future air pollution mortality time-series studies where only every-sixth-day PM measurements are available.
In conclusion, because in most of the largest cities in the United States PM measurements are available only every sixth day, the moving total model has the potential, in a large number of locations, to provide improved estimates of the effect of PM on mortality that have both smaller variance and smaller bias than the estimates that are currently obtainable using existing models. This means that in multicity studies on the health effects of PM, improved estimates could be obtained for the city-specific estimates and hence for the pooled regional and national effect estimates. These improved estimates would allow researchers to better understand the health effects of PM exposure and in turn allow more informed decisions about the public health significance of PM exposure. For these reasons and the ease at which the moving total model can be implemented, I believe that the moving total model is an important contribution to the current air pollution mortality time-series methodology.
I thank M. Martin and P. Switzer for their helpful comments.
Table 1 Standard deviation and bias for the estimates of the total PM effect (θ) obtained from the standard models and the moving total models.
Model fit to generated mortality
Standard
Moving total
Truth Lag 0 Lag 1 Lag 2 k = 2 k = 3 k = 4
0.00a 0.26b (−0.01)c 0.26 (−0.02) 0.28 (−0.01) 0.19 (0.08) 0.15 (0.11) 0.13 (0.07)
Lag 0d
0.25 0.27 (0.01) 0.26 (−0.21) 0.29 (−0.24) 0.18 (0.01) 0.15 (−0.02) 0.13 (−0.08)
0.50 0.25 (0.01) 0.28 (−0.38) 0.29 (−0.49) 0.18 (−0.07) 0.15 (−0.14) 0.13 (−0.24)
1.00 0.26 (0.01) 0.28 (−0.78) 0.27 (−1.00) 0.18 (−0.25) 0.15 (−0.41) 0.13 (−0.56)
2.00 0.26 (−0.02) 0.27 (−1.57) 0.29 (−1.97) 0.18 (−0.60) 0.14 (−0.96) 0.13 (−1.22)
Lag 1
0.25 0.27 (−0.17) 0.27 (−0.01) 0.29 (−0.19) 0.18 (0.00) 0.15 (0.00) 0.13 (−0.07)
0.50 0.26 (−0.37) 0.26 (0.00) 0.30 (−0.35) 0.19 (−0.11) 0.15 (−0.14) 0.13 (−0.22)
1.00 0.27 (−0.79) 0.27 (−0.01) 0.29 (−0.75) 0.18 (−0.33) 0.15 (−0.39) 0.13 (−0.52)
2.00 0.26 (−1.55) 0.26 (0.02) 0.28 (−1.44) 0.19 (−0.75) 0.15 (−0.89) 0.13 (−1.10)
Lag 2
0.25 0.28 (−0.26) 0.27 (−0.20) 0.27 (0.00) 0.18 (−0.14) 0.15 (−0.05) 0.13 (−0.08)
0.50 0.27 (−0.49) 0.26 (−0.40) 0.28 (−0.03) 0.19 (−0.35) 0.14 (−0.19) 0.12 (−0.22)
1.00 0.25 (−0.95) 0.26 (−0.73) 0.29 (0.00) 0.19 (−0.78) 0.16 (−0.47) 0.14 (−0.52)
2.00 0.26 (−1.98) 0.26 (−1.50) 0.28 (0.00) 0.19 (−1.69) 0.16 (−1.08) 0.13 (−1.14)
Lag 0–1
0.25 0.26 (−0.16) 0.25 (−0.18) 0.26 (−0.19) 0.18 (−0.08) 0.15 (−0.06) 0.12 (−0.09)
0.50 0.25 (−0.33) 0.28 (−0.32) 0.30 (−0.36) 0.18 (−0.24) 0.14 (−0.22) 0.13 (−0.26)
1.00 0.26 (−0.65) 0.28 (−0.66) 0.28 (−0.72) 0.18 (−0.55) 0.15 (−0.53) 0.14 (−0.59)
2.00 0.27 (−1.31) 0.29 (−1.34) 0.28 (−1.44) 0.19 (−1.19) 0.15 (−1.19) 0.13 (−1.27)
4.00 0.27 (−2.59) 0.25 (−2.63) 0.27 (−2.88) 0.18 (−2.46) 0.14 (−2.49) 0.13 (−2.62)
Lag 0–2
0.25 0.27 (−0.10) 0.27 (−0.14) 0.29 (−0.18) 0.18 (−0.01) 0.15 (0.00) 0.13 (−0.07)
0.50 0.28 (−0.21) 0.26 (−0.25) 0.28 (−0.38) 0.18 (−0.14) 0.15 (−0.16) 0.13 (−0.24)
1.00 0.26 (−0.43) 0.25 (−0.52) 0.28 (−0.73) 0.18 (−0.37) 0.15 (−0.42) 0.13 (−0.55)
2.00 0.27 (−0.85) 0.26 (−1.03) 0.27 (−1.46) 0.20 (−0.83) 0.16 (−0.96) 0.14 (−1.17)
4.00 0.26 (−1.68) 0.26 (−2.06) 0.30 (−2.93) 0.19 (−1.73) 0.15 (−2.01) 0.14 (−2.40)
Truth is the specification of the “true” effect of PM on mortality and 1,000 times the θ value that were used to generate mortality.
a 1,000 times the total effect of PM on mortality (θ) that was used to generate mortality.
b 1,000 times the standard deviation for the estimate of the total effect of PM on mortality (θ).
c 1,000 times the bias for the estimate of the total effect of PM on mortality (θ).
d The specification of the “true” effect of PM on mortality that was used to generate mortality.
Table 2 Standard deviation and bias for the estimates of the total PM effect (θ) obtained from the standard models and the moving total models.
Model fit to generated mortality
Standard
Moving total
Truth Lag 0 Lag 1 Lag 2 k = 2 k = 3 k = 4
DLM 1a
0.25b 0.27c (−0.16)d 0.26 (−0.11) 0.28 (−0.16) 0.19 (−0.06) 0.16 (−0.03) 0.13 (−0.08)
0.50 0.27 (−0.31) 0.27 (−0.27) 0.28 (−0.27) 0.19 (−0.20) 0.16 (−0.17) 0.13 (−0.24)
1.00 0.26 (−0.61) 0.28 (−0.52) 0.29 (−0.57) 0.18 (−0.47) 0.15 (−0.44) 0.13 (−0.55)
2.00 0.25 (−1.15) 0.27 (−0.99) 0.27 (−1.12) 0.18 (−0.99) 0.14 (−0.97) 0.12 (−1.15)
4.00 0.26 (−2.33) 0.26 (−2.03) 0.28 (−2.29) 0.18 (−2.10) 0.15 (−2.07) 0.13 (−2.39)
DLM 2
0.25 0.26 (−0.11) 0.27 (−0.08) 0.27 (−0.22) 0.20 (0.00) 0.15 (−0.01) 0.13 (−0.07)
0.50 0.27 (−0.22) 0.27 (−0.20) 0.27 (−0.42) 0.19 (−0.12) 0.16 (−0.15) 0.14 (−0.24)
1.00 0.27 (−0.40) 0.26 (−0.39) 0.28 (−0.85) 0.19 (−0.29) 0.16 (−0.41) 0.14 (−0.54)
2.00 0.27 (−0.79) 0.27 (−0.76) 0.29 (−1.70) 0.18 (−0.68) 0.15 (−0.92) 0.13 (−1.15)
4.00 0.26 (−1.56) 0.28 (−1.56) 0.28 (−3.39) 0.19 (−1.43) 0.15 (−1.96) 0.13 (−2.40)
DLM 3
0.25 0.25 (−0.22) 0.26 (−0.16) 0.26 (−0.14) 0.18 (−0.11) 0.15 (−0.07) 0.13 (−0.10)
0.50 0.27 (−0.46) 0.27 (−0.31) 0.29 (−0.35) 0.19 (−0.30) 0.16 (−0.24) 0.14 (−0.26)
1.00 0.28 (−0.92) 0.27 (−0.62) 0.27 (−0.63) 0.19 (−0.66) 0.16 (−0.58) 0.13 (−0.61)
2.00 0.27 (−1.81) 0.27 (−1.22) 0.27 (−1.23) 0.19 (−1.42) 0.15 (−1.27) 0.13 (−1.29)
4.00 0.26 (−3.64) 0.25 (−2.48) 0.27 (−2.46) 0.19 (−2.94) 0.15 (−2.67) 0.13 (−2.66)
DLM 4
0.25 0.26 (−0.13) 0.26 (−0.15) 0.29 (−0.22) 0.19 (−0.04) 0.15 (−0.04) 0.13 (−0.08)
0.50 0.25 (−0.21) 0.26 (−0.29) 0.27 (−0.41) 0.18 (−0.16) 0.15 (−0.17) 0.14 (−0.24)
1.00 0.27 (−0.40) 0.26 (−0.59) 0.29 (−0.77) 0.20 (−0.38) 0.16 (−0.44) 0.14 (−0.55)
2.00 0.28 (−0.80) 0.26 (−1.16) 0.27 (−1.53) 0.19 (−0.86) 0.15 (−1.00) 0.13 (−1.18)
4.00 0.26 (−1.61) 0.27 (−2.32) 0.29 (−3.11) 0.18 (−1.81) 0.16 (−2.11) 0.13 (−2.42)
Truth is the specification of the “true” effect of PM on mortality and 1,000 times the θ value that were used to generate mortality.
a The specification of the “true” effect of PM on mortality that was used to generate mortality.
b 1,000 times the total effect of PM on mortality (θ) that was used to generate mortality.
c 1,000 times the standard deviation for the estimate of the total effect of PM on mortality (θ).
d 1,000 times the bias for the estimate of the total effect of PM on mortality (θ).
Table 3 Results of fitting both the standard and moving total models to the actual data from Cook County, Illinois, and Allegheny County, Pennsylvania.
Model fit to mortality
Standard
Moving total
County Lag 0 Lag 1 Lag 2 k = 2 k = 3 k = 4 Baseline
Cook County 0.127a (0.264)b −0.042 (0.249) −0.441 (0.246) 0.150 (0.187) −0.047 (0.153) 0.009 (0.133) 0.462 (0.212)
Allegheny County 0.693 (0.437) 0.356 (0.423) 0.524 (0.415) 0.633 (0.310) 0.542 (0.255) 0.528 (0.221) 0.598 (0.351)
Baseline is the baseline estimate of the total effect of PM on mortality obtained from the DLM of PM fit to the daily data.
a 1,000 times the estimated effect of PM on mortality.
b 1,000 times the standard deviation of the estimated effect of PM on mortality.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7907ehp0113-00115316140620ResearchDietary Intake and Arsenic Methylation in a U.S. Population Steinmaus Craig 12Carrigan Kenichi 2Kalman Dave 3Atallah Raja 3Yuan Yan 1Smith Allan H. 11 Arsenic Health Effects Research Program, School of Public Health, University of California, Berkeley, California, USA2 Division of Occupational and Environmental Medicine, University of California, San Francisco, California, USA3 School of Public Health and Community Medicine, University of Washington, Seattle, Washington, USAAddress correspondence to A.H. Smith, Arsenic Health Effects Research Program, School of Public Health, 140 Warren Hall, University of California, Berkeley, CA 94720-7360 USA. Telephone: (510) 843-1736. Fax: (510) 843-5539. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 10 5 2005 113 9 1153 1159 6 1 2005 10 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Millions of people worldwide are exposed to arsenic-contaminated drinking water, and ingestion of inorganic arsenic (InAs) has been associated with increased risks of cancer. The primary metabolic pathway of ingested InAs is methylation to monomethyl arsenic (MMA) and dimethyl arsenic (DMA). However, people vary greatly in the degree to which they methylate InAs, and recent evidence suggests that those who excrete high proportions of ingested arsenic as MMA are more susceptible than others to arsenic-caused cancer. To date, little is known about the factors that determine interindividual differences in arsenic methylation. In this study, we assessed the effect of diet on arsenic metabolism by measuring dietary intakes and urinary arsenic methylation patterns in 87 subjects from two arsenic-exposed regions in the western United States. Subjects in the lower quartile of protein intake excreted a higher proportion of ingested InAs as MMA (14.6 vs. 11.6%; p = 0.01) and a lower proportion as DMA (72.3 vs. 77.0%; p = 0.01) than did subjects in the upper quartile of protein intake. Subjects in the lower quartile of iron, zinc, and niacin intake also had higher urinary percent MMA and lower percent DMA levels than did subjects with higher intakes of these nutrients. These associations were also seen in multivariate regression analyses adjusted for age, sex, smoking, and total urinary arsenic. Given the previously reported links between high percent MMA and increased cancer risks, these findings are consistent with the theory that people with diets deficient in protein and other nutrients are more susceptible than others to arsenic-caused cancer.
arsenicdrinking waterenvironmental healthmetabolismnutritional susceptibility
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Inorganic arsenic (InAs) occurs naturally in the groundwater of many parts of the world, and millions of people worldwide are exposed to drinking water containing this known carcinogen (Nordstrom 2002). Ingested arsenic causes cancers of the skin, bladder, and lung and has been associated with cancers of other organs [National Research Council (NRC) 1999, 2001]. The estimated risks associated with these exposures may be quite high. According to a subcommittee of the NRC, the cancer risks associated with lifetime exposures at the new U.S. standard of 10 μg/L may be as high as 1 in 300 (NRC 1999, 2001). The U.S. drinking water standards for other carcinogens have been set at levels associated with cancer risks that are about 30–3,000 times lower (Smith et al. 2002). Importantly, the new U.S. standard for arsenic applies only to public water systems. Approximately 15% of the U.S.population obtain their water from private wells (U.S. Geological Survey 2004), and arsenic concentrations > 10 μg/L have been documented in private wells throughout the United States (Ayotte et al. 2003; Steinmaus et al. 2003; Welch et al. 1999).
The primary metabolic pathway of ingested InAs in humans is methylation (Gebel 2002; Styblo et al. 2002; Vahter 2002). Ingested InAs is first methylated to monomethylarsonic acid (MMA5), which is reduced to monomethylarsonous acid (MMA3). MMA3 is then methylated to dimethylarsinic acid (DMA5), which is reduced to dimethylarsinous acid (DMA3). In humans, this process is not complete, and some arsenic remains as either InAs or MMA. Typically, ingested InAs is excreted as 10–20% InAs, 10–15% monomethyl arsenic (MMA), and 60–75% dimethyl arsenic (DMA) (Hopenhayn-Rich et al. 1993). However, large interindividual variations exist.
Until recently, methylation was thought to be primarily a detoxification pathway. This was based on the finding that MMA5 and DMA5—the most common forms of MMA and DMA found in exposed humans—are more readily excreted and less toxic than is InAs (Buchet et al. 1981; Gebel 2002; Hughes and Kenyon 1998; Moore et al. 1997). The trivalent forms of MMA and DMA are rapidly oxidized in urine and therefore are difficult to measure in human epidemiologic studies. Recently, however, methods have been developed to stabilize and measure MMA3 and DMA3 in urine, and these metabolites have been identified in urine samples from arsenic-exposed humans (Aposhian et al. 2000; Del Razo et al. 2001; Le et al. 2000; Mandal et al. 2001; Wang et al. 2004). Laboratory studies have shown that the trivalent forms of MMA and DMA are much more toxic than the pentavalent forms, and in vitro evidence suggests that MMA3 in particular may be more toxic than trivalent inorganic arsenic (InAs3) (Cullen et al. 1989; Lin et al. 1999, 2001; Mass et al. 2001; Petrick et al. 2000; Styblo et al. 1997, 1999, 2000). These findings suggest that methylation may not be strictly a detoxification pathway.
In fact, several epidemiologic studies have reported associations between elevated urinary proportions of MMA and increased risks of arsenic-associated health effects. In four studies from arsenic-exposed regions in Taiwan, subjects who excreted high proportions of urinary arsenic as MMA (percent MMA) or had high urinary MMA:DMA ratios had skin and bladder cancer odds ratios (OR) that were two to five times higher than did subjects who excreted low proportions of urinary MMA or had low MMA:DMA ratios (Chen et al. 2003a, 2003b; Hsueh et al. 1997; Yu et al. 2000). Associations between high levels of urinary percent MMA and increased bladder cancer risks were also found in studies on arsenic-exposed populations in the United States and Argentina (Steinmaus et al. 2004). Other studies have reported links between elevated urinary percent MMA or an elevated MMA:DMA ratio and increased risks of arsenic-caused skin lesions and increased rates of chromosomal aberrations (Del Razo et al. 1997; Maki-Paakkanen et al. 1998). The consistency of these associations, across different studies and different study populations, provides fairly strong evidence that individual differences in arsenic methylation patterns, and the environmental or genetic factors that cause these differences, play an important role in susceptibility to arsenic-caused disease.
To date, the environmental or genetic factors that control arsenic methylation are largely unknown. This is the first study to report on the impact of dietary protein, zinc, iron, thiamine, and several other potentially important macro- and micronutrients on arsenic methylation in humans.
Materials and Methods
Subjects were recruited from among residents of six counties in western Nevada and Kings County in California. These areas contain the cities of Hanford, California, and Fallon, Nevada, the largest populations in the United States with historically high water arsenic levels (Steinmaus et al. 2003). Historically, arsenic levels in the drinking water supplies in these cities had been near 100 μg/L, although levels in Hanford have dropped to < 50 μg/L over the past 15 years because of the development of new wells. In Fallon, an arsenic treatment plant has recently been installed to meet the new U.S. arsenic standard of 10 μg/L. Most other cities in the study area have public water supplies with arsenic levels < 20 μg/L. Approximately 20% of the study area residents obtain water from private wells where arsenic levels range from below detection to > 1,000 μg/L.
Most of the study subjects were recruited from the participants of a case–control study of bladder cancer and arsenic exposure (Steinmaus et al. 2003). Subjects with bladder cancer were obtained from state cancer registries and from local hospitals and physicians. Control subjects were selected through random digit dialing (RDD) and from randomly selected lists provided by the Health Care Financing Administration. Further details on the selection of subjects for the case–control study are described elsewhere (Steinmaus et al. 2003). All participants who had lived in the cities of Fallon or Hanford or the nearby surrounding areas for at least the 1 year preceding recruitment were invited to participate in the methylation study. Because the bladder cancer case–control study included mostly men > 60 years of age, 15 additional subjects, mostly females and subjects < 60 years of age (average age = 48, 53% female), were recruited for this study using RDD. These 15 subjects were recruited during the same time period as the controls in the larger case–control study, and the same methods were used to assess their diets and urine metabolites. Removing these subjects had little impact on our results.
Most arsenic ingested by humans is excreted in urine, and the relative distribution of arsenic metabolites in urine is commonly used as a biomarker of arsenic methylation patterns (NRC 1999). Two to three urine samples were collected from each participant over a 1-year period. Subjects were given screw-top polypropylene containers and asked to give a midstream sample of the first morning void. A previous study has shown strong correlations in arsenic excretion between single first-morning samples and samples collected over 24 hr (Calderon et al. 1999). Samples were then transported on ice to the field laboratory each day where they were kept frozen at −20°C. Urine samples were transported overnight on dry ice to the University of Washington, Seattle, for analysis. This study was approved by the University of California, Berkeley, Committee for Protection of Human Subjects.
The urinary concentrations of arsenic were measured using hydride generation atomic absorption spectroscopy (Crecelius 1978). Briefly, inorganic arsenic (InAs3 and InAs5), MMA, and DMA were reduced to the corresponding arsine in a batch reactor using sodium borohydride in 5-mL samples. The volatile reduction products (arsine, methyl arsine, and dimethylarsine) were removed by sparging with helium. Entrained arsines were concentrated in a chromosorb-filled cryogenic trap in liquid nitrogen temperatures until all arsine-forming arsenic in the sample had reacted. The cryotrap was then allowed to warm, and the collected arsines were separated on the basis of differential volatilization. We detected the separated volatile arsenic species using atomic absorption spectroscopy with a hydrogen microburner combustion cell to convert arsines to elemental arsenic (PerkinElmer, Inc., Wellesley, MA). To prevent interference by certain compounds (Del Razo et al. 1999), each urine sample was acidified with 2 M HCl and allowed to sit for at least 4 hr. Total arsenic was determined by flow injection analysis/atomic fluorescence spectrometry (PS Analytical, Inc., Orpington, Kent, UK), and this result was compared with the sum of the species detected. If a significant amount of arsenic remained undetected, additional digestion or assay for arsenobetaine was performed. Detection limits for InAs, MMA, and DMA were 0.5, 1.0, and 2.0 μg/L, respectively. Concentrations below the detection limit were set at one half the detection limit. The MMA and DMA measured in this study were in the pentavalent forms. The trivalent forms, MMA3 and DMA3, are rapidly oxidized to MMA5 and DMA5 during storage (Del Razo et al. 2001). Most samples in this study were frozen for 2–6 weeks before analysis. We analyzed a subsample of urine specimens for MMA3 and DMA3 but found no MMA3 and only trace amounts of DMA3.
We used the National Cancer Institute (NCI)’s Health Habits and History Questionnaire (HHHQ) (Block et al. 1986) to collect dietary information from each subject. The full HHHQ was administered over the telephone by trained study personnel. Subjects were asked about their typical frequency and portion sizes for each food item over the preceding year because our a priori hypothesis was that relatively long-term dietary patterns influence arsenic methylation. We assessed nutrient intake by multiplying the frequency of food consumption and the typical portion size by the nutrient content of each food using the HHHQ-Dietary Analysis Personal Computer System (DIET-SYS; version 4.01) and its accompanying dietary composition database (NCI 1997). Nutrient levels obtained using the HHHQ have been shown to correlate reasonably well with data obtained using 24-hr recall food records and serum nutrient levels (Block et al. 1990; Coates et al. 1991; Hartman et al. 1996). Our a priori hypotheses involved protein, folate, zinc, vitamin B12, and several of the other nutritional variables that have been linked to arsenic methylation and toxicity in laboratory studies (NRC 1999). However, results for all of the nutritional variables routinely calculated by the DIETSYS program are presented in this article. Selenium has been linked to arsenic methylation in several studies (Christian and Hopenhayn 2004; Hsueh et al. 2003), but we did not assess selenium in this study because of the potentially large inaccuracies in using food frequency questionnaire information to quantify selenium intake (Zhuo et al. 2004).
We calculated the relative proportion of each arsenic species (percent InAs, percent MMA, and percent DMA) by dividing the concentration of each species by the total arsenic concentration, defined as the sum of InAs, MMA, and DMA. Because two to three urine samples were collected from each subject, results from each sample were averaged to obtain a single value for each subject. The intraclass correlation coefficients (ICCs) for the proportions of each metabolite between samples taken at different points in time ranged from 0.45 to 0.68 (Steinmaus et al. 2005). The association of each arsenic species with variables such as age, sex, and smoking history were first assessed using univariate analyses. We also evaluated the association between species proportions and total urinary arsenic. Associations between arsenic dose and methylation patterns have been identified in previous studies, although these generally involve exposures that are much higher than in our study and these associations have typically been small (NRC 1999). The Student t-test and the Wilcoxon rank-sum test were used to compare category means. All analyses were initially done separately for cases and controls. However, because we identified no differences between these groups in the relationship between dietary factors and arsenic species in urine, cases and controls are pooled in the results presented here. Arsenic-caused cancer has an estimated latency of ≥ 20 years (NRC 1999). In many of our subjects, their current water source was not the same as their water source ≥ 20 years previously. Because we measured urinary arsenic levels near the time our cancer cases were diagnosed, we did not expect to find a correlation between cancer and the urinary arsenic levels in this study.
Associations between nutrient levels and the proportions of each arsenic species were assessed in two ways. First, the mean proportions of InAs, MMA, and DMA in subjects in the upper and lower quartile of each nutrient variable were compared using the Student t-test. Because the intake of most nutrients is strongly related to total calorie intake, we calculated energy-adjusted nutrient levels using the residual method described by Willett and Stampfer (1998). Second, we performed linear regression using the proportion of each arsenic species as the dependent variable and the energy-adjusted nutrient level as the independent variable. This was done with and without the addition of age (continuous), sex, smoking (current vs. noncurrent smoker), and total urinary arsenic (the sum of InAs, MMA, and DMA as a continuous variable) as independent variables. Entering age or total urinary arsenic as categorical rather than continuous variables had no impact on the results. Entering smoking as pack-years or number of cigarettes smoked per day also did not change the results. All data analyses were carried out using the SAS statistical program package (version 8.0e; SAS Institute Inc., Cary, NC).
Results
In total, 87 subjects agreed to provide urine samples and complete the dietary questionnaire. Table 1 shows the distribution of demographic and lifestyle variables among the study participants. Twenty-two subjects were female (25%), 14 were current smokers (16%), 23 had a history of bladder cancer (26%), and the average age was 68 (range, 21–98 years).
Table 1 also shows the relative proportions of arsenic species and the results of the univariate analyses comparing demographic variables and species proportions. Females excreted a lower percent InAs and percent MMA and a higher percent DMA than did men. Current smokers excreted a higher percent InAs and a lower percent DMA than did former and never-smokers, although these differences were not statistically significant. Increasing age was associated with decreasing percent InAs, but no association was seen between age and percent MMA or percent DMA. The proportion of each arsenic species was similar between cases and controls, and no significant association was seen between total urinary arsenic and the proportion of each arsenic species. Adjusting the total urinary arsenic levels for urine creatinine had no impact on our results.
Table 2 shows the mean level of each nutrient and the mean percent InAs, percent MMA, and percent DMA for the lower and upper quartile of each energy-adjusted nutrient residual. Subjects in the lowest quartile of protein, iron, thiamine, niacin, vitamin B6, zinc, and α-carotene intake had a higher mean percent InAs, a higher mean percent MMA, and lower mean percent DMA than subjects in the uppermost quartile of these nutrients, although in some of these comparisons the p-value for the differences was > 0.05. For subjects in the lower and upper quartiles of protein intake, respectively, the mean proportions of each arsenic species were 13.1 and 11.4% for percent InAs (p = 0.23), 14.6 and 11.6% for percent MMA (p = 0.01), and 72.3 and 77.0% for percent DMA (p = 0.01). The difference between the median nutrient values for subjects in the upper quartile and subjects in the lower quartile was 25.7 g for protein, 5.64 mg for iron, 0.67 mg for thiamine, 8.34 mg for niacin, 0.58mg for vitamin B6, and 545.3 μg for α-carotene. Similar findings were identified when the MMA:DMA ratio was assessed. For example, the MMA:DMA ratio in those in the lower and upper quartiles of protein intake were 0.21 and 0.15 (p = 0.008), respectively (data not shown). Clear and consistent threshold patterns were not seen in our analysis. For example, mean percent MMA and percent DMA values for subjects in the two middle quartiles of protein intake were 13.1 and 75.0%, respectively. These are approximately midway between values for subjects in the upper and lower quartiles. In analyses comparing the upper and lower quartiles of nutrient levels that were not adjusted for energy intake, no clear associations were seen between any nutrient and percent InAs, percent MMA, or percent DMA (data not shown).
Table 3 shows the results of the linear regression analysis, adjusted for age, sex, smoking, and total urinary arsenic. Increases in protein intake were associated with decreases in percent MMA [linear regression coefficient (b) = −0.075; p = 0.02]. This corresponds to an increase of 1.5% in percent MMA for every 20-g decrease in protein intake. High iron and niacin intakes were associated with increases in percent DMA, and increases in oleic acid intake were associated with decreases in percent InAs. Inclusion of age, sex, smoking, and total urinary arsenic in the linear regression model had relatively small impacts on these results. For example, the regression coefficient for protein and percent MMA was −0.084 (p = 0.01) in the model that did not include age, sex, smoking, and total urinary arsenic, and −0.075 (p = 0.02) in the model that included these variables.
Discussion
The findings of this study suggest that low intakes of dietary protein, iron, zinc, and niacin lead to a decreased production of DMA and increased levels of MMA in arsenic-exposed individuals. Links between methylation patterns and dietary intake of thiamine, vitamin B6, lutein, and α-carotene were also identified in the unadjusted analysis but were less clear after adjustment for age, sex, smoking, and total urinary arsenic levels. As a whole, the results of this study provide some evidence that certain dietary variables can affect arsenic methylation in humans. Although multiple comparisons were performed in this study and some of our findings could be due to chance, several of our results are consistent with those of previous investigations.
The impact of diet on arsenic metabolism and toxicity has been controversial because the risk assessment process used by the U.S. Environmental Protection Agency (EPA) to establish the U.S. drinking water standard for arsenic is based primarily on dose–response information from poorly fed populations in Taiwan (Morales et al. 2000; NRC 2001; U.S. EPA 2001). It has been hypothesized that the Taiwanese populations were particularly susceptible to the health impacts of arsenic as a result of their poor diets, and therefore, the results of studies done in Taiwan may not be relevant to better-fed populations such as those in the United States (Carlson-Lynch et al. 1994; NRC 2001). Although several dietary variables have been mentioned as part of this hypothesis, much of the past debate on this issue was based on whether or not people with low dietary intakes of protein had sufficient amounts of choline, methionine, or cysteine to fully metabolize InAs to DMA (Beck et al. 1995; Brown and Beck 1996; Carlson-Lynch et al. 1994; Engel and Receveur 1993; Mushak and Crocetti 1995, 1996; Slayton et al. 1996). Although the adequacy of the Taiwanese diet is debatable, studies done in experimental animals have shown that severe protein deficiencies can impair arsenic methylation and excretion (Tice et al. 1997; Vahter and Marafante 1987). However, the relevance of these studies to human arsenic exposures was unknown because most species of experimental animals metabolize and excrete arsenic much differently than humans (NRC 1999, 2001; Vahter 1999). Our study is the first to assess the role of dietary protein intake and arsenic methylation in humans, and our findings suggest that, despite these wide inter-species differences, the impacts of protein on arsenic metabolism that have been reported in experimental animals may also occur in human populations.
Protein deficiencies have been linked not only to changes in arsenic methylation but also to increased risks of arsenic-caused adverse effects. In two separate studies in mice, low dietary protein caused increases in DNA hypomethylation and increases in developmental toxicity (Lammon and Hood 2004; Okoji et al. 2002). Several human studies have identified associations between indicators of general malnourishment and the development of arsenic-caused skin lesions, skin cancer, and cardiovascular effects (Chen et al. 1988; Chen et al. 2003a; Guha Mazumder et al. 1998; Hsueh et al. 1995), although the specific role of protein was not addressed in these studies. Only one published human study has investigated the role of protein intake on arsenic-related disease. Mitra et al. (2004) investigated associations between arsenic-caused skin lesions and nutrient intakes, measured using 24-hr dietary recalls, in 238 subjects from West Bengal, India. Elevated odds ratios were seen in subjects with low intakes of calcium [OR = 1.89; 95% confidence interval (CI), 1.04–3.43], fiber (OR = 2.20; 95% CI, 1.015–4.21), and folate (OR = 1.67; 95% CI, 1.87–3.20). In addition, subjects in the lowest quintile of animal protein intake had a skin lesion odds ratio of 1.94 (95% CI, 1.05–3.59) compared with subjects in the highest quintile of animal protein intake. As a whole, the results of these studies, combined with the findings of our investigation, provide a small but emerging body of evidence that low intakes of dietary protein can affect arsenic methylation and may increase in arsenic-associated toxicity.
Although our findings regarding protein are consistent with those of a few other studies, the magnitude of the effect we identified is relatively small compared with the wide inter-individual variability typically seen in arsenic methylation patterns. The differences we identified in percent InAs, percent MMA, and percent DMA between subjects in the upper and lower quartiles of protein intake were 1.7, 3.0, and 4.7%. In comparison, the overall range in percent InAs, percent MMA, and percent DMA in our study population was 29, 23, and 39%, respectively. In an analysis of variance, energy-adjusted protein intake explained only 7.3% of the total variance seen in percent MMA in our subjects. The 3.0% difference in percent MMA we identified between the upper and lower quartile groups of protein intake is of similar magnitude to the impacts identified for some of the other variables most strongly linked to methylation status, including sex and certain genetic polymorphisms (Chiou et al. 1997; Hopenhayn-Rich et al. 1996b). However, studies linking arsenic methylation patterns to increased cancer risks have, to date, not provided sufficient information to estimate dose–response relationships. Thus, the exact impact that these relatively small changes in methylation patterns have on arsenic-caused cancer risks is currently unknown.
In addition to protein, we identified associations between arsenic methylation and iron intake. In the West Bengal study discussed above, the mean daily intake of iron was lower in subjects with arsenic-caused skin lesions than in controls, but this difference was relatively small (13.1 mg in cases and 14.6 mg in controls, p = 0.07) (Mitra et al. 2004). In one study, oral administration of iron reduced arsenic-caused DNA damage in mice, although it is unknown whether this effect is related to impacts on arsenic methylation (Poddar et al. 2000). Zinc has been linked to decreased arsenic toxicity in some studies (Milton et al. 2004; NRC 1999; Rabbani et al. 2003) but not in others (Mitra et al. 2004; Shimizu et al. 1998; Wang 1996). In our study, subjects with higher intakes of zinc had lower percent MMA and higher percent DMA, although these results are not statistically significant. We also identified associations between methylation patterns and dietary niacin but are not aware of any animal or human studies that have identified a similar association.
Several other dietary variables that have been directly or indirectly linked to arsenic metabolism in previous animal or in vitro studies, including β-carotene, vitamin E, folate, and vitamin B12, were not clearly associated with arsenic methylation patterns in our study (Brouwer et al. 1992; Buchet and Lauwerys 1985; Hsueh et al. 2003). There are several possible reasons why we may have underestimated or missed some associations. One possibility is that certain dietary variables may have substantial impacts only when nutritional deficiencies are severe. In our study, almost all subjects had intakes of protein, iron, vitamin A, thiamine, and other nutrients above U.S. recommended dietary allowance values. In the blackfoot region of Taiwan, where many of the early studies linking ingested arsenic to cancer took place, the mean intake of protein was similar to that of our study subjects (60 g/day in the Taiwanese and 64 g/day in our subjects) (Engel and Receveur 1993; Yang and Blackwell 1961). However, the proportion of subjects in Taiwan with severe deficiencies is unknown, and mean intakes of other variables, such as niacin and zinc, may have been below recommended levels (Engel and Receveur 1993; NRC 1999). In the West Bengal study discussed above, only 44% of subjects had protein intakes above recommended levels (Mitra et al. 2004). Although it is possible that the impacts of diet on arsenic methylation may be greater in populations where nutritional deficiencies are severe, high risks of arsenic-associated cancers and other diseases are not limited to malnourished populations and have been reported in populations where overall nutrition is good (Ferreccio et al. 2000; Hopenhayn-Rich et al. 1996a, 1998; Smith et al. 1998, 2000).
Errors in assessing diet or methylation status could have biased the effect estimates in this study. Although a validated diet questionnaire was used, we asked subjects to provide an estimate of their typical diet over a 1-year period. If methylation patterns depend more on day-today dietary choices than on long-term dietary trends, and subjects changed diets substantially from day to day, the magnitude of any true effects may have been biased. Large intraindividual variability in arsenic methylation patterns could have caused similar bias, although we may have diminished this somewhat by collecting multiple urine samples from each subject and basing methylation status on average values. In measuring both diet and methylation patterns, any misclassification would most likely have been nondifferential and therefore have biased our results toward the null rather than toward spurious associations.
Another explanation for the relatively small impacts we identified in this study is that the dietary variables we assessed may indeed play only a small role in arsenic methylation, and other environmental or genetic factors may have a more predominant role. The r2 values for the percent MMA and percent DMA regression models including each dietary variable with age, sex, smoking, and total urinary arsenic were all < 0.26 (Table 3), suggesting that these variables explain only a small portion of the total variance seen in percent MMA and percent DMA in our subjects. The results of several studies suggest that inherited genetic traits can have important influences on individual methylation patterns (Chung et al. 2002; Concha et al. 2002; Vahter 1999, 2000, 2002). For example, in a study of 11 families in Chile, the correlation in percent MMA in sibling–sibling pairs, whose genetic makeup is likely very similar, was greater than that in mother–father pairs, who would not necessarily share the same genetic traits (ICC = 0.69, p < 0.01 in sibling–sibling pairs; ICC = 0.01, p = 0.97 in mother–father pairs) (Chung et al. 2002). In a study of arsenic-exposed residents in Taiwan, subjects with the null genotype of glutathione S-transferase M1 had a higher proportion of urinary arsenic in the inorganic form than those with the non-null genotype (regression coefficient = 3.8, SD = 1.9, p < 0.05) (Chiou et al. 1997). Other studies have shown that arsenic methylation patterns may vary by ethnicity (Vahter 2000, 2002). Inheritance has also been shown to be a major factor in the individual variation of the activity of several other human methyltransferases (Weinshilboum 1992, 1988).
The trivalent form of MMA was not measured as part of this study. MMA3 is rapidly oxidized to MMA5 in human urine and could not be reliably measured in field investigations at the time this study was done. Several studies have shown that MMA3 is more acutely toxic than other arsenic species (Cullen et al. 1989; Lin et al. 1999, 2001; Mass et al. 2001; Petrick et al. 2000; Styblo et al. 1997, 1999, 2000). However, only a few studies have investigated the presence of MMA3 in nonchelated humans (Del Razo et al. 2001; Mandal et al. 2001; Valenzuela et al. 2005; Wang et al. 2004). Given the high toxicity of MMA3, and the links between total MMA and arsenic-associated cancer risks reported in several investigations (Chen et al. 2003a, 2003b; Del Razo et al. 1997; Hsueh et al. 1997; Maki-Paakkanen et al. 1998; Yu et al. 2000), future studies on MMA3 and its role in human toxicity could add important insights into the mechanisms of arsenic-caused health effects.
In conclusion, the data presented here suggest that dietary protein intake and possibly other nutritional deficiencies can affect arsenic methylation, although the impacts we identified in this well-fed population are small compared with the wide interindividual variability seen in this metabolic process. Additional research on dose–response relationships between arsenic methylation and chronic health effects, as well as further information on the environmental and genetic factors that control arsenic methylation, may help in the identification of susceptible subpopulations and could provide important insights into the carcinogenic mechanisms of this common drinking water contaminant.
Primary funding for this study was provided by the California Cancer Research Program grant 9900563V. Additional support was provided by National Institute of Environmental Health Sciences grants K23 ES11133 and P42 ES04705, and the Center for Occupational and Environmental Health.
Table 1 Demographic variables and proportions of arsenic species (mean ± SD).
Variable No. (%) Percent InAs Percent MMA Percent DMA
All 87 (100) 12.1 ± 4.9 13.1 ± 3.9 74.8 ± 7.0
Sex
Women 22 (25) 10.3 ± 2.7 10.7 ± 2.4 79.0 ± 3.6
Men 65 (75) 12.7 ± 5.3 13.9 ± 4.0 73.4 ± 7.4
p-Value 0.009 < 0.001 < 0.001
Smoking
Current 14 (16) 14.7 ± 7.5 13.0 ± 5.3 72.3 ± 11.2
Former 47 (54) 11.6 ± 4.1 13.5 ± 3.8 74.9 ± 6.0
Never 26 (30) 11.6 ± 4.0 12.4 ± 3.2 76.0 ± 5.8
p-Value 0.15 0.94 0.34
Age (years)
< 65 24 (28) 13.2 ± 4.7 11.9 ± 2.5 74.9 ± 6.4
65–75 34 (39) 13.0 ± 5.0 13.6 ± 4.5 73.3 ± 8.2
> 75 29 (33) 10.0 ± 4.3 13.4 ± 3.8 76.6 ± 5.9
R (p-value)a −0.24 (0.03) 0.15 (0.17) 0.08 (0.46)
Urinary arsenic (μg/L)b
< 9.9 29 (33) 12.9 ± 5.4 13.4 ± 4.2 73.8 ± 8.4
9.9–20.3 29 (33) 11.3 ± 3.6 12.5 ± 4.3 76.2 ± 5.8
> 20.3 29 (33) 12.0 ± 5.4 13.4 ± 3.0 74.6 ± 6.7
R (p-value)a 0.05 (0.63) −0.03 (0.76) −0.02 (0.87)
a Pearson correlation coefficient and associated p-value.
b Total urinary arsenic was defined as the sum of InAs, MMA, and DMA.
Table 2 Mean daily intake of each dietary variable, and the mean proportion of arsenic species in the upper and lower quartiles of each energy-adjusted dietary variable.
Percent InAs
Percent MMA
Percent DMA
Nutrient Nutrient levels (mean ± SD) Lower quartile Upper quartile p-Value Lower quartile Upper quartile p-Value Lower quartile Upper quartile p-Value
Protein (g) 64.1 ± 28.6 13.1 11.4 0.23 14.6 11.6 0.01 72.3 77.0 0.01
Fat (g) 77.3 ± 42.2 13.2 12.5 0.65 12.4 12.4 0.98 74.4 75.1 0.72
Carbohydrates (g) 188.1 ± 81.4 13.4 12.2 0.42 12.7 13.7 0.44 73.9 74.1 0.94
Calcium (mg) 746.8 ± 425.8 11.3 12.1 0.59 13.2 13.6 0.73 75.4 74.2 0.58
Phosphorus (mg) 1107.7 ± 521.1 12.8 10.9 0.25 12.8 12.4 0.72 74.4 76.6 0.29
Iron (mg) 11.6 ± 4.7 14.1 11.1 0.05 14.8 12.5 0.06 71.0 76.4 0.02
Sodium (mg) 2820.4 ± 1487.2 13.1 11.9 0.52 14.2 11.9 0.06 72.7 76.2 0.17
Potassium (mg) 2619.5 ± 971.3 11.8 10.4 0.30 12.6 12.3 0.74 75.5 77.2 0.30
Vitamin A (IU) 7201.8 ± 8269.1 13.4 11.5 0.20 14.0 12.6 0.30 72.6 75.9 0.16
B1/thiamine (mg) 1.22 ± 0.57 12.7 11.1 0.27 14.3 11.9 0.05 72.9 76.9 0.06
B2/riboflavin (mg) 1.66 ± 0.83 12.3 11.2 0.43 14.2 12.8 0.18 73.5 76.0 0.19
B3/niacin (mg) 16.0 ± 6.8 12.7 11.1 0.27 14.8 12.1 0.03 72.5 76.8 0.05
Vitamin C (mg) 114.2 ± 70.1 13.2 12.2 0.57 13.8 12.1 0.17 73.0 75.6 0.25
Saturated fat (g) 27.9 ± 16.3 13.5 11.1 0.18 13.3 12.7 0.59 73.2 75.6 0.24
Oleic acid (g) 27.9 ± 16.1 13.6 12.3 0.41 13.3 12.8 0.66 73.0 74.9 0.44
Linoleic acid (g) 13.7 ± 7.2 12.1 13.1 0.58 13.8 13.3 0.69 74.1 73.6 0.85
Cholesterol (mg) 328.2 ± 243.8 11.5 12.4 0.46 12.3 12.9 0.61 76.2 74.7 0.42
Fiber (g) 11.8 ± 5.2 13.7 11.7 0.19 13.7 13.4 0.79 72.6 74.9 0.30
Folate (μg) 239.2 ± 103.8 12.2 11.6 0.69 13.8 12.3 0.16 74.0 76.1 0.26
Vitamin E (ATE) 8.2 ± 3.5 12.7 13.8 0.49 13.1 12.7 0.75 74.2 73.4 0.77
Zinc (mg) 9.7 ± 4.4 12.9 12.0 0.54 14.1 12.5 0.17 73.0 75.5 0.18
Vitamin B6 (mg) 1.28 ± 0.56 12.4 11.9 0.75 14.8 11.8 0.004 72.8 76.3 0.08
Magnesium (mg) 259.5 ± 99.0 11.9 11.5 0.76 13.1 12.1 0.30 74.9 76.4 0.41
α-Carotene (μg) 408.6 ± 1032.0 14.1 10.3 0.02 13.9 12.3 0.13 72.0 77.5 0.02
β-Carotene (μg) 2805.4 ± 3810.7 13.1 12.1 0.47 14.1 12.3 0.20 72.8 75.6 0.23
Lutein (μg) 2152.2 ± 2868.2 10.9 12.4 0.19 14.3 12.1 0.04 74.8 75.6 0.66
Lycopene (μg) 1173.0 ± 1082.8 13.1 12.0 0.51 13.0 12.6 0.69 73.8 75.4 0.51
Retinol (μg) 577.7 ± 350.6 12.7 12.4 0.86 13.6 12.9 0.61 73.7 74.7 0.69
ProA-carotene (μg) 3159.0 ± 4761.5 14.0 11.6 0.15 13.6 11.9 0.13 72.4 76.5 0.08
Cryptoxanthin (μg) 59.5 ± 60.8 13.4 12.5 0.63 13.2 13.7 0.72 73.4 73.7 0.89
ATE, α-tocopherol equivalents.
Table 3 Adjusted linear regression coefficients (± SEs) for nutrient levels using percent InAs, percent MMA, and percent DMA as the dependent variables.a
Percent InAs
Percent MMA
Percent DMA
Nutrient b (SE) p-Value R2 b (SE) p-Value R2 b (SE) p-Value R2
Protein (g) −0.012 (0.040) 0.76 0.18 −0.075 (0.031) 0.02 0.20 0.088 (0.057) 0.12 0.21
Fat (g) −0.045 (0.028) 0.11 0.21 0.011 (0.023) 0.63 0.14 0.034 (0.041) 0.41 0.20
Carbohydrates (g) −0.011 (0.011) 0.33 0.19 0.003 (0.009) 0.71 0.14 0.008 (0.017) 0.64 0.19
Calcium (mg) 0.0024 (0.0015) 0.10 0.21 −0.0009 (0.0012) 0.48 0.15 −0.0016 (0.0022) 0.47 0.20
Phosphorus (mg) 0.0020 (0.0018) 0.26 0.20 −0.0016 (0.0015) 0.28 0.15 −0.0004 (0.0026) 0.87 0.19
Iron (mg) −0.32 (0.16) 0.05 0.22 −0.23 (0.13) 0.09 0.17 0.55 (0.23) 0.02 0.24
Sodium (g) −0.76 (0.69) 0.28 0.20 −0.31 (0.57) 0.58 0.14 1.07 (1.00) 0.28 0.20
Potassium (g) −0.18 (0.94) 0.84 0.18 −1.21 (0.75) 0.11 0.17 1.39 (1.34) 0.30 0.20
Vitamin A (1,000 IU) 0.038 (0.063) 0.55 0.19 −0.045 (0.051) 0.38 0.15 0.007 (0.091) 0.94 0.19
B1/thiamine (mg) −2.5 (1.6) 0.12 0.21 −1.7 (1.30) 0.18 0.16 4.2 (2.3) 0.07 0.23
B2/riboflavin (mg) 0.62 (0.92) 0.50 0.19 −0.91 (0.74) 0.22 0.16 0.29 (1.32) 0.83 0.19
B3/niacin (mg) −0.25 (0.13) 0.05 0.22 −0.20 (0.11) 0.07 0.18 0.45 (0.18) 0.02 0.25
Vitamin C (mg) −0.0002 (0.0078) 0.98 0.18 −0.0040 (0.0063) 0.53 0.15 0.0042 (0.0112) 0.71 0.19
Saturated fat (g) −0.088 (0.069) 0.21 0.20 0.049 (0.057) 0.39 0.15 0.039 (0.101) 0.70 0.19
Oleic acid (g) −0.16 (0.07) 0.02 0.23 0.02 (0.06) 0.75 0.14 0.14 (0.10) 0.17 0.21
Linoleic acid (g) −0.012 (0.109) 0.91 0.18 0.021 (0.089) 0.81 0.14 −0.009 (0.157) 0.95 0.19
Cholesterol (mg) 0.0009 (0.0033) 0.77 0.18 0.0016 (0.0026) 0.54 0.15 −0.0026 (0.0047) 0.58 0.19
Fiber (g) −0.045 (0.112) 0.69 0.19 −0.051 (0.091) 0.57 0.15 0.097 (0.160) 0.55 0.20
Folate (μg) −0.0003 (0.0060) 0.95 0.18 −0.0061 (0.0048) 0.21 0.16 0.0064 (0.0086) 0.46 0.20
Vitamin E (ATE) 0.020 (0.203) 0.92 0.18 −0.079 (0.165) 0.63 0.14 0.059 (0.292) 0.84 0.19
Zinc (mg) −0.22 (0.22) 0.32 0.19 −0.34 (0.18) 0.06 0.18 0.57 (0.32) 0.08 0.22
Vitamin B6 (mg) 0.76 (1.59) 0.63 0.19 −2.04 (1.28) 0.11 0.17 1.27 (2.28) 0.58 0.19
Magnesium (mg) −0.0005 (0.00760 0.94 0.18 −0.0066 (0.0061) 0.28 0.15 0.0072 (0.0108) 0.52 0.20
α -Carotene (mg) 0.13 (0.50) 0.79 0.18 −0.36 (0.41) 0.38 0.15 0.23 (0.72) 0.75 0.19
β -Carotene (mg) 0.11 (0.130) 0.41 0.19 −0.09 (0.11) 0.40 0.15 −0.02 (0.20) 0.92 0.19
Lutein (mg) 0.28 (0.17) 0.10 0.21 −0.15 (0.14) 0.31 0.15 −0.14 (0.25) 0.58 0.19
Lycopene (mg) −0.03 (0.48) 0.94 0.18 −0.73 (0.38) 0.06 0.18 0.77 (0.68) 0.26 0.20
Retinol (μg) 0.0019 (0.0017) 0.27 0.20 −0.0011 (0.0014) 0.44 0.15 −0.0008 (0.0024) 0.74 0.19
ProA-carotene (mg) 0.052 (0.109) 0.63 0.19 −0.072 (0.089) 0.42 0.15 −0.019 (0.157) 0.90 0.19
Cryptoxanthin (μg) −0.0037 (0.0086) 0.67 0.19 0.0054 (0.0070) 0.44 0.15 −0.0017 (0.0124) 0.89 0.19
ATE, α-tocopherol equivalents.
a Adjusted for age, sex, smoking, and urinary total arsenic.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7374ehp0113-00116016140621ResearchPesticide Exposure Alters Follicle-Stimulating Hormone Levels in Mexican Agricultural Workers Recio Rogelio 12Ocampo-Gómez Guadalupe 2Morán-Martínez Javier 2Borja-Aburto Victor 3López-Cervantes Malaquías 4Uribe Marisela 1Torres-Sánchez Luisa 4Cebrián Mariano E. 11 Sección Externa de Toxicología, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, México DF, México2 Departamento de Salud Ambiental, Centro de Investigación Biomédica, Facultad de Medicina de Torreón, Universidad Autónoma de Coahuila, Torreón, Coahuila, México3 Instituto Mexicano del Seguro Social, México DF, México4 Instituto Nacional de Salud Pública, Cuernavaca, Morelos, México.Address correspondence to M.E. Cebrián, Sección de Toxicología, CINVESTAV, P.O. Box 14-740 Avenida Instituto Politécnico Nacional No. 2508, Col. Zacatenco, Delegación Gustavo A. Madero, CP 07300, México DF, México. Telephone: 52-5-5061-3309. Fax: 52-5-5061-3395. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 10 5 2005 113 9 1160 1163 30 6 2004 10 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Organophosphorous pesticides (OPs) are suspected of altering reproductive function by reducing brain acetylcholinesterase activity and monoamine levels, thus impairing hypothalamic and/or pituitary endocrine functions and gonadal processes. Our objective was to evaluate in a longitudinal study the association between OP exposure and serum levels of pituitary and sex hormones. Urinary OP metabolite levels were measured by gas–liquid chromatography, and serum pituitary and sex hormone levels by enzymatic immunoassay and radioimmunoassay in 64 men. A total of 147 urine and blood samples were analyzed for each parameter. More than 80% of the participants had at least one OP metabolite in their urine samples. The most frequent metabolite found was diethylthiophosphate (DETP; 55%), followed by diethylphosphate (DEP; 46%), dimethylthiophosphate (DMTP; 32%), and dimethyldithiophosphate (DMDTP; 31%). However, the metabolites detected at higher concentrations were DMTP, DEP, DMDTP, and dimethylphosphate. There was a high proportion of individuals with follicle-stimulating hormone (FSH) concentrations outside the range of normality (48%). The average FSH serum levels were higher during the heavy pesticide spraying season. However, a multivariate analysis of data collected in all periods showed that serum FSH levels were negatively associated with urinary concentrations of both DMTP and DMDTP, whereas luteinizing hormone (LH) was negatively associated with DMTP. We observed no significant associations between estradiol or testosterone serum levels with OP metabolites. The hormonal disruption in agricultural workers presented here, together with results from experimental animal studies, suggests that OP exposure disrupts the hypothalamic–pituitary endocrine function and also indicates that FSH and LH are the hormones most affected.
alkylphosphatesendocrine disruptorsestradiolFSHLHorganophosphorous pesticidesprolactintestosterone
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Several pesticides have been considered endocrine disruptors because of their capacity to block or activate hormone receptors and/or to affect sex hormone levels (Vinggaard et al. 2000). Epidemiologic studies have suggested associations between OP exposure and long-term effects, such as reproductive disorders (infertility, birth defects, adverse pregnancy outcomes, perinatal mortality) and neurotoxicity (polyneuropathy, neurobehavioral hazards, Parkinson’s disease) (Baldi et al. 1998). Organophosphorous pesticides (OPs) are suspected to alter reproductive function by reducing brain acetylcholinesterase (AChE) activity and secondarily influencing the gonads. Studies on experimental animals have shown that the AChE inhibitor diisopropyl fluorophosphate (DFP), and repeated doses of OP significantly decreased brain AChE activity and significantly increased acetylcholine, gamma-aminobutyric acid, epinephrine, norepinephrine, dopamine, and 5-hydroxytryptamine concentrations (Glisson et al. 1974; Gupta et al. 1984). In addition, organophosphate and carbamate pesticides have been shown to alter pituitary–thyroid and pituitary–adrenal axes and affect prolactin serum levels (Clement 1985; Kokka et al. 1987). OP exposure was also reported to decrease brain AChE activity in mice, which in turn is associated with reduced serum levels of luteinizing hormone (LH) and progesterone and decreased egg production (Rattner and Michael 1985). Smallridge et al. (1991) reported that rats treated with low DFP doses showed increased serum LH levels whereas treatment with higher doses decreased LH and prolactin serum levels. The blunted LH and prolactin response to gonadotropin-releasing hormone (GnRH) induced by DFP led the authors to conclude that the effect was, at least in part, due to a suppressive effect at the pituitary level. More recently, Spassova et al. (2000) reported increased adrenocorticotropic hormone (ACTH) plasma levels and serum corticosterone and aldosterone levels in rats exposed to acephate and metamidophos and suggested that the excess acetylcholine stimulated the release of the hypothalamic corticotropin-releasing hormone, which in turn stimulated ACTH secretion. Sarkar et al. (2000) reported that subchronic oral administration of quinalphos (7–14 mg/kg/day for 15 days) to male rats resulted in increased serum concentrations of LH, follicle-stimulating hormone (FSH), prolactin, and testosterone, without significant effects on dopamine, noradrenaline, or serotonin levels in the hypothalamus or pituitary. Nag and Nandy (2001) reported a significant inhibition of monoamine oxidase-A (MAO-A) and MAO-B, the two main dopamine-degradative enzymes, in rat brain mitochondria exposed to OPs and also that the reversibility of the effect was dependent on the OP used. Similarly, Choudhary et al. (2002) showed that rats treated subcutaneously with the OP dichlorvos presented an increase in dopamine and norepinephrine levels accompanied by increases in the activity of tyrosine and dopamine β-hydroxylases and concomitant decreases in MAO activity, suggesting that the OP-induced decreased prolactin resulted from dopaminergic inhibition, because prolactin secretion is primarily under inhibitory control by dopamine. Thus, the information obtained from these experimental studies suggests that OP exposure alters brain neurotransmitter levels and that the hypothalamic–pituitary axis is a direct target for OP toxicity in rodents.
In contrast, little information is available on the endocrine effects of OP on humans. Güven et al. (1999) reported that, in suicidal individuals suffering from acute OP poisoning, ACTH, prolactin, and cortisol serum levels were significantly higher during poisoning, whereas FSH levels were significantly lower and hormone levels returned to normal after poisoning resolution. Straube et al. (1999) reported a decrease in testosterone and estradiol serum levels in pesticide sprayers 1 day after acute exposure to a wide variety of pesticides; however, chronic exposure resulted in higher LH and testosterone levels in chronically exposed men. In contrast, Padungtod et al. (1998) studied Chinese OP factory workers exposed to ethylparathion and metamidophos and reported that urinary p-nitrophenol levels 1 hr after the work shift were positively correlated with serum and urinary FSH levels. Multivariate analysis indicated that OP exposure significantly increased serum LH and FSH levels, whereas testosterone levels decreased. The authors suggested that the abnormal pituitary and sex hormonal patterns found were secondary to testicular damage. In contrast, Larsen et al. (1999) concluded that the use of a wide range of fungicides, insecticides, and herbicides by Danish farmers was not a likely cause of short-term effects on semen quality and reproductive hormones (testosterone, FSH, LH, and inhibin). However, little information is available on the associations between biologic markers of exposure and endocrine effects of OP on agricultural workers. Therefore, our objectives were to evaluate in a longitudinal study the association between OP exposure, as assessed by dialkylphosphate (DAP) urinary excretion, and serum levels of pituitary and sex hormones in Mexican agricultural workers.
Materials and Methods
Study population.
We conducted a longitudinal study in the agricultural community of Villa Juarez, State of Durango, Mexico, during 1997–1998. We selected this area because it is surrounded by agricultural fields whose main products are vegetables. Methyl parathion, metamidophos, endosulfan, dimethoate, and diazinon were the pesticides applied most frequently. We randomly selected 230 men from a household sampling frame. Eligible subjects were residents of this community for at least 15 years (average, 24 ± 11.8 years) and had no history of chemotherapy, radiotherapy, or chronic illnesses. One hundred thirty-two (56.4%) healthy men agreed to participate, but only 64 provided a complete set of samples. Each individual was interviewed directly regarding his sociodemographic characteristics, occupational activities, alcohol consumption, smoking habits, and clinical characteristics. Each participant signed an informed consent form and donated blood and urine samples. The study protocol was approved by the Ethics Committee of the School of Medicine, University of Coahuila, Mexico.
Sample collection.
Samples were collected during the main three periods of the agricultural cycle: crop preparation (November through February) in which small quantities of OP pesticides are regularly sprayed (henceforth called the low-exposure period), heavy spraying season (March through June) when large quantities of pesticides are sprayed (high-exposure period), and/or during the months of July through October in which medium quantities of OP are sprayed (medium-exposure period). A minimum of one and a maximum of five pairs of samples [1 (32.8%), 2 (29.7%), 3 (18.7%), 4 (14.1%), or 5 (4.7%)] were obtained from each subject during the study. Therefore, a total of 147 samples were analyzed for each parameter (51 obtained in the low-exposure period, 53 in the high-exposure period, and 43 in the medium-exposure period). The low- and medium-exposure groups were combined for the bivariate analysis in order to increase statistical power.
Hormone analysis.
Serum levels of pituitary hormones LH, FSH, and prolactin were measured by enzymatic immunoassay. Estradiol and testosterone were measured by radioimmunoassay. For both assays, we used methods and reagents provided by the World Health Organization (WHO 1999). Three internal and external WHO quality control samples were included in each assay run. Detection limits for LH and FSH were 0.01 IU/mL, and those for prolactin, estradiol, and testosterone were 0.01 ng/mL, 0.06 pg/mL, and 0.01 ng/mL, respectively. The mean intra- and interassay coefficients of variation for all hormones under study were between 8 and 13%. We used the normal ranges of WHO hormone levels as reference values (WHO 1999).
Urine collection and OP metabolite analysis.
A spot morning urine sample was collected from each participant before blood sample collection and frozen (–70°C) until analysis. We measured OP metabolites dimethylphosphate (DMP), diethylphosphate (DEP), dimethylthiophosphate (DMTP), dimethyldithiophosphate (DMDTP), diethylthiophosphate (DETP), and diethyldithiophosphate (DEDTP) by gas chromatography at Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional according to Aprea et al. (1996). Detection limits for the six metabolites ranged from 1 to 200 ng/mL. The mean intra- and interassay coefficients of variation for OP metabolites under study were between 6 and 11%. Total DAP was calculated as the sum of the six metabolites.
Statistical analysis.
General characteristics of the study population were described with arithmetic means, SEs, and proportions. Urinary metabolites and serum hormone mean levels were compared among agricultural periods by means of Student’s t-, Mann-Whitney, or chi-square tests, depending on the type of variable and its distribution. Dependent variables were transformed to normalize their distribution. Crude associations between individual metabolites and hormone levels were estimated by means of a generalized estimating equation (GEE) to account for the lack of independence of observations (McCullagh and Nelder 1989). Variables that changed the crude regression coefficients by > 10% were considered confounders [age, body mass index (BMI), occupation, smoking, and alcohol intake]. Multivariate adjusted GEE models were further calculated. To account for the multiple comparisons made in our statistical analyses and in an attempt to avoid type 1 statistical errors, we reduced the cutoff point for statistical significance from 0.05 to 0.01. All analyses were performed using STATA statistical software (version 7.0; Stata Corp., College Station, TX, USA).
Results
Subjects and urinary DAP levels.
Table 1 shows some characteristics of the population under study. There were no significant differences in relation to age, BMI, occupation, smoking, and alcohol intake between low/medium- and high-exposure groups (data not shown). Regarding DAPs, > 80% of the participants had at least one OP metabolite in their urine samples. The most frequent metabolite found was DETP (55%), followed by DEP (46%), DMTP (32%), and DMDTP (31%). However, the metabolites detected at higher concentrations were DMTP, DEP, DMDTP, and DMP (Table 2). Except for DETP, urinary metabolite levels were higher during the high-exposure period.
Hormone levels.
A high proportion of individuals (48%) had serum FSH levels outside the range of normality (1.2–5.0 IU/mL) proposed by the WHO (1999). The average FSH serum levels were higher (p < 0.05) during the heavy pesticide spraying season (high-exposure period) than during the low- and middle-exposure periods (Table 3). However, a multivariate analysis of data collected in all periods showed that serum FSH levels were negatively associated with both DMTP and DMDTP urinary concentrations (Table 4).
Most studied individuals (~92%) had prolactin serum levels within WHO (1999) normality range (1–19 ng/mL) without significant differences among periods or significant associations with urinary OP metabolites. Regarding LH, 71% of the individuals studied had serum levels within the WHO (1999) normality range (2.5–9.8 IU/L). A multivariate analysis of data collected in the three periods showed that LH serum levels were negatively associated (p = 0.008) with DMTP urinary concentrations (Table 4).
Regarding sex hormone levels, most studied individuals (72%) had estradiol serum levels within the WHO (1999) normality range (10–60 pg/mL) without significant differences among periods. We observed no significant associations between estradiol serum levels and urinary excretion of OP metabolites (Table 4). Most individuals (89%) had testosterone serum levels within the WHO (1999) normality range (2.7–9.0 ng/mL) without significant differences among periods. We found no associations between testosterone serum concentration and urinary OP metabolites.
Discussion
Our main findings in this study were the high proportion of agricultural workers with FSH serum levels outside the range of normality and the inverse relationship between DMTP urinary concentrations and FSH and LH levels. Total DAP urinary levels in nonoccupationally exposed Mexican individuals were twice as high as those reported for the Italian general population (Aprea et al. 1996). The levels in Mexican pesticide sprayers were 190 times higher than those reported for the Italian general population and 6 times higher than those in American greenhouse workers (O’Rourke et al. 2000), suggesting that Mexican individuals living or working in rural environments are highly exposed to OPs. The effects on FSH and LH in the present study were in partial agreement with those reported by Güven et al. (1999) in acutely OP-intoxicated individuals showing significant decreases in serum FSH levels and increases in prolactin without changes in LH levels. In contrast, Padungtod et al. (1998) reported that OP exposure, measured as urinary p-nitrophenol levels, significantly increased serum LH and FSH levels, whereas those of testosterone were decreased.
Our results were also in partial agreement with those obtained from studies on experimental animals that suggested the presence of dose-dependent stimulatory and inhibitory effects of OP on the endocrine pituitary function. For example, Smallridge et al. (1991) reported that rats treated with low DFP doses showed increased serum LH levels, whereas treatment with higher doses decreased LH and prolactin serum levels. In contrast, Sarkar et al. (2000) reported that subchronic administration of quinalphos increased serum levels of LH, FSH, prolactin, and testosterone. Despite evidence shown in experimental animals (Sarkar et al. 2000; Smallridge et al. 1991) and humans (Güven et al. 1999), our study did not show significant alterations in prolactin serum levels. The clinical significance of the disruptive effects of OP exposure on FSH and LH homeostasis in the present study could be related to an abnormal gametogenic testicular function.
Regarding effects on steroidal hormones, our findings were not in agreement with studies on OP-exposed individuals reporting decreases in testosterone and estradiol serum levels (Padungtod et al. 1998; Straube et al. 1999), which were in agreement with experimental studies reporting OP-induced alterations in testosterone and estradiol metabolism (Butler and Murray 1993; Murray and Butler 1995). Nonetheless, there is a need for further studies measuring urinary and/or serum concentrations of sexual hormones and their metabolites in human exposed populations. In addition to the dose-dependent stimulatory or inhibitory effects of OP on endocrine function mentioned above, the lack of consistency among studies assessing the effects of pesticide exposure on human hormone levels could also be due to differences in length and severity of exposures, protection equipment patterns of use, agrochemicals used, and/or agricultural practices, which play an important role in determining the characteristics of endocrine effects. Regarding the relationship between OP metabolites and endocrine effects, it is likely that urinary levels of OP metabolites reflect the magnitude of the exposure. However, further experimental studies are required to establish whether OP metabolites are able to induce alterations in the neuroendocrine system, notwithstanding that OP metabolites are assumed to have fewer biologic effects than do their parent compounds or their highly reactive oxons.
In summary, the hormonal disruption in agricultural workers in the present study, together with results from experimental animal studies, suggests that OP exposure disrupts the hypothalamic–pituitary endocrine function and also indicates that FSH and LH are the hormones most affected.
We thank C. Hernández, P. Nava, and R.M. García for chemical analysis and A. Gómez-Muñoz for statistical advice.
This study was partially supported by grants from the Consejo Nacional de Ciencia y Tecnología (CONA-CYT; 28403-M), the World Health Organization Human Reproduction Program (96349), University of California Institute for Mexico and the United States (UC-MEXUS), and the National Institutes of Health/Fogarty International Center Training and Research in Environmental and Occupational Health (TW00623).
Table 1 Selected characteristics of the study population (n = 64).
Characteristic Prevalencea
Age (years)
Mean ± SD (range) 28.6 ± 8.8 (18–50)
Occupation
Agricultural workers 25.4
Pesticide sprayers 19.0
Other 55.6
Body mass index (kg/m2)
Mean ± SD (range) 26.1 ± 4.2 (20.2–42.2)
Tobacco smoking (no. of cigarettes/day)
None 40.6
1–4 31.3
5–9 10.9
≥10 17.2
Alcohol intake (glasses of wine/month)
0–6 28.1
7–21 25.0
24–36 29.7
42–60 17.2
a Values are percent except where noted.
Table 2 Mean ± SD (range) of urinary OP metabolite levels (ppb) by agricultural period.
Period of exposure
Metabolite Low/medium (n = 104)a High (n = 43)a
DMP 56.2 ± 249.7 (ND–2169.1) 62.5 ± 169.1 (ND–994.1)
DMTP 694.6 ± 1531.3 (ND–7383.0) 3790.8 ± 19183.9 (ND–124441)
DMDTP 101.8 ± 273.4 (ND–2148.6) 333.4 ± 889.5 (ND–3971.3)
DEP 150.4 ± 630.4 (ND–4144.3) 217.1 ± 743.6 (ND–3492.3)
DETP 43.4 ± 54.5 (ND–394.3) 32.1 ± 56.1* (ND–202.1)
DEDTP 33.8 ± 153.0 (ND– 1062.0) 94.4 ± 251.1 (ND–938.1)
Total DAP 1170.3 ± 2318.3 (ND–11525.6) 4406.5 ± 19885.4 (ND–130430.7)
ND, nondetectable,
a Number of samples.
* p < 0.05 by Mann-Whitney’s test.
Table 3 Mean ± SD (range) of serum hormone levels by agricultural period.
Period of exposure
Hormone Low/medium (n = 104)a High (n = 43)a
LH (IU/L) 4.2 ± 2.7 (0.2–15.2) 4.8 ± 2.8 (0.1–10.3)
FSH (IU/L) 4.8 ± 2.7 (0.3–13.9) 6.1 ± 2.9* (0.5–19.1)
Prolactin (ng/mL) 7.5 ± 5.1 (0.2–25) 6.7 ± 4.3 (0.2–16.5)
Estradiol (pg/mL) 17.4 ± 11.9 (0.1–55.7) 20.2 ± 12.7 (1.8–46.0)
Testosterone (ng/mL) 4.5 ± 1.6 (1.3–9.6) 4.8 ± 2.0 (1.9–12.3)
a Number of samples.
* p < 0.05 by t-test on transformed variable.
Table 4 Associations between serum hormone levels and urinary OP metabolite concentrations (n = 147 samples).
LH (IU/mL)
FSH (IU/mL)
Prolactin (ng/mL)
Estradiol (pg/mL)
Metabolite β(SE) p-Value β (SE) p-Value β (SE) p-Value β (SE) p-Value
DMP –0.21a (0.55) 0.53 0.03b (0.6) 0.83 –0.66c (1.0) 0.42 –13.05d (7.6) 0.19
DMTP –0.0002 (0.0003) 0.008 –0.002e (0.0003) 0.007 –0.00004f (0.0005) 0.78 –0.018d (0.06) 0.60
DMDTP –0.24e (0.11) 0.13 –1.0 (0.09) 0.001 –0.02g (0.20) 0.77 –1.40h (2.34) 0.44
DEP 0.20 (0.06) 0.08 –0.15i (0.06) 0.11 –0.02c (0.12) 0.69 –1.59a (0.37) 0.04
DETP 0.0009g (10.1) 0.99 0.08g (9.4) 0.93 0.008g (17.9) 0.98 14.5j (58.6) 0.62
DEDTP –1.2e (0.8) 0.22 –0.085g (0.8) 0.74 –0.0005g (1.49) 0.99 –0.02k (5.78) 0.95
a Adjusted for occupation.
b Adjusted for age and smoking.
c Adjusted for age and group.
d Adjusted for alcohol intake and occupation.
e Adjusted for BMI.
f Adjusted for age, BMI, alcohol intake, and occupation.
g Adjusted for age, BMI, occupation, smoking, and alcohol intake.
h Adjusted for smoking, alcohol intake, and occupation.
i Adjusted for age.
j Adjusted for smoking and alcohol intake.
k Adjusted for BMI, occupation, smoking, and alcohol intake.
==== Refs
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Spassova D White T Singh AK 2000 Acute effects of acephate and metamidophos on acetylcholinesterase activity, endocrine system and amino acid concentration in rats Comp Biochem Physiol C Toxicol Pharmacol 126 1 79 89 11048668
Straube E Straube W Kruger E Bradatsch M Jacob-Meisel M Rose HJ 1999 Disruption of male sex hormone with regard to pesticides: pathophysiological and regulatory aspects Toxicol Lett 107 1–3 225 231 10414800
Vinggaard AM Hnida C Breinholt V Larsen JC 2000 Screening of selected pesticides for inhibition of CYP19 aromatase activity in vitro Toxicol In Vitro 14 227 234 10806373
WHO Special Programme of Research, Development and Training in Human Reproduction 1999. Programme for the Provision of Matched Assay Reagents for the Immunoassay and Radioimmunoassay of Hormones. Geneva:World Health Organization.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7843ehp0113-00116416140622ResearchDesign of a Microsphere-Based High-Throughput Gene Expression Assay to Determine Estrogenic Potential Naciff Jorge M. 1Richardson Brian D. 1Oliver Kerry G. 2Jump M. Lynn 1Torontali Suzanne M. 1Juhlin Kenton D. 1Carr Gregory J. 1Paine Jennifer R. 1Tiesman Jay P. 1Daston George P. 11 Miami Valley Innovation Center, Procter & Gamble Company, Cincinnati, Ohio, USA2 Radix BioSolutions, Georgetown, Texas, USAAddress correspondence to J.M. Naciff, Procter & Gamble Company, Miami Valley Innovation Center, P.O. Box 538707 #805, Cincinnati, OH 45253-8707 USA. Telephone: (513) 627-1761. Fax: (513) 627-0323. E-mail:
[email protected]., B.D.R., M.L.J., S.M.T., K.D.J, G.J.C., J.R.P., J.P.T., and G.P.D. are employed by the Procter & Gamble Co. K.G.O. is employed by Radix BioSolutions.
9 2005 12 5 2005 113 9 1164 1171 10 12 2004 12 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Recently gene expression studies have been multiplied at an accelerated rate by the use of high-density microarrays. By assaying thousands of transcripts at a time, microarrays have led to the discovery of dozens of genes involved in particular biochemical processes, for example, the response of a tissue/organ to a given chemical with therapeutic or toxic properties. The next step in these studies is to focus on the response of a subset of relevant genes to verify or refine potential therapeutic or toxic properties. We have developed a sensitive, high-throughput gene expression assay for this purpose. In this assay, based on the Luminex xMAP system, carefully selected oligonucleotides were covalently linked to fluorescently coded microspheres that are hybridized to biotinylated cRNA followed by amplification of the signal, which results in a rapid, sensitive, multiplexed assay platform. Using this system, we have developed an RNA expression profiling assay specific for 17 estrogen-responsive transcripts and three controls. This assay can evaluate up to 100 distinct analytes simultaneously in a single sample, in a 96-well plate format. This system has improved sensitivity versus existing microsphere-based assays and has sensitivity and precision comparable with or better than microarray technology. We have achieved detection levels down to 1 amol, detecting rare messages in complex cRNA samples, using as little as 2.5 μg starting cRNA. This assay offers increased throughput with decreased costs compared with existing microarray technologies, with the trade-off being in the total number of transcripts that can be analyzed.
endocrine disruptorestrogen-regulated gene expressionfluorescently coded microsphereshigh-throughput gene expression assaymultiplex
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The effectiveness of characterizing changes in gene expression in response to chemical exposure, therapeutic or toxic, or caused by changes in physiological status of tissues or organs (disease, development, etc.), has grown exponentially in the past few years because of the availability of cost-effective microarray technology. Microarrays allow the quantitative analysis of thousands of gene expression changes simultaneously in a single experiment, orders of magnitude more than the number that could be evaluated using older technologies for quantitative gene expression [i.e., Northern blot, RNase protection, quantitative real-time polymerase chain reaction (QRT–PCR)]. This approach has led to the discovery of multiple genes putatively involved in particular biochemical processes. Once a subset of relevant genes has been identified, the next step usually focuses on the response of that subset, for example, to confirm or refine potential therapeutic or toxic targets. This step has been performed largely using traditional methods, namely, QRT–PCR or Northern blot analysis. However, these methods are time- and sample-consuming because of their one-gene-at-a-time approach. What is needed is an intermediate technology to evaluate changes in the expression level of 10–100 genes (multiplexed), with high specificity, sensitivity, and reproducibility.
In previous studies using high-density oligonucleotide arrays, we have shown that exposure to various estrogen receptor (ER) agonists induced a characteristic gene expression profile in the developing reproductive system of the female rat (Naciff et al. 2002, 2003). Among these genes, there is a subset whose changes in expression are specific for estrogen exposure. Using this subset of genes (molecular fingerprint) rather than a single biomarker, we have developed a high-throughput gene expression assay based on the Luminex xMAP system (Luminex Corp., Austin, TX). The Luminex system is a multi-analyte bioassay detection system capable of performing up to 100 assays simultaneously in a single microtiter plate well. This system uses polystyrene microspheres internally dyed with red and infrared fluorophores that can be individually identified. The fluorescent microspheres can be coated with a reagent specific to a particular bioassay, allowing the capture and detection of specific analytes from a sample. This assay has been used to quantify proteins, genotype patients, and test viral loads in a multiplex platform (Brodsky and Silver 2002; Dunbar et al. 2003; Fulton et al. 1997; Gordon and McDade 1997; Morgan et al. 2004; Oliver et al. 1998; Smith et al. 1998; Vignali 2000). To date, there are many kits available to measure different proteins (Earley et al. 2002; Luminex 2005) in different samples based on the Luminex microspheres. However, there is only a single reference in the literature describing the use of this approach to quantify gene expression at the level of RNA transcripts (Yang et al. 2001). These authors have shown the ability to detect the expression level of up to 20 genes simultaneously with a lower detection limit of 100 amol. This detection limit allows the evaluation of only the moderately to highly expressed genes, a limitation to its broader applicability. We report here significant improvements to the assay that increases the sensitivity by two orders of magnitude, down to a single attomole of labeled target in a complex target mixture (for comparison, the detection limit of the Affymetrix microarray chip is about 15 amol per transcript). The assay we have developed is suitable for detection of up to 100 different transcripts with high throughput of hundreds to thousands of samples per day, with high accuracy, speed, sensitivity, and flexibility (add or subtract specific transcripts). It is particularly valuable for applications requiring the detection of a moderate number of transcripts in thousands of samples.
Our necessity for a high-speed multiplex assay has been driven by an imminent U.S. Environmental Protection Agency (EPA) requirement to determine the potential for the chemicals it regulates to interact with estrogen, androgen, or thyroid hormone system (U.S. EPA 2005). We believe that gene expression analysis has the greatest potential to evaluate these interactions in a sensitive and specific manner and is most amenable to the development of in vitro alternatives. We have used microarray analysis to determine changes in gene expression (to identify a “molecular fingerprint”) induced by estrogens, including the potent ER agonist, 17α-ethynyl estradiol (EE), in the female reproductive system of the rat. Previous studies in this laboratory have shown that transplacental exposure to various ER agonists (genistein, bisphenol A, and EE), or direct exposure (in prepubertal female rats) elicited a specific profile of gene expression changes in response to these chemicals (Naciff et al. 2002, 2003). From the set of affected genes, we have chosen a subset for which expression is strongly influenced by estrogen exposure that was used as a test case for evaluating the performance of the microsphere-based assay.
Materials and Methods
Animals, treatments, and target preparation.
Total RNA was isolated and purified according to the method previously reported by this laboratory (Naciff et al. 2003). Briefly, 15-day-old Sprague-Dawley female rats (Charles River, Raleigh, NC) were obtained and housed for 5 days before treatment. The experimental protocol was carried out according to Procter & Gamble’s approved protocols for animal care, and animals were maintained in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals (Institute of Laboratory Animal Resources 1996). On day 20 the rats were treated with either vehicle (peanut oil) or 0.1, 1.0, or 10.0 μg/kg EE each day for 4 consecutive days. This dose regimen was selected to elicit a graded (dose-dependent) uterotrophic response in the immature rat as indicated in Naciff et al. (2003). On day 24 the rats were sacrificed. The reproductive tissues (uteri with ovaries attached) were excised, trimmed of fat and connective tissue, and stored in RNAlater (Ambion, Inc., Austin, TX) at 4°C. We chose to evaluate the uterus and ovaries as a pool because they are two of the tissues most sensitive to estrogenic regulation. Although we realize that this may result in loss of information about gene expression in one or the other organ, we believe that from a screening perspective we have taken the best approach because these organs contain considerable variation in the expression levels of the two ER isoforms, ER-α and ER-β, and consequently have the potential to represent gene expression changes induced by activation of any of the isoforms of the ER in the target tissues. RNA was isolated using TriReagent (Molecular Research Center, Inc., Cincinnati, OH). Total RNA was further purified using the RNeasy Kit (Qiagen Inc., Valencia, CA) and stored at −80°C. Ten micrograms of RNA, from five independent samples (biologic replicates), was then converted into double-strand cDNA. The Enzo Bioarray RNA Transcription Kit (Affymetrix Inc., Santa Clara, CA) was then used to generate 50–75 μg of biotin-labeled cRNA. The cRNA was fragmented and hybridized to Affymetrix rat 34A chips. Unused fragmented cRNA was stored at −20°C until use for these experiments. The batch of cRNA was used in the many steps of optimizing the assay until none was left. The data shown here in the final assay are from a new batch of cRNA generated from the original stock of total RNA, which was used to generate the cRNA evaluated by microarray analysis. To fully assess the overall quality of each sample, the newly labeled cRNA samples were hybridized to the Affymetrix GeneChip Test 3 Array as previously described (Naciff et al. 2003).
Oligonucleotides.
To verify the data from the microarray experiments, we chose to use a single oligonucleotide to confirm the data from 16–20 oligos that are tiled for each feature on the U34A rat chips (for a full description of the rat U34A array content, see Affymetrix 2005). Briefly, in the rat genome U34A high-density oligonucleotide microarray, each gene or expressed sequence tag (EST) is represented by 16–20 pairs of 25-mer oligonucleotides that span the coding region. Each probe pair consists of a perfect match sequence that is complementary to the cRNA target and a sequence that is mismatched by a single base change at the middle of the nucleotide, a region critical for target hybridization. The mismatched oligonucleotide serves as a control for non-specific hybridization. We hypothesized that one probe could be used for the evaluation of the corresponding transcript. To identify the best possible oligonucleotide (probe), we designed an algorithm that allowed us to select the best-performing probe (oligonucleotide) from the entire probe set. This algorithm evaluates different parameters that best predict the results that were obtained from all probes in the feature. Another option was to select the best two or even three probes. Thus, the second- and third-best-performing probes, for each transcript selected, were identified using the different parameters evaluated by the algorithm. To test the validity of our approach, two oligonucleotides were identified as being the best-performing probes to evaluate the mRNA level for the rat glyceraldehyde 3-phosphate dehydrogenase (GAPDH). Additionally, because Affymetrix chips employ a set of perfect match–mismatch pair of oligonucleotides tiled onto the microarray to better evaluate the specific signal of any given mRNA, relative abundance, we decided to apply this strategy to the microsphere assay. To test this approach, a perfect match and a mismatch probe for 11β-hydroxysteroid dehydrogenase (Hsd11b2) were identified and used for the evaluation of the mRNA level of Hsd11b2 in the different samples. Empirical determination of the value of using the mismatch probe, paired with the data obtained from the perfect match probe, indicated that the specificity of the signal was not improved by its use (data not shown). The signal values from the perfect match probe are highly specific and are the only ones reported. The different probes selected are indicated in Table 1.
Coupling of oligonucleotides to microspheres.
Transcript-specific oligonucleotides, corresponding to the complementary sequence of the desired mRNA (Table 1), were covalently coupled to fluorescently distinct sets of carboxylate-modified polystyrene xMAP microspheres using water-soluble carbodiimide. All oligonucleotides were purchased from Integrated DNA Technologies Inc. (Coralville, IA) and were synthesized with a 5′-amino linker and a C12 spacer. These oligos were coupled covalently to 20 fluorescently distinct sets of carboxylated microspheres as previously detailed (Fulton et al. 1997) at a ratio of 1 nmol of modified oligo to 1 × 107 microspheres. The 20 sets of carboxylated polystyrene microspheres (5.6 μm in diameter) of differing fluorescence addresses (determined by its spectral signature determined by its red/infrared fluorophore ratio) were ordered from Luminex Corp. Specifically, 1 × 107 microspheres were pelleted in a microcentrifuge (12,000 × g) for 2 min, and the supernatant was carefully removed. The microspheres were resuspended in 50 μL of buffer containing 0.1 M MES [2-(N-morpholino)ethanesulfonic acid; Sigma-Aldrich Co., St. Louis, MO] at pH 4.5. The amino-substituted oligonucleotides were dissolved in double-distilled H2O to 1 mM and stored at −80°C. For the coupling reaction, 1 nmol of the appropriate oligonucleotide was added to the desired microsphere set. The reaction was initiated by adding 2.5 μL of freshly prepared 10 mg/mL 1-ethyl-3-(3-dimethyl-aminopropyl) carbodiimide hydrochloride (EDC; Pierce, Rockford, IL), followed by incubation of the mixture for 30 min at room temperature, in the dark. After the initial 30 min incubation, a second 2.5 μL of freshly prepared EDC solution (10 mg/mL) was added to the reaction and incubated for an additional 30 min. The coupling reaction was terminated by adding 1 mL of 0.02% Tween 20 (polyoxyethylenesorbitan monolaurate; Sigma-Aldrich Co.) to the microsphere suspension, vortexed, and then centrifuged (12,000 × g) for 4 min; the supernatant containing free oligonucleotides and unreacted EDC was removed. To ensure that all the uncoupled oligonucleotides were removed, the microspheres were washed with 1 mL of 0.1% SDS (Sigma-Aldrich Co.). The oligonucleotide-conjugated microspheres were resuspended in 1 mL of TE buffer (10 mM Tris, 1 mM EDTA, pH 8.0; Sigma-Aldrich Co.), counted on a hemacytometer, adjusted to a concentration of 1 × 107 microspheres/mL, and stored in TE at 4°C, protected from the light. Complementary sense-strand oligos were ordered with a 5′ biotin modification to be used for titration curves for each set of the coupled microspheres.
Hybridization.
To optimize the system, initial experiments used biotinylated complementary sense-strand probes to develop a titration curve to examine the sensitivity of the assay. In this assay five genes (analytes) were chosen to evaluate the sensitivity of this system. To this end 5,000 microspheres per analyte were combined in a well of a 96-well plate containing 1× TMAC [3 M tetramethyl-ammonium chloride, 0.1% SDS; 50 mM Tris–HCl, pH 8.0; and 4 mM EDTA, pH 8.0; Sigma-Aldrich Co.] in a total volume of 50 μL. Biotinylated complementary oligonucleotide (4 μL) at varying concentrations (one-half log serial dilutions for 100 fmol to 100 amol) diluted in TE (10 mM Tris and 1 mM EDTA, pH 8.0) was added and mixed. Samples were heated at 95°C for 2 min. The mixture was transferred to a 48°C shaking heat block (Eppendorf Thermomixer; Eppendorf North America, Inc., New York, NY) for 1 hr. The samples were spun at 2,500 × g, and the supernatant was removed by inverting the plate over the waste container and tapping one time on a table covered with absorbent paper. Samples were washed once with 1× TMAC and resuspended with 65 μL streptavidin–phycoerythrin (SAPE; Molecular Probes, Eugene, OR) stain (5 μg/mL SAPE in 1× TMAC) for 15 min at room temperature. The samples were then analyzed on a Luminex 100 instrument (Luminex Corp.).
For the amplification of the signal (see “Results”), the following steps were added to the protocol. After the washes after the SAPE stain, the microspheres were incubated for 60 min with biotinylated anti-streptavidin antibody (Vector Laboratories, Burlingame, CA) with goat IgG (Sigma-Aldrich Co.) to block nonspecific binding. The microspheres were spun, the supernatant was removed, and the microspheres were then stained again (Table 4) with 50 μL SAPE for 10 min. The microspheres were then washed a final time with PBS–1% BSA and analyzed.
Through empiric determination, the optimized protocol (Table 4) has incorporated the following changes: 1× TMAC was replaced with 0.5× TMAC, the two SAPE incubations and the anti-streptavidin incubation steps were performed with PBS–1% BSA as the buffer; and the bead concentration was reduced 10-fold. Because the flow cytometric analysis is done at a preset photomultiplier (PMT) setting, there is the possibility that when a given transcript is relatively abundant (high copy number), the signal values could reach saturation, whereas the signal values derived from the hybridization of uncommon transcripts (low copy number) to their specific microspheres could be relatively low. To avoid this potential problem, and taking advantage of the instrument used in our studies [Luminex 100 system from Luminex Corp. or Bio-Plex Suspension Array System from Bio-Rad Laboratories, Inc. (Hercules, CA)], we analyzed the fluorescence signal values at two PMT settings (low and high) to expand the dynamic range of the assay without losing sensitivity.
Instrumentation.
Samples run at Radix BioSolutions were read on a Luminex 100 (SP1 software, version 1.7.69; Luminex Corp.). Samples run at Procter & Gamble’s Miami Valley laboratories were read on the Bio-Rad Bio-Plex (Bio-Rad’s version of the identical Luminex 100 flow cytometer) using the Bio-Plex Manager software, version 2.0. Each system integrates lasers, optics, fluidics, electronics, and signal processing to identify each set of fluorescent-coded microspheres from each set and to measure the total fluorescence at the surface of each microsphere to quantify the amount of reporter bound to it. The median fluorescence intensity (MFI), derived from reading at least 100 microspheres from each set, was used as a representation of the whole population of microspheres of each set in any given sample. The MFI from five independent samples (biological replicas), from control or from each EE-treated animal, were determined and used to determine the average fold change on the expression of the transcript of interest.
QRT–PCR.
To verify the relative change in gene expression identified by both the microspheres and the oligonucleotide microarrays, we used a real-time (kinetic) QRT–PCR approach as previously described by this laboratory (Naciff et al. 2002, 2003). The primers used for the QRT-PCR have also been published (Naciff et al. 2003).
Results
Design of the transcript quantification assay.
Our goal was to quantify the amount of specific transcripts present in a complex mixture of cRNA with a sensitivity and specificity comparable with at least those obtained from the microarray analysis. To this end we chose oligonucleotides with unique sequences of 25 bases in length, based upon the best-performing probes tiled on the Affymetrix rat genome U34A high-density oligonucleotide microarray. Effective oligonucleotide probes for each transcript were selected based on the data from 44 chip hybridizations (Naciff et al. 2003), using a statistical algorithm (Juhlin KD, Carr GJ, Jump ML, Richardson BD, Torontali SM, unpublished data), and the analyzed data. The expression of 17 of the 20 target transcripts evaluated in this article is regulated by estrogens in a dose-dependent manner. We also included two internal control transcripts (vascular α-actin and cyclophilin B) that are also expressed in the uterus/ovaries of the immature rat but whose expression is not regulated by estrogens in these organs. An oligonucleotide specific for a bacteriophage M13 gene was also included as an external control. The sequences of the oligonucleotides are shown in Table 1.
Sensitivity and specificity of hybridization of the selected oligonucleotides coupled to fluorescent microspheres.
Each of the 20 oligonucleotides, derived from the 19 Rattus norvegicus genes and a control bacteriophage M13, was coupled to xMAP microspheres with a unique fluorescence emission wavelength. Each microsphere set was evaluated individually for specificity and sensitivity, after which the different microsphere sets were combined for multiplexed assays.
To determine the specificity and sensitivity of the assays and to optimize the system, initial hybridization experiments used a titration of biotinylated complementary sense-strand probes. Five specific microspheres sets were used to evaluate the sensitivity of this system. A complex mixture of different concentrations of synthetic complements, which included the antisense oligonucleotides specific for M13, Calb3, Cyp171a, Pp1b, and Hsd11b2 (sequences are given in Table 1), was mixed with oligonucleotides specific to GAPDH-5′ and GAPDH-3′ mRNA region (5′-CGTCAAGATCAAATGGGGTGAT GCT-3 and 5-ATCCTGGGCTACACT GAGGACCAGG-3′, respectively), and a single-base mismatch for Hsd11b2 (mutation on the central region, 5′-TCATGAGACC ATcTATACCCTACC-3′). Oligonucleotide-coupled microspheres were combined with different concentrations of biotinylated complementary oligonucleotides and allowed to hybridize at 48°C. The signal of each analyte bound to each type of microsphere present in the reaction was determined using SAPE as a reporter fluorescent tag. A negative control consisted of all assay reactants except for the biotinylated complementary oligonucleotides. The median fluorescent intensity of at least 100 microspheres of each set was calculated by the software. The intensity of the hybridization signals is linearly related to the amount of analyte being evaluated, from 0 to 36.1 fmol, for each oligonucleotide–microsphere set (Figure 1). The assay was no longer linear at higher concentrations of analyte (100 fmol). The specificity of the hybridization is not compromised as the number of analytes increases.
Evaluation of the sensitivity and specificity of the assay in experimental samples.
The transcripts to be analyzed (analytes or targets) were biotin-labeled cRNAs, prepared from the uterus/ovaries of prepubertal rats exposed to vehicle control or various doses of EE for 4 days (0.1, 1, or 10 μg EE/kg/day, low, mid, or high doses, respectively, n = 5 for each dose group), collected from a previous study (Naciff et al. 2003). Each sample from every dose group was analyzed independently and used as a biological replicate.
Initial sensitivity results of the microsphere-based assay were not within the range needed for the detection of all transcripts. The hybridization signal from one transcript, intestinal calcium-binding protein (Calb3), is shown in Figure 2. Although the assay clearly allows for the determination of the changes induced in Calb3 by EE exposure (control vs. low, mid, or high doses; C, L, M, and H respectively), the corresponding signal values were near the lower end of the sensitivity. The signal intensities obtained were similar to those obtained by Yang et al. (2001). The fluorescence intensity (signal value) corresponding to the transcript levels of Calb3 in the control sample (vehicle treated) was extremely low (44 units with 5 μg cRNA and 68 units with 10 μg cRNA). Calb3 is known to be a gene transcript that shows a moderately high expression in control samples and is stimulated to high expression levels with high doses of EE as shown by Affymetrix GeneChip data (4,700 units in control to 49,400 units in high-dose group with a chip scaling factor of 1,500). Although the fluorescence signal had high specificity (Figure 1) and it was well above the background level (the fluorescence of the microspheres not exposed to cRNA), in the context of a very complex cRNA background the small signal intensity left little room for the detection of gene transcripts that were expressed less abundantly than Calb3.
Signal amplification using biotin-labeled anti-streptavidin antibody.
To amplify the signal from our microspheres assay, we used a biotin-labeled anti-streptavidin antibody amplification system. The signal intensity was amplified, after hybridizing the microspheres with their targets (cRNA), by a second staining with a biotin-labeled anti-streptavidin antibody followed by a second streptavidin-R–phycoerythrin staining. Incorporating the antibody amplification step into the assay, as described in “Materials and Methods,” led to an average increase in signal intensity of approximately 5- to 10-fold (Figure 2). The fluorescent signal corresponding to the transcript levels of Calb3 in control samples increased approximately 4-fold from 68 units to 234 units using 10 μg cRNA, but the fluorescent signal of the sample derived from tissues exposed to EE in the high-dose group (10 μg/kg/day) increased from 980 to 10,632 units by using the amplification step.
To better understand the magnitude of increased sensitivity from the amplification step, we measured the increase in signal obtained with a titration curve of complementary biotinylated oligonucleotide. The saturation concentration is relative to the maximum signal value of the detector in the flow cytometer. In Figure 3A, the titration curve of a biotinylated complementary oligonucleotide for Calb3 is shown for the comparison of the nonamplified and amplified signal. The assay sensitivity increased approximately 10-fold in the amplified assay compared with the nonamplified assay.
Additional refinements to the assay, including changes in the hybridization buffer, the incubation time, and instrument PMT settings, have led to an additional 10-fold increase in sensitivity. Fluorescent signals are now at least 8- to 20-fold above background, which makes data calculations far more reliable (Figure 3B), for relatively low abundance as well as abundant transcripts.
The signal amplification steps do not change the linearity of the relationship between the amount of target quantified and the magnitude of the signal regardless of the complexity of the sample or the level of multiplexing (Figure 4A). The signal amplification step allows the simultaneous quantification of multiple analytes in the linear range from 1 up to 1,000 amol. In addition Figure 4B demonstrates that a titration of cRNA from rat uterus/ovaries (1, 2.5, 5, or 10 μg total) has no effect on the sensitivity of the assay for the control biotinylated complementary oligonucleotide for M13. As can be seen in Figure 4B, even in the context of the whole population of labeled uterine/ovarian cRNA of the rat, the amplification of the signal does not modify the specificity or the sensitivity of the assay
Gene expression changes induced by EE in the uterus/ovaries of the prepubertal rat: microarray versus microsphere-based approach.
To evaluate the overall performance of the multiplex assay, the expression levels of the 20 selected genes were evaluated under the optimized conditions. After hybridization and amplification of the signal, the expression levels of the 20 genes were measured simultaneously. Replicate measurements from five independent biological samples (biological replicas) from each dose group (0, 0.1, 1, and 10 μg EE/kg/day; control, low, mid, and high dose, respectively) were made. The reproducibility of the assay is represented in Figure 5 with the determination of the expression levels of Calb3, an up-regulated gene, and Cyp17a1, a down-regulated gene, in independent samples. The comparison of the signal values (arbitrary fluorescent units) obtained from the microarray analysis (Affymetrix) versus the ones obtained from the optimized xMAP assay for four representative transcripts is shown in Table 2. It is clear that there is a high concordance on the hybridization signal values between the two methods, which results in a one-to-one correspondence in the gene expression changes determined by microarray analysis and by the xMAP assay. In both cases, there is a clear dose response for genes that are up-regulated (C3, Scya11, and Ehhadh) or down-regulated (Star) by EE exposure. Further, the transcripts that are found at relatively low copy number, up-regulated genes in control samples or down-regulated in high-dose–treated samples, or at a high-copy-number, down-regulated genes in controls or up-regulated genes in EE-treated samples, can be quantified unambiguously.
The results from the evaluation of the 17 target transcripts representing the product of genes whose transcription is regulated by estrogens, as well as two genes not regulated by estrogens (internal controls: vascular α-actin and cyclophilin B), from the uterus/ovaries of the immature rats exposed to 10 μg EE/kg/day (high dose) are shown in Table 3. The Affymetrix microarray data for the indicated genes, and many others, have already been published (Naciff et al. 2003). The relative expression level was determined by calculating the ratio of high-dose EE-treated to control-treated samples. Some experiments (run 1, multiple quantifications in independent samples) were performed at Radix Biosolutions, where the hybridization signal was quantified using a Luminex 100 SP1 software, version 1.7.69. Replicate experiments (run 2) of the same samples were performed at Procter & Gamble, where the hybridization signal was quantified on a Bio-Rad Bio-Plex (Bio-Rad’s version of the Luminex 100) using the Bio-Plex Manager software, version 2.0 . For most of the analytes, the fluorescent signals were highly reproducible, particularly when the analyte represented low or moderately abundant transcripts (Table 3). However, there were some discrepancies with the quantitation of transcripts with high copy number, such as C3 (complement component 3). The samples read on the Bio-Plex instrument were taken at the high PMT setting to enhance the instrument’s overall sensitivity; however, this setting causes the PMT to reach signal saturation more readily. Thus, for highly expressed genes such as C3, signal saturation on the Bio-Plex instrument resulted in discordant data between the replicate experiments. Changing the PMT gain setting of the Bio-Plex (or the Luminex) instrument and rereading the same samples allowed the determination of analytes present in the samples at relatively high copy numbers and did not modify the quantitation of the other analytes (Table 3). A second reading of the same samples shows only a 10% (± 3%) loss of the signal intensity by photobleaching of the reporter (data not shown). Thus, reading the hybridization signal of the same test samples at two PMT settings (low and then high) results in the expansion of the dynamic range of the assay without losing sensitivity.
Discussion
We have developed a high-throughput gene expression profiling assay using fluorescent labeled microspheres coupled with gene-specific oligonucleotides to evaluate the effects of estrogen exposure in the prepubertal rat. Substantial improvements on existing bead-based assays have been made that result in an assay that is highly correlated in relative gene expression changes determined by established microarray technologies. These improvements include an anti-streptavidin amplification method that produces a 10-fold increase as well as changes to the hybridization buffer, assay kinetics, and hybridization temperature. Using these improvements, a detection limit of 1 amol of complementary RNA has been achieved, allowing the analysis of rare messages in complex cRNA samples using as little as 2.5 μg starting material. This assay offers increased throughput with decreased costs compared with existing microarray technologies, allowing the determination of gene expression changes specifically, rapidly, and with high sensitivity and resolution. This system is a rapid multiplexed assay platform that quantifies up to 100 distinct analytes simultaneously in a single sample in a 96-well plate format. The microsphere-based assay format provides the flexibility to rapidly customize the specific genes being examined in an assay simply by adding or removing individual bead sets from the assay’s mix. Our data demonstrate that this approach can be used to create a high-throughput screening assay to identify potential gene expression changes induced by a given treatment, such as the one used in this study to identify chemicals with estrogenic activity. The multiplexing capability offers the opportunity to screen large numbers of chemicals to determine their potential therapeutic and/or toxic properties, in a cost- and animal-effective manner using a customized set of genes. The complete optimized protocol is described in Table 4. The schematic representation of the microsphere-based high-throughput gene expression assay is shown in Figure 6.
The method presented in this article could be used in a variety of applications related to assaying for hybridization of a target polynucleotide to an oligonucleotide probe and amplification of a signal generated thereby. It can be used as a first- or second-tier assay to extract data of biomedical relevance for a wide range of applications. For example, it can be adapted to evaluate specific gene expression changes used as “biomarkers” to identify desired (therapeutic) versus undesired (toxic) outcomes in the identification of different pathogens present as potential contaminants of food stuffs, drinking water or even in the air, or in the determination of a pathological state (e.g., see Bau et al. 2002; Rhodes et al. 2004; Zhang et al. 2002).
Methods for the detection of specific transcripts have advanced at a relative fast pace. However, they have some drawbacks, including high background noise, extended time and labor requirements, lack of specificity, lack of sensitivity, and a high entry and operation cost. The method we have developed offers a cost-efficient system that demonstrates low background noise, high specificity and sensitivity, and after cRNA labeling can be completed in as little as 4 hr. In the available literature, there are some approaches to identify specific gene expression changes; however, they have some limitations overcome by the assay here described. Particularly, Yang et al. (2001) reported the use of microspheres linked to a capture probe that has sequence complementary to a first segment of a sequence of a single-strand target nucleic acid. However, this method is not as sensitive and can detect only relatively abundant transcripts. On a different approach, Fuja et al. (2004) described a four-plex assay to determine the expression of four genes using carboxyl–polystyrene particles of four sizes. The primary limitation with this approach is the limited number of different-sized microspheres to expand the assay to greater than four transcripts in a single reaction. With a different approach Georgieva et al. (2002) described a method based on magnetic-activated cell separation followed by a nested reverse-transcriptase–PCR to quantify tyrosinase and MART-1 mRNA. The data from these authors indicated a detection rate comparable to that of other established total RNA extraction methods. However, not all the results were concordant, and the assay does not seem to be very reliable. On a theoretical basis Hug and Schuler (2003) proposed a method to calculate the number of molecules of a single mRNA species in a complex mRNA preparation by cloning tagged nucleic acid molecules onto the surface of microbeads and amplifying them, followed by their quantification. However, to our knowledge this method has not been implemented. For another application of microsphere-based assay, Spiro and Lowe (2002) developed a procedure for multiplexed quantification of PCR products using the bead method in a manner that can lead to high-throughput testing, to determine microorganisms from contaminated groundwater. The major limitations of their approach are the amount of starting material they could quantify and the requirement for PCR amplification.
The assay we have developed overcomes the various limitations found with other methods and is highly flexible compared with current methods. In addition the duration of the assay hybridization step can vary from a minimum of 1 hr up to 18 hr. Because hybridization is between single-strand cRNA and single-strand oligonucleotide capture probes, increasing the hybridization time up to 18 hr can double the sensitivity of the assay. The enhancement of sensitivity described here permits the amount of input material (cRNA) to be as little as 1 μg in a complex mixture of cRNAs or polynucleotides such as total RNA, or mRNAs, according to the desired application. The oligonucleotide(s) used to create the assay may be preoptimized, as we have done, or the multiplexing capability of the system allows the optimized oligonucleotide to be selected empirically. For example, one or more oligonucleotides specific for the same gene may be coupled to different microsphere sets, combined, and subjected to a hybridization-based assay to determine the best signal (specificity and sensitivity) for the desired set of analytes.
Although in this study the amplified signal is detected using a flow cytometer, other means to detect the amplified signal are suitable and within the scope of the present method. The Luminex-based analyzers use standard 96-well plate formats, thereby providing this assay platform with higher sample throughput capabilities than other standard array formats. In addition, the use of the standard microtiter plate format makes the system amenable to automation.
In summary, we have developed an assay to quantify specific gene expression changes in a complex background with high sensitivity and specificity. We have made several improvements on existing bead-based assays that provide results that highly correlate with standard microarray technologies. We have achieved detection levels down to 1 amol (10−18 mol), detecting rare messages in complex cRNA samples, using as little as 2.5 μg. This assay offers sensitivity greater than that achieved by Affymetrix GeneChip microarrays with identical sample preparations. The assay can analyze up to 100 analytes simultaneously (validated with 20), is highly flexible (add/subtract any given analyte by adding or removing specific microsphere sets), and offers significant time savings over QRT–PCR and it has high throughput capabilities on a standard 96-well format (scaleable to 384-well format).
We thank M. Aardema and F. Gerberick for critical comments on the manuscript.
Figure 1 Sensitivity and specificity of the assay. To determine the specificity and sensitivity of the assays, a series of multiplexed hybridization reactions were performed. Four different biotinylated complementary oligonucleotides (Calb3, M13, Cyp17a1, and Ppib) were hybridized at different concentrations (0, 0.1, 0.316, 1, 3.16, 10, 31.6, 100 fmol) to eight different sequence-specific microsphere sets. The data demonstrate undetectable cross-hybridization. Calb3, intestinal calcium-binding protein, or calbindin 3; Cyp17a1, 17α-hydroxylase cytochrome P450; Ppib, cyclophylin B or peptidylprolyl isomerase B.
Figure 2 Comparison of the quantification of Calb3 expression with and without signal amplification. Biotinylated cRNA samples from control (C) vehicle-treated or EE-exposed tissues (0.1, 1.0, or 10.0 μg/kg/day; L, M, and H, respectively) were hybridized to microspheres coupled to the specific probe for Calb3, and the fluorescent intensity of at least 100 microspheres was determined (nonamplified). To increase the signal value, two equivalent sets of samples were hybridized to the specific Calb3 microspheres, washed, and then hybridization signal was amplified as indicated in ”Materials and Methods.” A clear dose response to EE exposure can be determined in both cases; however, the amplification step increases the sensitivity of the assay.
Figure 3 Example of the optimization of the hybridization assay for the quantification of Calb3. (A) Antisense oligonucleotide for Calb3, at various concentrations (1, 10, 100, 1000, 10,000, and 100,000 amol/sample), was hybridized to the specific Calb3 microspheres coupled to the specific probe for Calb3, and the mean fluorescence intensity of at least 100 microspheres was determined (standard). To increase the signal value, equivalent samples of antisense oligonucleotide for Calb3 were hybridized to the specific Calb3 microspheres and washed, and then hybridization signal was amplified as indicated in methods (amplified or base). (B) The optimization of the hybridization conditions, as indicated in “Materials and Methods,” resulted in a greater increase in sensitivity of the assay (optimized).
Figure 4 Specificity and dynamic range of detection with amplification. Signal amplification does not change the linearity of the relationship between the amount of target quantified and the magnitude of the signal, even when the analyte is found within a complex mixture of cRNA potential targets. (A) Antisense oligonucleotide for Calb3, Cyp17a1, Ppib, and M13 at various concentrations (1, 10, 100, 1000, 10,000, and 100,000 amol/sample) were mixed and hybridized to the specific Calb3-, Cyp17a1-, Ppib-, and M13-specific microspheres in a four-plex format, and the mean fluorescence intensity of at least 100 microspheres was determined after the hybridization signal was amplified as indicated in methods. (B) The indicated amounts of M13 antisense oligonucleotide were mixed with fragmentation buffer or with various amounts of a highly complex cRNA sample, and then hybridized with M13-specific microspheres.
Figure 5 Reproducibility of the assay, exemplified with the determination of the expression levels of (A) Calb3 (up-regulated gene) and (B) Cyp17a1 (down-regulated gene) in five independent samples (biologic replicas), in two independent experiments (Rep. 1 and Rep. 2) with the complete set of samples. Ten micrograms of biotinylated cRNA from control vehicle-treated (control) or EE-exposed tissues (0.1, 1.0, or 10.0 μg/kg/day; low, medium, and high, respectively) was hybridized to microspheres in a five-plex format (with the specific sets for Calb3, Cyp17a1, Ppib, Hsd11b2, and M13) under the optimized protocol as indicated in “Materials and Methods.” Duplicate results and the means from five samples for each treatment regimen are shown for Calb3 and Cyp17a1. The transcripts are identified in Table 1.
Figure 6 Schematic representation of the microsphere-based high-throughput gene expression assay. Fluorescent microspheres with covalently bound oligonucleotides specific to the cRNA to be quantified are incubated with cRNA and biotin (b) labeled. Hybridized cRNA is revealed by SAPE [phycoerythrin (PE)-conjugated streptavidin (SA)]. The signal is amplified by a second stain using the biotinylated anti-streptavidin, followed by a third staining step with SAPE.
Table 1 Genes selected for the bead-based high-throughput gene expression assay (20-plex format).
Accession no. Gene name Gene symbol Probe sequence
AB006007 steroidogenic acute regulatory protein Star 5′-ACGTGGCTGCTCAGTATTGACCTCA-3′
AF022147 uterus-ovary specific putative transmembrane protein or CUB and zona pellucida-like domains 1 Cuzd1 5′-CGTCATGCTCGTATCACAGCCTCAG-3′
K03249 peroxisomal enoyl-CoA-hydrotase-3-hydroxyacyl-CoA Ehhadh 5′-TGGATCTGTAACACATTGAGTTCAA-3′
L00191 fibronectin 1 Fn1 5′-TGGCCACACCTACAACCAGTATACA-3′
L11007 cyclin-dependent kinase 4 Cdk4 5′-TGGAGTGTTGGCTGTATCTTCGCAG-3′
L26292 FSH-regulated protein or Kruppel-like factor 4 Klf4 5′-TTTGTCTTCCGATCTACATTTATGA-3′
M14656 osteopontin or secreted phosphoprotein 1 Spp1 5′-AGAGAGTTCATCTTCTGAGGTCAAT-3′
M29866 complement component C3 C3 5′-GTCAAGGTCTACTCCTACTACAATC-3′
M31837 insulin-like growth factor-binding protein 3 Igfbp3 5′-TACAGAGCTTTCCTTGAGAGCACAA-3′
M57664 cretine kinase-B Ckb 5′-GTTTTTGATGTCTCCAACGCTGACC-3′
M86389 heat shock protein 27 Hspb1 5′-ATGAGTGGTCTCAGTGGTTCAGCTC-3′
X82396 cathepsin B Ctsb 5′-GCTGCACCTTGAAGCTAGTCACTTC-3′
Y08358 eotaxin or small inducible cytokine subfamily A11 Scya11 5′-GAAATAGGGTCTCACTGTATCACCC-3′
X06801 vascular alpha-actin VaACTIN 5′-TACTGCTGAGCGTGAGATCGTCCGT-3′
S64044 progesterone receptor Pgr 5′-TTTGCACCTGATCTAATCCTGAATG-3′
V00604 bacteriophage M13 M13 5′-AAGCAACCATAGTACGCGCCCTGTA-3′
K00994 intestinal calcium-binding protein or calbindin 3 Calb3 5′-CGACACCACCTACTGATTGAATCCT-3′
M21208 cytochrome P450, family 17, subfamily a, polypeptide 1 Cyp17a1 5′-CTCAACACCCACAGTACAATCTTAG-3′
U22424 11-beta-hydroxylsteroid dehydrogenase type 2 Hsd11b2 5′-TCATGAGACCATGTATACCCTACCA-3′
AF071225 cyclophilin B or peptidylprolyl isomerase B Ppib 5′-AGCAAGTTCCATCGTGTCATCAAGG-3′
Gene annotations are from GenBank (http://www.ncbi.nih.gov/GenBank) with the exception of VaACTIN and M13.
Table 4 Optimized protocol.
For duplicate wells
2.5 μg cRNA target per well
10 μL stock cRNA (0.5 μg/μL)
40 μL 1/2 × TMAC hybridization buffer containing 100 amol M13 oligo hybridization control spike
Microsphere mixture: 800 μL (enough for 20 samples)
2 μL each bead stock used in 20-plex (stock = 107 microspheres/mL)
760 μL 1/2 × TMAC hybridization buffer
Procedure
1. Add 25 μL diluted cRNA target.
2. Add 25 μL bead mix to each well according to step 1 above.
3. Incubate at 95°C for 2 min.
4. Transfer plate to thermo mixer, cover, and hybridize 3 hr up to overnight at 48°C while shaking at 500 rpm.
5. Spin samples in centrifuge at 2,250 × g for 2 min; flick and tap off solution.
6. Wash microspheres with 100 μL 1/2 × TMAC.
7. Spin samples in centrifuge at 2250 × g for 2 min; flick and tap off solution.
8. Wash microspheres with 100 μL PBS–BSA.
9. Spin samples in centrifuge at 2,250 × g for 2 min; flick and tap off solution.
10. Add 50 μL SAPE stain mix; shake at 500 rpm at 25°C for 15 min.
11. Spin samples in centrifuge at 2,250 × g for 2 min; flick and tap off solution.
12. Wash microspheres with 100 μL PBS–BSA.
13. Spin samples in centrifuge at 2250 × g for 2 min; flick and tap off solution.
14. Add 50 μL anti-streptavidin and goat IgG; shake at 500 rpm at 25°C for 60 min.
15. Spin samples in centrifuge at 2,250 × g for 2 min; flick and tap off solution.
16. Wash microspheres with 100 μL PBS–BSA.
17. Spin samples in centrifuge at 2,250 × g for 2 min; flick and tap off solution.
18. Add 50 μL SAPE; shake at 500 rpm at 25°C for 15 min.
19. Spin samples in centrifuge at 2,250 × g for 2 min; flick and tap off solution.
20. Wash microspheres with 100 μL PBS–BSA.
21. Spin samples in centrifuge at 2,250 × g for 2 min; flick and tap off solution.
22. Resuspend in 65 μL PBS–BSA and read on low- and then high-PMT settings of the instrument.
Table 2 Signal values (arbitrary fluorescent units) obtained from the microarray (Affymetrix) versus the xMAP.
Microarray
xMAP
Gene transcript Control 0.5 μg EE 1 μg EE 10 μg EE Control 0.5 μg EE 1 μg EE 10 μg EE
Star 2,558 2,626 370 (A) 296 (A) 1,169 1,366 384 397
Ehhadh 210 (A) 325 (A) 1,294 1,267 181 210 1,147 1,027
Scya11 293 (A) 855 (M) 4,542 10,419 843 1,296 5,039 10,210
C3 74 (A) 310 (A) 39,138 32,320 779 1,169 27,547 26,761
The average signal value of the indicated transcripts obtained from the uterus/ovaries from five females exposed to vehicle control or the indicated doses of EE for 4 days (μg EE/kg/day) as indicated in Naciff et al. (2003). Transcripts for which an absent (A) or marginal (M) call was determined (MAS 5.0, Affymetrix) in all the samples (n = 5) are noted even though there is a signal value.
Table 3 Relative expression levela of selected genes determined by microarray compared with xMAP technology.
Gene expression average fold change (n = 5)
Gene symbol Affymetrix microarrayb xMAP run 1 (high PMT) xMAP run 2 (high PMT) xMAP run 2 (low PMT)
VaACTIN 1.0 1.0 1.0 1.0
Ppib 1.0 1.1 1.1 1.1
Hsd11b2 4.0 3.3 4.2 3.9
Ctsb 2.6 3.8 3.2 3.6
Ckb 2.6 3.5 2.8 3.4
C3 295.0 144.0 36.2 100.2
Ehhadh 6.0 3.9 6.5 5.5
Scya11 35.5 14.4 12.7 14.6
Hspb1 2.1 2.2 2.0 2.1
Pgr 2.1 1.8 1.8 1.9
Calb3 10.5 46.5 10.7 35.2
Fn1 2.6 3.4 2.7 3.2
Cuzd1 12.9 38.3 16.7 38.2
Klf4 3.3 2.4 2.4 2.4
Star −8.7 −3.2 −3.1 −2.6
Igfbp3 −4.4 −2.5 −2.8 −2.6
Cyp17a1 −15.1 −6.1 −4.9 −4.8
Spp1 −3.5 −2.3 −2.3 −2.0
Cdk4 −1.6 −1.2 −1.2 −1.2
a The relative expression level is represented by the ratio of high-dose EE-treated to control-treated samples. Run 1 was done at Radix Biosolutions (read on a Luminex 100 flow cytometer using SP1 software, version 1.7), and run 2 was done at Procter & Gamble (read on the Bio-Rad Bio-Plex using the Bio-Plex Manager software, version 2.0).
b The Affymetrix microarray data for the indicated genes, and many others, have been published (Naciff et al. 2003).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7561ehp0113-00117216140623ResearchMicrowaves from GSM Mobile Telephones Affect 53BP1 and γ-H2AX Foci in Human Lymphocytes from Hypersensitive and Healthy Persons Markovà Eva 12Hillert Lena 34Malmgren Lars 5Persson Bertil R. R. 6Belyaev Igor Y. 171 Department of Genetics, Microbiology and Toxicology, Stockholm University, Stockholm, Sweden2 Laboratory of Molecular Genetics, Cancer Research Institute, Bratislava, Slovak Republic3 Occupational and Environmental Health, Stockholm County Council, Stockholm, Sweden4 Department of Public Health Sciences, Division of Occupational Medicine, Karolinska Institutet, Stockholm, Sweden5 MAX-lab, Lund University, Lund, Sweden6 Department of Medical Radiation Physics, Lund University Hospital, Lund, Sweden7 Laboratory of Radiobiology, General Physics Institute, Russian Academy of Science, Moscow, RussiaAddress correspondence to I.Y. Belyaev, Department of Genetics, Microbiology and Toxicology, Stockholm University, S-106 91 Stockholm, Sweden. Telephone: 46-8-16-41-08. Fax: 46-8-16-43-15. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 28 4 2005 113 9 1172 1177 9 9 2004 28 4 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The data on biologic effects of nonthermal microwaves (MWs) from mobile telephones are diverse, and these effects are presently ignored by safety standards of the International Commission for Non-Ionizing Radiation Protection (ICNIRP). In the present study, we investigated effects of MWs of Global System for Mobile Communication (GSM) at different carrier frequencies on human lymphocytes from healthy persons and from persons reporting hypersensitivity to electromagnetic fields (EMFs). We measured the changes in chromatin conformation, which are indicative of stress response and genotoxic effects, by the method of anomalous viscosity time dependence, and we analyzed tumor suppressor p53-binding protein 1 (53BP1) and phosphorylated histone H2AX (γ-H2AX), which have been shown to colocalize in distinct foci with DNA double-strand breaks (DSBs), using immunofluorescence confocal laser microscopy. We found that MWs from GSM mobile telephones affect chromatin conformation and 53BP1/γ-H2AX foci similar to heat shock. For the first time, we report here that effects of MWs from mobile telephones on human lymphocytes are dependent on carrier frequency. On average, the same response was observed in lymphocytes from hypersensitive and healthy subjects.
53BP1 and γ-H2AX focichromatinDNA double-strand breakshypersensitivity to electromagnetic fieldsstress response
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The growing public concerns about possible effects of microwave (MW) exposure from mobile telephones have been discussed in many countries because of increasing use of wireless communication systems. Two groups have reported that increased incidence of brain tumors and acoustic neuroma is correlated with exposure to mobile telephone MWs depending on duration of mobile telephone use (Hardell et al. 2003; Lonn et al. 2004). Negative findings were also reported by other groups, but the results of these epidemiologic studies are not directly comparable because of methodologic and other differences, as has recently been reviewed (Kundi et al. 2004). The intensity levels of exposure to MWs from mobile telephones are lower than the standards adopted by the International Commission for Non-Ionizing Radiation Protection (ICNIRP 1998). These standards are based on the thermal effects of MWs resulting in heating of exposed tissues or cells. There is also evidence for nonthermal effects of MWs that suggests a possible relationship between nonthermal MW exposure and both permeability of the brain–blood barrier (Persson et al. 1997) and stress response (de Pomerai et al. 2000). Recent studies have described neuronal damage in the brains of exposed rats (Ilhan et al. 2004; Salford et al. 2003). In other studies, no effects of nonthermal MWs were observed (Meltz 2003). However, experimental data suggested that MW effects occur only under specific parameters of exposure, depending on several physical parameters and biologic variables (Adey 1999; Belyaev et al. 2000; Binhi 2002; Blackman et al. 1989). Dependence of the MW effects on several physical parameters, including frequency, polarization, and modulation, and also several biologic variables could explain various outcomes of studies with nonthermal MWs (Belyaev et al. 2000).
MWs under specific conditions of exposure induce DNA strand breaks in rat brain cells as measured by single-cell electrophoresis (Lai and Singh 1996, 1997). The mechanisms of this effect are not understood, but they could be related to induced changes in the interaction of DNA with proteins, rather than DNA damage (Belyaev et al. 1999).
Several proteins, such as the tumor suppressor p53-binding protein 1 (53BP1) and phosphorylated H2AX (γ-H2AX), have been shown to produce discrete intranuclear foci, which are believed to colocalize with DNA double-strand breaks (DSBs) providing a scaffold structure for DSB repair (DiTullio et al. 2002; Schultz et al. 2000; Sedelnikova et al. 2002). According to the current model, this scaffold functions by recruiting proteins involved in the repair of DSBs (Fernandez-Capetillo et al. 2002; Iwabuchi et al. 2003; Kao et al. 2003). The scaffold is organized within a megabase-size chromatin domain around an actual DSB regardless of the type of repair that is involved (Paull et al. 2000). In an analysis of the 53BP1 foci in human lymphocytes after exposure to MWs from mobile telephones using the Global System for Mobile Communication (GSM) standard at 915 MHz, we did not find induction of 53BP1 foci (Belyaev et al. 2005). In contrast, we found that MWs similar to heat shock induced significant reduction in the background level of 53BP1 foci (Belyaev et al. 2005). In the present study, we analyzed the γ-H2AX protein in addition to the 53BP1 protein. We also applied the method of anomalous viscosity time dependence (AVTD) that is sensitive to various genotoxic effects (Belyaev et al. 1999, 2001).
So-called hypersensitivity to electromagnetic fields (EMFs) is a fairly new phenomenon, and etiology of the hypersensitivity to EMFs is not yet known. There are several symptoms that people experience in proximity to different sources of EMFs, such as video display terminals of personal computers, electrical appliances, or mobile telephones. The symptoms are not specific for this illness, and there is no known pathophysiologic marker or diagnostic test (Hillert et al. 1999).
There is a substantial lack of knowledge in the biophysical modeling of MW-induced nonthermal biologic effects. Resonance-like interactions of MWs with such targets as cellular membranes, chromosomal DNA, and ions in protein cavities have been proposed (Adey 1999; Belyaev et al. 1992a; Binhi 2002; Ismailov 1987).
Among other dependencies, dependence of nonthermal effects of MWs on frequency has been reported (Belyaev et al. 2000; Pakhomov et al. 1998). In a recent study of nonthermal effects of GSM MWs at various frequencies on the conformation of chromatin in human lymphocytes, Sarimov et al. (2004) found that MWs from GSM mobile telephones affect chromatin conformation in human normal and transformed lymphocytes at specific frequencies, 905 MHz and 915 MHz being most effective. The observed MW effects depended upon the initial state of chromatin as measured before exposure and were similar to stress responses induced by heating (Sarimov et al. 2004). In the present study, we analyzed the effects of MWs at different frequencies on chromatin conformation and 53BP1 and γ-H2AX foci in lymphocytes from healthy and hypersensitive subjects.
Materials and Methods
Subjects and blood samples.
Blood samples from five healthy subjects and five patients reporting hypersensitivity to EMFs were obtained at the Department of Occupational and Environmental Health, Stockholm County Council, Sweden. The group of hypersensitive persons was selected on the basis of self-reported hypersensitivity to EMFs and characterized regarding symptom profile, triggering factors, exposure–time relationships, and avoidance behavior (Hillert et al. 1999). The group reporting hypersensitivity to EMFs consisted of five men 32–60 years of age (Table 1). Control healthy subjects were matched by age (± 5 years) and sex (Table 1). All hypersensitive persons and controls were employed or students. None of the participants were smokers, and no subject was on any regular medication. All hypersensitive subjects reported that their symptoms were triggered by electrical devices that were not sources of light. Four of the participants reported that mobile telephones also triggered symptoms. The fifth subject did not use a mobile telephone and consequently did not know if this exposure triggered symptoms. In all pairs, the hypersensitive person scored higher than the matched control in the questionnaire on symptoms (29 symptoms scored for frequency and severity; maximum score, 232) (Hillert et al. 1998). In all persons reporting hypersensitivity to EMFs, neurovegetative symptoms such as headache, fatigue, and difficulties concentrating were more pronounced than skin symptoms. The mean scores per person for neurovegetative symptoms were 33 in the hypersensitive group and 1.2 in the control group. The corresponding scores for skin symptoms in the face and upper chest were 10 and 0.4, respectively. In all cases of reported hypersensitivity, the subjects reported experiencing symptoms 24 hr after exposure to a reported triggering factor, in most cases within 1 hr. All patients reported that they tried to avoid triggering factors.
Fresh blood samples from hypersensitive persons and from matched controls were coded and all data were analyzed blind. Ethical permissions were obtained from the Ethics Committee of the Karolinska Institutet, Stockholm, Sweden. All subjects volunteered for the study.
Chemicals and reagents.
We obtained reagent grade chemicals from Sigma (St. Louis, MO, USA) and Merck KgaA (Darmstadt, Germany). We purchased double cytoslides coated with polylysine and cytoslide chambers from ThermoShandon (Pittsburg, PA, USA). Anti-53BP1 antibody (monoclonal mouse) was kindly provided by T. Halazonetis (Wistar Institute; University of Pennsylvania, Philadelphia, PA, USA). The antibody recognizes the C-terminal domain of the protein that corresponds to the BRCT (BRCA-1 C-terminal) domains. Anti-γ-H2AX (monoclonal rabbit) was purchased from Trevigen-BioSite (Täby, Sweden).
Cells.
Lymphocytes were isolated from peripheral blood by density gradient centrifugation in Ficoll-Paque (Pharmacia LKB, Uppsala, Sweden) according to the manufacturer’s instructions. The cells were transferred to basal medium [BM: RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS), 2 mM l-glutamine, 50 IU/mL penicillin, and 50 μg/mL streptomycin (ICN Pharmaceuticals, Inc., Costa Mesa, CA, USA)] and incubated at 5% CO2 and 37°C in a humidified incubator. Adherent monocytes were removed by overnight incubation of the cell suspension in Falcon culture flasks (Becton Dickinson, Franklin Lakes, NJ, USA) at a cell density of 3 × 106 cells/mL in a volume of 10–40 mL. After this incubation, the cells remaining in suspension were collected by centrifugation. The cell density was adjusted to approximately 2 × 106 cells/mL in fresh BM, and the lymphocytes were pre-incubated for 2 hr at 37°C before exposure. The viability of cells was always > 98% as measured with a trypan blue exclusion assay.
Cell exposure.
In five independent experiments, coded samples from hypersensitive subjects and matched control subjects were exposed simultaneously. All exposures were performed for 1 hr at 37°C in a humidified CO2 incubator, in 14 mL round-bottom tubes (Falcon), 2.5 mL cell suspension per tube, 2 × 106 cells/mL. Lymphocytes were exposed to MWs using a GSM900 test mobile telephone (model GF337; Ericsson, Stockholm, Sweden) as previously described (Belyaev et al. 2005; Sarimov et al. 2004). Briefly, the output of the telephone was connected by the coaxial cable to a transverse electromagnetic transmission line (TEM) cell. The 124 different channels/frequencies that are used in GSM900 mobile communication differ by 0.2 MHz in the frequency range between 890 and 915 MHz. We used channels 74 and 124 with frequencies of 905 and 915 MHz, respectively. The signal included standard GSM modulations. No voice modulation was applied, and discontinuous transmission mode was off during all exposures. GSM signal is produced as 577 μsec pulses (time slots), with an interpulse waiting time of 4,039 μsec (seven time slots). The power was kept constant during exposures, at 2 W (33dBm > 1 mW) in pulse, as monitored online using a power meter (Bird 43; Bird Electronic Corporation, Cleveland, OH, USA). The specific absorption rate (SAR) was determined by measurement and calculation. We measured transmitted and reflected power using a power meter (Hewlett-Packard 435A; Hewlett-Packard Company, Palo Alto, CA, USA) and a coaxial directional coupler (Narda 3001-20; Narda, Hauppage, NY, USA). A signal generator (Agilent 8648C; Agilent, Santa Rosa, CA, USA) connected to a power amplifier (Mini-circuits ZHL-2-8-N; Mini-circuits, Brooklyn, NY, USA) was used. The SAR, calculated from the absorbed power and the mass of the sample, was 37 mW/kg. Good correlation between these measurements and calculations using the finite different time domain (FDTD) method has been confirmed (Sarimov et al. 2004). Because of nonequal distribution of SAR through the exposed volume, the minimal and maximal FDTD-derived SARs were 2.5-fold lower and 3.3-fold higher, respectively, compared with the mean values. All these SAR values were well below thermal effects. Temperature was measured in the MW-exposed samples before and after exposure with a precision of 0.1°C. No changes in temperature were induced during exposures.
At the place of exposure, static magnetic field was 18 ± 2 μT as measured by means of a magnetometer (Sam3, Dowty Electronics Ltd., Cannock, UK) and background extremely low-frequency magnetic field was not more than 200 nT, root mean square, as measured with a three-dimensional microteslameter (Field dosimeter 3, Combinova, Bromma, Sweden).
In each experiment, the cells from the same blood samples were exposed in the same TEM cell to MWs at 915 MHz and 905 MHz and sham-exposed with MWs off. The cells were exposed in sequence, and the order of exposure was randomized. Heat treatment in a water bath, at 41°C and 43°C, was used as a positive control for stress responses. As a positive control for genotoxic effect, the cells were irradiated with 137Cs γ-rays, at 3 Gy, using a Gammacell 1000 source (Atomic Energy of Canada Limited, Ottawa, Canada). The dose rate was 10.6 Gy/min.
AVTD measurements.
We studied the conformation of chromatin by the method of AVTD. Cell lysis was performed immediately after exposure as previously described (Belyaev et al. 1999). Briefly, lymphocytes were lysed in polyallomer centrifuge tubes (14 mm; Beckman, Fullerton, CA, USA) by addition of 3.1 mL lysis solution (0.25 M Na2EDTA, 2% wt/vol sarcosyl, 10 mM Tris-base, pH 7.4) to 0.1 mL cell suspension. The lysates were prepared in triplicate and kept at 23°C for 4 hr in darkness before AVTD measurements. The AVTDs were measured at a shear rate of 5.6/sec and shear stress of 0.007 N/m2. Normalized relative viscosity (NRV) was used to characterize condensation of chromatin (Belyaev et al. 1999).
Immunostaining and foci analysis.
Immediately after exposure, the cells were placed on ice for 1 hr to prevent the repair of eventual DSBs. Cytoslide samples were prepared by using cytospin centrifugation according to the manufacturer’s instructions (ThermoShandon, Pittsburgh, PA, USA). The immunostaining was performed according to Schultz et al. (2000), with some modifications. Cells were fixed in cold 3% paraformaldehyde in phosphate-buffered saline (PBS; pH 7.4), permeabilized with cold 0.2% Triton X-100 in PBS (for 15 and 10 min, respectively), stained with primary antibody 53BP1 (1:20) and γ-H2AX (1:100) prepared in 2% FBS in PBS for 1 hr, followed by three washes in cold PBS, and incubated for 1 hr with secondary Hexo goat anti-mouse IgG (H + L) antibody conjugated with Alexa fluor 488 (Molecular Probes, Inc., Eugene, OR, USA) together with Zymax goat anti-rabbit IgG Cy3 conjugate (Zymed, San Francisco, CA, USA), both in 2% FBS and in 1:200 dilution, followed by three washes in cold PBS. After 20 min DNA staining in ToPro-3-iodide (Molecular Probes; 10 μM in PBS, prepared from 1 mM stock solution in dimethyl sulfoxide) and 5 min of washing in PBS, cytoslides were mounted with equilibration solution and antifade reagent (SlowFade Light Antifade Kit; Molecular Probes) and sealed with coverslips. The images were recorded using a confocal laser scanning microscope Zeiss Axiovert 100M (Carl Zeiss Microscopy, Jena, Germany) from 5–10 fields of vision that were randomly selected from two slides per treatment condition. Through focus, maximum projection images were acquired from optical sections 1.00 μm apart and with a section thickness of 2.00 μm in the z-axis. Resolutions in x- and y-axes were 0.20 μm. Seven optical sections were usually acquired for each field of vision, and a final image was obtained by projection of all sections onto one plane. The foci were counted in the cells from these final images using LSM 510 software (Carl Zeiss Microscopy). For each experimental condition, we analyzed 300–600 cells. All images were analyzed blind regarding exposure parameters.
Statistical analysis.
We set the statistical power to 0.80 based on previously obtained data on effects of GSM MWs on human lymphocytes (Belyaev et al. 2005; Sarimov et al. 2004). We analyzed data using the Mann-Whitney U-test, Kruskal-Wallis test, or the Wilcoxon matched-pairs signed-rank test. A correlation analysis was performed using Spearman rank order correlation test. Results were considered as significantly different at p < 0.05.
Results
Viability.
The viability of unexposed cells as measured by the trypan blue exclusion assay varied between normal and hypersensitive subjects in the range of 0.01–2%. We found no statistical difference in the levels of viability between these groups.
Chromatin conformation.
We observed a statistically significant decrease in AVTDs corresponding to chromatin condensation in cells of 5 subjects (subjects 301, 302, 406, 606, and 607; Table 2) of 10 at the frequency of 915 MHz (p < 0.05, Mann-Whitney U-test; Table 2). In contrast, only in cells from subject 403, we observed a significant increase in AVTDs that corresponds to decondensation of chromatin after 915 MHz. MWs at 905 MHz resulted in either significant condensation (subject 607), or decondensation (subject 403), or no effects (Table 2). These data suggested that effects of MWs might be frequency dependent and that differing responses might be observed in cells from different individuals. Similar interindividual variability was observed in response to the heat shock, especially at 43°C, where two subjects responded by condensation (subjects 406 and 707) and two by decondensation (subjects 302 and 403). We found no statistically significant differences between the effects on chromatin conformation in cells from controls and hypersensitive groups as measured after either MW exposures or heat shock (p > 0.05, Wilcoxon matched-pairs signed-rank test). The data pooled from all subjects, normal and hypersensitive, were analyzed for each treatment condition. The analysis of these pooled data showed a statistically significant effect of MW exposure at 915 MHz (p < 0.0223, Mann-Whitney U-test).
Immunostaining.
Our 53BP1/γ-H2AX foci analysis included a positive control with 3 Gy γ-rays. We observed a significant increase in the number of foci 1 hr after irradiation (data not shown). In contrast, neither cells from control subjects nor cells from hypersensitive subjects responded to 915 MHz by induction of foci (Table 3). We observed a distinct MW-induced reduction in the level of 53BP1/γ-H2AX foci in cells from both control and hypersensitive subjects in response to 915 MHz (Figure 1, Tables 3 and 4). Very similar reductions in 53BP1/γ-H2AX foci were observed in lymphocytes from control and hypersensitive subjects in response to heat shock at 41°C and 43°C (Tables 3 and 4, Figure 2A,B). The response to 905 MHz was not consistent among subjects, and either increase, decrease, or no effect was observed in the number of foci, dependent on the subject (Tables 3 and 4).
For each subject, we verified the hypothesis that MW exposure affects formation of 53BP1 and γ-H2AX foci. For this purpose, we compared effects of MW exposures with sham (multiple comparisons of sham, 905 MHz, and 915 MHz) using Kruskal-Wallis analysis of variance (ANOVA) by ranks. This comparison showed that MWs affected both 53BP1 and γ-H2AX foci in cells from each tested person (Table 5).
We next verified the hypothesis that the effect of MW exposure was frequency dependent by comparing MW effects at 905 MHz and 915 MHz for cells from each subject by the Mann-Whitney U-test. This comparison showed that MW effects on 53BP1 foci depended on frequency in cells from nine subjects (all except subject 606), and effects on γ-H2AX foci depended on frequency in cells from six subjects (all except subjects 302, 406, 607, and 708) (Table 6).
Under identical conditions of treatment, the numbers of 53BP1 and γ-H2AX foci were not significantly different between cells from matched controls and hypersensitive subjects compared using the Wilcoxon matched-pairs signed-rank test. Therefore, we pooled the data from all experiments with cells from control and hypersensitive subjects. Statistical analysis of these pooled data showed that 915 MHz exposure significantly reduced the number of 53BP1 and γ-H2AX foci in human lymphocytes (Tables 3 and 4). Despite the fact that no heating was induced by MW exposure, the reduction in the number of 53BP1 and γ-H2AX foci was larger than after heat shock at 41°C (Tables 3 and 4). In the case of γ-H2AX foci, this reduction was even larger than after heat shock at 43°C (Table 4). Importantly, the pooled effects of MWs were statistically significantly different at 915 MHz and 905 MHz for both 53BP1 and γ-H2AX foci (p < 0.0125 and p < 0.0357, respectively, Wilcoxon matched-pairs signed-rank test).
For all treatment conditions, a correlation between 53BP1 and γ-H2AX foci was observed (R = 0.64, p < 0.00001, Spearman rank-order correlations test). However, most of the 53BP1 and γ-H2AX foci did not colocalize, and the colocalization did not exceed 7%.
Discussion
It has been previously shown that nonthermal MWs affected conformation of chromatin in Escherichia coli cells, rat thymocytes, and human lymphocytes under specific conditions of exposure (Belyaev et al. 1992b, 2000, 2002; Sarimov et al. 2004). Usually, in human lymphocytes, the AVTDs decreased transiently after exposure to nonthermal MWs as opposed to the increase in AVTDs observed immediately after genotoxic impacts, such as ionizing radiation or chemicals (Belyaev et al. 1999, 2001; Sarimov et al. 2004). Several experimental observations have suggested that the increase in the AVTDs is caused by the relaxation of DNA domains (Belyaev and Harms-Ringdahl 1996). Single-cell gel electrophoresis and halo assay have confirmed this suggestion (Belyaev et al. 1999, 2001). On the other hand, the decrease in AVTDs can be caused by either chromatin condensation or DNA fragmentation (Belyaev et al. 1999, 2001). Because no 53BP1/γ-H2AX foci were produced in response to 915 MHz, the decrease in the normalized maximum relative viscosity induced by the 915 MHz exposures was likely caused by chromatin condensation. Both decrease and increase in AVTDs were induced by heat shock at 41°C and 43°C, depending on the subject (Table 2). In contrast to a previous study (Sarimov et al. 2004) in which cells were exposed to MWs at room temperature, MW exposure was performed at 37°C in the present study. Bearing in mind the previously observed dependence of MW effects on temperature (de Pomerai et al. 2000), the data from these two studies should be compared with care. The AVTD data from both studies show that MWs and heat shock result in either condensation or decondensation of chromatin in human lymphocytes dependent on the subject and the duration and temperature of treatment. We detected no heating in samples exposed to MWs; therefore, the MW effects were not caused by heating.
The analysis of 53BP1/γ-H2AX foci is a more sensitive assay compared with other available techniques to measure DSBs, such as pulsed field gel electrophoresis or neutral comet assay. Using this sensitive technique, we did not find any genotoxic effects of 915 MHz under the specific conditions of exposure employed here. In contrast, this frequency persistently decreased the level of foci. Therefore, in the present study we confirm our previous finding that exposure at 915 MHz reduces 53BP1 foci in a manner similar to heat shock, suggesting that this frequency affects cells in a manner similar to a stress factor (Belyaev et al. 2002, 2005). The duration of exposure was 2 hr in the previous study (Belyaev et al. 2005). In the present study, we show that even shorter exposure, 1 hr, produces a significant reduction in the 53BP1 level.
In contrast to 915 MHz exposures, MWs at 905 MHz could either decrease or increase the number of foci depending on the subject. Does it mean that 905 MHz exposures induce DSBs in those cases in which foci increased? The data obtained here neither exclude nor directly support such a possibility. We should also state that we do not really know the details of the subjects’ physiologic status, and therefore this may be the determining factor.
Frequency-dependent inhibition of DNA repair by nonthermal MWs has previously been found (Belyaev et al. 1992a, 1992b). The novel result of the present study is that both 53BP1 and γ-H2AX foci can be decreased similarly by heat shock and MWs from mobile telephones. We hypothesize that stress-induced chromatin condensation either reduces availability of DNA breaks to enzymes and antibodies or disrupts DNA repair machinery that involves binding of 53BP1/γ-H2AX proteins to DSBs. If repair is affected, according to the second of these hypotheses, the obtained results may have a connection to genotoxicity and cancer.
We show here for the first time that the vast majority of 53BP1 and γ-H2AX foci do not colocalize in either sham-control or MW/heat-shock–treated lymphocytes. The formation of these foci deals with phosphorylation of 53BP1/γ-H2AX proteins (DiTullio et al. 2002; Fernandez-Capetillo et al. 2002). It is thus possible that the observed effects of MW and heat shock at the level of 53BP1/γ-H2AX foci formation was due to a change in phosphorylation. Recent evidence has indicated activation of stress-induced pathways in cultivated cells in response to MWs (Leszczynski et al. 2002). Their article indicated that mobile telephone MWs activate a variety of cellular signal transduction pathways, among them the hsp27/p38MAPK stress response pathway (Leszczynski et al. 2002). Whether activation of stress response pathways relates to apoptosis, brain–blood barrier permeability, or increased cancer in humans remains to be investigated.
The comparison of pooled data obtained with all treatments did not show significant differences between the groups of controls and hypersensitive subjects. This result might be explained by the heterogeneity in groups of hypersensitive and control persons. Even if there is such a difference, it would be masked by the large individual variation between subjects, which was observed in both control and hypersensitive groups. An additional problem may be the lack of any objective criteria for selection of a study group consisting of persons that are truly either insensitive or hypersensitive to EMFs (although this has yet to be proven).
For the first time, the data obtained in the present study clearly show that MWs from GSM mobile telephones affect simultaneously the formation of 53BP1 and γ-H2AX foci in human lymphocytes as function of carrier frequency. This result obtained in lymphocytes from both healthy and hypersensitive persons is of great importance. Such frequency dependence suggests a mechanism that does not deal with thermal heating. Investigation of this mechanism and the molecular targets of the frequency-dependent effects of MWs in the frequency range of mobile communication is a fundamental problem.
Another aspect of this finding is that criteria other than “thermal,” based on SAR and power density in acute exposures, may be needed for accurate safety standards. In particular, these safety standards certainly cannot be based on data obtained at one specific frequency.
Conclusions
Nonthermal MWs from GSM mobile telephones at lower levels than the ICNIRP safety standards affect 53BP1 and γ-H2AX foci and chromatin conformation in human lymphocytes. These effects suggest induction of stress response and/or DNA damage. For the first time, we report that mobile telephone MWs affect 53BP1 and γ-H2AX foci dependent on carrier frequency. We also show that heat shock induces similar responses. The same responses were observed in lymphocytes from healthy subjects and from subjects reporting hypersensitivity to EMFs.
We thank S.D. Smith and M. Harms-Ringdahl for critical reading of the manuscript, T. Halazonetis for donation of 53BP1 antibodies, L.-E. Paulsson and G. Anger for verification of the experimental unit for exposure to microwaves, R. Sarimov for help with statistical analysis, and E. Thunberg for obtaining and coding of blood samples.
The Swedish Council for Working Life and Social Research, the Swedish Animal Welfare Agency, and the Swedish Radiation Protection Authority supported these studies.
Figure 1 Images of fixed human lymphocytes (counterstained blue with ToPro-3-iodide) showing 53BP1 foci (stained green with Alexa fluor 488) and γ-H2AX foci (stained red with Cy3) as revealed by immunostaining and confocal laser microscopy of cells from subject 501. Significantly fewer foci were observed after 1 hr exposure to 915 MHz and heat shock (41°C) than in control cells. Exposure to 905 MHz resulted in a statistically significant increase in the number of 53BP1 foci in cells from this subject (Table 3). Bar = 10 μm.
Figure 2 53BP1 and γ-H2AX foci in human lymphocytes of matched controls (A; n = 5) and hypersensitive subjects (B; n = 5) after exposure to 905 MHz, 915 MHz, and heat shock at 41°C and 43°C, as measured by immunostaining and confocal laser microscopy after 1 hr of treatment. Values shown are mean and SD for amounts of foci per cell from five subjects. Similar reduction of foci level was seen after 915 MHz exposure and after heat shock. Exposure to 905 MHz led to either reduction or induction of foci dependent on subject, resulting in larger SDs for this treatment compared with 915 MHz.
Table 1 Information on hypersensitive male subjects (n = 5) and matched controls (n = 5).
Subject Age (years) Duration of hypersensitivity (years)
301a 32 5
302 33 —
403a 33 2
406 29 —
501a 47 8
502 44 —
606 45 —
607a 45 1
707 59 —
708a 60 2
a Cases of reported hypersensitivity to EMFs.
Table 2 Relative changes in chromatin conformation in response to MWs as analyzed by the AVTD assay immediately after exposure and normalized to sham (NRV).
905 MHz
915 MHz
41°C
43°C
Subject NRV SD p-Value NRV SD p-Value NRV SD p-Value NRV SD p-Value
301a 1.08 0.15 0.6122 0.45 0.04 0.0039** 0.75 0.10 0.0937 1.02 0.11 0.8485
302 1.33 0.18 0.1374 0.39 0.08 0.0018** 0.94 0.09 0.5422 1.31 0.10 0.0314*
403a 1.51 0.20 0.0256* 1.77 0.21 0.0034** 1.26 0.17 0.1486 2.43 0.39 0.0105*
406 0.72 0.15 0.1338 0.66 0.02 0.0003** 0.58 0.13 0.0304* 0.49 0.02 0.0001**
501a 0.81 0.29 0.5851 0.96 0.23 0.8613 0.84 0.20 0.6278 1.49 0.34 0.1008
502 0.78 0.14 0.2566 0.62 0.12 0.0844 0.56 0.12 0.0618 0.92 0.15 0.6411
606 0.90 0.07 0.2282 0.71 0.06 0.0137* — — — 0.67 0.12 0.0617
607a 0.68 0.08 0.0200* 0.83 0.05 0.0322* — — — 0.90 0.09 0.3154
707 1.16 0.14 0.3193 1.12 0.06 0.0990 1.10 0.03 0.0323** 0.80 0.03 0.0025**
708a 0.83 0.10 0.1814 0.97 0.12 0.8313 0.83 0.23 0.5085 0.77 0.10 0.1019
All subjects 0.98 0.28 0.4812 0.85 0.40 0.0232* 0.86 0.24 0.0831 1.08 0.56 0.4812
—, Not analyzed. Lymphocytes from subjects hypersensitive (n = 5) and matched controls (n = 5) were exposed to MWs at 905 MHz or 915 MHz during 1 hr. Means of three measurements and SD are shown along with p-values (Mann-Whitney U-test.)
a Cases of reported hypersensitivity to EMFs.
* p < 0.05.
** p < 0.01.
Table 3 Changes in 53BP1 foci in response to 1 hr MW exposure.
Sham
905 MHz
915 MHz
41°C
43°C
Subject 53BP1 SD 53BP1 SD p-Value 53BP1 SD p-Value 53BP1 SD p-Value 53BP1 SD p-Valuea
301a 0.95 0.73 1.54 0.65 0.0652 0.03↓ 0.06 0.0099** 0.06↓ 0.04 0.0013** 0.14↓ 0.09 0.0030**
302 1.45 0.81 2.44↑ 0.63 0.0020** 0.17↓ 0.27 0.00001** 1.80 0.93 0.3460 0.08↓ 0.09 0.0000**
403a 0.42 0.47 2.87↑ 1.48 0.0002** 0.00↓ 0.00 0.0015** 0.01↓ 0.03 0.0372* 0.00 0.00 0.0712
406 0.62 0.13 0.28↓ 0.23 0.0031** 0.08↓ 0.17 0.0002** 0.06↓ 0.09 0.0000** 0.04↓ 0.04 0.00001**
501a 1.06 0.22 1.67↑ 0.50 0.0028** 0.15↓ 0.11 0.0007** 0.18↓ 0.14 0.0000** 0.01↓ 0.01 0.00001**
502 0.66 0.25 1.35↑ 0.45 0.0079** 0.25 0.23 0.0556 0.24↓ 0.08 0.0066** 0.01↓ 0.01 0.0003**
606 0.84 0.26 0.08↓ 0.07 0.0079** 0.20↓ 0.14 0.0079** — — — 0.00↓ 0.00 0.0001**
607a 1.33 0.39 0.18↓ 0.08 0.0079** 0.35↓ 0.18 0.0079** — — — 0.01↓ 0.01 0.0001**
707 0.88 0.19 1.68↑ 0.44 0.0228** 0.52 0.25 0.0556 0.56↓ 0.08 0.0089** 0.11↓ 0.09 0.0000**
708a 1.62 0.39 1.09↓ 0.17 0.0159** 0.70↓ 0.13 0.0079** 0.85↓ 0.10 0.0028** 0.10↓ 0.04 0.0000**
CS 0.89 0.10 1.17 0.98 0.4728 0.24↓ 0.17 0.0176** 0.67 0.78 0.3235 0.05↓ 0.05 0.0037**
HE 1.08 0.10 1.47 0.98 0.5575 0.25↓ 0.29 0.0013** 0.28↓ 0.39 0.0072** 0.05↓ 0.07 0.0061**
All subjects 0.98 0.10 1.32 0.94 0.3150 0.24↓ 0.22 0.0001** 0.47↓ 0.61 0.0266** 0.05↓ 0.05 0.00001**
—, Not analyzed. Lymphocytes from subjects hypersensitive to EMF (HE; n = 5) and matched controls (CS; n = 5) were exposed to MWs at 905 MHz and 915 MHz or heat shocked. For each subject, the mean of measurements in 300–600 cells and SD are shown along with p-values for differences compared with sham-exposure by Mann-Whitney U-test. Arrows ↓and ↑ designate direction of effects, decrease or increase, respectively.
a Cases of reported hypersensitivity to electromagnetic fields.
* p < 0.05.
** p < 0.01.
Table 4 Changes in γ-H2AX foci in response to 1 hr MW exposure.
Sham
905 MHz
915 MHz
41°C
43°C
Subject γ-H2AX SD γ-H2AX SD p-Value γ-H2AX SD p-Value γ-H2AX SD p-Value γ-H2AX SD p-Value
301a — — — — — — — — — — — — — —
302 — — — — — — — — — — — — — —
403a 0.91 0.69 7.24↑ 1.54 0.00001** 0.10↓ 0.25 0.0015** 0.10↓ 0.25 0.0105** 0.00↓ 0.00 0.0130*
406 1.06 0.57 1.05 1.11 0.4173 0.10↓ 0.14 0.0002** 0.52↓ 0.40 0.0003** 0.02↓ 0.03 0.0003**
501a 1.30 1.11 0.09↓ 0.08 0.00001** 0.00↓ 0.00 0.0007** 0.00↓ 0.00 0.0231** 0.02↓ 0.02 0.0248*
502 0.06 0.02 0.03 0.04 0.0992 0.00↓ 0.00 0.0079** 0.00↓ 0.00 0.0002** 0.00↓ 0.00 0.0001**
606 0.53 0.32 0.01↓ 0.01 0.0079** 0.03↓ 0.02 0.0079** — — — 0.00↓ 0.00 0.0059**
607a 0.36 0.04 0.03↓ 0.03 0.0079** 0.04↓ 0.02 0.0079** — — — 0.00↓ 0.00 0.00001**
707 0.52 0.12 1.22↑ 0.38 0.0079** 0.12↓ 0.10 0.0079** 0.18↓ 0.18 0.0068** 0.68 0.34 0.3402
708a 1.34 0.14 1.09 0.34 0.4206 0.18↓ 0.05 0.0079** 0.80↓ 0.20 0.0011** 0.25↓ 0.05 0.00001**
CS 0.54 0.11 0.58 0.65 0.9033 0.06 0.06 0.0808 0.23 0.26 0.1546 0.18 0.34 0.2637
HE 0.98 0.11 2.11 3.45 0.5613 0.08↓ 0.08 0.0256* 0.30 0.43 0.0587 0.07↓ 0.12 0.0192*
All subjects 0.76 0.11 1.34 2.44 0.5737 0.07↓ 0.06 0.0019** 0.27↓ 0.32 0.0426* 0.12↓ 0.24 0.0029*
—, Not analyzed. Lymphocytes from subjects hypersensitive to EMF (HE; n = 5) and matched controls (CS; n = 5) were exposed to MWs at 905 MHz and 915 MHz or heat shocked. For each subject, mean of measurements in 300–600 cells and SD are shown along with p-values for differences compared with sham by the Mann-Whitney U-test. Arrows ↓and ↑designate direction of effects, decrease or increase, respectively.
a Cases of reported hypersensitivity to electromagnetic fields.
* p < 0.05.
** p < 0.01.
Table 5 MW effects on formation of 53BP1 and γ-H2AX foci as analyzed by the Kruskal-Wallis ANOVA by ranks (multiple comparisons of sham, 905 MHz and 915 MHz) in cells from hypersensitive subjects (n = 5) and matched controls (n = 5).
p-Value
Subject No. of images 53BP1 γ-H2AX
301a 24 0.0011** —
302 32 0.00001** —
403a 30 0.00001** 0.00001**
406 27 0.0002** 0.0003**
501a 25 0.0002** 0.00001**
502 20 0.0011** 0.0155*
606 16 0.0052** 0.0051**
607a 15 0.0034** 0.0075**
707 15 0.0098** 0.0019**
708a 15 0.0032** 0.0075**
—, Not analyzed. For each experimental condition, 300–600 cells were analyzed.
a Cases of reported hypersensitivity to EMFs.
* p < 0.05.
** p < 0.01.
Table 6 Comparison of MW effects on 53BP1 and γ-H2AX foci at different frequencies, 905 MHz and 915 MHz, in cells from hypersensitive subjects (n = 5) and matched controls (n = 5) as analyzed by the Mann-Whitney U-test.
No. of images
p-Value
Subject 905 MHz 915 MHz 53BP1 γ-H2AX
301a 10 5 0.0007** 0.00001**
302 10 10 0.00001** 0.4173
403a 10 10 0.00001** 0.00001**
406 10 10 0.0029** 0.0992
501a 10 5 0.0006** 0.0079**
502 10 5 0.0007** 0.0079**
606 5 5 0.1508 0.0079**
607a 5 5 0.0317* 0.4206
707 5 5 0.0159* 0.00001**
708a 5 5 0.0159* 0.4173
For each experimental condition, 300–600 cells were analyzed.
a Cases of reported hypersensitivity to EMFs.
* p < 0.05.
** p < 0.01.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7198ehp0113-00117816140624ResearchPhysiologic and Symptomatic Responses to Low-Level Substances in Individuals with and without Chemical Sensitivities: A Randomized Controlled Blinded Pilot Booth Study Joffres Michel R. Sampalli Tara Fox Roy A. Nova Scotia Environmental Health Centre, Fall River, Nova Scotia, CanadaAddress correspondence to M. Joffres, Nova Scotia Environmental Health Centre, 3064 Lake Thomas Dr., Fall River, NS B2T 1K6 Canada. Telephone: (902) 860-3069. Fax: (902) 860-2046. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 19 5 2005 113 9 1178 1183 21 4 2004 19 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We conducted a pilot study using a randomized, single-blind, placebo-controlled exposure among 10 individuals with and 7 without reported chemical sensitivities in a dedicated testing chamber. Objectives of the study were to explore the length of the adaptation period to obtain stable readings, evaluate responses to different substances, and measure the level and type of symptomatic and physiologic reactions to low-level exposures. Reported and observed symptoms, electrodermal response, heart rate, skin temperature, surface electromyogram, respiratory rate, contrast sensitivity, and the Brown-Peterson cognitive test were used and compared between cases and controls and between test substances (glue, body wash solution, dryer sheet) and control substances (unscented shampoo and clean air). Subjects with chemical sensitivities (cases) took longer to adapt to baseline protocols than did controls. After adaptation, despite small study numbers, cases displayed statistically significant responses (all measures, p < 0.02) in tonic electrodermal response to test substances compared with controls and compared with the control substance. Symptoms were also higher in cases than in controls for the body wash solution (p = 0.05) and dryer sheets (p = 0.02). Test–retest showed good agreement for both symptoms and tonic electrodermal responses (McNemar’s test, p = 0.32 and p = 0.33, respectively). Outside of skin conductance, other measures had no consistent patterns between test and control substances and between cases and controls. This study shows the importance of using an adaptation period in testing individuals with reported chemical sensitivities and, despite small numbers, raises questions about underlying mechanisms and level of reactivity to low-level chemical exposures in sensitive individuals.
complex mixtureselectrodermal responsemultiple chemical sensitivitypilot projectrandomized controlled trialskin conductance
==== Body
Despite the controversial nature of multiple chemical sensitivities (MCS)/environmental sensitivities (ES), the following consensus criteria (Bartha et al. 1999) have been proposed to define MCS/ES: The symptoms are reproducible with repeated chemical exposure; the condition is chronic; low levels of exposure (lower than previously tolerated) result in manifestations of the syndrome; the symptoms improve or resolve when the incitants are removed; responses occur to multiple chemically unrelated substances; and symptoms involve multiple organ systems. These criteria are based upon previous definitions (Cullen 1987; Nethercott et al. 1993; Randolph 1961).
Some researchers consider another term, “idiopathic environmental intolerances” (IEI), more appropriate [International Programme on Chemical Safety (IPCS) 1996] and believe it should be restricted to individuals with absolutely no abnormalities except a self-reported abhorrence to chemicals. Many people with chemical sensitivities have other conditions and symptoms such as asthma, rhinosinusitis, dermatitis, and headaches triggered by chemical exposures and would not meet this restrictive definition of idiopathic environmental intolerances. Our previous study (Joffres et al. 2001) identified a common set of consistent symptoms that follow exposure: difficulty in concentrating, fatigue, forgetfulness, irritability, itchy or burning eyes, sneezing, and hoarseness or loss of voice. The prevalence of ES is not negligible (Kreutzer et al. 1999; Meggs et al. 1996), and physicians, even those most familiar with these conditions, often overlook such a syndrome (Kutsogiannis and Davidoff 2001). Questions remain unanswered regarding the etiology, associated mechanisms, and inconsistency in pattern of symptoms. Several reports (Fiedler and Kipen 1997; Kipen and Fiedler 2002a, 2002b; Sparks et al. 1994) have outlined some of the different viewpoints on etiology and physiopathology. Meggs (1995) and Bascom et al. (1997) have proposed a neurogenic inflammation model. Sensitization or kindling of olfactory-limbic pathways by acute or chronic exposure has also been proposed (Bell et al. 1997, 1999; Miller 2001). Researchers for the IPCS (1996) proposed that
the key experiment is to determine in a double-blind challenge study whether subjects with “IEI” successfully discriminate between exposures to environmental factors (including to which illness is attributed) and placebo. If the subjective response (appearance of symptoms) of test subjects is able to discriminate between exposure to test chemicals and placebos, in a blinded design, this would suggest the operation of a toxicological mechanism in which culpable agents interact with tissue targets to trigger a receptor-mediated pathophysiological response.
A few studies have attempted to look at experimental testing of affected individuals (Fiedler et al. 2000; Fiedler and Kipen 2001; Rea et al. 1991; Staudenmayer et al. 1993). A critical factor that may have been overlooked in some of these studies is the adaptation of subjects to baseline study protocols. Adaptation is defined here as the period taken by the subjects in a study to get used to the general study protocols, obtaining stable physiologic (i.e., skin conductance and symptomatic) readings before the actual introduction of substances. We realized that with our patient population expressing a high level of reactivity, we would need to get stable readings before introducing any test substance. Otherwise, we might obtain erratic physiologic or symptomatic responses because of testing conditions, which would make it impossible to differentiate challenge substances from placebos. Surprisingly, we have not found any psychophysiologic literature discussing the importance of stabilizing readings before starting experiments other than the usual caution of a few minutes of relaxation. Most studies have either used a set period for adaptation (Fiedler et al. 2000) or not considered this factor at all (Staudenmayer et al. 1993).
Therefore, we hypothesized that individuals with ES may require more time to adapt to the experimental conditions compared with controls without chemical sensitivities. Also, we hypothesized that each individual may have a different adaptation period. We also wanted to explore the type of measure that would be the most useful to detect change and therefore included several psychophysiologic measures.
Addressing the issue of symptom development has also been critical to research on these illnesses. There is a need to see how symptoms develop and what triggers are associated with symptoms in individuals with ES. Is there a relation between symptoms and skin conductance response during exposure to chemicals? Estimating the level of reactivity of cases and controls to the substances was another major objective of this pilot study.
Hence, to answer these questions, we conducted a pilot, blinded, controlled booth study at the Nova Scotia Environmental Health Centre, a government-funded facility dedicated to research and management of individuals with ES.
Materials and Methods
Inclusion criteria.
Subjects with sensitivities were selected from the last 50 new patients enrolled at the Nova Scotia Environmental Health Centre who fulfilled the consensus criteria (Bartha et al. 1999) and who gave informed consent to participate in the study. The Dalhousie University Health Sciences Human Research Ethics Board approved the study protocol. Controls, without known chemical sensitivities, were recruited from friends of the patients and from advertisement in local churches.
Exclusion criteria.
Subjects were excluded from the study if they had any other major illnesses such as cancer (outside of skin cancer or past cancer without relapse in the preceding 5 years), insulin-dependent diabetes, stage 2 or 3 hypertension (systolic/diastolic blood pressure: stage 2, 160–179/100–109; stage 3, > 180/> 110), history of myocardial infarction, angina pectoris, stroke, or psychiatric disorders such as major depressive disorder, schizophrenia, shared psychotic disorder, dementia, or drug dependence.
A total of 12 cases (all women; mean age, 40 years; range, 25–60 years) and 7 controls (all women; mean age, 42 years; range, 26–59 years) gave an informed consent to participate and fulfilled the initial inclusion/exclusion criteria of the study. The subjects were matched within age, education, and ethnicity categories.
Challenge booth.
The study was conducted in a dedicated room that included the challenge booth (Figure 1) made from inert materials with no apparent odors, allowing introduction of materials that release various chemicals into the environment. The booth is a glass room with steel framing with dimensions of 2.2 m (7.2 ft) in height by 1.2 m (4 ft) in width by 1.8 m (6 ft) in depth. The construction materials and procedure used have received careful consideration toward making it suitable for research work on individuals with chemical sensitivities. The air-flow, temperature, and lighting in the facility can be varied to increase the comfort level of the subjects. A side box attached to the booth was used to introduce chemicals into the air stream. The door to the side box opens on one side of the booth so the subjects are unaware of the substance being introduced into the booth. Air is allowed to enter the side box into an inlet provided on the frosted side of the booth. The inlet height is at the breathing level of the occupant. The air entering the booth is 100% outdoor air that has been filtered before being distributed throughout the clinic, not recirculated, and is then directly vented to the outdoors through an exhaust located in the ceiling of the booth.
Test substances.
Test substances for the study were those commonly reported by affected people to cause reactions: common glue, a scented body wash solution, dryer sheet, and for control substances, unscented shampoo and clean air. These substances were contained in a closed metal box, introduced through the side box, and passively released in the booth airflow. A new dryer sheet was used for each session. Patients could not smell the substance because of nose plugs.
Outcome measures.
Before and after each booth session, subjects answered a symptom questionnaire on symptoms reported by Joffres et al. (2001; e.g., eye irritation, throat irritation, sleepiness, headache) as the top symptoms experienced by our patient population after an exposure. The questionnaire measured irritation on an ordinal scale of 0–10, with 0 being barely detectable and 10 being strongest detectable (Joffres et al. 2001). The booth environment (light, sound, and temperature) was also rated before and after each session on a scale of 0–10 (0 = poor, 10 = excellent). The purpose of the adaptation sessions was to reduce the number and severity of symptoms reported to the nurse among reactive subjects. The adaptation sessions identified symptomatic responses shown by subjects to the baseline protocols of the study such as wearing nose plugs, wearing respiratory belt, and the other BIOPAC (BIOPAC Systems Inc., Goleta, CA, USA) measures.
During the testing period, we compared pre- and postsymptom scores from that day, and coded any change in score or type of symptom as a positive response. We disregarded the symptoms that occurred consistently during the adaptation sessions (baseline symptomatic responses) while computing a response toward challenge substances.
After the booth session, subjects recorded and reported to the nurse any changes they observed during the next 8-hr period. They were instructed not to visit malls or other similar places where they may be exposed to other substances 8 hr before and 8 hr after each booth session. Because many of our patients reported effects lasting up to 4 or 5 days after a booth session, subjects were exposed to only one substance at a time and had a minimum of 1 week between two booth sessions.
We used the Brown-Peterson test (Peterson and Peterson 1962) to test a variation in the short-term memory span of the subjects after a booth exposure. Subjects were given a series of trigrams of letters; each trigram was followed by a number countdown challenge, after which subjects were asked to recall the trigram. Short-term memory for 9, 18, and 36 sec intervals was examined. The test was conducted before and after each exposure session.
Contrast sensitivity (Schreiber et al. 2002), which provides a detailed assessment of spatial vision and is sometimes recommended as a test to screen visual damage caused by chemical exposures such as solvents, was tested before and after each session. Peak flow was used as a pre- and postsession measure to determine impairment in lung function. All these measures were recorded pre- and postsession during all booth sessions for each subject.
Physiologic measures.
To assess physiologic measures of skin temperature, skin conductance, respiratory rate, heart rate, and surface electromyography (EMG), we used used the BIOPAC MP 100 data acquisition system (BIOPAC Systems Inc.) during each booth session. Surface EMG was collected at a rate of 1,000 samples/sec using the Biopac electrodes placed at the upper trapezius muscle. Disposable electrocardiogram electrodes were used to acquire heart rate at a sampling rate of 1,000 samples/sec. The positive electrode was placed on the right wrist, the negative on the left wrist, and the ground electrode on the right ankle. Finger temperature and skin conductance were measured at a rate of 3 sample/sec.
Of the physiologic measures, only skin conductance was a consistent indicator of adaptation and response to challenge substances postadaptation. Results and discussion in this article are therefore restricted to this physiologic measure.
Skin conductance recording and analysis.
We recorded skin conductance using Ag/AgCl electrodes filled with isotonic electrolyte jelly and attached to the fore and middle fingers of the left hand. Subjects were asked to wash their hands with lukewarm water before the start of each booth session. Skin conductance data acquisition and analysis were conducted using Acknowledge 3.2.4 software (BIOPAC Systems Inc.). The raw data collected were first smoothed using low-pass filter. The readings were compared with the baseline readings from that day.
Skin conductance response has been described in the literature as having two components: phasic and tonic responses (e.g., Lim et al. 1997). Phasic responses may be evoked even by a discrete stimulus such as subtle variations in environment or even thought processes. Tonic skin conductance response is the baseline level of skin conductance in the-absence of any stimulus. This is known to vary with time in the presence of a stimulus depending upon the psychological state of the individual and their autonomic regulation. We considered only the tonic responses while assessing a positive response to challenge substances in our pilot study. Recordings showed variations in the level of conductance because of artifacts or other factors, and the change in amplitude and the length of the tonic response cannot be easily used in a continuous form without arbitrary criteria about where and how the measures will be made. Therefore, we adopted a simple criterion that could be easily reproduced: Tonic responses were considered positive if there was a change in amplitude from the preexposure period (of the session) by at least 0.5 microsiemens (μS) about 20 sec after the introduction of the substance.
Booth session protocol.
In an orientation visit we discussed details of the study with the subjects and answered questions about the study. After consent, an adaptation period allowed subjects to get used to baseline study protocols, such as cognitive testing (Brown-Peterson test), answering questionnaires, getting used to the booth, and wearing nose plugs.
Each subject was given up to 10 individual booth sessions with a maximum of four sessions for adaptation to the baseline study protocols. Each booth session consisted of the same set of changes occurring at the same time, which included opening and closing of the side door through which substances could be introduced (2.5, 5, and 10 min), exhaust fan going on low speed (7 min), high speed (11.5 min), and then being turned off (12.5 min) (Figure 2). The stability in readings was judged only by the stability of tonic skin conductance responses (see above for criteria) and in reduction of symptomatic responses (symptom scores) based on the interview with the nurse.
After adaptation, the subjects were blindly challenged to the test substances, clean air, glue, body wash solution, and dryer sheet in a randomized sequence. Subjects received test substances only if an “open and close” door sequence (time, 2.5 min) in the pretest period, also done during the adaptation phase, did not elicit any change in conductance greater than our defined threshold of 0.5 μS approximately 20 sec after the introduction of the substance. The subjects received only one challenge substance (placebo, control, or test substances) in a session. Each subject was retested to at least one substance that they reacted to in a randomized sequence. Even if they did not react to any substance (as in the case of our control subjects), they were still retested on at least one substance to confirm their nonreactivity.
Of the 12 subjects with ES, two did not adapt to the baseline protocols and were excluded from further study. All seven controls completed the adaptation phase and were able to participate in the next phase of the study (Figure 3).
This pilot study was single blinded. Subjects were not aware of what substance was introduced and could not smell the substance because of nose plugs. The nurse monitoring subjects was not aware of what was being introduced into the booth. The researcher introducing the substance also monitored data recording and analyzed the results and was separated from the nurse and the subject by a partition. The order of administration of the three test substances (glue, body wash lotion, dryer sheet) and a control substance (unscented shampoo) was randomized using a table of random numbers.
Data analyses.
Heart rate variability was analyzed by the Institute of Heart Math (McCraty et al. 1995) and did not differ between test and control substances or between patients and controls. Respiratory rate showed erratic patterns that seemed to be influenced by presence of the nose plug, and we could not identify any specific patterns.
In addition to skin conductance, symptom scores before and after booth sessions were the only other measure that indicated completion of an adaptation period and responses to challenge period. No other measures are discussed in this article. Data were dichotomized into reaction versus no reaction to simplify presentation and allow the study to be easily reproduced.
We used SAS (version 9.1; SAS Institute Inc., Cary, NC, USA) for the statistical analyses. Fisher’s exact test statistic (two sided) was used to test differences in proportion using an alpha level of 0.05 between cases and controls, and McNemar’s statistic was used for paired data (between placebo and substance among cases and among controls). Because one column in the 2 × 2 table had 0 frequencies, we used a frequency of 0.001 to be able to estimate a p-value.
Results
Table 1 presents the measures that were collected during the booth sessions, and Figure 2 shows the timeline for the different sequences of the preexposure, exposure period, and post-exposure period.
Figure 3 shows skin conductance responses of cases and controls to baseline study protocols. The proportion of cases reacting to the different testing conditions (different sounds) using set criteria described above decreased with the number of sessions and was much higher in cases than in controls. In the ES group, 83% (10 of 12) adapted after four sessions, whereas 17% (2 of 12) did not adapt after four sessions. Most of the controls adapted in a single session (86%, 6 of 7).
An example of a tonic skin conductance response during the adaptation period is presented in Figure 4 by stimuli and by session. Although there were variations in skin conductance after the different stimuli in the first and second sessions (first and second window), there were no longer tonic responses in the third session. Figure 5 presents the percentage of individuals with ES having a specific symptomatic response to each of three challenge substances: glue, body wash solution, and dryer sheet. The most common type of reaction was burning eyes and headaches after exposure to the dryer sheet and glue.
The percentages of cases and controls presenting a skin conductance response or any specific symptom to the test substance are shown in Figure 6. The level of response was higher for all test substances in cases than in controls and higher for test substances (glue and dryer sheet) than for control substances (unscented shampoo and clean air) in cases. There was a relatively close match between physiologic and symptomatic responses during exposure to challenge substances. Only one control displayed a response in skin conductance to a test substance, and two controls showed symptom responses to test substances; none showed symptom responses to the control substances.
The most significant difference in skin conductance between cases and controls was for dryer sheets (p < 0.0001), followed by glue (p = 0.0004) and body wash solution (p = 0.02). In cases, comparing skin conductance between substances and control substances, there were similar patterns showing statistical differences between control substance and dryer sheets (p = 0.0007), glue (p = 0.006), and body wash solution (p = 0.02).
For symptomatic responses, there were no statistically significant differences among cases between any substances and the control substances, but there were statistically significant differences between cases and controls for the dryer sheet (p = 0.02) and for glue (p < 0.05). There were no statistically significant differences among controls.
Table 2 shows symptomatic and skin conductance test–retest results in selected cases and controls by subject and by substance. Table 3 presents all our test–retest data on selected individuals. There was an overall good agreement between test and retest for both symptoms and skin conductance responses (McNemar’s test, p = 0.32 and p = 0.33, respectively).
Discussion
The purpose of this pilot study was to shed some light on the experimental conditions, substances, and measures that could lead to better trials in chemically sensitive individuals. There was a clear difference in the time taken by cases to adapt to the experimental conditions, and two cases did not adapt after four sessions; however, more than half adapted after the second session, and six of seven controls adapted in only one session. We have not found any data that emphasize the need for an adaptation period in studies that are measuring physiologic responses outside of the few minutes used to stabilize readings. There is also no mention of the importance of stabilizing the reactions to the experimental conditions such as being observed, changes in temperature and airflow, and sounds such as opening and closing a door to introduce a substance. Although adaptation may not be an important issue in “normal” people, it is certainly an issue in people with ES who are usually in a state of hyper-reactivity. Reactions to test substances need to be differentiated from the effect of the experimental conditions. Otherwise, the experimental “noise” will result in misclassification, reducing statistical significance and resulting in negative findings.
Because we removed most of the noise due to the experimental conditions, the ability of individuals to detect the test substances, compared with placebo and compared with controls, raises several questions. First, EMG, heart rate, respiratory rate, skin temperature, cognition, and contrast sensitivity did not show any consistent patterns of reaction. Therefore, these measures may not be very sensitive or relevant to the pathophysiology of reactions in individuals with ES. Because this was a pilot study, a larger sample size might have shown some more subtle differences, and therefore we cannot totally reject the usefulness of these measures in their ability to discriminate between test and control substances.
Second, differences in skin conductance response bring into question what type of reaction takes place after exposure to a very low-level substance. Skin conductance responses have been widely used in the psychosocial field. Since the work of Edelberg (1972), research has shown that changes in skin conductance (or, conversely, resistance) are affected by the filling of sweat ducts and the number of sweat glands affected. This mechanism, under the autonomic nervous system control, seems cholinergic, regulated by the premotor cortex, the hypothalamus and limbic systems, and the reticular formation. The role of the hypothalamus/limbic system has been previously suggested in the pathophysiology of MCS (Bell et al. 1997). Nevertheless, as Bell and colleagues pointed out, there have been no controlled experiments to determine whether or not sensitization to low-level chemical exposures occurs in MCS patients.
Third, because the hypothalamus/limbic system is closely linked with emotions, is it possible that the perception of chemicals through the eyes (Millqvist et al, 1999) or the upper respiratory system (Shusterman 2002) provoked a rise in anxiety, activating the autonomic nervous system, resulting in a skin conductance response? In future experiments, we will be studying patterns of reactions to see if we can differentiate anxiety responses from the type of response observed in this study.
Fourth, in contrast to a pure anxiety response hypothesis, it could be argued that irritation of these chemosensitive structures (eyes and respiratory tract) could lead to neurogenic inflammation as hypothesized by Meggs (1995) and Bascom et al. (1997). Stimulation of the glossopharyngeal and vagal nerves via the hypopharynx and larynx could result in the type of symptoms described by patients and the observer. Most cases reported burning eyes, eye irritation, headaches, or sleepy or drowsy feelings, which fit with our previous study of symptoms after exposure in chemically sensitive patients (Joffres et al. 2001).
Some of our patients were not able to adapt to baseline protocols, showing erratic responses to the testing conditions. These subjects are therefore in a state of hyperreactivity and should not be included in such experiments. Some cases reacted to the unscented shampoo control substance, but none reacted to clean air. This suggests that clean air should be used as the control substance.
In subjects who adapted to the experimental setup, the most irritant substances (dryer sheets and glue) triggered a physiologic (skin conductance) response accompanied with symptomatic responses in many cases (Figure 6). When we looked at whether symptoms preceded or followed the skin conductance reaction, we found that most symptoms occurred at the same time or followed rather than preceded electrodermal response. Only 20% of subjects had symptoms preceding changes in skin conductance.
Several studies have shown that undetected chemicals can still induce brain activations (Lorig 1994; Sobel et al. 1999). Staudenmayer et al. (1993) used an olfactory masker with the test substances and as a control substance. Therefore, if the olfactory masker is not perceived by the sense of smell but is still able to alter neurophysiology, there should be no detectable differences between test and control substance (olfactory masker) because of the added noise of the olfactory masker. If patients had adapted to the olfactory masker, they might have been able to detect a difference, but the study protocol did not take this problem into account. Lorig (1994) and Sobel et al. (1999) also raised the question of why our controls did not detect the active substances. Perhaps they did not because we considered only tonic skin conductance responses in our study. All but one of our control subjects displayed only phasic responses, whereas almost all the cases showed tonic variations. We will be looking at the importance of phasic changes in our next study. In addition, we did not use measures as sensitive as the electroencephalogram. The pilot study has also helped us identify skin conductance ranges in which a specific type of response, phasic or tonic, is displayed if a stimulus is perceived as a stressor. This will be critical for such studies because these ranges will help us identify what type of response can be considered given a specific range of baseline. We will be confirming this with a larger sample size.
Reaction times varied by substance and were fastest with the dryer sheet, where most of the individuals (8 of 10) reacted in < 200 sec after the introduction of the substance. It is therefore important to allow enough time to observe a response and not introduce other substances that could create experimental noise. Introducing more than one challenge substance in one session, sham exposures, or maskers may not allow isolation of the delayed responses that we observed in a few cases for substances other than the dryer sheet.
Test–retest performed on five cases reacting to glue showed that in four cases the level of reaction was higher in the retest session (similar trends were also observed while retesting on two other test substances), and this fits with Bell et al.’s (1997
(1999) theory regarding sensitization of olfactory-limbic pathways and what Sorg and Newlin (2002) observed in rats, where repeated chemical exposure produced sensitization of the central nervous system circuitry. What was also interesting in the Sorg and Newlin (2002) study was that rats given repeated formaldehyde demonstrated increased fear conditioning to odor paired with footshock, suggesting amplification of neural circuitry guiding fear responding to a conditioned odor cue.
Therefore, whether or not these reactions are triggered by an unconscious anxiety response after the awareness by the nervous system of a situation perceived as threatening can still be argued. We tend to agree with Spurgeon (2002) that the reporting of symptoms may result from a complex set of interactions between aspects of personality, attitudes, culture, and social climate as well as any pathologic changes. Fiedler et al. (2000) noted that reactions to specific substances in individuals do not necessarily elicit physiologic responses. We noted this in a few cases in our study as well. The fact that, in other cases, the physiologic reaction was not followed by any symptomatic response raises the question of whether an anxiety response, recorded at the unconscious level, could still result in such an isolated physiologic reaction.
Symptomatic responses in sensitive individuals and not in controls also correspond to Fiedler et al.’s (2000) results. We are currently investigating whether and how we can differentiate anxiety responses from other types of responses using phasic and tonic responses. Even if we assume an anxiety response, the question remains of whether or not it is an initial, secondary (conditioning), or mixed response to low-level chemical exposure.
There are several limitations to this study. First, because it was a pilot study, the number of cases and controls was relatively small, and results need to be confirmed with a larger study. Then, to ensure a double-blind design, it will be necessary to have two separate individuals, one doing the data analysis and the other in charge of introducing the different substances. In addition, the observer, the nurse, might have been able to pick up the smell of some substances and give unconscious nonverbal cues to the patient inside the booth. We have since installed a video camera that allows remote observation of the patient inside the booth but have not detected any difference in the results due to this change. Although the air flow in the booth was maintained constant and substances were introduced every time in the same manner, it would be essential to measure (with gas chromatography) the actual levels reaching the individuals and ensure that delivery of the substances is constant through methods such as those described by Fiedler et al. (2000).
Conclusions
In terms of experimental design, this pilot study raised the significance of including an adaptation phase to get stable physiologic measurements and minimize noise in the results. This study also brings up questions regarding the significance of an electrodermal response to low-level chemical substances. Are we observing an unconscious anxiety response, or another type of response such as neurogenic inflammation? Other measures such as functional magnetic resonance imaging would certainly add understanding of the brain regions involved in the reactivity, but this will require unique laboratory conditions. Reproducibility of these results and understanding the pathophysiologic mechanism should be the next priorities.
This study was supported by the Nova Scotia Department of Health.
Figure 1 Challenge booth and testing conditions.
Figure 2 Time line for booth session. Sequence of changes: 1 and 2, opening and closing of side door of booth; 3, exhaust fan on low speed; 4, opening and closing of side door of booth; 5, exhaust fan on high speed; 6, exhaust fan off.
Figure 3 Number of sessions required for each individual to reach stable baseline skin conductance readings (adaptation) among (A) cases and (B) controls. Subjects 3 and 9 (black bars) did not adapt after four sessions.
Figure 4 Example of tonic skin conductance responses observed in a case during adaptation, by stimuli and by session. Sequence of changes: 1 and 2, opening and closing of booth side door; 3, exhaust fan on low speed; 4, opening and closing of booth side door; 5, exhaust fan on high speed; 6, exhaust fan off.
Figure 5 Percentage of individuals with ES presenting responses to challenge substances, by type of symptom and substance (sub).
Figure 6 Percentage of (A) cases and (B) controls presenting skin conductance or any symptom response by test substance. Placebo was clean air. Abbreviations: BWS, body wash solution; CS, control substance; DS, dryer sheet.
Table 1 Booth sessions measures.
Before During After
Interview with nurse Skin conductance Interview with nurse
Peak flow Skin temperature Peak flow
Contrast sensitivity EMG Contrast sensitivity
Brown-Peterson test Respiratory rate Brown-Peterson test
Symptoms rating Electroencephalogram Symptoms rating
Environment rating Nurse’s observation Environment rating
Table 2 Symptomatic and skin conductance test–retest results in selected cases and controls by subject and by substance.
Case/control Test symptom Retest symptom Substance Test skin conductance Retest skin conductance
Case 1 1 Glue 1 1
Case 1 1 Glue 1 1
Case 1 1 Glue 0 (EMG) 1 (EMG)
Case 0 1 Glue 1 (EMG) 0 (EMG)
Case 1 1 Glue 1 1 (EMG)
Case 0 0 Body wash 1 1
Case 1 1 Body wash 1 0 (EMG)
Case 0 0 Body wash 1 1
Case 1 1 Dryer sheet 1 1 (SKT)
Case 1 1 Dryer sheet 1 1
Case 1 1 Dryer sheet 1 1
Case 0 0 Dryer sheet 1 1 (EMG)
Case 0 0 Placebo 0 0
Case 0 0 Placebo 0 0
Case 0 0 Placebo 0 0
Case 0 1 Control 1 1
Case 1 1 Control 1 0 (EMG)
Case 1 1 Control 0 0
Case 1 1 Control 0 0
Case 0 0 Control 0 0
Control 1 0 Glue 0 0
Control 1 0 Glue 0 0
Control 0 0 Glue 0 0
Control 1 1 Body wash 1 1
Control 0 0 Body wash 0 0
Control 1 0 Dryer sheet 0 0
Control 0 0 Dryer sheet 0 0
Control 0 0 Dryer sheet 0 0
Control 0 0 Control 0 0
Control 0 0 Placebo 0 0
Control 0 0 Placebo 0 0
Abbreviations: 0, no response; 1 positive response; EMG, positive response by EMG; SKT, positive response by skin temperature.
Table 3 Examples of test and retest skin and symptomatic responses for selected substances.
Skin conductance
Symptomsa
Test
Retest
Test
Retest
Subject Test substance L A L A Symptom Scores Symptom Scores
1 Glue 210 1.00 600 0.95 Eye irritation 0–7 Ear pain 0–6
Nausea 1–6
2 Glue 253 0.86 265 1.38 Eye irritation 0–5 Headache 0–5
Tired 1–7 Tired 2–7
3 Dryer sheet 300 1.20 500 0.90 Tired 1–8 Tired 0–6
4 Dryer sheet 246 0.90 169 0.98 Headache 3–9 Burning eyes 0–5
5 Body wash 302 0.83 412 0.95 Eye irritation 0–7 Tired 1–6
6 (control) Body wash 256 1.03 425 1.4 Fatigue 3–9 Fatigue 0–8
7 Control 462 0.95 509 0.86 None Throat irritation 1–7
Brain fog 2–8
8 Control 324 1.05 — — Brain fog 1–6 Clumsiness 0–7
Abbreviations: —, no data; A, amplitude in microsiemens of tonic skin conductance responses; L, latency in seconds.
a Reported symptoms and symptom severity scores before and after introduction of test substance.
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Meggs WJ 1995 Neurogenic switching: a hypothesis for a mechanism for shifting the site of inflammation in allergy and chemical sensitivity Environ Health Perspect 103 54 56 7628426
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Rea WJ Ross GH Johnson AR Smilley RE Sprague DE Fenyves EJ 1991 Confirmation of chemical sensitivity by means of double-blind inhalant challenge of toxic volatile chemicals Bol Asoc Med P R 83 389 393 1807272
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Sobel N Prabhakaran V Hartley CA Desmond JE Glover GH Sullivan EV 1999 Blind smell: brain activation induced by an undetected air-borne chemical Brain 122 209 217 10071050
Shusterman D 2002 Review of the upper airway, including olfaction, as mediator of symptoms Environ Health Perspect 110 suppl 4 649 653 12194901
Sorg BA Newlin DB 2002 Sensitization as a mechanism for multiple chemical sensitivity: relationship to evolutionary theory Scand J Psychol 43 161 167 12004954
Sparks PJ Daniell W Black DW Kipen HM Altman LC Simon GE 1994 Multiple chemical sensitivity syndrome: a clinical perspective. I. Case definition, theories of pathogenesis, and research needs Occup Med 36 718 730
Spurgeon A 2002 Models of unexplained symptoms associated with occupational and environmental exposures Environ Health Perspect 110 suppl 4 601 605 12194893
Staudenmayer H Selner JC Buhr MP 1993 Double-blind provocation chamber challenges in 20 patients presenting with “multiple chemical sensitivity Regul Toxicol Pharmacol 18 44 53 8234918
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7493ehp0113-00118416140625ResearchCorrelating Agricultural Use of Organophosphates with Outdoor Air Concentrations: A Particular Concern for Children Harnly Martha 1McLaughlin Robert 1Bradman Asa 2Anderson Meredith 3Gunier Robert 11 California Department of Health Services, Oakland, California, USA2 University of California, Berkeley, California, USA3 Impact Assessment Inc., Oakland, California, USAAddress correspondence to M. Harnly, California Department of Health Services (CDHS), Environmental Health Investigations Branch, 1515 Clay St., Suite 1700, Oakland, CA 94612 USA. Telephone: (510) 622-4484. Fax: (510) 622-4505. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 13 5 2005 113 9 1184 1189 16 8 2004 5 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. For the organophosphate pesticide chlorpyrifos, median inhalation noncancer, acute children’s exposures in agricultural communities are elevated above reference doses; for diazinon, similar exposures are nearly elevated. We used multivariate linear regression analysis to examine the temporal and spatial associations between agricultural use and measured air concentrations of chlorpyrifos, chlorpyrifos oxon, diazinon, and malathion. Agricultural use within a 3-mile radius on the monitoring day and use on the 2–4 prior days were significantly associated with air concentrations (p < 0.01) for all analytes except malathion; chlorpyrifos oxon showed the strongest association (p < 0.0001). In the final models, which included weather parameters, the proportion of variance (r
2, adjusted for the number of model variables) for all analytes ranged from 0.28 (p < 0.01) for malathion to 0.65 (p < 0.0001) for diazinon. Recent cellular, animal, and human evidence of toxicity, particularly in newborns, supports the public health concern indicated by initial risk estimates. Agricultural applications of organophosphates and their oxon products may have substantial volatization and off-field movement and are a probable source of exposures of public health concern.
agricultureairchlorpyrifoschlorpyrifos oxondiazinoninhalation exposuremalathionorganophosphatespesticidesvolatilization
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Volatilization of pesticides from agricultural fields constitutes a large source of potential human exposure. The United States purchases a little more than 900 million pounds of active pesticidal ingredients annually for agricultural use (Donaldson et al. 2002), of which growers in California use approximately 16% [California Department of Pesticide Regulation (CDPR) 2005]. The amount volatized from agricultural fields can be considerable: for some pesticides, up to 90% of the application amount may volatize [reviewed by Bedos et al. (2002); Unsworth et al. (1999); Van den Berg et al. (1999)].
The pesticide air monitoring program of the California Environmental Protection Agency’s (Cal/EPA) Toxic Air Contaminant (TAC) Program is one of the few U.S. programs that monitor air in agricultural communities [Baker et al. 1996; U.S. Geological Survey (USGS) 1995]. In a health risk evaluation of the measured air concentrations, the broad-spectrum organophosphates chlorpyrifos and diazinon ranked highest in acute inhalation toxicity, after three fumigants with higher vapor pressures (e.g., methyl bromide). That evaluation derived the range (50th–95th percentiles) of inhalation risks, expressed as hazard quotients (HQs), the ratio of estimated intake to the reference dose (RfD). Estimated intakes were based on the distributions of air concentrations and inhalation rates for children (< 13 years of age) and adults. Organophosphate inhalation RfDs were based on the no- or lowest-observed-adverse-effect levels (NOAELs or LOAELs) in neurotoxicity animal studies of cholinesterase enzyme inhibition. For children’s acute and subchronic exposures, to derive the inhalation chlorpyrifos RfD (0.001 mg/kg/day), the NOAEL was divided by uncertainty factors totaling 1,000; to derive the diazinon RfD (0.00009 mg/kg/day), the LOAEL was divided by factors totaling 300. Risks were higher for children than for adults. For chlorpyrifos and diazinon, the HQs for the median of children’s acute exposures were 4.0 and 0.8, respectively. These results suggest a potential public health concern for residents and children of agricultural communities (Lee et al. 2002). The CDPR has placed both chlorpyrifos and diazinon on a high-priority list for risk characterization, which is a step toward listing pesticides as toxic air contaminants in California (CDPR and Patterson 2004).
Further specification of sources of ambient pesticide air concentrations is needed. For organophosphates, source or use patterns are complex. During the 1990s, use of chlorpyrifos and diazinon in residences or around public structures constituted up to 50 and 70%, respectively, of all agricultural and nonagricultural use [U.S. Environmental Protection Agency (EPA) 2000a, 2002].
Potential atmospheric dispersal and residence time of organophosphates after agricultural applications are not well understood and are driven by many interwoven factors, including application methods, tillage practices, irrigation techniques, temperature, sunlight, rainfall, and wind (Bedos et al. 2002; Racke 1993; Whang et al. 1993). Environmental fate estimates indicate that organophosphates volatize from surfaces and have short half-lives (i.e., a few days) on foliage, but in soil their half-lives vary widely (Table 1) [reviewed by Bradman et al. (1994); Miles et al. (1979); Racke (1993); Willis and McDowell (1987)]. Once in the air, the organophosphate phosphorothionates (a subclassification of organophosphates, including chlorpyrifos, diazinon, malathion, and others, e.g., azinphos-methyl, dimethoate, methidation, parathion, and phosmet) degrade within a matter of hours by reacting with photochemically produced hydroxy radicals (Hazardous Substances Data Bank 2002), degrading from thion (P = S) to oxon (P = O) compounds (Aston and Seiber 1997). The oxon degradation compounds are also formed in mammals, are more reactive, and are more potent inhibitors of acetylchlolinesterase than are the parent compounds (Chambers and Russell 1993; Huff et al. 1994; Poet et al. 2003). Spatially, pesticide air dispersion models estimate postapplication, off-field dispersal of ≤1 km (0.62 miles) after single applications of pesticides to specific agricultural fields (Raupach et al. 2001; Watanabe 2000). Yet deposition and some air measurements suggest that organophosphates may have an atmospheric dispersion of ≥100 km (62.1 miles) (Seiber et al. 1993; van Dijk and Guicherit 1999; Zabik and Seiber 1993).
In California, growers are required to report 100% of their agricultural pesticide use (CDPR 2000). For epidemiologic studies of chronic end points, such as childhood cancer and fetal outcomes, researchers have temporally and spatially aggregated the reported use as a broad indirect measure of many potential exposures (Bell et al. 2001; Reynolds et al. 2002). Yet no one has investigated whether there is a relationship between reported agricultural organophosphate use and human exposure by any pathway. For a health assessment of mothers and their children being conducted in California’s Salinas Valley [i.e., the CHAMACOS (Center for Health Analysis of Mothers and Children of Salinas) study (Eskenazi et al. 1999)], we plan to characterize media pathways of children’s organophosphate exposure from source to internal biologic dose. To address this aim, we used the chlorpyrifos, diazinon, and malathion air monitoring data collected by the Cal/EPA TAC program [California Air Resources Board (CARB) 1998a, 1998b, 1999]. Malathion is used widely in California but is lower in toxicity than other organophosphates because of mammalian metabolism (Ecobichon 1996). We characterized the associations between agricultural pesticide use and measured air concentrations at different temporal and spatial scales and examined the extent to which meteorologic conditions modify the relationships.
Materials and Methods
Air measurement data.
Air samples were collected by CARB for the CDPR and then analyzed by the Trace Analysis Laboratory at the University of California at Davis (UCD) Department of Environmental Toxicology, under contract with CARB. Their methods are described elsewhere (CARB 1998a, 1998b, 1999). In summary, during application season CARB placed monitors on the roofs of four accessible public buildings in populated high-pesticide-use areas. The areas monitored for chlorpyrifos and diazinon (Tulare and Fresno Counties, respectively) are in the southeastern portion of California’s Central Valley. Malathion monitoring occurred in Imperial County, an irrigated desert area. The distances between monitoring stations averaged 9.0, 9.1, and 13.8 miles for chlorpyrifos, diazinon, and malathion, respectively. At each monitoring location, the distance to nearby fields where target applications may occur was noted and varied between close (e.g., across the street) and 1 mile away.
Chlorpyrifos monitoring was conducted in the late spring of 1996, diazinon monitoring in the winter of 1998, and malathion monitoring in the early spring of 1998. CARB collected samples over 3-week (diazinon, malathion) and 4-week (chlorpyrifos) periods based on the use patterns for the respective pesticides. Generally, 24-hr samples were collected, using lipophilic polystyrene resin (XAD-4) beginning between 0800 hr and 1000 hr, Monday through Friday, with a flow rate of 15 L/min for chlorpyrifos and a lower flow rate (3–4 L/min) for diazinon and malathion (CARB 1998a, 1998b, 1999). Quality control included field and trip spikes, trip blanks, and replicate samples. Analytical measurements included spiking the resin tube with labeled analyte before extraction. In their analysis for chlorpyrifos and chlorpyrifos oxon, UCD used a gas chromatograph with a flame photometric detector and a filter for phosphorus detection; quantification was confirmed with an electrolytic conductivity detector and/or a mass selective detector operated in selective ion monitoring mode (CARB 1998a). For diazinon and malathion, UCD used a gas chromatograph with a DB-17 capillary column and a quadrapole mass spectrometer operated in selected ion mode (CARB 1998b, 1999). The method quantitation limits (QL) per sample were 68 ng for chlorpyrifos, 113 ng for chlorpyrifos oxon, 44.5 ng for diazinon, 17.3 ng for malathion, and 34.0 ng for maloxon. All blanks were less than the QL. Acceptable recoveries, ranging from 77 to 125%, were reported for all spiked analytes. Similar to other pesticide air measurements in California (Lee et al. 2002), results were not corrected for recovery. The relative differences of co-located samples, where amounts in both samples were above the QL, had mean relative differences of 12% for chlorpyrifos (16 pairs), 11% for chlorpyrifos oxon (16 pairs), 4% for diazinon (8 pairs), and 15% for malathion (11 pairs; CARB 1998a, 1998b, 1999).
Meteorologic data.
We extracted meteorologic data (temperature, wind speed, wind direction, and rainfall) from the California Weather Database, maintained by the University of California Statewide Integrated Pest Management Program (UC-SIPMS 2003). We selected the meteorologic station closest to each air monitoring location and for which appropriate weather data had been collected for the monitoring period. Two chlorpyrifos and two diazinon monitoring locations matched to meteorologic stations nearby (within 3 miles), whereas the two other chlorpyrifos and two other diazinon monitoring locations matched to stations farther away (9 and 15 miles). All four malathion monitoring locations matched to stations 5–9 miles away.
Pesticide data.
CDPR performs a number of validity checks—for example, duplicate records and valid pesticide and geographic identifiers—and maintains a public-use data set of the mandatory agricultural pesticide application reporting known as Pesticide Use Reporting. Growers report their use by date, application method (aerial vs. ground), and section of the Public Land Survey System (PLSS); a PLSS section is approximately 1 mi2. Nonagricultural applications (i.e., at residences, schools, and golf courses) are not reported by PLSS sections and could not be included in our analysis (CDPR 2000, 2005). To evaluate local impacts, we retrieved pesticide-use data for an approximate 49-mi2 area surrounding each of the 12 monitoring sites. The small percentage of records (< 1%) flagged by CDPR as probable errors or outliers, that is, inordinately high applications, were deleted (CDPR 2000). We divided each 49-mi2 area into two smaller areas: an inner circle encompassing the PLSS sections within a 1.5-mile radius of the monitoring site and a doughnut, or outer circle, encompassing the PLSS sections between 1.5 and 3 miles from the monitoring site. We summed pesticide use within the inner and outer circles on each sample collection day (day i). To evaluate how earlier use affected the air concentration on day i, we also summed use separately in the inner and outer circles on each of the 3 days before the monitoring day (day i – 1, day i – 2, and day i – 3). A 4-day temporal selection is consistent with the range of midpoints (3 and 5 days) of reported half-lives on foliage (Table 1). Except for chlorpyrifos models, which had a larger sample size, and where we included day i – 4 in the inner circle, we included neither earlier use nor use in a broader area because the limited number of samples collected would not support a larger number of variables in a multivariate analysis (Harrell 2001).
Statistical analyses.
Using SAS (version 8; SAS Institute Inc., Cary, NC), we generated stepwise multiple linear regression models. For collected air samples below the QL, we entered half that limit. Air concentrations followed a lognormal distribution and were log-transformed for regression analyses. To examine the impact of pesticide use, we developed two series of multiple regression models, entering use variables stepwise. The first series of models examined use applied per square mile in the inner and outer circles combined (pounds per square mile in PLSS sections within a 3-mile radius). We generated a univariate model of pounds applied in the combined inner and outer circles on day i. Then, to determine whether use on the day before the sample collection day explained additional variance in air concentrations, we generated a separate model with the two variables, use on day i and use on day i – 1. Continuing to build separate regression models with additional variables, we repeated this stepwise process for each of the days before the monitoring day (days i – 2 and i – 3). After adding each variable, we tested for the overall fit of the model with the F-test (where F is the ratio of the regression mean square to the deviation mean square). To determine the significance of an added variable (i.e., whether the coefficient for the added variable was nonzero), we performed a log-likelihood ratio test. We did not remove variables based on these p-values because p-value–based variable selection may violate statistical estimation principles (Harrell 2001). We did review the multivariate model: When the proportion of variance (r
2), adjusted for the number of variables in our analysis, decreased after addition of a pesticide use variable, or when the parameter estimates on any use variable in the model became negative, we removed the variable from subsequent stepwise models. To evaluate whether pesticide use in the inner and outer circles had independent impacts, we similarly generated a second series of models but entered use in the inner and outer circles as two variables.
Using the model series for each analyte that generated the highest r
2, we continued the described stepwise multiple regression modeling procedure, adding variables that might modify the relationship between use and air concentrations including: percent pounds applied upwind of monitor (percentage of pounds applied in the upwind PLSS section on day i, as determined by the average wind direction, of the total pounds applied in both circles), percent pounds applied aerially in the PLSS sections within both circles on day i, monitor location, maximum daily temperature, average daily wind speed, and average daily rainfall. To code each monitor location uniquely and parsimoniously, we created two dichotomous indicator variables: For chlorpyrifos and diazinon, this was north versus south and east versus west of the centroid of the four monitors; for malathion, this was north versus south and, because the two northern monitors were also both western monitors, the monitors closest to the centroid of the four monitors versus those farther away. Although for chlorpyrifos and diazinon, which were monitored at the base of the Sierra Nevada Mountains, the directional coding may capture the influence of diurnal wind patterns (e.g., nighttime cooling and movement of foothill air), we primarily considered these monitor variables as proxy indicator variables that could control for several unmeasured factors (i.e., structural or landscape pesticide use near the monitor, terrain, or other unknown factors related to the monitor location). Residuals of final models were confirmed to follow a normal distribution.
Results
Pesticide use in California, during the years that air monitoring occurred, was generally similar to use in other years (Figure 1). Sampling locations were in areas of heaviest pesticide use (Figure 2). Of the three pesticides we examined, chlorpyrifos had the highest use statewide and the widest geographic distribution, followed by diazinon and then malathion.
Table 2 shows the average use, air concentrations, and weather variables in each monitoring area. Chlorpyrifos had the highest use and the highest air concentrations. Chlorpyrifos samples were collected during the warm, late spring, and diazinon samples during the foggy, rainy winter dormant season. Malathion monitoring also occurred during the winter, but the desert conditions of the monitoring location were warm and dry.
For the oxon breakdown products, chlorpyrifos oxon was detected in 83% of samples. Diazoxon was not measured. Maloxon was measured but was above the QL in only 46% of samples—an insufficient number for statistical analysis.
All applications of chlorpyrifos and diazinon were ground applied. Malathion was applied both on the ground and by air. Because each of the three pesticides was applied predominantly to one crop (Table 2) in the monitored areas, we could not include a target-crop variable in our analysis.
Tables 3 and 4 show the proportion of variance explained (adjusted r
2) for our multiple regression models. For both chlorpyrifos and chlorpyrifos oxon, the associations between measured air concentrations and agricultural pesticide use are stronger for our second series of use models, where uses in the inner and outer circles are separate variables. Chlorpyrifos oxon air concentrations are significantly associated with use in the inner and outer circles on the monitoring day (day i) and adding use on the prior day (day i – 1) improves the adjusted r
2. Adding use on day i – 2, day i – 3, and day i – 4 in the inner circle but not the outer circle further improves the adjusted r
2. For chlorpyrifos, there is a similar relationship, although the overall fit of the model is less and not significant until most of the use variables are in the model. The better overall fit for the second series of models compared with the first suggests independent impacts of use in the inner compared with the outer circles.
Adding the location of the monitor to the chlorpyrifos model (Table 4) improves the adjusted r2. Adding weather parameters (i.e., wind speed), slightly improves the r2 for both chlorpyrifos and chlorpyrifos oxon. The significance of the log-likelihood tests that the coefficient of each added variable is nonzero ranges from 0.002 for the addition of use within inner circle day i – 3 to the chlorpyrifos oxon model (an increase in r2 from 0.31 to 0.39), to 0.17 for the addition of use within the outer circle, day i – 2, to the chlorpyrifos oxon model (an increase in r2 from 0.24 to 0.25). For the final models, the adjusted r2 is 0.30 for chlorpyrifos (p < 0.01 for overall fit) and 0.43 for chlorpyrifos oxon (p < 0.0001 for overall fit).
For diazinon and malathion, the associations between measured air concentrations and pesticide use are stronger for our first series of models, where use in the two use areas is combined as one variable (Table 3). For diazinon, the adjusted r2 improves both when use for day i – 1 is added and when use for day i – 2 is added. Notably, the addition of monitor location and daily temperature (positively correlated with air concentrations) increases the r2 for the diazinon model to 0.38 (for overall fit, p < 0.0001), and when either wind speed or rainfall (both negatively correlated with air concentrations and highly correlated with each other) is added, the final r2 increases to 0.65 (for overall fit, p < 0.0001) (Table 4). In the diazinon final model, the tests that the coefficient of each added variable is nonzero reveal statistical significance (p = 0.05 to < 0.0001) for each added variable.
For malathion, the adjusted r2 improves when pesticide use on day i – 1 is added and is similar to that seen for chlorpyrifos, although the overall fit of the multivariate model is not significant because of the smaller number of samples collected (Table 3). Adding use on day i – 2 and day i – 3 marginally improves the r2. Adding the percent pounds applied upwind, the monitor location, and weather parameters to the model (Table 4) each improves the adjusted r2. The p-values of the tests that the coefficient of each added variable are nonzero, ranging from 0.03 for the addition of wind speed to 0.24 for the addition of pesticide use, day i – 2. The adjusted r2 for the final model is 0.28 (for overall fit, p < 0.01).
Discussion
This study demonstrates associations between regional agricultural application quantities of organophosphates and measured air concentrations. National pesticide-use maps suggest that areas of high agricultural use for the three organophosphates we studied are found in many states (USGS 1997). Our results also suggest analyte priorities and potential spatial and temporal parameters for inhalation exposures to organophosphates.
More specifically, measured air concentrations of chlorpyrifos oxon showed a stronger association with reported agricultural use than did chlorpyrifos. This stronger relationship is consistent with the longer estimated air half-life of chlorpyrifos oxon (11 hr) compared with chlorpyrifos (4 hr) (Aston and Seiber 1997). The 24-hr sample collection period studied here allows for the photochemical reaction to occur.
The detectable impact of organophosphate use on community air concentrations may be brief, on the order of days: Inclusion of day i – 3 in our models often failed to improve the adjusted r2 over the association shown by inclusion of days i through i – 2 (Table 3). This finding is consistent with the lower range of the reported half-lives on foliage (Table 1). Other studies are also supportive: In a Canadian agricultural area, chlorpyrifos air levels dropped notably in the week after applications (Rawn and Muir 1999); on each of the 2 days after the day of urban aerial applications of malathion, air levels of the pesticide were approximately halved (Brown et al. 1993). Nonetheless, an initial period of high volatilization may be followed by a longer period of slower volatilization (Brown et al. 1993; Glotfelty et al. 1990b; Whang et al. 1993) that could not be studied here because of the limited sampling scope.
For all analytes, the adjusted r2 improves by including pesticide use in square-mile sections out to a 3-mile radius (5,000 m), whether as a single combined-use variable or independently as two separate variables. This distance is large compared with that studied after single applications on specific agricultural fields: Researchers have generally measured or estimated air concentrations at 10 feet to one-half mile (800 m) from the field (Raupach et al. 2001; Watanabe 2000; Woodrow et al. 1997). There is, however, considerable potential for the atmospheric persistence and long-range movement of vapor and droplets of semivolatile pesticides through vaporization of larger to smaller droplets, vertical air mixing resulting in increased droplet and vapor height, and deposition of droplets with subsequent re-entry into the atmosphere (Van den Berg et al. 1999; van Dijk and Guicherit 1999). In studies of organophosphate ambient air in the Sierra Nevada mountains and fog water of the Salinas Valley, oxon and thion parent organophosphates were detected 9–14 miles (15–22 km) from application sites, with oxons in higher concentration than the thion parent products (Aston and Seiber 1997; Schomburg et al. 1991).
Inclusion of monitor location, entered as proxy indicator variables controlling for unmeasured factors related to the location, improved the adjusted r2 for most of the pesticide models (Table 4). Several unmeasured factors are possible, including locational differences in application or other farming practices, topography that could affect diurnal wind flow, and pesticides applied to buildings near the monitors. Inclusion of weather parameters, most notably wind speed, also improved the models in the predicted direction, particularly for diazinon. Because diazinon monitoring was conducted in winter, during windy, rainy, and heavy fog conditions, inclusion of wind speed may account for washout or dilution of background air concentrations, making the impact of agricultural use more apparent. The water solubility of diazinon has been noted (Glotfelty et al. 1990b), and in our analysis, rainfall was highly correlated with wind speed; inclusion of either factor equally improved the r2.
There is considerable unexplained variance in our models. However, there are many inherent limitations of the data. The number of samples and locations was limited. Meteorologic data were not specific to the air monitoring location, and the amounts in colocated samples also varied some (on average, 4–15%), suggesting analytical variability in air measurements. The fraction of analyte bound to airborne particulates was not measured, although this has been shown for organophosphates to be < 5% of the mass (Glotfelty et al. 1990a).
Underreporting of pesticide use is another potential source of uncertainty. A systematic evaluation of underreporting (i.e., a field survey of applications to validate use reporting) has never been conducted. However, growers are required by law to report their pesticide use, and local agricultural commissioners may inspect use reporting when they conduct audits of pesticide applicator records or when investigating potential misapplication of pesticides (California Office of Administrative Law 2001). Another limitation is that we could not examine whether applications closer to the monitor (i.e., < 1 mile) had a greater impact, and we could not include in our models data on pesticide application methods (e.g., orchard blast vs. ground application) or farming practices such as tilling and irrigation. Given the limited sampling size and scope, as well as the many factors we could not evaluate, our models did explain a considerable fraction of the variability of measured pesticide air concentrations. Our results suggest that agricultural applications of organophosphates are a source of exposure, that significant impacts may be brief (on the order of days), that the spatial dispersion may be greater than that currently studied in single-pesticide-application air studies, and that oxon degradation compounds may be equally, if not more, important contributors to air concentrations than the parent thion products.
Others have noted the need for improved pesticide ambient air monitoring (Carey and Kutz 1985; USGS 1995). Improved air monitoring and enhanced pesticide-use reporting, including enhanced geographic resolution and farming practices, could not only validate estimated exposure zones and duration but also provide valuable regulatory tools for estimating the amount of pesticide use reduction necessary to achieve a desired air level.
Organophosphates have been the focus of a variety of regulatory efforts to reduce exposures. Based on estimated dietary risks, chlorpyrifos was recently withdrawn for use on tomato and apple crops (U.S. EPA 2000a). In California, educational outreach to growers has reduced organophosphate use on orchards (Epstein et al. 2001). Nonetheless, consideration of the air exposure pathway in the recent U.S. EPA chlorpyrifos reregistration risk assessment was waived based on estimated photodegradation; the more toxic oxon metabolite was not considered (U.S. EPA 2000b). In contrast, the recent U.S. EPA risk evaluation of diazinon suggested that greater consideration of the air pathway was needed (U.S. EPA 2002). In our study, chlorpyrifos had greater use and higher air concentrations than diazinon, and those concentrations represented a higher health risk than those for diazinon (Lee et al. 2002). These results suggest that the U.S. EPA environmental fate and pesticide-use estimates should be reexamined.
Whether initial estimates of elevated inhalation risks for chlorpyrifos and diazinon exposures to children (Lee et al. 2002) are adequately protective is uncertain. Although intra- and interspecies 10-fold uncertainty factors were used, the true range of mammalian response is unknown (Hattis et al. 1996). Children and adults may alone differ by several orders of magnitude in their susceptibility (Eskenazi et al. 1999; Faustman et al. 2000). Recent comparison of RfDs to risks quantified by examining the entire range of the animal dose–response curve (i.e., benchmark doses) demonstrates that RfDs generally underestimate health concern and do not represent negligibly small risks (Castorina and Woodruff 2003). Greater evidence of the vulnerability of embryos, fetuses, neonates, and adolescents to organophosphates has also recently emerged. In murine embryos, cell death has been induced at the chlorpyrifos RfD for drinking water, 0.003 mg/kg/day (Greenlee et al. 2004). Additional organophosphate neurodevelopmental toxicity mechanisms include gliogenesis, axonogenesis, and synaptogenesis (Qiao et al. 2002). Chlorpyrifos oxon also binds directly to some (i.e., muscarinic) cholinergic receptors (Huff et al. 1994). In humans, among 314 newborns in New York City, low levels (1–4 pg/g) of chlorpyrifos and diazinon in umbilical cord plasma were inversely associated with birth weight and length (Whyatt et al. 2004). Although these outcomes were not correlated with maternal personal air levels of chlorpyrifos and diazinon, the oxon metabolites were not measured in air. Further, the personal air levels were significantly correlated with the cord blood levels and were, for chlorpyrifos, lower (chlorpyrifos mean = 15 ng/m3) than that studied here (Table 2) (Whyatt et al. 2004).
We have studied only a few organophosphates. In 2003, in addition to the 2.7 million pounds of chlorpyrifos, diazinon, and malathion applied to California agriculture, 1.2 million lb of other organophosphorothionates, and a total of 7.9 million lb of cholinesterase inhibitors (i.e., all organophosphates and carbamates), were applied on California agriculture (CDPR 2005). Our findings suggest that reducing agricultural organophosphate use could reduce community air concentrations and lower inhalation risks to children and residents living in and around agricultural communities.
We thank S. Lee, P. Reynolds, and M. Lipsett of CDHS, and B. Eskenazi for their technical assistance.
This research was supported by U.S. Environmental Protection Agency Science to Achieve Results program grant R826709 and National Institute of Environmental Health Sciences grant 5P01 ES09605-04.
This research was not subjected to federal peer review. Opinions are those of the authors and do not necessarily reflect the official position of the CDHS or the funding agencies. No official endorsement should be inferred.
Figure 1 Agricultural use of organophosphates in California by year.
Figure 2 Map of organophosphate (chlorpyrifos, diazinon, malathion) use and locations of monitors, in California.
Table 1 Environmental fate parameters.
Parameter Chlorpyrifos Diazinon Malathion
Vapor pressure (mm Hg at 25°C) 2.0 × 10–5a 9.0 × 10–5a 1.8 × 10–4b
Water solubility (mg/L) 1.4 at 25°Ca 60 at 20°Ca 145 at 25°Ca
Half-life
Air as vapor (hr) 4c 4c 5c
Foliage (days) 1–9d 1–5e < 1–9f
Soil (days) 2–1,575d 3–87g 1–6f
a Data from Tomlin 1997.
b Data from Daubert and Danner 1989.
c Data from Hazardous Stubstances Data Bank 2002.
d Reviewed by Racke (1993).
e Reviewed by Willis and McDowell (1987).
f Reviewed by Bradman et al. (1994);
g Data from Miles et al. 1979.
Table 2 Monitoring conditions and median air concentrations.
Parameter Chlorpyrifos Diazinon Malathion
County of monitoring Tulare Fresno Imperial
Major crop Citrus Almond Alfalfa
Monitoring period 28 May–29 June 1996 12 Jan–2 Feb 1998 23 Feb–12 Mar 1998
No. of monitoring days 20 12 12
No. of monitoring locations 4 4 4
Percent samples above QLa 84 60 83
Median air concentrationb 33 ng/m3 17 ng/m3 9.9 ng/m3
Mean lb/mi2 per day
Within 1.5 mi 9.9 1.2 4.1
Within 1.6–3 mi 7.7 1.4 3.2
Mean (range) applications/day, within 3 mi 3.2 (0–16) 3.9 (0–27) 1.5 (0–11)
Average maximum daily temperature (°F) 89 58 73
Average daily wind speed (mi/hr) 3.8 4.1 4.9
Mean daily inches of rainfall 0 0.12 0.004
a Percentage of chlorpyrifos oxon samples above QL = 83%.
b Median air concentration for chlorpyrifos oxon = 22 ng/m3.
Table 3 Stepwise multiple regression models, adjusted r2,a of air concentrations and pesticide use (lb/mi2).
Variables added sequentiallyb Chlorpyrifos Chlorpyrifos oxon Diazinon Malathion
Model series 1c
Use within 3 miles: day i 0.07 0.13* 0.12* 0.08
Use within 3 miles: day i – 1 0.08 0.21# 0.17* 0.14
Use within 3 miles: day i – 2 0.09 0.24# 0.23**d 0.14
Use within 3 miles: day i – 3 Removede Removede Removede 0.15d
Model series 2f
Use within 1.5 miles: day i 0.05 0.16# 0.01 0.05
Use within 1.6–3 miles: day i 0.07 0.19# 0.05 0.07
Use within 1.5 miles: day i – 1 0.09 0.24# 0.06 Removede
Use within 1.6–3 miles: day i – 1 Removede 0.25# 0.1 0.13
Use within 1.5 miles: day i – 2 0.13* 0.31# 0.12 Removede
Use within 1.6–3 miles: day i – 2 Removede Removede 0.13 0.16
Use within 1.5 miles: day i – 3 0.18* 0.39# 0.14 Removede
Use within 1.6–3 miles: day i – 3 Removede Removede Removede Removede
Use within 1.5 miles: day i – 4 0.21d 0.41#d NA NA
NA, not applicable (i.e., analysis was not conducted).
a Adjusted for the number of variables in the model; statistical significance is a test of the overall significance of the multivariate model.
b Each row within a model series represents a regression model with the named variable (the added variable) and the variables named in the rows above it included. Day i is the sample collection day. Day i – 1, i – 2, i – 3, and i – 4 represent each of the 4 days before the sample collection day.
c For model series 1, pesticide use in PLSS sections within a 3-mi radius of monitor was entered as one variable.
d Pesticide use variables in this model selected for subsequent analysis.
e The adjusted r2 decreased compared with previous model or the use parameter coefficients were negative; the variable was eliminated from subsequent models.
f For model series 2, use in PLSS sections within a 1.5- and a 1.5- to 3-mi radius of monitor were entered as two separate variables.
* p < 0.01;
** p < 0.001;
# p < 0.0001.
Table 4 Addition of weather/locational variables to stepwise multiple regression models, pesticide use variables included: adjusted r2 for multivariate model.a
Additional variables added sequentiallyb Chlorpyrifosc Chlorpyrifos oxonc Diazinond Malathiond
Percent pounds applied upwind Removede Removede Removede 0.18
Percent pounds aerially applied NA NA NA Removede
Location of monitor 0.29# Removede 0.29** 0.22
Temperature (maximum daily) Removede Removede 0.38# Removede
Wind speed (average daily) 0.30#f 0.43#f 0.65#f 0.28*f
Rainfall (average daily) NA NA Removede Removede
NA, not applicable (i.e., there were either no applications applied aerially or there was no rainfall).
a Adjusted for the number of variables in the model; statistical significance is a test of the overall significance of the multivariate model.
b Each row within a model series represents a regression model with the pesticide use variables, the variable named in the row (the added variable) and the variables named in the rows above it included.
c Model series 2 (pesticide use out to 3-mi radius as two separate variables) generated improved adjusted r2 compared with model series 1 (pesticide use out to 3-mi radius as one variable), and the additional variables are stepwise added to model series 2.
d Model series 1 (use out to 3-mi radius as one variable) generated improved adjusted r 2, compared with model series 2, and additional variables are added stepwise to model series 1.
e Adjusted r2 decreased or remained the same, or any use parameter coefficients were negative; the variable was eliminated from subsequent models.
f Final model.
* p < 0.01;
** p < 0.001;
# p < 0.0001.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7691ehp0113-00119016140626ResearchTesting the Dose–Response Specification in Epidemiology: Public Health and Policy Consequences for Lead Rothenberg Stephen J. 1Rothenberg Jesse C. 21 National Institute of Public Health, Center for Research in Population Health, Cuernavaca, Morelos, Mexico2 University of Sydney, Faculty of Economics and Business, School of Economics and Political Science, Camperdown, New South Wales, AustraliaAddress correspondence to S.J. Rothenberg, Center for Research in Population Health, National Institute of Public Health, Avenida Universidad 655, Sta. María Ahuacatitlán, CP 62508 Cuernavaca, Morelos, México. Telephone/Fax: 52-739-395-0662. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 10 5 2005 113 9 1190 1195 22 10 2004 10 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Statistical evaluation of the dose–response function in lead epidemiology is rarely attempted. Economic evaluation of health benefits of lead reduction usually assumes a linear dose–response function, regardless of the outcome measure used. We reanalyzed a previously published study, an international pooled data set combining data from seven prospective lead studies examining contemporaneous blood lead effect on IQ (intelligence quotient) of 7-year-old children (n = 1,333). We constructed alternative linear multiple regression models with linear blood lead terms (linear–linear dose response) and natural-log–transformed blood lead terms (log-linear dose response). We tested the two lead specifications for nonlinearity in the models, compared the two lead specifications for significantly better fit to the data, and examined the effects of possible residual confounding on the functional form of the dose–response relationship. We found that a log-linear lead–IQ relationship was a significantly better fit than was a linear–linear relationship for IQ (p = 0.009), with little evidence of residual confounding of included model variables. We substituted the log-linear lead–IQ effect in a previously published health benefits model and found that the economic savings due to U.S. population lead decrease between 1976 and 1999 (from 17.1 μg/dL to 2.0 μg/dL) was 2.2 times ($319 billion) that calculated using a linear–linear dose–response function ($149 billion). The Centers for Disease Control and Prevention action limit of 10 μg/dL for children fails to protect against most damage and economic cost attributable to lead exposure.
child IQdose responsehealth benefithealth policylead
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Few researchers doubt that lead exposure has significant health consequences at levels below those considered medically acceptable just decades ago, although there is still debate over what levels of lead exposure, if any, can be considered harmless. Key to this debate is determining the form of the dose–response function describing how the amount of exposure is related to the magnitude of the health effect.
There are two basic forms of the dose–response function for lead: a simple linear model, in which the increase in health effect is a linear function of increasing blood lead concentration (BPb), and a nonlinear model, in which the amount of health effect change attributable to lead changes according the region of the dose–response curve studied. A special case of the nonlinear dose–response function is a threshold model in which the response to lead decreases as a function of decreasing dose until it reaches a lead dose below which there is no further detectable change in health. An alternative threshold model is one in which the response to lead changes as a function of increasing dose until an upper lead bound is reached, at which point the increase in health damage exceeds predictions, as in cases of high doses producing organ damage.
Although epidemiologists have become increasingly sophisticated in construction and diagnosis of models describing their data, as a whole, we generally pay much less attention to systematically and rigorously addressing the specification of the dose–response function. A number of public health issues depend on adequately specifying the form of the dose–response function for lead, chief among them regulatory action.
Cost–benefit analyses should form the backbone of regulatory decisions regarding permissible exposures or background concentrations of toxic substances in both population and occupational settings. In such an ideal world, the savings in health care, disability, and productivity gain realized from reducing exposure to a particular substance are compared to the cost required to achieve that reduction in exposure. Policy analysts seek the “sweet spot,” where the marginal costs of lead reduction equal the marginal benefits (i.e., where the slopes of the cost function and benefits function are equal) (Pacala et al. 2003). Even if in the real world less easily quantifiable factors affect regulatory decisions, all parties to regulation have some notion of costs and benefits in mind when presenting their cases to regulatory agencies.
One recent publication (Grosse et al. 2002) presented data on the economic benefits of nationwide lead reduction due to childhood IQ (intelligence quotient) loss attributable to lead over the last 25 years. These authors conservatively used a linear dose–response function of lead–IQ as part of their model, stating that there was insufficient evidence to determine the shape of the dose–response function. The economic savings predicted by their model were in the range of hundreds of billions of dollars over the lifetime of a yearly birth cohort.
The lead–health dose–response function selected for the benefits model has clear implications for policy decisions based on it. A threshold model suggests that once reductions of population level of lead reach the threshold, further lowering of lead would have no beneficial health or economic consequences. The current Centers for Disease Control and Prevention (CDC) action limit of > 10 μg/dL for children (CDC 1991) would be justifiable on health grounds alone if there were a threshold somewhere near that limit. A linear model suggests that equal reduction in population BPb is accompanied by equal reduction in health consequence from any starting level of lead. Under a linear dose–response model, even though the health benefit would continue to increase with further population lead reduction, the present CDC action limit might be justifiable on economic grounds if the cost of further population BPb reduction far exceeded the recoverable economic benefits. A nonlinear model, especially one in which health benefits are greater for lead reduction nearer the population’s zero lead point than farther from it, would argue for further reduction in population lead levels and CDC action limits if the accelerated health benefit at lower lead levels exceeded the increased costs of lead reduction to those levels.
In this article, we present a critical examination of the dose–response function in a widely studied area of epidemiologic research with lead: childhood IQ. We present easily accessible statistical techniques useful for deciding among alternative dose–response functions and for testing whether residual confounding resulting from possible misspecification of model control variables affects the dose–response function. We apply our dose–response modeling results to the benefit model noted above to calculate changes in economic benefits realized from using a statistically adequate dose–response function. The results are placed in the context of public health policy and regulation.
Materials and Methods
Data sets.
Eight prospective studies of lead exposure have used child IQ or developmental index as the outcome measure, with outcome measured at least to 5 years of age, of which seven (Baghurst et al. 1992; Bellinger et al. 1992; Canfield et al. 2003; Dietrich et al. 1993; Ernhart et al. 1989; Schnaas et al. 2000; Wasserman et al. 1997) agreed to participate in a pooled analysis study; combining the data sets produced a study sample of 1,333 with a 0.1–71.7 μg/dL range of lead exposure (Lanphear et al. 2005). All studies producing data for the pooled analysis were approved by an appropriate institutional review board. Child IQ as measured by one of several versions of the Wechsler Intelligence Scales for Children (Wechsler 1967, 1974, 1981, 1991) around 7 years of age was regressed on different indices of BPb (child BPb from 6 to 24 months, peak BPb during the first 7 postnatal years, average BPb over the same time, and contemporary BPb) in multiple regression models controlling for maternal IQ and education, quality of the home environment and child–caretaker interaction [Home Observation for Measurement of the Environment (HOME); Caldwell and Bradley 1984], birth weight, and study site. Other control and confounding variables, such as child’s sex, tobacco exposure during pregnancy, alcohol use during pregnancy, maternal age at delivery, marital status, and birth order, had no significant effect in the models, did not significantly alter the IQ–lead relationship, and were not included in the final models. All lead variables in the models were natural-log transformed. All lead variables had highly significant effects on IQ (p < 0.0005) in the models.
We selected BPb measured contemporaneously with IQ for further analysis using the pooled data set [adjusted estimate of natural-log lead (95% confidence interval; CI) on IQ = −2.70 (−3.74 to −1.66)], because this was the measure to which Lanphear et al. (2005) devoted most attention, even though it had the second smallest coefficient among the four measures presented.
Statistical analyses.
Multiple regression modeling.
The IQ data set was analyzed with the original model specifications, including log-transformed BPb, using multiple regression analyses (STATA, version 8.2; Stata Corp., College Station, TX, USA). The IQ multiple regression model was also respecified with a linear lead term.
Specification tests for the functional form of the lead variable (dose–response function).
The omitted variable test, or regression specification error test (Ramsey 1969), statistically tests change in model fit when any polynomial transformation of the variable in question is used in place of the original functional form of the variable. To test whether the polynomial form is superior to the original form of the variable, a chi-squared test is constructed by using the difference in two models’ chi-squared value (or the difference in two times the log likelihood of the two models), with the number of degrees of freedom determined by the number of additional variables added to the polynomial model. This is a maximum likelihood evaluation of changes in model fit and is a test of nested models, because the original specification is nested within the polynomial specification. Its principal disadvantage is that it only tests whether a polynomial specification is better than a simpler specification and does not allow direct comparison of two non-nested models each with a different specification, such as linear and logarithmic. On the other hand, it is easy to do even in the absence of “canned” statistical routines and quickly indicates whether the original variable specification can be improved by adding polynomial terms.
An accessible approach for comparing variable specification between two non-nested models is the J-test (Davidson and MacKinnon 1981). It can be realized by first obtaining predicted values for two models, each with a different specification of the same independent variable, and then adding the prediction of the first model to the specification of the second model and vice versa (Appendix). A clear indication in favor of one or the other specification would occur when one of these prediction-added models results in a significant value for one specification of the variable and the other prediction-added model results in a nonsignificant value. A disadvantage of this test is its low power to detect a significant improvement in variable specification. Hundreds or thousands of observations might be needed if the difference between two alternative variable specifications is subtle or the variable is measured over a limited range. Low power and limited range are not limiting factors in the present study (n = 1,333; BPb range, 0.1–71.7 μg/dL).
Testing for residual confounding.
When control or confounding variables either are omitted or their functional form is mis-specified, the resulting residuals in the model could cause an alteration in the apparent functional form of the dose–response relationship (Becher 1992). In the case of IQ, not accounting for variables such as the number of other family members, family socioeconomic status, birth or childhood trauma or serious illness, IQ of the father, or other variables that might control subject IQ could alter the measured form of the dose–response relationship for lead. If these variables are not accounted for in the experimental design by becoming part of the inclusion/exclusion criteria or they are not tested for and, where appropriate, included in the models, they may contribute to residual confounding of the dose–response curve.
Another potential cause of residual confounding occurs when the functional form of included control or confounding variables is not correctly specified. Because much statistical modeling in epidemiology is performed using some variant of generalized linear models (we used least-squares regression), modelers may assume that the linear specification of these other variables is correct. For instance, a truly nonlinear relationship between maternal and child IQ that is mistakenly modeled as a linear relationship, significant or not, will alter the residuals of child IQ over different parts of the maternal–child IQ relationship. Because the dose–response curve for lead–IQ is based on those residuals, this confounding can modify the modeled dose–response relationship.
When residual confounding is caused by a variable omitted from the design, there is little remedy available except to redesign the study and collect the data anew. Fortunately, we can account for residual confounding when it is due to misspecification of included variables. Generalized additive models (GAM) (Hastie and Tibshirani 1990) can use smoothing spline functions, among other smoothers, to fit continuous and ordinal independent variables to the dependent variable instead of predetermined linear fits as with linear regression models. Depending on the number of degrees of freedom allotted to the splines, the technique can follow complex nonlinearity in the relationship between independent variables and the dependent variable, nonlinearity that might be difficult to account for by parametric functions. The penalty for increasing the complexity of the spline fit is the use of more degrees of freedom in the model. GAM yields no parameters readily summarizing the relationship between independent variables and the dependent variable. There is no disadvantage, however, if we want to use GAM to characterize the possibly complex relationships among independent control variables and the dependent variable to avoid having incorrect residuals affect the parametric dose–response relationship, as has been previously shown with simulations (Benedetti and Abrahamowicz 2004).
GAM allows calculation of the gain from the spline fit over a linear fit by assessing the increase in deviance of the fit of the linear characterization of the variable compared with the spline fit characterization. Under the null hypothesis that nonlinearity of the smoothed function is an artifact, the gain is approximately a chi-squared distribution. Thus, approximate probability values can be calculated for improvement of fit using the spline function. A significant gain indicates that the original linear or any other specification of the variable was a poorer fit to the data than is the spline fit. The procedure also gives a total model gain and model gain significance value.
We used cubic-spline GAM modeling of IQ. We modeled all continuous and ordinal variables with cubic splines with 2, 3, and 4 degrees of freedom. We constructed three alternative models based on the basic model above. In the first series of GAM models, we used untransformed BPb (linear BPb) spline modeled with the same number of degrees of freedom as the control variables. A significant gain in the spline-modeled untransformed BPb term would indicate that the original linear BPb specification could be improved upon, after correcting for any nonlinearity in the control variables.
In the second series of GAM models, we substituted the natural-log–transformed BPb variable for the linear BPb variable of the first model, allowing the number of degrees of freedom of the spline fit to vary as in the first model. An insignificant gain of the natural-log–transformed lead variable would indicate there was no improvement detected in the fit to the dependent variable by spline modeling of the log-transformed lead variable, correcting for possible nonlinearity in the control variables.
Finally, the third series of GAM models was constructed as above, except that the natural-log–transformed lead variable was held to 1 degree of freedom. This tested the original natural-log specification of the lead variable in a model where residual confounding from possible misspecification of the control variables was corrected. Insignificant gains in the other variables would suggest that their original specifications were adequate. The size of the lead coefficient was compared between the third series and the original multiple regression model to determine how much residual confounding of misspecified control variables affected the estimated size of the relationship between lead and the health outcome dependent variable.
All statistical procedures were carried out using MATLAB (version 6.5.1; Mathworks, Natick, MA, USA) and STATA 8.2.
The benefits model.
We used a previously published model (Grosse et al. 2002) of economic benefits showing expected dollar savings produced by population lead declines in the United States from 1976 through 1999 solely through increased population cognitive ability as measured by lead effects on child IQ. The model posits that the dollar gain in the affected cohort is a simple product of reduction in BPb over the period (micrograms per deciliter), the IQ–BPb slope (IQ per micrograms per deciliter), the earnings–IQ slope (%), the present value of lifetime earnings of a 2-year-old child (in year 2000 dollars), and the size of the 2-year-old cohort. Grosse et al. (2002) used linear IQ–lead slopes of 0.185–0.323 IQ points for each 1 μg/dL, calculated from published meta-analyses.
Instead of the linear IQ–lead slope, we substituted the change in IQ expected over the estimated 15.1 μg/dL decrease in population lead in the United States, calculated by assuming both a linear–linear and a log-linear lead–IQ dose–response function using the results of the pooled analysis presented above and then recalculated the cohort benefit.
Results
Lead and IQ.
Table 1 shows the lead coefficients of the different IQ models. Both linear and natural-log lead specifications were highly significant (Table 1). The omitted variable test using the linear lead variable showed a significant improvement in fit using the polynomial lead specification (p = 0.020), whereas the same test showed that a polynomial form of the log lead variable offered no improvement (p = 0.258).
The J-test showed that the log lead specification was still significant (p = 0.009) in a model with the prediction from the linear lead model added (Table 1). The alternative model, the linear lead model with the prediction from the log lead model resulted in an insignificant linear lead variable (data not shown). The results indicate that the log lead specification described the data significantly better than did the linear lead specification.
Tables 2–4 show the results of the GAM analyses. Presented results are limited to the 2 degrees of freedom spline fits because they usually resulted in the largest gains and lowest probability values, although results were similar for the 3 and 4 degree of freedom spline fits. Spline fit gains are shown only for ordinal and continuous variables because dichotomous variables cannot be fit by splines and they remain in the model unmodified.
In Table 2 the linear lead model is entirely fitted by splines. Note that the gains of all control variables were nonsignificant, suggesting adequate specification of these variables as linear. The gain of the linear lead variable was highly significant (p = 0.006), and the total gain of the model was also significant (p = 0.0142). These findings indicate that both the linear lead specification and the model as a whole better fit the data when splines were used than when the original variables were fit by linear regression. The results from Table 1, that the linear lead term did not adequately fit the data, was confirmed in Table 2, and the nonlinear (spline) fit of the lead variable was not due to residual confounding with included variables.
Table 3 shows the same spline-fit model as Table 2, but the natural-log lead term is substituted for the linear lead term. Once again, no control variable showed significant gain using the spline fit, the log-transformed lead variable gain was also nonsignificant (p = 0.230), and the model itself was not significantly improved by fitting the variables with splines (p = 0.163). There was no significant improvement in the log-linear lead–IQ fit by adjusting for possible departures from that specification.
In Table 4 the natural-log–transformed lead variable is allowed to maintain its original specification while the remainder of the variables are fit with splines. Comparison of the coefficient of the log lead variable in this model (β= −2.62) with the coefficient of the multiple regression model (β = −2.70; second coefficient from Table 1) further supports the result that there was no important misspecification of the control variables in the original multiple regression model and the log-linear form of the dose–response curve was not affected by residual confounding of variables included in the model.
These results strongly support the hypothesis that an adequate description of the dose–response curve for the effect of lead on child IQ is log-linear, not linear, and that residual confounding of the dose–response specification by possible misspecification of included control variables played no role. The log-linear dose–response relationship is compared with the linear dose–response relationship in Figure 1.
Economic benefits model for lead–IQ.
Grosse et al.’s (2002) benefit model of economic gains due to lead reduction effect on IQ in the United States calculated the total year 2000 dollar savings as a result of the fall of BPb over a 23-year period. Their model postulated that the dollar benefit per cohort was benefit = A × B × C × D × E, where A is the reduction in BPb (micrograms per deciliter); B is the IQ–BPb slope; C is the earnings–IQ slope (%); D is the present value of earnings of a 2-year-old child (in 2000 US dollars); and E is the size of the 2-year-old cohort. We used their “base case” figures as follows: C, 2.0; D, $723,000; E, 3,800,000. In place of Grosse et al.’s A of 15.1 μg/dL, we used the difference in the natural-log BPb values in 1976 and 1999: BPb (1976), 17.1 μg/dL; natural-log BPb (1976), 2.84; BPb (1999), 2.0 μg/dL; natural-log BPb (1999), 0.69; difference in BPb (1976 –1999), 15.1 μg/dL; and difference in natural-log BPb (1976 – 1999), 2.15. In place of Grosse et al.’s B of 0.257 IQ–BPb slope (every decrease of 1 μg/dL BPb is associated with an increase of 0.257 IQ points) used in their “base case” analysis, we used the natural-log lead coefficient calculated from the pooled analysis study (Table 1, natural-log lead model), 2.70 (every natural-log unit decrease in BPb is associated with an increase of 2.70 IQ points).
The original benefits model used uncertainty in the reduction of BPb over the period studied (variable A) and the IQ–BPb slope (term B) to calculate upper and lower bounds on economic benefits. We used only the uncertainty in the IQ–BPb slope, calculated from the coefficients presented in Table 1 and from the reported meta-analysis Grosse and colleagues used (Grosse et al. 2002; Schwartz 1994) in their base case analysis. We present Grosse et al.’s original calculations based on their linear lead coefficient, the new calculations based on a log-linear dose–response relationship with 95% CIs of B (−3.74 to −1.66), and, for comparison, the dollar savings per cohort based on the demonstrably incorrect linear lead specification calculated from the pooled analysis study, 0.18 (95% CI, −0.26 to −0.10) (Table 1, linear lead model). These results are presented in Table 5.
Savings estimated using the correct log-linear dose–response relationship between BPb and IQ are nearly 2.2 times those estimated using a poorly fitting linear dose–response relationship for the same decrease in population BPb.
Discussion
Model specification.
Diagnosing model specification is an essential part of statistical modeling, particularly when ordinal and continuous variables are part of the model. Compared with the more commonly used diagnostic tests for general linear models, such as testing for distribution and homoskedasticity of residuals, formal tests of the assumed functional form of any independent variables against the dependent variable are scarcely reported in the epidemiologic literature. We often do not address functional form issues except as a by-product of adjusting residual diagnostics. For example, most lead–IQ studies in children, especially in the last 20 years, have used a natural-log–transformed lead variable to normalize the residual distribution of the model and correct for heteroskedasticity of residuals.
Although issues of the functional form of the lead–health effect relationship have occasionally been raised in the literature, notably by Schwartz (e.g., Schwartz 1994), and more recently and extensively by Grosse et al. (2002), it is common practice for the authors of applications of these studies to economic analysis to use linear approximations of the lead effect over a limited range of BPb, as did Grosse et al. (2002). Authors have not extrapolated health effects below the lower limits of lead in their data sets in the past. Data sets studying a wide range of BPb have only recently become available. Because linear and log lead specifications produce large differences in predictions only as BPb approaches zero (Figure 1), data sets including substantial numbers of very low BPb levels are required to notice, appreciate, and test for adequacy of alternative specifications. Implicit in the log-lead specification is that change in health effect with change in BPb at higher levels is small, save when lead toxicity associated with pathologic organ damage is reached.
Most studies using a log-linear dose–response relationship also neglect to comment on the public health implications of this functional form. As opposed to a linear dose–response relationship, where equal changes in health outcome are predicted for equal changes in BPb across the entire range of BPb, the log-linear relationship has the steepest slope at the lowest BPb. Health outcome changes are equal for equal proportional changes in BPb across the entire tested range of BPb. In the case of a log-linear dose–response relationship, the increase in population IQ predicted from a decrease in population BPb from 2 to 1 μg/dL is exactly the same as that predicted from a decrease in BPb from 20 to 10 μg/dL or from 40 to 20 μg/dL, although populations exposed to these different concentrations of lead will likely have different mean IQs.
We calculated the BPb change in the U.S. population between 1976 (17.1 μg/dL) and 1999 (2.0 μg/dL) used by Grosse et al. (2002) at 2.15 natural-log units change. The pooled analysis study has 38 subjects with BPb levels < 2 μg/dL. If we project the 1999 population BPb of 2.0 another 2.15 natural-log lead units down to a population lead level of 0.24 μg/dL in the indeterminate future, we can duplicate the health benefit of BPb reductions for the population already achieved by the reductions between 1976 and 1999, at least for IQ outcomes.
The log-linear dose–response function for lead, especially if it generalizes across other health outcomes, may also account for the failure of many older studies to find significant lead-related effects. In occupational studies of health effects of lead exposure, often the generally high mean BPb levels of the “exposed” groups and even the “nonexposed” control group place health comparisons among exposure levels on the flat end of the log-linear dose–response curve, where the dose–response curve approximates a nearly zero slope linear trend. Under such conditions, a very large sample size would be needed to detect significant differences among groups. If a linear model were used to specify the dose–response relationship at higher BPb, even significant effects detected in large studies would have small coefficients. The apparent “no-effect” relationship predicted by the near zero slope of a log-linear dose–response function at elevated BPb is especially notable in the occupational lead–blood pressure literature.
We do not propose that the log-linear dose–response function for BPb effect on child IQ is the “correct” dose–response function. Our analysis indicates only that it is superior to a linear–linear dose–response function. We examined two other nonlinear dose–response functions for this relationship, a third-order polynomial and a logit dose–response function. The logit function is attractive because it can model a reduction of the dose–response slope as BPb falls below currently modeled data, thus providing for the possibility of an ultralow threshold for lead effect on IQ. The polynomial function would permit modeling of a new increase of slope of the dose–response function beyond the upper limits of the data set modeled here. This would allow accounting for severe lasting effects of the pathologic changes associated with lead-induced encephalopathy. However, the alternative nonlinear dose–response functions both modeled the present data set no better than did the log-linear function, including the steeper slope at low BPb. There was a difference of < 0.2% of the variance in IQ accounted for by the lead variable among the three specifications. Because the log-linear dose–response relationship only required two parameters for complete specification and the alternatives required three parameters, we elected to use the most parsimonious specification for detailed analysis of the data set at hand.
Public health and policy implications.
There appears to be no support for a threshold model for BPb effect on IQ. On the contrary, instead of finding a no-effect lower limit, the present study strongly suggests that most of the damage attributable to BPb occurs within the first few micrograms per deciliter of BPb within the lead range studied. Any apparent threshold will appear at the upper ranges of BPb, where the dose–response curve flattens, at least until BPb reaches the range producing frank organ damage.
In prospective lead studies of child development, including the pooled study IQ effects cited here, history of exposure is always available in the form of sequential BPb measurements of each child. We have good evidence that the log-linear lead–IQ dose–response function is not an artifact of unmeasured history of exposure and represents the best available functional description of BPb on IQ.
The drop in population lead exposure from mean BPb of 17.1 μg/dL to 2.0 μg/dL over the last quarter century produced the large health benefits calculated in Table 5. Although lead in paint and food has been specifically regulated with the goal of reducing population lead exposure, the reduction of lead in air and eventually in dust, one of the major contributors to past urban population lead exposure (Mielke et al. 1983, 1997, 1999), was by and large due to the introduction of catalytic converters for automobiles. Thus, a large part of the drop in population lead was only coincidentally achieved in response to the stated policy of reducing gaseous automobile contaminants. Fortunate though we may have been to have benefited from this accidental process, it is unlikely that further reduction in population lead exposure will be achieved without increased targeted effort.
Although many hailed the Occupational Safety and Health Administration’s 1979 regulations seeking to limit occupational exposure to 40 μg/dL (Occupational Safety and Health Administration 1979) and the CDC’s promulgation in 1991 of action limits for childhood lead exposure to 10 μg/dL (CDC 1991), it appears very likely that these limits have prevented only a small percentage of the damage associated with lead exposure. The total modeled increase in IQ from the pooled data study over the BPb range of 0.1–71.7 μg/dL was 17.7 IQ points. The improvement in IQ predicted by the log-linear model down to the CDC action limit for children was 5.3 IQ points; the remainder of the IQ improvement (12.4 IQ points) was found below the CDC action limit. If we continue to permit children and, by extension, pregnant women to maintain up to 10 μg/dL BPb without aggressive intervention to lower exposure, we are still allowing most of the preventable subclinical damage to occur.
Economic benefits realized by lead exposure reduction under a log-linear dose–response function are more than twice that previously estimated using a linear dose–response function. Updated cost studies of further population and occupational exposure reduction are long overdue. Using updated cost and benefit models, epidemiologists and health economists can determine how much additional exposure reduction is economically warranted.
In this article we only address the form of the dose–response function for lead effect on child IQ. Another well-studied area of health effects of lead exposure is the effect of lead on adult hypertension and blood pressure. A recent meta-analysis of studies examining the effect of contemporary BPb on adult blood pressure (Nawrot et al. 2002) used a log-linear dose–response relationship and found a signifi-cant effect. For every doubling of BPb, Nawrot et al. (2002) calculated a 1.0 mm Hg increase in systolic blood pressure and a 0.6 mm Hg increase in diastolic blood pressure. The pattern of increased blood pressure with increase in BPb was exactly the same as the decrease in child IQ with increased BPb. The greatest changes in predicted blood pressure occurred in the first few micrograms per deciliter of BPb. Because the authors performed no formal testing of other forms of the dose–response function, the log-linear dose–response relationship for BPb on adult blood pressure should be examined in a pooled data study similar to that used for the IQ study detailed here. If the original data in the studies contributing to the meta-analysis for blood pressure were used in a pooled analysis study, there would be > 50,000 subjects in the study. Such a large study sample would allow testing other nonlinear forms of the dose–response relationship for lead and blood pressure against the log-linear form. Any nonlinear dose–response relationship of the same general form as the log-linear function for blood pressure would have economic and public health repercussions similar to those discussed above for IQ. Over the U.S. population, BPb change measured between 1976 and 1999, Nawrot’s coefficient translates into a 4.1 mm Hg decrease in population systolic blood pressure, a change with significant health and economic benefits. However, most of the health and economic benefits would be realized only by bringing population and occupational exposures well below currently permitted limits.
Conclusions
Correctly specifying the dose–response or exposure–health relationship in all epidemiologic and toxicologic studies has important scientific, economic, and policy implications. Authors of such studies could take the initiative and apply statistical techniques similar to those discussed in this article to test whether the presented functional form of the dose–response relationship cannot be ruled out by definable statistical criteria. Journal editors and their reviewers can also insist that authors provide such evidence regarding dose–response curves in submitted manuscripts. Adopting these practices will give toxicologists additional clues about mechanisms of effect, will give environmental economists more accurate data for their models, and will give regulators the needed information for evidence-based actions.
If the nonlinear form of the exposure–health effect curve is more appropriate to the data than a linear function, we still have most of our work ahead of us to protect the population from the effects of lead.
Appendix: Steps for Running J-test
Selection between two non-nested models
Linear Model
, where [β· X ] is the vector of all other variables in the model.
Generate the prediction of linear lead = Πlin.
Logarithmic Model
Generate the prediction of natural log lead = Πlog.
The
J-test:
Then test the probability of the predicted lead term and the original lead term in the two artificial regressions above.
We thank the pooled data study authors for permission to use the data. We especially thank R. Canfield, who reviewed early versions of the manuscript and suggested residual confounding issues and health benefit models.
This work was supported in part by the Secretariat of Health, Mexico; Consejo Nacional de Ciencia y Tecnología (CONACyT) grant SALUD-2002-C01-7159; and the U.S. Environmental Protection Agency.
Figure 1 Partial regression plot of adjusted IQ (adjusted for natural-log lead model) and BPb (from Lanphear et al. 2005). The two regression lines (bold) with 95% CIs (narrow lines) represent the best-fit estimates of the relationship between IQ and BPb for natural-log–transformed BPb and linear BPb. Note that the linear BPb term overestimates the slope (change in IQ with change in BPb) of the statistically superior natural-log lead function down to 15 μg/dL and underestimates the slope < 15 μg/dL. The scatter plot does not show all data points because the y-axis has been expanded to show differences in regression functions.
Table 1 Lead coefficients for IQ as a function of model.a
Variable Coefficient 95% CI p-Value
Linear lead modelb −0.18 −0.26 to −0.10 < 0.0005
Natural-log lead modelc −2.70 −3.74 to −1.66 < 0.0005
Quadratic lead model 0.005 0.001 to 0.009 0.020
Quadratic-log lead model −0.25 −0.76 to 0.26 0.258
Ln(lead) with linear lead prediction (J-test) model −2.47 −4.30 to −0.63 0.009
a Control variables for all models were HOME, birth weight, maternal IQ, maternal education, and site identification.
b Model with linear lead specification.
c Model with natural-log lead specification.
Table 2 GAM results for IQ.
Variable dfa Gain Probability of gain
HOME 2 2.621 0.106
Birth weight 2 2.587 0.108
Maternal IQ 2 0.596 0.440
Maternal education 2 0.961 0.327
Linear lead 2 7.467 0.006
df, degrees of freedom. Dichotomous variables (sites) not shown: spline fit of linear lead specification and all independent variables with two degree of freedom splines. Total gain (nonlinearity χ2) = 14.232 (5.003 df), p = 0.0142.
a Approximate.
Table 3 Spline fit of natural-log lead specification and all independent variables with two degree of freedom splines.
Variable dfa Gain Probability of gain
HOME 2 2.646 0.104
Birth weight 2 2.515 0.113
Maternal IQ 2 0.603 0.438
Maternal education 2 0.690 0.406
Natural-log lead 2 1.438 0.230
df, degrees of freedom. Total gain (nonlinearity χ2) = 7.894 (5.005 df), p = 0.1626.
a Approximate.
Table 4 Spline fit of all independent variables with two degree of freedom splines with original natural-log lead variable modeled as is.
Variable dfa Linear coefficient Gain Probability of gain
HOME 2 4.51 2.740 0.098
Birth weight 2 1.48 2.523 0.112
Maternal IQ 2 4.91 0.609 0.436
Maternal education 2 1.15 0.642 0.424
Natural-log lead 1 −2.62 — —
Abbreviations: —, not applicable (natural log lead was not modeled as a spline function; thus, there is no gain or probability of gain); df, degrees of freedom. Total gain (nonlinearity χ2) = 6.514 (4.006 df), p = 0.1644.
a Approximate.
Table 5 Economic savings (year 2000 dollars) per cohort estimated from the Grosse et al. (2002) IQ model according to dose–response specification.
Study Benefit/cohort (billions $) 95% CIa
Grosse et al. (2002) linear lead 213.83 147.27–280.39
Pooled analysis, linear lead 148.58 82.18–215.82
Pooled analysis, natural-log lead 318.98 196.30–441.67
a CIs cannot be used to compare linear and log lead specifications because the linear specification is incorrect and the 95% CI calculated from it suffers from uncorrected residual heteroskedasticity.
==== Refs
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Schwartz J 1994 Low-level lead exposure and children’s IQ: a meta-analysis and search for a threshold Environ Res 65 1 42 55 8162884
Wasserman GA Liu X Lolacono NJ Factor-Litvak P Kline JK Popovac D 1997 Lead exposure and intelligence in 7-year-old children: the Yugoslavia Prospective Study Environ Health Perspect 105 956 962 9410739
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Wechsler D 1974. Manual for Wechsler Intelligence Scale for Children—Revised. San Antonio, TX:The Psychological Corporation.
Wechsler D 1981. WISC-R-Español. Escala de intelligencia revisiada para el nivel escolar. Mexico City:El Manual Moderno, SA.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7974ehp0113-00119616140627ResearchThe Development and Use of an Innovative Laboratory Method for Measuring Arsenic in Drinking Water from Western Bangladesh Frisbie Seth H. 1Mitchell Erika J. 1Yusuf Ahmad Zaki 2Siddiq Mohammad Yusuf 2Sanchez Raul E. 3Ortega Richard 4Maynard Donald M. 5Sarkar Bibudhendra 671 Better Life Laboratories, Inc., East Calais, Vermont, USA2 Bangladesh Association for Needy Peoples Improvement, Chorhash, Kushtia, Bangladesh3 Green Mountain Laboratories, Inc., Montpelier, Vermont, USA4 Laboratoire de Chimie Nucléaire Analytique et Bioenvironnementale, Université de Bordeaux, Gradignan, France5 Johnson Company, Inc., Montpelier, Vermont, USA6 Department of Structural Biology and Biochemistry, Hospital for Sick Children, and7 Department of Biochemistry, University of Toronto, Toronto, Ontario, CanadaAddress correspondence to B. Sarkar, Department of Structural Biology and Biochemistry, Hospital for Sick Children, Department of Biochemistry, University of Toronto, 555 University Ave., Toronto, Ontario M5G 1X8, Canada. Telephone: (416) 813-5921. Fax: (416) 813-5379. E-mail
[email protected] authors declare they have no competing financial interests.
9 2005 19 5 2005 113 9 1196 1204 28 1 2005 19 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. All of Bangladesh’s approximately 10 million drinking-water tube wells must be periodically tested for arsenic. The magnitude of this task and the limited resources of Bangladesh have led to the use of low-cost, semiquantitative field kits that measure As to a relatively high 50 μg/L national drinking water standard. However, there is an urgent need to supplement and ultimately replace these field kits with an inexpensive laboratory method that can measure As to the more protective 10 μg/L World Health Organization (WHO) health-based drinking water guideline. Unfortunately, Bangladesh has limited access to atomic absorption spectrometers or other expensive instruments that can measure As to the WHO guideline of 10 μg/L. In response to this need, an inexpensive and highly sensitive laboratory method for measuring As has been developed. This new method is the only accurate, precise, and safe way to quantify As < 10 μg/L without expensive or highly specialized laboratory equipment. In this method, As is removed from the sample by reduction to arsine gas, collected in an absorber by oxidation to arsenic acid, colorized by a sequential reaction to arsenomolybdate, and quantified by spectrophotometry. We compared this method with the silver diethyldithiocarbamate [AgSCSN(CH2CH3)2] and graphite furnace atomic absorption spectroscopy (GFAAS) methods for measuring As. Our method is more accurate, precise, and environmentally safe than the AgSCSN(CH2CH3)2 method, and it is more accurate and affordable than GFAAS. Finally, this study suggests that Bangladeshis will readily share drinking water with their neighbors to meet the more protective WHO guideline for As of 10 μg/L.
arsenicarsenomolybdateBangladeshchronic arsenic poisoningdrinking watergraphite furnace atomic absorption spectroscopysilver diethyldithiocarbamatespectrophotometry
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The life expectancy in Bangladesh during the mid-1960s was only 46 years. Many premature deaths resulted from drinking surface water that was contaminated with bacteria causing diarrhea, cholera, typhoid, and other life-threatening diseases (Nyrop et al. 1975). Aid agencies, the Bangladesh government, and private individuals began installing 8–12 million tube wells to prevent these deaths by providing access to microbially safe groundwater for drinking [World Health Organization (WHO) 2003]. By 1995, Bangladesh had 120 million people (Monan 1995), approximately 97% of whom drank tube-well water (WHO 2000), and for a variety of reasons the life expectancy had increased to 55 years (Monan 1995).
Regrettably, this new source of drinking water was not tested for toxic metals. In 1993, Dhaka Community Hospital first diagnosed chronic arsenic poisoning caused by drinking Bangladesh’s groundwater [British Geological Survey (BGS) 1999a]. In 1997, our team produced the first national-scale map of As concentration in Bangladesh’s ground-water [Frisbie et al. 1999; U.S. Agency for International Development (USAID) 1997]. This map showed that 45% of Bangladesh’s area had groundwater with As concentrations greater than the 50 μg/L national standard (Frisbie et al. 1999; USAID 1997). In 2003, a risk assessment estimated that 28 million Bangladeshis were drinking water with As concentrations greater than this national standard (Yu et al. 2003). As a result of this exposure, skin cancer, melanosis, leukomelanosis, keratosis, hyperkeratosis, and nonpitting edema from chronic As poisoning are common in Bangladesh (BGS 1999b; Frisbie et al. 2002). In addition, the rates of bladder cancer, liver cancer, and lung cancer are expected to increase in Bangladesh based on an analysis of death certificates for As exposures in Taiwan (Morales et al. 2000).
Fortunately, our 1997 study (Frisbie et al. 1999; USAID 1997) also suggested that testing and sharing tube wells could rapidly and inexpensively provide drinking water with As concentrations less than the 50 μg/L national standard to 85% of Bangladesh’s population. That is, 85% of Bangladesh’s neighborhoods have at least one tube well that does not require treatment for As removal before drinking. Therefore, the vast majority of Bangladeshis with unsafe water could potentially get safe drinking water from their neighbors (Frisbie et al. 1999; USAID 1997). As a result of this discovery, groundwater testing has become a major component of an overall strategy for providing safe drinking water to the people of Bangladesh. To date, > 1 million of Bangladesh’s approximately 10 million tube wells have been tested for As with easy-to-use, relatively inexpensive, and semiquantitative field kits [UNICEF (United Nations Children’s Fund) 2004]. Tube wells are considered safe and marked with green paint if the As concentration is ≤ 50 μg/L, the national standard. Conversely, tube wells are considered unsafe and marked with red paint if the As concentration is > 50 μg/L. Those with safe water are asked to share with their less fortunate neighbors. In addition to this initial survey, periodic testing of tube wells has been recommended to ensure that the population has continued access to safe drinking water. This periodic testing is prudent because the As concentration in some of Bangladesh’s tube wells has changed dramatically over time (Frisbie et al. 1999; USAID 1997).
This urgent need to periodically test approximately 10 million tube wells and the limited resources of Bangladesh have led to the use of these semiquantitative field kits for measuring As. However, these field kits are not precise because the user must estimate the concentration of As from a color chart, similar to that used for measuring pH with pH paper. There are two commonly used field kits in Bangladesh. The first kit is not sensitive and must be modified to detect As at the 50 μg/L national standard. The second kit is inaccurate and must be modified to avoid underestimating the true concentration of As. After modification, this second kit overestimates the true concentration of As (Geen et al. 2005). As a result of these deficiencies, there is an urgent need to supplement and ultimately replace these field kits with a quantitative, accurate, precise, sensitive, inexpensive, and environmentally safe laboratory method for measuring As in Bangladesh’s drinking water. The development and evaluation of such a laboratory method are reported here for the first time.
This new method uses relatively inexpensive reagents and equipment that are easily obtained in Bangladesh and can be used to measure a wide variety of analytes. By design, it uses the same equipment as the silver diethyldithiocarbamate [AgSCSN(CH2CH3)2] method for measuring As [American Public Health Association (APHA) et al. 1989], which is commonly used in Bangladesh. Therefore, the new method can be readily implemented in Bangladesh, and it does not require expensive equipment. Such resources are rare in Bangladesh; for example, in 1997 there was only one atomic absorption spectrometer in the entire country that was used for the routine analysis of As (USAID 1997). Also, it does not require highly specialized equipment, such as devoted As analyzers, which have relatively high cost and limited utility (USAID 1997).
It is very important to realize that the WHO drinking water guideline for As of 10 μg/L is based on a 6 × 10−4 excess lifetime skin cancer risk for human males, which is 60 times higher than the 1 × 10−5 factor that is typically used to protect public health (WHO 1996). The WHO (1996) states that the drinking water guideline for As should be 0.17 μg/L based on the risk of death from skin cancer. However, the detection limit for most laboratories is 10 μg/L, which is why the less protective guideline was adopted: “Guideline values are not set at concentrations lower than the detection limit achievable under routine laboratory operating conditions” (WHO 1993, 1998).
Similarly, Bangladesh has limited access to atomic absorption spectrometers or other sophisticated instruments for measuring As and uses a much higher 50 μg/L drinking water standard, largely because of the poor accuracy of the AgSCSN(CH2CH3)2 method (USAID 1997). However, a 50 μg/L drinking water standard fails to protect against death from not only skin cancer but also bladder, liver, and lung cancers. Drinking water with 50 μg/L As may cause one extra death from skin cancer per 500 people and one extra death from bladder, liver, or lung cancer per 300 people (Morales et al. 2000). Therefore, > 150,000 Bangladeshis are expected to die from skin, bladder, liver, or lung cancer caused by drinking water with > 50 μg/L As. More than 120,000 of these lives could be saved if Bangladesh complied with the more protective WHO drinking water guideline for As of 10 μg/L by sharing safe water within affected neighborhoods. The present study suggests that Bangladesh could adopt this more protective 10 μg/L guideline for As if it used the arsenomolybdate method for As measurement.
Materials and Methods
Sample Collection, Preservation, Shipment, and Analyses
Groundwater samples were collected from four neighborhoods in western Bangladesh (Fulbaria, Bualda, Jamjami, and Komlapur) during 18–21 July 2002 (Figure 1). A total of 71 random samples were collected from 67 tube wells in these four neighborhoods. A total of 18 random samples were collected from 17 tube wells in each of three neighborhoods. In the fourth neighborhood, access was denied at 1 sampling location; therefore, a total of 17 random samples were collected from 16 tube wells in that neighborhood.
To the extent possible, the sampled tube wells in each neighborhood were distributed at 500-m intervals along perpendicular axes that radiated in four equal lengths from the center (Figure 2). Two samples were collected from the centermost tube well in each neighborhood. One sample was collected from each of the remaining tube wells. The latitude and longitude of these tube wells were determined using a Garmin Global Positioning System 12 Channel Personal Navigator (Garmin International, Inc., Olathe, KS, USA). The accuracy of this instrument was approximately 15 m.
We used established collection, preservation, and storage methodologies to ensure that each sample was representative of groundwater quality (APHA et al. 1995; Stumm and Morgan 1981). Accordingly, all sampled tube wells were purged by pumping vigorously for 10 min immediately before sample collection. All samples were collected directly into polyethylene bottles. These samples were not filtered. Samples were analyzed immediately after collection with pH paper, preserved by acidification to pH < 2 with 5.0 M hydrochloric acid, and stored in ice-packed coolers. The temperature of all stored samples was maintained at 0–4°C until immediately before analysis at laboratories in Dubai and Vermont. In Dubai, all of these samples were analyzed for As by both the arsenomolybdate and AgSCSN(CH2CH3)2 methods. The samples were subsequently shipped to Vermont and analyzed for As by graphite furnace atomic absorption spectroscopy (GFAAS). Contour maps of the As concentration in tube-well water from these four neighborhoods were drawn (Figure 3) as described under “Mapping.”
Quality Control of Laboratory Analyses
All three methods for determining As were calibrated daily. The calibration for the AgSCSN(CH2CH3)2 method used five different standards, including a blank. The calibrations for the arsenomolybdate and GFAAS methods used six different standards, including blanks. All calibration standards were prepared from the same stock As solution. The concentration of the most dilute nonblank standard was between 1 and 10 times the method detection limit. Calibration check standards were analyzed every 20 samples and as the last analysis of each day to assess data quality. An externally supplied standard was used to assess the accuracy of the calibration standards and calibration check standards (APHA et al. 1995).
We used the recovery of a known addition of standard to eight different samples to assess the matrix effects of all three methods of determining As. In addition, three types of precision were measured for each method from the analysis of seven different standards (precision of standards), the duplicate analyses of eight different samples (precision of samples), and the analysis of a known addition of standard to eight different samples (precision of known additions). The same eight samples were used to assess the matrix effects and precisions of all three methods of determining As (Table 1; APHA et al. 1995).
Arsenomolybdate Method
Apparatus.
The apparatus, shown in Figure 4, includes an arsine (AsH3) generator (a 125 mL Erlenmeyer flask), a scrubber (made from a 10 mL volumetric pipette and a rubber stopper), and an absorber (made from a 20 mL volumetric pipette, a rubber stopper, and a polyethylene cap). The cap of the absorber has four grooves cut along its side to vent H2 gas without the loss of liquid during AsH3 generation. The spectrophotometer (product no. 6305; Jenway, Essex, UK) was set at 835 nm and used a 1.0 cm glass cell. All glassware was acid washed in 1.00 M HCl.
Reagents.
We purchased concentrated sulfuric acid (H2SO4; product no. 102766H) and concentrated HCl (product no. 101256J) from BDH Laboratory Supplies and zinc (20-mesh granules; product no. 24,346-9) from Aldrich Chemical Company (Milwaukee, WI, USA).
As solutions.
We prepared the As solution by dissolving 4.0 g sodium hydroxide (product no. 131687; Panreac Química S.A., Barcelona, Spain) in 10 mL distilled water; arsenic trioxide (1.320 g; product no. 22,762-5; Aldrich) was added, and the solution was diluted to 1,000 mL with distilled water. An intermediate As solution was prepared by diluting 5.00 mL stock As solution to 500 mL with distilled water. This intermediate solution was then used to prepare a standard As solution (10.00 mL intermediate As solution diluted to 100 mL distilled water, which was used in the experiments.
Potassium iodide (50.0% wt/vol).
Fifty grams of KI (product no. 102123B; BDH Laboratory Supplies, Poole, UK) was diluted to 100 mL with distilled water and stored in the dark until use.
Stannous chloride dihydrate (40.0% wt/vol).
Forty grams of stannous chloride dihydrate (SnCl2·2H2O; product no. 102704Q; BDH Laboratory Supplies) was dissolved and diluted to 100 mL with concentrated HCl.
Lead(II) acetate trihydrate (10.0% wt/vol).
Pb(COOCH3)2·3H2O (10.0 g; product no. 21,590-2; Aldrich) was dissolved and diluted to 100 mL with distilled water.
Iodine/KI.
Twenty grams of KI was dissolved in approximately 250 mL distilled water; 12.5 g I2 (product no. 20,777-2; Aldrich) was added, and the solution was mixed for several hours using a Teflon-coated magnetic stir bar until all the I2 dissolved. The solution was then diluted to 500 mL with distilled water and stored in the dark. The solution was prepared fresh weekly.
Sodium bicarbonate (1.00 M).
NaHCO3 (product no. 102474V; BDH Laboratory Supplies) was dissolved and diluted to 100 mL with distilled water.
Sulfuric acid/ammonium molybdate tetrahydrate.
We prepared 100 mL of 6.50 M H2SO4 in distilled water. We then diluted 6.9 g (NH4)6Mo7O24·4H2O (product no. 100282H; BDH Laboratory Supplies) to 100 mL with distilled water. Finally, the 6.50 M H2SO4 and the 6.9% (wt/vol) (NH4)6Mo7O24·4H2O were mixed together.
Sodium metabisulfite (6.00%wt/vol).
Six grams of Na2S2O5 (product no. 301804E; BDH Laboratory Supplies) were diluted to 100 mL with distilled water; the solution was prepared fresh daily.
Stannous chloride dihydrate (0.20% wt/vol).
Fifty milliliters of 40.0% (wt/vol) SnCl2·2H2O was diluted to 100 mL with distilled water; the solution was prepared fresh daily.
Sample treatment.
In order to reduce As(V) to As(III), 35.0 mL of either sample or standard was mixed with 0.35 mL of 50.0% (wt/vol) KI and 0.35 mL of 40.0% (wt/vol) SnCl2·2H2O in a 125 mL Erlenmeyer flask (to be used as the AsH3 generator). The mixture was boiled 1.0 min to reduce As(V) to As(III), and a water bath was used to cool the mixture to room temperature.
Scrubber preparation.
The scrubber was prepared by placing 0.17 ± 0.03 g glass wool (product no. 18421; Riedel-de Hafin; Seelze, Germany) onto a piece of Whatman filter paper (Whatman, Kent, UK). Ten drops of 10.0% (wt/vol) Pb(COOCH3)2·3H2O was then added to this piece of glass wool, and the glass wool was squeezed in the filter paper to remove the excess solution. The glass wool was then fluffed and placed in the scrubber (Figure 4).
Absorber preparation.
The absorber was prepared by first adding 2.50 mL I2/KI solution to a 20 mL test tube; 0.50 mL 1.00 M NaHCO3 was then added. This solution was poured into the absorber and the cap was placed on the absorber (Figure 4).
Arsine generation, color development, and spectrophotometry.
The amount of time for each step of this procedure, from adding concentrated H2SO4 to measuring the absorbance, was consistent for all samples and all standards. Two milliliter of concentrated H2SO4 was mixed with the treated sample or treated standard in the 125-mL Erlenmeyer flask (the AsH3 generator); after adding and mixing 10.0 mL concentrated HCl and 1.0 mL 40.0% (wt/vol) SnCl2·2H2O, 5.0 g Zn was added. The scrubber and absorber were then connected to the AsH3 generator (Figure 4). We allowed 30 min for the AsH3 to completely evolve from the AsH3 generator to the absorber.
The liquid from the absorber was poured into a test tube calibrated to receive 5.00 mL; 0.50 mL of distilled water was then used to rinse the residual liquid from the absorber to the test tube. One milliliter of H2SO4/(NH4)6Mo7O24·4H2O solution was added to the test tube and mixed. Then 0.50 mL of 6.00% (wt/vol) Na2S2O5 solution was added to the test tube and mixed. The Na2S2O5 changed the mixture from deep reddish brown to faint yellow (the brown color must be eliminated). When necessary, distilled water was added to until the total volume of liquid was 5.00 mL. Then 0.50 mL 0.20% (wt/vol) SnCl2·2H2O was added and mixed. After waiting 30 min for the bluish green arsenomolybdate color to develop, we measured the absorbance at 835 nm.
Calibration.
For calibration, we added 0, 0.50, 1.00, 2.00, 4.00, and 8.00 mL of standard As solution into six separate 125 mL Erlenmeyer flasks and added distilled water until the total volume of liquid in each flask equaled 35.0 mL (Figure 4). The resulting standards contained 0, 0.50, 1.00, 2.00, 4.00, and 8.00 μg of As or 0, 14, 28.6, 57.1, 114, and 229 μg/L, respectively. We used the sample treatment, scrubber preparation, absorber preparation, AsH3 generation, color development, and spectrophotometry procedures described above to analyze these standards. We confirmed that the calibration results follow Beer’s law; that is, we tested for a higher order polynomial relationship to confirm linearity, and we tested the null hypothesis that the y-intercept goes through the origin (Neter et al. 1985).
Ultraviolet/Visible Spectrum of Arsenomolybdate
In order to determine the absorption maximum of arsenomolybdate we measured the absorption spectra of three arsenomolybdate samples using an Agilent 8453 ultraviolet/visible spectroscopy system (Agilent Technologies, Palo Alto, CA, USA). The wavelengths of these spectra ranged from 190 to 1,100 nm. Each arsenomolybdate sample was prepared from a 229 μg/L As standard solution using the sample treatment, scrubber preparation, absorber preparation, AsH3 generation, color development, and spectrophotometry procedures described above under “Arsenomolybdate Method.” The spectrum of each arsenomolybdate sample was measured relative to a blank. Similarly, each blank was prepared from a 0 μg/L As standard solution using these procedures. Each spectrum was measured after 30 min of color development.
AgSCSN(CH2CH3)2 Method
We analyzed all samples for As following the AgSCSN(CH2CH3)2 method of APHA et al. (1989). In this method, a 125-mL specimen jar was used for the AsH3 generator. The scrubber and absorber were identical to those used for the “Arsenomolybdate Method” shown in Figure 4. A 35.0 mL aliquot of sample or standard was placed in the AsH3 generator and treated with 5.0 mL concentrated HCl, 2.00 mL 15.0% (wt/vol) KI in distilled H2O, and 0.40 mL 40.0% (wt/vol) SnCl2·2H2O in concentrated HCl to reduce As(V) to As(III). This reduction was allowed 15 min for completion at room temperature. The scrubber was prepared as described above for the “Arsenomolybdate Method.” The absorber received 4.00 mL 0.50% (wt/vol) AgSCSN(CH2CH3)2 in pyridine (C5H5N). Then, 3.0 g Zn was added to the sample or standard to generate AsH3. After 30 min of AsH3 generation, the absorbate was measured spectrophotometrically at 535 nm.
GFAAS Method
All samples were analyzed for As by GFAAS with a Buck Scientific 220AS autosampler, 220GF graphite furnace, and 210VGP atomic absorption spectrometer (Buck Scientific, East Norwalk, CT, USA). A 1.00 mL aliquot of standard As solution, sample, or diluted sample was loaded onto the autosampler. The six standards contained 0, 1.0, 5.0, 15.0, 30.0, and 50.0 μg/L As, respectively. The matrix of each 1.00 mL aliquot was modified with 50.0 μL 10.0% (wt/vol) ammonium nitrate (NH4NO3; product no. A1216; Spectrum Chemicals and Laboratory Products, New Brunswick, NJ, USA) in deionized water, 50.0 μL 0.2% (wt/vol) palladium nitrate [Pd(NO3)2] in 2% (wt/vol) HNO3 (product no. K, Buck Scientific), and 50.0 μL 1.79% (wt/vol) magnesium nitrate hexahydrate [Mg(NO3)2·6H2O; product no. 5855-1; EM Science, Gibbstown, NJ, USA] in deionized water. The autosampler delivered a 20.0 μL aliquot of this mixture to the graphite furnace. The furnace tube was made from nonpyrolytic graphite (product no. BS300-1253; Buck Scientific). The furnace initialized at 100°C for 10 sec, heated to 250°C for 20 sec, dried the mixture at 250°C for 15 sec, heated to 750°C for 25 sec, ashed the mixture at 750°C for 10 sec, heated to 2,200°C for 1.5 sec, and atomized the mixture at 2,200°C for 3 sec. The sheath and internal flows of argon gas were 1,200 and 200 mL/min, respectively. The absorbance from a hollow-cathode lamp was read at 193.7 nm through a 0.7 nm slit and after deuterium (D2) background correction. This absorbance was measured for 2.4 sec during atomization. Finally, this absorbance over time was used to calculate As concentration (Buck Scientific Inc. 2002; Harris 1999).
Statistics
We measured all 71 samples from this study for As by the arsenomolybdate, AgSCSN (CH2CH3)2, and GFAAS methods. We used a paired t-test of the As concentrations from these samples to determine if the arsenomolybdate and AgSCSN(CH2CH3)2 methods gave equivalent or different results (Table 2); a second paired t-test to determine if the arsenomolybdate and GFAAS methods gave equivalent or different results (Table 3); and a third paired t-test to determine if the AgSCSN(CH2CH3)2 and GFAAS methods gave equivalent or different results (Table 4). Each of these three paired t-tests was evaluated at the 95% confidence level (Snedecor and Cochran 1982).
We used an F-test to determine if two precision values were equivalent or different. The standard deviation indicates precision (Table 1; APHA et al. 1995), and a variance is indicated by precision squared (Snedecor and Cochran 1982). A ratio of these variances in an F-test show the equality of the corresponding precisions (Snedecor and Cochran 1982). Each F-test was evaluated at the 95% confidence level.
Mapping
We used the As concentration by the arsenomolybdate method, the sample location by the Global Positioning System, and the Surfer Surface Mapping System (version 7; Golden Software Inc., Golden, CO, USA) to draw one contour map for each of the four neighborhoods (Figure 3). To map the contours shown in Figure 3, we used a variogram to select the equation that best matched the spatial continuity of the actual As concentrations in each neighborhood (Figure 2). We used inverse-distance weighted least-squares equations (Shepard’s method) for Fulbaria and Bualda and a logarithmic equation for Jamjami. We did not use an equation for Komlapur because all the samples in this neighborhood had As concentrations ≤ 10 μg/L, the WHO drinking water guideline (WHO 1996).
Societal Evaluation
Initial interview during sampling.
One principal user of each tube well was interviewed during groundwater sampling during 18–21 July 2002. Each interview was conducted in Bangla from a list of standard questions. Each interviewee was asked whether alternative drinking water sources were available (rain, ponds, rivers, or canals), and the following information was recorded: a) the number of users per tube well; b) the depth and age of each tube well; c) the results from previous As tests, if any; and d) whether any users were known to have melanosis, keratosis, gangrene, skin cancer, conjunctivitis, respiratory distress, or enlarged liver. Finally, the willingness of each interviewee to get safe drinking water from their neighbors or to give safe drinking water to their neighbors was evaluated.
Distributing As results.
Our field staff gave a Bangla-language form letter summarizing the neighborhood’s As results to each interviewee 6 months after their groundwater was sampled. In addition, the contents of each letter were explained in Bangla by our field staff. Therefore, each interviewee knew if his tube well had a safe or unsafe concentration of As. Furthermore, each interviewee knew which neighbor’s tube well had a safe or unsafe concentration of As.
Interviewees with As concentrations ≤ 10 μg/L were informed that their tube wells were safe with respect to this element. In addition, they were informed that their tube wells should be tested for As at least once a year because the concentration of As can change over time (Frisbie et al. 1999; USAID 1997). Finally, these interviewees were asked to share their drinking water with those who could not get safe water from their own tube wells.
Interviewees with As concentrations > 10 μg/L (WHO drinking water guideline) were informed that their tube wells were unsafe with respect to this element, and the imminent risk of serious health problems from continuing to drink this water was thoroughly explained. Finally, they were asked to get safe drinking water from their neighbors.
Final interview 1 year after sampling.
Each original interviewee, if available, was revisited 1 year after their groundwater was sampled and 6 months after they were given the As results for their neighborhood. If the original interviewee was not available, then a surrogate interviewee was identified. These people were interviewed in Bangla from a list of standard questions. The purposes of this final interview were to determine a) if the people with safe tube wells actually gave drinking water to their neighbors who did not have safe drinking water; b) whether the people with unsafe tube wells actually got safe drinking water from their neighbors; c) whether the people with safe water followed our instructions and retested their tube wells for As; and d) the health status of all tube-well users.
Results and Discussion
Rationale for sampling drinking water in western Bangladesh.
We chose western Bangladesh for this study because it has some of the widest ranges of groundwater As concentrations in the country, according to our two national-scale surveys (Frisbie et al. 1999, 2002; USAID 1997). This variability is associated with the complex mixture of flood plain deposits that form western Bangladesh (Yu et al. 2003). As a result, the neighborhoods in western Bangladesh usually have at least one tube well that does not require treatment for As removal before drinking (Frisbie et al. 1999; USAID 1997). For example, the As concentrations in our four randomly selected western Bangladeshi neighborhoods ranged from < 0.7 to 590 μg/L, with 30% of samples > 10 μg/L, the WHO drinking water guideline (WHO 1996). This makes western Bangladesh well suited for evaluating our laboratory method of measuring As in drinking water. It is also well suited for determining whether people will use the results from this method to share safe drinking water with their neighbors.
Independent evaluation of the arsenomolybdate method.
The arsenomolybdate calibration obeys Beer’s law; that is, the plot of absorbance versus As concentration is linear, and the y-intercept goes through the origin. A test for a higher order polynomial relationship was done for each daily calibration and routinely confirmed this linearity at the 95% confidence level (Neter et al. 1985). Similarly, a test of the null hypothesis that the y-intercept goes through the origin was performed for each daily calibration and routinely confirmed that the y-intercept was equivalent to 0, 0 at the 95% confidence level (Neter et al. 1985). The calibration equation from the arsenomolybdate method detection limit study is absorbance = 0.00169 × (micrograms As per liter), and the correlation coefficient (r) = 1.00. The slope of the equation and the 7 μg/L method detection limit shown in Table 1 suggest that the colorimetric measurement of As by arsenomolybdate is extremely sensitive. The 101.5 ± 3.6% recovery of known additions shown in Table 1 suggests that the results from the arsenomolybdate method are accurate. The respective 2.1, 4.7, and 7.1 μg/L precisions of standards, samples, and known additions shown in Table 1 suggest that the results from the arsenomolybdate method are reproducible.
In this method, As is removed from the sample by reduction to AsH3 gas. This gas is collected in an absorber by oxidation to arsenic acid (H3AsO4). All previous attempts at this separation have suffered from poor reproducibility because of the incomplete recovery of AsH3 (Sandell 1942, 1959). The sources of this incomplete recovery were insufficient concentrations of oxidant in the absorbate, the deterioration of absorbate with time, and improper absorber designs. This problem of incomplete recovery has been solved.
We discovered and corrected an error that has not been reported by previous researchers. This error was from drift caused by the arsenomolybdate absorption spectrum changing with time. Our method requires that the arsenomolybdate color develop for 30 min before its absorbance is measured. This color is relatively stable after 30 min. In addition, a loss of sensitivity from using polychromatic light to measure absorbance has been corrected. Our method requires that the absorbance be measured at 835 nm, the absorption maximum of arsenomolybdate. In addition, previous arsenomolybdate methods were not compared with established methods for measuring As (Milton and Duffield 1942; Sandell 1959). Comparisons of our method with the AgSCSN(CH2CH3)2 and GFAAS methods for measuring As are shown in Tables 1–4.
Finally, all routine analytical methods must use a rigorous quality control plan to identify and correct systematic errors. The quality control plan for the arsenomolybdate method should include daily calibrations and the frequent analysis of blanks, externally supplied standards, calibration check standards, duplicate samples, and known additions of standard to samples (APHA et al. 1995; Frisbie et al. 1999; USAID 1997).
Arsenomolybdate versus AgSCSN (CH2CH3)2.
We found no difference between the arsenomolybdate and AgSCSN(CH2CH3)2 methods for measuring As at the 95% confidence level, according to a paired t-test of all 71 samples from this study (p-value = 0.06; Table 2). As a result, these two methods are highly correlated (r = 0.993; Table 2). These two methods can quantify As to less than the WHO drinking water guideline of 10 μg/L without expensive or highly specialized laboratory equipment; the cost and required skill to implement these methods are comparable; the precisions of standards for the methods are equivalent at the 95% confidence level; and the precisions of samples for the two methods are equivalent at the 95% confidence level (Table 1).
However, the precision values of known additions for the arsenomolybdate and AgSCSN(CH2CH3)2 methods are different at the 95% confidence level (Table 1). These two precision values were measured using the same eight samples. This suggests that the AgSCSN(CH2CH3)2 method is imprecise at relatively high As concentrations because of the sample matrix. Additional evidence of this imprecision is the 16.6% relative percent difference of the mean As concentrations from all 71 samples measured by the arsenomolybdate and AgSCSN(CH2CH3)2 methods (Table 2). One source of this imprecision may be the incomplete reduction of As(V) to As(III) in Bangladesh’s tube-well water by the relatively mild sample treatment procedure of the AgSCSN(CH2CH3)2 method. More specifically, the arsenomolybdate method uses KI and SnCl2·2H2O at 100°C for this reduction, whereas the AgSCSN(CH2CH3)2 method uses KI and SnCl2·2H2O at room temperature. Another source of this imprecision may be the incomplete generation of AsH3 in Bangladesh’s tube-well water by the relatively mild AsH3 generation procedure of the AgSCSN(CH2CH3)2 method. More specifically, the arsenomolybdate method uses 0.18 g KI, 0.540 g SnCl2·2H2O, 2.0 mL concentrated H2SO4, 10.0 mL concentrated HCl, and 5.0 g Zn per 35.0 mL of sample for AsH3 generation. In contrast, the AgSCSN(CH2CH3)2 method uses 0.300 g KI, 0.16 g SnCl2·2H2O, 5.0 mL concentrated HCl, and 3.0 g Zn per 35.0 mL of sample for AsH3 generation.
Another important difference between the arsenomolybdate and AgSCSN(CH2CH3)2 methods is related to worker health and environmental protection. More specifically, the arsenomolybdate method uses H2O as a solvent, which is nontoxic, nonflammable, and easy to dispose of. In contrast, the AgSCSN(CH2CH3)2 method uses either chloroform (CHCl3) or C5H5N as a solvent (Table 1). All routes of exposure to CHCl3 are likely to cause cancer in humans [U.S. Environmental Protection Agency (EPA) 2004]. Moreover, with improper disposal, CHCl3 can persist in an aquifer for centuries (Pankow and Cherry 1996). Exposure to C5H5N may cause increased liver weight and hepatic lesions (U.S. EPA 2004). In addition, C5H5N is highly flammable and, as a result, presents an acute risk to laboratory workers (Sittig 1985).
In summary, the arsenomolybdate method is more accurate and precise than the AgSCSN(CH2CH3)2 method based on the recoveries of known additions (Tables 1 and 2). The arsenomolybdate method is safer than the AgSCSN(CH2CH3)2 method because it does not use toxic solvents (Table 1).
Arsenomolybdate versus GFAAS.
We found no difference between the arsenomolybdate and GFAAS methods for measuring As at the 95% confidence level, according to a paired t-test of all 71 samples (p-value = 0.24; Table 3). As a result, these two methods are highly correlated (r = 0.996; Table 3). Both methods can quantify As to less than the 10 μg/L WHO drinking water guideline (WHO 1996; Table 1). However, the equipment cost for the GFAAS method is much greater than that for the arsenomolybdate method (Table 1).
Despite its far lower cost, the arsenomolybdate method is more accurate than the GFAAS method. The recovery of known additions by the arsenomolybdate method is equivalent to 100% (101.5 ± 3.6%; Table 1). In contrast, the recovery of known additions by the GFAAS method is > 100% (103.3 ± 3.1%; Table 1). This suggests that the GFAAS method overestimated the true concentration of As in this matrix by approximately 3.3% (Table 1). This estimate of 3.3% bias by the GFAAS method is based on the recovery of known additions from eight samples. Additional evidence of this bias is the 3.6% relative percent difference of the mean As concentrations from all 71 samples measured by the arsenomolybdate and GFAAS methods (Table 3). This overestimation by the GFAAS method was likely caused by scattered light from sodium chloride or similar matrix salts that remained in the furnace during atomization (Frisbie et al. 1999, 2002; Harris 1999; USAID 1997). If so, the NH4NO3, Pd(NO3)2, and Mg(NO3)2·6H2O matrix modifiers and D2 background correction did not fully resolve this interference.
In contrast, the GFAAS method is more precise than the arsenomolybdate method. The standards, samples, and known additions are measured with greater precision by the GFAAS method than by the arsenomolybdate method (Table 1). Each of these three F-tests was evaluated at the 95% confidence level.
In summary, the arsenomolybdate method is more accurate and affordable than the GFAAS method (Tables 1 and 3).
AgSCSN(CH2CH3)2 versus GFAAS.
The AgSCSN(CH2CH3)2 and GFAAS methods for measuring As are different at the 95% confidence level, according to a paired t-test of all 71 samples (p-value = 0.01; Table 4). Furthermore, the As concentrations measured by the GFAAS method were approximately 20.1% greater than those measured by the AgSCSN(CH2CH3)2 method (Table 4). The difference between these methods was likely caused by a combination of two deficiencies: the observed imprecision of the AgSCSN(CH2CH3)2 method at relatively high As concentrations, and the observed bias of the GFAAS method at all As concentrations.
Societal evaluation.
Any society may accept or reject a given solution to a problem for a variety of reasons. Therefore, it is essential to evaluate the willingness of Bangladeshis to use the results from our method to obtain safe drinking water with As concentrations 10 μg/L from their own wells or from their neighbors, and to give this “safe” drinking water to their neighbors.
Water testing and sharing can provide access to safe drinking water for the millions of Bangladeshis who live in communities where some tube wells are safe and others are not (Frisbie et al. 1999, 2002; USAID 1997). Whether or not this testing and sharing will actually provide safe drinking water in these communities will depend on neighbors correctly understanding the As results for their tube wells and consistently sharing safe water with each other. However, water gathering and use are subject to cultural habits that are difficult to change. The nearly universal switch (97%) in Bangladesh since 1971 from using surface water for drinking to tube-well water has shown that water use traditions can be changed, provided there are extensive community education programs and support from both the government and nongovernmental organizations (WHO 2000).
In Bangladesh, as in most other areas where water must be gathered from a communal source, the chore of water gathering is generally considered to be women’s work. However, Bangladeshi women are constrained by society and their families to stay close to home, whereas unrelated men are generally not welcome inside family compounds. In order for water testing and sharing to be successful as a strategy for providing safe drinking water in Bangladesh, the neighbors as a community must be educated about the meaning of their As results. They must also be willing to use tube wells that may not be close to home or to share their own safe tube wells with unrelated neighbors or strangers.
In our initial survey, which was completed before the owner or regular user of each tube well knew the results from our testing, we asked respondents if they would be willing to use other water sources if their own tube well had unsafe levels of As. We also asked them if they would be willing to share water with their neighbors if their own tube well turned out to be safe. The results were very encouraging; 86% of respondents claimed they would permit family members to gather water from other tube wells if their own tube well had unsafe levels of As, and 94% said they would share water with neighbors if they had safe water and their neighbors did not have safe water.
However, what people say they will do is not always what they actually do in practice. For this reason, we conducted a follow-up survey 6 months after our testing results were distributed to the original respondents or their family members. In this second survey, we learned that almost all tube-well owners (91%) shared water with others. Many respondents noted that sharing water with strangers is customary in village communities and required as a matter of courtesy or charity, which seems to override issues of water safety. Generally, however, after As testing, the status of a tube well becomes known in the community. Once As test results become known in the community, only strangers continue to ask for water from contaminated tube wells. Owners of safe tube wells report that the largest group of non-family members gathering water at their tube wells are neighbors, and 26% of these owners claimed that one reason why they shared their water was because other tube wells had unsafe levels of As and theirs did not.
Instead of sending women out to get water from a stranger’s tube well, men in some families took over this chore. Some men go to public tube wells at mosques to gather water for their families. Others seek water from neighbors’ or relatives’ tube wells. However, this was cited as a source of conflict by 11% of tube-well owners, who did not like having unrelated men enter their family compounds. The location of a tube well is crucial. If the tube well is located on the street, then all may use it freely. In contrast, if the tube well is located inside a family compound, only family members and certain relatives have free access to the compound and the tube well, regardless of the stated willingness of the owner to share the tube well. Distance is also a factor for willingness to gather water from an outside tube well, with one family reporting that it continued to use As-contaminated water because other tube wells were too far away. The maximum distance required to get safe drinking water in each of these four neighborhoods was approximately 0 m in Komlapur, 490 m in Bualda, 1,400 m in Fulbaria, and 2,100 m in Jamjami (Figure 3).
Education programs concerning the dangers of As-contaminated water have achieved some measure of success, because only one owner of an unsafe tube well in our study appeared not to have understood the implications of his tube well’s As results. This owner, a 70-year-old male, professed during the follow-up interview that his water was safe, and he told us that he had continued to use it and share it freely with others.
Many of the tube-well owners (85%) followed our recommendation and had their tube wells retested. This retesting was not performed by our research team. Only one respondent told us that he thought retesting was not necessary (the one who believed his water was safe when it was not); the others who did not retest their water gave cost as the reason for not retesting. People were especially willing to retest their water if this could be done at no cost to them. Of the respondents who had their water retested, 96% reported that the retesting was done with no charge to them; however, two respondents paid to have their water retested.
Our survey suggests that the number of tube wells, especially private tube wells, is rapidly increasing in Bangladesh as more families acquire the economic resources to build them. Only 14% of tube wells were reported to have been constructed before 1983 (56% private); 27% were reported to have been constructed from 1983 to 1992 (83% private), and 59% from 1993 to 2002 (92% private), making the tube wells in our random sample from western Bangladesh only 9 years old on average. In addition, we asked whether respondents were aware of any As patients in the area. The number of nearby As patients was not statistically related to the concentration of As during our sampling event (p = 0.44). Similarly, the concentration of As during our sampling event was not statistically related to the age of the tube well (p = 0.51). In contrast, the number of nearby As patients was statistically related to the age of the tube well and hence the duration of exposure to tube-well water that was potentially contaminated with As. That is, the oldest tube wells were associated with the highest number of nearby As patients (p = 0.03). This stresses the fact that duration of exposure to As must be considered in addition to the concentration of As at any given time, because this can explain why the tube wells of some As patients are found to contain very low levels of As but have been used over many years (WHO 2001). For example, Figure 5 shows a female As patient with keratosis of the palms and blackfoot disease. When tested in 2002, the tube well used by this patient had an As concentration of 1.4 μg/L, but she had been drinking from this tube well for 34 years. The As concentration in some of Bangladesh’s tube wells has changed dramatically over time (Frisbie et al. 1999; USAID 1997). These results demonstrate the need to periodically test all drinking water tube wells so that the total lifetime exposure to As can be reduced. Because most of the tube wells in the country have been installed quite recently, the numbers of As patients may begin to increase dramatically as more people develop a history of using tube wells for longer than the 5–10 years it may take to develop symptoms of chronic As poisoning. Finally, as the ages of the tube wells and the length of exposure to As increase, it may become even more vital to adopt a drinking-water standard for As lower than the current Bangladesh standard of 50 μg/L, such as the 10 μg/L WHO guideline (WHO 1996).
Conclusions
A valid method for the determination of As by arsenomolybdate is now available. This method is more accurate, precise, and environmentally safe than the AgSCSN(CH2CH3)2 method (Tables 1 and 2), and it is more accurate and affordable than the GFAAS method (Tables 1 and 3).
Most important, the arsenomolybdate method is the only accurate, precise, and safe way to quantify As to less than the 10 μg/L WHO drinking water guideline (WHO 1996) without expensive or highly specialized laboratory equipment (Table 1). This suggests that developing countries such as Argentina, Bangladesh, Bolivia, Chile, China, India, Mexico, Nepal, Pakistan, Peru, and Thailand, with limited access per capita to atomic absorption spectrometers or other sophisticated instruments for measuring As, could lower their 50 μg/L drinking water standards to the more protective 10 μg/L guideline if they use the arsenomolybdate method (Bhattacharya et al. 2002; Ng et al. 2003; United Nations 2004). More than 32 million people from these countries drink water with As concentrations greater than their national standards of 50 μg/L (Bhattacharya et al. 2002; Ng et al. 2003; United Nations 2004; Yu et al. 2003). This suggests that > 170,000 people from these countries will die from skin, bladder, liver, or lung cancer caused by drinking water with > 50 μg/L As. If these countries complied with a 10 μg/L drinking water standard for As, perhaps by sharing safe water as was done in the four neighborhoods from this study, > 140,000 of these 170,000 lives could potentially be saved.
In particular, it is very important that the Government of Bangladesh lower its 50 μg/L drinking water standard for As. The rapidly increasing number of tube wells in Bangladesh suggests that the mortality rate from chronic As poisoning is also rapidly increasing. Lowering this standard will reduce exposure and save lives by encouraging the use of safer drinking water.
Finally, our surveys suggest that Bangladeshis will readily test and share their drinking water to meet the more protective WHO guideline for As of 10 μg/L. More specifically, 85% of tube-well owners were concerned enough to retest their water for As within 1 year, and 90% of the tube-well owners that had tube-well As concentrations > 10 μg/L actually gathered water from their neighbors.
We thank K. Adam, M. Quader, and M.A. El Shakankiri.
This study was supported by Better Life Laboratories, the Bangladesh Association for Needy Peoples Improvement, Green Mountain Laboratories, CNRS (Centre national de la recherche scientifique) at the Université de Bordeaux 1, the Hospital for Sick Children, SETU (Services for Education, Training and Unity), and L. Spence.
Figure 1 Map of western Bangladesh showing the four neighborhoods where groundwater samples were collected from tube wells. Kushtia is a major city.
Figure 2 As concentration (μg/L) in tube-well water at each sampling location by the arsenomolybdate method. (A) Fulbaria. (B) Bualda. (C) Jamjami. (D) Komlapur.
Figure 3 Contour maps of As concentration (μg/L) in tube-well water at each sampling location in the four neighborhoods by the arsenomolybdate method. (A) Fulbaria. (B) Bualda. (C) Jamjami. (D) Komlapur. The black contour line represents the 10 μg/L WHO drinking water guideline.
Figure 4 The AsH3 generator, scrubber, and absorber used for the two colorimetric determinations of As.
Figure 5 Photograph of a Bangladeshi female with keratosis of the palms and blackfoot disease. Her well contained 1.4 μg/L As on 21 July 2002; she had been drinking from this well for 34 years.
Table 1 Quality control results, equipment costs, and solvents for the determination of As by arsenomolybdate, AgSCSN(CH2CH3)2, and GFAAS methods.
Arsenomolybdate AgSCSN(CH2CH3)2 GFAAS
Method detection limit 7 μg/L 9 μg/L 0.7 μg/L
Equipment cost $6,700a $6,700a $37,000b
Recovery of known additionsc 101.5 ± 3.6% 103 ± 18% 103.3 ± 3.1%
Precision of standards 2.1 μg/L 2.6 μg/L 0.22 μg/L
Precision of samples 4.7 μg/L 4.5 μg/L 1.7 μg/L
Precision of known additions 7.1 μg/L 24 μg/L 2.0 μg/L
Solvent H2O CHCl3 or C5H5N H2O
a Includes a spectrophotometer, distillation unit for purifying laboratory water, analytical balance, top-loading balance, hot plate with stirrer, and glassware.
b Includes an atomic absorption spectrometer, distillation unit for purifying laboratory water, analytical balance, top-loading balance, and glassware.
c 95% confidence interval.
Table 2 Comparison of As concentrations determined by the arsenomolybdate and AgSCSN(CH2CH3)2 methods.
Measure Result
Calculated t 1.88
Critical two-tailed t at α = 0.05 (70 df) 1.99
p-Value of paired t-test 0.06
Correlation coefficient, r 0.993
Mean As concentration by arsenomolybdate 27.6 μg/L
Mean As concentration by AgSCSN(CH2CH3)2 23.4 μg/L
Relative percent difference of these means 16.6%
Table 3 Comparison of As concentrations determined by the arsenomolybdate and GFAAS methods.
Measure Result
Calculated t 1.19
Critical two-tailed t at α = 0.05 (70 df) 1.99
p-Value of paired t-test 0.24
Correlation coefficient, r 0.996
Mean As concentration by arsenomolybdate 27.6 μg/L
Mean As concentration by GFAAS 28.6 μg/L
Relative percent difference of these means 3.6%
Table 4 Comparison of As concentrations determined by the AgSCSN(CH2CH3)2 and GFAAS methods.
Measure Result
Calculated t 2.63
Critical two-tailed t at α = 0.05 (70 df) 1.99
p-Value of paired t-test 0.01
Correlation coefficient, r 0.995
Mean As concentration by AgSCSN(CH2CH3)2 23.4 μg/L
Mean As concentration by GFAAS 28.6 μg/L
Relative percent difference of these means 20.1%
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7542ehp0113-00120516140628ResearchMeeting Report: Summary of IARC Monographs on Formaldehyde, 2-Butoxyethanol, and 1-tert-Butoxy-2-Propanol Cogliano Vincent James Grosse Yann Baan Robert A. Straif Kurt Secretan Marie Béatrice Ghissassi Fatiha El International Agency for Research on Cancer, Lyon, Francethe Working Group for Volume 88 *Address correspondence to V.J. Cogliano, Carcinogen Identification and Evaluation, International Agency for Research on Cancer, 150 cours Albert Thomas, 69372 Lyon cedex 08, France. Telephone: 33-4-72-73-84-76. Fax: 33-4-72-73-83-19. E-mail:
[email protected]*The Working Group for Volume 88 of the IARC Monographs includes Ulrich Andrae (Germany), Sherwood Burge (UK), Rajendra Chhabra (USA), John Cocker (UK), David Coggon (UK), Rory Conolly (USA), Paul Demers (Canada), David Eastmond (USA), Elaine Faustman (USA), Victor Feron (the Netherlands), Michel Gérin (Canada, Chair), Marcel Goldberg (France), Bernard Goldstein (USA), Roland Grafström (Sweden), Johnni Hansen (Denmark), Michael Hauptmann (USA), Kathy Hughes (Canada), Ted Junghans (USA), Dan Krewski (Canada), Steve Olin (USA), Martine Reynier (France), Judith Shaham (Israel), Morando Soffritti (Italy), Leslie Stayner (USA), Patricia Stewart (USA), and Douglas Wolf (USA).
The authors declare they have no competing financial interests.
9 2005 12 5 2005 113 9 1205 1208 31 8 2004 12 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. An international, interdisciplinary working group of expert scientists met in June 2004 to develop IARC Monographs on the Evaluation of the Carcinogenic Risk of Chemicals to Humans (IARC Monographs) on formaldehyde, 2-butoxyethanol, and 1-tert-butoxy-2-propanol. Each IARC Monograph includes a critical review of the pertinent scientific literature and an evaluation of an agent’s potential to cause cancer in humans. After a thorough discussion of the epidemiologic, experimental, and other relevant data, the working group concluded that formaldehyde is carcinogenic to humans, based on sufficient evidence in humans and in experimental animals. In the epidemiologic studies, there was sufficient evidence that formaldehyde causes nasopharyngeal cancer, “strong but not sufficient” evidence of leukemia, and limited evidence of sinonasal cancer. The working group also concluded that 2-butoxyethanol and 1-tert-butoxy-2-propanol are not classifiable as to their carcinogenicity to humans, each having limited evidence in experimental animals and inadequate evidence in humans. These three evaluations and the supporting data will be-published as Volume 88 of the IARC Monographs.
1-tert-butoxy-2-propanol2-butoxyethanolcarcinogenformaldehydeglycol ethershazard identificationIARC Monographsleukemianasopharyngeal cancersinonasal cancer
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Twenty-six scientists from 10 countries met at the International Agency for Research on Cancer (IARC) in June 2004 to develop IARC Monographs on the Evaluation of the Carcinogenic Risk of Chemicals to Humans (IARC Monographs) on formaldehyde, 2-butoxyethanol, and 1-tert-butoxy-2-propanol (IARC, in press). This is the fourth IARC evaluation of formaldehyde and the first of the glycol ethers.
Formaldehyde is widely used in resins that bind wood products, pulp and paper, and glasswool and rockwool insulation. It is also used in plastics and coatings, textile finishing, and chemical manufacturing and as a disinfectant and preservative. High concentrations can be found in some work environments, and much lower concentrations in homes.
2-Butoxyethanol is a glycol ether widely used as a solvent in paints, paint thinners, glass-cleaning and surface-cleaning products (especially in the printing and silk-screening industries), and personal-care and other personal products and as a chemical intermediate. General-population exposure can occur through the use of consumer products, particularly cleaning agents.
1-tert-Butoxy-2-propanol is a glycol ether that has found increasing use as a solvent in coatings, glass-cleaning and surface-cleaning products, inks, adhesives, and nail-polish lacquers.
Materials and Methods
IARC convenes an international, interdisciplinary working group of expert scientists to develop each volume of the IARC Monographs. The working group writes a critical review of the pertinent scientific literature (published articles, articles accepted for publication, and publicly available documents from government agencies) and a consensus evaluation of each agent’s potential to cause cancer in humans.
The IARC Monographs are developed during an 8-day meeting whose objectives are review and consensus. Before the meeting, each member of the working group writes a portion of the critical review. At the meeting, four subgroups (exposure, cancer in humans, cancer in experimental animals, and mechanistic and other relevant data) review these drafts and develop consensus subgroup drafts. Then the working group meets in plenary session to review the subgroup drafts and develop a consensus evaluation. After the meeting, IARC scientists review the final draft for accuracy and clarity before publication.
The evaluation is developed in steps (IARC 2005). The subgroup of epidemiologists proposes an evaluation of the evidence of cancer in humans as sufficient evidence, limited evidence, inadequate evidence, or evidence suggesting lack of carcinogenicity. A subgroup of toxicologists and pathologists proposes an evaluation of the evidence of cancer in experimental animals, choosing one of the same descriptors. Combination of these two partial evaluations yields a preliminary default evaluation that the agent is one of the following: group 1, carcinogenic to humans; group 2A, probably carcinogenic to humans; group 2B, possibly carcinogenic to humans; group 3, not classifiable as to its carcinogenicity to humans; or group 4, probably not carcinogenic to humans.
When the epidemiologic evidence is sufficient, the final evaluation is carcinogenic to humans, regardless of the experimental evidence. In other cases, the mechanistic and other relevant data are considered to determine whether the default evaluation should be modified upward or downward. A subgroup of experts in cancer mechanisms assesses the strength of the mechanistic data and whether the mechanisms of tumor formation in experimental animals can operate in humans. The overall evaluation is a matter of scientific judgment, reflecting the combined weight of the evidence.
Working groups are selected to invite the best-qualified experts and to avoid real or apparent conflicts of interests. Consideration is given also to demographic diversity and a balanced representation of all scientific views. Each potential participant submits a Declaration of Interests [World Health Organization (WHO) 2005], which IARC assesses to determine whether there is a conflict that warrants some limitation on participation. An expert with a real or apparent conflict of interest may not serve as chairperson, draft text discussing cancer data, or participate in the evaluations. IARC strives to ensure that the working group is free from all attempts at interference, before and during the meeting. This includes lobbying, written materials, and meals or other favors offered by interested parties. Working group members are asked not to discuss the subject matter with anyone outside the meeting and to report all attempts at interference (Cogliano et al. 2004).
Results
Formaldehyde.
There was a statistically significant excess of deaths from nasopharyngeal cancer in the largest and most informative cohort study of industrial workers (Hauptmann et al. 2004), with statistically significant exposure–response relationships for peak and cumulative exposure. An excess of deaths from nasopharyngeal cancer was also observed in a proportionate mortality analysis of the largest U.S. cohort of embalmers (Hayes et al. 1990), and an excess of cases of nasopharyngeal cancer was observed in a Danish study of proportionate cancer incidence among workers at companies that manufactured or used formaldehyde (Hansen and Olsen 1995). Although other cohort studies reported fewer cases of nasopharyngeal cancer than expected (Coggon et al. 2003; Pinkerton et al. 2004; Walrath and Fraumeni 1983), the working group noted that the deficits were small and the studies had low power to detect an effect on nasopharyngeal cancer. Of seven case–control studies of nasopharyngeal cancer (Armstrong et al. 2000; Hildesheim et al. 2001; Olsen et al. 1984; Roush et al. 1987; Vaughan et al. 1986, 2000; West et al. 1993), five found elevations of risk for exposure to formaldehyde. The working group considered it “improbable that all of the positive findings for nasopharyngeal cancer that were reported from the epidemiologic studies, and particularly from the large study of industrial workers in the United States, could be explained by bias or unrecognized confounding effects.” The working group concluded that these studies provide “sufficient epidemiological evidence that formaldehyde causes nasopharyngeal cancer in humans.”
Excess mortality from leukemia, primarily of the myeloid type, has been observed relatively consistently in six of seven studies of embalmers, funeral parlor workers, pathologists, and anatomists (Hall et al. 1991; Hayes et al. 1990; Levine et al. 1984; Logue et al. 1986; Stroup et al. 1986; Walrath and Fraumeni 1983, 1984). A recent meta-analysis found that, overall, the relative risk for leukemia in these workers was increased and did not vary significantly among studies (Collins and Lineker 2004). There had been speculation that these findings might be explained by viruses; however, the working group found little evidence that these occupations have a higher incidence of viral infections or that viruses have a causal role in myeloid leukemia. Until recently, these leukemia findings received little attention because excess leukemia had not been observed in the studies of industrial workers. There is now, however, some evidence for an association between formaldehyde exposure and leukemia in the recent updates of two of the three major industrial cohorts. A statistically significant exposure–response relationship was observed for leukemia and, particularly, for myeloid leukemia in the study of industrial workers in the United States, based on peak exposure and, to a lesser degree, on average intensity of exposure to formaldehyde (Hauptmann et al. 2003).
There was no excess mortality from leukemia when the industrial workers were compared with the general U.S. population, but a comparison with the general population may be biased. In another study, excess mortality from leukemia was found in the recent update of garment workers in the United States (Pinkerton et al. 2004). This excess was statistically significant among workers with a longer duration of exposure and follow-up. In contrast, the updated study of industrial workers in the United Kingdom did not find excess mortality from leukemia (Coggon et al. 2003). This high-quality study had sufficient size and follow-up to have reasonable power for detecting an excess of leukemia, but it did not report on peak exposures or the risk of myeloid leukemia specifically. The working group concluded, “In summary, there is strong but not sufficient evidence for a causal association between leukaemia and occupational exposure to formaldehyde.” This conclusion, falling between sufficient and limited evidence, was based on a consistently increased risk in studies of embalmers, funeral parlor workers, pathologists, and anatomists and was present in two of the three most informative studies of industrial workers.
Several case–control studies have investigated the relationship between formaldehyde exposure and sinonasal cancer. A pooled analysis of 12 studies showed an increased risk of adenocarcinoma in men and women thought never to have been exposed to wood dust or leather dust, with an exposure–response trend for an index of cumulative exposure (Luce et al. 2002). One other case–control study (Olsen and Asnaes 1986) and a proportionate incidence study (Hansen and Olsen 1995) showed an increased risk of sinonasal cancer, particularly squamous cell carcinoma. Against these largely positive findings, the three most informative cohort studies of industrial workers showed no excesses of sinonasal cancer (Coggon et al. 2003; Hauptmann et al. 2004; Pinkerton et al. 2004). The working group noted that most studies did not distinguish tumors as originating in the nose or sinuses; thus, an increased risk of nasal cancer would be diluted if there were no corresponding effect on the sinuses. In the case–control studies, the working group also noted the potential for confounding by wood dust exposure, which is associated with adenocarcinoma. The working group concluded that there is limited evidence that formaldehyde causes sinonasal cancer in humans.
In experimental animals, several studies have shown that inhalation exposure induces squamous cell carcinomas of the nasal cavities in rats (Albert et al. 1982; Feron et al. 1988; Gibson 1984; Kamata et al. 1997; Kerns et al. 1983; Monticello et al. 1996; Morgan et al. 1986; Sellakumar et al. 1985; Woutersen et al. 1989), although single studies in mice (Kerns et al. 1983) and hamsters (Dalbey 1982) showed no carcinogenic effects. Four studies of formaldehyde administered to rats in drinking water gave varying results: One showed an increased incidence of forestomach papillomas in male rats (Takahashi et al. 1986); a second showed an increased incidence of gastrointestinal leiomyosarcomas in female rats and in both sexes combined (Soffritti et al. 1989); a third showed increased incidences of total malignant tumors, lymphomas and leukemias, and testicular interstitial-cell adenomas in male rats (Soffritti et al. 2002); whereas a fourth did not show a carcinogenic effect (Til et al. 1989). Formaldehyde also showed co-carcinogenic effects by inhalation, ingestion, and dermal exposure (Dalbey 1982; Iverson 1986; Takahashi et al. 1986).
The toxicokinetics of inhaled formaldehyde have been well studied (Agency for Toxic Substances and Disease Registry 1999). More than 90% of inhaled formaldehyde is absorbed in the upper respiratory tract (Heck et al. 1985). Absorbed formaldehyde can be oxidized to formate and carbon dioxide or can be incorporated into biologic macromolecules. Formaldehyde has a half-life of about 1 min in rat plasma (Rietbrock 1965). Inhalation exposure has not been found to alter the endogenous concentration of formaldehyde in the blood of rats, monkeys, or humans (Casanova et al. 1988; Heck et al. 1983, 1985). Oral exposure to 14C-formaldehyde resulted in some excretion in urine and feces within 12 hr (Galli et al. 1983). Dermal application of 14C-formaldehyde resulted in some urinary excretion in rats and monkeys (Jeffcoat et al. 1983).
Evidence shows that formaldehyde is genotoxic in multiple in vitro models and in exposed humans and laboratory animals. Human studies reported increased DNA–protein crosslinks in workers exposed to formaldehyde (Shaham et al. 1996, 2003), and this is consistent with studies in laboratory rats and monkeys. Cellular proliferation increases considerably at concentrations > 6 ppm and amplifies the genotoxic effects of formaldehyde. The working group concluded, “The current data indicate that both genotoxicity and cytotoxicity play important roles in the carcinogenesis of formaldehyde in nasal tissues.” On the other hand, with respect to the potential for formaldehyde to induce leukemia, the working group was not aware of any good rodent models for acute myeloid leukemia in humans. Several possible mechanisms were considered, such as clastogenic damage to circulating stem cells. There is a single study reporting cytogenetic abnormalities in the bone marrow of rats inhaling formaldehyde (Kitaeva et al. 1990). The working group concluded, “Based on the data available at this time, it was not possible to identify a mechanism for the induction of myeloid leukaemia in humans.” This is an area needing more research.
The working group concluded that formaldehyde is carcinogenic to humans (group 1), based on sufficient evidence in humans and sufficient evidence in experimental animals. Based on the information now available, this classification is higher than those of previous IARC evaluations (IARC 1982, 1987, 1995).
2-Butoxyethanol.
2-Butoxyethanol was tested for carcinogenicity by inhalation exposure in male and female mice and rats [National Toxicology Program (NTP) 2000]. Clear increases in tumor incidence were observed only in mice. In male mice exposed to 2-butoxyethanol, there was a dose-related increase in the incidence of hemangiosarcomas of the liver. In female mice, there was a dose-related increase in the incidences of combined forestomach squamous-cell papillomas and carcinomas (mainly papillomas). In female rats, there was a positive trend in the occurrence of benign or malignant pheochromocytomas (mainly benign) of the adrenal medulla, but this equivocal result could not be attributed with confidence to exposure to 2-butoxyethanol. No increases were observed in male rats. The epidemiologic data were inadequate for this compound.
Regarding mechanisms of carcinogenesis, the working group considered that hemolysis and associated oxidative stress in the liver have been proposed to be linked to the induction of mouse liver neoplasia. They also considered that, in view of lower sensitivity to hemolysis of human erythrocytes and higher human liver concentrations of the antioxidant vitamin E, the induction of liver tumors in humans would be improbable through this pathway, but it was noted that other potential mechanisms have not been investigated. The working group observed that the mouse forestomach tumors are associated with high local exposure to 2-butoxyethanol and high local concentrations of the toxic metabolite 2-butoxyacetic acid.
The working group concluded that 2-butoxyethanol is not classifiable as to its carcinogenicity to humans (group 3), with limited evidence in experimental animals and inadequate evidence in humans.
1-tert-Butoxy-2-propanol.
1-tert-Butoxy-2-propanol was tested for carcinogenicity by inhalation exposure in male and female mice and rats (Doi et al. 2004; NTP 2003). In a single study in both male and female mice, a dose-related increase in the combined incidence of liver tumors (hepatocellular adenomas and carcinomas), including hepatoblastomas, was observed. When hepatocellular carcinomas and hepatoblastomas were combined, there was a significant trend for the increase in malignant tumors in females. In male rats, there were marginal, nonsignificant increases in the incidences of renal tubule adenomas (with one carcinoma at the highest dose) and hepatocellular adenomas, but these findings were considered to be equivocal. In female rats, there were no dose-related increases in tumor incidence. No epidemiologic data were available for this compound.
With regard to mechanisms of carcinogenesis, the working group found the available data inadequate to elucidate a potential mechanism for the mouse liver tumors. They found the renal effects largely consistent with the α2u-globulin–associated nephropathy that occurs in male rats, but concluded that the available evidence satisfies only some, but not all, of the IARC criteria for the mechanism associated with accumulation of α2u-globulin. Regarding the potential for genotoxic effects, the working group was not able to draw any meaningful conclusion in view of the scarcity of the data available.
The working group concluded that 1-tert-butoxy-2-propanol is not classifiable as to its carcinogenicity to humans (group 3), with limited evidence in experimental animals and inadequate evidence in humans.
Discussion
A theme common to these three evaluations is the consideration of mechanistic information to develop and evaluate hypotheses about the sequence of steps leading to the induction of tumors in experimental animals. The hypothesized mechanisms described in these evaluations provide an interesting set of cases that range from a vast literature on respiratory-tract tumors in rats induced by inhalation of formaldehyde to some more tentative hypotheses about the various tumors observed in animals after exposure to glycol ethers. Both types of mechanistic data sets were of use in the evaluation process.
The evaluation of formaldehyde as carcinogenic to humans shows the importance of mechanistic information in the classification of carcinogens. For the nasopharyngeal tumors, the working group discussed the convergence of the epidemiologic, experimental, and mechanistic evidence. If the evidence in humans had been less than sufficient, the strong mechanistic evidence in exposed humans and sufficient evidence in experimental animals might still have led to classification as group 1. The extensive mechanistic data for formaldehyde-induced respiratory cancer provide strong support for the empirical observation of nasopharyngeal cancer in humans, although computer models that predict an anterior-to-posterior gradient of formaldehyde deposition in the upper respiratory tract would predict that formaldehyde would cause cancer in the nose as well as the nasopharynx in humans.
On the other hand, the lack of information on possible mechanisms by which formaldehyde might increase the risk of leukemia in humans tempered the interpretation of the epidemiologic data on that cancer type. The entire working group discussed at length this divergence between the epidemiologic and mechanistic conclusions for leukemia. Information to support a biologically plausible mechanism could have supported a stronger conclusion about the evidence of leukemia in humans.
In the evaluations of the glycol ethers, the working group grappled with questions of interpretation and scientific judgment. A recurring issue was the criterion for characterizing a rare tumor or an unusual set of observations that can carry greater weight than a typical bioassay result. A related matter was how to bring in additional information to resolve difficult questions—for example, how to consider the results of historical controls or alternative statistical tests. When the working group tried to, but could not, reach consensus on a question of interpretation or scientific judgment, the evaluation presented the differing positions favored by its members. For example, after thorough discussion, several members of the working group favored an evaluation of the carcinogenicity in experimental animals as sufficient for 1-tert-butoxy-2-propanol. This view emphasized the dose-related induction of hepatoblastoma in male and female mice, considering hepatoblastoma as a rare neoplasm with low spontaneous incidence in mice, especially in females. Most of the working group, nevertheless, considered the evidence to be limited, based on the interpretation of hepatoblastoma being a variant of hepatocellular carcinoma.
It is important to note that the evaluation of an agent as not classifiable as to its carcinogenicity to humans is not a determination of safety, with respect to both cancer and effects other than cancer. It indicates that the data did not meet the minimum standards developed by the IARC for sufficient evidence in experimental animals and suggests that further testing is needed, particularly when there is widespread human exposure or another reason for public health concern.
We gratefully acknowledge the important contributions of the administrative staff of the IARC Monographs: S. Egraz, M. Lézère, J. Mitchell, and E. Perez.
The IARC Monographs are supported, in part, by grants from the U.S. National Cancer Institute, the European Commission, the U.S. National Institute of Environmental Health Sciences, and the U.S. Environmental Protection Agency.
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Takahashi M Hasegawa R Furukawa F Toyoda K Sato H Hayashi Y 1986 Effects of ethanol, potassium metabisulfite, formaldehyde and hydrogen peroxide on gastric carcino-genesis in rats after initiation with N -methyl-N ′-nitro-N -nitrosoguanidine Jpn J Cancer Res 77 118 124 3082823
Til HP Woutersen RA Feron VJ Hollanders VHM Falke HE 1989 Two-year drinking-water study of formaldehyde in rats Food Chem Toxicol 27 77 87 2714719
Vaughan TL Stewart PA Teschke K Lynch CF Swanson GM Lyon JL 2000 Occupational exposure to formaldehyde and wood dust and nasopharyngeal carcinoma Occup Environ Med 57 376 384 10810126
Vaughan TL Strader C Davis S Daling JR 1986 Formaldehyde and cancers of the pharynx, sinus and nasal cavity. I. Occupational exposures Int J Cancer 38 677 683 3770995
Walrath J Fraumeni JF Jr 1983 Mortality patterns among embalmers Int J Cancer 31 407 411 6832852
Walrath J Fraumeni JF Jr 1984 Cancer and other causes of death among embalmers Cancer Res 44 4638 4641 6467219
West S Hildesheim A Dosemeci M 1993 Non-viral risk factors for nasopharyngeal carcinoma in the Philippines: results from a case–control study Int J Cancer 55 722 727 7503957
WHO 2005. Declaration of Interests for WHO Experts. Geneva:World Health Organization. Available: http://www.who.int/ipcs/publications/cicad/en/Declaration_of_interest.pdf [accessed 8 April 2005].
Woutersen RA van Garderen-Hoetmer A Bruijntjes JP Zwart A Feron VJ 1989 Nasal tumours in rats after severe injury to the nasal mucosa and prolonged exposure to 10 ppm formaldehyde J Appl Toxicol 9 39 46 2926095
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Environ Health Perspect. 2005 Sep 12; 113(9):1205-1208
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7986ehp0113-00120916140629ResearchEnvironmental Medicineδ-Aminolevulinic Acid Dehydratase Polymorphism and Risk of Brain Tumors in Adults Rajaraman Preetha 1Schwartz Brian S. 2Rothman Nathaniel 1Yeager Meredith 3Fine Howard A. 4Shapiro William R. 5Selker Robert G. 6Black Peter M. 7Inskip Peter D. 11 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA2 Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA3 Core Genotyping Facility, and4 Neuro-oncology Branch, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, Maryland, USA5 Barrow Neurological Institute, St. Joseph’s Hospital and Medical Center, Phoenix, Arizona, USA6 Western Pennsylvania Hospital, Pittsburgh, Pennsylvania, USA7 Brigham and Women’s Hospital, Boston, Massachusetts, USAAddress correspondence to P. Rajaraman, REB, National Cancer Institute, NIH, DHHS, 6120 Executive Blvd., EPS Room 7085, Bethesda, MD 20892-7238 USA. Telephone: (301) 496-8847. Fax: (301) 402-0207. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 10 5 2005 113 9 1209 1211 2 2 2005 10 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The enzyme δ -aminolevulinic acid dehydratase (ALAD), which catalyzes the second step of heme synthesis, can be inhibited by several chemicals, including lead, a potential risk factor for brain tumors, particularly meningioma. In this study we examined whether the ALAD G177C polymorphism in the gene coding for ALAD is associated with risk of intracranial tumors of the brain and nervous system. We use data from a case–control study with 782 incident brain tumor cases and 799 controls frequency matched on hospital, age, sex, race/ethnicity, and residential proximity to the hospital. Blood samples were drawn and DNA subsequently sent for genotyping for 73% of subjects. ALAD genotype was determined for 94% of these samples (355 glioma, 151 meningioma, 67 acoustic neuroma, and 505 controls). Having one or more copy of the ALAD2 allele was associated with increased risk for meningioma [odds ratio (OR) = 1.6; 95% confidence interval (CI), 1.0–2.6], with the association appearing stronger in males (OR = 3.5; 95% CI, 1.3–9.2) than in females (OR = 1.2; 95% CI, 0.7–2.2). No increased risk associated with the ALAD2 variant was observed for glioma or acoustic neuroma. These findings suggest that the ALAD2 allele may increase genetic susceptibility to meningioma.
ALADbraincase–controlmeningiomapolymorphismtumor
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The ALAD gene codes for the enzyme δ -aminolevulinic acid dehydratase (ALAD), which catalyzes the second step of heme synthesis involving the condensation of two molecules of aminolevulinic acid (ALA) to form porphobilinogen. The most commonly studied polymorphism in the gene, ALAD G177C (dbSNP ID: rs1800435) contains a G-to-C transversion at position 177 of the coding region, resulting in the substitution of asparagine for lysine. ALAD G177C has two codominant alleles: ALAD1 and ALAD2 (Battistuzzi et al. 1981), with an ALAD2 allele prevalence of approximately 10% (range, 6–20%) in Caucasian populations, 3–11% in Asian populations, and 3% in African-American populations (Kelada et al. 2001).
Although ALAD can be inhibited by a variety of chemicals, including lead, trichloroethylene, bromobenzene, and styrene (Fujita et al. 2002), polymorphic differences in enzyme binding or chemical uptake have been examined most extensively for lead. On average, individuals with the ALAD2 allele have higher blood lead levels than do ALAD1 homozygotes, probably due to tighter binding of lead by the ALAD2 enzyme (Alexander et al. 1998; Bergdahl et al. 1997; Fleming et al. 1998; Hsieh et al. 2000; Shen et al. 2001; Wetmur et al. 1991; Ziemsen et al. 1986). Previous studies in animals and humans indicate that exposure to lead may increase the risk of brain tumors [International Agency for Research on Cancer (IARC) 1987; Silbergeld 2003; Steenland and Boffetta 2000], particularly meningioma (Cocco et al. 1999; Hu et al. 1999; Navas-Acien et al. 2002).
Given that the ALAD G177C polymorphism affects the toxicokinetics of lead in the body, and that exposure to lead may increase the risk of adult brain tumors, we postulated a possible association of ALAD G177C genotype and risk of intracranial tumors of the brain and nervous system (hereafter referred to as brain tumors). Analyses were conducted using data from a hospital-based case–control study of brain tumors conducted by the National Cancer Institute (NCI) between 1994 and 1998.
Materials and Methods
Study population.
Subjects for the brain tumor study were enrolled from 1994 through 1998 from three hospitals specializing in brain tumor treatment, located in Phoenix, Arizona; Boston, Massachusetts; and Pittsburgh, Pennsylvania. The study protocol was approved by the institutional review board of each participating institution. Study methods have been described in detail elsewhere (Inskip et al. 2001).
Eligible cases for the parent study were ≥ 18 years of age with a first intracranial glioma, meningioma, or acoustic neuroma diagnosed during or within the 8 weeks preceding hospitalization. Ninety-two percent of eligible brain tumor patients agreed to participate in the study. All diagnosed cases of glioma and meningioma were confirmed by microscopy, as were 96% of acoustic neuroma cases. A total of 489 subjects with glioma, 197 subjects with meningioma, and 96 subjects with acoustic neuroma were enrolled.
Controls were patients admitted to the same hospitals as cases for a variety of non-neoplastic conditions, with the most common being injuries (25%), circulatory system disorders (22%), musculoskeletal disorders (22%), and digestive disorders (12%). More than 90% of patients were interviewed within 1 year of symptom onset. Control subjects were frequency matched in a 1:1 ratio to all brain tumor cases based on hospital, age, sex, race/ethnicity, and proximity of residence to the hospital; 799 controls (86% of all contacted controls) were enrolled.
Informed written consent was obtained from all cases and controls. Blood samples were collected and sent for genotyping for 73% of all subjects: 382 subjects with glioma (78%), 158 subjects with meningioma (80%), 71 subjects with acoustic neuroma (74%), and 540 control subjects (68%). The main obstacle to obtaining blood samples was subject refusal, with nonparticipation in the blood draw being higher for control subjects than for case subjects.
Shortly after hospitalization, a trained research nurse administered a structured in-person interview for each subject. Information on known or possible risk factors for brain tumors (including a detailed occupational history) was collected for all subjects.
Processing of blood samples.
DNA was extracted from the peripheral white blood cells (buffy coat or granulocytes) of blood samples using a phenol-chloroform method described by Daly et al. (1996). ALAD genotyping was conducted by the NCI’s Core Genotyping Facility using a medium-throughput TaqMan assay (Applied Biosystems, Foster City, CA).
Reactions for the assay were done in a 384 (96 × 4)-well plate format. Lyophilized sample DNA (10 ng) was used for a 5 μL TaqMan reaction. Four Coriell DNA controls (Coriell Cell Repositories, Camden, NJ) for each genotype as well as no-template controls (NTCs) were put on the plate along with the samples; 2.5 μL of the 2X Universal Master Mix (Applied Biosystems), 200 nM of each primer, and 900 nM of each probe was used in the reaction. Probe 1 (TGTGAAGCGGCTGG), specific to the ALAD1 allele, contained the FAM dye reporter. Probe 2 (TGTGAACCGGCTGG) was specific to the ALAD2 allele and contained the VIC dye reporter. The primers used were primers F (TGCCTTCCTTCAACCCCTCTA) and R (CAAGGGCCTCAGCATCTCTT). Step 1 of the assay-specific thermocycling process involved 2 min of UNG (uracil-DNA glycosylase) activation using AmpErase UNG (Applied Biosystems) at 50°C. This was followed by 10 min of enzyme activation at 95°C (step 2), 0.30 sec of template denaturation at 92°C if using 3′MGB quencher, or at 95°C if using 3′TAMRA quencher (step 3), and 1 min of assay-specific annealing at 60°C (step 4). Steps 3 and 4 were repeated 49 times, after which the reaction was held at 4°C. The plate was then read on the ABI 7900HT sequence detection system (Applied Biosystems), and the results of the allelic discrimination were graphed as a scatter plot of allele 1 reaction versus allele 2 reaction (SDS Software; Applied Biosystems), with each well of the 384-well plate represented as a spot on the graph. Four distinct clusters on the allelic plot represented the NTCs and three possible genotypes, ALAD1–1, ALAD2–2, and ALAD1–2, respectively. Calls were determined manually by a technician using SDS software.
Quality control specimens for the study included multiple samples from three individuals who were not study subjects (QC-A, n = 34; QC-B, n = 20; QC-C, n = 15) and 76 duplicates from study subjects. These specimens were submitted for genotyping in a masked fashion and were collected and processed in a manner identical to that for study samples. Ninety-eight percent agreement was achieved between the three non-study replicates. The concordance rate for study duplicates (study samples compared with masked relabels) was 87% for glioma, 100% for meningioma, 85% for acoustic neuroma, and 89% for controls.
Using a conservative call strategy that labeled a call as missing if the genotype was unclear, ALAD genotyping was successfully conducted for 94% of samples (93% of gliomas, 96% of meningiomas, 94% of acoustic neuromas, and 94% of controls). Missing values (noncalls) were generally equally likely to be from case or control samples.
Statistical analysis.
We assessed statistically significant departure from Hardy-Weinberg equilibrium for controls using the chi-square test. Unconditional logistic regression was used to estimate odds ratios (ORs) and calculate 95% confidence intervals (CIs) for the effect of the variant ALAD2 allele, adjusting for study matching factors. We entered adjustment variables as indicator variables in the following categories: age in years (18–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–99); race/ethnicity (non-Hispanic white, Hispanic, African-American, other); sex (male, female); hospital (Phoenix, Boston, Pittsburgh); and residential proximity to the hospital in miles (0–4, 5–14, 15–29, 30–49, ≥ 50). Because the small number of ALAD2–2 homozygotes precluded accurate estimation of risk for ALAD1–2 heterozygotes and ALAD2–2 homozygotes separately, these categories were combined for analysis. ALAD1–1 homozygotes were the reference group. In order to test for the influence of control group composition on the results, the models were run excluding each major category of control discharge diagnosis, one at a time.
Results
The distribution of demographic characteristics for genotyped subjects (Table 1) was comparable with the distribution for all study subjects (Inskip et al. 2001). Most demographic characteristics were distributed similarly for brain tumor cases and controls. Relative to controls, subjects with glioma were proportionately more often male, whereas subjects with meningioma and acoustic neuroma were more often female. Differences between individual tumor group and control group distributions were mostly due to the matching of controls for all tumors combined rather than for specific tumor subgroups.
Study participants in the youngest and oldest age brackets were less likely to have given a blood sample, as were control subjects who lived closest to the hospital. Brain tumor cases that were male, of “other” race/ethnicity, or from the Phoenix study site were also less likely to have given a blood sample (results not shown).
No significant departure from Hardy-Weinberg equilibrium was detected for controls (p = 0.3). Table 2 summarizes the association between ALAD G177C genotype and risk of each brain tumor type. The odds of meningioma were significantly higher for individuals possessing any ALAD2 allele compared with ALAD1–1 homozygotes (OR = 1.6; 95% CI, 1.0–2.6). This risk did not differ markedly when different control subgroups (musculoskeletal disorders, circulatory disorders, digestive disorders, or trauma) were excluded. Although the observed association between ALAD2 and meningioma appeared stronger in males (OR = 3.5; 95% CI, 1.3–9.2) than in females (OR = 1.2; 95% CI, 0.7–2.2), the sample size for the sex-specific estimates was small, with only 10 male and 25 female meningioma cases possessing the variant allele. No increased risk associated with the ALAD2 variant was observed for glioma or acoustic neuroma.
Discussion
We found that the ALAD2 allele of the G177C polymorphism was associated with increased risk of meningioma, especially in males. Confirmation of our findings will require replication in other studies with a larger number of meningioma cases. If risk of meningioma is truly increased in individuals with the ALAD2 allele, the question arises as to whether the effect depends upon exogenous chemical exposures that act on the heme synthesis pathway or is independent of such exposures. A direct effect of the ALAD2 polymorphism might be indicated if the ALAD2 allele has lower enzyme activity than the ALAD1 allele, given that the precursor ALA is thought to be neurotoxic and genotoxic (Silbergeld 2003). However, ALAD enzyme activity does not appear significantly different for the two alleles (Battistuzzi et al. 1981). Alternatively, it is possible that the increased risk of meningioma in ALAD2 individuals arises in the presence of chemicals that influence the heme synthesis pathway. Several chemicals have been shown to inhibit ALAD enzyme activity, including lead, trichloroethylene, bromobenzene, and styrene (Fujita et al. 2002). Polymorphic differences in enzyme binding or chemical uptake have been examined most extensively for lead, and individuals with the ALAD2 allele are generally reported to have higher blood lead levels than are individuals with the ALAD1 allele (Alexander et al. 1998; Bergdahl et al. 1997; Fleming et al. 1998; Hsieh et al. 2000; Shen et al. 2001; Wetmur et al. 1991; Ziemsen et al. 1986).
The observation that the relationship between ALAD2 and risk of meningioma was stronger in men than in women could be due to biologic differences or differential exposure to a chemical agent modified by ALAD genotype. Given the small number of male meningioma cases with the variant allele, we also cannot rule out the possibility that the observed effect modification is due to chance.
The specific question of ALAD genotype and brain tumor risk has not been addressed previously in the literature. In a previously published analysis of this same data set, we found elevated risk of meningioma in individuals who had worked in military occupations or as autobody painters, designers and decorators, industrial production supervisors, teachers, or managers (Rajaraman et al. 2004). Aside from teachers and managers, all of these occupations have potential exposure to lead, suggesting that lead might be implicated in meningioma risk. Our observation of increased risk with the ALAD2 variant for meningioma, but not for glioma or acoustic neuroma, parallels the observation that reports of increased risk of brain tumor with lead have been more consistent for meningioma (Cocco et al. 1999; Hu et al. 1999; Navas-Acien et al. 2002) than for glioma or for all brain tumors combined. However, the role of lead in the observed association between ALAD genotype and meningioma can be meaningfully addressed only when data on both ALAD genotype and individual lead exposure are available.
In a study with hospital controls, study results can be biased if the exposure under study is associated with conditions enrolled in the control series (Miettinen 1985). To assess for such a bias, we conducted a sensitivity analysis by excluding one major control subgroup at a time from the analysis. Systematically excluding control subgroups did not change observed ORs appreciably and resulted, if anything, in slightly stronger evidence of an association between ALAD2 and meningioma when circulatory or digestive disorders were excluded. If use of hospital controls introduced a bias in the observed OR, therefore, the likely direction of the bias was toward the null.
The relatively low concordance rate for study duplicates is another potential concern. However, the concordance for duplicates from meningioma cases was 100%, so our observed association for meningioma was probably not affected by genotyping concerns. Moreover, if we assume that 10% nondifferential misclassification did occur, this would have biased our findings toward the null, making our observed association a conservative estimate.
We chose to study the ALAD polymorphism based on a priori biologic and functional considerations and not by screening a large number of associations. Nonetheless, the possibility that our findings are due to chance cannot be ruled out. These findings should be viewed as hypothesis generating and need to be confirmed by replication in other studies.
Although our study had limited power for evaluating risk with respect to subtypes of tumor, it remains one of the largest case–control studies of brain tumors to date. Aside from a small percentage of brain tumors that can be explained by familial syndromes or exposure to ionizing radiation, very little is known about the etiology of brain tumors (Preston-Martin and Mack 1996; Wrensch et al. 2002). In order to clarify the role of lead (or other chemicals) in the observed relationship between ALAD genotype and risk of meningioma, it will be important to conduct a detailed exposure assessment and evaluate the joint effect of exposure and ALAD genotype in this, or another, study population.
Table 1 Demographic characteristics for individuals with glioma, meningioma, and acoustic neuroma and frequency-matched controlsa for genotyped individuals: NCI adult brain tumor study, 1994–1998.
Characteristic Glioma cases (n = 355) Meningioma cases (n = 151) Acoustic neuroma cases (n = 67) Controls (n = 505)
Sex
Male 192 (54.1) 32 (21.2) 23 (34.3) 234 (46.3)
Female 163 (45.9) 119 (78.8) 44 (65.7) 271 (53.7)
Race/ethnicity
White, non-Hispanic 323 (91.0) 123 (81.5) 61 (91.0) 450 (89.1)
Hispanic 19 (5.4) 12 (8.0) 5 (7.5) 36 (7.1)
Black 7 (2.0) 9 (6.0) 0 (0.0) 11 (2.2)
Other 6 (1.7) 7 (4.6) 1 (1.5) 8 (1.6)
Mean age (years) 51.3 55.0 51.6 49.4
Hospital site
Phoenix, AZ 162 (45.6) 75 (49.7) 49 (73.1) 258 (51.1)
Boston, MA 126 (35.5) 62 (41.1) 18 (26.8) 164 (32.5)
Pittsburgh, PA 67 (18.9) 14 (9.3) 0 (0.0) 83 (16.4)
Values are no. (%) except where indicated.
a Controls were matched to the total case group including glioma, meningioma, and acoustic neuroma.
Table 2 Association of ALAD2 polymorphism (ALAD1–2 or ALAD2–2 ) with risk of glioma, meningioma, and acoustic neuroma: NCI adult brain tumor study, 1994–1998.a
Glioma (n = 355)
Meningioma (n = 151)
Acoustic neuroma (n = 67)
Control (n = 505)
No. (%)b OR (95% CI) No. (%) OR (95% CI) No. (%) OR (95% CI) No. (%)
ALAD1–1 301 (84.8) 1.0 116 (76.8) 1.0 57 (85.1) 1.0 420 (83.2)
ALAD1–2 53 (14.9) 0.9 (0.6–1.3)c 32 (21.2) 1.6 (1.0–2.6)c 10 (14.9) 0.9 (0.4–1.9)c 79 (15.6)
ALAD2–2 1 (0.3) 3 (2.0) 0 6 (1.2)
a Models adjusted for matching factors (hospital, sex, race/ethnicity, age, residential proximity to hospital); results are only reported if number of exposed cases is ≥ 5.
b Percentages based on genotyped samples.
c Estimates are for having ≥ 1 copy of the ALAD2 allele.
==== Refs
References
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Battistuzzi G Petrucci R Silvagni L Urbani F Caiola S 1981 Delta-aminolevulinate dehydrase: a new genetic polymorphism in man Ann Hum Genet 45 Pt 3 223 229 7305279
Bergdahl I Grubb A Schutz A Desnick R Wetmur J Sassa S 1997 Lead binding to delta-aminolevulinic acid dehydratase (ALAD) in human erythrocytes Pharmacol Toxicol 81 4 153 158 9353844
Cocco P Heineman E Dosemeci M 1999 Occupational risk factors for cancer of the central nervous system (CNS) among US women Am J Ind Med 36 1 70 74 10361589
Daly A Steen V Fairbrother K Idle J 1996 CYP2D6 multi-allelism Methods Enzymol 272 199 210 8791778
Fleming D Chettle D Wetmur J Desnick R Robin J Boulay D 1998 Effect of the delta-aminolevulinate dehydratase polymorphism on the accumulation of lead in bone and blood in lead smelter workers Environ Res 77 1 49 61 9593628
Fujita H Nishitani C Ogawa K 2002 Lead, chemical porphyria, and heme as a biological mediator Tohoku J Exp Med 196 2 53 64 12498316
Hsieh L Liou S Chen Y Tsai L Yang T Wu T 2000 Association between aminolevulinate dehydrogenase genotype and blood lead levels in Taiwan J Occup Environ Med 42 2 151 155 10693075
Hu J Little J Xu T Zhao X Guo L Jia X 1999 Risk factors for meningioma in adults: a case-control study in northeast China Int J Cancer 83 3 299 304 10495419
IARC 1987 Overall Evaluations of Carcinogenicity: An Updating of IARC Monographs Volumes 1 to 42 IARC Monogra Eval Carcinog Risks Hum suppl 7 1 440
Inskip PD Tarone RE Hatch EE Wilcosky TC Shapiro WR Selker RG 2001 Cellular-telephone use and brain tumors N Engl J Med 344 2 79 86 11150357
Kelada S Shelton E Kaufmann R Khoury M 2001 Delta-aminolevulinic acid dehydratase genotype and lead toxicity: a HuGE review Am J Epidemiol 154 1 1 13 11427399
Miettinen O 1985. Theoretical Epidemiology: Principles of Occurrence Research in Medicine. New York:John Wiley & Sons.
Navas-Acien A Pollan M Gustavsson P Plato N 2002 Occupation, exposure to chemicals and risk of gliomas and meningiomas in Sweden Am J Ind Med 42 214 227 12210690
Preston-Martin S Mack WJ 1996. Neoplasms of the nervous system. In: Cancer Epidemiology and Prevention (Schottenfeld D, Fraumeni JF, eds). New York:Oxford University Press, 1231–1281.
Rajaraman P De Roos A Stewart P Linet M Fine H Shapiro W 2004 Occupation and risk of meningioma and acoustic neuroma in the United States Am J Ind Med 45 5 395 407 15095422
Shen X Wu S Yan C Zhao W Ao L Zhang Y 2001 Delta-aminolevulinate dehydratase polymorphism and blood lead levels in Chinese children Environ Res 85 3 185 190 11237505
Silbergeld E 2003 Facilitative mechanisms of lead as a carcinogen Mutat Res 533 1–2 121 133 14643416
Steenland K Boffetta P 2000 Lead and cancer in humans: where are we now? Am J Ind Med 38 3 295 299 10940967
Wetmur JG Lehnert G Desnick RJ 1991 The delta-aminolevulinate dehydratase polymorphism: higher blood lead levels in lead workers and environmentally exposed children with the 1–2 and 2–2 isozymes Environ Res 56 2 109 119 1769358
Wrensch M Minn Y Chew T Bondy M Berger MS 2002 Epidemiology of primary brain tumors: current concepts and review of the literature Neuro-oncol 4 4 278 299 12356358
Ziemsen B Angerer J Lehnert G Benkmann H Goedde H 1986 Polymorphism of delta-aminolevulinic acid dehydratase in lead-exposed workers Int Arch Occup Environ Health 58 3 245 247 3770964
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7751ehp0113-00121216140630ResearchChildren's HealthLocal Variations in CO and Particulate Air Pollution and Adverse Birth Outcomes in Los Angeles County, California, USA Wilhelm Michelle 1Ritz Beate 121 Department of Epidemiology and2 Center for Occupational and Environmental Health, School of Public Health, University of California at Los Angeles, Los Angeles, California, USAAddress correspondence to B. Ritz, Department of Epidemiology, School of Public Health, UCLA, P.O. Box 951772, 650 Charles E. Young Dr., Los Angeles, CA 90095-1772 USA. Telephone: (310) 206-7458. Fax: (310) 206-7371. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 10 5 2005 113 9 1212 1221 4 11 2004 10 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We extended our previous analyses of term low birth weight (LBW) and preterm birth to 1994–2000, a period of declining air pollution levels in the South Coast Air Basin. We speculated that the effects we observed previously for carbon monoxide, particulate matter < 10 μm in aero-dynamic diameter (PM10), and traffic density were attributable to toxins sorbed to primary exhaust particles. Focusing on CO, PM10, and particulate matter < 2.5 μm in aerodynamic diameter (PM2.5), we examined whether varying residential distances from monitoring stations affected risk estimates, because effect attenuation may result from local pollutant heterogeneity inadequately captured by ambient stations. We geocoded home locations, calculated the distance to the nearest air monitors, estimated exposure levels by pregnancy period, and performed logistic regression analyses for subjects living within 1–4 mi of a station. For women residing within a 1-mi distance, we observed a 27% increase in risk for high (≥ 75th percentile) first-trimester CO exposures and preterm birth and a 36% increase for high third-trimester pregnancy CO exposures and term LBW. For particles, we observed similar size effects during early and late pregnancy for both term LBW and preterm birth. In contrast, smaller or no effects were observed beyond a 1-mi distance of a residence from a station. Associations between CO and PM10 averaged over the whole pregnancy and term LBW were generally smaller than effects for early and late pregnancy. These new results for 1994–2000 generally confirm our previous observations for the period 1989–1993, again linking CO and particle exposures to term LBW and preterm birth. In addition, they confirm our suspicions about having to address local heterogeneity for these pollutants in Los Angeles.
air pollutionepidemiologylow birth weightpreterm birth
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Over the past few years, the number of reports linking outdoor air pollution to adverse birth outcomes including intrauterine growth retardation, preterm birth, and perinatal mortality increased considerably (Glinianaia et al. 2004; Maisonet et al. 2004). The fast expansion of this research worldwide was enabled by the existence of air monitoring stations and routinely collected birth certificate information in many populated urban areas. The studies conducted in many different locales and populations agree in one aspect: Outdoor air pollution seems to play some role in determining birth outcomes. Yet the differences in pollutants, outcomes, and pregnancy periods studied make causational interpretations of the observed associations a subject of ongoing debate. Although local monitoring resources and major emission sources may determine choices for pollutants studied, it is time to use all available data as comprehensively as possible and to consider asking some new questions to further expand and eventually integrate our knowledge base.
Our previous work focused on the South Coast Air Basin (SoCAB) of Southern California and examined adverse birth effects due to air pollution in infants born between 1989 and 1993. Exposure assessment was based on measurements taken at air monitoring stations located throughout the basin. We observed positive associations between average carbon monoxide concentrations during the third trimester of pregnancy and term low birth weight (LBW) (Ritz and Yu 1999) and between concentrations of CO and particulate matter < 10 μm in aerodynamic diameter (PM10) 6 weeks before birth and prematurity (Ritz et al. 2000). We also reported a dose–response relationship between CO concentrations during the second month of pregnancy and cardiac ventricular septal defects and between second-month ozone concentrations and aortic/pulmonary artery and valve anomalies and conotruncal defects (Ritz et al. 2002).
Here we not only extend our previous analyses of term LBW and preterm birth to a more recent period during which air pollution levels in the SoCAB generally declined (1994–2000), but also examine issues that previously could not be addressed. We reported that proximity to traffic sources were related to these birth outcomes, suggesting that smaller primary exhaust particles may play a role for the effects we observed in the SoCAB (Wilhelm and Ritz 2003). Ambient monitoring stations, however, may not adequately capture the effects of primary exhaust pollutants that are more heterogeneously distributed throughout neighborhoods such that exposure depends on proximity to sources. Recently we obtained two new data sources: electronic birth address data for Los Angeles (LA) County and fine particle [particulate matter < 2.5 μm in aerodynamic diameter (PM2.5)] monitoring data collected in the SoCAB since 1999. The address data allowed us to examine the potential for and magnitude of exposure misclassification resulting from local heterogeneity in pollutant exposures. To do so, we relied on residential distance to monitoring stations because localized exposure might be captured more accurately for residences in closer proximity to a monitoring station. It has been argued that smaller particles are of most relevance for human health (Englert 2004; Ibald-Mulli et al. 2002). Based on emission inventories, most fine and ultra-fine (PM<0.1) particles found in the urban atmosphere derive from engine combustion (Hitchins et al. 2000; Schauer et al. 1996; Shi et al. 1999; Zhu et al. 2002a), and most particles emitted directly in vehicle exhaust are in the ultrafine size range of 20–130 nm for diesel engines and 20–60 nm for gasoline engines (Morawska et al. 1999; Shi et al. 2001). Recent dosimetry studies indicate the total deposition fraction of ultrafine particles increases as particle size decreases, with the greatest fractional deposition in the deep lung occurring between 5 nm and 100 nm (Jaques and Kim 2000; Yeh et al. 1997). Unlike larger fine particles, ultrafines seem to escape phagocytosis by alveolar macrophages and translocate to extrapulmonary organs (Oberdörster and Utell 2002); thus, they may be able to transfer potentially toxic compounds sorbed to these particles—such as polycyclic aromatic hydrocarbons (PAHs)—to the fetus and the placenta. It has been suggested that these compounds may interfere with placental development and subsequent nutrient and oxygen delivery to the fetus (Dejmek et al. 1999, 2000).
Topinka et al. (1997) reported PAH–DNA adduct levels in placentas from nonsmoking women living in a polluted district in the Czech Republic to be significantly greater than those in placentas of women living in an agricultural area with lower air pollution levels. Perera et al. (1998) reported decreased birth weights, lengths, and head circumferences in Polish newborns with elevated PAH–DNA adduct levels in cord blood leukocytes, and in a more recent study (Perera et al. 2003) conducted in New York City, they observed lower birth weights and head circumferences in babies born to African-American women exposed to high PAH levels during pregnancy. For our large population-based study, neither ultrafine particle nor placental PAH–DNA adduct measurements were available; instead, we relied on PM10, PM2.5, and CO as exposure proxies. CO is released directly in motor vehicle exhaust and does not react readily in the atmosphere to form other compounds. Also, decreases in CO concentrations as one moves farther away from traffic sources in LA correlate almost perfectly with decreases in ultrafine particle number counts and black smoke concentrations (Zhu et al. 2002a, 2002b). However, significant amounts of PM2.5 are created secondarily through atmospheric reactions depending on season and location in the LA Basin (Kim et al. 2002). Thus, although the new PM2.5 measures allow us to examine the contribution of fine particles to the observed effects on adverse birth outcomes, they cannot be easily interpreted as a primary exhaust proxy, and CO may still be the better indicator/proxy of primary exhaust toxins’ contributions.
Materials and Methods
Subjects.
We used birth certificates, provided by the California Department of Health Services, to identify study subjects and to determine their gestational age, birth weight, and information on covariates included in our analyses. To allow comparisons with our previous results for the period 1989–1993 (Ritz and Yu 1999; Ritz et al. 2000), we performed a ZIP-code–level analysis in which we selected all births during 1994–2000 to mothers who resided in a ZIP code whose area fell at least 60% within a 2-mi radius of a monitoring station (31 SoCAB ZIP codes met this criterion in 1994–2000, resulting in a total of 146,972 births). The 2-mi criterion is based on the assumption that stationary air monitors may most accurately reflect air pollution exposures within a small area surrounding stations, especially for pollutants with concentrations that vary spatially according to local sources, such as CO.
In a second, address-level analysis, we identified all 1994–2000 births to women living in ZIP codes located within a broader 5-mi radius of a monitoring station in LA County (any portion of the ZIP code). We obtained electronic address data from the LA County Department of Health and linked these to the state-level data based on unique identifiers (local file number, date of birth, and ZIP code) for 930,681 (93.6%) of the 994,832 births in these ZIP codes. We geocoded these home locations using ArcView GIS software (version 3.2) and StreetMap (both from Environmental Systems Research Institute, Redlands, CA). After correcting addresses that could not be geocoded during the first round of automated processing in ArcView (n = 87,647) with ZP4 software (August 2002 data release; Semaphore Corporation, Aptos, CA), we were able to map 47,583 additional subjects based on corrected addresses. Thus, overall we mapped 840,472 subject homes (90.3% of homes that could be address matched); unsuccessful mapping was due to address errors or an inability to match recorded house numbers to street segments in the StreetMap.
Calculating the distance from each home to the nearest air monitoring station, we found that 518,254 subjects resided within 4 mi of a station. Of the 146,972 (2-mi ZIP-code approach) and 518,254 (4-mi address approach) subjects, 141,475 and 498,235 records, respectively, provided gestational age and birth weight data. We excluded infants with birth weights < 500 g (n = 139 for ZIP-code and 511 for address analyses, respectively) or ≥ 5,000 g (n = 265 and 891) and births for which gestational age was likely misreported [delivery occurred < 90 days (n = 56 and 213) or ≥ 320 days gestation (n = 1,639 and 6,086)]. We also restricted our sample to singleton births (excluding 3,242 and 11,365 multiple births, respectively). Finally, some subjects were excluded because of insufficient monitoring data available during the pregnancy periods of interest: < 30 or 10 days of measurements available for CO, NO2, and O3 during a given trimester or month/6 week period of pregnancy, respectively; < 5 or 2 days of measurements available for PM10 during a given trimester or month/6-week period of pregnancy, respectively; or < 10 or 4 days of measurements available for PM2.5 during a given trimester or month/6-week period of pregnancy, respectively. In our adjusted analyses, study subjects may also have been excluded because of missing data for individual-level covariates such as maternal age, infant sex, maternal race, prenatal care information, and maternal education; final sample sizes are reported along with the results.
The outcomes of interest were term LBW (< 2,500 g at ≥ 37 completed weeks gestation) and vaginal birth < 37 completed weeks gestation; for analyses of preterm birth, we excluded births delivered by cesarean section because we previously found no evidence that these were related to increased air pollution levels before delivery (Ritz et al. 2000). Outcomes were analyzed as dichotomous variables, such that term LBW or preterm babies were compared with all other infants who were born at term and weighed ≥ 2,500 g at birth. We generated odds ratio (OR) or risk ratio (RR) estimates for term LBW and preterm birth. This research was approved by the University of California at Los Angeles Office for Protection of Research Subjects and the California State Committee for the Protection of Human Subjects.
Exposure assessment.
Maternal exposure to air pollution during various pregnancy periods was estimated based on air monitoring data for CO, nitrogen dioxide, O3, PM10, and PM2.5 collected by the South Coast Air Quality Management District (SCAQMD) at 16 (2-mi ZIP-code approach) and 12 stations (4-mi address approach) between 1994 and 2000. For the ZIP-code–level analysis, O3 measurements were available at all 16 stations, CO and NO2 measurements were available at 15 stations, PM10 measurements at 8 stations, and PM2.5 measurements at 9 stations in 1999–2000. For the address-level analysis (focused on LA County), CO and O3 measurements were available at all 12 stations, and NO2, PM10, and PM2.5 measurements were available at 11, 6, and 8 stations, respectively. Based on the birth date and gestational age reported on the birth certificate, we calculated the start and end dates of various pregnancy periods for each subject (entire pregnancy, trimesters and months of pregnancy, and 6 weeks before birth) and averaged air pollution concentrations measured at the assigned station over these periods. The averages were based on hourly measurements for the gaseous pollutants (CO, NO2, and O3); 24-hr average measurements taken every 6 and 3 days were available for PM10 and PM2.5, respectively. We evaluated associations between risk of term LBW and average air pollution exposures during each trimester and over the entire pregnancy period. For preterm birth, we focused on exposures during the first month of pregnancy, the first and second trimesters of pregnancy, and 6 weeks before birth.
Statistical methods.
The association of air pollution with term LBW and preterm birth was evaluated using logistic regression analyses. We evaluated air pollution exposures as continuous measures and grouped them into categories according to their distribution in the total population (< 25th, 25th to < 75th, and ≥ 75th percentiles). Exposure to levels below the 25th percentile was used as the referent category for each pollutant.
We adjusted for several known risk factors for LBW and preterm birth that could potentially confound the relationship between adverse birth outcomes and air pollution. For all outcomes, we adjusted for maternal age (< 20, 20–29, 30–34, 35–39, ≥ 40 years), maternal race (African American, white, Hispanic, Asian, other races), maternal education (< 9, 9–11, 12, 13–15, ≥ 16 years), parity (first birth vs. second or subsequent birth), interval since the previous live birth (≤ 12 months vs. > 12 months), level of prenatal care (none, during first trimester, after first trimester), infant sex, previous LBW or pre-term infant (one or more vs. none), and birth season (Table 1). For birth weight, we also adjusted for gestational age (measured in weeks), entering a linear and quadratic term into the model to capture the leveling off of the slope for weight gain during the last weeks of pregnancy (Ritz and Yu 1999). Risk factors for LBW and preterm birth that are not registered on California birth certificates include maternal active and passive smoking, maternal weight and height, pregnancy weight gain, birth weight of mother, and marital status. We performed separate analyses for subjects living near stations that monitored CO but not PM10 versus those that provided measures for both CO and PM10.
Results
In Tables 1 and 2, we present mean birth weights, gestational ages, and the incidences of term LBW and preterm birth by known risk factors and by percentiles of air pollution exposure during various pregnancy periods. We found the highest incidence of term LBW and preterm birth among mothers who lacked prenatal care, were of African-American race, experienced previous low weight or preterm births, and were younger (< 20 years) or older (≥ 40 years) at delivery. In contrast, the incidence of term LBW and preterm birth was lower among women with higher educational levels, higher order parity, and at least 12 months since the previous live birth. In female infants, the incidence of term LBW was higher but the incidence of preterm birth was lower than in male infants, and more preterm babies were born during the winter months. Incidences based on the address-level cohort were similar.
Table 3 presents pollutant means and correlations based on the ZIP-code–level analyses; correlations based on the address-level analyses were very similar. Pregnancy averages for CO, NO2, and PM2.5 were strongly positively correlated with each other and inversely correlated with O3. In the SoCAB, this is due to well known seasonal and geographic patterns for these pollutants. PM10 averages were moderately correlated with PM2.5, NO2, and CO.
Term LBW
CO effects.
We observed a 12% increase in risk of term LBW per 1-ppm increase in third-trimester CO in ZIP-code–level analyses and a 10% increase for women living within 1 mi of a station based on single-pollutant models (Table 4). Beyond 1 mi of a station, the estimated effect sizes were smaller (~ 5% increase per 1 ppm CO). Adding NO2 and O3 average third-trimester concentrations to our models did not change the positive associations observed for CO, but adding PM10 had opposite effects at the ZIP-code and address level. The point estimates for CO were close to 1 in PM10-adjusted ZIP-code analyses, whereas for women living within 1 mi of a station the effects for CO persisted after adjustment for PM10. However, because fewer stations measure PM10, adding these averages reduced our sample size for each model considerably and resulted in a loss of precision for the 1-mi radius analyses. We performed analyses separately for stations measuring both pollutants versus CO only [referred to below as CO-only stations (Figures 1 and 2); results not shown in tables] and found that the effect for CO appeared isolated to women residing near stations measuring CO but not PM10. In fact, in ZIP-code–level analyses we observed an 18% [OR for the single-pollutant model (ORsingle) = 1.18; 95% confidence interval (CI), 1.09–1.29] increase in term LBW risk per 1-ppm increase in third-trimester CO for women residing near monitoring stations that measured CO but not PM10, whereas for residents living around stations measuring both pollutants, effect estimates were close to 1 in single- and multipollutant models (per 1-ppm increase: ORsingle = 0.99; 95% CI, 0.89–1.09; ORmulti = 0.99; 95% CI, 0.85–1.15). For women living within 1 mi of a station, our results also suggested some increases for CO at CO-only stations (per 1-ppm increase: ORsingle = 1.07; 95% CI, 0.93–1.24), whereas at stations also measuring PM10, CO was associated with term LBW only after adjustment for particles (per 1-ppm increase: ORmulti = 1.21; 95% CI, 0.85–1.74), suggesting confounding of CO associations by PM10 at these stations.
Effect estimates for CO concentrations averaged over the entire pregnancy period and term LBW were similar to the third-trimester results at the ZIP-code–level (per 1-ppm increase: ORsingle = 1.12; 95% CI, 1.04–1.20; adjusting for PM10: ORmulti = 0.93; 95% CI, 0.76–1.13) and for women residing within 1 mi of a station (per 1-ppm increase: ORsingle = 1.05; 95% CI, 0.91–1.22; adjusting for PM10: ORmulti = 1.00; 95% CI, 0.62–1.59). Again, the associations seemed isolated to women living near stations measuring CO only (per 1-ppm increase: ORsingle = 1.09; 95% CI, 0.91–1.30) versus those living within 1 mi of stations measuring both pollutants (per 1-ppm increase: ORmulti = 1.00; 95% CI, 0.62–1.59), yet these estimates suffered reduced precision because of the much smaller sample size within the 1-mi distance.
Particle effects.
Unlike the ZIP-code–level analysis that provided no evidence for an effect of PM10 concentrations on term LBW risk, a 48% increase in risk was observed for women with third-trimester PM10 averages of ≥ 44.0 μg/m3 and residing within 1 mi of an LA County station in a single-pollutant model (Table 4). The effect estimates for PM10 slightly increased to 58% when adding other pollutants to the model, but 95% CIs widened because of the reduction in sample size. Relatively strong associations were also observed for women residing within 1 mi of a monitoring station in multipollutant models for the third-trimester (per 10 μg/m3: ORmulti = 1.36; 95% CI, 1.12–1.65) and entire pregnancy period (per 10 μg/m3: ORmulti = 1.24; 95% CI, 0.91–1.70). Although CIs for percentile-based estimators were wide, the continuous variables suggested an exposure–response pattern. No associations were observed when the distance between subject homes and monitoring locations was greater than 1 mi. The sample size for PM2.5—only available for the years 1999–2000—was too limited and resulted in CIs too wide to derive conclusive results for this outcome.
Other pollutants and pregnancy periods.
No associations were observed between first- and second-trimester CO and PM10 concentrations and term LBW based on ZIP-code–level analyses or for first- and second-trimester PM10 concentrations based on address-level analyses. However, address-level analyses suggested effects for first-trimester CO for women living within 1 mi of a station, but only after adjusting for NO2 and O3 [per 1 ppm: OR adjusted for gaseous pollutants (ORadjusted) = 1.07; 95% CI, 0.90–1.28; no association when PM10 was added to the model]. Similarly, associations between second-trimester CO and term LBW were suggested for women living within 1 mi of a station (per 1 ppm: ORadjusted = 1.09; 95% CI, 0.99–1.19).
After adjusting for CO and/or PM10, we did not observe associations between NO2 and O3 and term LBW in any of our models.
Preterm birth
CO effects.
Focusing first on early pregnancy, in the ZIP-code and address-level analyses we observed a 4–8% increase in risk of preterm birth per 1-ppm increase in first-trimester CO that persisted when adjusting for gaseous pollutants; however, point estimates were close to 1 after adjustment for PM10 (Table 5). Stratifying on station type revealed that the associations again applied only to women who lived close to stations measuring CO and not PM10 (CO ≥ 2.2 ppm: RRadjusted = 1.24; 95% CI, 1.00–1.54) and not to women living within 1 mi of stations monitoring both pollutants (CO ≥ 1.9 ppm: RRmulti = 1.03; 95% CI, 0.78–1.36). Results based on a shorter averaging period to reflect time of fetal implantation into the uterus—that is, the first month of pregnancy—were similar to those for first-trimester exposures. Furthermore, a small risk increase suggested for second-trimester CO exposures for women residing within 1 mi of a station disappeared when adjusting for PM10 exposures.
Examining influences of pollutant exposures at the end of pregnancy, we observed a 4–9% increase in the risk of preterm birth when average CO concentrations 6 weeks before birth were ≥ 1.9 ppm based on ZIP-code–level analyses (Table 5). Again, all associations were reduced and close to 1 when we adjusted for PM10, and estimated effects were limited to women residing near stations measuring CO and not PM10. In ZIP-code–level analyses, we estimated a 21% increase in risk for women residing near CO-only stations when average CO concentrations 6 weeks before birth were ≥ 2.0 ppm (RRadjusted = 1.21; 95% CI, 1.06–1.38), whereas the estimate was close to 1 (CO ≥ 1.8 ppm: RRmulti = 0.94; 95% CI, 0.84–1.05) for women residing near stations measuring both pollutants. At CO-only stations, the effect was stronger and more consistent in address-level analyses as well: We observed a 26–30% increase in risk of preterm birth for women residing within 1–2 mi of a station (CO ≥ 2.1 ppm and residence within 1 mi: RRadjusted = 1.26; 95% CI, 1.03–1.55; CO ≥ 2.1 ppm and residence within 1–2 mi: RRadjusted = 1.30; 95% CI, 1.15–1.48), whereas at stations measuring both pollutants the CO point estimates were close to 1 (CO ≥ 1.8 ppm and residence within 1 mi: RRmulti = 0.85; 95% CI, 0.62–1.15; CO ≥ 1.8 ppm and residence within 1–2 mi: RRmulti = 0.97; 95% CI, 0.84–1.11).
Particle effects.
We did not observe a risk increase for first-trimester PM10 exposures and preterm birth based on the ZIP-code–level analyses. Yet women in the highest exposure quartile and residing within 1 mi of a station experienced a 17% increased risk during early pregnancy (PM10 ≥ 51.2 μg/m3: RRmulti = 1.17; 95% CI, 0.92–1.50). This effect decreased with increasing distance from a station, especially after 2 mi (Table 5). Negative effects were seen for PM2.5 in single-pollutant models for the first trimester, but these reversed in multipollutant models (per 10 μg/m3 PM2.5 for the 1–2 mi radius: RRmulti = 1.18; 95% CI, 0.84–1.65). Results based on first month average concentrations for both pollutants were similar to those observed for first-trimester concentrations.
We also did not observe associations between average PM10 concentrations 6 weeks before delivery and risk of preterm birth based on the ZIP-code–level analyses. For women residing within 1 mi of a station, our models suggested that PM10 exposures 6 weeks before birth have effects (17% increased risk for women in the highest exposure quartile), although our analyses were imprecise because of small sample sizes (Table 5).
Elevated PM2.5 levels 6 weeks before birth resulted in a 19% increase in risk of preterm birth (PM2.5 ≥ 24.3 μg/m3: RRsingle = 1.19; 95% CI, 1.02–1.40) based on the ZIP-code–level analysis, yet this estimate was reduced to 12% in a multipollutant model (PM2.5 ≥ 24.6 μg/m3: RRmulti = 1.12; 95% CI, 0.82–1.52) and was rather imprecise. Our continuous exposure measure suggested that the risk of preterm birth increased by 12% per 10-μg/m3 increase in PM2.5 averaged over 6 weeks before birth (RRsingle = 1.10; 95% CI, 1.00–1.21; RRmulti = 1.12; 95% CI, 0.90–1.40). Point estimates were stronger for PM2.5 exposures 6 weeks before birth for women living within 1 mi of a station, especially in multiple-pollutant models; yet again due to relatively small sample sizes, the 95% CIs were wide, especially when adjusting for all other pollutants.
Other pollutants and pregnancy periods.
We did not observe associations between first- and second-trimester NO2 concentrations and risk of preterm birth. We also observed no effects for second-trimester exposures to PM10 and PM2.5. When limiting the exposure period to the first month of pregnancy, O3 results for a model containing all pollutants showed strongly increased risks for preterm birth (per 1-pphm increase: RR = 1.23; 95% CI, 1.06–1.42; O3 ≥ 1.42 and < 2.97 pphm: RR = 1.45; 95% CI, 1.16–1.80; O3 ≥ 2.97 pphm: RR = 1.74; 95% CI, 1.31–2.32, based on the ZIP-code–level cohort); results for first-trimester exposures were similar but slightly smaller. Also, we observed a positive association between second-trimester O3 concentrations and risk of preterm birth, but only after including all pollutants in the model (per 1-pphm increase: RR = 1.38; 95% CI, 1.14–1.66). In general, models containing all pollutants (i.e., CO, NO2, O3, PM10, and PM2.5) were unstable because of collinearity between pollutant concentrations and the small sample size when including only 2 years of data for PM2.5. We observed no effects for NO2 and O3 concentrations 6 weeks before birth.
Discussion
Our new results for 1994–2000 births generally confirm our previous observations for the period 1989–1993, again linking air pollution—specifically, CO and particles—to term LBW and preterm birth in the SoCAB and also confirmed our suspicions about the importance of addressing local heterogeneity in concentrations of pollutants from traffic sources.
Specifically, our ZIP-code–level analyses provided renewed evidence for an exposure–response relation between third-trimester CO concentrations and term LBW (Table 4), yet we observed the greatest effects for women living within 1 mi of a monitoring station (29–36% increased risk for the highest exposure quartile), and effect estimates clearly diminished with increasing distance between homes and stations. In accordance with our earlier results, ZIP-code–based analyses again showed no association between PM10 and term LBW. However, for women residing within 1 mi of a PM10 station, we estimated a relatively large 48–58% increase in term LBW risk for the highest third-trimester exposure quartile, and an exposure–response pattern was suggested. Unfortunately, sample sizes for the more recently established PM2.5 monitoring stations were too small, rendering our analyses for term LBW and PM2.5 uninformative. Thus, we cannot determine whether effects are related to fine or coarse particles or both.
In Western societies, birth weight is generally determined by factors affecting pregnancy after the 28th week of gestation (Kline et al. 1989). However, several researchers have hypothesized that exposure to particles and/or PAHs sorbed to particle surfaces may directly modulate the proliferation of the trophoblast because of reactions between these pollutants and receptors for placental growth factors (Dejmek et al. 2000, 1999; Perera et al. 1998), and this has also been borne out in some experimental studies (Guyda 1991; Zhang et al. 1995). Such reactions may interfere with fetoplacental exchange of oxygen and nutrients and subsequently impair fetal growth (Dejmek et al. 2000). Although previously we focused on third-trimester exposures for term LBW (Ritz and Yu 1999)—the period of pregnancy during which most fetal weight gain occurs—here we also examined effects for other trimesters and for exposures averaged over the entire pregnancy period, allowing comparisons with other studies. Our address-level analyses suggested effects for first- and second-trimester CO concentrations for women living within 1 mi of a monitoring station, but point estimates were lower than those for third-trimester exposures, and CIs were wide. Clearer effects emerged when averaging CO exposures over the entire pregnancy, yet the effect sizes were somewhat smaller than for third-trimester exposures only. Similarly, effects were suggested for PM10 averaged over the entire pregnancy period and term LBW risk; again, these estimates were smaller than those based on third-trimester exposures, and CIs were wide and included null values. Thus, our present results suggest that not only the third trimester but also the entire pregnancy period may influence term LBW at least for CO—that is, that the accumulation of exposure throughout pregnancy may affect fetal growth possibly in addition to peak exposures during especially vulnerable periods. Recently, a chronic/cumulative effect for smoking throughout pregnancy on perinatal mortality has also been suggested with risk increasing from early- to late-pregnancy exposures (Platt et al. 2004).
The existing literature on air pollution and adverse birth outcomes is difficult to synthesize because of differences in fetal growth and outcome measures, exposure periods, and pollutants evaluated in each study, and we concentrate here on those studies that can be compared with our own results. An early study reported that pregnancies in Beijing, China, were at increased risk of term LBW when average third-trimester concentrations of sulfur dioxide and total suspended particles (TSP) were high (per 100-μg/m3 increase in SO2: OR = 1.11; 95% CI, 1.06–1.16; per 100-μg/m3 increase in TSP: OR = 1.10; 95% CI, 1.05–1.14) (Wang et al. 1997). The study lacked measurements for CO and other pollutants possibly correlated with SO2 and TSP, and the main source of air pollution in Beijing at the time was residential use of coal stoves. Thus, generalizations to other urban areas more affected by transportation sources, such as southern California, may be limited, although the results implicated particle exposures during the third trimester, similar to our own study. More comparable with southern California may be the following studies conducted in the United States and other industrialized nations. A study of six northeastern U.S. cities found associations between third-trimester CO and term LBW (Maisonet et al. 2001), and a study of births in Washoe County, Nevada, estimated a mean birth weight reduction of 11 g (95% CI, 2.3–19.8 g) per 10-μg/m3 increase in PM10 during the third trimester (Chen et al. 2002); however, the latter study lacked statistical power when examining term LBW. Another U.S.-based study reported increased risks of very LBW (infants < 1,500 g) and term LBW for women residing in New Jersey census tracts with high polycyclic organic matter (POM) concentrations (PAHs comprise a major portion of POMs) (Vassilev et al. 2001a). These authors relied on modeled POM concentrations from the U.S. Environmental Protection Agency Cumulative Exposure Project that only allowed them to derive annual average concentrations, precluding the examination of exposure influences on specific pregnancy periods.
In Seoul, South Korea, first-trimester concentrations of CO, TSP, NO2, and SO2 increased the risk of term LBW, yet no associations were observed for third-trimester exposures (Ha et al. 2001). However, a follow-up study extending this Korean birth cohort by 2 years reported positive associations between first-trimester CO and, in addition, second-trimester CO, PM10, SO2, and NO2 concentrations and term LBW risk (Lee et al. 2003). Corroborating our new results for effect of exposure on term LBW throughout pregnancy, Lee et al. (2003) also reported positive odd ratios for each of the four pollutants averaged over the entire pregnancy.
Studies using small for gestational age (SGA) as an end point reported effects for first-trimester exposures to carcinogenic PAHs, PM10, and PM2.5 in the Czech Republic (Dejmek et al. 1999, 2000) and for first-month SO2, NO2, and CO exposures and first-trimester SO2 and CO exposures in Vancouver, Canada (Liu et al. 2003). The New Jersey study (Vassilev et al. 2001b) also reported increased SGA risk with elevated annual average POM concentrations. Studies focusing on LBW while adjusting for gestational age reported effects for early pregnancy exposures. A Czech study of LBW conducted by Bobak (2000) observed effects for first-trimester SO2 and TSP; how-ever, low gestational age accounted for this relation. The Vancouver study reported effects for first-month SO2 exposures and LBW risk similar to what they reported for SGA (Liu et al. 2003). Finally, some studies treated birth weight as a continuous outcome. Estimating birth weight reductions, Gouveia et al. (2004) reported inverse relations between first-trimester CO and PM10 concentrations and term birth weight for women in São Paulo, Brazil, adjusting for gestational age; however, they did not observe consistent relationships between term LBW and pollutant exposures in any specific trimester of pregnancy. A Taiwanese study also observed birth weight reductions in women exposed to higher first-trimester concentrations of SO2 and PM10, the only pollutants with measurements available (Yang et al. 2003). High prenatal exposures to PAHs were associated with lower birth weights and smaller head circumferences in African-American women living in New York City (Perera et al. 2003). Personal PAH samples during a 48-hr period in the third trimester were collected; thus, it is unclear whether these measurements represent exposures only during the third trimester or during all of pregnancy.
Concordance with our previous results was also observed for preterm birth: New ZIP-code–level analyses suggested small risk increases for CO exposures during early pregnancy (6% increase for the highest first-trimester exposure quartile) and late pregnancy (9% increase for the highest 6 weeks before birth exposure quartile). Again, our address-level analyses produced much larger CO effect estimates for women residing within 1–2 mi of a station compared with those living farther away. We observed no association between PM10 and risk of preterm birth in ZIP-code–level analyses, but a 20% increase in risk was suggested for women residing within 1 mi of a station when average first-trimester PM10 concentrations were ≥ 45.1 μg/m3; a 17% increase in risk was suggested for women residing within 1 mi of a station when average PM10 concentrations 6 weeks before birth were ≥ 44.8 μg/m3, yet our estimates were imprecise. An effect for exposures during the last 6 weeks before birth but not the first trimester was also observed for fine particles (< 2.5 μm): ZIP-code–level analyses revealed a 19% increase in risk of preterm birth for women with PM2.5 levels ≥ 24.7 μg/m3, and further address-level analyses suggested the strongest PM2.5 effects for women residing within 1 mi of a station, especially when controlling for all other pollutants.
The literature evaluating preterm birth as an outcome is less prolific than the literature on growth retardation. Similar to our earlier analysis (Ritz et al. 2000), we observed the strongest associations between air pollution and preterm birth for CO and PM10 in early pregnancy (first trimester) and late pregnancy (6 weeks before birth); it also appears that PM2.5 exposures in late pregnancy may be important. The Chinese study also reported a late pregnancy effect for air pollution in Beijing: Short-term increases in SO2 and TSP concentrations 7–10 days before birth increased the risk of preterm birth (Xu et al. 1995). The Vancouver study reported that SO2 and CO increases during the last month of pregnancy increased prematurity risk (Liu et al. 2003). Others reported effects on preterm birth for first-, second-, and third-trimester NO2 concentrations (Maroziene and Grazuleviciene 2002), first-trimester SO2 and TSP concentrations (Bobak 2000), annual average POM concentrations (Vassilev et al. 2001b), and an air pollution exposure index that combined annual average measures of five criteria pollutants (CO, NO2, O3, PM10, and SO2) (Woodruff et al. 2003). These data suggest that some component of urban air pollution (and it may not necessarily be a routinely measured component) seems to be acting in either early pregnancy or late pregnancy, or both, to increase susceptibility and/or trigger preterm birth. The biologic pathways for such triggering events in late pregnancy are to date unknown but may include disturbances of the pituitary–adrenocortico–placental system or uterine blood flow, and/or maternal infections initiating premature contractions and/or premature rupture of membranes. Toxicologic data may help answer these questions. Several studies including our own suggest, however, that the risk due to air pollution is greatest for exposures experienced in the first trimester. Hobel et al. (1999) reported that patients who delivered preterm had elevated plasma levels of adrenocorticotropic hormone at all gestational ages and elevated cortisol levels were observed already at 18–20 weeks’ gestation, suggesting that factors involved in the causation of pre-term birth may exert their influence earlier in gestation. Wadhwa et al. (2001) proposed that chronic rather than acute stressors or defined stress events need to be considered in advancing the understanding of risk factors for pre-term deliveries.
In general, we observed stronger associations for CO and term LBW and preterm birth when restricting our analyses to women who resided within close proximity to stations measuring CO and not PM10. One explanation for this may be that CO concentrations in general tended to be higher at CO-only stations. For example, the 75th, 90th, and 95th percentiles for third-trimester CO averages based on CO-only stations at the ZIP-code level were 2.02, 2.87, and 3.52 ppm, respectively whereas for the stations measuring CO and PM10 these values were 1.70, 2.14, and 2.43 ppm, respectively. We examined the composition of the populations around both types of monitoring stations with respect to individual maternal characteristics such as age, race/ethnicity, and education, and no clear pattern distinguishing them emerged. Furthermore, we used U.S. Census data for the year 2000 (U.S. Census Bureau 2004) to look at factors such as percent living in poverty (based on block groups within 2 mi of a station) and ethnic composition and found no differences between the two types of stations except that two of the CO-only stations were located in wealthier areas.
Another possible explanation is that CO may be a better marker of traffic emissions in the geographic areas surrounding CO-only stations versus areas surrounding stations that measure both CO and PM10 and that some unmeasured component in traffic exhaust is in fact responsible for the observed effects attributed to CO in our models. We tried to assess this by examining correlations between station-specific distance-weighted traffic density (DWTD) values and pollutant concentrations measured at each station for the year 2000. A DWTD measure was derived for each station using methods described in our previous study (Wilhelm and Ritz 2003). Year 2000 annual average daily traffic counts on streets within 2,000 feet from each station were weighted by the distance from the station to the street using a Gaussian probability distribution. We accounted for the influence of wind direction on the dispersion of exhaust from roadways by incorporating the percentage of time each station was annually downwind of a street into the DWTD value. Correlations between DWTD and annual average concentrations of CO and NO2, pollutants typically considered indicative of traffic exhaust, were positive at CO-only stations (r = 0.54 for CO, r = 0.55 for NO2) compared with small and negative correlations seen for stations measuring both CO and PM10 (r = –0.17 for CO, r = –0.32 for NO2). Interestingly, annual average O3 was negatively correlated with DWTD at CO-only stations (r = –0.91) but not at CO+PM10 stations (r = 0.16). O3 is a secondary pollutant formed through photochemical atmospheric reactions, and NO released directly in motor vehicle exhaust scavenges O3 to form NO2. Therefore, the negative correlation between O3 and traffic density at CO-only stations may reflect the greater contribution of motor vehicle emissions to air pollution in these areas. These correlations for the 12 LA County monitoring stations (Figure 2) suggest that CO may be a better marker of traffic exhaust exposure (although still imperfect) in the areas surrounding the CO-only stations; thus, the associations we observed for women residing in the vicinity of these stations may in fact be due to some unmeasured traffic exhaust component. Additional toxicologic and monitoring data are needed to investigate this hypothesis further.
The most important source of bias in this study is exposure misclassification. We discussed the sources of this misclassification at length in previous reports (Ritz and Yu 1999; Ritz et al. 2000; Wilhelm and Ritz 2003). Restricting our analyses to women who lived in close proximity to a station (within 1 mi) increased our effect estimates. Assuming that the misclassification inherent in our analyses is nondifferential, our results suggest that CO and particulate concentrations at an ambient monitoring station are better predictors of actual exposure for subjects living in close proximity to the station. This held true for pollutants that are usually considered to have relatively homogeneous spatial distributions over larger areas, such as PM10 and PM2.5. Hypothesizing that the observed effects are due to specific traffic exhaust pollutants for which CO and particles are mere proxies, it seems that ambient monitoring stations do not adequately capture the effects of primary exhaust pollutants expected to be more heterogeneously distributed throughout neighborhoods, such that ambient monitors misrepresent exposures beyond a 1-mi radius. Thus, our new results confirmed our suspicions that nondifferential exposure misclassification would generally increase and effect estimates decrease if local heterogeneity was important and that effects would not be adequately captured for homes at greater distances from monitoring stations.
Another potential source of bias in this study is residual confounding due to risk factors we were unable to account for in our analyses (e.g., maternal stature and weight gain during pregnancy, active and passive tobacco smoke exposure, stress). We recently completed a survey of approximately 2,500 LA County women who gave birth during 2003 to collect information on such factors. Therefore, in future analyses we will be able to assess directly whether these factors are an important source of bias in our analyses. The survey also included information on residential and occupational history, amount of commuting, and exposure to indoor air pollution sources during pregnancy. In the future, we will be able to examine more closely the importance of these factors for our air pollution results.
Conclusions
As in our previous studies, we observed associations between elevated concentrations of CO and PM10 both early and late in pregnancy and risk of term LBW and preterm birth for women residing in the SoCAB and giving birth between 1994 and 2000. Thus, our previous results were generally confirmed for CO and PM10, even though concentrations of these two pollutants decreased in the SoCAB throughout the 1990s. We also observed some-what smaller effects for CO and PM10 averaged over the entire pregnancy period and risk of term LBW, similar to some previous reports in the literature. Restricting our analyses to women who lived within close proximity of monitoring stations appeared to reduce exposure misclassification and effect attenuation. Effects also were greater for women residing near stations measuring CO and not PM10, and we propose that this occurs because CO might be a better marker of traffic emissions in these LA locations. Improved exposure assessment methods may help to reduce misclassification and pinpoint important air pollution sources. Additional toxicologic or mechanistic studies may help shed more light on the effects observed in epidemiologic studies.
CORRECTION
In the section “Preterm birth” and in Table 5, several of the values were incorrect in the manuscript originally published online. They have been corrected here.
We thank C. Miller of the South Coast Air Quality Management District for providing air monitoring data and L. Rollins of the Los Angeles County Health Department for providing electronic birth certificate data.
This work was supported by the National Institute of Environmental Health Sciences (NIEHS grant R01 ES010960-01). We also acknowledge support from the Southern California Particle Center and Supersite (U.S. Environmental Protection Agency STAR grant R82735201 and California Air Resources Board contract 98-316) and the Southern California Environmental Health Sciences Center (NIEHS grant 5P30 ES07048-07).
Figure 1 Location of SoCAB monitoring stations measuring CO and PM10 and CO only: ZIP-code–level analysis.
Figure 2 Location of LA County monitoring stations measuring CO and PM10 and CO only: address-level analysis.
Table 1 Incidence of term LBW and preterm births by demographic characteristics: ZIP-code–level cohort.a
Term LBW
Preterm
Parameter No. of births or mean ± SD No. of cases or mean ± SD Incidence (95% CI) No. of births or mean ± SD No. of cases or mean ± SD Incidence (95% CI)
Mean gestational age (days) 275.5 ± 16.3 273.5 ± 10.8 276.0 ± 15.6 241.9 ± 20.3
Mean birth weight (g) 3366.1 ± 542.3 2255.2 ± 276.3 3363.3 ± 505.5 2865.58 ± 727.5
LBW (< 2,500 g) 136,134 2,778 2.0 (2.0–2.1) 4,382 2,400 54.8 (53.3–56.2)
Preterm (< 37 weeks) — — — 106,483 9,268 8.7 (8.5–8.9)
Infant sex
Male 70,015 1,188 1.7 (1.6–1.8) 54,086 5,022 9.3 (9.0–9.5)
Female 66,018 1,590 2.4 (2.3–2.5) 52,397 4,246 8.1 (7.9–8.3)
Prenatal care
None 919 35 3.8 (2.6–5.0) 774 179 23.1 (20.2–26.1)
During first trimester 110,662 2,174 2.0 (1.9–2.0) 85,810 6,929 8.1 (7.9–8.3)
After first trimester 23,793 555 2.3 (2.1–2.5) 19,315 2,063 10.7 (10.2–11.1)
Parity
First birth 51,831 1,275 2.5 (2.3–2.6) 39,795 3,546 8.9 (8.6–9.2)
Second or subsequent birth 84,303 1,503 1.8 (1.7–1.9) 66,688 5,722 8.6 (8.4–8.8)
Time since previous live birth
≤12 months 2,199 57 2.6 (1.9–3.3) 1,833 328 17.9 (16.1–19.6)
> 12 months 132,862 2,686 2.0 (1.9–2.1) 103,788 8,842 8.5 (8.3–8.7)
Maternal race/ethnicity
White 25,418 374 1.5 (1.3–1.6) 19,330 1,365 7.1 (6.7–7.4)
Hispanic 86,285 1,652 1.9 (1.8–2.0) 68,587 5,964 8.7 (8.5–8.9)
African American 11,624 426 3.7 (3.3–4.0) 8,572 1,110 12.9 (12.2–13.7)
Asian 7,687 182 2.4 (2.0–2.7) 6,138 451 7.3 (6.7–8.0)
Other 4,783 136 2.8 (2.4–3.3) 3,604 361 10.0 (9.0–11.0)
Maternal education (years)
< 9 25,766 470 1.8 (1.7–2.0) 20,547 1,884 9.2 (8.8–9.6)
9–11 32,103 765 2.4 (2.2–2.5) 25,812 2,454 9.5 (9.1–9.9)
12 37,885 830 2.2 (2.0–2.3) 29,487 2,615 8.9 (8.5–9.2)
13–15 21,604 410 1.9 (1.7–2.1) 16,416 1,311 8.0 (7.6–8.4)
≥16 17,658 277 1.6 (1.4–1.8) 13,328 895 6.7 (6.3–7.1)
Maternal age (years)
< 20 16,688 458 2.7 (2.5–3.0) 14,156 1,551 11.0 (10.4–10.5)
20–29 72,912 1,418 1.9 (1.8–2.0) 58,602 4,742 8.1 (7.9–8.3)
30–34 29,386 524 1.8 (1.6–1.9) 21,998 1,858 8.4 (8.1–8.8)
35–39 13,961 277 2.0 (1.8–2.2) 9,692 895 9.2 (8.7–9.8)
≥40 3,169 100 3.2 (2.5–3.8) 2,019 219 10.8 (9.5–12.2)
Previous LBW or preterm infant
One or more 1,426 92 6.5 (5.2–7.7) 783 150 19.2 (16.4–21.9)
None 134,708 2,686 2.0 (1.9–2.1) 105,700 9,118 8.6 (8.5–8.8)
Birth season
Winter 32,781 602 1.8 (1.7–2.0) 25,567 2,356 9.2 (8.9–9.6)
Spring 35,594 735 2.1 (1.9–2.2) 28,001 2,298 8.2 (7.9–8.5)
Summer 34,468 716 2.1 (1.9–2.2) 26,908 2,372 8.8 (8.5–9.2)
Fall 33,291 725 2.2 (2.0–2.3) 26,007 2,242 8.6 (8.3–9.0)
a Multiple births were excluded from the data set for term LBW (cohort size = 136,134); multiple births and births by cesarean section were excluded from the data set for preterm birth (cohort size = 106,483).
Table 2 Incidence of term LBW and preterm births by air pollution exposure: ZIP-code–level cohort.a
Parameter No. of births No. of cases Incidence (95% CI)
Term LBW: third trimester
Percentile of average CO exposure (ppm)b
< 0.91 32,510 604 1.9 (1.7–2.0)
0.91 to < 1.82 65,212 1,323 2.0 (1.9–2.1)
≥1.82 32,366 755 2.3 (2.2–2.5)
Percentile of average PM10 exposure (μg/m3)
< 32.8 19,805 404 2.0 (1.8–2.2)
32.8 to < 43.4 39,351 798 2.0 (1.9–2.1)
≥43.4 19,912 435 2.2 (2.0–2.4)
Percentile of average PM2.5 exposure (μg/m3)
< 17.1 5,593 134 2.4 (2.0–2.8)
17.1 to < 24.0 11,209 250 2.2 (2.0–2.5)
≥24.0 5,988 124 2.1 (1.7–2.4)
Percentile of average O3 exposure (pphm)
< 1.38 33,733 785 2.3 (2.2–2.5)
1.38 to < 2.87 66,990 1,329 2.0 (1.9–2.1)
≥2.87 33,814 643 1.9 (1.8–2.0)
Percentile of average NO2 exposure (pphm)
< 3.02 32,442 615 1.9 (1.7–2.0)
3.02 to < 4.40 64,308 1,334 2.1 (2.0–2.2)
≥4.40 32,207 712 2.2 (2.1–2.4)
Preterm birth: first trimester
Percentile of average CO exposure (ppm)
< 0.97 25,499 2,212 8.7 (8.3–9.0)
0.97 to < 1.87 51,206 4,371 8.5 (8.3–8.8)
≥1.87 25,427 2,335 9.2 (8.8–9.5)
Percentile of average PM10 exposure (μg/m3)
< 32.9 15,662 1,364 8.7 (8.3–9.2)
32.9 to < 43.9 31,388 2,758 8.8 (8.5–9.1)
≥43.9 15,793 1,353 8.6 (8.1–9.0)
Percentile of average PM2.5 exposure (μg/m3)
< 18.0 3,262 347 10.6 (9.6–11.7)
18.0 to < 25.4 6,352 560 8.8 (8.1–9.5)
≥25.4 3,416 309 9.0 (8.1–10.0)
Percentile of average O3 exposure (pphm)
< 1.36 26,461 2,338 8.8 (8.5–9.2)
1.36 to < 2.85 52,694 4,654 8.8 (8.6–9.1)
≥2.85 26,562 2,222 8.4 (8.0–8.7)
Percentile of average NO2 exposure (pphm)
< 3.05 25,434 2,183 8.6 (8.2–8.9)
3.05 to < 4.42 50,515 4,442 8.8 (8.5–9.0)
≥4.42 25,279 2,267 9.0 (8.6–9.3)
Preterm birth: 6 weeks before birth
Percentile of average CO exposure (ppm)
< 0.87 25,498 2,176 8.5 (8.2–8.9)
0.87 to < 1.82 50,964 4,353 8.5 (8.3–8.8)
≥1.82 25,466 2,350 9.2 (8.9–9.6)
Percentile of average PM10 exposure (μg/m3)
< 31.8 15,564 1,373 8.8 (8.4–9.3)
31.8 to < 44.1 31,121 2,686 8.6 (8.3–8.9)
≥44.1 15,722 1,383 8.8 (8.4–9.2)
Percentile of average PM2.5 exposure (μg/m3)
< 16.5 4,305 355 8.2 (7.4–9.1)
16.5 to < 24.7 8,257 726 8.8 (8.2–9.4)
≥24.7 4,378 420 9.6 (8.7–10.5)
Percentile of average O3 exposure (pphm)
< 1.29 26,299 2,338 8.9 (8.5–9.2)
1.29 to < 2.92 52,527 4,455 8.5 (8.2–8.7)
≥2.92 26,341 2,361 9.0 (8.6–9.3)
Percentile of average NO2 exposure (pphm)
< 2.96 25,236 2,232 8.8 (8.5–9.2)
2.96 to < 4.41 50,359 4,380 8.7 (8.5–8.9)
≥4.41 25,183 2,227 8.8 (8.5–9.2)
a Multiple births were excluded from the data set for term LBW (cohort size = 136,134); multiple births and births by cesarean section were excluded from the data set for preterm birth (cohort size = 106,483).
b Values listed are the < 25th, 25 to < 75th, and ≥75th percentiles.
Table 3 Pollutant averages (ranges) and Pearson correlation coefficients for all pollutants by pregnancy period: ZIP-code–level cohort.a
Pearson correlation coefficients
Trimester/pollutant Mean (range) CO NO2 O3 PM10
First trimester
CO (ppm) 1.42 (0.26–2.82) 1.0
NO2 (pphm) 3.91 (2.06–6.20) 0.81 1.0
O3 (pphm) 2.15 (0.43–4.12) −0.31 −0.47 1.0
PM10 (μg/m3) 42.2 (26.3–77.4) 0.12 0.29 −0.01 1.0
PM2.5 (μg/m3) 21.9 (11.8–38.9) 0.57 0.73 –0.55 0.43
Third trimester
CO (ppm) 1.21 (0.23–2.93) 1.0
NO2 (pphm) 3.73 (2.01–6.24) 0.84 1.0
O3 (pphm) 2.22 (0.38–4.18) −0.36 −0.51 1.0
PM10 (μg/m3) 41.5 (25.7–74.6) 0.32 0.45 −0.08 1.0
PM2.5 (μg/m3) 21.0 (11.8–38.9) 0.67 0.78 −0.60 0.52
Six weeks before birth
CO (ppm) 1.42 (0.02–5.88) 1.0
NO2 (pphm) 3.70 (0.76–7.46) 0.83 1.0
O3 (pphm) 2.11 (0.15–5.85) −0.37 −0.53 1.0
PM10 (μg/m3) 39.1 (13.0–103.7) 0.36 0.49 −0.16 1.0
PM2.5 (μg/m3) 21.0 (9.9–48.5) 0.63 0.74 −0.60 0.60
a Pollutant averages and correlation coefficients are based on the entire data set (i.e., singleton term LBW births, singleton, vaginal preterm births, and controls) for all averaging periods except for the third trimester in which preterm births were excluded.
Table 4 Results for singleton term LBW [ORs (95% CIs) (n = cases, noncases)]: third trimester.
CO
PM10
Measure Single-pollutant model Multipollutant model (CO, NO2, O3) a Multipollutant model (CO, NO2, O3, PM10) a Measure Single-pollutant model Multipollutant model (CO, NO2, O3, PM10) a
Distance ≤1 mib (n = 653, 28,144) (n = 628, 27,352) (n = 221, 10,160) Distance ≤1 mi (n = 247, 10,981) (n = 221, 10,160)
Per 1 ppm 1.10 (0.98–1.23) 1.15 (0.98–1.35) 1.21 (0.85–1.74) Per 10 μg/m3 1.22 (1.05–1.41) 1.36 (1.12–1.65)
0.96 to < 1.84c 1.08 (0.88–1.33) 1.07 (0.83–1.38) 1.10 (0.72–1.69) 33.4 to < 44.4 1.08 (0.76–1.52) 1.16 (0.77–1.74)
≥1.84 1.36 (1.04–1.76) 1.29 (0.92–1.81) 1.39 (0.77–2.49) ≥44.4 1.48 (1.00–2.19) 1.58 (0.95–2.62)
1 < distance ≤2 mi (n = 2,077, 87,049) (n = 2,058, 85,847) (n = 873, 39,497) 1 < distance ≤2 mi (n = 895, 40,803) (n = 873, 39,497)
Per 1 ppm 1.05 (0.99–1.13) 1.03 (0.94–1.13) 0.91 (0.76–1.10) Per 10 μg/m3 0.98 (0.90–1.06) 1.02 (0.92–1.14)
0.95 to < 1.83 1.05 (0.94–1.18) 1.03 (0.90–1.17) 1.05 (0.86–1.29) 33.4 to < 44.7 0.95 (0.80–1.13) 0.93 (0.77–1.12)
≥1.83 1.10 (0.95–1.28) 1.07 (0.89–1.28) 0.97 (0.73–1.30) ≥44.7 0.96 (0.78–1.18) 1.02 (0.79–1.32)
2 < distance ≤4 mi (n = 6,888, 293,904) (n = 6,857, 292,020) (n = 3,378, 143,981) 2 < distance ≤4 mi (n = 3,424, 146,347) (n = 3,378, 143,981)
Per 1 ppm 1.06 (1.02–1.10) 1.04 (0.99–1.10) 1.01 (0.92–1.11) Per 10 μg/m3 1.03 (0.99–1.08) 1.04 (0.98–1.09)
0.96 to < 1.85 1.06 (1.00–1.13) 1.04 (0.96–1.11) 1.08 (0.98–1.20) 33.9 to < 45.0 1.04 (0.96–1.14) 1.02 (0.92–1.12)
≥1.85 1.08 (1.00–1.18) 1.05 (0.95–1.17) 1.11 (0.96–1.29) ≥45.0 1.08 (0.97–1.20) 1.06 (0.93–1.21)
ZIP-code level: SoCABd (n = 2,596, 112,495) (n = 2,487, 107,053) (n = 1,473, 62,604) ZIP-code level: SoCAB (n = 1,592, 68,652) (n = 1,473, 62,604)
Per 1 ppm 1.12 (1.05–1.19) 1.10 (1.01–1.21) 0.99 (0.85–1.15) Per 10 μg/m3 1.03 (0.97–1.09) 1.07 (0.99–1.15)
0.90 to < 1.75 1.13 (1.02–1.25) 1.12 (0.99–1.28) 1.02 (0.86–1.20) 33.2 to < 43.6 0.98 (0.86–1.11) 0.97 (0.85–1.12)
≥1.75 1.28 (1.12–1.47) 1.29 (1.08–1.53) 0.97 (0.78–1.22) ≥43.6 1.03 (0.88–1.21) 1.09 (0.90–1.31)
a For multipollutant model continuous results, all pollutants are entered as continuous variables; for multipollutant model categorical results, all pollutants are entered as categorical variables using the following percentiles of the concentration distributions: < 25th (reference group), 25th to 75th, ≥75th.
b The address-level analyses included the following LA County stations: Azusa, Burbank, Long Beach, Reseda, Pomona, Lynwood, Central LA, Pasadena, Hawthorne, West LA, Pico Rivera, and Santa Clarita.
c Values listed are the 25 to < 75th, and ≥ 75th percentiles.
d Includes ZIP codes that fell ≥60% by area within a 2-mi radius of the following stations: Azusa, Burbank, Long Beach, Reseda, Pomona, Lynwood, Central LA, Pasadena, Hawthorne, West LA, Anaheim, La Habra, El Toro/Lake Forest (after 1999 becomes Mission Viejo), Costa Mesa, Upland, and San Bernardino. The following variables were included in the models: infant sex, maternal age, race/ethnicity, and education, interval since previous live birth, previous LBW or preterm infant, level of prenatal care, birth season, parity, gestational age, and gestational age squared.
Table 5 Results for singleton, vaginally-delivered preterm births—RRs (95% CIs) (n = cases, noncases).a
CO
PM10 PM2.5
Measure Single-pollutant model Multipollutant model (CO, NO2, O3) b Multipollutant model (CO, NO2, O3, PM10) b Measure Single-pollutant model Multipollutant model (CO, NO2, O3, PM10) b Measure Single-pollutant model
1st Trimester 1st Trimester 1st Trimester
Distance ≤1 mic (n = 2,073, 21,931) (n = 2,018, 21,277) (n = 735, 7,948) Distance ≤1 mi (n = 792, 8,622) (n = 735, 7,948) Distance ≤1 mi (n = 291, 2,701)
Per 1 ppm 1.06 (1.00–1.12) 1.10 (1.01–1.20) 0.99 (0.83–1.18) Per 10 μg/m3 1.00 (0.93–1.09) 1.00 (0.90–1.12) Per 10 μg/m3 0.85 (0.70–1.02)
1.05 to < 1.92d 1.00 (0.91–1.11) 1.05 (0.93–1.18) 0.96 (0.78–1.17) 33.3 to < 45.1 1.07 (0.90–1.26) 1.12 (0.92–1.36) 18.1 to < 25.2 0.91 (0.72–1.16)
≥1.92 1.18 (1.03–1.34) 1.27 (1.07–1.50) 1.03 (0.78–1.36) ≥45.1 1.12 (0.91–1.38) 1.17 (0.90–1.50) ≥25.2 0.83 (0.60–1.14)
1 < distance ≤2 mi (n = 6,662, 68,100) (n = 6,599, 67,236) (n = 2,997, 31,419) 1 < distance ≤2 mi (n = 3,067, 32,351) (n = 2,997, 31,419) 1 < distance ≤2 mi (n = 913, 8,763)
Per 1 ppm 1.06 (1.03–1.10) 1.04 (0.99–1.09) 0.94 (0.86–1.03) Per 10 μg/m3 1.01 (0.97–1.05) 1.04 (0.99–1.10) Per 10 μg/m3 0.85 (0.74–0.99)
1.03 to < 1.90 0.95 (0.90–1.01) 0.90 (0.84–0.96) 0.92 (0.83–1.01) 33.7 to < 45.3 1.03 (0.95–1.12) 1.07 (0.98–1.17) 18.3 to < 25.2 0.81 (0.69–0.94)
≥1.90 1.09 (1.01–1.17) 0.98 (0.90–1.08) 0.99 (0.86–1.14) ≥45.3 1.07 (0.97–1.19) 1.13 (1.00–1.27) ≥25.2 0.79 (0.65–0.97)
2 < distance ≤4 mi (n = 24,339, 229,969) (n = 24,274, 228,586) (n = 12,205, 113,902) 2 < distance ≤4 mi (n = 12,311, 115,594) (n = 12,205, 113,902) 2 < distance ≤4 mi (n = 4,025, 35,222)
Per 1 ppm 1.08 (1.06–1.09) 1.05 (1.02–1.07) 1.05 (1.01–1.10) Per 10 μg/m3 1.01 (0.99–1.03) 0.99 (0.97–1.02) Per 10 μg/m3 0.83 (0.78–0.88)
1.05 to < 1.90 0.98 (0.95–1.01) 0.93 (0.90–0.96) 0.94 (0.89–0.99) 34.1 to < 45.5 1.03 (0.99–1.08) 0.99 (0.95–1.04) 18.5 to < 24.9 0.79 (0.74–0.85)
≥1.90 1.11 (1.07–1.16) 1.03 (0.99–1.08) 1.06 (1.00–1.14) ≥45.5 1.02 (0.96–1.07) 0.94 (0.89–1.01) ≥24.9 0.76 (0.70–0.84)
ZIP-code level: SoCABe (n = 8,592, 88,869) (n = 8,244, 84,473) (n = 4,916, 50,087) ZIP-code level: SoCAB (n = 5,304, 54,888) (n = 4,916, 50,087) ZIP-code level: SoCAB (n = 1,059, 9,895)
Per 1 ppm 1.04 (1.01–1.07) 1.03 (0.98–1.08) 0.97 (0.90–1.04) Per 10 μg/m3 0.99 (0.96–1.01) 0.99 (0.96–1.03) Per 10 μg/m3 0.73 (0.67–0.80)
0.95 to < 1.81 0.97 (0.93–1.02) 0.95 (0.90–1.02) 0.92 (0.85–0.99) 33.3 to < 44.2 1.01 (0.95–1.08) 1.03 (0.97–1.11) 18.0 to < 25.4 0.70 (0.61–0.80)
≥1.81 1.05 (0.99–1.12) 1.01 (0.93–1.10) 0.95 (0.85–1.06) ≥44.2 0.98 (0.90–1.05) 1.01 (0.92–1.11) ≥25.4 0.64 (0.53–0.76)
Six weeks before birth Six weeks before birth Six weeks before birth
Distance ≤1 mic (n = 2,074, 21,930) (n = 2,017, 21,294) (n = 734, 7,964) Distance ≤1 mi (n = 792, 8,608) (n = 734, 7,964) Distance ≤1 mi (n = 378, 3,778)
Per 1 ppm 1.04 (0.98–1.09) 1.10 (1.03–1.18) 0.98 (0.83–1.16) Per 10 μg/m3 1.02 (0.95–1.10) 1.06 (0.97–1.16) Per 10 μg/m3 1.09 (0.91–1.30)
0.92 to < 1.84d 1.00 (0.91–1.11) 1.00 (0.89–1.12) 0.96 (0.77–1.19) 32.5 to < 44.8 1.09 (0.92–1.29) 1.09 (0.90–1.31) 16.8 to < 24.1 1.21 (0.97–1.51)
≥1.84 1.01 (0.89–1.15) 1.01 (0.85–1.18) 0.85 (0.62–1.15) ≥44.8 1.12 (0.92–1.37) 1.17 (0.91–1.49) ≥24.1 1.25 (0.93–1.68)
1 < distance ≤2 mi (n = 6,662, 68,054) (n = 6,589, 67,147) (n = 2,987, 31,325) 1 < distance ≤2 mi (n = 3,066, 32,293) (n = 2,987, 31,325) 1 < distance ≤2 mi (n = 1,185, 12,170)
Per 1 ppm 1.04 (1.01–1.08) 1.10 (1.05–1.14) 1.01 (0.93–1.09) Per 10 μg/m3 1.00 (0.96–1.03) 1.01 (0.97–1.06) Per 10 μg/m3 1.08 (0.97–1.21)
0.91 to < 1.85 1.04 (0.98–1.10) 1.08 (1.01–1.15) 0.97 (0.88–1.08) 32.3 to < 45.3 0.99 (0.91–1.07) 1.00 (0.92–1.10) 17.2 to < 24.5 0.94 (0.82–1.08)
≥1.85 1.14 (1.06–1.22) 1.22 (1.11–1.33) 0.97 (0.84–1.11) ≥45.3 0.99 (0.89–1.10) 1.02 (0.91–1.16) ≥24.5 1.04 (0.87–1.24)
2 < distance ≤4 mi (n = 24,313, 229,724) (n = 24,244, 228,335) (n = 12,175, 113,642) 2 < distance ≤4 mi (n = 12,282, 115,326) (n = 12,175, 113,642) 2 < distance ≤4 mi (n = 5,229, 48,855)
Per 1 ppm 1.01 (0.99–1.02) 1.03 (1.00–1.05) 1.03 (0.99–1.08) Per 10 μg/m3 0.99 (0.98–1.01) 1.00 (0.98–1.02) Per 10 μg/m3 1.05 (0.99–1.10)
0.93 to < 1.87 1.02 (0.99–1.05) 1.02 (0.99–1.06) 0.98 (0.94–1.04) 33.1 to < 45.6 1.00 (0.96–1.05) 1.01 (0.96–1.05) 17.3 to < 24.6 1.06 (1.00–1.13)
≥1.87 1.04 (1.00–1.08) 1.05 (1.00–1.10) 1.00 (0.94–1.08) ≥45.6 0.98 (0.93–1.03) 0.98 (0.92–1.04) ≥24.6 1.08 (0.99–1.17)
ZIP-code level: SoCABe (n = 8,589, 89,039) (n = 8,252, 84,678) (n = 4,898, 50,048) ZIP-code level: SoCAB (n = 5,285, 54,721) (n = 4,898, 50,048) ZIP-code level: SoCAB (n = 1,381, 14,047)
Per 1 ppm 1.03 (1.00–1.06) 1.08 (1.04–1.13) 0.99 (0.92–1.06) Per 10 μg/m3 1.02 (0.99–1.04) 1.02 (0.99–1.06) Per 10 μg/m3 1.10 (1.00–1.21)
0.87 to < 1.75 1.00 (0.95–1.05) 1.01 (0.95–1.07) 0.96 (0.89–1.04) 32.1 to < 44.3 1.01 (0.95–1.07) 1.02 (0.95–1.09) 16.5 to < 24.7 1.06 (0.94–1.20)
≥1.75 1.04 (0.98–1.11) 1.09 (1.00–1.18) 0.94 (0.84–1.05) ≥44.3 1.04 (0.96–1.12) 1.04 (0.95–1.14) ≥24.7 1.19 (1.02–1.40)
a ORs were adjusted to RRs.
b For multipollutant model continuous results, all pollutants are entered as continuous variables; for multipollutant model categorical results, all pollutants are entered as categorical variables using the following percentiles of the concentration distributions: < 25th (reference group), 25th to 75th, ≥75th.
c The address-level analyses included the following LA County stations: Azusa, Burbank, Long Beach, Reseda, Pomona, Lynwood, Central LA, Pasadena, Hawthorne, West LA, Pico Rivera, and Santa Clarita.
d Values listed are the 25th to < 75th, and ≥ 75th percentiles.
e Includes ZIP codes that fell ≥60% by area within a 2-mi radius of the following stations: Azusa, Burbank, Long Beach, Reseda, Pomona, Lynwood, Central LA, Pasadena, Hawthorne, West LA, Anaheim, La Habra, El Toro/Lake Forest (after 1999 becomes Mission Viejo), Costa Mesa, Upland, and San Bernardino. The following variables were included in the models: infant sex, maternal age, race/ethnicity, and education, interval since previous live birth, previous LBW or preterm infant, level of prenatal care, birth season, and parity.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7932ehp0113-00122216140631ResearchChildren's HealthUse of Di(2-ethylhexyl) Phthalate–Containing Medical Products and Urinary Levels of Mono(2-ethylhexyl) Phthalate in Neonatal Intensive Care Unit Infants Green Ronald 1Hauser Russ 1Calafat Antonia M. 2Weuve Jennifer 1Schettler Ted 3Ringer Steven 4Huttner Kenneth 5Hu Howard 161 Department of Environmental Health, Harvard School of Public Health, Boston, Massachusetts, USA2 Centers for Disease Control and Prevention, Atlanta, Georgia, USA3 Science and Environmental Health Network, Boston, Massachusetts, USA4 Neonatology Unit, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA5 Neonatology Unit, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA6 Channing Laboratory, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USAAddress correspondence to H. Hu, Harvard School of Public Health, Department of Environmental Health, Landmark Center East 3-110A, 401 Park Dr., Boston, MA 02215 USA. Telephone: (617) 384-8968. Fax: (617) 384-8994. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 8 6 2005 113 9 1222 1225 17 1 2005 26 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Objective: Di(2-ethylhexyl) phthalate (DEHP) is a plasticizer used in medical products made with polyvinyl chloride (PVC) plastic and may be toxic to humans. DEHP is lipophilic and binds non-covalently to PVC, allowing it to leach from these products. Medical devices containing DEHP are used extensively in neonatal intensive care units (NICUs). Among neonates in NICUs, we studied exposure to DEHP-containing medical devices in relation to urinary levels of mono(2-ethylhexyl) phthalate (MEHP), a metabolite of DEHP.
Design: We used a cross-sectional design for this study.
Participants: We studied 54 neonates admitted to either of two level III hospital NICUs for at least 3 days between 1 March and 30 April 2003.
Measurements: A priori, we classified the infants’ exposures to DEHP based on medical products used: The low-DEHP exposure group included infants receiving primarily bottle and/or gavage feedings; the medium exposure group included infants receiving enteral feedings, intravenous hyperalimentation, and/or nasal continuous positive airway pressure; and the high exposure group included infants receiving umbilical vessel catheterization, endotracheal intubation, intravenous hyperalimentation, and indwelling gavage tube. We measured MEHP in the infants’ urine using automated solid-phase extraction/isotope dilution/high-performance liquid chromatography/ tandem mass spectrometry.
Results: Urinary MEHP levels increased monotonically with DEHP exposure. For the low-, medium-, and high-DEHP exposure groups, median (interquartile range) MEHP levels were 4 (18), 28 (58), and 86 ng/mL (150), respectively (p = 0.004). After adjustment for institution and sex, urinary MEHP levels among infants in the high exposure group were 5.1 times those among infants in the low exposure group (p = 0.03).
Conclusion: Intensive use of DEHP-containing medical devices in NICU infants results in higher exposure to DEHP as reflected by elevated urinary levels of MEHP.
di(2-ethylhexyl) phthalatehospital equipment and suppliesmono(2-ethylhexyl) phthalateneonatal intensive care unitsnewborn infants
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Di(2-ethylhexyl) phthalate (DEHP) is an aromatic diester used primarily to soften and plasticize the rigid polymer polyvinyl chloride (PVC); DEHP may represent between 20 and 40% of the finished weight of the plastic (Jaeger and Rubin 1973). Among other properties, DEHP imparts to PVC flexibility, strength, optical clarity, and resistance to broad-range temperature variations (Shea 2003). In its pure form, DEHP is a clear, oily liquid, which is highly lipophilic (fat soluble) and poorly soluble in water. In PVC plastic, DEHP resides in the PVC matrix as a semisolid and readily migrates out of the plastic into blood or other lipid-containing solutions in contact with the plastic, a phenomenon observed with blood stored in PVC bags (Peck and Albro 1982; Rock et al. 1984). Because of its versatile properties, DEHP is also found in many commercial and household products such as vinyl floor and wall coverings, furniture, raincoats, and shower curtains, as well as cosmetics, personal care products, and food packaging [Agency for Toxic Substances and Disease Registry (ATSDR) 2002].
DEHP has been used as a plasticizer in a variety of medical products, such as bags containing blood, plasma, intravenous fluids, and total parenteral nutrition, tubing associated with their administration, nasogastric tubes, enteral feeding tubes, umbilical catheters, extra-corporeal membrane oxygenation (ECMO) circuit tubing, hemodialysis tubing, respiratory masks, endotracheal tubes, and examination gloves. The rate of DEHP leaching varies not only with the type of solution in contact with the plastic material but also with storage and temperatures at the time of use, storage time, and percent DEHP in the plastic product (Marcel 1973). The leaching rate of DEHP has been studied most rigorously for banked blood and plasma and, as reported, varies between 0.25 to 0.40 mg/100 mL/day for whole blood stored > 21 days at 4°C and 6 mg/unit of platelet concentrate stored at room temperature (Jaeger and Rubin 1973; Marcel 1973; Peck and Albro 1982). Leaching has also been demonstrated in a study in which endotracheal tubes were found to have 6–12% less (0.06–0.12 mg DEHP/mg tube) DEHP after use (Latini and Avery 1999).
Mono(2-ethylhexyl) phthalate (MEHP), one of the metabolites of DEHP, consistently produces developmental, reproductive, and hepatic toxicity in laboratory animals (ATSDR 2002; Gray and Beamand 1984), raising concern about whether human exposure to DEHP approaches the levels of adverse effect found in toxicologic studies. Medical devices containing DEHP are in extensive use in modern neonatal intensive care units (NICUs). It has been estimated that such use might be exposing infants to DEHP at levels that exceed the average daily adult exposure [median = 0.71 μg/kg body weight/day (Kohn et al. 2000)] by 2–3 orders of magnitude, approaching the lowest observed adverse effect level in animal studies (38–144 mg/kg body weight/day) (National Toxicology Program 2000). A recent study showed that MEHP urinary concentrations in neonates undergoing intensive therapeutic interventions in a NICU were several times higher than those from the general U.S. population (Calafat et al. 2004). In addition to potentially high neonatal exposure in NICUs from DEHP-containing products, there is additional concern regarding potential health risks because, until 3 months of age, infants have immature glucuronidation pathways. Because glucuronidation, via enzymes such as UDP-glucuronosyltransferase, facilitates urinary excretion of phthalates and other xenobiotics, a reduced potential for glucuronidation may lead to slower MEHP excretion and higher levels of MEHP in neonates than in older children and adults (Leeder and Kearns 1997).
Among neonates in NICUs, we conducted a study to characterize DEHP exposure and to quantitatively relate the use of DEHP-containing products to urinary levels of MEHP, a biologic marker of DEHP exposure.
Materials and Methods
Study population.
We studied a convenience sample of 54 infants enrolled from the level III NICUs at two major Boston-area hospitals. Level III NICUs provide all newborn care, including mechanical ventilation, high-frequency ventilation, surgery, and cardiac catheterization. We selected infants to reflect a range of diagnoses (including congenital anomalies and developmental and metabolic abnormalities) and NICU care requirements (including ventilation, enteral feedings, parenteral nutrition, and indwelling catheterization). Infants must have been in the NICU at least 3 consecutive days before enrollment, have had a corrected gestational age of ≤44 weeks, and have been born at or transferred to either hospital between 1 March and 30 April 2003. Concerns regarding the sensitivity of this research led to the design and implementation of a data and sample collection protocol that was based on visual inspection and that did not include inspection of the medical records. The study protocol and methods were approved by the institutional review boards of Harvard School of Public Health, Brigham and Women’s Hospital, and Massachusetts General Hospital.
Assessment of DEHP exposure.
Before data collection, we defined three DEHP exposure categories (low, medium, and high) based on a review of medical products typically used in both NICUs and information provided by their manufacturers with respect to DEHP content. Infants classified as having low DEHP exposure were those receiving primarily bottle and/or gavage feedings. The medium-DEHP exposure group included infants receiving enteral feedings by indwelling gavage tubes either continuously or by bolus feedings; intravenous hyperalimentation by indwelling percutaneous intravenous central catheter (PICC) line, broviac, or umbilical vessel catheter (UVC); and/or nasal continuous positive airway pressure by nasal prongs. The high-DEHP exposure group included infants receiving continuous indwelling UVC, endotracheal intubation, intravenous hyper-alimentation by the central venous route (i.e., PICC line, broviac, UVC), and an indwelling gavage tube (for gastric decompression).
One of the study investigators (R.G.) observed the care of each infant for 1–4 hr per day for 1–3 days per infant, for a total of 3–12 hr of observation per infant. Observations were made during a single day for 37 infants, on 2 days for 12 infants, and on 3 days for 5 infants. During the observations, the investigator took an inventory of products in use for the care of each infant. This information was used to categorize infants into low-, medium-, and high-DEHP exposure groups. None of the infants changed DEHP exposure groups over the course of observation.
Assessment of urinary MEHP.
The observing investigator collected spot urine samples at the end of each infant’s observation period(s). A total of 81 urine samples were collected from 54 infants. Two or more urine samples came from 17 infants, and of these, four samples were collected concurrently with the first samples, and 18 others were collected 6–72 hr after the first samples. We used these repeated samples to assess intraindividual variability in urinary MEHP levels.
Urine samples were collected by either squeezing the urine from a cotton gauze placed in the infant’s diaper at the beginning of the observation period or from the cotton filling of the diaper the infant wore during the period of observation. The cotton gauze or the removed cotton diaper filling was placed into a 3–5 cc polypropylene syringe (Becton-Dickinson, Franklin Lakes, NJ), the plunger was replaced, and the urine was squeezed into a 2- or 4-cc Nunc (Rochester, NY) cryovial. Urine samples were frozen within 4–6 hr at –35°C and shipped on dry ice to the Centers for Disease Control and Prevention (CDC; Atlanta, GA) for analysis.
Urine specimens were analyzed for 10 phthalate monoesters, including three DEHP metabolites [MEHP, mono(2-ethyl-5-hydroxyhexyl) phthalate, and mono(2-ethyl-5-oxohexyl) phthalate], using automated solid-phase extraction/isotope dilution/high-performance liquid chromatography/tandem mass spectrometry at the CDC. Monobutyl phthalate and monobenzyl phthalate were also frequently detected. In this report, we focus on levels of MEHP, because this metabolite has been most studied and is a valid biomarker of DEHP exposure.
The analytical method used for measuring phthalate metabolites in urine has been described in detail previously (Silva et al. 2004). Briefly, the method involved the enzymatic deconjugation of the phthalate metabolites from their glucuronidated form, followed by automated solid-phase extraction. We used reversed-phase high-performance liquid chromatography to separate the phthalate metabolites from other components in the extracted urine. We then quantified the metabolites by isotope dilution/tandem mass spectrometry. Samples, reagent blanks, and quality control (QC) materials were processed identically. QC materials were analyzed along with the study samples to ensure the accuracy and reliability of the data. QC materials with low concentrations (QCL) and high concentrations (QCH) were prepared from a base urine pool, obtained from multiple anonymous donors as described previously (Silva et al. 2004), dispensed in 5-mL aliquots, and stored at –20°C. Each QC material was characterized by repeated measurements, spanned over several weeks, to define the mean concentrations and the 95% and 99% control limits of MEHP. Each analytical run consisted of 50 (5 QCs, 5 blanks, and 40 unknown) samples. The concentrations of the replicate QCH and QCL materials, averaged to obtain one measurement of QCH and QCL for each run, were evaluated using standard statistical probability rules. All MEHP concentrations, reported in nanograms per milliliter of urine, were blank-corrected. However, MEHP is not typically found in the reagent blank samples, and when it is, it is at concentrations near the limit of detection (LOD; 0.87 ng/mL). Specimens with MEHP levels below the LOD were assigned a value of 0.435 ng/mL, the midpoint between 0 and the LOD.
Assessment of other variables.
We collected data, as available, on gestational age and length of stay in the NICU. However, data on these variables were incomplete for about half of the infants because, as noted, the sample collection was anonymous and not based on review of medical records. The interpretation of these data is likely to be tenuous because of missing data, and we therefore do not present these results.
Statistical analysis.
We used Fisher’s exact test to evaluate differences in sex and institution by DEHP exposure group. To evaluate the intraindividual variability of urinary MEHP levels, we computed Spearman correlations between repeated urine specimens. For unadjusted comparisons of urinary MEHP levels by sex, institution, and DEHP exposure group, we used the Mann-Whitney Kruskal-Wallis non-parametric test. We used multiple linear regression to compare urinary MEHP levels across DEHP exposure groups, adjusting for institution and infants’ sex. Because the distribution of urinary MEHP was skewed, we used log-transformed MEHP values in the regression models. In regression models of log-transformed MEHP, the regression parameters estimated MEHP levels in the medium- and high-DEHP exposure groups as a proportion of MEHP levels in the low-DEHP exposure group. Our primary analyses focused on the MEHP levels in the first urine specimen collected from each infant; in secondary analyses, we incorporated first and, where available, second and third specimens, using repeated-measures regression to compare MEHP levels across exposure groups.
We used quantile regression (Koenker and Hallock 2001) to estimate sex- and institution-adjusted quartiles of urinary MEHP for each DEHP exposure group.
Results
The 54 infants in our study were roughly equally distributed between the two institutions, and 34 (63%) were female (Table 1). Thirteen (24%) infants had low exposure to DEHP-containing products, 24 (44%) had medium exposure, and 17 (32%) had high exposure. Among the first set of urine samples collected from these infants, 11 (20%) had MEHP levels that were less than the LOD (0.87 ng/mL), and the maximum level was 758 ng/mL (geometric mean = 14 ng/mL; geometric SD = 8.6 ng/mL).
Correlation between urinary MEHP levels in repeated urine samples.
Among the 17 infants with multiple urine samples, urinary MEHP measurements from the same infants were highly correlated. Spearman correlations were 0.9 (p < 0.0001; n = 17) for urinary MEHP levels in the first and second urine specimens, 0.9 (p = 0.04; n = 5) for the second and third specimens, and 0.7 (p = 0.2; n = 5) for the first and third specimens. Although our data were limited, we observed no apparent dissimilarity in the correlation between specimens collected at the same time (correlation coefficient r = 0.80; n = 4) versus those collected at different times (r = 0.88; n = 13 first and second samples only).
Correlates of DEHP exposure group and urinary MEHP.
Compared with infants in the two lowest DEHP exposure groups, infants in the high-DEHP exposure group were more likely to be patients at institution B (Table 1). In unadjusted comparisons, urinary MEHP levels were significantly higher among infants at institution B (Mann-Whitney Kruskal-Wallis p = 0.002; Table 2).
Association of DEHP exposure group with urinary MEHP.
Progressively higher DEHP exposure group was associated with progressively higher urinary MEHP levels. Median urinary MEHP levels (and 25th and 75th percentiles) in infants in the low-, medium-, and high-DEHP exposure groups were 4 (< LOD, 18), 28 (3, 61), and 86 (21, 171) ng/mL, respectively. DEHP exposure group remained a substantial predictor of urinary MEHP levels even after adjusting for infants’ sex and institution of hospitalization (p = 0.09; Table 3, Figure 1). Compared with infants in the low-DEHP exposure group, infants in the medium-DEHP exposure group had urinary MEHP levels that were twice as high [95% confidence interval (CI) of the multiplication factor, 0.5–7.4; p = 0.3], and infants in the high-DEHP exposure group had levels that were 5.1 times as high (95% CI of the multiplication factor, 1.2–21.9; p = 0.03). These results were identical when we analyzed all first, second, and third urinary MEHP values together using repeated-measures regression.
Urinary creatinine levels were available for 67 samples, 45 infants in total. Using this restricted sample, we analyzed the association between DEHP exposure group and creatinine-adjusted urinary MEHP concentrations, and the results were nearly identical to the results from the analyses of unadjusted MEHP levels.
Discussion
Our study of 54 infants receiving care in two NICUs demonstrated that the use of DEHP-containing medical devices is associated with a monotonic increase in urinary levels of MEHP, a metabolite of DEHP. In particular, urinary MEHP levels among infants in the high-DEHP exposure group were five times as high as those in the low-DEHP exposure group, after adjustment for infants’ sex and institution of care. Urinary MEHP levels among infants in the medium-DEHP exposure group were twice as high as levels among infants in the low-DEHP exposure group. Adjustment for institution resulted in modest attenuation in the association between DEHP exposure group and urinary MEHP, which may reflect the potential influence of other unmeasured sources of DEHP exposure that differed between institutions. In addition, urinary MEHP levels were modestly higher among male infants. Although the length of observation varied across infants, because of the acuteness of care requirements and the indwelling (and therefore continuous) nature of many of the products used, potential DEHP exposure from medical devices in use was stable over each infant’s observation period(s).
To date, there are limited measurements on human neonatal exposures to phthalates (Calafat et al. 2004) and, to our knowledge, no data on whether such exposures are associated with adverse health effects in these neonates. Limited data describe the effects of neonatal exposures on health later in life. A study of 18 adolescents 14–16 years of age who had undergone ECMO as neonates found no apparent abnormalities in their growth and pubertal maturity, and the levels of luteinizing hormone, follicle-stimulating hormone, testosterone (in boys), and estradiol (in girls) were within the normal reference ranges for stage of pubertal development (Rais-Bahrami et al. 2004). It is difficult to draw definitive conclusions from this study, because it was very small, and normal reference ranges for reproductive hormones, physical growth, and sexual maturation are quite wide. Furthermore, the postnatal levels of DEHP or its metabolites at the time of the ECMO treatment were not known.
Our study is limited by the narrow range of descriptive data available on the infants and the small number of infants examined. Yet it is the first study to directly examine the relative use of DEHP-containing medical products among NICU infants in relation to urinary MEHP concentration. Because our study included infants with a range of diagnoses and NICU care requirements, the results of this research may be generalizable to infants of other level III NICUs.
Several studies have considered DEHP exposure or sources of DEHP exposure in NICU infants (Calafat et al. 2004; Karle et al. 1997; Latini and Avery 1999; Loff et al. 2000). Calafat et al. (2004) considered exposure to DEHP from medical procedures in six newborn infants of a university hospital NICU undergoing intravenous infusion for more than 2 weeks. The geometric mean level of MEHP (100 ng/mL) in 33 urine samples collected from these children was several times higher than that from the general U.S. population ≥6 years of age (geometric mean, 3.43 ng/mL) (CDC 2003). The urinary MEHP levels reported in that study were similar to those in the high-DEHP exposure group in our study. Brock et al. (2002) studied 19 toddlers 12–17 months of age in Imperial County, California. Twelve children provided two urine samples, and seven children provided a single sample. Eight urine samples from six children had detectable levels of MEHP. The mean urinary MEHP level was 4.6 ng/mL (SD, 6.4 ng/mL; range, LOD < 1.2 to 47.3). These levels were an order of magnitude lower than the mean in the present study on neonates.
Others have directly assessed sources of DEHP exposure from medical devices among infants. Among neonates, Karle et al. (1997) found that DEHP leached from ECMO circuit tubing at 10.5–34.9 mg/kg, depending on length, type, and size of PVC tubing. Interestingly, circuits with heparin coating did not leach DEHP. Latini and Avery (1999) directly observed the degradation of 44 indwelling PVC endotracheal tubes that had been used in NICU infants for as little as 12 hr. These tubes demonstrated a substantial loss of DEHP, 0.06–0.12 mg DEHP per mg of sample (6–12%), compared with unused tubes.
In our study, DEHP exposure differed significantly by institution. Specifically, most high-DEHP-exposure infants were from institution B, and infants from institution B had a greater median urinary MEHP concentration than did infants from institution A. Loff et al. (2000) studied DEHP leaching from parenteral nutrition infusion lines used for 24-hr fat emulsion administration in 2-kg neonates and found that 25 mL of standard 20% lipid emulsion administered by pump via polyethylene syringes and PVC infusion line yielded samples (after 24 hr at 27°C) having postinfusion DEHP concentrations of 422.8 μg/mL. The authors concluded that the total amount of DEHP for a 2-kg baby caused by lipid emulsions in a day, under these conditions, is approximately 10.2 mg. In our study, we speculate that the higher exposure to DEHP in institution B compared with institution A reflects the extensive use in particular of two DEHP-containing items at institution B: unsiliconized PVC indwelling endotracheal tubes, and a PVC indwelling hemodynamic monitoring UVC used for, among other things, parenteral nutrition. These DEHP-containing medical devices were sparingly used in institution A.
In conclusion, our study demonstrated an association between the use of DEHP-containing medical products and urinary levels of MEHP in NICU infants. This study was designed to evaluate the distribution of exposure to DEHP among NICU neonates only. Additional studies, larger and more comprehensive than the present one, with follow-up of the infants, are needed. Such studies would help determine potential health risks, if any, from DEHP exposure among NICU infants.
We acknowledge M. Silva, J. Reidy, and A. Herbert for the phthalate measurements.
This research was conducted with support from Health Care Without Harm, the Rasmussen Foundation, the Harvard–National Institute for Occupational Safety and Health Education and Research Center, and National Institute of Environmental Health Sciences ES00002. R.G. conducted this research while a Fellow in Occupational and Environmental Medicine at the Harvard School of Public Health.
Figure 1 Median and interquartile range of urinary MEHP, by DEHP exposure group, and adjusted for institution and infant sex using quantile regression.
Table 1 Characteristics, by DEHP exposure group [n (%)].
DEHP exposure group
Factor No. Low (n = 13) Medium (n = 24) High (n = 17) p-Valuea
Sex > 0.9b
Female 34 8 (62) 15 (63) 11 (65)
Male 18 4 (31) 8 (33) 6 (35)
Missing 2 1 (8) 1 (4) 0 (0)
Institution 0.01
A 28 11 (85) 12 (50) 5 (29)
B 26 2 (15) 12 (50) 12 (71)
a Fisher’s exact test comparing the distribution of sex and institution, respectively, across DEHP exposure group.
b Excludes infants with missing data on sex.
Table 2 Median concentrations (and 25th and 75th percentiles) of urinary MEHP, by sex, institution, and DEHP exposure group.
Urinary MEHP level (ng/mL)
Factor 25th percentile Median 75th percentile p-Valuea
Sex 0.15
Female 3 20 64
Male 19 39 75
Institution 0.002
A < LODb 12 29
B 18 58 92
DEHP exposure group 0.001
Low < LODb 4 18
Medium 3 28 61
High 21 86 171
a Mann-Whitney Kruskal-Wallis nonparametric test to evaluate differences in urinary MEHP distribution.
b LOD = 0.87 ng/mL.
Table 3 Relativea urinary MEHP levels (95% CIs), by DEHP exposure group, as a multiple of urinary MEHP in the low-DEHP exposure group (referent = low).
DEHP exposure group Not adjusted Adjusted for sex Adjusted for institution Adjusted for sex and institution
Low 1.0 (Referent) 1.0 (Referent) 1.0 (Referent) 1.0 (Referent)
Medium 3.4 (0.9–13.4) 3.1 (0.8–11.2) 2.2 (0.6–8.6) 2.0 (0.5–7.4)
High 12.4 (2.9–53.1) 9.7 (2.4–39.5) 6.1 (1.3–28.2) 5.1 (1.2–21.9)
Test of overall differences among groups, p-value 0.0004 0.008 0.06 0.09
a Estimates are multiplication factors derived from regression models of log-transformed urinary MEHP. They compare urinary MEHP levels in the medium- and high-DEHP exposure groups with levels in the low-DEHP exposure group. For example, after adjusting for sex and institution, urinary MEHP levels in the high-DEHP exposure group were about 5.1 times as great as those in the low-DEHP exposure group (95% CI, 1.2–21.9 times as great).
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References
ATSDR 2002. Toxicological Profile for Di(2-ethylhexyl)phthalate (DEHP). Atlanta, GA:Agency for Toxic Substances and Disease Registry.
Brock JW Caudill SP Silva MJ Needham LL Hilborn ED 2002 Phthalate monoesters levels in the urine of young children Bull Environ Contam Toxicol 68 3 309 314 11993803
Calafat AM Needham LL Silva MJ Lambert G 2004 Exposure to di-(2-ethylhexyl) phthalate among premature neonates in a neonatal intensive care unit Pediatrics 113 e429 e434 15121985
CDC 2003. Second National Report on Human Exposure to Environmental Chemicals. NCEH Pub. No. 02-0716. Atlanta, GA:Centers for Disease Control and Prevention.
Gray TJ Beamand JA 1984 Effect of some phthalate esters and other testicular toxins on primary cultures of testicular cells Food Chem Toxicol 22 2 123 131 6538161
Jaeger RJ Rubin RJ 1973 Extraction, localization, and metabolism of di-2-ethylhexyl phthalate from PVC plastic medical devices Environ Health Perspect 3 95 102 4735799
Karle VA Short BL Martin GR Bulas DI Getson PR Luban NL 1997 Extracorporeal membrane oxygenation exposes infants to the plasticizer, di(2-ethylhexyl)phthalate Crit Care Med 25 696 703 9142038
Koenker R Hallock KF 2001 Quantile regression J Econ Perspect 15 143 156
Kohn MC Parham F Masten SA Portier CJ Shelby MD Brock JW 2000 Human exposure estimates for phthalates Environ Health Perspect 108 A440 A442 11097556
Latini G Avery GB 1999 Materials degradation in endotracheal tubes: a potential contributor to bronchopulmonary dysplasia Acta Paediatr 88 1174 1175 10565474
Leeder JS Kearns GL 1997 Pharmacogenetics in pediatrics. Implications for practice Pediatr Clin North Am 44 55 77 9057784
Loff S Kabs F Witt K Sartoris J Mandl B Niessen KH 2000 Polyvinylchloride infusion lines expose infants to large amounts of toxic plasticizers J Pediatr Surg 35 1775 1781 11101735
Marcel YL 1973 Determination of di-2-ethylhexyl phthalate levels in human blood plasma and cryoprecipitates Environ Health Perspect 3 119 121 4704559
National Toxicology Program 2000. Expert Panel Report on DEHP. National Toxicology Program Center for the Evaluation of Risks to Human Reproduction. Available: http://cerhr.niehs.nih.gov/news/phthalates/report.html [accessed 15 January 2005].
Peck CC Albro PW 1982 Toxic potential of the plasticizer di(2-ethylhexyl) phthalate in the context of its disposition and metabolism in primates and man Environ Health Perspect 45 11 17 7140682
Rais-Bahrami K Nunez S Revenis ME Luban NL Short BL 2004 Follow-up study of adolescents exposed to di(2-ethylhexyl) phthalate (DEHP) as neonates on extracorporeal membrane oxygenation (ECMO) support Environ Health Perspect 112 1339 1340 15345350
Rock G Tocchi M Ganz PR Tackaberry ES 1984 Incorporation of plasticizer into red cells during storage Transfusion 24 493 498 6506180
Shea KM 2003 Pediatric exposure and potential toxicity of phthalate plasticizers Pediatrics 111 6 pt 1 1467 1474 12777573
Silva MJ Slakman AR Reidy JA Preau JL Herbert AR Samandar E 2004 Analysis of human urine for fifteen phthalate metabolites using automated solid-phase extraction J Chromatogr B 805 161 167
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7806ehp0113-00122616140632ResearchChildren's HealthBaseline Micronuclei Frequency in Children: Estimates from Meta- and Pooled Analyses Neri Monica 12Ceppi Marcello 3Knudsen Lisbeth E. 2Merlo Domenico Franco 1Barale Roberto 4Puntoni Riccardo 1Bonassi Stefano 31 Unit of Epidemiology and Biostatistics, National Cancer Research Institute, Genoa, Italy2 Institute of Public Health, University of Copenhagen, Copenhagen, Denmark3 Unit of Molecular Epidemiology, National Cancer Research Institute, Genoa, Italy4 Dipartimento di Scienze dell’Uomo e dell’Ambiente, University of Pisa, Pisa, ItalyAddress correspondence to S. Bonassi, Unit of Molecular Epidemiology, National Cancer Research Institute, Largo Rosanna Benzi 10, 16132 Genoa, Italy. Telephone: 39-010-5600924. Fax: 39-010-5600501. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 31 5 2005 113 9 1226 1229 30 11 2004 31 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The number of studies evaluating the effect of environmental exposure to genotoxic agents in children has rapidly increased in the last few years. The frequency of micronuclei (MN) in peripheral blood lymphocytes determined with the cytokinesis block assay is among the most popular biomarkers used for this purpose, although large inter- and intralaboratory variability of this end point has been observed in population studies. The availability of reference measures is therefore necessary for laboratories to validate protocols and analytical procedures, and for molecular epidemiologists, as well, to estimate the statistical power of studies and to assess the quality of data. In this article, we provide estimates of the baseline frequency of MN in children, conducting a meta-analysis of MN frequency reported by field studies in children and a pooled analysis of individual data [available from published studies and from the Human Micronucleus International Collaborative Study (HUMN) database]. Thirteen articles were selected for meta-analysis, and individual data included in the pooled analysis were retrieved from the databases of 12 laboratories. Overall means of 4.48 [95% confidence interval (CI), 3.35–5.98] and 5.70 (95% CI, 4.29–7.56) MN per 1,000 binucleated cells were estimated by the meta- and pooled analysis, respectively. A clear effect of age was detected, even within the restricted range of pediatric age considered, with significantly lower frequency values in newborns. No influence of sex was found. The study showed the advantage of using data from large collaborative studies and suggested a synergistic use of meta- and pooled analysis.
biomarkerchildrenenvironmental exposuregenetic damagemeta-analysismicronucleus assaymolecular epidemiologypooled analysis
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The number of studies evaluating the effect of environmental exposure to genotoxic agents in children has rapidly grown in the last years (Neri et al. 2005b; Suk et al. 2003), boosted by two main considerations: a) Children may be more sensitive than adults to genotoxic agents, and b) genetic damage occurring at young ages may affect the lifetime risk of delayed adverse health outcomes (Landrigan et al. 2003; Wild and Kleinjans 2003).
Among the several adverse health effects that have been studied in children exposed to environmental hazards, genetic damage has received a particular interest, especially after the recent publication of epidemiologic studies showing that a high frequency of chromosome damage predicts cancer in healthy adults (Bonassi et al. 2004).
The use of genetic biomarkers in children raises a number of issues (Neri et al. 2005a). On the other hand, studies in children are essential to make use of the findings in public and environmental health. Ethical issues are related to protection of privacy, causing no harm, and leaving the child with a “feel-good experience.” Legal issues of data protection, confidentiality, and autonomy of the child are also important. Further, a direct application to pediatric populations of biomarkers of genetic damage that have been proven useful in adults may be misleading. Differences in exposure to and intake of environmental agents, xenobiotic metabolism, and the role of infectious diseases may alter the reliability of some biomarkers when translated from adults to children. Finally, to allow a correct interpretation of findings from these studies, a basic question must be addressed: What is the estimate of the spontaneous occurrence of genetic damage in children?
The micronucleus (MN) test in peripheral blood lymphocytes with the cytokinesis block method is one of the most popular assays of genetic damage in human biomonitoring (Bonassi et al. 2005). Details about the assay can be found in the articles published by the Human Micronucleus International Collaborative Study (HUMN) (Fenech et al. 1999, 2003). The growing interest in this test, mostly due to the easy use of MN in monitoring exposure to genotoxic agents, is fueled also by the accumulating evidence that the frequency of MN in healthy subjects may be considered a marker of risk for cancer (Tucker and Preston 1996) and cardiovascular disease (Andreassi and Botto 2003). The performance of this biomarker in field studies involving children exposed to environmental agents has been recently reviewed (Neri et al. 2003, 2005b).
Despite the large number of advantages that justify the popularity of this assay, there is an evident limitation—the large inter- and intralaboratory variability in the MN frequency. This variability may be explained partially by technical reasons, genetic variability, or sampling error. However, interscorer discrepancy and protocol differences (when different laboratories are involved) have been shown to be the most important sources of variability (Bonassi et al. 2001).
The meta- and pooled analyses represent the ideal statistical tools for computing summary estimates of a biomarker frequency using data from different studies (Greenland 1987). The advantages and the limitations of meta- and pooled analysis have been discussed in many articles, and in many aspects these methods are complementary (Taioli and Bonassi 2002).
In this study, we identified the published studies reporting MN during childhood (age range, 0–18 years) with the aim of performing a meta-analysis of MN frequency for referent children and providing a meta-estimate of the MN baseline value. Moreover, a pooled analysis of individual data available from published studies and from the HUMN database (Bonassi et al. 2001) was performed with the same purpose.
Materials and Methods
Search strategy and studies selection.
Individuals from birth to late adolescence (age range, 0–18 years) were considered as children. The studies selected for inclusion in the meta-analysis were identified by systematically searching the MedLine/PubMed database (National Library of Medicine, National Institutes of Health, Bethesda, MD, USA; http://www.ncbi.nlm.nih.gov/PubMed). The terms “micronucleus tests” existing since 1989 and “micronuclei” existing since 1990 were used as medical subject heading (MeSH) key word. To cover previous years, a free text search with terms “micronucleus” and “micronuclei” was performed. The categories “All Child: 0–18 years,” “Human,” “Language: English,” and “Publication Date: from January 1st, 1985 to September 1st, 2004” were set as search limits. This allowed retrieving 168 citations.
Only studies measuring MN frequency in lymphocytes with the cytokinesis block method (Fenech and Morley 1985) and with at least 10 subjects in the referent group were included. Referent children exposed to genotoxic agents or affected by any disease were also excluded from the statistical analyses. MN frequency was always reported as the number of MN per 1,000 (‰) binucleated cells. According to these criteria, 13 studies (Table 1), accounting for 440 subjects, were found to be suitable for the meta-analysis (Barale et al. 1998; Bilban and Vaupotic 2001; Da Cruz et al. 1994; Dulout et al. 1996; Fellay-Reynier et al. 2000; Fenech et al. 1997; Livingston et al. 1997; Maluf and Erdtmann 2001; Migliore et al. 1991; Mikhalevich et al. 2000; Shi et al. 2000; Vleminckx et al. 1997; Zotti-Martelli et al. 1999). One study with a small number of 19-year-old subjects was also included (Barale et al. 1998). Three different referent groups identified in a single study (Vleminckx et al. 1997) were treated as independent in the analysis.
Six of the 13 studies included in the meta-analysis reported individual data for 103 children (da Cruz et al. 1994; Dulout et al. 1996; Fellay-Reynier et al. 2000; Maluf and Erdtmann 2001; Migliore et al. 1991; Mikhalevich et al. 2000) and were used also in the pooled analysis, together with 229 subjects from the HUMN database (Bonassi et al. 2001).
Statistical methods.
Meta-analysis.
We computed a summary estimate of the MN frequency applying the random-effects linear model to the natural logarithm of the MN mean of each study (DerSimonian and Laird 1986). This model takes into account two sources of variability: the error in estimating the ith MN mean in repeated samples from the same population the ith study belongs to, and the heterogeneity between studies. Although the former variability is assumed to be known and estimated by the standard error reported for each study, the latter (heterogeneity) was estimated from the model and found to be highly significant [Q = 308.14 (14 df), p < 0.001]. The analysis was carried out with STATA statistical software (STATA statistical software, release 7.0; Stata Corp., College Station, TX, USA); the random-effects model was performed with the procedure META, and the diagnostics, such as funnel plot, with the procedures METAINF and METABIAS.
Pooled analysis.
We obtained sex-specific and age-group–specific MN mean frequencies by fitting a negative binomial random-effects model to the MN count of each subject (Lindsey 1995). The negative binomial model was used to account for data overdispersion, whereas the clustered nature of data (i.e., the fact that the correlation between data within laboratories is likely to be larger than that between laboratories) was taken into consideration by introducing in the model a random effect due to the laboratory. The analysis was performed with MLwiN statistical software (version 1.10; Centre for Multilevel Modelling, Institute of Education, University of London, London, UK).
Results
Meta-analysis.
The MN frequency for each study included in the meta-analysis is reported in Table 1 along with the size of the study and the age range investigated. The overall meta-estimate of the MN frequency was 4.48 ‰ [95% confidence interval (CI), 3.35–5.98]. To evaluate the impact of single studies on this summary estimate of MN frequency, we performed a sensitivity analysis estimating the overall MN frequency after cyclically removing single studies. This approach showed the absence of influential studies, with meta-estimates of the MN frequency ranging from 4.77 to 4.22 ‰.
Pooled analysis.
The layout of data selected for the pooled analysis is reported in Table 2. This analysis is more powerful with respect to meta-analysis because potential confounding factors, such as age and sex, are accounted for. Age-group–specific and sex-specific pooled estimates computed from the random-effects model are shown in Table 3. Poor information was available about ethnic characteristics, although the large majority of children in the study came from European countries. The pooled mean estimate of the MN frequency was 5.70 ‰ (95% CI, 4.29–7.56) when all 332 referent subjects were considered. While pooled-mean values were similar for males (5.94 ‰; 95% CI, 4.39–8.04) and females (5.54 ‰; 95% CI, 4.13–7.4), MN frequency was clearly associated with age. Frequency values were very low in children < 1 year of age (3.27 ‰; 95% CI, 2.22–4.82) and increased significantly thereafter, reaching the level of 7.05 ‰ (95% CI, 5.01–9.94) in the 15- to 19-year-old age group (chi-square test for trend, p < 0.001).
Discussion
The main purpose of this study was to provide reference values for researchers planning studies on genomic damage in children. We used two complementary approaches to compute summary measures of baseline MN frequency in children. Meta-analysis provided summary measures that, although affected by a certain degree of heterogeneity, are considered to be representative of studies published in the literature. Pooled analysis, although limited to six published studies (those for which individual data were available), included 229 subjects (from six laboratories) from the HUMN database (Bonassi et al. 2001) and allowed the computation of pooled estimates adjusted for age and sex.
The estimates of the baseline MN frequency in children obtained by the two approaches were by large consistent, especially considering CIs. The lower mean value estimated by meta-analysis (4.48 vs. 5.70 ‰, adjusted for age and sex), considering that mean ages in the laboratories contributing data to meta-analysis and in those considered for the pooled analysis were similar, is likely to be attributable to a different distribution of absolute values of MN frequency in the two sets of data. This is not surprising because studies based on cytogenetic biomarkers suffer from a certain degree of heterogeneity among laboratories, and absolute values may differ even largely.
The availability of reference values is important for research teams and laboratories that need to validate protocols and analytical procedures as well as to estimate the statistical power of field studies and check the quality of data. For this purpose, baseline MN frequencies for the cytokinesis block assay have been published for an adult population (Bonassi et al. 2001).
Age is the most important predictor of MN frequency, as described by cooperative studies and reported in the literature (Bolognesi et al. 1997; Bonassi et al. 2001). However, despite the high number of subjects evaluated, only differences between the extent of genetic stability in adults and in children were described. Recently, data from a review of studies conducted in children exposed to a variety of mutagens, although limited, pointed to an influence of age even in the first two decades of life (Neri et al. 2003).
In the present study, the effect of age was evident even within the restricted age range considered in pooled analysis (0–19 years). The low frequency of MN detected in children < 1 year of age is noteworthy, given the growing number of studies performed on the cord blood, an easily accessible source of DNA. MN numbers were very low at birth (3.27 ‰) and increased by 66% in children 1–4 years of age (5.43 ‰), an increase that accounted for most of the age effect on MN frequency described in the literature. Besides the major challenges posed by the leaving of the protected intrauterine environment, other changes occurring in the first years of age, such as solid diet, vaccinations, and viral diseases, provide plausible explanations for this dramatic increase.
No difference in MN frequency was found by sex in our pooled analysis. The effect of sex has been repeatedly reported in adult populations, and a recently published pooled analysis estimated a 19% higher MN frequency in females than in males (Bonassi et al. 1995, 2001). However, the influence of sex was limited (and not statistically significant) in subjects ≤40 years of age and became more pronounced in older subjects (Bonassi et al. 2001). Possible biologic explanation include a sex-related aneuploidy phenomenon and the implication of sexual hormones (Bonassi et al. 1995). Findings reported by Neri et al. (2003) failed to show any clear effect of sex on MN frequency in children.
In conclusion, these results address an increasing request from researchers performing epidemiologic studies in children based on biomarkers—specifically, the availability of specific reference values in pediatric populations. The MN baseline values provided here are meant for the planning phase of a study and should not justify the conduct of future studies among children that exclude the identification of properly selected referent (control) subjects. Besides the main study findings, we showed here the advantage of using data from large collaborative research projects to improve the design of future field studies, including the efficiency of the statistical analysis.
We thank M. Kirsch-Volders, Brussels, Belgium, for useful discussion.
The study was supported by grants funded by the Associazione Italiana per la Ricerca sul Cancro and the European Union Fifth Framework Programme (QLK4-CT-2000-00628, QLK4-CT-2002-02831, and QLK4-CT-2002-02198).
Table 1 Field studies of MN in children included in the meta-analysis.
Reference No. of referent children (total no. of subjects in study) MN (mean ± SD) Age range (years)
Shi et al. 2000 20 (68) 1.70 ± 1.83 0–10
Barale et al. 1998 136 (1,650) 2.20 ± 2.22 0–19
Zotti-Martelli et al. 1999 30 (72) 2.26 ± 2.35 15 ± 2.0a
Fellay-Reynier et al. 2000 20 (41) 2.71 ± 2.60 0–18
Vleminckx et al. 1997b 33 (220) 2.94 ± 2.46 6–15
31 (220) 4.19 ± 3.50 6–14
25 (220) 4.76 ± 5.00 6–15
Migliore et al. 1991 15 (45) 4.14 ± 1.75 1–12
Maluf and Erdtmann 2001 30 (74) 4.65 ± 2.25 0–17
Livingston et al. 1997 31 (80) 5.16 ± 2.51 4–14
Dulout et al. 1996 12 (44) 5.58 ± 5.51 8–14
Da Cruz et al. 1994 16 (276) 7.33 ± 3.88 1–18
Bilban and Vaupotic 2001 20 (105) 9.00 ± 3.80 8–12
Fenech et al. 1997 11 (116) 9.80 ± 3.32 12–15
Mikhalevich et al. 2000 10 (30) 9.92 ± 2.70 14–17
Meta-estimate 440 (3,261) 4.48 ± 0.66c 0–19
a Mean ± SD.
b Three independently selected groups of referent children were included in the study (see “Materials and Methods” for details).
c SE approximate.
Table 2 Field studies of MN in children included in the pooled analysis and crude pooled estimates.
Author No. of referents MN (mean ± SD) Range Age range (years) Males (%)
Barale (HUMN) 119 2.43 ± 2.39 0.00–10.07 9–18 56
Fellay-Reynier et al. 2000 20 2.71 ± 2.60 0.00–6.78 0–18 30
Migliore et al. 1991 15 4.14 ± 1.75 1.88–7.53 1–12 40
Maluf and Erdtmann 2001 30 4.65 ± 2.24 1.50–12.00 0–17 60
Dulout et al. 1996 12 5.58 ± 5.47 0.00–19.00 8–14 25
Garcia (HUMN) 19 5.63 ± 3.32 0.00–10.00 7–15 32
Da Cruz et al. 1994 16 7.33 ± 3.88 1.00–18.00 1–18 44
Vorobtsova (HUMN) 8 7.38 ± 3.07 2.00–12.00 9–17 38
Chang et al. (HUMN) 54 8.54 ± 7.71 1.12–49.30 0–17 57
Scarfì et al. (HUMN) 22 9.44 ± 3.89 3.68–19.19 0–18 45
Mikhalevich et al. 2000 10 9.92 ± 2.70 5.77–14.00 14–17 40
Muller et al. (HUMN) 7 10.29 ± 9.76 3.00–31.00 7–14 14
Crude pooled estimate 332 5.23 ± 5.07 0.00–49.30 0–18 49
HUMN, data from the HUMN database (Bonassi et al. 2001).
Table 3 Pooled analysis: estimated mean values of MN by sex and age group (random-effects model) and pooled estimates adjusted by sex and age.
Females
Males
Pooled
Age (years) No. Estimated mean (95% CI) No. Estimated mean (95% CI) No. Estimated mean (95% CI)
< 1 25 3.21 (2.17–4.76) 26 3.40 (2.26–5.10) 51 3.27 (2.22–4.82)
1–4 13 5.34 (3.41–8.36) 8 5.64 (3.56–8.96) 21 5.43 (3.48–8.48)
5–9 27 5.47 (3.82–7.82) 22 5.78 (4.03–8.29) 49 5.62 (3.97–7.96)
10–14 54 5.88 (4.23–8.16) 52 6.21 (4.44–8.68) 106 6.02 (4.37–8.30)
15–18 51 6.90 (4.86–9.82) 54 7.30 (5.11–10.4) 105 7.05 (5.01–9.94)
Pooled 170 5.54 (4.13–7.4) 162 5.94 (4.39–8.04) 332 5.70 (4.29–7.56)
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7571ehp0113-00123016140633ResearchMini-MonographEarly Environmental Origins of Neurodegenerative Disease in Later Life Landrigan Philip J. 1Sonawane Babasaheb 2Butler Robert N. 3Trasande Leonardo 1Callan Richard 1Droller Daniel 11 Center for Children’s Health and the Environment, Department of Community and Preventive Medicine, Mount Sinai School of Medicine, New York, New York, USA2 National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, USA3 International Longevity Center, New York, New York, USAAddress correspondence to P.J. Landrigan, Center for Children’s Health and the Environment, Department of Community and Preventive Medicine, Box 1057, One Gustave L. Levy Pl., Mount Sinai School of Medicine, New York, New York 10029 USA. Telephone: (212) 241-4804. Fax: (212) 996-0407. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 26 5 2005 113 9 1230 1233 1 9 2004 10 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Parkinson disease (PD) and Alzheimer disease (AD), the two most common neurodegenerative disorders in American adults, are of purely genetic origin in a minority of cases and appear in most instances to arise through interactions among genetic and environmental factors. In this article we hypothesize that environmental exposures in early life may be of particular etiologic importance and review evidence for the early environmental origins of neurodegeneration. For PD the first recognized environmental cause, MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine), was identified in epidemiologic studies of drug abusers. Chemicals experimentally linked to PD include the insecticide rotenone and the herbicides paraquat and maneb; interaction has been observed between paraquat and maneb. In epidemiologic studies, manganese has been linked to parkinsonism. In dementia, lead is associated with increased risk in chronically exposed workers. Exposures of children in early life to lead, polychlorinated biphenyls, and methylmercury have been followed by persistent decrements in intelligence that may presage dementia. To discover new environmental causes of AD and PD, and to characterize relevant gene–environment interactions, we recommend that a large, prospective genetic and epidemiologic study be undertaken that will follow thousands of children from conception (or before) to old age. Additional approaches to etiologic discovery include establishing incidence registries for AD and PD, conducting targeted investigations in high-risk populations, and improving testing of the potential neurologic toxicity of chemicals.
Alzheimer diseasemanebmanganeseNational Children’s Studyneurodegenerative diseaseparaquatParkinson diseasepesticides
==== Body
Parkinson disease (PD) and Alzheimer disease (AD) are the two most common neurodegenerative diseases of the older American population. PD affects more than 500,000 Americans (National Institute of Neurological Disorders and Stroke 2004; Siderowf and Stern 2003). About 50,000 new cases are reported each year, and in recent years the annual number of deaths from PD has increased steadily (Lilienfeld et al. 1990). Internationally, the incidence rate for PD approximates 17 per 100,000 per year, although this is probably an underestimate (Twelves et al. 2003). AD has been diagnosed in an estimated 2.3 million persons in the United States, and there are approximately 360,000 newly diagnosed cases each year (Brookmeyer et al. 1998). It is estimated that by 2050, as the U.S. population continues to age, as many as 14 million Americans may have AD (Lewin Group 2001).
Causation of both PD and AD is complex. In a minority of cases, particularly in early onset AD and PD, etiology appears to be primarily genetic (Tanner et al. 1999). But in most cases, causation appears to involve interactions among multiple genetic and environmental factors (Foster 2002; Kennedy et al. 2003). We hypothesize that exposure of the developing brain to still undefined toxic environmental agents during windows of vulnerability in early life—in utero and in early postnatal life—may be an important contributor to causation.
Here we provide an overview of the emerging body of evidence on the environmental origins of neurodegenerative disease. We focus especially on environmental exposures that occur early in life during windows of developmental vulnerability. We offer recommendations for future research. This report and its recommendations are based on the conference “Early Environmental Origins of Neurodegenerative Disease in Later Life: Research and Risk Assessment” sponsored by the Mount Sinai Center for Children’s Health and the Environment. The conference was held in New York City on 16 May 2003.
The Pathology of PD and AD
PD presents clinically as a disorder of motor function characterized by tremor, slow and decreased movement (bradykinesia), muscular rigidity, poor balance, and problems in gait (Parkinson’s Disease Foundation 2004). Pathologically, PD patients show loss of dopaminergic neurons in the substantia nigra (SN) pars compacta and frequently have Lewy bodies, eosinophilic intracellular inclusions composed of amyloid-like fibers and α-synuclein (Dawson and Dawson 2003).
AD is characterized by a deterioration of cortical neurons, resulting in dementia. The two typical histopathologic features are a) plaques, which are clumps of insoluble β-amyloid protein fragments that accumulate extracellularly, and b) intracellular neurofibrillary tangles composed of altered tau protein (Alzheimer’s Association 2003).
Costs of Neurogenerative Disease
A 1997 economic study prepared for the Parkinson’s Disease Foundation estimated the annual cost of treatment per patient to be approximately $24,000 (John C. Robbins Associates 1997). The estimated total annual costs of treating PD in the United States range from $12 to 25 billion. These costs are spread across families, benefit providers, social security, Medicare, and Medicaid. In addition to the financial costs, there are the human costs of pain and suffering, sadness and despair, and reduction in overall quality of life.
Combined Medicare and Medicaid spending on AD amounted to more than $50 billion in 2000 and is anticipated to increase to nearly $83 billion by 2010 (Lewin Group 2001). Preliminary statistics from 2001—the most recent year for which these data are available—from the Centers for Disease Control and Prevention (CDC) list AD as the eighth leading cause of death in the United States, responsible for 62,000 deaths annually (CDC 2003a).
PD and AD may co-occur and may share some etiologic or predisposing factors. Elderly patients who develop rapidly progressive PD may be at up to 8 times increased risk of developing AD (Wilson et al. 2003). Although the risk of developing AD and PD increases with age, neither of these diseases nor the symptoms of dementia are part of normal aging. In the absence of disease, the human brain can function well into the tenth decade [National Institute on Aging (NIA) 2000].
The Barker Hypothesis
Through detailed reconstructions of neonatal and medical histories of birth cohorts in the United Kingdom, David Barker of the University of Southampton proposed what is now termed “the Barker hypothesis” (Osmond and Barker 2000), the concept that parameters of fetal, infant, and childhood growth may be predictors of disease in later life. Barker found that infants with low birth weight, small head circumference, and low ponderal index at birth are at increased risk of developing coronary heart disease, hypertension, stroke, insulin resistance, and diabetes as adults. He found also that reduced fetal growth and impaired development during infancy were associated with increased mortality from cardiovascular disease (CVD) in both men and women, independent of social class and other confounders such as smoking, alcohol consumption, and obesity (Barker et al. 1993; Osmond et al. 1993). This association is strong and graded, is observed in various populations, and is specific to CVD. In Barker’s studies, low birth weight followed by obesity in later life led to a particularly high risk of CVD and insulin resistance. Further analysis indicated that hypertension may begin in utero and become magnified with age (Law et al. 1993).
Barker hypothesized that fetal undernutrition during critical periods of vulnerability in early development leads to persistent changes in hormone levels and in altered tissue sensitivity to these hormones, permanently altering the metabolism and body structure (Hinchliffe et al. 1992; Lumbers et al. 2001).
The Expanded Barker Hypothesis
At the 2003 Mount Sinai Conference on Early Environmental Origins of Neurological Degeneration, we explored the plausibility of extending the Barker hypothesis to encompass brain development and to explore the impacts of toxic chemicals on brain development.
Conferees generally supported the hypothesis that early exposures to environmental toxicants could later affect the brain and that such associations are biologically plausible (De la Fuente-Fernandez and Calne 2002). This consensus was based on experimental studies of associations between early-life exposures to pesticides and PD (Thiruchelvam et al. 2000a, 2000b), as well as on epidemiologic studies of the toxic and apparently irreversible effects on the developing brain of in utero exposures to lead, methylmercury, and polychlorinated biphenyls (Grandjean et al. 1997; Jacobson et al. 1990; Needleman et al. 1990). A mechanistic hypothesis proposed (Langston et al. 1999) that early exposures to neurotoxic chemicals reduce the number of neurons in critical areas of the brain such as the SN to levels below those needed to sustain function in the face of the neuronal attrition associated with advancing age (Figure 1).
Evidence for the Environmental Origins of Parkinson Disease
Twin studies.
A large-scale study of twins designed to assess genetic versus environmental factors in the etiology of PD found a high degree of concordance within twin pairs for early-onset PD (onset before age 50) but much less concordance for disease of late onset (Tanner et al. 1999). This finding suggests that early onset PD may be of genetic origin in most cases (although the etiologic role of a shared environment can never be completely excluded), whereas beyond 50 years of age environmental factors become increasingly important (Tanner et al. 1999).
MPTP and PD.
Several clinical and epidemiologic studies have demonstrated that exposures to certain synthetic chemicals are associated with increased incidence of PD. The first of these studies was the description in 1982 of severe Parkinson-like symptoms among a group of drug users in northern California who had taken synthetic heroin contaminated with MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine; Langston et al. 1999). This episode strongly supported the concept that exogenous chemicals can cause or contribute to causation of PD (Priyadarshi et al. 2001). MPTP was subsequently shown to act selectively—specifically injuring dopaminergic neurons in the nigrostriatal system in humans as well as in experimental animals (Langston et al. 1999). Evidence also was found for ongoing dopaminergic nerve cell loss without Lewy body formation in these patients. This suggested a self-perpetuating process of neurodegeneration. Years later, consistent with that hypothesis, postmortem examination of persons who had been exposed to MPTP showed a marked microglial proliferation in the SN pars compacta (Orr et al. 2002). In some patients, MPTP-induced PD appeared almost immediately after exposure, whereas in others, onset became evident only months or years later, apparently reflecting progressive injury against a background of declining physiologic reserve.
Paraquat and PD.
An etiologic link has been suggested between PD and the herbicide paraquat (1,1′-dimethyl-4,4′-bipyridinium; Brooks et al. 1999; McCormack et al. 2002). Paraquat is structurally similar to MPP+, the active metabolite of MPTP. Epidemiologic data suggest a positive dose–response relationship between lifetime cumulative exposure to paraquat and risk of PD (Liou et al. 1997). In experimental studies in which paraquat has been administered to animals, researchers have observed loss of SN dopaminergic neurons, depletion of dopamine in the SN, reduced ambulatory activity, and apoptotic cell death (Liu et al. 2003).
Maneb and PD.
Exposure to the dithiocarbamate fungicide maneb has been reported to enhance uptake of MPTP and to amplify its neurotoxicity; both paraquat and maneb target brain dopamine. In animal studies, early-life exposure to a combination of paraquat and maneb produced destructive effects on the nigrostriatal dopaminergic system and abnormalities in motor response that were more severe than those produced by either agent alone. These effects were amplified by aging (McCormack et al. 2002; Thiruchelvam et al. 2000a, 2000b).
Rotenone and PD.
The insecticide rotenone induces clinical and pathologic features in rats similar to those induced by PD, including selective degeneration of the nigrostriatal dopaminergic system and movement disorders (Liu et al. 2003; Sherer et al. 2003). Synergistic effects have been observed in animals administered a combination of rotenone and lipopolysacchide, a molecule that stimulates inflammation (Gao et al. 2003; Thiruchelvam et al. 2000b).
Manganese and PD.
Although manganese is an essential trace element, chronic occupational exposure to high levels of this metal causes accumulation in the basal ganglia, resulting in manganism, a condition characterized by tremors, rigidity and psychosis (Mergler and Baldwin 1997). This condition has been reported in manganese miners. Concern exists that widespread introduction of the manganese-containing fuel additive MMT (methyl-cyclopentadienyl manganese tricarbonyl) to the U.S. gasoline supply may increase population exposure to manganese and thus increase risk of parkinsonism in sensitive populations (Needleman and Landrigan 1996).
Other chemicals and PD.
Exposures to pesticides and other organic compounds are widespread in the American population (CDC 2003b). Levels of organochlorines have been found to be elevated in the brains of persons with PD (Fleming et al. 1994). A study of French elderly individuals found an association between past occupational exposure to pesticides, low cognitive performance, and increased risk of developing AD or PD (Baldi et al. 2003). Other reported links between environmental factors and PD include increased risks from drinking well water, rural living, farming, and exposure to agricultural chemicals (Liou et al. 1997; Priyadarshi et al. 2001).
Epidemiologic studies have shown inverse, apparently protective relationships between cigarette smoking, coffee consumption, and PD (Hernan et al. 2002).
Inflammation and PD.
Inflammation of the brain in early life caused by exposure to infectious agents, toxicants, or environmental factors has been suggested as a possible cause or contributor to the later development of PD (Liu et al. 2003). The inflammatory process in such cases may involve activation of brain immune cells (microglia and astrocytes), which release inflammatory and neurotoxic factors that in turn produce neurodegeneration (Liu and Hong 2003). This concept first arose in the suggestion that infection with influenza virus in the pandemic of 1918 produced an increased risk of PD. More recently, infection with certain microorganisms such as the soil bacterium Nocardia asteroides has been proposed as a risk factor for PD (Kohbata and Beaman 1991). In animal experiments, exposure to bacterial endotoxin lipopolysaccharide in utero induced dopaminergic neurodegeneration (Gao et al. 2002; Liu et al. 2000, 2003).
Isolated populations of high risk for PD.
PD incidence and mortality rates differ among ethnic groups and exhibit strong regional variation, thus providing additional evidence that environmental factors may be involved in causation (Ben-Shlomo 1997; Foster 2002).
For example, the Chamorros population of Guam and Rota in the western Pacific have an unusually high prevalence of motor neuron disease, a syndrome that includes amyotrophic lateral sclerosis (ALS), parkinsonism, and progressive dementia. It has been proposed that this syndrome of parkinsonian dementia is related to the consumption of flour made from cycad seeds (Spencer 2003) or to inhalation of pollen from cycad plants (Seawright et al. 1995). Consumption of cycad flour may have been especially common on Guam in the famine years before and during World War II. The declining incidence and increasing age at onset of ALS and parkinsonism–dementia complex among the Chamorros over the past 50 years together with the decreasing prevalence of ALS over the same time in high-incidence areas of Japan and Indonesia suggests the disappearance of an environmental factor unique to these population groups (Kurland and Mulder 1954; Plato et al. 2003).
Evidence for the Environmental Origins of Dementia
Lead and cognitive function.
Childhood exposure to lead, even at relatively low levels (Canfield et al. 2003), results in a decline of cognitive function that persists into adulthood and that manifests as a persistent lowering of IQ score plus alteration in behavior (Needleman et al. 1990). Each increase of 10 μg/dL in the lifetime average blood lead concentration was found to be associated with a 4.6-point decrease in IQ (Schwartz et al. 2000). There appears to be no minimum threshold level below which lead does not cause brain injury (Canfield et al. 2003). In addition, elevated lead levels in childhood have been associated with lower class standing in high school, lower vocabulary and grammatical-reasoning scores, poorer hand–eye coordination, and self-reports of minor delinquent activity (Needleman et al. 1990).
Occupational exposure to lead among adults is associated with poorer neurobehavioral test scores and with deficits in manual dexterity, executive ability, verbal intelligence, and verbal memory (Schwartz et al. 2000). Recent data suggest that cognitive function can decline progressively in older lead workers in relation to cumulative past occupational exposure to lead (Stewart et al. 1999). Susceptibility to the persistent effect of lead on the central nervous system may be enhanced in persons who have at least one apolipoprotein E-4 allele (Stewart et al. 2002).
Recommendations
The conferees agreed on recommendations for future research into the environmental etiology of chronic neurodegenerative disease.
Conduct long-term prospective epidemiologic and genetic studies of the impact of environmental factors on the development of neurodegeneration.
Most previous research on the causation of the neurodegenerative disorders has been either cross-sectional or retrospective in design and thus has been extremely limited in its ability to discern environmental etiologic factors that may have been encountered in early life. Most previous studies have had to reconstruct past exposures from imperfect memory, from incomplete records, or from biologic markers of uncertain half-life. The conferees offered the suggestion that a large prospective cohort study would provide a most powerful tool to explore possible early environmental causes of neurodegenerative disease. If such a study were to include genetic analyses, it would provide a unique means for exploring the gene–environment interactions that likely are involved in the genesis of PD and AD. Ideally such a study should enroll subjects at or even before conception and follow them through old age and should incorporate numerous biologic makers of exposure as well as detailed evaluations of behavioral and lifestyle factors, including information on occupational exposures and pesticide use. Such a prospective design would permit the real-time assessment of exposures as they occur and avoid the need for retrospective re-creation of past exposures. These features are now incorporated into the proposed National Children’s Study.
Four factors that make this a propitious time to launch a massive prospective epidemiologic study of the impact of the environment on health and development, such as the National Children’s Study, are a) the development of better skills in conducting and analyzing data from large prospective studies; b) the refinement of highly sensitive, extremely accurate chemical analyses that permit detection and quantification of xenobiotics in body fluids even at very low levels; c) advances in information technology; and d) capacity for rapid, relatively inexpensive genetic analysis (Berkowitz et al. 2001).
Establish registries for Parkinson and Alzheimer patients.
Current data sources that rely principally on mortality statistics likely undercount the number of persons with neurodegenerative diseases. It is important to foster collaborations among agencies and to create new links across databases in different regions of the country to better track incidence rates of these disorders.
Pursue suspected links between environmental exposures and neurobehavioral disorders in unique, high-risk populations.
Targeted studies of persons with unique patterns of disease such as the residents of Guam (Kurland and Mulder 1954) or persons with unusual environmental exposures such as those exposed to MPTP (Langston et al. 1999) demonstrate the value of undertaking clinical and epidemiologic pursuit of disease clusters.
Improve toxicity test methods to better assess chronic neurodegeneration (Slotkin 2004).
Too few chemicals are tested for chronic neurotoxicity, and those that are examined are typically studied under test protocols in which the chemicals are administered during adolescence and the animals sacrificed and studied 12–24 months later. Functional assessment of neurologic function is often not included. This approach misses the opportunity to study possible late effects of early exposures. To overcome these limitations in design, conferees recommended that the duration of toxicity testing protocols should be extended to incorporate administration of chemicals in early life ideally in utero or even before conception, coupled with lifelong follow-up. Such expanded protocols may also incorporate functional neurobehavioral test batteries as well as neuropathologic examinations of relevant areas of the brain (Landrigan et al. 2003).
This article is part of the mini-monograph “Early Environmental Origins of Neurodegenerative Disease in Later Life: Research and Risk Assessment.”
We express our sincere thanks to L. Boni of the Center for Children’s Health and the Environment, Department of Community and Preventive Medicine, Mount Sinai School of Medicine, New York, New York.
The views expressed in this article are the opinions of the authors and do not represent endorsement or policy of their affiliated institutions or the U.S. Environmental Protection Agency.
The conference was co-sponsored by the U.S. Environmental Protection Agency (U.S. EPA CR X-83043201-0), the National Institute of Environmental Health Sciences (NIEHS 273-MH-310208), the Beldon Fund, the Baumann Family Foundation and the Bachmann-Strauss Dystonia and Parkinson Foundation Inc., NIEHS Superfund grant P42-ES07384, NIEHS Children’s Center (P01-ES009584), and U.S. EPA Children’s Center (RD-83171101-0).
Figure 1 Long-term consequences of early loss of critical neurons after developmental damage. DA, dopaminergic. The impact of early developmental damage is not immediately evident but produces disease years or decades later as the number of neurons decreases with advancing age.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7573ehp0113-00123416140634ResearchMini-MonographThe Role of Early Life Environmental Risk Factors in Parkinson Disease: What Is the Evidence? Logroscino Giancarlo Department of Epidemiology, Harvard School of Public Health, Boston, Massachusetts, USAAddress correspondence to G. Logroscino, Department of Epidemiology 3-819, Harvard School of Public Health, 677 Huntington Ave., Boston, MA 02115 USA. Telephone: (617) 432-2652. Fax: (617) 566-7805. E-mail:
[email protected] author declares he has no competing financial interests.
9 2005 26 5 2005 113 9 1234 1238 1 9 2004 24 3 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Parkinson disease (PD) is of unknown but presumably multifactorial etiology. Neuropathologic studies and animal models show that exposure to environmental neurotoxicants can determine progressive damage in the substantia nigra many years before the onset of clinical parkinsonism. Therefore, PD, like other neurologic diseases related to aging, may be determined by exposures present in the environment early during the life span or even during pregnancy. Recent epidemiologic studies have focused on the possible role of environmental risk factors present during adult life or aging. Smoking and coffee drinking have consistently been identified to have protective associations, whereas roles of other risk factors such as pesticide and infections have been reported in some studies but not replicated in others. Both genetic inheritance and sharing of common environment in the same family explain the increased risk of PD of relatives of PD cases compared with relatives of controls in familial aggregation studies. Much evidence indicates that risk factors that have a long latency or a slow effect could be important for late-onset PD. Further epidemiologic studies are warranted in this area.
early lifeParkinson diseasepesticidesplace of birthsmokingtoxicants
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Parkinson disease (PD) is of unknown but presumably multifactorial etiology. The main impetus for the environmental causation theory of PD came from the isolation of the chemical compound MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine) that was associated with marked parkinsonism in four young adults after using a Demerol derivative intravenously (Langston et al. 1984).
Toxic substances can cause static or progressive damage. The distinction between these two pathogenetic mechanisms is relevant to understanding whether early environmental risk factors play a role in the pathogenesis of PD. MPTP produces both acute and progressive cellular loss in the substantia nigra. MPTP exposure at one point in time can be responsible for a progressive clinical syndrome with an intervening latent period.
The neuropathologic study of the brains of three MPTP-exposed subjects showed microglial activation with an ongoing inflammatory reaction 3–6 years after the toxic exposure (Langston et al. 1999). In each case neuropathologic examination of the brain revealed depletion of pigmented neurons in the substantia nigra but without Lewy bodies, the pathologic hallmark of PD. These findings indicate an active, ongoing neuronal loss, suggesting that an event happening at a single point in time may determine a slowly progressive neurodegenerative process in the substantia nigra (Langston et al. 1999). In monkeys exposed to MPTP 5–14 years before death and free from MPTP exposure for 3 years before death, biopsy data showed reactive microglia and extracellular melanin (McGeer et al. 2003). The results of these two studies demonstrate that toxic exposure many years before the onset of clinical parkinsonism can result in a progressive neuronal loss and a permanent inflammatory reaction in the brain of patients with PD. Consistent with this model, a brain injury could produce a progressive loss of dopaminergic (DA) cells prenatally or early in life. Subsequently, a second toxic insult or normal aging could determine a selective neuro-degenerative process in the substantia nigra (Langston 1990).
Even if sporadic PD is a neurodegenerative disorder that characteristically begins after 50 years of age, the possibility that it could be determined by the early life environment has been proposed in the past (Martyn and Osmond 1995). In this review I focus on published evidence from both experimental and epidemiologic studies of a possible role of early life environmental risk factors for PD.
Infections in Early Life
Inflammation has been proposed as one of the possible pathogenetic mechanisms for PD (McGeer et al. 2001). The proinflammatory cytokine tumor necrosis factor-α (TNF-α) kills DA neurons in vitro and is elevated in the brains of patients with PD (Mogi et al. 1994). Intrauterine infection might lead to nigral cell loss in the fetal brain and a subsequent decrease in DA neurons in the substantia nigra secondary to inflammation. Bacterial vaginosis during pregnancy is characterized by the presence of lipopolysaccharide (LPS), a bacteriotoxin that is an inducer of TNF-α (Thorsen et al. 1998). These compounds are increased in the chorioamniotic environment of infected women and could interfere with the normal development of DA neurons. Ling et al. (2002) injected gravid female rats intraperitoneally with LPS, and the newborn rats were killed 3 weeks after birth. The pre-natal exposure to LPS produced lesions that were specific to DA cells. DA cells were counted in the mesencephalon using tyrosine hydroxylase–immunoreactive (THir) cells as a DA neuron marker, and a significant reduction of cell number was found (27%). The LPS treatment reduced the THir cells, but not microtubule-associated protein-2 (MAP-2), a marker of all neurons. The specificity of the lesions and the timing of the inflammatory response to LPS injection in the fetal brain suggest that LPS might interfere with normal DA neuron development.
These results outline the possibility that, after intrauterine infection, some individuals may be born with limited striatal neurochemical reserves and a reduced DA cell count. This reduced reserve might predispose adults to PD. Perinatal infections may therefore represent risk factors for PD. Most epidemiologic studies on infection during early life and risk for PD were conducted in England. A population-based study was conducted in 42 general practices in eastern Hertfordshire (Martyn and Osmond 1995). PD cases were subjects born after 1910 and diagnosed by a neurologist or a geriatrician, and controls were subjects in the same general practices matched by age and sex (172 cases, 343 controls). Records in the first part of the 20th century were used to obtain information about birth weight and growth during the first year of life. The subjects answered a questionnaire about sibship, position in the birth order, type of housing, other features of the environment in early life, and experience of the common infections of childhood. Subjects were more likely to have suffered croup [odds ratio (OR) = 4.1; 95% confidence interval (CI), 1.1–16.1] or diphtheria (OR = 2.3; 95% CI, 1.1–3.6). The results of this study do not suggest that poor growth in fetal life or infancy is important in the etiology of PD but do suggest that early infection might have a role in susceptibility to PD. A study conducted in a Harvard alumni cohort showed a negative association between measles infection and PD (Sasco and Paffenbarger 1985), and a reduced risk of PD was associated with most childhood viral infections. The authors speculate that a negative history of measles could be a marker for negative influenza history before 1918 and thus a higher risk of infection during the 1918 influenza epidemic because of the lack of partial influenza immunity. The data on infections in these studies were based on recall and therefore should be interpreted with caution (although a negative association in the Harvard study is not what would be expected in presence of recall bias).
Some investigators have examined the relationship between influenza pandemics and PD, testing the hypothesis that PD could follow intrauterine influenza (Mattock et al. 1988). Three groups of PD patients were identified through specialty clinics or general practitioners. Matched controls were selected so that they were born in the same 5-year period as the index patient. For each group and each calendar year, the ratio of parkinsonian to control births was calculated. This risk ratio was used to estimate the relative risk of each cohort developing PD in later life. Calendar years with a PD:control ratio of 1.66 or greater were defined as “cluster years.” Subjects born in 1892, 1904, 1909, 1918, 1919, and 1929 (years encompassing the influenza pandemics of 1890–1930) appeared to have had an increased risk of developing idiopathic PD compared with subjects born in other periods, and four of these years coincide with years of influenza pandemic. The PD:control birth ratio showed a significant correlation with the crude influenza mortality for the year of birth during the period from 1900 to 1930 (Mattock et al. 1988). People born around 1900 had a risk of PD 5 times that of people born around 1930 (Martyn 1997). A study conducted in Aberdeen, Scotland, failed to replicate these results (Ebmeier et al. 1989). A cohort of 243 subjects with PD resident in Aberdeen confirmed a peak of PD births in the year 1902, but there was no association between PD births and influenza deaths. The limited power of this cohort could be an explanation for the negative results. The strong variation of PD risk from year to year of birth shown in some of the previous studies would be consistent with the action during development of an infectious agent, more likely a virus that typically occurs in epidemics.
Season or month of birth could be used as surrogate of viral insult and has been associated with disorders such as schizophrenia (Mortensen et al. 1999). Mattock et al. (1988) reviewed the season of birth of 517 individuals with PD. They showed an excess of births in the period between March and June, with the greatest excess in May. These results were confirmed in a study in Japan on two different cohorts with an excess of PD births in winter and spring (Miura et al. 1987; Torrey et al. 2000).
All these studies were based on the hypothesis that intrauterine influenza infection or other viral insults may be cytotoxic to the developing fetal substantia nigra and could enhance the risk of PD after a latent period (Poskanzer and Schwab 1963; Takahashi and Yamada 2001).
Data on the season of birth are some of the least compelling arguments for a relation between influenza and PD because there are other potential variables that are characterized by a seasonal variation that could be responsible for the observed association. Epidemiologic studies on the relationship between infections early in life and PD are definitively inconclusive.
Place of Birth
Geographic epidemiology is defined as a description of the spatial pattern of disease occurrence (Elliot et al. 1992). Place of birth can be a surrogate for early-life exposure. State of birth in the United States was studied as an exposure of interest for PD (Betemps and Buncher 1993). Outcome was measured by death, using death certificates from U.S. death information in 1981. The measure of effect was the proportionate mortality ratio (PMR), using all the other deaths in the United States as the comparison group. Potential confounders such as age, gender, and whether the state of birth was the same as the state of death were included in a logistic model. Cerebrovascular disease deaths were used as a negative control group because there is no association that is postulated with childhood exposure; multiple sclerosis was used as a positive control because it has been found to be related to geography in childhood. The results showed a very high PMR for Utah (44.95/10,000), Idaho (37/10,000), Colorado (31/10,000), New Mexico (32/10,000), Kansas (32/10,000), and Nebraska (33/10,000), with a west to east gradient. The lowest PMR was registered in Delaware (7.3/10,0000). However, it is important to mention that PMR can be affected by the distribution of other causes of death. In contrast, a study conducted by Kurtzke and Goldberg (1988) using death certificates showed that age-adjusted death rates of PD presented a north–south gradient, similar to that found for multiple sclerosis. In this study only the state where the PD subject was resident at the time of death was considered in the analysis. Geographic studies based on death certificates may be affected by the underreporting of PD that can be inhomogeneous across different geographic areas. Studies of geographic variation need to be interpreted with caution because many factors beyond environmental exposure could determine the geographic pattern of a disease. Genetic factors and ethnicity are real sources of variation across different regions beyond environmental exposure. Chance, migration, differences in procedures used to diagnose/classify cases, and access to medical care are other possible alternative explanations (Elliot et al. 1992).
Toxic Exposure Early in Life
The distinction between secondary parkinsonism and PD is critical, particularly when toxicants are the exposure of interest. Therefore, in this context, it is important to underscore that the diagnosis of PD is clinical because no diagnostic biomarker is available. In every study on PD there is some degree of misclassification of cases. In a recent population-based study the initial diagnosis of PD was later confirmed in 83% of 202 diagnosis but rejected in 15% of the initial diagnosis (2% had diagnosis of possible PD; Schrag et al. 2002). The false-positive diagnosis is generally due to vascular parkinsonism, essential tremor, drug-induced parkinsonism, or other neurodegenerative disorders that include parkinsonism as one of the clinical features, such as multisystem atrophy, progressive sopranuclear palsy, and dementia. The use of more stringent criteria causes the rate of false positives to drop down to about 10% (Jellinger 2003). Missing the diagnosis of PD is particularly frequent. Recent door-to-door epidemiologic studies show that almost one of four prevalent cases of PD is detected during the survey in the community (de Rijk et al. 1997). The misclassification of cases if random and not related to the exposure of interest makes the detection of true association more difficult in analytic studies.
Clinical parkinsonism can be caused by a series of exogenous factors such as viruses, toxic substances such as MPTP and manganese, and trauma. These clinical observations suggest that environmental risk factors are important for the etiology of PD. Several environmental toxicants related to occupation in agriculture have been implicated as risk factors in PD. The chemical structure of 1-methyl-4-phenyl-pyridinium (MPP+), the toxic metabolite of MPTP, is similar to the herbicide paraquat (PQ; Snyder and D’Amato 1985).
Animal experiments have clearly shown the selective action of PQ on the DA system. Systemic PQ given to mice through repeated intraperitoneal injections induces selective damage of DA neurons in the pars compacta of the substantia nigra (McCormack et al. 2002). The cell loss in this model was dependent on dose and age. The presence of a different effect of the exposure at different ages in this model outlines a possible interaction of toxic environmental exposures with normal aging (McCormack et al. 2002). Chronic, continuous, and systemic exposure to rotenone, commonly used as an insecticide in gardening, selectively inhibits complex I, causing selective degeneration of the nigrostriatal system with accumulation of cytoplasmic inclusions containing ubiquitin and α-synuclein (Betarbet et al. 2000). In mice, PQ exposure determines an up-regulation of α synuclein, accelerating the formation of aggregates in the substantia nigra (Manning-Bog et al. 2002). This confirms previous observations in vitro that postulated a selective binding of PQ with partially folded α-synuclein (Uversky et al. 2001).
Toxic exposures that occur early in development could determine long-term pathology in the central nervous system. A model exploring this hypothesis should explore the possibility that early damage to the DA system could result in cell loss or high vulnerability to a second environmental risk factor associated with PD (two-hit model). A similar model was hypothesized for exposure to PQ and the fungicide maneb (MB) during development. The combined action could permanently change the nigrostriatal DA system and enhance its vulnerability to subsequent neurotoxicant challenges. Mice exposed to two neurotoxicants, PQ and MB, and subsequently exposed again as adults presented with the most severe damage. They showed a 70% reduction in motor activity 2 weeks after the last toxic dose (Thiruchelvam et al. 2002). Developmental exposure to PQ or MB alone produced minimal changes, whereas a significant decrease in nigral cell counts was observed after adult exposure. Striatal DA levels were reduced by 37% after only the developmental exposure to PQ plus MB, but after a second exposure during adulthood, striatal DA levels were reduced by 62%. This experiment suggests that the exposure during development to the two toxicants produced a state of masked toxicity that was revealed after adult re-exposure. In another experiment conducted by the same group, pregnant mice were treated with MB or PQ (Barlow et al. 2004). Offspring were normal when evaluated on locomotor activity. Subsequently offspring were treated with MB or PQ. Only males treated with MB during the prenatal period and with PQ during adulthood showed a significant reduction of locomotor activity (95%) and a selective DA loss in the pars compact of the substantia nigra. Exposure to specific pesticides during the prenatal period can produce a limited but selective amount of lesions in the nigrostriatal system. These developmental lesions might determine an enhanced susceptibility that, in combination with later exposure to other toxicants (PQ), may be involved in the induction of PD during aging.
The results of experimental studies on the role of pesticides are concordant with the results of epidemiologic studies. A series of epidemiologic studies have pointed out the importance of pesticides, in particular insecticides and fungicides. The problem with these studies is that they were mainly case–control, with a limited number of cases and without biologic measurement of the exposure. Most of the studies were based on retrospective collection of data with questionnaires (Checkoway and Nelson 1999). The only prospective study is the cohort study of Japanese men in Hawaii, originally established for the investigation of risk factors: 7,986 Japanese–American men born between 1900 and 1919 were enrolled in the longitudinal Honolulu Heart Program, with a median length of follow-up of 27 years. The relative risk of PD was 1.9 (95% CI, 1.0–3.5) for men who had worked more than 20 years on a plantation compared with men who never worked in a plantation (sugarcane and/or pineapple). The age-adjusted incidence of PD was higher in men exposed to pesticides than in men who never did plantation work (Petrovitch et al. 2002).
A recent meta-analysis on environmental risk factor and PD was performed using a large number of studies (16 studies for living in rural area, 18 studies for well water drinking, 11 studies for farming and 14 studies for pesticides) (Priyadarshi et al. 2001). Restricting the analysis to studies in the United States, the combined OR was 1.44 (95% CI, 0.92–2.24) for well water; 2.17 (95% CI, 1.54–3.06) for rural residence; 1.72 (95% CI, 1.2–2.46) for the combined exposure of living on a farm, farming, and exposure to animals; and 2.26 (95% CI, 1.95–2.39) for exposure to pesticides. The highest change in risk was for subjects living in rural areas for more than 40 years (OR = 4.9; 95% CI, 1.4–18.2). This might indicate that a cumulative exposure over time is needed but may also suggest that exposure during early life is important. In a study conducted in Saskatchewan, Canada, 20 of 22 cases of early-onset PD had exclusively rural exposure and drank well water during the first 15 years of life (Rajput et al. 1987). Well water consumed in childhood could be considered a potential vehicle for a toxic exposure. Analysis of pesticides and of metals in the well water did not reveal the role of any specific toxicant in this Canadian study.
Neurotoxicants such as MB, rotenone, and PQ interact with mitochondrial complex I (Dawson and Dawson 2003). Reduced activity of complex I has been described consistently in PD subjects (Schapira et al. 1998). A possible interaction between environmental risk factor and genetic polymorphism in mitochondrial genes and in genes involved in detoxification of metabolites has been intensively searched, with inconsistent results (Tan et al. 2000). The aggregation of α-synuclein and the subsequent inhibition of proteasome could be the consequence of complex I reduction in activity and may determine the death of DA neurons. The protection of proteasome function is one of the hypothesized roles of parkin (Feany and La Spada 2003).
Heterozygote mutations in the Parkin gene have been associated with late-onset PD (Oliveira et al. 2003). These parkin heterozygote mutations or other mutations could act as susceptibility genes in PD in conjunction with environmental risk factors, including neurotoxicants. Combined treatment with the two neurotoxicants PQ and Mn2+-ethylenebis-dithiocarbamate shows enhanced toxicity only in mice expressing human α-synuclein (Thiruchelvam et al. 2004). Only a mutant strain (hm2–α-Syn-39) presented enhanced vulnerability to neurotoxicants. This specific gene–environment interaction could be the source of different responses to neurotoxicants in different mice strains and in humans as well.
In conclusion, epidemiologic data suggest that the use of pesticides or surrogates of pesticide exposure increase the risk of PD. However, no epidemiologic studies specifically address the issue of toxic exposure early in life.
Coffee, Smoking, and Personality Trait
In a recent meta-analysis, Hernan et al. (2002) analyzed 44 case–control studies and 4 cohort studies on smoking and 8 case–control studies and 5 cohort studies on coffee drinking. The risk of PD was 60% lower among smokers than among nonsmokers and about 30% lower in coffee drinkers than in non-coffee drinkers. Smoking and coffee are complex exposures, but the primary candidate substances for the protective effects are nicotine and caffeine, respectively. There is some evidence to support a protective effect of caffeine on the DA system. Mice treated with caffeine before exposure to MPTP had reduced DA cell loss compared with mice unexposed to caffeine (Chen et al. 2001). The action of caffeine seems to be mediated through a blocking action on adenosine-2 receptors (Schwarzschild et al. 2002). Similarly, in animal models, nicotine increases the presence of factors that have a neuroprotective effect on DA neurons (Maggio et al. 1998). Nicotine is an antioxidant (Ferger et al. 1998) and is an inhibitor of monoamine oxidase B that activates MPTP neuorotoxicity (Yong and Perry 1986). Nevertheless, a different biologic basis for these findings cannot be excluded.
A premorbid personality in PD patients has been described in several studies (Paulson and Dadmehr 1991; Todes and Lees 1985). Smoking and coffee drinking may be surrogates of an underlying personality trait or behavior and be related to a characteristic pre-parkinsonian personality (Marder and Logroscino 2002). Smoking and coffee consumption generally begin in early adulthood. Subjects who later develop PD may be less likely to have experiences that are associated with novelty or that are socially inappropriate (Bharucha et al. 1986; Ward et al. 1984). In a case–control study conducted with data from the Rochester Epidemiology Project, inverse associations were reported between PD and tobacco chewing, snuff use, and alcoholism (Benedetti et al. 2000). These exposures may represent patterns of unusual behavior and may depend on the presence of a specific personality trait.
Personality traits are established quite early in life, and it is known that specific personality traits are genetically related to the DA system (Ebstein et al. 1996). Twin studies are useful for trying to disentangle environmental and genetic risk factors. In twins discordant for PD, the twins who did not develop PD smoked more than their twin (Tanner et al. 2002). Interestingly, this effect was more pronounced in monozygotic twins. The effect remained even when the analysis was conducted 10 years before the disease onset. This study suggests a true biologic effect of smoking, but a further effect of other traits that are present even 20 years before the disease onset cannot be excluded. In a nested case–control study from Kaiser Permanente on 626 subjects and 1,245 controls, Van Den Eeden et al. (2002) classified subjects according to their personality traits. The protective effect of smoking was present even when restricting the analysis to individuals who scored high for introversion and depression. Among these subjects, smoking reduced the risk of PD by 50%.
In the EUROPARKINSON Collaborative Study, the protective effect of smoking was present only in early-onset PD (onset before age 50) but not in late-onset PD (Tzourio et al. 1997) Similar findings had been previously reported in the Northern Manhattan Aging Study in New York (Mayeux et al. 1994).
None of the previous studies looked at the age when subjects began to smoke. In data from the Nurses Health Study and the Health Professional Study, subjects who started to smoke early (before age 20) had a stronger reduction in risk, independent of future smoking behavior (A. Ascherio, personal communication). This observation suggests that if smoking is not causal, the other possible cause should already be present in the causal pathway before age 20.
Again, many of these studies suggest that the analysis of behavior in early adulthood could be key to disentangling the role of smoking, drinking coffee, and personality traits on the occurrence of PD.
Familial Aggregation Studies
In early-onset PD, the mode of inheritance is autosomal dominant or recessive (Golbe et al. 1990; Kitada et al. 1998). The role of genes and the type of transmission are less clear for late-onset PD. It is well known that subjects with a family history of PD have an increased risk of disease (Lazzarini et al. 1994; Marder et al. 1996; Payami et al. 1994). Only a few of these studies have been population based, and none had complete genealogic information. The population-based design is important to avoid ascertainment bias because relatives of subjects with the disease are more likely to seek medical attention, and therefore families with more than one family member are more likely to be included. A recent study from Iceland coupled the genealogic information with a population-based study design (Sveinbjornsdottir et al. 2000). In Iceland a computerized database contains genealogic information for 11 centuries, including information on all the living Icelanders. PD cases (772 patients) were identified from several sources; 560 had late-onset PD. The risk ratio for PD was 6.7 for siblings, 3.2 for offspring, and 2.7 for nephews and nieces of patients with late-onset PD. The authors concluded that the pattern of inheritance was consistent with both genetic and environmental factors. In a subsequent comment on this study, it was noticed that the risk ratio for siblings and offspring was different, whereas the offspring and nieces and nephews had similar risk (de la Fuente-Fernandez and Calne 2001). This pattern of inheritance was not consistent with simple vertical or genetic clustering. These data might be explained by the presence of two birth cohorts (one for siblings and one for offspring and nephews and nieces) that had similar risk because of similar exposure in the same period of their life to some environmental risk factor. The results of the Icelandic study support the idea that environmental risk factors acting at different points in time during the life span (including early life) are critical for the development of PD.
There is evidence that some reported familial cases may be due to environmental factors. The age pattern within families with multiple cases can help to determine if there is a substantial involvement of environmental risk factors over a specific period of time (Calne et al. 1987). If an environmental exposure is present, a similar time of onset should be present in cases of the same family. If a genetic effect is the main causal factor, then a similar age of onset in different generations or a pattern of age of onset is expected. In six Canadian families with multiple cases, the time interval between onset of symptoms within the same family was relatively short (1–8 years), whereas the mean difference in age of onset of PD in children and parents in this series was 25.2 years. Also, no correlation was found between degree of consanguinity and occurrence of disease. This report underscores the importance of environmental risk factors in some of the familial PD cases.
In a case–control study conducted in Spain with 299 PD patients and 295 controls, the age of onset of PD was found to correlate with maternal age at birth of the patient, but not with paternal age. Both mothers and fathers had increased risk of PD compared with parents of controls (de la Fuente-Fernandez 2000). The author hypothesizes that environmental exposure may cause mitochondrial change in the mother that can then be transmitted to the child through the ovum. In this study, it is particularly interesting that the negative correlation of PD age of onset with maternal age was present also in PD probands with an affected father. This study suggests that environmental factors operating before birth may have a role in PD, even if this does not exclude the role of a genetic predisposition.
Conclusions
There is experimental evidence that PD and other neurologic diseases related to aging may have their origin during pregnancy or in early life, including young adulthood. The epidemiologic studies exploring this hypothesis are few, and the most were conducted many years ago. Similar to that for cancer and cardiovascular disease, the etiologic model for PD has explored mainly risk factors associated with adult life or in many cases after the age of 65 (Kuh and Ben-Shlomo 1997). Cohort studies exploring neurologic disease related to aging, with few exceptions, are based on cohorts of subjects 65 or more years of age. The limitations of the case–control approach when exploring questions on exposures that happened decades before disease onset are well known.
The role of aging in the pathogenesis of PD is not clear, but recent evidence suggests that it is less important than was thought in the past. In a recent neuropathologic study there was no significant loss of tyrosine hydroxylase positive neurons in the substantia nigra in normal subjects (44– 80 years of age). This result suggests that normal aging is not accompanied by significant catecholaminergic cell loss (Kubis et al. 2000). If these data are confirmed, it is conceivable that risk factors that have a long latency or a slow effect could be important for late-onset PD, independent from a specific effect of aging. PD could be “programmed” in utero according to the Barker hypothesis for chronic diseases (Kuh and Ben-Shlomo 1997), and aging may be only a period where there is a second hit or an interaction with other processes. The risk factors in early life could determine an increased risk for PD, an earlier age of onset, or a different clinical course. Testing these pathogenetic models should be one of the future focuses of neuroepidemiologic research. To investigate the role of neurodevelopment on neurodegeneration, investigators must develop new methods to characterize the early-life environment of the elderly. The alternative strategy is to build up new cohorts or use existing cohorts that examine the whole life span, as in the Barker approach to study cardiovascular diseases.
This article is part of the mini-monograph “Early Environmental Origins of Neurodegenerative Disease in Later Life: Research and Risk Assessment.”
I thank K. Marder and D. Hesdorffer for helpful comments and R. Chaput for help in the preparation of the manuscript.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7572ehp0113-00123916140635ResearchMini-MonographFetal Environment and Schizophrenia Opler Mark G. A. 1Susser Ezra S. 231 Department of Psychiatry, Columbia University, New York, New York, USA2 Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, New York, USA3 New York State Psychiatric Institute, New York, New York, USAAddress correspondence to M. Opler, Department of Epidemiology, Columbia University, 722 W. 168th St., New York, NY 10032 USA. Telephone: (646) 234-3607. Fax: (212) 305-9413. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 26 5 2005 113 9 1239 1242 1 9 2004 12 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Schizophrenia and related disorders are adult-onset illnesses with no definitively established risk factors. Several studies report that exposures to infection and nutritional deprivation during early development may elevate the risk of later developing schizophrenia, specifically during the prenatal period. Preliminary evidence implicates lead exposure as well, suggesting that chemical exposures during early development may constitute a new class of risk factors for schizophrenia that has not been adequately investigated. Exposure to lead is given as an example of a chemical agent for which some effects have been described throughout the life course on both general neurodevelopmental outcomes and now on a specific psychiatric diagnosis. Findings from prospectively collected birth cohorts are offered as examples of both innovations in methodology and opportunities for future generations of investigators.
developmentalleadPbprenatalprospectivepsychosisschizophrenia
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Schizophrenia and schizophrenia spectrum disorders (SSDs) are mental illnesses of unknown etiology, typically diagnosed in adolescence or adulthood. With no known cure, these diseases are frequently associated with long-term disability and staggering social and economic costs. Over the past 20 years, researchers have theorized that exposures that elevate the risk of later developing schizophrenia may occur during the prenatal period. A current version of the “neurodevelopmental hypothesis” of schizophrenia states that gene–environment interactions alter the structure and function of the developing brain, contributing to the onset of schizophrenia later in life (Murray and Lewis 1987; Waddington and Youssef 1987; Weinberger 1987). Although this working hypothesis is now widely used, the underlying mechanisms are the subject of ongoing debate. In this review we consider methods being used to study the prenatal environment and schizophrenia, particularly the relationship between prenatal lead (Pb) exposure and schizophrenia.
Early Antecedents of Schizophrenia
Schizophrenia is a mental illness that has been grouped among the psychotic disorders, that is, those chiefly characterized by delusions, sensory hallucinations, and severe impairments of speech organization. It has not been associated successfully with any specific physical finding universal to all cases. For some time, this led researchers to doubt that schizophrenia had any underlying physical cause. Many sought to find evidence of brain pathology but were unsuccessful, leading to the declaration that schizophrenia was the “graveyard of neuropathologists” (Plum 1972). During 1980s, that belief was gradually supplanted as subtle physical findings began to emerge. Neuroimaging techniques suggested structural changes, such as increased ventricular volume in the brains of patients (Kelsoe et al. 1988), and cohort studies found differences in neuromotor function and childhood behavior between patients who developed schizophrenia and the general population. This seemed to suggest early origins of the illness, predating clinically defined disease by decades. Although on brain imaging, certain regional changes such as increased ventricular and reduced cortical and hippocampal volume have been noted (Halliday 2001), findings in schizophrenia are comparatively subtle. Overall, these indicate mild structural disarray at the cellular level, changes in neural density (Chana et al. 2003), and decreased neuropil (Roberts et al. 1996) along with altered connectivity in multiple regions of the brain (Harrison 1999).
Prospective approaches.
The use of postmortem examination is problematic in studies of diseases that have early antecedents because it is difficult to determine whether findings are because of underlying dysfunction rather than degeneration after clinical progression or side effects of pharmacologic treatment. In the study of fetal origins of schizophrenia, it is also difficult to apply the case–control approach, because reliable evidence of events that occurred during fetal development cannot be obtained easily. Prospective techniques are one potential solution; such studies, based on cohorts of subjects identified before the onset of disease and before the exposures under investigation (e.g., at or before birth) and followed across the life course, have become key to investigations of early developmental events in schizophrenia. However, such investigations are complicated by the very long latency of the disease, since clinical onset may not be evident until adolescence or adulthood, decades after the putative prenatal exposures. Systems to track subjects, identify exposures, and diagnose disease must be maintained for decades. In addition, large numbers of subjects are required to accurately assess the relatively modest increases in risk that any single factor is likely to contribute to a multifactorial disease such as schizophrenia.
Recent investigations have built on these studies, using prospective cohorts identified before birth for studies of known or suspected neurodevelopmental disruptors. Several ascertain prenatal exposure through quantifiable measurements, for example, analysis of archived maternal biologic samples collected before birth. Various hypotheses have been advanced, and a number of studies have produced suggestive results. As an example, we describe one ongoing study that has examined toxic, nutritional, infectious, and other risk factors. After describing selected findings on infection and nutrition that illustrate the methods used, we then describe how this study has been used to investigate prenatal lead exposure as a risk factor.
The PDS study.
The Prenatal Determinants of Schizophrenia (PDS) study was initiated in the 1990s. It is based on a cohort of approximately 20,000 pregnant women identified in northern California between 1959 and 1966 as part of the Childhood Health and Development Study (Susser et al. 2000). This study includes aliquots of maternal sera drawn during prenatal visits. These samples were stored and maintained at National Institutes of Health facilities, frozen at −20°C in anticipation of future studies. They have been used in combination with hospital records and new diagnostic data (van den Berg et al. 1988).
Cases of schizophrenia and SSDs were identified from a database of inpatient, outpatient, and pharmacy records. Records for cohort members with diagnoses indicative of psychosis or prescriptions for antipsychotic medication were reviewed, abstracted, and rated by two psychiatrists for the presence or absence of psychosis. These ratings were then used to identify potential cases to be sought for a thorough diagnostic interview. Ultimately, 71 cases of schizophrenia and SSDs were identified. Controls were selected from the cohort and matched to cases on the basis of several factors, including date of membership in the cohort, date of birth, gender, timing of first maternal blood draw, and the number of available serum samples (for details see Susser et al. 2000).
Influenza and markers of infection.
Previous work describing associations between prenatal exposure to a variety of viral agents has been considered for some time and extensively reviewed elsewhere (Crow 1978; Mednick et al. 1988; Torrey and Peterson 1973). The PDS study is among the first studies capable of performing serologic measures for exposure to influenza. For this analysis in stored maternal serum from cases and matched controls, the hemagglutination inhibition test was performed on four antigens of influenza strains known to be prevalent between 1959 and 1966 in northern California, including A/H2N2/Japan/57, A/H2N2/Japan/62, A/H2N2/Taiwan/64, and B/Massachusetts/66. Exposure to influenza usually results in a rise in antibody titers, referred to as seroconversion. Typically, seroconversion is characterized as a 4-fold rise in antibody titers taken in serial samples. As most subjects in this study had single samples taken within each trimester, a single cutoff level was sought as a proxy of influenza exposure during pregnancy. Validity studies demonstrated that levels of ≥ 1:20 in a single serum sample were highly specific and sensitive.
First-trimester exposure was associated a 7-fold increase in risk of schizophrenia and SSDs, whereas second- and third-trimester exposure showed no increase in risk. However, although first trimester is usually defined as the period between zero and 90 days after the last menstrual period (post-LMP), the blood draws taken in this study only occur as early as 46 days post-LMP. Therefore, first trimester here signifies, in effect, assessment in the latter part of first trimester. Additional analyses were conducted analyzing exposure during the first and second halves of pregnancy defined as 0–142 days (in effect, 40–142 days post-LMP) and from 143 days post-LMP until termination of pregnancy, respectively. Exposure in the first half of pregnancy conferred a 3-fold increase in risk, whereas no increase was seen after exposure during the second half of pregnancy or when second-trimester exposure was considered.
Although clearly an advance over previous work, the PDS study has three key limitations. First, the number of cases of schizophrenia and SSDs with the required prenatal sera was small—64 cases and two matched controls per case. Although the study found a substantial association between prenatal influenza exposure and schizophrenia, the confidence limits of this association are wide. Second, influenza infection is typically documented by noting an increase in titers over time, and the measure used in this study represents a proxy of the established standard. Third, the increase in risk does not correspond exactly with previous findings concerning timing of exposure. Prior reports have indicated that second-trimester exposure is associated with increases in risk, whereas in this study, exposure during first trimester and first half of pregnancy confers risk. Further investigation is required to explain this difference.
Prenatal maternal nutrition and body mass index.
Nutritional factors has also been postulated to play a role in the etiology of schizophrenia. Both lack of specific micronutrients and general nutritional deprivation have been previously implicated as risk factors for broad developmental disruption and for schizophrenia specifically. In one landmark study of prenatal nutritional deprivation known as the Dutch Famine Study (Susser et al. 1998), neurodevelopmental outcomes were measured after severe caloric restriction. Rates of schizophrenia approximately doubled for individuals conceived under conditions of nutrient deprivation during early gestation (Susser et al. 1996). Early gestational exposure to famine conferred risk for schizophrenia, whereas late gestational exposure did not. Later studies that extended these findings to schizophrenia spectrum personality disorders also showed a 2-fold increase in risk for early gestational exposure to famine (Hoek et al. 1998). Two other studies found evidence that low maternal body mass index (BMI) or low birth weight is associated with schizophrenia (Done et al. 1991; Wahlbeck et al. 2001).
Recently, high rather than low maternal BMI has become a focus of concern because the number of women of reproductive age with above-average or high BMI has increased in industrialized societies. The PDS study used measures of prepregnant maternal BMI, categorized to low (< 19.9), average (20–26.9), above average (27–29.9), and high (≥ 30.0). Compared with average maternal prepregnant BMI, high BMI was significantly associated with schizophrenia and SSDs in the adult offspring (relative risk = 2.9; 95% confidence interval, 1.3–6.6). This finding was independent of maternal age, parity, race, education, or cigarette smoking during pregnancy.
Prenatal Lead Exposure and Neurodevelopment
For centuries lead has been known as a toxic agent but only recently has been recognized as having subtle but significant developmental effects. McKhann stated in 1926 that the “manifestations of Pb poisoning usually subside without serious consequences.” In 1943 Byers and Lord (1943) disproved this statement in a follow-up study of 20 children with documented Pb poisoning. They examined not only gross neurologic signs but also IQ scores and academic performance. Although based on a small sample of convenience, 19 of the children later exhibited serious difficulties in school. Since these initial studies, prenatal Pb exposure has been measured using maternal blood Pb (BPb) during pregnancy, neonatal BPb, amniotic fluid, and umbilical cord BPb (Korpela et al. 1986). Comparisons of maternal and umbilical BPb indicate that transfer of Pb from maternal to fetal blood during pregnancy is unimpeded by the placenta. Prospective approaches to Pb exposure and development have been used in a number of instances. They have focused primarily on developmental outcomes such as attention, academic achievement, and cognition, and have used maternal blood draws or postnatal measures in a variety of biologic media (Pocock et al. 1994). These studies and others generally have provided strong support for the role of Pb as a developmental neurotoxin (Bellinger et al. 1994). However, because they mostly have followed subjects into or through childhood, they are not informative regarding adult-onset disorders such as schizophrenia.
A few modestly sized studies have now followed subjects through adolescence. In one example, Needleman and colleagues recruited 312 first- and second-grade children in Chelsea and Somerville, Massachusetts. Dentin Pb levels were measured for each subject (Needleman et al. 1979). This measure was used to identify high exposure (those with dentin levels > 20 ppm), moderate exposure (10–19.9 ppm), and low exposure (< 10 ppm) (Needleman et al. 1990). Neurobehavioral testing was conducted at the time of collection in 1979 (mean age, 7.3 years) and again in 1988 (mean age, 18.4 years). Dentin Pb is useful as a measure of exposure averaged over the age of the tooth, although dentin Pb levels are associated with dental caries and fillings (Gil et al. 1996). Results showed an increased risk of not graduating from high school among those with increased dentin Pb levels. Reading difficulties sufficiently severe to be defined as a disability showed a similar distribution. Subjects who had been diagnosed with clinical Pb poisoning earlier in the study had the highest percentages of failure to graduate (42.9%) and reading disabilities (50%).
A study by Dietrich et al. (2001) presents data that show Pb exposure versus juvenile delinquency at different exposure levels. Based in Cincinnati, Ohio, the sample of 195 subjects is largely African American, disadvantaged, and urban. Using a prospective cohort with prenatal and postnatal BPb assessments collected every 3 months until 6.5 years, the study measures parental and self-report of delinquent behaviors including drug and alcohol use in adolescence. Subjects were given self-report questionnaires and assessed at 15 and 17 years of age. The results are categorized by lowest, low, medium, and highest BPb levels. When prenatal BPb, average childhood BPb, and 78-month BPb were estimated as predictors of delinquent behavior, increasing concentrations were associated with a modest increase in delinquent acts reported in adolescence. Prenatal BPb exposure > 10 μg/dL results in an increase of more than 2.3 delinquent acts compared with exposures ≤ 5 μg/dL. Significantly higher rates of delinquent behavior are related via a categorical BPb measured prenatally and at 78 months of age, although not by average childhood BPb (Dietrich et al. 2001).
Findings on Prenatal Lead Exposure and Schizophrenia
Many of the effects described in adolescence after early-life exposure, including decreased academic attainment, social deficits, and behavioral difficulties are comparable with the early antecedents of SSDs. Similarly, a number of factors that have been suggested as being associated with Pb exposure, such as urban residence, have also been studied as risk factors for SSDs. Although the samples from prospective studies described here do not have sufficient power to be definitive, the findings are suggestive, and the overall approach that these studies take may be used as a model. Principally, the combination of prospective collection of biologic samples can be combined with longitudinal assessments for the study of early-life exposures as they relate to adolescent and adult psychiatric diseases.
Although several techniques are available for assessing Pb exposure in biologic samples, the principal one used in studies of prenatal exposure is direct measurements on maternal blood. The PDS study has stored sera, not whole blood, containing the Pb-sequestering erythrocytes required for direct measurements in small volumes. Techniques for direct measurement of Pb could not be employed. However, a biologic marker of Pb exposure, δ-aminolevulinic acid (δ-ALA) may be detected in urine, plasma, and serum using high-pressure liquid chromatography with fluorescence detection.
Feasibility studies were conducted to assess the utility and predictive value of this technique in small volumes of stored maternal serum (Opler et al. 2004). It was determined that second-trimester serum was likely to be the best indicator of prenatal exposure because both Pb and corresponding δ-ALA levels are believed to be relatively stable at midpregnancy. Second-trimester samples were available for 44 cases and 75 matched controls (one to two controls per case).
A single 100-μL aliquot of second trimester serum was made available for each subject. A concentration of 9.5 ng/mL of δ-ALA, corresponding to a BPb level of 15 μg/dL, was used as a cut-off value to divide the sample into exposed and unexposed subjects. Samples were coded and blinded with respect to case status. Using this approach, Pb exposure as measured by elevated δ-ALA was associated with about a 2-fold increase in risk of SSDs in this sample (odds ratio = 2.43; 95% confidence interval, 0.99–5.96). The small numbers of subjects contribute to the wide confidence limits.
Some important limitations should be noted. First, the use of a biologic marker rather than direct measurements means that the observed increase in risk could be mediated by the effects of Pb on δ-ALA rather than by Pb exposure directly. Serum δ-ALA itself may be the exposure of interest. In experimental models, δ-ALA has been shown to be neurotoxic, interfering with GABA (γ-aminobutyric acid) neurotransmission [Emanuelli et al. 2001; also reviewed by Cory-Slechta (1995)]. Second, the findings of this study are also difficult to interpret conclusively because the sample size is relatively small and the result has a wide confidence interval. We have now obtained permission to analyze the one other existing data set of this type with a similar sample size. These results will be forthcoming.
Future Directions
The use of prospectively collected cohorts in combination with archived biologic samples is a proven and powerful method for studying disease–exposure relationships throughout the life course. This method has allowed schizophrenia research to move away from less refined definitions of prenatal exposure and into investigations that may someday focus on specific molecular agents in causal pathways. This longitudinal approach was made possible by the foresight of early generations of researchers in combination with the efforts of those who succeed them. The initial results from prenatal cohort studies are still preliminary, and the process they describe is still in its infancy. Every class of candidate exposures will benefit from continued technical and methodologic refinement. Using infection as an example, those agents or strains that cause the greatest increases in risk, specific physiologic responses to infection, and the timing of exposure during pregnancy may be further investigated. Nutritional deprivation might also be explored in greater detail, using methods to study the roles of individual micronutrients. Finally, chemical exposures could eventually be examined in terms of toxicokinetics and mechanisms of action, allowing proximal effects of exposure to be teased apart and considered separately from consequent physiologic responses.
Although life-course epidemiology is currently yielding important results, it is limited to the resolution and specificity that designers of prenatal cohorts create through the type, frequency, and periodicity of data collection. Presently, the biology that links exposures to causal mechanisms is nearly impossible to study in detail without the use of experimental models. Basic researchers have used clinical and neurochemical observations from humans to develop of animal models of schizophrenia and are now studying some of the exposures implicated in epidemiology using the same techniques. Although animal models of psychiatric disorders are imperfect and subject to a number of limitations, they are useful for testing the biologic plausibility of new hypotheses generated by epidemiology.
We believe that to reach the goal of effective prevention of schizophrenia, all available data on the disorder must be integrated, including observational and experimental findings. Investigators with interdisciplinary training and who are comfortable with the language and concepts of study design from the population level to the molecular level will play a crucial role in the future of the field.
This article is part of the mini-monograph “Early Environmental Origins of Neurodegenerative Disease in Later Life: Research and Risk Assessment.”
Work for this article was supported partly by National Institutes of Health training grant 5 T32 MH 13043 and National Alliance for Research on Schizophrenia and Depression Independent Investigator award.
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Emanuelli T Pagel FW Alves LB Regner A Souza DO 2001 5-Aminolevulinic acid inhibits [3 H]muscimol binding to human and rat brain synaptic membranes Neurochem Res 26 2 101 105 11478735
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van den Berg BJ Christianson RE Oechsli FW 1988 The California Child Health and Development Studies of the School of Public Health, University of California at Berkeley Paediatr Perinatal Epidemiol 2 265 282
Waddington JL Youssef HA 1987 Is schizophrenia a neurodevelopmental disorder? BMJ (Clin Res Ed) 295 997 998
Wahlbeck K Forsen T Osmond C Barker DJ Eriksson JG 2001 Association of schizophrenia with low maternal body mass index, small size at birth, and thinness during childhood Arch Gen Psychiatry 58 48 52 11146757
Weinberger DR 1987 Implications of normal brain development for the pathogenesis of schizophrenia Arch Gen Psychiatry 44 660 669 3606332
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7568ehp0113-00124316140636ResearchMini-MonographPharmacokinetic and Pharmacodynamic Factors That Can Affect Sensitivity to Neurotoxic Sequelae in Elderly Individuals Ginsberg Gary 1Hattis Dale 2Russ Abel 2Sonawane Babasaheb 31 Connecticut Department of Public Health, Hartford, Connecticut, USA2 Clark University, Center for Technology, Environment and Development, Worcester, Massachusetts, USA3 National Center for Environmental Assessment, Research and Development, U.S. Environmental Protection Agency, Washington, DC, USAAddress correspondence to G. Ginsberg, Connecticut Department of Public Health, P.O. Box 340308, Mail Stop 11CHA, Hartford, CT 06134 USA. Telephone: (860) 509-7750. Fax: (860) 509-7785. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 26 5 2005 113 9 1243 1249 1 9 2004 26 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Early-life exposure to agents that modulate neurologic function can have long-lasting effects well into the geriatric period. Many other factors can affect neurologic function and susceptibility to neurotoxicants in elderly individuals. In this review we highlight pharmacokinetic and pharmacodynamic factors that may increase geriatric susceptibility to these agents. There is a decreasing trend in hepatic metabolizing capacity with advancing years that can affect the ability to clear therapeutic drugs and environmental chemicals. This factor combined with decreased renal clearance causes prolonged retention of numerous drugs in elderly individuals. A geriatric pharmacokinetic database was developed to analyze changes in drug clearance with advancing age. This analysis shows that the half-life of drugs processed by hepatic cytochrome P450 enzymes or via renal elimination is typically 50–75% longer in those older than 65 than in young adults. Liver and kidney diseases are more common in elderly individuals and can further decrease the clearance function of these organs. Polypharmacy, the administration of numerous drugs to a single patient, is very common in elderly individuals and increases the risks for drug interaction and side effects. With advancing age the nervous system undergoes a variety of changes, including neuronal loss, altered neurotransmitter and receptor levels, and decreased adaptability to changes induced by xenobiotics. These changes in the central nervous system can make elderly individuals more susceptible to neurologic dysfunction when confronted with single pharmacologic agents, polypharmacy, or environmental toxicants. The many factors that affect elderly responses to neuroactive agents make environmental risk assessment for this age group a special concern and present a unique challenge.
adverse drug reactionsgeriatricmetabolismneurotoxicitypharmacokineticspolypharmacy
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Early-life exposures to heavy metals, pesticides, and other neurotoxicants can lead to long-lasting and/or delayed effects on the nervous system (Barone et al. 2001; Thiruchelvam et al. 2002). Such effects may accelerate the natural aging process and may be most prominent in elderly individuals (Moser 2001). However, early-life exposures are only one factor that may affect functioning of the nervous system during the geriatric period. The aging process itself has substantial effects on the brain and peripheral nervous system (Masoro and Snyder 2001; Weiss 2000). In addition, exposure of elderly individuals to neuroactive drugs or environmental chemicals can alter central nervous system (CNS) function and lead to a variety of symptoms (Hein et al. 1990; Overstreet 2000; Wozniak et al. 1991). Elderly individuals can have different sensitivity to these agents than younger adults because of pharmacokinetic factors (chemical absorption, distribution, metabolism, excretion) and pharmacodynamic factors that control cellular response to the chemical. Thus, although there has been much focus on how early-life exposures may affect later-life function, this influence needs to be seen within the context of myriad other factors that occur in elderly individuals.
This article is a review of some of the pharmacokinetic and pharmacodynamic differences between younger and older adults that may lead to altered sensitivity to chemical exposure. The review capitalizes upon pharmacokinetic data for therapeutic drugs in elderly individuals to estimate geriatric/younger adult differences in key pathways that can affect the handling of a wide array of xenobiotics. As shown in Figure 1, therapeutic treatment of illness requires information on both pharmacokinetics and pharmacodynamics to understand interpatient variability and how to titrate dosage for body size, age, or genetic traits. This leads to clinical pharmacokinetic drug trials, which when compiled for numerous drugs can reveal metabolism and clearance differences across age groups (Ginsberg et al. 2002). If sufficient clinical pharmacokinetic data are available, it may be possible to make inferences about the handling of environmental toxicants in a particular age group such as elderly individuals.
Given that it would be unethical to purposely expose a potentially sensitive population to even trace levels of an environmental toxicant, there is a lack of pharmacokinetic (termed “toxicokinetic” when referring to toxicants) data in elderly individuals. The approach of relying on therapeutic drug studies, as described in this review, can help address this gap. In addition a variety of exposure and pharmacodynamic factors that can lead to adverse drug reactions (ADRs) in elderly individuals are reviewed to help understand occurrences of drug-related neurologic dysfunction in this age group.
Basic Features of Aging That Can Affect Response to Xenobiotics
Most of us have witnessed the relatively large interindividual variability in the rate of aging that exists in the human population. This variability in the rate of aging can increase intersubject variability in response to xenobiotics compared with younger adults. Although it is clear that biologic age is more relevant than chronologic age, it is not always easy to obtain information showing the biologic age of an organ system. Fortunately, this is feasible for key pharmacokinetic factors such as hepatic metabolism and urinary clearance because these pathways are often determinants of clinical pharmacokinetic studies. Such indices are used in the titration of drug dosage to help achieve optimal activity and minimal side effects in elderly individuals who use drugs with a narrow therapeutic index (Cutler and Narang 1984; Tranchand et al. 2003).
“Normal” aging of organs and systems leads to diminished function in many areas, from decreased neuromuscular strength and reaction time to losses in memory and cognition (Masoro and Snyder 2001; Weiss 2000). These deficits can be accentuated under the influence of physiologic or chemical stressors due to the loss of functional reserve in aging, leading to the manifestation of a clinical disease process or ADR. For example, defenses against oxidant stress appear to decline with age, as seen in in vitro studies in which the basal rate of lipid peroxidation increased and the ability to scavenge oxygen radicals decreased with replicative age of the cell cultures (Sitte et al. 2001). Increasing age has been associated with increasing levels of lipid peroxidation as measured by pentane exhalation in human subjects (Zarling et al. 1993). Although antioxidant levels in the frontal lobes of the brain have been reported to decrease in an age-related fashion (Craft et al. 2004), blood antioxidant levels and enzymes may not be lower in older subjects unless there is poor nutrition or active disease processes (Vogel et al. 1997; Wouters-Wesseling et al. 2003; Zarling et al. 1993).
Another example of decreased function in elderly individuals is impaired clearance function in both the liver and kidney, leading to a greater potential for ADR from drug overdose (Muhlberg and Platt 1999; Sotaniemi et al. 1997). The effects of these declines in organ function on the pharmacokinetics of therapeutic drugs and toxicants are discussed in more detail below.
Superimposed on these “normal” age-related decreases in function are disease-related changes in organ structure and function. Diseases of the liver and kidney are more common and typically more advanced in elderly individuals than in younger age groups, leading to a greater likelihood for disease-related decreases in the pharmacokinetic processing of drugs or environmental chemicals (Lam et al. 1997; Regev and Schiff 2001). Disease in key clearance organs can increase intersubject variability in drug response and thus provide further rationale for careful dose titration in elderly individuals.
Another major factor that can affect an elderly subject’s response to administered drugs is the potential for drug–drug or drug–environmental chemical interactions due to polypharmacy. Polypharmacy is a general term describing the use of multiple drugs, often to address different conditions, in a single patient. The number of drugs prescribed can be especially high when certain drugs are used to combat the side effects of the primary drugs given to treat the disease. Polypharmacy is prevalent in elderly individuals because of the greater number of chronic diseases that manifest with increasing age and the greater reliance upon pharmacologic treatment options in this age group (Routledge et al. 2004). Polypharmacy can stress pharmacokinetic and pharmacodynamic systems that are already at a reduced level of function due to normal aging processes and the loss of functional reserve.
Physiologic Changes That Can Affect Pharmacokinetic Function during Aging
Body composition changes with advancing age as the percentage of muscle mass and body water decline by as much as 25% in women by age 70 (Masoro and Snyder 2001). These decreases occur while body lipid is increasing, with this compartment rising to > 40% of body weight in elderly women and to > 30% in elderly men (Figure 2).
These changes can be expected to cause a larger volume of distribution and longer half-life of lipophilic chemicals because of their increased sequestration in fat, a tissue that is not involved in body clearance. This has been suggested for dioxin-exposed Vietnam War veterans in whom sequential analysis over 15 years showed that those with higher body lipid had slower dioxin clearance (Michalek and Tripathi 1999). Lipid-soluble drugs have slower clearance in elderly individuals, as shown for certain benzodiazepine antianxiety drugs. For example, diazepam’s volume of distribution and half-life are greater in elderly individuals, which is a function of both slower metabolic oxidation and its high lipid solubility (Barclay 1985; Cutler and Narang 1984; Greenblatt et al. 1983). Diazepam’s prolonged half-life has led to its diminished use in elderly individuals, with replacement anxiolytics such as lorazepam preferred because of more rapid pharmacokinetics.
Another factor that affects drug distribution is the decreased plasma protein binding capacity in elderly individuals. This decrease is 15–25% and has been attributed to increased proteinuria (loss of serum proteins to urine due to changes in renal function) rather than to decreased plasma protein synthesis by the liver (Schmucker 2001). This nonspecific loss of plasma proteins can lead to higher free drug plasma concentration that for highly bound drugs and environmental chemicals can lead not only to a greater percentage available for uptake into target organs but also to a greater percentage available to clearance organs for metabolism or renal elimination (Masoro and Snyder 2001). The reduced protein binding capacity may lead to a greater potential for drug–drug interaction via competitive displacement from the limited number of serum binding sites (Girgis and Brooks 1994).
Aging and hepatic clearance.
Liver function is generally considered to be maintained fairly well into old age. Several liver function tests such as serum albumin, bilirubin, cholesterol, and alkaline phosphatase are not markedly different in 90-year-olds compared with young adults (Schmucker 2001). However, liver size and hepatic blood flow are diminished 25–35% in elderly individuals, and there is also a decrease in bile flow (LeCouteur and McLean 1998; Zeeh and Platt 2002). Histopathologic examination of livers of aged individuals reveals an accumulation of lipofuscin, a brown pigmented waste product that is formed from the oxidation of lipids and proteins (Anantharaju et al. 2002). This evidence suggests an increase in hepatic lipid peroxidation and/or a decreased capacity to remove this waste product in the liver at advanced age. Cell culture studies demonstrate the increased accumulation of lipofuscin in cells undergoing oxidative stress and with increasing replicative age, supporting the concept that senescent cells have diminished defenses against oxidative stress (Sitte et al. 2001).
The specific content of cytochrome P450 (CYP) enzymes in the liver also diminishes during aging. A large liver bank consisting of 226 biopsy specimens of similar histopathologic condition was probed for total CYP content (nmol CYP/gram liver), and antipyrine clearance was measured in vivo in the same subjects (Sotaniemi et al. 1997). Antipyrine is a non-specific CYP substrate and thus is a general index of hepatic CYP function. As shown in Figure 3, when the data were broken into age decade of donor, there is a decrease beginning at 40–49 years of age, with further decreases in the 60s and 70s. The maximal decline relative to the youngest age group is 50% for hepatic CYP content and 30% for antipyrine clearance. This is consistent with other evidence (Schmucker 2001) and suggests that multiple factors such as less blood flow to the liver, smaller liver mass, and decreased specific content of CYPs may combine in elderly individuals to decrease hepatic clearance of drugs.
A therapeutic drug pharmacokinetic data set in elderly individuals was developed on the basis of published studies to further evaluate in vivo drug clearance function with increasing age (Hattis and Russ 2003). Data for 46 drugs encompassing a variety of clearance pathways including CYP-mediated phase I metabolism, phase II conjugation, and renal excretion as parent drug were included in the database (Table 1). The database includes results for more than 4,500 subjects, reported as either individual or group mean data for drug clearance, half-life, volume of distribution or area under the curve (AUC) of drug concentration in blood × time after dosing (Tables 2, 3). Subjects were grouped in 5-year age increments following the reference age group, 18–24 years of age. Most age groups are represented by more than 100 subjects, with the minimum number being 45 subjects in the over-85 age group.
Analysis of changes in drug clearance across multiple age groups and drugs was performed using multiple regression techniques as described in a previous analysis of a pediatric pharmacokinetic data set (Ginsberg et al. 2002). Separate regression coefficients were derived for each age group and end point [half-life, clearance, AUC, volume of distribution (Vd)], with the regression coefficient representing the age group:young adult ratio of parameter values.
Figure 4 shows geometric mean drug clearance and half-life results across all drugs in the database, with results expressed as the ratio of clearance or half-life in the particular age group compared with the reference, young adult group. The figure shows that drug clearance and half-life remain similar to the young adult level through 60 years of age, but that greater half-life and slower clearance are seen in the subsequent age groups. The maximal change is in the 80- to 84-year-old group, with half-life increased by 60% and clearance decreased by 50%. All age groups older than 65 were significantly different than the reference group except the over-85 age group, for which the results are somewhat closer to the reference group. However, the data for the over-85 age group are the least robust, as they are based upon the smallest data set.
Figure 5 provides an analysis of across-age differences in half-life for drug groupings based on broad categories of clearance mechanism: CYP-mediated phase I metabolism, phase II conjugation without prior oxidation, and renal excretion. The results for CYP and renal elimination are consistent with the overall trend shown in Figure 4, with significant increases in half-life in the over-65 age group. Once again, the over-85 age group does not show a difference from the reference group, likely because of the small sample size available for each pathway. It is also possible that particularly robust individuals tend to be entered into clinical pharmacokinetic studies at this age. Phase II conjugation shows no evidence of decline with advancing age.
These results indicate that the physiologic changes in liver that occur during senescence translate into significantly less drug clearance in elderly individuals older than 65. This elderly/young adult pharmacokinetic difference is 50–75% for drugs cleared by a variety of CYPs or via renal excretion. The basis for the increase in half-life for drugs cleared by renal elimination is discussed further in the next section. Recognition of decreased drug clearance in elderly individuals has led to dose adjustment for numerous drugs to avoid overdose.
The geriatric pharmacokinetic database was also used to assess interindividual variability in drug clearance in elderly age groups compared with that in younger adults (Hattis and Russ 2003). Figure 6 is a scatter diagram depicting the variability in drug clearance for individual subjects. Data are shown as the difference in clearance from that expected based upon the overall data set regression. The figure shows substantial interindividual variability in each age group, but when statistically analyzed, there was no evidence for an increase in variability in older adults. It is unclear whether and to what degree enrollment in these various pharmacokinetic drug trials is constrained in terms of concurrent illnesses or drug therapy. Such enrollment constraints would tend to limit the degree of variability seen in the subjects in these studies compared with that in the general population, especially for older age groups where these factors are more prevalent.
Aging and renal clearance.
The aging process has deleterious effects on renal function, with decreases in renal weight and number of glomeruli in subjects older than 50. In addition, the basement membrane thickens and the number of schlerotic glomeruli increases. These changes can occur because of normal aging but are accelerated by the onset of disease processes (Muhlberg and Platt 1999). Decreases in renal blood flow have been approximated at 10% per decade beginning after the fourth decade (Muhlberg and Platt 1999); these decreases can lead to glomerular ischemia in elderly individuals. These factors combine to decrease glomerular filtration rate with data from 256 geriatric patients pooled across 17 pharmacokinetic studies indicating a steady decline in creatinine clearance in subjects older than 60 (Muhlberg and Platt 1999). Subjects who had the lowest creatinine clearance were also the ones in which drug plasma levels attained potentially toxic levels. Cisplatin and other drugs that are cleared via renal excretion are dosed in elderly individuals on the basis of renal clearance function to ensure that toxic levels do not build up in blood (Dollery 1999; Legha 1990). Thus, titration of drug dosage is particularly important for drugs that are cleared by or can damage the kidneys. For example, acyclovir-induced renal failure and concomitant neurotoxicity are more likely to occur in those whose creatinine clearance rate is already somewhat diminished (Johnson et al. 1994).
Liver and Kidney Disease in Elderly Individuals
The preceding discussion focused on subjects with no overt liver or kidney disease and so could be described as the natural course of aging of hepatic and renal function. However, disease-induced decrements in liver and kidney clearance of xenobiotics are an important overriding factor leading to altered pharmacokinetics and possibly increased variability in elderly individuals.
Although some organ diseases occur at other ages as well, their prevalence and severity are generally greater in elderly individuals because of more time for accumulation of damage and decreasing functional reserve (Mahon and James 1994). The senescent liver has reduced regenerative capacity such that recovery is impaired in response to viral or toxic insult or disease process (Regev and Schiff 2001). Diseases such as alcohol-induced cirrhosis, viral hepatitis–induced cirrhosis, hepatocellular carcinoma, diabetic-associated chronic liver disease, and biliary cirrhosis are more prevalent in the elderly and are associated with replacement of functional tissue with fibrotic or tumorous tissue or fatty lesions (Adler et al. 2002; Amarapurkar and Das 2002; Jansen 2002; Van Dam and Zeldis 1990). These changes can affect hepatic blood flow and the mass of metabolizing tissue available for drug clearance.
A variety of drugs can induce hepatotoxicity with the possibility that reduced cellular defenses and reserve capacity could make the elderly individuals’ liver more susceptible to these drugs. For example, serum transaminases were used as an index of hepatotoxicity from combined isoniazid/rifampin therapy in pulmonary tuberculosis patients (Van Den Brande et al. 1995). Before antituberculosis treatment, serum transaminase levels were similar in a group of 67 young patients (mean age, 38.6 years) and 64 elderly patients (mean age, 71.2 years); subjects with pre-existing hepatic disease were eliminated from the study. During the course of treatment, serum transaminase levels in the young group increased approximately 2-fold, whereas a statistically greater increase of 4- to 5-fold was seen in the elderly group. Another study found that the incidence and severity of hepatic side effects of antituberculosis therapy were high in the elderly individuals, especially those with pre-existing hepatitis (Schaberg et al. 1996).
Sensitivity to drug-induced hepatotoxicity is likely dependent on a number of factors, including capability of hepatocytes to activate and detoxify the particular drug or chemical. Thus, one cannot generalize that elderly individuals will always be more sensitive to the effects of hepatotoxicants. However, where drug-induced hepatotoxicity does occur in elderly individuals, as exemplified by the anti-tuberculosis drugs, one can expect there to be reduced hepatic extraction of other xenobiotics leading to the potential for toxic drug interactions.
Another chemical-induced hepatic effect that may be more prevalent in elderly individuals is liver cancer. This may be counterintuitive from the perspective that carcinogen exposure at a late stage in life leaves little time for expression of the chemical-induced genetic or biochemical change. However, a number of studies have shown a greater increase in liver tumors in response to promotional carcinogens (phenobarbital, peroxisome proliferators) when dosing was initiated in aged as opposed to young adult rats (Cattley et al. 1991; Kraupp-Grasl et al. 1991; Ward et al. 1988). The mechanism for this age-related vulnerability may be that promotors act on clones of cells that already have been transformed by an initiating (typically genotoxic) carcinogen. The number of initiated clones is believed to increase throughout the life span as a result of cumulative exposure to initiators, thus giving promotors a larger population of cells to act on in elderly individuals (Cattley et al. 1991).
Renal disease is also more common in elderly individuals. The chronic effects of hypertension and type 2 diabetes on the renal vasculature lead to renal diseases involving nephroschlerosis, atherosclerosis, and athero-embolism (Gomez et al. 1998; Mulder and Hillen 2001; Ritz and Tarng 2001). Nephropathy and reduced renal blood flow can progress to end-stage renal disease and the need for dialysis. Even before this point, renal disease can lead to decreases in renal blood flow, glomerular filtration, and tubular transport processes (Lam et al. 1997). This can be expected to decrease the clearance of water-soluble drugs and drug metabolites that rely upon glomerular filtration or tubular secretion mechanisms for entry into urine. Plasma protein binding can also be further diminished as greater amounts of these proteins are lost from blood into urine. Given that the effects of renal disease on the clearance of drugs are combined with the normal aging decrement in renal function, titration of drug dosage to renal function as estimated by creatinine clearance is especially important in elderly patients (Lam et al. 1997).
A variety of drugs can produce renal side effects, such as nonsteroidal anti-inflammatory agents, aminoglycoside antibiotics, amphotericin B, and acyclovir. The elderly are generally more sensitive to the renal toxicity caused by these agents because their elimination is already compromised because of aging-related decreased renal function, leading to greater concentrations in blood and kidney (Johnson et al. 1994; Muhlberg and Platt 1999). This drug-induced worsening of renal function can lead to interactions between these and co-administered drugs that also rely on renal elimination.
Polypharmacy
As mentioned above, certain drugs can affect pharmacokinetic function by producing toxic side effects in the liver or kidney to which elderly individuals may be more susceptible (Girgis and Brooks 1994; Larrey 2002; Muhlberg and Platt 1999). Additionally, a number of pharmacokinetic mechanisms can lead to drug–drug interaction to which elderly individuals may be more susceptible, for example, competition for serum protein binding sites or for metabolic or renal elimination pathways (Seymour and Routledge 1998). One example of this involves the selective serotonin reuptake inhibitors (SSRIs), drugs that are often prescribed to treat depression (Hemeryck and Belpaire 2002). Fluoxetine and paroxetine are SSRIs that are potent inhibitors of CYP2D6 and thus can prolong the half-life of numerous drugs. This is of concern because of the long half-life of these SSRI agents, particularly in elderly individuals, such that drug interactions are possible for weeks after SSRI treatment cessation (Bourin et al. 2001). For this reason, other SSRI agents are preferred in elderly individuals (Spina and Scordo 2002).
Although specific drugs may be risk factors for side effects and drug interaction in elderly individuals, the most prevalent factor for the increase in ADR in this age group may be polypharmacy. The elderly consume a disproportionate quantity of drugs, with those older than 65 taking, on average, two to six prescribed and one to three nonprescription drugs at any one time (Routledge et al. 2004). Analysis of French national statistics of reported ADRs and medication use found the expected increase in ADRs with advancing age beyond 55 years (Begaud et al. 2002). However, the increase was not apparent when the data were adjusted for the number of drugs consumed per individual in each age group (Figure 7). This finding agrees with a careful analysis of ADRs, prescription drug administration, age, and other factors in a multicenter clinical study in Italy (Carbonin et al. 1991). The doubling of ADRs between those younger than 50 versus those older than 70 was significantly related to taking numerous drugs, having numerous medical conditions, and longer hospital stays, but there was no independent correlation with age. It thus appears that the cumulative effect of taking numerous drugs can challenge normal physiologic and metabolic functions at any age, but this problem is most acute in elderly individuals because polypharmacy is so prevalent at this life stage.
Pharmacodynamic Aspects of Sensitivity to Neurotoxic Agents in the Elderly
In addition to the pharmacokinetic and polypharmacy factors described above, there are numerous pharmacodynamic factors in the CNS of elderly subjects that may affect sensitivity to neuroactive agents. Changes in central cholinergic pathways, including decreased number of brain acetylcholine postsynaptic receptors, have been demonstrated in rodent models of aging and may contribute to the progressive decline in memory and cognition in elderly individuals (Pedigo et al. 1984). Aged animals and humans also experience a decline in a number of other biochemical parameters that are critical to central cholinergic transmission. The activity of choline acetyl-transferase, an enzyme in acetylcholine synthesis, is diminished in older subjects. This may be a sign that central cholinergic neurons have diminished integrity (Overstreet 2000). The level of brain acetylcholinesterase also decreases during aging (Bartus et al. 1982). Thus, there are a variety of changes during aging that could affect sensitivity toward anticholinesterase agents (e.g., organophosphate and carbamate insecticides, chemical warfare agents). Some of these changes may tend to counterbalance one another; that is, decreased acetylcholinesterase levels may be offset by decreased levels of acetylcholine synthesis or acetylcholine receptors (Overstreet 2000). This makes it difficult to predict whether elderly individuals will be at higher risk to anticholinesterase agents, and there is insufficient animal data on this to draw conclusions. However, the general diminution of central cholinergic systems suggests decreased functional reserve for adaptation to chemical stresses affecting these systems. Consistent with this is the observation of decreased neurotransmitter receptor plasticity in senescent rats (Pedigo 1994). In these studies, aged rats were unable to up- or down-regulate the numbers of central cholinergic (muscarinic) receptors in response to cholinergic agonists or antagonists (Pedigo 1994). The cholinergic system decrements described above are most pronounced in those elderly with Alzheimer disease.
Another mechanism that may predispose elderly individuals to increased sensitivity to chemical-induced neurotoxicity is deficiency in plasma and tissue esterases. Carboxylesterases (CEs) and A-esterases (AEs) inactivate a wide variety of organophosphate pesticides, either by stoichiometric binding (CE) or via catalytic hydrolysis of the activated, neurotoxic form of the pesticide (AE). Studies in aged (24-month-old) rats demonstrated a markedly increased sensitivity to the acute effects of parathion but not chlorpyrifos (Karanth and Pope 2000). The increased sensitivity to parathion correlated with a 50% reduction of plasma CE in aged rats, whereas levels of AE were unaffected by aging.
Changes in other CNS pathways may predispose elderly individuals to the neurotoxic effects of drugs and environmental chemicals. Aging involves a decline in neuron density in the substantia nigra even in the absence of Parkinson disease (McGeer et al. 1988; Weiss 2000), and this may be related to the increased susceptibility of the dopaminergic system to chemical modulation. This has been seen in aged rats that experienced a greater dopamine depleting effect in the striatal region from acute methamphetamine dosing compared with younger rats (Bowyer et al. 1998). Another agent affecting the central dopaminergic system is 1-methyl-4-phenyl-1,2,3,6-tetrahydropryidine (MPTP). Its acute and residual effects on dopamine levels in the striatum were greatest in 12-month-old mice compared with 7-month-old (intermediate effect) and 23-day-old mice (least effect) (Ali et al. 1993). Additionally, the MPTP-induced effect was rapidly reversible in the younger animals but not in the 1-year-old age group. Similar increases in susceptibility of nigrostriatal neurons in aged mice has been found for paraquat and maneb neurotoxicity (Thiruchelvam et al. 2003).
Elderly humans and rats are more sensitive to the excitotoxic effects of domoic and kainic acids, structural analogs that are chemically related to the excitatory neurotransmitter glutamate (Perl et al. 1990; Teitelbaum et al. 1990; Wozniak et al. 1991). Domoic acid contamination of mussels in Canada in 1987 led to a food poisoning outbreak involving 107 patients, some of whom experienced severe gastrointestinal and neurologic symptoms and pathology of the hippocampus, the main target region of the brain. Acute and protracted neurologic effects were more pronounced in elderly subjects, which led to studies in young, middle-aged, and aged rats with kainic acid (Wozniak et al. 1991). The dose response for seizures was approximately 3 times greater in aged compared with young rats. A number of changes in excitatory and inhibitory pathways accompany aging, including decreases in kainic acid binding in the hippocampus and increased levels of glutamate (Masoro and Snyder 2001). However, the mechanism for increased sensitivity to domoic and kainic acid in elderly individuals has not been uncovered.
These examples point out that aging involves pharmacodynamic changes that can predispose to greater neurotoxic effects compared with earlier life stages. Another factor that may affect CNS function in elderly individuals is the long-term sequelae of earlier exposure to neurotoxic agents. For example, a study of Danish workers found that those whose occupations involved exposure to a mixture of organic solvents had a greater prevalence of memory loss and decreased ability to concentrate relative to a reference group (Hein et al. 1990). This pattern persisted into retirement, where these CNS deficits and an additional finding of increased headache manifested in solvent-exposed, retired workers. Similarly, there is evidence that occupational exposure to heavy metals such as mercury, manganese, aluminum, and lead can lead to long-term decrements in neurologic function because of the irreversible and cumulative effects of these neurotoxicants (Hochberg et al. 1996; Payton et al. 1998; Rifat et al. 1990; Weiss 2000).
Summary
Numerous types of exposures, either singly or in cumulative fashion, may lead to increased risk for neurologic deficits in elderly individuals. Early-life exposures and workplace exposures may have long-lasting effects on CNS function that may become more significant as the normal aging process decreases functional reserve. Therapeutic drugs are often a risk factor because many are neuroactive, and susceptibility in elderly individuals is increased because of decreased clearance or pharmacodynamic factors that increase vulnerability. The large number of drugs prescribed to an elderly patient at a single time (polypharmacy) and the existence of underlying pathology of the major clearance organs or CNS can increase the likelihood for neurotoxicity. These factors combine to increase the possibility that elderly individuals generally are more susceptible to the neurotoxic effects of environmental chemicals. The extent to which this is true and the possible size of age-related sensitivity differences has not been well studied.
Assessing risks to elderly individuals from neurotoxicant exposure that occurs in early, mid-, or late stages of life is challenging because of the various factors summarized in this article that can alter sensitivity and increase variability in this age group. Risk assessments can be tailored to geriatric populations by adjusting input parameters in standard physiologically based pharmacokinetic (PBPK) models to account for the changes in physiology, metabolism, and renal elimination that are known to take place during aging (Clewell et al. 2002). Such modeling approaches can point out whether the internal dose of a toxicant or its metabolites are likely to be elevated in healthy geriatric subjects and in those whose hepatic or renal function has been compromised by a disease process. This type of approach can also be useful in simulating chemical–chemical interactions that may affect clearance and other pharmacokinetic parameters (Liao et al. 2002), an especially important application given the numerous interactions possible from polypharmacy in elderly individuals. However, PBPK modeling of geriatric polypharmacy will be complex and may benefit from case studies that demonstrate how this approach can improve geriatric risk assessments. Another area needing further study is the relative sensitivity of elderly individuals to neurotoxic agents as affected by pharmacodynamic factors. The increasing emphasis being placed on understanding the responses of elderly individuals to drugs and environmental toxicants should improve our ability to assess risks for this age group in the future.
This article is part of the mini-monograph “Early Environmental Origins of Neurodegenerative Disease in Later Life: Research and Risk Assessment.”
The views expressed in this article are those of the authors and do not necessarily reflect the views or policies of the State of Connecticut, the U.S. Environmental Protection Agency, or Clark University.
Research was supported partly by U.S. Environmental Protection Agency contract CR829746-01 to Clark University.
Figure 1 Linkage between clinical pharmacology and environmental risk assessment.
Figure 2 Average percent body fat versus age in men and women: estimates from the National Health and Nutrition Examination Survey III body mass index data using the formulas of Lean et al. (1996).
Figure 3 Decline in hepatic CYP function with age. Cl, clearance. Data are mean ± SD. Adapted from Sotaniemi et al. (1997).
*p < 0.01.
Figure 4 Change in drug clearance and half-life with increasing age across all drugs in pharmacokinetic database. Data points are geometric mean ± SE.
Figure 5 Comparison of half-life across age groups for different clearance mechanisms. Data points are geometric mean ± SE.
Figure 6 Scatter plot of observed (Obs) versus model expected (exp) log(clearance/kg bw). bw, body weight. y = –0.0269 to 0.00050x; R2 = 0.005.
Figure 7 Age-specific rates of adverse drug reactions before and after adjusting for drug consumption. Modified from Begaud et al. (2002).
Table 1 Major routes of elimination of drugs in the geriatric pharmacokinetic database.
Chemical Route of elimination Chemical Route of elimination
Amikacin Renal Lisinopril Fecal
Amitriptyline CYP2D6 Lithium Renal
Ampicillin Renal Meperidine Esterase
Antipyrine CYP: mixed/unknown Mephobarbital-r CYP2C19
Atracurium Esterase Mephobarbital-s CYP: mixed/unknown
Benazepril Renal Mianserin CYP2D6
Bromazepam CYP: mixed/unknown Midazolam CYP3A or CYP3A4
Bupivicaine CYP1A2 Nortriptyline CYP2D6
Chlorpheniramine CYP2D6 Oxazepam Conjugation
Chlorzoxazone CYP2E1 Oxytocin Other: sulfhydryl reduction and aminopeptidase
Copper Renal
Diazepam CYP2C19 Paracetamol Conjugation
Dicumarol Unclassified Pethidine Esterase
Enalapril Renal Phenylbutazone Conjugation
Enalaprilat Renal Phenylpropanolamine Renal
Erythromycin CYP3A or 3A4 Piroxicam CYP2C9
Fentanyl CYP3A or CYP3A4 Propanolol CYP2D6
Gentamicin Renal Teicoplanin Renal
Grepafloxacin Unclassified Terfenadine Renal
Ibuprofen CYP2C9 Theophylline CYP1A2
Ketanserin Unclassified Valproic acid Conjugation
Ketoprofen Conjugation Vancomycin Renal
Ketorolac Renal Viloxazine Unclassified
Lidocaine Cyp3A or CYP3A4
Table 2 Overview of the database for various parameters.
Parameter No. of drugs Individual data points Data groupsa Subjects in data groups (n) Total no. of subjects
AUC 17 163 21 317 480
Clearance 31 210 59 1,003 1,213
Half-life 44 359 84 1,312 1,671
Vd 26 153 51 996 1,149
Total 46 885 215 —b —b
Vd, volume distribution.
a Data sets where pooled (e.g., mean, SD) rather than individual data are available for the indicated parameter.
b Not summed to avoid double counting.
Table 3 Summary of the database by predominant modes of elimination.
Route of elimination No. of drugs Individual data points Data groupsa Subjects in data groups (n) Total no. of subjects
All CYPs 19 389 126 2,315 2,704
Conjugation 5 122 6 60 182
Other metabolism 4 18 18 114 132
Renal and/or fecal elimination 14 278 51 892 1,170
Unclassified 4 78 14 247 325
Total 46 885 215 3,628 4,513
a Data sets where pooled (e.g., mean, SD) rather than individual data are available for the indicated parameter.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7567ehp0113-00125016140637ResearchMini-MonographNeurodegenerative Diseases: An Overview of Environmental Risk Factors Brown Rebecca C. 1Lockwood Alan H. 2Sonawane Babasaheb R. 31 Association of Schools of Public Health, National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, USA2 Departments of Neurology and Nuclear Medicine, Veterans Affairs Western New York Healthcare System and University at Buffalo, The State University of New York, Buffalo, New York, USA3 National Center for Environmental Assessment, Office of Research and Development, U.S. Environmental Protection Agency, Washington, DC, USAAddress correspondence to R.C. Brown, U.S. EPA, NCEA, 1200 Pennsylvania Ave. NW, Mailcode 8623D, Washington, DC 20460 USA. Telephone: (202) 564-3293. Fax: (202) 565-0079. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 26 5 2005 113 9 1250 1256 1 9 2004 5 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The population of the United States is aging, and an ever-increasing number of Americans are afflicted with neurodegenerative diseases. Because the pathogenesis of many of these diseases remains unknown, we must consider that environmental factors may play a causal role. This review provides an overview of the epidemiologic evidence for environmental etiologies for neurodegenerative diseases such as Alzheimer disease, Parkinson disease, parkinsonian syndromes (multiple system atrophy and progressive supranuclear palsy), and amyotrophic lateral sclerosis. Epidemiologic evidence for an association between environmental agents’ exposure and neurodegenerative diseases is not conclusive. However, there are indications that there may be causal links, and the need for more research is obvious.
Alzheimer diseaseamyotrophic lateral sclerosiselectromagnetic fieldsmetalsmultiple system atrophyneurodegenerationParkinson diseasepesticidesprogressive supranuclear palsysolvents
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The population of the United States is aging, and an ever-increasing number of Americans are afflicted with neurodegenerative diseases. Neurodegenerative diseases result from the gradual and progressive loss of neural cells, leading to nervous system dysfunction. According to the National Institute of Neurological Disorders and Stroke, there are more than 600 neurologic disorders, with approximately 50 million Americans affected each year.
These diseases cost the U.S. economy billions of dollars each year in direct health care costs and lost opportunities; it is estimated that $100 billion per year is spent on Alzheimer disease (AD) alone (Meek et al. 1998). In addition to the financial costs, there is an immense emotional burden on patients and their caregivers. As the number of elderly citizens increases, these costs to society also will increase.
Until recently, most concerns about environmental agents have centered on their potential for causing cancer. Cancer and neurodegeneration represent opposite ends of a spectrum: whereas cancer is an uncontrolled proliferation of cells, neurodegeneration is the result of the death of cells, whether due to direct cell death by necrosis or the delayed process of apoptosis. Attention is now being focused on environmental agents’ potential for damaging the developing and mature nervous system, resulting in neurodegenerative diseases.
Known risk factors for neurodegenerative disease include certain genetic polymorphisms and increasing age. Other possible causes may include gender, poor education, endocrine conditions, oxidative stress, inflammation, stroke, hypertension, diabetes, smoking, head trauma, depression, infection, tumors, vitamin deficiencies, immune and metabolic conditions, and chemical exposure. Because the pathogenesis of many of these diseases remains unknown, we must consider the role of environmental factors in these diseases.
In this review we examine the human evidence for environmental etiologies for some diagnosed neurodegenerative diseases. Epidemiologic literature, not case studies or experimental animal research, was searched for relevance to an association between neurodegenerative diseases and environmental agents. We briefly review genetics and lifestyle habits (e.g., smoking, coffee, alcohol) but not other potential risk factors such as age at onset, socioeconomic status, gender, ethnicity, and education. Although it is acknowledged that the etiology of neurodegenerative diseases is often multifactorial (particularly gene–environment interactions), the purpose of this review is to examine the extent of the available epidemiologic literature solely on environmental agents.
Study Type Selection
The greatest assurance for causality comes when the exposure to the environmental agent can be determined before the outcome. Because of the nature and variety of neurodegenerative diseases, the vast majority of cases are observed in the elderly population, yet the exposure could have occurred years or decades before the resulting effect. This long latency period makes it extremely difficult to track exposures before the outcome in a longitudinal fashion. A few groups have conducted studies of this type (Abbott et al. 2003; Baldi et al. 2003b; Hernan et al. 2001; Lindsay et al. 2002; Petrovitch et al. 2002; Preux et al. 2000; Rondeau et al. 2000; Tyas et al. 2001).
Because of these major constraints, many investigators have used a case–control study design that examines cases after diagnosis. The major limitation of this type of study is recall bias (see “Bias”). Ecologic studies are another type of study, with the major limitation being the inability to characterize exposure data to individuals (see “Exposure Definition”).
Disease Definition
Precise determinations of neurodegenerative disease can be elusive, although for approximately 80% of cases, a diagnosis made by a clinician is confirmed postmortem (Mok et al. 2004). For many cases, a confident diagnosis can be made only during an examination of brain tissue after death. Because autopsies are not always conducted, when using mortality data such as death records, there are obvious problems if the neurologic disease in question is not listed as a primary or secondary cause of death (Ritz and Yu 2000). Most studies focus on symptoms ascertained during life, and many of the criteria used to define neurologic disease are left to a certain extent to subjectivity, with inevitable associated misclassification of disease.
Also, there are difficulties associated in accurately differentiating one neurologic disease from another, particularly since many conditions may co-exist. For example Drayer et al. (1986) measured “Parkinson plus syndrome,” which was defined as Parkinson disease (PD) and multiple system atrophy (MSA), or PD and progressive supranuclear palsy (PSP). There is also co-morbidity of vascular conditions such as stroke or coronary heart disease. To complicate matters further, diagnostic criteria have changed or evolved over time. For example, those for MSA were revised as recently as 1998 (Wenning et al. 2004).
One solution to the problems associated with diagnosing neurodegenerative diseases would be to measure neurologic symptoms individually and early, before clinical diagnosis. Formal neuropsychologic tests can provide some objective support. Several lines of evidence have been published with this focus (Baldi et al. 2001; Engel et al. 2001; Farahat et al. 2003; Kamel and Hoppin 2004; Kamel et al. 2003; Pilkington et al. 2001). However, there may be some uncertainty with linking early subtle effects with clinical diagnosis. For this reason, we examine in this review only those studies where there was definitive clinical diagnosis for neurodegenerative disease.
Exposure Definition
A number of studies have examined the relationship between exposure to environmental agents and neurologic outcomes. Many studies involve a plethora of individual agents as a mixture, so it is understandable that some have not found an association for neurologic outcomes using a broad exposure definition. Similarly, the grouping of pesticides as an exposure category may be entirely too large. Many researchers have attempted to subdivide this class of chemicals into insecticides, herbicides, and fungicides, but even then a large number of substances could be classified within these clusters, and it remains difficult to tease out the associated environmental agent with the adverse outcome. For example, Baldi et al. (2003b) note the lack of information on specific pesticides because of trade secrets, although they could surmise the pesticides most often used in vineyards would be fungicides.
Accurate exposure assessment may be difficult to perform because of the retrospective aspect of case–control studies (see “Bias”). Also, assigning exposure categories may result in misclassification for ecologic studies (Gauthier et al. 2001; Ritz and Yu 2000) and when proxy respondents are used (Gauthier et al. 2001). There is often a lack of dose–response data, and similarly, the intensity of exposure may be missed. In environmental exposure data, there may be a peak exposure not captured in averaged exposure data, or there may be an accumulation of low-level exposure that may be below the detection limit. For biomonitoring data, nonpersistent exposures will be difficult to capture with accuracy, and the organ or tissue assessed may or may not be where the chemical is deposited or where it has an effect. Finally, if for some exposures there is an earlier critical window of exposure that sets the stage for later neurologic degeneration, a focus on later exposure history may not capture early developmentally relevant exposures.
Power
To find statistically significant results, investigators must have a sufficiently large study sample, or once the data are stratified the results will not be very telling. Likely this is why many investigators publish results on groups of chemicals (i.e., pesticides) rather than on specific chemicals, or on diagnosable diseases rather than on individual symptoms. Therefore, it is not surprising that little evidence exists to support an association between PD and specific pesticides (Engel et al. 2001; Hertzman et al. 1994). One study in particular did find an association for paraquat and PD (Liou et al. 1997), and another found an association between organochlorines and alkylated phosphates and PD (Seidler et al. 1996). However, the number of studies examining specific pesticides resulting in statistically significant conclusions is limited.
Additionally, many of the case–control studies use small numbers of study participants, particularly those studies measuring biomarkers of exposure (Bergomi et al. 2002; Drayer et al. 1986; Felmus et al. 1976; Fleming et al. 1994; Gellein et al. 2003; Kapaki et al. 1989; Kasarskis et al. 1995; Miyata et al. 1983; Moriwaka et al. 1993; O’Mahony et al. 1995; Rajput et al. 1986; Sood et al. 1990; Stober et al. 1983; Vinceti et al. 2002; Yasui et al. 1991a, 1991b, 1993). This limits the amount of power to detect a statistically significant association. Also, although not discussed in this review, the increasing focus on gene–environment interactions will have a major impact on the results because of the need to stratify results across genotype as well as chemical type.
Bias
Closely related to exposure assessment, a major difficulty for case–control studies generally is recall bias. Because the exposures tend to occur much earlier than the diagnosed outcome, the individual is asked to remember potential exposures over a long period; this is exacerbated for neurological diseases because the outcomes in question also tend to affect memory. However, those individuals with symptoms may overreport exposures because of the desire to determine a cause for their condition.
Selection bias may occur if those participating in the study are healthier than those who do not participate. This is known as the “healthy worker effect” because many occupational studies contain only those employees who have remained healthy and able to retain their job. For nonoccupational studies, those with more severe neurodegenerative diseases may participate to a lesser degree because of their reduced ability to communicate and travel to study centers.
Confounders
As mentioned above, other risk factors for neurodegenerative disease may include gender, endocrine conditions, oxidative stress, infection and inflammation, nutrition, vascular conditions, depression, head trauma, tumors, and level of education. Ethnicity and culture may also have implications and provide insights into the etiology of some diseases (Marder et al. 1998). In particular, smoking and caffeine and alcohol consumption have been postulated to have an association with neurodegenerative diseases.
Although the protective effect of smoking on PD is well known (Allam et al. 2004; Quik 2004; Ross and Petrovitch 2001), there is conflicting epidemiologic evidence regarding an association between smoking and risk of AD (Almeida et al. 2002; Letenneur et al. 2004). There is much less evidence for an association between smoking and parkinsonian syndromes, but one study showed a protective effect for MSA but not for PSP (Vanacore et al. 2000).
Similarly, caffeine consumption has generally been shown to be protective against the development of PD (Ross and Petrovitch 2001), although there is much less consistent information for an association with AD (Lindsay et al. 2002; Tyas et al. 2001). There is mixed evidence for a protective effect of alcohol and PD (Behari et al. 2001; Benedetti et al. 2000; Checkoway et al. 2002; Fall et al. 1999; Kuopio et al. 1999; Liou et al. 1997; Morano et al. 1994; Paganini-Hill 2001; Wang et al. 1993) and less for alcohol and AD (Letenneur et al. 2004; Tyas 2001).
Alzheimer Disease
Alzheimer disease is perhaps the prototypical degenerative disease affecting the central nervous system. AD is a chronic progressive disease characterized by memory loss and deficits in one or more of the following cognitive domains: aphasia (language disturbance), agnosia (failure to recognize people or objects in presence of intact sensory function), apraxia (inability to perform motor acts in presence of intact motor system), or executive function (plan, organize, sequence actions, or form abstractions). In addition, these deficits must be severe enough to interfere with daily life or work, and they must represent a significant decline from an earlier level of function. It is estimated that about four million Americans are currently diagnosed with AD. The prevalence rate is about 7% for individuals aged 65 or more, with the risk doubling every 5 years after age 65 (McCullagh et al. 2001; McDowell 2001).
Although most cases of AD are thought to be sporadic, there are at least four well-known risk factors for AD: increasing age, familial association, Down syndrome, and the apolipoprotein E4 allele (Cedazo-Mínguez and Cowburn 2001; McCullagh et al. 2001; Raber et al. 2004; Rubinsztein and Easton 2000; Weisgraber and Mahley 1996). Some examples of the association betwen exposure to environmental agents and AD are described briefly below.
Heavy metals are well-recognized environmental agents that affect brain development, leading to life-long impairment. Several epidemiologic studies have examined the possible link between aluminum (Al) and AD, with conflicting results. One study found Al in antiperspirants to be significantly associated with AD (Graves et al. 1990), but others showed no association for Al in antiperspirants (Lindsay et al. 2002) or in antacids (Broe et al. 1990; Graves et al. 1990; Lindsay et al. 2002; Tyas et al. 2001). Studies examining occupational exposures of Al found slightly elevated but nonsignificant risk (Graves et al. 1998; Salib and Hillier 1996) or no association (Gun et al. 1997). Also, no association was observed for Al in bone and AD (O’Mahony et al. 1995). One reason for this discrepancy in occupational studies is the inaccurate exposure assessment based on job description and rated for exposure to metals (Graves et al. 1998). Furthermore, these comparisons may not be appropriate because of the different types of exposures, for example, dermal, oral, and inhalation.
The findings for an association between Al in drinking water and AD are also inconclusive. Two studies demonstrated statistically significant results (McLachlan et al. 1996; Rondeau et al. 2000), although the latter did not find a dose–response relationship. Others have found no association (Forster et al. 1995; Martyn et al. 1997). Because of this variability, some researchers are now attempting to explore whether speciation of Al plays any role in causation of AD. For example, Gauthier et al. (2000) found an association with monomeric organic Al in drinking water and AD but not with other forms of Al.
Metals other than Al have also been studied for the relationship with AD but to a much lesser extent. No association was observed for occupational exposure to lead or mercury (Gun et al. 1997), mercury from dental amalgams (Saxe et al. 1999), or increased mercury in the pituitary gland of AD cases versus controls (Cornett et al. 1998a). However, a non-significant elevation of mercury in the brain was associated with AD (Cornett et al. 1998b). Similarly, a statisticially significant association was observed between AD and an elevation of iron in the brain (Cornett et al. 1998b) but not in the pituitary gland (Cornett et al. 1998a), although these studies may have suffered from low power. Related to the storage of iron, an increase of the protein ferritin was found to be higher in the cerebral spinal fluid of patients with AD than that of controls (Kuiper et al. 1994). A statisticially significant association was found between AD and an elevation of zinc in the brain (Cornett et al. 1998b) but not for concentrations of zinc in hair and serum (Shore et al. 1984) or pituitary gland (Cornett et al. 1998a), although these studies may have suffered from low power. No association was observed for hair and serum concentration of copper or magnesium and AD (Shore et al. 1984). An association was observed between AD and increased selenium levels in the brain (Cornett et al. 1998b) but not in the pituitary gland (Cornett et al. 1998a).
The neurologic effect of some pesticides (especially organophosphates and carbamates) on their intended targets is known, and the evidence is growing for the unintended consequences on humans. A significant association was observed between occupational exposure to pesticides in general and AD (Baldi et al. 2003b), and statistical significant risk was observed for fumigants and defoliants and AD (Tyas et al. 2001). Others have not found an association for occupational (Baldi et al. 2003b; Gun et al. 1997; Tyas et al. 2001) or residential exposure to pesticides and AD (Gauthier et al. 2001). Fleming et al. (1994) examined the biologic burden of pesticides in AD cases and found an association between pesticides measured in the brain and AD.
Electromagnetic fields (EMFs) have been suspected as a causal factor for the development of AD. Sobel et al. (1996) found a strong association for EMFs and AD, with an increased risk for males compared with females. Feychting et al. (1998) found a strong but non-significant association with AD. A dose–response trend was observed for both outcomes. However, Graves et al. (1999) observed no association between EMF exposure and AD.
The few studies examining solvent exposure and the development of AD are contradictory. One study using job description as proxy for exposure assessment found a non-significant but suggestive association (Graves et al. 1998), yet other studies (Gun et al. 1997; Tyas et al. 2001) and a meta-analysis found no association (Graves et al. 1991).
Parkinson Disease
Parkinson disease is unique from AD in that it is characterized by abnormalities of motor control, as opposed to intellectual and personality changes. PD is characterized by resting tremors, bradykinesia (slowness of voluntary movement), rigidity, and a loss of postural reflexes. Patients with PD typically have a flat, expressionless face and walk with a stooped gait characterized by small steps. Many patients also experience severe depression.
The lifetime risk for PD is estimated to be 2 and 1.3% for men and women, respectively, and between 3.7 and 4.4% for “parkinsonism,” a term used to characterize other clinical conditions characterized by akinesia and rigidity that do not meet clinical or pathologic criteria for idiopathic PD (Elbaz et al. 2002). Although a number of genetic polymorphisms are linked to PD (Checkoway et al. 1998), it is likely that the majority of cases of PD are not inherited but related to environmental factors. This is supported by a genetic study of twins by Tanner et al. (1999), who observed that monozygotic–dizygotic concordance rates are indistinguishable, implying a lack of genetic influence and a strong probability of an environmental influence. Examples of environmental risk factors for PD are discussed below.
Substantial numbers of epidemiologic studies have found positive associations between PD and exposure to pesticides (Abbott et al. 2003; Baldi et al. 2003a, 2003b; Fall et al. 1999; Herishanu et al. 2001; Hertzman et al. 1994; Liou et al. 1997; Petrovitch et al. 2002; Ritz and Yu 2000; Seidler et al. 1996), herbicides (Butterfield et al. 1993; Gorell et al. 1998; Semchuk et al. 1992, 1993), insecticides (Butterfield et al. 1993; Gorell et al. 1998), and for residence in a fumigated home (Butterfield et al. 1993). Similarly, Fleming et al. (1994) found increased levels of pesticides in the brains of PD cases versus controls.
There are also a number of studies that have shown no association between PD and pesticide exposure (Behari et al. 2001; Koller et al. 1990; Kuopio et al. 1999; McCann et al. 1998; Smargiassi et al. 1998; Stern et al. 1991), herbicide exposure (McCann et al. 1998; Smargiassi et al. 1998), and fungicide exposure (Gorell et al. 1998). Some studies use a broad exposure definition for pesticides, as mentioned above.
Farming occupation and farm residence are related to pesticide exposure. A significant association was found for PD and farmworkers (Gorell et al. 1998; Ho et al. 1989; Wechsler et al. 1991) and orchard workers (Hertzman et al. 1990). However, these positive findings have not been replicated by other investigators examining farming (Baldi et al. 2003b; Behari et al. 2001; Chan et al. 1998; Engel et al. 2001; Koller et al. 1990; Kuopio et al. 1999; Morano et al. 1994; Rocca et al. 1996; Wong et al. 1991) or residence on a farm (Butterfield et al. 1993; Gorell et al. 1998; Semchuk et al. 1991).
Residence in rural locations was found to be associated with PD in a number of studies (Butterfield et al. 1993; Golbe et al. 1990; Ho et al. 1989; Koller et al. 1990; McCann et al. 1998; Morano et al. 1994; Rajput et al. 1986; Stern et al. 1991; Wong et al. 1991). Yet no association was found in a few other studies (Baldi et al. 2003b; Behari et al. 2001; Chan et al. 1998; Semchuk et al. 1991) and decreased risk was reported in others (Tanner et al. 1989; Wang et al. 1993).
Also related to rural or farm living is the use of well water, possibly associated with runoff of pesticides or other environmental contaminants. A number of studies show a positive association (Koller et al. 1990; Morano et al. 1994; Rajput et al. 1986; Smargiassi et al. 1998; Wong et al. 1991). However, many others found no association for drinking well water and the development of PD (Behari et al. 2001; Chan et al. 1998; Engel et al. 2001; Gorell et al. 1998; Kuopio et al. 1999; Liou et al. 1997; Semchuk et al. 1991; Stern et al. 1991; Wang et al. 1993), and one found an inverse relationship (McCann et al. 1998), although many of these studies may have low power to detect outcomes, and the study designs vary.
The association between PD and exposure to metals has been intensely investigated. Welders exposed to multiple types of metals appear to be at increased risk of developing PD, particularly at an earlier age (Racette et al. 2001). One study showed a subjective association with increased frequency of heavy metal exposure; however, this could not be confirmed (Seidler et al. 1996). Other studies showed no association for occupational exposures to heavy metals in general (Gorell et al. 1999; Liou et al. 1997).
Results of epidemiologic studies examining exposure to specific metals have similarly been variable. Some studies showed a significant association with exposure to manganese alone (Engel et al. 2001; Gorell et al. 1997, 1998, 1999), and Powers et al. (2003) showed a joint effect with exposure to both manganese and iron, but others have not found any association between PD and manganese (Semchuk et al. 1993). This is supported by evidence showing no change in brain concentration of manganese (Dexter et al. 1989, 1991, 1992) in PD cases versus controls. No association was observed for exposure to iron alone (Gorell et al. 1998) but was observed for the combination of iron and copper (Gorell et al. 1997, 1998, 1999), iron and lead (Gorell et al. 1997, 1998, 1999), and iron and manganese (Powers et al. 2003). There is evidence, however, that iron (Dexter et al. 1989, 1991, 1992; Hirsch et al. 1991; Riederer et al. 1989; Sofic et al. 1988) or ferritin (Dexter et al. 1990) deposits to greater or lesser degrees in areas of the brain. Exposure to copper was found to be associated with PD (Gorell et al. 1997, 1998, 1999; Wechsler et al. 1991), although biomonitoring studies are variable, with some showing an increase (Dexter et al. 1989, 1992) and another showing no change in brain concentration (Riederer et al. 1989). Exposure to Al was found to be higher in male cases than in male controls in one study (Wechsler et al. 1991) but not in another (Semchuk et al. 1993). For mercury, one study found that a significantly larger number of PD cases had dental amalgams than did controls (Seidler et al. 1996), and an association was observed between the concentration of mercury in the blood and urine and PD (Ngim and Devathasan, 1989), but others showed no association with exposure to mercury (Gorell et al. 1998; Semchuk et al. 1993). Finally, although zinc was found in the brain tissue of patients with PD (Dexter et al. 1989, 1991, 1992), no association for zinc exposure and PD was observed in another study (Gorell et al. 1998).
Limited epidemiologic studies suggest an association between exposure to solvents and PD. One study examined exposure to organic solvents and found a statistically significant relationship to the development of PD (Smargiassi et al. 1998). In addition to the possibility of solvents causing PD, Pezzoli et al. (2000, 2004) found that exposure to hydrocarbon solvents increased PD severity and earlier age at onset; another showed suggestive evidence of an association between solvents and PD (Seidler et al. 1996).
There are claims of an increase of PD for those working with wood or in other forms of construction. One study found a nonsignificant but highly elevated occupational risk (Hertzman et al. 1990); another found a link to exposure to wood preservatives (Seidler et al. 1996); and a third found an increased risk for those having worked on construction sites (Herishanu et al. 2001). However, the agent associated with PD is not determined for this exposure.
Parkinsonian Syndromes
There are other neurodegenerative conditions with symptoms similar to PD, including MSA and PSP. These diseases often can be confused with PD because they have similar symptoms and may also co-exist (Drayer et al. 1986). Therefore, it is important to consider whether they have similar or distinct etiologies compared with PD and other neurodegenerative diseases.
MSA is a cluster of three related disorders, one of which is parkinsonian that is characterized by low blood pressure resulting in dizzy spells. The incidence rate is about 0.6 in 100,000 per year, with the incidence rate increasing to 3 in 100,000 in the population older than 50 years (Bower et al. 1997; Wenning et al. 2004). The evidence of an association with environmental agents is limited. Nee et al. (1991) found MSA to be significantly associated with metal dusts and fumes, plastic monomers and additives, organic solvents, and pesticides when compared with controls. Case–control studies of biomarkers of exposure show increased concentrations of iron in the brains of patients with MSA (Dexter et al. 1991, 1992) and in patients diagnosed with both PD and MSA (Drayer et al. 1986) versus controls. No change in brain concentration of manganese was observed (Dexter et al. 1991, 1992).
PSP is a neurologic condition affecting the brainstem that also has symptoms similar to those of PD. It is characterized by movement and visual abnormalities. Little is known about the incidence rate (Bower et al. 1997) or etiology of PSP (Davis et al. 1988; Golbe et al. 1996; Pezzoli et al. 2004; Rajput and Rajput 2001; Rehman 2000). Case–control studies examining the body burden of exposure shows iron was increased in the brains of patients with PD (Dexter et al. 1991, 1992) and patients with both PD and PSP (Drayer et al. 1986). However, neither iron nor Al in the brains of cases was found to be different from cases in another study (Hirsch et al. 1991). Copper was decreased in the brains of patients with PD (Dexter et al. 1991, 1992) and manganese had no effect (Dexter et al. 1991, 1992).
Amyotrophic Lateral Sclerosis
Amyotrophic lateral sclerosis (ALS), also known as motor neuron disease or Lou Gehrig’s disease, is a rare neuromuscular disease with an incidence rate of about 1 in 100,000. It is characterized by muscular weakness from the degeneration of motor neurons, and like PD, intellect and personality is often unaffected. The National Institute of Neurological Disorders and Stroke reports that only 5–10% of all ALS cases can be traced to genetics, particularly to a mutation related to the superoxide dismutase 1 enzyme. This leaves the vast majority of cases without a known etiology, with the potential for environmental association briefly outlined below.
Far fewer studies have examined the association of pesticides and ALS than for both AD and PD. McGuire et al. (1997) found that agricultural chemicals have a significant association with the development in ALS, with a stronger association for men than for women.
Metals may play a role in the development of ALS. Some studies have observed an association with occupation in welding or soldering (Armon et al. 1991; Gunnarsson et al. 1992), but not all have found metals to be related to ALS (Gresham et al. 1986; McGuire et al. 1997). More specifically, an association has been observed with exposure to lead (Armon et al. 1991; Chancellor et al. 1993; Felmus et al. 1976; Kamel et al. 2002), but no association was observed between ALS and lead levels in various tissues (Kapaki et al. 1989; Stober et al. 1983) or toenails (Bergomi et al. 2002); however, these studies had limited numbers of study participants. No association was observed between exposure to zinc and ALS (Vinceti et al. 2002), and the evidence from biomarker studies is inconclusive, with an increased (Gellein et al. 2003), decreased (Yasui et al. 1993), and no association observed for levels in brain tissue (Kapaki et al. 1997; Nagata et al. 1985) or toenails (Bergomi et al. 2002) compared with controls. However, these studies may have had limited power based on the size of the study population. Although one epidemiologic study showed no association between exposure to copper and ALS (Vinceti et al. 2002), there was decreased copper concentration observed in both cerebrospinal fluid and blood (Kapaki et al. 1997), and no association in toenails (Bergomi et al. 2002) among patients with ALS versus controls. Mercury was associated with ALS risk (Felmus et al. 1976) but was found in lower concentrations in the blood of ALS patients versus controls (Moriwaka et al. 1993).
Case–control studies examining biomarkers of iron, manganese, selenium, and Al and risk of ALS were found. Increased iron levels have been observed in brain tissue (Kasarskis et al. 1995; Yasui et al. 1993), although not in blood (Nagata et al. 1985) or toenails (Bergomi et al. 2002). An increase of manganese was observed in cervical cords (Miyata et al. 1983), both an increase (Kapaki et al. 1997) and decrease (Nagata et al. 1985) in blood levels, and no difference in toenail concentration (Bergomi et al. 2002) among cases versus controls. Selenium was found to be increased (Nagata et al. 1985) and decreased (Moriwaka et al. 1993) in blood cells, but no association was observed in toenails (Bergomi et al. 2002) of patients with ALS versus controls. An increase was observed in Al in central nervous system tissue (Yasui et al. 1991a, 1991b) and cerebrospinal fluid (Sood et al. 1990), yet others observed no association in spinal cords (Kasarskis et al. 1995) or toenails (Bergomi et al. 2002). However, the latter two studies had small numbers of study participants, possibly limiting the power to detect an association.
A few studies found a relationship between other exposures and ALS. Gunnarsson et al. (1992) found a nonsignificant association with solvents, but the association was stronger and statistically significant for males with family history of neurodegenerative disease or thyroid disease. Others found conflicting results (Chancellor et al. 1993; McGuire et al. 1997). One study found that those with a history of occupation in the manufacturing of plastics have a significant association with the development of ALS (Deapen and Henderson et al. 1986). Occupations in electrical work have been implicated in the development of ALS in a few studies (Deapen and Henderson 1986; Gunnarsson et al. 1992).
Conclusion
Epidemiologic evidence for an association between environmental agents and neurodegenerative disease is inconclusive. The amounts of xenobiotics released into the environment are huge by any measure, and the paucity of information about their effects on various physiologic systems, including neurodevelopmental processes, represents a major gap in knowledge. To close this gap, the following broad areas of research topics need attention: a) better health tracking and monitoring data for chronic diseases, b) more comprehensive and longitudinal biomonitoring of environmental agents that can be linked with specific molecular/biochemical markers of exposure and subsequent health outcome data, and c) more epidemiologic research and testing of environmental agents to better define their effects on the adult and developing brain, as well as other critical organ systems.
Until such time that ethically and scientifically well-designed epidemiologic studies can provide a reasonable certainty that specific environmental agents, either alone or in combination with other agents, cause a given neurodegenerative disease, research on the environmental contribution to neurodegenerative disease needs to continue.
This article is part of the mini-monograph “Early Environmental Origins of Neurodegenerative Disease in Later Life: Research and Risk Assessment.”
We thank L. Boni and L. Trasande of Mt. Sinai School of Medicine for their logistic support. We also thank E. Newell of the U.S. EPA for editorial support.
The opinions expressed in this article are the authors’ and do not necessarily represent those of their affiliated institutions. Any mention of trade names also does not represent endorsement of products by the U.S. EPA, Association of Schools of Public Health, or the University at Buffalo, The State University of New York, Buffalo, New York.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7569ehp0113-00125716140638ResearchMini-MonographAging and the Environment: A Research Framework Geller Andrew M. Zenick Harold National Health and Environmental Effects Laboratory, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina, USAAddress correspondence to A.M. Geller, U.S. EPA, 109 TW Alexander Dr., MD B305-02, Research Triangle Park, NC 27711 USA. Telephone: (919) 541-4208. Fax: (919) 541-1440. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 26 5 2005 113 9 1257 1262 1 9 2004 3 3 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The rapid growth in the number of older Americans has many implications for public health, including the need to better understand the risks posed to older adults by environmental exposures. Biologic capacity declines with normal aging; this may be exacerbated in individuals with pre-existing health conditions. This decline can result in compromised pharmacokinetic and pharmacodynamic responses to environmental exposures encountered in daily activities. In recognition of this issue, the U.S. Environmental Protection Agency (EPA) is developing a research agenda on the environment and older adults. The U.S. EPA proposes to apply an environmental public health paradigm to better understand the relationships between external pollution sources → human exposures → internal dose → early biologic effect → adverse health effects for older adults. The initial challenge will be using information about aging-related changes in exposure, pharmacokinetic, and pharmacodynamic factors to identify susceptible subgroups within the diverse population of older adults. These changes may interact with specific diseases of aging or medications used to treat these conditions. Constructs such as “frailty” may help to capture some of the diversity in the older adult population. Data are needed regarding a) behavior/activity patterns and exposure to the pollutants in the microenvironments of older adults; b) changes in absorption, distribution, metabolism, and excretion with aging; c) alterations in reserve capacity that alter the body’s ability to compensate for the effects of environmental exposures; and d) strategies for effective communication of risk and risk reduction methods to older individuals and communities. This article summarizes the U.S. EPA’s development of a framework to address and prioritize the exposure, health effects, and risk communications concerns for the U.S. EPA’s evolving research program on older adults as a susceptible subpopulation.
agingenvironmental healthexposurefrailtymicroenvironmentolder adultspharmacodynamicspharmacokineticspolypharmacy
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Demographics of the United States are changing rapidly. By 2030, the number of individuals older than 65 will more than double to 71.5 million [U.S. Administration on Aging (U.S. AoA) 2004]; one of every five Americans will be older than 65 [U.S. Department of Commerce (U.S. DoC) 1996]. This growth in the number of older Americans has major implications from both human and ecologic health perspectives. For human health, there will be an increasing need to understand the impact that environmental exposures and conditions might have on individuals as they enter later stages of life. Equally important will be the need to understand the implications for, or impact on, ecologic resources associated with accommodating the residential, medical, recreational, and transportation needs of this population. This document presents a preliminary framework for research to assist the U.S. Environmental Protection Agency (EPA) to better a) delineate the special susceptibilities associated with the aged compared with the healthy younger adult population, b) identify gaps in knowledge, and c) provide a starting point to establish research priorities. Given that the impact of the transition of “baby boomers” into senior citizens will rapidly accelerate in the next 5–10 years, opportunities exist to conduct research in the interim that will help to better inform decisions made by policy makers, institutions, and individual citizens. For the U.S. EPA, this work will provide a scientific rationale for decisions on how to appropriately incorporate the differential sensitivity of aging adults into environmental risk assessment, decisions, and actions. A parallel framework for considering the ecologic and resource use implications of the growing population of older adults is also being developed (U.S. EPA 2005b).
This article is a brief review based on primary and secondary sources that address the susceptibility of older adults to effects of environmental exposures and describes a wide array of research priorities. Two U.S. EPA-sponsored activities have also greatly informed this document. The first was the workshop “Differential Susceptibility and Exposure of Older Persons to Environmental Hazards’’ convened by the National Academy of Sciences (NAS) in December 2002 (NAS 2003). In addition, the U.S. EPA invited public comments on environmental hazards that may affect the health of older adults in states and local communities at six public listening sessions held throughout the United States in the spring of 2003 (U.S. EPA 2003a) and from comments sent directly to the U.S. EPA (2005a). The priorities that emerged from the NAS workshop and the public listening sessions were similar to those previously described by the NAS and the International Programme on Chemical Safety (IPCS) (Baker and Rogul 1987; IPCS 1993; National Research Council 1987).
Environmental Public Health Framework
A research program focused on the aging population is consistent with the priority that the U.S. EPA gives to susceptible subpopulations in its risk assessment/risk management processes. Several of the U.S. EPA’s statutes mandate such considerations [e.g., Clean Air Act of 1970 (1970), Food Quality Protection Act of 1996 (1996), Safe Drinking Water Act of 1974 (1974)]. As with the Agency’s well-established programs to assess risk to children (U.S. EPA 2000), a program focused on the aging population must consider factors that affect susceptibility associated with various life stages. For example, parallel assumptions are that individuals may be at greater risk at certain life stages as a result of modified pharmacokinetic and pharmacodynamic capacity and different exposure conditions. Susceptibility of older adults can be defined by qualitative or quantitative differences. Qualitative differences mean that exposure-related adverse health outcomes are present in older adults that are not present in younger individuals. Quantitative differences mean that a toxicologic outcome may be observed at lower doses, have a greater severity, or have a shorter latency to onset in older individuals than in young adults.
It is important to recognize that variability is a hallmark of the aging population (National Research Council 1987; Schmucker 1998, 2001; Vestal 1989), so it is likely that members of an aging population will exhibit variability in their responses to environmental agents. Thus, it follows that research will not generate a “one-size-fits-all” set of recommendations for risk management/health prevention actions. For example, at least three subpopulations can be identified: a) healthy individuals with normal but possibly diminishing capacities; b) individuals confronted with the emergence of disease/illness associated with later years (e.g., Alzheimer disease, age-related sensory losses); and c) individuals already afflicted with disease/illness entering this period of life (e.g., cardiovascular disease, respiratory disease, thyroid deficiency, diabetes).
One construct that may account for some of the variability of the aging population is that of the “fit” versus “frail” elderly (Crome 2003; O’Mahoney 2000). Fit refers to older individuals who are able to independently perform the daily activities in the community; frailty refers to older adults who may not be independently mobile and may be dependent on others to carry out daily activities, and are often in institutionalized care. The roots of frailty may lie in alterations to multiple physiologic systems (Walston 2004). It connotes diminished reserve capacity, diminished resistance to stressors, and increased health risk (Bortz 2002; Fried et al. 2001; Schuurmans et al. 2004). This construct may work to summarize the overall effects of the many conditions that affect health in the elderly because frailty has been shown to reduce both pharmacokinetic and pharmacodynamic functions (Kinirons and O’Mahoney 2004; Walston et al. 2002). It may also help to identify individuals or subgroups among the heterogeneous older adult population that might be susceptible to environmental agents because it has been shown to be a better predictor of adverse outcomes in older adults than chronologic age (Schuurmans et al. 2004).
Additional factors, including sex, socioeconomic status, cultural differences, lifestyle, nutrition, exposure history, and geographiclocation, may also be used to stratify or characterize the population of older adults. These sources of variability can be considered cross-cutting influences because they may be important determinants of the exposures experienced as well as the health outcomes. For example, these factors may modify the environmental exposure experienced by older individuals through effects on behavior and lifestyle choices, by influencing residential choices, daily habits, and activity patterns. They can also affect how the body responds to potentially threatening environmental exposures, influencing both what the body does to environmental toxicants (pharmacokinetics) and what those toxicants do to the body (pharmacodynamics) (Figure 1).
These cross-cutting factors are potential mediators of susceptibility, some of which are more the province of other federal agencies. One goal of this research framework is that it will be a basis for fostering collaborative research with these sister agencies.
An environmental public health continuum (Figure 2) that has been used previously by the U.S. EPA (2003b, 2003c) to aid in the development of broad research strategies has also been used in this article. Along this continuum are the cascade of events beginning with the source through adverse health effects. Research is directed to helping researchers understand the determinants influencing each component along the continuum and, equally important, the predictive linkages between components. The continuum is also of heuristic value in arraying ongoing research and identifying major gaps to help set priorities. The following sections concentrate on the contributions of the components identified as external exposure, internal dosimetry, and health outcomes (early biologic effects and the manifestation of disease) to the potential susceptibility of older adults. These data, in turn, provide a basis for risk management and health promotion.
Exposure
Exposure is the contact between an environmental agent and a target. In exposure assessments, exposure is usually quantified as the product of the concentration of the agent in environmental media with which an individual comes in contact (e.g., air, water, food) and the time the individual is in contact with the environmental agent. The behaviors that bring an individual into contact with an environmental agent are important determinants of the level of exposure. For example, inhalation exposures depend on the microenvironments where people spend their time. The term “microenvironment” refers to the immediate surroundings of an individual that can be treated as homogeneous or well characterized in the concentrations of an agent (e.g., home, office, automobile, kitchen, store). Understanding microenvironments is critical because the highest personal exposures may occur where little time is spent but contaminant levels are high. For example, up to 35% of an individual’s daily exposure to particulate matter (PM) may come from microenvironments where only 4–13% of time is spent (Rea et al. 2001). Recent data have been published on the personal exposures of older adults to PM (Lippman et al. 2003; Rodes et al. 2001; Williams and Wallace 2002).
The sources and pathways of exposure as well as exposure locations may differ in older adults compared with younger adults. Current characterizations of the older adult population suggest that older adults spend more time indoors than younger adults, particularly in residences, and show marked similarities to the very young (0–4 years of age) in where they spend their time and in their types of environmental exposures (Jenkins et al. 1992; Klepeis et al. 1996; Williams et al. 2000a, 2000b). Time spent indoors is important because many hazardous air pollutants occur at higher concentrations indoors, thus potentially exacerbating exposure to indoor air pollutants (Kinney et al. 2002; Saarela et al. 2003; Spengler et al. 1985; U.S. EPA 1998). There are, however, differences within the older population, again demonstrating that this is a heterogeneous group. For example, older adults in similar residential situations in different locations (Baltimore, Maryland vs. Fresno, California) spend different amounts of time indoors (Rea et al. 2001). Differences in activity patterns such as cooking, which may have implications for PM exposure, can also be seen between older adults in residential retirement centers (Williams et al. 2000b) and the broader population (Klepeis et al. 1996).
Dose
The goal of research on internal dosimetry is to understand the effects of physiologic and biochemical changes with age on target tissue dose for a given exposure. This work focuses on the pharmacokinetic processes of absorption, distribution, metabolism, and elimination (ADME) that determine the dose of an environmental agent that reaches a target organ. Many age-related differences in drug and toxicant responsiveness appear to be based on altered ADME (Table 1) (Birnbaum 1991; Clewell et al. 2002, 2004; Mayersohn 1994; O’Mahoney 2000; von Moltke et al. 1995). Changes in these processes mean that the same external dose may result in a very different internal dose or distribution to different target organs in older adults.
The current pharmaceutical literature indicates that fit older adults are quite similar to fit younger adults in pharmacokinetic parameters, with the general exceptions of decreased renal excretion and hepatic processing, secondary to changes in hepatic blood flow and liver volume (O’Mahoney 2000). It is notable, however, that pharmacokinetic function is decreased in frail older individuals. Disease, physical trauma, and changes in nutritional status (O’Mahoney 2000; Walston 2004) can alter many pharmacokinetic factors (Figure 3).
Absorption.
Absorption occurs principally via the gastrointestinal tract, the respiratory tract, or the skin. There are no marked age-related changes in gut absorption after oral exposure. One exception involves decreased acid production in the stomach, which reduces the dissolution of basic compounds (Mayersohn 1994; O’Mahoney 2000; Schmucker 1985). The inhalation pathway may show changes in absorption or deposition due to age- or disease-related changes in lung volume, ventilation rate, and alveolar elasticity (Clewell et al. 2002; Lippman et al. 2003). For example, changes due to airway obstruction that accompany chronic obstructive pulmonary disease result in deeper penetration of PM and a higher rate of particle deposition (Brown et al. 2002; Kim and Kang 1997). Changes in dermal structure and function with aging may alter dermal absorption such that the ability of the skin to exclude certain compounds may be reduced with aging. This reduction in barrier function is most likely to accompany pre-existing conditions that place the skin under stress—the skin of older adults recovers from stresses significantly more slowly compared with that of younger adults (Elias and Ghadially 2002; Ghadially et al. 1995; Ye et al. 2002).
Distribution.
Distribution of a chemical throughout the body can be affected by many factors, including body composition, blood flow, and plasma binding proteins (Clewell et al. 2002). Changes in body composition (Table 1) can result in reduced volume of distribution or increased half-lives for xenobiotic compounds, depending on whether compounds are soluble in lipids or water (O’Mahoney 2000; Schmucker 1985; von Moltke et al. 1995). Changes in plasma protein binding may also be critical (Table 1) because the main factor determining the effect of a compound in the body is the free, unbound fraction of that compound (Birnbaum 1991; O’Mahoney 2000; von Moltke et al. 1995). Age-related reductions in serum albumin can increase the serum-free fraction of lipophilic compounds, whereas age-or disease-related increases in α1-acid glycoprotein affect the binding of basic compounds (Clewell et al. 2002).
Another potential area of concern is changes in the blood–brain barrier with aging, resulting in increased permeability of the cerebral microvasculature to toxicants that could result in neurodegenerative disease. Most data currently indicate that there are no significant changes in permeability with normal aging (Shah and Mooradian 1997). Diseases often associated with aging, however, such as diabetes, hypertension, and cerebral ischemic events, may compromise this barrier function (Johansson 1998; Mooradian 1997; Starr et al. 2003; Wisniewski and Lossinsky 2002). This may be important in understanding the environmental etiology of conditions such as parkinsonism, which has been linked to exposure to some compounds that ordinarily show limited penetration of the blood–brain barrier (Brooks et al. 1999; Thiruchelvam et al. 2003).
Metabolism.
The liver is the major metabolic organ in the body, and studies show that levels of liver activity drop with aging (Youssef and Badr 1999). This decreased activity could result in slowed detoxification of some compounds and reduced excretion rates, which can result in higher effective circulating levels and longer half-lives.
Although it was initially thought that the age-related decrease in metabolism was due to changes in the activity of liver enzymes, current data indicate that most age-related changes in hepatic activity are due to declines in liver volume and blood flow with age (O’Mahoney 2000; Schmucker 2001; Vestal 1989). There are few known significant changes in the levels of activity of metabolic enzymes with normal aging (Schmucker 1998, 2001; Shimada et al. 1994), but many gaps still remain in the understanding of aging-related metabolic changes (Clewell et al. 2002). The disposition of xenobiotics is also affected by transporters such as P-glycoprotein (Pgp) and multidrug-resistance–associated protein. There is increased Pgp expression in lymphocytes of older adults; it has been suggested that this may have an effect on metabolism and drug interactions (Gupta 1995; Kinirons and O’Mahoney 2004; McLean and LeCouteur 2004). The effect of aging on Pgp function throughout the body is still unknown.
The role of the liver enzymes is critical to another aspect of the issue of age-related changes in metabolism: polypharmacy, the administration of two or more pharmaceutical compounds to an individual. Studies show that 90% of people older than 65 take one or more medications daily, with most taking two or more, and residents of nursing homes or care facilities average six to eight medications per individual (Prybys et al. 2002; Vestal 1997). Because the same biologic processes “clear” medications and environmental toxicants, there is concern that older adults who take multiple medications may be at increased risk of adverse reactions between medications and concurrent or subsequent environmental exposures. Either induction or inhibition of metabolic enzymes by environmental chemicals (Borlakoglu and Haegele 1991; Butler and Murray 1997; Thummel and Wilkinson 1998; Youssef and Badr 1999) could alter the body’s critical processing of pharmacologic agents (Shimada et al. 1994; Thummel and Wilkinson 1998). Conversely, metabolic processes can make some environmental chemicals more biologically active, as in the case of some carcinogens or pesticides (Buratti et al. 2003; Guengerich and Shimada 1991; Sams et al. 2000). Therefore, exposure to these compounds, in conjunction with medications that may induce higher levels of enzyme activity, could result in greater toxicity (U.S. Food and Drug Administration 2002). Polypharmacy may also affect plasma protein binding if competitive displacements occur (Herrlinger and Klotz 2001; Mayersohn 1994; Rolan 1994).
Elimination.
The elimination of toxicants and their metabolites is affected by age-related changes in hepatic function, described above, and by decreased kidney function. A decrease in the rate of renal clearance results in an increase in the elimination half-life of a compound. Renal changes observed with age include a decrease in mass of the kidneys, a reduction in the size and number of nephrons, a decrease in renal blood flow, and reductions in glomerular filtration rate, renal plasma flow, and tubular function (Mayersohn 1994; O’Mahoney 2000). In addition, the alterations in pulmonary function that affect absorption of gases and volatile compounds also will affect their excretion through the pulmonary route (Birnbaum 1991). There is also evidence that bile flow and biliary transport is reduced with aging, thus reducing excretion through that route (Birnbaum 1991).
Health Outcomes
There are clear examples of increased health risk associated with environmental exposures of older individuals. Research on PM has shown significant associations between cardiopulmonary morbidity and pollutant levels (Bateson and Schwartz 2004; Zanobetti et al. 2000). Older adults are also more vulnerable to gastrointestinal disease from waterborne pathogens (Naumova et al. 2003). However, aging-related changes in pharmacodynamic processes that may limit the body’s ability to maintain homeostasis and respond to injury have been studied less extensively than the pharmacokinetic changes (Vestal 1997). Older adults may be more susceptible to toxicants in the environment because they have a decreased ability to compensate for the effects of environmental insult, that is, a reduced “reserve capacity.” The mechanisms underlying a decreased compensatory ability may be similar across organ systems but may be expressed differently in those organ systems. For example, the same processes that play a major role in cancer initiation and promotion may also play an important role in cognitive decline with aging (Lu et al. 2004). These processes include DNA damage in promotor regions of genes with reduced expression and a reduction in base-excision DNA repair associated with oxidative stress and impaired mitochondrial function. Additional pharmacodynamic changes include age-related changes in receptor numbers, sensitivity, and up- and down-regulation, as well as altered signal transduction, reduced numbers of neurons, and changes in calcium homeostasis (Goldman et al. 1994; Michalek et al. 1990). Alterations to other mechanisms of plasticity or homeostasis include reduced immune response, altered response to oxidative stress (Kodavanti 1999), and reduced DNA repair and anti-proliferation mechanisms (Frohman 1994; Lu et al. 2004; Rehman and Masson 2001). Aging also results in changes in neuroendocrine and neurotransmitter levels (dopamine, GABA, glutamate) along with alterations in the thyroid–pituitary axis; decreases in the production of sex steroids, growth hormone, and insulin-like growth factor; and increases in glucocorticoids and cytokines (Frohman 1994; Rehman and Masson 2001 Smith et al. 2004). These age-related alterations in function may then contribute to the increased vulnerability of older individuals to a variety of environmentally linked adverse health outcomes. Examples include neurotoxicity (Kodavanti 1999; Spencer 1990; Weiss 1990), cancer (Akman 2003), cardiovascular and pulmonary morbidity (Lippman et al. 2003; Reaven 2003), inflammatory responses (McCord 2003), and gastrointestinal effects associated with increased susceptibility to waterborne pathogens (Naumova et al. 2003).
Research Recommendations
One of the challenges in conducting research on aging is addressing the great diversity of health and exposure conditions of the older adult population. To be responsive to public health needs, it is important to be able to predict which older adults, defined not only by age but also by the influence of the cross-cutting factors discussed above, will be most susceptible and to which environmental agents. Employing the environmental public health continuum (Figure 2), the following are research areas under initial consideration by U.S. EPA.
Exposure: provide data for use in source-to-dose exposure models applicable to older adult populations and information specific to older adults for the U.S. EPA Exposure Factors Handbook (U.S. EPA 1997).
Initial steps include the identification of susceptible subgroups of older adults on the basis of exposure and activity modeling. Exposure models will be derived from information on chemical and biologic stressors, geographic location, and health status drawn from existing databases at the U.S. EPA. Other resources to be “mined” include age-specific census, occupational, dietary, and product safety data. Research on activity patterns and microenvironments of importance to the elder population includes characterization of time spent indoors, recreational choices, occupation, control over the environment in group housing, and the effects of reduced mobility, lifestyle choices, and isolation. This initial modeling and compilation of data will help researchers identify and prioritize data gaps. This will, in turn, generate hypotheses and guide further development of the database and refinement of models for assessing the degree to which susceptibility in older adults is due to differences between older and younger adults in activity and in exposures to harmful environmental agents.
Dose: determine the contribution of age-related alterations in pharmacokinetics to the susceptibility of older adults.
The initial steps in this research will also be model-driven in that sensitivity analysis will help to determine which factors, such as changes in particular metabolic enzymes, transport processes, or excretion functions, are most important for particular classes of chemicals. Modeling should also define the magnitude of change necessary for a factor to alter the exposure–dose–response relationship for prototype chemicals. This will help to narrow the focus for empirical research on physiologic and biochemical parameters that will have the greatest effect in older adults by overlaying those factors on the ones that are known to change with aging and with diseases of aging. It may also help to define which diseases or medications might be expected to increase susceptibility to effects of environmental exposures (Figure 3). Susceptibility due to pharmaceutical use will be informed by our understanding of the common mechanisms underlying the metabolism of pharmaceutical compounds and environmental agents. In addition, identification of critical kinetic factors will aid in the evaluation and development of animal or in vitro models because effects noted in rodent models of aging have not always accurately reflected changes present in aging humans (e.g., Schmucker 2001).
Health outcomes: determine the relationship between exposure to environmental agents and adverse health effects in aging populations.
Data from mechanistic research conducted to understand the age-dependence of pharmacodynamic processes such as protective, repair, compensatory, and plasticity mechanisms across organ systems can be applied to the question of whether mode of action information can predict which subpopulations are susceptible to the effects of environmental agents. As with the pharmacokinetic approach, this work will identify and prioritize the processes or mechanisms that confer susceptibility on aging adults and match these with environmental agents presumed to operate through similar putative mechanisms.
Risk communication: develop a strategy for communication of risk, risk management, and public health intervention.
This will likely include the dissemination of information to and through environmental and health professionals, state and local governments, developers of senior communities, and the broad community of professionals, organizations, and associations involved with aging issues. Effective communication must consider the social and cognitive strategies most appropriate for older adults (Helmuth 2003; Park 2002).
Conclusion
The research framework we describe in this article focuses on the potential for interactions between aging and environmental exposures to produce adverse health effects in older adults. This research program will generate data on exposures that the aging population experiences and the subsequent pharmacokinetic and target organ responses, with the goal of providing a better understanding of the environmental health risks associated with aging in healthy or compromised older adults. These data will be used to generate models and guidance on how to appropriately incorporate the differential susceptibility of this heterogeneous subpopulation into health promotion and intervention strategies to ameliorate risk from environmental exposures.
Now, still a few years away from the cresting of this demographic wave, is the time to anticipate, accommodate, and manage the environmental risks associated with this inevitable shift in American demographics toward an aging society.
This article is part of the mini-monograph “Early Environmental Origins of Neurodegenerative Disease in Later Life: Research and Risk Assessment.”
We thank K. Thomas, B. Glenn, K. Hammerstrom, and E. Washburn of the Office of Research and Development Work Group for the Aging Initiative and R. Dewoskin for their additions to this research framework, and K. Sykes of the Office of Children’s Health Protection for her support. We also thank L. Birnbaum, S. Wright, and W. Boyes for their helpful comments on an earlier draft of the manuscript.
The information in this document has been funded wholly (or in part) by the U.S. EPA. It has been subjected to review by the National Health and Environmental Effects Research Laboratory and approved for publication. Approval does not signify that the contents reflect the views of the agency, nor does mention of trade names or commercial products constitute endorsement or recommendation for use.
Figure 1 The interactions of environmental health, exposure, and additional sources of variability with aging broadly define the proposed dimensions for research on the health effects of exposure to environmental agents in older adults. SES, socioeconomic status. The dashed arrow signifies that many more items could be included along with the sources of variability listed.
Figure 2 Environmental public health continuum used by the U.S. EPA for strategic planning of research. Modified from the U.S. EPA (2003c).
Figure 3 Predictive modeling to identify the pharmacokinetic parameters that most affect outcomes such as tissue dosimetry and toxicity will consider prototype toxicants chosen according to a set of criteria (left), physiologic compartments and processes (center), and additional sources of variability that affect physiologic function (right). GI, gastrointestinal. All of these contribute to the determination of the level of a toxicant at its biologic target. Shaded boxes indicate which of the body’s compartments are mainly involved in ADME of environmental exposures, recognizing that almost all tissues have some metabolic capacity.
Table 1 Pharmacokinetic changes that may contribute to increased susceptibility in older persons.
Process Pharmacokinetic changes in aging adults
Absorption No significant changes in gastric absorption; decline in gastric acid production
Changes in dermal absorption, barrier function
Changes in lung volume, elasticity, ventilation rate
Distribution Change in body composition
Decreased total body water in older adults results in decreased volume of distribution/higher serum levels for polar compounds
Decreased muscle mass and increased relative adipose levels result in higher accumulation of lipophilic compounds and slower clearance
Plasma protein binding—decrease in plasma albumin (which bind acidic compounds), increase in α1-glycoprotein (bind basic compounds)
Potential for increased permeability of blood–brain barrier with concurrent disease (diabetes, hypertension, cerebrovascular ischemia)
Metabolism Reduced liver volume and liver blood flow
Minor effects on phase I and II metabolism in healthy aging
Significant metabolic effects in conjunction with frailty/age-associated disease
Decline in specific cytochrome P450 content
Polypharmacy—interactions of environmental toxicants with therapeutic compounds, herbal supplements, and diet due to shared metabolic pathways, and/or induction or inhibition of metabolic enzymes and/or transporters
Excretion Reduced renal function
Reduced blood flow
Reduced glomerular filtration
Reduced renal MFO activity, inducibility
Reduced biliary excretion
Reduced pulmonary excretion
MFO, mixed-function oxidase.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7570ehp0113-00126316140639ResearchMini-MonographDevelopmental Pesticide Models of the Parkinson Disease Phenotype Cory-Slechta Deborah A. 123Thiruchelvam Mona 12Barlow Brian K. 2Richfield Eric K. 131 Environmental and Occupational Health Sciences Institute, University of Medicine and Dentistry of New Jersey and Rutgers University, Piscataway, New Jersey, USA2 Department of Environmental and Occupational Medicine, and3 Department of Pathology and Laboratory Medicine, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey, USAAddress correspondence to D.A. Cory-Slechta, Environmental and Occupational Health Sciences Institute, 170 Frelinghuysen Rd., Piscataway, NJ 07920 USA. Telephone: (732) 445-0205. Fax: (732) 445-0131. E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 26 5 2005 113 9 1263 1270 1 9 2004 31 3 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. It has been hypothesized that developmental insults could contribute to Parkinson disease (PD), a neurodegenerative disorder resulting from the loss of the dopamine neurons of the nigrostriatal pathway. Two models of developmental pesticide exposures in mice are presented here that yield PD phenotypes consistent with this possibility. Combined exposures to the herbicide paraquat (PQ) and the fungicide maneb (MB), both of which adversely affect dopamine systems, administered from postnatal days 5–19, produced selective losses of dopamine and metabolites and reduced numbers of dopamine neurons in the substantia nigra. Effects were greater than those produced by adult-only exposures. Moreover, developmental PQ + MB exposures enhanced vulnerability to this pesticide regimen when administered subsequently in adulthood. In a second model, exposure to MB from gestational days 10–17 markedly increased vulnerability to PQ exposures during adulthood, with reductions in dopamine and metabolites and numbers of dopamine neurons in the substantia nigra. Females evidenced protection in both models. Collectively, these models demonstrate that developmental exposures can produce progressive, permanent, and cumulative neurotoxicity of the nigrostriatal dopamine system and enhance vulnerability to subsequent environmental insults. Finally, effects of PQ + MB were greater than those of either pesticide alone in the postnatal model. This is consistent with a multiple-hit hypothesis predicting that multiple concurrent insults occurring at different target sites within a system (here nigrostriatal dopamine) may constrict the range and flexibility of compensatory mechanisms, thereby compromising the integrity and viability of the system. As such, this hypothesis presents a biologic strategy for identifying potentially significant neurotoxic mixtures for hazard identification in future studies.
developmentdopaminemanebnigrostriatal systemparaquatParkinson diseasepesticidessubstantia nigra
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Parkinson disease (PD) is a chronic neurodegenerative condition afflicting nearly one million people in the United States alone (McDonald et al. 2003). First described almost 200 years ago (Parkinson 1817), it is characterized clinically by resting tremor, rigidity, akinesia, and bradykinesia with a subsequent loss of postural stability. The sporadic form of the disease typically begins after 60 years of age. The basis for this movement disorder is a progressive loss of dopaminergic (DA) cells in the substantia nigra pars compacta, the neurons of the nigrostriatal DA system. The consequence is a loss of DA in the nigrostriatal system and thereby its control over motor function. Symptoms become evident after the loss of approximately 80% of these DA neurons. Characteristic of the accompanying pathology is the presence of Lewy bodies, which are intracytoplasmic inclusions containing neurofilament proteins and others such as α-synuclein and parkin. PD brings with it an extensive emotional and financial toll not only on the individuals affected but also on their families and the community. The disease is primarily idiopathic or sporadic; Mendelian-based genetic mutations account for less than 5% of the total cases of PD.
The cause(s) of PD remains unknown, and over the years the mechanism of genetic inheritance versus environmental exposures has been debated. It has become increasingly clear over the past several years, however, that multiple risk factors, both genetic and environmental, can produce parkinsonism (Figure 1). No single genetic mutation can account for most PD, an assertion underscored by the findings of a study by (Tanner et al. 1999) that examined more than 19,000 pairs of white male twins from the World War II veterans registry. The results showed no difference in concordance rates for PD in monozygotic and dizygotic twins 60 or more years of age. Nevertheless, at the current time, at least nine different genetic loci have been associated with the familial form of the disease (Hardy et al. 2003). These include mutations in the genes for α-synuclein and parkin, two of the proteins found in Lewy bodies. In environmental exposures, both pesticides and metals have been implicated as risk factors for PD, based on results from some epidemiologic studies (both metals and pesticides) as well as from experimental models (pesticides). A series of meta-analyses of such studies, including 16 based on living in rural areas, 18 for well-water drinking, 11 for farming, and 14 for pesticide exposures (Priyadarshi et al. 2001), concluded that all these variables may be risk factors for the development of PD.
Consistent with the potential for environmental exposures to contribute to the etiology of PD is the fact that the disease shows geographic variation in its mortality statistics. Such variation has been reported in Japan (Imaizumi 1995), Canada (Imaizumi 1995; Svenson 1990; Svenson et al. 1993), and the United States (Kurtzke and Goldberg 1988; Lanska 1997; Lilienfeld et al. 1990; Lux and Kurtzke 1987). Three studies using U.S. data show a north-to-south gradient for age-adjusted PD mortality. Figure 2 depicts results based on 1988 U.S. National Center for Health Statistics data (Lanska 1997). The highest PD rates occurred in the Northeast, Mid-Atlantic States, the Midwest, and the Pacific Coast states, with intermediate levels in the mountain states and in the Southwest and very low rates in the South, particularly from Texas to Florida. This is in contrast to other neurologic diseases: stroke mortality rates are particularly high in the Southeastern United States (Pickel et al. 1997), and cancers of the nervous system are lowest in Pacific states including California, Oregon, and Washington (Menck et al. 1998).
Also cited as evidence in support of environmental contributions to this disease is that PD occurs in greater frequency in industrialized countries. The PD prevalence rate is reportedly much lower in China than in the United States Li et al. 1985; Tanner et al. 1987, 1989), and even in China, PD appears to be associated with industrial chemical exposures (Tanner et al. 1989). A recent study examining PD mortality and pesticide exposure in California from 1984 through 1994 reported that mortality was increased in counties using agricultural pesticides after controlling for age, gender, race, birthplace, year of death, and education (Ritz and Yu 2000). The fact that the prevalence of PD in immigrant populations is comparable to the prevalence rate in the country of destination is also indicative of an environmental exposure basis of PD. For example, the prevalence of PD in Nigeria is lower than that for U.S. African Americans (Schoenberg et al. 1988); those of African Americans and whites in the United States are reported to be similar (Schoenberg et al. 1985), even though the African populations in the United States and Nigeria are largely homogeneous genetically after controlling for age and other variables that likely differ between countries. Similarly, Americans of Japanese or Okinawan ancestry have been reported to exhibit a PD incidence similar to that of white Americans, which is higher than that in Asian countries (Morens et al. 1996).
This apparent multiplicity of risk factors supports a growing belief that PD may be multifactorial in nature rather than a disease that can be ascribed to a unitary etiology. PD may be the result of the net interactions of multiple risk factors encountered over the lifetime, that is, a lifelong bionetwork of interactions, which in addition to those promoting risk, would also include factors that have shown to be protective against PD, such as caffeine and cigarette smoking (Baron 1986; Gorell et al. 1999; Kessler and Diamond 1971; Ross and Petrovitch 2001). Such a multifactorial etiology also would be consistent with PD because the disease exhibits marked heterogeneity with respect to signs and symptoms that manifest, the age of onset, and the rate of progression. For such reasons, it may be more appropriate to think of PD not as a unitary disease entity but rather as a broader phenotype. Such a premise may also explain why early studies that examined the potential for one such pesticide, paraquat (PQ), to produce parkinsonism were not particularly compelling (Bagetta et al. 1992; Markey et al. 1986; Perry et al. 1986). Investigators became interested in PQ particularly as a pesticidal risk factor because it shares a marked structural similarity to MPP+ (1-methyl-4-phenylpyridinium), the most widely used experimental model of PD.
However, PQ exposure does not occur in isolation but instead occurs in conjunction with many other risk factors, including other environmental chemicals. Indeed, one can consider a type of multiple-hit hypothesis for the impact of multiple risk factors targeting the brain. Specifically, the brain may readily be able to compensate for the effects of an individual chemical alone acting on a particular system of the brain. However, when multiple target or functional sites within that particular system are attacked by different mechanisms (i.e., multiple chemical exposures or chemical exposures combined with other risk factors), the system may no longer be able to homeostatically reregulate itself, thereby leading to sustained or cumulative damage. Figure 3 provides a hypothetical example featuring a DA terminal. Four concurrent insults are portrayed. Although all four insults target the DA terminal, they do so by different mechanisms, that is, at different sites of the same system. Here, for example, insult A targets the vesicular monoamine transporter in which DA is stored; insult B attacks the enzyme converting tyrosine to DOPA (3,4-dihydroxyphenylalanine) and thus the DA metabolic pathway; insult C strikes the metabolism of DOPAC (3,4-dihydroxyphenylacetic acid) to HVA (homovanillic acid); and insult D hits the DA transporter that takes DA back up from the synaptic cleft postrelease. Multiple insults occurring concurrently at multiple sites within the system may constrict the range and flexibility of compensatory mechanisms, thereby compromising the integrity and viability of the system. As a consequence, mixtures could have effects that are more robust and more rapid in onset and that differ in character from effects produced by single exposures.
On the basis of such considerations, we posited that concurrent exposures to multiple pesticides that target the nigrostriatal DA systems but that do so through different mechanisms might provide more significant neurotoxicity (Cory-Slechta, in press). Thus, an exposure model was developed in young adult mice based on combined exposure to the herbicide PQ and the fungicide maneb (MB), based on their DA effects (Bagetta et al. 1992; Calo et al. 1990; Liou et al. 1996; Markey et al. 1986; Miller et al. 1991; Morato et al. 1989; Perry et al. 1986; Takahashi et al. 1989; Walters et al. 1999; Yoshimura et al. 1993). PQ shares a remarkable structural similarity to MPP+, the most widely used experimental model of the PD phenotype. MB actually enhances the effects of MPTP (1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine; parent compound of MPP+). This model in young adult C57Bl/6 mice was produced by administration of doses of 10 mg/kg PQ, 30 mg/kg MB, or combined PQ + MB, intraperitoneally (ip) twice a week for 6 weeks, for a total of 12 doses, and was shown to result in a PD phenotype (Thiruchelvam et al. 2000a, 2000b). In this phenotype, effects of PQ + MB were found to be potentiated; that is, for some measures, effects of combined PQ + MB were found where neither pesticide administered alone had any impact. The observed effects were, moreover, highly selective for the nigrostriatal DA system and irreversible. Additionally, studies using the PQ + MB model have shown a greater vulnerability of males to the combined treatment, which is consistent with observations from epidemiologic studies of PD (Wooten et al. 2004). These studies suggest that the greater incidence of the disease reported in males in epidemiologic studies may not only be due to greater exposures to potential environmental risk factors such as pesticides but also may be related to gender-based physiologic differences. Our studies also now indicate that both aging (Thiruchelvam et al. 2003) and overexpression of mutant human α-synuclein (Thiruchelvam et al. 2004) can enhance the PD phenotype produced by PQ + MB.
Developmental Insults and Parkinson Disease
Although PD is a neurodegenerative condition with a late-life onset, the possibility that it could be related to insults that occur early in life has been raised. Indeed, the pattern of manifestation of signs and symptoms should not be presumed to coincide with the timing of etiologic factors. Figure 4 depicts hypothetical models in which events occurring early in life could contribute or lead to PD later in life. Normal age-related degeneration in the DA system has been repeatedly described, which includes the loss of DA cell bodies as well as alterations that compromise the function of residual DA neurons (Anglade et al. 1997; Cabello et al. 2002; Calne and Langston 1983; Roth and Joseph 1994). Insults during the adult stage of the life cycle superimposed on such normal aging could further decrease DA function and lead more rapidly to the symptomatic PD range (< 20%; shaded area of Figure 4). It could be posited that events occurring early in development have long-term, delayed consequences for DA systems that may become evident only as the DA system undergoes further adult-related cell loss. For example, a developmental insult alone could accelerate the loss of DA neurons across life (solid red line), such that the percentage of DA function reaches the symptomatic PD range more rapidly. Adult insults superimposed upon such an enhanced decline, moreover, could further accelerate such a process (dashed red line). Alternatively, or in addition to these events, developmental insults could result in an early loss of DA neurons such that the entire curve of degeneration is displaced downward (solid green line) and again the symptomatic range is reached earlier in life, again, with such a possibility further accelerated by adult insults (dashed green line).
The potential for adverse effects of pesticide exposures to infants and children is of significant concern (Kimmel and Makris 2001; Mendola et al. 2002; Tilson 1998). Many pesticides were specifically designed to affect the nervous system of pests, but the phylogenetic parsimony of nervous system structure and function across species leaves humans at risk as well. This must be coupled with the fact that numerous factors contribute to the particular vulnerability of the developing nervous system to environmental chemical exposures. One is the complex series of events associated with brain development, which starts during the embryonic period and actually extends through adolescence. During this time, progenitor cells in the brain must travel in a defined time frame to their appropriate location and establish functional connections that are the basis for signals that ultimately underlie complex human behavior. In humans this period is primarily during the prenatal period. An additional factor operative early in life that can contribute to enhanced vulnerability of the brain is the incomplete development of the blood–brain barrier, including possible enhanced permeability to toxicants, or other examples of altered toxicokinetics. Although the barriers to blood–brain and blood–cerebrospinal fluid interfaces (i.e., tight junctions) are present from early in development, permeability to small lipid-insoluble molecules is greater in developing brain, and mechanisms of ion and amino acid transfer develop only sequentially with brain growth (Saunders et al. 1999).
Such possibilities raise at least three questions: a) Can developmental environmental insults lead to progressive or permanent nigrostriatal DA neurotoxicity? b) Will this insult alter vulnerability to subsequent environmental insults? c) Can such environmental insults produce cumulative neurotoxicity? The answers to such questions were pursued using the combined PQ + MB model described previously.
Subsequent studies using PQ + MB were undertaken to address the questions posed above with respect to exposures during development. Findings from these efforts attest to the significance of early development as a period of vulnerability for insults that later lead to a PD phenotype, demonstrating positive answers to all three questions. Indeed, findings from these studies also raise new and important issues concerning the adequacy of current risk assessment strategies.
Developmental Environmental Insults: Dopamine Neurotoxicity and Altered Vulnerability
Can developmental environmental insults lead to progressive or permanent nigrostriatal DA neurotoxicity? Will this insult alter vulnerability to subsequent environmental insults? To address these questions related to the potential for developmental insults to lead to a PD phenotype, C57Bl/6 mice were exposed as shown in Figure 5 to the pesticides PQ + MB alone or in combination during postnatal days 5–19 at doses that were 1/30th of those used in the young adult studies (Thiruchelvam et al. 2002). Locomotor activity was assessed at 6 weeks and again at 6 months of age. In our studies, this measure has proven to be predictive of underlying nigrostriatal DA neuron loss. At 6.5–7.5 months of age, a subset of these mice were rechallenged with the same (adult) dosing regimen of the pesticides. Some mice were exposed to the pesticides only as adults. Approximately 2 weeks after the end of the adult rechallenge, locomotor activity was again evaluated, and brains were harvested thereafter for determinations of levels of DA and metabolites and for assessment of numbers of DA neurons using unbiased stereologic determinations.
Figure 6 depicts total locomotor activity (total horizontal activity counts across the duration of the session) measured in 45-min sessions at 6 weeks of age and again at 6 months of age, the latter determination occurring more than 5 months after pesticide exposure. Locomotor activity reductions were seen only in the PQ + MB group and not in response to either PQ or MB alone; that is, effects were potentiated. Reductions of 23% were evident in the PQ + MB group at 6 weeks of age, and by 6 months of age levels were further reduced by 38% relative to that of controls. Thus, these effects were progressive and could also be presumed to be permanent.
Changes in levels of DA and its metabolites were determined in striatum at the termination of the experiment, when mice were approximately 8–9 months of age. DA and DOPAC (3,4-dihydroxyphenylalacetic acid) levels are depicted in Figure 7 for groups that were exposed only during development or only as adults and those exposed developmentally and rechallenged as adults (developmental + adult). Relative to controls, developmental exposure to PQ alone decreased levels of DA, but these reductions were notably enhanced by combined PQ + MB, whereas MB alone had no impact. Similar but far less pronounced effects were seen in mice treated only as adults, where only PQ + MB produced significant reductions. The most dramatic effects were observed in groups treated developmentally and then rechallenged as adults, where reductions occurred in response to both PQ alone and MB alone, with even more marked effects in the group receiving PQ + MB, where levels of DA were reduced by 62% relative to those of controls. Moreover, developmental exposure followed by adult rechallenge unmasked silent toxicity apparently produced by MB treatment during development: although no changes were seen in response to developmental-only MB exposure, reductions did occur when it was followed by adult rechallenge with MB. A highly similar pattern of treatment-related effects was seen for DOPAC.
A similar but more marked profile of effects was observed for changes in TH+ neurons (Figure 8). In this case even developmental-only exposures to PQ alone and to MB alone produced modest but statistically significant decreases in numbers of DA neurons in the substantia nigra pars compacta, and even larger reductions were found for combined PQ + MB. In adult-only exposures, both PQ alone and PQ + MB produced small but significant reductions (10–15%) in numbers of TH+ neurons. The most dramatic effects were again seen in groups treated developmentally and then rechallenged as adults. Here, the reductions produced by PQ alone and MB alone were significantly greater than those observed in response to developmental-only exposures. So, too, was the reduction in the PQ + MB group, where the TH+ neuron count decreased approximately 67% relative to control. These effects were not mirrored by changes in numbers of TH− neurons in the nigra (not shown) or by reductions in TH+ neurons in the ventral tegmental area (region of cell bodies for the mesolimbic DA system; not shown); that is, these reductions were highly selective for the nigrostriatal DA system.
The protection of females from the effects of PQ + MB was particularly striking. For example, Figure 9 displays the changes in striatal levels of DA presented for males in Figure 6 and corresponding data for females. The reductions in DA levels produced by virtually all treatments were either attenuated or absent in females, with the exception of the reduction in DA in response to developmental exposure to PQ alone. The most obvious protection can be seen in comparing males with females in the groups exposed developmentally and rechallenged as adults. In these groups, females did show some reductions in levels of DA relative to those of their control group, but the magnitude of the effect was markedly reduced relative to the corresponding reductions observed in males.
This protection in females likewise extended to the reduction in numbers of TH+ neurons in the substantia nigra pars compacta produced by these pesticide exposures, as shown in Figure 10. Figure 10 shows that the reductions produced by adult exposures, and particularly by developmental exposures followed by adult rechallenge were significantly reduced in females compared with males. The reductions produced in response to developmental-only exposures were, however, of generally comparable magnitude in the two genders, suggesting a postpubertal change. Collectively, these findings showing that protection in females is conferred postpuberty suggests that events associated with maturation of reproductive systems may play a role in this response.
It is important to note that the effects described in response to these treatments were seen in the absence of any indications of systemic toxicity in treated mice, including the lack of any weight loss; indeed, mice gained weight across the experiment. Nor were any gross histopathologic changes found. Thus, the effects observed are not a reflection in any sense of a generalized toxicity.
Can Such Environmental Insults Produce Cumulative Neurotoxicity?
Human exposures occur to mixtures over the life span, with the specific components of that mixture no doubt changing across time. One question raised by such exposure scenarios is whether sequential exposures across the lifetime would result in cumulative neurotoxicity to the nigrostriatal DA system. This too can be posited in the context of the multiple-hit hypothesis described previously, in that sequential and permanent damage to different target sites within the system could also result in compromise of homeostatic regulatory capacities. This question was pursued using the experimental design depicted in Figure 11. In this study C57Bl/6 mice were exposed during gestation to saline or to MB only via administration to the dam of a dose of 1 mg/kg (1/10th the dose used in young adult studies) subcutaneously (sc) during gestational days 10–17, a time frame chosen to correspond to the emergence of the nigrostriatal DA system. Pups were weaned at 25 days of age, and locomotor activity was evaluated at 6 weeks of age, again as a preliminary gauge of underlying changes in the DA system. At approximately 2 months of age, a subset of mice was challenged with saline, with 5 mg/kg PQ alone, or with 30 mg/kg MB alone, the doses used with young adult mice, and these doses were administered daily for 8 days. One week later, locomotor activity was redetermined, and brains were harvested for determinations of levels of catecholamines and stereologic assessment of numbers of DA neurons (Barlow et al. 2004).
Findings from this study were largely unexpected. Changes in locomotor activity measured 7 days after the adult rechallenge are shown in Figure 12 for both males and females treated developmentally with saline or MB and rechallenged as adults with saline, PQ alone, or MB alone. With this particular exposure regimen, prenatal exposure to MB alone followed by adult rechallenge with PQ alone produced dramatic reductions in locomotor activity at this time point, effects that were seen only in males. Correspondingly, the reductions in levels of DA and of its metabolite DOPAC that were observed occurred only in response to prenatal MB exposure followed by adult rechallenge with PQ alone (Figure 13) and, again, in males but not in females. In addition, this regimen, that is, prenatal MB followed by adult rechallenge with PQ, produced a loss of TH+ neurons that occurred selectively in the substantia nigra pars compacta and not in the ventral tegmental area (compare Figure 13A,B). Also, there were no changes in numbers of TH− neurons. Furthermore, females were protected from this loss of TH+ substantia nigra neurons (Figure 14) as they were from the other adverse effects of prenatal MB followed by adult PQ exposure. As in the previous study (Thiruchelvam et al. 2002), these effects were seen in the absence of any indication of systemic toxicity, body weight loss, or gross histopathology.
Conclusions and Research Needs
These studies demonstrate the first examples of models of the PD phenotype based on developmental pesticide exposure. Our findings confirm that a developmental insult can have effects that appear to be progressive and permanent and ultimately lead to damage to the nigrostriatal DA system, including loss of TH+ neurons (Figures 10, 14), consistent with the hypothetical models posed in Figure 4. In addition, both the postnatal and gestational exposure experimental models examined here showed that pesticide exposures during development increased vulnerability to subsequent pesticide exposures occurring later in life. These findings indicate that vulnerability can accumulate across insults, such that the effects of successive insults may actually be enhanced. Indeed, repeated insults also revealed underlying silent toxicity, wherein effects of developmental exposures to pesticides alone manifested only after adult rechallenge with the same or even a different pesticide. These findings also underscore the need for inclusion of childhood pesticide exposures in epidemiologic studies and further evaluation in experimental models.
The basis for the protection observed in females in these studies, mainly conferred post-pubertally, is not yet clear. In addition to gender-related differences in time to onset of, magnitude of effects of and, perhaps, incidence that are reported in diseases and dysfunctions in which DA systems play a key role (e.g., attention deficit hyperactivity disorder, schizophrenia, PD), experimental studies have repeatedly demonstrated protective effects of estrogen in various experimental models, including protection against the DA toxicity associated with the experimental compound MPTP as well as against methamphetamine-induced DA toxicity (Dluzen and McDermott 2002; Dluzen et al. 1996; Miller et al. 1998). The mechanism(s) of such effects remains unknown and may be diverse but could include effects on DA release, on the DA transporter, and/or on the activity of tyrosine hydroxylase (TH), the rate-limiting enzyme in the DA synthesis pathway (Dluzen and McDermott 2000; Kuppers et al. 2000). This is a finding clearly warranting further experimental attention given the potential for therapeutic considerations.
One unexpected finding from these studies was the dramatic enhancement of the nigrostriatal dopaminergic toxicity associated with PQ treatment when it was administered in adulthood after developmental exposure to MB. The mechanisms responsible for this enhancement of toxicity, particularly given the time lag between treatments (~ 10 weeks), is not yet known. In adults the effects of PQ are potentiated by MB at least partly through a toxicokinetic interaction in which the levels of PQ in brain are increased and the rate of elimination from brain is decreased by co-administration with MB, effects that may be related to an inhibition of efflux transport of PQ by MB (Barlow et al. 2003). Clearly, such a mechanism cannot be operative in the current experiment using gestational MB followed by adult PQ given the time lag between exposures. Little is known about the neurotoxicity of MB, particularly during development. Another explanation eliminated by stereologic assessments is that the number of TH+ neurons is already reduced by gestational exposure to MB alone such that PQ acted to further decrease these numbers, because no decrease in TH+ neurons was produced by gestational MB followed by adult saline exposure (Figure 14). One possibility, however, is that MB established a “mutant steady state” (Clarke et al. 2000) in which the homeostatic state of those cells is abnormal and confers an increased rate of cell death in response to a subsequent challenge, a variant of the multiple-hit model posed in support of these experiments.
Even in the absence at the present time of an understanding of the specific mechanisms by which such augmentation of adverse effects can occur across delays of exposure between treatments, such findings raise serious questions about the adequacy of current risk assessment paradigms to encompass the patterns of toxicities observed in these studies. For example, the enhancement of the dopaminergic toxicity of PQ in adults by previous gestational exposure to MB would not necessarily have been predicted in advance; no structural similarities exist between these compounds, and they do not appear to act by identical mechanisms. Moreover, issues of cumulative toxicity, repeated insults, and exposures to mixtures are not addressed explicitly in the derivation of risk. Certainly, the question must be raised of whether the simple addition of uncertainty factors to a no-observed-adverse-effect levels in the risk assessment context would suffice to cover the toxicity produced by these sequential treatments. In addition the possibility that pharmaceuticals, herbal supplements, or food additives might interact with pesticides via toxicokinetic mechanisms, a phenomenon seen with MB potentiation of PQ effects, has not yet received sufficient consideration.
The brain, of course, consists of a complex network of highly interactive systems. This interactive matrix may account for the enhanced vulnerability of the brain under some conditions of neurotoxic chemical exposure. The occurrence of adverse effects anywhere within such circuits may result in disturbances across such networks and in associated behavioral output. Thus, the brain provides a more extensive matrix for damage than is seen in many other organs. In addition, direct effects at one point in such interactive systems may be amplified and produce indirect damage at other points within the network. Because different brain systems use common neurotransmitters for excitatory and inhibitory function, insults targeting neurotransmitter function per se, for example, receptors, transporters, or neurotransmitters, can have broad ramifications across the brain and thereby influence a wider array of behavioral functions.
The multiple-hit hypothesis of neurotoxicity upon which these studies were based postulates that insults to different target sites within a specific brain system, here the nigrostriatal DA system, when occurring concurrently or cumulatively, will compromise homeostatic and repair capacities of the system and thereby increase its vulnerability. Findings consistent with our hypothesis, as observed in these studies, suggest such a biologic plausibility rationale as a new strategy for defining potentially significant neurotoxic mixtures in a risk context for future studies, specifically mixtures of neurotoxicants acting on the same system of the brain but via different mechanisms of action.
This article is part of the mini-monograph “Early Environmental Origins of Neurodegenerative Disease in Later Life: Research and Risk Assessment.”
Special thanks to the technical assistance of B. Goodman, and to R. Baggs (University of Rochester Medical School) for histopathologic analyses.
This work was supported by grants ES P30ES01247 and ES10791 from the National Institute of Environmental Health Sciences to D.C-S. and DAMD17-98-1-8628 from the U.S. Army to E.K.R.
Figure 1 Multiple different insults lead to the PD phenotype: schematic depicting various risk factors that have been associated either with increased odds ratios for PD in epidemiologic studies or with producing characteristics of the phenotype in experimental models. Circles show genes associated with PD, rectangles depict metals implicated in PD, dotted rectangles depict pesticides implicated as risk factors, and dashed rectangles depict physiologic risk factors that contribute.
Figure 2 Age-adjusted mortality data for PD for states in the United States. Based on 1988 data and adapted from Lanska (1997).
Figure 3 Schematic depicting the multiple-hit hypothesis as applied to a DA terminal within the central nervous system. Four concurrent insults are depicted that occur at different target sites of the DA terminal: insult A affects the vesicular transporter; insult B, affects the metabolism of tyrosine to DOPA; insult C, affects the breakdown of DOPAC; and insult D, affects the DA transporter. The multiple-hit hypothesis here presumes that the brain may readily be able to compensate for the effects of an individual chemical itself acting on a particular target system of the brain. However, when multiple target or functional sites within that particular system are attacked by different mechanisms (i.e., multiple chemical exposures or chemical exposures combined with other risk factors), the system may no longer be able to homeostatically reregulate itself, thereby leading to sustained or cumulative damage. Modified from Cory-Slechta (in press).
Figure 4 Hypothetical models by which insults incurred during development with or without added insults later in life can accelerate the onset to a PD phenotype by reaching the point where only approximately 20% of DA function remains. The solid black line depicts the rate of normal aging of the DA system; an adult insult superimposed upon this function accelerates the development of the disease (dashed black line). An insult occurring during development (solid red line) could increase the slope of the normal decline such that the level of DA dysfunction is reached earlier in life. An adult insult(s) superimposed upon this function (dashed red line) would further accelerate the process. Another model would involve a developmental insult that results in a loss of DA neurons early in life (solid green line) such that the reserve capacity is diminished and the result is a more rapid onset of the disease, again, a process that could be hastened by additional insults incurred later in life (dashed green line). Modified from Thiruchelvam et al. (2002).
Figure 5 Depiction of the experimental design for the experiment examining postnatal exposure to PQ and/or MB in C57Bl/6 mice. PN, postnatal days. Pups were treated with saline, 0.3 mg/kg PQ, 1.0 mg/kg MB, or the combination administered ip from PN 5–19 of gestation. Offspring were tested for locomotor activity at 6 weeks of age and again at 6 months of age. A subset of mice were rechallenged at 6.5–7.5 months of age with saline, 10 mg/kg PQ, 30 mg/kg MB, or the combination administered twice per week for 6 weeks, for a total of 12 doses. Another set of mice were given these treatments only as adults. Approximately 2 weeks after the adult treatments, locomotor activity was evaluated and brains harvested for determinations of levels of DA and metabolites and for determinations of numbers of neurons using unbiased stereology. Modified from Thiruchelvam et al. (2002).
Figure 6 Locomotor activity changes after postnatal exposure: total locomotor activity counts in 45 min sessions measured at 6 weeks of age and again at 6 months of age in mice exposed developmentally to saline, 0.3 mg/kg PQ, 1.0 mg/kg MB, or the combination via ip administration postnatally as shown in Figure 5. Modified from Thiruchelvam et al. (2002).
Significant differences were found compared with acorresponding saline control; bcorresponding PQ group; and ccorresponding MB alone.
Figure 7 Levels of DA (A) and the metabolite DOPAC (B) measured at 2 weeks after adult rechallenge in mice exposed developmentally only to saline (Sal) or to 0.3 mg/kg PQ, 1.0 mg/kg MB, or the combination via ip administration postnatally (Developmental); developmentally followed by adult rechallenge with saline, 10 mg/kg PQ, 30 mg/kg MB, or the combination administered twice per week for 6 weeks for a total of 12 doses (Developmental + Adult); or only as adults to saline, 10 mg/kg PQ, 30 mg/kg MB, or the combination administered twice per week for 6 weeks for a total of 12 doses (Adult), as shown in Figure 5. Modified from Thiruchelvam et al. (2002).
Significant differences were found compared with acorresponding saline; bcorresponding adult only; ccorresponding developmental only; dMB alone; eand PQ alone.
Figure 8 Total numbers of TH+ neurons in the substantia nigra pars compacta measured using unbiased stereology at 2 weeks after adult rechallenge in mice exposed as described in Figure 7. Sal, saline. Modified from Thiruchelvam et al. (2002).
Significant differences were found compared with acorresponding saline; bcorresponding adult only; ccorresponding developmental only; dMB alone; ePQ alone.
Figure 9 Levels of DA in male compared with female offspring measured at 2 weeks after adult rechallenge in mice exposed as described in Figure 7. Sal, saline. Male data modified from Thiruchelvam et al. (2002); female data from Thiruchelvam M, Cory-Slechta DA, Barlow BK, Richfield EK (unpublished data).
Significant differences were found compared with asaline; bsaline, PQ alone, and MB alone; cfemale; dsaline and MB alone.
Figure 10 Total numbers of TH+ neurons in the substantia nigra pars compacta in male compared with female offspring measured using HPLC at 2 weeks after adult rechallenge in mice exposed as described in Figure 7. Sal, saline. Male data modified from Thiruchelvam et al. (2002); female data from Thiruchelvam M, Cory-Slechta DA, Barlow BK, Richfield EK (unpublished data).
Significant differences were found compared with asaline; bsaline, PQ alone, and MB alone; cfemale; dsaline and MB alone.
Figure 11 Depiction of the experimental design for the experiment examining gestational exposure to MB in C57Bl/6 mice. Abbreviations: GD, gestational days; PN, postnatal day. Dams were treated from GD 10–17 with either saline or 1.0 mg/kg MB administered sc. Offspring were tested for locomotor activity at 6 weeks of age and rechallenged at 7–8 weeks of age with saline, 5 mg/kg PQ or 15 mg/kg MB administered ip every day for 8 days. Locomotor activity was assessed 7 days after the end of the adult rechallenge, and brains were then harvested for determinations of levels of DA and metabolites and for determinations of numbers of neurons using unbiased stereology. Modified from Barlow et al. (2004).
Figure 12 Total locomotor activity counts measured in 45 min sessions 7 days after the last adult rechallenge as described in Figure 11. Sal, saline. Values are shown for both male and female offspring. Modified from Barlow et al. (2004).
aSignificantly different from all other comparisons.
Figure 13 Levels of (A) DA and its metabolites (B) DOPAC and (C) HVA measured by HPLC 7 days after the last adult rechallenge with saline, 5 mg/kg PQ, or 15 mg/kg MB administered ip every day for 8 days after prenatal exposure to either saline or 1.0 mg/kg MB administered sc during gestational days 10–17 as shown in Figure 11. Sal, saline. Values are shown for both male and female offspring. Modified from Barlow et al. (2004)
aSignificantly different from same-gender saline–saline, same-gender MB–saline, and same-gender PQ–saline.
Figure 14 Numbers of TH+ and TH− neurons in the substantia nigra pars compacta (A) and ventral striatum (B) measured using unbiased stereology 7 days after the last adult rechallenge with saline, 5 mg/kg PQ, or 15 mg/kg MB administered ip every day for 8 days after prenatal exposure to either saline or 1.0 mg/kg MB administered sc during gestational days 10–17 as shown in Figure 11. Values are shown for both male and female offspring. Modified from Barlow et al. (2004).
Significant differences were found compared with asame-gender Sal–Sal; bsame-gender MB–Sal; csame-gender Sal–PQ; and dopposite-gender same-exposure condition.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0057416140601PerspectivesEditorialEditorial: The Future of Environmental Medicine in Environmental Health Perspectives: Where Should We Be Headed? Schwartz Brian S. Johns Hopkins Bloomberg School of Public Health and School of Medicine, Baltimore, Maryland, E-mail:
[email protected] Gary Oregon Health Sciences University, Portland, OregonHu Howard Harvard School of Public Health and School of Medicine, Boston, MassachusettsThe authors declare they have no competing financial interests.
Brian S. Schwartz is currently a professor at the Johns Hopkins Bloomberg School of Public Health and director of the Division of Occupational and Environmental Health in the Department of Environmental Health Sciences. He joined the faculty at Johns Hopkins in 1990 and is a past director of the Occupational and Environmental Residency there. He is jointly appointed in Medicine and Epidemiology.
Gary Rischitelli is an assistant scientist at the Center for Research on Occupational and Environmental Toxicology and an associate professor in the Department of Public Health and Preventive Medicine at the Oregon Health & Science University. He is board certified in both occupational medicine and medical toxicology by the American Board of Preventive Medicine and is a fellow of the American College of Occupational and Environmental Medicine. His research interests are in the areas of occupational and environmental epidemiology, injury prevention, toxicology, and the ethical, legal, and social issues surrounding these disciplines.
Howard Hu is a professor of Occupational and Environmental Medicine at the Harvard School of Public Health and an associate physician at the Channing Laboratory, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School. He directs the Center for Children's Environmental Health and Disease Prevention Research, the Occupational/Environmental Medicine Residency, and the Metals Epidemiology Research Group, all at the Harvard School of Public Health.
9 2005 113 9 A574 A576 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In 1998, Environmental Health Perspectives (EHP) began “Grand Rounds in Environmental Medicine” as a regular feature (Hu 1998; Hu and Woolf 2003). This soon led to an expanded Environmental Medicine section that aimed to regularly publish articles in the Grand Rounds format as well as reviews, commentaries, case reports, and research articles, all of relevance to environmental medicine, with a focus mainly on clinical practice. The Grand Rounds series has been a resounding success, as reflected by the wide range of environmental medicine topics, the diversity of reporting sources, and the increased physician readership of the journal. Howard Hu served as the first Medical Editor from 1996 to 2004 and was then succeeded by Brian Schwartz; also in 2004, the journal appointed two Associate Medical Editors: Howard Hu and Gary Rischitelli.
With this change in medical editorship, we wish to take the opportunity to present our views on the scope of the “environment” and “environmental medicine” and what we believe is the expanding nature of the field. We present this as a sounding board for ideas and not in any way as a definitive discussion of these issues. Our overall goal is to increase physician readership of the journal and engage EHP ’s audience on a wide variety of environmental medicine topics relevant to both clinical and public health practice; this editorial is a first step in achieving this goal.
We are in a changing world. The world’s population continues to grow and the proportion of the elderly is increasing, with recognition of the susceptibility to health risks not only in early life but also in late life. The United States, once the greatest manufacturing nation in history, has become primarily a service economy. Many of the high-intensity hazardous exposures that were the traditional focus of early environmental and occupational health practitioners and social reformers are not the threat to large U.S. populations that they once were. Some exposures of concern remain as pockets of high-intensity hazards or as widespread chronic low-level hazards; new threats are being identified; and environmental factors and gene–environment interactions are thought to play a greater role in the etiology of many diseases than previously believed (Newton and Schwartz 2005; Schwartz 2005a, 2005b).
We are quickly approaching a global economy, in which there is free movement of capital, goods, information, and services, if not free movement of labor. A great deal of manufacturing has shifted to developing countries, accompanied by levels of hazardous exposures not seen in the developed world in decades (LaDou 1994, 2002, 2003), and these countries frequently have few occupational or environmental health resources to address such hazards (LaDou 2002, 2003). With this global economy, we have witnessed a global movement of exposures and other environmental hazards. Toxicants released in one geographic area can influence health at great distances, and agents not previously considered to be pollutants, such as carbon dioxide, are having profound impacts on global climate patterns. Global environmental health threats now include not only climate change but also ecosystem decay, species loss, deforestation and desertification, sea level rise, fisheries decline, and stratospheric ozone depletion, to name but a few.
Traditionally, environmental health specialists have thought about the ways that the environment can influence human health in terms of the hazardous agents, sources, and routes of exposure. This led to a focus on how human activities have resulted in contamination of air, food, water, and soil, and, in turn, how the contaminants can adversely affect human health. The purview of environmental health has more recently been expanded to bear on the potential impact on health of other components of the environment and on multiple scales, from local to global.
Although the definition of “environment,” as it pertains to environmental health, is not our main focus in this editorial, it has clearly been evolving and needs to be articulated, so its effects on human health and medical practice in relation to each other can be articulated more clearly. To that end, we discuss the environment in terms of four main domains: the natural, anthropogenic, social, and cultural environments.
The natural environment includes those features that did not arise from human activities, and can include radon; earthquakes, volcanism, and other natural disasters; cosmic ionizing radiation; certain infectious diseases; and other similar hazards. The anthropogenic environment is what has been created or altered by humans; it includes hazards from toxicants—the traditional practice core of environmental medicine—as well as the built environment, and how this may influence health-related behaviors. In contrast to declines in manufacturing and traditional hazardous exposures in the United States, land use has been dramatically outstripping population growth in most parts of the country (Frumkin et al. 2004). A growing literature describes how urban sprawl and the local food environment can influence health (Ewing 2005; Ewing et al. 2003a; Ewing et al. 2003b; Handy et al. 2002).
The social environment involves the interactions between people in various places, and can include factors such as social disorganization, safety, physical disorder, commercial vitality, and economic deprivation, measured at the neighborhood level. A growing literature has documented a contextual effect of the social environment on a variety of health outcomes after control for critical individual-level risk factors (Pickett and Pearl 2001); perhaps more important, the social environment may affect several underlying biologic pathways that can modify how the body responds to toxicants or other traditional environmental hazardous exposures (McEwen 2000a, 2000b, 2000c, 2002).
Finally, the cultural environment is important because, for example, cultural norms can influence an individual’s interaction with the environment; if the cultural norm is to engage in physical activity, it is more likely that individual community members will also do so. Without consideration of this influence, for example, studies of land use and physical activity could reach erroneous conclusions.
Clearly, as the concept of “environment” evolves from the traditional notions of hazards to the built, social, and cultural environments on several scales, it has expanded or created new interfaces with the fields of individual and social behavior, psychology, sociology, ecology, land use, architecture, and other disciplines. This suggests to us that the role of physicians in environmental health and medicine must also evolve if they are to remain relevant and for physicians to have an impact in addressing individual and population health threats that can arise from these old and new environmental challenges.
In the face of these trends, and given the evolution of the concept of “environment” as it pertains to health, what, then, is “environmental medicine”? While one primary focus of environmental medicine should continue to include the health concerns of individual patients resulting from hazardous exposures, it should also expand to encompass the larger notion of “environment” and include a particular focus on population health.
In the mainstream scientific literature, environmental medicine is the work of clinicians and has been generally defined as the evaluation, management, and study of detectable human disease or adverse health outcomes from exposure to external physical, chemical, and biologic factors in the general environment [Ducatman 1993; Ducatman et al. 1990; Institute of Medicine (IOM) 1995]. Over a decade ago, the IOM specified competency-based objectives for environmental medicine education (IOM 1995). Although this report and its predecessors (IOM 1990, 1991, 1993) included discussion of epidemiology, population health, and nonmedical interventions, the overwhelming focus was on the clinical assessment and medical management of patients with individual illness due to hazardous exposures (IOM 1995). The tension between the focus on the clinical evaluation of patients and the broader goals of environmental medicine was recognized (IOM 1993).
Unfortunately, education in environmental medicine remains a low priority in U.S. medical schools and postgraduate clinical training programs (Burstein and Levy 1994; Schenk et al. 1996). Moreover, it is clear that the landscape of what can be claimed to be environmental medicine in practice has become complex and diverse. Although its roots are in traditional allopathic clinical practice, many environmental medicine physicians are primarily involved in public health practice and use quantitative and management skills that focus on populations rather than the clinical paradigm that focuses on individuals.
The traditional, allopathic practice of clinical environmental medicine has evolved in developed countries with a steady decline in the need for diagnosis and treatment of disease caused by high-level exposures as these countries have made the epidemiologic transition from short-latency, acute effects of high-intensity doses to long-latency, chronic effects of low-intensity cumulative doses. In the United States, the legal implications of exposures have increased, whereas making causal connections has become more challenging, more contentious, and more often opposed by well-funded industry groups (Michaels 2005).
Perhaps as a result of the aforementioned epidemiologic transition and the failure of allopathic practitioners in caring for patients with nonspecific, symptom-based disorders that the patients believe are caused by low-level environmental exposures, alternative medicine practice for patients with environmental concerns has become increasingly common. Nontraditional use of diagnostic and treatment procedures that have little medical scientific justification for their use has become a regular part of this practice. The scientific community has not shed much light on understanding the causes and optimal management approaches for these conditions; therefore, it is probably no surprise that the management of such patients has become the purview of alternative systems of care. To confuse matters even more, the term “environmental medicine” has been adopted by practitioners of a branch of alternative medicine also known as “clinical ecology.” To date, this practice has not been evidence-based and cannot be considered a validated approach to such patients.
On the other hand, environmental health practitioners long ago showed that we could prevent environmental disease without completely understanding the cause, the specific toxic agent, genetic polymorphisms, or mechanistic pathways. For example, in the 18th century, Sir Percival Pott stopped an epidemic of scrotal cancer in chimney sweeps by asking them to improve their genital hygiene (Pott 1775), while knowing little about the cause, biology or mechanism of this disease. Environmental health specialists involved in public health practice thus have a right to ask, “Must we wait for incontrovertible scientific evidence of health effects and causes in the face of impressive trends in environmental degradation?” If so, how does this assist the goal of protecting the public’s health?
Clinical environmental medicine practice is at a crossroads, especially in the United States. Traditional hazardous exposures that formed the base of practice continue to decline in the United States, while health concerns have become aroused by lower and lower levels of exposures, with many patients often finding their way to non-traditional practice. There are still high-intensity exposures, especially in the developing world and in poor and minority communities in the United States. Although traditional exposures on average are declining in the United States, new concerns do arise, and research and public health practice expertise must be brought to bear on these issues.
We believe that environmental medicine must continue to have a patient-based arm of practice, but it must increasingly encompass newer and broader concerns, especially regarding the health of populations and public health practice. In addition to clinical expertise, population, communication, and policy-change skills must be increasingly used. To assess risks and help effect change, practitioners must possess biomedical, epidemiologic, and management skills, and these practitioners should include physicians involved in environmental medicine.
EHP is the ideal vehicle for providing information to the diverse group of physicians practicing in the expanding field of environmental medicine. What are these diverse physician “practices,” and what knowledge and skills do these need? We have identified five categories of “practices”: environmental and occupational medicine (EOM) clinical practice specialists; EOM public health practitioners; international EOM practitioners (in the developing world); community-based primary care providers; and academic EOM physician-scientists.
EOM clinical practice specialists must be able to diagnose and provide medical and nonmedical management of all environmental diseases, translate new research results to practice, and make complex causal inferences. By contrast, EOM public health practitioners have the clinical skills to diagnose environmentally related disease and interpret clinical data, but they are mainly focused on population, not individual health, and are thus interested in policy change and the management of environmental exposures and issues. They are primarily interested in prevention, not diagnosis and treatment, and acknowledge that a case of EOM disease represents a failure of prevention and thus often a failure of EOM policy. Such practitioners embrace the importance of the precautionary principle: while researchers are waiting for the last bit of scientific evidence to be generated, climate is changing, habitat is being destroyed, species are being lost, and disease is being caused. For many environmental health issues, the time to act is now.
In the developing world, international EOM practitioners (LaDou 2003) still face traditional hazards and high-level exposures. Many lack specialty training or access to expertise. They must be able to diagnose and treat common EOM diseases and assess and manage emerging health threats from rapid industrialization.
Primary care providers must be able to recognize sentinel cases in the community—which requires making inferences about cause—and then take appropriate steps, often by getting relevant public health authorities involved, to prevent additional cases. Making inferences about cause requires skills in exposure and dose estimation, and getting public health authorities involved requires knowledge of reporting requirements and responsible parties. Such practitioners also must be able to provide knowledgeable advice to their patients about what is known about new causes; patients want to know what must be done about the myriad environmental concerns that appear in the lay press and other sources of such information. The challenge is how to provide information on these topics to these practitioners in an efficient and easily learned way.
Finally, academic EOM physician/scientists are directing or collaborating in multidisciplinary research that capitalizes on both clinical knowledge in individuals and epidemiologic information from populations and results in new knowledge of clinically translatable value. They work on a wide variety of research topics and use a full range of clinical, epidemiologic, and environmental health science skills to shed light on the environmental exposures and complex causal pathways that underlie the pathogenesis and progression of disease.
We have described the expanding scope of environmental medicine and physician practice in it. As editors we want to be encompassing and not exclusionary about what we believe are appropriate articles for the Environmental Medicine section. We want to continue the highly successful Grand Rounds in Environmental Medicine and encourage continued submissions of such articles, focused on either clinical practice or population-based practice, especially from developing countries. We welcome submissions that evaluate interventions of potential importance in individuals or in populations. Environmental medicine is not just about clinical practice for patients concerned about environmental diseases; thus, we welcome submissions of epidemiologic and community-based studies that have relevance to the public health practice of environmental medicine. We hope our ideas stimulate thought and discussion on these topics and look forward to receiving your submissions.
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McEwen BS 2002 Protective and damaging effects of stress mediators: the good and bad sides of the response to stress Metabolism 51 2 4 12040533
Michaels D 2005 Doubt is their product Sci Am 292 96 101 15934658
Newton S Schwartz DA 2005 NIEHS priorities: the process of strategic planning Environ Health Perspect 113 A362 15929874
Pickett KE Pearl M 2001 Multilevel analyses of neighbourhood socioeconomic context and health outcomes: a critical review J Epidemiol Community Health 44 111 122 11154250
Pott P 1775. Chirurgical Works of Percival Pott, F.R.S. and Surgeon to St. Bartholomew’s Hospital. ed. London:Hawes, W. Clarke, and R. Collins, in Paternoster Row.
Schenk M Popp SM Neale AV Demers RY 1996 Environmental medicine content in medical school curricula Acad Med 71 499 501 9114870
Schwartz DA 2005a Scientific vision: setting forth a strategy Environ Health Perspect 113 A292 15866748
Schwartz DA 2005b A vision for the future Environ Health Perspect 113 A220
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0057816140602PerspectivesDirector's PerspectiveTraining the Next Generation Schwartz David A. MDDirector, NIEHS and NTP, E-mail:
[email protected] 2005 113 9 A578 A578 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Many factors go into achieving the highest-quality research: a clear vision, sufficient funding, and supportive facilities, to name just a few. But without question, the most critical factor is the human one. All great research starts with a creative idea, which comes from an individual or a group of people working together to solve a problem. The mission of the NIEHS is to understand how the environment influences the development and progression of human disease, and so it is imperative that we bring the best minds to bear on these problems. Recruitment, retention, and career development are challenges for every aspect of biomedical research, including environmental health sciences. Thus, as we reconceptualize the opportunities and challenges in our field, we at the NIEHS will critically evaluate our approach to research training and career development. To produce the best science, we must nurture the best scientists.
Training and career development in environmental health sciences is clearly one of our most profound responsibilities. However, this is not a simple process. Although research training and career development are the most obvious in the early stages of mentored research, this process should be viewed as a continuum from initial recruitment and engagement in biomedical research through the initial and subsequent discoveries that lead to an established and productive career in environmental health sciences. Our institute needs to establish a clear path to support the development of young scientists throughout their progression from student to senior investigator.
We at the NIEHS will critically evaluate our approach to research training and career development. To produce the best science, we must nurture the best scientists.
The first step in this progression is engagement in scientific research. Students at the high school and college levels must be introduced to the field of environmental health sciences, shown its relevance and potential for understanding human disease and solving the real-world problems that affect all of us, and provided both the opportunity and the encouragement to enter into the sometimes daunting realm of scientific research. To accomplish this, we will use and expand the tools we have—such as summer internship programs, undergraduate work–study programs, partnerships with professional and educational societies, and outreach through publications—to create an open door to welcome the best and brightest young minds to our field.
And we must realize that engagement in biomedical research is just the first step. In order to retain the best scientists in our field, we must create an environment in which young scientists can become ever more excited by their work, where they feel supported both intellectually and personally, and where they have the flexibility to move seamlessly within and outside of our intramural and extramural scientific communities.
Given the breadth of environmental health sciences, interdisciplinary research is an absolute necessity, especially in our training programs. This range of diversity creates an enormous opportunity to bring together teams of scientists with different backgrounds, skills, and ideas to more effectively tackle today’s critical problems in environmental health. An interdisciplinary approach will undoubtedly lead to more profound achievements in biomedical research, which will then translate into more substantial advances in human health. Our young scientists need to be trained in interdisciplinary research so that they can more effectively work with other scientists to ask and answer the toughest questions in environmental health sciences.
In addition, we must work with budding scientists at critical junctures in their careers, such as between college and graduate school or between mentored and independent research, to facilitate these transitions so that the movement into or retention in environmental health sciences becomes a natural progression that is supported by the NIEHS. This could be accomplished by a combination of mentoring as well as established and evolving extramural programs including career transition awards, loan repayment programs, and awards for new investigators to support not just research costs but also the startup costs for new laboratories.
However, career development is fundamentally dependent on mentorship. When I think back to my own career development, I am astonished by the profound impact of my outstanding mentors. For instance, throughout the past 15 years, Gary Hunninghake, professor of medicine at The University of Iowa and one of my many mentors, has continually prodded and guided me to the next level of achievement through independent research support, scientific program development, and encouragement to become the director of the NIEHS. Gary and other great mentors instill in their trainees a passion for their work that embraces creativity, individuality, dedication, fearlessness, and personal balance. The NIEHS needs to improve it’s ability to develop and support such mentors.
And while investing in the training of researchers at our own institute, we should renew and expand our efforts to become a central location for the continued education and training of scientists from around the world. NIEHS scientific director Lutz Birnbaumer recently suggested that we seek to establish the NIEHS as a “campus of learning,” somewhat in the model of Cold Spring Harbor or the Jackson Laboratory, whereby we would offer continuing education and training in environmental health sciences through programs such as summer fellowships, short courses, and longer sabbatical opportunities. Such programs would be taught by a combination of NIEHS and outside scientists who would bring a wide variety of skills, ideas, perspectives, and expertise.
One definition of “institute” is a place for instruction. The process of strategic planning under way at the NIEHS will aid us in focusing on how we can achieve our future research and training goals as we strive to fulfill the promise of our name and become a true institute for scientific learning and discovery.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0058016140603PerspectivesCorrespondenceIARC Carcinogen Update Rousseau Marie-Claude INRS-Institut Armand-Frappier, Université du Québec Montréal, Québec, Canada, E-mail:
[email protected] Kurt International Agency for Research on Cancer, Lyon, France, E-mail:
[email protected] Jack Université de Montréal Montréal, Québec, Canada, E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 113 9 A580 A581 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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We recently published an article in which we presented a list of occupational carcinogens (Siemiatycki et al. 2004), based on the International Agency for Research on Cancer (IARC) Monographs Program. Our review covered Volumes 1–83 of the IARC Monographs. However, because the IARC Monograph Program is ongoing, the list of occupational carcinogens will need to be periodically updated. Since we completed our article, there have been three Monograph meetings that addressed substances that can be classified as occupational; therefore, we would like to notify readers of some important changes in the list of occupational carcinogens. Table 1 shows summary information about occupational substances and mixtures that were recently evaluated by IARC as human carcinogens (group 1), probable human carcinogens (group 2A), or possible human carcinogens (group 2B). As we did in our earlier article (Siemiatycki et al. 2004), we added to the IARC evaluations our assessment of the main occupations in which the agent may be found and the target organ for carcinogenicity.
Volume 86 focuses on cobalt in hard-metals and cobalt sulfate, gallium arsenide, indium phosphide, and vanadium pentoxide (IARC, in press a) In our article (Siemiatycki et al. 2004), cobalt and cobalt compounds were listed as Group 2B human carcinogens. In IARC’s recent evaluation (IARC, in press a), cobalt metal with tungsten carbide is classified in Group 2A, whereas cobalt metal without tungsten carbide, cobalt sulfate, and other soluble cobalt(II) salts remain in Group 2B. Three substances for which there were no previous IARC evaluations have now been evaluated and classified: gallium arsenide is classified as a Group 1 human carcinogen, indium phosphide as a Group 2A (probable) human carcinogen, and vanadium pentoxide as a Group 2B (possible) human carcinogen (IARC, in press a).
Volume 87 (IARC, in press b) updates the prior evaluations on inorganic and organic lead compounds, which were included in Volume 23 (IARC 1980) and in Supplement 7 (IARC 1987). Previously, lead and inorganic lead compounds were classified in Group 2B, whereas organic lead compounds were classified in Group 3. The most recent IARC evaluation results in an upgrading of inorganic lead compounds to Group 2A; organic lead compounds remain in Group 3 (IARC, in press b). The Working Group, however, noted that part of the organic lead is metabolized into ionic lead, which would be expected to present the same toxicity as inorganic lead.
In Volume 88, formaldehyde was upgraded from a Group 2A (probable) to a Group 1 human carcinogen (IARC, in press c; Cogliano et al. 2005). The other two substances covered by this monograph, 2-butoxyethanol and 1-tert-butoxy-2-propanol, are evaluated as Group 3 (not classifiable).
Table 1 Substances and mixtures that have been evaluated by IARC as human carcinogens and that are occupational exposures, based on Monograph Volumes 84–90.
Substance or mixture Occupation or industry in which the substance is founda Site(s) IARC classification IARC Monograph
Cobalt metal with tungsten carbide Production of cemented carbides (hard-metal industry); tool grinders; saw filers; diamond polishers Lungb 2A 86
Cobalt metal without tungsten carbide Miners; production of alloys; processing of copper and nickel ore; glass and ceramic production; welders of cobalt-containing alloys Uncertain 2B 86
Cobalt sulfate and other soluble cobalt(II) salts Electroplating and ceramic industries Uncertain 2B 86
Gallium arsenide Production; microelectronics industry (integrated circuits and optoelectronic devices) Uncertain 1c 86
Indium phosphide Production; microelectronics industry (integrated circuits and optoelectronic devices) Uncertain 2Ad 86
Vanadium pentoxide Ore refining and processing; chemical manufacturing industry; maintenance of oil-fired boilers and furnaces Uncertain 2B 86
Inorganic lead compounds Lead smelters; plumbers; solderers; occupations in battery recycling smelters; production of lead-acid batteries; printing press occupations; pigment production; construction and demolition Lungb
Stomachb 2A 87
Formaldehyde Production; pathologists; medical laboratory technicians; plastics; textile and plywood industry Nasopharynxe
Leukemiab
Nasal sinusesb 1 88
a Not necessarily an exhaustive list of occupations/industries in which this agent is found; not all workers in these occupations/industries are exposed. The term “production” is used to indicate that this substance is man-made and that workers may be exposed in the production process.
b We judged that the evidence for an association with this site was suggestive.
c In reaching an overall evaluation of Group 1, the working group noted the potential for gallium arsenide to cause cancer through releases of a small amount of its arsenic, which behaves as inorganic arsenic at the sites where it is distributed. Arsenic and arsenic compounds have been evaluated as IARC Group 1, carcinogenic to humans. For arsenic in drinking water, the most recent IARC evaluation of arsenic [Volume 84; (IARC 2004)], there was sufficient evidence in humans that arsenic causes cancers of the urinary bladder, lung, and skin; the evidence for cancers of the liver and kidney was limited.
d Absence of data on cancer in humans; the final evaluation for carcinogenicity was upgraded from 2B to 2A based on evidence of carcinogenicity in experimental animals.
e The evidence was sufficient.
==== Refs
References
Cogliano VJ Grosse Y Baan RA Straif K Secretan MB El Ghissassi F the Working Group for Volume 88 2005 Summary of IARC Monographs on Formaldehyde, 2-Butoxyethanol, and 1-tert -Butoxy-2-Propanol Environ Health Perspect 113 1205 1208 16140628
IARC 1980 Some Metals and Metallic Compounds IARC Monogr Eval Carcinog Risk Chem Hum 23
IARC 1987. Overall Evaluations of Carcinogenicity: An Updating of IARC Monographs Volumes 1 to 42. IARC Monogr Eval Carcinog Risk Chem Hum(suppl 7).
IARC 2004 Some Drinking-Water Disinfectants and Contaminants, Including Arsenic IARC Monogr Eval Carcinog Risk Hum 84
IARC In press a. Cobalt in Hard-metals and Cobalt Sulfate, Gallium Arsenide, Indium Phosphide and Vanadium Pentoxide. IARC Monogr Eval Carcinog Risks Hum 86.
IARC In press b. Inorganic and Organic Lead Compounds. IARC Monogr Eval Carcinog Risks Hum 87.
IARC In press c. Formaldehyde, 2-Butoxyethanol and 1-tert-Butoxy-2- propanol. IARC Monogr Eval Carcinog Risks Hum 88.
Siemiatycki J Richardson L Straif K Latreille B Lakhani R Campbell S 2004 Listing occupational carcinogens Environ Health Perspect 112 1447 1459 15531427
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00583AnnouncementsErratumErratum 9 2005 113 9 A583 A583 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In "Decrease in Anogenital Distance among Male Infants with Prenatal Phthalate Exposure" [
Environ Health Perspect 113:1056–1061 (2005)], Shanna Swan’s affiliation was listed as University of Rochester, Rochester, Minnesota. The correct location is Rochester, New York.
EHP regrets the error.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0058816140607EnvironewsForumSustainable Development: Empowering Indigenous Peoples Kessler Rebecca 9 2005 113 9 A588 A588 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Deforestation, erosion, and loss of biodiversity all directly affect Central American indigenous peoples’ sustenance, health, and way of life. The new Integrated Ecosystem Management in Indigenous Communities Regional Program (IEM) aims to alleviate these problems and the extreme poverty of many indigenous groups by helping communities manage their lands sustainably. The IEM will help communities establish and manage conservation areas and finance income-generating projects like sustainable tourism, sustainable forestry, and production of handicrafts, organic coffee and cocoa, and other traditional products. The program emphasizes traditional land management practices to combat declining bio-diversity, soil, and water quality.
“One objective is to strengthen local groups to prepare strategies to help with these problems,” says Alberto Chinchilla, regional facilitator for the Central America Indigenous and Peasant Coordination Association for Community Agroforestry. This group, along with the Central American Indigenous Council and Central American Commission for Environment and Development (CCAD), will implement the program.
The IEM will support small projects in some 550 communities in Belize, Costa Rica, El Salvador, Guatemala, Honduras, Nicaragua, and Panama, where nearly 7 million indigenous people account for about a quarter of the population. The Global Environment Facility granted the program $9 million through the Inter-American Development Bank (IDB) and the World Bank, cofinanced through other projects from both banks. Indigenous groups and CCAD will contribute another $2.5 million.
Indigenous groups’ environmental problems stem from their tenuous rights to the land they occupy, advocates say. Not all of the region’s nations enforce or even legally recognize indigenous land rights. Nor do governments or the World Bank require indigenous groups’ consent before approving projects that affect their lands or require their forced relocation, say advocates.
Poverty and encroachment onto their lands by ranchers, farmers and loggers wielding environmentally devastating practices has led many indigenous groups to forsake traditional land use practices for often unsustainable hunting, agriculture, and timber harvest methods. In agriculture, for instance, practices such as letting plots lay fallow for years at a time, intensive hand weeding, and cultivating a diversity of crops, often in the shade of fruit- or lumber-producing trees have in certain places given way to shorter crop rotation cycles, increased chemical use, and crop monocultures. The new practices may offer bountiful harvests in the short term, but they ultimately degrade the soil and are expensive to perpetuate. Struggling communities may also sell off timber or land for negligible sums to outsiders, who then clear the land for agricultural use, according to IEM documents.
Indigenous groups’ political influence is growing. But they continue to suffer from worse poverty, more disease, greater discrimination, and less education than other sectors of society, the World Bank concluded in its May 2005 report Indigenous Peoples, Poverty and Human Development in Latin America: 1994–2004.
Some indigenous activists question the IEM. “How will the [program] create sustainable development if the majority of the governments in the region don’t recognize the ability of indigenous communities to administer their lands, territories, and natural resources?” asks Hector Huertas, a lawyer from the Kuna tribe with the Centro de Asistencia Legal Popular, an indigenous advocacy group. He and others cast a wary eye on the World Bank and the IDB, whose projects, they say, typically leave a heavy environmental and cultural footprint. Many also believe the agencies charged with implementing the IEM may not truly represent indigenous peoples’ interests.
Some even argue that drawing indigenous communities into the cash economy through development projects threatens their autonomy. Rudolph C. Rÿser, chairman of the Center for World Indigenous Studies in Washington and a Cowlitz tribe member, says communities that provide for their own needs best exemplify sustainability. “People can say it’s unrealistic for indigenous communities to take care of themselves as autonomous economic units. They’d better realize it’s been going on for fourteen thousand years.”
Sustaining themselves.
Street vendors sell weavings in Guatemala (left), and a laborer picks coffee beans in Costa Rica (above). A new Central American project will finance such sustainable money-making activities to help indigenous groups prosper.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00591EnvironewsForumEHPnet: Millennium Ecosystem Assessment Dooley Erin E. 9 2005 113 9 A591 A591 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The Millennium Ecosystem Assessment (MA) is the largest assessment to date of the health of the world’s ecosystems. Launched in 2001 by United Nations (UN) secretary-general Kofi Annan and authorized by governments through four international conventions, the MA is intended as a tool to inform decision makers and the public. The documents flowing forth from this work, which was completed in March 2005, have been prepared by 1,360 experts from 95 countries, with an 80-person independent board of review editors. The documents draw on information gathered from the scientific literature, existing data sets, and scientific models, and incorporate knowledge gleaned from the private sector, workers in the field, indigenous peoples, and local communities. Information about the MA, as well as the documents it has released, are available online at http://www.millenniumassessment.org/.
The findings of the MA are grim. Over the past 50 years, humans have changed ecosystems faster and more extensively than during any other comparable time period in human history. These rapid changes have grown out of increasing demands for natural goods and services, such as food, fresh water, timber, fiber, and fuel. The MA also finds that ecosystem changes have brought about substantial gains in human well-being and in economic development, but that these gains have come at the cost of degrading ecosystem services and increasing poverty for some groups of people. The report predicts that ecosystem change could accelerate during the next 50 years and contribute to nonachievement of the UN Millennium Development Goals.
Yet, there is some hope that this situation can still be reversed, and the report sets forth options for improving ecosystems by 2050. These fall under three scenarios: “Global Orchestration,” “Adapting Mosaic,” and “TechnoGarden.” The Global Orchestration scenario reflects a globally connected society focused on international trade and economic liberalization that also takes strong steps to reduce problems such as poverty and inequality and to invest in public infrastructure and education. The Adapting Mosaic scenario focuses on local-scale activities, and investments in human and social capital emphasize education to bring about a better understanding of the nature of ecosystems. At the core of the TechnoGarden scenario is the use of technology and highly managed, often engineered ecosystems to deliver ecosystem services. A fourth scenario, “Order from Strength,” emphasizes heightened security and a fragmented society, to the detriment of the environment.
The MA homepage provides the latest news related to the project, while links along the right side of the page access the numerous partners in the MA. These partners include the UN Development Programme, the UN Environment Programme, the World Bank, multiple universities, and others.
The Reports section of the site provides links to the major documents produced by the MA. Each report can be downloaded for free in English and several other languages; there is also information on how to order printed copies. The Resources section assembles slide presentations, figures, tables, maps, posters, logos, and brochures that can be used by the media. All are available to download for free.
The About the MA section of the website provides a thorough history of how the work came about, how it was funded, how it was undertaken, and how it may continue in the future. This section also includes a page devoted to the many subregional assessments that are being carried out in conjunction with the MA. Links to each provide details of the areas covered by the assessments, the institutions carrying out the assessments, the features of the ecosystem being assessed, key features of the assessments, and the time frame and budget for the work.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0059216140608EnvironewsNIEHS NewsDwelling on Differences in Health Medlin Jennifer 9 2005 113 9 A592 A595 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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While millions of Americans suffer from illnesses such as cardiovascular disease, diabetes, high blood pressure, and cancers of the breast and prostate, these maladies affect certain population groups more than others, for a host of complex reasons suspected but not confirmed by hard scientific data. Observation of these health disparities (defined as differences in incidence, prevalence, mortality, and burden of disease among specific population groups) led the NIH to fund eight different research centers with a total of $60.5 million over five years to study what factors might mediate the onset or outcomes of these common diseases.
Two years ago investigators at the eight Centers for Population Health and Health Disparities (CPHHDs) began the complicated task of sorting out these disparity-inducing influences. Today they are just beginning to evaluate how biological, social, cultural, environmental, and economic factors overlap and combine in such a way as to influence the rate of disease—and in some cases the outcomes of disease—in certain populations.
Breast Cancer in Chicago’s South Side
Researchers at the University of Chicago CPHHD are focusing primarily on how both mind and body may interact with genes to create breast cancer differences between whites and blacks. Center director Sarah Gehlert and her co-investigators are following women in 15 predominately black neighborhoods on Chicago’s South Side. Although white women have higher incidence of breast cancer, black women have higher mortality from the disease. Gehlert’s colleague, medical professor Funmi Olopade, is helping the group address the question of why black women in both the United States and West Africa develop breast cancers that are more aggressive and more often lethal than tumors in white women, and do so at a younger age.
They hope to answer this question by analyzing the RNA and DNA of breast tumors from women from Ibadan, Nigeria, and the Chicago South Side. According to Olopade, genetic factors contribute to 5–10% of breast cancers overall, but in women younger than 40, they are responsible for about 25% of all breast tumors. In West Africa, breast cancer is considered a disease of young women, with 43 being the average age of diagnosis in Ibadan, some 10–15 years younger than in industrialized countries.
Olopade is examining the molecular characterization of the tumors, while Gehlert is following the women in terms of their psychosocial functioning. Meanwhile, center co-director Martha McClintock is using rats to test the hypothesis that social isolation—and the resulting hypervigilance that isolated animals exhibit—may lead to the development of spontaneous mammary tumors. The investigators suspect that social isolation among women living in dilapidated areas with high rates of violent crime may contribute to poor cancer outcomes.
Gehlert and her colleagues have spent a significant amount of time working to improve access to—and quality of—useful health information for women in the South Side community. To recruit the 503 women who participated in the initial focus groups, “we literally stood at bus stops and church parking lots,” Gehlert recalls. “We asked them about health messages that reach them in the community and found that these messages had been reduced primarily to ‘what not to do,’” instead of useful direction on how to be well for the long term.
Local health advocate and long-time South Side resident Annie Pope agrees: “We found there is an absence of information about breast cancer; these women know more about AIDS than breast cancer,” she says. “They need access to mammograms, but they also need to know how to develop a relationship with their physician.”
Prostate Cancer Disparities
At the University of Pennsylvania CPHHD, investigators want to find out why black men differ noticeably from white men in incidence of and death from prostate cancer. According to the National Cancer Institute, 73 of every 100,000 black men died from prostate cancer for the period of 1996 to 2000, compared to 30.2 of every 100,000 white men.
Center director Timothy Rebbeck and his colleagues are studying behavioral and environmental factors associated with prostate cancer among black men, including whether genes involved in testosterone metabolism may predict a good or bad prostate cancer outcome. They will also explore possible discrimination in screening practices (reflecting biases of both physicians and patients) as well as treatment after diagnosis, which may affect prostate cancer outcomes. Still another study focuses on social, cultural, behavioral, and environmental factors on quality of life after black and white patients receive their prostate cancer diagnosis.
“Most men develop prostate tumors sooner or later,” Rebbeck points out. “The key is not to figure out who has prostate cancer, but who has a bad outcome and then focus on how to avoid [that outcome].” Center investigators also hope to document how socioeconomic factors (such as neighborhood, income level, and education level) may influence how and whether cancer is treated. “We can guess that [an impoverished environment, lower income, and lower educational attainment] have a negative influence, but it’s not well known,” Rebbeck explains. “We need the data to show if they’re really influencing disease outcome.”
Cervical Cancer in Ohio Appalachia
Studies under way at The Ohio State University CPHHD target health disparities associated with yet another form of cancer. Center director Electra Paskett and her team of colleagues want to know why cervical cancer incidence is higher among women living in the state’s Appalachian country, which comprises 29 counties. Indeed, cervical cancer rates in this area range from 11.4 to 20.3 cases per 100,000 women, compared with a national rate of 9.6 cases per 100,000 women.
The goal of one study is to boost early detection of cervical cancer (which increases rate of survival) by increasing the proportion of women in this area aged 18 and older who receive regular Pap smears and return for follow-up care if necessary. Because smoking, a known cervical cancer risk, is more common among this population than in the general U.S. population, a second study will test the effectiveness of a smoking cessation program. A third study of 1,200 women will explore how variables such as behavior, economic conditions, and barriers to health care, as well as biological factors (including infection with the sexually transmitted human papillomavirus, which may be a precursor to cervical cancer) may interact to contribute to cervical abnormalities.
Paskett says the greatest challenge so far has been recruiting participants across such a large geographic area as well as outfitting the 16 participating clinics with resources to support the study. “We’ve had to provide clinics with special equipment they didn’t have, such as centrifuges and freezers,” she explains. “Some of the clinics were short-staffed and not up to date with computer technology.”
When Many Diseases Burden One Population
Research under way at the Wayne State University Center for Urban and African American Health targets several health problems burdening the black population in the Detroit area. Three research projects share the common themes of obesity, diet, lifestyle factors such as physical activity, and obesity-related cancer and cardiovascular disease. According to center director John Flack, blacks as a group have higher incidence not only of cancer, but also of high blood pressure, stroke, kidney disease, and obesity; they also tend to exercise less.
A team lead by Zora Djuric is seeking to better understand why weight gain is more common in black breast cancer survivors than in whites, a statistic of great concern because weight gain is linked to breast cancer recurrence. Another study explores the link between sodium intake (and a possible resulting rise in blood pressure) and weight gain. The researchers also hope to find optimal ways to improve outcomes of black patients undergoing cardiovascular rehabilitation.
Although Flack suspects that environmental differences and lower birth weights in black babies may contribute to the disparity in health problems among this population compared to whites, he cautions against assigning too much importance to the role of genetics. “Most genetic variations do not occur between ethnic groups,” he says. “My guess is that the genetic contribution is much smaller than some have speculated.”
Boston Puerto Ricans Beleaguered by Stress
Researchers at the Tufts University CPHHD aim to uncover the factors that make older Puerto Ricans living in the greater Boston area significantly more likely to suffer from physical disability, depression, cognitive impairment, type 2 diabetes mellitus, and other chronic health conditions than do non-Hispanic white elders living in the same neighborhoods. Center director Katherine Tucker suspects that higher levels of stress—possibly resulting from poverty, migration, acculturation (including adopting a U.S. diet), and perceived discrimination—leads to greater long-term physical expressions of stress (or “allostatic load”) and eventually adverse health outcomes. Investigators are collaborating with a community organization, La Alianza Hispana, to offer social intervention, health care, and nutrition information to local communities.
So far, investigators have completed more than 400 interviews with Puerto Rican adults aged 50–75 living in the Boston area and have gathered information on poverty, language isolation, urban environments, nutritional intake, and measures of stress. The latter was assessed both by questionnaires and by physiological measures, such as levels of stress hormones including the catecholamines and cortisol. The investigators plan to follow up with two forms of intervention—one group will receive multivitamins and compliance reminders, while the other will receive social interaction activities to relieve stress. Each intervention will continue for two years, and postintervention measures of health will be compared to baseline for these groups in relation to the remaining participants who did not receive the interventions.
A companion study is under way to assess genetic interactions with diet on the risk of cardiovascular disease and diabetes. According to Tucker, 40% of Puerto Rican immigrants aged 60 or older have type 2 diabetes, compared with fewer than 20% of whites, numbers that Tucker says are “out of control.” Though their long-term goal is to document disparities and provide information to target improved services that will promote health in this high-risk group, the researchers have seen some immediate benefits already. “We’re having a very positive response from patients who didn’t know they were ill and who have followed up with their physicians to start a treatment plan,” Tucker says.
Health Disparity: A Positive Thing?
In at least one case, belonging to a minority group may confer health benefits. That’s the thinking behind what James Goodwin, director of the CPHHD at the University of Texas Medical Branch in Galveston, calls “the Hispanic paradox,” a finding that the health of many Hispanic populations in the United States is similar to that of whites, even though the Hispanic groups suffer disadvantages in income, health insurance, housing, education, and other factors that correlate strongly with health.
For Hispanics, health varies in relation to neighborhood composition. Hispanics living in largely homogeneous census tracts enjoy lower cancer incidence and lower cancer mortality than those living in neighborhoods with low percentages of Hispanics. For example, data from the Hispanic Established Populations for Epidemiologic Studies of the Elderly, a population-based longitudinal study of 3,050 older Mexican Americans living in the Southwest, showed a more than threefold difference in cancer prevalence among the subjects as a function of the percentage of Mexican Americans in their respective census tracts. Goodwin and his colleagues want to first find out what it is about high-density Hispanic neighborhoods that promotes good health, as well as the pathways or mechanisms that seem to transmit good health to residents of those neighborhoods. They suspect nutrition and buffers against stress both play some role.
“The role of stress in disease has been way, way underestimated and ignored,” Goodwin says. “As doctors, we tend to focus only on things we can measure. We don’t have a stress-o-meter, so we can’t easily measure stress. We focus on what we can objectify—pollution, for example, as opposed to concerns about pollution.”
The researchers are merging readily available data sets—including the Hispanic Established Populations for Epidemiologic Studies of the Elderly as well as the Surveillance, Epidemiology, and End Results cancer registry—with census data, and then analyzing them to better understand the role of neighborhood in cancer incidence.
Neighborhoods and Negative Health Influences
Researchers at the RAND CPHHD in Santa Monica have also noted how neighborhoods can affect health outcomes, including infant mortality, life expectancy, and the development of chronic diseases such as heart disease and asthma. Center director Nicole Lurie says it may be possible to understand how neighborhood environments influence the development of disease by examining predisease markers of cumulative biological stress, including hormonal reactions and inflammatory and endocrine markers.
A major accomplishment for this center has been the development of the Contextual Data Library, a core data library for use in future studies. Researchers have layered publicly available health, socioeconomic, and census data with segregation and cost-of-living indices as well as measures of street connectivity, air pollution, and land use. The result is a detailed look, statistically speaking, of any given individual’s neighborhood characteristics. The data can be downloaded for free (see http://www.rand.org/labor/aging/dataprod/cdl/listdata.html).
Studies planned or under way at the RAND center will evaluate numerous neighborhood variables, including how the presence of parks shapes physical activity and health; whether different types of neighborhoods and neighborhood features produce different biological “footprints” (patterns of biological markers); how elements of the built environment may influence mental health; how physical and social aspects of a neighborhood may contribute to the disabling process in the elderly; whether neighborhood characteristics correlate with obesity, physical activity, and diet; and how outdoor air pollution affects the worsening of asthma.
“There continues to be a major debate about what factors make people sick,” Lurie says. “We want to find out how much is caused by neighborhood factors, particularly those that could be modified by public policy.”
Neighborhood Effects on Breast Cancer
The impact of neighborhoods on breast cancer is the focus of research under way at the CPHHD at The University of Illinois at Chicago. C enter director Richard Warnecke and his colleagues are studying the relationship between patients’ social environment and access to early detection and diagnosis of breast cancer in black, Hispanic, and white women. According to Warnecke, stage at diagnosis is the best predictor of survival.
Related studies will examine the influence of social networks on patients’ use of health care services and response to symptoms, identify factors and beliefs that may delay a patient’s seeking medical attention (for example, the fear that touching the breast too often or getting too many mammograms will itself cause cancer), and explore factors from discovery through treatment that influence breast cancer prognosis.
Investigators are evaluating data from the state breast cancer registry coded by census variables and information about where individual participants live, collecting blood samples to analyze DNA markers and stress measures, and conducting extensive interviews with both patients themselves and members of each patient’s primary social support network.
Long term, Warnecke hopes the studies will have a positive “systems effect” on breast cancer screening and treatment. “In Chicago, if you’re poor, there can be as much as a six-month waiting time for screening; if an anomaly is found, there can be a [further] six-month waiting time for a biopsy,” he says. The reason for this lag time is the lack of facilities that are accessible at a cost that poor women can afford. The wait could, for some women, mean the difference between life and death.
Like Warnecke, scientists working at all the CPHHDs hope not only for answers to their questions about disease causes and interventions at both the population and individual levels, but also for the tools to promote change and significantly reduce health disparities altogether.
Pieces of the puzzle.
The NIEHS Centers for Population Health and Health Disparities are working to uncover why the factors that make populations unique may also work to make them more—or less—vulnerable to environmentally related disease.
Connecting the dots.
Researchers at the eight centers are studying how biological, sociocultural, environmental, and economic factors combine to contribute to disparities in disease among local populations across the United States.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00595EnvironewsNIEHS NewsHeadliners: Proteins: Timeless Protein Plays a Role in Coupling Cell Cycle and Circadian Rhythm Phelps Jerry 9 2005 113 9 A595 A595 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Ünsal-Kaçmaz K, Mullen TE, Kaufmann WK, Sancar A. 2005. Coupling of human circadian and cell cycles by the Timeless protein. Mol Cell Biol 25:3109–3116.
In mammals, the Timeless protein is necessary for proper functioning of circadian rhythm, the predictable “internal body clock” that regulates the 24-hour cycle of biological processes in animals and plants. The protein is also evolutionarily related to many cell cycle control proteins, which mediate cellular pathways that are activated by environmental changes or cellular injury, resulting in a protective response. In the current study, NIEHS grantee William K. Kaufmann and colleagues at the University of North Carolina–Chapel Hill set out to determine whether the Timeless protein may itself function as a core component of the human cell cycle.
Disruptions in circadian rhythm have been implicated in a variety of diseases and conditions from common jet lag to cancer. The cell cycle is the orderly sequence of events by which a cell duplicates its contents and divides into two. Both systems have pervasive effects on physiology at the levels of the cell, organ, and organism.
Although the two systems have distinct regulatory mechanisms, there is growing evidence that they are linked. Most mammalian cells function on an approximate 24-hour cell cycle, and the circadian clock has been implicated in regulating the phases of cell division. This linkage is important for a new field of research and medicine known as chronotherapy, which aims to coordinate the delivery of chemotherapeutic drugs with the circadian and cell cycles to maximize drug efficacy while minimizing side effects.
Kaufmann and colleagues found that the human Timeless protein interacted with both Cry2 (a confirmed circadian clock protein) and Chk1 kinase (a cell cycle checkpoint protein). Timeless also appeared to play a role in the DNA damage checkpoint response, a process that arrests cell division and activates DNA repair mechanisms. Other experiments demonstrated that inhibiting production of Timeless protein seriously compromised checkpoint-regulated coordination of cell division, “indicating an intimate connection between the circadian cycle and DNA damage checkpoints.”
Although there is still much to be learned about the function and control of the Timeless protein, these results indicate that it does indeed act in the control of both the circadian clock and the cell cycle by interacting with circadian clock proteins and playing an important role in the DNA damage response. However, the authors point out that the circadian cycle operates normally in the absence of the cell cycle.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0059616158523EnvironewsNIEHS NewsBeyond the Bench: Cultivating Environmental Leadership in the Midwest Tillett Tanya 9 2005 113 9 A596 A596 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Today’s youth are the environmental health leaders of tomorrow. The Environmental Health Sciences Research Center (EHSRC) at The University of Iowa, in conjunction with its partner, the Belin–Blank International Center for Gifted Education and Talent Development (also a component of The University of Iowa), is helping some of these future leaders understand the environment and their role in it, with the goal of inspiring the next generation of environmental health advocates.
Each summer since 1997, the two partners have joined forces to conduct the Environmental Health Sciences Institute for Rural Youth (EHSI), an intensive, full-scholarship, one-week residential experience for rising tenth-graders from small, rural Midwest communities. By giving high school students access to a wealth of environmental health information and helping them translate that information for dissemination to their own communities, the EHSI helps foster leadership qualities that will help them apply those skills to their future careers and their personal lives.
Each summer the EHSI accepts about 15 students to the program, and houses them in student residence halls on the Iowa campus. According to David Osterberg, director of the EHSI, the primary goal of the program is to inspire students to consider the environmental health sciences as a possible future career.
“We have students for a week, so we can aspire to do many things,” he says. “We help develop mentoring relationships between smart high school students and our scientists, expose students to cutting-edge research, show them a full range of environmental health topics, and give them some career options. I especially like to emphasize policy so students realize there are potential solutions to problems that impact the environment and human health.”
Throughout the week, the students are exposed to information on environmental health and related research through lectures, interactive lab sessions, one-on-one mentoring, and field trips. In this year’s session, students attended lectures on such diverse topics as the relationship between cancer and the environment, nanotechnology in environmental health science, global climate change, and the connection between agriculture and health. The mentoring and lab sessions then give the students a first-hand glimpse of current research related to the lecture content.
This summer’s lab activities included a pathology session in which students examined specimens of human organs to compare cancerous and healthy tissues. Another session focused on inhalation toxicology. Students dissected mouse lung tissue and examined the cells under a microscope to determine the effects of grain dust exposure on the lung. Afterwards, they watched a dust measurement and quantification demonstration in the EHSRC’s environmental modeling and assessment facilities.
The program also exposes the students to initiatives taking place in the Iowa community that encourage environmental responsibility. One of this year’s field trips was a visit to the Amana Lily Pond, a wetland that has been planted with poplar trees to act as natural filters to prevent herbicides, insecticides, and fertilizers from entering the creek and emptying into the pond. The students also saw a demonstration by the Iowa Renewable Energy Association of the “solar traveler,” a mobile demonstration unit that produces electricity via solar power.
Since public speaking is a crucial skill for scientists and public health workers, the program also includes a session on speaking in front of groups that helps the students improve their body language and voice projection to deliver an effective presentation. Once the summer session ends, each student chooses an environmental heatlh science topic and organizes information learned over the week into a presentation that is delivered to a school group and to a community group in their hometown.
The presentations, as well as the other aspects of the EHSI experience, give the students the opportunity to become environmental health science ambassadors who can potentially impact the lives of their families and neighbors. “We hope that in the course of EHSI Week, students gain an appreciation for environmental issues as well as their own personal stake in how these issues affect their health, their families, and their communities,” says Osterberg.
Budding environmental scientists.
(left) EHSI students explore the Devonian Fossil Gorge in Coralville, Iowa. A 1993 flood washed away layers of earth and exposed bedrock containing a multitude of fossils. The field trip followed a lecture on effects of global climate change. (above) Students perform a chemical analysis on water collected at the Amana Lily Pond phytoremediation site.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0059816140609EnvironewsFocusPopulation Equation: Balancing What We Need With What We Have Dahl Richard 9 2005 113 9 A598 A605 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Planet Earth, now home to about 6.5 billion human beings, has thus far disproved the doomsayers. In 1798, Rev. Thomas Robert Malthus predicted that population would outrun food supply on the assumption that human numbers would increase at a geometric rate while food would be limited to arithmetic increases. Then, in 1968, Stanford University professor Paul R. Ehrlich issued a similar warning in his book The Population Bomb, in which he predicted that hundreds of millions of people would die of starvation in the 1970s and 1980s.
Both men underestimated humanity’s resourcefulness—as well as its scientific and technological acumen—in figuring out how to provide for its growing numbers. Still, there’s little doubt that the Earth’s human carrying capacity has a limit. And growth can’t continue indefinitely without more of the significant environmental health impacts we are already seeing. In addition to documenting exactly how much growth is occurring, scientists are now interested in trends reflecting where such growth is occurring and the effect of factors such as consumption rates and migration on sustainability of the Earth’s resources.
Maximum Capacity
Nobody really knows what the planet’s human carrying capacity is. Some, like Cornell University ecology and agriculture professor David Pimentel, contend that the Earth has already passed that point. Citing high malnutrition rates in the world, Pimentel estimates that the Earth’s carrying capacity—providing a quality life for all inhabitants—would appear to be about 2 billion. Other estimates go to both extremes. In a 1995 Cato Institute essay titled “The State of Humanity: Steadily Improving,” Julian L. Simon, the late University of Maryland economist, wrote, “We have in our hands now—actually in our libraries—the technology to feed, clothe and supply energy to an ever-growing population. . . . Even if no new knowledge were ever gained . . . we would be able to go on increasing our population forever.” On the other end of the spectrum, in 1971—three years after writing The Population Bomb—Ehrlich placed the limit at 500 million.
Others suggest that humans are already finding a way to take care of the population problem as evidenced by declining birth rates everywhere in the world. Declining birth rates don’t necessarily translate into declining populations, however. The United Nations (UN) Population Division projects that by 2050, global population could reach 9.1 billion.
This greater global population will differ from the current one in several ways. The population growth of the developed world has slowed to a crawl; fertility rates are on the decline and in some countries, such as Italy and Japan, population itself is projected to peak in five years. But poor countries will experience large increases for decades to come. Meanwhile, the UN points out that in 2007, for the first time in history, the global population will cross over from being predominantly rural to mostly urban, and that that trend will continue indefinitely.
“Most of the growth that’s going to happen in the next twenty, thirty years is going to be happening in the poor countries—it’s going to happen mostly in the cities, and mostly in the slums of the cities,” says John Bongaarts, vice president of policy research at the nonprofit Population Council. “Most of the next two or three billion people will end up in the slums of the poorest countries.”
Like many demographers, Bongaarts sees the decline in fertility rates, mostly in the industrial world, as the emerging worldwide norm. This means, he says, that at some point the poorer countries will reach the same stabilization point that the developed world has achieved and that global population will one day decline. He projects that peak will be reached at about 9.5 billion people.
Perhaps surprisingly, population’s relationship to health and environmental impacts is often ignored or glossed over by policy makers. In part, says Robert Engelman, vice president for research at the policy action group Population Action International, there’s a belief that “population will take care of itself.” But there’s also a reticence to talk about population because it gets tied up in politics, including the abortion debate.
Julie Starr, a population and environment specialist with the National Wildlife Federation, says she was surprised to see that the eight UN Millennium Development Goals that were set in 2000 failed to make any mention of population growth and family planning. These goals summarize all the development goals agreed to at international conferences and summits during the 1990s, with a target achievement date of 2015. “Each of the goals has specific targets, and population is mentioned nowhere—not even in goals that deal with maternal health and poverty,” she says. “Our message is that you can’t do anything about environmental sustainability if we don’t address population.”
“There’s been a lack of attention to the fact that population continues to grow in the world at a rate that is certainly unsustainable,” Engelman says. “And population is connected to environmental conditions everywhere. There really isn’t any environmental area that you can look at and say that it’s completely irrelevant to the number of people living in a particular ecosystem or watershed.”
Marking the Trends
An international group of scientists who took part in a major new international study, however, apparently wants to see greater attention paid to population in future discussions about environmental sustainability. The Millennium Ecosystem Assessment, launched by UN secretary-general Kofi Annan in 2000 to assess the impact that environmental changes would have on achieving the Millennium Development Goals, involved the work of 1,360 scientific experts who aspired to measure the environmental impact that people are having on the Earth.
One document to emerge from the assessment process is Ecosystems and Human Well-Being: Synthesis, released in March 2005, which is one of several periodic reports scheduled for release through the end of 2005. This report examined the “services” that ecosystems provide (for example, fish from the ocean and pollution filtration from wetlands) and concluded that 15 of the 24 services are being degraded or used unsustainably. It suggested that the various environmental declines comprise a roadblock to achieving many of the Millennium Development Goals, including those calling for ensurance of global environmental stability, poverty alleviation, and food security.
The role of population in causing these declines is implicit throughout Ecosystems and Human Well-Being: Synthesis and explicit in a section in which it is identified as one of five “indirect drivers” that are altering ecosystems. Walter V. Reid, director of the assessment project, says ecosystem health is affected by two kinds of pressures that humans exert: changes in demand for (and consumption of ) an ecosystem’s specific services, and changes in emissions that might harm the ecosystem. “Obviously, both change in demand and change in emissions are closely tied to the combination of population change and economic growth,” he says.
To Reid, the most troubling development regarding population trends and their environmental impact is the fact that the greatest population growth is now occurring in environmentally fragile areas, like drylands and mountainous regions, where water is scarce and the soil is generally poor. In those areas, he says, “if you have high population growth that is overtaxing the capacity of the soils to provide food, you have high rates of soil erosion and depletion, and there’s just no buffer. And if you need more water, there’s just no buffer of water even to begin with.”
Demographers and social scientists use the term “poverty trap” to describe such areas, which are characterized by classic vicious cycles. “The pressure to degrade resources is insurmountable,” Reid says. “People don’t have other options. And when they degrade resources, that leads in the long run to higher levels of poverty and infant mortality and lower income, which leads to greater pressure to degrade resources.”
Another population trend emphasized in the March report is the movement of people to coastal areas around the world. Coastal ecosystems—marshes, mangroves, reefs—are extremely important contributors to human well-being, serving as breeding and nursery grounds for many species and as erosion prevention buffers between land and sea. Yet these benefactors are rapidly being destroyed. According to Reid, 35% of the world’s mangroves and 20% of its coral reefs have disappeared in the last two to three decades due to human pressure.
The assessment makes a variety of recommendations for policy makers—remove environmentally harmful subsidies to agriculture and fisheries, improve management of ecosystem services in regional planning decisions, provide public education about the importance of ecosystems, promote greener technologies, and more. But Reid believes that if the report is to have an impact, there must be some kind of repeating assessment process. He thinks that a mechanism should be created so that the subject is revisited in similar fashion every 10 years.
The Role of Consumption
Roger-Mark De Souza, technical director of the Population, Health, and Environment program at the Population Reference Bureau, points out that another important trend in the developing world is its high and growing proportion of young people. In sub-Saharan Africa, for example, the proportion of people under 15 to people over 65 is 44% to 3%, according to the bureau. In Latin America, the numbers are 32% younger people compared to 6% older people. “That means that we will have continued population growth for some period of time because those young people of today are tomorrow’s parents,” he says. “We call that ‘population momentum.’”
In addition to their raw numbers, De Souza says, ever-increasing globalization means the growing ranks of young people in the developing world may be driven to consume more than their parents do. “They access images about life in other parts of the world on television and the Internet, and they desire to live that way,” he explains.
Geographer Robert Kates, a visiting scholar at the Harvard Center for International Development, contends that consumption rates are actually more important than population. Currently, a huge per-capita consumption disparity exists between rich and poor nations. According to the September 2003 Population Bulletin, published by the Population Reference Bureau, in 1999 the average North American consumed more than 15 times the energy of the average African (230 gigajoules—equivalent to about 143 barrels of oil—in North America compared with 15 gigajoules in Africa).“Most people accept the notion that major, long-term environmental problems will stem more from consumption than from population growth,” Kates says. “Population growth is one of the forces that drives consumption. But there are a whole host of other forces as well—growing income, changing diets, the creation of transnational markets.”
Kates argues that potential growth rates for consumption around the world are much greater than the better-known predicted rates for population growth. Therefore, he suggests, the number of people isn’t as important as what those people do. “The increase in the number of people is clearly slowing down everywhere in the world,” he says. “But the increase in consumption by those people is going up everywhere, except in Africa, and there’s no sign of diminution in the future. So there will be a shift from long-term historic concern about population to a growing concern about how, what, and where we consume.”
Others, however, say while paying attention to consumption is indeed a critical force, its importance should not sideline the question of where and at what level population growth will end. “If our [global] population had stabilized where it was in antiquity, at about two hundred fifty to three hundred million, our consumption probably wouldn’t make too much difference,” says Engelman. “But it’s precisely because human population has gone where it is that consumption has the global impacts that it has. How much ‘environmental space’ each of us has to consume sustainably has everything to do with how many of us there are.”
The Impact of Population
Whether one chooses to attribute impacts to human numbers or human behavior, the fact remains that the world’s population—its numbers, its movement, its actions—is having a profound impact on human and environmental health. A variety of organizations and individuals, including the UN and other international agencies, nongovernmental organizations, scientists, and demographers, have identified many of the ways in which this is happening.
Water availability.
Engelman points out that the amount of fresh water on Earth is roughly the same today as it was 3,000 years ago, while population has increased 40-fold. Declining water tables are a growing problem in much of the world. According to the Population Reference Bureau, 12 of the world’s 15 water-scarce countries are in the Middle East and North Africa, comprising an area that experienced more than a doubling of population—from 173 million to 386 million people—between 1970 and 2001. Growing additional food to nourish growing populations will rely heavily on irrigation, placing greater strain on water tables. The Millennium Ecosystem Assessment reports that usage levels of fresh water for drinking, industry, and irrigation are “unsustainable.” The American Association for the Advancement of Science (AAAS), in its 2000 AAAS Atlas of Population and Environment, predicted that the situation is “likely to be worsened by the deteriorating quality of water, polluted by industrial wastes and sewer discharges.”
Deforestation.
According to the Population Reference Bureau, human activities during the 1990s resulted in the deforestation of 563,709 square miles of land, roughly the equivalent of Colombia and Ecuador combined. Most of the deforestation occurred in Africa and South America, where forests have been cleared for cropland, fuel use, and commercial sale of wood products. The environmental and human health impacts of deforestation are varied, including increased propensity for flooding, loss of medicinal species and fuel wood, soil erosion, and exacerbation of climate change as carbon is released back into the atmosphere. Related to deforestation is the issue of biodiversity loss. The World Conservation Union estimates that nearly one-fourth of the mammals and one-eighth of the birds on Earth are now threatened with extinction.
Fisheries.
“The fishery story is a sad case of overuse by humans,” says Bongaarts. “Fish populations have collapsed in many parts of many oceans, and lower-quality fish are replacing them.” According to the AAAS atlas, the world’s marine catch increased fivefold between 1950 and 1990, but has remained stagnant ever since. The Millennium Ecosystem Assessment took an even bleaker view, finding that harvests have been declining since the late 1980s (Reid says the discrepancy relates to how one interprets the official statistics reported by different countries).
Climate change.
The link between population growth and climate change is less clear. Engelman points out that the vast majority of climate change is driven by emissions from industrialized countries, the populations of which will soon peak or have already done so. But poor countries are rapidly expanding their industrial capacity in response to out-sourcing by industrialized countries, and their share of climate change–related emissions will increase rapidly in coming years, raising the need for international agreements on emissions reductions, Engelman says.
Air quality.
The World Health Organization (WHO), in its 1999 Air Quality Guidelines, said that outdoor air pollution in Western Europe and North America has improved since 1970, but in less developed countries air pollution in the large cities—including Delhi, Jakarta, Mexico City, and many Chinese cities—is severe. So is its impact on public health. The World Resources Institute studied the health effects of air pollution in cities in poor nations and said in the 1999 report Urban Air Pollution Risks to Children: A Global Environmental Health Indicator that it was responsible for 50 million cases per year of chronic cough among children under age 14.
Infectious disease.
Human population growth and migration has also fostered the emergence of many infectious diseases by increasing population density. This is especially true in urban areas, where illnesses such as dengue and cholera are becoming more common, the Population Reference Bureau reported in the September 2003 Population Bulletin. Encroachment into wildlife habitats also exposes humans to new diseases. “Increased contact with wildlife and associated diseases, combined with international trade in livestock, has led to outbreaks of diseases such as rinderpest [a viral disease affecting ungulates] in Africa and foot-and-mouth disease in Europe,” stated the report.
The U.S. Situation
The Center for Environment and Population (CEP), a nonprofit research and public policy organization, will be releasing a national report this fall that will explore the relationship between U.S. population trends and their impact on health and the environment. Victoria Markham, director of the CEP and executive editor of the AAAS Atlas of Population and Environment, says one of the reasons for the study is that the United States, in a departure from other industrialized nations, is experiencing significant population growth and will continue to do so.
Where the AAAS atlas was one of the first large efforts to tie known data about environmental change to population, the upcoming CEP report will do the same sorts of comparisons within American borders. Markham says the latter report will focus on several human population variables that relate to environmental impact—population growth, distribution, movement, and makeup, as well as household demographic trends and consumption rates—and apply them to the nation’s four census regions.
With a population of 298 million, the United States is the third most populous country in the world, behind China (population 1.3 billion) and India (population 1.1 billion). Projections in the Population Reference Bureau’s 2004 World Population Data Sheet call for the United States to remain third behind China and India for decades to come, while two other current industrialized countries, Russia and Japan, will be dropping out of the top 10 and leaving the United States as the only currently industrialized country on that list by 2050.
“Couple our growing population with our disproportionately high rate of resource consumption, and you have a volatile combination,” Markham says. “The United States turns out to be a world leader in terms of per-capita global environmental impact.”
Like the rest of the world, the United States is becoming ever more urbanized, but at a more advanced level, as 80% of Americans now live in metropolitan areas, according to the 2001 U.S. Census Bureau report Population Change and Distribution, 1990 to 2000. But while more Americans than ever are living in metro areas, most of the growth is occurring outside center cities, in outlying suburban areas.
Markham says this outcome—sprawl—can be illustrated by the fact that while the American population has grown by 17% in the last two decades, the land area converted to metropolitan use grew by 50%. “Air pollution is very closely tied to population,” she says. “Transportation is the fastest growing energy-use sector in the United States, and it’s particularly tied to this sprawled development because people have to drive more and drive farther. The result is increased carbon dioxide emissions.”
Another trend is a continuing higher rate of population increase in the South and West, compared with the Midwest and Northeast. This trend largely reflects the movement of the industrial infrastructure from the North to the South and West starting in the 1960s for various economic reasons, such as lower taxes and lower labor costs. In terms of environmental impact, the population growth in the West is especially worthy of concern because of the region’s fragile water supply. “Population growth couldn’t be happening in a more environmentally vulnerable place in the United States,” Markham says. The Ogallala aquifer, which lies under eight western states and is the largest groundwater system in North America, accounting for 20% of all irrigated land in the United States, is down one-third of its capacity and is shrinking at the rate of a foot per year, according to Markham.
Meanwhile, Americans are living in ever larger per-capita household space, which exacerbates energy consumption. The CEP report will describe the continuing decline in number of persons per household, which translates into more households. At the same time, the physical size of American homes is growing ever larger. According to Markham, the proportion of houses of at least 3,000 square feet more than doubled between 1988 and 2003; during that same time, the number of new houses smaller than 1,200 square feet declined. And lot sizes of new one-family houses outside the country’s metropolitan areas rose by 6% in the past 10 years, according to the Census Bureau. The increase in the number of houses overall combined with larger lot sizes means more land is being used for residential development than ever before.
Reasons for Hope, Possible Futures
Discussions about burgeoning human populations and their impact on health and the environment abound in gloomy data and prospects of doom. But experts also suggest there are reasons to be somewhat optimistic. First, they say, humanity has proven itself to be more resourceful than Malthus and Ehrlich gave it credit for being. “Basically, forty or fifty years ago, the whole world was growing rapidly,” Bongaarts says. “There was a huge concern about potential food shortages and environmental problems. But birth rates have declined, so growth is not as rapid as people thought it would be.”
Even though the rates are declining in poor countries, they’re still higher than the acknowledged replacement figure of 2.1 children per woman. Still, Asian, Latin American, and Caribbean women are bearing children at a rate of 2.6 children per woman in 2004 compared to about 5 per woman in 1970, according to the UN Population Division. African women still have 5 children on average, but that’s down from 6.7 in 1970. Europe has dropped from 2.2 children per woman to a population-slashing 1.4. In the major world regions, only North America has not seen declining birth rates. North American women averaged 2.0 children in 1970 and the figure was the same in 2004.
To many observers, the decline in global birth rates is clear proof of the effectiveness of family planning programs. “I think the greatest proportion of demographic research points to the worldwide effort to make contraception available, which was clearly desired and was in fact picked up and used,” Engelman says.
Lars Bromley, a senior program associate in the AAAS Office of International Initiatives, has come to the same conclusion. “If a country works to reduce its birth rate, it’s not a foregone conclusion that they’re destined to have twelve children per woman,” he says. “Places like Bangladesh and elsewhere have really performed miracles over the last generation.” According to Bangladesh Demographic and Health Survey 2004, published by the U.S. Agency for International Development, birth rates in that country have declined from 6.3 children per woman in the early 1970s to 3.0 children in 2004.
Another improvement, Kates points out, is that although the total amount of energy consumed continues to rise, the world is reducing its “energy intensity”—that is, the amount of energy it uses per unit of production—at a rate of about 1% per year. This is mostly due to improved energy-saving technology.
But as the scientists who conducted the Millennium Ecosystem Assessment conclude, a broad international response is necessary to deal with the environmental declines caused by increasing human pressure. They didn’t make predictions about what may happen, but they did offer four possible future scenarios. The first, “Global Orchestration,” depicts a world that makes economic development a priority and emphasizes solving environmental problems rather than preventing them in the first place. The second, “Order from Strength,” represents a fragmented world concerned primarily with security and protection, where the approach to the environment again is reactive. The third, “Adapting Mosaic,” would deemphasize economic development and put priority on the health of ecosystems, largely through the strengthening of local management strategies. The fourth scenario, “TechnoGarden,” describes a future in which a unified world relies on environmentally sound technology and highly managed, often engineered, ecosystems to deliver ecosystem services, and that achieves both strong economic growth and a healthier world.
Reid believes that the work on which direction the world should go must start soon. And he believes the debate must focus more on population than it has to date. “Population is one of those issues that’s so central and so politicized,” he says. “Sometimes you worry that people are ignoring it because of the political side of it, but it’s critical that people keep thinking about it and about steps that can be taken to address population problems.”
Signs of Ecological Change
Over the past century and especially over the past 40 years, people have effected vast changes in the global environment. Those people most directly affected by environmental challenges, from water pollution to climate change, are also the poorest and least able to change livelihoods or lifestyles to cope with or combat ecological decline. Some signs of ecological change include:
Source: UNFPA. 2004. State of World Population 2004: The Cairo Consensus at Ten—Population, Reproductive Health and the Global Effort to End Poverty. New York, NY: United Nations Population Fund.
Deforestation.
Farmers, ranchers, loggers, and developers have cleared about half the world’s original forest cover, and another 30% is degraded or fragmented.
Climate change.
As a result of fossil fuel consumption, carbon dioxide levels today are 18% higher than in 1960 and an estimated 31% higher than at the onset of the Industrial Revolution in 1750. Accumulation of greenhouse gases (including carbon dioxide) in the atmosphere is tied to rising and extreme change in temperatures as well as more severe storms.
Food insecurity.
Over the past half-century, land degradation has reduced cropland by an estimated 13% and pasture by 4%. In many countries, population growth has raced ahead of food production in recent years. Some 800 million people are chronically malnourished, and 2 billion lack food security.
Water scarcity.
Since the 1950s, global demand for water has tripled. Groundwater quantity and quality are declining due to overpumping, runoff from fertilizers and pesticides, and leaking of industrial waste. Half a billion people live in countries defined as water-stressed or water-scarce; by 2025, that figure is expected to surge to between 2.4 billion and 3.4 billion.
Overfishing.
Three-quarters of fish stocks are now fished at or beyond sustainable limits. Industrial fleets have fished out at least 90% of large ocean predators—including tuna, marlin, and swordfish—in the last 50 years.
Sea level rise.
Sea level has risen an estimated 10–20 centimeters, largely as a result of melting ice masses and the expansion of oceans linked to regional and global warming. Small island nations and low-lying cities and farming areas face severe flooding or inundation.
Globally based trade reform lifts populations out of poverty, freeing up resources to respond to environmental problems as they become apparent.
Nations are concerned primarily with security. Powerful countries shift burdens to weaker nations, and ecosystem services become increasingly vulnerable.
Political activity targets regional ecosystems, and investments are geared toward better understanding of these systems. Some areas thrive while others continue to degrade.
A globally connected world relies on highly managed ecosystems to provide services and solutions to environmental problems. Ecological engineering flourishes.
The Millennium Ecosystem Assessment examined how each scenario would increase or decrease material well-being, health, security, social relations, and freedom of choice and action.
Countries Accounting for About 75% of the World Population by Order of Population Size
1950 2005 2050
Rank Populationa Cumulated % Rank Populationa Cumulated % Rank Populationa Cumulated %
1. China 555 22.0 1. China 1,316 20.4 1. India 1,593 17.5
2. India 358 36.2 2. India 1,103 37.4 2. China 1,392 32.9
3. United States 158 42.5 3. United States 298 42.0 3. United States 395 37.2
4. Russia 103 46.6 4. Indonesia 223 45.5 4. Pakistan 305 40.6
5. Japan 84 49.9 5. Brazil 186 48.4 5. Indonesia 285 43.7
6. Indonesia 80 53.0 6. Pakistan 158 50.8 6. Nigeria 258 46.6
7. Germany 68 55.7 7. Russia 143 53.0 7. Brazil 253 49.4
8. Brazil 54 57.9 8. Bangladesh 142 55.2 8. Bangladesh 243 52.0
9. United Kingdom 50 59.9 9. Nigeria 132 57.3 9. Dem Rep Congo 177 54.0
10. Italy 47 61.7 10. Japan 128 59.2 10. Ethiopia 170 55.9
11. France 42 63.4 11. Mexico 107 60.9 11. Mexico 139 57.4
12. Bangladesh 42 65.0 12. Vietnam 84 62.2 12. Philippines 127 58.8
13. Ukraine 37 66.5 13. Philippines 83 63.5 13. Uganda 127 60.2
14. Pakistan 37 68.0 14. Germany 83 64.8 14. Egypt 126 61.6
15. Nigeria 33 69.3 15. Ethiopia 77 66.0 15. Vietnam 117 62.9
16. Spain 28 70.4 16. Egypt 74 67.1 16. Japan 112 64.1
17. Mexico 28 71.5 17. Turkey 73 68.2 17. Russia 112 65.3
18. Vietnam 27 72.6 18. Iran 70 69.3 18. Iran 102 66.5
19. Poland 25 73.6 19. Thailand 64 70.3 19. Turkey 101 67.6
20. Egypt 22 74.4 20. France 60 71.2 20. Afghanistan 97 68.7
21. United Kingdom 60 72.2 21. Kenya 83 69.6
22. Italy 58 73.1 22. Germany 79 70.4
23. Dem Rep Congo 58 73.9 23. Thailand 75 71.3
24. Myanmar 51 74.7 24. United Kingdom 67 72.0
25. Tanzania 67 72.7
26. Sudan 67 73.5
27. Colombia 66 74.2
28. Iraq 64 74.9
a In millions.
Source: UN. 2005. World Population Prospects: The 2004 Revision. Highlights. New York, NY: United Nations; Table VIII.3.
Countries Accounting for About 75% Average Annual Population Increase in the World
1950–1955 2005–2005 2045–2050
Rank Pop Increasea Cumulated % Rank Pop Increasea Cumulated Rank Pop Increasea Cumulated %
1. China 10,849 22.8 1. India 16,457 21.7 1. India 4,994 14.8
2. India 7,507 38.6 2. China 8,373 32.7 2. Dem Rep Congo 2,935 23.5
3. United States 2,652 44.1 3. Pakistan 3,057 36.8 3. Uganda 2,855 32.0
4. Brazil 1,782 47.9 4. United States 2,812 40.5 4. Nigeria 2,523 39.5
5. Russia 1,740 51.5 5. Nigeria 2,784 44.2 5. Pakistan 2,498 46.9
6. Indonesia 1,382 54.5 6. Indonesia 2,721 47.7 6. Ethiopia 1,999 52.8
7. Japan 1,238 57.1 7. Bangladesh 2,581 51.1 7. Afghanistan 1,699 57.9
8. Bangladesh 852 58.8 8. Brazil 2,509 54.5 8. Bangladesh 1,493 62.3
9. Pakistan 837 60.6 9. Ethiopia 1,781 56.8 9. United States 1,489 66.7
10. Mexico 800 62.3 10. Dem Rep Congo 1,499 58.8 10. Kenya 1,058 69.9
11. Nigeria 750 63.9 11. Philippines 1,458 60.7 11. Niger 1,007 72.9
12. Philippines 645 65.2 12. Mexico 1,388 62.5 12. Yemen 881 75.5
13. Thailand 627 66.5 13. Egypt 1,349 64.3
14. Turkey 625 67.8 14. Afghanistan 1,226 65.9
15. Egypt 572 69.0 15. Vietnam 1,113 67.4
16. Ukraine 560 70.2 16. Turkey 992 68.7
17. Vietnam 537 71.4 17. Uganda 901 69.9
18. South Korea 513 72.4 18. Iraq 747 70.9
19. Poland 491 73.5 19. Kenya 713 71.8
20. Iran 435 74.4 20. Tanzania 713 72.8
21. Colombia 696 73.7
22. Sudan 666 74.6
WORLD 47,586 100.0 WORLD 75,835 100.0 WORLD 33,697 100.0
a In thousands.
Source: UN. 2005. World Population Prospects: The 2004 Revision. Highlights. New York, NY: United Nations; Table VIII.6.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0060616140610EnvironewsSpheres of InfluenceKeeping Afloat: A Strategy for Small Island Nations Schmidt Charles W. 9 2005 113 9 A606 A609 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Looking for some adventure? Then how about a trip to Fiji, the Caribbean, or the Seychelles? With their palm trees, lazy beaches, and clear turquoise waters, few places have more appeal than the world’s island nations. Roughly 40 such countries, some sovereign and others not, lie scattered across the planet, ranging in size from Papua New Guinea, with a land area of 452,860 square kilometers and more than 5 million citizens, to Tokelau, a tiny, self-administering territory of New Zealand with a land mass measuring just 10 square kilometers and a population of 1,400.
Perhaps surprisingly, there is no clear official definition for what constitutes an island nation. The Alliance of Small Island States (AOSIS), an ad hoc lobbying and negotiation organization that coordinates the most prominent grouping, refers to its 45 members as “small island developing states,” or SIDS. The criteria that define SIDS are somewhat ambiguous, however. AOSIS members include Belize, Guinea-Bissau, Guyana, and Suriname, which are all coastal—although not technically island—nations. The upper population limit for SIDS, which is 10 million, also appears flexible: AOSIS counts Cuba as a member, despite the country’s population of 11.3 million. AOSIS has even received an application from Madagascar, despite that country’s population of 18 million.
Island nations may be beautiful, but their isolation makes them vulnerable to outside forces that increasingly threaten their survival. Rising sea levels linked to global warming could submerge some altogether. Tuvalu, a West Pacific nation whose peak height rises just 5 meters over sea level, could be uninhabitable within 50 years, some experts say. A similar fate could also doom the Maldives, the Marshall Islands, Kiribati, and Tokelau.
Meanwhile, for reasons not entirely understood, hurricane activity in the tropics appears to be increasing. Hurricane Ivan—the sixth most intense Atlantic hurricane on record—blasted the Caribbean with 160-mile-per-hour winds in 2004, devastating most of Grenada’s housing and virtually destroying its economy within just a few hours. The 2005 Caribbean hurricane season made headlines early on with Dennis in July. This earliest powerful hurricane ever recorded in the region caused an estimated US$5–9 billion in damage.
Most island nations struggle daily with depleted fish stocks, inadequate waste management, ship pollution, degraded reefs, dwindling freshwater supplies, and poverty. In general, the farther an island nation is from global markets, the poorer it is. Distance makes everything more expensive; oil prices in particular tend to be extremely high owing to transportation costs. The more remote islands also tend to lack communications infrastructure, access to information technology, and adequate numbers of trained professionals, including engineers, doctors, and teachers. These technical limitations slow economic development and exacerbate environmental problems caused by poor management of island resources.
Island nations are also uniquely threatened by price shocks from economic globalization. Most island nations depend heavily on tourism, foreign aid, and a limited range of exports like sugar and bananas. Declines in any of these sectors can decimate revenue streams, exacerbating the poverty that already plagues these countries. In St. Lucia, export revenue from bananas plunged from US$46 million in 1996 to US$22 million in 2002, largely because cheaper bananas from Central America flooded world markets during that same period. According to Kishan Kumarsingh, technical coordinator at the Environmental Management Authority in Trinidad and Tobago, climate change can also alter growing conditions, affecting agricultural productivity.
Price shocks emanate in part from the World Trade Organization’s (WTO) trade liberalization policies, which are dismantling arrangements that traditionally have guaranteed markets for island exports. A preferential trade link between the European Union and African, Caribbean, and Pacific Island exporters known as the EU–ACP Agreement is being phased out by the WTO after 30 years in existence as part of a deliberate effort coordinated by the organization to reduce protectionism and open economies to more globalized trade. Its elimination already hurts Caribbean banana exports, as indicated by St. Lucia’s experience. Sugar exports from Pacific countries could suffer similar fates, says François Coutu, a spokesperson in the United Nations (UN) Department of Public Information.
With environmental and economic problems mounting, island nations are turning increasingly to donor countries for aid. But these sources, too, are dwindling: donor nations’ development aid to island nations decreased from a high of US$2.3 billion in 1995 to US$1.7 billion in 2005, according to the UN Small Island Development Unit.
The Rising Sea
Of all the threats facing island nations, the rise in sea level could be the most catastrophic. Traditionally, sea level was measured with tide gauges including simple graduated measuring staffs and more complex devices that produce continuous graphic descriptions of tide height over time. But in the early 1990s, satellites began generating more comprehensive profiles of global sea level. Thanks to these orbiting systems, scientists now know that the average global rate of sea level rise has increased 50% during the last 12 years—up to 3 millimeters per year from a 50-year annual average of 2 millimeters, according to the National Aeronautics and Space Administration (NASA). Experts attribute rising sea level to global warming and its influence on two key parameters: ocean warming (which causes water to expand) and glacial melting (which is discharging increasing amounts of freshwater directly into the sea).
According to Kevin Trenberth, who heads the Climate Analysis Section at the National Center for Atmospheric Research, the global mean sea surface temperature has risen by roughly 1.5°F since the beginning of the twentieth century. Of that increase, roughly half has occurred since 1970, reflecting the growing effects of global warming from human-induced climate change, he says. The increase in sea surface temperature causes oceans to expand. This thermal expansion, Trenberth says, produces a roughly 1.6-millimeter global rise in sea level every year.
Melting glaciers account for most of the residual rise in sea level, Trenberth says. In a worst-case scenario, enormous ice sheets in Greenland and West Antarctica could melt and raise sea levels by up to 1 meter, suggests Waleed Abdalati, who heads the Cryospheric Sciences Branch at NASA’s Goddard Space Flight Center. Scientists emphasize there currently aren’t enough data to predict when—or even whether—such a scenario might occur. But if it does, many island nations and coastal communities would be inundated: “It’s estimated that more than a hundred million lives are potentially impacted by a one-meter increase in sea level,” Abdalati says.
In the meantime, even the comparatively small increases in sea level seen today can produce large effects, particularly when superimposed on high tides and storm surges, says Laury Miller, who heads NASA’s Satellite Altimetry Laboratory. Miller adds that as a first impact, rising seas contaminate freshwater resources.
This isn’t news to Enele Sosene Sopoaga, AOSIS vice chair and Tuvalu’s UN ambassador. While his country’s water storage capacity has improved, Sopoaga says, the freshwater tables just beneath Tuvalu’s atolls have become brackish and poisonous to root crops including pulaka, a yam-like vegetable that was once a dietary staple. Sea level rise is a constant presence in Tuvalu, he says. Waves crashing just a meter from the main road bring rocks and debris, slowing traffic and endangering lives. And many homes experience flooding whenever tides are high. “Our island is sinking together with our hearts,” he says.
Tuvalu is also battered by tropical storms of increasing ferocity, Sopoaga says, a view supported by new research from the Massachusetts Institute of Technology indicating that hurricanes in both the Atlantic and Pacific oceans have become 50% stronger during the last 50 years. These findings, produced by professor Kerry Emanuel of the Department of Earth, Atmospheric, and Planetary Sciences, appear in the 31 July 2005 online edition of Nature.
According to Trenberth, the main factor driving storm intensity is sea surface temperature. Higher sea surface temperatures raise the water vapor retention capacity of the lower atmosphere, which increases rainfall during hurricanes and other tropical storms. Because sea surface temperatures are rising with global warming, more intense weather events may be a direct consequence of human activity, Trenberth says.
Citing their vulnerability to climate change, island nations have long championed the Kyoto Protocol, which seeks mandated reductions in the greenhouse gases linked to global warming. But Tom Wigley, a senior scientists at the National Center for Atmospheric Research, says the greenhouse gases in the atmosphere now will likely linger for up to 100 years. Even if future emissions were limited to current levels, greenhouse gases would continue to rise and pose ongoing threats, he says. This is due to the lag-time response of the atmospheric and climate system—the climate does not react immediately or in the short term to greenhouse gas emissions, but rather over long periods.
Adapting to Climate Change
Small island nations faced with the consequences of climate change must somehow adapt to it. Only a few options are available, however, and none of them are attractive. According to Kumarsingh, islanders can either abandon threatened areas, retreat to higher ground, or build walls to hold back the sea. The Maldivian capital of Male is partially ringed by a system of protective walls that was built at a cost of US$4,000 per meter, financed largely by Japan. These walls saved the capital from the great tsunami that struck the Indian Ocean in December 2004. But seawalls are not always so effective; according to Sopoaga, most built in Tuvalu are damaged and in urgent need of repair.
Kumarsingh says coastal retreat could be highly challenging. “It’s not so simple,” he explains. “You have to relocate communities, amenities, and services, which is expensive. Island cultures are rooted in coastal living, so relocation has many socioeconomic impacts.”
Further, he adds, some islands, such as the Maldives, do not have higher ground to which to retreat. The size of many islands also limits how far people can go to escape: “When you retreat from one coast, chances are you may end up on the other coast. So where do you go?”
Perhaps most importantly, these strategies address only one aspect—sea level rise—of the consequences of global warming. Among the other direct and indirect consequences are changes in agriculture and food production, biodiversity loss, damage to coastal coral reefs (which are sensitive to warm sea temperatures and which act as natural coastal defense structures by dampening wave energy), saltwater intrusion to coastal aquifers (making potable water production more expensive), and increases in certain disease vectors due to increased humidity.
The Mauritius Strategy
Today, island nations are trying to promote global awareness of their plight. Earlier this year, the world spotlight shone on island nations during a large international meeting held in Port Louis, Mauritius. The January meeting, sponsored by the UN, brought together 2,000 delegates and numerous heads of state to review progress on a 10-year action plan for island nations called the Barbados Programme of Action (BPOA). Stakeholders acknowledged that the BPOA—which since 1994 has sought to improve environmental management and economic development in island nations—has fallen far short of its goals. One UN official attributed the BPOA’s failures in part to bureaucratic inertia. “The UN never set up an appropriately sized implementation office, and we had problems with no new funds being pledged [by donor countries],” the official explains. “Furthermore, the [island nations] never made the plan a cornerstone of their aid dealings.”
Hoping to redress these shortcomings, delegates pushed forward by drafting a new plan called the Mauritius Strategy. “The Mauritius Strategy has clear recommendations that if taken seriously will make up for the deficiencies of the last ten years,” says Anwarul K. Chowdhury, under-secretary-general for the High Representative for the Least Developed Countries, Landlocked Developing Countries, and Small Island Developing States (UN-OHRLLS). The 30-page strategy lays out a detailed agenda focused on issues such as climate change, sea level rise, natural disasters, waste management, water resources, energy, technology, sustainable development, and tourism.
Stakeholders hope the renewed momentum will fuel concrete progress. According to Om Pradhan, chief of the UN-OHRLLS Policy Development and Coordination Monitoring and Reporting Unit, international delegates appeared receptive to the view that island nations face unique challenges. The European Union in particular, he says, was forthcoming in its support for the islands—which is critical because European countries provide aid and comprise key markets for island exports.
Responsibility for the strategy’s implementation falls to the UN Department of Economic and Social Affairs (UN-DESA) and the UN-OHRLLS, among other divisions. As such, the UN will help island nations prepare to claim that they deserve special attention from the international community, says Diane Quarless, who heads the UN-DESA Small Island Developing States Unit.
But these claims could be met with some resistance, she admits. This is in part because some island nations are relatively well off: Singapore and Aruba, for instance, each boast per-capita incomes of US$28,000, values that in Quarless’s opinion, are skewed high by the wealth of a minority. “The international community looks at per-capita income, and says, ‘These guys are rich, and they live in paradise. Why should we give development assistance to them?’” Quarless says. “But we say that it’s not all paradise in the island nations. Economic and environmental vulnerability are universal across the board—even the richer countries could be wiped out by a single natural disaster. The international community puts an inordinate weight on income; we want them to emphasize environmental vulnerability when they decide if island nations are worthy of preferential treatment.”
A key objective, Quarless says, is to convince the UN General Assembly that environmental vulnerability—not income—should determine island eligibility for developing country assistance. As it stands now, the UN selects nations for its list of Least Developed Countries (LDCs) on the basis of income. LDCs are eligible for special concessions, such as access to duty-free export markets and high levels of developmental aid. Several island nations—including the Maldives, Cape Verde, and Samoa—may soon be “graduated” from this list because of their rising per-capita income. Quarless argues that, because they face extraordinary environmental challenges, these countries should be allowed to retain their LDC status. “A country like Maldives, which is quite literally under threat of disappearance, should not have to face the challenge of fending for itself,” she says.
Additional UN advocacy on behalf of island nations now targets institutions including the World Bank and the WTO, Pradhan adds. These institutions don’t currently accept “islandness” as a special category, he says. “This is something we’re trying to change in view of [island nations’] greater vulnerabilities to social, economic, and environmental shocks,” he says.
Inasmuch as island nations need global support, however, they must also promote their own sustainable development, making the most productive use of island resources. Stakeholders in Fiji, for instance, have recently developed automotive fuels using coconut oil.
Lelei LeLaulu, president of Counterpart International, a U.S.–based nonprofit company that promotes sustainable development projects around the world, argues that island nations need to emphasize sustainable tourism as a cornerstone of development. With this approach, island nations recognize that resource attractions—for instance, coral reefs—can’t be protected in isolation from their larger ecosystems. Conservation should address large natural areas rather than small pockets with tourist appeal, such as specific beaches, LeLaulu says. Moreover, he adds, island nations should strive for greater community ownership of tourist enterprises to ensure that natural resources are available for future generations. With a greater stake in tourism, islanders may do more to prevent coastal pollution and to regenerate the coral reefs that attract visitors and sustain local ecosystems.
Other experts point out that economic diversification is also essential for islands that depend heavily on tourism. “The traditional generic island tourism product that readily attracts foreign earnings—sand, sea, coastal hotels, and so forth—are the very amenities that are under threat from sea level rise and tropical cyclones. . . . The challenge here is the development of a tourism product that is attractive outside of the traditional attractions,” says Kumarsingh. He argues that economic diversification is essential for those economies that are heavily dependent on tourism. “Otherwise,” he says, “[island nations] may be setting up themselves for a greater fall—putting all their eggs in a very vulnerable basket.”
Ultimately, island nations are world treasures whose health may portend the future of the planet. Both their inhabitants and the millions who come visiting every year have a stake in their survival. Postcards sent today depict peaceful serenity, but behind these nations’ attractive façades lie many difficult challenges. As the waters and the pressures rise it remains to be seen whether the world’s island nations will sink or swim.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00615EnvironewsScience SelectionsAir Pollution in Space and Time: Birth Outcomes Are Complicated by Exposure Variations Weinhold Bob 9 2005 113 9 A615 A616 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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The association between air pollution and adverse effects on human birth outcomes is drawing increasing attention worldwide. In one of the latest developments, two epidemiologists at the University of California, Los Angeles, have found that the typical use of air pollution data from fixed monitoring stations may be inadequate for accurately pinpointing the links between air pollution and birth outcomes [EHP 113:1212–1221]. They also corroborate earlier findings that timing of pollution exposures is significant and that studying different pollutant combinations substantially complicates analysis.
Using new data in a follow-up on previous work, the researchers discovered that carbon monoxide (CO) and particulate matter (PM10) had significant adverse effects—and at concentrations well below U.S. federal standards—on preterm and low-birth-weight (LBW) births for women living within one mile of an air pollution monitoring station. However, they measured substantially less or no effect for women living just two to four miles away. They also found the effects were most pronounced in association with exposure during early and late gestation, but less apparent for the full pregnancy.
The researchers used state and county databases documenting births to mothers living in the Los Angeles area from 1994 to 2000. To analyze LBW at full term, they studied a zip code cohort of 136,134 births, of which 2,778 were LBW. To analyze preterm births, they used 106,483 of the same births (minus births by cesarean section), of which 9,268 were preterm. Data from 18 government air pollution monitors documented CO, PM10, nitrogen dioxide, and ozone levels. There were two years of data for fine particulate matter (PM2.5), but the researchers found that wasn’t a long enough period to provide significant findings (although many other studies have found that PM2.5 is more of a health concern than PM10).
The researchers discovered that for the first trimester, the highest quartile of CO concentrations observed—never more than two-thirds of the U.S. Environmental Protection Agency (EPA) 8-hour standard—was associated with a 27% increase in risk of preterm birth for women living within one mile of a pollution monitoring station. Similar CO exposures and distances during the third trimester were associated with a 36% increase in LBW following a full-term pregnancy. Parallel effects were seen for PM10—which never exceeded two-thirds of the EPA 24-hour standard—early and late in pregnancy. This generally confirmed findings from an earlier study using data from 1989 to 1993. No significant relationships were seen for nitrogen dioxide or ozone.
The researchers were able to account for many potentially confounding factors, including maternal age, level of prenatal care, and infant’s sex. However, they had no data for other factors known to influence birth outcomes, such as maternal occupation, height, weight, weight gain during pregnancy, and smoking status. Folding in such data could affect the outcome of this study, the team acknowledges. The team’s finding that effect estimates diminished for women living farther than one mile from a station suggests that the air monitoring stations may not provide accurate measures of exposure for these women; development of ways to better capture spatial variability in pollutant concentrations is therefore an important goal.
The researchers concluded that improved air pollutant data reflecting both geographic variation and the specific substances in the ambient air mixture will lead to much better understanding of air pollutants on birth effects. Also important are the use of finer breakdowns of the pregnancy period and more detailed background information on the parents and child.
Born of necessity.
A study of Los Angeles mothers shows that more detailed exposure information is critical for accurately drawing links between air pollution and adverse birth outcomes.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00616EnvironewsScience SelectionsWhen Lead Goes to Your Head: Genotype May Link Exposure and Meningioma Szpir Michael 9 2005 113 9 A616 A616 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Scientists know very little about the causes of most brain tumors. A small percentage of cases can be explained by familial syndromes, or by exposure to ionizing radiation, but the precise roles of specific genes or other environmental factors, such as lead, remain largely unexplored. A research team now reports an association between a genetic variant for δ-aminolevulinic acid dehydratase (ALAD)—an enzyme involved in the synthesis of heme—and an increased risk of developing meningioma, a tumor that occurs in the membranes covering the brain and the spinal cord [EHP 113:1209–1211].
Some previous studies suggested that people who carry an ALAD polymorphism known as ALAD2 tend to have higher concentrations of lead in their blood. Other research has indicated that occupational exposure to lead may increase the risk of meningioma. The findings of the current study suggest a possible link between these two results.
The team discovered a connection between ALAD2 and meningioma in a study of 573 patients with brain tumors from hospitals in Arizona, Massachusetts, and Pennsylvania. The patients were compared to 505 control subjects who were admitted to the same hospitals for conditions that did not involve tumors. Of the brain tumor patients, 151 had meningioma, 355 had glioma (a cancer that grows from glial cells in the brain), and 67 had acoustic neuroma (a tumor of the auditory nerve).
The ALAD genotype—based on the ALAD1 and ALAD2 alleles—was determined for each patient and each control subject. Possible links between the ALAD2 allele and the brain tumors were investigated using unconditional logistic regression.
The statistical analyses revealed that people who carried the ALAD2 allele (heterozygotes and homozygotes) were 1.6 times more likely than the ALAD1 homozygotes to develop meningioma. This modest but significant association was stronger in males, who were 3.5 times more likely to develop meningioma if they had the variant allele. However, the authors caution that their sample size may be too small to draw conclusions about gender-related effects. They saw no increased risk linked with the ALAD2 allele for glioma or acoustic neuroma.
These results raise the question of how the ALAD2 allele might increase the risk of meningioma. Previous work by the same team, based on the same study subjects, found an elevated risk of meningioma for occupational groups that may be exposed to lead, including auto body painters and industrial production supervisors. Certain other studies have shown that individuals who carry the ALAD2 allele have higher levels of lead in their blood. Together, these results suggest that lead may play a role in the link between the ALAD2 allele and meningioma. The researchers recommend that future investigations should consider the combined effects of exposure to lead and the ALAD2 allele on the incidence of this cancer.
Chasing leads on brain tumors.
Information on what causes brain tumors is fragmented; however, new data may tie together clues about lead exposure and a predisposition to develop meningioma.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00617AnnouncementsNIEHS Extramural UpdateScience Education: The Future Begins Today! 9 2005 113 9 A617 A617 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Today, more than ever, science education is essential for increasing our science literacy and cultivating the next generation of scientists. Science literacy is key to helping us make sense of the information we receive in this fast-paced world where technologic and scientific advances are made at an ever increasing rate. At a time when fewer U.S. students are pursuing careers in science and U.S. students are not faring as well in the sciences and mathematics as their counterparts around the world (Martin et al. 2004; National Center for Education Statistics 2004), science education can nurture student enthusiasm for science and ensure that the United States continues to contribute to scientific, technologic, and economic advances. Business and science leaders highlight that should these downward trends continue, the United States may experience adverse economic, social, and scientific consequences.
The NIEHS supports a kindergarten through 12th grade (K–12) science education grant program to increase student awareness of environmental health, stimulate student interest in science and academics, improve student academic performance, and enhance teachers’ ability to engage students. The NIEHS supports nine innovative education research projects to develop and disseminate inquiry-based curricula that use environmental health sciences as a central theme across different subjects (e.g., math, science, language arts, history, geography, civics). Key highlights of these projects demonstrate how the NIEHS science education program is working to prepare the next generation of scientifically literate and scientifically engaged citizenry.
Improved student achievement: Preliminary data from several projects indicate that their curricular materials improve students’ performance, especially for special-needs students. All projects report increased enthusiasm for science when students are exposed to the integrative curriculum.
Increased understanding of environmental health: Several projects report increased student understanding regarding the link between human health and the environment. In particular, students gained awareness of how science relates to their personal lives.
Social responsibility: Using a problem-based learning (PBL) curriculum, students learn from real-world experiences. They learn how to identify questions, conduct research, analyze data, and communicate recommendations. Projects using PBL show that students gain an appreciation about the link between science and social responsibility. Students are able to answer the question “How am I ever going to use this?”
Teacher participation: Teachers have limited time to learn and implement new curricula. However, several projects highlight how working closely with teachers from the very beginning to develop integrative environmental health curricula has led to greater buy-in from teachers and increased the use and sustainability of the materials.
For more information on the NIEHS science education program and the nine projects mentioned, visit http://www.niehs.nih.gov/translat/k12/ehsic.htm.
Contact
Liam O’Fallon | [email protected]
==== Refs
References
Martin MO Mullis IVS Gonzelez EJ Chrostowski SJ 2004. TIMSS 2003 International Science Report. Chestnut Hill, MA:TIMSS & PIRLS International Study Center.
National Center for Education Statistics 2004. International Outcome of Learning in Mathematics Literacy and Problem Solving: PISA 2003 Results From the U.S. Perspective. Washington, DC:National Center for Education Statistics.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a00618AnnouncementsFellowships, Grants, & AwardsFellowships, Grants, & Awards 9 2005 113 9 A618 A619 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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Tools for Zebrafish Research
This Program Announcement (PA) is to encourage investigator-initiated applications designed to exploit the power of the zebrafish as a vertebrate model for biomedical and behavior research. Applications proposing to develop new tools or genetic or genomic resources of high priority to the zebrafish community that will advance the detection and characterization of genes, pathways, and phenotypes of interest in development and aging, organ formation, behavior, and disease processes are welcome. This effort stems from an NIH initiative developed by the Institutes and Centers of the Trans-NIH Zebrafish Coordinating Committee (TZCC; http://www.nih.gov/science/models/zebrafish/) under the co-chairmanship of NICHD and NIDDK. Since its formation in 1997, the committee has played an active role as an advocate for the zebrafish as an important model for development and disease research. PAR-02-142, “Tools for Genetic Studies in Zebrafish” (http://grants.nih.gov/grants/guide/pa-files/PAR-02-142.html) was issued in August 2002 because it was clear that there was a critical need for non-hypothesis driven, tool development proposals to be reviewed as a group, within a single framework. It focused on identifying additional mutants and developing new genetic tools in zebrafish. Ongoing dialog with the zebrafish research community, most recently at the Sixth International Meeting on Zebrafish Development and Genetics in June 2004, suggested a continuing need for not only tools, but high priority resources as well. Therefore, this PA is a continuation of the program initiated by PAR-02-142. The objective is to continue to broaden the range, power, and utility of tools for biomedical and behavioral research using zebrafish, and to develop genetic and genomic resources of high priority to the zebrafish community. Methodology developed and data and mutants generated as a result of this PA are expected to be made widely available to the research community as described by NIH Grants Policy (Principles and Guidelines for Recipients of NIH Research Grants and contracts on Obtaining and Disseminating Biomedical Research Resources: Final Notice, December 1999, http://ott.od.nih.gov/NewPages/Rtguide_final.html and the NIH Model Organism Sharing Policy, http://grants.nih.gov/grants/policy/model_organism/index.htm).
Objectives to be addressed in applications submitted in response to this PA include, but are not limited to, the following: 1) development and/or application of novel methods of mutagenesis (e.g., insertional, site-specific, conditional knockout vectors or systems); 2) development of techniques supporting more efficient targeting of induced local lesions in genomes (TILLING); 3) development of technologies for gene inactivation and for gene expression manipulation including, but not limited to, morpholino oligonucleotides, new types of antisense technology, techniques for homologous recombination, techniques for gene trapping, and strategies for directing gene misexpression, or other transgenic methodologies; 4) development of high throughput small molecule screens; 5) development of new genetic or genomic resources that are of high priority for the zebrafish community; 6) development and/or application of novel screens for mutants; these may be refinement of phenotypic analyses preparatory to screening, or phenotypic screens based on observation of alterations in morphology, physiology, or behavior; 7) screens focusing on identifying novel developmental genes; and pathways, including those mediating sensitivity or resistance to environmental toxicants; 8) screens to analyze the genetic basis of adult phenotypes including behavior, aging, organ disease, cancer, and responses to environmental toxicants, alcohol, and drugs of abuse.
The participating NIH Institutes and Centers have provided a brief outline of their interests as they relate to the goals of this PA. These brief mission statements are intended to indicate the breadth of the biomedical areas of interest in which zebrafish are likely to be a useful model.
NCI: Generation and study of zebrafish models to identify and place genes in functional pathways that effect growth and development, in particular, genes/pathways that, when altered, result in uncontrolled or cancerous growth.
NCRR: The NCRR supports research projects that broaden the utility of the zebrafish model for cross-cutting biomedical research that is not encompassed within a single NIH Institute or Center. Interests include, but are not limited to, development of new methods for mutagenesis and/or phenotypic characterization that would be of use in research on a wide range of diseases or organs, particularly if these methods could be applied to other animal models as well as the zebrafish.
NEI: Research on the normal and abnormal visual system, including eye development, optic nerve guidance and the visual centers of the brain. This research might include the use of mutants to elucidate the cellular and molecular processes that control normal eye development and function and to provide models for the investigation of the genetic bases of inherited eye diseases.
NHLBI: Cellular and molecular functions of zebrafish genes that have potential to model human cardiovascular, blood, and pulmonary, or sleep disorders. Genetic basis of disorders of cardiovascular development and function; effect of mutations on subsequent organ development leading to such disorders as arrhythmia, cardiac hypertrophy, dilated cardiomyopathy, and heart failure; developmental aspects of endothelial dysfunction as the basis for vascular disorders; developmental defects in hematopoiesis and the relationship to disorders of the hematopoietic system; genetic basis of angiogenesis, and vasculogenesis; and, the genetic basis, regulation, and role of biological clock mechanisms in development and circadian behavior.
NIA: Basic research on the genetic and molecular basis of aging and longevity. Generation and analysis of late-onset disease models or long-lived mutants that can be used to identify, clone, and characterize genes involved in normal and pathological aging. Cellular and molecular function of genes expressed, for example, in the aging nervous system, cardiovascular, immune, and musculoskeletal systems. Such genes include, but are not limited to, those involved in neurodegenerative disorders, neuroplasticity, cell death, damage and repair of DNA and proteins, oxidative stress, and maintenance of differentiated cell function.
NIAAA: Mechanistic studies of ethanol-induced teratogenesis, behavioral impairments, and organ damage. These studies may include screening methods for alcohol-related phenotypes, gene identification, and functional analyses of these genes.
NIAMS: Mutations that have the potential to illuminate the development and function of the vertebrate musculoskeletal system and skin. The musculoskeletal system includes muscle, bone, articulated joints, cartilage, tendon, and ligament. Priority will be given to the establishment of collaborations between investigators with expertise in the zebrafish and investigators with expertise in the musculoskeletal systems and skin of mammals and humans.
NICHD: Identification, cloning, and characterization of the genes important in normal development as well as those mutant genes that cause developmental defects. Elucidation of the cellular, biochemical, molecular, and genetic mechanisms underlying normal and defective development. This includes, but is not limited to, the study of general mechanisms of pattern formation and cell lineage, neural crest development, cell specification, differentiation, migration, and fate in early development of many organs/systems such as limb, nervous system, immune system, and heart.
NIDCD: Identification and cloning of genes/proteins involved in the normal and disordered development in the areas of hearing, balance, smell, taste, voice, speech, and language. Elucidation of the cellular, molecular, and biochemical and sensory processing mechanisms governing the proliferative, regenerative, lineage determination, and developmental capacities of these sensory cells and tissues.
NIDCR: All aspects of normal and abnormal craniofacial development, including genetics, complex origins of craniofacial disorders, cell lineages and differentiation, cell signaling and gene regulation, embryonic patterning, imaging, biomimetics, and new technologies for high-throughput genetic and protein screens.
NIDDK: Research on diabetes, particularly studies on pancreatic beta cell function and development, obesity and mechanisms underlying satiety, other endocrine and metabolic diseases, hematologic disorders, physiology and diseases of the digestive system, liver, kidney, and urinary tract. Studies aiming to clarify the cellular and molecular events that dictate tissue and organ formation in all these systems are considered of relevance. In addition, studies that exploit the zebrafish to model physiological processes such as renal function, fluid and electrolyte balance, are relevant to NIDDK. These studies could include, but need not be limited to, studies to develop cell lines from any of the tissues or organs of interest, studies to characterize normal or abnormal function of tissues or organs of interest, methods to screen and identify additional mutations in these systems, and studies to define the molecular mechanisms that dictate cell-specific gene expression in relevant cell types.
NIDA: Identification of mechanisms underlying tolerance, sensitization, and addiction to drugs of abuse such as nicotine, amphetamine, cocaine, opiates, barbiturates, and hallucinogens. Identification of genetic suppressors and enhancers of the teratological effects of drugs of abuse on behavior and the nervous system. Processes involved in the development of brain regions and neurotransmitter systems mediating the hedonic and addictive properties of drugs of abuse.
NIEHS: Studies to examine the mechanism whereby environmental factors/agents alter any aspect of development. This includes the screening for mutants that ameliorate the toxicity of environmental agents, and the subsequent identification and characterization of the genes and pathways involved in their action. Characterization of the interactions among genetics, environmental agents, and time during development that lead to structural or functional abnormalities. Studies to examine the mechanistic pathways involved in developmental exposure to environmental agents and subsequent increased susceptibility to adult onset disease (developmental imprinting). Development of a mechanistically based model for testing environmental agents for developmental toxicity.
NIGMS: Development of novel methods for mutagenesis and manipulation of gene expression. Mutagenesis screens to identify and characterize genes that control fundamental biological mechanisms such as those that underlie gene regulation, chromosome organization and mechanics, cell growth and differentiation, pattern formation, sex determination, morphogenesis, cell cycle control, and behavior. Small molecule screens for phenotypes that are relevant to those fundamental biological mechanisms.
NIMH: Investigations that examine molecular, cellular, and biochemical bases of genetic mutations affecting neurogenesis, biological rhythms, learning, memory, and other cognitive functions and behaviors of the nervous system. These studies include, but are not limited to, development of screening methods for such mutations, identification, isolation, mapping, and functional analyses of the genes underlying mutations.
NINDS: Research on the development, normal function, and diseases of the nervous system. This research might include the use of mutants to understand the mechanisms controlling the following processes: neurogenesis, nervous system patterning, cell lineage, cell migration, formation of neural circuits, programmed cell death, axon pathfinding and regeneration, myelination, and motor and sensory function. In addition, the utility of mutants as models for neurodegenerative diseases for use in translational research, including therapeutic drug screens, functional neuroanatomy of the developing and adult nervous system, and use of optical imaging techniques to visualize neural activity, is of particular interest. The areas of interest listed above are not presented in any order of priority, they are only examples of areas of research to consider. Applications representing areas of interest to more than one Institute or Center will be assigned to multiple Institutes or Centers for funding consideration. Applicants are encouraged to propose work in other areas that are related to the objectives and scope of this PA.
This funding opportunity will use the NIH Individual Research Project Grant (R01) award mechanism(s). As an applicant, you will be solely responsible for planning, directing, and executing the proposed project.
This funding opportunity uses just-in-time concepts. It also uses the modular as well as the non-modular budget formats (see http://grants.nih.gov/grants/funding/modular/modular.htm). Specifically, if you are submitting an application with direct costs in each year of $250,000 or less, use the modular budget format described in the PHS 398 application instructions. Otherwise follow the instructions for nonmodular research grant applications.
The PHS 398 application instructions are available at http://grants.nih.gov/grants/funding/phs398/phs398.html in an interactive format. For further assistance contact GrantsInfo at 301-435-0714, (telecommunications for hearing impaired: TTY 301-451-0088) or by e-mail:
[email protected].
Applications must be prepared using the most current PHS 398 research grant application instructions and forms. Applications must have a D&B Data Universal Numbering System (DUNS) number as the universal identifier when applying for Federal grants or cooperative agreements. The D&B number can be obtained by calling 866-705-5711 or through the web site at http://www.dnb.com/us/. The D&B number should be entered on line 11 of the face page of the PHS 398 form.
Letters of intent are requested but not required; the letter of intent deadline for the latest cycle has passed. Letters of intent for future cycles are due August 19, 2006, 2007. Applications are due September 19, 2005, 2006, 2007. The complete version is available at http://grants.nih.gov/grants/guide/pa-files/PAR-05-080.html.
Contact: Lorette Javois, Center for Developmental Biology and Perinatal Medicine, National Institute of Child Health and Human Development, 6100 Executive Boulevard, Room 4B01, MSC 7510, Bethesda, MD 20892-7510 USA, Rockville, MD 20852 USA (for express/courier service; non-USPS service) 301-496-5541, fax: 301-480-0303, e-mail:
[email protected].
Reference: PAR No. PAR-05-080
Innate Immunity to NIAID Category B Protozoa
This initiative will support basic research to define the mechanisms of action by which the innate immune system recognizes and responds to the food and water-borne eukaryotes classified as NIAID Category B priority protozoan pathogens (Cryptosporidium parvum, Cyclospora cayetanensis, Giardia lamblia, Entamoeba histolytica, Toxoplasma, and Microsporidia) (http://www2.niaid.nih.gov/Biodefense/bandc_priority.htm).
The NIH and other agencies in the Department of Health and Human Services (DHHS) are currently supporting extramural research to develop new products to protect the public from the health consequences of biological agents that might be used in acts of terrorism or war. The research supported by this RFA will contribute to meeting the goals for host defense described in the NIAID Strategic Plan for Biodefense, which is located at: http://www2.niaid.nih.gov/Biodefense/Research/strat_plan.htm, by increasing our understanding of the mechanisms by which the innate immune system recognizes, responds to, and neutralizes the complex defense systems of protozoan pathogens. The complexity of the interactions between the host innate immune system and protozoan pathogens requires in-depth knowledge of innate immunity, mucosal immunity, and protozoan microbiology/biochemistry.
There is evidence that the innate immune system recognizes and responds to protozoan parasites. Recent studies have shown that Toxoplasma gondii stimulates host cells through TLR2 and TLR4, activates B lymphocytes, NK and NKT cells, and stimulates Interferon-gamma and NO production. In other studies, Cryptosporidium parvum infection activated both CD4+ and CD8+ gamma/delta T cells, and Giardia species stimulated the release of several effector molecules, including defensins, cryptdins, and indolicidin. Utilizing the NIAID Category B protozoa to better understand the innate immune response to eukaryotic pathogens may lead to new broad spectrum immunotherapeutics and adjuvants for protozoan vaccines.
Eukaryotic pathogens are a serious public health problem in developing countries. Exposure to pathogens such as Giardia, Cryptosporidium, and Entamoeba has been greatly diminished in the U.S. due to effective regulation of public water supplies and commercial food processing. Thus, interest in protozoan vaccine development has lagged behind that for prokaryotic pathogens. With the recently increased threat of biowarfare, the potential for adulteration of U.S. food and water supplies has increased. In-depth understanding of innate immune responses to protozoa is important because their genomic complexity affords them a greater variety of immune evasion mechanisms than those displayed by prokaryotes.
The major goal of this RFA is to support research focused on the mechanisms of action by which the mammalian innate immune system responds to the food and waterborne protozoa from the NIAID Category B Priority Pathogens list. Studies utilizing human cells or clinical samples are not required, but are strongly encouraged. Appropriate areas of research include, but are not limited to: 1) Characterization of host cells involved in the innate immune response to protozoa; 2) Identification of novel pathogen-associated molecular pattern recognition receptors on host cells; 3) Characterization of mediators of innate immunity that are produced by host cells stimulated by protozoa; 4) Elucidation of the intracellular signaling pathways in the mammalian innate immune cells that are stimulated by protozoa; 5) Comparison of human versus animal model molecular responses to protozoan pathogens or their components; 6) Human or animal model gene mutations or polymorphisms associated with distinctive innate immune responses to protozoa.
This initiative will unite interdisciplinary teams of researchers with expertise in innate immunity, mucosal immunity, and protozoan microbiology to stimulate scientifically sound, original research that will advance the field and encourage productive interactions among Principal Investigators. In the long term, these efforts will form a basis for the rational design of pathogen- or class-specific immunotherapeutics, as well as adjuvants that will enhance the future development of vaccines against potential bioweapons.
In some cases, immunological mechanisms relevant to biodefense are broadly applicable for many pathogens and may be most efficiently studied using model systems. Immunological research that is directed at protozoa other than those listed as NIAID Category B Priority Pathogens is responsive to this announcement if the research specifically addresses a practical approach to inducing, controlling, or improving the effectiveness of the innate immune response to NIAID Category B protozoan infection. Applicants must justify how the proposed research is applicable to immune responses against the listed agents.
Note: This RFA will NOT support clinical trials; applications requesting support for clinical trials will be considered nonresponsive and will be returned to the applicant without review. However, utilization of human-derived material in studies is considered responsive.
This funding opportunity will use the individual Research Project Grant (R01) and Exploratory/Developmental Research Grant (R21 ) award mechanisms.
This funding opportunity uses just-in-time concepts. It also uses the modular as well as the nonmodular budget formats (see http://grants.nih.gov/grants/funding/modular/modular.htm). Specifically, if you are submitting an application with direct costs in each year of $250,000 or less, use the modular budget format described in the PHS 398 application instructions, available at http://grants.nih.gov/grants/funding/phs398/phs398/398.html in an interactive format. Otherwise, follow the instructions for nonmodular research grant applications. For further assistance contact GrantsInfo, 301-435-0714, (telecommunications for the hearing impaired: TTY 301-451-0088) or by e-mail:
[email protected].
Applications must be prepared using the most current PHS 398 research grant application instructions and forms. Applications must have a D&B Data Universal Numbering System (DUNS) number as the universal identifier when applying for Federal grants or cooperative agreements. The D&B number can be obtained by calling 866-705-5711 or through the web site at http://www.dnb.com/us/. The D&B number should be entered on line 11 of the face page of the PHS 398 form.
The deadline for receipt of letters of intent is October 24, 2005, with November 22, 2005 the deadline for receipt of applications. The complete version of this RFA is available at http://www.niaid.nih.gov/ncn/budget/QA/fra-05-042.htm.
Contact: David B. Winter, Division of Allergy, Immunology, and Transplantation, National Institute of Allergy and Infectious Diseases, Room 3014, MSC-6601, 6610 Rockledge Drive, Bethesda, MD 20892-6601 USA, (for express mail use 20817), 301-496-7551, fax: 301-480-2381, e-mail:
[email protected]. Reference RFA-AI-05-042
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0581a16140605PerspectivesCorrespondenceIndoor- and Outdoor-Generated Particles and Children with Asthma Moshammer Hanns Institute of Environmental Health Medical University of Vienna, Vienna, Austria, E-mail:
[email protected] author declares he has no competing financial interests.
9 2005 113 9 A581 A581 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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In their article “Pulmonary Effects of Indoor- and Outdoor-Generated Particles in Children with Asthma,” Koenig et al. (2005) made use of their really interesting model that enables them to discern exposure from indoor- and outdoor-generated particles. They concluded that
The ambient-generated component of PM2.5 [particulate matter ≤2.5 μm in aerodynamic diameter] exposure is consistently associated with increases in eNO [exhaled nitric oxide] and the indoor-generated component is less strongly associated with eNO.
This finding should not lead to the assumption that particles generated indoors are in general not able to induce endogenous NO production. The authors themselves pointed out one limitation of their study:
Children in the Seattle panel study spent an average of 66% of their time indoors at home and 21% indoors away from home (primarily at school) … (Koenig et al. 2005)
However, contribution of indoor sources to PM exposure was only estimated on the basis of measurement data from the subjects’ residences. This could have led to uncertainties in the exposure assessment, biasing the effect estimates toward null.
I also want to mention the great variability in possible indoor sources of PM. In their article, Koenig et al. (2005) provided no information on the smoking status of household members. If the 19 children under study lived in nonsmoking households, the results might be true for this setting but not for others.
Finally, I suggest that time of measurement and exposure should be considered. If the children attend school in the morning, they might go home (maybe in high traffic) at noon or early afternoon for lunch.
[NO] samples were collected in the afternoon or early evening at the child’s residence. Children were asked to forgo food intake for 1 hr before collection of exhaled breath.
If NO production peaks several hours after exposure, it could be possible that the children’s exposure on their way home from school was the most influential one (not because of the origin of the particles but because of the study’s lag structure). It would be interesting and rather rewarding to study the short-term lag structure between PM exposure and both exhaled NO and lung function [for which Koenig et al. (2005) found an association with exposure due to sources in the residents’ homes]. I would expect an increase of exhaled NO to be a rather late reaction to inflammatory stimuli. For example Rolla et al. (2004) reported that after aspirin inhalation by subjects with aspirin-inducible asthma, NO increased significantly reaching the peak value 4 hr after bronchoconstriction.
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References
Koenig JQ Mar TF Allen RW Jansen K Lumley T Sullivan JH 2005 Pulmonary effects of indoor- and outdoor-generated particles in children with asthma Environ Health Perspect 113 499 503 15811822
Rolla G Di Emanuele A Dutto L Marsico P Nebiolo F Corradi F 2004 Effect of inhalation aspirin challenge on exhaled nitric oxide in patients with aspirin-inducible asthma Allergy 59 827 832 15230814
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0581bPerspectivesCorrespondenceIndoor- and Outdoor-Generated Particles: Koenig et al. Respond Koenig Jane Allen Ryan Larson Tim Liu Sally University of Washington Seattle, Washington, E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 113 9 A581 A581 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
We appreciate Moshammer’s comments and his interest in our research. We have several points to raise in reply.
In our article (Koenig et al. 2005), we stated that indoor sources are known to affect airway inflammation. We recognized that indoor sources vary greatly and that 19 homes may not provide a sufficient sample size to allow for a robust association. It is true that the children in our study spent substantial time away from home. We now have additional data from a panel of 16 adults (average age of 75 years) who did not commute or leave home regularly; in these adults we found the same coefficient with eNO (exhaled nitric oxide) versus outdoor PM2.5 (particulate matter ≤2.5 μm in aerodynamic diameter) as in the research in question (Jansen et al. 2004). In addition, Ebelt et al. (2005) found lung function decrements only with ambient particles in a group of non-smoking 54- to 86-year-old adults. These results provide additional evidence of an ambient-only pulmonary effect among individuals who spent relatively little time away from home.
Regarding smoking status, one inclusionary criterion for our study was to be a non-smoker and live with nonsmokers; thus smoking is not an important indoor source of particles in these residences. Children in the Seattle school district do not go home for lunch. However, it is true that our exhaled breath samples were taken 1–2 hr after the commute home (Liu et al. 2003). On average, the time between morning commute and eNO collection was 9 hr; between afternoon commute and breath collection was about 2 hr. We are now looking at the short-term lag structure. Using a polynomial distributed lag model, we found that PM2.5 was associated with the eNO for up to 10–12 hr before the eNO measurement (Mar et al., in press).
==== Refs
References
Ebelt ST Wilson WE Brauer M 2005 Exposure to ambient and nonambient components of particulate matter: a comparison of health effects Epidemiology 16 396 405 15824557
Jansen K Koenig JQ Larson TV Fields C Mar TF Stewart J 2004 Nitric oxide in subjects with respiratory disease is associated with PM2.5 and black carbon in Seattle [Abstract] Am J Respir Crit Care Med 169 A282
Koenig JQ Mar TF Allen RW Jansen K Lumley T Sullivan JH 2005 Pulmonary effects of indoor- and outdoor-generated particles in children with asthma Environ Health Perspect 113 499 503 15811822
Liu L-JS Box M Kalman D Kaufman J Koenig J Larson T 2003 Exposure assessment of particulate matter for susceptible populations in Seattle, WA Environ Health Perspect 111 909 918 12782491
Mar TF Jansen K Shepherd K Lumley T Larson TV Koenig JQ In press. Exhaled nitric oxide in children with asthma and short term PM exposure in Seattle. Environ Health Perspect.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0581c16140604PerspectivesCorrespondenceBlood Lead and IQ in Older Children Carpenter David O. Institute for Health and the Environment University at Albany, Rensselaer, New York, E-mail:
[email protected] author declares he has no competing financial interests.
Editor’s note: In accordance with journal policy, Chen et al. were asked whether they wanted to respond to this letter, but they chose not to do so.
9 2005 113 9 A581 A582 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
In their article about blood lead and IQ in older children, Chen et al. (2005) made the very important observation that IQ (intelligence quotient) in older children correlates better with their current blood lead level than with levels determined at 2 years of age. This observation has enormous public health implications in terms of defining who is at risk of cognitive decrement upon exposure to lead, and challenges the widely held assumption that the effects of lead on neurobehavioral function are exclusively developmental. My colleagues and I (Carpenter et al. 2002) previously studied the effects of gestational and lactational exposure of rats to lead with measurement of long-term potentiation in hippocampal brain slices. Long-term potentiation is an electrophysiological measure of synaptic plasticity that is widely viewed as being at least one component of learning and memory, and it is reduced upon in vivo lead exposure. To our surprise we also found that acute perfusion of low concentrations of lead onto brain slices from control animals resulted in reduction of long-term potentiation, suggesting that the effect is more pharmacologic than developmental. The results of Chen et al. (2005) in humans and our studies in rats suggest that lead causes both developmental and pharmacologic impairment of cognitive function. If this is true, then steps should be taken to prevent exposure to lead and to reduce lead levels in individuals of any age, not just young children.
==== Refs
References
Carpenter DO Hussain RJ Berger DF Lombardo JP Park HY 2002 Electrophysiologic and behavioral effects of perinatal and acute exposure of rats to lead and polychlorinated biphenyls Environ Health Perspect 110 suppl 3 377 386 12060832
Chen A Dietrich KN Ware JH Radcliffe J Rogan WJ 2005 IQ and blood lead from 2 to 7 years of age: are the effects in older children the residual of high blood lead concentrations in 2-year-olds? Environ Health Perspect 113 597 601 15866769
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0582a16140606PerspectivesCorrespondenceComparison of Study Controls Ohsako Seiichiroh Environmental Health Sciences Division National Institute for Environmental Studies, Tsukuba, Japan, E-mail:
[email protected] Chiharu Center for Disease Biology and Integrative Medicine, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan, E-mail:
[email protected] authors declare they have no competing financial interests.
9 2005 113 9 A582 A582 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
In the article “Natural Variability and the Influence of Concurrent Control Values on the Detection and Interpretation of Low-Dose or Weak Endocrine Toxicities,” Ashby et al. (2004) discounted a number of studies reporting low-dose effects caused by endocrine-disrupting chemicals, including bisphenol A (BPA), based on variability in control values in experiments conducted at different times. They cited data from four experiments that we reported in three published studies (Ohsako et al. 2001; Sakaue et al. 1999, 2001). Ashby et al. stated that the marked reduction in daily sperm production (DSP) caused by BPA that we observed in two experiments (Sakaue et al. 2001) should be discounted because the DSP values in BPA-exposed males were not significantly different from those in controls we reported in another study (Ohsako et al. 2001). Ashby et al. (2004) proposed that our conclusion that BPA significantly decreased DSP is incorrect because of a difference in control values for DSP from different experiments conducted at different times.
We would like to point out that it is absolutely inappropriate to compare these sets of data not only because the experiments were conducted at different times but also because they included different experimental conditions and different animals (Table 1). First, we used Sprague-Dawley rats in the two experiments in the BPA study (Sakaue et al. 2001) and Holtzman rats in another study of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) (Ohsako et al. 2001). Second, the Sprague-Dawley rats used in the BPA study were purchased from a laboratory animal breeder, whereas the Holtzman rats used in the TCDD experiment were bred at the National Institute for Environmental Studies (NIES). Third, animal maintenance conditions were not the same for the three studies: Holtzman rats were housed in polycarbonate cages with wooden chips, and Sprague-Dawley rats were kept in hanging stainless wire-mesh cages.
There are many reasons why males from different rat strains obtained from different breeding facilities using different animal feed and housing conditions might differ in DSP. We thus disagree with the interpretation by Ashby et al. (2004) that this variability between rats from different strains invalidates the conclusion drawn by Sakaue et al. (2001) that exposure to BPA reduces DSP in Sprague-Dawley rats. We also disagree with their attempt to discount effects caused by BPA and other endocrine-disrupting chemicals due to variability in control animals from entirely different experiments in which low-dose effects were reported by other researchers.
Table 1 Comparison of conditions of the three experiments.
Sakaue et al. 1999 Ohsako et al. 2001 Sakaue et al. 2001
Rat strains Holtzman Holtzman Sprague Dawley
Place of birth Animal breeding facility (NIES) Chemical hazard area (NIES) CLEA Japan
Age at the time of purchase NA NA PND77
Cage type Stainless wire-mesh Polycarbonate plastic Stainless wire-mesh
Bedding None Wood chip None
Age at the time of tissue collection PND126 PND120 PND126
Place for examination Animal breeding facility (NIES) Chemical hazard area (NIES) Animal breeding facility (NIES)
Test compound BPA TCDD BPA
Vehicle Corn oil Corn oil/4% n-nonane Corn oil/6.5% ethanol
Control DSP valuea (× 106/testis) 39.0 ± 1.6 (4) 34.6±2.0 (12) 44.0±2.2 (5)b
49.3±5.3 (8)c
Abbreviations: NA, not applicable; PND, postnatal day.
a Mean ± SE (number of animals).
b First experiment.
c Second experiment.
==== Refs
References
Ashby J Tinwell H Odum J Lefevre P 2004 Natural variability and the influence of concurrent control values on the detection and interpretation of low-dose or weak endocrine toxicities Environ Health Perspect 112 847 853 15175171
Ohsako S Miyabara Y Nishimura N Kurosawa S Sakaue M Ishimura R 2001 Maternal exposure to a low dose of 2,3,7,8-tetrachlorodibennzo-p -dioxin (TCDD) suppressed the development of reproductive organs of male rats: dose dependent increase of mRNA levels of 5-reductase type 2 in contrast to decrease of androgen receptor in pubertal ventral prostate Toxicol Sci 60 132 143 11222880
Sakaue M Ohsako S Ishimura R Kurosawa S Kurohmaru M Hayashi Y 1999. Preliminary BPA experiment [Abstract]. In: 127th Annual Meeting of the Japanese Society of Veterinary Science, 2–4 April, Fujisawa, Japan. Abstract 47.
Sakaue M Ohsako S Ishimura R Kurosawa S Kurohmaru J Hayashi Y 2001 Bisphenol A affects spermatogenesis in the adult rat even at a low dose J Occup Health 43 185 190
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0582b16140606PerspectivesCorrespondenceStudy Controls: Ashby Responds Ashby John Syngenta Central Toxicology Laboratory Alderley Park, Cheshire, United Kingdom E-mail:
[email protected] author is employed as a research scientist by Syngenta PLC.
9 2005 113 9 A582 A583 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Figure 1 is the relevant summary figure (Figure 8) from our article (Ashby et al. 2004). Our point in the article, as well as now, is that it is incumbent upon each investigator to accept, to study, and where possible, to understand the extent, nature, and origins of variability (within and between experiments) of the critical assay parameter. If you do not know why the assay parameter varies naturally with time, or between experiments, it becomes difficult to interpret small perturbations of the parameter induced in a chemical toxicity study. This was the problem we faced when we tried to explain our inability, over four extensive studies (Ashby et al. 2003), to confirm the effects that Sakaue et al. (2001) reported for bisphenol A (BPA). The control values for daily sperm production (DSP) in Sprague-Dawley rats over our four experiments (Figure 1) varied little, despite the use of three different rodent diets and a variety of physical test conditions (changes in bedding and caging). We also noted (Ashby et al. 2004) that Sakaue et al. reported similar control DSP values for Holtzman rats (Sakaue et al. 1999) and Sprague-Dawley rats (Sakaue et al. 2001; Figure 1). The most interesting aspect of the data in Figure 1 is the extent of variability in control DSP values reported by Sakaue et al. (2001) for their two experiments on BPA in Sprague-Dawley rats. It is important to understand the origins of these variations in control DSP values between similar experiments before interpreting small chemically induced perturbations in DSP values with confidence. Equally, by paying attention to the origins of control variability, we were able to show that two chemicals we had previously considered to be negative in the rodent uterotrophic assay were, in fact, weakly positive (Ashby et al. 2004). Stable control values for an assay lead to the generation of sound assay data.
Figure 1 Comparison of control DSP (mean ± SD) reported from the same laboratory [Ohsako et al. (2001) and Sakaue et al. (1999, 2001)] and a different laboratory (Ashby et al. 2003) with the greatest effect induced by BPA (Sakaue et al. 2001). A range of BPA doses was used in these experiments, and only the dose that induced the greatest effect in each experiment is shown: 20 μg/kg (Sakaue et al. 1999); 200 μg/kg (Sakaue et al. 2001); 200 mg/kg (Ashby et al. 2003). The effect of BPA is not significantly different from the control reported by Ohsako et al. (2001; bar 2: one- or two-sided Student's t-test). Sakaue et al. (1999) and Ohsako et al. (2001) used Holtzman rats, and Sakaue et al. (2001) and Ashby et al. (2003) used Sprague-Dawley rats. However, the identical control DSP values for Holtzman rats (Sakaue et al. 1999, bar 1) and Sprague-Dawley rats (Sakaue et al. 2001; bar 3) indicate that rat strain is not a key variable on control DSP values and that, consequently, it is possible to compare data across strains and experiments for that laboratory. Reprinted from Ashby et al. (2004) with permission from Environmental Health Perspectives. *Reported by Sakaue et al. (2001) as statistically different from the concurrent control (bars 3 and 4).
==== Refs
References
Ashby J Tinwell H Lefevre PA Joiner R Haseman J 2003 The effect on sperm production in adult Sprague-Dawley rats exposed by gavage to bisphenol A between postnatal days 91–97 Toxicol Sci 74 129 138 12773777
Ashby J Tinwell H Odum J Lefevre PA 2004 Natural variability and the influence of concurrent control values on the detection and interpretation of low-dose or weak endocrine toxicities Environ Health Perspect 112 847 853 15175171
Ohsako S Miyabara Y Nishimura N Kurosawa S Sakaue M Ishimura R 2001 Maternal exposure to a low dose of 2,3,7,8-tetrachlorodibennzo-p -dioxin (TCDD) suppressed the development of reproductive organs of male rats: dose dependent increase of mRNA levels of 5-alpha reductase type 2 in contrast to decrease of androgen receptor in pubertal ventral prostate Toxicol Sci 60 132 143 11222880
Sakaue M Ohsako S Ishimura R Kurosawa S Kurohmaru M Hayashi Y 1999. Preliminary BPA experiment [Abstract]. In: 127th Annual Meeting of the Japanese Society of Veterinary Science, 2–4 April, Fujisawa, Japan. Abstract 47.
Sakaue M Ohsako S Ishimura R Kurosawa S Kurohmaru J Hayashi Y 2001 Bisphenol A affects spermatogenesis in the adult rat even at a low dose J Occup Health 43 185 190
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0589a16158522EnvironewsForumDrug Abuse: Meth’s Pollution Epidemic Potera Carol 9 2005 113 9 A589 A589 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Methamphetamine problems are soaring nationwide. Nearly 60% of county officials report that meth is the largest drug problem in their county, and 87% saw jumps in meth arrests in the past three years, according to a survey released in July 2005 by the National Association of Counties. But meth isn’t just a hard drug; it’s also an environmental hazard.
Illicit drug makers can cook small batches of meth anywhere they can plug in a stove, microwave, or electric skillet (heat is not required, but speeds the manufacture). The ingredients include common cold medicines, ammonia fertilizer, and muriatic acid. Cooking generates a variety of noxious solvents and gases, such as hydrogen chloride, phosphine, and meth itself. According to an 8 August 2005 Newsweek article, for each pound of meth produced, five pounds of toxic waste are left behind.
Police and firemen report breathing problems and headaches when they bust meth labs, but no one has quantified the hazards they face. So John Martyny, an industrial hygienist at National Jewish Medical and Research Center in Denver, teamed up with law enforcement officials in Colorado. They set up controlled cooks in an abandoned motel (which was later razed) and measured the resulting pollutants.
In the unpublished studies, phosphine gas reached 2.9 parts per million (ppm), three times the occupational short-term exposure limit. Phosphine causes headache, pulmonary edema, and death. Martyny says cook fatalities are probably linked to this chemical. Hydrogen chloride fumes reached 155 ppm, more than three times the level considered by the National Institute for Occupational Safety and Health to be “immediately dangerous to life or health.” Hydrogen chloride causes respiratory tract damage. Ammonia, which causes lung edema, also soared to three times the “immediately dangerous to life or health” level.
Anyone who is present during cooks is exposed to these and likely other toxicants; a third of all meth busts find children present. “The health costs to children may not be identified for years to come,” says Martyny, who predicts long-term respiratory and neurological problems.
More research is needed to determine the best ways to clean up meth labs. Meth becomes airborne during production and settles on surfaces at up to 16,000 micrograms per 100 square centimeters (μg/100 cm2). Even six months after a staged cook, Martyny found meth levels of 300 μg/100 cm2 on surfaces. Carpets trap meth and other pollutants, yet vacuuming dra-meth levels. So Martyny recommends discarding carpets.
Further, after grinding contaminated wallboard in separate unpublished studies, Stephen Lee, who supervises the Emergency Response Team at the Minnesota Pollution Control Agency in St. Paul, learned that washing walls removes less than 10% of the total meth. The rest is trapped deeper. Whether it bleeds back out to the surface and poses an exposure risk is unknown. Lee is evaluating whether a covering of oil-based paint seals meth within wallboard.
Few states have guidelines for cleaning up meth labs. “As more states deal with remediation of meth properties, they turn to us,” says Carolyn Comeau, manager of the Clandestine Drug Lab Program at the Washington State Department of Health. Washington requires remediation by contractors certified by the state to decontaminate meth labs, which are often found in low-income rental properties. The state also requires that surface meth be at or below 0.1 μg/100 cm2 before new residents can move in. Scientific evidence like Martyny’s and Lee’s should yield more effective guidelines, says Comeau.
Lee adds that a health-based standard for meth residues on building surfaces is needed to determine which properties need remediation and when a property has been adequately cleaned. All three experts support national remediation standards like those proposed in the Methamphetamine Remediation Research Act of 2005, which would establish a federal research program at the Environmental Protection Agency to study the environmental and health effects of meth labs and coordinate cleanup efforts. The bill has 55 cosponsors, and floor passage in the House is expected later this year.
Basement time-bomb.
A home meth lab produces toxic waste along with illegal drugs.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0589b16158522EnvironewsForumThe Beat Dooley Erin E. 9 2005 113 9 A589 A591 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Watching Mines in Eastern Europe
In January 2000, cyanide from a Romanian gold mine spilled into the Tisza River, killing nearly all the aquatic life and fouling the drinking water of millions of people. To help avoid such incidents in the future, government officials from a dozen southeastern European countries came together in May 2005 in Cluj-Napoca, Romania, and signed on to a new strategy calling for detailed site assessments for mines of concern, higher health and environmental standards for new mines, and plans for their eventual closure. The agreement also calls for early warning systems to warn countries downstream of mining-related pollution incidents. More than 150 mining operations exist in the area; more than a third have been labeled by the UN Environment Programme as posing a serious risk to human health, the environment, and regional stability.
Smoking Ends Up on Cutting-Room Floor
“Bollywood,” the Indian film industry and the world’s largest producer of films, has been ordered by the Indian government to cut movie and TV scenes showing actors smoking, effective October 2005. Announcement of the ban set off a flurry of dissent from Indian film makers, who see it as censorship, although some actors have expressed support for the decision. Health minister Anbumani Ramadoss said the ban could save millions of children who would otherwise start smoking “under the influence of movies.” This new law, which also requires listing of tar and nicotine content on cigarette packaging, comes just a year after India banned smoking in public places and forbade tobacco firm advertising in and sponsorship of sporting events. Each year more than 800,000 Indians die smoking-related deaths.
The Power of Pachyderm Poo
The Rosamund Gifford Zoo of Syracuse, New York, is investigating a potential new source of renewable energy, one that is based on the daily output of the zoo’s own residents, especially its six elephants—the zoo is looking at the half-ton of elephant manure produced each day as a feed-stock to produce methane or hydrogen for a fuel cell or generator. The zoo is also studying whether it could use the manure from a number of its other large animals. Using the animal waste would not only provide fuel, but also save the zoo money in disposal fees as well as the fossil fuels used to transport the waste. Many U.S. farms already use animal waste for power production.
Herbal Answers for Deadly Diseases
Ohio State University researchers have found that extracts from two Mojave Desert plants can kill the parasites that cause leishmaniasis and African sleeping sickness. These diseases afflict millions, primarily in developing nations, and are usually fatal if left untreated. Drugs based on chemicals from the dotted dalea and the Mojave dalea may offer a cheaper, safer, and more expedient alternative to the costly and sometimes nephrotoxic drugs currently used to treat the diseases. About 2 million new cases of leishmaniasis are reported each year. Sleeping sickness affects an estimated 50,000–500,000 people, mainly in rural sub-Saharan Africa.
Nanotech to the Rescue?
A new study by the University of Toronto Joint Centre for Bioethics shows just how useful new nanotechnologies could be in helping developing countries overcome urgent problems such as extreme poverty, hunger, environmental degradation, and diseases such as malaria and HIV/AIDS. The study, published in the April 2005 PLoS Medicine, ranks nanotechnology applications by their potential contribution to development and meeting the eight UN Millennium Development Goals. The top 10 applications were deemed to be energy storage, production, and conversion; agricultural productivity enhancement; water treatment and remediation; disease diagnosis and screening; drug delivery systems; food processing and storage; air pollution and remediation; construction; health monitoring; and vector and pest detection and control. The study also noted that nanotechnology research and development initiatives have been launched in several developing countries.
Africa Afire
By 2030, smoke from wood-fueled cooking fires will cause about 10 million premature deaths among African women and children, and by 2050, such fires will release 7 billion tons of carbon into the environment, according to a study published 1 April 2005 in Science. Sub-Saharan Africans consumed nearly 470 million tons of wood (in the form of firewood and charcoal) in 2000. Moving to petroleum-based fuels such as kerosene and propane gas would prevent the most premature deaths, but a more feasible strategy would be to adopt more modern methods of producing cleaner-burning charcoal. Such a shift could prevent 1–2.8 million premature deaths.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0590aEnvironewsForumMetal Toxicity: Tattoos: Safe Symbols? McGovern Victoria 9 2005 113 9 A590 A590 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
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A 2003 Harris Poll reported that 16% of U.S. adults are tattooed, including over a third of those aged 25–29. Despite the art’s growing popularity, the toxicology of tattoos is poorly understood. Now some ink components—particularly heavy metals—have raised concerns. A lawsuit set to go to trial in October 2005 has been filed against nine tattoo ink companies for violations of California’s Proposition 65, which requires that Californians be warned before exposure to chemicals causing cancer, birth defects, or other reproductive harm.
“One reason we started looking at tattoos is that the research we’ve done suggests teenage girls in particular are a huge market now for tattoos,” says Deborah Sivas, president of the nonprofit American Environmental Safety Institute (AESI), which filed the suit. The concern is not that the inks are acutely harmful, but rather that chronic exposure to some metals—especially lead—is a known problem.
Titanium and aluminum are often used as colorants in tattoos; more worrisome, inks using nonmetal colorants may include traces of antimony, arsenic, beryllium, chromium, cobalt, lead, nickel, and selenium (AESI filed over the latter eight metals). Sivas says the ink used for a 3 by 5 inch tattoo contains 1–23 micrograms of lead, versus the 0.5 micrograms per day permitted under Proposition 65.
Understanding exposure to lead and other metals once incorporated into a tattoo is not simple. A healed tattoo is a complicated array of ink particles trapped within dermal fibroblasts, macrophages, and mast cells. “One of the biggest problems is, over the period of time, how is exposure evaluated?” says Westley Wood, president of Unimax Supply, a tattoo equipment supplier and ink producer, which settled out of court in the AESI lawsuit. “Should it be counted every single day for the rest of your life, or is it dissipated in the body within a month?”
“Metal toxicity has not been an observed problem,” asserts physician Linda Dixon, president of the American Academy of Micropigmentation, a cosmetic tattooing trade group and manufacturer of Kolorsource brand of cosmetic ink. However, she adds, “Information about pigments in traditional tattoo products is usually a trade secret and not shared. We need information which is scientifically based.”
Dixon suggests publishing a list of pigments that are known to be safe and those known to be toxic. “Know your colors, know your pigments,” she says. “The scientists know what to avoid, and this should be common knowledge in the tattoo industries.” Though tattoo inks are subject to regulation by the Food and Drug Administration as cosmetics and color additives, that agency does not currently attempt to actually regulate tattooing or the pigments involved.
Despite the upcoming court battle, among the 17% of tattooed Americans the Harris Poll say regret their indelible marks, the greatest reason for dissatisfaction is not the safety of the tattoo but having been inscribed with the wrong person’s name.
No clear picture.
There are few health data for tattoo inks; with the growing popularity of the art, some see this as a cause for concern.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0590bEnvironewsForumAgriculture: Green Farming Equipment Stemp Graeme 9 2005 113 9 A590 A590 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
California’s San Joaquin Valley, known for its rich harvests of grapes, tomatoes, and oranges, is also prone to smog and ground-level ozone created when the equipment that works the land combines with the natural topography. Now farmers can do their part to clear the air by using “green” farm machinery that boasts greater efficiency and cleaner fuels.
In July 2004, California legislators set new air quality regulations for farmers, forcing them to significantly reduce their emissions of potential greenhouse gases and fine particles. Farmers—a tough lobbying group—were previously exempt from state air regulations. But the machinery, dust, pesticide use, and other facets of farming make this industry one of the worst polluters.
The American Lung Association’s State of the Air 2005 report ranked three California farm counties (Kern, Fresno, and Tulare) among the five worst in the nation for ozone and particle pollution. Such poor air quality has a major health impact, especially for young children. A study by the Central California Children’s Institute found that 15.7% of San Joaquin Valley children had asthma. Statewide, Fresno and Kings counties had the worst asthma rates, with more than 20% of children diagnosed.
The “Optimizer,” developed by Kevin McDonald, founder and president of Tillage International, offers one way to make farm-work more efficient. This multipurpose tiller comes in two models and does all the necessary tilling, planting, and herbicide application in one step. Farmers who once had to do multiple passes can work their fields in one or two passes, cutting down on the amount of tractor fuel needed.
McDonald says growers currently using the Optimizer estimate that the tiller, at $149,000–$189,000 depending on model, could pay for itself in under a year. Researchers at the University of California, Davis, tested the machinery and found that it saved 50% on fuel and 72% on time. Furthermore, the Optimizer is eligible for a Natural Resources Conservation Service grant through the U.S. Department of Agriculture, which could significantly cut the one-time purchase cost.
Another innovative way to cut emissions is to replace petroleum fuel with biodiesel. Biodiesel is made by refining vegetable oils such as those found in soybeans and rape-seed, and can be mixed with regular diesel in varying concentrations. In October 2002 the U.S. Environmental Protection Agency analyzed the emissions of a 20% biodiesel/ 80% petroleum diesel blend and found reduced emissions of particulate matter (–12%), unburned hydrocarbons (–20%), and carbon monoxide (–12%).
While a diesel engine will run using 20% or even 100% biodiesel, equipment manufacturers like New Holland and John Deere recommend only 2–5% biodiesel. But even a small amount counts when you’re as big as John Deere; the company announced in February 2005 it would begin using a 2% biodiesel blend as the preferred factory fill for all its diesel machinery.
If air concerns don’t convince farmers to invest in new products, simple economics may. A 1989 report by the California Air Resources Board noted that grapes, cotton, oranges, lemons, and beans grown in 1985 levels of air pollution lost 16–29% in yield and size as a direct result of smog.
Easier on the air.
The Optimizer is part of a new generation of green farm equipment.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0614aEnvironewsScience SelectionsRocking the Cradle: Phthalate Exposure in NICU Infants Barrett Julia R. 9 2005 113 9 A614 A614 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Animal studies have linked di(2-ethylhexyl) phthalate (DEHP) with reproductive and developmental toxicity, and have demonstrated an especially pronounced effect on testicular development when administered postnatally. Previous research has shown that newborns treated at neonatal intensive care units (NICUs) may receive doses of DEHP at 2–3 times the average daily adult exposure, and that these infants have relatively high urinary levels of the DEHP metabolite mono(2-ethylhexyl) phthalate (MEHP). Now researchers, using urinary MEHP as a biomarker of DEHP exposure, demonstrate for the first time that the more DEHP-containing devices are used in treating an infant, the more DEHP makes its way into the infant’s body [EHP 113:1222–1225].
Human DEHP exposure is widespread but generally much lower than the levels causing harm in animal studies. However, certain circumstances such as intensive medical treatment can result in higher-than-average exposure, which may be a particular risk for newborn males. DEHP is added to polyvinyl chloride (PVC) plastics for use in medical equipment including IV bags, blood bags, and various types of tubing, as well as many industrial and consumer PVC products. DEHP does not chemically link to PVC and leaches into fluids (such as blood and saline solution) that contact the plastic. The amount of leaching depends upon factors such as type of fluid, length of storage, and temperature.
The study involved 54 newborn girls and boys receiving treatment at two Boston-area NICUs between 1 March and 30 April 2003. The infants had been admitted for various reasons, and treatment included procedures such as mechanical ventilation, enteral feedings, and cardiac catheterization.
Prior to visiting the NICUs, the researchers defined low, medium, and high DEHP exposure categories based upon typical NICU procedures and equipment. Infants whose treatment consisted primarily of bottle and/or gavage feedings composed the low-exposure group. Infants in the medium-exposure group received more invasive therapies involving equipment such as an indwelling gavage tube or umbilical vein catheter. High-exposure infants experienced multiple and simultaneous invasive treatments, including endotracheal intubation and continuous umbilical vein catheterization.
One researcher visited the NICUs and observed each infant for 3–12 hours over the course of 1–3 days (more than one infant was observed at a time). During the observational visits, the researcher noted the equipment being used for each infant, then assigned the infant to an exposure group accordingly. At the end of each visit, urine samples were collected for MEHP measurement.
The researchers detected 10 phthalate metabolites in the samples, including 3 associated with DEHP, but focused on MEHP for data analysis since this metabolite is well studied and a proven biomarker of DEHP exposure. MEHP levels ranged from less than the level of detection to 758 nanograms per milliliter and did not vary substantially between multiple individual samples.
Between the two NICUs there were 13, 24, and 17 infants in the low-, medium-, and high-exposure groups, respectively. The researchers found that infants in the high-exposure group had MEHP levels five times higher than those in the low-exposure group. MEHP levels for medium-exposure infants were twice those of the low-exposure group.
The researchers indicate that the MEHP levels seen in this study are similar to those previously reported for NICU infants and higher than those reported for older children; no data are available for infants who did not need NICU care. The relevance of these exposures to health effects is unknown, and the researchers urge larger, more comprehensive studies with follow-up to determine consequences of DEHP exposure related to NICU treatment.
Double jeopardy?
Babies in neonatal intensive care units, already a high-risk group, are likely to have greater exposure to potentially harmful phthalates than other children.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0614bEnvironewsScience SelectionsArsenic and Prostate Cancer: Acquiring Androgen Independence Tibbetts John 9 2005 113 9 A614 A615 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Scientists already suspect that prostate cancer, the second-leading cause of cancer death in U.S. men, is linked with chronic arsenic exposure. Now a team of researchers reports that human prostate cells that underwent chronic, low-level arsenic exposure not only exhibited aggressive carcinoma-like growth, but also showed an increased incidence of androgen independence, a state often linked to advanced or fatal prostate cancers, and one that makes these cancers more difficult to treat [EHP 113:1134–1139].
Androgen, a sex hormone that stimulates and maintains masculine characteristics, is a necessary component in normal prostate function that can also encourage the survival and growth of prostate cancer. That is why some current treatments, such as pharmaceutical androgen blockers or removal of the testes, focus on making androgen less available to prostate cancer cells. However, some patients experience androgen independence, in which prostate cancer cells no longer need the male hormone to differentiate and grow out of control.
The mechanisms behind androgen independence are not completely understood, but scientists do know that this phenomenon sometimes occurs when androgen receptors go functionally awry. In some cases, the androgen receptors are “overexpressed,” greatly increasing in number. In other cases, the receptors mutate, which can result in hyperresponsiveness to androgens. Still other receptor mutations allow nonandrogens (such as the estrogens) and even androgen inhibitors to stimulate cancer growth. Sometimes androgen receptors are bypassed completely, and cell growth is activated by other naturally occurring compounds including insulin-like growth factor-1, epidermal growth factor, and others.
In the current study, researchers examined the growth of normal and arsenic-transformed human prostate epithelial cells. The arsenic-transformed cells had been exposed continuously to sodium arsenite and, after 29 weeks of exposure, produced malignant tumors when inoculated into nude mice. Both cell lines were observed in two different media. One medium included the complete range of steroids, including ample amounts of androgen and growth factors. The other lacked normal amounts of sex hormones and growth factors.
The experiment showed that, consistent with malignant cell growth, prostate cells chronically exposed to arsenic grew more rapidly than control cells in both media. In the steroid-rich medium, the arsenic-transformed cells proliferated approximately twice as fast as the unexposed control cells. In the steroid-depleted medium, the arsenic-transformed cells proliferated about 2.5 times faster than control cells.
Arsenic exposure therefore appeared to be associated with the acquisition of androgen independence in prostate cells. However, the observed arsenic-induced androgen independence did not occur by any previously studied mechanism; it was not linked to overexpression of androgen receptors or receptor mutations that facilitate cell growth via nonandrogens.
A clue may lie in earlier experiments, in which the same researchers observed a marked increase in production of K-ras (an oncogene associated with prostate cancers) in arsenic-transformed cells. K-ras is a key part of a growth-stimulating pathway in the prostate that is stimulated by androgens. K-ras was clearly correlated with arsenic-induced carcinoma-like growth and androgen independence. The authors speculate here that arsenic may bypass the androgen receptor and directly cause aberrant K-ras activation.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0632a16140612AnnouncementsBook ReviewPrescription for a Healthy Nation: A New Approach to Improving Our Lives by Fixing Our Everyday World Rull Rudolph Rudolph Rull is an assistant researcher at the Center for Health Policy Research at the University of California, Los Angeles. He is an epidemiologist whose research focuses on using geographic information systems for assessing environmental exposures to pesticides and air pollution and characterizing the built environment.9 2005 113 9 A632 A632 Farley Tom . and Cohen Deborah . .
Prescription for a Healthy Nation: A New Approach to Improving Our Lives by Fixing Our Everyday World .
.
2005 . . Beacon Press . : Boston, MA . . ISBN: ISBN: 0-8070-2116-4. . $24.952005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
By Tom Farley and Deborah Cohen
Boston, MA:Beacon Press, 2005. 243 pp. ISBN: 0-8070-2116-4, $24.95 cloth
Much of our daily news is devoted to health care crises. Spiraling health care costs, which currently consume one-seventh of the U.S. economy, are mostly devoted to treating a modern epidemic of preventable chronic conditions such as obesity, diabetes, lung cancer, and heart disease; however, treatment of these chronic conditions does little to prevent future cases. Preventive interventions designed to educate people about the health risks of overeating, tobacco use, alcohol abuse, and sedentary lifestyles have had limited success in modifying behaviors and have not translated into a logical reversal in the prevalence of these chronic diseases.
In Prescription for a Healthy Nation, Tom Farley and Deborah Cohen suggest solutions for reversing this trend and preventing new cases by changing the social and physical environment. In “The Leading Causes of Health,” the authors argue that the environment is a powerful force in influencing human behavior, although changing and redesigning the environment to promote health is not necessarily a “new approach.” The sanitation and hygiene revolution of the 19th and 20th centuries—the greatest success of modern public health practice—was largely carried out by constructing sewers, mandating solid waste disposal and access to clean water in cities, and other interventions to prevent the spread of infectious agents. The success of these preventive measures lies in the fact that the benefits affect the entire society. However, according to the authors, many current preventive interventions that attempt to reduce risk through education tend to ignore the aspects of our environment that encourage risky behavior, such as the overabundance of high-fat junk food and pervasive advertising of alcohol and tobacco. In addition, preventive education has largely targeted individuals at high risk such as people with high-fat diets who are thus at a higher risk for having a heart attack, while neglecting the much larger population of individuals with moderate- or low-fat diets who have a lower risk for heart attack. By absolute numbers, more lower-risk people will suffer heart attacks than high-risk individuals; therefore, consuming less fat will reduce everyone’s risk. The authors propose a return to this “curve-shifting” approach to prevention that encourages everyone to improve their health behavior; they substantiate these arguments with examples from public health and cognitive psychology research, historical and current events, and personal anecdotes.
In “Curve Shifters,” Farley and Cohen identify four modifiable components that influence our health environment or “healthscape”: a) accessibility of healthy (e.g., fruits and vegetables) and unhealthy items (e.g., tobacco); b) physical structures that promote or endanger health (e.g., guardrails) and neighborhood designs that discourage crime or promote physical activity; c) social structures that influence the acceptability of our health behaviors (e.g., bans on indoor smoking); and d) the popular media that influences our behavior through advertising and the broadcasting of influential images in movies.
In the final section, “Healthscaping America,” solutions are proposed for altering the environment to promote health, for example, mandating the display of fresh fruits and vegetables at checkout counters in supermarkets and convenience stores and banning the sale of junk food in schools and the advertising of alcohol on television. Workplaces can encourage employees to take exercise breaks, and neighborhood streets can be designed to encourage walking and bicycling for travel and recreation. The authors concede that these are not complete solutions and that these proposals will generate controversy and be viewed both as radical and as an excuse for personal irresponsibility by policymakers and businesses with a financial stake in their implementation. However, throughout the history of public health, interventions such as sanitation and indoor smoking bans that were once deemed radical are now commonly accepted as a responsibility of the state.
Farley and Cohen present these provocative ideas in a clear and highly readable manner with contemporary examples that address the urgency of this crisis. This book is an instructive resource for scientists, policymakers, community health advocates, and anyone with an interest in improving the health of our society.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences ehp0113-a0632b16140612AnnouncementsNew BooksNew Books 9 2005 113 9 A632 A632 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright.
==== Body
Children’s Health and the Environment: Developing Action Plans
L. Licari, L. Nemer, G. Tamburlini
Geneva:World Health Organization, 2005. 96 pp. ISBN: 92-890-1374-5, $36
Computational Genome Analysis: An Introduction
Richard C. Deonier, Simon Tavaré, Michael S. Waterman
New York:Springer-Verlag, 2005. 540 pp. ISBN: 0-387-98785-1, $79.95
Deliberative Environmental Politics: Democracy and Ecological Rationality
Walter F. Baber, Robert V. Bartlett
Cambridge, MA:MIT Press, 2005. 288 pp. ISBN: 0-262-02587-6, $60
Dynamics of Mercury Pollution on Regional and Global Scales: Atmospheric Processes and Human Exposures Around the World
Nicola Pirrone, Kathryn R. Mahaffey, eds.
New York:Springer-Verlag, 2005. 748 pp. ISBN: 0-387-24493-X, $129
Encyclopedia of Biostatistics, 2nd ed.
Peter Armitage, Theodore Colton, eds.
Hoboken, NJ:John Wiley & Sons, Inc., 2005. 6,100 pp. ISBN: 0-470-84907-X, $2,995
Environmental Citizenship
Andrew Dobson, Derek Bell, eds.
Cambridge, MA:MIT Press, 2005. 312 pp. ISBN: 0-262-02590-6, $60
Generation Extra Large: Rescuing Our Children from the Epidemic of Obesity
Chris Woolston, Lisa Tartamella, Elaine Herscher
New York:Basic Books, 2005. 255 pp. ISBN: 0-465-08390-0, $25
Handbook of Urban Health: Populations, Methods, and Practice
Sandro Galea, David Vlahov, eds.
New York:Springer-Verlag, 2005. 650 pp. ISBN: 0-387-23994, $89.95
Hazardous Materials Characterization: Evaluation Methods, Procedures, and Considerations
Donald A. Shafer
Hoboken, NJ:John Wiley & Sons, Inc., 2005. 400 pp. ISBN: 0-471-46251-8, $74.95
Industrial Transformation: Environmental Policy Innovation in the United States and Europe
Theo de Bruijn, Vicki Norberg-Bohm, eds.
Cambridge, MA:MIT Press, 2005. 376 pp. ISBN: 0-262-54181-5, $27
Inventing for the Environment
Arthur Molella, Joyce Bedi, eds.
Cambridge, MA:MIT Press, 2005. 424 pp. ISBN: 0-262-63328-0, $17.95
Microarrays for an Integrative Genomics
Isaac S. Kohane, Alvin Kho, Atul J. Butte
Cambridge, MA:MIT Press, 2005. 326 pp. ISBN: 0-262-61210-0, $25
Ontologies for Bioinformatics
Kenneth Baclawski, Tianhua Niu
Cambridge, MA:MIT Press, 2005. 440 pp. ISBN: 0-262-02591-4, $45
Polluted Promises: Environmental Racism and the Search for Justice in a Southern Town
Melissa Checker
New York:New York University Press, 2005. 288 pp. ISBN: 0-8147-1658-X, $22
Power, Justice, and the Environment: A Critical Appraisal of the Environmental Justice Movement
David Naguib Pellow, Robert J. Brulle, eds.
Cambridge, MA:MIT Press, 2005. 352 pp. ISBN: 0-262-16233-4, $62
Precautionary Tools for Reshaping Environmental Policy
Edited by Nancy J. Myers, Carolyn Raffensperger
Cambridge, MA:MIT Press, 2005. 400 pp. ISBN: 0-262-13458-6, $62
Scarcity and Growth Revisited: Natural Resources and the Environment in the New Millennium
R. David Simpson, Michael A. Toman, Robert U. Ayres, eds.
Washington, DC:RFF Press, 2005. 320 pp. ISBN: 1-93311-510-6, $70
Sustainable Development, Energy and the City: A Civilisation of Concepts and Actions
Voula Mega
New York:Springer-Verlag, 2005. 288 pp. ISBN: 0-387-24354-2, $99
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-2451621612910.1186/1471-2105-6-245Research ArticleExtension of Lander-Waterman theory for sequencing filtered DNA libraries Wendl Michael C [email protected] W Brad [email protected] Genome Sequencing Center, Washington University, St. Louis MO 63108, USA2 Donald Danforth Plant Science Center, St. Louis MO 63132, USA2005 10 10 2005 6 245 245 2 5 2005 10 10 2005 Copyright © 2005 Wendl and Barbazuk; licensee BioMed Central Ltd.2005Wendl and Barbazuk; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The degree to which conventional DNA sequencing techniques will be successful for highly repetitive genomes is unclear. Investigators are therefore considering various filtering methods to select against high-copy sequence in DNA clone libraries. The standard model for random sequencing, Lander-Waterman theory, does not account for two important issues in such libraries, discontinuities and position-based sampling biases (the so-called "edge effect"). We report an extension of the theory for analyzing such configurations.
Results
The edge effect cannot be neglected in most cases. Specifically, rates of coverage and gap reduction are appreciably lower than those for conventional libraries, as predicted by standard theory. Performance decreases as read length increases relative to island size. Although opposite of what happens in a conventional library, this apparent paradox is readily explained in terms of the edge effect. The model agrees well with prototype gene-tagging experiments for Zea mays and Sorghum bicolor. Moreover, the associated density function suggests well-defined probabilistic milestones for the number of reads necessary to capture a given fraction of the gene space. An exception for applying standard theory arises if sequence redundancy is less than about 1-fold. Here, evolution of the random quantities is independent of library gaps and edge effects. This observation effectively validates the practice of using standard theory to estimate the genic enrichment of a library based on light shotgun sequencing.
Conclusion
Coverage performance using a filtered library is significantly lower than that for an equivalent-sized conventional library, suggesting that directed methods may be more critical for the former. The proposed model should be useful for analyzing future projects.
==== Body
Background
Over the last few decades, DNA sequencing has firmly established its role in the broader enterprises of scientific and medical research. Enabled by ongoing development and refinement of laboratory techniques, instruments, and software, investigators are now studying a wide array of genomes at a level of sophistication not before possible. While a number of sequencing approaches have been devised, experience indicates that the efficacy of any particular one depends strongly upon the context of the target sequence. For instance, the whole genome shotgun (WGS) procedure has proved especially suited to microbes [1]. Conversely, mammalian projects are being completed using large-insert mapped clones, which are better able to resolve long-range assembly issues related to DNA repeats [2].
Repetitive sequences are especially abundant in many of the economically and agriculturally important plant species. For example, the maize genome (Zea mays) is comparable in size to the human genome, yet up to 80% of it consists of retroelements [3,4]. The degree to which established sequencing techniques will be successful for such cases is not clear [5]. Two notable methods have been proposed to address high-repeat projects. Both seek to filter out repetitive regions, leaving primarily low-copy "islands" to be amplified in a DNA library. Methyl filtering excludes repetitive elements based on their elevated levels of cytosine methylation [6]. Conversely, high-Cot purification preferentially selects low-copy genic regions based upon characteristic re-association rates [7,8]. In this context, it can be considered a form of normalizing procedure. Methyl filtering appears to be compatible only with plant genomes [9], while Cot selection can be applied broadly.
Genomic projects are generally guided by probabilistic models of the underlying random processes. The seminal work of Lander and Waterman [10] has long served as the theoretical foundation for standard fingerprint mapping and shotgun sequencing methods. Although not strictly correct, the coverage model first used by Clarke and Carbon [11] is also treated as a de facto part of Lander-Waterman (LW) theory. These formulations are predicated upon an infinitely long genome, whose sequence is completely represented in the form of a non-biased clone library. In mathematical terms, these clones and their resulting sequence reads are taken to be independently and identically distributed (IID). The LW model allows one to estimate parameters of interest, e.g. sequence coverage and the number of gaps, as functions of the number of reads processed (Fig. 1, top).
Figure 1 Schematic of the covering process for the conventional continuous library (top) versus the filtered discontinuous library (bottom).
Filtered libraries are, however, an incomplete representation of the target sequence. Specifically, they are punctuated by fixed gaps of unknown size (Fig. 1, bottom). When using the filtering schemes mentioned above, the number of such gaps is expected to be large and this introduces additional modeling issues. Consider, for example, a de novo assembly. Without independent linking information, it is not strictly possible to distinguish between the fixed gaps native to the library and the sequence gaps that evolve stochastically as a part of the coverage process. Under ideal conditions, library gaps would clearly manifest themselves only in the limit of an infinite number of clones, because all sequence gaps should vanish.
So-called "edge effects" must also be considered for filtered libraries. Demonstrating this phenomenon is a matter of simple probability. Suppose a genic island of size σ is being covered by random sequence reads of length λ, where λ ≪σ. The terminal base position has only about a 1/σ chance of being covered by any individual read, while the associated probability for an interior base position (far from the terminus) is roughly λ/σ. Such differences can be regarded as a form of position-based sampling bias because preference for coverage is clearly shifted toward the interior island regions. The fraction of an island affected in this way can have significant implications on the evolution of coverage and gaps.
Here, we report an extension to the standard LW theory for filtered library configurations. It describes not only the analogs of established LW parameters, but also several new quantities of interest that arise as a consequence of the fragmented nature of the library. Preliminary experimental results have been favorable [12-14], suggesting that filtering procedures will be applied on a broader scale to the most recalcitrant genomes. For such projects, investigators must currently rely on a casual, but unproven adaptation of LW theory. Here, all genic islands are artificially concatenated into a single "super-island" and the size of this island is taken as the effective genome size. We will refer to this idealization as the Lander-Waterman Super Island (LWSI) model. Because this representation neglects library gaps and the associated edge effects, the degree to which it is applicable to actual projects is not known.
Results and discussion
The mathematical model
A gap consists of any genomic region following a read that is not manifested as coverage. Two types of gaps arise under this definition. If the uncovered region is represented in the library it is called a "sequence gap", otherwise it is called a "library gap" (Fig. 1, bottom). The numbers of sequence and library gaps are denoted by the random variables S and L, respectively. We also define the following random variables: C is the number of bases covered, I is the number of islands hit by at least one sequence read, and R denotes the number of reads hitting a particular island. (Table 1 summarizes the mathematical notation used in the model.)
Table 1 Mathematical notation
symbol type or formula meaning
C random variable number of bases covered
I random variable number of islands hit by at least one read
L random variable number of library gaps
R random variable number of reads hitting a particular island
S random variable number of sequence gaps
λ parameter length of a sequencing read
σ parameter size of a filtered island
i parameter number of filtered islands
n parameter number of sequencing reads processed
π σ - λ + 1 number of placements for a read on an island
Π i(σ - λ + 1) number of possible placements over whole target
Let the filtered library consist of i islands, each of which is σ base-pairs in size. Reads are taken to be of length λ base-pairs and are assumed to be IID, as in the standard LW model. Read length may include a reduction factor to account for the number of bases effectively lost in detecting overlap with another read [10]. We presume, as an upper bound, that read length does not exceed island length, i.e. λ ≤ σ
There are π = σ - λ + 1 possible placements of a read on each island, and consequently, Π = iπ total placements within the library. Assuming n reads have been processed, the expected values of the random variables are given by the following theorems.
Theorem 1 (library gaps). The expected number of library gaps is
E〈L〉 = i(1 - e-n/Π),
where e ≈ 2.71828 is Euler's number.
Theorem 2 (sequence gaps). The expected number of sequence gaps when λ ≤ (σ + 1)/2 is
where n/Π is constrained according to Lemma 2, ρ = nλ/Π, and δ = σ - 2(λ - 1).
Theorem 3 (coverage). The expected number of bases represented by the library that are covered by at least one sequence read is
where λ ≤ σ/2.
Theorem 4 (reads per island). The number of sequence reads placed on a specific island follows a Poisson distribution with an average value (rate) of E〈R〉= n/i. In particular, the probability that the island is not hit by any reads is exp(-n/i).
Theorem 5 (number of islands hit). The distribution of the number of genic islands hit by one or more sequence reads is
where μ = i exp(-n/i) and the expected value is
E〈I〉 = i(1 - e-n/i).
Theorems 2 and 3 have been derived according to parameters of current biological interest (Lemma 2). They also adhere to their respectively stated, but less-restrictive mathematical conditions. However, it is straightforward to modify them when λ is larger relative to σ (see Methods).
These results enable one to probabilistically characterize the shotgun sequencing process for filtered DNA libraries in much the same way that standard LW theory is used for conventional libraries. Filtering is expected to play a significant role for the most difficult, repeat-laden genomes, where cost and assembly issues may limit the success of conventional techniques.
Investigators have had to rely on a rudimentary adaptation of LW theory, whereby the fragmented library is modeled as a single "super-island" [7,12,14]. Here, there are i σ - λ + 1 ≈ i σ possibilities for placing clones of length λ. In actuality, significantly fewer placements exist, i(σ - λ + 1), owing to the discontinuities between islands. Some statistics will be dramatically skewed as a consequence, for example the expected contig size will not converge to the correct value of σ. Accuracy of other quantities is not clear. Also, there is no provision to estimate island-specific statistics, such as the number of islands hit by at least one read. The idealized LWSI model is correct only for the special case i = 1, although errors for some of the variables will be minimal if λ/σ is sufficiently small.
Here, we examine the sequencing process over a range of parameters to discern the general trends that one should be aware of. We also characterize some of the practical applications relevant to filtered libraries and assess the applicability of the "super island" (LWSI) assumption. Our model can readily be applied to specific projects, as well.
Coverage characteristics
Maize can be taken as a representative high-repeat genome. Whitelaw et al. [12] describe shotgun sequencing from filtered libraries. They report an average read length of 719 bases and 50 base minimal overlap, so that the effective read length is λ ≈ 669 bp. Genome size and repeat content are taken as 2.7 Gb and 80%, respectively [15]. If, for the moment, we assume perfect filtering, the resulting library would comprise about 540 Mb of DNA sequence.
Island size, unlike read length, cannot readily be characterized a priori. Maize genes are thought to reside predominantly in small, roughly 3 kb regions of unmethylated DNA, which are surrounded by tracts of 20–200 kb highly methylated, high-copy sequence [3,16,17]. Thus, the maize gene space appears to be well-dispersed across the physical genome with most genes being distinctly separated from one another [18-22]. Recent analysis of BAC clones supports this view [23]. In order to demonstrate trends of interest, we will assume a representative island size of 3,000 bases, but will additionally examine several hypothetical islands that are multiples of this value (Table 2).
Table 2 Island characteristics for an idealized 540 Mb filtered maize library
nominal island type σ i
1 gene 3,000 180,000
2 genes (hypothetical) 6,000 90,000
4 genes (hypothetical) 12,000 45,000
Evolution of the coverage process is shown in Fig. 2 for the various island lengths, as well as the idealized LWSI model [11]. Evidently, there is little difference in performance up to about 1× sequence redundancy. That is, coverage is largely independent of the size of islands in the library. This reflects the tendency of reads to generate new coverage early in a project, rather than increasing overlaps of existing coverage. Gaps in the library appear to have little influence in this stage. Even in the case of small islands, it is likely that reads are preferentially populating the uncovered, central portions of the various islands.
Figure 2 Coverage evolution for both the discontinuous island model and the LW "super-island" (LWSI) model.
As more reads are processed, we would expect this trend to change. Recall that the probability of a read covering a specific base position decreases closer to the edge of an island. This is the "edge effect". In this case, reads will tend to generate commensurately higher rates of overlap in the central regions, while the end regions will be covered at a slower pace. Indeed, Fig. 2 shows that behavior begins to diverge appreciably above 2× redundancy. Coverage becomes a strong function of island size.
Fig. 2 also indicates that the fraction of a filtered library that one can reasonably hope to obtain via random methods depends upon island size. For example, in the typical case of 3,000 bp islands, one would still expect to be missing about 4% of the sequence after processing 8× worth of reads. This figure contrasts with a 4% vacancy rate at slightly more than 3× redundancy with conventional libraries. Here, we would anticipate essentially complete coverage at the 8× milestone. For libraries consisting of 6,000 bp and 12,000 bp islands, the situation is more favorable. The model predicts vacancy rates of only about 2.5% and 1.3%, respectively, at 8× redundancy. Directed methods may be necessary for resolving the sequence at island edges.
The above observations call attention to a somewhat puzzling difference between filtered and conventional libraries. It is well-known that longer reads yield improved coverage performance for the latter. Specifically, coverage goes exponentially according to the redundancy, defined as nλ/G, where G is the genome size. Increasing the read length, in particular the ratio λ/G, implies that commensurately higher coverages could be obtained with a given number of reads. However, we have just observed that increasing the analogous ratio λ/σ in filtered libraries seems to slow the overall coverage rate.
This rather paradoxical behavior can be explained precisely in terms of the edge effect. In examining Thm. 3 more closely, we see that the first term (the one having a coefficient 2i) quantifies the coverage dynamics of the end regions. The coverage probability for any specific base in this region is not a function of read length (see Proof of Thm. 3), but the fraction of the island affected in this way is. Thus, longer reads impart edge effects over a larger percentage of each island. Moreover, the average difference in coverage probability between boundary and interior regions for a read is λ/2. Thus, the disparity in coverage probability between the two regions also grows in proportion to read length. Again paradoxically, this effect starts to diminish if reads become sufficiently long and finally vanishes in the limit of λ → σ. However, this is simply a consequence of the fact that all base positions once again have an equal chance of being covered, so edge effects disappear. Although perhaps not obvious, this limiting case is described by Thm. 5.
Gap census and contig length trends
Again using maize parameters as an example, Fig. 3 shows evolution of sequence gaps for the three island lengths in Table 2, as well as the idealized LWSI model. These curves are computed from Thm. 2 and are shown in the usual units of i σ/λ [10].
Figure 3 Evolution of gap census for both the discontinuous island model and the LWSI model.
As with coverage, performance appears to be mostly independent of island size up to about 1× sequence redundancy, but the cases differ appreciably after that. The rates of gap closure decline significantly as the islands become smaller. Underlying dynamics are similar to those discussed above for coverage. It is worth noting that these trends are fundamentally different from what one realizes when varying effective read length. In that instance, the apparent number of gaps rises as reads become effectively shorter. We find a similar behavior here when island length is held fixed, although the convergence point is independent of read length (data not shown). This effect is a rather subtle consequence of the original method devised for modeling detection thresholds. It is discussed extensively in ref. [10], primarily in the context of fingerprint mapping. However, the phenomenon is not as relevant to shotgun sequencing because detection thresholds are small relative to read length and largely constant. Here, we expect island size to be the more influential variable.
Strictly speaking, library and sequence gaps are not completely independent of one another as we have implied here. For instance, the generation of a library gap is synonymous with placing a read in the end position of an island. This event may inadvertently eliminate a sequence gap, as well. We cannot rigorously claim that the total number of gaps at any given point is simply the sum of the two gap types. However, according to Thm. 1, the actual number of library gaps should always be small compared to the number of sequence gaps (data not shown). To be more specific, the rate of library gap formation is very slow; there are only i placements of a possible i (σ - λ + 1) for which a read will spawn such a gap. Consequently, we can take the sequence gap census alone as a good approximation for the total number of gaps.
Assuming independence of the variables, we can approximate the expected length of contiguous segments as E〈C〉/E〈S〉. This expression is plotted in Fig. 4. Note that curves derived from the filtered model converge essentially to their respective island lengths, while the LWSI model diverges. This is a well-known anomaly in the basic Lander-Waterman formulation [24], although it has since been resolved [25]. Convergence to maximum contig length also appears to be faster for shorter islands. For example, for 3,000 bp islands there is little increase in average contig length after 5× sequence redundancy, while the 12,000 bp case is still developing even at 7× redundancy. Given the fundamental difference in longer-term behavior, it is somewhat surprising that the LWSI seems to be a better short-term indicator for contig length as compared to coverage and gaps. Specifically, predicted lengths seem to be independent of island size up to about 2× sequence redundancy, rather than the 1× limit observed for the other variables.
Figure 4 Evolution of average contig length for both the discontinuous island model and the LWSI model.
Application for gene tagging
One of the growing applications we anticipate for filtered libraries is as a sampling method to rapidly prototype gene sets. This procedure is referred to as "gene tagging" [13,14]. Here, one simply obtains a light random sampling of the filtered library and assesses gene hits via homology searching. A number of fundamental questions revolve around how gene hits will be distributed for a given number of sequencing reads. If we take island hits as an analog of gene hits, Thms. 4 and 5 are useful for formulating predictions. Conversely, the LWSI model is not suited to such calculations because there is no consideration of how islands are actually separated from one another.
Investigators are often interested in rudimentary estimates of the number of genes hit, for which we can apply either of these theorems. Here, the governing parameter is exp(-n/i), so that island and read lengths are irrelevant. Data from two recent projects are available for comparison: a methyl-filtered sorghum (Sorghum bicolor) library sampled at roughly 1.1× redundancy [14] and a combination methyl-filtered high-Cot maize (Zea mays) library sampled at roughly 1.2× redundancy [13]. In the former case, library size and average gene size were estimated as iσ ≈ 262 Mb and σ ≈ 3 kb, respectively. Tagging results are based on comparisons to 137 genes annotated from finished sorghum BAC clones [14]. For the latter case, we calculate theoretical performance using the maize estimates described above. Maize tagging results are based on WU-BLASTN (W. Gish, personal communication) comparisons to 151 highly-annotated maize B73 genes [26] at a minimum identity of 98%.
Fig. 5 shows the expected fraction of genes hit according to both Thm. 5 and the experimental data. Theoretical curves depend on the number of islands, as calculated from parameter estimates. In particular, the sorghum library is modeled as having i = 262 × 106/3,000 = 87, 333 islands, while the number of maize islands is estimated at 540 × 106/3,000 = 180,000. Agreement is relatively good in both cases up to about 60–70% of the gene space, after which the theory begins to over-predict the actual gene representation. Here, each empirical curve lies >20 standard deviations below its respective theoretical prediction (data not shown). This suggests systematic rather than stochastic factors account for the difference. Specifically, biases in the data are assumed to be present, although they are difficult to characterize at this stage. For example, Bedell et al. [14] speculate that perhaps 10% of sorghum repeats may be under-methylated, and thus able to survive the filtering process to some degree. Similarly, Whitelaw et al. [12] found a non-trivial number of retrotransposons in their combined methyl-filtered high-Cot maize library. Tagging also depends on the ability to identify suitable genes to assess, which itself is difficult and subject to error.
Figure 5 Comparison of Thm. 5 to experimental gene-tagging results in sorghum and maize.
A more sophisticated calculation can be made with the probability distribution given by Thm. 5. Again using the parameters cited by Bedell et al. [14], we plot the tail probability of hitting various fractions of the gene space as a function of sequence redundancy in Fig. 6. As we would intuitively expect, the required redundancy increases with the fraction of the gene space desired. The curves are surprisingly sharp in all cases. That is, the theoretical milestones for gene-tagging appear to be very-well defined. For example, the probability of tagging at least 95% of the gene space is vanishingly small below 0.71× sequence redundancy, but approaches unity upon reaching 0.73× redundancy. These analyses are clearly subject to the biases discussed above. For example, Fig. 6 suggests that the 1.1× sequencing depth should probably have captured about 99% of the sorghum genes. However, Bedell et al. [14] calculated the actual value to be about 95%. The gene tagging process becomes more efficient as gene size increases because the number of genic islands is commensurately less for a given library size (data not shown).
Figure 6 Tail probabilities for tagging various fractions of the gene space in Sorghum bicolor [14].
Estimating genic enrichment
Whitelaw et al. [12] proposed the idea of using the gap census predicted by LW theory, specifically the LWSI adaptation, to compute effective filtered genome size GL from preliminary shotgun data. The LW equation can readily be solved for GL as
so that the number of sequence gaps E〈S〉 serves as an indicator of GL. One can then estimate a genic enrichment factor G/GL, where G is the full genome size. Whitelaw et al. [12] performed such calculations for methyl-filtered and high-Cot maize libraries at less than 0.5× redundancy. Bedell et al. [14] also applied this concept to a methyl-filtered sorghum library at about 1× redundancy.
These calculations are founded on speculation that library gaps and edge effects could be ignored. We already described how performance is essentially independent of such factors when sequence redundancy remains below 1×. It therefore appears that these two particular computations are reasonable. However, this is clearly not the case in general. Standard LW theory will tend to under-estimate gaps, and consequently to under-estimate GL for higher redundancies. Genic enrichment factors would be artificially high. From a practical standpoint, light shotgun redundancy in conjunction with Eq. 1 seems to be a legitimate and convenient way to characterize enrichment; there is little penalty in neglecting edge effects and one need not estimate island size.
We note that GL in Eq. 1 can be further characterized in terms of lower and upper bounds using the appropriate distribution moments [25]. For example, Whitelaw et al. [12] calculated the size of the combination methyl-filtered high-Cot maize library to be roughly 413 Mb. Performing similar computations at 3 standard deviations above and below the mean, we estimate that the lower and upper limits for library size are approximately 406.6 and 420.5 Mb, respectively.
Peterson et al. [7] proposed a method for the complementary task; they compute the number of reads needed to cover a given filtered library fraction based on the "super-island" assumption inherent in the Clarke-Carbon equation [11]. According to the above discussion, this is, in principle, a reasonable approach. However, their specific calculations are synonymous with a redundancy exceeding 4× (99% coverage), making their estimates for n too low. In fact, Fig. 2 suggests that edge effects will make 99% random coverage difficult to achieve for any filtered library.
Conclusion
The primary assumption associated with DNA processing models is that entities are distributed in an IID fashion. Because there is little in the broad spectrum of experimental data that corroborates this supposition [27], we must regard our results in the context of upper bounds of performance. Actual projects should generally fall somewhat short of predictions. Moreover, it can be difficult to a priori estimate input parameters, especially the number and average size of islands. Consequently, theoretical results for specific projects should be interpreted with these limitations in mind.
According to the trends discussed here, it is clear that if island size is sufficiently large compared to read length, the LWSI model will be sufficient for predicting a number of relevant parameters. However, with the exception of enrichment estimation discussed above, it does not appear that this will be the case for most projects. For example, we examined island sizes up to 12 kb, but edge effects were still noticeable for reads ~700 bp in length. It is unclear whether there are species whose average island length would be substantially larger than this. Moreover, there is an ongoing trend toward longer reads [28]. Coupled with the need to calculate island-specific parameters, we feel the model described here will play a role for filtered library projects analogous to the one already established by standard LW theory for conventional libraries.
We also suggest potential application of this model for other non-traditional sequencing scenarios. For example, there is increasing interest in sequencing ciliated protozoa [29,30]. The macronuclear genomes of such organisms consist of >20,000 distinct "nano-chromosomes", with an average length of less than 3,000 base pairs. Because most of these chromosome structures are too long to be traversed with end-sequences, it is likely that a shotgun approach will be necessary.
The observations made here have a number of practical implications for the planning and execution of future filtered library shotgun projects. In general, the progress realized when using standard "full-length" reads will be less than that of the equivalent WGS project. In many cases, this implies stopping at what are conventionally considered to be only moderate redundancies. For example, results shown in Figs 2, 3, and 4 suggest little is gained in sequencing 3 kb islands past about 5×. Likewise, they indicate that assemblies would have less sequence coverage and less contiguity as compared to equivalent WGS projects. Improved economy and performance of directed methods become commensurately more important.
The model establishes λ/σ as the primary parameter governing edge effects. By varying island size, we found that results for a given value of sequence depth improved as λ/σ decreases. The same effect can clearly be obtained by decreasing read length for a given island size. Pyro-sequencing platforms immediately suggest themselves as a good potential match for this application. For example, current effective read lengths of about 150 bp [31] imply λ/σ = 0.05 for 3 kb islands. Here, results would be roughly equivalent to what is shown for the 12 kb islands in Figs 2, 3, and 4 using full-length reads. That is, contiguity and sequence coverage would be much improved. Because islands correlate with low-copy sequence content, we would not expect reduced read lengths to substantially impede the assembly process.
Methods
This section describes the mathematical proofs for the theorems reported in the Results section. First, we define a nucleotide-based island coordinate system x ∈ {1, 2, 3, ..., σ} whose origin is the left-hand boundary (Fig. 7). Coordinate locations for sequencing reads refer to the starting location of the read, i.e. the position of its left-most base. Each read falls into one of three classifications.
Figure 7 Island coordinate system and nomenclature.
1. Domain Read: A read for which no overlapping read will extend past the right end of the island. The coordinate range is x ∈ {1, 2, 3,..., σ - 2(λ - 1)}.
2. Boundary Read: A read for which one or more overlapping reads can extend past the right boundary. The coordinate range is x ∈ {σ - 2(λ - 1) + 1,..., σ - λ}.
3. Terminal Read: A read that resides on the extreme right of the island. Its position is x = σ - λ + 1.
There are no read starting positions for x > σ - λ + 1. In LW theory, all reads are of the domain type because the target sequence is considered infinitely long. The additional concepts of boundary and terminal reads allow us to account for library gaps and edge effects in a structured fashion.
Proof of theorem 1
A library gap on an island is manifested by the presence of a terminal read, which can be placed in exactly one way. By Lemma 1, the associated probability is ξ (1). Considering this event over all i islands in conjunction with the IID assumption yields theorem 1.
Proof of theorem 2
This proof is based on the presumption 2λ ≤ σ + 1, which we anticipate would characterize most library filtering projects. That is, islands are long enough such that domain reads actually exist. For shorter islands, appropriate results can be derived by simply omitting the consideration of domain reads.
Let us define event Θx as the start of a contig of reads at position x on an island, where x is constrained according to the above definitions of the read types. Also, define the sub-events , whereby the contig is initiated by reads 1, 2,..., n, respectively. Now, , so that
As in LW theory, we may consider the sub-events to be mutually-exclusive of each other (Lemma 2), from which we find
This expression represents the probability that a contig starts at position x on an island. For cases of biological interest, n ≪ Π, as described in Lemma 2. If this condition is not met, one must instead utilize the full binomial placement model as discussed in Lemmas 1 and 2.
Following Lander and Waterman [10], we observe that a contig begins with the initiation of a "base read" at x and continues until no overlapping reads are detected. This event is denoted by Φx and can be taken to represent a gap associated with position x on the island. For domain reads of the type considered in LW theory, overlapping reads can be found along the entirety of the base read. Here,
where the overbar represents the complement of the specified event. In light of the IID assumption, the corresponding probability is
For boundary reads, the number of possible overlapping reads depends explicitly on the distance of the base read from the end of the island. Analysis reveals a similar expression, except where the power λ in the previous expression is replaced by σ - λ + 2 - x.
Asymptotic approximation can be applied to P (Φx) for domain reads according to the argument discussed in Lemma 2 for λ and the ratio nΠ-1. However, it is not valid for boundary reads because σ - λ + 2 - x is not sufficiently large, in general. Considering P (Φx) for every domain or boundary read position x on each one of the i islands in conjunction with the IID assumption then yields theorem 2.
Proof of theorem 3
This proof is based on the presumption 2λ ≤ σ, which we anticipate would characterize most library filtering projects. That is, there are no base positions that would be covered with complete certainty for any read that hits the island. For shorter islands, appropriate results can be derived in a similar fashion to that shown here.
According to Lemma 1, the probability P (Θx) of a clone traversing a specific position x on an island can be deduced by simply counting the number of ways a segment can cover this position. If x <λ, the left boundary constrains the position, giving exactly x successful placements. Likewise, symmetry dictates the same behavior at the right boundary, so that the number of placements is σ - x + 1 when x > π. For each of the remaining positions, no boundary constraints exist, so that there are λ successful placements. Therefore,
where again, function ξ is defined by Lemma 1. Considering P (Θx) for every position x on each one of the i islands in conjunction with the IID assumption then yields theorem 3.
For segment lengths λ > σ/2, probability is identical to the above for the first two cases, but their limits are changed to x <π and x > λ, respectively, and the last case becomes ξ (π). Expected value of coverage could then be found by similar algebraic operations.
Proof of theorem 4
The number of reads placed on an island does not depend upon position. Each read either hits a specific island with probability p = i-1, or it does not hit this island with = 1 - i-1. The process is binomial since the n reads are IID. However, as i and n are both large for cases of interest, it essentially behaves according to a Poisson distribution having a rate n/i.
Proof of theorem 5
By Thm. 4, the probability that an island is hit by at least one read is 1 - exp(-n/i). The expected number of islands hit is obtained by considering this case for all i islands. Since position is irrelevant, this process is equivalent to the classical occupancy problem having i bins. Feller [32] reports the asymptotic distribution.
Supporting lemmas
The following Lemmas represent simplifications of binomial processes that are valid for typical filtered genomic libraries, i.e. those having many islands. Scenarios in which the number of islands is small can readily revert to the underlying binomial descriptions.
Lemma 1. The probability that a particular event occurs on a specific island is
ξ (β) = 1 - e-β n/Π,
where β denotes the number of local read placements on the island associated with realizing the event.
Proof. Let Φj denote realization of the event on the island in question for a specific read j. We have Φj = Θ1 ∩ Θ2, where Θ 1 and Θ2 denote, respectively, that read j lands on the required island and that it instantiates the event. All islands are identical, so Θ2 does not depend on Θ1. If there are β placements that instantiate the event on an island, the probability is
which can be written more succinctly as P (Φj) = β/Π. The probability of not realizing the event for a specific read j is simply the complement .
Reads are of uniform length and are independent of one another, thus satisfying the IID assumption. Consequently, this scenario is binomial over the collection of n reads; each read either instantiates the event, or it does not. The probability of not realizing the event for any of the n reads is clearly
The probability of the main event itself is simply the probability that it is caused by one or more reads: .
To complete the proof, we must show that the asymptotic form is valid. The relevant functions expand as
and
which are clearly equivalent in an asymptotic sense as n becomes large. In the limit β → σ - λ + 1, all placements on an island instantiate the event, so that β/Π → i-1. This represents the worst case for the approximation's accuracy. For the typical filtered library, we expect i > 104, so that the exponential form would be valid for characteristic values of n. For example, after n = 104 reads the approximation error would be limited to a maximum value of about 0.005%.
Lemma 2. The start of a contig can be considered according to a mutually-exclusive read placement process having a probability n/Π.
Proof. Let denote the event where read j starts at position x and thereby instantiates a contig at that location. The probability of this event is clearly binomial; the read either starts a contig at x with probability 1/Π, or it does not with complementary probability 1 - 1/Π. The probability of at least one of the n reads starting a contig is given by Lemma 1 as ξ (1). This quantity expands as
Let us define ζ = λ/σ, so that the quotient n/Π can be recast as
Here, Δ = nλ (iσ)-1 is the conventional sequence redundancy, which is usually less than 10. Read lengths are typically λ > 500, while we anticipate ζ < 0.25 for most biologically-relevant cases. Also, σ-1 ≪ 1, making its contribution negligible. Consequently, n/Π is, at most, on the order of about 0.02. This implies the above expansion is well-approximated by its first term alone, with the maximum error being about 1% for the stated parameters. This one-term approximation is identical to what one obtains from a strict model of a mutually-exclusive starting process, i.e.
Authors' contributions
MCW and WBB conceived the model. MCW performed the mathematical derivation of the model and drafted the manuscript. WBB obtained and analyzed the maize comparison data and edited the draft manuscript. Both authors read and approved the final manuscript.
Acknowledgements
The authors gratefully acknowledge J. Bedell of Orion Genomics and K. Schubert of the Donald Danforth Plant Science Center for discussions and comments on the manuscript. This work was partially supported by grants from the National Human Genome Research Institute (HG003079) and the National Science Foundation (0221536).
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Feller W An Introduction to Probability Theory and Its Applications 1968 3 New York NY: John Wiley & Sons
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-2471622130410.1186/1471-2105-6-247SoftwarePhydbac "Gene Function Predictor" : a gene annotation tool based on genomic context analysis Enault François [email protected] Karsten [email protected] Jean-Michel [email protected] Structural and Genomic Information, CNRS – UPR 2589, 31 chemin Joseph Aiguier, 13009 Marseille, France2005 12 10 2005 6 247 247 2 6 2005 12 10 2005 Copyright © 2005 Enault et al; licensee BioMed Central Ltd.2005Enault et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The large amount of completely sequenced genomes allows genomic context analysis to predict reliable functional associations between prokaryotic proteins. Major methods rely on the fact that genes encoding physically interacting partners or members of shared metabolic pathways tend to be proximate on the genome, to evolve in a correlated manner and to be fused as a single sequence in another organism.
Results
The new "Gene Function Predictor", linked to the web server Phydbac proposes putative associations between Escherichia coli K-12 proteins derived from a combination of these methods. We show that associations made by this tool are more accurate than linkages found in the other established databases. Predicted assignments to GO categories, based on pre-existing functional annotations of associated proteins are also available. This new database currently holds 9,379 pairwise links at an expected success rate of at least 80%, the 6,466 functional predictions to GO terms derived from these links having a level of accuracy higher than 70%.
Conclusion
The "Gene Function Predictor" is an automatic tool that aims to help biologists by providing them hypothetical functional predictions out of genomic context characteristics. The "Gene Function predictor" is available at .
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Background
Annotating proteins of unknown biological function is still a major bottleneck in the exploitation of genomic information. The main approaches are all based on the recognition of sequence similarity, from which functional homology is inferred with various levels of confidence. Methods such as BLAST, PSI-BLAST [1] or Pfam [2] are used to automatically generate functional annotations to a sizable fraction of the genes in newly sequenced genomes. However, from 20% to 50% of genes [3] are still annotated as being of unknown function, either because they have no statistically significant matches in current databases or because they only match uncharacterized protein sequences from other organisms. To provide putative functional assignments to those proteins, comparative genomic approaches are now reaching beyond the simple recognition of sequence similarity [4-6]. The reliability of these new methods, often referred to as genome context analysis, is now steadily improving, due to the almost exponential increase in the number of fully sequenced genomes. They allow the detection of functionally linked proteins, either physically interacting partners or members of shared metabolic pathways or cellular processes. The functional association of proteins may cause their encoding genes (i) to be part of a shared transcriptonal unit (Operon or Gene Cluster method), [7-9] or to exhibit a chromosomal proximity conserved in several genomes (Gene Neighbor method) [10,11], (ii) to have evolved in a correlated manner (Phylogenetic Profiles method) [12] or (iii) to have fused as a single gene in another organism (Rosetta Stone method) [13,14].
Here we introduce the new "Gene Function Predictor" of our web software Phydbac [15] based on the results given by a combination of these non-homology based methods. This database proposes putative associations between Escherichia coli K-12 proteins as well as functional GO term predictions derived from these associations. A blast mode is also available to apply the method to any protein sequence. In this study, we first describe separate improvements to the three major genomic context methods. An integrated score combining their results is defined and shown to predict protein pairwise associations more accurately than the ones already proposed in established databases such as Predictome [16], Prolinks [17] and String [18]. We then take advantage of the pre-existing functional annotations of the putatively associated proteins to assign them to GO categories [19]. The "Gene Function Predictor" proved to be particularly useful for the «conserved hypothetical protein» subset, as shown on a specific example.
Implementation
This web tool is designed as a CGI script written in Perl running on an Apache web server. This script first retrieves genes through the process of the information entered into a HTML Form. A target gene can either be retrieved by its name or by the presence of a keyword in its annotation. The putative associations and functional predictions are then extracted by running a number of Perl scripts on a database of pre-computed blast hits and auxiliary information. Results for the query are then displayed through HTML pages. The "Gene Function Predictor" is accessible through any browser.
Results and discussion
Data sources and scoring
In this study, genomic context analysis is applied to the well annotated bacterium Escherichia coli K-12 (Figure 1). This analysis is performed using the 150 completely sequenced organisms available in Refseq, including 130 bacteria, 17 archaeal bacteria and 3 unicellular eukaryota. E. coli protein associations available in Phydbac's "Gene Function Predictor" are generated by three genomic methods : the phylogenetic profile, the colocalization and the Rosetta Stone methods. Improvements to these different methods and their implementation are described in the following.
Figure 1 Description of the methods used in the "Gene Function Predictor". The protein coding genes of our target organism E. coli are compared to the ORFs of 150 genomes. (A) The P score applied to E. coli protein phylogenetic profiles allows to identify protein pairs that evolved in a similar manner. For example, genes A and E are present in genomes 1, 2 and 150 and absent in genome 3. (B) The C score is associated to gene pairs nearby in, at least, one genome. This score is computed from the intergenic distances between E. coli genes and their respective homologs in all other genomes. The genes B and F (respectively red and green) are found only separated by 30 bp in genome 1 and by 5 bp in genome 3, resulting in a C score of 0.8 between those two genes. (C) The F score is computed for each domain fusion detected. In the example, domains of E. coli genes C and D are found fused in a gene of genome 3. (D) Significant P, C and F scores are combined in an integrated score. (E) Functional predictions are made out of the annotations of associated partners.
Consensus phylogenetic profiles (P)
It is established that proteins evolving in a correlated manner tend to participate in common metabolic pathways or constitute multi-molecular complexes. Using the simplest type of phylogenetic profile, the co-occurrence of genes is represented by a string of bits, each bit recording the presence or absence of an ortholog to a given gene in a genome [12]. In an earlier work [20], we proposed to replace this binary scale by continuous values derived from alignment scores.
Let Sab be the best Blastp bit score between a target protein a and all proteins of a bacteria b and saa the self-score of the a protein aligned with itself. Each point of the phylogenetic profile of a protein a is computed as : Rab = Sab/saa.
Each point of a profile is weighed proportionally to the length and quality of the corresponding alignment. Although this method was shown to improve on the binary method of Pellegrini et al. [12], the profiles are noisy. As we want no orthologs to be missed, even low sequence similarities are considered, bringing back a certain amount of false positives.
To improve their quality, profiles now use the information contained in the profiles of genes from other species. Introduced as a display feature in our web software Phydbac [15], Consensus Phylogenetic Profiles (CPP) are built from the profiles of a target gene and of its putative orthologs. The CPP of a gene has a non-zero score in a given column (corresponding to a bacterium) if more than half of its best matches in the other species has a match in this bacterium. The score of the profile for this column will then be the mean of the non-zero scores of the different putative orthologs with the corresponding bacteria. Figure 2 shows the profile of E. coli protein phoR, the ones of its best homologs in different organisms and the CPP of phoR built out of all those profiles. We note that the CPP of phoR is similar to its simple profile except at the columns corresponding to the two Neisseria meningitidis strains. Unlike phoR for which low sequence similarity matches are found in these strains, its best homologs do not exhibit any matches in these two organisms, suggesting that no orthologs of phoR are present in them.
Figure 2 Profiles of the E. coli protein phoR and of its best homologs in different organisms. The consensus profile (CPP) of phoR is derived from these profiles as described in the text.
From the CPP of the 4271 protein coding genes of E. coli, a pairwise P score is then computed. The P score is a correlation coefficient computed without the mean between each pair of profiles, profiles being N-dimensional vectors, with N the number of sequenced genomes (here N = 150). The profiles are stored in a matrix R where Rik is the value of the gene i profile at the column k corresponding to bacteria k. The score Pij reflecting the coevolution level between two genes i and j is then given by :
Pairs of gene products exhibiting the highest P scores are the most likely to be functionally linked.
Detection of co-localizations (C)
The identification of pairs of genes part of the same operon in a genome can also lead to their functional associations. Indeed, genes organized into operons, i.e. genes transcribed into a single mRNA, are co-regulated and tend to fill related roles in cellular processes of the organism. Such genes are identified either by using intergenic distances separating genes [8], by analyzing conserved chromosomal adjacency between genes in a set of genomes [9-11] or more recently by combining these two sources of information [21]. Because the assumption that genes separated by small intergenic distances are likely to belong to a shared operon is true in all prokaryotic organisms [8,22], the intergenic distances in all the genomes are more informative than conserved chromosomal adjacencies. Our score C is based on intergenic distances separating colocalized gene pairs across all the genomes. Two genes are said to be colocalized in a genome if these two genes and the genes between them on the chromosome are on the same strand and if all adjacent gene pairs of the string are separated by less than 300 bp [11]. In E. coli, more than 98% of the gene pairs being on an operon described in RegulonDB [23] are separated by less than this threshold of 300 bp. The distance associated to a colocalized gene pair is the maximal intergenic distance found between them. For example, for three adjacent genes A, B and C on the same strand, if A and B are separated by 5 bp and B and C by 75 bp, we consider A and C to be colocalized with a distance of 75 bp.
To avoid artefacts due to the presence of redundant strains and of evolutionary close species in the 150 genomes, we restrict our analysis to 87 groups of similar organisms, made on the basis of the multiple alignments of the 150 homologs of three conserved genes [15]. A pair of genes found colocalized in Xanthomonas campestris and in the two Xylella fastidiosa strains will only be considered as colocalized in the group containing these organisms, the minimal distance separating this couple in a genome of this group being recorded.
The colocalization score C reflects the degree of confidence in the fact that genes are colocalized because of functional relationships, ie that genes are part of an operon in a genome. The score between each gene i and j of a target genome is :
where Gij are the groups of genomes where i and j are colocalized and dij(g) the minimal distance in base pairs that separates i and j in a genome of the group g. The definition of C was derived from observed characteristics of colocalized genes. A colocalization score between two genes must always increase as groups in which these genes are co-localized are found. Our C score was built in order to verify this point. Indeed, C is equal to 1 minus a product of elements, each element, involving an intergenic distance, being comprised between 0.5 and 1. To calculate these elements, an exponential function is used because the information gained from an intergenic distance must not be proportional to the length of this distance. Different formulas were tested and this C definition is the one that gives the better results.
In contrast to the Operon or Gene Cluster method, our score C is able to detect gene pairs distant in E. coli that form an operon in other organisms (like genes B and F in Fig. 1). Unlike the Gene Neighbor method, it can detect operons present in only one organism (like genes A and E in Fig. 1). Of course, not all of the E. coli gene pairs are separated by less than 300 bp in at least one genome. Only 199,262 of the possible 9,118,580 gene couples of E. coli are found separated by less than 300 bp in at least one genome, with an average score C of 0.48. A C average of 0.87 is found when considering the 2219 pairs of genes present in the same operon described in RegulonDB [23].
Identification of gene fusion events (F)
Associations of genes can also be deduced with the Rosetta stone technique [13,14] by detecting gene fusion events. Two distinct genes of a given organism that are found fused as a continuous sequence (referred to as the Rosetta Stone sequence) in another genome tend to physically interact. Non-homologous proteins fused as a single sequence are identified with the aid of the Pfam protein domain database [2]. Rpsblast of all the Pfam domains against all the proteins of 150 genomes and E. coli were computed, using a threshold of significance of 10e-10 for the expectation value of the alignments. Two E. coli proteins are determined to be fused if at least one domain of each protein is found separately in a third protein of another organism. As domains are relatively short compared to protein sequences, we did not consider overlaps larger than 10 residues between the alignments of the two domains on the Rosetta Stone sequence. The presence of two domains in different proteins as well as on the same coding sequence is of course not enough to be sure that a real fusion event took place between genes coding these proteins. But as such domains are likely to be functionally linked, it is also true for the proteins in which the domains appear separately. A score F is deduced from the probability that two genes are found fused by chance in another single sequence described in [17]. This score F depends on the number of sequences with which the two domains considered exhibit a significant sequence similarity and the number of sequences in which these domains are found fused. The score F is computed for each of the 22,100 E.coli protein pairs for which a putative domain fusion is detected.
Evaluation and comparison between P, C and F and the integrated score
As the three scores P, C and F are based on different concepts, they are supposed to be independent and to provide different valuable information. To integrate them appropriately into a unique score, we have to scale them by their respective accuracy to predict genes that are functionally linked. As P, C and F are continuous scores, a list of ranked significant associations given by each approach allows to calculate the fraction of associations involving two genes annotated in the same COG category among associations linking COG-annotated genes [24].
This success rate also allows us to compare the quality of each score (Figure 3). First of all, we note that the information on coevolution is better retrieved when Consensus Profiles are used (P) compared to our previous simple profiles (P old). The increase of accuracy between P and P-old is higher by more than 30% for any number of predicted pairs. C gives even better results than P, with 15,600 predictions with an accuracy higher than 0.5 (12,800 for P). 1,743 of the 2,219 gene pairs in shared operons of RegulonDB [23] have a C score higher than the threshold corresponding to an accuracy of 0.5. The score F associates 5,500 different E. coli protein pairs with a success rate higher than 50%.
Figure 3 Cumulated accuracy for the different methods. The cumulated accuracy is the fraction of gene pairs associated by a method and being in the same COG category. The different curves represent this accuracy among the best associations for the P score based on Consensus Profiles, for the P-old based on simple profiles, for the score of colocalization C, for the F score detecting fusion events and for the integrated score S.
The success rate is used to establish normalized scores across the different approaches. This normalization procedure then allows the individual scores to be merged into an integrated score in a simple way :
Sij = 1 - [(1-Pij) × (1 - Cij) × (1 - Fij)]
where i and j are two genes and with Pij, Cij and Fij set to 0 when no significant score is found for i and j. The quality of predictions made with this integrated score S is significantly better than each of P, C and F on their own (figure 3). For the 10,000 best associations between COG -annotated genes given by each method, the score S has a cumulated accuracy 21% better than the score C, 30 % better than the score P and more than 60% better than the score F alone. For an expected success rate of at least 80 %, 9,379 pairwise associations are derived from the S score, involving 2,500 E. coli genes. A coverage of 70 % (2,975 of the 4,278 genes) is obtained when considering an accuracy of 70 %.
Comparative benchmarking of databases
There are three major databases of putative associations between prokaryotic genes derived from genomic context analysis : Predictome, String and Prolinks. Each one implements different methods with personal flavours. In Predictome [16], phylogenetic profiling, gene neighbor and domain fusion are implemented in their traditional way and applied to orthologous families of genes defined in COG. One of its major limitation is the absence of a quality score for each prediction. In the earlier releases of String [25], genomic analysis was also relying on COGs. A protein mode is now available, based on continuous phylogenetic profiling, gene neighbor, fusion, as well as experimental data and literature mining [18]. Prolinks [17] uses binary profiles not based on COG data. For pairwise alignments, the homology is considered significant if the e-value associated is lower than 10e-10. Text mining, gene fusion, gene neighbor as well as a gene cluster method are also implemented. For each method, they developed their own probabilistic score. Prolinks and String scale the different methods separately and then compute a confidence score.
To compare those three databases to Phydbac, the associations were downloaded from their respective web sites. As the accuracy of the putative links given by each database is tested against Gene Ontology data [19], we only keep associations involving GO-annotated genes. 18,760 different associations between GO-annotated genes are found in Predictome, 57,266 in String and 59,260 in Prolinks. For each database, each target gene annotated in at least a GO class has a certain number of GO-annotated genes associated to it. We select the same number of our best GO-annotated predictions involving this target gene and determine which database has the best accuracy for each gene (Figure 4). Associations given by our method more often imply genes belonging to the same GO category than associations of other databases.
Figure 4 Comparison of the databases. Comparison of the results given by Phydbac and those found in the three existing databases based on non-homology methods.
We can note that Predictome gives better results for only 10 % of the genes (74% for Phydbac). This point is not surprising as the release of Predictome is the oldest of the three databases. String is the database that gives the most different results to ours (only 11% of genes having similar results). As we have seen, additional information, different to genomic information, has recently been added and the gene cluster method is not used. In figure 4, we note that results of Phydbac are more accurate than those of String for 54% of the 2,907 GO-annotated genes that have at least one association in String. Our putative associations are also better than those found in Prolinks. For 46% of the 3,137 GO-annotated genes of Prolinks, putative associations predicted by Phydbac imply two genes of the same GO category more often than those found in Prolinks. A surprising result described in the Prolinks paper (Bowers et al. 2004) is the fact that the integration of their 5 methods do not give better results than their Gene Neighbor method on its own. Their final score for a gene couple is the maximum value found with the 5 methods. As we have seen, the different methods are supposed to give independent information, and although this is not strictly true, a combination of the different scores (as in String and Phydbac) works better.
Assignment to GO categories
In addition to putative associations, we developed an annotation procedure meant to assign genes to Gene Ontology categories [19]. GO provides structured classifications that cover several domains of molecular and cellular biology. Gene products are described throughout three non-overlapping domains : (i) Molecular Function describes activities at the molecular level, (ii) Biological Process describes biological goals accomplished by one or more molecular functions and (iii) Cellular Component describes locations at the level of subcellular structures and macromolecular complexes. GO can be viewed as a directed acyclic graph that represents a network in which each term may be a "child" of one or more "parents". For example, the function term "peptidyl-serine ADP-ribosylation", from the biological process vocabulary, is a child of both terms "protein amino acid ADP-ribosylation" and "peptidyl-serine modification".
In our annotation procedure, each term is considered as an independent class. For a target gene and for a fixed accuracy threshold for S, a certain number of genes are potentially linked to the target, associated to a total of t annotations. Each GO term A appears nA times in the t annotations (cases where A is a parent of one of the t annotations are also counted) and NA times in the total pool of the T annotations of E. coli genes. The probability to draw at least nA annotations of the GO term A or of child terms of A by chance out of t annotation is given by :
For each target gene, GO terms with a value for this probability lower than 10e-10 are considered as putative functional annotations. The same procedure is repeated for decreasing accuracy thresholds of S.
Considering E. coli genes already annotated with GO terms, 1,725 GO term predictions are derived from the 4,006 links with expected success rate of 90%. 80% of these 1,725 predictions are correct, i.e. already appear in the annotations of genes. Out of these 1,725 predictions, the 974 best, corresponding to a probability lower than 10e–13, have an accuracy greater than 85 %. When using links with an expected success rate of 80% (9,379 pairwise links), 70% of the 6,466 functional predictions are correctly inferred. Of course, predicted GO terms that do not appear in the gene's annotation cannot be considered systematically as false predictions. Annotated genes may have additional – yet unknown – functions or predictions may represent the gene function on another level. For example, yaeT, annotated in GO term as an "outer membrane"protein, which describes its location, is predicted to participate in lipid A biosynthesis and metabolism, which describes the biological process it may be involved in. For an accuracy threshold of 60%, 16,280 GO term predictions are made for more than 1,500 E. coli genes.
Web interface and example
The "Gene Function Predictor" emulates two main different modes of operation. In the first mode, the predictions can be made for any protein sequence pasted by the user in a similar manner to Plex [26]. In this Blast mode, the consensus profile of the given sequence is dynamically created and the most similar profiles are determined among the genes of the organisms processed in Phydbac. The conserved neighbors on the chromosomes are also determined by comparing the sequences found proximate to the pasted sequence in all organisms. Genes associated to the query by Rosetta Stone are identified by the presence of conserved domains in this sequence. If some associated partners are determined, an annotation procedure similar to the one described above is applied, even though all the partners do not come from the same organism. This mode of operation is useful for genes of organisms whose sequence is not complete or not public.
The second mode of operation of the "Gene Function Predictor" is a database gathering the results described in the study for processed organisms. Currently limited to E. coli, this mode will be extended to all fully sequenced micro-organisms. E. coli genes can be retrieved by their names or by the presence of a keyword in their annotations. For any gene queried, its most probable association partners as well as its significant GO term predictions are displayed on a single page. The confidence we have in the different predictions is depicted through keywords and colours. For example (Figure 5), yjgI, a protein annotated as "putative oxidoreductase"is associated to a reductase (fabG) and to other putative oxidoreductase (ucpA, ygfF) by coevolution (P) and 4 of its 7 best associated partners are acyl-carrier proteins and are significantely linked with yjgI by each of the three methods (P, C and F). As acyl carrier proteins are fundamental components of fatty acid biosynthesis, the best GO term predicted for yjgI is "fatty-acid synthase activity"and "fatty-acid biosynthesis"(Figure 5). The specific biochemical activity of yjgI cannot be deduced from these results, but like its most probable partners, yjgI is likely to be involved in fatty acid synthesis. Furthermore, acyl carrier protein as well as acyl carrier protein synthase are known to be essential for E. coli viability. Maybe this is also the case for yjgI. For such proteins annotated as "putative ..."or uncharacterized proteins, our tool provides hypothetical functions, either new or on another level of description. The "Function Predictor" is fully linked with the software Phydbac, as a closer analysis and additional information can be retrieved through the display of the profiles, of the conserved gene neighbors and of the gene fusion.
Figure 5 Typical output of the « Gene Function Predictor ». Predictions for the E. coli gene yjgI. Significant predicted GO terms are displayed as well as the associations from which these predictions are derived.
Conclusion
Although the huge amount of data provided in the past few years from genome sequencing allows a large spectrum of research axes, the most serious problem of modern bioinformatics is still the quality and degree of completeness of the annotation of sequenced genomes [3]. Earlier versions of Phydbac made a step in this direction by providing an interactive resource on prokaryotic proteins and on their context that may help microbiologists. But the different sources of information contained in genomic data may not always be trivially extracted by hand. The new "Gene Function Predictor"integrates the different concepts to automatically predict putative functions for E. coli genes.
We have shown that the integrated score, from which the putative pairwise associations are derived, gives better results than any intermediate approach on its own. We also compared our results to the best associations found in major databases based on the same concepts. Our protein linkages proved to be more accurate.
GO assignments were also benchmarked and are highlighted with distinct colors when displayed on the web. As GO is an annotation standard, the same procedure can be computed for any prokaryotic organism. A future version of the "Gene Function predictor", currently limited to E. coli, will be extended to all fully sequenced micro-organisms, even though a Blast mode of operation is already available.
Availability and requirements
Project name: Phydbac "Gene Function Predictor"
Project home page:
Operating system(s): Web server
Programming language: Perl and HTML
Authors' contributions
FE and KS analyzed the data and designed the methodology. FE developed the programs, the web interface and wrote the manuscript. KS contributed with ideas on overall design, feature requirements, and implementation. JMC coordinated the research and JMC and KS assisted with drafting the manuscript.
Acknowledgements
The research reported here was supported in part by the french Ministry of Education and Research and by the french National Center for Scientific Research (CNRS).
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Salgado H Gama-Castro S Martinez-Antonio A Diaz-Peredo E Sanchez-Solano F Peralta-Gil M Garcia-Alonso D Jimenez-Jacinto V Santos-Zavaleta A Bonavides-Martinez C Collado-Vides J RegulonDB (version 4.0): transcriptional regulation, operon organization and growth conditions in Escherichia coli K-12 Nucleic Acids Res 2004 32 D303 6 14681419 10.1093/nar/gkh140
Tatusov RL Fedorova ND Jackson JD Jacobs AR Kiryutin B Koonin EV Krylov DM Mazumder R Mekhedov SL Nikolskaya AN Rao BS Smirnov S Sverdlov AV Vasudevan S Wolf YI Yin JJ Natale DA The COG database: an updated version includes eukaryotes BMC Bioinformatics 2003 4 41 12969510 10.1186/1471-2105-4-41
von Mering C Huynen M Jaeggi D Schmidt S Bork P Snel B STRING: a database of predicted functional associations between proteins Nucleic Acids Res 2003 31 258 61 12519996 10.1093/nar/gkg034
Date SV Marcotte EM Protein function prediction using the Protein Link Explorer (PLEX) Bioinformatics 2005 21 2558 9 15701682 10.1093/bioinformatics/bti313
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BMC BioinformaticsBMC Bioinformatics1471-2105BioMed Central London 1471-2105-6-2541622568210.1186/1471-2105-6-254Research ArticlePrediction of β-barrel membrane proteins by searching for restricted domains Mirus Oliver [email protected] Enrico [email protected] Botanisches Institut der Ludwig-Maximilians-Universität München, Menzinger Str. 67, 80638 München, Germany2005 14 10 2005 6 254 254 20 6 2005 14 10 2005 Copyright © 2005 Mirus and Schleiff; licensee BioMed Central Ltd.2005Mirus and Schleiff; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The identification of β-barrel membrane proteins out of a genomic/proteomic background is one of the rapidly developing fields in bioinformatics. Our main goal is the prediction of such proteins in genome/proteome wide analyses.
Results
For the prediction of β-barrel membrane proteins within prokaryotic proteomes a set of parameters was developed. We have focused on a procedure with a low false positive rate beside a procedure with lowest false prediction rate to obtain a high certainty for the predicted sequences. We demonstrate that the discrimination between β-barrel membrane proteins and other proteins is improved by analyzing a length limited region. The developed set of parameters is applied to the proteome of E. coli and the results are compared to four other described procedures.
Conclusion
Analyzing the β-barrel membrane proteins revealed the presence of a defined membrane inserted β-barrel region. This information can now be used to refine other prediction programs as well. So far, all tested programs fail to predict outer membrane proteins in the proteome of the prokaryote E. coli with high reliability. However, the reliability of the prediction is improved significantly by a combinatory approach of several programs. The consequences and usability of the developed scores are discussed.
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Background
Genomes of numerous organisms are sequenced. Computer-assisted assignment of coding regions of the organism of interest is the first important step for the understanding of the complex proteomic network [1]. Even though the quality of such predictions will be satisfying in future, the knowledge of the sequences of the gene products alone will not provide insight into their function or localization in the cell. In addition, the emphasis has switched from the study of individual molecules to a large-scale, high-throughput examination of genes and gene products of an organism with the aim of assigning their functions [2] and placing them into the complex biochemical networks. One kind of information comes from the structural classification of gene products. Since genome and proteome projects result in a rapid increase of information, the biochemical analysis has to be accomplished by in silico predictions [3]. One of the central questions is the localization of proteins since up to 50% of the proteins of a cell have to traverse at least one membrane in order to reach their place of function within organellar compartments [4]. In the past, several prediction programs have been developed for this purpose [5]. However, the analysis of the intracellular localization of a protein is not only limited to the question to which organelle the protein is targeted. One important functional aspect is the distribution of the protein within this cell compartment. For some sub-organellar compartments like thylakoids predictions can be performed based on the targeting signal [6].
However, to date no differentiation of the signal is found for most sub-organellar localizations. So far, various approaches exist to identify helical transmembrane proteins [7,8]. More recently, however, the focus was shifted slightly to include the prediction of β-barrel membrane proteins. Initially, structure prediction was applied with reasonable success when proteins already known to form β-barrel structures were modeled [9]. Now, four alternative directions are used in order to newly identify β-barrel proteins out of a genomic/proteomic data set. In the first approach, sequence profile based HMMs for predicting β-barrel membrane proteins were developed [10-12]. The second methodology is based on the alternating hydrophobicity as a measure for β-barrel transmembrane segments [13]. Thirdly, the structural data of the β-barrel membrane proteins were statistically analyzed and certain criteria developed for a linear prediction [14,15]. The fourth methodology is based on a modified k-nearest neighbor algorithm of the whole sequence amino acid composition [16,17]. Recently, the combination of several independent procedures for β-barrel membrane protein prediction [18,19] or their combination with other procedures, e.g. signal sequence prediction [15,19], was employed to improve the prediction quality.
To evaluate the performance of the developed procedures, test pools are commonly used to derive parameters that discriminate proteins of interest from those of structurally different classes. To avoid an overrepresentation of certain protein families, sequences are removed until each pair of proteins in the pool shares a degree of identity below a certain user defined threshold. Several algorithms have been published to solve this global optimization problem [e.g. [20-22]]. Based on such test pools a comparison of the above mentioned strategies revealed a differential behavior. For example, Deng and co-workers [23] demonstrated that the linear predictor has a very low false positive but a high false negative rate. In contrast, a broader comparison of the predictors performed by Bagos and co-workers [24] manifested that the different predictors perform with a similar quality of about 25% false prediction.
We now improved the linear prediction by implementing new parameters and alterations of the previously established parameters based on test pools to increase the reliability and to avoid manual selection. Here, our main goals were to maintain a very low false positive rate and to reduce the high false negative rate of about 51% as reported by Deng and co-workers [23] for the original prediction method by Wimley [14]. We present parameters for β-barrel membrane protein identification and their prediction performance on the proteome of the prokaryote E. coli.
Results
First, the published set of parameters (Fig. 1A) [14,15] was used to analyze proteomic data. The parameter set is defined as following (Table 1): the statistical values of the probability for an amino acid to be present in either the lipid tail or head group region and facing the membrane or channel interior in membrane-inserted β-strands were taken from Wimley [14]. The β-strand length for the calculation of the exact β-strand score (EBSS, see Methods) and for the hairpin score (HPS) was chosen to be 10 amino acids following the original argumentation [14]. The loop length for calculation of the HPS was set to cover the range from the initial strand up to 14 amino acids distance. Previously, a minimal loop length of four [14] or five amino acids [15] was considered. For the calculation of the β-barrel score (BBS) of a protein the selection criterion for the HPS value was set to >6.0. For the calculation of the β-strand number (BSN) all independent EBSS peaks >2.0 were counted. For the final selection of β-barrel proteins, all proteins with a BBS of at least 0.7 and all proteins with a BSN above 13 were collected [15]. For comparison, the amount of sequences predicted to be β-barrel membrane proteins of the E. coli proteome using this parameter set was analyzed. Employing the original proposed set – selecting all proteins with a BBS greater than or equal to 1.0 – about ~4% proteins of the E. coli proteome were selected [14]. Applying the modified set [15], about 12.2% of all sequences of the E. coli proteome were predicted to form a membrane inserted β-barrel. The larger number observed with the new criteria might represent an increase of the false positive prediction. Especially the introduction of the BSN criterion, even though essential for a reduction of the false negative rate of β-barrel membrane protein selection in eukaryotic background, revealed the prediction of soluble proteins [15]. As a consequence, we have focused on the BBS and BSN to refine the prediction procedure without altering the calculation of the EBSS.
Figure 1 The selection criteria. (A) Schematic view of the scores for prediction. (B) The amount of sequences in all test pools with 0 or 1 transmembrane helix (TMH) predicted by TMHMM. (C) The amount of sequences in the pools with NOM proteins (lane 1) greater than 79 amino acids (lane 2) and with less than 2 predicted transmembrane helices (lane 3).
Table 1 Summary of the parameters for β-barrel prediction
scorea definition of the parameterb value usedc
EBSS β-strand length 10 aa
Core region 4 aa
BSNd Number of individual peaks EBSS >2.0
β-strand length 10 aa
HPS β-strand length 10 aa
Loop length 0–14 aad
BBS HPS cut off >6.0
BBS275d HPS cut off >6.0
Sliding window 275 aa
BSHS225d EBSS cut off for strands >2.0
Sliding window 225 aa
For detailed descriptions see text. a ... The different intermediate scores necessary for final calculation of the β-barrel score; b ... the scores are calculated by various physicochemical assumptions; c ... the parameters for the different scores were used as suggested by Wimley, if not stated differently [14]; d ... described in here
Preceding parameters for selection
Three global rules were defined for all subsequent predictions. First, at least 8 transmembrane β-strands [25] are required to form a β-barrel embedded in a lipid bilayer. In general, the average length of a single transmembrane β-strand is 10 amino acids [14]. Therefore, a membrane inserted β-barrel contains at least 80 amino acids and consequently the first cut off defined is a rejection of all sequences below 80 amino acids (Fig. 1C, Table 2). Second, we found that a pre- or post-selection by TMHMM [26] improves the performance. Previously was suggested, that two predicted transmembrane helices are an indication of a helical membrane protein [27]. Therefore, sequences with more than one predicted transmembrane helix are considered as helical anchored and rejected from the β-barrel prediction (Fig. 1B and 1C; Table 2). Here, the reliability of this step is defined by the false positive rate of TMHMM in regard to β-barrel membrane proteins. However, screening the PSort OM (outer membrane) test pool, PilQ, probably a β-barrel membrane protein involved in the assembly or modification of pili [28], is – besides two small cysteine-rich proteins – the only protein with more than one predicted transmembrane helix. Third, in order to select a sequence as a β-barrel membrane protein all scores defined in the following have to be above zero.
Table 2 The new parameter set for linear prediction
Parameter olda newb newc newd
aa - >79
TMHMM <1 <2
BSN >13 >10
BSN/aa - >0.026
BBS275 - >2.5 >1.35 >1.35
BSHS225 - >0.12 >0.11 >0.04
BBS >0.7 - - -
Shown are the parameters used in here for linear prediction (new). They are compared to previously used parameters (old). a ...values as previously described [15], b ...values for a prediction with 0% false positive rate (BBS275 OR BSHS225), c ...values for prediction with lowest false prediction rate considering independent selection by BBS275 and BSHS225 (OR), d ...values for prediction with lowest false prediction rate considering dependent selection by BBS275 and BSHS225 (AND).
Control of the incorporated β-strand number
One of the selection criteria for membrane β-barrel proteins was based on the β-strand number (BSN) of the proteins [15]. The previous BSN was calculated by selecting each individual region with EBSS values above 2.0. Hence, in a stretch of 10 amino acids considered as β-strand several counts can exist if values above 2.0 are separated by values equal or less than 2.0. We changed this algorithm as follows. The first predicted strand now starts at the amino acid with the highest EBSS. The preceding and succeeding nine amino acids are excluded from the search for the highest EBSS in the remaining values to account as starting amino acid of another strand. The β-strand selection procedure stops when no EBSS above 2.0 is left or all amino acids have been assigned to β-strand or pre-β-strand regions.
Hence, the number of counted strands is reduced in prokaryotic sequences (Fig. 2A). In addition, analyzing the BSN in regard to the sequence length of the proteins revealed a clear dependence on the amino acid length (Fig. 2B). For most of the sequences of the E. coli proteome (Fig. 2A, grey line, about 89%) at least one strand is proposed documenting that a selection by strand appearance alone is not possible. This might be understood as ~2% (2.001*1011) of all possible amino acid combinations (1.024*1013) analyzing a 10 amino acid window for the EBSS calculation [14] lead to a value above 2.0. Assuming a random distribution within an amino acid sequence, one peak exists about every 60 amino acids. Taking the previous result [15] suggests an even higher number of selected strands leading to the over-representation of large proteins in the first selection, which had to be excluded manually. Analyzing the ratio between BSN and sequence length of the proteins revealed that for most of the sequences of the E. coli proteome (56%) one to three membrane inserted β-strands per 200 amino acids are predicted. As the amino acids within sequences are not entirely randomly distributed, the amino acid stretch is found to be a little longer than in the statistical calculation. However, the result is in line with the discussion above and further documents that BSN selection should be controlled by the statistical occurrence of β-strands.
Figure 2 The BSN selection. (A) The relationship between BSN calculated by the old and new procedure is shown for the sequences of E. coli (circle, bottom x-axis). The percentage of sequences with a certain BSN is shown as line plot (top x-axis). (B) The sequence length (in amino acids) dependence of the new BSN for sequences of E. coli is shown. (C-D) Sequences with a BSN value above 6 (solid), 8 (dashed), 10 (dashed-dotted) or 12 (dotted) were selected from the PDB (C) or PSort (D) pools. Subsequently, for the generated sequences pools the percentage of false positive selected sequences from NOM protein pools (black lines) and the false negative selected sequences from OM protein pools (grey lines) in relation to the BSN/aa cut off was determined. (E) The numbers of structurally determined strands and of predicted strands are shown; the line indicates a similar detection value. (F) The amount of strands predicted at identical position (maximum 1 amino acid mismatch; identical), of strands predicted at identical or overlapping position (maximum 5 amino acids mismatch; overlap) and the amount of false negative and false positive predicted strands (false) is shown.
To establish the selection criteria, we analyzed several test pools (described in Methods). First, the percentage of selected sequences from the sequence pools containing non-barrel proteins (Fig. 2C,D, black lines) with different BSN cut offs in relation to the BSN/aa cut off was determined. For these proteins, a BSN selection cut off of 10 in combination with a BSN/aa cut off of 0.026 results in a 0% false positive selection (Fig. 2C,D). This corresponds to one peak of the EBSS above 2.0 every 40 amino acids, which is above the calculated statistical expected frequency (see above). Previously, the existence of at least one transmembrane α-helix per 100 amino acids was defined as cut off for helical transporters [29]. Comparing the length of the β-strand (10 amino acids on average) and of the membrane inserted helix (20–24 amino acids) as well as the number of the inserted membrane segments (statistically membrane β-barrels contain at least twice as many membrane inserted segments compared to helical transporters) supports the defined BSN/aa score of 0.026. This set of parameters leads to a selection of all sequences in the PDB pool of β-barrel transmembrane proteins (Fig. 2C, grey). For the sequences in the PSort pool of OM proteins a false negative rate of at least 43% is achieved (Fig. 2D, grey dotted). However, the further selection by BBS reduces the false negative rate as described below.
We next analyzed whether the new algorithm for BSN calculation could be used for the generation of topological models of the analyzed proteins. As already discussed above, an over prediction of strands is obtained, especially for larger proteins (Fig. 2E). A detailed analysis of the strands predicted (Fig. 2F) revealed a 68% identical positioning allowing one amino acid mismatch and 80% overlapping positioning of the strands requiring at least five amino acids overlap between structural determined and predicted strands. However, the rate of false positioned strands (false negative and false positive selected strands) is 45%. This analysis suggests that the positioning is not as much the problem as the over prediction and the prediction should be combined with the analysis of other physicochemical parameters.
Development of a new criterion based on the localization of the pore-forming domain
Detailed analysis and topological modeling revealed that the pore-forming regions are mostly located within a compact domain (Fig. 3A). Prokaryotic pore-forming proteins/domains are typically of a size between 30 and 35 kDa. The topological models based on the solved structures of OmpF and Nalp are shown as examples (Fig. 3A). Guided by this observation, the scores were now calculated for a defined region of the sequences. We used different scanning windows starting with 75 amino acids as it is below the smallest possible pore unit as discussed above and incremented the window size in steps of 25 amino acids. This window is subsequently moved across the protein and the highest calculated score was selected. For sequences with less amino acids than the window size, the BBS value of the entire sequence is considered. The false positive rates for the combined pools of the non-β-barrel proteins and the false negative rates for the combined pools of β-barrel proteins for each BBS calculation window for different BBS-x (x reflects window size) cut off values were calculated.
Figure 3 Analysis of the BBS and BSHS. (A) The β-strand locations of a N. meningitidis (NalP) and an E. coli (OmpF) OM protein are shown. The window for calculating the BBS or a domain based BBS (BBS-x) is indicated. (B-C) The false prediction rate for BBS-x (B) or BSHS-x (C) calculation using different amino acid windows and different cut off scores is shown. The regions with the lowest false prediction rates (black) for the three times weighted pool of the NOM proteins is shown. (D) The percentage of sequences above a certain threshold value of BBS275 minus BBS is shown for the sequences of E. coli.
In vivo, a ratio between soluble and helical membrane proteins of at least 10:1 is expected [27]. It is further reasonable to assume that cells do not contain more β-barrel membrane proteins compared to helical membrane proteins. Indeed, other publications discuss a β-barrel membrane protein content within the entire proteome of 2 to 4% [12,14]. However, our pools represent a ratio of 3.8:1 of NOM (non-outer membrane) to OM proteins. To match the proteomic situation, the false positive prediction rate of the NOM proteins was weighted three times higher than the false negative prediction rate of the OM proteins (Fig. 3B). Hence, a region with lowest false prediction in windows of 250 to 375 amino acids and a BBS-x score of 0.6 to 1.0 could be obtained (Fig. 3B). The lowest false prediction rate was achieved utilizing a window of 275 amino acids with a cut off value of 0.8. Therefore, for the subsequent analysis the BBS is replaced by the BBS in a 275 amino acid window (BBS275). Analysis of the score performance when applied to sequences from E. coli (Fig. 3D) shows, that about 70% of all sequences have a smaller BBS275 compared to the old BBS even though the highest value obtained for BBS275 of E. coli sequences is similar to the highest BBS value (not shown).
Guided by the development and performance of the new BSN score, we developed and analyzed a new score taking into account the alternating hydrophobicity for each predicted strand. Here, for each predicted transmembrane β-strand its alternating hydrophobicity according to equation 1 (E1) was calculated and multiplied with its EBSS value (E2). The final score BSHS (β-strand based hydrophobicity score) is calculated according to equation 3 (E3). The analysis of the performance of the score was performed as described for BBS275. Here, we identified a window of 225 amino acids as best performing (Fig. 3C). This is in line with a homo-oligomeric complex formation of most of the β-barrel membrane proteins, since strands on the protein-protein interface do not necessarily reveal an alternating hydrophobicity as the strands involved in complex formation are not exposed to the membrane lipids [30].
Development of scores for the linear predictor
After development of three scores for the linear predictor (BSN, BSHS225 and BBS275) we went on to establish selection procedures. They include the three discussed preceding steps by size, TMHMM [26] prediction and score analysis as discussed above.
First, scores for the selection with a low false positive rate had to be established. Hence, the cut offs of BSN and BSN/aa, 10 and 0.026, warrant a low false positive selection according to the analysis of the test pools (Fig. 2C,D; Table 2). Analyzing the BBS275 and BSHS225 distribution of the NOM proteins in the test pools revealed cut off values of 2.5 and 0.12 (independent selection, BBS275 OR BSHS225, Fig. 4A, Table 2), respectively. This procedure selects 62.5% of the OM proteins of the test pools and therefore, the false negative prediction rate is 37.5%.
Figure 4 Score definition for the linear predictor. (A) The false positive rate for the NOM protein pool in dependence on the BBS275 cut off (grey) or BSHS225 cut off (black) was calculated. (B, C) The false positive selection rate for the NOM protein pool and the false negative rate of the OM protein pool was calculated for a sliding window for BBS275 and BSHS225 considering BSN>10 and BSN/aa>0.026 as preselection rule. The false prediction rate was calculated using a three times higher weight of the false positive rate of NOM proteins. In (B) the false prediction rates of the individual selection by BBS275 and BSHS225 is shown. In (C) the false prediction rate of the dependent selection by BBS275 and BSHS225 is shown.
However, a 0% false positive predictor does not perform with a low false negative prediction rate. We therefore went on to derive scores for the lowest false prediction rate as well. Since the BSN algorithm leads to an over-prediction of strands, we considered the BSN:BSN/aa selection as an initial step and did not alter the cut off values for selection. Subsequently, the selection by the two scores was performed individually by each score (OR selection) or in combination of both scores (AND selection). Again, we weighted the false positive rate of the NOM pools three times higher as the false negative rate of the OM proteins for the discussed reason. Analyzing the selection performance by the individual BBS275 and BSHS225 (Fig. 4B, Table 2) revealed a score cut off combination of 1.35 and 0.11, respectively. BBS275 and BSHS225 in combination (Fig. 4C, Table 2) result in cut off values of 1.35 and 0.04, respectively. For both procedures a false negative rate of 27.5% and a false positive rate of about 1.2% were obtained based on the analyzed test pools.
Comparison of predictors applied to proteome wide prediction
To further confirm the quality of the developed cut off values we analyzed their performance by prediction of β-barrel proteins from the prokaryotic E. coli proteome. Here, 108, 160 or 150 sequences were selected by the cut off values defined for 0%, OR or AND approach. This accounts for 2.1%, 3.1% or 2.9% of the entire proteome, respectively (Fig. 5A). For 83/111/106 sequences (0%, OR, AND) a (proposed) function could be assigned (Fig. 5B). Hence, we found 15/32/27 (0%, OR, AND) sequences not encoding for OM proteins (Fig. 5B). Interestingly, most of the selected NOM proteins are secreted proteins or proteins of the periplasmic space (Fig. 5B, white section). Assuming a similar distribution of the localization of the hypothetical proteins as found for the annotated sequences, we obtain a false positive rate of 18% for the 0% selection, of 29% for the OR procedure and of 26% for the AND procedure (Fig. 5C).
Figure 5 Identification of β-barrel protein sequences from the E. coli proteome. (A) Sequences were selected from the E. coli proteome by the three parameter sets developed (Table 2). The percentage of selected sequences in comparison to the proteome size is shown (bars 1–3). Also shown are the percentage of sequences selected by MCMBB (bar 4), MCMBB filtered by TMHMM (bar 5, MCMBB*), by BOMP (bar 6; please note, that only two sequences were selected by BOMP with αTM >1 according to TMHMM), by TMB-Hunt, BBTM protein score >0 and E-value <1 (TMB-Hunt°, bar 7), by TMB-Hunt, BBTM protein score >0 and E-value <1 controlled by TMHMM (TMB-HUNT°*, bar 8) and by the global procedure (bar 9). (B) The sequences selected by the three procedures proposed in here were analyzed for known or assigned function or localization. The percentage of the sequences either classified as hypothetical, outer membrane, extra-cellular or soluble intracellular is shown. (C) The false positive rate for the three in here generated sequence pools (bars 1–3), for the sequence pool generated by MCMBB (bar 4), by MCMBB controlled by TMHMM (bar 5), by BOMP (bar 6), by TMB-Hunt, BBTM protein score >0 and E-value <1 (TMB-Hunt°, bar 7) or by TMB-Hunt, BBTM protein score >0 and E-value <1, controlled by TMHMM (TMB-Hunt°*, bar 8) is shown.
To achieve an impression of the performance quality, we compared our selection with the performance of MCMBB [31], BOMP [19], TMB-Hunt [16,17] and a predictor just based on the global amino acid distribution of β-barrel proteins [32]. MCMBB selects 10% of the E. coli proteome (Fig. 5A, MCMBB, 565 sequences). Application of the pre- or post-selection by TMHMM (see above) revealed only a slight reduction of the selected pool (Fig. 5A, MCMBB*, 530 sequences). For both selections we found a very high false positive rate of about 70% (Fig. 5C). Interestingly, for the predictor based on the global amino acid composition an even higher number of sequences was selected (Fig. 5A, Global), which was not drastically altered when post-screened with TMHMM (not shown). Even though it was estimated that about 30% of all proteins are helical membrane proteins [27], it is not considered to be likely that more than 10% of all proteins are β-barrel membrane proteins as discussed above. Therefore, these results raise the question, how reliable scores based on prediction performance on test pools are when transferred to proteome wide prediction.
Using BOMP (Fig. 5A) or TMB-Hunt controlled by the E-value (Fig. 5A, TMB-Hunt°) results in a similar pool size compared to the 0% selection established in here. At default settings BOMP selected 2.23% of the E. coli proteome (Fig. 5A, BOMP) with a false positive rate of 26.4% (Fig. 5C, BOMP) and only two proteins with more than one transmembrane helix according to TMHMM prediction (not shown). TMB-Hunt predicted 1.9% of the E. coli proteome as integral outer membrane proteins (Fig. 5A, TMB-Hunt°) with a false positive rate of 24.1% when requiring both a BBTM protein score >0 and an E-value <1 (Fig. 5C, TMB-Hunt°). A post-selection by TMHMM reduced the ratio of predicted sequences to 1.8% (Fig. 5A, TMB-Hunt°*) and the false positive rate to 19.2% (Fig. 5C, TMB-Hunt°*).
Previously, the combination of several predictors was suggested to improve the selection reliability [18,19]. This strategy allows an increase of the prediction quality as tested on proteomic data of the OM proteome of Nostoc sp. PCC7120 [18]. We subsequently analyzed the overlap of our procedures with the output of the other programs. The amount of sequences selected in the overlap of our selection and that of the other programs depends on the size of the selected sequence pools by the individual programs (Fig. 6A). Therefore, this combinatory approach revealed the most sequences in combination with MCMBB and the least number of sequences in combination with TMB-Hunt after E-value selection (Fig. 6A). To see, whether an improvement of the selection quality was achieved, we have analyzed the false positive rate after the combinatory approach. The false positive rate is dependent on the number of sequences in the selected pool (Fig. 6B). Using BOMP, 91 sequences (OR) and 90 sequences (AND) were selected in combination with our procedure showing a false positive rate of 9.6 and 8.6%, respectively (Fig 6A and 6B, light grey bar). The lowest false positive rate of about 6% out of 60 selected sequences was achieved combining our AND or OR method with TMB-Hunt (Fig. 6B, dark grey bar). We next analyzed, whether the same result would be obtained increasing the threshold of BOMP or TMB-Hunt. Analyzing the overlap of the BOMP and the AND selection, we discovered that only sequences of the rank 1–3 were omitted (Fig. 6C). The integral β-barrel score [19] of these proteins is rather low (rank 1 and 3) or even below threshold (rank 2, proteins are only selected by pattern match [19]).
Figure 6 The performance of the combinatory approach. (A) The percentage of sequences selected from the E. coli proteome by our three methods in combination with MCMBB (black bar), BOMP (light grey bar) and TMB-Hunt, BBTM protein score >0 and E-value <1 (dark grey bar) is given. (B) The false positive rate for combinatory approach performed as under (A) is shown. (C) The percentage of sequences selected by BOMP and our AND selection were analyzed in comparison to the BOMP selection sorted according to the BOMP rank (BR) assigned (top panel, grey). The percentage of the (putative) outer membrane β-barrel proteins (black bar) and (putative) non-outer membrane β-barrel proteins (white bar) in relation to the total amount of rejected sequences is given on the bottom. (D, E) The percentage of sequences selected by TMB-Hunt° and our AND selection were analyzed in comparison to the TMB-Hunt° selection sorted according to the BB score (D) or E-value (E) assigned ({explained in [17]} grey). In (E), the percentage of the (putative) outer membrane β-barrel proteins (black bar) and (putative) non-outer membrane β-barrel proteins (white bar) in relation to the total amount of rejected sequences sorted according to the E-value is given on the bottom. (F) The percentage of the E. coli proteome selected by the combinatory approach between TMB-Hunt° & BOMP, TMB-Hunt° & MCMBB, and BOMP & MCMBB is given on the left side. The right side shows the false positive rate as explained in Fig. 5C.
However, simply rejecting all sequences of rank 1–3 from the BOMP selection would not reveal the same result as the overlap procedure. Furthermore, analyzing the rejected sequences we found that most of the sequences rejected from the BOMP prediction are indeed non-β-barrel outer membrane proteins (Fig. 6C, bottom, white). Analyzing the overlap between TMB-Hunt and our AND prediction shows that the BB score [17] does not show any clear preference for rejection (Fig. 6D), whereas all sequences with an E-value above 0.8 were rejected (Fig. 6E). Interestingly, sequences with very low E-values were rejected as well (Fig. 6E). Analysis of the rejected sequences shows that again mostly non-β-barrel proteins are rejected although the amount of β-barrel proteins removed from the selected pool seems to be increased. Finally, we went on to compare the combinatory approach including our predictor with the combinatory approach among the other programs. Again, utilizing MCMBB resulted in a larger number of selected sequences than the combination of TMB-Hunt and BOMP (Fig. 6F, left). However, to our surprise, the false positive rate was not significantly changed in comparison to the individual programs (compare Fig. 6F right and Fig. 5C). This might be explained as all other programs analyze the entire sequence as such, whereas our prediction is based on a defined region of the sequence.
Summarizing, the combination of our procedure with other predictors increased the quality of the performance. However, this improvement is only achieved by a consensus approach of a domain and a full length sequence based predictor.
Conclusion
The aim of the presented work was to develop better tools or rules for the prediction of β-barrel membrane proteins. In a recent proposal [15] we obtained a significant false prediction of soluble proteins. First, we went on to optimize the developed scores by implementing a new definition of the BSN and a control parameter for this score (BSN/aa, Fig. 2 and Table 2). Further, we analyzed the domain size optimum for β-barrel discrimination (Fig. 3). Here we learned that the best performance was achieved in a window below 300 amino acids. The latter result is in line with the observation that most porins are about 30–35 kDa [25]. Furthermore, for β-barrel proteins of larger size, clustered pore regions were found. For example the structural modeling of FhaC [33], ShlB [34] or Toc75 [15,35] suggests a soluble domain or long loops in the N-terminal region, whereas only the C-terminal portion seems to be involved in pore formation. It might therefore be suggested that an evolutionary prolongation of the membrane β-barrel proteins occurred facilitating the interaction with other proteins or substrates as seen for Toc75 [36]. This result is interesting for the understanding of the evolutionary development of such proteins. It might point to the fact that a minimal structural unit was the starting seed for the development of larger pores as discussed for helical transporters ("hairpin theory") [37]. Finally, we used a combination of an amino acid distribution based score and the theory that membrane facing strands should reveal an alternating hydrophobicity and calculated a combined score in a 225 amino acid window (BSHS). The window size might reflect that strands involved in homo-oligomerisation do not contain as many hydrophobic amino acids compared to those facing the exterior.
By visual inspection of the structures we determined the average sizes of the continuous region exposed to the lipid membrane and of the region containing the β-barrel. The obtained sizes are ~275 and ~325 amino acids on average, respectively. This corresponds quite well to the window sizes determined for BSHS225 and BBS275. Theoretically, the smallest possible β-barrel membrane domain, an 8-stranded β-barrel of about 80 aa length, should represent the optimal screening window size. But as we are not analyzing each protein separately but a whole pool of proteins, also the larger β-barrels – mostly assembled into homo-oligomeric complexes – have their influence. Here, three major factors contribute to the window sizes determined for BSHS225 and BBS275: (i) The β-barrel has a N-terminal and/or C-terminal extension, (ii) one or more long loops break the compact β-barrel domain into two or more parts and (iii) in homo-oligomeric complexes certain parts of the β-barrel domain are involved into protein-protein binding and therefore do not necessarily show an alternating hydrophobicity which results in a smaller scanning window for the BSHS225 compared to the BBS275. Remarkably, according to Wallin and von Heijne [27] most of the in there investigated proteins of eubacterial organisms have a local maximum at six transmembrane helices within a segment of about 225 to 275 residues. The average domain sizes of β-barrel and above mentioned helical membrane proteins lie within the same range. Therefore, the best discrimination between the two structurally different classes might be possible within this domain. This finding further supports our approach to identify β-barrel membrane proteins by searching for the transmembrane domain only.
Subsequently, scores for β-barrel membrane protein prediction were developed using test pools (Fig. 4, Table 2) and three preceding rules. First, a selected protein has to be larger than 80 amino acids, since the smallest monomeric transmembrane β-barrel structure consists of 8 strands [38]. Second, if more than one transmembrane helix is identified by TMHMM, the current protein is not considered as a transmembrane β-barrel protein (Fig. 1) and finally, all scores calculated for a sequence have to be larger than zero, regardless of a performed individual or combined selection. In comparison to the previous procedure [15] we achieved a significant increase of the prediction performance of the E. coli proteome (Fig. 5). Certainly, a factor contributing to this achievement was the greater flexibility of the HPS calculation. Wimley [14] originally set the loop length to a minimum of four amino acids. By this slight simplification, as Deng and co-workers [23] also noticed, some hairpins might be missed, because about 28% of the loops are up to three amino acids short [14]. Thus, we kept the window of 25 amino acids for the HPS calculation, but searched for the start of the second β-strand from position 11 to 25, thereby allowing a loop length of 0 to up to 14 amino acids. However, the discriminative power of the linear predictor is limited by the availability of crystal structures of β-barrel membrane proteins. Although about 20 non-redundant structures of this type are currently available in the PDB, they only represent a few families of the diverse group of β-barrel membrane proteins. For example, the important family of β-barrel shaped polypeptide transporters [35] is still missing. A crystal structure of a member of this family would certainly help to improve the predictive power.
In terms of the prediction performance on the sequence pools we have met our goal of reducing the high false negative rate reported by Deng and co-workers [23] for Wimley's [14] original method. Deng and co-workers [23] developed a HMM for discriminating β-barrel membrane proteins. For screening proteomes they raised the threshold score in order to increase the chance of true positive hits. For our procedures we included in the development of the prediction parameters an optimization for a proteome wide scan by taking care of the proposed in vivo ratio of OM proteins to NOM proteins. Thus, a direct comparison of the performance on proteomes regarding the test pool derived parameters is not possible. This raises the question, if test pools alone are sufficient to receive an impression of the prediction performance on real proteomes. Regarding the generation of test pools not only a broad and diverse collection of proteins but especially the algorithm to reduce the redundancy of the gathered sequences is of central importance. To keep or not to keep a protein – this is here the question. However, there is possibly still a need for improvement of such redundancy removal algorithms. As a consequence, we suggest testing β-barrel membrane protein prediction procedures also on a real proteome. The very well annotated proteome of the prokaryote E. coli [39] is a good candidate for such a model proteome. This additional testing gives the user a better impression of the reliability of the predictions for prokaryotic proteomes and would allow a better comparison of the scores developed.
The combination of different independent procedures for β-barrel membrane protein prediction [18,19] was employed to improve the prediction quality. In here we have analyzed and compared several programs and program combinations. These programs can be classified according to their training sets, to the mathematical procedure taken as basis for the prediction or the size restriction for the sequence analysis window. Therefore, the combination of these programs could be achieved based on the difference of one of the named properties. However, we found that predicting sequences with programs differing in the size restriction for the sequence analysis window revealed the lowest false positive rate based on the E. coli proteome. We therefore speculate that the prediction of β-barrel membrane proteins could be further improved employing knowledge based limitations toward the domains, which have to be identified, and global selection approaches in combination.
Methods
Test pool generation
In order to evaluate the prediction, the following sequence pools were created with a redundancy of maximal 50%. From the TMPDB [40] databank we retrieved the file TMPDB_alpha_nr_PR.dat [41] which contains a set of α-helical transmembrane proteins. We further collected all OM proteins from the experimentally verified ePSortdb dataset v2.0 [42] and removed proteins that are clearly no integral OM proteins and proteins marked as hypothetical. From the same databank all available proteins of the cytoplasmic membrane and the cytoplasm of Gram-negative bacteria were downloaded.
From PDB [43,44] (version 01/11/2005) we retrieved globular proteins. By SCOP [45] classification we downloaded from PDB proteins with transmembrane helices and all available transmembrane β-barrels.
As mentioned above, we removed all proteins with more than one transmembrane helix predicted by TMHMM and with less than 80 amino acids. Of the initially 1.235 proteins, 782 survived these steps.
All proteins that are not β-barrel membrane proteins and are not from PDB were accumulated in one sequence pool. They are referred to as PSort NOM protein pool. The OM proteins of PSort and PDB were kept each in separate pools.
Proteome
For testing our in here developed procedure on a real proteome, the genomic derived sequences deposited at [46] (from 01/07/2005) for E. coli were used.
Definition of the scores
The algorithms for EBSS, HPS, BBS and BSN were previously described [14,15]. In brief: The EBSS gives the TM beta-strand probability within a 10 aa sliding window which corresponds to the average length of a TM beta-strand [14]. Approximately 6 aa are required to cross the hydrophobic core of the membrane and further 3 aa for the lipid head group regions of each membrane leaflet [14]. Collecting crystal structures of all β-barrel membrane proteins available in 2002 at a maximum sequence identity of 50% Wimley [14] calculated the statistical occurrence of amino acids belonging to TM β-strands in the above mentioned membrane regions further differentiating between amino acids exposed to the membrane or oriented towards the interior of the pore. Taking into account the typical β-barrel architecture, the HPS is derived by applying a sliding window calculation to the EBSS values. The HPS is calculated by summing up the greatest EBSS of the first 10 and the following 15 residues. The BBS is calculated by adding all HPS values above 6 normalized to the amino acid number of the sequence.
Calculation of the scores
The BSHS value is derived by calculating the individual score for the β-strand starting at amino acid z. The β-strand position was assigned by the in here redesigned BSN algorithm.
Xstrand(aa=z)=∑i=04(<H>aa(z+(i*2+1))−<H>aa(z+(i*2)))/10 (E1)
MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=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@6E37@
using the water/octanol transfer free energy scale [47], multiplying the EBSS value
Ystrand(aa=z) = |Xstrand (aa=z)| * EBSS(aa=z) (E2)
adding all values in a defined amino acid window (w) and normalize to that window
BSHS(w)=∑aa=1w−9Ystrand(aa)/w (E3)
MathType@MTEF@5@5@+=feaafiart1ev1aaatCvAUfKttLearuWrP9MDH5MBPbIqV92AaeXatLxBI9gBaebbnrfifHhDYfgasaacH8akY=wiFfYdH8Gipec8Eeeu0xXdbba9frFj0=OqFfea0dXdd9vqai=hGuQ8kuc9pgc9s8qqaq=dirpe0xb9q8qiLsFr0=vr0=vr0dc8meaabaqaciGacaGaaeqabaqabeGadaaakeaaieaacqWFcbGqcqWFtbWucqWFibascqWFtbWudaWgaaWcbaGaeiikaGIaee4DaCNaeiykaKcabeaakiabg2da9maaqahabaGaeeywaK1aaSbaaSqaaiabbohaZjabbsha0jabbkhaYjabbggaHjabb6gaUjabbsgaKjabcIcaOiabbggaHjabbggaHjabcMcaPaqabaaabaGaeeyyaeMaeeyyaeMaeyypa0JaeGymaedabaGaee4DaCNaeyOeI0IaeGyoaKdaniabggHiLdGccqGGVaWlcqqG3bWDcaWLjaGaaCzcamaabmaabaGaeeyrauKaeG4mamdacaGLOaGaayzkaaaaaa@54D1@
All scores were described earlier [15]. For transmembrane helix prediction TMHMM v. 2.0 was used [26,48].
Abbreviations
aa, amino acids; BBS, β-barrel score; BSN, β-strand number; EBSS, exact β-strand score; HPS, hairpin score; BSHS, β-strand based hydrophobicity score; HMM, Hidden Markov Model; MCM, Markov Chain Model; OM, outer membrane; NOM, non-outer membrane
Acknowledgements
Special thanks to L. Eichacker for helpful discussions. This work was supported by grants to E.S. from the Deutsche Forschungsgemeinschaft (SFB-TR01), from the Fonds der Chemischen Industrie and from the Volkswagenstiftung.
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TMPDB FTP
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RCSB Protein Data Bank
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NCBI FTP
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BMC DermatolBMC Dermatology1471-5945BioMed Central London 1471-5945-5-111625962910.1186/1471-5945-5-11Research ArticleEvaluation of the profile of alopecia areata and the prevalence of thyroid function test abnormalities and serum autoantibodies in Iranian patients Seyrafi Hassan [email protected] Maryam [email protected] Hamed [email protected] Sahar [email protected] Ali [email protected] Department of Dermatology. Tehran University of medical sciences. Tehran. Iran2005 31 10 2005 5 11 11 22 7 2005 31 10 2005 Copyright © 2005 Seyrafi et al; licensee BioMed Central Ltd.2005Seyrafi et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The study aimed at evaluating the prevalence of thyroid function abnormalities in patients with alopecia areata (AA) and its association with other autoimmune diseases and various autoimmune antibodies.
Method
We retrospectively analyzed medical records of 123 patients with AA. The main site of involvement, pattern, and extent of alopecia as well as presence of the similar disease in first-degree family members and serologic status of patients were recorded.
Results
Participating in the study were 57 males and 66 females (6 to 59 years old). In the majority of patients (69.9%) the disease was manifested in the first two decades of life. Patients with family members having alopecia were recorded in 24.4%. Thyroid function abnormalities were found in 8.9% of patients. Positive autoimmune antibodies were associated with AA in 51.4% of patients with no significant association between the severity and duration of disease and presence of these antibodies.
Conclusion
The incidence of positive auto-immune antibodies in Iranian patients is higher than previous reports. Concerning the female:male ratio, thyroid function tests and the prevalence of alopecia in first-degree relatives, our results are compatible with previous data obtained from different ethnic populations. Previous reports documented that a greater severity and longer duration of AA were seen in the early onset forms; however our result are relatively different which could be explained by differences in genetic factors.
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Background
Alopecia areata (AA) is a non-scarring hair disorder, the etiology of which is minimally understood. Since human hair has an important communicational role and also because it is predominantly a disease of the youth, this disorder is able to cause significant psychological distress. Therefore, it would be extremely grateful to find appropriate measures to overcome this relatively stressful disorder. This aim is only when achieved that the main underlying causes of the disorder have been discovered.
Although many different pathogenic causes have been proposed, the determination of the exact underlying etiology of AA is extremely problematic. In fact, these difficulties are in part due to variable extent of the disease and the heterogonous and poorly defined nature of the patients studied. Of the numerous pathogenic processes which have been proposed as the underlying pathogenic causes of the AA, immunological, environmental, psychological, and genetic factors [1,2] are the most powerful explanations, but the relative significance of each is not completely known. For example, the genetic basis is explained by a higher familial occurrence, with a positive family history in 10–42% of patients in different populations [3]. There are also lots of data concerning the contribution of autoimmune processes in the pathogenesis of AA and in fact these evidences are more convincing [4]. The association of AA with other auto-immune processes, such as auto-immune thyroiditis and diabetes mellitus has been widely reported and has been considered as a potent indicator of the contribution of auto-immunity in the pathogenesis of the AA [5]. Although all these evidences suggest that the hair can be considered a target organ for autoimmune processes, presence of remarkable data concerning the contribution of psychological, environmental and genetic predisposing factors make it difficult to determine the exact cause of the disorder.
Also there is a lack of agreement on the overall prevalence of thyroid disease and thyroid function abnormalities in alopecia areata [6] and the prevalence of thyroid disease in patients with alopecia areata in previous reports varies from 8 to 28% [7]. Unfortunately, no study is available from the Iranian subcontinent.
The aim of our study was to evaluate the frequency of thyroid function abnormalities, antithyroid auto-antibodies and few other well-known autoimmune antibodies [antinuclear antibody (ANA), anti-smooth muscle antibody (SMA), anti-parietal cell antibody (PCA), anti-thyroglobulin antibodies (anti-Tg)] in Iranian patients affected by AA. Moreover, we intended to assess the prevalence of AA between the first degree relatives of our patients.
Methods
The study was carried out at the dermatology department (Tehran University of medical sciences) between February 2002 and July 2004. The data were collected retrospectively and systematically in a pre-established questionnaire. AA was diagnosed according to the definition of Olsen et al. [8]. The extent of hair loss was classified as < 50% (S1–S2) involvement, 50–99% (S3–S4) involvement, alopecia totalis (AT), and alopecia universalis (AU) at the time of presentation. All patients with AA were entered in the analysis. During the data collection, the main site of involvement, pattern, and extent of alopecia were recorded. Also a physical examination directed toward signs of other systemic or autoimmune diseases and the history of AA in first degree relatives were stated.
The data of serologic status [ANA, SMA, PCA, anti-Tg, and routine thyroid function tests (including Free T3, Free T4, and TSH)] were collected from the medical records of patients. The serum level of SMA, ANA and PCA (positive titre ≥1:80) were measured by indirect immunofluorescence (IIF) according to the standard protocols (Iason Labormedizin, Graz, Austria). Free T3 (FT3, normal range: 3–7.5 pmol/l), TSH (normal range: 0.5–3.5 mU/l), and anti-Tg (positive titer > 1:100) were measured by the chemiluminesence fluorescence method (Bayer Diagnostics, Leverkusen, Germany).
Statistical analysis
The associations were analyzed by chi-square, Fischer bicaudal exact test and T test. A probability of less than 0.05 was considered significant. SPSS for Windows (Release 11.5.0) was used for statistical analysis.
Results
A total of 123 patients with Alopecia disorders, including 57 (46.3%) males and 66 (53.7%) females were entered in our analysis (female: male ratio = 1.15:1). Of these, 57 patients (46.3%) had AT, 12 patients (9.8%) AU and 54 patients (43.9%) patchy alopecias. The age of patients at the onset of the disease had a wide range from 6 to 59 years (24.05 ± 9.98, mean ± SD). The peak age at the onset of the disease for either sex was 15 to 25 years, constituting 45.5% of the whole population of the patients. 86 patients (69.9%) experienced their first episode of AA before 20 years old. Although involving with the severe forms of the disease was seen more frequently in those patients who had early onset alopecias, but considering the severity and the duration of the disease no significant difference was found between the different age groups of patients.
Involvement of the family members with AA was found in 30 of 123 patients (24.4%). Abnormalities of thyroid function were detected in 11 (8.9%) patients included 7 (63.6%) male and 4 (36.4%) female. Of these, 3 patients (2.4%) had T3, 4 patients (3.2%) T4 and 10 patients (8.2%) TSH abnormalities. Presence of auto-immune antibodies (ANA, SMA, Anti-Tg and PCA) was established in 62 patients (51.4%). Of the 123 patients investigated for Anti-Tg, 36 patients (29.3%) had positive titer, including 27 men (22%) and 9 women (7.3%). Of these, positive titers were detected in 16 patients with the patchy form of the disease (13%) and 20 patients with AU (16.3%) (Table 1). Although positive titer was observed in 22 patients (21.1%) affected with AA before 20 years, but there was no statistically significant difference between the positivity of Anti-Tg test and the age of patients at the onset of the disease (Table 2). A higher proportion of patients (18.2%) with a longer duration of disease (>5 years) compared with patients with length of disease less than 1 year (1.9%) have positive anti-Tg titer, but differences did not attain statistical significance. Of 19 (15.5%) patients with positive PCA titer, 13 patients (10.8%) presented with AU and 6 (4.9%) with patchy forms but there was no significant association between the severity of disease and positivity of PCA. Of 19 patients who have positive PCA titer, 11 patients (6.9%) were afflicted longer than 5 years. There were also no significant differences between duration of disease and presenting positive anti-parietal antibody. 5 (4.8%) of 123 patients presented with SMA, all of them were between 15 to 25 years and the onset of disease was before 10 years old (Table 3). Two (1.8%) women in the patchy form group were seropositive for ANA. Both were involved before 20 years old and afflicted by disease for more than 5 years (Table 4).
Table 1 The frequency of positive anti-Tg antibody among the different forms of AA.
Anti-Tg Antibody/Clinical form Positive Negative
n % n %
Patchy 16 13 14 11.4
AT 0 0 57 46.3
AU 20 16.3 16 13
Total 36 29.3 87 70.7
Table 2 The frequency of positive anti-Tg antibody among the different age groups.
Anti-Tg Antibody/Age group Positive Negative
n % n %
5–14 5 4.1 13 10.5
15–24 20 16.2 33 26.9
25–34 6 4.9 21 17
35–44 5 4.1 11 9
>44 0 0 9 7.3
Total 36 29.3 87 70.7
Table 3 The frequency of positive anti-smooth muscle antibody among the different age groups.
Anti SM Ab/Age group Positive Negative
n % n %
5–14 0 0 21 17
15–24 6 4 38 31
25–34 0 0 26 21.1
35–44 0 0 21 17
>44 0 0 11 9
Total 5 4.8 118 95.1
Table 4 The frequency of positive ANA among the different age groups.
ANA/Age group Positive Negative
n % n %
5–14 0 0 18 16.2
15–24 1 0.9 49 44.1
25–34 0 0 26 23.4
35–44 0 0 13 11.8
>44 1 0.9 3 2.7
Total 2 1.8 109 98.2
Discussion
Alopecia is an ancient disease and was known to Egyptians even before Christ [9]. Despite its long history, our knowledge is actually limited. Generally, significant differences have been identified in the profile of the disease among different societies [10].
Previous studies have revealed that AA affects both sexes equally [11,12] with females slightly more predominanated [3,13,14]; Similar findings have been obtained in our study with slight predominance of the females (female:male ratio = 1.15:1).
AA may begin as early as the fourth month of the life [15] or as late as in the late seventies [12]. Our findings were not very different and the youngest patient in our group of patients was a 6 years and the oldest was 59. The prevalence of AA presenting before 20 years was reported previously to be between 27–44% [16], whereas the result in the current study was interestingly higher (69.9%). This disparity is difficult to explain and could be due to racial and genetic factors.
AA has been considered as an auto-immune disease, due to an aberrant T cell response against hair follicle self-antigens [17]. This auto-immune etiology has been also proposed on the basis of its association with various auto-immune diseases, the presence of auto-antibodies and various underlying immunologic abnormalities in the affected sites of these patients [10].
One of the main associations is with thyroid abnormalities [5]. The incidence of thyroid disease has varied from 8 to 28% in patients with AA [16]. Milgraum et al. also found an apparent association between thyroid disease and AA [4]. Subsequently Lewinski et al confirmed the frequent co-existence of AA and thyroid abnormalities [18]. Conversely, in 1994 Puavilai et al. estimated that the prevalence of thyroid disease is relatively low (7.2%) and they were not statistically different from patients with AA and control group [7]. In our study, 8.9% of patients had abnormal thyroid function tests, which relatively correlate with previous reports. We should note that the prevalence of thyroid disease in the Iranian normal population is 2.97% [19], hence our patients show higher incidence of thyroid diseases in comparison with normal individuals. Many reports found significant associations between alopecia areata and autoimmune endocrinopathies [5], but other studies do not confirm this [20]. In a report of large number of cases from North America [3], auto-immune diseases were closely associated with AA in 17.1% of patients. However, in the study of Vinod et al in 1996, the corresponding features were much lower (5%) [16].
In previous studies SMA and PCA were found in 34.6% and 42.3% of the patients, respectively, followed by antithyroglobulin antibody in 2.8% [21]. In our study, these auto-immune antibodies were associated with AA in 15.5% and 4.8% of patients, which are much lower than the previous report of Kumar et al. These authors have themselves also interested in their results and mentioned that AA in India is associated more often with antismooth muscle and antiparietal cell antibodies.
Generally the frequency of autoantibodies in Iranian patients (51.4%) is higher than the other populations [3,16]. It is next to impossible to offer an adequate explanation of the real causes of these differences and may partly be attributed to racial and genetic factors.
It is well-known that various kinds of auto-antibodies are found in each population of healthy individuals. The limitation that forced itself upon our research study was the fact that because of retrospective nature, we did not posses any control group in order to compare the results obtained from the study of antibodies; however, it must be admitted that a report concerning the rate of antithyroglobulin and ANA antibodies in normal Iranian population exists [19]. The result of this study has reported the rate of anti-Tg antibody and ANA, 16.2% and 1.7% of the normal Iranian population, respectively. We have made a comparison of the anti-Tg and ANA rate in our patients with the above result. In our study the prevalence of positive anti-Tg antibodies in patients was higher than normal Iranian population (29.8% versus 16.2%), but the frequency of ANA titer is relatively equal in our patients and normal individuals (1.8% versus 1.7%). On the other hand, the prevalence of these antibodies in our patients is not consistent with the previous study of Okamoto et al [22], in which the prevalence of positive ANA in the patients afflicted with AA was remarkably higher than our patients (53% versus 1.8%). Over all, on account of genetic factors, the prevalence of auto-antibodies is not similar in the different societies, even in the normal population. For example the positive titer of ANA in the normal Iranian population is 1.8%, while it was estimated that in the normal Japanese it is about 15% [22]. This difference emphasize on the fact that we could not compare the results of different communities. Unfortunately, we could not find any information about the value of SMA and PCA in normal populations.
Family members having AA has been reported in 10 to 20% of patients [12-14]. Our figure of 24.4% is close to the upper limit of the above reports and this higher prevalence of the disease in the first degree relatives of the patients as compared to the previous reports of the other communities, emphasizes again on the importance of the genetic factors in our population. However, it should be also considered that the patients in the clinic of a University Hospital may have disease more severe than the average and this may be the reason for the high frequency of a positive family history. On the other hand, in previous publications, the highest family history rates have often been in small series and bias in ascertainment of the cases is often the reason for this.
Competing interests
The author(s) declared that have no competing interest.
Authors' contributions
HS and MA participated in the design of the study and supervised the study progress. HA performed the data collection. SM and AG participated in the statistical analysis and drafted the manuscript. All authors read and approved the final manuscript.
Pre-publication history
The pre-publication history for this paper can be accessed here:
Acknowledgements
This study was carried out with the sponsorship of Tehran University of Medical Sciences.
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Hedstrand H Perheentupa J Ekwall O Gustafsson J Michaelsson G Husebye E Rorsman F Kampe O Antibodies against hair follicles are associated with alopecia totalis in autoimmune polyendocrine syndrome type I J Invest Dermatol 1999 113 1054 8 10594751 10.1046/j.1523-1747.1999.00778.x
Roselino AM Almeida AM Hippolito MA Cerqueira BC Maffei CM Menezes JB Vieira RE Assis SL Ali SA Clinical epidemiologic study of alopecia areata Int J Dermatol 1996 35 181 4 8655233
Shellow WV Edwards JE Koo JY Profile of alopecia areata: a questionnaire analysis of patient and family Int J Dermatol 1992 31 186 9 1568816
Milgraum SS Mitchell AJ Bacon GE Rasmussen JE Alopecia areata, endocrine function, and autoantibodies in patients 16 years of age or younger J Am acad Dermatol 1987 17 57 61 3301924
Friedman PS Alopecia areata and autoimmunity Br J Dermatol 1969 81;105 153 7
Sharma VK Dawn G Kumar B Profile of alopecia areata in Northern India Int J Dermatol 1996 35 22 7 8838924
Puavilai S Puavilai G Charuwichitratana S Sakuntabhai A Sriprachya-Anunt S Prevalence of thyroid diseases in patients with alopecia areata Int J Dermatol 1994 33 632 3 8002158
Olsen E Hordinsky M McDonald-Hull S Price V Roberts J Shapiro J Stenn K Alopecia areata investigational assessment guidelines. National Alopecia Areata Foundation J Am Acad Dermatol 1999 40 242 6 10025752
Ebel B The papyrous Ebers The greatest Egyptian medical document 1937 Copenhage: Leivin and Munksgaard
Nanda A Alsaleh QA Al-Hasawi F Al-Muzairai I Thyroid function, autoantibodies, and HLA tissue typing in children with alopecia areata Pediatr Dermatol 2002 19 486 91 12437547 10.1046/j.1525-1470.2002.00216.x
Anderson I Alopecia areata: a clinical study Br Med J 1950 4691 1250 2 14791998
Muller SA Winkelmann RK Alopecia areata. An evaluation of 736 patients Arch Dermatol 1963 88 290 7 14043621
De Weert J Temmerman L Kint A Alopecia areata: a clinical study Dermatologica 1984 168 224 9 6724078
De Waard-van der Spek FB Oranje AP De Raeymaecker DM Peereboom-Wynia JD Juvenile versus maturity-onset alopecia areata-a comparative retrospective clinical study Clin Exp Dermatol 1989 14 429 33 2605805
Switzer SE Alopecia areata in an infant Arch Dermatol Syphilol 1947 55 143 5
Vinod K Sharma VK Goutan D Bhushan K Profile of alopecia areata in northern India Int J Dermatol 1996 35 22 7 8838924
Tobin DJ Orentreich N Fenton DA Bystryn JC Antibodies to hair follicles in alopecia areata J Invest Dermatol 1994 102 721 4 8176253 10.1111/1523-1747.ep12375477
Lewinski A Broniarczyk-Dyla G Sewerynek E Zerek-Melen G Szkudlinski M Abnormalities in structure and function of the thyroid gland in patients with alopecia areata J Am Acad Dermatol 1990 23 768 9 2229515
Heydarian P Azizi F Thyroid dysfunction and autoantibodies 10 years after implementation of universal salt iodization: Tehran Thyroid Study Irn J Endcorinol Metab 2003 4 229 41
Sharma VK Sialy R Kumar B Gupta S Evaluation of thyroid function in north Indians with alopecia areata: response to intravenous injection of 100 micrograms thyrotropin releasing hormone (TRH) J Dermatol 1999 26 339 42 10405476 10.1159/000053516
Kumar B Sharma VK Sehgal S Antismooth muscle and antiparietal cell antibodies in Indians with alopecia areata Int J Dermatol 1995 34 542 5 7591433
Okamoto M Ogawa Y Watanabe A Sugiura K Shimomura Y Aoki N Nagasaka T Tomita Y Muro Y Autoantibodies to DFS70/LEDGF are increased in alopecia areata patients J Autoimmun 2004 23 257 66 15501396 10.1016/j.jaut.2004.07.004
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BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-1491626289510.1186/1471-2164-6-149Research ArticleSNiPer: Improved SNP genotype calling for Affymetrix 10K GeneChip microarray data Huentelman Matthew J [email protected] David W [email protected] Albert D [email protected] Jason J [email protected] Diane [email protected] John V [email protected] Dietrich A [email protected] Neurogenomics Division, The Translational Genomics Research Institute (TGen) Phoenix, Arizona 85004, USA2005 31 10 2005 6 149 149 23 5 2005 31 10 2005 Copyright © 2005 Huentelman et al; licensee BioMed Central Ltd.2005Huentelman et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
High throughput microarray-based single nucleotide polymorphism (SNP) genotyping has revolutionized the way genome-wide linkage scans and association analyses are performed. One of the key features of the array-based GeneChip® Mapping 10K Array from Affymetrix is the automated SNP calling algorithm. The Affymetrix algorithm was trained on a database of ethnically diverse DNA samples to create SNP call zones that are used as static models to make genotype calls for experimental data. We describe here the implementation of clustering algorithms on large training datasets resulting in improved SNP call rates on the 10K GeneChip.
Results
A database of 948 individuals genotyped on the GeneChip® Mapping 10K 2.0 Array was used to identify 822 SNPs that were called consistently less than 75% of the time. These SNPs represent on average 8.25% of the total SNPs on each chromosome with chromosome 19, the most gene-rich chromosome, containing the highest proportion of poor performers (18.7%). To remedy this, we created SNiPer, a new application which uses two clustering algorithms to yield increased call rates and equivalent concordance to Affymetrix called genotypes. We include a training set for these algorithms based on individual genotypes for 705 samples. SNiPer has the capability to be retrained for lab-specific training sets. SNiPer is freely available for download at .
Conclusion
The correct calling of poor performing SNPs may prove to be key in future linkage studies performed on the 10K GeneChip. It would prove particularly invaluable for those diseases that map to chromosome 19, known to contain a high proportion of poorly performing SNPs. Our results illustrate that SNiPer can be used to increase call rates on the 10K GeneChip® without sacrificing accuracy, thereby increasing the amount of valid data generated.
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Background
Single nucleotide polymorphisms (SNPs) are fast becoming the markers of choice for genome-wide linkage scans, loss of heterozygosity (LOH), comparative genomic hybridization (CGH) and whole-genome association studies [1]. This is due to the existence of high throughput technologies like the GeneChip® Human Mapping Array from Affymetrix coupled with the abundant and uniform distribution of SNPs throughout the human genome [2-6]. The GeneChip® Mapping Array relies on the hybridization of biotin-tagged fragments of SNP-containing DNA to complementary DNA oligomers chemically tiled on a silicon wafer in order to genotype 10,204 SNPs with a mean inter-marker spacing of 258 Kb [7]. The assay utilizes a relatively minor amount of genomic DNA (250 ng) and a series of reactions called fragment selection by PCR (FSP). The FSP reactions involve an Xba I restriction enzyme digest of genomic DNA followed by a universal adaptor ligation step and then PCR using parameters designed to selectively amplify DNA less than 1 Kb in size. After purification, the PCR products are digested to a size of ~50 bp with DNase I, end-labeled with biotin, and hybridized to the microarray wafer.
Successful hybridizations are detected fluorescently using a streptavidin-phycoerythrin conjugated molecule and an antibody-mediated signal amplification technique. Each SNP is interrogated in both the sense and antisense direction by multiple "quartets" of 25-mer oligonucleotide probes. These probe quartets consist of both perfect match (PM) and mismatch (MM, probes containing a single non-complementary base offset from the SNP interrogation position in the up or downstream direction) conformations for the major (A) and minor (B) SNP alleles being investigated. SNP genotype calls are ultimately made using the integration of fluorescent signal intensities at each location across the quartets.
To make each individual SNP genotype call the Affymetrix software employs a key mathematical filter, a feature extraction calculation, and finally fits each SNP into a trained statistical model. We will briefly review the Affymetrix calling approach on Affymetrix 10K Mapping Array. A more detailed description is available through Affymetrix or through previous publications [8]. The mathematical filter is termed the detection filter, which essentially determines if the MM fluorescence signal is greater than the PM signal. Such a result indicates a general inability of the tiled oligonucleotides to resolve the SNP from the background of mismatches whose sequences are nearly identical. SNPs that pass the detection filter are further utilized for feature extraction. It is during this calculation that the fluorescent signal intensities at each location on the microarray are indexed to calculate relative allele signal (RAS) values. Two RAS values are calculated for each SNP, one using the sense (RAS1) probes and a second using the antisense (RAS2) probes. The basic equation for RAS is as follows: RAS = A/(A+B), in which A represents the relative fluorescence intensities at the PM spots for the major SNP allele subtracted from the MM spots while B represents the same values for the minor allele. When plotted, the RAS1 and RAS2 values are used to infer a genotype call. For example, if a SNP has RAS1 and RAS2 values near 0,0 then the genotype call should be BB. If the RAS values are near 1,1 the genotype is AA. Unfortunately, the RAS values and the acceptable variance in each must be determined empirically for each SNP. Affymetrix genotyped 108 ethnically diverse DNA samples and utilized the corresponding RAS scores in a modified partitioning around medoids (MPAM) classification algorithm to delimit the boundaries of call silhouettes or zones for each SNP [8]. These call silhouettes are essentially statistical models for each SNP genotype based on the classification results of the training data set. They are used to make future experimental genotype calls. For further in-depth description of how calls are made on the 10K GeneChip® array, see the manuscript by Liu et al. [9]. If a SNP's probe intensity values do not pass the detection filter score (DS) or the RAS scores fall outside the boundaries of the statistical model then the SNP is assigned a "NoCall" value. The overall call rate of a sample is equal to the number of SNPs receiving an AA, AB, or BB genotype call divided by the total number of SNPs on the chip.
After completing thousands of 10K GeneChip® assays it is clear that even in samples with the highest overall call rates there are some SNPs consistently called less than other SNPs. In this article we report that infrequently called SNPs on the GeneChip® Mapping 10K 2.0 Array are primarily due to problems associated with the boundaries of the statistical model call zone and therefore are related to suboptimal training of the MPAM algorithm for those particular SNPs. We detail the creation of an application, SNiPer, which utilizes two training-based clustering algorithms to increase overall call rates thereby increasing the amount of usable genotype data on each chip.
Results
Identification and characterization of poorly behaving SNPs on the 10K GeneChip®
In order to identify those SNPs that frequently result in a "NoCall" on the 10K GeneChip® we compiled a database of 948 individuals that were genotyped in the last two months in our laboratory. The call rate of these samples was required to be greater than 90%. The frequency at which each SNP was not called – the "NoCall" rate – was calculated (Figure 1). SNP identifiers and their observed "NoCall" rates are included as Additional File 1 and can be downloaded directly from our supplementary data site [10]. An arbitrary "NoCall" rate of 25% across the entire sample set was used to identify SNPs considered to be poor performers. The percentage of poorly performing SNPs on each chromosome as determined by the Affymetrix MPAM algorithm and the SNiPer algorithm are detailed in Figure 2.
Figure 1 Percentage of "NoCall" for SNPs on the 10K GeneChip. SNP performance was investigated for 948 individual genotypes on the 10K GeneChip® Mapping Array. SNPs were grouped based on their overall percentage of "No Call" signals.
Figure 2 Percentage of SNPs by chromosome with "No Call" rates greater than 25%. SNPs having "No Call" rates greater than 25% were identified after processing with the MPAM (white bars) or SNiPer (black bars) algorithms. The total number of these poor performing SNPs was then divided by the total number of SNPs on the respective chromosome. The worst performing chromosome was 19 which is also known to have the highest gene density.
To investigate why certain SNPs behave poorly we examined four parameters: Detection filter scores (DS), G-C content of the tiled probe, PCR amplicon size, and the distribution of calls for each SNP in relation to the statistical model call zone. Comparison of the DS values clearly indicated that when well-performing SNPs (i.e. those with low "NoCall" rates) fail to be called they do so primarily because of the detection filter, while the majority of poorly performing SNPs fail for other reasons. For SNPs with "NoCall" rates less than 25%, the average "NoCall" rate was determined to be 5.5% ± 4.7% and the detection filter failure rate (the number of times across all 948 samples that the SNP fails the detection filter) was 2.9% ± 1.6%. Alternatively, for SNPs with "NoCall" rates greater than 25%, the average NoCall rate was 35.8% ± 11.6% and the detection filter failure rate was only 9.4% ± 10.3%. Failure of the detection filter causes ~50% of the total failures for the top performing SNPs but only ~25% of the total failures for the worst performing SNPs. Probe G-C content was not found to impact call rate. Interestingly, PCR amplicon size does play a role in the frequency at which a SNP is called. The Affymetrix specified PCR cycling parameters favor the production of amplicons less than 1 kb. The average amplicon size for the top 100 worst performing SNPs was 696 bp ± 181 bp while the 100 best performing SNPs were found on amplicons of 521 bp ± 87 bp (two-tailed t-test = p < 0.01). This finding underscores the fact that degraded sample DNA will result in lowered call rates, especially for those SNPs residing on larger sized amplicons. However, the samples used in our study consisted primarily of genomic DNA of high quality as determined by agarose gel electrophoresis. Therefore, while amplicon size can be linked to call rate, further investigation yielded that the more critical factor is the location of the MPAM model silhouette for each SNP. As indicated above, SNP failure of the detection filter is not the primary reason that the worst performing SNPs are not called. As an example one can look at the twenty worst performers. Only six of these SNPs fail the Affymetrix detection filter in at least one-third of the samples. Visual inspection of the GDAS call zones for the remaining SNPs suggests that the majority of the other poor performers are due to inadequate localization of the particular SNP model silhouette, a probable result of inadequate training of the Affymetrix MPAM algorithm for these SNPs. In other words, the RAS1/RAS2 intersection point was closely clustered for the SNP allele but still resulted in a "NoCall" because this cluster was primarily located outside the boundary of the silhouette. We were also able to find examples of widely varying RAS1 values in conjunction with tightly clustered RAS2 values and the opposite case as well. These findings are illustrated in Figure 3.
Figure 3 A graphical representation of the performance of 6 example SNPs for 948 individuals. Screen shots of the call zones (ellipses) and respective calls (solid shapes) for select SNPs from 948 individual genotypes. Blue represents call zone and calls of "B/B", Green represents "A/B", and Purple represents "A/A". Red represents those individuals that produced a "No Call" for the SNP. RAS1 and RAS2 scores are indicated on the x and y-axis respectively. Panel (a) SNP_A-1517236 and (b) SNP_A-1510986 represent SNPs with tightly clustered RAS scores, but inadequately trained call zones. An infrequently called SNP (SNP_A-1606312) with no systematic explanation is illustrated in panel c. Some SNPs cluster tightly at their RAS2 values, but have widespread RAS1 values (SNP_A-1513739) as in panel d. The opposite effect is seen in panel e (SNP_A-1508518). Panel f shows a SNP that is called >99% of the time (SNP_A-1511517) in these 948 individuals.
SNiPer as a tool to call poorly performing SNPs
The ability to call these poorly performing SNPs was investigated using the algorithms discussed in the methods section. Through the use of real-time clustering we were able to decrease the average overall "NoCall" rate from 5.22% ± 0.03% to 0.97% ± 1.27% (Table 1). This was achieved by maintaining a 98.61% ± 0.21% genotype concordance compared to the Affymetrix genotypes (Table 1). Mendelian inheritance error was assessed using individually genotyped trios and was found to be comparable to the MPAM accuracy (99.94% for MPAM vs. 99.80% for SNiPer, Table 1).
Table 1 Comparison of the Affymetrix MPAM and SNiPer algorithms.
ALGORITHM %NOCALLS CONCORDANCEVS. MPAM INHERITANCE ACCURACY
MPAM 5.22% ± 0.03% ------ 99.94%
SNiPer 0.97% ± 1.27% 98.61% ± 0.21% 99.80%
Discussion
In this article we identified 822 SNPs with "NoCall" rates of 25% or greater on the GeneChip® 10K Mapping Array. Additionally, we report the application of clustering algorithms to call these poorly performing SNPs at an increased rate without significantly compromising the concordance.
In regard to linkage studies on the 10K GeneChip®, the consequences of accurately adding 10% of SNPs which were previously not calculated include improved information content and filling gaps in the genetic map. As Figure 2 illustrates, the MPAM algorithm poorly calls over 18% of SNPs on chromosome 19. In fact, there are two stretches of SNPs on chromosome 19 where 5 out of 10 adjacent SNPs are poor performers. Additionally, chromosome 19 has the highest gene density of all human chromosomes, more than double the average for all other chromosomes [11]. It is unfortunate that this chromosome contains the lowest density of SNPs of all the autosomes on the 10K GeneChip® platform. Importantly, only 2% of the SNPs on chromosome 19 exhibit "NoCall" rates greater than 25% after running the samples through the SNiPer algorithm.
There are 12 regions in the genome where three consecutive SNPs exhibit "NoCall" rates greater than 25% of the time, three of these regions occurring on chromosome 1. Processing of samples using the SNiPer algorithm resolves this issue. After running SNiPer the highest number of poorly performing neighboring SNPs is 2 in a window size of 10 and there now exists no regions in the genome with consecutive SNPs with "NoCall" rates greater than 25%.
Interestingly, it appears that SNPs can fail the MPAM calling algorithm in four different ways. A widely dispersed RAS1 (Figure 3d) or RAS2 (Figure 3e) value can lead to a poorly performing SNP. Tightly clustered RAS1 and RAS2 values complemented with an inadequately trained call zone (Figure 3a,b) are the more frequent reason a SNP performs poorly. Also, a small percentage of SNPs fail to elicit clustered RAS scores for no clear systematic reason (Figure 3c).
Even though the genomics community is moving towards denser SNP genotyping platforms for both linkage and association analysis there are still a large number of funded studies currently being performed using the 10K GeneChip. For this reason it still remains important to improve upon the performance of the assay whenever possible. Additionally, even though the SNiPer algorithms detailed in this manuscript were designed for use on the 10K GeneChip® it could be applied to the denser genotype platforms from Affymetrix with little modification. One future direction of study may include the comparison of the SNiPer algorithm with the dynamic modeling algorithm currently in use on the 100K and 500K GeneChips.
Conclusion
SNPs called less than 75% of the time occur at a frequency of 8% on the GeneChip® 10K Mapping Array. While there is a relationship between frequency of calling and PCR amplicon size we have concluded that the primary reason for a high "NoCall" rate is inadequate training of the calling algorithm. These poorly performing SNPs could play a confounding role in linkage analysis studies especially on chromosomes 19, 21, and X, where the proportion of poorly performing SNPs is greater than 10% of the total interrogated SNPs on the entire chromosome. The SNiPer algorithms now successfully call these poorly performing SNPs, resulting in increased performance of the 10K GeneChip.
Methods
10K GeneChip® Mapping Array Genotyping
10K SNP genotyping was performed as detailed by Affymetrix on the GeneChip® Mapping 10K 2.0 Array [12]. In short, 250 ng of genomic DNA was digested with 10 units of Xba I (New England Biolabs, Beverly, MA) for 2 hours at 37°C. Adaptor Xba (P/N 900410, Affymetrix, Santa Clara, CA) was then ligated onto the digested ends with T4 DNA Ligase for 2 hours at 16°C. After dilution with water, samples were subjected to PCR using primers specific to the adaptor sequence (P/N 900409, Affymetrix) with the following amplification parameters: 95°C for 3 minutes initial denaturation, 95°C 20 seconds, 59°C 15 seconds, 72°C 15 seconds for a total of 35 cycles, followed by 72°C for 7 minutes final extension. PCR products were then purified and fragmented using 0.24 units of DNase I at 37°C for 30 minutes. The fragmented DNA was then end-labeled with biotin using 100 units of terminal deoxynucleotidyl transferase at 37°C for 2 hours. Labeled DNA was then hybridized onto the 10K Mapping Array at 48°C for 16–18 hours at 60 rpm. The hybridized array was washed, stained, and scanned according to the manufacturer's instructions.
SNiPer
The SNiPer program was implemented in Java using Sun Microsystem's free Java 2 Standard Edition 5.0 (J2SE 5.0) compiler [13]. The user interface was constructed using Swing and Abstract Windowing Toolkit components; both standard class libraries provided in the Java Foundation Classes as part of J2SE 5.0. Java was chosen for the portability of the Java Virtual Machine.
SNiPer was created to increase call rates without sacrificing accuracy. Two main approaches were explored; the creation of new static models based on our large database of individual genotypes or the development of a way to cluster new samples against an existing library of data. The second option was investigated further because it affords the end user the ability to adapt the clustering as new data is generated much more easily. However there are two major problems facing real-time clustering. The first is prohibitively long runtimes and the second is the elucidation of the proper input parameters for the algorithm variables. The runtime issue can be solved by proper algorithm choice and optimization of the algorithm for increased efficiency. The second hurdle is relatively straightforward for individual genotyping purposes due to the knowledge that the data should cluster in three separate groups.
Algorithm choice began with the investigation of PAM, CLARANS, and WAVECLUSTER. PAM and CLARANS are both medoid-based partitioning algorithms and both were found to produce high quality clusters. However, they were abandoned because of extremely poor runtime efficiency on large data sets that make real-time clustering time-consuming. WAVECLUSTER is a wavelet transformation algorithm known to scale extremely well to very large data sets because it requires only one pass through the data. We focused on the sequential use of two algorithms known as PANN (Partitioning Around Nearest Neighbors) and MDBSCAN (Modified Density Based Spatial Clustering of Applications with Noise) since they are less sensitive to input parameters.
PANN is a partitioning algorithm similar to K-Means except it utilizes the Affymetrix distance between groups correction in place of the typical distance to the nearest centroid calculation. K-Means clustering fails because it tends to split high-density clusters while PANN takes advantage of the fact that the number of clusters and their approximate locations can be predicted. PANN uses a naïve approach for its initial assignment and reassigns points with a correction based upon the ideal that a point should belong to the cluster with the nearest neighbor. The steps of PANN can be summarized as following:
1. Calculate the three centroids representative of the three clusters.
2. Assign each point to the cluster with the nearest centroid.
3. For each point, find its nearest neighbor in each of the three clusters. Assign each point to the cluster with the smallest nearest neighbor distance.
4. Repeat Step 3 until results converge (i.e. no points are moved to different clusters).
MDBSCAN is a modified version of the DBSCAN algorithm that includes a pre-processing filter for calculating input parameters and a post-processing filter to assign points considered noise [14]. MDBSCAN is designed to discover a variable number of clusters, which allows it to easily discover and avoid calling SNPs which do not have three clear clusters. The steps of MDBSCAN can be summarized as following:
1. Calculate the value epsilon, . Experiments determined that results converged for the value λ = 35.
2. For each point, find the epsilon neighborhood NEps, the set of all points that are within Eps distance from the current point.
3. For each point, if size(NEps) ≥ MinPts, then mark it as a core point. For our purposes the value MinPts = 4 was used.
4. Find a random core point and add it to a new cluster.
5. For each core point in the cluster, add all the points in its NEps to the cluster and remove them from the database.
6. Repeat Step 5 until no more new points can be added to the cluster.
7. Repeat Step 4 until no more core points remain in the database.
8. For the remaining points in the database, assign them to the cluster with the nearest centroid.
Our investigations found that the best performance is derived through the sequential use of the PANN and MDBSCAN algorithms. The input data required by these algorithms is the same; columns containing Affymetrix SNP ID numbers, predetermined (via Affymetrix GDAS) or dummy genotype calls, and columns denoting the RAS scores in both the antisense and sense direction for each SNP. A file is then designated as the data output location and the data is clustered using both algorithms. If a SNP does not pass the Affymetrix DS threshold then it also receives a "NoCall" from SNiPer and is not clustered. SNiPer is designed to handle multiple samples at once and we have successfully clustered and called 96 samples in ~60 minutes time. After generating a data set from each algorithm a "strict" filter is applied whereby if the genotype calls did not agree between PANN and MDBSCAN the final output for that SNP was a "NoCall". SNiPer can be downloaded freely from the supplementary data page [10].
PCR amplicon script
Amplicon sizes were determined by taking the chromosomal location of each SNP on the microarray chip and finding the nearest upstream and downstream cut sites for the Xba nuclease. The SNP chromosomal locations were extracted from chromosome report data files downloaded from NCBI's FTP site [15]. Xba cut sites were determined by software, developed in-house in Perl, that processed chromosomal FASTA sequence files downloaded from UCSC's Genome Browser FTP site [16].
List of abbreviations used
SNP: single nucleotide polymorphism
PCR: polymerase chain reaction
FSP: fragment selection by PCR
RAS: relative allele signal
Authors' contributions
MJH performed SNP genotyping, participated in the conceptualization of SNiPer, and drafted the manuscript. DWC helped conceptualize and write code for SNiPer, performed the statistical analysis of the SNP data, and helped draft the manuscript. ADS helped conceptualize and write code for SNiPer. JJC performed analysis of SNiPer output data. DH-L performed SNP genotyping. JVP performed statistical analysis of the SNP data and implemented the PCR amplicon script. DAS provided oversight and funding for the project. All of the authors have read and approved the final manuscript.
Supplementary Material
Additional File 1
SNP identifiers and their observed No Call Rates.
Click here for file
Acknowledgements
The authors acknowledge support from NIH Neuroscience Blueprint Grant 1U24NS043571.
==== Refs
Craig DW Stephan DA Applications of whole-genome high-density SNP genotyping Expert Rev Mol Diagn 2005 5 159 170 15833046 10.1586/14737159.5.2.159
Kennedy GC Matsuzaki H Dong S Liu WM Huang J Liu G Su X Cao M Chen W Zhang J Liu W Yang G Di X Ryder T He Z Surti U Phillips MS Boyce-Jacino MT Fodor SP Jones KW Large-scale genotyping of complex DNA Nat Biotechnol 2003 21 1233 1237 12960966 10.1038/nbt869
Sachidanandam R Weissman D Schmidt SC Kakol JM Stein LD Marth G Sherry S Mullikin JC Mortimore BJ Willey DL Hunt SE Cole CG Coggill PC Rice CM Ning Z Rogers J Bentley DR Kwok PY Mardis ER Yeh RT Schultz B Cook L Davenport R Dante M Fulton L Hillier L Waterston RH McPherson JD Gilman B Schaffner S Van Etten WJ Reich D Higgins J Daly MJ Blumenstiel B Baldwin J Stange-Thomann N Zody MC Linton L Lander ES Altshuler D A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms Nature 2001 409 928 933 11237013 10.1038/35057149
Broman KW Feingold E SNPs made routine Nat Methods 2004 1 104 105 15782170 10.1038/nmeth1104-104
Sellick GS Longman C Tolmie J Newbury-Ecob R Geenhalgh L Hughes S Whiteford M Garrett C Houlston RS Genomewide linkage searches for Mendelian disease loci can be efficiently conducted using high-density SNP genotyping arrays Nucleic Acids Res 2004 32 e164 15561999 10.1093/nar/gnh163
Hirschhorn JN Daly MJ Genome-wide association studies for common diseases and complex traits Nat Rev Genet 2005 6 95 108 15716906 10.1038/nrg1521
Fidanza J Glazer M Mutnick D McGall G Frank C High capacity substrates as a platform for a DNA probe array genotyping assay Nucleosides Nucleotides Nucleic Acids 2001 20 533 538 11563070 10.1081/NCN-100002329
Matsuzaki H Loi H Dong S Tsai YY Fang J Law J Di X Liu WM Yang G Liu G Huang J Kennedy GC Ryder TB Marcus GA Walsh PS Shriver MD Puck JM Jones KW Mei R Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array Genome Res 2004 14 414 425 14993208 10.1101/gr.2014904
Liu WM Di X Yang G Matsuzaki H Huang J Mei R Ryder TB Webster TA Dong S Liu G Jones KW Kennedy GC Kulp D Algorithms for large-scale genotyping microarrays Bioinformatics 2003 19 2397 2403 14668223 10.1093/bioinformatics/btg332
TGen Division of Neurogenomics Supplementary Data Page
Grimwood J Gordon LA Olsen A Terry A Schmutz J Lamerdin J Hellsten U Goodstein D Couronne O Tran-Gyamfi M Aerts A Altherr M Ashworth L Bajorek E Black S Branscomb E Caenepeel S Carrano A Caoile C Chan YM Christensen M Cleland CA Copeland A Dalin E Dehal P Denys M Detter JC Escobar J Flowers D Fotopulos D Garcia C Georgescu AM Glavina T Gomez M Gonzales E Groza M Hammon N Hawkins T Haydu L Ho I Huang W Israni S Jett J Kadner K Kimball H Kobayashi A Larionov V Leem SH Lopez F Lou Y Lowry S Malfatti S Martinez D McCready P Medina C Morgan J Nelson K Nolan M Ovcharenko I Pitluck S Pollard M Popkie AP Predki P Quan G Ramirez L Rash S Retterer J Rodriguez A Rogers S Salamov A Salazar A She X Smith D Slezak T Solovyev V Thayer N Tice H Tsai M Ustaszewska A Vo N Wagner M Wheeler J Wu K Xie G Yang J Dubchak I Furey TS DeJong P Dickson M Gordon D Eichler EE Pennacchio LA Richardson P Stubbs L Rokhsar DS Myers RM Rubin EM Lucas SM The DNA sequence and biology of human chromosome 19 Nature 2004 428 529 535 15057824 10.1038/nature02399
Affymetrix 10K GeneChip website
Sun Microsystem's Java site
Yue SH Li P Guo JD Zhou SG Using Greedy algorithm: DBSCAN revisited II J Zhejiang Univ Sci 2004 5 1405 1412 15495334 10.1631/jzus.2004.1405
NCBI dbSNP Human Chromosome Reports FTP site
UCSC Genome Browser FTP site
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BMC Genomics. 2005 Oct 31; 6:149
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10.1186/1471-2164-6-149
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==== Front
BMC GenomicsBMC Genomics1471-2164BioMed Central London 1471-2164-6-1491626289510.1186/1471-2164-6-149Research ArticleSNiPer: Improved SNP genotype calling for Affymetrix 10K GeneChip microarray data Huentelman Matthew J [email protected] David W [email protected] Albert D [email protected] Jason J [email protected] Diane [email protected] John V [email protected] Dietrich A [email protected] Neurogenomics Division, The Translational Genomics Research Institute (TGen) Phoenix, Arizona 85004, USA2005 31 10 2005 6 149 149 23 5 2005 31 10 2005 Copyright © 2005 Huentelman et al; licensee BioMed Central Ltd.2005Huentelman et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
High throughput microarray-based single nucleotide polymorphism (SNP) genotyping has revolutionized the way genome-wide linkage scans and association analyses are performed. One of the key features of the array-based GeneChip® Mapping 10K Array from Affymetrix is the automated SNP calling algorithm. The Affymetrix algorithm was trained on a database of ethnically diverse DNA samples to create SNP call zones that are used as static models to make genotype calls for experimental data. We describe here the implementation of clustering algorithms on large training datasets resulting in improved SNP call rates on the 10K GeneChip.
Results
A database of 948 individuals genotyped on the GeneChip® Mapping 10K 2.0 Array was used to identify 822 SNPs that were called consistently less than 75% of the time. These SNPs represent on average 8.25% of the total SNPs on each chromosome with chromosome 19, the most gene-rich chromosome, containing the highest proportion of poor performers (18.7%). To remedy this, we created SNiPer, a new application which uses two clustering algorithms to yield increased call rates and equivalent concordance to Affymetrix called genotypes. We include a training set for these algorithms based on individual genotypes for 705 samples. SNiPer has the capability to be retrained for lab-specific training sets. SNiPer is freely available for download at .
Conclusion
The correct calling of poor performing SNPs may prove to be key in future linkage studies performed on the 10K GeneChip. It would prove particularly invaluable for those diseases that map to chromosome 19, known to contain a high proportion of poorly performing SNPs. Our results illustrate that SNiPer can be used to increase call rates on the 10K GeneChip® without sacrificing accuracy, thereby increasing the amount of valid data generated.
==== Body
Background
Single nucleotide polymorphisms (SNPs) are fast becoming the markers of choice for genome-wide linkage scans, loss of heterozygosity (LOH), comparative genomic hybridization (CGH) and whole-genome association studies [1]. This is due to the existence of high throughput technologies like the GeneChip® Human Mapping Array from Affymetrix coupled with the abundant and uniform distribution of SNPs throughout the human genome [2-6]. The GeneChip® Mapping Array relies on the hybridization of biotin-tagged fragments of SNP-containing DNA to complementary DNA oligomers chemically tiled on a silicon wafer in order to genotype 10,204 SNPs with a mean inter-marker spacing of 258 Kb [7]. The assay utilizes a relatively minor amount of genomic DNA (250 ng) and a series of reactions called fragment selection by PCR (FSP). The FSP reactions involve an Xba I restriction enzyme digest of genomic DNA followed by a universal adaptor ligation step and then PCR using parameters designed to selectively amplify DNA less than 1 Kb in size. After purification, the PCR products are digested to a size of ~50 bp with DNase I, end-labeled with biotin, and hybridized to the microarray wafer.
Successful hybridizations are detected fluorescently using a streptavidin-phycoerythrin conjugated molecule and an antibody-mediated signal amplification technique. Each SNP is interrogated in both the sense and antisense direction by multiple "quartets" of 25-mer oligonucleotide probes. These probe quartets consist of both perfect match (PM) and mismatch (MM, probes containing a single non-complementary base offset from the SNP interrogation position in the up or downstream direction) conformations for the major (A) and minor (B) SNP alleles being investigated. SNP genotype calls are ultimately made using the integration of fluorescent signal intensities at each location across the quartets.
To make each individual SNP genotype call the Affymetrix software employs a key mathematical filter, a feature extraction calculation, and finally fits each SNP into a trained statistical model. We will briefly review the Affymetrix calling approach on Affymetrix 10K Mapping Array. A more detailed description is available through Affymetrix or through previous publications [8]. The mathematical filter is termed the detection filter, which essentially determines if the MM fluorescence signal is greater than the PM signal. Such a result indicates a general inability of the tiled oligonucleotides to resolve the SNP from the background of mismatches whose sequences are nearly identical. SNPs that pass the detection filter are further utilized for feature extraction. It is during this calculation that the fluorescent signal intensities at each location on the microarray are indexed to calculate relative allele signal (RAS) values. Two RAS values are calculated for each SNP, one using the sense (RAS1) probes and a second using the antisense (RAS2) probes. The basic equation for RAS is as follows: RAS = A/(A+B), in which A represents the relative fluorescence intensities at the PM spots for the major SNP allele subtracted from the MM spots while B represents the same values for the minor allele. When plotted, the RAS1 and RAS2 values are used to infer a genotype call. For example, if a SNP has RAS1 and RAS2 values near 0,0 then the genotype call should be BB. If the RAS values are near 1,1 the genotype is AA. Unfortunately, the RAS values and the acceptable variance in each must be determined empirically for each SNP. Affymetrix genotyped 108 ethnically diverse DNA samples and utilized the corresponding RAS scores in a modified partitioning around medoids (MPAM) classification algorithm to delimit the boundaries of call silhouettes or zones for each SNP [8]. These call silhouettes are essentially statistical models for each SNP genotype based on the classification results of the training data set. They are used to make future experimental genotype calls. For further in-depth description of how calls are made on the 10K GeneChip® array, see the manuscript by Liu et al. [9]. If a SNP's probe intensity values do not pass the detection filter score (DS) or the RAS scores fall outside the boundaries of the statistical model then the SNP is assigned a "NoCall" value. The overall call rate of a sample is equal to the number of SNPs receiving an AA, AB, or BB genotype call divided by the total number of SNPs on the chip.
After completing thousands of 10K GeneChip® assays it is clear that even in samples with the highest overall call rates there are some SNPs consistently called less than other SNPs. In this article we report that infrequently called SNPs on the GeneChip® Mapping 10K 2.0 Array are primarily due to problems associated with the boundaries of the statistical model call zone and therefore are related to suboptimal training of the MPAM algorithm for those particular SNPs. We detail the creation of an application, SNiPer, which utilizes two training-based clustering algorithms to increase overall call rates thereby increasing the amount of usable genotype data on each chip.
Results
Identification and characterization of poorly behaving SNPs on the 10K GeneChip®
In order to identify those SNPs that frequently result in a "NoCall" on the 10K GeneChip® we compiled a database of 948 individuals that were genotyped in the last two months in our laboratory. The call rate of these samples was required to be greater than 90%. The frequency at which each SNP was not called – the "NoCall" rate – was calculated (Figure 1). SNP identifiers and their observed "NoCall" rates are included as Additional File 1 and can be downloaded directly from our supplementary data site [10]. An arbitrary "NoCall" rate of 25% across the entire sample set was used to identify SNPs considered to be poor performers. The percentage of poorly performing SNPs on each chromosome as determined by the Affymetrix MPAM algorithm and the SNiPer algorithm are detailed in Figure 2.
Figure 1 Percentage of "NoCall" for SNPs on the 10K GeneChip. SNP performance was investigated for 948 individual genotypes on the 10K GeneChip® Mapping Array. SNPs were grouped based on their overall percentage of "No Call" signals.
Figure 2 Percentage of SNPs by chromosome with "No Call" rates greater than 25%. SNPs having "No Call" rates greater than 25% were identified after processing with the MPAM (white bars) or SNiPer (black bars) algorithms. The total number of these poor performing SNPs was then divided by the total number of SNPs on the respective chromosome. The worst performing chromosome was 19 which is also known to have the highest gene density.
To investigate why certain SNPs behave poorly we examined four parameters: Detection filter scores (DS), G-C content of the tiled probe, PCR amplicon size, and the distribution of calls for each SNP in relation to the statistical model call zone. Comparison of the DS values clearly indicated that when well-performing SNPs (i.e. those with low "NoCall" rates) fail to be called they do so primarily because of the detection filter, while the majority of poorly performing SNPs fail for other reasons. For SNPs with "NoCall" rates less than 25%, the average "NoCall" rate was determined to be 5.5% ± 4.7% and the detection filter failure rate (the number of times across all 948 samples that the SNP fails the detection filter) was 2.9% ± 1.6%. Alternatively, for SNPs with "NoCall" rates greater than 25%, the average NoCall rate was 35.8% ± 11.6% and the detection filter failure rate was only 9.4% ± 10.3%. Failure of the detection filter causes ~50% of the total failures for the top performing SNPs but only ~25% of the total failures for the worst performing SNPs. Probe G-C content was not found to impact call rate. Interestingly, PCR amplicon size does play a role in the frequency at which a SNP is called. The Affymetrix specified PCR cycling parameters favor the production of amplicons less than 1 kb. The average amplicon size for the top 100 worst performing SNPs was 696 bp ± 181 bp while the 100 best performing SNPs were found on amplicons of 521 bp ± 87 bp (two-tailed t-test = p < 0.01). This finding underscores the fact that degraded sample DNA will result in lowered call rates, especially for those SNPs residing on larger sized amplicons. However, the samples used in our study consisted primarily of genomic DNA of high quality as determined by agarose gel electrophoresis. Therefore, while amplicon size can be linked to call rate, further investigation yielded that the more critical factor is the location of the MPAM model silhouette for each SNP. As indicated above, SNP failure of the detection filter is not the primary reason that the worst performing SNPs are not called. As an example one can look at the twenty worst performers. Only six of these SNPs fail the Affymetrix detection filter in at least one-third of the samples. Visual inspection of the GDAS call zones for the remaining SNPs suggests that the majority of the other poor performers are due to inadequate localization of the particular SNP model silhouette, a probable result of inadequate training of the Affymetrix MPAM algorithm for these SNPs. In other words, the RAS1/RAS2 intersection point was closely clustered for the SNP allele but still resulted in a "NoCall" because this cluster was primarily located outside the boundary of the silhouette. We were also able to find examples of widely varying RAS1 values in conjunction with tightly clustered RAS2 values and the opposite case as well. These findings are illustrated in Figure 3.
Figure 3 A graphical representation of the performance of 6 example SNPs for 948 individuals. Screen shots of the call zones (ellipses) and respective calls (solid shapes) for select SNPs from 948 individual genotypes. Blue represents call zone and calls of "B/B", Green represents "A/B", and Purple represents "A/A". Red represents those individuals that produced a "No Call" for the SNP. RAS1 and RAS2 scores are indicated on the x and y-axis respectively. Panel (a) SNP_A-1517236 and (b) SNP_A-1510986 represent SNPs with tightly clustered RAS scores, but inadequately trained call zones. An infrequently called SNP (SNP_A-1606312) with no systematic explanation is illustrated in panel c. Some SNPs cluster tightly at their RAS2 values, but have widespread RAS1 values (SNP_A-1513739) as in panel d. The opposite effect is seen in panel e (SNP_A-1508518). Panel f shows a SNP that is called >99% of the time (SNP_A-1511517) in these 948 individuals.
SNiPer as a tool to call poorly performing SNPs
The ability to call these poorly performing SNPs was investigated using the algorithms discussed in the methods section. Through the use of real-time clustering we were able to decrease the average overall "NoCall" rate from 5.22% ± 0.03% to 0.97% ± 1.27% (Table 1). This was achieved by maintaining a 98.61% ± 0.21% genotype concordance compared to the Affymetrix genotypes (Table 1). Mendelian inheritance error was assessed using individually genotyped trios and was found to be comparable to the MPAM accuracy (99.94% for MPAM vs. 99.80% for SNiPer, Table 1).
Table 1 Comparison of the Affymetrix MPAM and SNiPer algorithms.
ALGORITHM %NOCALLS CONCORDANCEVS. MPAM INHERITANCE ACCURACY
MPAM 5.22% ± 0.03% ------ 99.94%
SNiPer 0.97% ± 1.27% 98.61% ± 0.21% 99.80%
Discussion
In this article we identified 822 SNPs with "NoCall" rates of 25% or greater on the GeneChip® 10K Mapping Array. Additionally, we report the application of clustering algorithms to call these poorly performing SNPs at an increased rate without significantly compromising the concordance.
In regard to linkage studies on the 10K GeneChip®, the consequences of accurately adding 10% of SNPs which were previously not calculated include improved information content and filling gaps in the genetic map. As Figure 2 illustrates, the MPAM algorithm poorly calls over 18% of SNPs on chromosome 19. In fact, there are two stretches of SNPs on chromosome 19 where 5 out of 10 adjacent SNPs are poor performers. Additionally, chromosome 19 has the highest gene density of all human chromosomes, more than double the average for all other chromosomes [11]. It is unfortunate that this chromosome contains the lowest density of SNPs of all the autosomes on the 10K GeneChip® platform. Importantly, only 2% of the SNPs on chromosome 19 exhibit "NoCall" rates greater than 25% after running the samples through the SNiPer algorithm.
There are 12 regions in the genome where three consecutive SNPs exhibit "NoCall" rates greater than 25% of the time, three of these regions occurring on chromosome 1. Processing of samples using the SNiPer algorithm resolves this issue. After running SNiPer the highest number of poorly performing neighboring SNPs is 2 in a window size of 10 and there now exists no regions in the genome with consecutive SNPs with "NoCall" rates greater than 25%.
Interestingly, it appears that SNPs can fail the MPAM calling algorithm in four different ways. A widely dispersed RAS1 (Figure 3d) or RAS2 (Figure 3e) value can lead to a poorly performing SNP. Tightly clustered RAS1 and RAS2 values complemented with an inadequately trained call zone (Figure 3a,b) are the more frequent reason a SNP performs poorly. Also, a small percentage of SNPs fail to elicit clustered RAS scores for no clear systematic reason (Figure 3c).
Even though the genomics community is moving towards denser SNP genotyping platforms for both linkage and association analysis there are still a large number of funded studies currently being performed using the 10K GeneChip. For this reason it still remains important to improve upon the performance of the assay whenever possible. Additionally, even though the SNiPer algorithms detailed in this manuscript were designed for use on the 10K GeneChip® it could be applied to the denser genotype platforms from Affymetrix with little modification. One future direction of study may include the comparison of the SNiPer algorithm with the dynamic modeling algorithm currently in use on the 100K and 500K GeneChips.
Conclusion
SNPs called less than 75% of the time occur at a frequency of 8% on the GeneChip® 10K Mapping Array. While there is a relationship between frequency of calling and PCR amplicon size we have concluded that the primary reason for a high "NoCall" rate is inadequate training of the calling algorithm. These poorly performing SNPs could play a confounding role in linkage analysis studies especially on chromosomes 19, 21, and X, where the proportion of poorly performing SNPs is greater than 10% of the total interrogated SNPs on the entire chromosome. The SNiPer algorithms now successfully call these poorly performing SNPs, resulting in increased performance of the 10K GeneChip.
Methods
10K GeneChip® Mapping Array Genotyping
10K SNP genotyping was performed as detailed by Affymetrix on the GeneChip® Mapping 10K 2.0 Array [12]. In short, 250 ng of genomic DNA was digested with 10 units of Xba I (New England Biolabs, Beverly, MA) for 2 hours at 37°C. Adaptor Xba (P/N 900410, Affymetrix, Santa Clara, CA) was then ligated onto the digested ends with T4 DNA Ligase for 2 hours at 16°C. After dilution with water, samples were subjected to PCR using primers specific to the adaptor sequence (P/N 900409, Affymetrix) with the following amplification parameters: 95°C for 3 minutes initial denaturation, 95°C 20 seconds, 59°C 15 seconds, 72°C 15 seconds for a total of 35 cycles, followed by 72°C for 7 minutes final extension. PCR products were then purified and fragmented using 0.24 units of DNase I at 37°C for 30 minutes. The fragmented DNA was then end-labeled with biotin using 100 units of terminal deoxynucleotidyl transferase at 37°C for 2 hours. Labeled DNA was then hybridized onto the 10K Mapping Array at 48°C for 16–18 hours at 60 rpm. The hybridized array was washed, stained, and scanned according to the manufacturer's instructions.
SNiPer
The SNiPer program was implemented in Java using Sun Microsystem's free Java 2 Standard Edition 5.0 (J2SE 5.0) compiler [13]. The user interface was constructed using Swing and Abstract Windowing Toolkit components; both standard class libraries provided in the Java Foundation Classes as part of J2SE 5.0. Java was chosen for the portability of the Java Virtual Machine.
SNiPer was created to increase call rates without sacrificing accuracy. Two main approaches were explored; the creation of new static models based on our large database of individual genotypes or the development of a way to cluster new samples against an existing library of data. The second option was investigated further because it affords the end user the ability to adapt the clustering as new data is generated much more easily. However there are two major problems facing real-time clustering. The first is prohibitively long runtimes and the second is the elucidation of the proper input parameters for the algorithm variables. The runtime issue can be solved by proper algorithm choice and optimization of the algorithm for increased efficiency. The second hurdle is relatively straightforward for individual genotyping purposes due to the knowledge that the data should cluster in three separate groups.
Algorithm choice began with the investigation of PAM, CLARANS, and WAVECLUSTER. PAM and CLARANS are both medoid-based partitioning algorithms and both were found to produce high quality clusters. However, they were abandoned because of extremely poor runtime efficiency on large data sets that make real-time clustering time-consuming. WAVECLUSTER is a wavelet transformation algorithm known to scale extremely well to very large data sets because it requires only one pass through the data. We focused on the sequential use of two algorithms known as PANN (Partitioning Around Nearest Neighbors) and MDBSCAN (Modified Density Based Spatial Clustering of Applications with Noise) since they are less sensitive to input parameters.
PANN is a partitioning algorithm similar to K-Means except it utilizes the Affymetrix distance between groups correction in place of the typical distance to the nearest centroid calculation. K-Means clustering fails because it tends to split high-density clusters while PANN takes advantage of the fact that the number of clusters and their approximate locations can be predicted. PANN uses a naïve approach for its initial assignment and reassigns points with a correction based upon the ideal that a point should belong to the cluster with the nearest neighbor. The steps of PANN can be summarized as following:
1. Calculate the three centroids representative of the three clusters.
2. Assign each point to the cluster with the nearest centroid.
3. For each point, find its nearest neighbor in each of the three clusters. Assign each point to the cluster with the smallest nearest neighbor distance.
4. Repeat Step 3 until results converge (i.e. no points are moved to different clusters).
MDBSCAN is a modified version of the DBSCAN algorithm that includes a pre-processing filter for calculating input parameters and a post-processing filter to assign points considered noise [14]. MDBSCAN is designed to discover a variable number of clusters, which allows it to easily discover and avoid calling SNPs which do not have three clear clusters. The steps of MDBSCAN can be summarized as following:
1. Calculate the value epsilon, . Experiments determined that results converged for the value λ = 35.
2. For each point, find the epsilon neighborhood NEps, the set of all points that are within Eps distance from the current point.
3. For each point, if size(NEps) ≥ MinPts, then mark it as a core point. For our purposes the value MinPts = 4 was used.
4. Find a random core point and add it to a new cluster.
5. For each core point in the cluster, add all the points in its NEps to the cluster and remove them from the database.
6. Repeat Step 5 until no more new points can be added to the cluster.
7. Repeat Step 4 until no more core points remain in the database.
8. For the remaining points in the database, assign them to the cluster with the nearest centroid.
Our investigations found that the best performance is derived through the sequential use of the PANN and MDBSCAN algorithms. The input data required by these algorithms is the same; columns containing Affymetrix SNP ID numbers, predetermined (via Affymetrix GDAS) or dummy genotype calls, and columns denoting the RAS scores in both the antisense and sense direction for each SNP. A file is then designated as the data output location and the data is clustered using both algorithms. If a SNP does not pass the Affymetrix DS threshold then it also receives a "NoCall" from SNiPer and is not clustered. SNiPer is designed to handle multiple samples at once and we have successfully clustered and called 96 samples in ~60 minutes time. After generating a data set from each algorithm a "strict" filter is applied whereby if the genotype calls did not agree between PANN and MDBSCAN the final output for that SNP was a "NoCall". SNiPer can be downloaded freely from the supplementary data page [10].
PCR amplicon script
Amplicon sizes were determined by taking the chromosomal location of each SNP on the microarray chip and finding the nearest upstream and downstream cut sites for the Xba nuclease. The SNP chromosomal locations were extracted from chromosome report data files downloaded from NCBI's FTP site [15]. Xba cut sites were determined by software, developed in-house in Perl, that processed chromosomal FASTA sequence files downloaded from UCSC's Genome Browser FTP site [16].
List of abbreviations used
SNP: single nucleotide polymorphism
PCR: polymerase chain reaction
FSP: fragment selection by PCR
RAS: relative allele signal
Authors' contributions
MJH performed SNP genotyping, participated in the conceptualization of SNiPer, and drafted the manuscript. DWC helped conceptualize and write code for SNiPer, performed the statistical analysis of the SNP data, and helped draft the manuscript. ADS helped conceptualize and write code for SNiPer. JJC performed analysis of SNiPer output data. DH-L performed SNP genotyping. JVP performed statistical analysis of the SNP data and implemented the PCR amplicon script. DAS provided oversight and funding for the project. All of the authors have read and approved the final manuscript.
Supplementary Material
Additional File 1
SNP identifiers and their observed No Call Rates.
Click here for file
Acknowledgements
The authors acknowledge support from NIH Neuroscience Blueprint Grant 1U24NS043571.
==== Refs
Craig DW Stephan DA Applications of whole-genome high-density SNP genotyping Expert Rev Mol Diagn 2005 5 159 170 15833046 10.1586/14737159.5.2.159
Kennedy GC Matsuzaki H Dong S Liu WM Huang J Liu G Su X Cao M Chen W Zhang J Liu W Yang G Di X Ryder T He Z Surti U Phillips MS Boyce-Jacino MT Fodor SP Jones KW Large-scale genotyping of complex DNA Nat Biotechnol 2003 21 1233 1237 12960966 10.1038/nbt869
Sachidanandam R Weissman D Schmidt SC Kakol JM Stein LD Marth G Sherry S Mullikin JC Mortimore BJ Willey DL Hunt SE Cole CG Coggill PC Rice CM Ning Z Rogers J Bentley DR Kwok PY Mardis ER Yeh RT Schultz B Cook L Davenport R Dante M Fulton L Hillier L Waterston RH McPherson JD Gilman B Schaffner S Van Etten WJ Reich D Higgins J Daly MJ Blumenstiel B Baldwin J Stange-Thomann N Zody MC Linton L Lander ES Altshuler D A map of human genome sequence variation containing 1.42 million single nucleotide polymorphisms Nature 2001 409 928 933 11237013 10.1038/35057149
Broman KW Feingold E SNPs made routine Nat Methods 2004 1 104 105 15782170 10.1038/nmeth1104-104
Sellick GS Longman C Tolmie J Newbury-Ecob R Geenhalgh L Hughes S Whiteford M Garrett C Houlston RS Genomewide linkage searches for Mendelian disease loci can be efficiently conducted using high-density SNP genotyping arrays Nucleic Acids Res 2004 32 e164 15561999 10.1093/nar/gnh163
Hirschhorn JN Daly MJ Genome-wide association studies for common diseases and complex traits Nat Rev Genet 2005 6 95 108 15716906 10.1038/nrg1521
Fidanza J Glazer M Mutnick D McGall G Frank C High capacity substrates as a platform for a DNA probe array genotyping assay Nucleosides Nucleotides Nucleic Acids 2001 20 533 538 11563070 10.1081/NCN-100002329
Matsuzaki H Loi H Dong S Tsai YY Fang J Law J Di X Liu WM Yang G Liu G Huang J Kennedy GC Ryder TB Marcus GA Walsh PS Shriver MD Puck JM Jones KW Mei R Parallel genotyping of over 10,000 SNPs using a one-primer assay on a high-density oligonucleotide array Genome Res 2004 14 414 425 14993208 10.1101/gr.2014904
Liu WM Di X Yang G Matsuzaki H Huang J Mei R Ryder TB Webster TA Dong S Liu G Jones KW Kennedy GC Kulp D Algorithms for large-scale genotyping microarrays Bioinformatics 2003 19 2397 2403 14668223 10.1093/bioinformatics/btg332
TGen Division of Neurogenomics Supplementary Data Page
Grimwood J Gordon LA Olsen A Terry A Schmutz J Lamerdin J Hellsten U Goodstein D Couronne O Tran-Gyamfi M Aerts A Altherr M Ashworth L Bajorek E Black S Branscomb E Caenepeel S Carrano A Caoile C Chan YM Christensen M Cleland CA Copeland A Dalin E Dehal P Denys M Detter JC Escobar J Flowers D Fotopulos D Garcia C Georgescu AM Glavina T Gomez M Gonzales E Groza M Hammon N Hawkins T Haydu L Ho I Huang W Israni S Jett J Kadner K Kimball H Kobayashi A Larionov V Leem SH Lopez F Lou Y Lowry S Malfatti S Martinez D McCready P Medina C Morgan J Nelson K Nolan M Ovcharenko I Pitluck S Pollard M Popkie AP Predki P Quan G Ramirez L Rash S Retterer J Rodriguez A Rogers S Salamov A Salazar A She X Smith D Slezak T Solovyev V Thayer N Tice H Tsai M Ustaszewska A Vo N Wagner M Wheeler J Wu K Xie G Yang J Dubchak I Furey TS DeJong P Dickson M Gordon D Eichler EE Pennacchio LA Richardson P Stubbs L Rokhsar DS Myers RM Rubin EM Lucas SM The DNA sequence and biology of human chromosome 19 Nature 2004 428 529 535 15057824 10.1038/nature02399
Affymetrix 10K GeneChip website
Sun Microsystem's Java site
Yue SH Li P Guo JD Zhou SG Using Greedy algorithm: DBSCAN revisited II J Zhejiang Univ Sci 2004 5 1405 1412 15495334 10.1631/jzus.2004.1405
NCBI dbSNP Human Chromosome Reports FTP site
UCSC Genome Browser FTP site
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16250910
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PMC1280926
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CC BY
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2021-01-04 16:32:52
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no
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BMC Med Res Methodol. 2005 Oct 26; 5:34
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latin-1
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BMC Med Res Methodol
| 2,005 |
10.1186/1471-2288-5-34
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oa_comm
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BMC NeurosciBMC Neuroscience1471-2202BioMed Central London 1471-2202-6-621622569910.1186/1471-2202-6-62Research ArticleDisentangling the effects of phonation and articulation: Hemispheric asymmetries in the auditory N1m response of the human brain Tiitinen Hannu [email protected]äkelä Anna Mari [email protected]äkinen Ville [email protected] Patrick JC [email protected] Paavo [email protected] Apperception & Cortical Dynamics (ACD), Department of Psychology, P.O.B. 9, FIN-00014 University of Helsinki, Finland2 BioMag Laboratory, Engineering Centre, Helsinki University Central Hospital, Finland3 Laboratory of Acoustics and Audio Signal Processing, Helsinki University of Technology, Espoo, Finland2005 15 10 2005 6 62 62 27 4 2005 15 10 2005 Copyright © 2005 Tiitinen et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
The cortical activity underlying the perception of vowel identity has typically been addressed by manipulating the first and second formant frequency (F1 & F2) of the speech stimuli. These two values, originating from articulation, are already sufficient for the phonetic characterization of vowel category. In the present study, we investigated how the spectral cues caused by articulation are reflected in cortical speech processing when combined with phonation, the other major part of speech production manifested as the fundamental frequency (F0) and its harmonic integer multiples. To study the combined effects of articulation and phonation we presented vowels with either high (/a/) or low (/u/) formant frequencies which were driven by three different types of excitation: a natural periodic pulseform reflecting the vibration of the vocal folds, an aperiodic noise excitation, or a tonal waveform. The auditory N1m response was recorded with whole-head magnetoencephalography (MEG) from ten human subjects in order to resolve whether brain events reflecting articulation and phonation are specific to the left or right hemisphere of the human brain.
Results
The N1m responses for the six stimulus types displayed a considerable dynamic range of 115–135 ms, and were elicited faster (~10 ms) by the high-formant /a/ than by the low-formant /u/, indicating an effect of articulation. While excitation type had no effect on the latency of the right-hemispheric N1m, the left-hemispheric N1m elicited by the tonally excited /a/ was some 10 ms earlier than that elicited by the periodic and the aperiodic excitation. The amplitude of the N1m in both hemispheres was systematically stronger to stimulation with natural periodic excitation. Also, stimulus type had a marked (up to 7 mm) effect on the source location of the N1m, with periodic excitation resulting in more anterior sources than aperiodic and tonal excitation.
Conclusion
The auditory brain areas of the two hemispheres exhibit differential tuning to natural speech signals, observable already in the passive recording condition. The variations in the latency and strength of the auditory N1m response can be traced back to the spectral structure of the stimuli. More specifically, the combined effects of the harmonic comb structure originating from the natural voice excitation caused by the fluctuating vocal folds and the location of the formant frequencies originating from the vocal tract leads to asymmetric behaviour of the left and right hemisphere.
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Background
A voiced speech signal such as a vowel is created in the human sound production system through phonation and articulation [1]. In normal phonation, the vibrating vocal folds produce a periodic excitation, termed the glottal flow. Due to this inherent periodicity, the spectra of vowels produced by normal phonation are characterized by a harmonic comb structure, i.e., distribution of energy at the fundamental frequency (F0, ranging from 100 Hz in males up to 400 Hz in infants) and its harmonic integer multiples (2 × F0, 3 × F0, etc.) located regularly in frequency [2]. This comb structure is then locally weighted in frequency by the resonances caused by the vocal tract. These resonances, termed the formants (F1, F2, F3, etc.), determine the vowel category. Changing the shape and the length of the vocal tract results in different formant frequency settings and, consequently, in variations of the perceived phoneme category. The F0 and its harmonics are the primary acoustical cues underlying pitch perception and the lowest two formants are regarded as the major cues in vowel categorization [1].
The auditory N1(m) response of the electro- and magnetoencephalography (EEG & MEG, respectively), generated in the auditory cortices of the left and right hemisphere, reflects the acoustic properties of auditory stimuli [[3-10], see [11] for a review]: its amplitude is largely determined by stimulus onset characteristics and stimulus intensity and its latency varies according to both stimulus intensity and frequency. An increase in stimulus intensity decreases the latency of the N1m and, in the 500 – 4000 Hz range, the N1m is elicited at a roughly invariant latency. Interestingly, in the frequency range of speech F0, sinusoidal stimuli result in longer-latency N1(m) responses and this latency delay increases monotonically as stimulus frequency is lowered [12,13].
With respect to phonation, the latency delay of the N1m is observable both when the F0 is present [14] and absent [11,15,16]; in the latter case, provided that the harmonic structure of the high-frequency components is intact, the result is the virtual perception of the fundamental frequency (i.e., the missing fundamental). With regard to articulation, the categorization of vowels might be based on temporal encoding of the formant frequencies [6,7,17,18]. For instance, the vowel /u/, which has relatively low F1 and F2 values (approx. 300 & 800 Hz, respectively), elicits the N1(m) at a longer latency than the vowel /a/, which has higher F1 and F2 values (700 & 1100 Hz, respectively). Previous studies have related these effects either to the F1 [11,18] or F1 and F2 values [6,7,17] of these vowels.
These latency effects of the N1m elicited by vowels have been documented to occur symmetrically in the two hemispheres [6,7,11,17,18]. This symmetry appears rather interesting when considering that speech stimuli comprising consonants [4,19] have been found to elicit asymmetric N1m response behavior. However, given that vowels are the core phonemes of speech utterances [2], and that they comprise spectral energy preferred by either the left or the right hemisphere (i.e., formant frequencies and glottal periodicity, respectively; [20]), one would expect that isolated vowel sounds should result in hemispheric asymmetries as indexed by the auditory N1m response. Hemispheric specificity of speech processing notwithstanding, no consensus has been reached on whether cerebral asymmetries are brought about only by attentional top-down modulation of cortical activity [21] or whether they might be found already in the passive recording condition when the subject is not engaged in the attentive processing of vowel stimuli.
To summarize, the effects of voice excitation and articulation on cortical activity elicited by vowels have been studied extensively – but, more often than not, in isolation. This, obviously, might be considered a shortcoming in cognitive brain research, further emphasized by the fact that the two issues are inseparable in real speech communication. In addition, studies addressing the combined effects of phonation and articulation have typically used a much too narrow perspective in characterizing voice excitation; it is often quantified in terms of F0 alone while the role of the type of the excitation, and thereby also the set of underlying spectral cues, is ignored. This limited perspective, again, can be criticized from the point of view of natural speech communication: As an example, two representatives of the vowel /a/ can be created with equal F0s but with greatly different types of the voice excitation waveform. This results in two speech sounds, both perceived as the phoneme /a/ and, importantly, of the same pitch. However, their voice quality can be clearly different due to differences in the type of the excitation waveform. It is, for example, possible that the one /a/ sounds breathy due to use of a soft pulseform in the glottal excitation whereas the voice quality of the other /a/ is perceived as pressed resulting from the use of a sharper shape in the glottal excitation pulseform [22,23].
Besides the above-mentioned, restricted view on the role of the voice excitation type, we hasten to emphasize another, equally overlooked an issue in studies of speech production and perception: because of the wide range of their F1 and F2 values, vowels are also fundamentally different in terms of the distribution of energy over frequency. For instance, due to its high F1 and F2, the sound energy in the vowel /a/ is distributed across a wide, 0–2 kHz range of high-energy harmonics. However, in the case of, say, vowel /u/, the low positions of F1 and F2 strongly attenuate the higher harmonics and most of the sound energy is actually allocated at frequencies below 1 kHz. This, then, results in variations in the perceived loudness of the stimuli, despite attempts to adjust the intensity of the stimuli using objective measures such as the sound pressure level (SPL).
Recent studies conducted in the passive recording condition indicate that the overall harmonic structure of vowels should perhaps not be overlooked in descriptions of speech-evoked cortical activity. For one, the amplitude of the N1m is already modulated by the presence of periodic glottal excitation in vowel sounds: a vowel with this kind of excitation elicits larger-amplitude N1m responses than the same vowel with an aperiodic, intensity-matched noise excitation [24]. Further, the amplitude of the N1m reflects temporal changes in the harmonic structure of speech created by glides in F0 while corresponding glides in pure tones do not affect the N1m amplitude [25]. Contrasting these observations, both the amplitude and latency of the N1m are unaffected by the identity of loudness-matched vowels (/a/, /o/, & /u/) [26] and by the lack of phonetic F1,F2-content in natural, periodically excited vowels [27]. Regardless of the formant frequencies, the latency of the N1m elicited by speech sounds with different F0-values appears to be invariant and shorter than the latency of the N1m elicited by pure tones whose frequencies are adjusted to match the F0 of the speech sounds [25,27]. Thus, these findings tentatively suggest that the presence of periodic glottal excitation in auditory stimulation might be an important prerequisite for the elicitation of speech-specific cortical activity.
Given the lack of data on the combined effects of phonation and articulation, the present study was designed to investigate how different combinations of voice excitation (phonation) and formant frequencies (articulation; for a description of the stimuli, see Fig. 1) are reflected in the cortical processing of vowels as indexed by the auditory N1m response. To investigate the effects of phonation, we used the periodic glottal excitation extracted from a natural utterance and contrasted its effects with those of an aperiodic noise waveform and a tonal excitation represented by two sinusoids. The effects of articulation, in turn, were analyzed by introducing two natural-sounding vowels with an intact harmonic structure (/a/per & /u/per) and located in the opposite corners of the F1,F2-space. Hence, as illustrated in Fig. 1, the study comprised two phonemes with known formant values, but created by three substantially different variants of excitation. The spectra of the vowels excited by aperiodic noise (/a/aper & /u/aper) were similar to their periodic counterparts, both in terms of the formant frequencies and the overall spectral envelope structure but, importantly, they lacked the comb structure of natural speech. Further impoverishing the stimulation, we also utilized two-tone complexes /a/tone and /u/tone, where the sound energy was concentrated at two distinct frequency peaks corresponding to the F1 and F2 of /a/ and /u/.
Perceptually, the vowels /a/per and /u/per were of normal voice quality while their aperiodic, noise-excited counterparts matched for intensity resembled whispered speech. Both had a rich spectral structure and were recognizable as speech. In contrast, the tonal stimuli had an extremely sparse spectral structure not perceivable as speech. Based on previous research [11,12,14-16,24-27], we hypothesized that the type of phonation (voice excitation) should be reflected in latency variations of the N1m response. With regard to articulation, we expected that the different sound energy distributions of the vowels /a/ and /u/, caused by the different articulatory settings as explained above, should result in variations in the amplitude of the N1m. With regard to amplitude, latency, and source localization of the N1m, we were specifically interested to see whether asymmetries in the left- vs. right-hemispheric brain activity might arise already in the passive recording condition. Finally, in line with the tentative findings reported in [24], the experimental design allowed us to study whether human speech consisting of an intact, natural harmonic structure leads to a different spatial distribution of cortical activation than unnatural utterances.
Results
As illustrated in Figures 2 and 3, the temporal dynamics of cortical activation as indexed by the latency of the N1m varied asymmetrically in the right and left hemispheres according to vowel category and type of excitation. This observation was confirmed by statistical analysis which showed a significant hemisphere by vowel by excitation type-interaction (F(2,18) = 9.55, p < 0.01): In the right hemisphere, the periodic, aperiodic, and tonal variants of /a/ elicited the N1m at an invariant latency (119, 118, and 119 ms for /a/per, /a/aper, and /a/tone, respectively; p = n.s. in all comparisons), and, interestingly, some 10 ms earlier than the three variants of /u/ (130, 130, and 127 ms for /u/per, /u/aper, and /u/tone, respectively; p = n.s.). There were significant differences in all comparisons of the latency of the N1m elicited by the vowels /a/ and /u/ (p < 0.01 for /a/per vs. /u/per; p < 0.001 for /a/aper vs. /u/aper; p < 0.05 for /a/tone vs. /u/tone).
In the left hemisphere, the three variants of /u/ elicited the N1m at comparable latencies (126, 130, and 133 ms for /u/per, /u/aper, and /u/tone, respectively; p = n.s. in all comparisons), although the N1m tended to peak earlier as stimulus complexity was increased (/u/per vs. /u/tone, p = 0.07). Variations in the type of voice excitation had a marked effect on the latency of the N1m elicited by the vowel /a/: both the periodic and the aperiodic vowel elicited the N1m at a significantly longer latency than the two-tone complex (122, 123, and 114 ms for /a/per, /a/aper, and /a/tone, respectively; p < 0.05 for both /a/per and /a/aper vs. /a/tone). The 4-ms latency difference between the N1m responses to /a/per and /u/per was statistically non-significant, whereas the responses to /a/aper and /a/tone were faster than those to /u/aper and /u/tone (p < 0.05 for /a/aper vs. /u/aper; p < 0.001 for /a/tone vs. /u/tone).
With regard to response amplitude, the right-hemispheric N1m responses were more prominent than the left-hemispheric ones (40 and 24 fT/cm; F(1,9) = 14.69, p < 0.01). In both hemispheres, the amplitude of the N1m varied according to both vowel category (F(1,9) = 5.54, p < 0.05; hemisphere-vowel-interaction (F(1,9) = 0.74, p = n.s.) and excitation type (F(2,18) = 17.35, p < 0.001; hemisphere-type of excitation – interaction (F(2,18) = 0.41, p = n.s.): As depicted in Fig. 4, the vowel /a/ elicited larger N1m responses than the vowel /u/ (35 and 29 fT/cm for /a/ and /u/, respectively, p < 0.05). Furthermore, the vowels with periodic excitation elicited larger-amplitude N1m responses (38 fT/cm) than the vowels with aperiodic (27 fT/cm) or tonal excitation (30 fT/cm; p < 0.001 for both periodic vs. aperiodic and periodic vs. tonal excitation). The vowels with aperiodic and tonal excitation, however, resulted in equally large N1m responses (p = n.s.).
Corroborating previous observations [24-27], the sources of the N1m were confined to a restricted area in both hemispheres (displaying location shifts up to 7 mm), and the right-hemispheric ECD locations were more anterior than the left-hemispheric ones (Fig. 5). The N1m responses to stimuli with natural, periodic structure were anterior to those elicited by stimuli with impoverished stimulus structure. In both hemispheres, the ECDs for the periodic vowels (/a/per & /u/per) were roughly 3 mm anterior to those for the aperiodic vowels (/a/aper & /u/aper; F(2,4) = 15.98, p < 0.05 & F(2,6) = 6.62, p < 0.05 for the left and right hemispheres, respectively). The ECDs for the two-tone complexes (/a/tone & /u/tone) were located between those for the periodic and aperiodic vowels, differing statistically from neither. Also, there were no differences between the ECD locations either along the medio-lateral or the superior-inferior-dimension.
Discussion
Here we studied the combined effects of phonation (i.e., voice excitation) and articulation (i.e., formant frequencies) on cortical activity elicited by vowels with carefully controlled acoustic properties. Brain activity elicited by natural, periodic speech sounds was contrasted with that elicited by the deficient harmonic structure of aperiodic speech sounds and two-tone complexes. Both the type of excitation of the vowels and their formant settings resulted in hemispheric asymmetries with regard to the latency behavior of the auditory N1m response, suggesting that the left and right auditory areas of the human brain employ different strategies for extracting information from speech signals. Further, given that the data revealing cortical asymmetries were derived in the passive recording condition, it appears that these extraction processes takes place without requiring, for example, top-down attentional engagement.
Firstly, we were able to establish that vowels comprising the periodic glottal excitation elicited distinctly different time courses of the auditory N1m in the left and right hemisphere: the vowel /a/ activated the right-hemispheric auditory cortex some 10 ms earlier than the vowel /u/, whereas both of these vowels activated the left-hemispheric auditory cortex at the same latency. This indicates that the right hemisphere treats differentially vowels with different formant settings and may therefore be involved in the processing of articulatory cues. The right-hemispheric 10-ms latency difference occurred regardless of the type of voice excitation and is compatible with previous observations [6,7,11,17,18] which have shown that the latency of the N1m is determined by the F1 and/or F2 frequency of the vowels, with the low-formant vowel /u/ eliciting a longer-latency N1m than the high-formant vowel /a/.
This latency effect of the N1m was complemented by modifications in the N1m amplitude according to both phonation and articulation. Phonation had a straightforward effect, with the natural periodic stimulation always resulting in more prominent brain activity than aperiodic or tonal stimulation. With regard to articulation, however, matters become more complicated because it appears that the N1m amplitude depends on both the locations of formant frequencies and the overall spectral distribution of the stimulus energy. Here, intensity matching was used to objectively normalize the overall energy (i.e., the energy integrated over all frequency components) to the same value for all the stimuli. This procedure is typically used in laboratory settings to ensure that different stimuli represent the same sound pressure level. Thus, using two clearly different articulatory settings, we were able to study the behavior of N1m evoked by speech sounds of equal phonation and overall energy but with different sound energy spectral distributions and established that the high-frequency periodic vowel /a/ elicits a larger-amplitude N1m than the periodic vowel /u/. The present data suggests that this could be attributed to differences in sound energy distributions: the periodic vowel /u/per, endowed with much lower frequency values of F1 and F2, has sound energy mainly at these frequencies, thus resulting in amplitude-diminished N1m response compared to the periodic vowel /a/per which has sound energy distributed across a wider range of high-energy harmonics. This interpretation gains further support if one considers the N1m amplitudes in Figure 4: the N1m amplitudes to the periodic vowel /u/ and the two-tone complexes, which have relatively similar distributions of spectral energy, are quite close to each other, whereas the large difference in N1m amplitudes elicited by the periodic vowel /a/ vs. the other five stimuli might reflect their large spectral discrepancy. Understanding the effects of sound energy distribution on the behavior of N1m obviously requires further experimentation and this could be done, for instance, by studying the processing of speech sounds representing the same phoneme, such as /a/, but excited by different shapes of the periodic glottal excitation. The present observations already indicate that the amplitude of the N1m is sensitive to the energy distribution of the stimulus which can be affected, importantly, both by changes in phonation and in articulation, and any violation in the natural structure of speech sounds is carried over to N1m amplitude and latency dynamics.
The present observations also suggest that the processing of periodic vowels with different spectral energy distributions results in latency changes in the right hemisphere whereas the left hemisphere responds to these vowels at an invariant latency. Therefore, we propose that the left-hemispheric constant-latency brain process in response to vowels with periodic glottal excitation is related to the ability to correctly categorize vowel identity irrespective of the considerable variations in their acoustic structure. This conclusion gains further support from a recent study [27] showing that the periodic vowel /a/ elicits the N1m at a constant latency regardless of whether the voice pitch is that of a male, a female, or a child. Here, the origin of speech-specific invariance in the left hemisphere is further narrowed down to the effects introduced by phonation, that is, the presence of the natural glottal excitation in stimulation: When the spectral comb structure provided by the periodic glottal excitation is replaced by an aperiodic one, the vowel with high-frequency F1 and F2 activate the auditory cortex at a significantly shorter latency than the vowel with low-frequency F1 and F2. When the spectral structure of the excitation is further impoverished, this latency difference becomes even more pronounced: the two-tone complex /a/tone activates the auditory cortex at a very short latency, characteristic of high-frequency tonal stimulation [11-13].
Finally, it appears that stimuli with a periodic spectral structure are processed in slightly different brain areas than stimuli with an aperiodic structure, there being shifts in the ECD locations in the anterior-posterior direction. Although the present observations provide corroborating evidence that the effect, despite being only of the order of 2–3 mm, is a reliable one [24], we are still lacking a proper explanation of the underlying neuronal mechanisms. Tentatively, one might suggest that stimuli with a natural harmonic structure evoke activity across larger neuronal populations than stimuli with an impoverished structure. Consequent changes in the centre of gravity of the activated cortical areas would show up as shifts in the ECD location as well as in larger response amplitudes for natural sounds. Alternatively, the more anterior activation for natural sounds might reflect the processing of speaker identity (present in the periodically excited vowels) which has been suggested to take place in anterior auditory areas (with posterior areas specializing in the processing of language content of stimulation [28,29]).
Conclusion
The present study suggests that in human auditory cortex, categorization of speech sounds takes place irrespective of attentional engagement and is based on cues provided by both phonation (periodic glottal excitation) and articulation (the formants of voiced speech) which, consequently, lead to hemispheric asymmetries as indexed by the auditory N1m response. More specifically, the effect of the locations of the F1,F2 frequencies on the amplitude composition of the harmonics plays a major role in the categorical perception of vowels: The amplitude of the N1m in both hemispheres probably reflects the distribution of sound energy at different frequencies, and varies according to vowel category and the type of voice excitation. The latency variations of the right-hemispheric N1m appear to be attributable to the spectral energy distribution of the speech sound, while the invariant latency of the left-hemispheric N1m might be related to the ability of humans to categorize vowels irrespective of variations in pitch and loudness. The present study indicates that the simultaneous presence of the natural glottal excitation and formant frequencies is a prerequisite for the emergence of the speech-specific cortical activation as reflected in the auditory N1m response. Therefore, based on the above, we propose that speech-specificity should be understood as specificity to the acoustic structure of natural speech.
Methods
Subjects
Ten right-handed subjects (age 20 – 44 years, 6 females) participated in the study with informed consent. All the subjects reported being right-handed and having normal hearing. The experiment was approved by the Ethical Committee of the Helsinki University Central Hospital. During the experiment, the subjects, instructed not to pay attention to the auditory stimuli, were concentrating on reading a self-selected book or watching a silent video.
Stimulus preparation and presentation
The stimuli (Fig. 1) were created by using the Semi-synthetic Speech Generation method [30]. Firstly, a natural glottal excitation (F0 = 115 Hz) was extracted from an utterance produced by a male speaker. By using this natural periodic glottal waveform as an input to an artificial vocal tract model, the vowels /a/per and /u/per of normal voice quality were synthesized. The lowest four formant frequencies of the vocal tract model were set at 670 Hz (F1), 1000 Hz (F2), 1950 Hz (F3) and 3440 Hz (F4) for /a/per and at 330 Hz (F1), 580 Hz (F2), 1900 Hz (F3) and 2900 Hz (F4) for /u/per. Secondly, the aperiodic counterparts of the vowels, /a/aper and /u/aper, were produced by replacing the glottal excitation with a noise sequence whose spectral envelope matched that of the glottal excitation. Thirdly, the two-tone complexes /a/tone and /u/tone were synthesized by exciting the vocal tract model with a composite of two sinusoidals. The frequencies and amplitudes of the tones were adjusted so that the spectrum of the synthesized tone complex matched the two strongest harmonics in the vicinity of F1 and F2 of the vowels /a/per and /u/per. This resulted in F1 and F2 values of 670 Hz & 1000 Hz for /a/tone and 330 Hz & 580 Hz for /u/tone, respectively. All the stimuli were smoothed during their onsets and offsets with a 5-ms Hanning-window. Finally, sound energy (computed as the squared sum of the digital time-domain signals) was equalized across the stimuli and the sound pressure level was adjusted for each subject by using the vowel /a/per as a reference stimulus resulting in a between-subject intensity range of 70–75 dB SPL(A). The 200-ms stimuli were delivered to the subject's ears through plastic tubes and ear pieces at an inter-stimulus interval of 800 ms. Each stimulus type was presented in its own sequence and the six sequences were presented in pseudorandom order counterbalanced across subjects. The presentation order was chosen randomly during each measurement and for each subject, and the order of stimulus presentation was controlled for to avoid possible short-term adaptation effects in the amplitude of the N1m.
MEG data-acquisition and analysis
Cortical activation elicited by the stimuli was registered by using a 306-channel whole-head MEG measurement device (Elekta Neuromag Oy, Finland) in a magnetically shielded room. At the beginning of each stimulus sequence, the head position with respect to the sensor array was determined by using head position indicator coils attached to the subjects scalp, with the locations of the coils with respect to the left and right preauricular points and the nasion having been determined prior to the measurement. In order to cancel out the cortical activity not time-locked to stimulus presentation (e.g., activity related to muscle artefact, eye-movements caused by reading or watching the video), for each stimulus, 150 evoked responses were averaged over a period of 700 ms including a 100-ms pre-stimulus baseline, and passband-filtered at 1–30 Hz. Epochs exceeding 3000 fT/cm were excluded online, and electrodes monitoring horizontal and vertical eye movements were used in removing artefacts (>150 μV) online.
The auditory N1m, defined as the response maximum in the registered waveform at around 100 ms, was studied for effects in amplitude and latency. In each hemisphere and for each subject, response latency was determined from the pair of planar gradiometers exhibiting N1m response maxima (which was the same for all stimulus types) for all the waveforms elicited by the different stimulus types. Response amplitude was defined as the average of the field gradient vector sums from six pairs of planar gradiometers displaying maximum N1m responses. Source localization was done by using unrestricted single equivalent current dipoles (ECDs). The ECDs were fitted to a single time point defined as the moment of the N1m reaching its peak amplitude in the averaged waveform of all the 66 sensors located above the left or right temporal brain areas. The ECD locations were estimated in a three-dimensional coordinate system defined by the x-axis passing through the preauricular points (positive to the right), the y-axis passing through the nasion, and the z-axis as the vector cross-product of the x and y unit vectors. Statistical analyses were performed by using repeated measures ANOVA (2 hemispheres × 2 vowels × 3 excitation types for the response waveforms; 2 vowels × 3 types of excitation separately in the right and the left hemispheres for the ECD locations) and Newman-Keuls post hoc -tests when appropriate.
Authors' contributions
HT, AMM and PA designed the experimental setup of the study, and PA prepared the auditory stimuli. AMM and VM acquired the data. AMM performed the data & statistical analyses. All authors participated in the writing process, and have approved the final version of the manuscript.
Acknowledgements
This study was supported by the Academy of Finland (proj. no 1201602, 1206360 & 200859).
Figures and Tables
Figure 1 The spectra of the stimuli for the vowels /a/ (upper row) and /u/ (lower row), representing how articulation modifies stimulus structure. The stimuli were created using three different types of phonation: the natural periodic glottal pulseform (sounds /a/per and /u/per in the left column), the aperiodic noise sequence (/a/aper and /u/aper, center column), and tonal excitation (/a/tone and /u/tone, right column). The vowels excited by the natural periodic glottal pulseform are characterized by a harmonic comb structure, that is, distribution of sound energy at multiple integers of the fundamental frequency. This regular spectral fine structure is absent from the spectra of the vowels produced by the aperiodic excitation. The spectra of the sounds generated by tonal excitation are further impoverished, comprising only two spectral components. The spectral characteristics of the stimuli of all three excitation types are affected by the formant structure of the underlying vowel. Due to this, the vowel /a/ comprises more high frequencies than the vowel /u/.
Figure 2 Grand-averaged (N = 10) responses elicited by periodic (thick line), aperiodic (dashed line), and tonal (dotted line) excitation of the vowel /a/ and /u/, calculated over the pair of planar gradiometers displaying N1m response maxima above the left and right hemisphere. In all cases, the stimuli comprising natural periodic structure elicited a prominent N1m response peaking at around 120 ms after stimulus onset.
Figure 3 The grand-averaged latency of the left- and right-hemispheric N1m for the vowels /a/ and /u/ with three different types of phonation (periodic, aperiodic & tonal). In both hemispheres, the N1m for the vowel /a/ was elicited, on the average, 10 ms earlier than that for /u/. The latency behavior of the N1m was asymmetric across the two hemispheres: In the right hemisphere, N1m latency was determined by articulation (vowel category), whereas the latency of the left-hemispheric N1m depends on both phonation and articulation. Notably, in the left hemisphere, there were no significant latency differences between the N1m responses elicited by the periodic vowels /a/per and /u/per. Bars indicate standard error of the mean.
Figure 4 The grand-averaged amplitude of the N1m elicited by the vowels /a/ and /u/ with periodic, aperiodic, and tonal excitation (due to hemispheric symmetry, the left- and right-hemispheric data has been averaged). The vowels with periodic glottal excitation (/a/per & /u/per) elicited the most prominent N1m responses, and the amplitude difference between the two was statistically significant. In all cases, the vowels with aperiodic (/a/aper & /u/aper) and tonal (/a/tone & /u/tone) excitation resulted in N1m responses with significantly smaller amplitudes than did vowels with periodic excitation. Bars indicate standard error of the mean.
Figure 5 The ECD locations of the N1m responses in the anterior-posterior and superior-inferior dimensions. These were located in a restricted cortical area in both the left and the right hemisphere. The ECDs for the vowels with periodic (/a/per & /u/per) excitation were anterior to those for aperiodic (/a/aper & /u/aper) and tonal (/a/tone & /u/tone) excitation. Bars indicate standard error of the mean.
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Neukirch M Hegerl U Kötitz R Dorn H Gallinat U Herrmann VM Comparison of the amplitude/intensity function of the auditory evoked N1m and N1 componenets Neuropsychobiology 2002 45 41 48 11803241 10.1159/000048672
Roberts TPL Ferrari P Stufflebeam SM Poeppel D Latency of the auditory evoked neuromagnetic field components: stimulus dependence and insights toward perception J Clin Neurophysiol 2000 17 114 129 10831104 10.1097/00004691-200003000-00002
Roberts TPL Poeppel D Latency of auditory evoked M100 as a function of tone frequency Neuroreport 1996 7 1138 1140 8817518
May P Tiitinen H Ilmoniemi RJ Nyman G Taylor JG Näätänen R Frequency change detection in human auditory cortex J Comput Neurosci 1999 6 99 120 10333158 10.1023/A:1008896417606
Crottaz-Herbette S Ragot R Perception of complex sounds: N1 latency codes pitch and topography codes spectra Clin Neurophysiol 2000 111 1759 1766 11018489 10.1016/S1388-2457(00)00422-3
Pantev C Hoke M Lütkenhöner B Lehnertz K Tonotopic organization of the auditory cortex: pitch versus frequency representation Science 1989 246 486 488 2814476
Ragot R Lepaul-Ercole R Brain potentials as objective indexes of auditory pitch extraction from harmonics Neuroreport 1996 7 905 909 8724670
Obleser J Lahiri A Eulitz C Magnetic brain response mirrors extraction of phonological features from spoken vowels J Cogn Neurosci 2004 16 31 39 15006034 10.1162/089892904322755539
Roberts TPL Flagg EJ Gage NM Vowel categorization induces departure of M100 latency from acoustic prediction Neuroreport 2004 15 1679 1682 15232306 10.1097/01.wnr.0000134928.96937.10
Gage N Roberts TPL Hickok G Hemispheric asymmetries in auditory evoked neuromagnetic fields in response to place of articulation contrasts Brain Res Cogn Brain Res 2002 14 303 306 12067704 10.1016/S0926-6410(02)00128-3
Zatorre RJ Evans AC Meyer E Gjedde A Lateralization of phonetic and pitch discrimination in speech processing Science 1992 256 846 849 1589767
Obleser J Elbert T Eulitz C Attentional influences on functional mapping of speech sounds in human auditory cortex BMC Neurosci 2004 5 24 15268765 10.1186/1471-2202-5-24
Stevens K Acoustic Phonetics 1998 The MIT Press: Cambridge, MA
Alku P Vilkman E A comparison of glottal voice source quantification parameters in breathy, normal, and pressed phonation of female and male speakers Folia Phoniatr Logop 1996 48 240 254 8828282
Alku P Sivonen P Palomäki K Tiitinen H The periodic structure of vowel sounds is reflected in human electromagnetic brain responses Neurosci Lett 2001 298 25 28 11154827 10.1016/S0304-3940(00)01708-0
Mäkelä AM Alku P Mäkinen V Tiitinen H Glides in speech fundamental frequency are reflected in the auditory N1m response Neuroreport 2004 15 1205 1208 15129175
Mäkelä AM Alku P Tiitinen H The auditory N1m reveals the left-hemispheric representation of vowel identity in humans Neurosci Lett 2003 353 111 114 14664913 10.1016/j.neulet.2003.09.021
Mäkelä AM Alku P Mäkinen V Valtonen J May P Tiitinen H Human cortical dynamics determined by speech fundamental frequency NeuroImage 2002 17 1300 1305 12414269 10.1006/nimg.2002.1279
Belin P Zatorre R "What", "where" and "how" in auditory cortex Nat Neurosci 2000 3 965 966 11017161 10.1038/79890
Zaehle T Wüstenberg T Meyer M Jäncke L Evidence for rapid auditory perception as the foundation of speech processing: a sparse temporal sampling fMRI study Eur J Neurosci 2004 20 2447 2456 15525285 10.1111/j.1460-9568.2004.03687.x
Alku P Tiitinen H Näätänen R A method for generating natural-sounding stimuli for cognitive brain research Clin Neurophysiol 1999 110 1329 1333 10454267 10.1016/S1388-2457(99)00088-7
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Dyn MedDynamic medicine : DM1476-5918BioMed Central London 1476-5918-4-101625577910.1186/1476-5918-4-10CommentaryTime course of exercise induced alterations in daily activity in chronic fatigue syndrome Black Christopher D [email protected] Kevin K [email protected] Department of Kinesiology, The University of Georgia, Athens, GA, USA2005 28 10 2005 4 10 10 6 10 2005 28 10 2005 Copyright © 2005 Black and McCully; licensee BioMed Central Ltd.2005Black and McCully; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
In a previous study we demonstrated that while people with CFS had lower daily activity levels than control subjects, they were able to increase daily activity via a daily walking program. We reanalyzed our data to determine the time course of activity changes during the walking program. Daily activity assessed via an accelometer worn at the hip was divided into sleep, active, and walking periods. Over the first 4–10 days of walking the subjects with CFS were able to reach the prescribed activity goals each day. After this time, walking and total activity counts decreased. Sedentary controls subjects were able to maintain their daily walking and total activity goals throughout the 4 weeks. Unlike our previous interpretation of the data, we feel this new analysis suggests that CFS patients may develop exercise intolerance as demonstrated by reduced total activity after 4–10 days. The inability to sustain target activity levels, associated with pronounced worsening of symptomology, suggests the subjects with CFS had reached their activity limit.
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We have previously published data suggesting that individuals with chronic fatigue syndrome (CFS) could increase their total daily physical activity over a period of four weeks [1]. Six individuals with CFS were prescribed a daily walking program ranging from 15–25 minutes per day with the hopes of increasing their daily activity to a level approximating that of a healthy sedentary person. Daily activity was measured by an accelometer worn at the waist [1]. We found that while our CFS subjects were able to increase their daily activity, they were unable to reach daily levels similar to sedentary controls. Unlike previous studies, our daily exercise program was accompanied by a worsening of CFS symptomology. Overall mood, daily fatigue, and time spent each day with fatigue all worsened over the course of four weeks as the exercise program progressed. Based upon the observation that our most "active" CFS subjects were the least able to increase their daily activity, we proposed a "daily activity limit" as a possible explanation for the worsening of fatigue related symptoms and the inability to reach activity levels of sedentary controls.
In order to further examine the idea of a "daily activity limit" a detailed analysis of each subject's activity each day was performed. Daily activity was broken into 3 categories – exercise, active, and sleep based upon activity per minute and time of day. Four of the six subjects appeared to perform a single bout of exercise each day, and were chosen for further analysis. When daily activity data was viewed in this manner, a distinct pattern was observed. During the first 4–10 (average of 7) days of exercise, our CFS subjects spent, on average, approximately 23 minutes each day exercising. This indicates that the subjects were not only complying with the prescribed exercise, but also were able to reach the daily exercise target. This resulted in total daily activity of the CFS patients being approximately equal to the baseline activity of the control sedentary subjects (Figure 1). However, over the final 3 weeks of prescribed exercise, the average time spent each day exercising fell to approximately 8 minutes per day. Interestingly, average time and counts per day spent sleeping, and during non-exercise "active" periods did not change over the course of the study. In contrast, sedentary control subjects responded to a similar daily walking program by increasing their total daily activity 25–30% for the first week, and were subsequently able to maintain this increase for three weeks additional weeks.
Figure 1 The figure shows average data for 4 CFS and 4 control subjects. Accelorometer counts at the waist were recorded every two minutes continuously for 6 weeks (2 weeks of Pre with no walking, 4–10 days of initial walking, and ~3 weeks of continued walking-Final). Activity is in counts per day.
In light of these new findings, we feel a new interpretation of our data is warranted. Unlike our initial interpretation that CFS subjects could maintain an activity increase over four weeks, it is now apparent that the CFS subjects were only able to sustain the prescribed increase in daily activity for 4–10 days. We believe the reduction in total daily activity levels, primarily from a reduction in time spent exercising, observed during the following 3 weeks were related to greater symptoms of fatigue. These results indicate that the CFS patients in the current study were more likely than controls to develop exercise intolerance. This conclusion is supported by a previous case report, and is often suggested in review articles [2,3]. Our results also provide information on the time course by which people with CFS may develop exercise intolerance. Whether CFS patients can train to increase their exercise tolerance is currently unknown.
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Black CD O'Connor P J McCully KK Increased daily physical activity and fatigue symptoms in chronic fatigue syndrome Dyn Med 2005 4 3 15745455 10.1186/1476-5918-4-3
Snell CR VJMSSRPSGDWL Jason L FPTR Exercise Therapy Handbook of Chronic Fatigue Syndrome 2003 Hoboken, NJ , J. Wiley, and Sons 561 579
MacDonald KL Osterholm MT LeDell KH White KE Schenck CH Chao CC Persing DH Johnson RC Barker JM Peterson PK A case-control study to assess possible triggers and cofactors in chronic fatigue syndrome Am J Med 1996 100 548 554 8644768 10.1016/S0002-9343(96)00017-4
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Health Qual Life OutcomesHealth and Quality of Life Outcomes1477-7525BioMed Central London 1477-7525-3-641624889710.1186/1477-7525-3-64ResearchAdolescent distinctions between quality of life and self-rated health in quality of life research Zullig Keith J [email protected] Robert F [email protected] J Wanzer [email protected] Health Education, Department of Physical Education, Health, & Sport Studies, Miami University, Oxford, OH 45056, USA2 Arnold School of Public Health, Department of Health Promotion, Education & Behavior, University of South Carolina, Columbia, SC 29208, USA3 School of Medicine, Department of Family & Preventive Medicine, University of South Carolina, Columbia, SC 29208, USA4 Arnold School of Public Health, Department of Biostatistics & Epidemiology, University of South Carolina, Columbia, SC 29208, USA2005 25 10 2005 3 64 64 13 6 2005 25 10 2005 Copyright © 2005 Zullig et al; licensee BioMed Central Ltd.2005Zullig et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In adult quality of life (QOL) research, the QOL construct appears to differ from self-rated health status. Although increased QOL continues to be recognized as an important outcome in health promotion and medical intervention, little research has attempted to explore adolescent perceptual differences between self-rated health and QOL.
Methods
Correlational analyses were performed between self-rated health, physical health days and mental health days, and QOL. Data were collected from two different public high school adolescent samples during two different time periods (1997 & 2003) in two different geographic regions in the USA (a southern & midwestern state) with two different sample sizes (N = 5,220 and N = 140, respectively) using the CDC Youth Risk Behavior Survey (YRBS). The Centers for Disease Control and Preventions' health-related quality of life scale (HRQOL) provided estimates of self-rated health, physical health days and mental health days, and QOL.
Results
All correlation coefficients were significant in both samples (p ≤ .0001), suggesting sample size was not a contributing factor to the significant correlations. In both samples, adolescent QOL ratings were more strongly correlated with the mean number of poor mental health days (r = .88, southern sample; r = .89, midwestern sample) than with the mean number of poor physical health days (r = .75, southern sample; r = .79, midwestern sample), consistent with adult QOL research. However, correlation coefficients in both samples between self-rated health and the mean number of poor physical health days was slightly smaller (r = .24, southern, r = .32, midwestern) than that between self-rated health and the mean number of poor mental health days (r = .25, southern, r = .39 midwestern), which is contrary to adult QOL research.
Conclusion
Similar to adults, these results suggest adolescents are rating two distinct constructs, and that self-rated health and QOL should not be used interchangeably. QOL, in the context of public high school adolescents, is based largely upon self-reported mental health and to a lesser extent on self-reported physical health. Conversely, although self-reported mental health and self-reported physical health both contribute significantly to adolescent self-rated health, mental health appears to make a greater contribution, which is contrary to observations with adults. Health promoting efforts for adolescents may need to focus more on mental health than physical health, when considering population needs and type of micro or macro intervention.
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Background
It has become accepted that increasing one's quality of life (QOL) via health promotion efforts and medical care interventions is a desirable outcome for both adolescents and adults [1-3] and monitoring adult QOL continues to be of interest in the United States [4,5]. Monitoring adolescent QOL is also beginning to receive attention in some adolescent literature [6,7].
Although definitions vary, QOL has been defined as "a popular term that conveys an overall sense of well-being, including aspects of happiness and satisfaction with life as a whole. "It is broad and subjective rather than specific and objective" [[8], p.5]. Through this definition, self-rated health status is viewed as an important domain for overall QOL, but how important self-rated health status is in regard to QOL has been difficult to quantify, partly because prior research has not adequately defined what QOL means to individuals. For example, Gill and Feinstein [9] found in their literature review, researchers seem to switch QOL with other terms such as "health status" or "functional status" in their definitions. Further complicating the definition of QOL is the term health-related QOL (HRQOL).
McHorney [10] suggests HRQOL has evolved to encompass those aspects of overall QOL that can be clearly shown to affect health – either physical or mental. This is supported by Carr et al. [11] who suggest that while QOL encompasses those aspects of an individual's subjective experience that relate both directly and indirectly to health, disease, disability, and impairment, "HRQOL is the gap between our expectations of health and our experience of it." (p.1240). More succinctly, the Centers for Disease Control and Prevention (CDC) defined HRQOL as "an individual's or group's perceived physical and mental health over time" [[8], p8]. However, in attempting to address the conceptual confusion between QOL, HRQOL, and self-rated health status, Smith, Avis, & Assmann's [12] conducted a meta-analysis of 12 chronic disease studies and concluded "that only two domains – mental health and physical functioning – are key determinants of QOL judgments" (p.457). Thus, for clarification purposes in this paper, QOL is defined using those two domains: perceived physical and mental health days. Nevertheless, it is important to define self-rated, of which its importance has been delineated in a number of resources and deserves further attention.
QOL and self-rated health status
An extensive body of literature exists in regard to self-perceived, rated, or assessed health, particularly in reference to its predictive ability of morbidity and mortality as more detailed health status indicators for both adolescents and adults [13-24]. When asked "Would you say your health is excellent, very good, good, fair, or poor?" or a variation thereof, a significant association has been established with the risk of mortality over a four to nine-year period [16,17,24] and with risk behaviors such as smoking, exercise, sleep, body weight, and alcohol consumption in adults [13] and with personal, socio-environmental, behavioral, and psychological factors (e.g., health problems, disability, age, female gender, income, smoking, and higher BMI) in adolescents [20-22]. In addition, recent evidence suggests a measure of self-rated health was able to discriminate between risk factors and diabetes care among adolescents with Type I diabetes [23]. For example, male gender, higher parental socioeconomic level, a younger age of diagnosis, shorter diabetes duration, an no hospitalization in the preceding 6 months were all associated with better self-rated health.
As noted by Smith et. al [12] when clarifying the distinction between QOL and self-rated health status, these authors noted when rating QOL, patients give much greater emphasis to mental health (r = 0.47) than to physical functioning (r = 0.28). However, this pattern is reversed for appraisals of self-rated health in which physical functioning is more important than mental health. Their study provides evidence that adults are evaluating two different constructs. Their model also included social functioning, yet only weak correlations were established between QOL (0.14) and self-rated health (0.11). When social functioning was removed from the model, the contribution of mental health to QOL was 1.6 times as large as the contribution of physical functioning, suggesting perhaps the impact of social isolation or small social networks on the mental health of individuals. In other words, social functioning may be reflective of an individual's mental health, which in turn has greater effects on QOL than it does on self-rated health.
The finding that social functioning may overlap with mental health is an important finding when attempting to measure QOL, particularly among adolescents. It has been argued that in addition to measuring physical and mental health, adolescent QOL measurements should contain a social health component as well [14,25-27]. Socialization may be viewed as an important component for adolescents as they attempt to "fit in" among their peers, but it may not be as important when attempting to measure QOL. For example, Eisen, Ware, Donald, & Brook [28] found more overlap than expected between social and mental health in the RAND Corporation Health Insurance Study (HIS) of adolescents. They concluded:
"Whether the HIS social relations items are indicative of social health is open to question; they may instead be assessing a positive aspect of mental health. Although further study is required to clarify this issue, these analyses suggest either that HIS social relations items may not adequately measure the social component of child health or that mental and social components of child health are more substantially interrelated than hypothesized" [[28], p.919].
Therefore, it appears that social functioning may not be as important a determinant for QOL for either adults or adolescents. However, further investigation is warranted in the study of adolescent QOL and self-rated health status and begs the question as to whether QOL and self-rated health status measures are viewed as the same construct by adolescents, or do they represent differing constructs, as has been determined among adults? Furthermore, does mental health play a larger role for adolescent QOL and self-rated health ratings? Existing evidence suggests rising rates in adolescent conduct problems, depression, and suicide in nearly all developed countries since the Second World War [29,30]. Moreover, these trends are observed both among females and males, in all social classes, and among all family types [31]. Among American adolescents, Zullig et al. [32] found that although 60–62% of adolescents reported one or more poor physical or mental health days, respectively, in the past 30 days, as the number of reported poor health days increased to six or more days, poor mental health days significantly outweighed the number of poor physical health days (23% vs. 11% days, respectively). In addition, elevated levels of poor mental health days may last until about age 24 before declines are observed [5]. In light of these observations, it may be that adolescents give greater emphasis to mental health when reporting both their QOL perceptions and self-rated health.
Therefore, the purpose of this study was to examine the relationships between adolescent self-rated health, physical health, mental health, and QOL. Specifically, we seek to answer the question of whether mental health is more salient in both adolescent QOL and self-rated health ratings when compared physical health. Like adults, we hypothesized adolescent QOL would be more strongly related to mental health than physical health, but unlike adults, adolescent self-rated health would also be more strongly related to mental health than physical health. This research could have important implications for health practitioners, medical personnel, and researchers working with clinical populations, because when a measure of self-rated health is potentially used to assess QOL, the findings could be misleading. Bradley [33], for example, makes the distinction that asking participants how they feel about their health is different from asking them how they perceive their quality of life because, although people may feel their health is poor, their quality of life may be excellent or vice versa. Thus, efforts to achieve excellent health may actually damage QOL. Therefore, questioning self-rated health alone can have a confounding effect.
Methods
This study used data collected from two different adolescent samples at two different time periods in two different geographic regions in the USA to provide multiple indicators of model validity. The first sample of public high school adolescents originated in a southern state via the 1997 CDC-Youth Risk Behavior Survey (YRBS). The second sample utilizes a randomly selected sample of public high school adolescents in a Midwestern state and was collected in 2003. The Midwestern state adolescent data were collected as part of another program evaluation using the 2003 YRBS in the same fashion as the southern state sample.
Instrumentation
The core CDC health-related QOL scale was used for this study because it contains a measure of self-rated health and measures of physical and mental health days, both of which have been determined to be the key components of QOL among adults [12]. The CDC scale is based on research with adults age 18 or greater and initially began with the 4 core questions on the Behavioral Risk Factor Surveillance System (BRFSS) in 1994 [34,35]. Item 1 focuses on self-rated health "In general, how would you rate your health?" Consistent with previous research, response options for this item are excellent, very good, good, fair, and poor. Items 2 and 3 relate to recent physical and mental health symptoms, are considered mutually exclusive, and are worded as such "Now thinking about your physical (or mental) health, for how many days during the past 30 days was your physical (or mental) health not good?" Item 4 is conceptualized as a global measure of disability that explicitly incorporates both physical and mental health: "During the past 30 days, on how many days did poor physical or mental health keep you from doing your usual activities...?" For the purposes of this study, this item was omitted from all analyses.
All response options to the scale "days" items were identical and assessed the number of days symptoms were experienced: 0 days, 1–2 days, 3 to 5 days, 6 to 9 days, 10 to 19 days, 20 to 29 days, and all 30 days. This scale was determined to be valid among adults [36-38] and recently in a large, randomly selected population of adolescents through paper and pencil administration [32]. Two telephone-based reliability studies have also been conducted on the scale, revealing considerable test-retest reliability [39,40]. In both samples, this 4-item scale was amended to the end of the standard YRBS, eliminating any potential instrumentation bias.
Sampling
Southern Subjects
The YRBS used a sampling and weighting procedure designed to obtain a representative sample of all public high school students in grades 9–12 in a southern state, with the exception of students in special education schools. The survey was previously determined to have adequate test-retest reliability [41] for six major areas of health risk behaviors: behaviors leading to intentional and unintentional injuries (e.g., violent, aggressive, or suicidal behaviors); use of tobacco, alcohol, and other drugs; sexual behaviors; dietary behaviors; and physical activity [42]. For the YRBS, the initial sampling frame consisted of 215 schools, stratified by enrollment size into three categories: small, medium, and large. Eighty-seven schools were randomly selected, and 63 agreed to participate (72% response rate).
Parent notification forms were distributed at least five days in advance of survey administration; parents who did not want their children to participate were required to return the form. Surveys were conducted by trained data collectors, who emphasized anonymity, privacy, and confidentiality. This research was approved by the referent university's review board for the rights of human subjects in research.
Midwestern Subjects
The 2003 national YRBS survey instrument was used to collect data on these adolescents, which was also determined to display adequate test-retest reliability recently [43]. Second period classes were randomly selected from each school until the total potential survey population reached approximately 10% of the total school student population. A total of 17 classes were selected to participate at two high schools (N = 244), of which 140 students participated (57% response rate).
Parent notification forms were distributed at least one week in advance of survey administration. However, unlike in the southern sample, parents who did want their children to participate were required to return to the form. The principal author of this paper along with trained data collectors who emphasized anonymity, privacy, and confidentiality collected all data. This research was approved by the referents university's review board for the rights of human subjects in research.
Data analysis
With the southern sample, data analyses were conducted via SUDAAN [44], which accounts for the complex sampling design of the YRBS. Since the sampling design for the Midwestern sample was randomized, but not as complex as a statewide YRBS administration, all data analyses for the Midwestern sample were conducted with SAS (Cary, NC). Correlation analyses were performed to test the hypothesis that correlation coefficients for mental health (during the past 30 days) would correlate more strongly with self-rated health and QOL for both samples of adolescents when compared to physical health (during the past 30 days).
Results
Sample characteristics: Southern sample
There were a total of 5,220 observations in the 1997 YRBS. Respondents included 1,061 non-Hispanic white females (20.3%), 1,336 non-Hispanic black females (25.6%), 1,340 non-Hispanic white males (25.7%), 1,119 non-Hispanic black males (21.4%), 182 "other" females (3.5%) (Hispanic or Latino, Asian or Pacific Islander, American Indian or Alaskan Native), and 182 "other" males (3.5%). The "other" group was collapsed for reporting purposes.
As expected, the distribution of responses for each scale item was skewed toward the positive (Table 1). However, 785 (14.4%) adolescents still perceived their health as fair or poor, while 631 (11.4%) reported having 6 or more poor physical health days, and 1,291 (23.4%) reported having 6 or more poor mental health days.
Table 1 Responses to CDC scale items
Southern Sample Midwestern Sample
HRQOL item Number Percent Number Percent
Self-rated health
Excellent 1,088 19.96 22 15.7
Very good 1,652 30.30 39 27.9
Good 1,927 35.34 53 37.9
Fair 706 12.95 23 16.4
Poor 79 1.45 3 2.1
Number of days physical health not good in past 30 days
0 2,229 40.41 48 34.3
1–2 1,739 31.53 51 36.5
3–5 917 16.62 24 17.2
6–9 339 6.15 9 6.4
10–19 160 2.90 3 2.1
20–29 41 0.74 2 1.4
30 91 1.65 3 2.1
Number of days mental health not good in past 30 days
0 2,063 37.39 40 28.6
1–2 1,323 23.98 35 25.0
3–5 841 15.24 21 15.0
6–9 424 7.68 15 10.7
10–19 447 8.10 14 10.0
20–29 166 3.01 10 7.1
30 254 4.60 5 3.6
Sample characteristics: Midwestern sample
There were a total of 140 observations in the Midwestern sample of adolescents. Respondents included 58 males (41.4%) and 82 females (58.6%). The majority of the sample described themselves as white (n = 127, 90.7%), while 13 students described themselves as non-white (9.3%).
As expected, the distribution of responses for each scale item was also skewed toward the positive (Table 1), and generally consistent with the southern sample. However, 26 (18.6%) adolescents still rated their health as fair or poor, while 17 (12.1%) reported having 6 or more poor physical health days, and 44 (31.4%) reported having 6 or more poor mental health days. Although the Midwestern sample reported poorer self-rated health and greater percentages of adolescents reporting poor physical and mental health days than the southern sample, similar trends are observed for each scale item between each sample, that is, both samples reported greater impairment in mental health days than physical health days, with the percentage of those who reported fair/poor self-rated health falling in between. Thus, these observations can be considered as validation of the general pattern of QOL reporting for this scale.
Correlational analyses
Southern sample
Overall, correlation coefficients between self-rated health status and physical and mental health were modest, but significantly greater than zero at p < .0001 (Figure 1). The correlation coefficient between self-rated health status and the mean number of poor physical health days was slightly smaller (r = .24) than that between self-rated health status and the mean number of poor mental health days (r = .25). However, the correlation coefficient between the mean number of poor mental health days and the mean number of poor physical health days was larger (r = .42) than both coefficients with self-rated health status.
Figure 1 Relationship Between QOL and Self-Rated Health Status among Southern Adolescents.
Since the correlation coefficients were similar between self-rated health status and physical and mental health, PROC GLM was employed to test whether physical or mental health was contributing in a greater degree to self-rated health status. It was hypothesized that if both variables were contributing equally to self-rated health status, then taking the difference between the two would not be significant. The Type III sums of squares (from PROC GLM) for the difference between physical and mental health (F = 4.408, P = .0295) suggest that although both physical and mental health contribute significantly to self-rated health status, significantly greater contributions are made from mental health for adolescents in this sample. However, the correlations between mean number of poor mental health days, mean number of poor physical health days, and self-rated health were still modest at best.
The next objective was to determine whether physical or mental health was contributing greater variance to QOL. Adolescent QOL ratings were more strongly correlated with mental health days(r = .88) than with physical health days (r = .75) (Figure 4). These results suggest that for this random sample of adolescents, while both physical and mental health are significant contributors to QOL, mental health (specifically mental health during the past 30 days) is a more significant correlate of QOL than physical health (during the past 30 days).
Midwestern sample
All correlation coefficients were significant in the Midwestern sample (p < .0001), suggesting that sample size was not a significant contributing factor to the significant observed correlations between all scale items in the southern sample. Correlation coefficients between self-rated health status and the mean number of poor physical and mental health days were still moderate, but significantly greater than zero at p < .0001, and larger than those observed among the southern sample (Figure 2). In addition, the correlation patterns between self-rated health status and physical health (r = .32), and self-rated health status and mental health (r = .39), was consistent with the southern sample. However, the greater contributions of mental health to self-rated health, as opposed to physical health are more clearly defined in the Midwestern sample. Also consistent with the southern sample, the correlation coefficient between the mean number of poor mental health days and the mean number of poor physical health days was larger (r = .44) than both coefficients with self-rated health status.
Figure 2 Relationship Between QOL and Self-Rated Health Status among Midwestern Adolescents.
Results obtained from the southern sample regarding the strength of the correlation coefficients between mean number of poor physical health days and the mean number of poor mental health days and QOL were also duplicated in the Midwestern sample. In this sample, adolescent QOL ratings were more strongly correlated with mental health days (r = .89) than with physical health days (r = .79) (Figure 2). These results suggest that for this random sample of adolescents, while both physical and mental health are significant contributors to QOL, mental health (specifically mental health during the past 30 days) is a more significant predictor of QOL than physical health (during the past 30 days). Furthermore, although the strength of correlations differ slightly in strength between the southern and Midwestern samples, both models are consistent in their findings that mental health appears to be a greater contributor to self-rated health and QOL among adolescents.
Discussion
The correlation coefficients between the mean number of poor mental health days and the mean number of poor physical health days was larger in both the southern (r = .42) and Midwestern (r = .44) samples than both coefficients with self-rated health status in these analyses, which is consistent with Smith's et al. [12] findings. In the adult QOL literature utilizing this same scale, the mean number of poor mental health days was more highly correlated with the mean number of poor physical health days (r = 0.66) [45], suggesting that adolescents view physical and mental health with a greater degree of separation than adults. In addition, when rating QOL, although both physical and mental health are important evaluations for adolescents, mental health appears to be a greater contributor, which is consistent with what has been observed among adults [12].
It is important to understand whether mental or physical health evaluations contribute more heavily to adolescent QOL. In this study, the contributions of mental health days to QOL exceeded the contributions of physical health days (r = .88 and r = .75, respectively in the southern sample; r = .89 and r = .79, respectively in the Midwestern sample) for adolescents, which can better assist health practitioners, counselors, medical personnel, researchers, and other human service personnel working with adolescents and in program resource allocation. However, contrary to adults with chronic disease [12], adolescent self-rated health status in these samples is based more strongly on mental health and to a lesser extent on physical health. These findings suggest mental health issues are more salient among adolescents than among adults, whether rating their QOL or health status, and adolescent QOL is significantly less likely to be improved if only self-rated health status indicators are utilized in research designs. Therefore, if Healthy People 2010 objectives are going to be attained among adolescents, efforts to improve mental health need more emphasis on health promoting efforts and in clinical applications.
Secondary findings generated by this study are the correlation strengths among the variables analyzed. Although similar results were obtained between QOL and mental and physical health as in other investigations [12], the modest to moderate correlation coefficients between self-rated health and QOL (r = .28 in the southern sample; r = .38 in the Midwestern sample) and correspondingly strong coefficients between QOL and both physical health days and mental health days, this study provides additional evidence that adolescents may be evaluating two different constructs, as was concluded by Smith et al. [12] and recently by Zullig et al [32]. These results suggest that although self-rated health appears to be a component of adolescent QOL assessment, adolescents may be accounting for other factors other than mental or physical health days when self-rating their health. However, what these other factors are when adolescents are determining their self-rated health remains largely unexplained by these results. Evidence garnered from this preliminary study suggests that adolescents are clearly giving only modest attention to physical and mental health status when self-rating their health, but not when determining their QOL.
These findings carry important implications for selecting instruments for QOL research among adolescents. First, caution needs to be exercised when choosing adolescent QOL instruments. Our results suggest that self-rated health status measures should not be construed exclusively as QOL assessments. As an example, Huang et al [23] used the standard ordinal-scaled self-rated health measure as their outcome measure of quality of life in their sample of adolescents with diabetes. These results suggest that Huang and colleagues may have misrepresented their QOL study findings substantially because better self-rated health does not necessarily equate to increased quality of life. Thus, favorable intervention effects on self-rated health status may be significantly less effective for QOL among adolescents. Based on these results, QOL, in the context of public high school adolescents, is the subjective appraisal of one's current life based largely upon mental health and to a lesser extent on physical health.
Limitations
One limitation to this study is not having a concrete measure of social functioning for these samples of adolescents. Although one can conjecture the importance of including a measure of social functioning, without a measure(s) of social functioning, it is difficult to ascertain what other possible domains adolescents are rating when determining their self-rated health and the potential contributions of social functioning to mental health. Second, although these items from the CDC scale have performed preliminarily well in validity analyses with adolescents [32], scale reliability has not been tested. Third, a response rate of only 57% was obtained for Midwestern sample, which may have biased the results. Active consent procedures utilized in the Midwestern sample likely decreased this sample size. However, owing to a clear pattern of findings across both samples, it is unlikely any significant sampling bias occurred.
This study also has several study strengths. First, this study utilized two very different samples based on racial composition, sample timing (1997 & 2003), and geography (the south vs. midwest). Second, the Midwestern sample (N = 140) was much smaller than the southern sample (N = 5,220), yet the correlation patterns are both significant and similar in each sample. The fact that the Midwestern sample retained the significance levels observed among the much larger southern sample alleviates any concern that sample size is driving significance.
Conclusion
The general correlational patterns observed among these two geographically different and racially diverse study populations warrants attention. First, similar to adults, adolescent QOL determinants appear to be primarily related to mental and physical health. Second, although mental and physical health both contribute significantly to adolescent self-rated health, mental health appears to make greater contributions, which is opposite what has been observed with adults. However, adolescents may also be considering other health-related constructs in addition to their physical and mental functioning when self-rating their health. Coping styles, social support, social bonding, and personality characteristics are only a few possible mediators that may be considered. In this regard, further research is needed with a more comprehensive approach to self-rated health for teenagers. Finally, the separation of QOL and self-rated health measures appears to be justified by these analyses, as has been posited in adult QOL research.
Authors' contributions
KJZ conceived and designed the study, collected and analyzed the Midwestern state data, and coordinated all aspects of the manuscript. RFV helped draft the manuscript and was Principal Investigator from the southern state YRBS. JWD formatted and performed statistical analyses on the southern state data. All authors read and approved the final manuscript.
Acknowledgements
Southern state data was funded by Cooperative Agreement #U63/CCU 802750-04, US Centers for Disease Control & Prevention, National Center for Disease Prevention and Health Promotion, Division of Adolescent and School Health, Atlanta, GA and Cooperative Agreements with the State's Department of Education.
Midwestern state data was funded by the first author's university's President's Academic Enrichment Award, Office of the Provost.
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Part Fibre ToxicolParticle and Fibre Toxicology1743-8977BioMed Central London 1743-8977-2-101624204010.1186/1743-8977-2-10ReviewCombustion-derived nanoparticles: A review of their toxicology following inhalation exposure Donaldson Ken [email protected] Lang [email protected] Luis Albert [email protected] Rodger [email protected] David E [email protected] Nicholas [email protected] William [email protected] Vicki [email protected] ELEGI Colt Laboratory, Queens Medical Research Institute, University of Edinburgh, 47 Little France Crescent, Edinburgh EH16 4TJ, UK2 Institute of Occupational Medicine, Research Park North, Riccarton, Edinburgh EH14 4AP, UK3 Cardiovascular Research, Division of Medical and Radiological Sciences, The University of Edinburgh, Chancellor's Building, 49 Little France Crescent, Edinburgh EH16 4SU, UK4 Napier University, School of Life Sciences, 10 Colinton Rd, Edinburgh EH10 5DT, UK2005 21 10 2005 2 10 10 18 4 2005 21 10 2005 Copyright © 2005 Donaldson et al; licensee BioMed Central Ltd.2005Donaldson et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
This review considers the molecular toxicology of combustion-derived nanoparticles (CDNP) following inhalation exposure. CDNP originate from a number of sources and in this review we consider diesel soot, welding fume, carbon black and coal fly ash. A substantial literature demonstrates that these pose a hazard to the lungs through their potential to cause oxidative stress, inflammation and cancer; they also have the potential to redistribute to other organs following pulmonary deposition. These different CDNP show considerable heterogeneity in composition and solubility, meaning that oxidative stress may originate from different components depending on the particle under consideration. Key CDNP-associated properties of large surface area and the presence of metals and organics all have the potential to produce oxidative stress. CDNP may also exert genotoxic effects, depending on their composition. CDNP and their components also have the potential to translocate to the brain and also the blood, and thereby reach other targets such as the cardiovascular system, spleen and liver. CDNP therefore can be seen as a group of particulate toxins unified by a common mechanism of injury and properties of translocation which have the potential to mediate a range of adverse effects in the lungs and other organs and warrant further research.
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Introduction
Particulate matter (PM) is a complex mixture of different particle types, much of which are unlikely to cause any adverse effects and so the hypothesis has arisen that a sub-component(s) of PM drives the adverse effects. Much research has been, and is being, undertaken to test various hypotheses regarding which of the components in fact drives adverse effects. Combustion has been recognised as a potential source of harmful particles as well as gaseous pollutants [1,2]. Epidemiological studies do not readily allow associations of adverse effects with sub-components of PM, dependent as they usually are on mass measures of PM. However several epidemiological studies have been able to identify combustion-derived particles as an important component in driving adverse effects of PM [3-7]. Toxicology can more readily study the components of PM and there has been considerable amount of research demonstrating the toxicity of combustion-derived particles such as diesel soot [8,9], welding fume [10], carbon black [11] and nanoparticles coal fly-ash [12]. The workplace is also a site of exposure to combustion-derived nanoparticles as in the case of welding fume and in the manufacture of carbon black. We focus here on the toxicology of combustion-derived nanoparticles (CDNP) produced in a range of situations because reports on their mechanisms of toxicity suggest similarities.
CDNP present a diverse group of materials which gain commonality because of their origin in combustion processes and their demonstrated toxicity in various models. We have attempted to be systematic and have described the CDNP in terms of their physicochemistry then their adverse effects and finally their molecular toxicology; however there are gaps of various sizes in the available information and so we fall short of a truly systematic approach. We feel that this review is timely because the molecular toxicology of these materials is becoming better understood and the final common pathways of oxidative stress-mediated inflammation are now considered to underlie the effects of a range of CDNP. This is outlined in Figure 1 where the link between oxidative stress and inflammation is shown. In addition we review the evidence that CDNP and their components can migrate, from their site of deposition in the lungs, to other target organs.
Figure 1 Combustion-derived nanoparticles
Nanoparticles are defined as primary particles with at least one dimension < 100 nm, while ultrafine particles are defined as particles < 100 nm in all dimensions (W Kreyling, personal communication) and are commonly produced by combustion processes [1,2]. Like other nanoparticles, CDNP agglomerate readily and move into the accumulation mode which decreases the particle number but probably leaves the surface area dose unaffected. NP have the ability to cause inflammation and also, in the case of insoluble CDNP, have potential to escape from the site of deposition in the lungs and translocate to the blood and to other target organs [13]. The exemplar CDNP discussed here (Table 1) include welding fume and nanoparticulate carbon black, which are both occupational hazards, coal fly-ash which is an environmental hazard and diesel soot which is both an environmental and an occupational hazard. CDNP are primary in the sense that they arise directly from the combustion process, although their chemistry may change with aging as the particles undergo chemical interactions with components of the ambient air pollution cloud. The process of burning concentrates metals, due to combustion and hence degradation of the organic fraction to a degree that is dependent on the efficiency of the combustion. At the same time pyrolysis chemistry generates other complex organic molecules, some of which may persist along with elemental carbon nanoparticles. Nanoparticles also form from atmospheric chemistry e.g. sulphate and nitrate nanoparticles but these will not be discussed here as they are not derived directly from combustion. The combustion materials and the mode of combustion will ultimately determine the characteristics of the CDNP, including chemical composition, particle size and particle solubility. The large surface area of CDNP presents maximal opportunity for dissolution of soluble species from the surface of the insoluble core. For insoluble NP, the large surface area provides a surface on which catalytic chemistry can occur that favours the formation of free radicals. These free radicals are responsible for driving oxidative stress, the underlying mechanism that promotes an inflammatory response to CDNP. For a range of low toxicity, low solubility particle types the surface area alone is the driver for lung inflammation following instillation in rats [14]. CDNP may be soluble and release transition metals or organics as their primary pro-inflammatory mechanism. Both transition metals and organics can undergo complex cyclical chemical reactions in the milieu of the lungs that lead to the production of free radicals such as superoxide anion or hydroxyl radical [15-17]. By contrast low toxicity insoluble particles cause inflammation because of their surfaces; some types of CDNP have both soluble components and an insoluble core.
Table 1 Characteristics of the CDNP considered in this review
CDNP Origin Reported health effects
animals humans
Diesel exhaust particles Combustion of diesel oil Inflammation, fibrosis, cancer, Inflammation, cancer?
Welding fume Welding processes Inflammation; translocation of metals to the brain Metal fume fever, fibrosis, cancer, bronchitis
Fly-ash Combustion of coal or oil inflammation no data available
NP Carbon black Combustion of heavy fuel oil Inflammation, lung cancer; translocation of particles to the brain no data available
As detailed in this review, exposure to CDNP of various types is associated with a range of adverse health effects including fibrosis, chronic inflammatory lung disease, metal fume fever and cancer. These endpoints are found across a number of exposure conditions and to different kinds of CDNP but are not unique for CDNP.
This paper examines the evidence for harmful effects of CDNP and puts these in the context of a unifying hypothesis based on observations that the generic ability of CDNPs to cause inflammation is via oxidative stress and activation of redox-sensitive transcription factors that can lead to the adverse health effects listed below. The ability of CDNP and their associated metals to translocate to the blood and the brain are also discussed. These unusual toxic properties unite these materials and suggest that they can usefully be seen as a group of particulate lung toxins that act through similar pathways.
Health effects associated with exposure to CDNP
CDNP are generated in a number of scenarios including internal combustion engines, large scale burning of coal for power generation and in industrial processes where they often could be produced along with larger particles. The CDNP considered here are described in Table 1 along with their salient effects in humans and animals.
Diesel exhaust particulate
Both petrol and diesel fuels undergo combustion in automobile engines and give rise to CDNP [18,19] but diesel produces more particles per unit fuel than petrol and is by far the most-studied of the two regarding adverse health effects; therefore diesel CDNP are discussed here. Diesel fuel is a middle distillate of petroleum which contains paraffins, alkenes and aromatics[20]. On combustion in automobile engines it produces low solubility carbon-centred nanoparticles with complex chemical and physical structure, containing sulphates and an organic fraction comprising unburnt fuel, lubricating oil and polycyclic aromatic hydrocarbons along with a range of other chemicals, which can condense on the particles [20,21]. Singlet Diesel nanoparticles are 5–20 nm but readily form complexes chains and aggregates of 60 to 100 nm and larger [22]. Diesel exhaust particles (DEP) are usually the most common CDNP in urban environmental air and in environmental particulate air pollution (PM10) in conurbations generally; they also occur in an occupational setting. In the ambient environment the concentration of DEP in PM10 is likely to range from 5–30 μg/m3 while in occupational settings levels up to 1000 μg/m3 have been experienced [20]. The adverse health effects of exposure to DEP have been extensively studied epidemiologically, in animals and in cells. Epidemiological studies have been reviewed and show that there is a strong link between occupational exposure to diesel soot and lung cancer [20]. Animal studies generally support these findings and demonstrate that exposure to DEP and other nanoparticulate forms of carbon are carcinogenic [23] but these findings are complicated by the issue of rat lung overload [24]. Rat lung overload is a condition when very high lung surface area burden [25] of low toxicity, low soluble particles leads to failure of clearance, rapid accumulation of dose with concomitant inflammation and proliferation, which culminates in fibrosis and cancer. Humans are unlikely to experience overload levels of diesel soot, even in occupational settings, and there is a question over whether overload can occur at all in humans. It is therefore unlikely that cancer associated with DEP-exposure in humans results from mechanism similar to rat lung overload. Exposure to DEP has also been shown to be highly inflammatory in rats and mice in non-overload conditions [26-28] and to induce pro inflammatory effects on cells in vitro [29-31]. The well-documented link between inflammation and lung cancer [32-34] supports the idea that diesel exhaust may indeed be carcinogenic via an inflammatory pathway (see below).
Many studies have demonstrated profound adjuvant effects of diesel particles on development and intensity of allergic responses and these effects are mediated by direct effects of DEP on a wide range of cell types involved in allergy [35]. These effects could also be mediated indirectly through inflammation and oxidative stress [35]. The inflammatory effects of DEP appear to be driven by the particulate component i.e. the surface area effect [23] although the organic [36] and metal components [37] also appear to play a role in oxidative and pro-inflammatory effects and thereby affect pathogenicity.
Welding fume
Welding is an industrial technique that involves the joining of metal pieces using a filler metal. The filler metal is produced from an electrode wire that is consumed during the welding fusion process. High temperatures are involved, generating a welding fume as well as radiation, noise and gases [38] but we focus her on the fume particles. The vaporized metal produced by the heat of the welding process oxidises to produce a fume containing particles of metal oxide such as aluminium, cadmium, chromium, and copper [38], many of which are water soluble. The exact composition of the welding fume is determined by the metals involved in the weld and the composition of the electrode. Welding fume particles are comprised of a large proportion of nanoparticles [10].
Exposure to welding fume has been associated with both pulmonary and systemic health endpoints reviewed in [38]. These include decreases in pulmonary function, increased airway responsiveness, bronchitis, fibrosis, lung cancer and increased incidence of respiratory infection; in addition to these pulmonary effects metal fume fever is frequently observed in welders [38,39]. This systemic condition is considered to be caused by inhalation of zinc oxide fumes and it is characterised by acute onset of a flu-like illness accompanied by a dry cough, dyspnea, muscle aches, headaches and fever [40]. Metal fume fever is usually experienced in the first periods of exposure and on Mondays, with the symptoms declining as the working week progresses. Welding fume has been studied in both animals and in cells in culture, and in both it produces marked pro-inflammatory effects [10,41,42]. These effects are driven largely by the transition metals [10,42,43] which undergo redox-cycling resulting in oxidative stress.
Nanoparticulate carbon black
Carbon black (CB) is a low solubility particle produced industrially from incomplete thermal decomposition of hydrocarbons [44] in which the process is controlled to achieve pre-defined and reproducible particle sizes and properties suitable for a diverse range of industrial applications. Unlike the other CDNP described here NPCB is not accidentally produced, and is an industrial product but it clearly classifies as a CDNP. In thermal-oxidative processes such as the furnace black process, various types of hydrocarbon are sprayed into a natural gas-fired furnace and quenched with water to prevent complete burning [45]. The carbon black particles so-formed are complex, with a degenerated graphitic crystallite structure and high power electron micrographs clearly show irregular layered graphitic plates. [44]. The structure of carbon black is described as nodules, the roughly spherical primary structural elements, aggregates which comprise fused, connected particles and agglomerates, which are undispersed clusters of aggregates. CB has been studied extensively as to its toxicology, especially as an example of a low toxicity, low solubility particle not complicated by harmful levels of toxicologically-relevant organics or metals [23]. In long-term animal studies CB was found to be a carcinogen although rat lung overload very likely plays a role in this affect [46].
The smallest nodule-or particle-sized CB comprises primary particles in the low tens of nanonetres size range. CB with smallest primary particle sizes produces the highest optical density (jetness) compared to larger particle sizes, placing this material in demand for colouring enamels, acrylics and plastics, as well as inks and paints [44]. This nanoparticulate CB (NPCB), as it has come to be known, comprises a portion of the overall CB industry. NPCB was able to cause detectable but low level pro-inflammatory effects in rats following 7 hours inhalation exposure [47] and also following instillation [11] and this appears to be a consequence of the high surface area area per unit mass [14]. In cell studies NPCB has been shown to cause oxidative stress, pro-inflammatory gene transcription [48] stimulation of phagocytosis at low doses and inhibition at higher doses [49]. In studies of the health status of individuals working in the carbon black industry there is evidence of abnormalities in chest radiographs and respiratory morbidity, but equivocal findings on lung cancer [50-52]. However, none of these studies analysed a worker population exposed solely to NPCB. NPCB has been used quite extensively in particle toxicology as a model particle and nanoparticles, so there is a considerable existing database on its toxicity in vitro and in vivo [53].
Coal fly ash
Pulverised coal combustion is a commonly-used and efficient method of coal burning in power stations. In pulverized coal power stations the pulverised coal is blown into the furnace and burned off producing a fly ash emission. This particulate emission is controlled by number or methodologies including electrostatic precipitators, filters scrubbers and mechanical collectors [12]. However, these control measures are not 100% effective and some particles are released into the environment.
Toxicology studies have in general examined unfractionated pulverised coal fly ash (CFA) and these have shown generally low toxicity [54], however bioavailable iron has been reported to underlie an ability to generate oxidative stress [55,56]. Coal fly ash-exposed rats have been shown to exhibit increased susceptibility to infection [57], while a specially-prepared cloud of ultrafine (nanoparticulate) coal fly ash induced adverse effects on guinea pig lung function [58]. A recent study systematically examined the effect of fractionated coal fly ash in pulmonary inflammation and a nanoparticle fraction was available [12]. This study showed greatly enhanced potency of the nanoparticulate fraction compared to the fine and coarse fractions, as seen by enhanced ability to cause lung inflammation and kill macrophages in culture. The nanoparticle fraction was not especially enriched for toxic metals and the increased toxicity of this fraction may be a result of the high surface area, allowing redox reactions to take place.
Molecular toxicology mechanisms driving the inflammatory effects of CDNP in lungs
Based on published studies we hypothesise that CDNP have their effects through common pathways that produce inflammation and that oxidative stress is the lead effect driving the adverse health effects. Table 1 shows the CDNP studied in this paper along with a description of their origin and reported health effects in animals and humans.
There is considerable mechanistic data describing the molecular events flowing from the deposition in the lungs of the different CDNP under discussion here and this is outlined in Figure 2. Figure 2 shows that different components of different CDNP can cause oxidative stress that acts through well-documented redox-sensitive pathways, such as MAPK and NF-κB, to cause inflammation. Although the components that mediate these effects differ greatly between the different CDNP, there is commonality through their ability to cause oxidative stress and inflammation.
Figure 2 Diesel exhaust particles
As described above DEP causes inflammation in rat lungs [28,59] and in human lungs [60] following short-term, high level exposure. Evidence of the oxidative properties of DEP in vivo is shown by increased level of 8 OH dG, the oxidative adduct of hydroxyl radical, in the lungs of rats following exposure and in cells in culture treated with DEP [61,62]. DEP causes oxidative stress in a number of models in vitro such as oxidation of low density lipoprotein (LDL) [63] and in exposed epithelial cells [9,64]. The component responsible for the oxidative stress and subsequent pro-inflammatory signaling is principally the organic fraction [9,30,64,65], although transition metals may also be involved [37,66]. The organic fraction either contains, or can be metabolized to, species such as quinones that can redox cycle in cells to generate reactive oxygen species [17]
Activation of signaling pathways for pro-inflammatory gene expression is seen in a number of studies using DEP; these include MAPK activation [30,67-69] and NF-κB activation [30,70]. As would be anticipated, activation of these pathways culminates in transcription of a number of pro-inflammatory genes such as IL-8 in epithelial cells treated in vitro [71] and in human lungs exposed by inhalation [72]. TNFα has been reported to be increased in macrophages exposed to DEP in vitro [73] and IL-6 is released by primed human bronchial epithelial cells exposed to DEP [74]. Increased expression of the GM-CSF gene is reported in human epithelial cells exposed to DEP; in humans exposed to short-term high levels of DEP similar to those encountered in a busy garage, bronchial biopsies showed increased GROα and RANTES expression in the bronchial wall [75].
Nanoparticulate Carbon black (NPCB)
As described previously, at high exposures carbon black causes overload tumours in rats [27,46]. NPCB causes inflammation and the onset of rat lung overload tumours at a lower lung mass burden than larger, respirable CB [76]. This reflects the reliance of rat lung overload on the particle surface area burden [25], which is much greater for a given mass of NPCB than the same mass of non-NP respirable CB. Even at low lung burden NPCB showed evidence of mild pro-inflammatory effects whilst respirable CB did not [47]. Similar greater inflammogenicity of NPCB than respirable CB has been described with instillation models, [11,77].
Reactive oxygen species production has been measured with NPCB using in vitro cell-free systems [78,79] and oxidative stress has been demonstrated in exposed cells [48,80]. The chemical basis of the ability of NPCB to cause oxidative stress [78] is unknown, but unlike highly soluble welding fume, ROS production is not related to metal or any other soluble component [77]. In a cell free system the NPCB particles and similarly polystyrene NP, induce ROS production [81], suggesting that the surface reactivity is sufficient. However, in cells this ability may also be related to increased influx of extracellular Ca++ ions seen with NPCB [82]. Oxidative stress raises intracellular calcium by increasing release of Ca2+ from the endoplasmic reticulum, by enhancing ingress of Ca2+ through the plasma membrane calcium channels and through inhibition of Ca2+ transport out through the ATPase pumps in the plasma membrane [83]. Oxidative stress caused by NPCB is translated into activation of NF-κB and IL-8 gene expression in epithelial cells in vitro [48], while both oxidative stress and calcium are implicated in activation of AP-1 and TNFα production in macrophages [84]. A recent study reports that NPCB causes oxidative stress-mediated proliferation of airway epithelium, involving the Epidermal Growth Factor Receptor and the ERK cascade [85].
Welding fume
Exposure to welding fume nanoparticulate in humans is associated with inflammatory cytokine increases in the bronchoalveolar lavage (BAL) [86-88] and systemic oxidative stress [43]. The ability of welding fume to generate free radical is abundantly clear, even in a cell-free environment with only H2O2 acting as a reductant [42]. Rats exposed to welding fume show marked pulmonary inflammatory responses [42,89,90] and lipid peroxidation indicative of oxidative stress [42]. In a comprehensive study of the molecular signaling pathways leading to inflammation with welding fume, McNeilly et al demonstrated that the pro-inflammatory effects of welding fume in vitro [10] and in vivo [91] were entirely driven by oxidative stress arising from the soluble transition metal component. Epithelial cells treated with welding fume or the soluble transition metals from them showed oxidative stress leading to MAPK-dependent (manuscript in preparation) NF-κB and AP-1 activation leading to IL-8 gene transcription [10]. For welding fume nanoparticles, therefore, the soluble transition metals appear to be the primary mechanism of oxidative stress and inflammation.
Coal fly ash
In the past coal fly ash has been shown to have relatively low toxicity, for example lower than coal or quartz [54]. Recently there has been increasing interest in the ability of CFA to release bioavailable iron which can redox cycle to produce oxidants [55,92]. One study showed that the ability of a CFA standard to induce IL-8 release from epithelial cells was dependent on size, with the smallest size fraction (<1 μm) containing the most IL-8-stimulating activity [92]; this was due to the fact that the bioavailable iron was concentrated in this fraction. There was no attempt in this study to collect a nanoparticle fraction. In a study especially relevant to our review of CDNP, Gilmour et al [12] demonstrated that the nanoparticulate fraction of sub-bituminous coal was much more potent than any other fraction in causing lung inflammation and cytotoxicity in vitro, when compared on a mass basis. This was not obviously linked to enrichment of Fe or any other toxic metals in the nanoparticulate fraction. Electron microscopic examination of the nanoparticulate fraction of coal fly-ashes from bituminous and low-rank coals showed abundant discrete crystalline particles rich in Fe, Ti, and Al crystalline phases down to 10 nm in size whilst low-rank samples contained considerable amounts of alkaline-earth element aggregates in the form of phosphates, silicates, and sulfates and mixed species. Importantly, all coal fly-ash samples exhibited carbonaceous particles in the form of soot aggregates with primary particle size typically between 20 and 50 nm sometimes mixed or coated with multi-element inorganic species [93]. It seems possible that the soot particles were an important component in driving the adverse effects in ways analogous to the effects of diesel soot and NPCB.
There are no further studies on the ability of the NP fraction of CFA to cause oxidative stress or signal for inflammatory gene expression but such studies are warranted and we would predict that, along with the other CDNP discussed here, the pathway shown in Figure 2 would be activated, leading to inflammation.
CDNP and the cardiovascular system
Large scale epidemiological studies suggest that inhaled ambient air pollution particles (PM10) may also have effects on the cardiovascular system. Small increases in particulate levels are associated with more cardiovascular deaths and hospital admissions in both time-series [94,95] and population studies [96,97]. Cohort studies have documented an association between elevated particulate and the onset of acute myocardial infarction [98,99], an increase in heart rate [100] and a decrease in heart rate variability [101]. Human chamber studies delivering concentrated ambient particles (CAPs) have confirmed that particulate can have direct effects on cardiovascular physiology with alterations in heart rate variability [101] and brachial artery diameter [102].
This CAPS work has not been able to discriminate which size fraction is responsible for any effects but the hypotheses relating to cardiovascular effects of CAPS (and PM in general) are as follows 1) particle-induced lung inflammation affects the endothelium, thrombotic potential, fibrinolytic balance and atheromatous plaque activity in ways that favour plaque rupture and thrombosis; 2) particles enter the interstitium and/or cause inflammation which affects the autonomic nerve endings that regulate the heart rhythm leading to dysrhythmia; 3) particles translocate to the blood and have direct effects on the endothelium, plaques and thrombogenic mechanism. In various models NP are shown to be highly potent in these three areas of effect i.e. NP are very potent at causing inflammation, they interstitialise readily and they can gain access to the blood. For these reasons CDNP, the principal NP in ambient air, are implicated in the cardiovascular effects of PM in these CAPS studies.
These studies address the population risks associated with ambient particulate, but do not allow any assessment of the contribution of individual air pollutants. In the Copenhagen Male Study the influence of occupational exposure on cardiovascular risk was assessed. In these men, 5 years or more of occupational exposure to welding fumes doubled the risk of myocardial infarction with exposure to solder and plastic fumes conferring similar increases in risk [103].
Figure 3 shows the two predominant mechanistic pathways hypothesised to mediate the adverse cardiovascular effects of CDNP [104,105]. On the right of Figure 3 inflammation caused by the CDNP is seen to affect the systemic inflammatory response and cause destabilisation of atheromatous plaques. On the left, bloodborne CDNP affect endothelial cells, platelets and plaques directly to enhance thrombogenesis.
Figure 3 CDNP are capable of eliciting an inflammatory response in the lung which could have stimulatory effects on leukocytes and other cells in the atherosclerotic plaques, leading to their rupture. This could occur is in the absence of any transfer of CDNP from the lungs to the circulation as it might rely on cytokines and other mediators which were released into the circulation in response to events in the lungs, affecting events in the plaques. However, CDNP could also have effects on the cardiovascular system by virtue of their ability to gain access to the bloodstream. This has been demonstrated in animal studies for a range of nanoparticles delivered by inhalation and instillation [106-111]. Once circulating, CDNP may interact with the vascular endothelium, or have direct effects on atherosclerotic plaques by entering them and causing local oxidative stress and pro-inflammatory effects similar to those caused in the lungs. Increased inflammation could destabilise the coronary plaque, resulting in rupture, thrombosis and acute coronary syndrome [104]. Furthermore, particles may interact with circulating coagulation factors to promote thrombogenesis. There is, as yet no published data demonstrating that the CDNP described here gain access to the blood in humans, but the animal studies suggest that this is a plausible hypothesis.
CDNP and the brain
Recent work by Oberdorster and colleagues has demonstrated the transfer of radiolabelled nanoparticulate carbon from the nose of rats directly into the brain [112]. It is postulated that this transfer occurs via the olfactory nerves which run from the roof of the nasal cavity in to the olfactory lobes of the brain. However, this part of the brain is well vascularised, providing a potential systemic portal of deposition. If size is the factor that drives these effects then there is some concern that CDNP may have the general property of tropism to the brain. A search of relevant terms showed no published studies pertaining to brain transfer of diesel or coal fly-ash. The best-studied of the CDNP in regard to brain transfer is welding fume. Rats exposed to stainless steel welding fume over 60 days showed accumulation of manganese in the blood and liver but, most importantly, also in various areas of the brain [113]. Erikson et al [114] showed that inhalation exposure to manganese sulphate and manganese phosphate produced oxidative stress in the brains of rats as shown by metallothienin and glutamine synthetase levels; this varied between sexes and with age. In a sub-chronic exposure study with manganese phosphate there was accumulation of Mn in the brain but there was no associated loss of neurons or neurobehavioural effects [115]. Studies of workers exposed to welding fume, however, show clear evidence of neurological disease [116] and Mn is implicated in these effects [117]. It is not known whether the welding fume particles themselves are transferred to the brain or only the soluble Mn and other metals. However, soluble metals are very rapidly lost from welding fume particles [10] and a soluble salt of Mn was more efficient than an insoluble Mn salt in gaining access to the brain following inhalation exposure in rats [118]. Further work is required to improve our understanding of the factors dictating the transfer of CDNP and their associated soluble contaminants to the brain.
CDNP and the liver and spleen
As discussed above, NP of various types are reported to gain access to the blood. Stuart showed in 1970 that the liver and the spleen turn black following the instillation of carbon particle into the blood because the spleen and the liver have sinusoidal phagocytes which are in contact with the blood – the 'littoral' macrophages [119]. There is existing evidence that particles can gain access to the blood since coalworkers, who receive considerable exposure to particles, show greater numbers of particles in their spleen and liver at autopsy than non-coalworkers [120]. The amount of particulate in the spleen and liver, which can be assumed to have travelled via the blood, was greater in coalworkers with more severe lung disease, suggesting that inflamed/damaged lungs may be more susceptible to egress of particles into the blood than normal lungs. The normal function of the littoral macrophages of the spleen and liver is probably to 'sample' for antigens and xenobiotics in the blood and to quickly remove any bacteria that gain access to the blood. We may therefore anticipate that any NP that gain access to the blood will be taken up by these littoral macrophages in the spleen and the liver. Particles may also reach hepatocytes and other spleen cells with consequences that are presently unknown. However, in line with the above arguments pertaining to the potential role of NP in the adverse effects of PM10, we may anticipate that increases in acute phase proteins during periods of high PM [121] could be due to direct particle effects on the liver, the primary source of acute phase proteins [122]. In the single study that has so far been published concerning the effects of bloodborne NP on liver function of healthy mice, CDNPs induce platelet accumulation in the hepatic microvasculature that was associated with pro-thrombotic changes on the endothelial surface of hepatic microvessels. [123]. The accumulation of particles in the liver exerted a strong pro-coagulatory effect but did not trigger an inflammatory reaction. The effects of a particle burden on the spleen are unknown but could include adjuvant effects as observed with diesel particles and antigens in the lung [124].
CDNP and genotoxicity
The genotoxic properties of various particle types has been the focus of several studies concerned with elucidating the role such properties play in particle-associated pathogenicity [34]. However, the mechanisms involved in particle-induced genotoxicity remain poorly understood as particles are uniquely complex compared with soluble genotoxic/carcinogenic compounds, due to their physical and chemical characteristics [125]. There is evidence that 3 of the 4 CDNP studied in this review (diesel, NPCB and welding fume) are carcinogenic in humans or rats [20,38,126,127]. As mentioned earlier, DEP consist of a carbon core with adsorbed PAHs, quinones and transition metals. Genotoxicity, may therefore be caused by the direct (primary) interaction of PAHs which are known to cause DNA adduct formation [128] or alternatively via DNA strand breakage due to the production of reactive oxygen species generated by associated transition metals [8]. Carbon black particles are generally almost free of adsorbed organic compounds; however they have been shown to produce lung tumours in rats following chronic inhalation and instillation studies [127,129]. This indirect (secondary) genotoxicity pathway involves the phenomenon of lung particle overload resulting in a chronic inflammation and hence excessive ROS production leading to DNA damage. Studies by Knaapen et al, have demonstrated that co-incubation of rat lung epithelial cells with activated neutrophils in vitro stimulate the formation of the oxidative DNA lesion 8-OH-dG [32]. Less research has been carried out on the genotoxic effects of welding fumes. Some of the major components of welding fumes include iron, manganese, chromium and in particular hexavalent chromium (CrVI) chromium which has been shown to increase levels of 8-OH-dG in rats after inhalation exposure [130]. Yu and co-workers showed that rats exposed for 30 days to manual metal arc stainless steel (MMA-SS) welding fumes, exhibited increased DNA damage as measured by the comet assay and immunohistochemistry for 8-OH-dG [131]. Studies investigating the genotoxic capacity of coal fly ash have shown a role for particulate size and iron release leading to radical generation and oxidative DNA damage [132,133] as well as increased sister-chromatid exchange (SCE) frequencies in peripheral blood lymphocytes from workers occupationally exposed to coal fly ash [134].
Conclusion
Combustion is considered a source of toxic chemicals and particles [1] and this review has focused solely on the toxicology of the particulate component. Emanating, as they do, from very diverse combustion scenarios, CDNP have received variable and piecemeal research attention. This review has used diesel soot, welding fume, carbon black and fly-ash as exemplar CDNP to demonstrate that different CDNP in fact have many properties in common that suggest that they can be viewed as a coherent class of particulate toxins. They are unified by their combustion origin, small size, universal mechanism of injury and common properties of translocation which have the potential to mediate a range of adverse effects in the lungs and other organs. Notably, the CDNP studied here all have the potential to cause oxidative stress as an integral part of their pathogenic mechanism. This oxidative stress can cause inflammation and its local and systemic acute and chronic sequelae, as well as causing oxidative adducts in epithelium that can contribute to carcinogenesis. CDNP originating from any source can therefore be considered a potential hazard to the lungs and other systems through the pathways of oxidative stress, inflammation and carcinogenesis. This is summarised in Figure 4 where the link between oxidative stress and inflammation-related effects are shown along with carcinogenic effects of oxidative stress.
Figure 4 Of course the temperature, conditions and substrate for combustion mean that there is considerable heterogeneity in composition between, for example, welding fume and diesel soot. Therefore the key oxidative stress event may originate from different components depending on the particle under consideration. Components that may cause oxidative stress include CDNP-associated surfaces, metals or organics; this oxidative stress then acts through oxidative stress-responsive signalling pathways to affect responses such as inflammation and proliferation. In addition, oxidative stress can also cause oxidative genotoxic DNA adducts such as 8-OH-dG whilst bulky PAH-derived adducts may also form. Both adduct types can lead to mutation. Different components of CDNP may interact to enhance the level of oxidative stress, as in the case of metals and organics interacting in the redox-cycling of quinoid organics [17] or CDNP surfaces and transition metals interacting additively in their ability to cause inflammation [81].
In addition to the local inflammatory effects of CDNP at their sites of deposition they have the potential to translocate away from their site of deposition to the blood and brain. Bloodborne particles will be delivered to the cardiovascular system, spleen and liver. The cardiovascular system has emerged as a target for the effects of PM10 [104, 135] and it is likely that the CDNP in PM in fact mediate this effect [3-6]. They may do this through causing inflammation in the lungs which then impacts on inflammatory processes in the atheromatous plaques that govern their stability and development. Inflammation in the lungs may also affect the thrombotic potential of the blood. Alternatively, direct effects of bloodborne particles on the endothelium, clotting system and on atheromatous plaques could be responsible. Bloodborne CDNP may deposit in the spleen, liver and heart and in these situations they may have numerous additional adverse effects.
Combustion is a ubiquitous in the modern world and the generation of CDNP is correspondingly omnipresent. Much research emphasis has been placed on traffic-derived CDNP in PM and rightly so as they are the source of most CDNP in our cities where the greatest potential for human exposure exists. However it is also clear that there is considerable potential for mixed exposures to occur in specific scenarios, e.g. a welder working in a busy street. The interactions between different particle types are unknown but the common pathway of oxidative stress means that there is potential for additive or synergistic effects. Furthermore the involvement of oxidative stress in a number of chronic diseases such as asthma, COPD and coronary artery disease argues a powerful case for existence of susceptible populations, already well-studied in the adverse effects of PM.
CDNP represent an interesting and ubiquitous category of pathogenic particles whose adverse effects are substantial and additions to the list of candidate CDNP are to be anticipated. More research is warranted into the effects of CDNP at numerous levels from factors dictating their translocation between organs and tissue to their effect in sub-cellular signalling. Viewing CDNP as a class of particles with common origins and a strong hypothesis-based understanding of their toxic mechanisms should provide impetus and direction to research on existing and new CDNP, leading to a greater understanding.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
KD planned the study and contributed to all the sections; LT contributed to all of the sections; LAJ contributed to all of the sections except the cardiovascular section; RD wrote the section ongenotoxicity and contributed to the other sections; DN and NM wrote the section on cardiovascular effects and contributed to all of the sections; WMacN contributed to all of the sections; VS contributed to the planning of the paper and contributed to all of the sections.
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Plant MethodsPlant Methods1746-4811BioMed Central London 1746-4811-1-81627090610.1186/1746-4811-1-8MethodologyA streamlined method for systematic, high resolution in situ analysis of mRNA distribution in plants Drea Sinéad [email protected] Julia [email protected] Brian [email protected] Peter [email protected] Liam [email protected] John H [email protected] John Innes Centre, Norwich NR4 7UH, UK2 Department of Molecular, Cellular and Developmental Biology, P.O. Box 208104, Yale University, 266 Whitney Ave., New Haven, CT 06520-8104, USA2005 6 10 2005 1 8 8 31 8 2005 6 10 2005 Copyright © 2005 Drea et al; licensee BioMed Central Ltd.2005Drea et al; licensee BioMed Central Ltd.This is an Open Access article distributed under the terms of the Creative Commons Attribution License (), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
In situ hybridisation can provide cellular, and in some cases sub-cellular, resolution of mRNA levels within multicellular organisms and is widely used to provide spatial and temporal information on gene expression. However, standard protocols are complex and laborious to implement, restricting analysis to one or a few genes at any one time. Whole-mount and reverse transcriptase-PCR (RT-PCR) based protocols increase throughput, but can compromise both specificity and resolution. With the advent of genome-wide analysis of gene expression, there is an urgent need to develop high-throughput in situ methods that also provide high resolution.
Results
Here we describe the development of a method for performing high-throughput in situ hybridisations that retains both the high resolution and the specificity of the best manual versions. This refined semi-automated protocol has the potential for determining the spatial and temporal expression patterns of hundreds of genes in parallel on a variety of tissues. We show how tissue sections can be organized on microscope slides in a manner that allows the screening of multiple probes on each slide. Slide handling, hybridisation and processing steps have been streamlined providing a capacity of at least 200 probes per week (depending on the tissue type). The technique can be applied easily to different species and tissue types, and we illustrate this with wheat seed and Arabidopsis floral meristems, siliques and seedlings.
Conclusion
The approach has the high specificity and high resolution of previous in situ methods while allowing for the analysis of several genes expression patterns in parallel. This method has the potential to provide an analysis of gene expression patterns at the genome level.
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Background
In situ hybridisation (ISH) is the method of choice for describing the spatial expression pattern of a given gene. High resolution protocols provide cellular and even subcellular resolution. In multicellular organisms, ISH complements northern blotting, RT-PCR and microarrays, where the extraction of RNA from whole tissues invariably results in the loss of spatial information. Microarrays allow many genes to be studied in parallel and are currently one of the most powerful tools to study gene expression. However, microarray outputs often need to be verified by independent methods, such as ISH [1,2], and because these downstream methods have a much lower capacity, verification is usually limited to one or a few genes. ISH must therefore be made more efficient and less time-consuming.
A number of variations on the traditional in situ protocols have been reported, including whole-mount ISH (WISH) [3], in situ PCR [4,5] and the use of vibratome sectioned tissues [6]. The main shortcoming of ISH is undoubtedly the low-throughput nature of the technique. In situ PCR (ISPCR) and RT-ISPCR are elegant techniques that can increase both sensitivity and throughput but they are at best only semi-quantitative [5] and it is desirable first to ascertain the expression pattern by conventional means in order to establish suitable conditions for each probe.
Efforts to make the ISH technique into a highly parallel, systematic process have been successful in flies and primitive chordates [7-9]. Attempts have been made to address this issue in plants using WISH and in situ PCR techniques [10,11] although actual throughput remains undetermined.
High-throughput protocols used for animal embryos normally involve whole-mount methods [7,8,12], thus avoiding the need to section material. The challenges in applying similar techniques to plants include the large size of the tissues and the variable nature of the cell wall. These factors can variably compromise the penetration of probe and make microscopic examination more difficult and time-consuming. WISH is a possibility for Arabidopsis roots and seedlings [11], at least for low- and medium-throughput. However, when performed on other larger tissues, such as seeds, WISH may require embedding and sectioning after the in situ has been performed to evaluate the results [13]. Therefore, the high-throughput advantages gained in the early stages of such procedures are effectively cancelled out.
Promoter fusions with reporter genes are another option for cellular localisation of transcripts but this approach has recognised shortcomings [14]. Elements controlling gene expression are known to be located not only in the traditional promoter region upstream of the coding region, but intergenically and, potentially, a considerable distance from the gene [15,16]. The resources required for mass transformation and the fact that not all plant species are amenable limits the application of this approach to well-studied model species.
As well as providing an independent means of screening genes for the desired expression profiles (differential expression, domain specific expression etc.) in gene discovery efforts, we envisage that high-throughput mRNA ISH is entirely feasible and will complement the ever-growing microarray data resources available [2,29]; . Recently real-time RT-PCR has been adapted for high-throughput processing [30]. While these approaches provide a wealth of expression data for functional genomics, they are unable to provide the spatial resolution that often directly reflects functional involvement in developmental processes. This inherent drawback in microarray technology has been elegantly addressed using cell sorting to isolate pure populations of a given cell type from the Arabidopsis root. However, this innovative approach is limited to species where suitable and diverse cell line markers are available [31]. It is further limited to tissues whose cells can be separated and sorted: roots are susceptible to protoplasting enzymes but shoots and many other tissues are not.
With these considerations in mind, we have deconstructed the "traditional" ISH protocol and developed a protocol for ISH that retains high resolution and specificity but integrates a degree of automation to a standardised and streamlined protocol. We have used wheat grain and Arabidopsis floral meristems as tests for this new high-throughput protocol and show that it is capable of highly parallel processing.
Results and discussion
Generating an integrated protocol
One of the main challenges in mRNA ISH is developing an economical protocol that is applicable to large batches of different probes while maintaining a high level of specificity, sensitivity and resolution. The level of economy must be maintained throughout the process to provide a systematic high-throughput level of work. Figure 1 summarises the ISH protocol as described in previous reports [17,18] and describes the five main components of the entire procedure. We have examined each of these components individually, but in context of the overall technique, with the view to (i) simplification, (ii) automation and (iii) optimisation. As a practical accompaniment to the following description we have included a step-by-step version of the protocol as used at the bench (Additional file 1).
Figure 1 Flowchart summarising mRNA in situ methodology.
(i) Plant tissue preparation
Plants grown under desired conditions were harvested, trimmed to allow penetration of solutions, and immediately fixed. These steps were carried out manually in both the standard and new protocols but, in order to automate the new protocol, the samples were placed in a Tissue-Tek Vacuum infiltration processor for further processing.
The Tissue-Tek machine permits use of a combination of vacuum and pressure to exchange solutions at defined times and temperatures, relieving the operator of a large number of tedious steps as well as standardising the process. The initial fixation, dehydration and wax infiltration steps can take over a week to complete using the manual protocol, whereas the automated protocol reduces this to 24 hours. We evaluated various plant tissues prepared by both methods for tissue integrity and preservation of mRNA (signal strength). Arabidopsis tissues are equally well preserved and stained by both protocols but the automated procedure provided enhanced preservation in developing wheat seeds (data not shown). Many plant tissues, such as mature leaves, are very difficult to embed in wax using the manual protocol but even these recalcitrant tissues can be accommodated by altering the timing or pressure of processing.
Orientating and mounting in block for sectioning are skilled steps that normally require manual processing and these steps are therefore identical in both manual and automated protocols. Blocking up was carried out by hand to ensure favourable orientation but is facilitated by a dedicated embedding station. Sectioning cannot be automated due to the need to continuously assess section quality. However, the arrangement and number of tissue sections on the slide was made uniform using adherent, but removable, silicone isolators (Figure 2). This allowed the parallel screening of multiple probes on the same slide containing up to eight sections, each section in an isolated well.
Figure 2 Arrangement of sections and organisation of probes: (A) Silicone isolators to position sections on slides. Positions are shown 1 to 8. (B) Sections placed in postions 1 to 8, dried and shown after removal of silicone isolator. (C) Sections after de-waxing step. (D) Sections in hybridisation chambers. A-H cross refers to plate position of the probes (F). (E) Sections after colour development. (F) Organisation of probes in 96 well plate. (G) Organisation of Arabidopsis sections showing larger format hybridisation chamber on lower slide.
When the sections had adhered to the slide, and the silicone isolators were removed, the slides were treated to remove wax and to make the sections receptive to the hapten-labelled probes. These down-stream treatments are universal to all tissues and appropriate for automation. However, these treatments are complex and we evaluated which ones were functional (i.e. produced an enhanced, yet specific signal) and which were redundant. Using a training set of probes and the traditional manual protocol, we systematically eliminated or reduced each step in the protocol and visually evaluated the final result. Slides were processed in parallel but with a proportion subjected to a protocol that omitted one or more steps normally used. This led to a reduction in the number of ethanol dehydration steps and elimination of the 're-fixation" step after proteinase K treatment. Some steps, although not absolutely essential, appeared to enhance the reliability of the process and these were retained: for example, acetic anhydride treatment was found to reduce background (especially on poly-lysine coated slides) and we increased the time allowed for de-waxing in xylene while applying agitation using the VP2000 slide processor (see below).
Finally, using a basic open-plan slide processor (VP2000), common in medical cytology labs, a reduced section-processing protocol with essential steps only was automated and made completely hands-off. This also eliminates much of variation possible in a multi-step, closely timed procedure and led to more reproducible signals.
(ii) Probe-making
In the manual protocol, individual probes were made from linearized plasmids. This necessitates the analysis of each clone for suitable restriction sites. To eliminate time-consuming individual analysis, we used a PCR strategy to produce linear plasmid inserts for probe transcription. This allows the production of large number of probes in parallel, particularly if the original clones are in a common vector. For a collection of genes inserted in the same orientation in a common vector, as found in most gene libraries, a common pair of primers can be designed to the flanking regions of the vector and used to prepare all probes. Thus, for the wheat cDNAs we designed a pair of primers against the flanks of the polylinker region for the pINCY vector (a derivative of the pSPORT vector from Invitrogen). The 3' primer contained a T7 RNA polymerase transcription site (see Figure 3). Thus, all the antisense probes were amplified using the same primer pair and subsequently all transcribed with T7 RNAP (T7 RNA Polymerase). This strategy was scalable: templates were produced in batches of 96 (using a liquid dispensing Q-bot) and yield was estimated on a 96 well multi-slot E-gel (Invitrogen).
Figure 3 Schematic showing the production of templates for probe labelling. PCR reactions used cDNAs in pSPORT-derived vectors as template with a T7-linked 3' primer and a 5' primer based on vector sequence. T7 RNA polymerase was used to make all antisense probes.
We also assessed the yield of different RNA polymerases. Although in vitro transcription is possible with any one of T7, T3 or SP6 RNAPs, T7 was undoubtedly the most efficient providing high yields of almost every cDNA. SP6 was found to be the least consistent, but could produce reasonable yields for a low proportion of clones. We did not investigate this further and routinely used T7 RNAP.
Next, we minimised the protocol for the in vitro labelling of the PCR products to the following essential steps only: transcription with dig-UTP (digoxigenin uridine triphosphate) for two hours, immediate hydrolysis for 30 minutes for all probes and immediate precipitation with ammonium acetate (which also neutralises the carbonate hydrolysis buffer) and ethanol for 30–60 minutes. The labelling procedure was performed in 96-well plate format and took a total of four hours. To monitor transcription efficiency, a small aliquot from each batch of transcription products was run on an agarose gel to ensure that transcription is working and we repeated this test after the hydrolysis step.
The manual protocol describes individual hydrolysis times for each probe depending on the length of the DNA but we have found that once probes are hydrolysed to below a certain length, more detailed definition of an optimal probe size is not required. Hydrolysis fragments the probe and, in theory, the small fragments can access the RNA within tissue sections. However, subjecting the same transcript to several various hydrolysis incubations had little effect on signal strength. As template lengths ranged from 0.5 to 1.5 kb, we subjected each labelled transcript to the same hydrolysis treatment. When assessed on an agarose gel, all probes produced small fragments within a comparable range. Labelled transcription products were resuspended in TE and could be stored at -70°C where they remain usable for at least several months.
Accurate quantification of probes by dot-blot requires serial dilution but a simple qualitative test using a 1:100 dilution of each probe is usually sufficient to assess probe quality. If labelling is detected at this dilution, the same dilution in Hybridisation Solution (HS) can be denatured briefly and added to the slide for hybridisation. Since the probes are single stranded RNA, they are very prone to degradation during storage. Stability is much enhanced by diluting probes directly into the HS and storing them at -20°C: the HS contains 50% formamide which inactivates RNases.
(iii) Hybridisation and signal detection
At the end of the pre-treatment in the VP2000, the slides were dried and the silicone incubation chambers were carefully applied (Figure 2D). 40 μl of HS/probe mix was applied to each chamber in a standardised order from 96 well plate to slide (i.e., plate A1-A8 to slide 1 etc.,) such that 96 probes (including controls) can be applied to 12 slides (Figure 2F). Hybridisation was overnight at a standard 50°C in a conventional incubator on horizontal slide racks.
Use of hybridisation chambers with the same dimensions as the isolator allowed at least 8 different probes to be applied to a single slide. A range of sizes of isolator and hybridisation chambers are available and different sizes may be better suited to different tissue types (Figure 2G). Use of glass coverslips for incubation steps in the manual protocol normally requires prolonged, but gentle, washing to remove coverslips without damaging the underlying sections. However, the chambers can be quickly removed and the slides were loaded immediately for a short washing step in the VP2000. In addition, we also evaluated the various post-hybridisation steps and found that the RNase treatment step could be omitted completely. The RNAse step is thought to decrease background staining in ISH but slides prepared with or without RNase are essentially identical.
After standard incubations in blocking solution and Anti-DIG-AP (alkaline phosphatase) antibody, the slides were developed colorimetrically and a sample of the output is seen in Figure 2E. Using the colorimetric system rather than fluorescence means the progress of development can be monitored under a dissecting microscope and the problem of plant tissue autofluorescence is avoided. All reactions were stopped simultaneously, and all slides mounted permanently and could be stored for image-capture on the microscope.
(iv) Processing of results
Previous methods using one probe per slide meant that microscopic analysis was time-consuming and involved positioning many individual sections for optimal picture quality. In the automated protocol, with uniformly arranged sections representing multiple probes on every slide, image capture can be streamlined and has the potential for further automation.
(v) Data collection and storage
Images are collected in order and were directly linked to spreadsheet or database records of the probes used in the screening. In a recently published experiment on wheat seed development [19], batches of 96 probes were used to generate 288 images of 3 developmental stages. These images were labelled in order A1-96, B1-96 and C1-96 for each plate and stored accordingly. Representative results from the wheat project are shown in Figure 4A–F and are taken from different stages screened to show signal detection in the varied cell layers in the endosperm and surrounding tissues. These results illustrate that the automated protocol provides at least cellular resolution and in many cases, subcellular. Very specific patterns are defined within even thick-walled cells (such as the transfer cells of the nucellar projection and modified aleurone), which are likely to be recalcitrant to whole-mount procedures. Various other cell types are equally well stained with appropriate probes: the small cuboid cells of the young integument layers, the highly-vacuolate cells of the 9 DAA (days after anthesis) endosperm and the early multi-nucleate, but unicellular, coenocyte.
Figure 4 Examples of gene expression patterns in developing wheat grains. ID numbers indicate the Incyte gene code and can be used to search the wheat in situ database at SCRI (user: guest; password: wheatinsitu). (A) transcript detection specifically in the nucellar epidermis at 3DAA (ID 702007486). Arrowheads indicate no expression in the innermost nucellar lysate or the coenocytic endosperm. (B) coenocytic endosperm at 3DAA (ID 702038349) (C) peripheral cells of the modified aleurone only show transcript accumulation of an unknown gene at 9DAA (ID 701965703) (D) gene expression of a plantacyanin orthologue throughout the modified aleurone at 9DAA (ID 702044644). This section is from the same grain as in (C) and the arrowhead indicates that the most peripheral cells showing signal in (C) are not expressing plantacyanin. (E) a proteinase inhibitor is expressed strongly in the outer layers of the central endosperm at 9DAA (ID 701965839) (F) in contrast to (E), a gliadin storage protein gene is expressed throughout the central endosperm but not in the outermost layers (indicated with arrowhead; ID 702007003)
We also evaluated the protocol on other species, including Arabidopsis. Floral meristems were fixed and processed using the automated procedure and probes prepared in 96-well format. Using a training set of 4 previously characterised genes histone H4, AP3, AG and stm [18,20-22] (Figure 5) alongside genes encoding a variety of other cellular functions, we show that this approach has the potential for systematic spatial analysis of gene expression in a model organism, with at least cellular resolution (Figure 6). The expression patterns of a set of ten genes are shown in figure 6 includes some previously characterised genes including AtREM1, encoding a plant-specific regulatory protein and expressed in the floral meristems [23] (Figure 6A), CRABS CLAW, encoding a helix-loop-helix regulator of carpel development [24] (Figure 6J) and the recently described CORONA gene encoding a leucine zipper regulator of vascular tissue [25] (Figure 6K). These patterns are very similar to previously published results, indicating that the new protocol is robust and can be used for a range of genes without specific tailoring to each gene.
Figure 5 The automated ISH protocol on Arabidopsis tissues. Developing flowers (A-C), developing siliques (D), ovules (E) and transverse sections of the shoot apical meristem (SAM) of 10-day seedlings (F-G). Probes used are for histone H4 (A, D, E, G), AP3 (B), AG (C) and stm (F). (A-C) were counterstained with the cell wall dye, Calcofluor, which produces a light blue colour. Expression of AP3 and AG in serial sections (B and C) shows the distinct patterns of expression in the petal/stamen primordia for the class B AP3 gene and in the carpel primordia for the class C AG. In (D) arrows indicate expression of histone H4 in the developing ovules but by this stage there is no expression in the silique/carpel wall. Expression is also detectable in the endosperm of the developing ovule as well as in the cotyledons and root meristem of the embryo (E). In (F) an arrow indicates absence of stm expression in the leaf promordia but histone is expressed here and in the slightly older leaves (G). IM, inflorescence meristem; FM, floral meristem, number indicates the approximate flower stage; s, sepal; ca, carpel; st, stamen; pe, petal; cw, carpel wall; ov, ovule; en, endosperm; em, embryo; sam, shoot apical meristem; lp, leaf primordium.
Figure 6 A selection of gene expression patterns (mostly transcription factors) in young Arabidopsis flowers (A-L). AGI gene annotations accompany each figure in the panel. Expression in young floral meristems is strong in A, B, C, G, J and L. The arrow in H indicates weaker but specific expression in the floral meristem and in K there is strong expression of CNA in the vasculature (VA). A, B D, E, F, H, and I have been counterstained with the cell wall stain, Calcofluor, which produces a light blue colour.
Conclusion
We describe a semi-automated system for highly parallel processing of ISH. We have introduced a substantial degree of automation to produce a system for performing high-throughput RNA-ISH on hundreds of plant tissue sections simultaneously without loss of resolution, specificity or sensitivity. This slide processing system has a capacity of at least 96 probes per week/per person for multiple developmental stages or experimental treatments and, with the exception of imaging, is scalable. Therefore, it is now feasible to contemplate genome-wide spatial analyses of gene expression at cellular resolution in both crop and model plant species.
To achieve genome-wide coverage in any species, certain prerequisites are necessary. First, sequences representing the expressed genes must be suitable for making probes and be available in an organized format. Second, the remaining manual steps must be further streamlined and, ideally, automated. Finally, automated image collection, analysis and quantification methods need to be developed.
Several projects are currently underway whose collective aim is to provide the expressed Arabidopsis genome as organized libraries of clones. Thus, a large proportion of Arabidopsis genes are currently available as trimmed ORFs (open reading frames) from SALK and ESTs (expressed sequence tags) from ABRC (Arabidopsis Biological Resource Centre) , or as 3' UTRs (untranslated regions) or as groups of structurally related genes from special projects such as REGIA (Regulatory Gene Initiative in Arabidopsis) . These collections tend to present genes in a consistent format that is amenable to automation. In most crop species, where the genomes are as yet incomplete, large EST collections are available. However, even these can provide useful information when used in conjunction with microarrays, where gene expression data can be confirmed and resolved to individual cell types and tissue layers [19].
Throughput is somewhat lower in Arabidopsis than in wheat, largely due to the time required for sectioning the smaller and more heterogeneous tissues. The proportion of usable sections is less and the tissue complexity requires greater imaging times. However, the smaller size of Arabidopsis facilitates whole mount approaches [3,11] and, used with confocal microscopy and fluorescent imaging, this could completely eliminate manual sectioning without compromising either resolution or throughput.
Further automation is therefore required. Recent developments in slide processing have provided improved and more cost-effective slide processors that automate all the steps from de-waxing to mounting, including hybridisation (unpublished results) but imaging and analysis remain significant rate-limiting factors. Automated imaging and analysis involving machine-learning, are essential to extend this approach to the analysis of whole genomes. Such approaches have been developed for the analysis of tissue microarrays in the analysis of protein expression in various cancers, though these technologies still involve protein immunohistochemisty more than mRNA in situ hybridization [26-28]. However, as they employ a similar colour-based detection system, the technology should be transferable.
Methods
Preparation of plant material
Wheat plants (variety Savannah) were grown under controlled environment conditions (16°C, 16 h light) and ears tagged daily at anthesis. Arabidopsis Col-0 was grown in glasshouse under a 16-hour light regime. Wheat grains harvested at 3, 6 and 9 DAA were trimmed and Arabidopsis floral meristems were removed just after bolting. All tissues were fixed in 4% paraformaldehyde, then transferred to the Tissue Tek VIP (Vacuum Infiltration Processor, Sakura) for an automated fixation/dehydration/infiltration process as follows: fixative 6 h 35°C; 70% ethanol 1 h 35°C, 80% ethanol 1.5 h 35°C; 90% ethanol 2 h 35°C; 100% ethanol 1 h 35°C; 100% ethanol 1.5 h 35°C (repeat for 2 h); xylene 0.5 h 35°C (repeat for 1 h and again for 1.5 h); wax 1 h 60°C (repeat once, then again twice for 2 h). All steps were performed under vacuum. Samples were then transferred to the Tissue Tek Embedding Console for embedding in paraffin wax.
Section preparation
Sections (14 μm) from wheat samples at the required stages and 8 μm sections from floral meristems were cut on a Leica Microtome (RM2125RT) and ordered on polysine slides containing the silicone isolators (Grace Biolabs). After drying down at 42°C overnight suitable sections were selected for pretreatment.
Pretreatment steps were performed using the VP2000 Slide Processor (Vysis) using the following program: xylene 20 min (twice); 100% ethanol 10 min, then through a 95%, 85%, 50%, 30% ethanol series (2 min each), PBS (3 mM NaH2PO4, 7 mM Na2HPO4, 130 mM NaCl) 3–4 min; proteinase K (2–3 μg/ml in 100 mM Tris, 10 mM EDTA pH7.5) 30 min at 37°C; glycine (0.2%) 2 min; PBS 3–4 min; acetic anhydride (0.5% in 0.1 M triethanolamine pH 8) 10 min; PBS 3–4 min, then back through the ethanol series. Slides were completely dry at this stage and could be stored at 4°C until hybridisation.
Generating templates and labeling probes
Wheat cDNAs for screening are supplied as inserts in a vector derived from pSPORT1. Primers were designed in order to append a T7 RNAP site to the 3' end of the insert with the other primer nested inside the native vector T7 RNAP site; T7.2 5' GAATTGTAATACGACTCACTATAGGGCCAGTGAATTGAATTTAGG 3' and R7.2 5'AGGGAAAGCTGGTACGCCTGC 3' (T7 RNAP promoter binding site underlined). Arabidopsis histone H4 was amplified from pBluescript and AP3, AG, REM from pGEM vectors with universal forward and reverse primers for subsequent transcription with T7 RNAP. PCR reactions were performed with the following cycle: 94°C 3 min, then 30 cycles of 94°C 45 s, 63°C 45 s and 72°C 1.5 min, final extension of 72°C for 6 min. For 96-well plates PCR-product purification was done using the Montage Clean-up Kit (Millipore).
In vitro transcription was performed in 10 μl reactions for 2 h at 37°C in the presence of digoxigenin-UTP (Dig-UTP)-nucleotides (0.35 mM). Hydrolysis was carried out immediately in 100 mM carbonate buffer pH10.2 at 60°C for 30 min, and products precipitated in 2.5 M ammonium acetate and 3 vol absolute ethanol for 1 h at 4°C. Plates were centrifuged at 4000 rpm for 30 min and pellets resuspended in 30 μl TE (100 mM Tris, 10 mM EDTA) buffer. Dilutions (100 x) were made in water and 1 μl of each spotted on nitrocellulose for dot-blot: 30 min in blocking solution (Sigma), 30 min in anti-DIG-alkaline phosphatase (Roche); 5 min wash in TBS (10 mM Tris, 250 mM NaCl); 5 min in AP-buffer (100 mM Tris, 100 mM NaCl pH 9.5; 50 mM MgCl2) and developed as described above until signal was sufficient. All probes were then diluted 100-fold in hybridisation solution (300 mM NaCl, 10 mM Tris pH 6.8, 10 mM NaPO4, 5 mM EDTA, 50% formamide, 5% dextran sulphate, 0.5 mg/ml tRNA, 1 × Denhardt's, 0.1 mg/ml salmon testis DNA) and maintained stably at -20°C until hybridization.
Hybridisation and washing
Chambers (Grace Biolabs) were applied securely to the slides (after pre-treatment) and probes (diluted in hybridisation solution) were applied to one well (2 sections) for the 3 stages individually. Coverslips were placed on the chambers to prevent evaporation and hybridisation was performed overnight in a 50°C incubator.
Chambers were removed and slides arranged in the VP2000 for washing program: 15 min in 2 × SSC (0.3 M NaCl, 0.03 M Na citrate), 50% (v/v) formamide at 40°C; 40 m in the same at 50°C; 20 min in 1 × SSC, 50% (v/v) formamide at 50°C (all steps with constant agitation); 5 min in 1 × SSC at room temperature; 5 min in 1 × TBS at room temperature. Then slides were transferred into trays for staining: 1% blocking solution (Roche) in TBS 1 h, 1 × TBS containing 1/3000 dilution of Anti-DIG AP and 0.05% (v/v) Tween-20 1 h; 4 × 10 min washes in 1 × TBS; 5 min in AP-Buffer (0.1 M Tris, 0.1 M NaCl, 50 mM MgCl2); developed in AP-Buffer containing NBT (0.1 mg/ml) and BCIP (0.075 mg/ml) for a maximum of 24 h. Slides were then washed several times in water to stop the reaction followed by sequential washes in 70% and 100% ethanol to remove excess stain (the duration of the ethanol washes depends on the level of colour development which was monitored by eye). Slides were then allowed to dry and permanently mounted in Entellan (Merck).
Image capture and analysis
One section for each stage for each probe screened was photographed on a Nikon E800 microscope using a digital camera under brightfield conditions for wheat sections and with UV filter for the calcofluor-counterstained Arabidopsis sections. Images were recorded sequentially as ordered on the slides. Magnifications and camera settings remained unchanged for all images through all stages for wheat section and likewise for floral meristems.
Competing interests
The author(s) declare that they have no competing interests.
Authors' contributions
SD developed the system described, performed all work for the wheat experiments and wrote the manuscript; JC and BC provided technical assistance on adapting the technique to Arabidopsis; JHD, PS and LD were co-supervisors; JHD co-wrote the manuscript.
Supplementary Material
Additional File 1
Detailed high-throughput in situ hybridisation protocol. An illustrated text document is attached with a protocol written in step by step detail for people working at the bench.
Click here for file
Acknowledgements
The authors wish to thank Sarah Collier and Mary Byrne (John Innes Centre) for expertise working with Arabidopsis; Cathie Martin, Jan Traas, and Robert Sablowski for probes; Steve Evans and Gawain Bennett (Syngenta) for help with the wheat probe preparation; David Leader, Wolfgang Schuch and Simon Bright for facilitating the collaboration with Syngenta and the BBSRC, Syngenta and the John Innes Centre for funding.
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Saline Syst
Saline Syst
Saline Systems
1746-1448
BioMed Central
1746-1448-1-9
16250916
10.1186/1746-1448-1-9
Research
Determination of biological and physicochemical parameters of Artemia franciscana strains in hypersaline environments for aquaculture in the Colombian Caribbean
Camargo William N [email protected]
Durán Gabriel C [email protected]
Rada Orlando C [email protected]
Hernández Licet C [email protected]
Linero Juan-Carlos G [email protected]
Muelle Igor M [email protected]
Sorgeloos Patrick [email protected]
1 Fisheries and Illinois Aquaculture Center, Southern Illinois University Carbondale, Carbondale, IL 62901, USA
2 Escuela Superior Politécnica del Litoral (ESPOL), Guayaquil, Ecuador
3 Grupo de Investigación de la Artemia (GIA), Universidad del Atlántico y Fundación Universitaria San Martín, Barranquilla, Colombia
4 Departamento de Biología, Universidad del Atlántico, Barranquilla, Colombia
5 Artemia Reference Center and Laboratory of Aquaculture, University of Ghent, Rozier 44, Ghent B-9000, Belgium
2005
26 10 2005
1 99
8 4 2005
26 10 2005
Copyright © 2005 Camargo et al; licensee BioMed Central Ltd.
2005
Camargo et al; licensee BioMed Central Ltd.
https://creativecommons.org/licenses/by/2.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Background
Artemia (Crustacea, Anostraca), also known as brine shrimp, are typical inhabitants of extreme environments. These hypersaline environments vary considerably in their physicochemical composition, and even their climatic conditions and elevation. Several thalassohaline (marine) environments along the Colombian Caribbean coast were surveyed in order to contribute to the knowledge of brine shrimp biotopes in South America by determining some vital biological and physicochemical parameters for Artemia survival. Additionally, cyst quality tests, biometrical and essential fatty acids analysis were performed to evaluate the economic viability of some of these strains for the aquaculture industry.
Results
In addition to the three locations (Galerazamba, Manaure, and Pozos Colorados) reported in the literature three decades ago in the Colombian Caribbean, six new locations were registered (Salina Cero, Kangaru, Tayrona, Bahía Hondita, Warrego and Pusheo). All habitats sampled showed that chloride was the prevailing anion, as expected, because of their thalassohaline origin. There were significant differences in cyst diameter grouping strains in the following manner according to this parameter: 1) San Francisco Bay (SFB-Control, USA), 2) Galerazamba and Tayrona, 3) Kangarú, 4) Manaure, and 5) Salina Cero and Pozos Colorados. Chorion thickness values were smaller in Tayrona, followed by Salina Cero, Galerazamba, Manaure, SFB, Kangarú and Pozos Colorados. There were significant differences in naupliar size, grouping strains as follows (smallest to largest): 1) Galerazamba, 2) Manaure, 3) SFB, Kangarú, and Salina Cero, 4) Pozos Colorados, and 5) Tayrona. Overall, cyst quality analysis conducted on samples from Manaure, Galerazamba, and Salina Cero revealed that all sites exhibited a relatively high number of cysts.g-1. Essential fatty acids (EFA) analysis performed on nauplii from cyst samples from Manaure, Galerazamba, Salina Cero and Tayrona revealed that cysts from all sites exhibited high arachidonic acid:20:4(n-6) (ArA) and eicosapentaenoic acid: 20:5(n-3) (EPA) levels comparable to the control sample (SFB). In contrast, most cysts collected (including SFB) at different locations, and during different months, presented low docosahexaenoic acid: 22:6(n-3) (DHA) levels (Manaure was the only exception with high DHA levels). Some variations in EPA and ArA levels were observed in all sites, contrasting with the much lower DHA levels which remained constant for all locations, except for Manaure which exhibited variable DHA levels. DHA/EPA ratio was overall very low for all sites compared to SFB cysts. All strains had a low DHA/ArA, but a high EPA/ArA ratio, including the control.
Conclusion
The Colombian A. franciscana habitats analyzed were determined to be thalassohaline, and suitable for A. franciscana development. EFA profiles demonstrated that Tayrona, Galerazamba, Manaure and Salina Cero strains are suitable food for marine fish and crustacean culture because of their high EPA/ArA ratio, but might have to be fortified with DHA rich emulsions depending on the nutritional requirements of the species to be cultured, because of their overall low DHA content. The relatively small nauplii are appropriate for marine larvaeculture. In contrast, the strains from Tayrona, Kangarú, Salina Cero, and Pozos Colorados may be of use but limited to Artemia small biomass production quantities, because of the small surface area of their respective locations; Artemia could be exploited at these locations for local aquaculture applications. In general, cyst quality evaluation for Manaure, Salina Cero and Galerazamba cysts revealed that cysts from these three locations could improve their quality by concentrating efforts on cyst processing techniques. Finally, most locations had great A. franciscana production potential and require different degrees of water quality and/or infrastructure management.
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pmcBackground
Members of the genus Artemia (Crustacea, Anostraca), also known as brine shrimp, are typical inhabitants of extreme environments that have low species diversity and simple trophic structures [1]. These hypersaline environments vary considerably in terms of ionic composition, climatic conditions and altitude. As a general rule, chloride-rich lakes are the most adequate for Artemia development [2]; however, some strains require carbonate- (Mono lake Artemia, USA) or sulfate-rich waters (Tso Kar Lake Artemia, Tibet) for survival [1,3-5]. Conversely, some other ions may be deleterious to Artemia; potassium could be very toxic because of its occurrence with sodium [6,7]. Since water composition is important for Artemia survival, ecosystems where it occurs were classified in three categories based on their anionic composition: chloride-, sulfate- and carbonate-rich [2].
Artemia persimilis (Piccinelli and Prosdocimi 1968) and A. franciscana (Kellogg 1906) occur in waters of the American continent; the latter is the most cosmopolitan [8,9]. However, A. persimilis was reported in Sardinia, Italy [10]. Molecular (RAPD-randomly amplified polymorphic DNA) [11] and morphometric characters [12] indicate that A. franciscana occurs in the Colombian Caribbean.
To characterize Artemia strains the aquaculture industry employ an array of evaluation tools [13,14]: i) Cyst and nauplii biometry: cyst biometry assists in the determination of number of cysts.g-1; generally, 1 g from strains that produce small cysts contains more cysts.g-1, thus usually produce more nauplii.g-1. Similarly, naupliar biometry is also an essential tool for quality evaluation. Shorter naupliar length is important, particularly, to feed fish larvae which contrary to crustaceans, has to engulf prey in a single bite; ii) Cyst hatching characteristics: it can be affected by environmental factors, genotypical conditions and/or improper processing/storage. An acceptable cyst product should contain minimal quantities of impurities (i.e. sand, salt crystals, etc.) [13]. Hatching efficiency (HE) and hatching percentage (H%) vary greatly among commercial batches and account for most of the price difference [13]. However, HE may be a better criterion than H% since HE considers impurities content (i.e. empty cyst shells). Hatching values for a commercial product may be as low as 100,000 nauplii.g-1, but might yield ideally near 300,000 nauplii.g-1 (with H% > 90). Hatching synchrony must be high (Ts = 12–16 h), and the last nauplii should hatch within 8 h after T90 [13]. When Ts is low (T0–T100 > 10 h), first hatched nauplii will have consumed most of their energy reserves by the time that the last nauplii hatched and harvesting has ended [13]; and iii) Essential fatty acid (EFA) profiles: particular attention has been given by marine larvaeculture production facilities to search for EFA rich Artemia strains, i.e. arachidonic acid:20:4(n-6) (ArA), eicosapentaenoic acid: 20:5(n-3) (EPA), and docosahexaenoic acid: 22:6(n-3) (DHA) [15]. Since ArA and DHA are vital in marine fish nutrition [16-18] great effort has been devoted to incorporate high levels of ArA, DHA, and high ratios of DHA/EPA/ArA in live food. ArA is generally conserved during periods of starvation in marine fishes [19], and serves as the preferred precursor for eicosanoid biosynthesis [20]. Artemia fed with enriched n-3 and n-6 highly unsaturated fatty acids (HUFA) in turn results in better larval growth and survival of several marine species fed with it [18,21-25]. The DHA/EPA ratio is very variable in non-enriched Artemia, with values often lower than 1. Through the addition of DHA rich emulsions the DHA/EPA ratio increases up to 7 [15]. Enrichment success is strain dependent (i.e. particular Chinese strains), and linked to variations in DHA catabolism. Further, EFA nutritional requirements may vary between species and developmental stages [18]. In white bass larvae, the optimal DHA/ArA and EPA/ArA dietary ratios have been established at 2:1 and 1:1, respectively [26]. This contrasts with flat fish larvae (i.e. turbot and Atlantic halibut) which require much higher ratios of over 10:1 [27,28]. However, high ArA levels have been implicated in the malpigmentation of various flatfish species [29]. An additional consideration is the possibility of DHA deficiency in neural tissues (i.e. vision) in larvae fed fish oil-based diet, as has been observed in Atlantic halibut larvae and juvenile herring eyes [30,31].
Our objective was to characterize Artemia franciscana biotopes in Colombia and to evaluate the viability of some strains for commercial exploitation. The data presented here may be of importance for the aquaculture industry to find new Artemia sources.
Results
Along the Colombian Caribbean coast, nine potential Artemia habitats were explored with variable quantities (from very few cysts in the sediment to several pounds dispersed along the pond edges) of cysts and/or biomass and variable surface area (2.5 Tayrona to 4000 ha Manaure).
There were significant differences (Table 1) in cyst diameter (P = 0.00001), grouping strains in the following manner according to this parameter: 1) San Francisco Bay (SFB-Control ARC1258, USA), 2) Galerazamba and Tayrona, 3) Kangarú, 4) Manaure, and 5) Salina Cero and Pozos Colorados. Chorion thickness from Tayrona was the thinnest, followed by Salina Cero, Galerazamba, Manaure, SFB, Kangarú, and Pozos Colorados.
Table 1 Biometric determination of Artemia franciscana cysts and Instar I nauplii samples from several strains in the Colombian Caribbean and from San Francisco Bay (control ARC 1258) (units in μm).
Location Cysts diameter Chorion thickness Nauplii length
SFB (ARC 1258) 201.0a ± 15.8 / 183.4 ± 6.9 8.8 432.1a ± 24.6
Galerazamba 232.1b ± 16.5 / 214.8 ± 19.3 8.6 390.3b ± 24.5
Tayrona 233.4b ± 12.4 / 227.2 ± 8.2 3.1 451.9c ± 25.1
Kangarú 236.8c ± 12.3 / 212.0 ± 11.0 12.4 426.1a ± 26.4
Manaure 241.1d ± 12.1 / 223.9 ± 10.2 8.6 414.2d ± 29.3
Salina Cero 249.8e ± 10.5 / 234.2 ± 10.1 7.8 431.7a ± 31.4
Pozos Colorados 252.9e ± 10.7 / 226.5 ± 10.2 13.2 442.0e ± 24.0
Superscripts (a, b, c, d, e) per column denote significant differences among strains (P < 0.05).
Note: Cyst diameter represents both non-decapsulated and decapsulated values, respectively.
There were significant differences (Table 1) in naupliar size (P = 0.0001) where strains grouped as follows (smallest to largest): 1) Galerazamba, 2) Manaure, 3) SFB, Kangarú, and Salina Cero, 4) Pozos Colorados, and 5) Tayrona.
Overall, Manaure: had a high number of cysts.g-1 (Table 2) and HE, but a low H% and Ts. Galerazamba: had a low number of cysts.g-1 compared to other commercial cyst types, and a low H%, contrasting with a high HE and Ts. Salina Cero: had a high number of cysts.g-1, but a low H%, HE and Ts. SFB (control): had a high number of cysts.g-1, HE and H%, but a low Ts.
Table 2 Quality evaluation results for Artemia franciscana cyst from three major saltworks in the Colombian Caribbean (H%: hatching percentage, HE: hatching efficiency, HR: hatching rate, Ts: hatching synchrony).
Location Number of cysts/g* H% (nauplii from 100 full cysts) HE (nauplii/g of cyst) HR (hrs)
T 0 T 10 T 90 T s
Manaure 267,970.3 ± 5,639a 51.4 ± 0.6a 155,555.6 ± 6.3a 12 13 23.0 10.0
Galerazamba 208,260.4 ± 7,485b 53.1 ± 8.3a 125,888.9 ± 10.9b 12 13 26.0 13.0
Salina Cero 230,680.3 ± 4,474c 46.7 ± 2.1b 98,666.7 ± 2.2c 12 13 23.0 10.0
SFB (ARC1258) 283,556.1 ± 3,967d 67.4 ± 14.9c 127,222.2 ± 22.8d 15 16 25.5 9.5
* Mean values.
Superscripts (a, b, c, d) per column denote significant differences among strains (P < 0.05).
From the three (Cl-, SO42- and CO3 2-) characteristic anions used to classify hypersaline ecosystems [2], Cl- was the most abundant anion (Table 3) in all locations evaluated. The physicochemical parameters monitored (Table 4) presented some tendencies inherent to each site. Salinity in Pozos Colorados and Salina Cero had a tendency to maintain low salinities (rarely crystallizing), contrasting with Manaure that presented salinities close to crystallization in the evaporation portion of the salt production circuit. Similarly, pH in Galerazamba, Salina Cero and Manaure was towards the low end pH for Artemia production; while for Tayrona and Pozos Colorados it was towards the ideal pH (8.0 to 8.5). Percent O2 saturation was overall normal in most sites, with the exception of Tayrona which was rather low in some months. Water temperature was at the upper limit in most sites, and extremely high only in Pozos Colorados. Nitrite was overall low in all sites, contrasting with high nitrate concentration in all sites. Phosphate was also low, except in Pozos Colorados where it was too high. Primary production (chlorophyll a) was rather at the low end for hypersaline ecosystems. Precipitation was high in the southern sites explored (as expected) and low at northernmost locations (dessert-like sites).
Table 3 Characteristic anion composition of all extreme environments where Artemia franciscana has been reported in the Colombian Caribbean. (Gz: Galerazamba saltwork, SC: Salina Cero lagoon, Kan: Kangarú salt pond, PC: Pozos Colorados saltwork, Tay: Chengue salt pond in the Tayrona Natural National Park, Ma: Manaure saltwork, BH: Bahía Hondita saltern, Pu: Pusheo saltern, Warrego was dried, thus not in table) (units in g/l).
Anions Gz SC Kan PC Tay Ma BH Pu
Cl- 55.00 11.86 8.00 60.00 75.00 137.50 11.50 35.00
SO42- 12.90 3.36 * 3.47 3.78 11.14 8.24 3.98
HCO3- 0.11 0.19 0.14 0.29 0.97 0.23 0.11 0.21
CO32- * * 0.176 * * * * *
* Below detection limit.
Note: Ionic concentrations represent one single sample per pond or salt concentration basin at the sites where Artemia was reported and must not be used for comparison purposes (between locations), since ionic concentration might vary periodically especially in managed (saltworks with several concentration levels as well as different viscosities in the system) compared to unmanaged ecosystems (single evaporation basin).
Table 4 Physicochemical parameters of seven locations in the Colombian Caribbean where Artemia franciscana strains inhabit. Salinity range, pH range, temperature range, nutrients range (NO2-, NO3- and PO4-3), max. precipitation (month), and Chl a (sites sampled monthly between July 1998 and June 2000) (n = 20 stations per location).
Parameters Galerazamba Salina Cero Pozos Colorados Tayrona Manaure Pusheo Bahía Hondita
Salinity (g/l) 65 – 295 19 – 204 5 – 291 34 – 330 148 – 275 40 15
pH 7.2 – 8.1 6.7 – 8.6 7.4 – 8.9 7.9 – 8.8 7.6 – 7.9 8.5 8.4
Percent O2 sat. 70 – 150 53 – 131 66 – 212 23 – 131 56 – 99 ND ND
Temp. (C) 26.6 – 35.5 27.5 – 35.1 26.7 – 38.5 23.4 – 33.8 24.9 – 31.3 27.2 26.5
NO2-(mg/l) 0.005 – 0.120 0.003 – 0.115 0.001 – 0.077 0.002 – 0.018 0.005 – 0.025 0.007 0.073
NO3-(mg/l) 1.4 – 33.7 0.4 – 18.8 1.7 – 19.5 2.3 – 22.1 0.3 – 20.5 4.7 13.6
PO4-3 (mg/l) 0.33 – 1.98 0.21 – 5.05 0.01 – 18.5 0.32 – 2.83 0.05 – 1.27 1.03 2.52
Chl. a (mg/m3) 0.01 – 0.11 0.09 – 3.04 0.002 – 2.72 0.01 – 0.39 0.09 – 0.10 ND ND
Max. Precipitation (mm/month) 326.7 326.7 288.2 288.2 79.6 79.6 79.6
Total months sampled 24 22 13 18 24 1 1
Cyst samples from locations (Table 5) where enough cysts were collected to perform FAME analysis (FAME was actually done on freshly hatched nauplii from cysts), exhibited high EPA and ArA levels comparable to control sample (SFB-ARC1258). In contrast, most cysts collected (including SFB) at different locations, and during different months, presented low DHA levels (Manaure was the only exception with high DHA levels). Some variations in EPA and ArA levels were observed in all sites, contrasting with much lower DHA levels which remained constant for all locations, except for Manaure which exhibited variable DHA levels. DHA/EPA ratio was overall very low for all sites compared to SFB cysts. All strains had a low DHA/ArA ratio, but a high EPA/ArA ratio, SFB included.
Table 5 Intra-strain variability of ArA 20:4(n-6), EPA 20:5(n-3), and DHA 22:6(n-3) of some freshly hatched Artemia franciscana nauplii. Cysts samples collected in the Colombian Caribbean from 1998 to 2000 (Tay: Tayrona, Gz: Galerazamba, Ma: Manaure, SC: Salina Cero, SFB: San Francisco Bay control ARC1258) (values expressed in area %).
EFA Tay Gz Ma SC SFB
EPA 20:5(n-3) 2.7 – 3.6 0.3 – 8.6 1.7 – 3.1 2.2 – 5.9 0.3–2.4
DHA 22:6(n-3) 0.1 0.1 – 0.3 0.1 – 1.3 0.1 0.4
ArA 20:4(n-6) 2.8–3.4 0.1–3.9 0.9–1.1 1.3–3.2 0.9–1.3
DHA/EPA 0.03–0.04 0.03–0.33 0.06–0.42 0.02–0.05 0.03–1.33
EPA/ArA 0.96–1.06 2.21–3.00 1.89–2.82 1.69–1.84 0.33–10.23
DHA/ArA 0.03–0.04 0.08–1.00 0.11–1.18 0.03–0.08 0.31–0.44
Months sampled* (2) (9) (3) (4) (2)
*Each sample was taken from different months.
Note: Values express max.-min. Ranges expressed as percentages of total EFA.
Discussion
Vanhaecke and Sorgeloos [32] reported cyst diameter as small as 224 μm for the San Francisco Bay strain (California, USA), while Abatzopoulos et al. [33] reported cyst diameters as large as 330 μm for the bisexual species A. tibetiana, surpassing even the well known large cyst diameters of the polyploid parthenogenetic strains with a typical diameter near 280 μm. Cysts from Great Salt Lake (GSL-Utah, USA) have a larger cyst diameter (244.2 – 252.5 μm) compared with those from SFB (California, USA) (223.9 – 228.7 μm) [32]; cyst diameters in the Colombian Caribbean are more similar to cysts from GSL than SFB (control). Thus, a cyst diameter grouping in the following order is possible for strains (smallest to largest): 1) San Francisco Bay (SFB-Control ARC1258, USA), 2) Galerazamba and Tayrona, 3) Kangarú, 4) Manaure, and 5) Salina Cero and Pozos Colorados. However, within the same species, strains present different cyst diameter as well as different chorion thickness. The chorion thickness for GSL ranges from 4.7 to 5.7 μm and for SFB it is 7.1 to 8.3 μm [32]. In the case of cyst samples examined in this study, Tayrona were the thinnest, followed by Salina Cero, Galerazamba, Manaure, SFB (USA), Kangarú and Pozos Colorados (see Table 1).
The biometric analysis grouped strains according to naupliar size (Instar I) as follows (smallest to largest): 1) Galerazamba, 2) Manaure, 3) SFB, Kangarú, and Salina Cero, 4) Pozos Colorados, and 5) Tayrona. However, it is remarkable that the naupliar length measured by Vanhaecke and Sorgeloos [32] for the Galerazamba strain collected in 1977 greatly differed from that length in our study (480 ± 31.1 vs. 390.3 ± 24.5 μm); maybe because of some physicochemical effects (i.e. salinity) on the strain manifested over time, and/or food conditions and/or cysts harvested from different pond sizes (widely separated harvest sites), which have been reported [32] to also affect cyst size and chorion thickness. The same authors reported that SFB cysts produced at 180 mg.l-1 in vitro are significantly smaller than cysts produced at lower salinities. Biometrical studies performed on several strains from different geographical origins concluded that Artemia biometrical parameters were mainly strain specific [32]. These authors revealed that the length of Instar-I nauplii (both bisexual and parthenogenetic) may vary between 430 and 520 μm. Moreover, the North American Instar I nauplii (bisexual) tend to be at the lower previously given range. Thus, naupliar length for SFB (California) is between 428 ± 28.8 to 431 ± 23.7 μm, and for GSL strain (Utah) is between 486 ± 30.6 to 489 ± 29.2 μm [32]. Naupliar size is non-critical for the feeding of crustacean larvae, which can capture and manipulate nutritional particles with their feeding appendages [18]. In contrast, prey size is very critical for fish larvae, which do not have feeding appendages and must engulf particles. The correlation between naupliar size and fish larvae mortality indicates that 20% of the larvae die of hunger when being offered nauplii greater than 480 μm in the first stages of feeding [34]. Thus, depending on the developmental stage of the cultured fish larvae, selecting an appropriate naupliar size as live feed is critical.
Overall, samples from Manaure, Galerazamba and Salina Cero, according to the cyst quality test, exhibited a relatively high number of cysts.g-1. H% might have been affected (low in all samples evaluated) because the cyst processing method used (1/3 HP air blower with no heating element or temperature control) could not maintain a constant drying temperature/airflow, or some impurities were still present in samples. H% is dependent on degree of diapause termination, cysts energy content and amount of dead/non-viable/abortic embryos due to improper processing and/or storage [13]. Furthermore, HE reflects three factors: 1) H%, 2) presence of other components (i.e. empty shells, salt, sand, cysts water content), and 3) individual cyst weight. The low hatching synchrony of cysts from Manaure and Salina Cero could be attributed to environmental factors (i.e. raining after cysts were dehydrated, salinity, etc.), and/or as mentioned before improper processing. Significant interactions have been reported among some physicochemical-biotic factors (salinity, percent O2 saturation and chlorophyll a) and Artemia cyst production [35], factors which might affect consequently cyst quality.
The ionic analysis of all locations registered a Cl- anion predominance (Table 3), as expected [36], because of their thalassohaline origin. All hypersaline environments analyzed are suitable habitats for A. franciscana development [2]. Colombian hypersaline ecosystems sampled are similar to other American Cl- dominant hypersaline biotopes such as Leslie saltworks (California – USA) [37], La Sal del Rey (Texas – USA) [38] and GSL (Utah – USA) [39].
In nature Artemia is found at salinity levels between 60 to 220 g.l-1 (depending on the strain and/or species) and in neutral to alkaline waters, at temperatures generally below 34°C, and at rather low O2 levels. The low salinities measured for several months at Pozos Colorados and Salina Cero could hinder overtime Artemia production by favoring nauplii production (tolerant to low salinities and pHs) and affecting adults survival [35]. Similarly, the low end pH registered in Galerazamba, Salina Cero and Manaure could affect Artemia biomass, and even cyst production overtime. As pH decreases below 7.0 naupliar growth decreases and in adults the overall appearance deteriorates [40]. The same authors concluded that the optimum pH for Artemia growth was from 8.0 to 8.5. In the case of cyst, hatching efficiency is greatly compromised at pHs below 8.0 [41]. The low O2 levels registered at the Tayrona site could be attributed to the high accumulation of organic matter in the pond from surrounding vegetation. Water temperature was at the upper limit in most sites, with extreme temperatures at Pozos Colorados due to the lack of water circulation and small ponds size (< 0.5 ha). The nutrients (N:P, ideally 15:1) ratio was generally maintained within expected limits, except in Pozos Colorados where it was too high (1:1). The low primary production (chlorophyll a) determined in all sites was characteristic of hyperhaline environments. The most photosynthetically productive hypersaline environments [36] are the hypohalines and mesohalines. Furthermore, primary productivity in Salina Cero and Pozos Colorados was the highest among all sites, because of their low salinities. It is widely accepted that salinities higher than 50 g.l-1 hinder considerably primary productivity in hypersaline ecosystems perhaps because of an ionic complex formation of the dissolved macronutrients or because of a generic biologic phenomenon of a drastic specific reduction of microalgae, also occurring at higher salinities [42]. The high precipitation in the southern sites explored (Salina Cero, Galerazamba, Pozos Colorados and Tayrona) affected constantly Artemia cyst and biomass production by decreasing salinity and affecting light intensity.
The high EPA and ArA levels (Table 5) for Colombian cysts, determined by EFA analysis, compared to the control (SFB) have great potential for the aquaculture industry. In contrast, the low DHA level content in cysts from the Colombian sites (except Manaure) might be of concern if fed to marine larvae without further DHA enrichment, since DHA deficiency affects neural tissues development [30,31], survival and growth [18], particularly at the larval stage. The very low DHA/EPA ratios (<0.5) for all Colombian sites were as expected with values often lower than 1, but this ratios could be increased up to 7 [15] through the addition of DHA rich emulsions. All strains had a low DHA/ArA ratio, contrasting with a high EPA/ArA ratio (close to dietary ratio 2:1 and 1:1 for marine fish-white bass larvae, respectively [26]). Artemia ArA storage/usage mechanism might be similar to that of other marine organisms (marine fishes) which conserve ArA even during periods of starvation [19]. The observed fluctuations in ArA and EPA levels according to FAME analysis between, and even within strains (collected in different months), may be due to year-round variations in the biochemical composition of the primary producers available to adult Artemia [15].
Conclusion
The Colombian A. franciscana habitats analyzed are of marine (thalassohaline) origin; thus, all locations were expected to be Cl- rich.
The relatively small nauplii are appropriate for marine larvaeculture. In contrast, the strains from Tayrona, Kangarú, Salina Cero, and Pozos Colorados may be of use but limited to Artemia small biomass production quantities, because of the small surface area of their respective locations; Artemia could be exploited at these locations for local aquaculture applications.
In general, cyst quality evaluation for Manaure, Salina Cero and Galerazamba strains determined that cysts from these three locations could improve their quality by concentrating efforts on cyst processing techniques. Further, cyst quality might have been affected by interactions among some physicochemical-biotic factors and Artemia cyst production in their ecosystem which could be improved by managing some of these key physicochemical-biotic factors and/or infrastructure management (e.g. brine concentration in the different basins and nutrients).
EFA profiles demonstrated that Tayrona, Galerazamba, Manaure and Salina Cero strains are suitable for marine aquaculture because of their high EPA/ArA ratio, but might have to be fortified with DHA rich emulsions depending on the nutritional requirements of the species to be cultured, because of their overall low DHA content.
Methods
Study area
Galerazamba (10° 47' 38'' N, 75° 14' 48'' W): is a 220 ha thalassohaline saltwork with five ponds, three for brine and two for crystallizers. It is located approximately 20 km North of Cartagena city, at the borderline of the Bolívar department (Fig. 1). Studies have been conducted by several authors in the past using samples from this location [5,11-15,32,35,43-55]. This saltwork, built in a natural saline lagoon and surrounded by mangroves, formed by a sandy-clay and loamy-clay soil type, floods with seawater during high tide throughout the year [45-47].
Figure 1 Location of Artemia franciscana collection sites: SC: Salina Cero, Gz: Galerazamba, Kan: Kangarú, PC: Pozos Colorados, Tay: Chengue in the Tayrona National Natural Park, Ma: Manaure, Wa: Warrego, BH: Bahía Hondita, and Pu: Pusheo.
Salina Cero or Ciénaga Prieto (10° 46' 29''N, 75° 15' 55''W): is an 18 ha thalassohaline lagoon 3 km of Galerazamba, Bolivar department (Fig. 1) [11,12,35], studied in September 1998. For many decades, salt has been manually extracted once or twice per year, and fishermen noted the presence of Artemia for over five decades.
Kangarú (11° 59' 28''N, 74° 32' 21''W): is less than a 4 ha natural thalassohaline saltwork comprised of three small ponds located in the northern region of the Salamanca Island National Natural Park, Magdalena department (Fig. 1). It was explored in July 2000. Salt has been occasionally exploited for decades. This locality, is an important bird migration spot, however, it lost importance because of mangrove destruction as consequence of building a highway through the park.
Pozos Colorados (11° 09' 45''N, 74° 13' 34''W): is an approximately 65 ha very old artificial thalassohaline saltwork, currently abandoned. Few studies have been conducted by local researchers in the past using samples from this location. It is located near Santa Marta city, Magdalena department (Fig. 1), contiguous to the road connecting to Barranquilla to Santa Marta city [11]. This saltwork consists of only five irregularly shaped, shallow ponds with only 4 ha water surface.
Tayrona National Natural Park (Chengue natural saltwork where the 'Tayrona' Artemia population was first reported) (11° 19' 03''N, 74° 08' 13''W): This natural thalassohaline saltwork (Fig. 1) of approximately 2.5 ha is hypersaline due to a closure pattern of dynamic sedimentation of the communication channel with the inlet [54]. It is located in the Magdalena department [11,12,35,53-56]. Tayrona NNP encompasses a small number of saline non crystallizing ponds, with the exception of Chengue, where Artemia has been reported to occur. The salt pond is flooded during most of the year and serves as a saltwork during summer [57]. Chengue Inlet, is located in the middle of the Tayrona NNP, it presents a series of small bays and inlets extending from Santa Marta to Cañaverales to the east. Chengue salt exploitation existed long before the prehispanic period [58].
Manaure (11° 46' 32''N, 72° 29' 27''W): is located to the west, contiguous to the town of Manaure, in the center of La Guajira department, near Riohacha city (Fig. 1). Studies have been conducted by several authors in the past using samples from this location [5,11-13,35,43,47,48,50,52,54,55,59]. This saltwork is a thalassohaline, shallow water body extending over 4,000 ha. Water movement through the saltwork system is achieved both by pumping and through gravity. There are six pumping stations that increase water volume to a predetermined water level, thereafter water will flow by gravity. This zone was originally a natural lagoon surrounded in some areas by mangroves. The deposits were constructed using the natural topography of the terrain with some modifications. The levees were built by compacting large amounts of clay material brought from the margins of the saltwork [47].
Warrego (12° 19'N, 71° 54'W): is an approximately 600 ha (2 miles long) thalassohaline saltern located in the northern tip of La Guajira department, near Puerto Nuevo village (Fig. 1). Occasionally, the Wayu Indians extract salt when the brine crystallizes. Since it was completely dried up when we visited it (January 18, 2000), no water samples were collected from this location and found few Artemia cysts.
Bahía Hondita (12° 19' 28''N, 71° 44' 13''W): is a natural thalassohaline saltern, approximately 3000 ha, located in La Guajira department (Fig. 1). The Wayu Indians also extract salt in this saltern when the brine crystallizes. We visited the area on January 18, 2000 and only found Artemia cysts.
Pusheo (12° 20' 47''N, 71° 44' 17''W): is an approximately 400 ha thalassohaline saltern located in the northern tip of La Guajira department (Fig. 1), near Punta Gallinas. Occasionally, the Wayu Indians extract salt when the brine crystallizes. We visited the area on January 18, 2000, and only found Artemia cysts.
Preparation and sampling
Sampling was conducted monthly and cysts batches were collected irregularly (whenever available) in nine thalassohaline locations aforementioned in the northern region of the Colombian Caribbean, from July 1998 to June 2000. Cyst processing was done following these steps: (i) size separation with brine, (ii) density separation in brine, (iii) washing in freshwater, (iv) density separation in freshwater, (v) drying below 40°C, and (vi) vaccum packing and refrigerating cyst at 4 ± 2°C.
Cyst diameter and chorion thickness were recorded from sites where sufficient cysts were collected, using SFB (USA, ARC1258) cysts as reference material. Cysts were incubated for 3 hr in 10 g.l-1 artificial sea water (Instant Ocean®) at 25 ± 0.5°C and pH 8.3 [13]. One percent lugol's solution (5 %) was added to the sea water to stop embryos from hatching and cysts were in the dark overnight. Cyst diameter (μm) was measured in 200 cysts with a precalibrated microscope. Mean value and standard deviation were calculated using the predetermined conversion factor. Decapsulated cyst diameter (μm): a small sample of cysts was hydrated in tapwater for 2 h. Cysts were then decapsulated with a NaOH and NaOCl solution. Cysts were rinsed well and incubated in 10 g.l-1 artificial sea water (Instant Ocean ®) with 1 % lugol for 1 hr, at 25 ± 0.5°C, and pH 8.3 and was incubated for 1 h more. Afterwards, 1 % lugol was added again to the incubating solution and cysts were left overnight in the dark.
Decapsulated cyst diameter was measured for 200 cysts with a precalibrated microscope. Mean value and standard deviation were calculated using the predetermined conversion factor. Chorion thickness was calculated using this formula:
(cyst diameter - decapsulated cyst diameter)/ 2
Naupliar length was determined on Instar I nauplii, following this procedure [13]: cysts were incubated and hatched under controlled conditions (25 ± 0.5°C, pH 8.3 and illumination: 1000 lux) in artificial sea water (Instant Ocean®) at 35 g.l-1 salinity [32]. Nauplii were sampled at Instar I considering the protopodite of each antennae which bears two endites with a single long bristle attached to each and their brownish-orange color due to yolk presence (Instar II is translucent) as the traits defining this stage [60,61]. Nauplii were harvested when 90% of the total number of hatchable nauplii had been produced [22]. Two hundred nauplii were fixed in lugol's solution (5%) and the length determined using a microscope with a pre-calibrated projection system. Cyst quality studies [13] were performed only on major saltworks. The following parameters were used to evaluate cyst quality:
i) Hatching percentage (H%): number of nauplii that can be produced under standard hatching conditions from 100 full cysts (with embryos).
H% = (N × 100)(N+U+E)-1
Cysts (1.6 g) were incubated in 800 ml of 32 g.l-1 microfiltered (<1 μm) seawater (Instant Ocean®) under continuous illumination (2000 lux) at 28°C, pH = 8.3, in a cylindroconical vessel (test was run in triplicate per strain) with bottom aeration (>2 mg.l-1). Vessels were suspended in a water bath in a 100 gal aquarium with a water heather and a mixer to maintain a well distributed temperature (± 1°C). After 24 h incubation six 250 μl subsamples were taken from each cone with a micropipet. Each subsample was pipetted into a small vial and nauplii were fixated by adding a few drops of lugol's solution (5%). Nauplii (ni) and umbrella (ui) stages were counted in each subsample under a disection microscope. Mean values (N = nauplii and U = umbrella) were calculated each for these two stages. Unhatched cysts were decapsulated and empty cyst shells were dissolved with a drop of NaOH solution (40 g.100 ml-1 distilled water) and five drops NaOCl (5.25% NaOCl) added to each vial. Unhatched (orange colored) embryos (ei) were counted per cone (i = 6) and mean value (E) was calculated for each cone. H% value was calculated per cone, and mean value and standard deviation was calculated for three cones (final H% value).
ii) Hatching efficiency (HE): number of nauplii/g dry cysts that can be produced under standard hatching conditions.
HE = (N × 4 × 800 ml)(1.6 g)-1
HE value was calculated, for each strain evaluated, per cone, and mean value and standard deviation was calculated for three cones (final HE value). Hatching vessels were left for another 24 h, subsequently subsamples were again taken to calculate H% and HE for 48 h incubation period.
iii) Hatching rate (HR): period from incubation (cyst hydration) to nauplii release (hatching). The following HR time intervals are considered:
T0 = Incubation time untill appearance of first free swimming nauplii
T10 = Incubation time untill appearance of 10 % of total hatchable nauplii
T90 = Incubation time untill appearance of 90 % of total hatchable nauplii
TS = T90 - T10 ; this value gives an indication of the hatching synchrony
Six 250 μl samples, for each strain evaluated, were taken 12 h after incubation and HE was calculated every 3 h until HE mean value remained constant for three consecutive sampling periods. Mean values per period were then expressed as percentages of the maximal HE. A hatching curve was plotted for each strain, and T10 and T90 were extrapolated from the graph.
iv) Number of cysts.g-1: this parameter is dependent on cysts diameter. Cysts (4 g) were placed in an aluminum plate and weighted and dried in a drying oven set at 60°C for 24 h. Cysts were then cooled down to room temperature for 4 h in a tightly sealed glass drying chamber with fresh desiccant. One g of cyst sample (triplicates) was then weighted (0.1 mg accuracy) in an aluminum plate; to determine average cyst weight, for a single cyst, ten subsamples (10.0 ± 0.1 mg) were taken from each replicate and counted; to find how many cysts were in the 1 g sample average cyst number in 10 mg was then extrapolated to the 1 g cyst sample. This procedure was repeated for each strain evaluated.
At each location we measured: salinity (temperature compensated refractometer), percent O2 saturation and temperature (Oxymeter WTW® 330), pH (pH meter WTW® 330), nitrates, nitrites and phosphates (Hatch® DREL 2010 spectrophotometer), and chlorophyll a. For the latter, we used the Seston method and read (Hatch® DREL 2010 spectrophotometer). The ionic composition (Table 3) was determined using a Unicam 939/959 atomic absorption spectrophotometer. All samples were diluted with deionized water because of the high ionic concentration. A sample of nauplii from Galerazamba, Manaure, Salina Cero and Tayrona was taken for FAME; for this analysis we followed Sorgeloos et al. [13]: cysts were incubated and hatched under controlled conditions (25 ± 0.5°C, pH 8.3 and illumination: 1000 lux) in artificial sea water (Instant Ocean®) at 32 g.l-1 salinity. FAME methodology for freshly hatched nauplii (0.25 g) was a modification of the direct esterification described by Lepage and Roy [62]. The latter implicates a direct acid catalized transesterification without prior extraction of total fat, on dry sample (triplicates) amounts ranging from 10 to 150 mg. An internal standard 20:2 (n-6) was added prior to the reaction. FAME were extracted with hexane. After solvent evaporation FAME were prepared for injection by redissolving them in iso-octane (2 mg/ml). Quantitative determination was done by a Chrompack CP9001 gas chromatograph equipped with an autosampler and a TPOCI (Temperature programmable on-column injector). Injections (0.5 μl) were performed on column into a polar 50 m capillary column, BPX70 (SGE Australia), with a diameter of 0.32 mm and a layer thickness of 0.25 μm, connected to a 2.5 m methyl deactivated precolumn. The carrier gas was H2, at a pressure of 100 kPa and the detection mode FID. The oven was programmed to rise from the initial temperature of 85 to 150°C at a rate of 30°C/min, from 150 to 152°C at 0.1°C/min, from 152 to 172°C at 0.65°C/min, from 172 to 187°C at 25°C/min and to stay at 187°C for 7 min. The injector was heated from 85 to 190°C at 5°C/sec and stayed at 190°C for 30 min. Identification was based on standard reference mixtures (Nu-Chek-Prep, Inc., USA). Integration and calculations were done on computer with a software program Maestro (Chrompack).
Any experimental research on animals that was reported in this study was performed with the approval of an appropriate ethics committee regulating animal research.
Calculations and statistics
Standard deviations were calculated for all cyst diameter and naupliar length measurements. Data obtained were analyzed using one-way ANOVA, and averages compared with Duncan's test (SPSS V10.0).
Authors' contributions
WNC co-designed and carried out the experiment, participated in data collection, performed data analyses, and wrote manuscript.
GCD, LCH, OCR, JGL and IMM participated in data collection.
OCR evaluated cysts quality.
PS co-designed the experiment, FAME analysis performed at Artemia Reference Center.
Acknowledgements
This study was financed by a doctorate scholarship and a research project "Evaluación y aprovechamiento del recurso natural Artemia en las salinas de Manaure y Galerazamba, Caribe colombiano", directed by William Camargo (code 1116-09-343-97) and granted by the Colombian Council of Science and Technology "Francisco José de Caldas" (COLCIENCIAS) and by the Universidad del Atlántico, Barranquilla, Colombia. Fieldwork was possible thanks to valuable cooperation by J. Bolaño, T. Acuña, K. Coha, J. Garcia, S. Pereira, and V. Escorcia, as well as all members of the Artemia Research Group (GIA), Uniatlántico. Ionic analyses were made thanks to the cooperation from AAA, Barranquilla. We express our most sincere gratitude to E.V. Berghe for her very constructive recommendations on this paper.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7811ehp0113-00127116203233Commentaries & ReviewsFundamental Flaws of Hormesis for Public Health Decisions Thayer Kristina A. 1Melnick Ronald 1Burns Kathy 2Davis Devra 3Huff James 11 National Institute of Environmental Health Sciences, National Institutes of Health, Department of Health and Human Services, Research Triangle Park, North Carolina, USA2 Sciencecorps.org, Lexington, Massachusetts, USA3 H. John Heinz III School of Public Policy & Management, Carnegie Mellon University, Pittsburgh, Pennsylvania, USAAddress correspondence to K. Thayer, NIEHS, MD A3-01, PO Box 12233, Research Triangle Park, NC 27709 USA. Telephone: (919) 541-5021. Fax: (919) 541-0295. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 15 6 2005 113 10 1271 1276 1 12 2004 14 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Hormesis (defined operationally as low-dose stimulation, high-dose inhibition) is often used to promote the notion that while high-level exposures to toxic chemicals could be detrimental to human health, low-level exposures would be beneficial. Some proponents claim hormesis is an adaptive, generalizable phenomenon and argue that the default assumption for risk assessments should be that toxic chemicals induce stimulatory (i.e., “beneficial”) effects at low exposures. In many cases, nonmonotonic dose–response curves are called hormetic responses even in the absence of any mechanistic characterization of that response. Use of the term “hormesis,” with its associated descriptors, distracts from the broader and more important questions regarding the frequency and interpretation of nonmonotonic dose responses in biological systems. A better understanding of the biological basis and consequences of nonmonotonic dose–response curves is warranted for evaluating human health risks. The assumption that hormesis is generally adaptive is an oversimplification of complex biological processes. Even if certain low-dose effects were sometimes considered beneficial, this should not influence regulatory decisions to allow increased environmental exposures to toxic and carcinogenic agents, given factors such as interindividual differences in susceptibility and multiplicity in exposures. In this commentary we evaluate the hormesis hypothesis and potential adverse consequences of incorporating low-dose beneficial effects into public health decisions.
biphasic dose responsehormesisindividual susceptibilitylow-dose exposuresnonmonotonic dose responsenonlinear dose responsepublic healthregulationrisk assessment
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The concept of hormesis has received considerable attention over the past several years (Kaiser 2003a, 2003b). A recent literature search in the PubMed database on the term “hormesis” yielded 215 papers published between 2000 and 2004 compared to 116 published in 1999 and earlier (PubMed 2005). In several commentaries and reviews, hormesis—defined as low-dose stimulation, high-dose inhibition—has been used to promote the notion that low-level exposures to known toxic chemicals could be “beneficial” to human health (Calabrese and Baldwin 2003c; Renner 2004). For example, it has been proposed that
if low-dose stimulatory responses were assumed to be beneficial, the decision maker could view hormesis as adding potential benefit to society and could estimate an optimized population-based exposure standard. (Calabrese and Baldwin 2003a, p. 188)
Some proponents of this view claim hormesis is an adaptive, broadly generalizable phenomenon and argue that in the absence of contradictory information, the default assumption for risk assessments should be that at low exposures, toxic chemicals induce stimulatory effects (Calabrese and Baldwin 2003a). We argue that many examples used to support the widespread frequency of hormesis are better described by the more general term “nonmonotonic” dose responses. Nonmonotonic is used to describe dose–response relationships in which the direction of a response changes with increasing or decreasing dose. Use of the term hormesis, with the associated descriptors of low-dose stimulation and high-dose inhibition, can only be justified if there is an understanding of the biological processes underpinning that specific dose response. We agree that there is a need to address nonmonotonic dose–response relationships in the risk assessment process. However, even if certain low-dose effects were sometimes determined to be beneficial, this finding should not be used to influence regulatory decisions to increase environmental exposures to toxic agents, given factors such as variability in individual susceptibility, variability in individual exposures, and the public’s regular exposure to complex mixtures. Our commentary focuses on the evaluation of the hormesis hypothesis and consequences of incorporating low-dose beneficial effects into public health decisions, with special emphasis on the following issues:
The concept of hormesis is based largely on empirical observations and does not adequately consider underlying mechanism(s) of action. Without an understanding of the mechanisms underlying a hormetic response, it is not appropriate to conclude that hormesis is a uniformly adaptive phenomenon.
Stimulatory responses are not always beneficial, and some may be harmful. There is no scientific support for the assumption that stimulatory responses such as increased growth, enzyme activity, hormone concentration, and cell proliferation are beneficial.
Health decisions based on beneficial effects must address all the induced effects by that agent. Examples cited to support the incorporation of low-dose beneficial effects into exposure standards ignore other adverse effects that are induced by different mechanisms and that occur at similar or lower dose levels.
Health decisions based on beneficial effects must address interindividual differences in exposure and susceptibility, including genetic, life-stage, and health status factors. Susceptibilities and exposure levels vary among people over the course of a lifetime. In many cases timing of exposure can be more important than dose in determining health outcomes. Fundamental physiological differences stemming from genetic heterogeneity and differences in health status will also influence susceptibility.
Health decisions based on beneficial effects must address the fact that other environmental and workplace exposures may alter the low-dose response of a single agent. Exposures in the real world do not occur to single substances but to mixtures of toxicants that can interact with each other or affect different steps of multistage disease processes. The mix of chemicals that individuals are exposed to varies depending on the nature of their work, indoor home environment, drinking water supply, food sources, school environment, and where they socialize, in addition to lifestyle choices such as diet, hobbies, hygiene practices, and other factors such as the use of prescription and over-the-counter drugs. Moreover, many of these compounds can affect the same target tissues by either similar or different mechanisms of action.
The Concept of Hormesis As an Adaptive Response Does Not Adequately Consider Underlying Mechanisms of Action
As already stated, hormesis is generally described as low-dose stimulation and high-dose inhibition, producing a nonmonotonic dose response. This may be visualized in the situation in which low-dose exposure to an agent stimulates growth and high-dose exposure inhibits growth (Figure 1, solid line) (Renner 2004). U- or J-shaped dose responses (Figure 1, dashed line) can also be considered hormetic. A more recent definition of hormesis by Calabrese and co-workers considers the phenomenon to be an “adaptive” and frequently observed response resulting from exposure to a perturbing agent (Calabrese and Baldwin 2002b). Many of the recent publications on hormesis and its application to risk assessment are coauthored by Calabrese or reference his work. Thus, the works of Calabrese and his colleagues at the University of Massachusetts-Amherst are cited frequently in this article. These authors assert that
the hormetic phenomenon response is a common, evolutionary-based strategy to carefully regulate resource allocation in a definable range within the context of the re-establishment and maintenance of homeostasis. (Calabrese and Baldwin 2002a, p. 333)
In general this definition has positive (i.e., beneficial) connotations in that it implies that by adapting or coping with a stressor one will not suffer ill consequences. Yet, the evaluation criteria used to conclude that hormesis is a widespread, adaptive phenomenon are based on empirical observations of dose–response relationships with no regard for underlying mechanism(s) (Calabrese and Baldwin 2001, 2003).
Calabrese and Baldwin attempted to evaluate the frequency of hormesis by first reviewing studies published in three journals [Environmental Pollution (1970–1998), Bulletin of Environmental Contamination and Toxicology (1966–1998), and Life Sciences (1962–1998)] that they believed to represent a broad range of experimental models (Calabrese and Baldwin 2001). Epidemiologic and field studies were excluded from this analysis, as were non-English language articles. The authors evaluated 668 dose–response relationships from 195 published articles for evidence of hormesis that met the following inclusion criteria: a) presence of a concurrent control; b) capacity to achieve responses greater than (or less than) the control response; c) at least stwo doses below the no observed adverse effect level (NOAEL); and d) at least one dose showing a priori criteria-based inhibition (Calabrese and Baldwin 2001). The NOAEL was defined as either a) the highest dose with a response not statistically different with respect to adverse response from the control or b) the highest dose with a response ≥90% of control for inverted U-shaped dose–response relationship or as the highest dose with a response ≤110% of control for U- or J-shaped dose–response relationships. A priori criteria-based inhibition occurs when a) the response for at least one dose higher than the NOAEL is statistically different from controls, b) the response for at least one dose higher than the NOAEL shows a change of twice the value of the SD or SEM compared to the control group (for studies where only data distribution is reported); and c) the response for at least two doses higher than the NOAEL is < 90% of the control for inverted U-shaped dose–response relationships or > 110% of the control for U- or J-shaped dose–response relationships.
A dose–response relationship was considered hormetic if a) at least one dose at or below the NOAEL was statistically increased (for inverted U-shaped dose–response relationships) or decreased (for J- or U-shaped dose–response relationships); b) at least three doses at or below the study NOAEL had responses ≥110% of the control (for inverted U-shaped dose–response relationships) or ≤90% of the control (for J- or U-shaped dose–response relationships); or c) for studies in which only data distribution is reported, variability in response (2 times the SD or SEM) did not overlap with variability in the control group.
Using a study NOAEL to determine whether there are stimulatory effects at or below that dose is problematic because the determination of a NOAEL whether by selecting one of the actual doses in a study (non-significant change from control) or by modeling the dose–response data is influenced by the variability in the experimental data, sample size, the statistical power of the study, the end point being evaluated, the duration and route of exposure, and so forth. Because of variability in the control response (reflected in historical control data), a difference in response between the current control group and the dose groups below the presumed NOAEL may give the false appearance of a hormetic response. Thus, in some cases an apparent hormetic response may simply reflect data variability (Figure 2) rather than low-dose stimulation and high-dose inhibition.
The evaluation criteria used by Calabrese and Baldwin to determine whether a dose response is hormetic do not require statistically significant changes from control. Many of the dose responses classified as hormetic were identified based on the criteria that at least three doses at or below a study NOAEL differ by ≥10% of the relative control response (Calabrese and Baldwin 2001). For example, a change in incidence from 20 of 100 (20%) to 18 of 100 (18%) would be interpreted as a 10% change from control response [(20–18) of 20] and not a 2% change (20–18) in response. This approach can lead to a large change relative to the control with only a one-count change in response (e.g., the difference between 3 of 20 and 2 of 20 would amount to a 33% change). In this manner even small changes in incidence that reflect data variability would be interpreted incorrectly as evidence to support the widespread occurrence of hormesis.
In some cases the apparent hormetic response reported in animal studies may be largely an artifact of the evaluation methodologies. For example, 2,3,7,8-tetrachloro-p-dibenzodioxin (TCDD) has been frequently cited as an environmental carcinogen that produces low-dose beneficial effects (Calabrese and Baldwin 2003c;Kaiser 2003). In the carcinogenicity study of TCDD (Kociba et al. 1978), the incidence of tumors of the liver, lung, tongue, and nasal turbinates were increased, and the incidence of tumors of the pituitary, uterus, mammary glands, pancreas, and adrenal gland were decreased. In no case was an individual tumor response nonmonotonic; however, by calculating the total number of tumors, Calabrese presents the overall tumor response as hormetic (Kaiser 2003b). We argue that this should not be considered hormesis because none of the specific tumor responses contributing to the shape of the total tumor dose response can be considered hormetic or nonmonotonic. A simplified version of this scenario is presented in Figure 3.
There are additional issues regarding the interpretation of the dose–response data for total tumor incidence in the TCDD study. In that study (Kociba et al. 1978), mortality was increased in the high-dose group, and body weights were decreased relative to that of controls. Because adjustments were not made for early mortality, estimations of total tumor rates relative to controls are not reliable. In addition, it is well known that lower body weight is associated with reduced tumor incidence at several sites (Rao et al. 1987). Further, histologic examinations in the low-dose and mid-dose groups were not as extensive as those performed for the control and high-dose groups. Thus, the apparent hormetic response is not based on reliable data.
Stimulatory Responses Are Not Always Beneficial, and Some May Be Harmful
Although Calabrese and Baldwin (2002b) state that the adaptive response should not be interpreted a priori as being either beneficial or harmful, in other publications they claim that dose stimulatory responses are generally beneficial. For example:
Acceptance of hormesis will be difficult, therefore, because agencies will need to accept the possibility (actually, the likelihood) that toxic substances, even the most highly toxic (e.g., cadmium, lead, mercury, dioxin, PCBs, etc.) can cause beneficial effects at low doses [emphasis added]. (Calabrese and Baldwin 2003a, p. 191)
In any case, adaptive responses may be beneficial or harmful depending on the life stage or circumstances under which they occur. For example, natural hormones are responsible for maintaining homeostasis and controlling normal development; hence, exposure to agents that interfere with homeostatic control processes, especially those that stimulate growth at inappropriate or vulnerable times, can lead to abnormal development.
The concept of hormesis is based on experimental observations, but the assumption that stimulatory effects are always or usually beneficial is unproven. Many low-dose stimulatory responses with equally likely adverse consequences include increased cell replication, DNA synthesis; blood pressure, heart rate, interleukin-2 release, prolactin release, testosterone concentration, luteinizing hormone concentration, and dopamine outflow (Calabrese and Baldwin 2003b).
The concept of radiation hormesis is based on the hypothesis that low-dose ionizing radiation induces adaptive responses that enhance the repair of DNA damage from endogenous and exogenous sources and stimulate cell removal (Pollycove and Feinendegen 2003). However, this hypothesis needs to be tested. In addition, it is necessary to recognize that adaptive stress responses such as enhanced cell death may be beneficial or harmful depending on the circumstance of the response, and interpretations of hormetic effects of radiation exposure may be influenced by experimental designs. For example, the report of a negative correlation between domestic radon exposure and lung cancer mortality (Cohen 1995) was likely due to failure to account for confounding by cigarette smoking (Puskin 2003). Moreover, two recent reports refute the credibility of “radiation hormesis” by concluding that low doses of radiation present a cancer risk [National Research Council (NRC) 2005; International Agency for Research on Cancer 2005]. Regarding the possibility of low dose beneficial effects, the NRC concluded that
the assumption that any stimulatory hormetic effects from low doses of ionizing radiation will have a significant health benefit to humans that exceeds potential detrimental effects from the radiation exposure is unwarranted. (NRC 2005, p. 585)
Studies reviewed in support of the radiation hormesis hypothesis were “found either to be based on ecologic studies or to cite findings not representative of the overall body of data” (NRC 2005, p 19).
There are other clear examples where a stimulatory effect would not be considered beneficial. For example, agents that induce cytochrome P450 activities to enhance the rate of elimination of xenobiotics will also increase the mutagenic potential of chemicals that are activated to DNA-reactive intermediates by these enzymes. Glutathione S-transferase (GST) is usually considered to be a detoxifying enzyme. However, GST-mediated glutathione conjugation of trichloroethylene and other haloalkenes produces mutagenic intermediates. Thus, in some cases increased GST activity may be beneficial while in other cases it may be harmful. Polymorphisms in genes coding for metabolizing enzymes contribute to interindividual variability discussed below and may vary by more than 50-fold in humans (Guengerich et al. 1991).
In utero exposure to low and high doses of the synthetic estrogen diethylstilbestrol (DES) has opposite effects on uterine response to hormonal stimulation in adulthood (Alworth et al. 2002). Although at low doses the effect is stimulatory (increased uterine size) and therefore fits within the original definition of hormesis, this effect is not beneficial. In fact, a chemically induced positive uterotropic response is used as a screen for estrogenicity and raises concern about the toxicity of the agent [U.S. Environmental Protection Agency (EPA) 1998].
Health Decisions Based on Beneficial Effects Must Address All Induced Effects
The idea of focusing primarily on purported beneficial hormetic responses when making decisions for exposure standards is greatly weakened when all the toxicologic and epidemiologic evidence for a given compound or agent is considered. A major concern is that an agent may produce an apparent low-dose beneficial response for one effect but also induce an adverse effect at that same dose in a different organ or another species (Figure 4). For example, cadmium has been touted as a model hormetic agent (Calabrese and Baldwin 2003c), partly because low experimental doses (1–10 μmol/kg) have been associated with-nonstatistically significant decreases in testicular tumors in rats (Waalkes et al. 1988). However, a significant increase in the incidence of prostatic neoplasias and an increase in the number of prostate tumors per animal were observed in this same study within the hormetic dose range (Waalkes et al. 1988, 1997). Notably, cadmium has been long recognized as being carcinogenic to humans, associated with prostate, lung, renal, and bladder cancers (National Toxicology Program 2002).
Moreover, three epidemiologic studies indicate that current exposures to cadmium in the general population are associated with adverse health outcomes (Matsuda et al. 2002; Satarug and Moore 2004; Schwartz et al. 2003). One of the studies reported that increasing levels of urinary cadmium are associated with impaired fasting glucose (pre-diabetes) and diabetes after adjusting for age, ethnicity, sex, and body mass index in a sample of more than 8,700 adults (Schwartz et al. 2003). These findings are consistent with animal data showing that cadmium causes damage to the pancreas and alters glucose regulation in laboratory animals (Han et al. 2003; Kanter et al. 2003; Merali and Singhal 1980). Cadmium and many other heavy metals are also fundamentally toxic to the kidneys, with chronic low-level exposure leading to tubular damage (Goyer 1991). This damage is associated with increased mortality (standardized mortality ratios) in areas such as Jinzu, Japan (Matsuda et al. 2002). Among individuals with limited kidney function and among many elderly people whose kidney function declines as they age, exposure to cadmium and other nephrotoxins, even at very low levels, can prove extremely dangerous. When all these findings are considered, it is improbable that allowing higher levels of cadmium in the environment would provide an overall health benefit for the general population.
Other purported hormetic agents such as radiation present the same concern. Noncancer health concerns include decreased birth weight (Hujoel et al. 2004) and cognitive impairment after prenatal radiation exposure (Hall et al. 2004; Otake and Schull 1998; Yamazaki and Schull 1990).
Health Decisions Must Address Interindividual Differences in Exposure and Susceptibility
Regulating to achieve a purported beneficial response would require standards to be set at a specified level rather than below an exposure level. This would require that exposure levels in the general population be maintained within a narrow window which would be impossible. Even at a given environmental standard, differences in body mass can result in significant differences in exposure. For example, on a body-weight basis compared to adults, children breathe 3 times as much air, drink up to 7 times as much water, and ingest 3 times as much dust and soil because they put their hands in their mouths frequently (U.S. EPA 1997, 2002). The National Academy of Sciences Committee on Pesticides emphasized the importance of exposure in accounting for the differences in pesticide-related health risks between children and adults (National Academy of Sciences 1993).
Susceptibilities vary among individuals and over the course of a lifetime, making it difficult to identify a beneficial hormetic exposure at the population level. Based on numerous intrinsic and extrinsic factors that affect interindividual susceptibility to toxic agents, a dose that may appear to be beneficial for one subgroup (e.g., healthy young males) may produce adverse health effects in other subgroups (e.g., children, the elderly, immune-compromised individuals, or workers exposed to other toxic agents; Figures 5 and 6).
Consider ethanol, which is cited as a classic hormetic agent because low or moderate drinking is associated with beneficial outcomes including reduced overall mortality and reduced risk of coronary heart disease (CHD) and stroke, whereas high consumption is associated with other types of heart diseases, neurological disorders, cancer, liver cirrhosis, and traffic accidents (Agarwal 2002). But low to moderate drinking in pregnant women (defined as 1.2–2.2 drinks per day) is discouraged because even small amounts of alcohol during pregnancy (0.5 drinks per day) have been associated with adverse behavioral outcomes in children, including aggressive behavior (Sood et al. 2001). Because no evidence exists for thresholds of risk-free drinking during pregnancy, the American Academy of Pediatrics and the American College of Obstetrics and Gynecologists recommend abstinence for preconceptional and pregnant women (Sokol et al. 2003). Health decisions based on a limited characterization of variability in hormetic responses among exposed individuals may result in excessive health risks for susceptible subpopulations who do not experience the same dose-related effects.
A recent analysis of experimental animal studies for four types of ionizing radiation (Cs-137 gamma rays, X rays, neutrons, and internal βrays resulting from the injection of tritiated water) estimated a 3.5- to 5.3-fold increase in carcinogenic sensitivity per dose when exposure occurred in the fetal to birth–weaning period relative to comparable doses in adults (Hattis et al. 2004). In addition to lifestage differences in susceptibility to radiation-induced cancer, tumor response to radiation in adult animals varies depending on strain (Broerse et al. 1986), hormone status (i.e., estrogen levels; Bartstra et al. 2000), and whether the dose of radiation is a single or fractionated exposure (Maisin et al. 1988). There are many reasons that fetuses, infants, and children are more sensitive to chemicals than are adults. These range from the well-known susceptibilities of developing organ systems, such as the nervous system to neurotoxins including lead (Agency for Toxic Substances and Disease Registry 1999) and mercury (NRC 2000), as well as to age-related differences in metabolism and elimination (Ginsberg et al. 2002).
In addition to differences in exposure, age and genetic variabilities are relevant to consideration of the toxicity of organophosophate (OP) pesticides that are present in food and pet treatments. The enzyme paraoxonase (PON) metabolizes toxic breakdown products of OPs. People with higher than average PON levels due to genetic polymorphisms metabolize OPs more quickly (Hulla et al. 1999). Infants are especially vulnerable to OPs because adult levels of PON are not produced until approximately 2 years of age (Chen et al. 2003; Ecobichon and Stephens 1973). Other exposures such as alcohol, cigarette smoke, and certain medications also affect the level of PON-1 activity (Gouedard et al. 2003; Wang et al. 2004). Similarly, OP detoxification by malaoxonase differs between adults and children and varies at least 7-fold among adults (Sams and Mason 1999). Health decisions that do not adequately account for human variability will not sufficiently protect vulnerable segments of the general population.
Health Decisions Must Address Other Environmental and Workplace Exposures
Advocates of incorporating beneficial hormetic responses into risk assessment fail to recognize that people are exposed to hundreds of compounds each day, and these vary depending on our environmental and occupational exposures. According to Calabrese, maximal low-dose hormetic response stimulation for a given chemical occurs on average at a dose 5-fold below the NOAEL (Renner 2004). Thus, it follows that simultaneous exposure to other compounds that elicit similar toxic responses would be enough to move an individual from the low-dose supposed beneficial range to the range where adverse effects are expected (Figure 7). For example, a decision based on an apparent low-dose beneficial effect for TCDD would increase health risks because the general population is exposed to numerous dioxin-like compounds that also induce disease through activation of the aryl hydrocarbon receptor. Given that residues of hundreds of chemicals have been measured in humans (3M 2002; Centers for Disease Control and Prevention 2003; Environmental Working Group 2003; Olsen et al. 2002; Schecter et al. 2003), with many of them affecting the same tissues and fluctuating in concentration over the course of a lifetime, titrating exposure to achieve a relatively narrow beneficial hormetic range is untenable and clearly a poor public health policy.
Conclusions
Only after careful consideration of the biological underpinnings of a truly beneficial response can an exposure be considered for the general population, such as the addition of folic acid to cereals. If a toxic or hazardous pollutant were found to have truly beneficial effects at low dose, then that agent should be tested clinically, go through the U.S. Food and Drug Administration (FDA) approval process, and be regulated as a pharmaceutical for those who might benefit from its use. Certainly, the general population should not be exposed to chemotherapeutic agents that benefit cancer patients. For pharmaceuticals, it is understood that there are trade offs between benefits and risks. For example, although aspirin is a generally well-tolerated pain reliever and is increasingly advocated as a preventative tool for heart attacks and colorectal cancer (Vainio and Miller 2003; Werner et al. 2004), it is also linked to increased risk of gastrointestinal bleeding, cerebral hemorrhage (Werner et al. 2004), and asthma attacks (Jenkins et al. 2004). In addition, aspirin is not recommended for children or teenagers who have or are recovering from chicken pox or flulike symptoms because it can cause debilitating and sometimes lethal Reyes syndrome (U.S. FDA 2003). Individual risks to pharmaceutical agents can be controlled with proper usage; however, increased exposure to environmental toxins presents additional involuntary risks for the general population. Under the latter condition, exposure is inadequately controlled, and there is no mechanism to correct for individual circumstances (e.g., medical condition or age) that may result in harm.
Although hormetic effects may occur in some instances, it is indeed rare that exposures to toxic, mutagenic, teratogenic, and carcinogenic chemicals, even at low exposure levels, would be risk free and provide health benefits for the general public. Portraying chemicals with numerous adverse effects as having benefits while ignoring their hazards is irresponsible and does not provide full and objective disclosure. In the 1950s doctors prescribed DES to pregnant women to prevent miscarriage and premature births and to produce “bigger and stronger babies” even though DES had been shown to cause damage to reproductive tissues in animals (Dinusson et al. 1948; Dunn and Green 1963; Takasugi and Bern 1964). Human use of DES was banned in the United States in 1971 after the discovery of high rates of rare, clear-cell adenocarcinomas of the vagina and cervix in DES-exposed daughters (Herbst 1981), and later studies showed elevated breast cancer risk in women who took DES during pregnancy (Titus-Ernstoff et al. 2001). Certainly, health policy decisions should be based on scientific evidence and not on speculation of health benefits in order for the general population to avoid repeating the mistakes of the past similar to that of the DES tragedy.
The claims and projections of health benefits from exposures to environmental toxicants and carcinogens are based on untested assumptions and disregard numerous well-established scientific principles that underpin a public health–protective approach to regulating exposure to toxic substances. If hormesis were used in the decision-making process to allow higher exposures to toxic and carcinogenic agents, this would substantially increase health risks for many, if not most, segments of the general population.
We thank N. Walker and S. Taylor for their thoughtful review of the manuscript.
Figure 1 Nonmonotonic dose response for growth or cancer incidence.
Figure 2 An apparent hormetic response may reflect data variability. Some responses may appear to be hormetic but actually be an artifact of the experimental and analytical methodology because of data variability (shown here), small group size, large number of end points analyzed, unequal evaluations in all dose groups, effects of the agent on body weight and survival, and the underlying mechanism of the nonmonotonic dose response. Criteria for listing a response as hormesis must address all these potential confounding factors.
Figure 3 Three nonhormetic responses do not equal hormesis. The increase in tumor response at site 1 and the decreases at sites 2 and 3 are monotonic and therefore nonhormetic. Although the total tumor response appears to be nonmonotonic, this is not hormesis.
Figure 4 An agent induces multiple effects. An apparent beneficial hormetic dose for disease 1 (at dose 1×) increases disease incidence for disease 2. For example, an agent may induce liver tumors at the same low dose that is associated with a decrease in pituitary tumors.
Figure 5 Interindividual variability. An apparent maximal beneficial hormetic dose for adult 3 (at dose 3×) is toxic to adult 1 and adult 2. Because of genetic differences and extrinsic factors, people may respond differently to environmental toxicants. In this figure, adult 1 receives no benefit with any exposure to the agent and the dose response for adult 2 is maximal at a lower dose than that for adult 3.
Figure 6 Life stage differences in susceptibility. If the fetus, children, elderly, or other groups do not experience a beneficial hormetic response, health decisions based on hormesis will result in higher risks for these populations.
Figure 7 Exposure to mixtures. If agents A, B, and C act by similar mechanisms (e.g., activate the same receptor), then exposure to apparent beneficial hormetic doses of each of these together is toxic. In this example low-dose exposure to two agents may not produce a beneficial effect, but low-dose exposure to three agents is toxic. Because we are all exposed to different mixtures of toxic agents, beneficial health effects in the real world cannot be assumed based on responses of individual agents.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7745ehp0113-00127716203234ResearchImpact of Polychlorinated Biphenyls Contamination on Estrogenic Activity in Human Male Serum Plíšková Martina 1Vondráček Jan 12Canton Rocio Fernandez 3Nera Jiřií 1Kočan Anton 4Petrík Ján 4Trnovec Tomáš 4Sanderson Thomas 3van den Berg Martin 3Machala Miroslav 11 Veterinary Research Institute, Brno, Czech Republic2 Institute of Biophysics, Czech Academy of Sciences, Brno, Czech Republic3 Institute of Risk Assessment Sciences, University of Utrecht, Utrecht, the Netherlands4 Slovak Medical University, Bratislava, SlovakiaAddress correspondence to M. Machala, Department of Chemistry and Toxicology, Veterinary Research Institute, Hudcova 70, 62132 Brno, Czech Republic. Telephone: 420-533331813. Fax: 420-541211229. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 26 5 2005 113 10 1277 1284 12 11 2004 26 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Polychlorinated biphenyls (PCBs) are thought to cause numerous adverse health effects, but their impact on estrogen signaling is still not fully understood. In the present study, we used the ER-CALUX bioassay to determine estrogenic/antiestrogenic activities of the prevalent PCB congeners and PCB mixtures isolated from human male serum. The samples were collected from residents of an area with an extensive environmental contamination from a former PCB production site as well as from a neighboring background region in eastern Slovakia. We found that the lower-chlorinated PCBs were estrogenic, whereas the prevalent higher-chlorinated PCB congeners 138, 153, 170, 180, 187, 194, 199, and 203, as well as major PCB metabolites, behaved as anti-estrogens. Coplanar PCBs had no direct effect on estrogen receptor (ER) activation in this in vitro model. In human male serum samples, high levels of PCBs were associated with a decreased ER-mediated activity and an increased dioxin-like activity, as determined by the DR-CALUX assay. 17β-Estradiol (E2) was responsible for a major part of estrogenic activity identified in total serum extracts. Significant negative correlations were found between dioxin-like activity, as well as mRNA levels of cytochromes P450 1A1 and 1B1 in lymphocytes, and total estrogenic activity. For sample fractions containing only persistent organic pollutants (POPs), the increased frequency of anti-estrogenic samples was associated with a higher sum of PCBs. This suggests that the prevalent non-dioxin-like PCBs were responsible for the weak antiestrogenic activity of some POPs fractions. Our data also suggest that it might be important to pay attention to direct effects of PCBs on steroid hormone levels in heavily exposed subjects.
CYP1A1CYP1B1dioxin-like activityestradiolestrogenicityhuman serumpolychlorinated biphenyls
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Polychlorinated biphenyls (PCBs) are a group of structurally diverse and persistent environmental pollutants, widely distributed as complex mixtures. Mechanisms of toxicity of individual PCB congeners depend on the planarity of a molecule (Safe 1994), as well as on molecular weight and biotransformation rate (Rose et al. 2002). Similarly to 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD), the coplanar non-ortho-substituted PCBs activate aryl hydrocarbon receptor (AhR) and AhR-dependent signal transduction pathways (van den Berg et al. 1998). A majority of the adverse effects of these compounds is thought to be mediated through AhR activation. Therefore, the toxic potencies of dioxin-like PCBs can be expressed in terms of toxic equivalency factors (TEFs) relative to TCDD as the reference toxicant. The TEF values of individual PCBs multiplied by their respective concentrations can be used to yield TCDD toxic equivalents (TEQs) (van den Berg et al. 1998). In contrast, a distinct set of AhR-independent effects, including neurotoxicity, (anti)estrogenicity, and tumor promotion, has been found after exposure to noncoplanar ortho-substituted PCBs (Brouwer et al. 1999; Hansen 1998; Machala et al. 2003; Robertson and Hansen 2001); however, the modes of action of nondioxin-like PCBs are often not clear.
The biological activities of PCBs have been reported to include both estrogenic and anti-estrogenic effects in various in vitro and in vivo models (Cooke et al. 2001; Hansen 1998). TCDD and other AhR agonists, including dioxin-like PCBs, have been frequently reported to have antiestrogenic activity (Buchanan et al. 2000, 2002; Oenga et al. 2004; Safe and Wörmke 2003). Several modes of antiestrogenic action of AhR agonists might include repression of 17β-estradiol (E2)-dependent gene expression by interactions of activated AhR with DNA regions of E2 responsive gene promoters (see Oenga et al. 2004, Safe and Wörmke 2003), inhibition of E2-induced cell cycle proteins and uterine epithelial mitogenesis (Buchanan et al. 2002; Wang et al. 1998), or effects of PCBs on E2 metabolism (Pang et al. 1999; van Duursen et al. 2003). In contrast, the exact mechanisms of estrogenic or antiestrogenic activities of nondioxin-like PCBs are still not fully characterized. The reported results are often contradictory, derived from data obtained in different in vitro or in vivo models (Hansen 1998). The majority of studies found that low-molecular-weight PCBs elicit estrogenic activity both in vitro and in vivo (Arcaro et al. 1999; Nesaretnam and Darbre 1997; Rogers and Denison 2000; Rose et al. 2002). In contrast, the three most prevalent nondioxin-like PCBs, 2,2′,3,4,4′,5′-hexachlorobiphenyl (PCB 138), 2,2′,4,4′,5,5′-hexachlorobiphenyl (PCB 153), and 2,2′,3,4,4′,5,5′-heptachloro-biphenyl (PCB 180), have been reported to be antiestrogenic in MCF-7 cells (Bonenfeld-Jorgensen et al. 2001). However, estrogenic/ antiestrogenic potencies of a large set of PCB congeners have not yet been determined in a single in vitro bioassay. Taken together, there is only limited information on effects of prevalent nondioxin-like PCBs and complex PCB mixtures in mammalian blood and tissues.
One essential question is whether the chronic exposure to low doses of environmental persistent organic pollutants (POPs), including PCBs, has endocrine-disrupting effects on exposed human populations (Brouwer et al. 1999; Daston et al. 2003). There are only limited data on estrogenic and dioxin-like activities of complex samples of organic compounds collected from human blood. Sonnenschein et al. (1995) and Soto et al. (1997) reported the development of a serum extraction method for separation of POPs and endogenous steroids. Recently, this extraction and fractionation technique has been adapted for combined chemical and in vitro assay analysis in human blood, allowing for discrimination of effects of endogenous hormones and xenoestrogens (Fernandez et al. 2004). However, results of direct measurements of estrogen receptor (ER)-mediated activity of serum extracts or total POPs fractions in a comprehensive set of human subjects have not yet been published. More information is available concerning in vitro bioassays of dioxin-like activity in human blood contaminated with PCBs. The total TEQ values determined in human female serum and follicular fluid by the DR-CALUX (dioxin receptor–chemically activated luciferase expression) assay have been reported to correlate well with the sum of four major PCB congeners: 153, 138, 180, and 118 (Pauwels et al. 2000). The possible impact of environmental endocrine disruptors on breast cancer, male reproductive tract problems, or prostate cancer is questionable (Chen et al. 2003; Safe 2004). Nevertheless, estrogens play a significant role in, for example, testicular function (O’Donnell et al. 2001). Because the levels of endogenous estrogens in males are considerably lower than in females, possible estrogenic/ antiestrogenic impact of high levels of contamination could be more pronounced in males. Therefore, determination of in vitro estrogenic/antiestrogenic activities of extracts of human male blood samples collected from a PCB-contaminated area could yield more information about the impact of PCBs and/or other POPs on estrogen-dependent signaling.
Since 1959, several thousand tons of residues from the Chemko Strážske chemical plant in the Michalovce district, Slovakia, have been deposited in the nearby river and water reservoir sediments. This has resulted in widespread contamination of the environment, leading to high human exposure. Serum PCB concentrations in subjects from six different districts of Slovakia suggest that levels are three to six times higher in subjects from the Michalovce district (Kočan et al. 2001). When serum levels of 15 PCBs were compared in residents of two districts in eastern Slovakia, one with extensive environmental contamination from a former PCB production site (Michalovce) and the other matched on geography but with background PCB levels, the age-adjusted geometric means for the sum of 15 measured PCB congeners were statistically significantly higher in subjects from the Michalovce district for both sexes: 3327.6 versus 1331.4 ng/g lipid in males, 2751.8 versus 992.2 ng/g lipid in females (Pavúk et al. 2004).
As a part of a large epidemiologic study, the PCBRisk project (Trnovec et al. 2000), we investigated effects of extensive contamination with PCBs on human serum dioxin-like, estrogenic, and antiestrogenic activities of serum extracts from subjects living in the contaminated area. In this study, the ER-mediated activities of individual PCB congeners, which were identified as principal contaminants present in serum of human population in the studied area, were investigated using the T47D breast cancer cell line stably transfected with the luciferase reporter gene under control of estrogen-responsive elements, detecting the direct activation of ER (the ER-CALUX assay) (Legler et al. 1999). In the second step of the study, effects of chronic PCB exposure on antiestrogenic/estrogenic and dioxin-like activities exerted by extracts of human male sera (150 human male serum samples) were assessed and compared to concentrations of major POPs and levels of E2 in serum.
Materials and Methods
Chemicals.
The PCB nomenclature used here is from the International Union of Pure and Applied Chemistry (IUPAC). PCBs 74, 156, 170, 187, 199, and 203 were purchased from Ehrenstorfer (Augsburg, Germany); PCBs 28, 52, 66, 99, 101, 105, 118, 126, 138, 153, 180, and 194 were supplied by Promochem (Wesel, Germany). Purity of all compounds was > 99%. The chemical structure and nomenclature of the PCB congeners we studied is presented in Figure 1. TCDD was supplied by Cambridge Isotope Laboratories, (Andover, MA, USA); E2, cell culture media, and solvents were obtained from Sigma-Aldrich (Prague, Czech Republic). Stock solutions were prepared with dimethyl sulfoxide (DMSO) and stored in the dark. The final concentrations of solvent in the medium did not exceed 0.2% (vol/vol).
Blood sampling, extraction, and clean up.
We collected 150 individual male blood samples from residents of two areas of eastern Slovakia, which are differently contaminated with PCBs: the Michalovce district, where commercial PCB mixtures were produced for a number of years (Kočan et al. 2001), and the Stropkov district, which represented the background area. The samples of human male serum (5 mL) were treated with 2 mL methanol and extracted three times with n-hexane:diethyl ether (1:1); the extracts were evaporated and dissolved in 1 mL dichloro-methane (Horander et al. 2004). For determination of overall ER-mediated activity, we replaced the solvent with DMSO in one-half of the crude extract; the second half of the sample was placed on a sulfuric acid-activated silica column and eluted with n-hexane:diethyl ether mixture, evaporated, and redissolved in DMSO (Murk et al. 1997). Using these experimental settings, only persistent compounds were eluted, including PCBs and polychlorinated dibenzo-p-dioxins (PCDDs) and dibenzofurans (PCDFs).
Chemical analysis of POPs.
We determined concentrations of prevalent (non-coplanar) PCB congeners, hexachlorobenzene, and p,p′-DDE by gas chromatography/mass spectrometry (GC/MS) (Kočan et al. 2001, 2004). We calculated TEQs from high performance GC/MS data on blood concentrations of PCDD/PCDFs and non-ortho- and mono-ortho-chlorinated PCBs. The sum of PCBs (∑PCBs) used in the correlation and multivariate statistical analysis was based on the data on concentrations of 17 indicator coplanar and mono-ortho-chlorinated PCB congeners, including PCBs 28, 52, 66, 74, 77, 99, 101, 105, 118, 126, 138, 153, 156, 169, 170, 180, and 189.
Determination of effects associated with AhR activation.
We determined the levels of cytochrome P450 (CYP) 1A1 and CYP1B1 mRNA in human peripheral lymphocytes by RNA extraction and a quantitative reverse-transcriptase-polymerase chain reaction (RT-PCR) method using TaqMan technology (Canton et al. 2003; van Duursen et al. 2005). The in vitro potencies of POPs present in serum to activate AhR were measured in sulfuric acid/silica-treated extracts by a luciferase reporter gene assay (DR-CALUX; BioDetection Systems, Amsterdam, the Netherlands) as described previously (Murk et al. 1996).
ER-mediated activity and determination of E2 in male blood samples.
We determined estrogenic activities of 17 prevalent PCB congeners using the ER-CALUX bioassay (BioDetection Systems) using the human breast carcinoma T47D.Luc cell line, stably transfected with pEREtataLuc construct (Legler et al. 1999; Machala et al. 2004). ER-mediated activity was also determined in the cells treated with either total serum hexane/diethyl ether extracts or with a fraction of POPs obtained by a consequent sulfuric acid/silica fractionation. We determined anti-estrogenicity as a decrease in response to E2 in the cells co-treated with the individual PCB or POPs fraction. Concentrations of E2 were determined by ELISA (ADVIA Centaur Estradiol-6 III assay; Bayer HealthCare, Tarrytown, NY, USA) in 60 samples selected according to stratified PCB levels. We determined cytotoxicity of extracts or individual PCBs by a neutral red uptake assay after a 24-hr exposure.
Data analyses.
All calculations were performed with Microsoft Excel, SlideWritePlus 3.0 for Windows, or Statistica 6.1 for Windows (Microsoft Corporation, Redmond, WA, USA). Nonparametric statistical analyses (Kruskal-Wallis analysis of variance and the Mann-Whitney U test) were used for data analysis. We determined the relationships among biological and chemical data by correlation analysis and multivariate principal component analysis (PCA). We assessed the correlations among the compared parameters using nonparametric Spearman’s rank coefficient (Rs). For the PCA analysis, all the data were normalized using the transformation log (X + 1).
Results
Estrogenic and antiestrogenic potencies of a series of individual PCB congeners, found to be prevalent in the human male blood samples, were determined in the ER-CALUX assay. Lower-molecular-weight PCBs 28, 52, 66, and 74 elicited a significant ER-mediated activity at micromolar concentrations. Pentachlorobiphenyls (PCBs 99 and 105) were only partial ER agonists (Figure 2A). The ER activation by its natural ligand E2 was potentiated when cells were co-treated with trichlorobiphenyls (PCB 28 or 52) (Figure 2B). The most prevalent PCB congeners, 138, 153, 170, 180, and 187, as well as octachlorobiphenyls (PCBs 194, 199, and 203) did not induce the ER-dependent luciferase activity (data not shown), but they all significantly decreased the E2-induced luciferase activity (Figure 3). The most potent inhibitors of ER activation were PCBs 199, 203, and 153; however, the IC50 (concentration that inhibits 50% of maximal E2 response) values of all tested congeners were within a narrow concentration range, 2.9–16.0 μM. A partially reconstituted mixture of the most prevalent PCBs, reflecting a typical ratio of concentrations of individual congeners, showed a significantly higher antiestrogenic activity when compared with inhibition potency of PCB 153 (Figure 4). Potent AhR agonists (dioxin-like PCBs 126, 118, 105, and 156) did not significantly affect the ER activation in the ER-CALUX assay. The estrogenic and antiestrogenic effects of PCB congeners, including the data on their cytotoxicity and calculated median effective concentration (EC50) values, are summarized in Table 1.
In the second step of this study, we determined the estrogenic activities of 150 human male serum samples, collected in Michalovce and Stropkov districts, Slovakia, using the ER-CALUX bioassay. The total hexane/ diethyl ether extracts of human male serum samples, containing both endogenous steroids and POPs, showed significant estrogenic responses in the ER-CALUX assay, ranging from 12.5 to 59.2 pg E2 equivalents (EEQ) per milliliter (Table 2). Dioxin-like activities, measured in the POPs fractions by the DR-CALUX bioassay, ranged from 0.2 to 2.9 pg TEQs/mL (Table 2). Weak estrogenic or antiestrogenic activities were found in the fractions of POPs, but only in part of the samples. The POPs fractions from the less polluted background area elicited ER-mediated activity with a higher incidence (18 of 75 samples vs. 8 of 75 samples), whereas anti-estrogenic activity was detected more frequently in the samples from the PCB-polluted region (5 and 17 samples, respectively). However, the ER-mediated activities did not overcome 2 pg EEQs/mL, and only partial estrogenic or antiestrogenic responses (< 40%) were found in positive samples (data not shown). Conversion of concentration units showed that only submicromolar concentrations of prevalent PCBs were present in cultivation medium when serum extracts were applied to cells (data not shown); therefore, only partial antiestrogenic effects of PCBs could be expected in the sample mixtures.
The in vitro bioassay data were compared with data on concentrations of major POPs in the samples obtained in the PCBRisk project. In this large epidemiological study, > 2,000 human blood samples were evaluated for concentrations of PCBs, PCDD/PCDFs, and p,p′-DDE (Kočan et al. 2004). The analytical data for the subset of 150 male samples, which were used here for statistical analysis of in vitro bioassay data only, are summarized in Table 2. Additionally, data on induction of AhR-dependent expression of CYP1A1 and CYP1B1 mRNA in blood lymphocytes, as determined by real-time PCR (Canton et al. 2003; van Duursen et al. 2005), and E2 concentration determined in a substantial part of blood sample extracts were included in the statistical analysis. PCDDs did not contribute significantly to higher levels of TEQs, and concentrations of PCDFs, which might also contribute to the dioxin-like activities, were only marginally increased in highly exposed male subjects (Kočan et al. 2004). High concentrations of p,p′-DDE were found in a majority of samples (Table 2); however, only weak estrogenic and no dioxin-like activity was found for this compound (data not shown). Therefore, modulations of biological effects might be attributed mainly to differences in the concentrations of PCBs.
Total estrogenic activity was moderately decreased, while the dioxin-like activity was increased, in samples with high PCB levels ranging from 13.9 to 175.5 ng/mL serum (i.e., 1865.7–32509.4 ng/g lipid; Figure 5). Quartiles presented in Figure 5 are based on concentrations expressed per milliliter of serum. The alternative data set (expressed on a gravimetric basis) showed a very similar pattern of effects. The levels of E2 decreased in the fourth quartile of PCB concentrations, but the decrease was not significant.
Based on concentration data summarized in Table 2, we performed multivariate PCA to more precisely characterize statistical associations between in vitro bioassay data and levels of major organic contaminants in the blood. The PCA is one of a set of ordination techniques used in data reduction and summarization. As shown in Figure 6A, the first two components explained 55% of the variability of the original data. The axes were aligned with the directions of greatest variation in the data set. The other components were neglected because they did not contribute significantly to the meaningful interpretation of the relationships among biological and chemical parameters. The first principal component axis represented the chemical variables of the male serum extracts, such as ∑PCBs, PCB 153 concentration, p,p′-DDE content, and the biological variable AhR-mediated activity (DR-CALUX assay). The second principal component was associated with the ER-mediated activity of serum extracts (ER-CALUX assay), E2 concentrations, and CYP1A1 and CYP1B1 mRNA expression level. The length and direction of the lines represent the significance of the associated variables. The PCA confirmed a positive relation between overall estrogenicity (12.5–59.2 pg EEQ/ml) and E2 levels (1.0–43.5 pg/ml; Table 2). Further, weak negative relations between ER-mediated activity and expression of CYP1A1 and CYP1B1 mRNAs and between estrogenic and dioxin-like activities were observed (Figure 6A). The associations of the variables were confirmed by bivariate rank correlations computed on original untransformed data. The estrogenic activity of serum extracts correlated with E2 concentrations (Rs = 0.510, p < 0.001). Weak but statistically significant negative correlation between ER-mediated activity and levels of CYP1A1 mRNA (Rs = −0.241, p < 0.05), as well as between E2 concentrations and dioxin-like activity (Rs = −0.227, p < 0.1), were revealed. No correlation was found between E2 concentrations and total PCB levels (Rs = 0.078).
The PCA for the fractions of persistent compounds explained 60% of the total variability in the data set (Figure 6B). The first principal component was associated with a number of fractions with antiestrogenic activity (anti-ER), ∑PCBs, PCB 153 content, and AhR-mediated activity (DR-CALUX assay). The second principal component axis represented only the estrogenic activity of fractions (ER). PCA analysis showed that anti-estrogenic activity of fractions depended on the concentrations of PCB congeners and TCDD equivalents obtained in the DR-CALUX assay (Rs = 0.246–0.275, p < 0.01).
Discussion
PCBs have been reported to be both estrogenic and antiestrogenic, based on various in vitro and in vivo models. Lower-molecular-weight PCBs are reportedly estrogenic, with the exception of dioxin-like 3,3′,4,4′-tetrachlorobiphenyl (PCB 77), which elicited anti-estrogenicity in vivo and also in some in vitro models (Ramamoorthy et al. 1999). 2,2′,6,6′-Tetrachlorobiphenyl (PCB 54), a fully ortho-substituted compound not occurring in the environment at significant levels (Hansen 1998), was estrogenic both in the MCF-7 cell focus assay and in the rat uterotrophic assay (Arcaro et al. 1999). 3,3′,5,5′-Tetrachlorobiphenyl (PCB 80), another model congener, was a weak ER agonist both in vivo and in vitro, while, surprisingly, PCB 52 was inactive in the same models (Nesaretnam and Darbre 1997). PCB 66 and PCB 95 (2,2′,3,5′,6-pentachlorobiphenyl) have been reported to be estrogenic in BG1LucE2 cells at 10 μM concentrations, whereas coplanar PCB 77 elicited no ER-mediated activity in this cellular model (Rogers and Denison 2000). PCB 52 and PCB 77 caused a modest transient uterotrophic effect in weaning rats (Rose et al. 2002); however, PCB 77 attenuated the increase in uterine weight and cell proliferation in another study (Jansen et al. 1993). The uterotrophic effects after exposure to less persistent PCB congeners showed nonlinear dose responses, and they decreased rapidly (Rose et al. 2002). However, all the above data have been obtained from various models and assays, and estrogenic/ antiestrogenic effects of both lower chlorinated and higher chlorinated PCBs present in the environment have not yet been examined systematically in one assay.
In our study, PCBs 28, 52, 66, 74, 99 and 105, all found at significant levels in male serum samples, induced the ER-mediated activity at micromolar concentrations (Table 1), suggesting that ER activation could be one of the potential modes of action of low-molecular-weight PCBs. However, the decrease of total estrogenic activity and E2 levels observed in human serum samples of males exposed to high PCB levels (Figure 5A) indicated that PCB mixtures elicited an overall antiestrogenic effect. Therefore, the ER-mediated activity of lower-chlorinated PCBs appears to have only a limited toxicologic significance, perhaps with the exception of acute transient exposure to PCBs (Rose et al. 2002).
Unlike low-molecular-weight PCBs, the dioxin-like and prevalent high-molecular-weight PCB congeners are considered to be antiestrogenic. TCDD, a model toxicant for dioxin-like PCBs, exhibits potent anti-estrogenic activity (Buchanan et al. 2000, 2002; Cooke et al. 2001; Safe and Wörmke 2003). TCDD has little effect on total ER levels (Gierthy et al. 1996), and no direct binding to ER has been reported (see Safe and Wörmke 2003). Recently, inhibition of ER-mediated cell proliferation by coplanar PCBs has been reported in breast cancer cell lines (Oenga et al. 2004). TCDD or coplanar PCBs did not inhibit E2-induced activity of a reporter construct containing the promoter insert from creatine kinase B in T47D cells, while dioxin-like compounds, including PCB 77 and PCB 126, prevented activation of other reporter constructs in both MCF-7 and T47D cells, although only at levels as high as 10 μM (Ramamoorthy et al. 1999). This suggests that a type of reporter construct can affect detection of antiestrogenic activity. One possible mechanism of antiestrogenic activity of AhR ligands is the direct inhibition of E2-responsive genes through binding to inhibitory dioxin responsive elements (iDRE) in their promoter regions. Functional iDREs have been identified in promoter regions of pS2, c-fos, Hsp27 and cathepsin D genes (reviewed by Safe and Wörmke 2003). In the present study, anti-estrogenicity was not elicited by coplanar PCB 126 (Table 1) in the T47D.Luc cells used in the ER-CALUX assay. The lack of anti-estrogenic activity of coplanar PCBs observed in the T47D.Luc cell line might be explained by the missing iDREs in the reporter construct, which contains three tandem repeats of the consensus estrogen-responsive element (ERE) oligonucleotide (Legler et al. 1999).
On the other hand, this cellular model allowed us to investigate a direct activation of ER and/or perturbation of E2-induced ER activation. While the low-molecular-weight PCBs elicited ER activation and ER-dependent gene expression, prevalent and more persistent high-molecular-weight PCB congeners were antiestrogenic (Table 1, Figures 3 and 4). Pulses of exposure to more labile mixtures of lower chlorinated PCBs may contribute to transient endocrine disruption, including an increase in estrogenic activity (Hansen 1998; Rose et al. 2002). PCB 153 was estrogenic in the acute 2-day immature rat uterine weight assay, albeit at very high concentrations (Li et al. 1994). Antiestrogenic effects of three prevalent congeners (PCBs 138, 153, and 180) have been found both in a reporter gene and cell proliferation MCF-7 assays (Bonenfeld-Jorgensen et al. 2001). This is in accordance with our data on antiestrogenicity of high-molecular-weight PCBs in the ER-CALUX assay (Table 1). Inhibition of ER activation by hexa-, hepta- and octachlorinated biphenyls and suppression of estrogenic signaling found in serum of males chronically exposed to PCBs (Figures 3–5) suggest that PCB 153 and other prevalent congeners could contribute to overall antiestrogenic response. Contribution of hydroxy- and methylsulfonyl-PCB metabolites, p,p′-DDE and methylsulfonyl-p,p′-DDE metabolites to antiestrogenic activities of POPs might also be of importance. Because both low- and high-molecular-weight PCBs and POPs metabolites elicited their effects on estrogenic activity at similar micromolar concentrations (Bonenfeld-Jorgensen et al. 2001; Letcher et al. 2002; Machala et al. 2004; this study), it might be expected that the anti-estrogenic effects of prevalent higher chlorinated PCBs would prevail in human male blood. Nevertheless, antiestrogenic effects of PCBs detected in human male serum appeared to be less important, when compared with the levels of E2, the major contributor of the overall estrogenic activity.
In vitro bioassays are a suitable tool for exposure assessment of dioxin-like and (anti)estrogenic compounds (van den Berg et al. 1998; Zacharewski 1997). However, currently only limited data are available on dioxin-like activities found in human female serum and follicular fluid; the TEQs determined by the DR-CALUX assay have been reported to correlate well with the sum of four major PCB congeners 153, 138, 180, and 118 (Pauwels et al. 2000). The data on the ER-mediated activity in human blood samples are still limited. Rasmussen et al. (2003) reported estrogenic activity in female serum by E-Screen assay, however, they did not observe any correlation between estrogenicity and concentrations of individual endocrine disruptors. In the present study, a decrease of total estrogenic activity and increased dioxin-like activity were found in serum samples from human males chronically exposed to PCBs. However, as shown in Figure 5, correlations with PCB concentrations were significant only in subjects with high exposure levels.
Our data suggest that exposure to high levels of PCBs might affect E2 blood levels, although this was not significant. Currently, there is little information available about a possible modulation of steroid hormone levels after exposure to PCBs. In a recently published study, a weak but significant negative correlation was found between serum levels of the prevalent PCB 153 congener and testosterone in young men, and E2 concentrations (within a concentration range of 43–144 pM) were also slightly decreased in the more exposed subjects (Richthoff et al. 2003). The concentrations of PCB 153 (23–250 ng/g lipid) found in these subjects were significantly lower that those observed both in the present study and in previous studies in eastern Slovakia (Kočan et al. 2001, 2004). The concentrations of PCB 153 ranged from 115 to 8,631 ng/g lipid in 150 Slovak male serum samples included in the present study. Another experimental study in rats exposed to PCB mixtures also reported lower testosterone and E2 serum levels and suppression of brain aromatase activity (Hany et al. 1999). Decreased E2 concentrations could be associated with AhR activation by dioxin-like PCBs, leading to enhanced CYP1A/CYP1B1-catalyzed metabolism of E2 (Gierthy et al. 1996; Spink et al. 1990). Induction of CYP1A1 and CYP1B1 mRNAs in lymphocytes is considered to reflect increased exposure to dioxin-like compounds (Canton et al. 2003; Hanaoka et al. 2002). Within the epidemiologic study, Canton et al. (2004) found increased levels of CYP1A1 and CYP1B1 mRNA only in lymphocytes of males exposed to very high levels of PCBs (fourth quartile). This finding suggests that a physiologically significant AhR-dependent induction of E2-metabolizing CYP enzymes might occur in liver and other target tissues.
Besides CYP1A1, 1A2, and 1B1 iso-enzymes, CYP3A4 has been suggested to play a major role in hydroxylation of E2 (Badawi et al. 2000; Hayes et al. 1996; Pang et al. 1999; Spink et al. 1990; Takemoto et al. 2004; Yamazaki et al. 1998). Induction of CYP3A4 is a consequence of exposure to prevalent nondioxin-like PCBs (Gillette et al. 2002; Parkinson et al. 1981). Therefore, both coplanar and noncoplanar PCBs could increase E2 metabolism and reduce blood E2 concentrations.
Both dioxin-like and nondioxin-like PCBs might affect estrogen signaling by multiple mechanisms, as summarized in Figure 7. Obviously, this list of modes of action is not complete; PCBs might also potentially disrupt the pathways associated with the perturbation of hypothalamus–pituitary–gonadal axis hormone signaling and steroidogenesis, as another potential mechanism of E2 modulations by PCBs.
As outlined in the recent review by Safe (2004), it is currently not possible to directly attribute increased incidence of breast cancer or disorders of the male reproductive tract, for example, to endocrine disruption associated with organochlorine exposure. A number of adverse impacts of high PCB contamination have been identified, including perturbations of thyroid function, immunity, or neuro-developmental processes (Robertson and Hansen 2001). However, it was not possible to associate any of the adverse effects observed within the frames of the PCBRisk project with antiestrogenicity of PCBs.
In summary, significant associations between exposure to PCBs and overall (anti)estrogenic and dioxin-like activities in the present study were found only at high exposure levels. Although the prevalent noncoplanar PCBs elicited antiestrogenicity in the ER-CALUX assay, when tested as individual compounds or as a partially reconstituted mixture, a significant estrogenic activity was determined in whole-serum extracts. Moreover, only weak or negligible anti/estrogenic activities were found in serum extract fractions containing exclusively POPs including PCBs. Due to the presence of E2 in human male blood and its dominant role in total estrogenic activity of serum samples, reduction of E2 levels might be a more significant antiestrogenic effect of high PCB exposure. This mode of action, associated with induction of CYP1A1, CYP1A2, CYP1B1 and/or CYP3A4 enzymes or perturbation of steroidogenesis and endocrine signaling, preceding the biosynthesis of estrogens, deserves further attention.
This work was presented in part at the PCB Workshop, 13–15 June 2004, Urbana-Champaign, Illinois, USA, and at the Dioxin2004 Symposium, 6–10 September 2004, Berlin, Germany.
We thank all collaborators involved in the PCBRisk project for their enormous effort in collection of samples and for fruitful discussion and support, especially Å. Bergman and L. Hovander (Stockholm University, Sweden), M.B.M. van Duursen (IRAS, University of Utrecht, the Netherlands), and S. Jursa (Slovak Medical University, Bratislava, Slovakia). We thank M. Gájová for her assistance with extraction and fractionation of male blood samples.
This work was supported by the European Union (project no. QLK4-CT-2000-00488) and by the Czech Ministry of Agriculture (MZE 0002716201).
Figure 1 Chemical structures of selected PCB congeners examined for the antiestrogenic/estrogenic activities in human breast carcinoma T47D.Luc cells (ER-CALUX assay). (A) Lower molecular-weight PCBs present in low concentrations in male blood samples. (B) Non-ortho-chlorinated PCB. (C) PCB congeners occurring in relatively higher concentrations in male blood. (D) Prevalent high-molecular-weight PCB congeners.
Figure 2 Dose-dependent estrogenic effect of the individual PCB congeners (A) and the combined effect of two estrogenic PCB congeners (PCBs 28 and 52) plus 3 pM E2 (B) on induction of luciferase activity in the ER-CALUX assay. Results are expressed as mean ± SD.
*p < 0.05, and **p < 0.01 compared with control.
Figure 3 Dose-dependent effect of four indicator PCB congeners (A) and six higher chlorinated PCBs (B) on 3 pM E2-induced luciferase activity in T47D.Luc cells. Results are expressed as mean ± SD.
Figure 4 Antiestrogenic potencies of PCB 153 and an artificial mixture of the most prevalent PCBs (ratio of 6:3:5:2 of PCBs 153, 138, 170, and 180). Results are expressed as mean ± SD.
*p < 0.05 compared with an equimolar concentration of PCB 153.
Figure 5 Estrogenic activities (A) and dioxin-like activities (B) of extracts of human male serum samples. Median values of quartiles were stratified according to PCB concentrations.
*Significantly different from groups with lower PCB levels (p = 0.02; Mann-Whitney U test); concentrations of PCBs (μg/mL serum) are as follows: first quartile, 0.0020–0.0055; second quartile, 0.0055–0.0078; third quartile, 0.0079–0.0138; fourth quartile, 0.0139–0.1755.
Figure 6 Principal component analysis of the measured parameters of the serum samples. Abbreviations: Fraction (ER), number of estrogenic samples of the fraction of POPs; fraction (anti-ER), number of anti-estrogenic samples of the fraction of POPs; ∑PCBs, serum of 17 PCB congeners.
Figure 7 Potential mechanisms of antiestrogenic effects of coplanar and noncoplanar PCBs.
Table 1 PCB congeners under study, including molecular weights and estrogenic/antiestrogenic and cytotoxic effects determined in ER-CALUX assay using human breast carcinoma T47D.Luc cells.
ER-activated activity
Antiestrogenicity
PCB (IUPAC) Structure IECa (μM) IEFb IC50c (μM) IhEFd Cytotoxicity LOECe (μM)
28 2,4,4′-Trichlorobiphenyl 8.23 1.65 × 10−7 NI NI > 20
52 2,2′,5,5′-Tetrachlorobiphenyl 9.52 1.42 × 10−7 NI NI > 20
66 2,3′,4,4′-Tetrachlorobiphenyl 24.31 8.56 × 10−8 NI NI > 40
74 2,4,4′,5-Tetrachlorobiphenyl 17.00 1.24 × 10−7 NI NI > 40
99 2,2′,4,4′,5-Pentachlorobiphenyl WI WI NI NI > 40
105 2,3,3′,4,4′-Pentachlorobiphenyl WI WI NI NI > 40
118 2,3′,4,4′,5-Pentachlorobiphenyl NI NI NI NI > 20
126 3,3′,4,4′,5-Pentachlorobiphenyl NI NI NI NI > 40
138 2,2′,3,4,4′,5′-Hexachlorobiphenyl NI NI 10.12 4.94 × 10−6 20
153 2,2′,4,4′,5,5′-Hexachlorobiphenyl NI NI 5.89 8.50 × 10−6 20
156 2,3,3′,4,4′,5-Hexachlorobiphenyl NI NI WI WI 40
170 2,2′,3,3′,4,4′,5-Heptachlorobiphenyl NI NI 16.03 3.12 × 10−6 25
180 2,2′,3,4,4′,5,5′-Heptachlorobiphenyl NI NI 9.32 5.36 × 10−6 20
187 2,2′,3,4′,5,5′,6-Heptachlorobiphenyl NI NI 7.48 6.68 × 10−6 20
194 2,2′,3,3′,4,4′,5,5′-Octachlorobiphenyl NI NI 14.14 3.54 × 10−6 25
199 2,2′,3,3′,4′,5,6,6′-Octachlorobiphenyl NI NI 2.85 1.75 × 10−5 20
203 2,2′,3,4,4′,5,5′,6-Octachlorobiphenyl NI NI 3.20 1.56 × 10−5 20
Abbreviations: IEC, induction equivalency concentration; IEF, induction equivalency factor; IhEF, inhibitory equivalency factor; NI, no significant induction/inhibition; WI, weak induction/inhibition (< 50% of estradiol maximum induction; < 50% decrease in induction of 3 pM E2).
a Concentration of PCB congener inducing the same level of luciferase activity as the EC50 of the reference inducer E2 (2.08 pM).
b Calculated as the ratio between the EC50 of E2 and the concentration of the selected PCB congener inducing the same level of luciferase activity.
c Concentration of PCB congener causing 50% decrease in luciferase activity induced by 3 pM E2.
d Calculated as the ratio between the IC50 of the synthetic antiestrogen ICI 182,780 (IC50 = 50 pM) and the concentration of the selected PCB congener causing the same level of decrease in luciferase activity induced by 3 pM E2.
e Lowest (experimental) concentration of PCB congener causing a significant decrease of cell viability (24-hr exposure Neutral Red uptake assay).
Table 2 Summary of data from human male serum samples used in multivariate statistical analysis.
Concentration/mL serum
Concentration/g lipid
No. Range Median Mean Range Median Mean
Estrogenic activity (pg EEQs) 150 12.5–59.2 28.2 29.2 1.3–11.6 4.1 4.3
E2 (pg) 60 < 1.0–43.5 15.5 15.8 0.1–5.4 2.0 2.1
Dioxin-like activity (pg TEQs) 144 0.2–2.9 0.6 0.7 11.9–434.0 83.6 92.0
∑ PCBs/PCDD/PCDFs (pg TEQs) 100 0.05–0.5 0.1 0.2 7.5–57.9 18.2 20.8
∑ PCBs (μg) 150 0.0020–0.1755 0.0078 0.0147 0.3458–32.51 1.124 2.040
p,p′-DDE (μg) 150 0.0017–0.1165 0.0119 0.0171 0.2689–11.16 1.800 2.219
Sum of PCDD/PCDFs and dioxin-like PCBs was calculated as TEQs according to World Health Organization TEF values (van den Berg et al. 1998).
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7899ehp0113-00128516203235ResearchCigarette Smoking and Effects on Hormone Function in Premenopausal Women Windham Gayle C. 1Mitchell Patrick 2Anderson Meredith 3Lasley Bill L. 41 Division of Environmental and Occupational Disease Control, California Department of Health Services, Oakland, California, USA2 California Department of Health Services, Sacramento, California, USA3 Impact Assessment Inc., La Jolla, California, USA4 Institute of Toxicology and Environmental Health, School of Medicine, University of California, Davis, California, USAAddress correspondence to G.C. Windham, California Department of Health Services, Environmental Health Investigations Branch, 850 Marina Bay Parkway, Building P, Richmond, CA 94804 USA. Telephone: (510) 620-3620. Fax: (510) 620-3720.E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 3 6 2005 113 10 1285 1290 31 12 2004 2 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Cigarette smoke contains compounds that are suspected to cause reproductive damage and possibly affect hormone activity; therefore, we examined hormone metabolite patterns in relation to validated smoking status. We previously conducted a prospective study of women of reproductive age (n = 403) recruited from a large health maintenance organization, who collected urine daily during an average of three to four menstrual cycles. Data on covariates and daily smoking habits were obtained from a baseline interview and daily diary, and smoking status was validated by cotinine assay. Urinary metabolite levels of estrogen and progesterone were measured daily throughout the cycles. For the present study, we measured urinary levels of the pituitary hormone follicle-stimulating hormone (FSH) in a subset of about 300 menstrual cycles, selected by smoking status, with the time of transition between two cycles being of primary interest. Compared with nonsmokers, moderate to heavy smokers (≥ 10 cigarettes/day) had baseline levels (e.g., early follicular phase) of both steroid metabolites that were 25–35% higher, and heavy smokers (≥ 20 cigarettes/day) had lower luteal-phase progesterone metabolite levels. The mean daily urinary FSH levels around the cycle transition were increased at least 30–35% with moderate smoking, even after adjustment. These patterns suggest that chemicals in tobacco smoke alter endocrine function, perhaps at the level of the ovary, which in turn effects release of the pituitary hormones. This endocrine disruption likely contributes to the reported associations of smoking with adverse reproductive outcomes, including menstrual dysfunction, infertility, and earlier menopause.
cigarette smokingendocrine disruptionestrogenfollicle-stimulating hormonehormonesmenstrual dysfunctionprogesteronesteroidswomen’s health
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Cigarette smoke contains known reproductive toxicants, and smoking has been associated with adverse reproductive outcomes in women such as infertility, subfecundity, younger age at menopause, and menstrual disorders (Department of Health and Human Services 2001). Previously (Windham et al. 1999), we reported that smokers had different menstrual cycle characteristics compared with nonsmokers; heavy smoking was associated with shorter and more variable cycle lengths, with the shortening occurring primarily during the follicular phase. There was some suggestion of increased risk of short luteal phase (< 11 days) and anovulation as well, but the confidence intervals were quite wide. The mechanism of these and other reported effects is not known but may reflect alterations in hormone function by components of tobacco smoke, with smoking suggested as having antiestrogenic effects (Baron et al. 1990). However, studies examining estrogen and its metabolites by smoking status have found somewhat mixed results (Berta et al. 1992; Key et al. 1996; Longcope and Johnston 1988; Zumoff et al. 1990), perhaps partly due to limited sampling points.
Hormone function is difficult to study in non-clinic-based populations because of the cyclical nature of excretion and day-to-day variation in premenopausal women. Under the control of the complex hypothalamic–pituitary–ovarian (HPO) axis, the steroids estrogen and progesterone are released from and reflect ovarian activity and in turn modulate release of gonadotropins from the pituitary via a negative feedback loop. Rising levels of the pituitary hormone follicle-stimulating hormone (FSH) during the luteal to follicular phase transition between menstrual cycles are critical for follicle recruitment and development in the subsequent cycle (van Santbrink et al. 1995). Declining levels during the follicular phase are important for selection of a dominant follicle and its maturation, and FSH peaks again around ovulation. FSH level is considered a marker of ovarian reserve or fertility (Scott et al. 1989; Scott and Hofman 1995) and is elevated among women approaching menopause (Backer et al. 1999; Burger et al. 1995). As such, it may also be useful for identifying ovarian toxicants (Marcus et al. 1993). A few studies have suggested that smokers have higher levels of FSH than do non-smokers (Backer et al. 1999; Cooper et al. 1995; Cramer et al. 1994). These studies were based on a single serum sample, collected either very early in the cycle or with the cycle timing unknown, and tended to include older women, some of whom were perimenopausal or postmenopausal. Therefore, the purpose of the present study was to examine smoking in relation to the patterns of urinary hormone metabolites throughout the menstrual cycle in premenopausal women to determine whether smoking may exert some of its deleterious effects via an endocrine mechanism. In particular, we were interested in measuring whether estrogen excretion appeared reduced and identifying at what level such an effect may occur by examining the pituitary gonadotropin FSH. Ours is the first study to examine hormone dynamics, with daily metabolite levels of the steroids throughout the cycle and FSH during the luteal–follicular transition, in relation to smoking that was verified by bioassay.
Materials and Methods
The data for this investigation are derived from the Women’s Reproductive Health Study, a prospective study of menstrual function and early pregnancy loss conducted among 403 premenopausal women, whose data collection and analytic methods have been described previously (Waller et al. 1998; Windham et al. 1999) and are summarized briefly below. The institutional review boards of both Kaiser Permanente Medical Group and the California Department of Health Services approved the protocol, and participants provided written informed consent.
Women 18–39 years of age enrolled in the Kaiser Permanente Medical Care Program in northern California during 1990–1991 were screened by a short telephone interview to determine eligibility (based on possibility of becoming pregnant) and willingness to collect and freeze first morning urine samples daily for up to 6 months (Waller et al. 1998). About half of those eligible agreed to participate, but some later dropped out (16%) or became ineligible (11%), leaving 403 women who completed urine collection. On average, women collected urine on 92% of appropriate study days during 5.6 menstrual cycles, but because urine collection was not timed to the cycle start dates, a mean of 3.6 complete cycles were collected per woman. Steroid metabolites were measured daily, and FSH was measured in a subset of 300 cycles after additional funding was obtained. Participants completed a detailed baseline telephone interview that asked about demographics, reproductive history, lifestyle factors, and various exposures, including past and current cigarette smoking. Women filled out daily diaries during urine collection, recording vaginal bleeding, intercourse, and contraception, as well as the number of cigarettes smoked each day.
Smoking assessment.
The diary was used for determining amount smoked because the daily levels reported provided cycle-specific measures. The average number of cigarettes smoked per day for each cycle was calculated and then categorized; for the FSH subset, we primarily used none, low (1–9 cigarettes/day), and moderate smoking (≥ 10 cigarettes/day). In the full data set with larger numbers, we also delineated a heavy smoking category (≥ 20 cigarettes/day). To validate self-reported smoking, pooled urine samples of 5 days from two to three cycles of each woman were assayed for nicotine and its metabolite cotinine (Elkin et al. 1999; Windham et al. 1999). All of the nonsmokers had urinary cotinine levels < 25 ng/mL, a cut point well within the range used in other studies (Benowitz 1996). Two women who reported less than daily smoking, but with measured cotinine levels > 200 ng/mL, were excluded as potential misreporters, as was a nonsmoker who used nicotine gum.
Hormone end points.
Steroids.
The primary estradiol metabolites estrone sulfate and estrone glucuronide [estrone conjugates (E1C)] and the progesterone metabolite pregnanediol-3-glucuronide (PdG) were measured daily by enzyme-linked immunoassays and then adjusted for creatinine concentration, as described previously (Munro et al. 1991; Waller et al. 1998). We determined ovulatory status for each cycle based on a relative rise in progesterone above baseline levels (Kassam et al. 1996; Waller et al. 1998). The day of ovulation (or luteal transition) was estimated using a previously validated algorithm based on the ratio of E1C to PdG during 5-day windows where E1C was declining and PdG was increasing (Baird et al. 1990; Waller et al. 1998; Windham et al. 2002). In a small proportion (5.6%), we reassigned the day of ovulation to better correspond to individual steroid hormone plots. The cycle was divided into the follicular phase (calculated from the first day of menses through the estimated day of ovulation) and the subsequent luteal phase (day after ovulation to day before next menses). Cycle and phase lengths were categorized as short and long based on the 5th and 95th percentiles of their distributions (Windham et al. 1999). For examining and graphing mean daily values of the steroids by smoking status, the cycles were centered at the estimated day of ovulation.
As noted above, urine was collected during many partial cycles, so we generally excluded these, as well as cycles where ≥ 30% of days of urine were missing midcycle (~ 700 total). We only examined steroid parameters in the remaining 1,560 cycles for which a day of ovulation was assigned, so we could identify the follicular and luteal phases. Some incomplete first cycles are included in the analyses of luteal-phase measures if there was at least 20 days of urine collection and urine collection started within 14 days of the reported last menstrual period (n = 112). Cycles with incomplete luteal phases (which would include successful pregnancies) were not included in analyses of luteal-phase parameters, but 89 met the inclusion criteria for follicular-phase parameters (including 39 pregnancies). The primary hormone parameters calculated (Windham et al. 2002) for 1,451 follicular phases and 1,459 luteal phases include the following:
Baseline: the E1C baseline value calculated as the mean over the first 5 days of the cycle. To avoid including elevated “spillover” progesterone values from the previous luteal phase or rising levels of the next, the PdG baseline is the minimum 5-day average occurring before the luteal-phase 5-day maximum.
Daily average: Mean E1C and PdG levels calculated over the follicular phase or the luteal phase.
Area under the curve, or “total”: the sum of the daily E1C values during the follicular phase or the sum of daily PdG values during the luteal phase.
Peak: A 3-day average around the maximum E1C or PdG value. For the estrogen metabolite, the maximum value was selected within a 6-day window around the day of ovulation to capture the periovulatory peak. For progesterone, the maximum value during the luteal phase was selected. If values were missing for any of the 3 days, the peak variable was not calculated (18–20% of cycles).
Follicle-stimulating hormone.
After procuring additional funding, cycles were selected 2 years later for FSH assay, based on smoking status, to reach a goal of 300 cycles in total. Initially, all cycles of smokers (defined as an average of ≥ 1 cigarette reported/day or cotinine > 25 ng/mL) were selected, and two contiguous cycles of nonsmokers (cotinine ≤ 0.5 ng/mL) were randomly selected, targeting cycles early in urine collection to correspond to the timing of a saliva sample. The FSH assay, based on heat dissociation of the FSH heterodimer and measurement of the FSH β-subunit (Qiu et al. 1998), was conducted blind to smoking status at the University of California, Davis, laboratory where it was developed. Previous studies from the laboratory have shown good correspondence between the circulating FSH heterodimer and the urinary β-subunit (Li et al. 2002). The increase in FSH during the transition between menstrual cycles (e.g., luteal phase of one cycle to follicular phase of the next) is the initiator of events leading to ovulation, so this was identified as the time period of primary interest. FSH was measured in daily samples from 7 days before the first bleed day of a cycle through 17 days afterward to catch the periovulatory rise. Samples were run in duplicate and the average value used, unless the duplicates varied by > 20%, in which case they were rerun. FSH values were also creatinine adjusted. For final analyses, we used only cycles that had adequate specimen remaining to measure FSH during the time period of interest. If there was inadequate sample for nonsmokers, other cycles were substituted, but these were not available for smokers, so the final sample with FSH measures included 112 menstrual cycles among 32 smokers (9 smokers did not have sufficient urine for FSH analyses) and 209 cycles among 93 non-smoking comparisons.
Because there had been few prior studies examining daily FSH levels, we calculated the daily mean and the slope values for seven different 4–8 day windows of time within the luteal–follicular transition, which were consistent with earlier work from the laboratory (De Souza et al. 1998; Qiu et al. 1997). For FSH analyses, the first bleed day of the cycle was considered day 1 (with no day zero), and we primarily report results for the following windows: days −7 to −1, −5 to 1, −3 to 1, and −3 to 3. For comparison with other studies based on serum FSH, we also examined a basal level as the mean of days 1 to 5. We also examined the maximum FSH value midcycle (periovulatory) and the cycle day on which it occurred (within days 6–17). An FSH parameter was considered missing for a cycle if 25–30% of values within the window were missing (e.g., > 1 day of 4-day windows or 2 days of 7-day windows). This eliminated about 10% of sampling periods.
Statistical analysis.
Numerous covariates from the baseline interview were examined as potential confounders, including demographics, reproductive history, body mass index (BMI), and other lifestyle factors (caffeine and alcohol consumption, physical activity). In a previous report, we identified variables associated with the steroid parameters (Windham et al. 2002) and examined these as categorical variables (Table 1) in relation to FSH level and smoking status by analysis of variance. Because the primary analysis is at the cycle level and a woman’s cycles are not independent observations, we used mixed models that account for repeated measures for multivariate modeling (Laird and Ware 1982; Zeger and Liang 1986), effectively increasing the standard error of the estimates. Variables identified as potential confounders were included in regression models with each covariate removed one at a time to determine if the association between smoking and a selected FSH parameter (slope or mean of days −5 to 1) changed. If so, that variable was included in final multivariate models. Similar methods were used to build models for examining smoking in relation to steroid levels. In final regression models, we weighted the hormone parameters by the proportion of nonmissing values in the appropriate time frame during each cycle (Windham et al. 2002). Thus, if there were no missing values, the weight was 1, with missing values resulting in down-weighting; mean weights for each parameter ranged from 0.77 to 0.91.
Results
Overall, participants were predominantly white (71%), well educated (40% had a college degree), and parous (88%), with a mean age (± SD) of 31 ± 4.2 years (Waller et al. 1998; Windham et al. 2002). On the baseline questionnaire, 9.2% of women reported being current regular smokers, and the daily diaries indicated that 10% smoked an average of ≥ 1 cigarettes/day, with 5% smoking less frequently. Compared with nonsmokers, smokers were significantly less educated, more likely to be of a race other than Asian or white, drank more alcoholic and caffeinated beverages, and were more likely to have had any pregnancy and to have had a pregnancy loss or therapeutic abortion.
The characteristics of the FSH subset are shown in Table 1; smokers did not vary much from those in the overall study except they were even less likely to have a college degree. Nonsmokers in the subset were less likely to be Hispanic (p = 0.04) and more likely to be older (p = 0.10) than were all nonsmokers. The distribution of cycle characteristics (short cycle, long follicular phase, etc.) was similar in the subset and the overall study, and the association of shorter cycle length with smoking was also observed [crude risk ratio = 2.1; 95% confidence interval (CI), 1.1–4.0].
FSH findings.
Among nonsmokers, mean FSH levels (for days −5 to 1) tended to be higher in nonwhite, older women with greater caffeine consumption, and levels were lower among women with a history of pregnancy loss and with greater alcohol consumption (Table 1). Education, BMI, and other reproductive history variables were not strongly associated with FSH levels. All the mean FSH parameters during the luteal–follicular phase transition were significantly inversely associated with the length of the next cycle and follicular phase, either adjusting for smoking status or among nonsmokers only. The slope parameters that included 5–7 days of the previous cycle were also inversely associated with cycle length.
As shown in Figure 1, the urinary FSH levels reflect the rise during the luteal–follicular transition, with the highest levels occurring early in the follicular phase at days 3–5 and then again around ovulation. Smokers tended to have higher daily FSH levels than non-smokers during the luteal–follicular phase transition period. Consistent with that, mean FSH parameters in moderate to heavy smokers (≥ 10 cigarettes/day) compared with non-smokers were statistically significantly increased for all seven of the original time windows (and including the early follicular-phase mean) examined. Light smokers had levels more similar to nonsmokers or very slightly lower. The slopes of FSH during the corresponding periods did not appear to vary consistently by smoking level. Smokers did tend to reach the midcycle peak FSH level > 1 day earlier than did nonsmokers, with an intermediate value among light smokers, but this was less strong after adjustment. One woman had very high FSH levels, which were confirmed on reassay; she was older, a moderate smoker, and a heavy caffeine consumer. She was excluded to determine the degree to which her values were influencing results. This reduced the mean differences between moderate smokers and nonsmokers by about half, so we present these to be conservative in Table 2 showing adjusted differences. Adjustment (for age, race, pregnancy history, BMI, and alcohol and caffeine consumption) changed the magnitude of differences in means only very slightly but increased the estimates for difference in slope parameters among moderate smokers. In general, when including the woman with the high FSH values, the mean FSH levels during the cycle transition were significantly elevated by 52–57% in moderate smokers, whereas excluding her, the means were elevated about 30–35% (Table 2). The effect of excluding her was not consistent for the FSH slope parameters.
Limiting the analyses to only average length cycles (25–35 days) slightly strengthened the elevations in FSH associated with moderate smoking. We examined the smoking level in the previous cycle as well, that is, the one that included the luteal phase of interest, because this would precede FSH measurements. A few cycles drop out because the smoking data are missing, but results were very similar, presumably because smoking habits did not vary greatly across cycles.
Steroid findings.
Figure 2, of daily E1C and PdG mean levels, shows the characteristic cyclic patterns of estrogen and progesterone secretion. The steroid metabolites were examined in separate models that included age, race, education, pregnancy history, metabolic equivalence (MET) score (exercise levels), and caffeine consumption. The baseline levels of both steroid hormones were elevated among the heaviest smokers in multivariate models, but not statistically significantly: 22% for E1C and nearly 40% for PdG (Table 3). These elevations were significant at the moderate smoking cut point (≥ 10 cigarettes/day), where there is more power; the mean baseline E1C was elevated > 25% (β = 6.3; 95% CI, 0.40–12.3), and baseline PdG was increased about 35% (β = 0.20; 95% CI, 0.03–0.38), compared with nonsmokers. Progesterone metabolite levels during the luteal phase were somewhat lower among smokers in general (Figure 2B) and consistently about 25% lower among the heaviest smokers (Table 3).
Discussion
The present analysis showed that moderate to heavy smokers had elevated baseline (e.g., early follicular phase) levels of the steroid metabolites and heavy smokers had somewhat dampened progesterone metabolite levels during the luteal phase. Further, we found that mean urinary FSH levels during the time of the luteal–follicular phase transition were higher among moderate to heavy smokers than among nonsmokers. Combined with our previous findings of shorter cycle and follicular-phase lengths among heavy smokers (Windham et al. 1999), an alteration in the endocrine pattern with smoking is indicated.
Because of the nature of its association with various hormonally related diseases, smoking has been considered potentially anti-estrogenic. However, only a few studies have provided metabolic evidence to support this, and these studies are hampered by having few biosampling points, a small number of subjects, or inclusion of postmenopausal women. MacMahon et al. (1982) reported reduced urinary excretion of estrone, estradiol, and estriol in the luteal phase among smokers, suggesting that this may be due to reduced estrogen production. Michnovicz et al. (1986) found that smoking induced the 2-hydroxylation of estrone to relatively inactive metabolites and decreased excretion of estriol. However, several studies have not reported differences in serum estradiol concentrations with smoking in premenopausal women (Berta et al. 1992; Key et al. 1996; Longcope and Johnston 1988; Zumoff et al. 1990).
Some of the disease patterns observed with smoking may actually reflect increases in androgens or progesterone. A few studies have reported that smoking increases adrenal activity, with elevations in adrenal androgens seen mostly among postmenopausal smokers (Baron et al. 1995; Friedman et al. 1987; Key et al. 1991; Khaw et al. 1988). Zumoff et al. (1990) measured serum levels at multiple points during the cycle and reported elevated serum progesterone levels during the early follicular phase among smokers, when most progesterone is of adrenocortical origin. This is consistent with our finding of elevated baseline progesterone levels among heavier smokers. However, those authors did not report differences in progesterone levels during the luteal phase. Estrogen was increased in the follicular phase among smokers in that study, which we tended to observe as well. Similar to our finding, Berta et al. (1992) found that regular moderate smokers (≥ 10 cigarettes/day for at least 5 years) had lower plasma progesterone levels on a single sample day during the midluteal phase. With an increased baseline PdG reflecting more progesterone of adrenal origin in smokers, the decreased luteal-phase PdG levels we observed may indicate even lower corpus luteum contribution of progesterone to total excretion. Some in vitro studies (Bodis et al. 1992; Gocze et al. 1999; Miceli et al. 2005) have found inhibition of progesterone production by granulosa cells or luteal cells that were treated with cigarette smoke extract or the alkaloids found in smoke (e.g., nicotine, cotinine, anabasine).
The serum FSH level during the first 3–4 days of the cycle is useful clinically to assess fertility and predict success of in vitro fertilization, as well as to identify the perimenopausal transition (Burger et al. 1995; Mausher et al. 1988; Scott et al. 1989). The few other studies that examined FSH in relation to smoking were based on single serum samples and included women at older ages when FSH may be increasing perimenopausally. Two studies that measured FSH at the beginning of the cycle found higher levels associated with smoking (Cooper et al. 1995; Cramer et al. 1994), as did a study in which the timing of the serum draw was not known (Backer et al. 1999). These studies support our findings of elevated FSH levels with smoking, but our results expand upon them by examining the dynamics, showing that the elevation in FSH levels among smokers is observable at the end of the prior luteal phase. Furthermore, we observed this effect among reproductive-age women, before onset of the perimenopausal transition.
There are some limitations of the present study that should be considered. Women who comply with the labor-intensive urine collection protocol may not be entirely generalizable, and the eligibility criteria would tend to exclude women with chronic menstrual cycle disturbances. We measured estrone metabolites, which may vary by woman in how well they reflect serum estrogen levels. Furthermore, we cannot establish whether secretion or metabolism is affected by smoking. Our power was somewhat limited for examining FSH levels, because of limited funding and inadequate remaining urine sample for some participants. Thus, for example, we could not examine heavier smoking levels in relation to FSH. We did not examine the effects of passive smoking in this study. The FSH subset should exclude most women exposed to environmental tobacco smoke (ETS) from nonsmokers based on the cotinine level criteria we used (< 0.5 ng/mL), but they would be included in steroid hormone analyses. If ETS causes effects in the same direction as active smoking, but presumably to a lesser extent, this would tend to dilute effects we observed because of ETS exposure being included in the comparison group. Therefore, our results may underestimate the magnitude of the true association with steroid levels.
In conclusion, the present data are consistent with some previously published reports but extend them and present for the first time the effect of smoking on steroid and gonadotropin patterns across cycles. This approach permits the evaluation of the integrity of the HPO axis during the entire period of follicular recruitment and maturation rather than just analyzing hormone patterns during individual menstrual cycles. Because progesterone modulates FSH in the endocrine feedback loop, the lower progesterone metabolite levels in smokers during the luteal phase are consistent with decreased entrainment of FSH during the luteal–follicular phase transition, leading to the elevations we observed. The shortening of the follicular phase may be a direct consequence of the increased FSH, consistent with other findings (Cramer et al. 1994; De Souza et al. 1998). The increase in FSH may accelerate the recruitment and development of follicles, moving ovulation earlier. Short follicular phase has been associated with decreased fecundity or in vitro fertilization rates in several studies (Check et al. 1992; Fukuda et al. 2001; Kolstad et al. 1999; Liss et al. 2002). Shorter follicular phase may result in inadequate follicle development, followed by inadequate corpus luteum function. Because progesterone controls endometrial response, it is critical for early pregnancy maintenance; luteal-phase deficiency or decreased progesterone has been implicated as a cause of infertility and fetal loss (Pittaway et al. 1983; Tulppala et al. 1991; Wuttke et al. 2001). This pattern of higher FSH levels and shorter cycles in smokers is also consistent with the observation that smokers tend to experience earlier menopause (Cooper et al. 1999; Midgette and Baron 1990). Thus, the decreased progesterone and perturbation of FSH suggest both a target and one mechanism by which cigarette smoke may alter ovarian function and reduce female fertility. Because cigarette smoke contains thousands of chemicals, this pathway may serve as a model for some endocrine effects of other environmental exposures.
We thank the staff at Kaiser DOR and the California Department of Health Services, especially S. Swan, L. Fenster, and K. Waller for contributions to the original study; we also thank E. Elkin for initial analyses. Cotinine was measured at the laboratory of N. Benowitz, University of California San Francisco.
This work was supported by Tobacco-Related Disease Research Program grants 7RT-0119 and 3RT-0093, and the California Department of Health Services.
Figure 1 Mean daily levels of urinary FSH (ng/mg creatinine) by smoking status during the luteal–follicular phase transition. Day 1 is the first bleed day of a cycle, or the last menstrual period (LMP); negative days are in the previous cycle. Smokers exclude one woman with very high FSH values (see “FSH findings”); with her included, differences would be greater and extend farther into days 7–12. Data from Women’s Reproductive Health Study, California Department of Health Services.
Figure 2 Mean daily levels of urinary E1C (ng/mg creatinine; A) and PdG (μg/mg creatinine; B), by smoking status, in one representative cycle per participant. Cycles are centered on the estimated day of ovulation (labeled day 0), so negative days are in the follicular phase and positive days are in the luteal phase. Data from Women’s Reproductive Health Study, California Department of Health Services.
Table 1 Participant characteristics and mean FSH level in FSH subset, by smoking status.
Nonsmokers
Smokers
Variable No. of women (%) FSH (mean ± SD)a No. of women (%) FSH (mean ± SD)a
Race*
White 72 (77.4) 0.36 ± 0.17 24 (75.0) 0.50 ± 0.48
Asian 15 (16.1) 0.51 ± 0.35 2 (6.3) 0.90 ± 0.50
Other 6 (6.5) 0.48 ± 0.20 6 (18.8) 0.32 ± 0.14
Age (years)
< 30 26 (27.9) 0.33 ± 0.22 12 (37.5) 0.36 ± 0.14
30–34 34 (36.6) 0.42 ± 0.20 12 (37.5) 0.40 ± 0.21
≥ 35 33 (35.5) 0.42 ± 0.22 8 (25.0) 0.81 ± 0.74
Pregnancy history
0 pregnancies 7 (7.5) 0.49 ± 0.38 1 (3.1) 0.46 ± 0.21
≥ 1 pregnancy, 0 losses 59 (63.4) 0.41 ± 0.22 20 (62.5) 0.52 ± 0.55
≥ 1 pregnancy, ≥ 1 loss 27 (29.0) 0.34 ± 0.15 11 (34.4) 0.45 ± 0.22
Education*
No college 21 (22.6) 0.44 ± 0.23 16 (50.0) 0.55 ± 0.62
Some college 29 (31.2) 0.38 ± 0.16 13 (40.6) 0.43 ± 0.22
College graduate 43 (46.2) 0.38 ± 0.25 3 (9.4) 0.53 ± 0.14
BMI (kg/m2)
< 19.1 6 (6.5) 0.47 ± 0.44 2 (6.3) 0.29 ± 0.08
19.1–27.3 66 (71.0) 0.39 ± 0.21 19 (59.4) 0.58 ± 0.54
> 27.3 21 (22.6) 0.38 ± 0.18 11 (34.4) 0.41 ± 0.31
MET score
0 42 (45.2) 0.43 ± 0.26 10 (31.2) 0.45 ± 0.25
> 0 to < 40 37 (39.8) 0.36 ± 0.19 16 (50.0) 0.55 ± 0.63
≥ 40 14 (15.0) 0.37 ± 0.16 6 (18.8) 0.45 ± 0.21
Caffeine (mg/day)*
0 35 (37.6) 0.35 ± 0.23 3 (9.4) 0.39 ± 0.19
< 300 48 (51.6) 0.41 ± 0.21 19 (59.4) 0.44 ± 0.22
≥ 300 10 (10.8) 0.48 ± 0.21 10 (31.2) 0.63 ± 0.76
Alcohol* (drinks/week)
0 26 (28.0) 0.45 ± 0.29 5 (15.6) 0.64 ± 0.91
1–3 64 (68.8) 0.38 ± 0.19 17 (53.1) 0.41 ± 0.18
≥ 4 3 (3.2) 0.29 ± 0.09 10 (31.3) 0.55 ± 0.37
MET, metabolic equivalence.
a Mean FSH for days −5 to 1 calculated for cycles (vs. woman basis). Data from Women’s Reproductive Health Study, California Department of Health Services.
*p < 0.05 for test of independence between smoking status and covariate.
Table 2 Adjusteda difference in FSH metabolic parameters by smoking level and 95% CIs.
Cigarettes/dayb
Parameter and cycle daysc None (n = 186) Intercept ≤ 9 (n = 49) β (95% CI) ≥ 10 (n = 48) β (95% CI)
FSH slope
−7 to −1 0.02 −0.02 (−0.04 to 0.00) 0.02 (−0.003 to 0.04)
−5 to 1 0.04 −0.02 (−0.05 to 0.01) 0.02 (−0.015 to 0.06)
−3 to 1 0.09 −0.04 (−0.10 to 0.01) 0.02 (−0.04 to 0.07)
Mean daily FSH
−7 to −1 0.31 −0.03 (−0.12 to 0.06) 0.09 (−0.01 to 0.20)
−5 to 1 0.37 −0.06 (−0.17 to 0.05) 0.12 (0.00 to 0.24)
−3 to 1 0.40 −0.08 (−0.21 to 0.04) 0.14 (0.003 to 0.28)
−3 to 3 0.50 −0.08 (−0.22 to 0.06) 0.10 (−0.06 to 0.26)
1 to 5 0.68 −0.09 (−0.29 to 0.10) 0.006 (−0.22 to 0.23)
a Adjusted for age, race, pregnancy history, BMI, and alcohol and caffeine consumption in mixed models for repeated measures, with weighting of FSH parameter by proportion of nonmissing within the window.
b Smoking as reported during cycle starting with day 1; n is for the parameter with the largest numbers. These vary by a few cycles because of missing data; one outlier was excluded.
c Days are counted with first bleed day of a cycle numbered as day 1. Data from Women’s Reproductive Health Study, California Department of Health Services.
Table 3 Adjusteda difference and 95% CIs in steroid hormone metabolite parameters by smoking level.
Cigarettes/day
Hormone parameter None (nb = 1,313) Intercept ≤ 19 (n = 117) β (95% CI) ≥ 20 (n = 25) β (95% CI)
Estrogen (ng/mg creatinine)
Baseline 24.9 3.9 (0.06 to 7.6) 5.7 (−3.5 to 14.8)
Total FP 584.6 6.8 (−66.8 to 80.3) 29.1 (−159.6 to 217.8)
Daily average FP 39.0 1.2 (−3.1 to 5.5) 4.3 (−6.3 to 14.9)
Peak to periovulatory 70.4 −0.3 (−10.0 to 9.4) −11.5 (−32.7 to 9.7)
Progesterone (μg/mg creatinine)
Baseline 0.54 −0.03 (−0.14 to 0.08) 0.21 (−0.04 to 0.47)
Total LP 56.7 −1.7 (−9.4 to 5.9) −15.5 (−32.2 to 1.1)
Daily average LP 4.66 −0.17 (−0.80 to 0.47) −1.34 (−2.72 to 0.04)
Peak LP 6.64 −0.36 (−1.35 to 0.63) −1.63 (−3.65 to 0.40)
Abbreviations: FP, follicular phase; LP, luteal phase.
a Adjusted for age, race, education, prior pregnancies, caffeine, and MET score (exercise level).
b n indicates the parameter with the largest numbers (e.g., follicular-phase estrogen and luteal-phase progesterone); others vary because of missing data, with peaks having smaller n values by 260–300 cycles overall. Data from Women’s Reproductive Health Study, California Department of Health Services.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8133ehp0113-00129116203236ResearchDevelopmental Exposure of Rats to Chlorpyrifos Elicits Sex-Selective Hyperlipidemia and Hyperinsulinemia in Adulthood Slotkin Theodore A. 1Brown Kathleen K. 2Seidler Frederic J. 11 Department of Pharmacology and Cancer Biology, Duke University Medical Center, Durham, North Carolina, USA2 GlaxoSmithKline Inc., Research Triangle Park, North Carolina, USAAddress correspondence T.A. Slotkin, Box 3813 DUMC, Duke University Medical Center, Durham, NC 27710 USA. Telephone: (919) 681-8015. Fax: (919) 684-8197. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 2 6 2005 113 10 1291 1294 21 3 2005 2 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Developmental exposure to chlorpyrifos alters cell signaling both in the brain and in peripheral tissues, affecting the responses to a variety of neurotransmitters and hormones. We administered 1 mg/kg/day chlorpyrifos to rats on postnatal days 1–4, a regimen below the threshold for systemic toxicity. When tested in adulthood, chlorpyrifos-exposed animals displayed elevations in plasma cholesterol and triglycerides, without underlying alterations in nonesterified free fatty acids and glycerol. This effect was restricted to males. Similarly, in the postprandial state, male rats showed hyperinsulinemia in the face of normal circulating glucose levels but demonstrated appropriate reduction of circulating insulin concentrations after fasting. These outcomes and sex selectivity resemble earlier findings at the cellular level, which identified hepatic hyperresponsiveness to gluconeogenic inputs from β-adrenoceptors or glucagon receptors. Our results thus indicate that apparently subtoxic neonatal chlorpyrifos exposure, devoid of effects on viability or growth but within the parameters of human fetal or neonatal exposures, produce a metabolic pattern for plasma lipids and insulin that resembles the major adult risk factors for atherosclerosis and type 2 diabetes mellitus.
Barker hypothesischlorpyrifoscholesteroldiabetes mellitus type 2insulinorganophosphatestriglycerides
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Chlorpyrifos, one of the most widely used organophosphorus pesticides, is increasingly restricted in the United States because of its adverse effects on fetal and neonatal brain development [Barone et al. 2000; Landrigan 2001; Slotkin 2004; U.S. Environmental Protection Agency (EPA) 2000, 2002]. Nevertheless, organophosphates, including chlorpyrifos, still account for up to 50% of all insecticide application worldwide (Casida and Quistad 2004). Although the systemic toxicity of these pesticides resides in their ability to inhibit cholinesterase, other mechanisms contribute to their developmental neurotoxicity, notably the targeting of cell signaling cascades governing neuronal and hormonal signals that are essential to cell differentiation and homeostatic regulation (Barone et al. 2000; Casida and Quistad 2004; Gupta 2004; Pope 1999; Schuh et al. 2002; Slotkin 2004). Of these, the pathway synthesizing cyclic AMP (cAMP), controlled by adenylyl cyclase, appears to be among the most prominent sites for disruption by chlorpyrifos (Meyer et al. 2003, 2004a, 2004b; Olivier et al. 2001; Ward and Mundy 1996; Zhang et al. 2002).
Adenylyl cyclase and cAMP also participate in important metabolic, cardiovascular, and hormonal functions in the periphery, and we recently found that neonatal chlorpyrifos exposure leads to disruption of cardiac and hepatic cell signaling in adulthood (Meyer et al. 2004b). Perhaps most critically, chlorpyrifos-exposed males showed hyperreactivity of hepatic adenylyl cyclase to stimulation of β-adrenoceptors or glucagon receptors, inputs that are responsible for promoting gluconeogenesis and the consequent rise in circulating glucose levels. If these cellular alterations do indeed result in changes in hepatic function and responsiveness, then neonatal chlorpyrifos exposure might be expected to elicit long-term hyperglycemia and associated metabolic abnormalities, or alternatively, insulin hypersecretion might be required to offset the promotion of gluconeogenic signals. In the present study, we demonstrate that male rats exposed neonatally to chlorpyrifos display hyperinsulinemia and hyperlipidemia in adulthood, two of the major risk factors for type 2 diabetes mellitus and atherosclerosis. Our working hypothesis thus complements the Barker hypothesis, which draws a connection between low birth weight and the subsequent risk of coronary artery disease and diabetes (Barker 2003; Phillips 2002), extending the same outcomes into the realm of exposure to environmental toxicants even in the absence of growth retardation.
Materials and Methods
Animal treatments.
All experiments were carried out in accordance with the Guide for the Care and Use of Laboratory Animals as adopted and promulgated by the National Institutes of Health (Institute of Laboratory Animal Resources 1996). Timed-pregnant Sprague–Dawley rats (Charles River, Raleigh, NC) were housed in breeding cages, with a 12/12 hr light/dark cycle and free access to food and water. On the day of birth, all pups were randomized and redistributed to the dams with a litter size of 10 to maintain a standard nutritional status. Randomization within the respective treatment groups was repeated at intervals of several days, and in addition, dams were rotated among litters to distribute any maternal caretaking differences randomly across litters and treatment groups. Chlorpyrifos (Chem Service, West Chester, PA) was dissolved in dimethyl sulfoxide to provide consistent absorption (Whitney et al. 1995) and was injected subcutaneously at a dose of 1 mg/kg in a volume of 1 mL/kg once daily on postnatal days 1–4; control animals received equivalent injections of the dimethyl sulfoxide vehicle. This regimen has been shown previously to produce developmental neurotoxicity without eliciting growth retardation or any other signs of systemic toxicity (Aldridge et al. 2004; Meyer et al. 2004a, 2004b; Slotkin 2004; Song et al. 1997; Whitney et al. 1995). Indeed, neonatal brain cholinesterase inhibition is only about 25% (Song et al. 1997), well below the 70% threshold necessary for symptoms of cholinergic hyperstimulation (Clegg and van Gemert 1999), thus resembling the nonsymptomatic exposures reported in pregnant women (De Peyster et al. 1993). Moreover, the dose used here is well within the range of typical fetal and childhood exposures after routine application (Gurunathan et al. 1998; Ostrea et al. 2002).
Animals were weaned on postnatal day 21. Tests were performed using eight rats per sex per treatment group, with no more than one male and one female from each litter. Starting immediately after weaning, animals were handled every few days to accustom them to removal from the cage and contact with the investigators. At 110 days of age, during the active (dark) cycle, animals were placed in a Plexiglas restrainer, and two blood samples (total volume, 500 μL) were obtained from each animal by tail vein venipuncture, using a 23-gauge butterfly, one without added anticoagulant and the other containing EDTA. Handling was then maintained for the ensuing 10 days, at which time animals were fasted 8 hr, and a second pair of samples were obtained. Sera were analyzed using an Olympus Au640 Clinical Chemistry Analyzer (Olympus America Inc., Melville, NY), and insulin was determined using a commercial radioimmunoassay kit (Linco Research, St. Charles, MO). Samples containing EDTA were analyzed for total and glycosylated hemoglobin using a ColumnMate Analyzer (Helena Laboratories, Beaumont, TX). At no time during the restraint or venipuncture did the animals show overt signs of distress such as struggling or vocalizations.
Data analysis.
Data are presented as means and standard errors. To establish the effects of chlorpyrifos and its relationship to other variables, a multivariate analysis of variance (ANOVA; data log-transformed because of heterogeneous variance) was first conducted, encompassing neonatal treatment, sex, and feeding status (nonfasted vs. fasted, treated as a repeated measure, because each animal was evaluated sequentially for both states). Where chlorpyrifos treatment interacted with the other variables, data were then subdivided to permit lower-order ANOVAs, followed where appropriate by Fisher’s protected least significant difference to identify individual values for which the chlorpyrifos groups differed from the corresponding control; however, in the absence of interactions, only main chlorpyrifos treatment effects are reported. For all tests, significance was assumed at p < 0.05.
Results and Discussion
Neonatal chlorpyrifos exposure had no effect on growth or viability (data not shown), or on body weights of male or female rats in adulthood, nor were there any significant alterations in plasma levels of nonesterified free fatty acids or glycerol (Table 1). Nevertheless, cholesterol and triglycerides displayed significant elevations that were distinctly sex selective (treatment × sex interaction, p < 0.03), with a preferential effect in males (Figure 1). Both cholesterol and triglycerides were increased by about 35%, an effect that persisted even when the animals were fasted.
In contrast to the robust effect on plasma lipids, glucose concentrations in chlorpyrifos-exposed animals remained entirely within the normal range for either males or females (Figure 2A), nor did we see any change in the percentage of glycosylated hemoglobin (data not shown). The chlorpyrifos group also showed the typical reduction in glucose levels when fasted and values for females were lower than for males, just as in the control group. Nevertheless, the concentration of insulin was markedly elevated in nonfasted male rats, > 60% higher than in controls (Figure 2B). Fasting restored the insulin level to normal. In contrast, female rats exposed to chlorpyrifos did not show an elevation in insulin and actually showed a slight decrease at the margin of statistical significance. The normal sex differences in these measures as seen in the control groups reproduce those reported previously (O’Regan et al. 2004).
Our results thus indicate that apparently subtoxic neonatal chlorpyrifos exposure, devoid of effects on viability or growth and within the parameters of human fetal or neonatal exposures, produces a metabolic pattern for plasma lipids and insulin that resembles the known major risk factors and predictors for the appearance of atherosclerosis and type 2 diabetes mellitus in adulthood (Davis and Edelman 2004; Reaven et al. 2004). The sex selectivity and response to fasting both point to specific mechanisms underlying these effects. In our earlier work, we found that neonatal chlorpyrifos exposure produces hyperresponsiveness of hepatic cell signaling mediated through adenylyl cyclase, an effect specific to males (Meyer et al. 2004b). The promotional effect extends to two receptors linked to gluconeogenesis, the β-adrenoceptor and the glucagon receptor, and accordingly, it might be expected that these animals would show hyperglycemia. However, as shown here, circulating glucose levels remain essentially normal but only because the effects on gluconeogenic signals are offset by a sustained elevation in post-prandial circulating insulin concentrations. This counterbalanced relationship is reinforced by the fact that fasting restored insulin levels to normal: the feedback regulation of insulin release in response to reduced glucose availability during fasting is intact in the chlorpyrifos group. Under normal dietary conditions, the chlorpyrifos-exposed animals thus display hyperinsulinemia, a characteristic of the prediabetic state in type 2 diabetes mellitus, particularly in obese individuals (Davis and Edelman 2004; Reaven et al. 2004); however, in this case, the hyperinsulinemia exists even in animals with a normal body weight.
Just as found for insulin, male rats exposed to neonatal chlorpyrifos showed elevated plasma cholesterol and triglycerides in adulthood, thus sharing one of the major risk factors for human atherosclerosis. Furthermore, elevated postprandial triglycerides are yet another component of the metabolic syndrome that confers a significant risk of vascular disease. In the present study, there was no evidence of increased in vivo lipolysis (i.e., insulin resistance at the level of the adipocyte) because nonesterified fatty acids and glycerol were within normal limits. Unlike the situation for insulin, fasting did not reverse the effects on plasma cholesterol and triglycerides, likely reflecting their longer biologic half-life. Again, the main finding is that an otherwise subtoxic developmental exposure to chlorpyrifos leads to hyperlipidemia similar to that found in individuals prone to cardiovascular disease.
The present results point to a distinct sex disparity in the metabolic consequences of neonatal chlorpyrifos exposure, findings in keeping with effects on hepatic cell signaling as reported previously (Meyer et al. 2004b). Indeed, many other aspects of the developmental toxicity of chlorpyrifos similarly display sex differences, including neurochemical and behavioral effects (Aldridge et al. 2004, 2005; Levin et al. 2001; Moser et al. 1998). The specific mechanisms underlying the targeting of males or females is not yet known, but it is important to note that the impact on brain development targets both males and females, albeit with different patterns of effects. In contrast, the present results indicate metabolic effects restricted to males, so it is unlikely that the underlying mechanisms are the same as those involved in the neurobehavioral effects of chlorpyrifos. In keeping with the present findings, many other developmental disruptors similarly produce sexually dimorphic changes in cardiovascular and/or metabolic outcomes in males and females, both in animal studies and in humans (Adair and Cole 2003; Khan et al. 2003; O’Regan et al. 2004). Taken together with our findings, sex differences clearly need to be taken into account in future evaluations of similar outcomes from developmental chlorpyrifos exposure.
In its original formulation, the Barker hypothesis related prenatal factors leading to reduced birth weight with the subsequent development of cardiovascular disease and diabetes (Barker 2003). The present results point to similar outcomes evoked by an apparently “safe” exposure to a common environmental toxicant, even when normal growth parameters are maintained. In conjunction with our earlier findings for effects of neonatal chlorpyrifos exposure on hepatic cell signaling related to metabolic function (Meyer et al. 2004b), we can now provide a mechanistic link between cellular events and the appearance of hyperinsulinemia and hyperlipidemia in adulthood. Of course, because atherosclerosis and type 2 diabetes are multifactorial diseases, it is unlikely that chlorpyrifos exposure by itself would elicit these disease outcomes; nevertheless, the fact that chlorpyrifos evokes two of the metabolic changes most highly associated with these diseases implies that such exposures may increase risk or vulnerability to other contributory components. The standard view of chlorpyrifos, and potentially other organophosphorus insecticides, as developmental toxicants that specifically target the nervous system may thus require substantial revision.
This work was supported by National Institutes of Health grants ES10387 and ES10356.
Figure 1 Effects of neonatal chlorpyrifos exposure on plasma lipids in adulthood. NS, not significant. Values shown are mean ± SE. ANOVA across all measures, feeding status, and sex appears at the top; data were subdivided into males and females because of the significant treatment × sex interaction. The main chlorpyrifos treatment effect for each sex is shown within the panel; separate lower-order tests for cholesterol or triglycerides in fed and fasted states were not carried out because of the absence of interactions of treatment × feeding status or treatment × lipid subtype. Note the different scales for cholesterol and triglycerides.
Figure 2 Effects of neonatal chlorpyrifos exposure on plasma glucose (A) and insulin (B) in adulthood. NS, not significant. Values shown are mean ± SE. ANOVA across feeding status and sex appears at the top of each panel. For insulin levels, lower-order analyses were run separately for males and females and for fed and fasted states because of the interaction of treatment with both feeding status and sex. Across both treatment groups, glucose and insulin levels were significantly lower in females than in males (main effect of sex, p < 0.002 for glucose, p < 0.0001 for insulin) and were reduced by fasting (main effect of feeding status, p < 0.0003 and p < 0.0001, respectively).
*Significantly different from the corresponding control.
Table 1 Body weights, nonesterified free fatty acids, and glycerol.
Male
Female
Measure, age, and feeding status Control Chlorpyrifos Control Chlorpyrifos
Body weight (g)
110 days, fed 539 ± 19 547 ± 19 301 ± 7 298 ± 9
120 days, fasted 548 ± 16 568 ± 20 317 ± 6 302 ± 9
Nonesterified free fatty acids (μEq/dL)
110 days, fed 70 ± 14 67 ± 11 60 ± 8 87 ± 14
120 days, fasted 116 ± 9 112 ± 9 121 ± 11 130 ± 8
Glycerol (mg/dL)
110 days, fed 24 ± 3 22 ± 3 26 ± 2 31 ± 3
120 days, fasted 28 ± 2 25 ± 1 31 ± 1 27 ± 1
μEq, microequivalents. Values shown are mean ± SE. ANOVA for each set of measures indicates no significant treatment differences. By itself, fasting increased nonesterified free fatty acids (main effect of feeding status, p < 0.0001); glycerol values in females were significantly higher overall than in males (main effect of sex, p < 0.03).
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References
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Gupta RC 2004 Brain regional heterogeneity and toxicological mechanisms of organophosphates and carbamates Toxicol Mech Methods 14 103 143 20021140
Gurunathan S Robson M Freeman N Buckley B Roy A Meyer R 1998 Accumulation of chlorpyrifos on residential surfaces and toys accessible to children Environ Health Perspect 106 9 16 9417768
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Landrigan PJ 2001 Pesticides and polychlorinated biphenyls (PCBs): an analysis of the evidence that they impair children’s neurobehavioral development Mol Genet Metab 73 11 17 11350178
Levin ED Addy N Christopher NC Seidler FJ Slotkin TA 2001 Persistent behavioral consequences of neonatal chlorpyrifos exposure in rats Dev Brain Res 130 83 89 11557096
Meyer A Seidler FJ Aldridge JE Tate CA Cousins MM Slotkin TA 2004a Critical periods for chlorpyrifos-induced developmental neurotoxicity: alterations in adenylyl cyclase signaling in adult rat brain regions after gestational or neonatal exposure Environ Health Perspect 112 295 301 14998743
Meyer A Seidler FJ Cousins MM Slotkin TA 2003 Developmental neurotoxicity elicited by gestational exposure to chlorpyrifos: when is adenylyl cyclase a target? Environ Health Perspect 111 1871 1876 14644659
Meyer A Seidler FJ Slotkin TA 2004b Developmental effects of chlorpyrifos extend beyond neurotoxicity: critical periods for immediate and delayed-onset effects on cardiac and hepatic cell signaling Environ Health Perspect 112 170 178 14754571
Moser VC Chanda SM Mortensen SR Padilla S 1998 Age- and gender-related differences in sensitivity to chlorpyrifos in the rat reflect developmental profiles of esterase activities Toxicol Sci 46 211 222 10048124
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Slotkin TA 2004 Cholinergic systems in brain development and disruption by neurotoxicants: nicotine, environmental tobacco smoke, organophosphates Toxicol Appl Pharmacol 198 132 151 15236950
Song X Seidler FJ Saleh JL Zhang J Padilla S Slotkin TA 1997 Cellular mechanisms for developmental toxicity of chlorpyrifos: targeting the adenylyl cyclase signaling cascade Toxicol Appl Pharmacol 145 158 174 9221834
U.S. EPA 2000. Chlorpyrifos: Re-evaluation Report of the FQPA Safety Factor Committee. HED Doc. No. 014077. Washington, DC:U.S. Environmental Protection Agency.
U.S. EPA 2002 Chlorpyrifos: End-Use Products Cancellation Order Fed Reg 67 3698 3700 Available: http://www.epa.gov/fedrgstr/EPA-PEST/2002/January/Day-25/p1764.htm [accessed 6 December 2004].
Ward TR Mundy WR 1996 Organophosphorus compounds preferentially affect second messenger systems coupled to M2/M4 receptors in rat frontal cortex Brain Res Bull 39 49 55 8846108
Whitney KD Seidler FJ Slotkin TA 1995 Developmental neurotoxicity of chlorpyrifos: cellular mechanisms Toxicol Appl Pharmacol 134 53 62 7545834
Zhang HS Liu J Pope CN 2002 Age-related effects of chlorpyrifos on muscarinic receptor-mediated signaling in rat cortex Arch Toxicol 75 676 684 11876500
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8479ehp0113-00129516203237ResearchDeclining Sex Ratio in a First Nation Community Mackenzie Constanze A. 1Lockridge Ada 2Keith Margaret 31 Faculty of Medicine, University of Ottawa, Ottawa, Ontario, Canada2 Aamjiwnaang Environment Committee, Aamjiwnaang, Ontario, Canada3 Occupational Health Clinics for Ontario Workers Sarnia-Lambton, Pt. Edward, Ontario, CanadaAddress correspondence to C.A. Mackenzie, c/o 171 Kendall St., Point Edward, ON, Canada, N7V 4G6. Telelephone: (519) 337-4627. Fax: (519) 337-9442. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 17 8 2005 113 10 1295 1298 7 7 2005 16 8 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Members of the Aamjiwnaang First Nation community near Sarnia, Ontario, Canada, voiced concerns that there appeared to be fewer male children in their community in recent years. In response to these concerns, we assessed the sex ratio (proportion of male births) of the Aamjiwnaang First Nation over the period 1984–2003 as part of a community-based participatory research project. The trend in the proportion of male live births of the Aamjiwnaang First Nation has been declining continuously from the early 1990s to 2003, from an apparently stable sex ratio prior to this time. The proportion of male births (m) showed a statistically significant decline over the most recent 10-year period (1994–2003) (m = 0.412, p = 0.008) with the most pronounced decrease observed during the most recent 5 years (1999–2003) (m = 0.348, p = 0.006). Numerous factors have been associated with a decrease in the proportion of male births in a population, including a number of environmental and occupational chemical exposures. This community is located within the Great Lakes St. Clair River Area of Concern and is situated immediately adjacent to several large petrochemical, polymer, and chemical industrial plants. Although there are several potential factors that could be contributing to the observed decrease in sex ratio of the Aamjiwnaang First Nation, the close proximity of this community to a large aggregation of industries and potential exposures to compounds that may influence sex ratios warrants further assessment into the types of chemical exposures for this population. A community health survey is currently under way to gather more information about the health of the Aamjiwnaang community and to provide additional information about the factors that could be contributing to the observed decrease in the proportion of male births in recent years.
community-basedendocrine disruptionenvironmental exposureFirst Nationsex ratio
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There is increasing evidence that the human live birth sex ratio can be altered by a number of environmental and occupational chemical exposures. For example, lower proportions of male offspring have been observed in populations exposed to dioxin (Mocarelli et al. 2000), mercury (Sakamoto et al. 2001), pesticides (Garry et al. 2003; Goldsmith 1997; Jarrell et al. 2002), polychlorinated biphenyls (PCBs) (del Rio Gomez et al. 2002; Weisskopf et al. 2003), and parental smoking (Fukuda et al. 2002). It has been hypothesized that some of these environmental and occupational chemicals may act as endocrine-disrupting compounds (EDCs), influencing the sex ratio by changing the hormonal milieu of the parents (James 1996), or by inducing sex-specific mortality in utero (Sakamoto et al. 2001).
The birth sex ratio (male:female) of a population is often reported as the male proportion (m = number of male births divided by the total of all births). Worldwide, the human live birth sex ratio is remarkably constant, ranging between 102 and 108 male to 100 female live births (m = 0.504–0.519) (Bartleby.com 2003). In Canada, the sex ratio is generally reported to be 105:100 (M:F) (m = 0.512) (Allan et al. 1997). Although the sex of the human embryo is genetically controlled and determined at the time of conception, there is evidence that the sex ratio can be partially influenced by both endogenous and exogenous factors. Endogenous parental hormone concentrations of gonadotropins and/or testosterone at the time of conception are suspected to play a role in determining the sex of offspring (James 2004). Exogenous factors such as stress, parental disease, and exposure to certain chemicals appear to have some influence on the live birth sex ratio and may act by altering the parental hormone status (James 2004).
Sex ratios have been suggested as a non-invasive monitor of the reproductive health of a population (Davis et al. 1998; James 1997a). Changes in the sex ratio have been used to assess the reproduction of populations with demonstrated exposures to EDCs (Mocarelli et al. 2000), as well as in communities near hazardous chemical sites (Williams et al. 1995). Altered live birth sex ratios may also be a useful indicator of public health, in that they reflect death at earlier stages of development than traditional indicators such as perinatal and infant mortality (Williams et al. 1995).
Materials and Methods
We assessed the live birth sex ratios for the Aamjiwnaang First Nation community in Ontario, Canada, in response to concerns voiced by members of the community regarding the perception of fewer male children in recent years. This birth sex ratio assessment was part of a broader community-based investigation undertaken by the Aamjiwnaang in collaboration with the Occupational Health Clinics for Ontario Workers (OHCOW) along with scientific consultants, professionals, and students from a wide range of disciplines. The exploration included such quantitative measurements as soil, sediment, wildlife, fish, and air sampling, along with a door-to-door health survey and interviews. In keeping with the principles of community-based participatory research (Hall 1979, 2003; Hagey 1997; Hills and Mullett 2000; Keith and Brophy 2004), the community itself has been involved in all major decision-making about the direction of the research and has participated in much of the data collection.
Aamjiwnaang First Nation.
The Chippewas of Aamjiwnaang have approximately 850 band members residing on the Aamjiwnaang reserve land (Information Management Branch Department of Indian Affairs and Northern Development 2001). This reserve is located within the area identified as the St. Clair River Area of Concern by the Canada–U.S. Great Lakes International Joint Commission (Environment Canada 2005) and is situated immediately adjacent to the Sarnia-Lambton Chemical Valley—one of Canada’s largest concentrations of industry. The reserve is surrounded by several large petrochemical, polymer, and chemical industrial plants. The community provided informed consent and assistance to collect live birth sex ratio data from the Department of Indian and Northern Affairs database (Indian and Northern Affairs Canada, Ottawa, Ontario, Canada) for the period 1984–2003 (representing the full length of record). Births and deaths of members of the Aamjiwnaang First Nation are reported on a monthly basis to the Department of Indian and Northern Affairs by the Aamjiwnaang Lands and Memberships clerk.
Statistical analysis.
We calculated the proportion of male births for the Aamjiwnaang community by dividing the number of male live births by the total of all live births for each year 1984–2003. We used linear regression to examine the trend in the proportion of male births over time. Based on the data, we produced two linear regression lines to estimate a point in time where slopes of the regression lines deviate and the trend in the proportion of male births begins to decline. We then calculated the proportion of live male births for both 5- and 10-year intervals over the duration of the study period and compared these data to the expected proportion of males for Canada (m = 0.512), as well as a “control” First Nation community (m = 0.520) using Pearson’s chi-square analysis. The “control” community sex ratio was calculated from comparable data for a genetically similar, yet geographically distinct, Chippewa First Nation band that has requested to remain anonymous. Because the male proportion in the “control” community (m = 0.520) was not statistically different from the expected Canadian male proportion (χ2 = 0.098, df = 1, p = 0.754), all analyses shown were performed using the Canadian male proportion (m = 0.512) as the expected value.
Results
Altered sex ratios.
Examination of the proportion of male births for the Aamjiwnaang community over the study period 1984–2003 (Figure 1) shows that the proportion of male births appears to be relatively stable for the period 1984–1992; linear regression (r2 = 0.000) shows a slope not significantly different from zero (p = 0.990). A second linear regression for the period 1993–2003 (r2 = 0.547) shows a declining trend in the proportion of male births with a statistically significant deviation of slope from zero (p = 0.009).
When the sex ratio data were categorized into 5- and 10-year periods, we found a highly significant decrease in the proportion of male live births (m = 0.348, χ2 = 7.472, df = 1, p = 0.006) for the Aamjiwnaang community during the most recent 5-year interval (1999–2003) compared with the expected sex ratio (Table 1). We also observed a statistically significant decrease in the proportion of male births when the data were categorized into 10-year intervals over the period 1994–2003 (m = 0.412, χ2 = 7.100, df = 1, p = 0.008) (Table 1).
Discussion
Evidence of chemical exposures influencing sex ratios.
Normal variation in sex ratio can be expected in any population, especially with a small sample size; however, the extent of the sex ratio deviation for Aamjiwnaang appears to be outside the range of normal. Following relatively stable sex ratios from 1984–1992, there was a significant decline in the proportion of male live births for the period 1993–2003 (Figure 1). The continuing reduction in the proportion of male births is most apparent in the most recent 5-year period (1999–2003, m = 0.348; Table 1), indicating that there may be an ongoing process manifesting as a reduction in sex ratio starting in the early to mid-1990s. Previous studies have demonstrated that populations exposed to environmental contaminants such as endocrine disruptors, either through their close proximity to industrial plants or through other sources such as food, can have significant changes in the reproductive ability of the community, including the sex ratio. Table 2 summarizes some findings on the influence of environmental and occupational exposures on sex ratios.
There has been speculation that declining trends in the proportion of male births during the later part of the 20th century in industrialized countries including Canada, the United States (Allan et al. 1997), Sweden, Germany, Finland, Denmark, and the Netherlands (Davis et al. 1998) could be attributed to environmental contaminants and endocrine disruption. Conversely, other studies have shown increases in sex ratio (Lancaster and Day 1998), or no change (Grech et al. 2003), and it is unlikely that a single mechanism can account for the changes observed in any one country, given the scale of these studies (James 1998). However, changes in sex ratios of small populations can be used more reliably as a sentinel indicator of altered reproduction, especially when there is a documented exposure to environmental or occupational chemicals (James 1998).
There are several possible routes of exposure to chemicals that may affect the reproductive ability of a community. Populations can be exposed to contaminants through industrial accidents such as in Seveso, Italy, where young men exposed to high concentrations of dioxins sired significantly more female children than male (Mocarelli et al. 2000). Several studies have examined the sex ratios of communities exposed to different types of air pollution with conflicting results: a decrease in sex ratio was observed for residential areas exposed to air pollution from local incinerators (Williams et al. 1992); an increase in sex ratios was observed in areas close to a steel foundry (Lloyd et al. 1984, 1985), communities close to natural gas (Saadat et al. 2002) and petrochemical industry (Yang et al. 2000b); and no effect on sex ratio was observed for general air pollution (Williams et al. 1995) and in another (less powerful) study of municipalities close to a petroleum refinery plant (Yang et al. 2000a). Other routes of exposure to contaminants such as PCBs include food sources. Decreased sex ratios have been associated with maternal consumption of Great Lakes fish (Weisskopf et al. 2003) and fish contaminated with methylmercury (Sakamoto et al. 2001). To make matters more complicated, paternal Great Lakes fish consumption appears to increase the offspring sex ratio (Karmaus et al. 2002).
Although there is mounting evidence that environmental and occupational exposures to contaminants can affect sex ratios, the results to date can be difficult to interpret because of conflicting results and the number of variables that appear to be involved. The effect of a chemical exposure on a population’s sex ratio appears to depend on a number of factors, including parental age at the time of exposure, total exposure level, and whether it is a maternal or paternal exposure (Table 2). For example, similar exposures in men and women may have different effects on the sex of offspring, as observed with PCBs (Table 2).
Based on an assessment algorithm used by Jarrell (2002), there is reasonably strong evidence linking reduced sex ratios and environmental exposures of dioxin, dibromochloropropane, and hexachlorobenzene (HCB). Although the mechanism of action of these compounds on sex ratio is not entirely clear, both dioxin and HCB bind to the aryl hydrocarbon receptor and may alter sex ratios by changing the hormonal status of the parents. HCB is also associated with pregnancy loss in women (Jarrell et al. 1998). Similarly, other compounds such as methylmercury appear to increase the number of spontaneous abortions and stillbirths in exposed populations, ultimately altering the sex ratio of the surviving offspring (Sakamoto et al. 2001). The overall effects of other compounds such as PCBs on sex ratios will continue to be clarified with further research.
A 1996 assessment of soil and sediment contaminants on Aamjiwnaang reserve land has identified high concentrations [many exceeding sediment guidelines of the Ontario Ministry of the Environment and Energy (2004)] of several contaminants including PCBs, HCB, mirex, polycyclic aromatic hydrocarbons (PAHs), and metals (copper, nickel, lead, mercury, arsenic, chromium, manganese, iron) (Leadley and Haffner 1996). Pollutants released by the petrochemical industry surrounding the Aamjiwnaang reserve, as reported by the National Pollutant Release Inventory (Environment Canada 2002), are too numerous to name in entirety but include volatile organic compounds, ethylene, phthalates, dioxins and furans, HCB, vinyl chloride, PAHs, ammonia, acrylonitrile, and metals (nickel, mercury, lead, cadmium, zinc, manganese). Because of the close proximity of this community to the large aggregation of petrochemical industry and potential exposures to compounds with known effects on sex ratios, further investigations into the types and routes of chemical exposures (air, water, food, soil, and sediment) are warranted for this population.
Evidence of altered wildlife reproduction in the area.
A large body of literature has accumulated detailing the adverse effects of EDCs on the reproductive ability and sexual development of fish, amphibians, reptiles, and birds. Numerous studies indicate that wildlife populations in the Great Lakes area are being adversely affected by the level of contamination, and that evidence from wildlife research could be used as a sentinel for human health effects (Fox 2001). In the Great Lakes area close to the Aamjiwnaang reserve, fish with intersex gonads (both male and female) have been reported in Lake St. Clair (Kavanagh et al. 2004). There is also ongoing research in the St. Clair River Area of Concern region of the Great Lakes that is documenting reduced hatching success and altered sexual development in turtles as well as changes in the sex ratios of birds (Environment Canada Canadian Wildlife Service 2003).
Other population factors influencing sex ratio.
Numerous biological and environmental factors appear to have a minor influence on sex ratios, including parental age, parity, birth order, coital rates, infertility, maternal nutrition (James 1996, 2004), illness such as insulin-dependent diabetes mellitus (Rjasanowski et al. 1998), stress, war (Ansari-Lari and Saadat 2002), and selective reproductive practices (Allahbadia 2002). These influences on sex ratio are generally considered to play a small role, but cannot be ruled out completely without additional information about the study population.
One study has looked at the influence of race on sex ratios with North American Indian couples having slightly higher sex ratios (more boys) than Caucasian couples (Khoury et al. 1984). Comparison of the “control” Chippewa First Nation community to the average sex ratio for Canada showed no significant difference between the two populations, indicating that race is likely not playing a role in the observed altered sex ratio of the Aamjiwnaang community.
Future studies.
The Department of Indian and Northern Affairs database is the most accurate source of information for First Nations vital statistics in Canada. However, there are some limitations to this database because it potentially includes births of band members that are not residing on reserve land and does not provide additional information about, for example, parental age, parity, or stillbirths. A community health survey is under way to explore a broad range of health concerns among the residents of the Aamjiwnaang reserve, as well as to gather information on covariates that may influence live birth sex ratio, including parental age, length of residency in the community, sex of stillbirths, and lifestyle factors such as parental smoking.
The initial assessment of the sex ratios of the Aamjiwnaang community over the 20-year period 1984–2003 presented here indicates that there is a significant ongoing decrease in the proportion of male live births beginning in the early 1990s and continuing to the end of the study period 2003. Although several potential factors may be contributing to the observed decrease in sex ratio, the close proximity of this community to the large aggregation of petrochemical industry and potential exposures to compounds that may influence sex ratios warrants further assessment into the types of chemical exposures for this population. Because of the complexities of any population exposure, causality between a single compound and adverse effect is always difficult to assess. It is possible that the Aamjiwnaang community has had multiple chemical exposures over the years that may be contributing to the overall picture of a reduced sex ratio. Further assessment must include the identification of exposures to compounds that may already be associated with adverse effects on sex ratio, determination that the timing of exposure is appropriate for the observed change in sex ratio, and elimination of other potential influences on sex ratio.
We thank the Aamjiwnaang Environmental Committee for their interest and assistance. We also thank T. Colborn, M. Gilbertson, and N. Birkett for comments and encouragement, and W. Teel and J. Brophy for facilitating this study.
C.M. received funding from the McConnell Foundation, Ecosystem Health Program, Faculty of Medicine, University of Western Ontario.
Figure 1 Proportion of live male births (male live births/total live births) for Aamjiwnaang First Nation 1984–2003. The dotted line is the expected male proportion for Canada (0.512). The dashed line is the linear regression line for the period 1984–1992; r2 = 0.000; slope not significantly different from zero (p = 0.990). The solid line is the linear regression line for the period 1993–2003; r2 = 0.547; statistically significant deviation of slope from zero (p = 0.009).
Table 1 Total live births, proportion of live male births (male live births/total live births), χ2, and p-value for Aamjiwnaang First Nation 1984–2003 arranged in 5- and 10-year periods.
Period Total live births Proportion male births χ2a p-Value
5-Year
1984–1988 173 0.538 0.185 0.667
1989–1993 185 0.551 0.532 0.466
1994–1998 215 0.451 1.574 0.210
1999–2003 132 0.348 7.472 0.006*
10-Year
1984–1993 358 0.545 0.807 0.369
1994–2003 347 0.412 7.100 0.008*
a Chi-square was performed using an expected male proportion equal to 0.512; df = 1.
*Highly significant statistical deviation (p < 0.01) from the expected proportion of males using Chi-square analysis.
Table 2 The influence of environmental and occupational exposures on sex ratio.
Exposure type Decreased sex ratio (fewer boys) Increased sex ratio (fewer girls) No effect
Dioxin Paternal environmental exposure postindustrial accident (Mocarelli et al. 1996, 2000)
Paternal exposure as pesticide producers (Ryan et al. 2002) Paternal occupational exposure (Schnorr et al. 2001)
PCBs Paternal consumption of rice oil contaminated with PCBs at < 20 years of age (del Rio Gomez et al. 2002)
Maternal exposure to PCBs in Great Lakes fish (Weisskopf et al. 2003) Paternal exposure to PCBs in Great Lakes fish eaters (Karmaus et al. 2002) Consumption of rice oil contaminated with PCBs and PCDFs (Yoshimura et al. 2001)
Pesticides Paternal exposure to nematocide DBCP (Goldsmith 1997)
Pesticide applicators (Garry et al. 2003)
HCB exposure (Jarrell et al. 2002) Maternal exposure to HCB (Jarrell et al. 1998)
Methylmercury Maternal exposure to methylmercury-contaminated fish (Sakamoto et al. 2001)
Petroleum Municipalities exposed to petrochemical air pollution (Yang et al. 2000b)
Natural gas exposure (Saadat et al. 2002) Municipalities adjacent to a petroleum refinery plant (Yang et al. 2000a)
Air pollution Air pollution from incinerators (Williams et al. 1992) Air pollution from local steel foundry (Lloyd et al. 1984, 1985) General air pollution (Williams et al. 1995)
Radiation Maternal exposure to non-ionizing radiation (electromagnetic radiation, strong static) and paternal exposure to high voltage (James 1997b) Paternal occupational exposure to ionizing radiation (Dickinson et al. 1996) Background ionizing radiation (Saadat 2003)
Occupation Paternal exposure carbon type setters (Milham 1993)
Infertility treatment Maternal exposure to clomiphene citrate (Jarrell 2002)
Lifestyle Parental smoking (Fukuda et al. 2002)
Abbreviations: DBCP, dibromochloropropane; PCDFs, polychlorinated dibenzofurans.
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Yang CY Cheng BH Hsu TY Tsai SS Hung CF Wu TN 2000a Female lung cancer mortality and sex ratios at birth near a petroleum refinery plant Environ Res 83 33 40 10845779
Yang CY Tsai SS Cheng BH Hsu TY Wu TN 2000b Sex ratio at birth associated with petrochemical air pollution in Taiwan Bull Environ Contam Toxicol 65 126 131 10874090
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7921ehp0113-00129916203238ResearchAssessing the Sensitivity of Different Life Stages for Sexual Disruption in Roach (Rutilus rutilus) Exposed to Effluents from Wastewater Treatment Works Liney Katherine E. 1Jobling Susan 2Shears Jan A. 1Simpson Peter 3Tyler Charles R. 11 Environmental and Molecular Fish Biology Group, School of Biosciences, Hatherly Laboratories, Exeter, United Kingdom2 Aquatic Ecotoxicology Research Group, Department of Biological Sciences, Brunel University, Middlesex, United Kingdom3 Environment Agency, Waterlooville, United KingdomAddress correspondence to K.E. Liney, Environmental and Molecular Fish Biology Group, School of Biosciences, Hatherly Laboratories, Prince of Wales Rd., Exeter, EX4 4PS, UK. Telephone: 44-1392-264389. Fax: 44-1392-263700. E-mail:
[email protected] authors declare they have no competing financial interests
10 2005 14 6 2005 113 10 1299 1307 12 1 2005 14 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Surveys of U.K. rivers have shown a high incidence of sexual disruption in populations of wild roach (Rutilus rutilus) living downstream from wastewater treatment works (WwTW), and the degree of intersex (gonads containing both male and female structural characteristics) has been correlated with the concentration of effluent in those rivers. In this study, we investigated feminized responses to two estrogenic WwTWs in roach exposed for periods during life stages of germ cell division (early life and the postspawning period). Roach were exposed as embryos from fertilization up to 300 days posthatch (dph; to include the period of gonadal sex differentiation) or as postspawning adult males, and including fish that had received previous estrogen exposure, for either 60 or 120 days when the annual event of germ cell proliferation occurs. Both effluents induced vitellogenin synthesis in both life stages studied, and the magnitude of the vitellogenic responses paralleled the effluent content of steroid estrogens. Feminization of the reproductive ducts occurred in male fish in a concentration-dependent manner when the exposure occurred during early life, but we found no effects on the reproductive ducts in adult males. Depuration studies (maintenance of fish in clean water after exposure to WwTW effluent) confirmed that the feminization of the reproductive duct was permanent. We found no evidence of ovotestis development in fish that had no previous estrogen exposure for any of the treatments. In wild adult roach that had previously received exposure to estrogen and were intersex, the degree of intersex increased during the study period, but this was not related to the immediate effluent exposure, suggesting a previously determined programming of ovotestis formation.
differentiationeffluentendocrinefishwastewater treatment works
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A widespread occurrence of sexual disruption has been reported in freshwater and marine fish in Europe (Hecker et al. 2002; Jobling et al. 1998; Lye et al. 1997; Minier et al. 2000; Vethaak et al. 2002), Japan (Hashimoto et al. 2000), and the United States (Folmar et al. 2001; Munkittrick et al. 1998), and some of these effects have been linked with exposure to endocrine-disrupting chemicals emanating from wastewater treatment works (WwTW) effluents. Sexual disruption is defined as disruption of gonadal development in terms of either altered duct formation and/or development of ovotestis. The most commonly documented feminizing effect of WwTW effluents in fish is induction of vitellogenin (VTG; an estrogen-dependent yolk precursor) (Purdom et al. 1994). Other feminizing effects of WwTW effluents include altered sex steroid hormone levels in adult and juvenile fish (Folmar et al. 1996; Hecker et al. 2002), impaired gonadal development in adults and juveniles (Hemming et al. 2001; Jobling et al. 2002a), altered timing of sexual differentiation in early life stages (Rodgers-Gray et al. 2001), and gonadal intersex (gonads containing both male and female characteristics) (Jobling et al. 1998). Gonadal intersex includes disruption of the gonadal duct, where the male duct is feminized to form a female-like ovarian cavity, and/or the presence of both male and female germ cells within the same gonad (Nolan et al. 2001). Intersex has been reported in a wide variety of gonochoristic species of wild freshwater and marine fish worldwide (Allen et al. 1999; Harshbarger et al. 2000; Hashimoto et al. 2000; Jobling et al. 1998; Kavanagh et al. 2004; van Aerle et al. 2001).
The most extensive studies on the feminizing effects of WwTW effluents in wild fish have been conducted on the roach (Rutilus rutilus) living in U.K. rivers where surveys of > 50 river sites have found a high incidence of intersex in roach populations living downstream from WwTW effluent outlets (Jobling et al. 1998). The incidence and severity of the intersex condition were correlated with the proportion of WwTW effluent in the river and the population equivalent of the WwTWs (strength of the effluent) (Jobling et al. 1998). Recently, the predicted amount of steroid estrogen discharged from WwTWs was shown to correlate with the degree of feminization in wild intersex roach living immediately downstream of the WwTW discharge (Jobling et al. in press). Importantly, intersex roach have been shown to have both reduced milt volume and sperm density (Jobling et al. 2002a) and reduced fertility (Jobling et al. 2002b); thus, there is the potential for the intersex condition to lead to population-level effects.
Laboratory studies have shown that it is possible to induce gonadal duct disruption and germ cell disruption in early-life-stage (ELS) fish by exposing them to some of the estrogenic chemicals found in WwTW effluent, but generally this occurs only at concentrations higher than those found in the environment (Gimeno et al. 1997, 1998; Gray et al. 1999; Knorr and Braunbeck 2002; Koger et al. 2000; Yokota et al. 2000). Our previous work has shown that exposure of roach to an estrogenic WwTW effluent during early life [50–150 days posthatch (dph)] induced VTG synthesis and gonadal duct disruption (feminization) but not the simultaneous presence of male and female germ cells in the same gonad (Rodgers-Gray et al. 2001). The precise timing of the genetic programming for sex (germ cell) differentiation in the roach, however, is not known, and it may be initiated before 50 dph, and thus the relevant window for sexual disruption may not have been captured in that study.
Less is known about the potential plasticity of differentiated germ cells and thus the sensitivity of these cells for estrogenic disruption in gonochoristic fish. Juvenile medaka (that had undergone sexual differentiation) have been shown to undergo sex reversal when exposed to steroids, both male to female and female to male (Yamamoto 1953, 1958). Induction of intersex in sexually mature male medaka has also been reported after treatment with estradiol (Egami 1955a, 1955b; Kang et al. 2002; Shibata and Hamaguchi 1988). The conclusions drawn from these studies were that testis-ova were formed during the process of spermatogenesis and arose as a consequence of the transformation of spermatogonia B into female sex cells (oocytes) (Shibata and Hamaguchi 1988). In roach during the post-spawning period, there is a short interval of gonadal regression before further germ cell proliferation and renewed gonadal growth. It is possible that this window of germ cell proliferation in adult roach offers a window of enhanced sensitivity for germ cell disruption, when circulating endogenous steroid hormones are at low concentrations (Vainikka et al. 2004).
Estrogenic chemicals commonly detected in WwTW effluent and receiving waters include natural steroid estrogens, synthetic estrogens, alkylphenolic compounds, phthalates, bisphenols, and various pesticides and herbicides (Azevedo et al. 2001; Desbrow et al. 1998; Zhang et al. 2004). Steroid estrogens and some alkylphenolic chemicals are often found in WwTW effluents at concentrations sufficient to induce vitellogenic responses in laboratory exposures (Routledge et al. 1998; Tyler and Routledge 1998). Natural steroid estrogens (Kang et al. 2002; Koger et al. 2000), the synthetic steroid estrogen ethinylestradiol (Metcalfe et al. 2001), and alkylphenolic compounds (Gray and Metcalfe 1997; Gronen et al. 1999) have also been shown to induce intersex in fish, but generally at concentrations higher than those present in WwTW effluent discharges. Steroidal estrogens and alkylphenolic chemicals (and other environmental estrogens) have also been shown to be interactive (additive) in their effects (Silva et al. 2002; Thorpe et al. 2001, 2003). Together, these data strongly support the hypothesis that steroidal estrogens and alkylphenolic chemicals are key compounds in the development of feminized responses, including intersex in wild roach populations living in U.K. rivers.
Feminized responses in roach exposed to two estrogenic WwTWs were investigated during life stages of potential sensitivity for intersex induction (periods encompassing germ cell division). The developmental stages studied encompassed the period of sexual differentiation, from fertilization through the completion of the period of gonadogenesis and the postspawning period in adult fish. Given the likely role of steroidal estrogens and alkylphenolic chemicals in the induction of intersex in wild roach, these chemicals were measured in the effluents for each of the exposure studies conducted.
Materials and Methods
Experimental design.
We set up experiments to investigate the effects of treated wastewater effluent on sexual development in roach during two life history stages of perceived sensitivity for the disruption of sexual development, the first encompassing the period of sexual differentiation in early life and the second in adult fish during germ cell proliferation after the annual spawning event.
Treated wastewater effluents.
Roach were exposed to effluents from two U.K. WwTWs (WwTW A and WwTW B) with different population equivalents and treatment processes. At WwTW A, industrial influent to the works included approximately 6% of the total influent, and the population equivalent of the works influent was approximately 137,000. WwTW B had a higher industrial component in the influent (24%), and the population equivalent was approximately 312,700, double that at WwTW A. Influents at both works received secondary treatment. At WwTW A, secondary treatment consisted of trickling filters and bubble-diffused air-activated sludge treatment. Secondary treatment at the WwTW B plant consisted of bubble-diffused air-activated sludge (60% of the flow) and biological phosphorus-removal–activated sludge (40% of the flow), which were then combined before discharge into the receiving rivers. Extensive studies on the effluent from WwTW A have shown it to be estrogenic to fish (Harries et al. 1999; Rodgers-Gray et al. 2001). The effluent from WwTW A is pumped into an estuary where roach do not live, and hence the possible effects of this effluent on sexual development and function in wild roach have not been investigated. Direct studies on the estrogenicity of the effluent from WwTW B had not been previously undertaken, but there is a high incidence of intersex in wild roach living in the river below the discharge of this effluent (Jobling et al. 1998).
Feminized responses in roach exposed to WwTW effluent during early life (fertilization to 300 dph).
Figure 1A shows the experimental design for investigation of the effects of the two WwTW effluents in roach during early life. In this study, fish were exposed to graded concentrations of effluent continually from fertilization until 300 dph to include the full period for germ cell differentiation in this species. Each exposure system consisted of six tanks supplied with graded concentrations of treated sewage effluents and diluent (river water for WwTW A and dechlorinated tap water for WwTW B). Nominal concentrations of effluent at WwTW A were 100, 40, 20, and 10%, with river water and dechlorinated tap water as controls. At WwTW B, nominal effluent concentrations were 100, 80, 40, 20, and 10%, with a control tank receiving dechlorinated tap water. The flow rate through each of the tanks was 5 L/min, and flow rate and water temperature were monitored daily. The tanks were aerated to ensure sufficient oxygen supply. Fertilized roach eggs were allowed to hatch in 50-L glass-reinforced plastic tanks held within the larger exposure tanks. From hatching, fry were fed newly hatched Artemia until approximately 60 dph, when commercial (estrogen-free) cyprinid food pellets were introduced (Calverton Fish Farm, Nottingham, UK). Fish were later released into the larger 600-L mesocosm tanks at approximately 70 dph. At 60 dph, 60 fish from each treatment were transferred to clean water to depurate in order to compare a short-term exposure to treated WwTW effluent in early life (fertilization to 60 dph) with a chronic exposure from fertilization through to the completion of sexual differentiation (300 dph). Biological sampling was carried out (where numbers of surviving fish were sufficient) at 200 dph and 300 dph.
Feminized responses in adult postspawning roach exposed to WwTW effluents.
We carried out two experiments to investigate the effects of exposure to WwTW effluent on feminized responses, including germ cell development, during the postspawning period in adult male roach. These experiments were particularly focused on assessing whether exposure to WwTW effluents at this time was capable of inducing intersex (ovotestis). The study included roach that had no previous exposure to estrogen (naive fish) and had been hatched and reared to maturity in captivity and clean water (adult exposure 1), and wild roach that hatched and grew to maturity in the wild (and likely had some previous exposure to estrogen; adult exposure 2; Figure 1B).
Adult exposure 1 (WwTW A only).
One hundred twelve spermiating male roach (aged 3+) that had been reared throughout their lives in borehole water were obtained from the Calverton Fish Farm. Groups of 25 male fish were exposed to 100, 50, and 25% effluent (mixed with river water) or to river water alone at WwTW A for a 2-month period from July to September 2003. The flow rate through each of the tanks was 6 L/min, and this was monitored daily. The tanks were aerated to ensure sufficient oxygen supply. Fish were fed daily on commercial (estrogen-free) cyprinid pellets throughout the exposure. At the start of the study, 12 fish were sampled for biological analyses (preexposure sample), and the remaining fish were deployed into the tanks and subsequently sampled at the end of the study only.
Adult exposure 2.
In this study, spermiating wild roach were deployed into five tanks at WwTW A that were supplied with graded concentrations of effluent and river water. Nominal effluent concentrations were 100%, 50%, and 25%, with further tanks supplied with river water and dechlorinated tap water as controls. At WwTW B, four tanks were supplied with graded concentrations of effluent, and an additional tank received tap water only as a control. Nominal effluent concentrations were 100%, 50%, and 25%. Groups of 20 mature spermiating male roach of mixed age classes were exposed to each of the treatments at WwTWs A and B for periods of 4 months (WwTW A) and 2 months (WwTW B) beginning in July. The flow rate through each of the tanks at both WwTWs was 6 L/min, and this was monitored daily. The tanks were aerated to ensure sufficient oxygen supply. Fish were fed daily with commercial cyprinid pellets. At the start of the experiment, 20 fish were sampled for biological analyses (preexposure sample)
Measurement of steroid estrogens, alkylphenolic chemicals, and bisphenol A in the effluents.
Full-strength WwTW effluent samples from both WwTWs were collected during the studies. Seven-day composite WwTW effluent samples were collected from both WwTWs (Figure 1) and analyzed for the steroid estrogens 17β-estradiol, estrone, and 17α-ethinylestradiol and for the alkylphenolic compounds octylphenol, nonylphenol, and nonylphenol mono- and diethoxylates. Bisphenol A was also measured. Daily samples (2.5 L) of full-strength WwTW effluent were collected and refrigerated until processing (within 24 hr). The methods used to measure these chemicals were described by Kelly (2000) and Rodgers-Gray et al. (2000). In brief, the estrogenic chemicals were immobilized on a C18 silica-bonded solid-phase extraction column, eluted, and analyzed by gas chromatography/mass spectrometry. Chemical analysis was carried out by the Centre for Environment, Fisheries and Aquaculture Science (Burnham on Crouch, UK).
Biological sampling.
In the ELS roach study, at each sampling point fish from each treatment group were sacrificed with a lethal dose of anesthetic [MS-222, according to Home Office recommendations; Animals (Scientific Procedures) Act 1986]. For VTG analysis, 30 fish were placed into cryovials, frozen on dry ice, and then transferred for storage at −20°C. For gonadal histology, 30 fish were fixed in toto for 24 hr in Bouin’s solution and stored in 70% industrial methylated spirits (IMS) before processing for histology.
In the adult fish trials, individual fish were anesthetized [MS-222, according to Home Office recommendations; Animals (Scientific Procedures) Act 1986], and blood was collected via the caudal sinus with heparinized syringes. The fish were then sacrificed [Schedule 1 method, according to Home Office recommendations; Animals (Scientific Procedures) Act 1986]. Aprotinin (2 trypsin inhibitor units/mL) was added to each blood sample; the blood was then centrifuged at 15,000 rpm, and the supernatant was removed and frozen on dry ice for transportation and subsequently stored at −20°C. Total length, body weight, and gonadal weight were determined for each fish. Gonads were removed and fixed in Bouin’s solution for 6–8 hr before storage in 70% IMS before processing for histologic analysis. Scales were collected from fish during the second adult trial to determine the age of individual fish.
In adult fish, we calculated condition factor (K ) for each individual using the formula K = [weight (g) × 100] ÷ (length)3. Gonadosomatic index (GSI) was calculated in these fish using the formula GSI = (gonad weight × 100) ÷ (total body weight – gonad weight).
Measurement of VTG.
VTG was quantified in whole-body extracts in ELS fish and in plasma in adult fish using a carp-VTG enzyme-linked immunosorbant assay (ELISA) that has been validated for use in the roach (Tyler et al. 1999). For ELS fish, whole-body homogenates were prepared by defrosting the bodies of individual fish on ice and homogenizing with a phosphate-buffered saline, 0.05% Tween, and 1% bovine serum albumin, pH 7.4 (1 mL buffer/g of fish). After centrifugation at 15,000 rpm, the supernatant was removed and stored at −20°C until assayed in the VTG ELISA.
Gonadal histology.
For ELS roach, tissue blocks for gonad sectioning were prepared by cutting the fish trunk either side of the dorsal fin. Samples were then embedded in paraffin wax, sectioned at 5 μm, mounted, and stained with hematoxylin and eosin. For adult roach, three sections were prepared from each gonad, one section from each of the anterior, middle, and posterior areas, providing a total of six gonad sections per fish. The sections were embedded in paraffin, sectioned at 5 μm, mounted, and stained with hematoxylin and eosin. All sections were analyzed by light microscopy.
Statistical analyses.
All statistical analyses were carried out using Sigmastat (version 2.0; SPSS Inc. Chicago, IL, USA). Statistical significance was accepted at p < 0.05 for all comparisons. We assessed intergroup differences using one-way analysis of variance (parametric, for normalized data) or Kruskal-Wallis test (non-parametric). Multiple comparisons tests were performed, where appropriate, using post hoc analyses for parametric (Student-Newman-Keuls test) or nonparametric data (Dunn’s method). Histopathology data sets were analyzed for differences between treatment groups using chi-square analysis.
Results
Concentrations of steroid estrogens and alkyl-phenolic chemicals in the exposure effluents.
The concentrations of known estrogenic chemicals in the two effluents varied during the periods of study. In general, we observed that concentrations of steroid estrogens were higher in the effluent at WwTW A compared with that at WwTW B (Figure 2A). We detected the synthetic estrogen 17α-ethinylestradiol intermittently and only at WwTW A (up to 1.5 ng/L; limit of detection = 0.5 ng/L).
Feminized responses in roach exposed to WwTW effluent during early life (exposed from fertilization to 300 dph).
Measured concentrations of treated wastewater effluent.
Measured concentrations of WwTW effluent in the mesocosm tanks remained close to nominal in all treatments. At WwTW A, measured concentrations of effluent during the roach exposures between fertilization and 60 dph were 0 ± 0% (tap water control), 0 ± 0% (river water control), 15.0 ± 1.3%, 24.9 ± 1.4%, 42.6 ± 2.2%, and 100 ± 0% (mean ± SEM). From 50 dph to 200 dph, the only remaining effluent treatment group was the full-strength effluent (100 ± 0%). At WwTW B, measured concentrations of treated effluent during the roach exposure between fertilization and 60 dph were 0 ± 0%, 8.0 ± 0.7%, 17.8 ± 0.9%, 36.6 ± 1.6%, 78.3 ± 1.0%, and 100 ± 0%. Between 50 dph and 100 dph, the measured exposure regimes were 0 ± 0%, 17.0 ± 1.0%, 33.6 ± 1.3%, 77.8 ± 0.8%, and 100 ± 0%. For the period between 100 dph and 200 dph, measured concentrations were 0 ± 0%, 12.4 ± 0.8%, 33.9 ± 1.3%, and 81.0 ± 2.5%, and for the period between 200 dph and 300 dph, 0 ± 0%, 15.7 ± 0.6%, 35.1 ± 0.6%, and 77.0 ± 0.4% (mean ± SEM). The temperature of the effluent/water fluctuated with the ambient temperature in all exposure tanks at both exposure WwTWs. There were no significant differences in water temperature between the various treatments at any one time point at either WwTWs (p > 0.05). There were differences in growth of the fish between treatments at each sampling point, but this was not related to the concentration of the effluents.
VTG induction.
Analysis of whole-body VTG at 200 dph (Figure 3A) showed that fish exposed to full-strength effluent at WwTW A had significantly higher titers of VTG (8-fold higher; 5,684 ± 546 ng/mL) compared with fish in the highest effluent concentration exposure group at WwTW B (80% effluent; 771 ± 122 ng/mL). This difference in plasma VTG in roach mirrored the differences in the effluent concentrations of steroid estrogens. At 300 dph, there was a concentration-dependent induction of VTG in roach at WwTW B, and in the 80% effluent a doubling in the body content of VTG (1709 ± 211 ng/mL), compared with fish at 200 dph. Fish that had been exposed to the various effluent concentrations for 60 days (to 60 dph) and then held in clean water to 300 dph contained little body VTG (between 29 ± 8 and 108 ± 42 ng/mL).
Gonadal development.
Gonadal phenotypes of fish at both 200 dph and 300 dph included males and females and fish that had not completed sexual differentiation. The gonad of undifferentiated fish contained several primordial germ cells surrounded by stromal cells, and the gonad was attached to the body wall by a single point of attachment, the mesogonium. Definitive female fish (Figure 4A) contained ovaries with the gonad attached to the mesentery by two points of attachment forming the ovarian cavity (female reproductive duct). Two types of germ cells were observed in phenotypic female fish. In some individuals, ovaries contained only oogonia, but in other females at a more advanced stage of development and with larger ovaries, both oogonia and primary oocytes in the perinucleolar stage were present. There were no obvious differences in the development of ovaries in the controls compared with the effluent-exposed females. In presumptive males in controls (Figure 4B), the testes had a single point of attachment to the mesentery, forming the sperm duct. The testes of phenotypic males in the controls contained either spermatogonia A or both spermatogonia A and B. Several individuals had testes at a very advanced stage of development containing spermatogonia A and B, spermatocytes, and spermatids. Some male fish that had been exposed to either WwTW A or WwTW B effluent had feminized reproductive ducts. These testes contained male germ cells (spermatogonia A, and both spermatogonia A and B in more advanced fish) but were connected to the body wall by two distinct points of attachment forming a “female-like” duct or ovarian cavity (Figure 4C). There was no evidence of disruption of germ cell development in any of the fish examined in any of the treatments.
The frequency of ovarian cavity or “female-like” reproductive ducts in sexually differentiated fish containing male germ cells is shown in Figure 3B. At 200 dph in 80% (WwTW B) and 100% (WwTW A) effluent exposure groups, all fish with male germ cells had feminized reproductive ducts. At 300 dph (WwTW B), the presence of an ovarian cavity in fish with male germ cells was positively correlated with the concentration of effluent. The only statistically significant difference, however, was for the 80% effluent fish (compared with controls, p < 0.001). Many of the male fish exposed to WwTW effluent for 60 days and then maintained in clean water for a further 240 days contained an ovarian cavity. At WwTW A, all “males” exposed to 40% and 100% effluent during early life retained a feminized reproductive duct (Figure 3C). At this WwTW, low numbers of depurated males that had been exposed to 10% and 20% effluent during early life had an ovarian cavity at 300 dph. None of the male fish in the river water control at WwTW A had a feminized duct. At WwTW B, again none of the males in the controls, and in this case also none of the males in the depurated 10% effluent exposure group, contained an ovarian cavity.
Feminized responses in adult postspawning roach exposed to WwTW effluents: adult exposure 1 (WwTW A only).
Measured concentrations of treated wastewater effluent.
Measured concentrations of WwTW effluent in the mesocosm tanks remained close to nominal in all treatments. The measured concentrations of treated effluent were 0 ± 0% (river water control), 26.5 ± 0.9%, 48.7 ± 1.0%, and 100 ± 0% (mean ± SEM).
Somatic and gonad growth.
The condition factor of the fish increased during the trial across all the treatments. There was a significant difference in condition factor between control and 50% effluent-exposed fish only (enhanced condition in the 50% effluent fish, p < 0.05; Figure 5A). The GSI of the fish decreased during the trial across all the treatments in line with normal seasonal patterns for sexual development (Figure 4B). At the end of the trial, the GSI in all of the effluent exposure groups was significantly lower than the GSI in the males kept in river water (p < 0.05; Figure 5B).
Plasma VTG concentrations.
The concentration of plasma VTG in male fish before the effluent exposures in July was 226 ± 67 ng/mL; at the end of the trial in September in the river control fish, it was 91 ± 22 ng VTG/mL. Plasma VTG in the fish exposed to 25% effluent was 38 ± 15 ng/mL (no induction). In fish exposed to 50% and 100% effluent, the plasma VTG was significantly higher than in the controls, at 2,332 ± 329 ng/mL and 2,223 ± 561 ng/mL, respectively (p < 0.05; Figure 5C).
Gonad development.
All of the 12 spermiating male fish sampled in July had large amounts of spermatozoa contained in the testis lobules. A section through a typical testis of a 3+ years spermiating male roach is shown in Figure 6A. We identified a single intersex fish, and it contained a small number of primary oocytes in the perinucleolar stage surrounded by testicular tissue.
Spermiation had ceased in males sampled in September. These testes contained spermatogonia A, spermatogonia B, and spermatocytes but no spermatozoa (Figure 6B). One fish sampled from the control river water exposure group was identified as intersex and contained a small number of primary oocytes in the perinucleolar stage. There were no obvious differences between the testes of fish sampled from the effluent exposure groups and the river water controls.
Adult exposure 2 (WwTWs A and B).
Measured concentrations of treated sewage effluent.
Measured concentrations of WwTW effluent in the mesocosm tanks remained close to nominal in all treatments.
At WwTW A, the measured concentrations of effluent between May, when the exposure started, and July were 0 ± 0% (tap water control), 0 ± 0% (river water control), 24.4 ± 0.9% (25% effluent), and 100 ± 0% (full-strength effluent; mean ± SEM). Between July and September, the measured concentrations of effluent were 0 ± 0% (tap water control), 0 ± 0% (river water control), 27.5 ± 1.7% (25% effluent), and 100 ± 0% (full-strength effluent; mean ± SEM). At WwTW B, measured concentrations of treated sewage effluent between May and July (when the exposure at this WwTW was terminated) were 0 ± 0% (tap water control), 25.1 ± 1.3% (25% effluent), 46.6 ± 1.5% (50% effluent), and 100 ± 0% (full-strength effluent; mean ± SEM).
Somatic and gonad growth.
The roach used in this trial were of mixed age, ranging between 3+ and 8+ years, with most in the 5+ year class. We found no clear correlates between age and any of the effects observed. The condition factor of the fish in the control group and all the treatment groups was higher at the termination of the experiments compared with the fish sampled at the start of the study (1.29 ± 0.02; Figure 5A). At WwTW A in both July and September, there were no significant differences in condition factor between control river-water–exposed and effluent-exposed fish. At WwTW B, the only significant difference in condition factor occurred between the controls and 100% effluent-exposed fish (an enhanced condition in the effluent-exposed fish; p < 0.05). As in adult exposure 1, the GSI decreased during the experiment across all the treatments in line with normal seasonal patterns for sexual development (Figure 5B). At WwTW A in July, the GSI in the 100% effluent-exposed fish was higher than in the river-water–exposed controls (p < 0.05), but there were no differences in GSI between the effluent-exposed fish and the control fish sampled in September. At WwTW B, the GSI was lower in the 50% and 100% effluent-exposed fish compared with the controls, indicating suppression in gonadal recovery after spermiation.
Plasma VTG concentrations.
The plasma VTG concentrations in fish at the start of the study were elevated (1,531 ± 426 ng/mL), indicating a previous exposure to an estrogenic stimulus (Figure 5C). At WwTW A in July, 2 months after the start of the experiment, males in the control river water and in the 25% effluent had plasma VTG concentrations of 62 ± 9 ng/mL and 94 ± 27 ng/mL, respectively, showing a clearance of VTG from the circulation. Fish exposed to 100% effluent had an elevated concentration of plasma VTG (~ 3-fold above the preexposure concentration, at 4,017 ± 1,013 ng/mL; p < 0.05). A similar pattern of effect occurred for these fish in September. At WwTW B in July, the plasma VTG concentration in male fish in the 100% effluent was 525 ± 136 ng/mL (significantly higher than in the controls; p < 0.05).
Gonad development.
We found intersex fish in both control and effluent treatment groups in exposure 2. The degree of disruption in the testes was classified into one of three categories: 0 = normal (no oocytes), 1 = a few oocytes (≤10 per section and principally composed of primary oocytes), and 2 = many oocytes (> 10 oocytes per testes section, many of which were often in the cortical alveolus stage of development). We found no differences between treatments and sampling points, and numbers and severity of the intersex condition (Figure 5D; p > 0.05). Furthermore, there was no effect of age on severity of the intersex condition (p > 0.05).
All fish sampled in May were producing sperm. All testis sections contained spermatozoa within the lobules and dispersed cysts of spermatogonia A and spermatogonia B. A section through a typical testis of a normal spermiating male roach for this study group is shown in Figure 6C. All intersex fish also had spermatozoa contained within lobules (a typical testis of an intersex roach is shown in Figure 6D). In fish sampled in July and September, both normal males and intersex fish were found in every effluent exposure group and in the controls. Testes in normal males were histologically very similar to those analyzed in adult exposure 1 (Figure 6B). The intersex condition included clusters of primary oocytes nested in an otherwise normal testicular tissue and, in the more severe condition, contained secondary oocytes (Figure 6E).
Discussion
The concentrations of both steroid estrogens and alkylphenolic chemicals in the two study effluents were comparable with those measured in other WwTW effluents in the United Kingdom and worldwide (Lee and Peart 1998; Spengler et al. 2001; Ternes et al. 1999). Differences in concentrations of the estrogenic chemicals measured in the two study effluents reflect either differences in the influent, differences in effluent dilution, and/or the different treatment processes employed in the works, as established in other studies (Kirk et al. 2002; Svenson et al. 2003; Ternes et al. 1999). The effluent content of both steroid estrogens and alkylphenolic chemicals was also variable within each of the works studied, even for samples collected as 7-day composites and even over the relatively short interval of only 2 months. At WwTW A, for example, the estrone content varied by more than 3-fold, and the concentration of nonylphenol mono-and diethoxylates varied by more than 9-fold. Seasonal differences may, at least in part, be due to changes in ambient temperature, which can affect microbiological populations and therefore the efficacy of biological treatments and/or high rainfall, which can result in dilution of influent to the WwTW and a reduced retention time through the works. This finding emphasizes the need for caution when single-point measures are used (as they often are) to assess steroidal estrogen and alkylphenolic chemical contents in WwTW effluents.
Both effluents induced a vitellogenic response in both juveniles and adults; however, the responses in fish at WwTW A were greater than those in fish at WwTW B, mirroring the higher concentrations of steroid estrogens at WwTW A. In the ELS exposure study, the vitellogenic response to full-strength (100%) effluent at WwTW A (at 200 dph) was an order of magnitude higher than the response to 80% effluent at WwTW B. Further exposure of the juvenile roach in the 80% effluent at WwTW B to 300 dph resulted in a further 2-fold increase in the VTG titer. This increase in VTG may have been as a consequence of a higher content of estrogenic chemicals during the 200–300 dph exposure period; however, no analytical chemistry was carried out at this time to confirm this. Alternatively, the increased titer of VTG in these fish may have resulted from the bio-concentration of the estrogenic chemicals in the fish from the effluent, which is known to occur for both steroid estrogens and alkylphe-nolic chemicals (Larsson et al. 1999). In the adult exposures, for males derived from the wild and exposed to full-strength effluent at WwTW B, the concentration of plasma VTG was lower (~ 33%) than in the preexposure fish, suggesting that the environment from which these fish were derived was more strongly estrogenic than was the effluent at WwTW B. The transient nature of the VTG induction was demonstrated in the ELS fish. At the end of the depuration study, there was a subsequent clearance of VTG (to concentrations not significantly different from controls) in fish previously exposed to effluent.
In the ELS exposure, there were differences in growth of fish between the treatments. Differences in growth rate may affect the timing of sexual differentiation and affect sex ratios (reviewed by Devlin and Nagahama 2002). However, at the end of the trial (300 dph), not all fish had completed sexual differentiation, and therefore we were not able to determine any differences in sex ratios between the treatments.
Adult fish across all treatment groups had an increased condition factor in both of the postspawning studies. The increased condition of the fish with time across the treatments was expected because energy directed to fuel rapid gonad growth during recrudescence is directed to support somatic growth in postspawning fish. The higher concentrations of effluent appeared to enhance condition recovery, and this may have occurred as a consequence of a greater availability of natural food, or because less energy was partitioned to gonad recovery (an associated inhibitory effect on testis recrudescence). Other studies have demonstrated that exposure to estrogenic chemicals, particularly alkylphenolic compounds commonly found in WwTW effluent, has a negative effect on testicular growth (Cardinali et al. 2004; Jobling et al. 1996; Magliulo et al. 2002). The reduced gonad mass (GSI) in the adult postspawning trials was in line with normal seasonal patterns for sexual development (the fish ceased producing sperm). There was no consistent pattern in the effect of effluent exposure on gonad recrudescence postspawning. In some cases, there appeared to be an inhibitory effect however, in others, a positive effect was observed, which may be due to an increased condition factor of these fish.
Exposure of juvenile roach to high concentrations of treated WwTW effluent from fertilization to 200 dph and 300 dph resulted in the feminization of the male reproductive duct. At 200 dph, all males in the higher effluent concentrations at both WwTWs had feminized ducts. At WwTW B, there was no concentration-related effect on duct feminization at 200 dph, but there was at 300 dph, when more fish had completed sexual differentiation (and thus their gonadal sex could be determined). Fish exposed to effluent at both WwTWs during early life and then transferred to clean water at 60 dph to depurate had disrupted ducts at the end of the study, indicating this effect was permanent. This supports previous studies that have shown feminized ducts induced by estrogen and estrogenic mixtures persist (Gimeno et al. 1997; Rodgers-Gray et al. 2001; van Aerle et al. 2002). The data presented here show that the window for disruption of duct development in roach includes the period between fertilization and 60 dph, before gonadal sexual differentiation is visible histologically. Feminization of the reproductive duct in fish has been induced in male fish in laboratory exposure to various estrogenic chemicals, including estradiol (Brion et al. 2004; Gimeno et al. 1996), ethinylestradiol (van Aerle et al. 2002), and the alkylphenolic compound 4-tert-pentylphenol (Gimeno et al. 1997, 1998), but only at relatively high exposure concentrations. The concentrations of steroid estrogens and alkylphenolic chemicals in WwTW B effluent were considerably lower than for those found to induce feminization of reproductive ducts in laboratory studies (Gimeno et al. 1996, 1998; van Aerle et al. 2002). The functional significance of duct feminization in males has been shown to depend on the severity of the condition, and feminized roach with severely disrupted ducts have a decreased fertility (Jobling et al. 2002b).
The field evidence that WwTW effluents induce germ cell disruption in wild roach is substantial (Jobling et al. 1998). The lack of germ cell disruption in any of the fish examined in the ELS study or in effluent-exposed adult males derived from estrogen-free environments indicated that either the estrogenic potency of effluents tested was not sufficient to induce this feature of feminization, or that germ cell disruption is a consequence of a longer term exposure to treated wastewater effluent. The longevity of exposure hypothesis is strongly supported by our recent findings that the severity of intersex in wild roach is age related (our unpublished data). In fish that had received exposure to estrogen before the exposure to the WwTW effluents, there was an apparent increase in the severity of the intersex condition, for both treatments and controls. Thus, this effect was not a function of the constituents in the WwTW effluents, but was possibly the consequence of a previously determined programming of germ cell disruption. The available data on intersex in roach thus suggest that oocytes in the testis of male roach arise either as a consequence of long-term exposure to estrogenic chemicals or as a consequence of programming during early life that manifests later in development as fish undergo sexual maturation.
We thank members of the Environmental and Molecular Fish Biology Research group at the University of Exeter, the Fish Physiology Research Group at Brunel University, Essex and Suffolk Water, and the Centre for Environment, Fisheries and Aquaculture Science (CEFAS; Burnham on Crouch, UK) for help in maintaining the mesocosms and fish and support in sampling. P. Roberts at CEFAS carried out the analytical chemistry. We are especially grateful for the support and advice throughout this project from A. Henshaw and staff at Calverton Fish Farm, U.K. Environment Agency.
This work was funded by UK Water Industry Research, the U.K. Environment Agency, and the Department for Environment, Food and Rural Affairs on a grant to C.R.T. and S.J.
Figure 1 Experimental design for the effluent exposure studies. Bullets indicate sampling points for analytical chemistry (collections of 7-day composite samples of effluent). Blue arrows indicate sampling points for biological analyses. (A) ELS study in which fertilized roach eggs were deployed into graded concentrations of WwTW effluent in mesocosm systems at both sites and maintained until 200 dph (WwTW A) and 300 dph (WwTW B). At both sites at 60 dph (July 2001), 60 fish from each effluent concentration and from controls were transferred to clean water for depuration and were sampled at 300 dph. (B) Postspawning roach studies in which sexually maturing male roach that had previously received exposure to estrogen were exposed to effluent from WwTW A for 4 months and WwTW B for 2 months (adult exposure 2), and another group of males with no previous exposure to estrogen was exposed to effluent from WwTW B for 2 months (adult exposure 1).
Figure 2 Measured concentrations of steroidal estrogens (A) and alkylphenolic chemicals (B) in full-strength (100%) effluent at both study sites during the experiments.
Figure 3 ELS study. (A ) Mean measured concentrations of VTG in whole-body homogenates of fish exposed from fertilization to 200 dph and 300 dph. (B) Percentage of fish with male germ cells and “female-like” reproductive duct exposed to WwTW effluents from fertilization to 200 dph and 300 dph. (C) Percentage of fish with male germ cells and “female-like” ovarian cavities after exposure to WwTW effluent from fertilization to 60 dph followed by depuration in clean water to 300 dph.
*p < 0.05, and **p < 0.001 compared with controls.
Figure 4 Gonadal histology of ELS roach after exposure to 100% effluent. (A) Control female. (B) Control male. (C ) Effluent-exposed male. Abbreviations: M, mesentery; O, oogonia; OC, ovarian cavity; PO, primary oocytes; S, spermatogonia; SD, sperm duct. Large arrows indicate points of attachment of the gonad to the mesentery.
Figure 5 Pre/postspawning studies showing effects of exposure to WwTW effluents on (A) condition factor (K ) and (B) GSI. (C ) VTG concentrations in blood plasma in adult male roach. (D) Intersex index of adult fish in exposure 2.
*p < 0.05 compared with control.
Figure 6 Pre/postspawning study (adult exposures 1 and 2) showing gonadal histopathology of male and intersex roach. (A and B) Gonads of males with no previous exposure to estrogen. In July (A), the testis was filled with spermatozoa (SZ), and cysts of spermatogonia A (SGA) and spermatogonia B (SGB) were also visible. In September (B), testes of male roach were normal, containing cysts of SGA, SGB, and spermatocytes (SC); there were no obvious differences between the testes of effluent-exposed fish and river water controls. (C–E ) Gonads of males with previous exposure to estrogen. In July, the testis was filled with SZ (C ), and some males were intersex (D). The testis contained SZ, oogonia (O), primary oocytes (PO), and larger oocytes in the cortical alveolus stage (CA). In September (E ), the gonads of these intersex fish contained cysts of SGA, SGB, and SC, together with PO.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7985ehp0113-00130816203239ResearchHistory of Inuit Community Exposure to Lead, Cadmium, and Mercury in Sewage Lake Sediments Hermanson Mark H. 1Brozowski James R. 21 Department of Chemistry, University of Pennsylvania, Philadelphia, Pennsylvania, USA2 Department of Soil, Water, and Climate, University of Minnesota, St. Paul, Minnesota, USAAddress correspondence to M.H. Hermanson, Department of Chemistry, University of Pennsylvania, 231 S. 34th St., Philadelphia, PA 19104-6323, USA. Telephone: (215) 573-8727. Fax: (215) 573-2112. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 31 5 2005 113 10 1308 1312 1 2 2005 31 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Exposure to lead, cadmium, and mercury is known to be high in many arctic Inuit communities. These metals are emitted from industrial and urban sources, are distributed by long-range atmospheric transport to remote regions, and are found in Inuit country foods. Current community exposure to these metals can be measured in food, but feces and urine are also excellent indicators of total exposure from ingestion and inhalation because a high percentage of each metal is excreted. Bulk domestic sewage or its residue in a waste treatment system is a good substitute measure. Domestic waste treatment systems that accumulate metals in sediment provide an accurate historical record of changes in ingestion or inhalation. We collected sediment cores from an arctic lake used for facultative domestic sewage treatment to identify the history of community exposure to Pb, Cd, and Hg. Cores were dated and fluxes were measured for each metal. A nearby lake was sampled to measure combined background and atmospheric inputs, which were subtracted from sewage lake data. Pb, Cd, and Hg inputs from sewage grew rapidly after the onset of waste disposal in the late 1960s and exceeded the rate of population growth in the contributing community from 1970 to 1990. The daily per-person Pb input in 1990 (720,000 ng/person per day) exceeded the tolerable daily intake level. The Cd input (48,000 ng/person per day) and Hg input (19,000 ng/person per day) were below the respective TDI levels at the time.
cadmiumCanadaexposurehistoryInuitlakesleadmercurysedimentssewage
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The presence of toxic heavy metals in the Arctic is evidence of long-range atmospheric transport (LRAT) of emissions from industrial and urban areas in temperate regions. Aquatic, marine, and terrestrial organisms—the “country foods” of the Inuit—accumulate these metals from the environment, subjecting native populations to high levels of exposure.
Lead, cadmium, and mercury are all naturally occurring but have no human nutritive function and are considered to be toxins. LRAT has moved atmospheric industrial emissions of these metals around the world for more than a century. The atmospheric residence times of Pb (~ 9 days), Cd (~ 4 days) (Pilgrim and Hughes 1994), and Hg (~ 4 days for particulate and 223 days for gas) (Šakalys and Kvietkus 2002) are long enough for them to reach the Arctic, where they are enriched [Arctic Monitoring and Assessment Program (AMAP) 1998; Hermanson 1991, 1998; Outridge et al. 2002] and are of particular concern because of possible human health effects (Chan et al. 1995; Pacyna 1995). The broad geographic distribution of these toxins has led to efforts to reduce atmospheric emissions, particularly with Pb because of its wide range of industrial uses and addition to gasoline worldwide (Nriagu 1990). Gasoline Pb content declined significantly in North America in the 1970s and 1980s (e.g., from 0.53 g/L in 1974 to 0.026 g/L in 1988 in the United States) [U.S. Department of Interior, Bureau of Mines (USDIBM) 1972, 1982]. In Canada during the late 1980s, however, Pb emissions were still 22 times those of Cd and 48 times those of Hg (Allen 1996). Pb emissions in the United States and Canada in the late 1980s were mostly from industrial processes and waste treatment (AMAP 1998). Cd emissions from nonferrous metal smelters have been reduced, shifting the primary anthropogenic emissions to fossil fuel combustion in the United States and industrial processes in Canada (AMAP 1998; Nriagu 1979; Pacyna 1995; Pilgrim and Hughes 1994; Skeaff and Dubreuil 1997). Hg emissions from nonferrous metal smelters have been reduced to the point that refuse incineration and fossil fuel combustion—particularly coal—are now considered to be the major atmospheric sources (AMAP 1998; Sunderland and Chmura 2000).
Various investigations have identified Inuit exposure to these metals (Dewailly et al. 2001; Dietz et al. 1996), but community exposure history has never been investigated because early data were never collected. As a result, changes in Pb, Cd, and Hg exposures in an Inuit population from reduced or altered emissions are not known.
Our research objective is to identify the history of Pb, Cd, and Hg exposure to the average individual in an Inuit community by measuring inputs of these metals to sewage lake sediments. Many small arctic communities rely on lakes, ponds, or lagoons for natural, facultative treatment of domestic wastes. Because Pb, Cd, and Hg concentrations in urine and feces are considered to be good indicators of exposure to these metals (Bardoděj et al. 1985; Bederka et al. 1985; Choudhury et al. 2001; Claeys-Thoreau et al. 1987; Engqvist et al. 1998; Iwao et al. 1981; Kjellström et al. 1978; Lauwerys et al. 1994; Müller et al. 1993; Sandborgh-Englund et al. 1998; Schouw et al. 2002; Tsuchiya and Iwao 1978), changing metal inputs to sediment in these lakes over time should be an estimate of the exposure history of the contributing community when background effects are considered.
Materials and Methods
Study site.
The study site is the Hamlet of Sanikiluaq on the Belcher Islands in southeastern Hudson Bay, Canada (Figure 1). At the time of sampling, about 550 persons, mostly Inuit, lived in the hamlet. The community has a heavy reliance on country foods that are known to be a contaminant source (Wein et al. 1996). No industrial or agricultural sources of Pb, Cd, or Hg are known to exist on the islands. Local activities are not considered to be a source of anthropogenic Pb to the environment other than use of Pb shot for hunting.
We sampled sediment cores collected from two lakes near Sanikiluaq. Annak has been used for disposal of raw domestic waste from the community since about 1968; Imitavik is located 1.5 km to the southeast and is immediately south of the community (Figure 2). The whole-lake focus-corrected flux of Pb, Cd, and Hg in a given period to Annak in excess of that observed at Imitavik reflects the exposure of Sanikiluaq residents to metals in products ingested and inhaled, including food and cigarette smoke—two well-documented metals sources (Benedetti et al. 1992, 1994; Dietz et al. 1996; Gamberg and Scheuhammer 1994; Luoma et al. 1995; Rey et al. 1997; Rickert and Kaiserman 1994; Scheuhammer et al. 1998).
Annak is a small lake (2-ha surface area, 4.5-m maximum depth). Like all small lakes in the area, it is very well mixed by strong surface winds and never stratifies thermally. Nutrients from wastewater have made it eutrophic (open-water pH ~ 10) and anaerobic under ice. Wastewater inputs are quickly dispersed throughout the lake during the open-water season. During winter, wastewater freezes on the eastern shore, entering the lake during thaw in May. Surface sediments, composed mostly of dead phytoplankton, are about 70% organic (Hermanson 1990, 1998). The treatment system is facultative—both aerobic and anaerobic—because of the diel photosynthetic cycle in summer. No engineering structures control its operation. It has no influent streams: its source water is seepage from a small drainage area (42 ha) and wastewater. The metals deposited into the lake are from background, atmosphere, and sewage. Because there are no local industrial or agricultural sources of Pb, Cd, or Hg input to the Sanikiluaq waste stream, excess inputs relative to Imitavik are assumed to be associated with human waste.
Only human waste was discharged to Annak in “honey bags” until 1983. A community-wide remodeling project starting in 1983 transformed the system to tank collection of all household waste. Tanks are now pumped regularly, with the waste transferred to Annak. We assume that household wastes, including cleaning and personal care products, are not significant sources of Pb, Cd, or Hg. Table 1 shows that the estimated fraction of inputs of these products, based on literature values, is not > 1.1% for all categories and each metal except Cd in laundry powder (8.9%).
Imitavik is a multiple basin lake (141-ha surface area, 17.5-km2 drainage basin) fed by many small surface drainage catchments to the south. It is mesotrophic: surface sediments are approximately 25% organic matter and the water pH is approximately 6.5 all year.
Methods.
In 1993 three sediment cores were collected each from Annak and Imitavik and composited to form one core from each lake. Annak cores were collected from maximum water depth in the lake. Imitavik cores were retrieved near the deepest part of the north basin in water approximately 5.5 m deep. The Imitavik collection area is isolated from most direct drainage basin inputs, so the observed Pb, Cd, and Hg inputs greater than natural fluxes are considered atmospheric in origin. The core collection areas in both lakes are near sites where cores were collected in previous lake sediment studies (Hermanson 1990, 1991, 1993).
Cores were sectioned at 1-cm intervals from the surfaces to a depth of 20 cm and at 2-cm intervals from there to the core bottoms, which exceeded a depth of 40 cm. Sections from a given depth in each of the three cores from a lake were combined to form the composite. Each composite section was analyzed for 210Pb, 137Cs, porosity, and loss on ignition—a surrogate measure for organic carbon. Dating and sedimentation rates were calculated from Pb-210 activity in sediment layers and the constant rate of supply model (Appleby and Oldfield 1978). These values have been reported previously (Hermanson 1998).
A 0.5-g sample of each composite section was digested using 50% nitric acid with microwave heating. Pb and Cd were analyzed from the digestate using graphite furnace atomic absorption spectrophotometry (AAS); Hg was analyzed by manual cold vapor AAS. Accuracy (recovery) from analysis of National Institute for Standards and Technology Standard Reference Material 2704 was 99.9% for Pb, 97.1% for Cd, and 95.0% for Hg. Precision (relative percent difference) was 1.7, 5.4, and 8.6%, respectively. Detection limits were 3.6, 0.16, and 23 ng/g. This digestion includes only those metals associated with organic matter in sediment and contains no mineral fractions.
Fluxes of Pb, Cd, and Hg to individual sections of cores (in nanograms per square centimeter per year) are products of particle deposition rates calculated from Pb-210 (in grams per square centimeter per year) and contaminant concentrations on those particles (in nanograms per gram). Calculating fluxes accounts for changes to sedimentation rates, a particular issue in Annak (Hermanson 1990, 1998). The calculations are converted to whole-lake deposition rates using focus factors (FFs) to correct for postdepositional sediment movement within the lake: the sedimentation rates and metals fluxes thus apply anywhere in the lake. FFs are a comparison of observed Pb-210 inventory in the sediment core to the established focus corrected input of 0.25 becquerel/g (Hermanson 1990). FFs were 2.91 for Imitavik and 1.74 for Annak (Hermanson 1998).
Our goal was to estimate changes in the per-person daily metal excretion in the community over time and compare that with tolerable daily intakes (TDIs) at the time of sampling. We calculated a 1-year input for each metal to Annak for 1970, 1980, and 1990 using the standard units of measure (nanograms per square centimeter per year), shown in Table 2. The background amounts from Imitavik for the same years were subtracted from the Annak values. The net amount for each metal is the total community human contribution to Annak resulting from urinary and fecal excretion. We calculated the average per-person daily contribution (excretion) by dividing these values for each of the 3 years by the community population at the time and number of days in the year.
Results and Discussion
Imitavik Lake.
To understand the community exposure recorded in Annak, we need to identify historic atmospheric metal inputs to the area measured in Imitavik.
The input of Pb to Imitavik in the 1700s was about 50 ng/cm2 per year (Figure 3) and grew exponentially during the Industrial Revolution from about 64 ng/cm2 per year in 1880 to 325 ng/cm2 per year in 1981. After 1981 the input stopped growing and appears to have declined slightly to about 300 ng/cm2 per year in 1993. Analysis of stable Pb isotopes in the same Imitavik sediment core shows that the area has received a mix of U.S. and Canadian industrial and urban Pb via LRAT since the 1800s (Outridge et al. 2002).
Cd deposition to Imitavik was about 1.1 ng/cm2 per year in the early 1700s and increased slowly until 1900, when the total deposition was about 1.8 ng/cm2 per year (Figure 4). Soon after 1900 the annual rate of increase grew rapidly, reaching about 4 ng/cm2 per year near the end of World War II. The Cd deposition growth rate was rapid enough that it doubled between 1900 and 1930, half the time required for doubling of Pb inputs. Unlike Pb, however, the Cd input had not doubled again as of 1993. In the 1940s Cd deposition appears to have stopped increasing until about 1952. From then until 1984, Cd inputs increased at an annual average rate similar to that of 1900–1940 until it reached 5.4 ng/cm2 per year in 1984. Between 1984 and 1993, the Cd deposition appeared to decline approximately 5%.
Preindustrial Hg inputs were about 0.2 ng/cm2 per year (Figure 5). Between 1800 and 1867, inputs increased about 2-fold, and then about 3-fold by 1980. Between 1980 and 1990 the input grew 44%. These growth factors are consistent with observations elsewhere in the Canadian Arctic (Lockhart et al. 1995). Input history of Hg to Imitavik has been previously reported (Hermanson 1998).
Annak Lake.
Pb inputs to Annak were lower than those to Imitavik until about 1950 and then show a trend similar to those to Imitavik up to the 1960s (Figure 3). Annak data show accelerating Pb inputs beginning about 1970, shortly after the beginning of sewage disposal into the lake. Pb input was about 250 ng/cm2 per year in 1970 and doubled within 12 years. It doubled again by about 1989, and peaked at 1,242 ng/cm2 per year about 1991. The decline that appears in Pb and other metals from 1991 through 1993 is an indication that inputs have dropped. However, more recent cores need to be collected to identify longer term trends.
Cd inputs to Annak are identical to the Imitavik inputs until the 1950s (Figure 4). Although Cd deposition to Imitavik stopped growing between the 1940s and about 1952, the input to Annak was unchanged between about 1944 and 1948. By 1953 Cd input in Annak increased to about 5 ng/cm2 per year and by 1960 increased to about 7 ng/cm2 per year. The higher inputs from the 1960s, apparently before sewage inputs began, were also observed in an earlier investigation (Hermanson 1991). Cd is known to be released from sediments after deposition, so there is a possibility that some Cd migrated downward in the core from greater inputs after the 1970s. However, Cd release is unlikely in reducing conditions (Khalid 1980) that are characteristic of Annak bottom waters during all times of year. After sewage inputs began, Cd inputs doubled by 1980, again by 1988, and again by 1991.
Hg inputs to Annak track the Imitavik record closely from about 1880 to the 1940s. In the late 1940s the inputs to Annak appear to be slightly higher than those to Imitavik (Figure 5). Hg inputs to Annak grew faster than Pb or Cd input after the onset of sewage disposal and between 1975 and 1991 grew from 1.6 ng/cm2 per year to 30 ng/cm2 per year (Hermanson 1998).
The results for both lakes agree with data from cores collected in 1983 and 1990 (Hermanson 1990, 1993). The Imitavik data show that the Belcher Islands region is affected by LRAT of contaminant trace metals.
The atmospheric Pb and Cd inputs to these lakes and to the Belcher Islands region stopped growing in the early 1980s and then appear to have declined. But the average human exposure as measured at Annak appears to have continued growing to 1990, suggesting the presence of other sources of Pb and Cd not related to LRAT. Pb exposure in some Inuit communities results from Pb shot ingested in country foods (Dewailly et al. 2001; Johansen et al. 2004; Scheuhammer et al. 1998) and inhaled in cigarette smoke (Dewailly et al. 2001; Rickert and Kaiserman 1994), neither of which is influenced by LRAT. The drop in Pb deposition to Annak sediments after 1990 may be associated with an intended ban on Pb shot in Canada (Scheuhammer et al. 1998). For Cd, the exposure represented in Annak may be entirely from cigarette smoke, which is known to contain high amounts of Cd and which has contributed to high body burdens in other parts of the North (Benedetti et al. 1994; Rey et al. 1997). This is also unrelated to LRAT.
Changes in daily per-person deposition of Pb, Cd, and Hg to Annak sediments.
The calculated values of person per day contribution to Annak sediment show that between 1970 and 1990 the average member of the community had growing excretion of—and apparent exposure to—all three metals, assuming that net metals deposition to Annak reflects intake and excretion (Figure 6).
The Pb value grew from 60,000 ng/person per day in 1970 to 162,000 ng/person per day in 1980 and 702,000 ng/person per day in 1990. A food study in five Canadian cities by Dabeka and McKenzie (1995) from 1986 through 1988 estimated Pb intake at 24,000 ng/person per day, considerably less than our Sanikiluaq excretion estimate. In a survey of Belgium, Malta, Mexico and Sweden, Claeys-Thoreau et al. (1987) noted a high variability in Pb excreted in feces, depending on country and its typical diet, ranging from 22,000 to 361,000 ng/person per day. Bederka et al. (1985) found an average of 180,000 ng/person per day of Pb excreted in feces in an Illinois group in the early 1980s. The results from Sanikiluaq suggest that the community has higher exposures to Pb than other groups and are generally consistent with ingestion values observed by Hansen (1990) in Greenland Inuit, which range from 69,000 to 369,000 ng/person per day and vary with meat consumption. The World Health Organization (WHO) has established a provisional tolerable weekly intake (PTWI) that calculates to a TDI for Pb of 214,000 ng/person (60-kg person) (van Oostdam et al. 1999). Values from Sanikiluaq in 1990 and from Greenland exceed this amount by > 3-fold in the worst case.
The Cd net deposition to Annak increased from 6,000 ng/person per day in 1970 to 12,000 ng/person per day in 1980 and 48,000 ng/person per day in 1990 (Figure 6). A food study in Canadian cities in 1986–1988 showed an average individual daily intake of 13,200 ng/person per day (Dabeka and McKenzie 1995). Another Canadian study by Newhook et al. (1994) showed food intake to be 12,600 for a 60-kg adult > 20 years of age. Although net Cd growth we observed in Annak sediments represents an increase of 8-fold over a 20 year period, the amounts are consistent with normal daily intake values from around the world that range from 15,000 to 79,000 ng/person per day (Kjellström 1979; Thornton 1992). They are considerably less than those in areas with known high Cd contamination in seafood, which range up to 281,000 ng/person per day (Elinder 1985; McKenzie-Parnell et al. 1988). A dietary Cd intake of 75,000 ng/person per day was considered a maximum safe level in the 1980s (Naylor and Loehr 1981), whereas the Canadian TDI in the 1990s was 60,000 ng/person per day (van Oostdam et al. 1999). The amounts observed in Annak sediments suggest that the average Sanikiluaq community member may not have been in danger of suffering from ill effects of Cd exposure from 1970 to 1990.
Hg net deposition to Annak increased from 700 ng/person per day in 1970 to 7,300 ng/person per day in 1980, to 19,000 ng/person per day in 1990, again, the fastest growth of these three metals (Figure 6). An urban Canadian food study from 1998 through 2000 found an average intake of 1,320 ng/person per day, just 7% of the Annak value in 1990 (Dabeka et al. 2003). The Canadian TDI for total Hg in the 1990s was 42,840 ng/person per day, but for methyl-Hg it was 28,260 ng/person per day for a 60-kg person, the same as that calculated from the WHO PTWI in 1999. In 2003 a Joint Food and Agriculture Organization (FAO)/WHO Expert Committee on Food Additives decreased this TDI level to an equivalent of 13,714 ng/person per day (IPCS-INCHEM 2004). In 1990 the average resident of Sanikiluaq would not have ingested or inhaled Hg beyond the TDI at the time. The recent reduction of the TDI level suggests that the community may be in the range of exposure values where ill effects may be experienced. However, the Annak values are lower than some food ingestion values in the Arctic: an Inuit food survey on Baffin Island by Chan et al. (1995) in the early 1990s showed 65,000 ng/person per day for women and 97,000 ng/person per day for men. Hg mean daily intake in Hansen’s (1990) study of four Greenland regions ranged from 25,000 to 128,000 ng/person per day and was related to meat consumption. Clearly, many Inuit Hg ingestion values exceed recent FAO/WHO limits.
Conclusions
Our results show that after sewage disposal to Annak began in the late 1960s, inputs of Pb, Cd, and Hg from domestic sewage grew rapidly and that the community has greater exposure to Pb than to either Cd or Hg.
Between 1970 and 1990 the average exposure to each community member to Pb increased > 10-fold, Cd about 8-fold, and Hg about 27-fold. Members of the Sanikiluaq community are exposed to Pb at levels that exceed TDI levels, and Hg intake is likely also above TDIs, although those limits vary depending on the health agency that defines them. These increased exposures occurred up to 1990 despite efforts to reduce industrial and urban emissions in the mid latitudes.
Sources of metals to the community during 1970–1990 probably included the effects of LRAT, Pb from gunshot, and a considerable amount of Cd, some Pb, and Hg from tobacco smoke.
These results show that undisturbed sediments from sewage lakes can be used to estimate community exposure to contaminants if there are no agricultural or industrial inputs to the waste stream.
Assistance in Sanikiluaq came from B. Fleming, J. Meeko Jr., M. Weesk, L. Kittosuk, and P. Kattuk. P. Kiry assisted with laboratory analysis.
This work was conducted under license 12419R from the Science Institute of the Northwest Territories. It was partially funded by the Hamlet of Sanikiluaq.
Figure 1 Map of the Hudson Bay area. Reprinted from Hermanson (1991) with permission from the American Chemical Society.
Figure 2 Annotated air photo of the study site. From National Air Photo Library, Canada (1982); reprinted with permission.
Figure 3 Pb inputs to Imitavik and Annak since the 1700s. CRS, constant rate of supply.
Figure 4 Cd inputs to Imitavik and Annak since the 1700s. CRS, constant rate of supply
Figure 5 Hg inputs to Imitavik and Annak since the 1700s. CRS, constant rate of supply. Modified from Hermanson (1998).
Figure 6 Average per capita excretion of Pb, Cd, and Hg in Sanikiluaq, 1970, 1980, and 1990.
Table 1 Estimated personal care product contributions of Pb, Cd, and Hg to Annak [ng/person per day (% of individual community member contribution in 1990)].
Personal care products Pb Cd Hg
Laundry powdera 1,700 (0.24) 4,300 (8.9) 210 (1.1)
Cosmeticsb 753 (0.10) 527 (1.1) 87 (0.46)
Shampooc 210 (0.03) 10 (0.02) 45 (0.24)
a Assumes 16.7 g/person per day (Jenkins and Russell 1994).
b Assumes 2.5 g/person per day (Demanz et al. 1984).
c Assumes 50 mL/person per day use (LeBlanc et al. 1999).
Table 2 Background (Imitavik) and sewage-enriched (Annak) focus-corrected fluxes of Pb, Cd, and Hg from 1960 (presewage), 1970, 1980, and 1990 (ng/cm2 per year), Belcher Islands.
Imitavik Annak
Pb
1960 180 193
1970 220 250
1980 310 426
1990 297 963
Cd
1960 4.1 6.8
1970 4.5 7.1
1980 5.1 13.4
1990 5.1 51.2
Hg
1960 1.1 1.5
1970 1.1 1.3
1980 1.2 6.3
1990 1.8 19.6
==== Refs
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Van Oostdam J Gilman A Dewailly E Usher P Wheatley B Kuhnlein H 1999 Human health implications of environmental contaminants in Arctic Canada: a review Sci Total Environ 230 1 82 10466227
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7904ehp0113-00131316203232ResearchPossible Influence of δ-Aminolevulinic Acid Dehydratase Polymorphism and Susceptibility to Renal Toxicity of Lead: A Study of a Vietnamese Population Chia Sin Eng 1Zhou Huijun 1Tham Mei Theng 1Yap Eric 1Dong Nguyen-Viet 2Tu NguyenThi Hong 3Chia Kee Seng 11 Department of Community, Occupational and Family Medicine, National University of Singapore, Singapore, Republic of Singapore2 Center of Occupational Health and Environment, Ministry of Industry, Hanoi, Vietnam3 General Department of Preventive Medicine and Control of HIV/AIDS Control, Ministry of Health, Hanoi, VietnamAddress correspondence to S-E. Chia, Department of Community, Occupational and Family Medicine (MD3), Faculty of Medicine, National University of Singapore, 16 Medical Dr., Singapore 117597, Republic of Singapore. Telephone: 65-68744970. Fax: 65-67791489. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 1 6 2005 113 10 1313 1317 3 1 2005 1 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. We examined six newly identified polymorphisms in the δ-aminolevulinic acid dehydratase (ALAD) single-nucleotide polymorphisms (SNPs) to determine if these SNPs could modify the relationship between blood lead (PbB) and some renal parameters. This is a cross-sectional study of 276 lead-exposed workers in Vietnam. All workers were measured for PbB, urinary retinol-binding protein (URBP), urinary α1-microglobulin (Uα1m), urinary β2-microglobulin (Uβ2m), urinary N-acetyl-β-d-glucosaminidase (NAG), urinary aminolevulinic acid (ALAU), serum α1-microglobulin (Sα1m), serum β2-microglobulin (Sβ2m), and urinary albumin (Ualb). The six SNPs were Msp and Rsa in exon 4, Rsa39488 in exon 5, HpyIV and HpyCH4 in intron 6, and Sau3A in intron 12. Analysis of covariance (ANCOVA) with interaction of PbB × SNPs were applied to examine modifying effect of the SNPs on the association of renal parameters and PbB, adjusting for potential confounders of age, gender, body mass index, and exposure duration. HpyCH4 was found to be associated with certain renal parameters. For HpyCH4 1-1, an increase of 1 μg/dL PbB caused an increase of 1.042 mg/g creatinine (Cr) Uα1m, 1.069 mg/g Cr Uβ2m, 1.038 mg/g Cr URBP, and 1.033 mg/g Cr Ualb, whereas in HpyCH4 1-2, an increase of 1 μg/dL PbB resulted in an increase of only 1.009 mg/g Cr Uα1m, 1.012 mg/g Cr Uβ2m, 1.009 mg/g Cr URBP, and 1.007 mg/g Cr Ualb. HpyCH4 SNP appeared to modify the lead toxicity to kidney with wild-type allele being more susceptible than variants. The mechanism for this effect is not clear. Further studies are needed to confirm this observation.
δ-aminolevulinic acid dehydratase (ALAD)HpyCH4intronleadSNP (single-nucleotide polymorphism)urinary albumin (Ualb)urinary retinol-binding protein (URBP)urinary α1-microglobulin (Uα1m)urinary β2-microglobulin (Uβ2m)
==== Body
The first and most common δ-aminolevulinic acid dehydratase (ALAD) polymorphism studied was the Msp single-nucleotide polymorphism (SNP) in exon 4. Since it was first reported (Battistuzzi et al. 1981), many reports (e.g., Onalaja and Claudio 2000) have been published on its association with inorganic lead (henceforth referred to as lead). Epidemiologic studies have tried to determine if the Msp polymorphism is important in human susceptibility to lead regarding various lead-targeted systems (Alexander et al. 1998; Bergdahl et al. 1997; Hu et al. 2001; Schwartz et al. 1995, 2000).
In recent years, reports have been published on the association between the Msp SNP and renal effects of lead exposure. Smith et al. (1995) suggested that the Msp-2 variant is a more susceptible allele because Msp-2 carriers had higher concentrations of blood uric acid and blood urea nitrogen (BUN). The differences achieved only borderline significance after adjusting for potential confounders. Bergdahl et al. (1997) found that serum creatinine is higher for Msp-2 carriers than for Msp 1-1 homozygotes in a sample of 89 lead workers. A linear relationship was observed between patella lead concentration, using X-ray fluorescence method, and serum uric acid when patellar lead passes 15 μg/g bone mineral for Msp-2 carriers, but the similar linear relation can be seen only when patella lead was > 101 μg/g bone mineral for the Msp 1-1 group (Wu et al. 2003). Wu et al. (2003) postulated that lead toxicity became apparent at a lower exposure level for Msp-2 carriers than for Msp 1-1 persons. These studies suggested that Msp-2 carriers were more susceptible to lead effects than were Msp 1-1 homozygotes. However, the reports were not consistent. Some studies had reported that the Msp-2 variant was associated with higher creatinine clearance as well as lower mean serum creatinine and BUN (Weaver et al. 2003).
Renal parameters such as creatinine clearance, BUN, and serum creatinine are insensitive to renal function changes. As much as 50–70% of kidney nephrons must be damaged before variation can be detected (Goyer 1989). The consensus may not be so clear, however, regarding the effects of lead on the renal system because no good longitudinal studies have been conducted to establish the predictive value of early biologic exposure markers in lead-exposed workers. Roels et al. (1994) studied 76 male lead smelter workers and 68 controls matched for age, gender, socioeconomic state, residence, and work shift characteristics. Although the tibia bone lead, blood lead (PbB), and urinary lead levels of the exposure group were significantly higher than those of the control group, no significant differences were observed in either common and stimulated creatinine clearance. More recently, however, Weaver et al. (2003) reported that higher lead levels were associated with lower BUN and serum creatinine levels and higher calculated creatinine clearance among those with the ALAD 1-2 genotype.
Renal parameters such as low-molecular-weight proteins [e.g., retinol-binding protein, α1-microglobulin, β2-microglobulin, N-acetyl-β-d-glucosaminidase (NAG)] representing proximal tubule injury are considered good alternatives because early lead-induced nephropathy usually involves damage of proximal tubule cell (Nolan and Shaikh 1992). To date, several studies have shown that these renal parameters have good correlation with lead exposure indices. NAG has been found to be the only marker elevated in early nephropathy in five studies reviewed by Bernard and Lauwerys (1989). One study in Japan (Endo et al. 1993) claimed that urinary α1-microglobulin (Uα1m) can be a useful indicator of renal impairment. A study of 128 lead workers (Chia et al. 1995) reported that Uα1m appears to be the most sensitive parameter compared with urinary β2-microglobulin (Uβ2m) and urinary retinol-binding protein (URBP). Another study of environmental lead exposure in children showed that URBP is increased by lead with good dose–effect and dose–response relations with PbB (Bernard et al. 1995).
To date, 111 SNPs have been reported in the National Center for Biotechnology Information website (NCBI 2005). But when we assessed the website in June 2004, 46 SNPs were reported. What are the relationships of these 46 SNPs with the renal effect of lead exposure? We used the Helix Tree software (Golden Helix, Inc., Bozeman, MT, USA; http://www.goldenhelix.com/pharmhelixtreefeatures.html) to test for linkage disequilibrium among the 46 SNPs; 6 SNPs were shown not to be in linkage disequilibrium. In this study we examined these 6 SNPs (Msp and Rsa in exon 4, Rsa39488 in exon 5, HpyIV and HpyCH4 in intron 6, Sau3A in intron 12) and examined their association with certain renal functions among a group of lead-exposed workers in Vietnam.
Materials and Methods
Study population.
The study population consisted of 323 workers from a battery factory in Hai Phong City, Vietnam. All workers from the production line as well as in the division of management and quality control were recruited into this study. Of these 323 workers, 246 were occupationally exposed to lead, and the remaining 77 were not directly exposed to lead. Although these 77 workers were not directly exposed to lead, many had a previous history of exposure. Some were still exposed, albeit less than the 246 group. Therefore, these workers were also included in the study. Signed consent was obtained for each worker before blood and urine samples were taken for subsequent analysis. The workers also filled out a questionnaire (in Vietnamese) with the help of a Vietnamese interviewer. After completing the questionnaire, a spot urine sample and 10 mL of blood were collected from each worker during the medical examination. Twenty of the exposed lead workers were not present during the study period, and 17 did not want to participate. Of the 77 workers who were not directly exposed to lead, 10 were unwilling to give their urine and blood for analysis. Because the study was strictly on a voluntary basis, the workers’ decisions in not giving urine and/or blood were respected. Hence, the 47 workers were excluded in the study, giving a response rate of 88.2% (276 of 323).
Questionnaire.
Information gathered included age, years of education, detailed occupational history, and current and previous smoking habits. Alcohol intake was also documented carefully, as it could confound the findings. Actual amount of alcohol consumption per day (i.e., types of drink consumed and the amount estimated by number of bottles consumed) and number of years of drinking were noted. Six workers gave a history of diabetes mellitus and/or hypertension. These workers were excluded from the study because diabetes mellitus and hypertension are known to affect renal functions.
Laboratory analysis.
We obtained blood samples by venipuncture with lead-free disposable syringes and stored the samples in heparinized lead-free polypropylene tubes. PbB was analyzed using atomic absorption spectrophotometry (Varian Spectra AA-30; SiberHegner Pte Ltd., Victoria, Australia) with a graphite furnace. External quality control was performed yearly under the National External Quality Assurance Scheme (England) and Inter-laboratory Comparison Programme (Canada). We obtained spot urine samples, and those used for analysis of ALAU, URBP, Uα1m, Uβ2m, urinary albumin (Ualb), and NAG were buffered (pH 7.2). Serum and urine samples were stored at –30°C until analysis. We analyzed all samples within 1 month of arrival in Singapore at our laboratory and adjusted all urine parameters for variability in urine flow using urinary creatinine that was expressed per gram of creatinine. Urine creatinine concentrations were determined using standard laboratory techniques. Ualb, ALAU, URBP, Uα1m, Uβ2m, and NAG were measured by enzyme-linked immunosorbent assay using commercially available polyclonal antibodies or test kits (Roche Diagnostic Corp., Indianapolis, IN, USA). The details of these tests have been reported previously (Green et al. 2004). NAG was determined using Noto’s method (Chia et al. 1995)
Identification of ALAD polymorphisms.
Six polymorphisms located on the ALAD gene were selected for analyses in our study. They are MspI and RsaI polymorphisms, which span a 248-bp region of the genomic ALAD sequence, as well as Rsa39488 (232 bp), HpyIV (234 bp), HpyCH4 (213 bp), and Sau3AI (282 bp) polymorphisms. The products of interest were determined through amplification by polymerase chain reaction (PCR), using primers designed with Primer3 (Whitehead Institute for Biomedical Research, Cambridge, MA, USA; http://frodo.wi.mit.edu/cgi-bin/primer3/primer3_www.cgi). The amplification cycles were performed on the PTC-100 MJ Thermal Cycler (PerkinElmer, Inc., Boston, MA, USA) and optimized to ensure specific amplification of the products of interest. Each PCR reaction was performed in a final volume of 25 μL, which included 10 ng of pure genomic DNA, 1.0 μM each primer, 200 μM each dNTP, 1× Taq buffer, 1.5 mM MgCl2, and 1.0 U of Taq DNA polymerase (all from Promega Corp., Madison, WI, USA), and topped up to the required volume with sterile distilled water. We used the restriction fragment length polymorphism method to locate the site of mutation; the fragments were determined on a 2.0% agarose gel (Seakem agarose; Cambrex North Brunswick Inc., North Brunswick, NJ, USA) and visualized on the Biorad Gel Doc 2000 system(BioRad Laboratories, Hercules, CA, USA).
Statistical analysis.
We determined that 28 people did not have any genotype measurements and had to exclude them from the analysis, leaving a sample of 248 workers. The data set of these 248 workers was used in all subsequent analyses. For some of the biologic samples, we were not able to analyze all the parameters because these samples ran out before we could complete the test.
The distributions of variables were checked individually with histogram and one-sample Kolmogorov-Smirnov (K-S) test for normality. Necessary transformation was undertaken to achieve normal distribution for all important variables. In the end, ALAU, Uα1m, Uβ2m, Ualb, URBP, serum α1-microglobulin (Sα1m), and serum β2-microglobulin (Sβ2m) went through logarithmic transformation. NAG went through square root transformation. The transformed variables all follow normal distribution with an insignificant p-value from the one-sample K-S test.
Statistical analysis addressed two issues: a) Is there any difference in the mean PbB concentrations and renal parameters between genotypes of each SNP after adjusting for known confounders? b) Within each single SNP, is the effect of PbB concentration on the renal parameters similar across genotype subgroups? Analysis of covariance (ANCOVA; Kutner et al. 2003) was the main statistical technique used. We initially examined scatter plots of each renal parameter (y-axis transformed value) and PbB concentration (x-axis) to identify linearity and extreme points.
We addressed the first issue using ANCOVA to adjust for age, exposure duration, body mass index (BMI), PbB (where appropriate), and gender. For the second issue, an interaction term of PbB × SNP was included while adjusting for the known confounders. Multiply linear regression equation models were used in the generation of regressions lines for Figures 1–4. All data are processed by SPSS (version 12.0; SPSS Inc., Chicago, IL, USA). Significance level was set as p < 0.05 (two-sided).
Results
Of the 248 workers, 184 (74.2%) were male and 64 (25.8%) were female, with a mean age of 39 (range, 20–66) years. The range of exposure duration was 1–41 years. Demographic features by SNP are summarized in Table 1. The allelic and genotypic frequencies are summarized in Table 2. For Msp, Sau3A, HpyIV, and HpyCH4, the wild-type allele is more predominant, whereas for Rsa and Rsa39488, the variant allele is more prevalent. The basic characteristics of the measurement variables are shown in Table 3. Table 4 shows the comparison of mean renal parameters by SNPs. The HpyIV variant group had higher mean ALAU and Ualb levels than the group with the wild-type genotype (1.15 vs. 0.95 mg/g Cr and 7.75 vs. 7.37 mg/g Cr, respectively). The wild-type genotype group for the Sau3A marker (1-1) had lower ALAU levels compared with those of the variant group (0.93 vs. 1.08 mg/g Cr) but higher NAG concentrations (2.93 vs. 2.53 U/g Cr). Only Rsa39488 subgroups had significant differences in PbB, with means of ALAD 1-1, ALAD 1-2, and ALAD 2-2 being 21.87 μg/dL, 20.43 μg/dL, and 25.54 μg/dL, respectively. The group with the Rsa39488 variant genotype, 2-2, had much higher PbB levels than the 1-1 and 1-2 genotype groups (Table 4).
Multiple linear regression equation models were constructed for each of the six SNPs. Of these six SNPs, only HpyCH4 showed significant association with URBP, Uα1m, and Uβ2m in the multiple regression analysis. Table 5 shows the relationship between different renal parameters and the HpyCH4 SNP. PbB concentrations significantly affect URBP, Uα1m, Uβ2m, and Ualb even after adjusting for age, gender, BMI, and exposure duration. PbB is an important predictor for these renal indices. There are significant interactions between the HpyCH4 marker and PbB concentration (Table 5). The associations of PbB concentrations and renal parameters are presented in Figures 1–4.
The increment in renal parameters corresponding to unit increase in PbB concentration is higher for HpyCH4 1-1 homozygotes than for HpyCH4 1-2 workers. For Uα1m, each increase of 1 μg/dL PbB corresponds to an increase of 0.016 log Uα1m (anti-log 1.042 mg/g Cr) for HpyCH4 1-1 workers, whereas for HpyCH4 1-2 workers the increase is 0.004 (anti-log 1.009 mg/g Cr). The same trend is shown in the relations of Uβ2m, URBP, and Ualb to PbB concentrations. The contrast between HpyCH4 1-1 and HpyCH4 1-2 is 1.069 versus 1.012 mg/g Cr, 1.038 versus 1.009 mg/g Cr, and 1.033 versus 1.007 mg/g Cr for Uβ2m, URBP, and Ualb, respectively.
Discussion
For ALAD SNPs, this study is the first to address more than one SNP. In addition to Msp in exon 4, we also examined Rsa SNP in exon 4, Rsa39488 in exon 5, HpyCH4 and HpyIV in intron 6, and Sau3A in intron 12. It has been shown by linkage analyses that these six SNPs are not in linkage disequilibrium.
We did not find any correlation of lead with Sα1m or Sβ2m, which is consistent with other studies (Cardenas et al. 1993; Chia et al. 1994; Endo et al. 1993). In our study, Msp SNP in exon 4 constitutes 95.9% of ALAD1 and 4.1% of ALAD2 alleles. This pattern is similar to those in most studies conducted on Asian populations (Hsieh et al. 2000; Shen et al. 2001). The allele composition of the other five SNPs had not been reported. To our knowledge, this is the first report of genetic distribution of the ALAD polymorphism in a Vietnamese population.
Although we determined some positive findings with Rsa, Rsa39488, HpyIV, and Sau3A, our findings were not consistent. Therefore, these SNPs are not discussed further; we concentrate our discussion on HpyCH4 and its association with renal function. HpyCH4 is a SNP that involves a G/C transversion in gene position 12,916 in intron 6. This SNP was first reported by Olson (2000). Allele frequency in his sample (North American population) was 0.818 for HpyCH4-1 and 0.182 for HpyCH4-2, and genotype frequency is 0.727 for HpyCH4 1-1, 0.182 for HpyCH4 1-2, and 0.091 for HpyCH4 2-2. Nakamura (2002) reported the frequencies of HpyCH4-1 to be 0.808 and of HpyCH4-2 to be 0.191 in a sample of 734 Japanese. In our study, HpyCH4-1 has a frequency of 0.942, and HpyCH4-2 has a frequency of 0.058. HpyCH4 1-1 homozygotes accounted for 88.4% (191), HpyCH4 1-2 heterozygotes accounted for 11.6% (25). Variant HpyCH4 2-2 homozygotes were not detected in this study population. Our study frequencies of the HpyCH4 allele were fairly similar to those reported by Nakamura (2002), whose sample was also drawn from an Asian population. However, the proportion of HpyCH4-1 in our study was relatively higher.
Several studies have shown that URBP (Bernard et al. 1995), Uα1m (Chia et al. 1995; Endo et al. 1993), Uβ2m (Nolan and Shaikh 1992), and urinary NAG (Bernard and Lauwerys 1989) are good indicators of early renal effects due to lead exposure. A study of 128 lead workers in Singapore (Chia et al. 1995) showed that Uα1m appears to be the most sensitive parameter compared with Uβ2m and URBP.
Our study found that a newly identified ALAD polymorphism, HpyCH4 in intron 6, was able to modify the association of PbB concentrations with certain renal parameters. For HpyCH4 1-1 homozygotes, 1 μg/dL PbB caused an increase of 1.042 mg/g Cr Uα1m, 1.069 mg/g Cr Ub2m, 1.038 mg/g Cr URBP, and 1.033 mg/g Cr Ualb, whereas in HpyCH4 1-2 heterozygotes, an increase of 1 μg/dL PbB resulted in an increase of only 1.009 mg/g Cr Uα1m, 1.012 mg/g Cr Uβ2m, 1.009 mg/g Cr URBP, and 1.007 mg/g Cr Ualb. These findings suggest that the HpyCH4 1-2 variant could be more resistant to the effects of lead toxicity on some renal functions than HpyCH4 1-1.
We showed that in the relationship between PbB and some renal parameters (Figures 1–4), two regression lines (HpyCH4 1-1 and HpyCH4 1-2) intersect around 25 μg/dL PbB. This point highlights the importance of stratifying the data by PbB when we study the modification effects of ALAD polymorphism on associations between lead exposure and renal function. Otherwise, problems would arise and cause bias in the findings. In our study, for workers with PbB < 25 μg/dL, HpyCH4 2-2 workers have higher renal function values than HpyCH4 1-1 workers. The opposite is true for workers with PbB > 25 μg/dL. Thus, one may conclude that ALAD2 is more susceptible when the samples have low lead exposure. Conversely, one might find that ALAD1 is more susceptible when the samples have higher exposure (i.e., PbB > 25 μg/dL). Our findings may explain, to some degree, why there were contradicting reports on the association between PbB concentrations and ALAD alleles in some studies. Wetmur et al. (1991) found a significant over-representation of Msp-2 isozymes among individuals with PbB in excess of 30 μg/dL. Schwartz et al. (1995) reported that the over-representation of Msp-2 allele can be present only when the PbB was > 40 μg/dL. Smith et al. (1995) failed to show any association of Msp-2 to PbB and he ascribed the nonassociation with the low lead exposure of the study population (mean PbB, 7.78 μg/dL).
For the Msp polymorphism, the differences noted in relation to the susceptibility of effects of lead have been attributed to the differential binding abilities of the ALAD isozymes. However, the HpyCH4 SNP, located in the intron region of the ALAD gene, technically does not have any protein-coding functions involved. George and David (1998) observed that some DNA behaves as an exon when expressed by one pathway but as an intron when expressed by another pathway. Both pathways can operate simultaneously, resulting in greater protein product variety. Perhaps the ALAD enzyme encoded by HpyCH4 1-1 and HpyCH4 1-2 could have been produced through one such pathway (George and David 1998). Regulatory functions of introns may involve controlling gene activity in different developmental stages or responding to immediate biologic needs by controlling local gene expressions. This function of introns could occur if exons code for a domain, a polypeptide unit that has a discrete function such as binding to a membrane, the catalytic site of an enzyme, or a structural unit of a protein (Jerry 2001). However, few studies have reported that polymorphisms located in introns can be associated with lead toxicity. One study that examined Msp polymorphism in intron 2 reported that variant workers were associated with decreased bone lead but not PbB (Kamel et al. 2003). Recent work has demonstrated that intronic mutations can have functional consequences in some genes such as p53 (Lehman et al. 2000). Similarly, intronic mutations may also have functional consequences in the ALAD genes.
Some limitations are inherent in this study. It is possible that individuals with certain ALAD polymorphisms may be more susceptible to the effects of lead toxicity on the renal system and thus would have been excluded from the workforce even before this study was conducted (healthy worker effect). But these susceptible workers will need to have obvious clinical renal diseases to be removed from the workforce. This is first time that factory workers in Vietnam were tested with these renal tests. Therefore, it is highly unlikely that we are dealing with a healthy worker effect. In Vietnam, most factories are state owned, as is this factory. Workers work in a factory all their lives (as can be seen by the long exposure history in Table 1). Schwartz et al. (1995) reported differences in the Msp ALAD genotype frequencies among workers in three lead factories with different lead exposures. They postulated that workers with the more susceptible ALAD genotype may moved to a lower lead-exposed job. Conversely, workers with the protective ALAD genotype may continue working because they have fewer symptoms even in higher exposure areas. We are unable to study this possible “population stratification” bias because our workers were rotated through the different departments/sections in the factory depending on the job schedule and manpower requirement.
We did not measure the body burden of lead and thus could not examine the effects of the ALAD SNPs in relation to lead accumulation in the body. We did use exposure duration as a surrogate, and this factor has been adjusted for in our analysis. In spite of factoring in the duration of exposure to lead, workers with HpyCH4 1-2 genotype had significantly better renal (certain) parameters than workers with the HpyCH4 1-1 genotype. The sample size of our study is also not large. There were only 25 workers with HpyCH4 1-2 compared with 191 workers with HpyCH4 1-1. Cadmium is also known to affect some of the renal parameters that were measured in this study (Chia et al. 1989). It has been reported that urinary cadmium of 4 μg/g Cr is the critical value for cadmium to have a significant effect on Uα1m, Uβ2m, URBP, and NAG (Roels et al. 1993). There was no measurement of cadmium levels for all the workers. However, we randomly selected a sample of 25 workers and determined their urine cadmium level; the mean was 0.57 μg/g Cr with a range of 0.34–0.98 μg/g Cr. These values were far below the reported urinary cadmium of 4 μg/g Cr that would affect some of the studied renal parameters.
In conclusion, HpyCH4 SNP located in intron 6 may have modifying effects on the relationship between lead exposure and renal function, with individuals carrying the HpyCH4 1-1 genotype being more susceptible to lead toxicity of the kidneys. The mechanism for this is unclear. Further studies are needed to confirm this observation.
We express our appreciation to T.B. Choo, G. Wei, and C.Y. Huak for their generous assistance in the statistical analysis. We thank the management and workers for their participation.
This study was supported by research grant from the Agency for Science, Technology and Research, Biomedical Research Council, Singapore (grant 01/1/21/19/186).
Figure 1 Regression lines of Uβ2m versus PbB by HpyCH4 genotype, adjusted for age, exposure duration, gender, and BMI. Data points are specified by value pairs of blood lead (PbB) and log-transformed renal parameters (Uα1m, Uβ2m, URBP, Ualb).
Figure 2 Regression lines of URBP versus PbB by HpyCH4 genotype adjusted for age, exposure duration, gender, and BMI.
Figure 3 Regression lines of Uα1m versus PbB by HpyCH4 genotype adjusted for age, exposure duration, gender, and BMI.
Figure 4 Regression lines of Ualb versus PbB by HpyCH4 genotype adjusted for age, exposure duration, gender, and BMI.
Table 1 Demographic characteristics of the study population by SNPs.
Gender
Age (years)
Exposure (years)
SNP Genotype Male [n (%)] Female [n (%)] n (%) Mean ± SD n Mean ± SD Range
Msp 1-1 168 (92.8) 58 (92.1) 226 (92.6) 39.5 ± 10.6 214 15.4 ± 10.2 1–41
1-2/2-2 13 (7.2) 5 (7.9) 18 (7.4) 36.4 ± 9.8 18 10.8 ± 9.1 1–27
Rsa 1-1 40 (22.1) 16 (26.2) 56 (23.1) 39.8 ± 11.0 54 15.8 ± 11.2 1–41
1-2 86 (47.5) 28 (45.9) 114 (47.1) 38.4 ± 10.3 107 14.3 ± 9.9 1–39
2-2 55 (30.4) 17 (27.9) 72 (29.8) 39.7 ± 10.7 69 15.0 ± 9.9 1–36
Rsa39488 1-1 41 (23.0) 15 (23.8) 56 (23.2) 40.0 ± 11.0 55 16.0 ± 11.3 1–41
1-2 81 (45.5) 32 (50.8) 113 (46.9) 39.3 ± 10.2 105 14.9 ± 10.0 1–39
2-2 56 (31.5) 16 (25.4) 72 (29.9) 39.0 ± 10.7 69 14.5 ± 9.5 1–36
HpyCH4 1-1 136 (87.2) 55 (91.7) 191 (88.4) 39.2 ± 10.4 184 15.5 ± 10.2 1–41
1-2 20 (12.8) 5 (8.3) 25 (11.6) 40.9 ± 10.7 23 13.9 ± 9.3 1–30
HpyIV 1-1 131 (74.9) 42 (67.7) 173 (73.0) 39.8 ± 10.4 163 15.3 ± 10.2 1–41
1-2/2-2 44 (25.1) 20 (32.3) 64 (27.0) 38.1 ± 10.9 62 14.4 ± 10.3 1–39
Sau3A 1-1 106 (66.7) 31 (51.7) 137 (62.6) 38.3* ± 10.7 131 14.9 ± 10.1 1–39
1-2/2-2 53 (33.3) 29 (48.3) 82 (37.4) 41.4* ± 9.7 77 16.05 ± 10.0 1–41
*p < 0.05.
Table 2 Frequencies of alleles and genotypes for each SNP [% (n)].
SNP ALAD1 ALAD2 1-1 1-2 2-2
Msp 95.9 4.1 92.6 (226) 6.6 (16) 0.8 (2)
Rsa 46.7 53.3 23.1 (56) 47.1 (114) 29.8 (72)
Rsa39488 46.7 53.3 23.2 (56) 46.9 (113) 29.9 (72)
HpyCH4 94.2 5.8 88.4 (191) 11.6 (25) 0
HpyIV 85.2 14.8 73 (173) 24.5 (58) 2.5 (6)
Sau3A 76.9 23.1 62.6 (137) 28.8 (63) 8.7 (19)
The distribution of genotypes and alleles within each SNP has been proven to follow the Hardy-Weinberg equilibrium.
Table 3 Mean of exposure indices and renal parameters.
Biologic parameter na Mean ± SD Minimum–maximum Range
PbB (μg/dL)b 247 24.4 ± 13.6 2.0–66.9 64.9
ALAU (mg/g Cr) 236 0.95 ± 1.55 0.34–3.63 3.30
Sα1m (mg/L) 241 41.8 ± 1.29 20.84–74.25 53.42
Sβ2m (mg/L) 244 1.45 ± 1.31 0.77–2.85 2.09
NAG (U/g Cr)c 236 2.82 ± 0.18 0.10–7.60 7.50
Uα1m (mg/g Cr) 244 6.82 ± 1.91 1.29–27.32 26.03
Ualb (mg/g Cr) 239 7.06 ± 1.73 1.23–34.12 32.89
Uβ2m (mg/g Cr) 237 0.19 ± 2.30 0.02–1.81 1.79
URBP (mg/g Cr) 239 0.16 ± 1.95 0.03–0.86 0.83
Cr, creatinine. Data are geometric mean, except as indicated.
a Numbers differ between tests because some samples were too small for all the tests to be performed.
b Arithmetic mean.
c Square root mean.
Table 4 Mean exposure indices and renal parameters by SNP genotypes.
HpyCH4
HpyIV
Msp
Rsa
Rsa39488
Sau3A
Adjusted meana 1-1 1-2 1-1 1-2/2-2 1-1 1-2/2-2 1-1 1-2 2-2 1-1 1-2 2-2 1-1 1-2/2-2
PbB (μg/dL)b 22.68 17.89 23.32 20.52 22.68 17.89 21.13 21.89 24.22 21.87* 20.43* 25.54* 23.22 20.8
ALAU (mg/g Cr) 1 1 0.954** 1.150** 1.01 0.95 1.07 0.98 0.99 1.03 1.02 0.96 0.93** 1.08**
Sα1m (mg/L) 42.02 43.9 42.32 42.02 42.21 40.97 42.23 41.99 42.24 42.29 42.04 42.58 42.04 42.6
Sβ2m (mg/L) 1.41 1.43 1.4 1.4 1.41 1.49 1.36 1.43 1.4 1.37 1.42 1.46 1.4 1.39
NAG (U/g Cr)c 2.81 2.46 2.78 2.77 2.77 2.76 3.12 2.73 2.66 3.07 2.81 2.51 2.93* 2.53*
URBP (mg/g Cr) 0.15 0.13 0.15 0.15 0.15 0.14 0.16 0.14 0.14 0.16 0.14 0.14 0.15 0.14
Uβ2m (mg/g Cr) 0.18 0.21 0.18 0.19 0.18 0.24 0.18 0.19 0.18 0.18 0.17 0.2 0.18 0.18
Ualb (mg/g Cr) 7.35 6.92 7.374* 7.750* 7.29 7.19 7.37 6.84 8.1 7.55 6.98 7.79 7.6 6.79
Uα1m (mg/g Cr) 6.21 5.29 6.15 6.07 6.11 6.8 6.49 5.69 6.58 6.5 5.72 6.53 5.89 6.36
Data are geometric means adjusted for age, exposure duration, gender, BMI, and PbB, unless otherwise specified.
a Adjusted for age, exposure duration, gender, and BMI.
b Arithmetic mean.
c Square root mean.
*p < 0.05.
**p < 0.01.
Table 5 Models of HpyCH4 regression by renal parameters.
Variable β(95% CI) p-Values
Log URBP
R2 = 0.1473
PbB 0.016 (0.006, 0.027) 0.002
HpyCH4 –0.02 (–0.140, 0.100) 0.742
HpyCH4 × PbB –0.012 (–0.022, –0.003) 0.008
Log Uα1m
R2 = 0.2366
PbB 0.018 (0.008, 0.027) < 0.001
HpyCH4 –0.067 (–0.177, 0.043) 0.233
HpyCH4 × PbB –0.014 (–0.022, –0.005) 0.002
Log Uβ2m
R2 = 0.1417
PbB 0.029 (0.016, 0.042) < 0.001
HpyCH4 0.047 (–0.103, 0.197) 0.537
HpyCH4 × PbB –0.024 (–0.035, –0.012) < 0.001
Log Ualb
R2 = 0.1417
PbB 0.014 (0.005, 0.023) 0.002
HpyCH4 0.000 (–0.103, 0.103) 0.998
HpyCH4 × PbB –0.011 (–0.019, –0.003) 0.007
CI, confidence interval.
The model adjusts for age, gender, BMI, and exposure duration. 0 represents the HpyCH4 1-1; 1 represents HpyCH4 1-2. The reference group is HpyCH4 1-1; the β-coefficient of PbB is the slope for the association between PbB and renal parameters in participants with this genotype. The corresponding slope in workers with ALAD 1-2 is the sum of the β-coefficient of PbB and that of HpyCH4 × PbB in each model (i.e., in the URBP model, the β-coefficient of PbB for HpyCH4 1-1 group is 0.016; the corresponding βfor HpyCH4 1-2 is 0.004 = 0.016 + –0.012). p-Values for the HpyCH4 × PbB reflect the statistical significance of the difference between the slopes of the regression line for HpyCH4 1-1 and for HpyCH4 1-2 workers.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7970ehp0113-00131816203240ResearchBiomarker Measurements in a Coastal Fish-Eating Population Environmentally Exposed to Organochlorines Ayotte Pierre 12Dewailly Éric 1Lambert George H. 3Perkins Sherry L. 4Poon Raymond 5Feeley Mark 6Larochelle Christian 1Pereg Daria 11 Unité de Recherche en Santé Publique, Centre Hospitalier Universitaire de Québec, Université Laval, Québec, Canada2 Laboratoire des Biomarqueurs, Institut National de Santé Publique du Québec, Québec, Canada3 Environmental and Occupational Health Sciences Institute, Robert Wood Johnson Medical School, University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey, USA4 Division of Biochemistry, Ottawa Hospital, Ottawa, Ontario, Canada5 Environmental Health Science Bureau, and6 Bureau of Chemical Safety, Health Canada, Ottawa, Ontario, CanadaAddress correspondence to P. Ayotte, Unité de Recherche en Santé Publique, Centre Hospitalier Universitaire de Québec, 945 Ave. Wolfe, Québec, QC, Canada G1V 5B3. Telephone: (418) 650-5115 ext. 4654. Fax: (418) 654-2148. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 1 6 2005 113 10 1318 1324 27 1 2005 1 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. The Lower North Shore region of the St. Lawrence River is home to a fish-eating population that displays an unusually high body burden of several organochlorines, including polychlorinated biphenyls (PCBs) and dioxin-like compounds (DLCs). We measured biomarkers indicative of liver enzyme induction and investigated the relationship with organochlorine body burden in adult volunteers from this population. We determined plasma concentrations of PCBs and chlorinated pesticides by high-resolution gas chromatography (HRGC) with electron capture detection. DLC concentrations were measured by the dioxin-receptor chemically activated luciferase expression (DR-CALUX) assay and in a subset of participants, by HRGC/high-resolution mass spectrometry. We measured cotinine, d-glucaric acid, and porphyrins in morning urine samples and determined liver CYP1A2 activity in vivo using the caffeine breath test. Neither DLC concentrations as measured by the DR-CALUX nor PCB-153 concentrations, the latter representing total PCB exposure, were correlated with biomarkers of effects. Smoking (morning urinary cotinine concentration) was positively related to CYP1A2 activity as measured by the caffeine breath test (p < 0.01). Liver CYP1A2 activity was in turn negatively correlated with PCB-105:PCB-153 and PCB-118:PCB-153 congener ratios (p < 0.05). Hence, despite the relatively high body burden of PCBs and DLCs in this population, only smoking had a significant correlation with biomarkers of hepatic enzyme induction. Our data are consistent with smoking-induced liver CYP1A2 activity altering heme metabolism and increasing the biotransformation of mono-ortho PCB congeners.
cytochrome P450 CYP1A2d-glucaric aciddioxinsenzyme inductionfood chainorganochlorine insecticidespolychlorinated biphenylsporphyrinssmoking
==== Body
Organochlorines (OCs) constitute a family of persistent, lipid-soluble compounds that includes industrial chemicals [e.g., polychlorinated biphenyls (PCBs), hexachlorobenzene (HCB)], pesticides (e.g., DDT, methoxychlor, mirex), and by-products of combustion and various industrial processes [e.g., polychlorodibenzo-p-dioxins (PCDDs) and polychlorodibenzofurans (PCDFs)]. These compounds are released into the environment at southern latitudes and transported to northern regions by long-range oceanic and atmospheric transport processes (Barrie et al. 1992; Macdonald et al. 2000). OCs bio-accumulate in adipose tissues of various species from northern aquatic ecosystems. Biomagnification occurs in food webs, leading to relatively elevated concentrations in species located at the highest trophic levels, including human populations that consume large amounts of sea products (Dewailly et al. 1993; Kiviranta et al. 2002; Sjödin et al. 2000).
The Lower North Shore of the St. Lawrence River, a remote coastal region of Québec, consists of 15 communities spread over a 400-km shoreline extending from Kegaska to Blanc Sablon (Figure 1). A large proportion of the 6,000 residents relies on fishing for subsistence and consequently consumes large amounts of seafood (Dewailly et al. 1992). Results from surveys conducted since 1990 have indicated that this population is highly exposed to PCBs and to dioxin-like compounds (DLCs) such as 2,3,7,8-substituted PCDDs and PCDFs as well as nonsubstituted and mono-ortho–substituted PCBs, compared to the southern Québec population (Dewailly et al. 1992; Muckle et al. 1998; Ryan et al. 1997). Food items that contribute the most to this exposure are sea-bird eggs and possibly cod and seal liver (Dewailly et al. 1992; Ryan et al. 1997).
Exposure to DLCs produces a wide variety of biologic and toxic effects such as teratogenesis, immunosuppression, and tumor promotion, most of them dependent on the activation of the aryl hydrocarbon receptor (AhR) (Mimura and Fujii-Kuriyama 2003; Poland and Knutson 1982; Safe 1990; Van den Berg et al. 1998). We previously reported on a group of 25 individuals from the Lower North Shore of the St. Lawrence River that had a mean total DLC concentration of 250 ng of 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) toxic equivalents (TEQ)/kg plasma lipids, including PCDDs, PCDFs, and non-substituted and mono-ortho PCBs (Ryan et al. 1997). Assuming a 20% body fat content in humans, the corresponding body burden would be 50 ng TEQ/kg body weight. In laboratory animals exposed to TCDD, adverse effects (hormonal, reproductive, and developmental) have been observed at body burdens in the range of 28–73 ng TCDD/kg body weight (van Leeuwen et al. 2000), hence suggesting that DLC exposure in this fish-eating population might induce adverse health effects.
To refine the assessment of the health risks possibly related to the body burden of DLCs and other OCs in this fish-eating population, investigators can measure early biologic events such as the induction of drug-metabolizing enzyme activities. Activation of genes coding for biotransformation enzymes such as cytochrome P4501A1 (CYP1A1) and CYP1A2 is a well-known consequence of TCDD binding to the AhR (Walker et al. 1999; Whitlock 1999). Liver CYP1A2 activity in humans can be measured in vivo by a noninvasive method, the caffeine breath test (CBT; Kotake et al. 1982). In this test, CYP1A2 activity is monitored by following the appearance of 13CO2 in exhaled breath resulting from the oxidative demethylation of [3-13C-methyl]caffeine (Abraham et al. 2002; Brambilla et al. 1995; Kotake et al. 1982; Lambert et al. 1990). The concentration of d-glucaric acid in urine has also been proposed as a biomarker of exposure to xenobiotics (Brewster 1988; Hogue and Brewster 1991; Poon et al. 1993). Glucuronidation is a major biotransformation pathway for a wide variety of xenobiotics and drugs, and its induction results in increased excretion of d-glucaric acid (Brewster 1988). Urinary d-glucaric acid excretion has also been shown to increase in humans exposed to OC pesticides (Hunter et al. 1972; Notten and Henderson 1977), PCBs (Maroni et al. 1984), and TCDD (Ideo et al. 1982). Urinary porphyrin excretion can also be altered by exposure to xenobiotics such has PCBs (Colombi et al. 1982), polybrominated biphenyls (Strik et al. 1979), and TCDD (Hoffman et al. 1986; Jung et al. 1994). Provided sufficient exposure dose, all these chemicals induce chronic disturbances in hepatic synthesis of porphyrins and thus lead to excess total porphyrin excretion and skin symptoms in the final stage (Hogue and Brewster 1991). Presumably, these chemicals would also alter urinary porphyrin profile at early stages before overt toxicity, and therefore, the latter constitutes a much more sensitive biomarker than total porphyrin determination (Johnson et al. 1988).
In the present study, we measured concentrations of porphyrins and d-glucaric acid in urine samples and performed the CBT in volunteers from two communities located on the Lower North Shore of the St. Lawrence River. We measured plasma lipid concentrations of PCBs and chlorinated pesticides by high-resolution gas chromatography (HRGC) with electron capture detection (ECD) and DLCs by the dioxin-receptor chemical-activated luciferase gene expression (DR-CALUX) bioassay. Finally, because tobacco smoking can induce liver enzyme activities, smoking status was assessed by questionnaire and validated using urinary cotinine measurements.
Materials and Methods
Study population and measurements.
We recruited participants in this study from two Lower North Shore settlements: Tête-à-la-Baleine and La Tabatière (Figure 1). Potential subjects were selected from the list of those who participated in a previous survey (Dewailly et al. 1992), and therefore, their concentration of OCs in plasma lipids was already known. We tried to recruit individuals with both high and low exposure to PCBs. Our research nurse contacted potential subjects by telephone and asked interested individuals to visit their local health center the week after for an information meeting. After a brief medical examination, subjects with cardiac arrhythmia, uncontrolled hypertension, or with a history of caffeine intolerance were excluded from the study. An 80-mL venous blood sample was drawn from a cubital vein and collected in Vacutainer (Becton Dickinson, Franklin Lakes, NJ, USA) tubes (10 mL) containing EDTA as the anticoagulant. Tubes were centrifuged (10 min) immediately after blood sampling, and plasma samples were kept frozen (−15°C) in plain Venoject tubes (10 mL) until OC analysis at the Laboratoire de Toxicologie (Institut National de Santé Publique du Québec, Québec, QC, Canada). Volunteers were also asked to collect their first urine on the next morning in a plastic vial containing sodium bicarbonate (0.5 g/10 mL urine). Urine samples were kept frozen (−15°C) until analysis for d-glucaric acid and cotinine (Environmental Health Science Bureau, Health Canada, Ottawa, Ontario, Canada) and porphyrins and creatinine (Division of Biochemistry, Ottawa Hospital, Ottawa, ON, Canada). Finally, volunteers were asked to fast for 6 hr and not to drink or eat methylxanthine-containing foods (chocolate, coffee, tea, cola) for 12 hr before the CBT, which was conducted the next morning.
We obtained anthropometric measurements (weight, height), and administered a short questionnaire to obtain information on current medication and smoking and drinking habits. We obtained informed consent from participants before administering the questionnaire and collecting biologic samples. This study was approved by the Ethics Committee from Laval University Medical Center (Centre Hospitalier Universitaire de Québec).
Caffeine breath test.
The CBT was administered as previously described by Kotake et al. (1982). Briefly, [3-13C-methyl]caffeine (Cambridge Isotopes, Cambridge, MA, USA) dissolved in water (10 mg/mL) was administered orally at a dose of 3 mg/kg body weight (up to a maximum of 200 mg). Subjects were asked to remain seated for 15 min before the test and during the 2-hr period after caffeine administration. Before drinking the caffeine solution, volunteers exhaled into an air collection device made of a modified 20-mL syringe. This device allows the subject to exhale the air contained in the lower respiratory tract and to trap the alveolar air that is needed for the CO2 analysis. Air samples were also collected at 30, 60, 90, and 120 min postcaffeine administration. Duplicate samples were taken at each time. Air samples (16 mL) were transferred into evacuated glass containers for transport and storage to the laboratory. After cryogenic purification of the CO2 present in exhaled air samples, the 13CO2:12CO2 ratio was determined by differential gas-isotope ratio mass spectrometry according to Schoeller and Klein (1979). The quantity of labeled CO2 exhaled was expressed as percent labeled dose exhaled per hour (Schneider et al. 1978). The excess 13CO2-exhaled/mmol 12CO2 for the dose was determined and multiplied by the basal CO2 production rate (assuming 300 mmol/m2 of body surface per hour) (Schneider et al. 1978; Schoeller and Klein 1979). Results were expressed as the 2-hr cumulative exhalation of labeled CO2, because it is the best monitor of caffeine clearance in the adult (Kotake et al. 1982).
Urine analyses.
Porphyrins (hepta-, hexa-, pentacarboxylic porphyrins; uroporphyrins; and coproporphyrins) in urine samples were measured by high-performance liquid chromatography with a fluorescence spectrophotometer (Johnson et al. 1988). The detection limit is 0.5 pmol (25 nmol/L). The within-run coefficients of variation for 25 nmol/L and 300 nmol/L were 4–12% and 3–5%, respectively.
d-Glucaric acid was measured by high-performance liquid chromatography with an ultraviolet detector after pretreatment of urine with boronic acid gel to remove interfering substances such as l-ascorbic acid and d-glucuronic acid (Poon et al. 1993). The detection limit of this method is 10 μmol/L d-glucaric acid, and the run-to-run precisions were 9.1 and 7.7% at concentrations of 60 and 310 μmol/L, respectively.
Smoking has been shown to induce CYP1A2 activity in humans. Kotake et al. (1982) reported that the 2-hr cumulative exhalation of 13CO2 for smokers was almost 2-fold the value for nonsmoking controls. We measured cotinine concentration in urine samples by an ELISA method (COTI-TRAQ, Serex Inc., NJ, USA). Creatinine concentration was determined by a modified Jaffe reaction, using an automated colorimetric analyzer.
Plasma OC analyses.
Concentrations of ten chlorinated pesticides or metabolites (aldrin, dieldrin, cis-chlordane, trans-chlordane, p,p′-DDE, HCB, mirex, cis-nonachlor, trans-nonachlor, oxychlordane) and 13 PCB congeners (IUPAC nos. 28, 52, 99, 101, 118, 128, 138, 153, 156, 170, 180, 183, 187) were determined by HRGC/ECD. Plasma samples were mixed with an aqueous solution of ammonium sulfate and ethanol (1:1) and then extracted with hexane. Extracts were concentrated and cleaned by elution through two columns containing activated Florisil. We performed the analysis by dual-column gas chromatography, using an HP 5890 gas chromatograph equipped with two capillary columns (HP Ultra 1 and Ultra 2, both 50 m long, 0.2-mm inner diameter, 0.33-μm film thickness) and twin electron capture detectors (Hewlett Packard, Palo Alto, CA, USA). We achieved identification and quantification of the specific congeners by comparing responses and retention times with calibration standards for each analyte of interest. Recovery of OCs varied between 90 and 103%. The detection limit of the method was 0.05 μg/L for all compounds. The within-run coefficients of variation for PCB congeners and chlorinated pesticides ranged from 5.1 to 13.5% (at 0.4 μg/L).
OC concentrations were expressed on a plasma lipid basis because blood samples were not collected after a fasting period, which may affect plasma OC concentrations (Phillips et al. 1989). To adjust OC concentrations on a lipid basis, we used standard enzymatic procedures to determine total and free cholesterol and triglycerides. We measured phospholipids containing choline by the enzymatic method of Takayama et al. (1977), using a commercial kit (Wako Pure Chemicals Industries, Richmond, VA, USA). We estimated total plasma lipids (TL) by adding the concentrations of cholesterol esters [obtained by subtracting free cholesterol from total cholesterol (TC)], free cholesterol (FC), triglycerides (TG), and phospholipids (PL), according to the following formula (Phillips et al. 1989):
Quantification of 2,3,7,8-chloro-substituted PCDD/PCDF congeners and coplanar non-ortho PCB congeners (IUPAC nos. 77, 126, and 169) in plasma samples was performed by AXYS Analytical Services Ltd. (Sydney, BC, Canada). A 5–10 g plasma sample was spiked with labeled surrogate standards (13C-labeled analogs of the target PCDDs, PCDFs, and coplanar PCBs) and extracted with an ethanol, hexane, and saturated ammonium sulfate mixture (1:1:1), and the hexane extract was washed with concentrated sulfuric acid and distilled water. The extract was then dried over anhydrous sodium sulfate and cleaned up using the following column chromatography procedures: a) layered acidic-basic silica gel column, eluted with hexane; b) activated alumina column, eluted with hexane (discarded) followed by elution with 1:1 dichloromethane: hexane (collected); c) carbon on celite column (4.75% carbon), eluted with hexane (discarded), followed by 1:1 dichloromethane: cyclohexane and then 10:1 ethyl acetate:toluene (collected together for coplanar PCB congener analysis), followed by inversion of the column and elution with toluene (for collection of PCDDs and PCDFs); d) activated alumina column, each fraction (coplanar PCB and PCDD/PCDF) applied to a new alumina column and eluted with hexane (discarded) and 1:1 dichloromethane:hexane (collected for gas chromatograph/mass spectrometry analysis). In preparation for gas chromatograph/mass spectrometry analysis, an aliquot of 13C-labeled recovery standard was added to each fraction collected. Each fraction was analyzed by HRGC/high-resolution mass spectrometry (HRMS) using a VG Autospec Ultima high-resolution mass spectrometer equipped with an HP5890 gas chromatograph (Hewlett Packard, Palo Alto, CA, USA). The chromatographic separation was carried out using an Agilent/J&W DB-5 capillary column (60 m long, 0.25 mm inner diameter, 0.1 μm film thickness; Agilent Technologies Canada Inc., Mississauga, ON, Canada).
DR-CALUX assay.
Plasma samples (2 mL) were mixed with an aqueous solution of ammonium sulfate and ethanol (1:1) and then extracted with hexane. Extracts were concentrated and cleaned by elution through two columns containing activated Florisil. The cleaned extracts were dissolved in dimethyl sulfoxide (DMSO) for CALUX measurements in 96-well plates using H4IIE.Luc cells (kindly donated by A. Brouwer, Vrije Universiteit, Amsterdam, the Netherlands). Cells were grown in Dulbecco’s modified Eagle medium with 10% fetal calf serum (Wisent Inc., St. Bruno, QC, Canada) in 96-well cell culture plates (Sarstedt Inc., Montreal, QC, Canada) to 60–80% confluence and exposed during 24 hr in triplicate to the plasma extracts, TCDD standards (AccuStandard, New Haven, CT, USA), or DMSO alone (vehicle; final concentration, 0.5% vol/vol). Cells were then washed with PBS, lysed in 50 μL lysis buffer (Passive Lysis 5× buffer; Promega, Madison, WI, USA) for 30 min, and the lysate was frozen at 80°C. For measurement of luciferase activity, samples were thawed, and 20 μL lysate was pipetted into a 96-well microtiter plate (Dynex Technologies Ltd., Worthing, UK). The plate was placed in a Lmax luminometer (Molecular Devices Corporation, Sunnyvale, CA, USA), and the following sequence of events was programmed for each well: a) injection of 100 μL luciferin assay mix (Promega); b) 4-sec delay; c) light production measured over a 10-sec period. The limit of detection was approximately 5 pg TEQ/g lipid for a 2-mL plasma sample containing 7 g lipids/L.
Statistical analysis.
Distributions of values for OC concentrations as well as urinary glucaric acid and porphyrins concentrations were skewed. Hence, we included the median in descriptive statistics. We limited statistical analyses to contaminants that were detected in > 50% of the samples. For contaminants and urinary measurements, a concentration equal to half the detection limit was assumed for samples with concentrations below the detection limit. Relations between biomarkers and contaminants were assessed using Spearman’s correlation coefficients. We used the Mann-Whitney U-test to compare biomarker values between age groups (≤45, > 45 years), smoking categories (smokers, nonsmokers), and alcohol consumption categories (occasional drinkers, abstainers). Nonparametric analyses were selected because of the skewed distributions and the small sample sizes. The level of statistical significance was set at 0.05. We used the SPSS for Windows statistical software package (version 11.5; SPSS Inc., Chicago, IL, USA) to perform all statistical analyses.
Results
Characteristics of participants.
Forty volunteers (23 women, 17 men) were recruited in the two Lower North Shore communities. The mean age of the participants was 47 years (range, 25–75 years). Ten subjects were on continuous medication at the time of the study, including two taking anticonvulsants (phenobarbital and phenytoin). The group consisted of 17 smokers and 23 nonsmokers, as determined by self-declared cigarette consumption and urinary cotinine results (urine cotinine concentration > 0.8 μg/mL). Fourteen participants drank alcohol occasionally, whereas 26 were abstainers. Mean body mass index was 27.0 (SD = 4.0).
Biomarkers of exposure.
Plasma concentrations of the various OCs that were detected in most participants are presented in Table 1. Major PCB congeners were congeners 153, 180, and 138. These three congeners represented 68% of the total PCB concentration in plasma lipids. Simple correlation analysis revealed strong associations (Spearman’s r > 0.78; p < 0.001) between PCB-153 concentrations and those of congeners 99, 138, 156, 170, 180, 183, and 187. Correlations between PCB-153 concentrations and those of mono-ortho PCBs 105 and 118 were somewhat lower (Spearman’s r = 0.52 and 0.58, respectively; p < 0.001). Among chlorinated pesticides, p,p′-DDE was the most abundant compound in plasma, followed by HCB, trans-nonachlor, oxychlordane, and mirex, which showed much lower concentrations (> 10-fold).
DLC concentrations determined by the DR-CALUX assay were moderately correlated with total PCB concentrations (Spearman’s r = 0.45; p = 0.003). Similar correlations were noted between DLC concentrations and either PCB-153 concentrations or total concentrations of mono-ortho congeners (105, 118, and 156) expressed in nanogram TEQs per kilogram plasma lipids. In a subset of participants (n = 15), concentrations of PCDDs/PCDFs and non-ortho coplanar PCBs were measured by HRGC/HRMS and expressed as total TEQ concentrations using the World Health Organization’s toxic equivalency factors (Van den Berg et al. 1998). The latter was significantly correlated with the TEQ concentration measured by the DR-CALUX assay in the same samples (r = 0.66; p = 0.008; Figure 2).
Biomarkers of hepatic enzyme induction.
Results for hepatic enzyme induction biomarkers are presented in Table 2. d-Glucaric acid was detected in urine samples from 95% of participants (38 of 40). The coefficient of variation (CV) exceeded 100% for this parameter, indicating that the distribution was highly skewed. Two participants, one being treated with phenobarbital and another with phenytoin, exhibited the two highest concentrations (26.0 and 14.9 mmol/mol creatinine, respectively). Eliminating these two values reduced the mean from 4.4 to 3.6 mmol/mol creatinine and the CV to 61%.
Coproporphyrins were present in all urine samples, whereas uroporphyrins were above the limit of detection in only 15% of samples (6 of 40). Penta-, hexa-, and heptaporphyrins were not detected in any urine sample. The distribution of coproporphyrin concentrations was slightly skewed. The highest value was observed for the individual taking phenobarbital. Eliminating the two subjects on anticonvulsant medication slightly reduced the mean (from 11.5 to 11.1 μmol/mol creatinine) and the CV (from 30 to 28%).
The CBT was successfully administered to 21 participants. This parameter followed a normal distribution with the mean very close to the median value and a CV of 41%. Only one subject that underwent the CBT was on antiepileptic medication, and eliminating this subject did not substantially modify the statistical descriptors of the distribution.
We examined relations between biomarkers of effects and found that urinary d-glucaric acid concentrations were weakly correlated with urinary coproporphyrin concentrations (Spearman’s r = 0.36; p = 0.02), whereas the latter were moderately correlated with the CBT (r = 0.57; p = 0.006). d-Glucaric acid concentrations were not correlated with CBT results.
Biomarkers of exposure versus biomarkers of hepatic enzyme induction.
We explored the relationship between biomarkers of exposure and biomarkers of hepatic enzyme induction. To this end, we used correlation analyses after excluding results from the two individuals taking anticonvulsants, in view of the known enzyme-inducing effects of these agents (phenobarbital and phenytoin). Urinary cotinine concentration was positively related to the CBT (Table 3). We selected PCB-153 to represent the group of persistent PCB congeners in those analyses and did not observe statistically significant correlations with any biomarkers of effects. DLC concentrations as measured by the DR-CALUX were also not correlated with biomarkers of effects. Age, gender, and alcohol consumption were not associated with the biomarkers of effects (data not shown).
Next, we tested correlations between effects biomarkers and concentrations of mono-ortho PCB congeners 105 and 118, which did not exhibit strong collinearity with PCB-153. We found moderate inverse correlations between these mono-ortho congeners and the results from the CBT, which reached statistical significance for PCB-105 (p = 0.05). We hypothesized that the latter results may indicate increased biotransformation of PCB-105 and PCB-118 in participants with increased liver CYP1A2 activity. We further tested correlations between CYP1A2 activity and PCB-105: PCB-153 and PCB-118:PCB-153 concentration ratios to take into account the difference in PCB exposure between participants, which is best represented by concentrations of PCB-153 (a very persistent congener). We observed stronger negative correlations between CYP1A2 activity and these congener ratios than those noted above (Figure 3), further suggesting that PCB-105 and PCB-118 are biotransformed at an increased rate in participants exhibiting high CYP1A2 activities. Similar correlation coefficients were observed after stratifying for age (≤45, > 45 years) and gender (data not shown).
Biomarkers of effects and smoking status.
Results for biomarkers of effects were stratified according to the smoking status because of the known effect of smoking on liver enzyme activities (Table 4). There was a tendency toward higher CBT values in smokers than in non-smokers, with the median value of smokers being 70% higher than that of nonsmokers (p = 0.07). The other biomarkers were not influenced by the smoking status of the participants.
Discussion
We measured biomarkers of effects, more specifically, markers of hepatic microsomal enzyme induction, in residents of a remote coastal region of Québec who rely partly on sea products for sustenance. Our hypothesis was that biomarkers of hepatic enzyme induction would be increased as a result of the chronic exposure to OCs in this fish-eating population. Participants displayed high concentrations of several persistent, lipophilic compounds that are biomagnified in aquatic food chains such as PCBs, chlorinated pesticides, and DLCs. However, despite this high exposure, we did not find any statistically significant relations between biomarkers of OC exposure and markers of hepatic enzyme induction in this population.
Urinary d-glucaric has been suggested as a biomarker of effect induced by PCB exposure. Apostoli et al. (2003) reported a median concentration of 5.5 mmol/mol creatinine, with values ranging from 1.7 to 12.4 mmol/mol creatinine in 73 subjects who were exposed to PCBs through eating farm produces contaminated by runoff water from a nearby dielectric fluid plant. Somewhat lower values were obtained in our group: the median was 3.4 mmol/mol creatinine and the range spread from < 0.8 to 9.8 mmol/mol creatinine, when we excluded the two highest values displayed by subjects taking enzyme-inducing anticonvulsants. In results similar to ours, Apostoli et al. (2003) did not observe any relation between plasma PCB levels and urinary d-glucaric acid concentrations; smoking was also not related to this biomarker of enzyme induction. Median total PCB plasma concentration in their participants was 27 μg/L (range, 1.1–394 μg/L), compared with 18 μg/L (range, 2.5–61 μg/L) in our group. In workers with higher PCB exposure (total PCB plasma concentrations ranging from 88 to 1,359 μg/L), Maroni et al. (1984) noted a higher d-glucaric concentration than in an unexposed comparison group. However, the correlation of PCB plasma concentrations with d-glucaric concentrations was not statistically significant. Children and adults from Seveso, Italy, who were highly exposed to TCDD after its release to the atmosphere because of a malfunction in a chemical plant showed a statistically significant increase of d-glucaric acid elimination compared to the control groups, even 3 years after the accident (Ideo et al. 1985). Hence, although d-glucaric acid concentrations are elevated in urine samples of individuals who have been highly exposed to dioxins, it does not appear to be sensitive enough in situations involving low-level environmental PCB exposure.
With regard to urinary porphyrins, coproporphyrins were detected in all participants, whereas the other porphyrins were rarely if ever present in concentrations exceeding the detection limit of the analytical method (25 nmol/L). Furthermore, we did not observe any correlation between OCs and urinary coproporphyrin levels. Provided a sufficient exposure dose, several halogenated aromatic hydrocarbons can lead to increased urinary porphyrin excretion (Hogue and Brewster 1991). However, although acute poisoning and high occupational exposure to these compounds are clearly porphyrogenic (Daniell et al. 1997), environmental exposure has not been linked to porphyrinuria. A recent study conducted among 241 residents of Flix (Catalonia, Spain), a village located near an electrochemical factory where high atmospheric levels of HCB were detected, revealed lower urinary levels of coproporphyrins in participants exhibiting the highest HCB plasma levels (Sunyer et al. 2002). With regard to DLCs, Calvert et al. (1994) did not find an increased risk of showing out-of-range urinary uroporphyrin or coproporphyrin concentrations in U.S. workers exposed to TCDD, compared to referents. Moreover, a subgroup of 10 workers who had a mean serum TCDD concentration of 559 pg/g lipids (range, 769–1,593 pg/g lipids) at the time of the study had similar coproporphyrin levels as those of the referents (Calvert et al. 1994). Considering the lower exposure to DLCs documented in our fish-eating population (range, 37–287 pg TEQs/g lipids), the observed lack of porphyrogenic effect in relation to DLC exposure is consistent with evidence in the literature.
During the last 15 years, the CBT has been used to assess in vivo liver CYP1A2 activity in various population subgroups. Hepatic microsomal caffeine N-3-demethylation, the initial major step in caffeine biotransformation in humans, is selectively catalyzed by CYP1A2 (Butler et al. 1989). Among subjects without any known exposure to pharmaceutical or environmental enzyme inducers, CYP1A2 activity in smokers is 1.5–2 times that of non-smokers (Abraham et al. 2002; Kotake et al. 1982; Lambert et al. 1986). A group of 51 Michigan farmers, who were exposed to polybrominated biphenyls when a flame retardant was accidentally added to dairy cattle feed, showed a greater median CYP1A2 activity that that of unexposed Illinois residents (Lambert et al. 1990). The authors also reported a weak positive correlation between serum polybrominated biphenyl concentrations and CBT values, although close inspection of the data indicates that this correlation is mainly due to five individuals with high plasma concentrations (> 300 μg/L). The median CBT value for Taiwanese who were accidentally exposed to PCB/PCDF through the ingestion of contaminated rice oil (Yu-Cheng patients) was 440% higher than that of the unexposed control group (Lambert et al. 1992). In contrast, subjects exposed to TCDD after the Seveso incident did not exhibit high CBT values when tested 16–18 years after their exposure (Brambilla et al. 1995). Initially, concentrations of TCDD up to 6,300 ppt had been measured in plasma samples from these individuals, but contemporary levels were not mentioned by the authors. Abraham et al. (2002) also used the CBT to measure CYP1A2 activity in two women highly exposed to TCDD, one man moderately exposed, and 50 control subjects (30 nonsmokers and 20 smokers). Results indicated that in the women with very high TCDD exposure (> 10,000 ppt), CYP1A2 activity (as measured by the CBT) was at least 5 times the mean activity observed in non-smoking controls. The individual moderately exposed (1,000 ppt) exhibited a CYP1A2 activity similar to that of smokers in the control group, and at the high end of values displayed by nonsmoking controls (Abraham et al. 2002). With total DLC concentrations not exceeding 300 ppt in our participants, we conclude that their exposure level was too low to induce liver CYP1A2 activity above baseline, as measured by the CBT.
We observed a moderate correlation between liver CYP1A2 activities (CBT values) and urinary coproporphyrin concentrations in our participants. In the study by Sunyer et al. (2002) discussed above, smokers had a higher concentration of coproporphyrin III, which was the major coproporphyrin in urine samples. The authors mentioned that this finding agreed with a possible indirect effect of cigarette smoking through CYP1A2 induction. CYP1A2 has been involved in disturbances of porphyrin metabolism in mice but not in humans (Gorman et al. 2002; Sinclair et al. 2000). Our findings do support a possible link between altered porphyrin metabolism and liver CYP1A2 activity in humans.
A major strength of our study is the use of a cell-based assay, the DR-CALUX assay, to measure the total concentration of DLCs in plasma samples of participants. A moderate correlation was observed between results from this bioassay and the analytical chemistry method, indicating that the DR-CALUX assay is indeed responding to AhR agonists extracted from plasma samples. We did not expect a strong correlation with the analytical chemistry data because the bioassay integrates the contributions from all AhR agonists extracted from plasma, including several that are not measured by the HRGC/HRMS method that is, xenobiotics such as polycyclic aromatic hydrocarbons (Seidel et al. 2002), dietary components such as flavonoids (Amakura et al. 2003), or endogenous compounds such as bilirubin and biliverdin (Phelan et al. 1998). In addition, mixtures of DLCs extracted from the plasma may not induce responses that are additive in the DR-CALUX, unlike the assumption made by summing the contributions of all DLCs measured by analytical chemistry to yield a total TEQ concentration (Safe 1990).
Because of the small number of participants in this study, one could question its power to detect associations between effect biomarkers and exposure variables. With 20 subjects, power was sufficient to detect the fairly strong positive association between smoking and liver CYP1A2 activity (urinary cotinine concentrations vs. CBT values, r = 0.60; p = 0.005). In contrast, correlation coefficients between biomarkers of hepatic enzyme induction and biomarkers of exposure (DLCs and PCB-153) ranged from 0.07 to 0.23, indicating weak associations between these variables at best. Although we cannot rule out an effect of OC exposure on hepatic enzyme activity, our results suggest that, in this population, OC exposure is less important than smoking as an inducing factor of CYP1A2 activity. Similar results were obtained by Pereg et al. (2002), who measured CYP1A1 activity in placenta samples obtained from Inuit women giving birth in Nunavik (northern Québec, Canada). These authors noted a statistically significant relationship between placental CYP1A1 activity and smoking but not with OC body burden, hence suggesting, as in the present study, that environmental OC exposure was not high enough in this population to induce AhR-mediated effects.
The inverse correlation noted in the present study between liver CYP1A2 activity and plasma concentrations of PCB-105 and PCB-118 is of interest for two reasons. First, it supports a role for CYP1A2 in mediating the biotransformation of these mono-ortho congeners in humans. Brown et al. (1989) previously reported a particular profile of PCB congeners in plasma samples of PCDF-exposed individuals that featured relatively low concentrations of these mono-ortho congeners compared with concentrations of di-ortho congeners. Second, the negative relation of CYP1A2 to mono-ortho PCBs emphasizes the importance of considering other factors linked to CYP1A enzyme activities as possible causal agents whenever an association between plasma concentrations of these mono-ortho congeners and disease is observed in epidemiologic studies. Indeed, we expect plasma concentrations of these congeners in an individual to be influenced by dietary, environmental, or lifestyle exposures to compounds that either induce (i.e., polycyclic aromatic hydrocarbons in cigarette smoke) or inhibit CYP1A enzymes, or by functional genetic polymorphisms for these enzymes. Hence, the plasma concentration of mono-ortho PCBs might be a surrogate of CYP1A enzyme activity in the individual, the latter being directly or indirectly linked to the disease. In a recent case–control study in which we noted an association between breast cancer risk and plasma concentrations of mono-ortho PCBs (Demers et al. 2002), we speculated that the association could be explained by CYP1A enzyme activities in women altering both plasma concentrations of mono-ortho PCBs and estradiol levels, the latter being the causal agent.
In summary, we found no relation between biomarkers of OC exposure and markers of hepatic enzyme induction in this highly exposed group of fish eaters from the Lower North Shore of the St. Lawrence River. Our results suggest that smoking induces liver CYP1A2 activity, which in turn alters porphyrin metabolism and increases the biotransformation of mono-ortho PCBs.
Many thanks to J.-P. Weber, A. Leblanc, and É. Pelletier from the Laboratoire de Toxicologie of the Institut National de Santé Publique du Québec for performing organochlorine analyses.
This study was supported by Health Canada’s Great Lakes Health Effects and St. Lawrence Vision 2000 Programs. The authors declare they have no competing financial interests.
Figure 1 The Lower North Shore region of the St. Lawrence River (Québec, Canada).
Figure 2 Correlation between concentrations of DLCs in plasma lipids determined by the DR-CALUX bioassay and those determined by HRGC/HRMS. r = 0.66; p = 0.008
Figure 3 Inverse correlations between liver CYP1A2 activity (CBT values) and plasma concentrations of (A) PCB-105 (r = −0.62; p = 0.003) and (B) PCB-118 (r = −0.53; p = 0.02) in 20 residents of the Lower North Shore of the St. Lawrence River.
Table 1 Plasma OC concentrations in 40 residents of fish-eating communities along the Lower North Shore of the St. Lawrence River (Québec, Canada).
OC Median Arithmetic mean SD Minimum Maximum
PCBs
PCB-99 127 137 80 12 308
PCB-105 32 37 25 < 7a 107
PCB-118 145 167 103 32 437
PCB-138 533 553 274 36 1,164
PCB-153 766 817 398 90 1,668
PCB-156 87 91 51 15 218
PCB-170 151 167 91 24 359
PCB-180 629 645 361 85 1,447
PCB-183 64 70 35 < 7a 136
PCB-187 177 194 94 17 378
Total PCBsc 2,820 2,897 1,372 334 5,880
Chlorinated pesticides
p,p′-DDE 1,086 1,681 1,576 56 6,262
HCB 104 101 42 35 223
Oxychlordane 43 53 48 < 7b 182
Mirex 46 47 32 < 7b 115
trans-Nonachlor 87 98 47 16 223
DLCsc 93 102 57 37 287
PCBs and chlorinated pesticides were analyzed by HRGC/ECD and are reported in units of microgram per kilogram lipids.
a The limit of detection is 0.5 μg/L, which corresponds to approximately 7 μg/kg lipids.
b Sum of 13 congeners.
c DLCs were determined by the DR-CALUX bioassay and are reported as nanogram TEQs per kilogram lipids.
Table 2 Biomarkers of effects in 40 residents of fish-eating communities along the Lower North Shore of the St. Lawrence River (Québec, Canada).
Biomarkers No. detected Median Arithmetic mean SD Minimum Maximum
d-Glucaric acid (mmol/mol creatinine) 38 3.4 4.4 4.5 < 0.8 26.0
Uroporphyrins (μmol/mol creatinine) 6 1.2 1.2 0.4 < 0.4 2.2
Coproporphyrins (μmol/mol creatinine) 40 11.0 11.5 3.5 5.7 19.2
CBT (% total)a 21b 5.3 5.4 2.2 1.9 9.6
a Percentage of total 13C dose exhaled as 13CO2 over a 2-hr period.
b The CBT was administered to the 40 participants, but for 19 of them CO2 concentrations in air samples were too low for isotopic analysis to be performed.
Table 3 Correlationsa between biomarkers of exposure and biomarkers of effects in residents of fish-eating communities along the Lower North Shore of the St. Lawrence River (Québec, Canada).
Urinary d-glucaric acid (n = 38)
Urinary coproporphyrins (n = 38)
CBT (n = 20)
Biomarker exposure/effects r p-Value r p-Value r p-Value
Cotinine 0.28 0.09 0.10 0.57 0.60 0.005
PCB-153 0.20 0.22 0.06 0.73 0.07 0.76
PCB-105 −0.05 0.75 −0.24 0.15 −0.45 0.05
PCB-118 0.01 0.99 −0.22 0.18 −0.41 0.07
DLCs 0.23 0.18 0.01 0.98 0.12 0.61
a Spearman’s correlation coefficient. Correlation analyses were performed after excluding two participants taking anticonvulsants.
Table 4 Biomarkers of effects in relation to the smoking status in residents of fish-eating communities along the Lower North Shore of the St. Lawrence River (Québec, Canada).
Nonsmokers
Smokers
Biomarker Median Mean SD n Median Mean SD n p-Valuea
d-Glucaric acid (mmol/mol creatinine) 3.6 3.6 1.7 21 2.9 3.6 2.6 17 0.33
Coproporphyrins (μmol/mol creatinine) 9.8 10.7 3.3 21 11.3 11.7 2.9 17 0.28
CBTb 3.6 4.7 2.3 13 6.1 6.7 1.8 7 0.07
a p-Value for Mann-Whitney U-test. Comparisons were effected after excluding two individuals taking anticonvulsants.
b Results of the CBT are expressed as the percent of total 13C dose exhaled as 13CO2 over a 2-hr period.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7980ehp0113-00132516203241ResearchFish Consumption and Advisory Awareness in the Great Lakes Basin Imm Pamela Knobeloch Lynda Anderson Henry A. Consortium the Great Lakes Sport Fish Division of Public Health, Wisconsin Department of Health and Family Services, Madison, Wisconsin, USAAddress correspondence to P. Imm, Department of Health and Family Services, 1 West Wilson St., Room 150, Madison, WI 53703 USA. Telephone: (608) 267-3565. Fax: (608) 267-4853. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 13 6 2005 113 10 1325 1329 31 1 2005 13 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. More than 61 million adults live in the eight U.S. states bordering the Great Lakes. Between June 2001 and June 2002, a population-based, random-digit-dial telephone survey of adults residing in Great Lakes (GL) states was conducted to assess consumption of commercial and sport-caught fish and awareness of state-issued consumption advisories for GL fish. On the basis of the weighted survey data, approximately 84% of the adults living in these states included fish in their diets. Seven percent (an estimated 4.2 million adults) consumed fish caught from the Great Lakes. The percentage of residents who had consumed sport-caught fish (from any water source) varied regionally and was highest among those who lived in Minnesota (44%) and Wisconsin (39%). Consumption of GL sport fish was highest among residents of Michigan (16%) and Ohio (12%). Among residents who had eaten GL fish, awareness of consumption advisories varied by gender and race and was lowest among women (30%) and black residents (15%). However, 70% of those who consumed GL sport-caught fish twice a month or more (an estimated 509,000 adults across all eight states) were aware of the advisories. Findings from this survey indicate that exposure to persistent contaminants found in GL fish is likely limited to a relatively small subpopulation of avid sport-fish consumers. Results also underscore the public health importance of advisories for commercial fish because an estimated 2.9 million adults living in these states consume more than 104 fish meals per year and may be at risk of exceeding the reference doses for methylmercury, polychlorinated biphenyls, and other bioaccumulative contaminants.
advisoryawarenessfishGreat Lakessport fish
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Consumption advisories for sport-caught fish were first issued by Great Lakes (GL) states during the 1970s. These advisories were based on findings from investigations of the methyl-mercury poisonings that had occurred in Minamata, Japan, and on fish tissue analysis. Since that time, researchers have discovered that a variety of other persistent environmental contaminants, including PCBs (polychlorinated biphenyls), DDT (dichlorodiphenyltrichloroethane), and polybrominated diphenyl ethers, had found their way into the aquatic food chain and might pose a risk to frequent consumers of large, predatory fish. Currently, health departments and/or state environmental agencies in 48 states issue consumption guidelines for local sport-caught fish—fish that is caught and not purchased.
The recent methylmercury reference dose revision from 0.5 μg/kg/day to 0.1 μg/kg/day triggered states to review their sport-fish advisories and federal agencies to assess the need for a commercial fish advisory. In 2004 the U.S. Environmental Protection Agency (EPA) and the U.S. Food and Drug Administration (FDA) jointly issued consumption advice for commercial fish that was intended to protect women of childbearing age and young children against the neurodevelopmental effects of methylmercury. It became apparent to some states that there was a need for a holistic methylmercury fish consumption advisory that combined advice forlocal sport-caught fish and commercial fish. Up until this time sport-caught fish advisories largely ignored exposures from commercial fish. Current advisories are intended to assist anglers and consumers of commercial fish in selecting fish low in chemical contaminants as part of a healthy, balanced diet.
In 2001 an estimated 1.85 million fishermen purchased licenses to fish on the Great Lakes (U.S. Department of the Interior 2002). Although this figure reflects an almost 30% decline from 2.55 million in 1991, GL sport fishing continues to be a popular recreational activity for many families. Frequent ingestion of fish from these lakes has been associated with higher body burdens of PCBs, DDT, and DDE (dichlorodiphenyldichloroethylene) (Anderson et al. 1998; Fiore et al. 1989; Humphrey 1983; Schwartz e al. 1983; Sonzogni et al. 1991; Tilden et al. 1997). These persistent contaminants accumulate in the body over time and increase the risk of a variety of health problems such as liver disease (Yu et al. 1997), reproductive (Dar et al. 1992; Weisskopf et al. 2003) and neurologic problems (Rogan and Gladen 1992), endocrine changes (Braathen et al. 2004; Persky et al. 2001), and developmental delays (Jacobson et al. 1990; Jacobson et al. 1986; Kimbrough and Krouskas 2003; Longnecker et al. 1997; Tilson and Kodavanti 1998). PCBs, DDT, and DDE have been classified as probable human carcinogens by the U.S. EPA, and sport-fish consumption has recently been associated with an increased risk of breast cancer among young, premenopausal women (McElroy et al. 2004).
Prenatal exposure to methylmercury has been associated with subtle learning delays and blood pressure changes (Grandjean et al. 1998; Sorenson et al. 1999). Methylmercury exposure during adulthood has recently been linked to higher rates of cardiovascular disease and acute myocardial infarction (Guallar et al. 2002; Salonen et al. 1995).
Until the early 1990s sport-fish consumption advisories were developed independently by each state. Development of these advisories may have been based on policy considerations as well as science. This led to confusion because neighboring states often provided different advice for the same, shared body of water. This situation was confusing for anglers and may have reduced confidence in the advisories. At the direction of the Council of Great Lakes Governors, the states that border the Great Lakes (Illinois, Indiana, Michigan, Minnesota, New York, Ohio, Pennsylvania, and Wisconsin) developed a protocol for a Uniform Great Lakes Sport Fish Consumption Advisory (Anderson et al. 1993). That 1993 advisory protocol provides information on the health benefits of fish; adverse effects of contaminants; recommended quantity, frequency, and types of fish to consume; recommended fishing locations; and preparation methods that can be used to reduce exposure to bioaccumulative contaminants such as PCBs and DDE (Anderson et al. 1993).
In 1991 the Great Lakes Sport Fish Consortium of health departments in six of the eight GL states (Michigan and Pennsylvania were not members of the original consortium) was formed and received competitive funding from the Agency for Toxic Substances and Disease Registries. In 1993–1994 the consortium conducted a random-digit-dial telephone survey of 8,306 residents of the eight GL states to evaluate their total fish and GL sport-fish consumption habits, define at-risk subpopulations, and assess the effectiveness of state-issued consumption advisories. Households were selected by a computerized random-digit-dial system, and an adult was then randomly selected among those in each household. The survey found that 50% of consumers of GL sport-caught fish were aware of the consumption advisory issued by their state of residence (Tilden et al. 1997). Awareness rates varied by gender and race and were lowest among women and minorities. Those results prompted a reevaluation of GL sport-caught fish advisory programs. Previously, information was targeted almost exclusively to anglers who were predominantly male, but in recognition that advice was not reaching women or minorities, the consortium expanded program outreach materials specificallly to include materials targeted to women of childbearing age. Although the focus of the consortium was on PCB and DDE in GL fish, each state also provided consumption advice for fish caught from inland lakes and rivers based on PCB and methylmercury fish tissue levels.
Between June 2001 and June 2002 the consortium conducted a follow-up randomized telephone survey of 4,106 adults to evaluate changes in awareness and fish consumption patterns among residents of these states. In this article we summarize findings from that survey and changes that occurred between the 1993 and 2001 surveys.
Materials and Methods
Between June 2001 and June 2002 a population-based, random-digit-dial telephone survey of adults (≥18 years of age) residing in Indiana, Illinois, Minnesota, Michigan, New York, Ohio, Pennsylvania, and Wisconsin was conducted by the Wisconsin Survey Research Laboratory (Madison, Wisconsin). This study was designed as a follow-up to the 1993–1994 study conducted in these states and involved 4,106 adults who were randomly selected from each household. Although the same basic survey instrument was used as a follow-up to the original survey, a new random sample among adult residents of these states was drawn. The total sample size was nearly half that of the original 1993–1994 study because of funding restraints. The overall Conference of American Survey Research Organizations (CASRO) response rate was 56% (CASRO 1982).
Trained telephone interviewers used standardized questionnaires to collect information on demographic characteristics and fish consumption during the preceding 12 months. Respondents were asked about their fish consumption habits in a stepwise pattern. Those who included fish in their diets were asked about sport-caught fish ingestion, specifically, any fish not purchased that was caught by the respondent or by someone else and given to the respondent. Fish purchased at a restaurant or store did not qualify as sport caught. Sport-caught fish consumers were asked about GL sport-caught fish intake, and consumers of GL sport-caught fish were asked about advisory awareness. GL sport-caught fish included fish caught in the mouths of rivers that feed into the Great Lakes. Because of the popularity of tuna in the U.S. diet and recent concerns regarding tuna as a source of mercury exposure, this survey also included a series of questions about tuna (any type) consumption, which the original 1993–1994 survey did not include. Consumers of “commercial fish only” were defined as fish consumers who reported eating no sport-caught fish in the previous 12 months. In this article commercial fish includes any type of tuna. However, where tuna is specifically referenced, it refers only to this type of commercial fish and no other.
Statistical analysis of prevalence estimates, odds ratios, and chi-square and t-tests were conducted using SAS statistical software (version 9.1 for Windows; SAS Institute Inc., Cary, NC). Survey data were weighted before analysis to reflect state-specific selection probability for each household and adjusted for the number of telephone lines serving the residence using 2000 Census data. Data from each state were weighted to reflect the population age (four age groups were used) and gender distribution.
Results
Demographic characteristics of the sample.
Of the 4,106 Great Lakes Basin residents who participated in this random-digit-dial telephone survey, 56% were female, 86% reported their race as white, 91% were high school graduates, and 50% were ≥45 years of age (Table 1). By design, the sample was stratified such that approximately 500 residents were sampled in each of the eight GL states. Data were weighted for age, gender, and state of residence to reflect the 2000 Census demographics. Our study cohort underrepresented black residents and lower income households.
Fish consumption.
On the basis of weighting of the survey data, > 80% of the adults living in this region had eaten some type of fish during the previous 12 months. Most of the population consumed only commercial fish (any type of fish purchased and not caught; Table 2). Nearly 70% specifically reported consumption of canned or fresh tuna, revealing the popularity of this type of commercial fish (Table 1). Fewer than one quarter (22%) had eaten any sport-caught fish, and only 7% (~ 4.2-million residents) had eaten fish that were caught from one of the Great Lakes.
Although the percentage of men and women who consume fish (any type) was nearly the same (85% vs. 83%, respectively), men were more likely to eat sport-caught fish (p < 0.0001) and GL sport-caught fish (p = 0.0064) than were women. Conversely, women were more likely to have ingested tuna than were men (p < 0.0001). Regardless of whether the fish was commercial or sport caught, consumption prevalence was positively correlated with household income (p-values < 0.0001). Consumption prevalences for “any type of fish” and tuna were correlated with education (p < 0.0001); however, sport-caught fish and GL sport-caught fish consumption prevalences were not correlated with educational attainment. The percentage of residents who included fish and sport-caught fish in their diets varied from state to state. Consumption of any type of fish ranged from 80% among Indiana residents to 87% among residents of Wisconsin. Sport-caught fish consumption was much more common in the Midwest than in the eastern states, ranging from 44% in Minnesota and 39% in Wisconsin to 15% in New York and 16% in Pennsylvania (Table 1).
Although the sample was too small to support extensive analysis by race or ethnicity, overall fish consumption rates were similar among black and white adults and lower among other/unknown races. This difference was statistically significant between white adults and those of other or unknown races (p < 0.0002). White residents were significantly more likely than black residents and residents of other/unknown races to have consumed tuna (p < 0.01) or sport-caught fish (p < 0.05). Adults reporting other/unknown races were significantly less likely to eat GL sport fish than were white or black adults (p < 0.05).
As shown in Table 2, most adults in these states (63%) consumed commercial fish but had not eaten any sport-caught fish during the 12-month recall period. This did not differ significantly from the 1993–1994 study (62%). Among those who ate fish, the average number of meals eaten (from all sources) ranged from 44/year among those who consumed only commercial fish to 53/year among those who had eaten sport-caught fish from the Great Lakes. Based on a t-test of the means of the log of the number of fish meals per year, the consumption rates reported by consumers of “commercial fish only” were significantly lower than those who included “GL sport-caught fish” in their diets (p = 0.044). Although the difference in means for these two fish consumer groups was less in the 1993–1994 study (46 meals/year vs. 48 meals/year), this difference also proved statistically significant when conducting a t-test of means of the logs (p =0.0007). There was, however, no statistically significant difference across time periods.
For those who ate GL sport-caught fish, the average number of GL sport-fish meals consumed per year was 13. This consumption rate was higher among men than among women (14 vs. 11, respectively), but the difference was not statistically significant.
Most respondents who consumed GL sport-caught fish did so fewer than 12 times a year (range, 1–126). Depending on the type and size of the fish consumed, this rate is likely to comply with most GL fish consumption advisories. As shown in Figure 1, a small percentage of the men (10%) and women (3%) in this group ate GL fish > 35 times a year, or about 3 times a month (this gender difference was not statistically significant). Between 1993–1994 and 2001–2002, the proportion of women who ate > 35 GL sport-fish meals/year decreased significantly from 8 to 3% (p = 0.0418). This equates to a decrease from approximately 158,000–59,000 women who live in the Great Lakes Basin. The percentage of men in this high-consumption group did not change significantly over this time period. Overall, the estimated number of GL sport-fish consumers who ate GL sport-caught fish > 35 times per year declined from 402,000 in 1993–1994 to 286,000 in 2001–2002.
The most popular types of GL sport-caught fish were walleye number of people (n) = 156), perch, smelt (n = 152); rainbow trout, chinook, coho salmon (n = 139); lake trout (n = 121); and “other” sport-caught fish (n = 78). Brown trout (n = 40) and carp and catfish (n = 33) were less frequently ingested by GL sport-fish consumers.
As shown in Table 3, most adults who live in the GL states eat fish up to once a week (52 meals/year). Table 4 provides demographic descriptors and population estimates for people who consume fish more than twice a week. This subgroup comprises a high-risk population for exposure to PCBs, methylmercury, and other persistent contaminants found in large, predatory fish. As shown in Table 4, > 2.8 million residents fall into this subgroup. Most were female, college educated, and ≥ 45 years of age. People reporting a household income > $50,000/year were more likely to fall into this category than were those with lower incomes. Residents of New York were three times more likely to fall into this high-intake subgroup than were residents of any other state and approximately eight times more likely than residents of Wisconsin and Minnesota. Fifty-one percent of these high-intake individuals ate tuna at least once a week.
Advisory awareness.
All GL states have issued consumption advisories for GL fish. Approximately half of adults who consumed fish from the Great Lakes were aware of the health advisory that had been issued by their state health department (Table 5). This awareness rate had not changed since the 1993–1994 survey. On the basis of multivariate logistic regression analysis, advisory awareness varied significantly (p < 0.05) by gender, black/white race, and fish consumption rate (Table 6). Whites were > 6 times more likely to be aware of their state’s advisory than blacks and men were four times more likely to be aware than women. Also, advisory awareness was positively associated with annual fish consumption rates.
Most GL fish consumers who were aware of the advisories issued by their state complied with them. Compliance rates for the types and sizes of fish that were safe to eat, preparation methods, and fishing locations ranged from 63 to 77%. The least popular recommendation was the restriction on the amount of fish that should be eaten in a given time period. Compliance with this guideline was only 52% (Table 7).
Discussion
Although persistent, bioaccumulative contaminants in the Great Lakes Basin continue to be a public health concern, our survey results indicate that sport fishing in these lakes remains a popular activity. According to the survey results, 7% of adults living in the GL states had eaten at least one meal of GL sport fish during the previous 12 months. This percentage corresponds to an estimated population of 4.2 million adult residents in these states. Compared with national dietary estimates, residents of these states appear to consume more fish than do people living in other regions. Including residents who did not eat fish, our study revealed an average fish consumption rate of 38 meals/year. The U.S. Department of Agriculture 1994–1996 and 1998 Continuing Survey of Food Intakes by Individuals found that the average number of fish meals (grams per day converted to 6-oz prepared fish meals per year) consumed by adults ≥ 18 years of age was approximately 32/year (U.S. EPA 2002). Comparison of responses to our 1993–1994 and 2001–2002 surveys indicates that fish consumption rates and awareness prevalence have remained stable over this time period. As in 1993–1994, the 2001–2002 study suggests that most GL sport-fish consumers choose to eat fish that are low in contaminants, such as perch, smelt, and walleye.
Findings from this survey suggest that significant exposure to GL contaminants from fish is limited to a small subpopulation of avid sport-caught fish consumers. The mean number of sport-caught fish meals reported by GL sport-caught fish consumers was 13 meals/year. Only one person (a man from Michigan, 35–44 years of age) consumed more than 2 GL fish meals/week. These consumption rates are similar to those reported by respondents of the 1993–1994 survey (Tilden et al. 1997).
The overall percentage of GL sport-caught fish consumers who were aware of the advisory in their state was similar to that observed in 1993–1994. In both studies, awareness prevalence was approximately 50%. However, women and black residents reported the lowest awareness rates. In 1993–1994, less than half (38%) the women who ate GL sport-caught fish were aware that their state had issued a consumption advisory for contaminants in these fish. As a result of the 1993–1994 survey of the GL states, the health departments of these states chose as a priority to augment their ongoing activities with the development of outreach materials aimed at young women and children. This group was targeted because of methods available to reach this population and because of an emphasis on the health of this more vulnerable population because of concerns regarding the effects of fish contaminants on the developing nervous system. The first group effort produced the “Hook into Healthy Fish” campaign where the states customized a set of common outreach materials that were distributed at WIC (Women, Infants and Children) clinics and public health fairs and sent to pediatricians. These materials included coffee mugs, posters, fact cards, T-shirts, memo pads, and refrigerator magnets produced by the Wisconsin Department of Health and Femily Services, Division of Public Health and funded the the Agency for Toxic Substances and Disease Registry. Despite efforts that were intended to raise awareness among this group, from 1993 to 2002 awareness prevalence among women had slipped from 38 to 30% (although this was not a statistically significant difference). However, on a positive note, the estimated number of women who ate GL sport fish > 35 times a year declined by approximately 99,000 over this same time period. The overall effectiveness of the outreach cannot be directly evaluated because of other community and environmental action group campaigns that ran concurrently during this time period.
Awareness rates also slipped among minorities and younger age groups but rose slightly from 58 to 65% among men and continued to be highest in men ≥45 years of age (67% in 2001–2002). Although temporal changes related to awareness among these groups were not statistically significant, our findings suggest that outreach to GL fishermen and their families may not be reaching some segments of the population. This study does not allow for extensive analysis by race because of the small number of minorities who reported consumption of GL sport fish. Additional research focusing on minority populations or oversampling areas with larger minority populations is needed.
Comparisons of advisory awareness versus state of residence were not made because only a small number of respondents in each state had consumed GL sport-caught fish during the period of our study.
The need to educate the adult population about persistent toxins present in some commercial fish is evident from these survey data. Not only do most of the adult population of these GL states consume only commercial fish (63%), but also, most people in our survey who exceeded the U.S. EPA/U.S. FDA recommendation of no more than two fish meals per week consumed only commercial fish. Conversely, most of the outreach that has been conducted in these states has been targeted at licensed anglers and their families. State-issued brochures are often specific to local species and water bodies. Written advisory information, such as fishing regulation booklets and advisory brochures, has been distributed primarily to recreational fishermen and health care providers. Until recently, very little information has been available to the general public regarding contaminant levels in fish that are sold in restaurants, fish markets, and grocery stores. Although contaminant levels vary greatly among fish depending on their diets, age, and habitat, most dietary guidance has encouraged fish consumption as a healthy alternative to red meats, and advisories have limited consumption restrictions to a few highly contaminated species such as shark and swordfish. As a result, many consumers assume that all other fish is safe for unlimited consumption. Few realize that frequent, prolonged consumption of canned tuna and other predator species can lead to a high body burden of methylmercury. The U.S. FDA has published a website with methylmercury levels in commercial fish and shellfish (U.S. FDA 2004). Those with the highest levels include king mackerel, shark, swordfish, and tilefish (all close to or above 1 ppm, except king mackerel at 0.73 ppm). Other commercial fish that have average methylmercury levels above 0.5 ppm include grouper and orange roughy. Average levels in canned albacore tuna and fresh/frozen tuna are 0.35–0.38 ppm, respectively. According to recent research conducted by Hites et al. (2004), total PCBs, dioxins, toxaphene, and dieldrin levels were significantly higher in farm-raised salmon than in wild salmon. This finding is significant because more than half the salmon sold in Northern Europe, Chile, Canada, and the United States is farm raised.
Advisories that focus only on sport-caught fish miss much of the fish-consuming population. Based on our survey, > 2 million residents of the Great Lakes Basin who eat only commercial fish eat enough commercial fish to exceed safety guidelines for exposure to a variety of persistent, bioaccumulative pollutants.
This work was funded by U.S. Public Health Service Agency for Toxic Substances and Diseases Registry grant 75/ATH598322-03.
Figure 1 GL sport-fish meals per year among GL sport-fish consumers (weighted).
Table 1 Fish consumption patterns among Great Lakes Basin residents.
% Weighted population that consumes:
Demographic characteristic No. of respondents (%) Any type of fish Tuna Sport-caught fish GL sport fish
Age (years)
18–34 1,040 (25) 77 59 19 7
35–44 904 (22) 88 74 29 10
≥ 45 2,065 (50) 87 73 22 6
Gender
Male 1,801 (44) 85 66 26 8
Female 2,305 (56) 83 72 19 6
Race
White 3,539 (86) 85 71 23 7
Black 259 (6) 84 60 19 10
Other/unknown 302 (8) 78 59 15 4
Education
Less than high school 386 (9) 72 54 22 7
High school graduate 1,412 (34) 80 64 23 7
Some college 995 (24) 87 72 24 8
College graduate 1,295 (32) 90 77 20 7
Household income ($)
< 15,000 488 (12) 71 54 14 4
15,000–24,999 447 (11) 86 68 22 5
25,000–34,999 432 (11) 84 69 23 6
35,000–49,999 723 (18) 86 69 24 8
≥ 50,000 1,255 (31) 89 77 26 9
Unknown 761 (18) 81 66 18 5
State
Illinois 508 (12) 86 68 24 5
Indiana 513 (13) 80 64 21 4
Michigan 502 (12) 82 68 25 16
Minnesota 510 (12) 84 69 44 8
New York 516 (13) 85 75 15 3
Ohio 529 (13) 83 62 21 12
Pennsylvania 508 (12) 86 71 16 3
Wisconsin 512 (12) 87 70 39 10
Total 4,106 (100%) 84% 69% 22% 7%
Percentages within groups may not total 100% because of rounding error. Percentages are based on weighted data. There were missing values for state, age, and education demographics because of partial completion of survey or refusal.
Table 2 Fish consumption and average number of fish meals by type of fish eaten.
Type of fish consumed No. of respondents % Who consume Average no. of fish meals per year Average no. of tuna meals per year
Commercial fish only (no sport fish) 2,442 63 44 28
Non-GL sport fish (may include commercial fish) 685 15 46 22
GL sport fish (may include commercial and/or non-GL sport fish) 299 7 53 35
None 628 16 0 0
Percentages and averages are based on weighted data. Average number of fish meals per year is calculated based on any and all types of fish, including tuna.
Table 3 Frequency of fish consumption by gender.
Weighted population estimate (%)
No. of meals per year Men Women
0 4,097,000 (14) 5,297,000 (17)
1 to < 12 3,452,000 (12) 4,054,000 (13)
12–24 9,074,000 (32) 8,141,000 (25)
25–52 7,762,000 (27) 9,269,000 (29)
53–104 2,960,000 (10) 3,740,000 (12)
> 104 1,342,000 (5) 1,513,000 (5)
Total 28,687,000 32,014,000
Table 4 Description of GL state residents who consume fish more than twice a week ( > 104 meals/year).
Demographic characteristic n Population estimate Weighted (%)
Age (years)
18–34 39 641,000 23
35–44 33 503,000 18
≥ 45 112 1,611,000 58
Gender
Male 81 1,342,000 47
Female 109 1,513,000 53
Education
Less than high school 13 219,000 8
High school graduate 38 589,000 21
Some college 45 803,000 28
College graduate 93 1,223,000 43
Household income ($)
< 15,000 19 276,000 10
15,000–24,999 25 345,000 12
25,000–34,999 19 223,000 8
35,000–49,999 22 377,000 13
≥ 50,000 76 1,162,000 41
Unknown 29 471,000 17
State
Illinois 24 376,000 13
Indiana 19 161,000 6
Michigan 24 324,000 11
Minnesota 14 107,000 4
New York 46 1,054,000 37
Ohio 23 352,000 12
Pennsylvania 22 342,000 12
Wisconsin 18 140,000 5
Type of consumer
Commercial only 132 2,034,000 73
Sport fish (non-GL) 30 425,000 15
GL sport fish 25 343,000 12
Total 190 2,855,000 100
Consumption of more than two fish meals per week exceeds the U.S. EPA/U.S. FDA recommended amount. There were missing values for education and age demographics because of partial completion of survey or refusal. Type of consumer categories match those of Table 2 and are mutually exclusive.
Table 5 Advisory awareness among GL sport-fish consumers.
2001–2002
1993–1994
Demographic characteristic n % Aware n % Aware
Age (years)
18–34 73 38 217 49
35–44 93 56 190 56
≥ 45 131 52 276 49
Race
White 258 55 638 53
Black 24 15 38 23
Gender
Male 153 65 355 60
Female 146 30 337 38
Education
Less than high school 25 33 50 33
High school graduate 91 50 278 50
Some college 86 55 195 50
College graduate 97 48 165 61
Consumption
0–5 meals/year 107 41 291 45
6–23 meals/year 135 57 248 53
≥ 24 meals/year 34 70 120 61
Total 299 49 692 51
Percentages are based on weighted data. Statistics are not provided for other races because of small sample sizes.
Table 6 Multivariate logistic regression model for advisory awareness among GL sport-fish consumers.
Demographic characteristic Odds ratio (95% confidence interval)
Race
Black Referent
White 6.6 (2.0–21.5)
Gender
Female Referent
Male 4.0 (2.3–7.1)
Fish consumption
< 6 meals/year Referent
6–23 meals/year 2.3 (1.3–4.1)
≥ 24 meals/year 5.0 (1.7–14.6)
All odds ratios reported in table were statistically significant at p < 0.05 level. Regression calculated using weighted data. Statistics are not provided for other races because of small sample sizes.
Table 7 Self-reported compliance with advisories.
Advisory component n % Always complying
Cooking/cleaning methods 81 77
Consumption frequency 92 52
Fish species and size 65 63
Fishing locations 57 71
Percentages are based on weighted data. n = number of GL sport-fish consumers who reported awareness of each guideline.
==== Refs
References
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Humphrey HD 1983. Population studies of PCBs in Michigan residents. In: PCBs Human and Environmental Hazards (D’Itri FM, Kamrin MA, eds). Ann Arbor, MI:Ann Arbor Science Publishers, 299–310.
Jacobson JL Jacobson SW Humphrey HE 1990 The effect of intrauterine PCB exposure on cognitive functioning in young children J Pediatrics 116 38 45
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U.S. EPA 2002. Estimated per Capita Fish Consumption in the United States. Washington, DC:U.S. Environmental Protection Agency. Available: http://www.epa.gov/waterscience/fish/consumption_report.pdf [accessed 19 May 2005].
U.S. FDA 2004. Mercury Levels in Commercial Fish and Shellfish. Rockville, MD:U.S. Food and Drug Administration. Available: http://www.cfsan.fda.gov/~frf/sea-mehg.html [accessed 19 May 2005].
Weisskopf MG Anderson HA Hanrahan LP 2003. Decreased sex ratio following maternal exposure to polychlorinated biphenyls from contaminated Great Lakes sport-caught fish a retrospective cohort study. Environ Health 10.1186/1476-069X.
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8000ehp0113-00133016203242ResearchInorganic Arsenite Potentiates Vasoconstriction through Calcium Sensitization in Vascular Smooth Muscle Lee Moo-Yeol 1Lee Young-Ho 2Lim Kyung-Min 1Chung Seung-Min 1Bae Ok-Nam 1Kim Heon 3Lee Choong-Ryeol 4Park Jung-Duck 5Chung Jin-Ho 11 College of Pharmacy, Seoul National University, Seoul, Korea2 College of Medicine and BK21 Project for Medical Sciences, Yonsei University, Seoul, Korea3 College of Medicine, Chungbuk National University, Cheongju, Korea4 Ulsan University Hospital, Ulsan, Korea5 College of Medicine, Chung-Ang University, Seoul, KoreaAddress correspondence to J.-H. Chung, College of Pharmacy, Seoul National University, Shinrim-dong San 56-1, Seoul 151-742, Korea. Telephone: 82-2-880-7856. Fax: 82-2-885-4157. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 14 6 2005 113 10 1330 1335 7 2 2005 14 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Chronic exposure to arsenic is well known as the cause of cardiovascular diseases such as hypertension. To investigate the effect of arsenic on blood vessels, we examined whether arsenic affected the contraction of aortic rings in an isolated organ bath system. Treatment with arsenite, a trivalent inorganic species, increased vasoconstriction induced by phenylephrine or serotonin in a concentration-dependent manner. Among the arsenic species tested—arsenite, pentavalent inorganic species (arsenate), monomethylarsonic acid (MMAV), and dimethylarsinic acid (DMAV)—arsenite was the most potent. Similar effects were also observed in aortic rings without endothelium, suggesting that vascular smooth muscle plays a key role in enhancing vasoconstriction induced by arsenite. This hypercontraction by arsenite was well correlated with the extent of myosin light chain (MLC) phosphorylation stimulated by phenylephrine. Direct Ca2+ measurement using fura-2 dye in aortic strips revealed that arsenite enhanced vasoconstriction induced by high K+ without concomitant increase in intracellular Ca2+ elevation, suggesting that, rather than direct Ca2+ elevation, Ca2+ sensitization may be a major contributor to the enhanced vasoconstriction by arsenite. Consistent with these in vitro results, 2-hr pretreatment of 1.0 mg/kg intravenous arsenite augmented phenylephrine-induced blood pressure increase in conscious rats. All these results suggest that arsenite increases agonist-induced vasoconstriction mediated by MLC phosphorylation in smooth muscles and that calcium sensitization is one of the key mechanisms for the hypercontraction induced by arsenite in blood vessels.
arsenicarseniteblood vesselscalcium sensitizationcardiovascular diseasemyosin light chain phosphorylationvasoconstriction
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Arsenic is a ubiquitous element distributed in the environment, and millions of people are chronically exposed to arsenic worldwide (Abernathy et al. 1999). Naturally contaminated drinking water is the main source of arsenic exposure, posing potential risk to human health (Nordstrom 2002; Schoen et al. 2004; Smith et al. 2002). Chronic arsenic exposure has been associated with a wide range of illnesses including cancer, hyperkeratosis, diabetes, and cardiovascular disease (Engel et al. 1994; Rossman 2003; Tseng 2004; Yu et al. 1984). Cardiovascular effects of arsenic exposure include hypertension, atherosclerosis, cerebrovascular disease, ischemic heart disease, and peripheral vascular disorders such as black-foot disease (resulting from gangrene caused by obstruction of peripheral blood vessels) in humans (Chen et al. 1995, 1996; Chiou et al. 1997; Rahman et al. 1999; Simeonova et al. 2003; Wang et al. 2002).
Lee et al. (2002) recently suggested that the mechanism for arsenic-induced cardiovascular disease is the increased susceptibility of platelets to aggregate, resulting in enhanced arterial thrombosis. Other mechanisms may also be responsible for the diversity of human cardiovascular disease from chronic arsenic exposure. One possibility is that arsenic may alter the normal vasomotor tone of blood vessels, which rises from contractility of vascular smooth muscle cells.
The contraction of smooth muscle is regulated by mediators such as neural and humoral factors, mechanical forces, and vasoactive substances from endothelial cells. Vascular smooth muscle contraction is triggered primarily by a rise in intracellular free Ca2+ concentration (Sanders 2001). Ca2+ binds to calmodulin (CaM), allowing Ca2+–CaM complex formation, which binds to and activates myosin light chain kinase (MLCK) (Horowitz et al. 1996). The active MLCK catalyzes the phosphorylation of the regulatory myosin light chain (MLC), which then triggers myosin–actin interaction, leading to the shortening of muscle and generation of force. When the intracellular Ca2+ concentration returns to a lower level, myosin is dephosphorylated by myosin phosphatase and the muscle relaxes (Somlyo and Somlyo 1994; Walsh et al. 1994).
In addition to the contraction mediated by Ca2+-dependent MLCK, it has been recently suggested that smooth muscle contraction is modulated by Ca2+ sensitization (Somlyo and Somlyo 2000). This implies that Ca2+-dependent contractions occur, but at a lower Ca2+ concentration than would be expected for that mediated directly via MLCK (Somlyo and Somlyo 2000). An increase in MLC phosphorylation due to reduced activity of myosin phosphatase appears to result in the hypercontraction of blood vessels, which is a characteristic feature of Ca2+ sensitization (Fukata et al. 2001; Uehata et al. 1997). This abnormal hypercontraction is generally known to cause acute vasospasm, micro-circulatory ischemia, increased systemic blood pressure, and ultimately, possible vascular diseases (Bohr et al. 1991; Mohri et al. 1998; Mulvany and Aalkjaer 1990).
Previous epidemiologic studies have reported that peripheral vascular resistance and systemic blood pressure were elevated in populations that had ingested the arsenic-contaminated drinking water (Chen and Yen 1964; Tseng et al. 1995). Carmignani et al. (1985) observed that chronic administration of arsenite to rats and rabbits caused a significant increase in vascular peripheral resistance. These studies suggest the possibility that arsenic may disrupt normal vasomotor function, leading to hypercontraction of blood vessels. Indeed, our previous study demonstrated that arsenic could inhibit acetylcholine-induced vascular relaxation via inhibition of nitric oxide synthase in vascular endothelial cells (Lee et al. 2003). In the process of investigating the effect of arsenic on blood vessels, we observed that arsenic could enhance agonist-induced contraction in an aortic ring organ bath system, suggesting that arsenic could disrupt contractile function in vascular smooth muscles as well. Therefore, in the present study we investigated the mechanism of arsenic-induced vascular dysfunction and its possible contribution to cardiovascular diseases.
Materials and Methods
Chemicals.
The following chemicals were purchased from Sigma (St. Louis, MO, USA): sodium arsenite (trivalent inorganic arsenic), sodium arsenate (pentavalent inorganic arsenic), dimethylarsinic acid (DMAV), phenylephrine, and serotonin creatinine sulfate. We obtained monomethylarsonic acid (MMAV) from Chem Service (West Chester, PA, USA) and anti-MLC and anti-phospho-MLC antibody from Santa Cruz Biotechnology (Santa Cruz, CA, USA). Fura-2/AM was supplied by Molecular Probe (Eugene, OR, USA), and all other reagents used were of the highest purity available.
Animals.
The entire animal protocol was approved by the Ethics Committee of Animal Service Center at Seoul National University. We used male Sprague-Dawley rats (Dae Han BioLink Co., Seoul, Korea) weighing 300–400 g throughout all experiments. Before the experiments, animals were acclimated for 1 week in the laboratory animal facility maintained at constant temperature and humidity with a 12-hr light/dark cycle. Food and water were provided ad libitum.
Measurement of vasoconstriction in organ bath.
Rats were sacrificed by decapitation and then exsanguinated. We carefully isolated the thoracic aorta and cut it into ring segments. Aortic rings without endothelium were prepared by gently rubbing the intimal surface of the aortic rings with a wooden stick. We then mounted the rings in four-channel organ baths filled with Krebs-Ringer (KR) solution (115.5 mM NaCl, 4.6 mM KCl, 1.2 mM KH2PO4, 1.2 mM MgSO4, 2.5 mM CaCl2, 25.0 mM NaHCO3, and 11.1 mM glucose, pH 7.4). The organ bath was continuously gassed with 95% O2/5% CO2 and maintained at 37°C. The rings were stretched gradually to an optimal resting tension of 2 g and equilibrated for 30 min. The change in tension was measured isometrically with Grass FT03 force transducers (Grass Instrument Co., Quincy, MA, USA) and recorded using the AcqKnowledge III computer program (BIOPAC Systems Inc., Goleta, CA, USA).
To investigate the effect of arsenic on vasoconstriction, we treated the aortic rings with arsenic or the vehicle (saline) in minimum essential media (MEM) with 100 U/mL penicillin and 100 μg/mL streptomycin in a 95% air/5% CO2 incubator for 14 hr at 37°C. After mounting the aortic rings pretreated with arsenic in organ baths, we induced the contraction by cumulatively adding phenylephrine or serotonin to obtain concentration-contraction curves. In the experiments of high K+-induced contraction, 100 mM KCl-containing KR solution prepared by substituting K+ with Na+ was cumulatively added to the bath to obtain the indicated final concentrations.
Measurement of MLC phosphorylation.
We measured the extent of MLC phosphorylation using polyacrylamide gel electrophoresis (PAGE) and immunoblot analysis using an anti-phospho-MLC antibody, as previously described (Sakurada et al. 1998). After the aortic rings were pretreated with various concentrations of arsenite for 14 hr, 10−8 M phenylephrine was added to the organ bath for 2 min, and then ice-cold acetone with 10% trichloroacetic acid and 10 mM dithiothreitol was immediately added to stop the reaction. The aortic rings were washed with the acetone solution three times, lyophilized overnight, and stored at −70°C until protein extraction. After the dried tissues were cut into small pieces, we extracted proteins in a 50 μL sample buffer containing 8 M urea, 2% sodium dodecyl sulfate, 5% β-mercaptoethanol, 0.01% bromophenol blue, and 62.5 mM Tris-HCl by vortexing for 3 hr at room temperature. Protein extracts were electrophoresed in a 15% polyacrylamide mini-slab gel (Bio-Rad, Hercules, CA, USA) and then transferred onto nitrocellulose membranes in Tris/glycine buffer (25 mM Tris, 192 mM glycine). We detected the MLC level and the extent of phosphorylation with immunoblotting using anti-MLC antibody and anti-phospho-MLC antibody (Santa Cruz Biotechnology), respectively. Immunoreactive bands were visualized by horseradish peroxidase-conjugated secondary antibody (Santa Cruz Biotechnology) and an enhanced chemiluminescence kit (ECL; Amersham, Buckinghamshire, UK). For densitometric analysis of MLC-P/MLC, we determined densities of the corresponding bands using TINA software (Raytest, Straubenhardt, Germany).
Measurement of intracellular calcium level.
To measure intracellular Ca2+ levels, we cut the thoracic aorta without endothelium into spiral strips approximately 8 mm in length and 1 mm in width under a dissecting microscope. After the aortic strips were treated with arsenite for 14 hr, they were exposed to 10 μM acetoxymethyl ester of fura-2 (fura-2/AM) and 0.1% cremophor EL in a Krebs-Henseleit solution [(KH) solution: 119 mM NaCl, 4.6 mM KCl, 1.2 mM KH2PO4, 1.5 mM MgSO4, 2.5 mM CaCl2, 25.0 mM NaHCO3, and 11 mM glucose, pH 7.4] for 4 hr at room temperature.
We measured the intracellular free Ca2+ level based on the method described by Ozaki et al. (1991). Fure-2-loaded muscle strips were held horizontally in the organ chamber of a fluorimeter (CAF-110; Jasco, Tokyo, Japan) filled with KH solution. One end of the muscle strip was connected to a force-displacement transducer to monitor vessel tones. The KH solution was maintained at 37°C and continuously aerated with 95% O2/5% CO2. Passive tension of 2 g was applied and allowed to equilibrate before measurement. We elicited muscle contractions by changing the media with the KH solution containing 12.5, 25, and 90 mM KCl. Muscle strips were illuminated alternatively at 48 Hz in excitation wavelengths of 340 and 380 nm. We measured the intensity of 500 nm fluorescence emitted by 340 nm excitation (F340) and that emitted by 380 nm (F380) successively. We calculated the ratio of F340 to F380 [R(F340/F380)] as an indicator of intracellular Ca2+.
Measurement of blood pressure change induced by phenylephrine.
Rats were anesthetized with phenobarbital (50 mg/kg, intraperitoneal). We placed a catheter of polyethylene PE-50 tubing (Clay Adams, Sparks, MD, USA) filled with heparinized saline (100 U/mL) in the carotid artery to measure blood pressure, and we placed a catheter of polyethylene PE-10 fused to PE-50 tubing in the jugular vein to administer drugs. Catheters were tunneled subcutaneously and exteriorized at the back of the neck. Wounds were sutured and cleaned with alcohol. Experiments were performed after a 1-day recovery period. On the day of the experiment, the arterial catheter was connected to a pressure transducer (BIOPAC Systems Inc.) and blood pressure was measured using the AcqKnowledge III computer program. We allowed blood pressure to stabilize for a minimum of 30 min before beginning treatment. To determine the effects of arsenite on blood pressure increase induced by phenylephrine, we administered arsenite solution (1.0 mg/kg) by an intravenous bolus injection into the jugular vein. In the controls, equivalent amounts of saline were injected. After 2 hr, we infused the rats with 2.5 μg/kg/min phenylephrine for 3 min via the jugular vein and monitored the change of blood pressure in response to phenylephrine simultaneously. Infusions were performed with a Harvard syringe pump (South Natick, MA, USA) at a rate of 0.1 mL/min.
Statistical analysis.
We calculated the means and standard errors for all treatment groups. The data were subjected to one- or two-way analysis of variance (ANOVA) followed by Duncan’s multiple range test to determine which means were significantly different from the control. Statistical analysis was performed using SPSS software (SPSS Inc., Chicago, Il, USA). In all cases, we used a p-value of < 0.05 to determine significance.
Results
To investigate whether arsenic affects contraction of blood vessels, we treated intact aortic rings with various concentrations of arsenite (trivalent inorganic arsenic) for 14 hr and then added phenylephrine and serotonin cumulatively to obtain concentration-contraction curves. Arsenite alone did not cause any changes in basal vascular tone (data not shown). Arsenite treatment, however, enhanced the contraction induced by both phenylephrine and serotonin in a concentration-dependent manner (Figure 1). We investigated the effects of pentavalent inorganic species (arsenate) and two major metabolites, MMAV and DMAV, on phenylephrine-induced constriction (Figure 2). Arsenate did enhance phenylephrine-induced vasoconstriction, but the concentration required was higher than that of arsenite. MMAV and DMAV up to 100 μM failed to affect the phenylephrine-induced vasoconstriction. These results suggest that agonist-induced contraction in blood vessels can be enhanced by arsenic and that arsenite is the most potent form tested.
To investigate whether the enhanced contraction induced by arsenite was an endothelium-dependent effect, we performed experiments using aortic rings without endothelium. Treatment with arsenite to aortic rings without endothelium still resulted in a concentration-dependent increment of agonist-induced contraction in blood vessels (Figure 3), suggesting that the enhanced contraction by arsenite is primarily due to hypercontraction of smooth muscles. To examine whether hypercontraction by arsenite is mediated by the phosphorylation of MLC in smooth muscles, we evaluated the effect of arsenite on phenylephrine-induced MLC phosphorylation in aortic rings without endothelium. Arsenite treatment alone did not alter the basal levels of MLC phosphorylation (Figure 4A). Addition of phenylephrine alone did not affect the total MLC levels, but it increased MLC phosphorylation significantly (Figure 4B,C). However, when the aortic rings without endothelium were stimulated with phenylephrine, arsenite treatment resulted in a significant increase in MLC phosphorylation without a concomitant change in MLC levels (Figure 4B,C). These results indicate that arsenite enhances agonist-induced vasoconstriction through MLC phosphorylation in smooth muscles.
To examine whether the increased MLC phosphorylation was mediated by intracellular Ca2+ elevation in vascular smooth muscles, we investigated the effects of arsenic on intracellular Ca2+ levels when vasoconstriction was initiated by high K+ concentration. We used a high concentration of K+ to induce contraction in blood vessels, since K+ directly induces rapid influx of extracellular Ca2+ through voltage-gated Ca2+ channels in plasma membrane without involving other signal transduction pathway and thus could serve as a simple alternative tool to investigate the current premise. As shown in Figure 5A, 25 μM arsenite enhanced vasoconstriction induced by 10–50 mM K+ in aortic rings without endothelium, which is consistent with the results shown in Figure 3. The next experiment was performed using aortic strips loaded with fura-2 fluorescent dye to investigate whether arsenic increased intracellular Ca2+ in the presence of high K+. Arsenite, however, did not induce intracellular Ca2+ elevation, but rather decreased Ca2+ concentration significantly in the presence of 90 mM K+ (Figure 5B). These results show that arsenite enhances vasoconstriction without concomitant increase of intracellular Ca2+ levels, suggesting that arsenite might increase the contractility of blood vessels via Ca2+ sensitization.
To investigate this assumption, we measured smooth muscle contraction and the change of intracellular Ca2+ levels simultaneously using fura-2–loaded aortic strips. After the muscle strips were treated with 25 μM arsenite, contraction was induced successively by 12.5, 25, and 90 mM K+. Figure 6A and 6B shows a scanned reduction of the tracings from aortic strips. Compared with the control group, arsenite-treated aortic strips showed enhanced contraction without any concomitant increase in intracellular Ca2+ elevation (Figure 6A,B). Plotting this result reveals that the aortic strips treated with arsenite showed a steeper slope in the intracellular Ca2+ elevation versus contraction relationship (Figure 6C). These results suggest that arsenite might potentiate vasoconstriction by enhancing the Ca2+ sensitivity of contractile machinery in smooth muscle.
To verify the effects of arsenite on blood vessels in vivo, we monitored changes in blood pressure after intravenous infusion of phenylephrine into conscious rats (Figure 7). An intravenous bolus of arsenite had no effect on basal blood pressure. When rats were treated with arsenite 2 hr before phenylephrine infusion, the hypertensive effect of phenylephrine was significantly potentiated (23.0 ± 3.2 vs. 36.8 ± 3.6 mmHg, p = 0.029; Figure 7B,C). These results suggest that arsenite could induce the enhancement of agonist-induced vasoconstriction in vivo and this confirms the previous in vitro results (Figure 1).
Discussion
In the present study we demonstrate the ability of arsenic to enhance contraction of isolated aortic rings from rats. We have shown that arsenite enhances the vascular contraction induced by phenylephrine, serotonin, and high K+ in a concentration-dependent manner and that the Ca2+ sensitization in smooth muscle largely contributes to arsenite-induced hypercontractility. These in vitro results were consistent with in vivo results in which arsenite potentiated the hypertensive effect of phenylephrine. Recently, the effect of arsenic on platelets has been suggested as a key mechanism in the development of cardiovascular diseases (Lee et al. 2002). Blood vessels, however, are another important tissue in cardiovascular system. Hyper-contractility of blood vessels disrupts vasomotor tone regulation and makes vasoconstriction predominate, which can induce hypertension, complicating cardiovascular disease. Elevated peripheral resistance has been reported in populations exposed to arsenic-contaminated drinking water and in animals treated with arsenite (Carmignani et al. 1985; Chen and Yen 1964). Our data provide evidence that arsenite could enhance vascular smooth muscle contractility, suggesting that arsenic-induced hypercontraction of blood vessels might be another mechanism for arsenic-associated cardiovascular disease observed in human populations.
Suppression of nitric oxide production in endothelium results in the loss of vasodilator activity, causing vasoconstriction (Moncada et al. 1991), and we previously demonstrated that arsenite disrupts endothelium-dependent vasorelaxation via inhibition of endothelial nitric oxide production (Lee et al. 2002). Pi et al. (2003) also reported that vasoconstriction was increased in aortic rings isolated from arsenate-exposed rabbits. They explained the phenomena as a result of impaired nitric oxide formation. To examine whether the hypercontraction of blood vessels observed (Figure 1) was also mediated by the inhibition of endothelium-derived vasorelaxation activity, we performed experiments in aortic rings without endothelium. Surprisingly, contraction by phenylephrine or serotonin was still enhanced by arsenite treatment (Figure 3), suggesting that the hypercontraction was dependent on smooth muscles in blood vessels. Because arsenite not only interferes with endothelium-dependent vasorelaxation but also enhances smooth muscle-dependent contraction, arsenite treatment could result in overall hypercontraction of blood vessels, leading to a possible increased risk for development of vascular diseases such as hypertension and atherosclerosis (Bohr et al. 1991; Mulvany and Aalkjaer 1990; Rubanyi 1993; Vanhoutte 1997).
As shown in Figure 2, treatment with arsenate (pentavalent inorganic arsenic) also potentiated phenylephrine-induced contraction, whereas MMAV and DMAV showed no significant effect. Arsenate is generally known to exert its toxic effects by replacing phosphate in various biochemical reactions because it has a similar structure and properties to phosphate (Oremland and Stolz 2003). However, it is not certain that such properties are engaged in the enhancement of vasoconstriction shown in this study. Indeed, arsenate can be reduced to arsenite at a comparable rate in cell systems, and the biological effect of arsenate may in part result from its reduction to arsenite (Huang and Lee 1996). Therefore, considering that the effective concentration of arsenate is higher than that of arsenite, it is possible that the effect of arsenate arises from arsenite formed by reduction of arsenate.
Phenylephrine and serotonin act on the α1-adrenoceptor and the 5-HT2 serotonin receptor, respectively, on aortic smooth muscle cells and thus result in increasing intracellular Ca2+ (Bruckner et al. 1984; Peroutka 1984). In contrast to these receptor agonists, a high concentration of K+ bypasses receptor-signaling pathways and leads directly to intracellular Ca2+ elevation by opening Ca2+ channels following membrane depolarization (Chiu et al. 1987). These intracellular Ca2+ increases by phenylephrine, serotonin, and high K+ result in phosphorylation of MLC leading to vascular smooth muscle contraction. It is widely accepted that the degree of MLC phosphorylation is the essential factor that determines the extent to which smooth muscle contracts (Fukata et al. 2001; Walsh 1994). As shown in Figure 4B and 4C, MLC phosphorylation to phenylephrine was augmented by arsenite, from which it can be concluded that hypercontraction by arsenite is an MLC phosphorylation-dependent effect. In contrast, arsenite alone did not induce the MLC phosphorylation significantly (Figure 4A), consistent with the fact that basal tones of blood vessels were not changed by arsenite treatment alone.
In addition to intracellular Ca2+ elevation, the level of responsiveness of contractile machinery to intracellular Ca2+ plays a key role in regulating the contractility of smooth muscle cells (Somlyo and Somlyo 2000). Simultaneous measurement of contraction and intracellular Ca2+ increase in aortic strips revealed that arsenite potentiated the magnitude of contraction without concomitant increase in Ca2+ elevation (Figure 6), suggest- significant contribution of Ca2+ sensitization to the hypercontraction induced by arsenite. Previous studies suggested that the mechanism for Ca2+ sensitization was mainly due to modulation of MLC phosphatase activity (Fukata et al. 2001; Uehata et al. 1997). That is, if MLC phosphatase was inhibited, the enhanced MLC phosphorylation could be elicited at the same Ca2+ concentration, which resulted in hypercontraction. However, it is currently uncertain whether arsenite could inhibit MLC phosphatase directly.
In this study, we showed that arsenite causes enhanced vasoconstriction in vitro and demonstrated that Ca2+ sensitization in smooth muscle is responsible for MLC phosphorylation-dependent hypercontraction arsenite (Figure 8). In our in vivo study, arsenite treatment potentiated the hypertensive effect of phenylephrine in rats. These results confirm our in vitro observations and suggest that increased vasocontraction may be a contributing factor in the development of cardiovascular diseases in populations exposed to arsenic.
This work was supported by the Ministry of Science and Technology National Research and Development Program and by the Eco-Technopia 21 project of the Ministry of Environment, Korea.
Figure 1 Enhancement of phenylephrine- and serotonin-induced vasoconstriction in aortic rings by arsenite. (A) Phenylephrine. (B) Serotonin. See “Materials and Methods” for details. Values shown are mean ± SE of four independent experiments.
*Significantly different from the corresponding control (p < 0.05).
Figure 2 Effect of arsenic species on contraction of aortic rings induced by phenylephrine. See “Materials and Methods” for details. Values shown are mean ± SE of five independent experiments.
*Significantly different from the control (p < 0.05).
Figure 3 Enhancement of phenylephrine- and serotonin-induced contraction by arsenite in aortic rings without endothelium. (A) Phenylephrine (3 x 10−8 M). (B) Serotonin (10−6 M). See “Materials and Methods” for details. Values shown are mean ± SE of four independent experiments.
*Significantly different from the control (p < 0.05).
Figure 4 Effect of arsenite on MLC phosphorylation in aortic rings. (A) Basal levels of MLC-P. (B) MLC and MLC-P levels stimulated by 10−8 M phenylephrine. See “Materials and Methods” for details. (C) Densitometric analysis of MLC-P/MLC (typical results of one of three independent experiments).
Figure 5 Effect of arsenite on vasoconstriction and intracellular Ca2+ levels in the presence of high K+. (A) Contraction induced by various concentrations of K+ in Ca2+-free KR solution in aortic rings without endothelium after treatment with arsenite. (B) Intracellular Ca2+ levels determined in the presence of K+ in fura-2 loaded aortic strips without endothelium after treatment with arsenite; values shown are mean ± SE of more than three independent experiments. See “Materials and Methods” for details.
*Significantly different from the control group (p < 0.05).
Figure 6 Simultaneous measurement of Ca2+ increase and contraction by KCl in aortic strips without endothelium after treatment with arsenite. Representative tracings of intracellular Ca2+ increase (A) and contraction (B) induced by KCl. See “Materials and Methods” for details. (C) Ca2+ elevation versus the magnitude of contraction; values shown are mean ± SE of seven independent experiments.
Figure 7 Effect of intravenously administered arsenite on phenylephrine-induced blood pressure increase in rats. (A) Saline 2 hr after arsenite exposure. (B) Phenylephrine 2 hr after saline exposure (ΔmmHg = 23.0 ± 3.2; mean ± SE of four animals). (C) Phenylephrine 2 hr after arsenite exposure (ΔmmHg = 36.8 ± 3.6; mean ± SE of four animals). See “Materials and Methods” for details. Periods of infusion are indicated in each panel. Data are representative tracings of four independent experiments.
Figure 8 Proposed mechanism for arsenite-induced vasoconstriction. Abbreviations: Ca2+–CaM, calcium calmodulin; IP3, inositol 1,4,5-trisphosphate; PIP2, phosphatidylinositol-4,5-bisphophate; PLC, phospholipase C; PPase, phosphatase; SMC, smooth muscle cells; SR, sarcoplasmic reticulum. Arsenite enhances the contraction of SMC by agonists such as phenylephrine or serotonin mediated through MLC phosphorylation. Arsenite also enhances vasoconstriction induced by high K+. The mechanism for this effect is due not to alteration of intracellular Ca2+ levels, but to Ca2+-sensitization (shaded area) possibly via inhibition of PPase.
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Wang CH Jeng JS Yip PK Chen CL Hsu LI Hsueh YM 2002 Biological gradient between long-term arsenic exposure and carotid atherosclerosis Circulation 105 1804 1809 11956123
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7414ehp0113-00133616203243ResearchTetrachloroethylene (PCE, Perc) Levels in Residential Dry Cleaner Buildings in Diverse Communities in New York City McDermott Michael J. 1Mazor Kimberly A. 1Shost Stephen J. 1Narang Rajinder S. 2Aldous Kenneth M. 2Storm Jan E. 11 Center for Environmental Health, New York State Department of Health, Troy, New York, USA2 Wadsworth Center for Laboratories and Research, New York State Department of Health, Albany, New York, USAAddress correspondence to J.E. Storm, New York State Department of Health, Center for Environmental Health, 547 River St., Troy, NY 12180 USA. Telephone: (518) 402-7820. Fax: (518) 402-7819. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 21 6 2005 113 10 1336 1343 13 7 2004 21 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Fugitive tetrachloroethylene (PCE, perc) emissions from dry cleaners operating in apartment buildings can contaminate residential indoor air. In 1997, New York State and New York City adopted regulations to reduce and contain perc emissions from dry cleaners located in residential and other buildings. As part of a New York State Department of Health (NYSDOH) study, indoor air perc levels were determined in 65 apartments located in 24 buildings in New York City where dry cleaners used perc on site. Sampling occurred during 2001–2003, and sampled buildings were dispersed across minority and nonminority as well as low-income and higher income neighborhoods. For the entire study area, the mean apartment perc level was 34 μg/m3, 10-fold lower than mean apartment levels of 340–360 μg/m3 documented before 1997. The maximum detected perc level was 5,000 μg/m3, 5-fold lower than the maximum of 25,000 μg/m3 documented before 1997. Despite these accomplishments, perc levels in 17 sampled apartments still exceeded the NYSDOH residential air guideline of 100 μg/m3, and perc levels in 4 sampled apartments exceeded 1,000 μg/m3. Moreover, mean indoor air perc levels in minority neighborhoods (75 μg/m3) were four times higher than in nonminority households (19 μg/m3) and were > 10 times higher in low-income neighborhoods (256 μg/m3) than in higher income neighborhoods (23 μg/m3). Logistic regression suitable for clustered data (apartments within buildings) indicated that perc levels on floors 1–4 were significantly more likely to exceed 100 μg/m3 in buildings located in minority neighborhoods (odds ratio = 6.7; 95% confidence interval, 1.5–30.5) than in nonminority neighborhoods. Factors that may be contributing to the elevated perc levels detected, especially in minority and low-income neighborhoods, are being explored.
dry cleanersenvironmental justicePCEpercrace/ethnicitysocioeconomic statustetrachloroethylene
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Tetrachloroethylene (PCE), commonly referred to as perc, is the most frequently used solvent in the dry cleaning industry (Earnest 1996). In New York City and many other urban areas, dry cleaners using perc are sometimes colocated with residences, offices, retail businesses, or food establishments and emit fugitive perc emissions that contaminate indoor air throughout the buildings where they are located (Schreiber et al. 1993, 2002; Wallace et al. 1995). Perc levels in buildings with an operating dry cleaner, or simply near a dry cleaner, have ranged up to 55,000 μg/m3 (Altmann et al. 1995; Schreiber et al. 1993, 2002; Wallace et al. 1995).
In the workplace, air perc levels averaging about 30,000–80,000 μg/m3 have been associated with alterations in color vision and cognitive function (Gobba 2000), and levels of 1,800–2,400 μg/m3 have been reported to decrease visual contrast sensitivity (VCS) (Schreiber et al. 2002). Residential indoor air perc levels averaging about 5,000 μg/m3 have been associated with small but statistically significant deficits in cognitive performance (e.g., deficits in short-term memory, decreased reaction time) (Altmann et al. 1995), and residential indoor air levels averaging about 700 μg/m3 have been associated with decreases in visual function, although decreases were not significant, and residents’ function was still within a normal range (Schreiber et al. 2002; Storm and Mazor 2004).
These observations together have raised concern that residents of buildings where dry cleaners are using perc on site (i.e., residential dry cleaner buildings) may experience long-term, involuntary, and possibly harmful perc exposures. Based on this concern and evaluation of visual and other health effects associated with perc exposure, the New York State Department of Health (NYSDOH) derived a health-based guideline of 100 μg/m3 perc for residential air, considering continuous lifetime exposure and sensitive people (NYSDOH 1997, 2003). The NYSDOH currently considers this level to be a useful guideline in aiding decisions about the nature and urgency of efforts to reduce residential exposures to perc. Actions to reduce exposure are recommended by the NYSDOH if perc levels are above background even if they are < 100 μg/m3, but an increase in the scale and urgency of such actions is recommended when air levels are > 100 μg/m3. The NYSDOH recommends immediate action when an air level is ≥ 1,000 μg/m3.
Perc exposures have also been addressed by the federal government. In 1993, the U.S. Environmental Protection Agency issued regulations to control air emissions of perc from dry cleaners (U.S. Environmental Protection Agency 1993). However, these regulations did not specifically address fugitive perc emissions from dry cleaners in residential buildings. Hence, the New York State Department of Environmental Conservation (NYSDEC) and the New York City Department of Environmental Protection (NYCDEP) adopted additional dry cleaner regulations intended to reduce and contain fugitive perc emissions in 1997 and 1998, respectively, which specifically addressed dry cleaners in residential buildings (New York City 1998; NYSDEC 1997). Deadlines for compliance with specific components of the regulations were staggered over several years depending upon the type (“generation”) of dry cleaning equipment being used and the type of building (commercial or mixed use) where the dry cleaner was located. The dry cleaner regulations also mandated training and required submission of annual inspection reports by state-approved, third-party inspectors that are used to help document compliance.
Concurrent with adoption of these additional dry cleaner regulations, the NYCDEP and the New York City Department of Health and Mental Hygiene (NYCDOHMH) initiated a process to specifically address complaints from apartment building residents concerned about perc emissions from dry cleaners. Upon receipt of a citizen complaint regarding perc, the NYCDOHMH determines indoor air perc levels in complainants’ residences. Depending upon the level of perc detected, the dry cleaning equipment is sealed (perc > 1,000 μg/m3) or a notice of violation to the dry cleaner operator is issued (100 μg/m3 < perc < 1,000 μg/m3). In either case, the NYCDEP conducts an on-site investigation of the dry cleaner to determine compliance with dry cleaner regulations and to identify remedial actions required to reduce fugitive perc emissions. This complaint response process is a valuable component of dry cleaner regulation enforcement in New York City while also providing anecdotal information on perc levels in “complaint” buildings.
In 2000, the NYSDOH began recruitment for the New York City Perc Project (NYC Perc Project), a study intended to document perc exposures and possible associated visual function effects among residents of dry cleaner buildings. Indoor air perc levels and biologic (exhaled breath, blood) measures of perc exposure were obtained for residents in buildings with and without dry cleaners, and visual function was assessed using measures of VCS and color vision, previously shown to be adversely affected by perc or solvent exposure (Frenette et al. 1991; Gobba 2000; Iregren et al. 2002; Mergler 1991; Mergler and Blain 1987; Mergler et al. 1987, 1996; Schreiber et al. 2002).
Indoor air sample collection and analyses for the NYC Perc Project began in 2001, coincidentally midway through full implementation of the state and city dry cleaner regulations adopted in 1997–1998. The earliest sampled dry cleaner buildings had indoor air perc levels that were markedly below levels reported before 1997 (Schreiber et al. 2002; Wallace et al. 1995), with the unexpected exception of buildings located in neighborhoods with large minority and/or low-income populations. Although the NYC Perc Project was not specifically designed to evaluate the influence of neighborhood socioeconomic characteristics or state and city dry cleaner regulations on indoor air perc level in residential dry cleaner buildings, the results of this sampling effort provide a valuable initial basis for doing this and are reported here. The findings described should prove helpful in continuing federal, state, and local efforts to ensure that residential perc exposures are appropriately limited for all those residing in buildings with dry cleaners using perc.
Materials and Methods
Study area and building selection.
Eleven ZIP code areas surrounding Central Park in the borough of Manhattan in New York City comprised the main study area. These areas were selected largely based on their high density of residential dry cleaner buildings, the presence of some buildings where residential perc levels up to 5,000 μg/m3 had been previously documented (NYSDOH, unpublished data; Schreiber et al. 2002), and their close proximity to the location of participant visual function evaluations at the Mount Sinai School of Medicine. Coincidentally, these ZIP code areas also encompass neighborhoods characterized by markedly different income and minority characteristics.
Most dry cleaners in residential buildings included in this study were identified from registration certificates submitted to the NYSDEC as required by the 1997 dry cleaner regulations. Some others were identified from NYSDEC National Emissions Standards for Hazardous Air Pollutants (NESHAP) for Perchloroethylene Dry Cleaners records and from Internet-based business directories (ReferenceUSA, InfoUSA Inc., Omaha, NE; InfoSpace, InfoSpace Inc., Bellevue, WA). Internet-based business directories were cross-referenced against NYSDEC records to ensure that all dry cleaners in the study area were identified, because not all dry cleaners complied with NESHAP or NYSDEC reporting requirements. Dry cleaners identified were contacted by telephone to ascertain whether they were still in business and whether they identified themselves as using perc on site or as drop-off facilities (i.e., locations where items to be dry cleaned are dropped off and picked up but no dry cleaning occurs on site).
Identified dry cleaner buildings were visited and characterized from the sidewalk to verify that the dry cleaner was operating on site and that occupied residences were present in the same building. Numbers of residential floors were also noted for each building. Because NYC Perc Project inclusion criteria required that participants have no exposure to volatile organic compounds (VOCs) other than perc that might influence visual function, dry cleaner buildings where other businesses using VOCs (e.g., nail salons, shoe repair stores, photography developing) were present were excluded from further consideration. At least three other residential buildings with no dry cleaner or other business possibly using VOCs, and located at least one city block away from each dry cleaner building meeting inclusion criteria, were identified as reference buildings.
Early analytical results indicated that indoor air perc levels in most apartments in dry cleaner buildings sampled were below, or only slightly above, the NYSDOH residential air guideline of 100 μg/m3. Higher levels were found in dry cleaner buildings located in low-income, minority neighborhoods and in buildings elsewhere that had been the subject of a resident complaint. Because successful completion of the NYC Perc Project required that as many apartments as possible with elevated perc levels be identified, the strategy for identifying buildings for inclusion was modified so that buildings located in minority or low-income ZIP code areas and those that had been the subject of a complaint were prioritized.
Several residential buildings with dry cleaning drop-off facilities were inadvertently included early in the study before phone calls to ascertain whether dry cleaners were using perc on site were instituted. Although not meeting study criteria for inclusion in the NYC Perc Project, indoor air perc levels associated with drop-off facilities are of interest and so are also reported here.
Household recruitment and participant activities.
Buildings sampled include residential buildings where at least one household met NYC Perc Project eligibility criteria and enrolled in the study. Eligible households included those with one adult (20–55 years of age) and at least one child (5–14 years of age) residing in their building for at least 1 year. Adult–child pairs meeting these criteria and willing to participate were initially screened to exclude those with current or previous exposures to VOCs and/or medical conditions that could possibly interfere with visual function evaluation (i.e., diabetes, cataracts, glaucoma). Indoor air in five households without children was also sampled because residents were concerned and adamant about having their indoor air tested or because residents participated early in the study to help optimize study procedures. During screening, participants were asked to categorize their household race/ethnicity into one or more (up to four) of the following categories: white, African American, American Indian, Chinese, Japanese, Korean, Native Hawaiian, Samoan, Hispanic, or other. Adult participants were also asked to categorize their annual household income into one of the following ranges: < $15,000, $15,000–30,000, $30,000–45,000, $45,000–60,000, or > $60,000.
In most ZIP code areas, written material describing the NYC Perc Project was mailed to apartments in targeted residential dry cleaner buildings using addresses obtained during visits to the building or through the New York State Zip+4 Directory (U.S. Postal Service 2000). Listed telephone numbers associated with targeted buildings were obtained through reverse address queries from Internet-based residential telephone directories (ReferenceUSA, InfoSpace). Up to five calls to every residential telephone number were made at different times of day and on different days of the week beginning 5 days after study information had been mailed to addressees. Messages describing the study were left on all answering machines encountered. When a telephone call was answered, an attempt was made to determine whether an adult–child pair was present. If so, the respondent was asked to complete the screening questionnaire.
In ZIP code areas that have large minority (either predominately Hispanic or predominately African American) populations, recruitment was conducted through door-to-door visits by bilingual (Spanish/English) community health workers. This approach is consistent with recommendations for recruiting minority and lower-income populations (Cabral et al. 2003; Fitzgibbon et al. 1998; Grunbaum et al. 1996; Harris et al. 2003). Community health workers visited all residences in targeted buildings during afternoon and evening hours on different days of the week. Adults responding to door knocks were given a verbal description of the study and a written fact sheet describing the project, in Spanish or English, whichever was appropriate, and were administered the screening questionnaire. Written information urging residents to call the NYC Perc Project to enroll or obtain more information was left on doorsteps or slipped under doors when residents were not at home.
Residences of all eligible participants were visited to collect 24-hr indoor air samples. During these home visits, other activities associated with the NYC Perc Project also occurred (e.g., collection of exhaled breath samples, completion of residential/occupational/medical history questionnaires). All participants volunteered and signed adult consent and/or child assent forms approved by the NYSDOH and the Mount Sinai School of Medicine institutional review boards. Participants received $100 to compensate them for their participation in the NYC Perc Project, screening for glaucoma and other eye diseases, and a prescription for corrective lenses, if warranted, at no cost.
Indoor air sample collection and analysis.
Indoor air samples were collected using 3M organic vapor monitors (3M, St. Paul, MN) deployed in duplicate in the main living areas. Monitors were placed approximately 6 feet high and away from any direct sources of ventilation such as windows, air conditioners, fans, or heating/cooling vents. Air sampling occurred for 21–27 hr during weekdays beginning between 1500 and 2100 hr. A hard plastic, impermeable lid provided by the manufacturer was affixed to each monitor at the end of the collection period.
Monitors were analyzed for perc by the NYSDOH Wadsworth Center for Laboratories and Research in Albany, New York, as described by Amin et al. (1998). Analytical results were reviewed at the laboratory in accordance with approved quality assurance/quality control procedures and entered into the NYS-DOH Environmental Laboratory Data Accessioning and Reporting System. Sample results at or below the detection limit of 5 μg/m3 are reported as present but less than 5 μg/m3 (PL). Both the participating household and the NYCDOHMH were notified as soon as possible when apartment perc levels were above background, and follow-up activities were initiated by the NYCDOHMH.
Geographic information system application.
Buildings were geocoded according to street address using MapInfo (professional version 7.0; MapInfo Corporation, Troy, NY) and were assigned Census 2000 (U.S. Census Bureau 2002) block group characteristics for the census block group where they were located. Census block groups were categorized as minority or low income according to criteria for New York State urban areas and New York State urban poverty thresholds, respectively, outlined in the NYSDEC Environmental Justice and Permitting Policy (NYSDEC 2003). Census block groups with a population ≥51.1% Hispanic, African American, Asian and Pacific Islander, or American Indian (or < 51.1% non-Hispanic white) were classified as minority. Census block groups in which ≥23.59% of the population fell below the poverty threshold were classified as low income.
Analyses.
Quantities of perc in indoor air present but below the detection limit of 5 μg/m3 were assigned half the detection limit, and duplicate samples were averaged to determine apartment perc level. We evaluated apartment perc levels qualitatively against background levels of perc and against the NYSDOH residential air guideline of 100 μg/m3. Background was considered to be ≤11 μg/m3, the 75th percentile of indoor air perc levels detected in homes and offices sampled throughout the United States (Shah and Heyerdahl 1988). We also qualitatively compared perc levels with those measured in residential dry cleaner buildings before 1997 before adoption of state and city dry cleaner regulations.
We used Pearson’s correlation coefficients to estimate the association between resident self-reported race/ethnicity [minority (i.e., non-Hispanic white), nonminority (i.e., not non-Hispanic white)] and annual income range (< $30,000/year, > $30,000/year), and census block group assignment of residents’ building. We used logistic regression using generalized estimating equations appropriate for clustered observations, and SAS software (release 9.1; SAS Institute, Cary, NC) to evaluate associations between the occurrence of indoor air perc levels greater than the NYS-DOH residential air guideline of 100 μg/m3 and building census block group income or minority category.
Results
Both building and household inclusion criteria influenced which buildings and apartments were sampled as illustrated in Table 1. Overall, 180 dry cleaner facilities reported using perc on site. Of these, 136 were characterized to determine whether they met building inclusion criteria. Eighty-three met inclusion criteria, recruitment of households was attempted in 67, and sampling occurred in at least one apartment in 24. Although there were comparatively fewer dry cleaner buildings present in minority, low-income ZIP code areas, they accounted for a third of all dry cleaner buildings sampled. This reflects the comparatively larger proportion of households in these buildings meeting household inclusion criteria, as also noted in Table 1 and discussed further below. Also, nine sampled buildings had been the subject of a prior complaint, all of which were located in nonminority, higher income ZIP code areas. At least one household in 36 residential buildings without a dry cleaner was also sampled.
The study requirement that sampled households include an adult and child clearly influenced the sample obtained. As illustrated in Table 1, only about 1 in 10 households contacted included an age-eligible child (i.e., were potentially eligible). A higher proportion of contacted households in minority, low-income ZIP code areas had age-eligible children, so this study requirement contributed to a higher proportion of potentially eligible households being identified in minority, low-income ZIP code areas. This, combined with comparatively higher eligibility and participation rates, contributed to the final sample in which one-third of sampled households in dry cleaner buildings were in minority, low-income ZIP code areas even though they accounted for only about 1/10th of total contacted households. Recruitment and enrollment of contacted households in buildings without dry cleaners showed similar patterns. Also, 21 sampled households in nonminority, higher income ZIP code areas were in buildings that had been the subject of a prior complaint.
To assess the potential for selection bias given the low household contact and eligibility rates illustrated in Table 1, every dry cleaner building in the study area was assigned socioeconomic and demographic characteristics of the census block group where it was located. Population characteristics associated with dry cleaner buildings that were sampled and those that were not sampled were similar. In nonminority, higher income ZIP code areas, sampled dry cleaner buildings were located in census block groups averaging 74% nonminority and 7% low-income populations, and in which 7% of households included children 5–15 years of age. Dry cleaner buildings characterized, meeting inclusion criteria, and subjected to recruitment that were not sampled were in census block groups averaging 78% nonminority and 8% low-income populations and in which 7% of households included children 5–15 years of age. In minority, low-income ZIP code areas, sampled dry cleaner buildings were located in census block groups averaging 25% nonminority and 21% low-income populations and in which 14% of households included children 5 and 15 years of age. Dry cleaner buildings characterized, meeting inclusion criteria, and subjected to recruitment that were not sampled were in census block groups averaging 43% nonminority and 15% low-income populations and in which 11% of households included children 5–15 years of age. In all ZIP code areas, census block group characteristics assigned to dry cleaner buildings that were not characterized, that did not meet inclusion criteria, and/or that were not subjected to recruitment were similar to characteristics assigned to buildings that were sampled. Thus, within ZIP code areas, population characteristics of the dry cleaner buildings sampled are similar to those that were not sampled. Additionally, building census block group assignment and self-reported household minority and income categories were significantly correlated for building and household minority category (r = 0.55, p < 0.0001) and for building and household low-income category (r = 0.48, p = 0.005). Thus, socioeconomic characteristics of building census block group assignment and building residents appear to be equivalent, and characteristics associated with sampled buildings appear to be similar to other dry cleaner buildings in the same ZIP code areas.
Table 2 details minority and income census block group assignment for each dry cleaner building sampled as well as whether it had ever been the subject of a complaint, number of floors in each building, and perc levels for each household sampled. Table 2 conveys the following information pertinent to interpreting indoor air perc levels in the dry cleaner buildings sampled. First, the buildings sampled are dispersed throughout minority, low-income and nonminority, higher income neighborhoods and thus provide information for buildings in socioeconomically diverse areas. Second, the six highest perc levels detected, ranging between 695 and 5,000 μg/m3, are in six different dry cleaner buildings located in census block groups characterized as minority or as both minority and low income. These buildings are also among the smallest buildings sampled, only one having more than four floors (Table 2). Third, perc levels in “complaint” buildings, ranging from 5 (PL) to 372 μg/m3, were not among the highest in the study area, although they were among the highest in nonminority, higher income census block groups. None of the nine “complaint” buildings sampled was in a minority or low-income area. Fourth, all residences with perc > 100 μg/m3, with one exception (building e47), occurred on floors 1–4 of sampled buildings (Table 2). Finally, 12 of the 24 sampled dry cleaner buildings had at least one apartment where perc levels were > 100 μg/m3, with four of them also having at least one apartment where perc levels were > 1,000 μg/m3 (Table 3).
Importantly, when only buildings located in minority and/or low-income neighborhoods are considered, mean (geometric) perc levels are close to or exceed the NYSDOH residential air guideline of 100 μg/m3. Table 4 shows that indoor air perc level in 29 apartments in 10 dry cleaner buildings located in a minority census block group averaged 76 μg/m3, compared with 19 μg/m3 in 36 apartments in 14 buildings located in nonminority census block groups. The mean perc level in 11 apartments in 5 dry cleaner buildings located in a low-income census block group was 256 μg/m3, compared with 23 μg/m3 in 54 apartments in 19 buildings located in non-low-income census block groups. Thus, residents of dry cleaner buildings in minority, low-income areas appear to have disproportionately elevated exposures to perc even though, overall, perc levels have decreased since adoption of the 1997 dry cleaner regulations.
Discussion
We determined indoor air perc levels in residential buildings with on-site dry cleaners and in nearby residential buildings without dry cleaners in the borough of Manhattan, New York City. Buildings sampled included only those that were evaluated for NYC Perc Project inclusion and that met building inclusion criteria (e.g., no other source of VOCs present, occupied residences present). Additionally, individual apartments sampled included mostly those meeting NYC Perc Project household inclusion criteria (i.e., presence of an adult and child residing in the same household for at least 1 year with no other VOC exposures or certain medical conditions), although five sampled apartments included only adult residents. Thus, the sample obtained is not a truly random sample of all dry cleaner buildings in the study area. However, socioeconomic characteristics of the census block groups where sampled buildings are located reflect socioeconomic characteristics of their larger ZIP code area, are equivalent to census block groups where buildings that were not sampled are located, and are correlated with sampled household self-reported socioeconomic characteristics. Thus, conclusions drawn with respect to sampled building neighborhood characteristics and indoor air perc level are likely to be applicable to other residential buildings matching NYC Perc Project building inclusion criteria (e.g., dry cleaner using perc on site; no other source of VOCs).
Results demonstrate that mean indoor air perc levels in residential dry cleaner buildings in the study area have decreased by about 10-fold overall since adoption of state and city dry cleaner regulations (Table 3) and related enforcement activities (e.g., the complaint response process) in 1997. Maximum indoor air perc values have decreased about 5-fold over the same period. The range of perc levels observed in this study is also lower than the range of levels recently found in a jurisdiction without additional, nonfederal dry cleaner regulations in place. In eight residences in dry cleaner buildings in Hudson County, New Jersey, selected randomly from a list of 82 dry cleaners colocated with residences, indoor air perc levels ranged from 470 to 4,200 μg/m3 when sampled in 1998 (Garetano and Gochfeld 2000). By comparison, perc levels in most residences in dry cleaner buildings reported here were < 400 μg/m3, although eight apartments had perc levels > 400 μg/m3 (Table 2).
Thus, the findings reported here indicate that state and city dry cleaner regulations that specifically address the control of fugitive perc emissions from dry cleaners operating in residential buildings have apparently contributed to a substantial decrease in indoor air perc levels in those buildings. It is not clear how large a role, if any, the complaint response process has played in this decrease. Data were not obtained in this study that would support analysis of this. Moreover, despite the overall decrease in perc levels, mean levels in dry cleaner buildings remain elevated above levels in buildings with only drop-off facilities or without a dry cleaner (Table 4). Additionally, half the residential dry cleaner buildings sampled still had at least one apartment where indoor air perc levels exceeded the NYSDOH residential air guideline of 100 μg/m3, and four of them had at least one apartment where perc levels exceeded 10 times the NYSDOH residential air guideline (Tables 2 and 3). Of the 65 individual apartments sampled, 17 had perc levels > 100 μg/m3, and 4 had a perc level > 1,000 μg/m3 (Table 3). Thus, despite the evident success of additional dry cleaner regulations adopted in 1997 in reducing residential exposures to perc, involuntary residential perc exposures continued in the study area, at least through 2003, when sampling for this study was completed.
Importantly, the decrease in perc levels occurred unevenly. Perc levels were disproportionately higher in residential dry cleaner buildings located in minority, low-income neighborhoods compared with nonminority, higher income neighborhoods (Tables 2 and 4). All 4 apartments with perc levels > 1,000 μg/m3 are located in 4 different dry cleaner buildings in minority neighborhoods (3 of which are also low income), whereas none of 36 apartments in 14 dry cleaner buildings in nonminority, higher income neighborhoods had perc levels > 1,000 μg/m3 (Table 2). Further, mean perc levels in dry cleaner buildings in low-income or minority neighborhoods are about 10 and 4 times higher than mean levels in higher income and nonminority neighborhoods, respectively (Table 4). Finally, logistic regression indicated a significantly increased likelihood that apartments on lower floors in residential dry cleaner buildings located in minority neighborhoods would have perc levels > 100 μg/m3 compared with apartments in residential dry cleaner buildings located in nonminority neighborhoods. Individual household race/ethnicity and annual income were significantly correlated with residents’ building census block group minority and income assignment, providing corroborative evidence that minority, low-income residents of dry cleaner buildings have disproportionately elevated exposures to perc compared with nonminority, higher income residents.
Such disproportionate exposures of minority, low-income subpopulations is consistent with other recent reports that minority and low-income communities experience greater exposures to hazardous environmental contaminants than do other communities (Bowen 2002; Maantay 2002). However, most such reports of inequities in environmental exposures rely on estimates of exposure to hazardous substances based on geographic proximity of minority and low-income neighborhoods to potential sources of hazardous substances (e.g., Superfund sites, industrial facilities, etc.). Here, spatial analyses of small-area contaminant sources (e.g., dry cleaners) were combined with information about neighborhood minority and income characteristics (e.g., census block group data) and individual exposure estimates (e.g., apartment perc level) to document that, indeed, individual minority, low-income residents of dry cleaner buildings are likely to have greater perc exposure than are other residents of dry cleaner buildings.
It is not known why indoor air perc levels exceeded 100 μg/m3, and even 1,000 μg/m3, in some residential dry cleaner buildings 6 years after adoption of regulations intended to control them. One possible contributing factor is inconsistent or poor compliance with dry cleaner regulations by dry cleaners in affected buildings. Information provided to the NYSDEC by dry cleaners in the buildings sampled, as required by the dry cleaner regulations, indicates that dry cleaners in 22 of the 24 sampled buildings were using equipment that was in compliance with the regulations at the time of sampling (information was unavailable for dry cleaners in two sampled buildings, e368 and e6). Thus, it does not appear that a failure to use approved dry cleaner equipment accounted for these observations. Work practices (e.g., maintenance of effective vapor barrier/room enclosure, proper use of exhaust fans) can influence fugitive perc emissions, and prior complaints associated with some of the dry cleaner buildings sampled, even though equipment met regulatory requirements, suggests that poor work practices may have contributed to some of the elevated perc levels observed. This is consistent with another recent report that elevated perc levels (420–7,200 μg/m3) occurred in residences colocated with dry cleaners even though dry cleaning equipment met or exceeded federal dry cleaner regulatory requirements, and that positive work practices were associated with comparatively lower perc levels (Garetano and Gochfeld 2000). Regulatory agencies involved in dry cleaner regulation enforcement in New York (NYSDEC, NYSDOH, NYCDEP, NYCDOHMH) are currently evaluating these possibilities.
Another possible contributing factor to the higher perc levels found in some residential dry cleaner buildings is the existence of undesirable air flow and ventilation characteristics, especially in older buildings. Indoor air quality investigations in residences colocated with dry cleaners completed by state and city staff frequently note higher perc levels where there are structural conditions providing pathways for perc migration (e.g., poorly sealed pipe chases, cracks in walls or ceilings). Associations between substandard housing and increased exposure to environmental tobacco smoke, lead, mold, and pesticides is well recognized (Breysse et al. 2004; Krieger and Higgins 2002), but associations between substandard residential building quality and levels of indoor air contaminants originating from a source outside the home, such as a nearby dry cleaner, have not yet been thoroughly investigated. The findings here should encourage such an examination.
Finally, residents of buildings in minority, low-income neighborhoods may be less likely to complain to the city about fugitive perc emissions from a dry cleaner in their building. As noted above, the complaint response process is a valuable tool the city health department uses to help identify instances where residential perc levels are elevated and consequently where dry cleaners may not be operating in compliance with regulations. The observation that none of the sampled dry cleaner buildings in minority, low-income areas had ever been the subject of a prior complaint whereas 9 of the 16 sampled dry cleaner buildings in the remainder of the study had been, is consistent with this notion. On the other hand, some of the “complaint” buildings had some of the highest perc levels in nonminority, higher income areas. Thus, it is not clear whether the complaint response process contributed to reductions of perc to ≤100 μg/m3. Unfortunately, additional data were not gathered during this study that would allow an evaluation of the relationship between resident complaints and residential perc levels.
Bias in the selection of households sampled could have influenced the results in the observed direction if recruitment methods reduced the likelihood of including apartments with elevated perc levels in nonminority, higher income neighborhoods. However, it appears unlikely that this occurred to a major extent. Although not all residential dry cleaner buildings were targeted for recruitment in nonminority, higher income neighborhoods, many of those that were targeted were “complaint” buildings and were therefore thought most likely to have elevated perc levels. Nine of the 17 buildings sampled in these areas had been the subject of a prior complaint, and indeed, they were among the 4 buildings in these areas with the highest perc levels (Table 2). Bias may also have influenced results in the observed direction if recruitment methods increased the likelihood of including apartments with elevated perc in minority, low-income neighborhoods. This also appears unlikely to have significantly influenced results. Although a higher proportion of apartments on floors 1–4 in minority and/or low-income neighborhoods were sampled compared with nonminority, higher income neighborhoods, similar numbers of samples on floors 1–4 were obtained in both areas and the highest absolute levels of perc were consistently observed in minority, low-income areas (Table 2). Further, participation rates were similar for eligible households in both socioeconomic neighborhoods, providing no suggestion that those with comparatively higher or lower levels of perc were more or less likely to participate (Table 1). Still, the possibility that differences in recruitment strategies or other characteristics differentiating minority, low-income households from nonminority, higher income households may have influenced these findings is an acknowledged limitation of this study.
It is not known whether adverse health effects are associated with the levels of residential indoor air perc reported here, or whether adverse health effects may be associated with them in the future. In one study, 14 adults living in apartments near dry cleaning shops had significantly reduced scores on tests of cognitive function compared with age- and sex-matched controls (Altmann et al. 1995). The range of indoor perc level was 8–23,000 μg/m3, the median (and geometric mean) was 1,400 μg/m3, and the arithmetic mean was 5,000 μg/m3. Another study of 13 adult residents of dry cleaner buildings found that VCS and color discrimination ability were decreased, although they did not differ significantly from that of age- and sex-matched controls (Schreiber et al. 2002; Storm and Mazor 2004). Perc levels in apartments of tested adults averaged 1,800 μg/m3 (geometric mean) before vision testing, and 700 μg/m3 (geometric mean) at the time of vision testing (NYSDOH unpublished data). Based on these reports, effects on cognitive and/or visual function might be hypothesized to occur among individuals exposed to the levels of perc encountered in some apartments included in this study. Visual function assessments (VCS, color vision) and biologic measures of exposure (blood, breath perc levels) have been obtained for participants in the NYC Perc Project. Analyses of these data will allow us to relate environmental and biologic measures of perc exposure to each other and to the occurrence of visual function effects. This, in turn, will allow us to assess whether the evident inequity in perc exposure documented here contributes to an inequity in health outcome.
Conclusions
Mean indoor air perc levels in residential dry cleaner buildings in New York City (Manhattan) have decreased by about 10-fold since 1997, when additional dry cleaner regulations were adopted to reduce and contain fugitive perc emissions. By 2001–2003, the mean apartment perc level was 34 μg/m3, 10-fold lower than mean apartment levels of 340–360 μg/m3 documented before 1997. The maximum detected perc level was 5,000 μg/m3, 5-fold lower than the maximum of 25,000 μg/m3 documented before 1997. Despite these accomplishments, many residences in dry cleaner buildings still have levels above the NYSDOH air guideline of 100 μg/m3, and some have levels above 1,000 μg/m3. Moreover, buildings located in low-income and minority neighborhoods have disproportionately elevated perc levels. Logistic regression suitable for clustered data indicated that apartment indoor air perc level is significantly more likely to exceed 100 μg/m3 in dry cleaner buildings located in minority neighborhoods (OR = 6.7; 95% CI, 1.5–30.5) compared with buildings located in nonminority neighborhoods. Factors that may be contributing to the elevated perc levels detected, especially in minority and low-income neighborhoods, are being explored.
We thank C. Escorbore, M. Cespedes, S. Anderson, R. Lewis, E. Rodriguez, N. Mancebo, and S. Fleary of the Community Health Worker Program at the Northern Manhattan Perinatal Partnership Inc. for their hard work and dedication to the project. We also gratefully acknowledge the assistance of S.P. House, E.J. Prohonic, N.M. Walz, J.A. Hunt, P.M. Palmer, S.L. Kern, M.S. Force, S. Lin, and L.J. Gensburg of the New York State Department of Health (NYSDOH); E.M. Bell of the University at Albany (SUNY); T.J. Gentile and S.M. Byer of the New York State Department of Environmental Conservation; and R. Nieves of the New York City Department of Health and Mental Hygiene.
Although the research described in this article has been funded wholly or in part by the U.S. Environmental Protection Agency through grant R827446010 to the NYSDOH, it has not been subjected to the agency’s required peer and policy review and therefore does not necessarily reflect the views of the agency, and no official endorsement should be inferred.
Table 1 Summary of buildings and households sampled, by predominant population.
Buildings with on-site dry cleaners
Buildings without dry cleaners
Minority low income Nonminority higher income Total Minority low income Nonminority higher income Total
Buildings
Identifieda 16 164 180 — — —
Characterizedb 16 120 136 — — —
Met criteriac 11 72 83 57 236 293
Contactedd 11 56 67 — — —
Samplede 8 16 (9)f 24 15 21 36
Apartments
Identifiedg 169 2,611 2,780 485 2,730 3,215
Contactedh 102 1,159 1,261 273 979 1,252
Potentially eligiblei 31 101 132 63 112 175
Eligible j 23 66 89 29 51 80
Participatedk 22 43 (21)l 65 22 39 61
—, not applicable.
a Dry cleaners reporting using perc on site.
b Dry cleaner buildings surveyed for presence of occupied residences; absence of other VOC sources.
c Dry cleaner buildings with occupied residences; no other VOC sources.
d Dry cleaner building where household recruitment was attempted.
e Dry cleaner building where at least one apartment was sampled.
f Number of buildings that had received a prior resident complaint.
g Estimated total apartments present.
h Presence of age-eligible child(ren) determined.
i Age-eligible adult and child present.
j Met screening level NYC Perc Project household inclusion criteria.
k Apartment indoor air sampled for perc.
l Number of apartments located in buildings that had received a prior complaint.
Table 2 Perc levels (μg/m3) in residential dry cleaner buildings.
Building census block group category
Perc (μg/m3)
Building designation Low income Minority Building prior complaint No. of floors Floor(s) sampled Mean apartment levela Maximum building level
e368 X 15 14 5 (PL) 5 (PL)
e702 X 6 1, 4, 5, 6 5 (PL), 5 (PL), 5 (PL), 10 10
e56 14 3, 3 5 (PL), 12 12
e103 11 7 13 13
e369 X 4 3 27 27
e107 X 11 5, 11, 11, 11 8, 28, 13, 39 39
e41 X 16 15, 16, 16 9, 42, 10 42
e432 X 17 15, 15 49, 36 49
e53 26 3, 5 61, 8 61
e63 16 4, 5, 7, 10, 17, 17 5 (PL), 5 (PL), 5 (PL), 5 (PL), 80, 13 80
e252 X 6 1 84 84
e64 X 13 3, 6, 7, 8 99, 5 (PL), 28, 22 99
e47 X 12 2, 3, 4, 5, 6, 8, 11 5 (PL), 12, 92, 5 (PL), 25, 69, 194 194
e703 X X 7 1, 3, 4, 6, 7, 7 216, 41, 130, 12, 45, 78 216
e404 X 16 2, 2, 3 5 (PL), 322, 5 (PL) 322
e249 X 4 2 352 352
e431 X 7 2 372 372
e152 13 2, 7, 8, 11 400, 5 (PL), 15, 17 400
e18a X 4 3 695 695
e4 X X 4 3 760 760
e6 X X 4 2, 4 215, 2,100 2,100
e700 X X 3 3 2,135 2,135
e22 X 6 1, 4, 4, 4, 4, 6 84, 710, 4,600, 225, 335, 8 4,600
e5 X X 4 3 5,000 5,000
a Mean of duplicate values for main living space; quantities of perc PL were assigned half the detection limit (2.5 μg/m3) for all quantitative analysis. Perc values correspond to floors sampled.
Table 3 Summary of apartments and buildings sampled.
No. Percent
Apartments sampled 65
Mean < background (11 μg/m3) 21 32
Background (11 μg/m3) < mean ≤100 μg/m3 27 42
100 μg/m3 < mean ≤1,000 μg/m3 13 20
Mean > 1,000 μg/m3 4 6
Buildings sampled 24
Building maximum < background (11 μg/m3) 2 8
Background (11 μg/m3) < building maximum ≤100 μg/m3 10 42
100 μg/m3 < building maximum ≤1,000 μg/m3 8 33
Building maximum > 1,000 μg/m3 4 17
Table 4 Current and previously reported perc levels (μg/m3) in apartments and buildings with and without dry cleaners.
Perc (μg/m3)a
Study (location) Sampling period Dry cleaner type Buildings sampled Apartments sampled GM Median Range
Current NYC Perc Project (New York City) 2001–2003 On-site 24 65 35 28 3–5,000
Minority 10 29 75 78 3–5,000
Nonminority 14 36 19 14 3–400
Low income 5 11 256 215 12–5,000
Higher income 19 54 23 16 3–4,600
Drop-off 5 9 6 3 3–29
None 36 61 3 3 3–92
Before adoption of state dry cleaner regulations (NYSDEC 1997)
NYSDOH, unpublished data (New York City)b 1996–1997 On site 8 18 336 530 19–5,500
Wallace et al. 1995 (New York City) 1994–1995 On site 12 29 361 441 7–25,000
None 8 10 3 6 1–19
NYSDOH, unpublished data (New York City) 1991–1993 On-site, morning 16 20 1,326 2,091 6–24,667
On-site, evening 1 5 4,629 5,900 400–48,000
Schreiber et al. 1993 (Albany, NY) 1991–1992 On-site, morning 6 6 3,061 2,790 300–55,000
On-site, evening 6 6 212 4,865 100–36,500
None, morning 6 6 35 44 10–103
None, evening 6 6 46 56 22–77
GM, geometric mean.
a Values below the detection limit (5 μg/m3) were assigned one-half the detection limit (2.5 μg/m3) before log transformation and derivation of summary statistics; sampling times varied by study and ranged from 4 to 24 hr.
b Subset of buildings included in Schreiber et al. (2002).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.8009ehp0113-00134416203244ResearchLead Contamination in Cocoa and Cocoa Products: Isotopic Evidence of Global Contamination Rankin Charley W. 1Nriagu Jerome O. 2Aggarwal Jugdeep K. 3Arowolo Toyin A. 4Adebayo Kola 5Flegal A. Russell 11 Environmental Toxicology, WIGS, University of California, Santa Cruz, California, USA2 Department of Environmental Health Sciences, School of Public Health, University of Michigan, Ann Arbor, Michigan, USA3 Earth Sciences, University of California, Santa Cruz, California4 Department of Environmental Management and Toxicology, and5 Department of Agricultural Extension and Rural Development, University of Agriculture, Abeokuta, NigeriaAddress correspondence to C.W. Rankin, WIGS Laboratory, ETOX Department, UC Santa Cruz, 1156 High St., JBEB 269, Santa Cruz, CA 95064 USA. Telephone: (831) 566-8981. Fax: (831) 459-2088. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 26 5 2005 113 10 1344 1348 10 2 2005 26 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. In this article we present lead concentrations and isotopic compositions from analyses of cocoa beans, their shells, and soils from six Nigerian cocoa farms, and analyses of manufactured cocoa and chocolate products. The average lead concentration of cocoa beans was ≤ 0.5 ng/g, which is one of the lowest reported values for a natural food. In contrast, lead concentrations of manufactured cocoa and chocolate products were as high as 230 and 70 ng/g, respectively, which are consistent with market-basket surveys that have repeatedly listed lead concentrations in chocolate products among the highest reported for all foods. One source of contamination of the finished products is tentatively attributed to atmospheric emissions of leaded gasoline, which is still being used in Nigeria. Because of the high capacity of cocoa bean shells to adsorb lead, contamination from leaded gasoline emissions may occur during the fermentation and sun-drying of unshelled beans at cocoa farms. This mechanism is supported by similarities in lead isotopic compositions of cocoa bean shells from the different farms (206Pb/207Pb = 1.1548–1.1581; 208Pb/207Pb = 2.4344–2.4394) with those of finished cocoa products (206Pb/207Pb = 1.1475–1.1977; 208Pb/207Pb = 2.4234–2.4673). However, the much higher lead concentrations and larger variability in lead isotopic composition of finished cocoa products, which falls within the global range of industrial lead aerosols, indicate that most contamination occurs during shipping and/or processing of the cocoa beans and the manufacture of cocoa and chocolate products.
chocolatecocoacontaminationisotopesleadnatural foods
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Lead contamination in candies is a longstanding problem that has evolved with time. Fred Accum (1820) was the first person to systematically investigate the widespread contamination of confectionaries with metallic poisons. His study of 100 sweets sold in Britain during the early part of the 19th century found that 59 contained lead chromate, 12 contained red lead, and 10 contained Brunswick green (a mixture of Prussian blue and lead chromate). Most of the lead observed at that time was attributed to intentional adulteration or wraps that were glazed, colored, or printed with lead compounds. Since the middle of the 19th century, various measures including regulations and public education were implemented to minimize the contamination of candies from such sources (Nriagu 1985). Today, industrial activities dominate the global flux of lead in the environment (Flegal and Smith 1995; Nriagu and Pacyna 1988) and have become the predominant sources of contaminant lead in many food items, including candies. This remains true despite recent measures taken to reduce environmental lead contamination and to minimize human exposure to lead that have lowered the concentrations of this metal in foods and human populations (Egan 2002; Pirkle et al. 1998; Thomas et al. 1999; von Storch et al. 2003).
Specific focus on the source of lead in cocoa, the principal material used to make chocolate, began during the late 1970s. Despite subsequent marked reduction in the release of lead into the environment, due primarily to removal of lead from gasoline (Nriagu 1990), recent market-basket surveys still indicate continued lead contamination in some foods, notably manufactured cocoa and chocolate products. For example, in the 2000 U.S. Food and Drug Administration (FDA) Total Diet Survey (TDS), the average lead content for milk chocolate candy bars (27 ng/g) was the fourth highest reported for all food items (FDA 2000). This observation was corroborated by both the 20th Australian TDS, where milk chocolate had the second highest value of 65 foods, with a mean value of 21 ng/g and a maximum value of 40 ng/g (Food Standards Australia New Zealand 2003), and the 1997/1998 New Zealand TDS report, where the lead concentration in chocolate biscuits (15 ng/g) was 3-fold greater than those of cracker (5.2 ng/g) and plain sweet (5.2 ng/g) biscuits (Vannoort et al. 2000). In a recent study of cocoa-based chocolates sold in India, Dahiya et al. (2005) found the average lead concentrations to be 1.92 μg/g (range, 0.05–8.3 μg/g), and Onianwa et al. (1999) found the average lead content of cocoa powders sold in Nigeria to be 310 ng/g with a range of 80–880 ng/g.
The latter measurements are consistent with reports of elevated levels of lead in cocoa by the Cocoa Producer’s Alliance (COPAL), which is based in Nigeria. COPAL is the supplier of 75% of all cocoa beans to the world market (COPAL 2004a). The sources of lead in Nigerian cocoa products, which have become a concern, may conceivably include lead from local soils and rocks where the cocoa plant is grown; farming practices (e.g., the application of fertilizers, lead-containing pesticides, composts and other soil additives); atmospherically deposited lead; handling and processing of cocoa beans after harvesting (including drying in open air, transportation, and storage); grinding and manufacturing processes (wear and tear of lead-soldered machine parts); mixtures and additives to final products; and packaging and wrapping material.
The presence of relatively high concentrations in a consumer product that is heavily marketed to children is a special concern, because children are particularly susceptible to lead poisoning (Silbergeld 1997). The maximum permissible level (MPL) of lead recently proposed by Codex Alimentarius Commission was 0.1 μg/g for cocoa butter (a key ingredient in chocolate) and 1.0 μg/g for cocoa mass and cocoa powder (COPAL 2004b). In India, the lead content of chocolates (1.92 μg/g) exceeds the MPL for cocoa powder and cocoa butter. The provisional tolerable weekly intake (PTWI) of lead has been set at 25 μg/kg body weight for children [World Health Organization (WHO) 1993], equivalent to 3.6 μg/kg body weight/day. Examination of labels on various chocolate powders sold in the United States show that the serving sizes are typically in the range of 15–25 g. Assuming an absorption of 40% [Agency for Toxic Substances and Disease Registry (ATSDR) 1999], by consuming cocoa powder of average serving size (20 g) with an average lead content of 0.2 μg/g, a child weighing 15 kg would be acquiring approximately 3% of his or her PTWI from this source. By consuming cocoa powder with the maximum lead content (0.79 μg/g) reported by Mounicou et al. (2003), a child would receive about 12% of the PTWI from this exposure route. In India consumption of lead in chocolate products would account for approximately 28% of the PTWI for lead for the average child and exceed this threshold for many children.
A potentially important source of lead contamination in cocoa beans and cocoa is the tetra-ethyl lead (TEL) additive in gasoline, which is still common in many African countries (Nriagu 1992; Nriagu et al. 1996). For example, Nigerian gasoline contains 0.4–0.8 g/L lead, which is among the highest in the world (Ogunsola et al. 1994a); approximately 90% of the lead pollution in Nigeria is derived from the combustion of leaded gasoline, with total estimated annual lead aerosol emissions of 2,800 metric tons (Obioh et al. 1993). Those emissions are reflected in the contamination of Nigerian dusts, plants, and foods (Ajayi and Kamson 1983; Ndiokwere 1984; Nriagu 1992; Odukoya and Ajayi 1987; Ogunsola et al. 1994b; Onianwa and Egunyomi 1983) and the elevated blood lead concentrations noted in several studies of Nigerian people (Ademuyiwa et al. 2002; Nriagu 1992; Nriagu et al. 1997; Omokhodion 1994). This ongoing contamination from leaded gasoline emissions is consistent with the report by Bahemuka and Mubofu (1999) that lead concentrations of some foods grown on the African continent still exceed both WHO and the United Nation’s Food and Agricultural Organization permissible levels of 5 ng/g.
Unfortunately, the contribution of different natural and industrial leads in cocoa beans, cocoa, and chocolate products has not been resolved. To the best of our knowledge, there has been no systematic auditing of the sources of lead during the manufacture of chocolate products—from the harvesting of cocoa beans to the finished products. An objective of the present study was to determine the concentration and isotopic composition of lead in cocoa beans and soils from cocoa plantations of Nigeria to establish the baseline lead concentration of the beans and determine the relative contribution of soil lead to that concentration. Our other objective was to determine the contribution of other lead sources in cocoa bean products, cocoa, and chocolates, again using lead concentration and stable isotopic composition analyses.
Materials and Methods
Sample collection and preparation.
We collected cocoa bean and sediment samples in November and December 2002 from six farms in the three highest cocoa-producing states in Nigeria (Ondo, Osun, and Ogun), which were identified from statistics presented in the Central Bank of Nigeria Annual Reports (CBN 2002). Soil samples were collected at four depths in the soil profile: 0–10 cm, 0–20 cm, 35–50 cm, and 85–100 cm. At each farm, six separate samples were taken from the first and third profiles, and three samples were taken from the other profiles. Ripe cocoa bean samples were taken from each farm, as well as a sample of cocoa beans that had been fermented and dried in their shells, ready for export.
To create representative samples for each Nigerian farm, we made composites for each soil profile, both types of cocoa beans and the shells corresponding to the ripe cocoa beans collected. This process yielded homogenized composites of at least four soil, two cocoa bean, and two cocoa bean shell samples per farm for analysis. In addition to the Nigerian samples, cocoa beans from other countries and finished chocolate products including processed cocoa were collected for analysis.
All sample processing was conducted with established trace metal clean techniques (Scelfo and Flegal 2000) in trace metal clean rooms with HEPA (class 100) filtered air. Digestions in aqua regia (3:1 HCl:HNO3) were conducted using optima grade (Seastar Chemicals Inc., Sidney, British Columbia, Canada) reagents. Procedural blanks and reference materials from the National Institute of Standards and Technology [Standard Reference Material (SRM) 1547; NIST, Gaithersburg, MD, USA] and the National Research Council of Canada (MESS-3; Ottawa, Ontario, Canada) were digested concurrently with all samples to assess the efficacy of the method.
Analysis of lead content.
We analyzed lead concentrations using the same protocols delineated for previous analyses of lead in calcium supplements (Scelfo and Flegal 2000), with a Finnegan MAT Element high-resolution magnetic sector inductively coupled plasma mass spectrometer (HR-ICPMS) (Thermo Electron Corporation, Waltham, MA). Lead concentrations were derived from instrumental scans of the three major lead isotopes (206Pb, 207Pb, and 208Pb) and that of bismuth (209Bi). The sum of intensities for the stable lead isotopes was normalized to the bismuth internal standard to correct instrumental variations in sensitivity. Percent recoveries of MESS-3 and SRM 1547 averaged 94% and 98%, respectively.
Isotopic measurements.
In addition to the concentration measurements made with the HR-ICPMS, we made preliminary isotopic measurements from instrumental scans of all stable lead isotopes (204Pb, 206Pb, 207Pb, and 208Pb). Fractionation corrections were derived from concurrent analyses of SRM 981 (NIST; common lead standard reference material). Corrections for 204Pb/206Pb, 207Pb/206Pb, and 208Pb/206Pb averaged +0.006, +0.001, and +0.003, respectively.
We followed the initial HR-ICPMS measurements with thermal ionization mass spectrometry (TIMS) measurements of selected sample aliquots, using a VG Sector 54-WARP TIMS (GV Instruments, Wythenshawe, Manchester, England) and established protocols (Steding et al. 2000). Before these analyses, samples were dried and purified using Dowex AG1-X8 anion exchange resin (50–100 mesh) (Bio-Rad Labs, Hercules, CA) and high-purity (Seastar) hydrobromic acid. Soil samples were passed through the columns once, whereas other samples (cocoa bean shells, processed cocoa, and chocolate products) were passed through the columns a second time to further improve their purity and optimize their isotopic composition analyses. The eluates were dried and loaded onto rhenium filaments with a phosphoric acid and silica gel ionization enhancer. Procedural blanks were determined with a 208Pb spike and were < 0.1% of the samples analyzed. Fractionation corrections were calculated from concurrent analyses of SRM 981 using the linear law, with the mass bias per atomic mass unit correction averaging 0.0011 ± 0.0002. The precision of reproducibility for NIST 981 was ± 0.020 for 206Pb/204Pb, ± 0.0005 for 206Pb/207Pb, and ± 0.0009 for 208Pb/207Pb.
Results and Discussion
As shown in Table 1, lead concentrations in cocoa beans ranged from ≤ 0.103 to 1.78 ng/g, with an average concentration of 0.512 ng/g. This average is comparable with the lowest reported concentrations of lead in food (Flegal et al. 1990; Tahvonen and Kumpulainen 1995). Moreover, that average is considered to be conservatively high because it uses the detection limit (0.103 ng/g) for three samples with lead concentrations below that limit and includes the relatively high (order-of-magnitude) concentration of another sample (Kango Village Farm; sample taken directly from husk) that appears to have been contaminated when compared with the concentrations of the other 11 samples. Therefore, we assumed that the average lead concentration of cocoa beans from the Nigerian farms is < 0.5 ng/g and may well be < 0.1 ng/g.
Although the lead content of cocoa beans is as low as or lower than those of hundreds of different foods in the United States and elsewhere, lead concentrations of manufactured cocoa are among the highest of all foods. The values, displayed in Table 2, are similar to the value (280 ng/g) independently determined by West Coast Analytical Service (WCAS; Santa Fe Springs, CA) for processed cocoa (Northington J, personal communication) and the range of values (140–297 ng/g) reported by Mounicou et al. (2002). More recently, Mounicou et al. (2003) reported lead concentrations in cocoa powder ranging from 11 to 769 ng/g, with an average of 255 ng/g. Our average concentration, 197 ng/g, about 3% of a child’s PTWI, is also comparable with the highest concentration reported for any food (boiled shrimp maximum, 210 ng/g) in the U.S. TDS (FDA 2000). Similarly, the average lead concentration for 23 chocolate products measured by WCAS, 32.5 ng/g, is indicative of contamination, and the individual values, displayed in Table 3, are similar to the mean (27 ng/g) reported in the U.S. TDS for a plain milk chocolate bar (U.S. FDA 2000). Most notably, the average lead concentration of those chocolate products is approximately 60-fold higher than the average lead concentration of the Nigerian cocoa beans. A comparison of lead concentrations in the analyzed source material and the finished products is shown in Figure 1.
Although the lead content of cocoa beans is as low as or lower than those of hundreds of different foods in the United States and elsewhere, lead concentrations of manufactured cocoa are among the highest of all foods. The values, displayed in Table 2, are similar to the value (280 ng/g) independently determined by West Coast Analytical Service (WCAS; Santa Fe Springs, CA) for processed cocoa (Northington J, personal communication) and the range of values (140–297 ng/g) reported by Mounicou et al. (2002). More recently, Mounicou et al. (2003) reported lead concentrations in cocoa powder ranging from 11 to 769 ng/g, with an average of 255 ng/g. Our average concentration, 197 ng/g, about 3% of a child’s PTWI, is also comparable with the highest concentration reported for any food (boiled shrimp maximum, 210 ng/g) in the U.S. TDS (FDA 2000). Similarly, the average lead concentration for 23 chocolate products measured by WCAS, 32.5 ng/g, is indicative of contamination, and the individual values, displayed in Table 3, are similar to the mean (27 ng/g) reported in the U.S. TDS for a plain milk chocolate bar (U.S. FDA 2000). Most notably, the average lead concentration of those chocolate products is approximately 60-fold higher than the average lead concentration of the Nigerian cocoa beans. A comparison of lead concentrations in the analyzed source material and the finished products is shown in Figure 1.
Possible origins of contaminant lead in both the manufactured cocoa and chocolate products are identified by their lead isotopic ratios, which are also shown in Tables 2 and 3. Figure 2 provides a comparison of the lead isotopic ratios for chocolate products and manufactured cocoa with the ratios of the world’s industrial aerosols, as compiled by Bollhöfer and Rosman (2000, 2001, 2002). The plot shows that isotopic compositions of all of the chocolate products overlap with those of lead aerosols measured by Bollhöfer and Rosman (2000, 2001, 2002), but the isotopic compositions of manufactured cocoa and chocolate products are variable. Consequently, there is no single, identifiable source of contaminant lead in either processed cocoa or chocolate products, which is consistent with reports of geographic differences of lead concentrations in cocoa powder (Mounicou et al. 2003).
One of those sources may be cocoa bean shells, which have been shown to be very efficient in removing lead from solutions (Meunier et al. 2003a, 2003b, 2003c, 2004). Meunier et al. (2003a, 2003b) showed extraneous lead adsorption onto cocoa bean shells of up to 17,000 μg/g, or approximately 35 million times greater than our conservatively high calculation of the average lead concentration in cocoa beans. The shells can thus be regarded as an excellent protective shield against intrusion of lead into the bean from external sources before the beans are harvested. Furthermore, the removal of lead results in an increase in solution pH (removal of protons) and the release of calcium, magnesium, potassium, and sodium from the cocoa shells (Meunier et al. 2003c). The modification of ion balance may result in the transfer of lead from the bean to the shell, a decontamination process. Because of their capacity to scavenge lead, the shells may become a source of contamination after the beans are harvested.
That potential is indicated by the high lead concentrations of cocoa bean shells listed in Table 4. The average of our measurements of lead in cocoa bean shells (160 ng/g) is approximately 320-fold greater than the average values of lead measured in Nigerian cocoa beans (Figure 1). The disparity between lead levels in the cocoa beans and shells is consistent with the literature on lead contamination in foods, which have shown that contamination is greatest on plant surfaces that are subject to the direct deposition of industrial lead aerosols. For example, two decades ago it was determined that lead concentrations of spinach in the United States were elevated 30-fold from a baseline value of 0.0015 μg/g to 0.045 μg/g by atmospheric depositions of industrial lead, whereas the lead concentrations of peanuts were only elevated 2-fold from a baseline value of 0.005 μg/g to 0.010 μg/g from atmospheric contamination (Flegal et al. 1990). This potential source of contamination is further evidenced by the absence of a measurable increase in lead concentration between the beans sampled directly after removal from the husk of the plant and those that had been fermented in banana and plantain leaves and sun-dried (Oke Osun Farm; directly from husk, 0.846 ng/g; fermented and dried, 0.839 ng/g).
The presence of contaminant lead in bean shells from the cocoa farms is substantiated by the concentrations we observed in the various soil profile composites of this study (Table 5). The average lead concentration was 14.2 μg/g, which is consistent with the survey of Chukwuma (1997), who reported the lowest lead value measured in Nigerian soils as 10 μg/g. The lead isotopic ratios of the soil profiles, listed in Table 6 and displayed in Figure 3, are variable, indicating that multiple sources (e.g., historically different sources of TEL, pesticides, fertilizers, machinery) are responsible for the contamination observed. Although the isotopic area encompassed by the soil profiles overlaps the lead isotopic compositions of the cocoa bean shells and may be a source of current contamination, as shown in Figure 3, the similarities in the isotopic compositions of cocoa bean shells are indicative of a single predominant source of lead contamination at the cocoa farms. In light of the numerous published reports on the predominance of gasoline emissions as a source of lead contamination in Nigeria, this source is tentatively attributed to TEL, although we were not able to obtain samples of Nigerian gasoline for isotopic composition analysis in the United States for this study.
As shown in Figure 4, lead isotopic ratios of cocoa bean shells overlap those of manufactured cocoa and chocolate products. Because the FDA limits cocoa bean shells to comprise a maximum of 1.75% in finished chocolate products (Beckett 2002), lead concentrations observed for cocoa bean shells are too low to account for the contamination observed in the finished products. Coupling the previously discussed capacity for cocoa bean shells to adsorb lead with their lead isotopic composition suggests that further contamination of cocoa bean shells during the fermentation and drying stages at the farm is a possible source of some of the contaminant lead observed in the finished products. However, the larger spread of lead isotopic compositions for the manufactured cocoa and chocolate products indicates other sources of contamination occurring after the cocoa farms. Further studies investigating bean storage and intermediate phases of shipping and processing are needed to isolate the predominant source of lead found in the chocolate and cocoa products.
In summary, chocolate products and manufactured cocoa contain relatively high levels of contaminant lead compared with the baseline value for Nigerian cocoa beans used to make those products. Isotopic composition analyses of the products indicate multiple sources of contamination of industrial origin, which is consistent with the observation that there are numerous sources of lead contamination during the production of cocoa that have yet to be identified (COPAL 2004a, 2004b). Similar lead isotopic composition in contaminated cocoa bean shells from Nigeria, together with the high ability of cocoa bean shells to adsorb lead, suggest that contamination during cocoa processing at each farm may be responsible for some of the contamination in cocoa products; we propose that the ongoing use of leaded gasoline in Nigeria contributes to that contamination. However, the low lead concentration in cocoa beans compared with those of manufactured cocoa and chocolate products indicates that most lead contamination in those products occurs after the beans are harvested and dried, during the shipping of those beans and/or the manufacturing of cocoa and chocolate products.
R. Franks, S. Hibdon, D. Steding, and the WIGS laboratory were instrumental in completing the research. We thank P. Mascharak, D. Smith, Z. Zhu, and the anonymous reviewers for their suggested revisions of the manuscript.
This study was funded by the W.M. Keck Foundation, University of California Toxic Substances Research and Teaching Program, and the American Environmental Safety Institute.
Figure 1 A comparison of average lead concentrations (ng/g) for analyzed cocoa beans, cocoa bean shells, chocolate products, and manufactured cocoa.
Figure 2 Isotopic compositions of analyzed chocolate products and manufactured cocoa compared with those in global aerosols measured by Bollhöfer and Rosman (2000, 2001, 2002).
Figure 3 Isotopic compositions of cocoa bean shells compared with those of soil profile composites.
Figure 4 Isotopic compositions of cocoa bean shells compared with those of chocolate products and manufactured cocoa.
Table 1 Lead concentrations (ng/g) of cocoa beans taken directly from husk and after being fermented and dried at Nigerian farms.
Directly from husk
Fermented and dried
State Farm [Pb] % RSD [Pb] % RSD
Ogun Oke Osun, Ibese 0.846 9.81 0.839 7.73
Kango Village 1.78 62.4 0.941 3.83
Ondo Igbo Eleruku, Ita Ogbolu 0.213 5.16 0.182 4.45
Ase Igbo — — < DL 7.06
Osun Idi Obi I 0.313 8.30 < DL 10.1
Aba Arawense, Modakeke < DL 8.06 0.211 9.51
Abbreviations: % RSD, percent relative standard deviation, reported as the internal error (σ) in the HR-ICPMS measurements; DL, detection limit (0.103 ng/g).
Table 2 Lead isotopic ratios and concentrations (ng/g) of manufactured cocoa.
Sample 206Pb/207Pb 208Pb/207Pb [Pb] % RSD
Baking chocolate 1A 1.149 (4) 2.428 (4) 251 11.8
Baking chocolate 1B 1.148 (2) 2.425 (9) 241 0.75a
Baking chocolate 1C 1.150 (5)b 2.426 (8)b 263 0.75a
Baking chocolate 2A 1.160 (6) 2.429 (5) 188 6.93
Baking chocolate 2B 1.160 (1) 2.423 (1) 200 0.4a
Baking chocolate 2C — — 181 0.5a
Cocoa powder 1A 1.158 (3) 2.431 (11) 188 5.17
Cocoa powder 1B — — 186 0.8a
Cocoa powder 1C — — 183 0.7a
Cocoa powder 2 1.183 (3) 2.467 (1) 147 28.2
% RSD, percent relative standard deviation. Except where noted, isotopic compositions from multiple digests and analyses were averaged, and numbers in parentheses are the error (2σ) of these averages. % RSD is the error (2σ) from multiple analyses except where noted.
a Reported as the internal error (2σ) from the HR-ICPMS counting statistics.
b Reported as the internal error (2σ) calculated from the average RSD from concurrent SRM 981 analyses on the HR-ICPMS.
Table 3 Lead isotopic ratios and concentrations (ng/g) of chocolate products.
Sample [Pb] 206Pb/207Pb 208Pb/207Pb
Bittersweet chocolate 1 69.8 1.1712 (2) 2.4282 (1)
Bittersweet chocolate 2 29.4 1.1797 (1) 2.4357 (1)
Chocolate candy 1 23.0 1.2029 (1) 2.4542 (1)
Chocolate candy 2 27.9 — —
Chocolate candy 3 18.7 1.1716 (1) 2.4242 (2)
Chocolate candy 4 20.0 — —
Chocolate candy 5 11.9 — —
Chocolate pudding 1 14.8 — —
Chocolate pudding 2 15.9 — —
Dark chocolate 1 49.6 1.1768 (1) 2.4276 (1)
Dark chocolate 2 40.9 1.1774 (1) 2.4271 (1)
Dark chocolate 3 57.6 1.1688 (1) 2.4252 (1)
Dark chocolate 4 29.4 1.1992 (3) 2.4405 (2)
Dark chocolate 5 26.0 — —
Dark chocolate 6 35.1 1.1866 (1) 2.4400 (1)
Milk chocolate 1 23.4 1.1617 (1) 2.4201 (1)
Milk chocolate 2 14.9 1.1619 (8) 2.4163 (5)
Semisweet chocolate 1 42.1 1.1720 (1) 2.4294 (1)
Semisweet chocolate 2 41.7 1.1826 (1) 2.4330 (1)
Semisweet chocolate 3 31.1 1.1638 (1) 2.4198 (1)
Semisweet chocolate 4 56.1 1.1836 (11) 2.4456 (12)
Semisweet chocolate 5 36.2 1.1766 (1) 2.4275 (1)
Semisweet chocolate 6 — 1.1820 (5) 2.4376 (4)
Numbers in parentheses are the error (2σ) recorded from TIMS counting statistics. Isotopic ratios were obtained at the University of California, Santa Cruz, and concentrations were determined at WCAS.
Table 4 Lead concentrations (ng/g) and isotopic compositions of cocoa bean shells from Nigerian farms.
State Farm Sample 206Pb/207Pb 208Pb/207Pb [Pb] % RSD
Ogun Oke Osun, Ibese Shell 1 1.155 (5) 2.434 (9) 61 1.92
Shell 2 1.156 (5) 2.412 (8) 72 0.28
Kango Village Shell 1 1.158 (5) 2.439 (9) 74 0.75
Shell 2 1.156 (5) 2.438 (9) 82 0.24
Ondo Igbo Eleruku, Ita Ogbolu Shell 1 1.1566 (1)a 2.4336 (2)a 417 0.46
Shell 2 1.153 (5) 2.425 (9) 409 0.22
Ase Igbo Shell 1 1.158 (5) 2.436 (9) 73 0.46
Shell 2 1.158 (5) 2.431 (9) 144 0.29
Osun Idi Obi I Shell 1 1.156 (5) 2.428 (9) 185 0.22
Shell 2 1.155 (5) 2.432 (9) 132 0.26
Aba Arawense, Modakeke Shell 1 1.155 (5) 2.434 (9) 120 0.24
Shell 2 1.156 (5) 2.429 (9) 157 0.18
% RSD, percent relative standard deviation. Except where noted, numbers in parentheses are the internal error (2σ) calculated from the average relative deviation from concurrent analyses of SRM 981 on the HR-ICPMS. The percent relative deviation is reported as the internal error (σ) from the HR-ICPMS counting statistics.
a Isotopic compositions and the error (2σ) are from TIMS analysis.
Table 5 Lead concentrations (μg/g) of soil profile composites from Nigerian farms.
0–10 cm
0–20 cm
35–50 cm
80–100 cm
State Farm [Pb] % RSD [Pb] % RSD [Pb] % RSD [Pb] % RSD
Ogun Oke Osun, Ibese 3.54 2.4 3.00 0.7 2.94 0.6 3.46 0.7
Kango Village 17.0 0.5 21.5 0.4 40.6 0.5 29.8 0.4
Ondo Igbo Eleruku, Ita Ogbolu 11.2 2.2 12.1 1.2 12.6 0.4 — —
Ase Igbo 30.8 0.6 17.4 0.7 25.5 0.7 17.7 0.4
Osun Idi Obi I 10.8 2.3 11.7 0.4 10.6 0.4 14.9 0.4
Aba Arawense, Modakeke 7.55 1.3 6.86 2.4 6.25 0.5 8.21 0.4
% RSD, percent relative standard deviation reported as the internal error (σ) from HR-ICPMS counting statistics.
Table 6 Lead isotopic compositions of soil profile composites from Nigerian farms.
0–10 cm
0–20 cm
35–50 cm
80–100 cm
State Farm 206Pb/207Pb 208Pb/207Pb 206Pb/207Pb 208Pb/207Pb 206Pb/207Pb 208Pb/207Pb 206Pb/207Pb 208Pb/207Pb
Ogun Oke Osun 1.168 (5)a 2.469 (9)a 1.1226 (1)b 2.6942 (2)b 1.1953 (1)b 2.4988 (1)b 1.1959 (1)b 2.4972 (1)b
Kango Village 1.130 (5)a 2.760 (10)a — — 1.1237 (1)b 2.7179 (2)b 1.1259 (1)b 2.6860 (1)b
Ondo Igbo Eleruku 1.138 (5)a 2.493 (9)a 1.141 (5)a 2.492 (9)a — — — —
Ase Igbo 1.2558 (1)b 2.6417 (2)b 1.161 (5)a 2.428 (9)a 1.2561 (1)b 2.6269 (2)b 1.2493 (1)b 2.6248 (4)b
Osun Idi Obi I 1.1922 (1)b 2.4911 (6)b — — 1.1983 (1)b 2.3329 (1)b 1.1671 (1)b 2.4527 (1)b
Aba Arawense 1.203 (5)a 2.479 (9)a 1.200 (5)a 2.482 (9)a — — — —
a Numbers in parentheses are the internal error (2σ) calculated from the average relative standard deviation from concurrent SRM 981 analyses on the HR-ICPMS.
b Numbers in parentheses are the internal error (2σ) from the TIMS counting statistics.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7479ehp0113-00134916203245ResearchSecond-Hand Smoke–Induced Cardiac Fibrosis Is Related to the Fas Death Receptor Apoptotic Pathway without Mitochondria-Dependent Pathway Involvement in Rats Kuo Wei-Wen 1Wu Chieh-Hsi 1Lee Shin-Da 2Lin James A. 3Chu Chia-Yih 4Hwang Jin-Ming 4Ueng Kwo-Chang 5Chang Mu-Hsin 6Yeh Yu-Lan 7Wang Chau-Jong 8Liu Jer-Yuh 8Huang Chih-Yang 81 Department of Biological Science and Technology, China Medical University, Taichung, Taiwan2 Department of Physical Therapy, Chung-Shan Medical University, Taichung, Taiwan3 Department of Veterinary Medicine, National Chung-Hsing University, Taichung, Taiwan4 School of Applied Chemistry, and5 Department of Internal Medicine, Chung-Shan Medical University, Taichung, Taiwan6 Division of Cardiology, Armed Forces Taichung General Hospital, Taichung, Taiwan7 Department of Pathology, Changhua Christian Hospital, Changhua, Taiwan8 Institute of Biochemistry and Biotechnology, Chung-Shan Medical University, Taichung, TaiwanAddress correspondence to C.-Y. Huang, Institute of Biochemistry and Biotechnology, Chung-Shan Medical University No. 110, Section 1, Chien Kuo N. Rd., Taichung 402, Taiwan, ROC. Telephone: 886-4-24730022 ext. 11682. Fax: 886-4-24739030. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 1 6 2005 113 10 1349 1353 9 8 2004 1 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Exposure to environmental tobacco smoke has been epidemiologically linked to heart disease among nonsmokers. However, the molecular mechanism behind the pathogenesis of cardiac disease is unknown. In this study, we found that Wistar rats, exposed to tobacco cigarette smoke at doses of 5, 10, or 15 cigarettes for 30 min twice a day for 1 month, had a dose-dependently reduced heart weight to body weight ratio and enhanced interstitial fibrosis as identified by histopathologic analysis. The mRNA and activity of matrix metalloprotease-2 (MMP-2), representing the progress of cardiac remodeling, were also elevated in the heart. In addition, we used reverse-transcriptase polymerase chain reaction and Western blotting to demonstrate significantly increased levels of the apoptotic effecter caspase-3 in treated animal hearts. Dose-dependently elevated mRNA and protein levels of Fas, and promoted apoptotic initiator caspase-8 (active form), a molecule of a death-receptor–dependent pathway, coupled with unaltered or decreased levels of cytosolic cytochrome c and the apoptotic initiator caspase-9 (active form), molecules of mitochondria-dependent pathways, may be indicative of cardiac apoptosis, which is Fas death-receptor apoptotic-signaling dependent, but not mitochondria pathway dependent in rats exposed to second-hand smoke (SHS). With regard to the regulation of survival pathway, using dot blotting, we found cardiac insulin-like growth factor-1 (IGF-1) and IGF-1 receptor mRNA levels to be significantly increased, indicating that compensative effects of IGF-1 survival signaling could occur. In conclusion, we found that the effects of SHS on cardiomyocyte are mediated by the Fas death-receptor–dependent apoptotic pathway and might be related to the epidemiologic incidence of cardiac disease of SHS-exposed non-smokers.
cardiac survival IGF-1 signalingcaspasesdeath-receptor-dependent pathwaymitochondria-dependent pathwaysecond-hand smoke (SHS)
==== Body
Second-hand smoke (SHS), a mixture of smog produced from a burning cigarette and exhaled smoke, contains thousands of chemical constituents, most of which are harmful and cause human diseases such as lung cancer and cardiovascular disease (Boyle and Maisonneuve 1995; Bronnum-Hansen 2000; De Cesaris et al. 1992; Haugen 2002; MacDougall et al. 1983). However, most studies on this subject focus on the association of SHS with respiratory symptoms, impaired lung function, and increased bronchial responsiveness. Actually, epidemiologic studies have showed that exposure to SHS increases the incidence of cardiac disease 4-fold (Ciruzzi et al. 1998; Stefanadis et al. 1998), and the mortality of cardiac failure among passive smokers is 38% higher than in those who do not smoke or who are not exposed to SHS (Baud et al. 1991; Ecanow and Blake 1978). Pope et al. (2001) found that 2 hr of exposure to SHS significantly destroyed cardiac autonomic function, as evidenced by decrements in heart rate variability in 16 adult nonsmokers. However, so far the mechanisms behind cardiac disease in subjects exposed to SHS are poorly understood.
Apoptosis is a recognized mechanism for the elimination of redundant cells, although it may also inhibit cell proliferation. In fact, it has been suggested that apoptosis plays a critical role in the pathogenesis of human diseases, including cardiac disorders (Haunstetter and Izumo 1998). Apoptosis has been reported to contribute to the loss of cardiomyocytes, after which the collagen secreted by fibroblasts replaces the space of damaged cardiomyocytes in cardiomyopathy. Hence, the fibrosis following apoptosis is recognized as a predictor of adverse outcomes in subjects with cardiomyopathy (Kim and Iwao 2000; Narula et al. 1996). Therefore, the evaluation of the apoptosis and/or fibrosis process could be an excellent way of predicting the development of cardiomyopathy induced by SHS, although the specificity of the related signaling pathways involved in the development of apoptosis and/or fibrosis needs to be identified.
The induction of apoptosis is associated with the activation of aspartate-specific cysteine protease, including caspase-3 (Fernandes-Alnemri et al. 1994). Several studies have demonstrated that mitochondria may play an important role in apoptosis by releasing cytochrome c and activating caspase-9, which activates caspase-3 that is responsible for DNA-cleavage action (Liu et al. 1996; Reed and Paternostro 1999). In end-stage cardiomyopathy, cytosolic cytochrome c is also accumulated (Narula et al. 1999). In addition, the death-receptor–induced apoptotic pathway is reportedly involved in the pathogenesis of cardiac disease (Scheubel et al. 2002). This pathway is initiated by death-receptor agonists, including the Fas ligand. After ligand binding, Fas receptor oligomerization results in the activation of caspase-8, which is upstream of caspase-3, causing the activation of apoptosis (Haunstetter and Izumo 1998). Therefore, as a common component of apoptotic signaling, caspase-3 mediates both mitochondria-dependent and death-receptor–dependent apoptotic pathways. Furthermore, the down-regulation of a survival pathway is another possible factor in the promotion of apoptosis in cells. In cardiomyocytes, insulin-like growth factor-1 (IGF-1), the survival factor through which IGF-1 receptor (IGF-1R) activates the phosphatidylinositol-3 kinase/protein kinase B (Parrizas et al. 1997) and the Ras-Raf-MEK-ERK (Ras–Raf–mitogen-activated protein kinase–extracellular receptor kinase) pathways, should be taken into consideration for preventing myocyte apoptosis (Hoshijima et al. 1998). Particularly, activated PI3K enhances the phosphorylation of Akt (Simoncini et al. 2000), which in turn regulates the activity of Bad and BCl2 to control the apoptosis of cardiomyocytes (Campbell et al. 2001).
To understand whether the effects of SHS on rat hearts are mediated through activating apoptotic pathways, including mitochondria-dependent and Fas death-receptor–dependent signalings, or through suppressing survival pathways, we examined the cardiac levels of signaling proteins and gene expression in these pathways by performing reverse-transcriptase polymerase chain reaction (RT-PCR), Western blotting, or dot blotting. We used these results to explore the molecular mechanisms of the pathogenesis of cardiac disease induced by cigarette smoke.
Materials and Methods
Animal model and cigarette smoke exposures.
We purchased male Wistar rats (6 weeks of age; body weight, 120 ± 10 g) from National Science Council Animal Center (Taipei, Taiwan). The animals were housed six per cage in an environmentally controlled animal room, and water was provided ad libitum. All animals were handled according to the guidelines of the Taiwan Society for Laboratory Animals Sciences for the care and use of laboratory animals (Institute of Laboratory Animal Resources 1996). We divided 24 rats into four exposure groups. The rats were placed in whole-body exposure chambers and exposed to 0, 5, 10, or 15 cigarettes (New Paradise, Taiwan), representing control, low, medium, and high doses, respectively. Filtered air was introduced into the chamber at a low rate of 200 L/min. Rats were exposed to cigarette smoke for 30 min, twice a day, 6 days/week for 1 month. Room temperature was maintained at 22–25°C, and relative humidity was approximately 40%. After 1 month, rats were weighed and killed. After removal from the thorax, the hearts were cleaned with double-distilled water and dried before weighing. The left and right atria and the right ventricle were then removed, and the left ventricle was weighed. We calculated the ratios of total heart weight and left ventricle weight to body weight.
Hematoxylin-eosin and Masson trichrome staining.
Hearts were fixed in formalin, embedded in paraffin, and sectioned. Slides were hydrated through a series of graded alcohols (100, 95, and 75%), 15 min each. The slides were then stained with hematoxylin and eosin (H&E) or Masson trichrome. After gently rinsing with water, slides were dehydrated through graded alcohols for 15 min each, cleared in xylene, and coverslipped. Photomicrographs were obtained using Zeiss Axiophot microscopes (Zeiss, Oberkochen, Germany).
Tissue extraction.
Cardiac tissue extracts were obtained by homogenizing the left ventricle samples in phosphate-buffered saline (PBS; 0.14 M NaCl, 3 mM KCl, 1.4 mM KH2PO4, 14 mM K2HPO4) at a concentration of 1 mg tissue/10 μL PBS for 5 min. The homogenates were placed on ice for 10 min and then centrifuged at 12,000 rpm for 30 min. The supernatant was collected and stored at –70°C for further experiments.
Protein contents.
We determined the protein content of cardiac tissue extract using the Bradford protein assay (Bradford 1976) using the protein-dye kit (Bio-Rad, Richmond, CA, USA). We used a commercially available bovine serum albumin (Sigma Chemical, St. Louis, MO, USA) as a standard. Changes in optical density were monitored at 595 nm.
Zymography protease assay.
We mixed the cardiac tissue extracts (40 μg) thoroughly with a suitable volume of PBS buffer and 4 μL dye. We carried out gelatin zymography analysis by loading 20 μL of the extracts on 8% SDS-PAGE gels containing 0.1% gelatin and run by electrophoresis at 140 V for 2.5 hr. The gels were washed in a 2.5% Triton X-100 solution with shaking for 30 min and then incubated in 50 mL reaction buffer (40 mM Tris-HCl, pH 8.0; 10 mM CaCl2, 0.01% NaN3) at 37°C for 12 hr before staining with 0.25% Coomassie brilliant blue R-250 in 50% methanol and 10% acetic acid for 1 hr. Quantitative analysis was preformed after discoloring the stain in a destaining solution (10% acetic acid, 20% methanol) twice for 30 min.
Electrophoresis and Western blot.
We prepared the tissue extract samples as described above. SDS-PAGE was carried out with 10% polyacrylamide gels. The samples were electrophoresed at 140 V for 3.5 hr and equilibrated for 15 min in 25 mM Tris-HCl, pH 8.3, containing 192 mM glycine and 20% (vol/vol) methanol. Electrophoresed proteins were transferred to nitrocellulose paper (Hybond-C Extra Supported, 0.45 μm; Amersham, Piscataway, NJ, USA) using a Hoefer Scientific Instruments Transphor unit (Hoefer Scientific, San Francisco, CA, USA) at 100 mA for 14 hr. We incubated nitrocellulose papers in blocking buffer for 2 hr at room temperature and then in blocking buffer containing 100 mM Tris-HCl, pH 7.5, 0.9% (wt/vol) NaCl, and 0.1% (vol/vol) fetal bovine serum for 2 hr at room temperature. Monoclonal antibodies (Santa Cruz Biotechnology, Santa Cruz, CA, USA) were diluted 1:200 in antibody binding buffer containing 100 mM Tris-HCl, pH 7.5, 0.9% (wt/vol) NaCl, 0.1% (vol/vol) Tween-20, and 1% (vol/vol) fetal bovine serum. Incubations were performed at room temperature for 3.5 hr. We washed the immunoblots three times in 50 mL blotting buffer for 10 min and then immersed in the second antibody solution containing alkaline phosphatase goat anti-rabbit IgG (Promega, Madison, WI, USA) for 1 hr and diluted 1,000-fold in binding buffer. The filters were then washed in blotting buffer for 10 min three times. Color development was presented in 20 mL of a mixture consisting of 7 mg nitro blue tetrazolium, 5 mg 5-bromo-4-chloro-3-indolyl-phosphate, 100 mM NaCl, and 5 mM MgCl2 in 100 mM Tris-HCl, pH 9.5. Signal intensity of Western blots was quantitated using a PhosphoImager (Kodak, Rochester, NY, USA).
RNA extraction.
We extracted total RNA using the Ultraspec RNA Isolation System (Biotecx Laboratories, Inc., Houston, TX, USA) according to the manufacturer’s instructions. Each heart was thoroughly homogenized in 1 mL Ultraspec reagent/100 mg tissue using a Polytron homogenizer (Kinematica AG, Lucerne, Switzerland). The homogenates were washed twice with 70% ethanol by gentle vortexing. RNA precipitates were then collected by centrifugation at 12,000 × g and dried under vacuum for 5–10 min before dissolving in 50 μL diethylpyrocarbonate-treated water; precipitates were then incubated at 55–60°C for 10–15 min.
RT-PCR.
Total RNA (1 μg) was reverse transcribed and then amplified (30 cycles) by PCR using a Super Script preamplification system for first-strand cDNA synthesis and Taq DNA polymerase (Life Technologies, GIBCO BRL, Rockville, MD, USA). RT-PCR products (45 μL) were separated on a 1.25% low-melting-point agarose gel (Life Technologies). Amplimers were synthesized based on cDNA sequences from GenBank (2004). We used the human pHe7 gene as an internal standard.
RNA dot blotting.
We used RNA dot blotting for the hybridization and detection of IGF-1 and IGF-1R mRNAs as described previously (Huang et al. 1998). The corresponding digoxigenin-labeled antisense RNA probes were prepared from pGEM-1 containing a BamH1–EcoR1 956-bp insert consisting of exon 3 and flanking intron sequences from the rat IGF-1 gene, and the pTRI-IGFR-human transcription template containing a 236-bp cDNA fragment of the human IGF-1R gene spanning exons 8–7 (Ambion, Austin, TX, USA).
Statistical analysis.
We compared the data between groups of animals using one-way analysis of variance. We used Fisher’s least significant difference test to determine differences. p-Values < 0.05 were taken as significant.
Results
Cardiomyopathic alteration of rats administered different doses of SHS.
After 1-month administration of different doses SHS, all rats showed a generally healthy appearance. They were then weighed and killed, and the hearts were removed and weighed. We observed dark spots in the right ventricle of SHS-treated rats, and the ratio of whole heart weight to body weight showed significant reduction in the high-dose group (Figure 1A). To understand the possible cause of the decreased heart weight, we performed a histopathologic analysis of ventricular tissue stained with H&E as well as Masson trichrome. We found that the ventricular myocardium of healthy controls showed normal architecture and orderly alignment of myocytes with minimal interstitial fibrosis in tissues observed at both 100× and 400× magnification. In contrast, disarray with markedly enlarged interstitium of myocytes is evident in the SHS-administered group, and the most significant change was observed in the high-dose group (Figure 1B). Hearts from SHS-treated rats stained with Masson trichrome showed extensive fibrosis and myofibril disarray at the 200× magnification (Figure 1C). Masson trichrome staining also qualitatively revealed increased collagen deposition at minor and moderate levels in low-and medium-dose groups, respectively, but at a very strong level in the high-dose group. In addition, we evaluated the activity of matrix metalloprotease-2 (MMP-2), a gelatinase that can degrade extracellular matrix and that is associated with the morphologic changes. The treated groups had higher levels of both MMP-2 mRNA and protein activity than did the control group, although there was a decline in the high-dose group (Figure 1D,E).
Changes of caspase-3 mRNA and active protein levels in the cardiac tissues of rats.
Exposure to SHS could lead to cardiomyocyte-apoptosis–related heart failure. In order to identify the promotion of apoptosis, we measured mRNA and the active protein levels of the executive apoptotic protein caspase-3. Both mRNA and the active protein levels of treated groups were higher than those of the controls (Figure 2).
Alterations of component protein levels of Fas-receptor–dependent and mitochondria-dependent apoptotic pathways in the cardiac tissues of rats.
To further understand the upstream signaling pathways associated with the activation of caspase-3, we examined the levels of the components of the Fas death-receptor–dependent and mitochondria-dependent/death-receptor–independent apoptotic pathways. Compared with control animals, both Fas mRNA and protein levels increased in a dose-dependent manner (Figure 3A–C). Additionally, all the treated groups showed significantly higher levels of active caspase-8 than did the control group (Figure D,E). Conversely, there was no difference in expression of cytosolic cytochrome c protein in any of the groups, and there was a significant decrease in active caspase-9 protein level in the medium- and high-dose groups (Figure 4A,B). Furthermore, both cardiac Bad and BCl2 levels were significantly higher in all treated groups, compared with the control group (Figure 4C,D).
Induction of IGF-1 and IGF-1R mRNA in cardiac tissues of rats.
We also examined cell survival. Except for the mRNA level of IGF-1R in the low-dose group, dot blotting analysis of cardiac survival factor showed mRNA of both IGF-1 and IGF-1R to have significantly greater increases than in the control group. This suggested that the survival pathway, IGF-1 signaling, might be up-regulated in cardiac tissue of rats exposed to SHS (Figure 5).
Discussion
In the present study, after the exposure of 5, 10, or 15 cigarettes for 30 min, twice per day for 1 month, the rat hearts showed increased weight loss, interstitial fibrosis, and MMP-2 activity. After the confirmation of the activated caspase-3, a marker of apoptosis development, and the elevated levels of active caspase-8 and Fas protein, we did not observe increased levels of active caspase-9 or released cytochrome c in SHS-exposed rat hearts. This indicates that SHS-induced biologic cardiac responses associated with the activated caspase-3 may be involved with the Fas-signaling pathway. Furthermore, we demonstrated that cardiac expressions of IGF-1 and IGF-1R mRNA found by dot blotting were increased, suggesting that the compensatory effect of IGF-1 survival signaling also occurred in the hearts of the SHS-exposed rats.
Brilla et al. (1990) reported an association between the turnover of collagens and remodeling of the rat ventricles. The remodeling progresses immediately after myocardial damage with an increased level of collagenases (Janicki et al. 1995). Because MMP-2 is a member of gelatinase-A family, this enzyme is able to hydrolyze collagens I, IV, V, and VII (Dollery et al. 1995). Therefore, the level of MMP-2 might be predicted to increase during cardiac remodeling. Weber et al. (1992) reported that the severity of cardiac fibrosis may become significantly apparent with the development of remodeling by increasing MMP activity. That is, once the heart is damaged, extracellular matrix that connects cardiomyocytes will be degenerated by increased MMP for the adaptation of cardiomyocyte enlargement and fibroblast invasion. After hypertrophy, the dead apoptotic myocytes will be replaced by collagens, which are secreted by the invaded fibroblasts and will accumulate, resulting in ventricular fibrosis (Anversa and Nadal-Ginard 2002; Kim and Iwao 2000; Pacifico and Henry 2003; Takemura and Fujiwara 2003). The myocardial interstitial changes resulting from increased collagen deposition lead to cardiac stiffness and pathologic cardiac dysfunction (Anversa et al. 1996). Accordingly, the accumulated collagen, an extracellular matrix protein, will further contribute to the development of heart failure (Kim and Iwao 2000). In our study, the registered alignment of the normal myocardium characteristics became disordered by the administration of SHS (Figure 1B). Masson trichrome staining also showed extensive fibrosis and myofibril disarray in the SHS-exposed heart, with wider interstitial space and higher expression and activity levels of MMP-2 (Figure 1B–E). These results suggest the development of cardiac premature death characterized by the distortion in myocardium architecture and fibrosis. We believe that pathologic cardiac dysfunction can be predicted to occur if the experiment period is prolonged. Additionally, the data on IGF-1 signaling up-regulation in the present study might provide another explanation for the occurrence of cardiac fibrosis. The up-regulated IGF-1 signaling might promote the proliferation of noncardiomyocytes, such as fibroblasts, which may grow to fill in the space originally occupied by apoptotic cardiomyocytes, and again result in myocardial interstitial changes and cardiac fibrosis (Kim and Iwao 2000).
Apoptosis of cardiomyocytes has an important implication on cardiac dysfunction because of the reduced number of cardiomyocytes per functional units. The weight loss we found in smoking-induced rat ventricle in a dose-dependent manner (Figure 1A) could be associated with the progression of heart failure. The expression levels and activity increases of caspase-3 by RT-PCR and Western blotting (Figure 2) raise the possibility of cardiomyocyte apoptosis. Actually, caspase-3 is an important molecular marker of apoptotic signaling in that it modulates both mitochondria-dependent and Fas death-receptor–dependent apoptotic pathways. Further evidence confirming which of them is involved in the activation of caspase-3 is provided by the findings of elevated levels of caspase-8 and Fas, coupled with no alteration or even reduced levels of caspase-9 activity and cytosolic cytochrome c in ventricles of rats exposed to SHS (Figures 3 and 4). This demonstrates that the Fas-signaling pathway, but not mitochondria-dependent pathway, is associated with the increased cas-pase-3 level that may indicate apoptosis. Additionally, because of the activation of the Fas-signaling apoptotic pathway, to contend with cell death, cardiac IGF-1 survival signaling was also increased (Figure 5) in SHS-exposed rat hearts. Furthermore, it is notable that SHS-induced dose-dependent responses of heart weight loss and interstitial fibrosis are not demonstrated in that of other cardiac gene/protein levels. The results of Masson trichrome staining showed minor to moderate fibrosis in low- and medium-dose groups. However, more severe fibrosis responses were observed in the high-dose group (Figure 1C). We conjecture that apoptotic enzyme actions are still under control in low- and medium-dose groups. However, cells severely damaged by high doses may be too weak to produce enough enzymes. This makes the gene/protein level alteration fail in a dose-dependent manner in SHS-exposed hearts.
The elevated level of the proapoptotic protein Bad was also observed in SHS-treated rat hearts (Figure C,D). Based on the observation of the 2.3-fold enhancement of BCl2 promoter activity by IGF-1 (Pugazhenthi et al. 1999), it is possible that the promoted IGF-1 signaling resulted in the activation of the antiapoptotic protein BCl2, which might counterbalance the action of elevated pro-apoptotic protein Bad and prohibit the release of cytochrome c from the mitochondria. Another important study (Makin et al. 2001) reported that the induction of apoptosis by Bax, whose exertions are promoted by the translocation of Bad to mitochondria and can cause cytochrome c release, may limit the process of the mitochondria-dependent apoptotic pathway in some cancer cells. Makin et al. (2001) conjectured that some events besides Bax oligomerization/complex formation to the mitochondria surface were absent in the cells. Hence, the abnormality of Bax action in these events may also have occurred in the SHS-stimulated cardiac tissue, explaining why the elevated Bad level did not result in cytochromec release from mitochondria.
Our findings on the SHS effects in cardiac tissue include reduction of weight, alteration of morphology, possible progression of remodeling, fibrosis, and the apoptosis-related effects predicted by the activation of caspase-3 and Fas death-receptor-pathway–dependent signaling. They may be, at least partially, the possible molecular mechanisms behind how exposure of nonsmokers to SHS leads to the increased risk of cardiac events reported in epidemiologic studies. Particularly, because cardiomyocyte apoptosis and/or fibrosis is a more typical end-stage condition, it is more beneficial to alleviate these problems before the end-stage is reached. Therefore, it would seem appropriate to block cardiac Fas signaling when considering possible agents that might be helpful to control the development of apoptosis and/or fibrosis-related cardiac disease induced by SHS.
Correction
In the manuscript originally published online, there were errors in authors’ names and affiliations; they have been corrected here.
This work was supported by grant NSC 89-2320-B-040-053 from the National Science Council of the Republic of China.
Figure 1 Cardiomyopathic changes in rats exposed to different doses of SHS. Abbreviations: C, control; H, high dose; L, low dose; M, medium dose; numbers beside the treatment represent the repetition. (A) The heart weight (wt) to body wt ratio (mean ± SD) of rats exposed to SHS (at least five rats per group). (B) Histopathologic analysis of cardiac tissue sections stained with H&E. Magnification: top, 100×; bottom, 400X; bars = 15 μm. Enlarged interstitium was observed in SHS-administered animal hearts, with the high-dose group showing the most significant changes; arrows indicate the myocardial interstitium. (C) Representative cross-sections with trichrome staining; fibrotic areas are stained blue. Bar = 15 μm; magnification, 200X. Extensive fibrosis and myofibril disarray are present in hearts from SHS-treated rats, particularly the high-dose group. Cardiac fibrosis induced by SHS is described in more detail in the “Discussion.” Alteration of MMP-2 levels in cardiac tissues as shown by MMP-2 mRNA levels analyzed by semiquantitative RT-PCR, using GAPDH as a loading control (D) and MMP-2 activities analyzed by zymography assay (E).
*Significantly different compared with the control group (p < 0.05).
Figure 2 Activation of caspase-3 in cardiac tissues of rats. Abbreviations: C, control; H, high dose; L, low dose; M, medium dose; numbers beside the treatment represent the repetition. (A) Semiquantitative RT-PCR analysis of caspase-3 from left ventricles, using GAPDH as a loading control. (B) Western blot analysis of the activated form of caspase-3 from left ventricles, using α-tubulin as a loading control. (C) Caspase-3 shown as percent of control (mean ± SD of three independent experiments).
**Significantly different compared with the control group (p < 0.01).
Figure 3 Activation of Fas and caspase-8 in cardiac tissues of rats. Abbreviations: C, control; H, high dose; L, low dose; M, medium dose; numbers beside the treatment represent the repetition. Fas activation in left ventricles shown by (A) semiquantitative RT-PCR analysis, (B) Western blot analysis, and (C) quantitation of signal intensity of FAS (mean ± SD of three independent experiments). Caspase-8 activation in left ventricles shown by (D) Western blot analysis and (E) quantitation of the signal intensity of the activated form of caspase-8 (mean ± SD of three independent experiments).
*Significantly different compared with the control group (p < 0.05). **Significantly different compared with the control group (p < 0.01).
Figure 4 Activation of proteins related to apoptotic pathways in cardiac tissues. Abbreviations: C, control; H, high dose; L, low dose; M, medium dose; numbers beside the treatment represent the repetition. Cytosolic cytochrome c and the activated form of caspase-9 in left ventricles shown by (A) Western blot analysis and (B) quantitation of signal intensity of cytosolic cytochrome c and the activated form of caspase-9 (mean ± SD of three independent experiments). Bad and BCl2 in left ventricles shown by (C) Western blot analysis and (D) quantitation of the signal intensity of Bad and BCl2 (mean ± SD of three independent experiments).
*Significantly different compared with the control group (p < 0.05). **Significantly different compared with the control group (p < 0.01).
Figure 5 Induction of IGF-1 and IGF-1R gene expression in left ventricles from rats. Abbreviations: C, control; H, high dose; L, low dose; M, medium dose. (A) RNA dot blots prepared with 240 ng RNA/dot and probed with labeled oligonucleotide probes specific for the indicated genes. 18S RNA was used as a loading control. (B) Fold induction of gene expression (mean ± SD) for SHS-treated animals relative to control from three independent experiments.
*Significantly different compared with the control group (p < 0.05).**Significantly different compared with the control group (p < 0.01).
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7690ehp0113-00135416203246ResearchMolecular Characterization of Thyroid Toxicity: Anchoring Gene Expression Profiles to Biochemical and Pathologic End Points Glatt Christine M. 1Ouyang Ming 2Welsh William 2Green John W. 1Connor John O 1Frame Steven R. 1Everds Nancy E. 1Poindexter Greg 1Snajdr Suzanne 1Delker Don A. 11 DuPont Haskell Laboratory, Newark, Delaware, USA2 Department of Pharmacology, Robert Wood Johnson Medical School and Informatics Institute of the University of Medicine and Dentistry of New Jersey, Piscataway, New Jersey, USAAddress correspondence to D.A. Delker, Environmental Carcinogenesis Division, U.S. Environmental Protection Agency, 109 TW Alexander Dr. (B143-06), Durham, NC 27711 USA. Telephone: (919) 541-7639. Fax: (919) 541-0694. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 12 5 2005 113 10 1354 1361 22 10 2004 12 5 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Organic iodides have been shown to induce thyroid hypertrophy and increase alterations in colloid in rats, although the mechanism involved in this toxicity is unclear. To evaluate the effect that free iodide has on thyroid toxicity, we exposed rats for 2 weeks by daily gavage to sodium iodide (NaI). To compare the effects of compounds with alternative mechanisms (increased thyroid hormone metabolism and decreased thyroid hormone synthesis, respectively), we also examined phenobarbital (PB) and propylthiouracil (PTU) as model thyroid toxicants. Follicular cell hypertrophy and pale-staining colloid were present in thyroid glands from PB-treated rats, and more severe hypertrophy/colloid changes along with diffuse hyperplasia were present in thyroid glands from PTU-treated rats. In PB-and PTU-treated rats, thyroid-stimulating hormone (TSH) levels were significantly elevated, and both thyroxine and triiodothyronine hormone levels were significantly decreased. PB induced hepatic uridine diphosphate-glucuronyltransferase (UDPGT) activity almost 2-fold, whereas PTU reduced hepatic 5′-deiodinase I (5′-DI) activity to < 10% of control in support of previous reports regarding the mechanism of action of each chemical. NaI also significantly altered liver weights and UDPGT activity but did not affect thyroid hormone levels or thyroid pathology. Thyroid gene expression analyses using Affymetrix U34A GeneChips, a regularized t-test, and Gene Map Annotator and Pathway Profiler demonstrated significant changes in rhodopsin-like G-protein–coupled receptor transcripts from all chemicals tested. NaI demonstrated dose-dependent changes in multiple oxidative stress–related genes, as also determined by principal component and linear regression analyses. Differential transcript profiles, possibly relevant to rodent follicular cell tumor outcomes, were observed in rats exposed to PB and PTU, including genes involved in Wnt signaling and ribosomal protein expression.
excess iodidegene expressionmicroarraysoxidative stressphenobarbitalpropylthiouracilthyroidWnt signaling
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Thyroid cancer, a fairly uncommon form of cancer in the human population, has causal links to environmental radiation exposures (Meirmanov et al. 2003). Although there are multiple epidemiology studies that associate radiation exposure with thyroid cancer, there have been no studies to associate human thyroid cancer with environmental chemical exposures (Hard 1998). The human thyroid responds toxicologically to multiple antithyroid drugs, including excess iodide, propylthiouracil (PTU), and thionamides, but the progression to cancer after repetitive administration has not been observed (Hard 1998; Hill et al. 1998; Markou et al. 2001). In contrast, multiple antithyroid drugs administered to the rat have demonstrated an increase in thyroid tumors, including PTU, thionamides, and the hepatic enzyme inducer phenobarbital (PB) (Capen 1997; Hurley et al. 1998; McClain et al. 1988). Other xenobiotics, including several organic iodides such as amiodarone and erythrosine, have also been associated with thyroid tumor development in the rat. Many organic iodides alter rat thyroid homeostasis, causing thyroid hypertrophy and alterations in colloid that may potentially lead to thyroid tumors after chronic administration (Capen 1997; Hurley et al. 1998).
The mechanisms by which organic iodides induce thyroid toxicity are varied and may include excess iodide being released into the blood during xenobiotic metabolism, toxicity to the liver that alters thyroid hormone metabolism, and/or direct thyroid toxicity that inhibits the release of thyroid hormones into the circulation (Capen 1997; Hurley et al. 1998). In subchronic toxicology studies it has been difficult to predict whether early changes in thyroid pathology could lead to thyroid cancer in the rat after chronic administration of organic iodides. Excess iodide alone can be toxic to thyroid cells in culture and cause thyroid hypertrophy and changes in colloid in vivo in the rat model (Capen 1997; Vitale et al. 2000). It has also been reported that administration of excess iodide promotes thyroid tumor development in rats initiated with N-bis(2-hydroxypropyl)-nitrosamine (Kanno et al. 1992). However, iodide excess alone has not been sufficient in inducing follicular cell hyperplasia and thyroid tumors in rats but more commonly causes hypothyroidism (Backer and Hollowell 2000; Kanno et al. 1994). This is believed to be due partly to the escape from the Wolff-Chaikoff effect (acute inhibition of iodine organification) that is seen within days of exposure to excess iodide (Wolff and Chaikoff 1948). This escape phenomenon is associated with the down-regulation of the sodium iodide (NaI) symporter (NIS) thereby reducing the amount of inorganic iodine in the thyroid so that thyroxine (T4) and thyroid-stimulating hormone (TSH) secretions are returned to normal physiological levels (Eng et al. 1999).
Chronic elevation of TSH levels has been associated with an increased risk of thyroid tumors in the rat, which may be due, in part, to the high turnover of the circulating thyroid hormone triiodothyronine (T3) in this species compared with the lower T3 turnover rate in humans (Capen 1997). Many antithyroid drugs and PB mediate their carcinogenic properties by elevating circulating TSH levels in the rat albeit by different mechanisms (Hood et al. 1999; McClain et al. 1988). Although PTU reduces thyroid hormone production by inhibiting thyroglobulin organification in the thyroid and inhibiting the peripheral conversion of T4 to active T3, PB reduces circulating T4 by increasing its hepatic metabolism and excretion via glucoronidation. Reductions in thyroid hormone (T4 and T3) by either mechanism causes an elevation in TSH that is sufficient in causing thyroid tumors in rats after prolonged exposure without any evidence of thyroid DNA damage (McClain 1992). Organic iodides may also increase TSH levels by similar mechanisms; however, it is unknown what contribution iodide excess has in their overall toxicity to the rat thyroid. To this end we have implemented biochemical, pathological, and molecular analyses to characterize the rat thyroid response to three model toxicants: excess iodide by using NaI a noncarcinogen, and the rodent thyroid carcinogens PB and PTU, in a modified 2-week endocrine battery (O’Connor et al. 2002). The goals of this study were to identify dose-dependent gene expression profiles induced by excess iodide in rats, determine whether gene expression profiles could be obtained that correlate with clinical and pathological end points in rats, and determine whether profiles are predictive of the carcinogenic potential of each chemical in rats.
Materials and Methods
In Vivo Studies
Adult male Crl:CD (SD)IGS BR rats, approximately 8 weeks of age, were treated with NaI, PB, and PTU for 14 consecutive days. NaI, PB, and PTU were purchased from Sigma Chemical Company (St. Louis, MO). Rats (n = 20/group) were dosed by oral gavage with vehicle (water or 0.25% methylcellulose), NaI (0.1, 1, 10, or 100 mg/kg/day), PB (100 mg/kg/day), or PTU (10 mg/kg/day) at a dose volume of 5 mL/kg. NaI was dissolved in water, whereas PB and PTU were dissolved in methylcellulose. On day 15, all rats were euthanized by carbon dioxide anesthesia and exsanguination. Blood samples were collected from the inferior vena cava of each animal at necropsy to measure serum levels of TSH, T4, T3, and reverse T3 (rT3). Terminal body, thyroid gland, and liver weights were recorded for the first 10 animals of each dose group. The thyroid gland and surrounding tissue from the first 10 animals of each dose group were processed for histopathological evaluation. A liver sample from the first five animals of each dose group was processed to measure 5′-deiodinase I (5′-DI) and uridine diphosphate-glucuronyltransferase (UDPGT) activity. Thyroid glands from the last 10 animals (five from methylcellulose group) from each dose group were removed and placed in RNALater (Ambion, Austin, TX) overnight at 4°C. The next day, thyroids were removed from the RNALater and stored at –80°C until processed for total RNA.
The research described in this publication was conducted in a laboratory accredited by the Association for the Assessment and Accreditation of Laboratory Animal Care International, and the investigators complied with the regulations and standards of the Animal Welfare Act and adhered to the principles of the Guide for the Care and Use of Laboratory Animals (National Research Council 1996).
Pathological Evaluations
After euthanization the thyroid glands and surrounding tissue from the first 10 animals from each group were removed and placed into formalin fixative for at least 48 hr before trimming and weighing. After fixation, one individual performed a final dissection under a dissecting microscope. This was done in order to reduce the variability of the dissection procedure, thereby reducing the variability of the thyroid gland weights. Organ weights were calculated relative to body weight. The formalin-fixed thyroid glands were examined microscopically.
Hormonal Measurements
Blood was collected at the time of euthanization from all animals. Serum was prepared and stored between –65°C and –85°C until analyzed for serum hormone concentrations. Serum TSH (Amersham Biosciences Corp., Piscataway, NJ), T3 and T4 (Diagnostic Products Corp., Los Angeles, CA), and rT3 (Polymedco Corp., Cortlandt Manor, NY) concentrations were measured using commercially available RIA kits.
Microsomal Preparations
At necropsy, a section of the liver from the first five animals from each group was removed, and hepatic microsomes were prepared for biochemical evaluation. A portion of the liver was homogenized (1 g tissue/8 mL buffer) in buffer containing 50 mM Tris–HCl, 0.25 M sucrose, and 5.4 mM EDTA, pH 7.4. The homogenates were centrifuged at 15,000 × g for 15 min at 4°C. The resulting supernatants were removed and centrifuged at 100,000 × g for 70 min at 4°C; these pellets contained the microsomal fractions. The microsomal pellets were resuspended in the homogenization buffer at a protein concentration of 10–20 mg/mL, aliquoted, and stored between –65°C and –85°C until analyzed for UDPGT and 5′-DI. The protein content of the microsomes was measured before and after analyses by the BioRad method (Bradford 1976). Final calculations were based on the postassay protein determination.
5′-Deiodenase I Measurements
Microsomal 5′-DI activity was determined using modifications of the methods of Pazos-Moura et al. (1991) and Leonard and Rosenberg (1980). Briefly, 50 μL of reaction mixture [0.1 M potassium phosphate, 1 mM EDTA, 0.5 mM DTT, 14.5 μL [125I]rT3 (~ 4.9 μCi, 0.05 nmol)] was preincubated at 37°C for approximately 1 min. Fifty micro-liters of microsomes, diluted with liver homogenization buffer to achieve a final protein concentration of 0.5 mg/mL, was added to the reaction mixture. The tubes were vortexed and incubated for 10 min at 37°C. The reaction was stopped by the addition of 33 μL of ice-cold BSA–PTU solution (4% BSA, 5 mM PTU) and 133 μL of 20% TCA, and the tubes were placed on ice until centrifugation. All tubes were centrifuged for 3 min at 12,000 × g, 4°C. Two hundred microliters of the resulting supernatant (75% of the reaction volume) was applied to a Poly-Prep column equilibrated with 10% acetic acid, and eluted with 1.7 mL of 10% acetic acid. The resulting eluate was measured on a gamma counter to determine 5-DI activity (nmoles [125I]rT3 deiodinated per hour per milligram protein).
UDPGT Measurements
Microsomal UDPGT activity was determined spectrophotometrically using a modification of the method of Bock et al. (1983). Briefly, 15 μL of p-nitrophenol (66.7 mM) was added to 460 μL of microsomes, which had been resuspended with assay buffer [66 mM Tris–HCl, 10 mM magnesium chloride, 0.05% Brij 58 (polyoxyethylene ether 2 cetyl ether; CAS no. 9004-95-9), pH 7.5] to achieve a final protein concentration of 0.5–1.0 mg/mL. The tubes were preincubated at 37°C for 2 min before the addition of 25 μL UDPGA (uridine diphosphate glucuronic acid; 200 mM) to start the reaction. The tubes were vortexed and incubated for 10 min at 37°C. A reaction blank tube for each sample was run concurrently by substituting the UDPGA with assay buffer. The reaction was stopped by the addition of 0.5 mL ice-cold methanol, and the tubes were placed on ice until centrifugation. All tubes were centrifuged for 10 min at 2,000× g at 4°C. Three hundred microliters of the supernatant was combined with 2.7 mL 0.1 N sodium hydroxide, and the absorbance at 405 nm was measured. Rate of UDPGT activity is expressed as nmoles per minute per milligram protein.
Microarray Analysis
RNA preparation and analysis was done according to the Affymetrix-recommended protocol (Affymetrix 2002). Briefly, total RNA from four animals from each dose group was prepared individually using the TRIzol procedure (Invitrogen, Carlsbad, CA) and cleaned using the Qiagen RNeasy mini RNA cleanup protocol (Qiagen, Valencia, CA). The integrity of each RNA sample was determined using an Agilent 2100 Bioanalyzer (Agilent, Foster City, CA). After this, double-stranded cDNA from three of the four samples was prepared from 16 μg of total RNA using Superscript II reverse transcriptase (Invitrogen) and a T7 primer (Genset, Boulder, CO) for first-strand synthesis, and DNA polymerase and ligase (Invitrogen) for second-strand synthesis. Subsequently, labeled cRNA was synthesized from the cDNA using the Enzo RNA transcript labeling kit (Affymetrix, Santa Clara, CA) according to the manufacturer’s instructions. Approximately 20 μg of biotin-labeled cRNA was then fragmented in a solution of 40 mM Tris–acetate, pH 8.1, 100 mM KOAc, and 30 mM MgOAc at 94°C for 35 min. Labeled cRNA was hybridized to the Affymetrix GeneChip Test2 Array (Affymetrix) to verify the quality of labeled cRNA. After this, cRNA was hybridized to the Affymetrix Rat Genome U34A GeneChip Probe Array (RG-U34A; Affymetrix). The cRNA in hybridization cocktail was incubated overnight at 45°C while rotating in a hybridization oven. After approximately 16 hr of hybridization, the cocktail was removed and the arrays were washed and stained in a Fluidics Station 400 (Affymetrix) according to the Affymetrix-recommended protocol (Affymetrix 2002). Briefly, several cycles of washes were done initially with a nonstringent buffer (1 M NaCl, 67 mM NaH2PO4, 6.7 mM EDTA, 0.01% Tween 20) at 25°C and then with stringent buffer [100 mM MES, 0.1 M Na+, 0.01% Tween 20] at 50°C. The arrays were then stained in streptavidin phycoerythrin (SAPE) solution (10 μg/mL SAPE, 2 mg/mL acetylated BSA, 100 mM MES, 1 M Na+, 0.05% Tween 20) at 25°C, washed in nonstringent buffer, stained in antibody solution (2 mg/mL acetylated BSA, 100 mM MES, 1 M [Na+], 0.05% Tween 20, 0.1 mg/mL normal goat IgG, 3 μg/mL anti-streptavidin biotinylated antibody) at 25°C, stained again in SAPE solution at 25°C, and then washed again in nonstringent buffer at 30°C. Arrays were then scanned on a GeneArray scanner (Agilent). Image analysis, quantification of raw gene expression values, mismatched probe background subtraction, and present/absent calls were performed using the Microarray Suite software (version 5.0; Affymetrix).
Data Analysis
Differential gene expression was determined by the regularized t-test, which uses a Bayesian procedure (Baldi and Long 2001). Briefly, the expression level of each gene is assumed to be from a normal distribution with μ and σ2. Using a conjugate prior, the mean of the posterior (MP) estimate of μ is the sample mean. The MP estimate of σ2 is
where n is the sample size, s
2 is the sample variance, v0 is the degrees of freedom of the prior (a value of 10 is used in the analysis), and σ 02 is the mean of sample variances of genes in the neighborhood of the gene under consideration. The neighborhood is the 50 genes with sample means immediately above and below the sample mean of the gene under consideration; that is, the neighborhood consists of the 101 genes centered on the gene. After the MP estimates of μ and σ2 are obtained, the t-test of unequal variances is used to calculate a p-value of differential expression.
Multiple linear regressions are used to determine dose-dependent expression after NaI treatments of 0.1, 1, 10, or 100 mg/kg/day. Some genes respond to NaI linearly, but for other genes, the induction or repression of expression may become saturated after some dose levels. Therefore, two types of multiple linear regressions were performed. The first type was the linear regression of the gene expression levels and the dose levels, and the other type was the linear regression of the gene expression levels and the logarithms of the dose levels.
A principal component analysis was also performed on the data. Three animals were measured within each treatment for each gene. The treatment means were then subjected to principal component analysis. The components were thus determined on a per-treatment basis rather than a per-gene basis, as in Raychaudhuri et al. (2000). For each of the treatments, the mean of the transcription signals was computed. Then, for each treatment, the ratio of the treatment mean to the corresponding control mean was computed. The logarithms of these six signal ratios were then analyzed. There was no need to standardize these log-ratios, because they were essentially standardized without further attention.
Results
Liver Weights and Hormone Metabolism
After the 2-week exposure period, liver weights were increased in a dose-dependent manner and were significantly higher in rats administered 10 and 100 mg/kg/day NaI (8–13% increase) and 100 mg/kg/day PB (44% increase) compared with control rats that received water alone (Figure 1). Liver weights were slightly reduced in animals that received 10 mg/kg/day PTU, and this was attributed to concomitant reductions in body weight in this treatment group (data not shown).
UDPGT activity was significantly higher (99% increase) in rats administered 100 mg/kg/day PB compared with controls (Figure 1). In contrast, UDPGT activity was reduced (28% decrease) in rats that received 100 mg/kg/day NaI for 2 weeks. 5′-DI activity was dramatically reduced in PTU-treated animals (93% decrease), whereas all other treatments had no significant change in activity compared with controls (Figure 1).
Thyroid Hormone Levels and Histopathology
Treatment-related effects on thyroid hormone levels were observed in the 100 mg/kg/day PB and 10 mg/kg/day PTU groups. Compared with controls, T3, T4, and rT3 levels were reduced 23, 40, and 28%, respectively, in PB-treated rats and 80, 99, and 56%, respectively, in PTU-treated rats (Table 1). TSH levels were increased approximately 2- and 4-fold in PB- and PTU-treated rats, respectively. There were no treatment-related effects on thyroid hormone levels observed with NaI at any concentration tested.
Treatment-related changes in thyroid gland histopathology were observed in the PB and PTU treatment groups (Table 2). Thyroid follicular cell hypertrophy and pale-staining colloid were observed in both PB and PTU treatment groups, and diffuse hyperplasia was observed in the PTU group. Relative thyroid gland weights (percent of body weight) were also significantly increased (~ 3-fold) in the PTU treatment group compared with controls (Table 2). No treatment-related changes in thyroid gland histopathology were observed after NaI administration at any of the doses tested.
Thyroid Gland Gene Expression
Principal component analysis.
Thyroid gene expression data were analyzed using principal component analysis, a regularized t-test and multiple linear regressions. Principal component analysis of gene expression data from all 24 samples demonstrated grouping according to treatment. Six principal components were identified (Table 3). Of these, the first four account for approximately 85% of the total variation in the data. Neither of the remaining two meets the 70/N rule used by Raychaudhuri et al. (2000). The general methodology of that reference was followed in the present analysis. To understand these principal components, it is helpful to express each as a linear combination of the means of the six treatments (Table 4). The first component is essentially the average of the NaI treatment log-signal ratios. Large values of this component tend to be associated with up-regulation in one or more NaI treatments. Large negative values (i.e., negative numbers large in absolute value) of this component tend to be associated with down-regulation in one of more NaI treatments. The second component is an indicator for an effect due to PB and PTU. A large value of Pcomp2 (principal component 2) indicates an up-regulation, whereas a large negative value indicates a down-regulation. Component 3 is primarily a contrast between the PB and PTU treatments. The fourth principal component is primarily an indicator of effect at low doses of NaI. Based on these principal component analysis findings, further gene ontology work was directed to the first two principal components, namely, genomic profiles associated with NaI exposure or PB and PTU exposure.
Multiple linear regressions.
Dose-dependent expression, as determined by multiple linear regressions, was observed after NaI treatment. Transcript levels most influenced by dose (p ≤0.001), included the NIS [Slc5a5; GenBank accession no. U60282; (http://www.ncbi.nlm.gov)] and antioxidant enzymes such as glutathione peroxidase 2 (Gpx2), thioredoxin reductase (Txnrd1), and glutathione S-transferase pi (GST-pi; Gstp2) (Table 5). NIS mRNA was down-regulated in a dose-dependent manner to 80 (not statistically significant), 40, 30, and 20% of control values after 0.1, 1, 10, and 100 mg/kg/day NaI, respectively (Figure 2A). However, unlike NaI, PTU increased NIS expression by greater than 300% of control, and PB only slightly reduced (70% of control) NIS transcript levels. The most statistically significant of the antioxidant enzymes, GST-pi, was up-regulated to 154, 196, 263, and 250% of control values after the same NaI dosing regimen (Figure 2B). Similar to NaI, PB and PTU increased GST-pi expression levels to 198 and 265% of control, respectively.
Regularized t-test (Bayesian procedure).
In a separate analysis using the regularized t-test, 872, 948, and 1552 gene transcripts (of 8,740 transcripts present in all samples) were significantly (p < 0.01) changed by 100 mg/kg/day NaI, 100 mg/kg/day PB, and 10 mg/kg/day PTU administration compared with controls, respectively. To further characterize these genomic changes according to biological function and identify molecular pathways involved in the mode of action of each chemical, these gene lists were uploaded into GenMAPP (Gene Map Annotator and Pathway Profiler, version 1.0; Dahlquist et al. 2002). Two molecular pathways found to be highly influenced by NaI were rhodopsin-like G-protein receptors and oxidative-stress–related genes (Table 5). These pathways were also influenced by PB and PTU treatment, although the spectrum of gene transcripts was unique for each chemical. Although all chemicals affected the expression of one or more adrenergic receptors, dopamine and chemokine receptor transcripts were only affected by PB and PTU treatment, respectively. PB and PTU also caused a down-regulation of TSH receptor mRNA (Table 5) correlating with the increased circulating TSH observed with these chemicals from the serum hormone analysis. Interestingly, NaI induced specific changes in multiple olfactory receptor and oxidative stress transcripts, including Txnrd1, Gpx2, superoxide dismutase 2 (Sod2), and nitric oxide synthase 2 (Nos2), that were not affected by PB or PTU treatment (Table 5). NaI also altered the levels of multiple transcripts involved in apoptosis that were not affected by PB or PTU treatment including receptor-interacting serine-threonine kinase 3 [Ripk3, GenBank accession no. AF036537 (http://www.ncbi.nlm.nih.gov)], BH3 interacting domain 3 [Bid3; GenBank accession no. AI102299 (http://www.ncbi.nlm.nih.gov)], and scavenger receptor class B [Scarb1; GenBank accession no. D89655 (http://www.ncbi.nlm.nih.gov); p < 0.001]
To further identify discriminating molecular pathways that might be predictive of thyroid pathology and cancer risk, additional emphasis was placed on molecular pathways affected by both PB and PTU but not NaI. Interestingly, large differences were observed in transcripts from ribosomal protein and Wnt signaling gene families. Although not observed after NaI exposure, multiple 40S ribosomal protein transcripts were significantly reduced by PTU and PB treatment, including S4 (Rps4), S6 (Rps6), and S8 (Rps8) proteins (Table 6). NaI-dependent changes in ribosomal transcripts were observed in the large 60S subunit that included L22 (Rpl22) and L18 (Rpl18a) ribosomal proteins. Wnt target gene transcripts cyclin D1 (Ccnd1), c-jun (Jun), insulin-like growth factor binding protein (Igfbp2), VEGF receptor (Kdr), and protein kinase C (PKC) ζ(Prkcz) were up-regulated 50–300% in thyroids from PB- and PTU-treated animals, whereas no treatment-related changes in these transcripts were observed after NaI exposure (Table 6; Figure 3). PTU also altered the expression of other Wnt target genes, including multiple cyclin D3 (Ccnd3) transcripts and urokinase plasminogen activator receptor (Plau) mRNA (Table 6, Figure 3). Decreased expression of the adenomatous polyposis coli (Apc) gene and/or its homologs was also observed after exposure to PB and PTU. Finally, a modest increase (40%) in frizzled protein (Fzd1) mRNA was observed on a single gene target from the NaI treatment group. Multiple protein kinase transcripts were affected by PB and/or PTU exposure, including the up-regulation of Prkcz (PB and PTU) and PKCα(Prkca), and down-regulation of protein kinase A [Prkaa; GenBank accession no. X57986 (http://www.ncbi.nlm.nih.gov); PTU only]. In contrast, NaI did not significantly alter the transcript levels of any protein kinase (Table 6; Figure 3).
Discussion
Many organic iodides alter rat thyroid homeostasis that may potentially lead to thyroid tumors after chronic administration (Capen 1997; Hurley et al. 1998). Although chemically induced changes in thyroid hormone metabolism and thyroid hormone release have been associated with increased thyroid tumor incidence in rodents, no such correlation with cancer risk has been associated with excess iodide (Capen 1997; Markou et al. 2001). To better characterize thyroid toxicity associated with increased cancer risk in the rat, genomic, clinical, and pathological end points were examined in response to excess NaI (non-carcinogen) and the rodent thyroid carcinogens PB and PTU in a modified 2-week endocrine battery (O’Connor et al. 2002).
Similar to previous reports, PB and PTU caused hormonal alterations (reduced serum T3, rT3, and T4 levels and increased TSH) and induced thyroid follicular cell hypertrophy in male rats after a 2-week exposure period (De Sandro et al. 1991). PB also increased relative liver weights (percent of body weight) and hepatic UDPGT activity, suggesting the involvement of altered thyroid hormone metabolism in PB-induced thyroid toxicity (Barter and Klassen 1992). In contrast, PTU reduced hepatic 5′-DI activity, increased relative thyroid weights (percent body weight), and induced thyroid follicular cell hyperplasia in male rats, suggesting an alternative mechanism of toxicity, including peripheral conversion of T4 to T3 (Oppenheimer et al. 1972). These effects elicited by PB and PTU in this short-term study, specifically, increases in TSH levels resulting in microscopic alterations of the thyroid gland, are suggestive of a potential thyroid tumor response after longer-term exposures in male rats (Capen 1997).
No biologically significant effects were observed after NaI exposure using the same treatment regimen. NaI caused no changes in thyroid hormone levels or thyroid histopathology. UDPGT activity was statistically significantly reduced in rats dosed with 100 mg/kg/day NaI, although liver weights were significantly increased in male rats receiving 10 and 100 mg/kg/day NaI. The significance of these effects on hepatic UDPGT activity is unclear but suggests an alternative effect on liver metabolism despite the concomitant increase in liver weights. These dose-dependent effects demonstrate for the first time that excess iodide can elicit a biological/toxicological response on the liver. The importance of these observed liver effects are also supported by the fact that iodide doses used in this study (1–10 mg/kg/day) are obtainable in high-dose toxicology studies with iodinated compounds.
We found multiple gene transcripts that were significantly altered by linear regression, regularized t-test, and principle component analysis models in response to NaI treatment. The transcripts most sensitive to iodide exposure included the thyroid NIS and GST-pi. NIS mRNA was significantly reduced to 40% of control levels at NaI doses as low as 1 mg/kg/day. A similar reduction in NIS mRNA has been reported previously after 6 days of exposure to 0.05% NaI in the drinking water of rats (Eng et al. 1999). In that study a concomitant reduction in protein level was also demonstrated suggestive of reduced iodide transport into the thyroid. In our study, GST-pi mRNA was significantly increased (150% of control) in the thyroid of rats exposed to the lowest dose of NaI tested (0.1 mg/kg/day). The biologic significance of increased GST-pi (Gstp2) transcripts in the rat thyroid is uncertain but may be predictive of increased oxidative stress. Other antioxidant gene transcripts were induced in the rat thyroid after NaI exposure, including Gpx1 and Gpx2 (1.4- and 2.4-fold, respectively), Sod2 (1.6 fold), Txnrd1 (1.5-fold), heme oxygenase (Hmox1, 1.9-fold), and NAD(P)H dehydrogenase quinone (Nqo1, 2.2-fold; Table 5). Iodide increases oxidative stress in cultured thyroid cells as evidenced by increased intra-cellular reactive oxygen species and lipid peroxidation (Vitale et al. 2000). It has been suggested that I2, the molecular form of ionic iodide, is highly reactive with protein, lipids, and nucleic acids and that generation of iodocompounds may disrupt cellular membrane functions, increase reactive oxygen species, and cause programmed cell death in thyroid cells. Dose-dependent increases in the antioxidant enzyme transcripts Gpx2 and Gstp2 observed in the rat thyroid are consistent with a compensatory response to the generation of lipid peroxides as reported by Vitale et al. (2000) using iodide exposures in vitro. The observed alterations in multiple transcripts involved in apoptosis in our studies are also consistent with this proposed mechanism of iodide-induced thyroid toxicity.
NaI exposure also influenced the mRNA expression of a wide range of rhodopsin-like G-protein–coupled receptors including adrenergic receptors. NaI increased β-adrenergic receptor (Adrb2) gene expression in a dose-dependent manner, whereas PB and PTU increased α1-adrenergic receptor (Adra1d) gene expression. In previous in vitro studies, thyrotropin (TSH) increased α1-adrenergic receptor mRNA in rat thyroid FRTL-5 cells as a compensatory mechanism to down-regulate TSH-induced cAMP levels (Corda and Kohn 1985). cAMP is considered the primary second messenger in thyroid follicular cell growth and maintenance (Medina and Santisteban 2000; Richards 2001). Together with the observed reduction in TSH receptor mRNA observed in the thyroids of rats exposed to PB and PTU, these gene expression changes are suggestive of an adaptive response to high levels of circulating TSH seen in rats treated with PB and PTU but not NaI. Further investigation is needed to understand the biological significance, if any, of β-adrenergic receptor mRNA induction by NaI.
To further identify gene expression changes that may be predictive of cancer risk, additional emphasis was placed on signaling pathways influenced by PB and PTU (carcinogens) but not NaI (noncarcinogen). Interestingly, significant changes were observed in gene expression patterns associated with Wnt signaling in PB and PTU exposed animals. Up-regulation of Ccnd1, Ccnd3, and Jun mRNA (all transcriptional targets of Wnt signaling) are suggestive of increased risk to human cancer (Behrens 2000; Ishigaki et al. 2002). Cyclin D1 protein is overexpressed in human thyroid cancers that have causal links to environmental radiation exposures at nuclear test sites (Meirmanov et al. 2003). Cyclin D1 and cyclin D3 both facilitate entry into the cell cycle, and increased transcription of these genes is associated with increased cell proliferation and hyperplasia (Baldassarre et al. 2003; Ishigaki et al. 2002). Thyroid hyperplasia was only observed in PTU animals and not PB animals (hypertrophy was observed in both groups), suggesting that increased expression of these genes precedes visible histopathological changes. Finally, the proto-oncogene Jun, which is also associated with multiple types of human cancer, mediates its oncogenic activity by stimulating AP-1–mediated transcription and cell proliferation (Shaulian and Karin 2001). Up-regulation of Jun has been associated with thyroid cancer, but its importance in mediating Wnt signaling in the thyroid has yet to be characterized (Battista et al. 1998).
Increased β-catenin protein is a common marker for Wnt signaling in multiple types of cancer, including thyroid cancer (Behrens 2000). β-Catenin complexes with the adenomatous polyposis coli (Apc) gene and axin to regulate its phosphorylation by glycogen synthase kinase (Gsk3b) and subsequent proteosomal degradation (Henderson and Fagotto 2002). Wnt activation inhibits the phosphorylation of β-catenin, resulting in its nuclear accumulation, binding to the lymphoid enhancer factor-1/T-cell–specific transcription factor (LEF-1/TCF), and transcription of multiple target genes as previously discussed (Kikuchi 2000). Interestingly, significantly lower gene expression levels of Apc and/or two other tumor suppressor proteins with homologous sequence mRNAs were observed in the thyroid glands of animals treated with PB and PTU but not NaI. In addition, mRNA expression of Prkcz, an inhibitor of Gsk3b kinase activity, was up-regulated in response to PB and PTU animals but not NaI (Oriente et al. 2001). Inhibition of Gsk3b results in decreased phosphorylation of β-catenin and causes its nuclear accumulation. Lastly, a DNA damage protein (Gadd45a) and a MAP kinase (Mapk14), two other proteins recently associated with Wnt activation, were also differentially expressed in PB- and PTU-treated animals but not in NaI-treated animals (Hildesheim et al. 2004).
In further support of our findings, recent investigations have also demonstrated silencing of Wnt signaling in mammalian cells after exposures to thyroid hormone (T3; Miller et al. 2001; Natsume et al. 2003). In these studies T3 inhibited β-catenin accumulation and transcriptional activity in rat pituitary and human kidney cells, respectively. Although T3 promotes cellular growth in many cellular systems, it is suggested that T3 involvement in normal organ development and cellular differentiation also implies antitumor activity of this critical hormone. In our studies, reductions in thyroid hormone were only observed in PB-and PTU-treated animals with the most significant changes after PTU exposure. This correlates well with the magnitude and number of Wnt signaling target genes changed in the microarray analyses of each treatment group.
Finally, significant changes in ribosomal protein mRNA were also observed in PB- and PTU-exposed animals. Alterations in ribosomal protein expression have been associated with increased cell proliferation and hyperplasia in multiple tissue types (Chou and Blenis 1995; Hamadeh et al. 2002). The ribosomal protein Rps6, regulated by p70 ribosomal S6 kinase (pp70s6k), is important in transcription and translation events precluding entry into the cell cycle (Cass and Meinkoth 1998). The mRNA of this protein was down-regulated in PB- and PTU-treated animals, correlating with other cAMP signaling responses (protein kinase mRNA regulation) observed in this study.
In summary, we present new findings with regard to thyroid gene expression in the rat after subchronic exposure to NaI, PB, and PTU. Although no significant changes in biochemical and pathological measurements of thyroid function were observed in the rat after NaI exposure, significant changes in thyroid gene expression were observed even at the lowest concentration of NaI tested (0.1 mg/kg/day). Many of these genes, namely, multiple antioxidant enzymes have not been characterized previously in the rat thyroid and may prove useful as biomarkers of iodide exposure. We also demonstrate gene expression changes associated with thyroid histopathology and hormone status after PB and PTU exposures. Expression patterns of Wnt signaling genes correlated with circulating thyroid hormone levels, thyroid histopathology, and the carcinogenic potential of PB and PTU in the rat. The potential interactions between these mechanisms of thyroid toxicity are summarized in Figure 4. We suggest that organic iodides may elicit changes in circulating hormone levels as well as cause increases in oxidative stress in the rat thyroid. Although excess iodide may not cause follicular cell transformation via activation of Wnt signaling, it may enhance cytotoxicity in the rat thyroid exacerbated by thyroid pathology caused by fluctuations in thyroid hormone levels. These findings, in addition to further elucidating signaling pathways that correlate with rat thyroid pathology, may provide useful biomarkers for the prediction of thyroid tumorigenesis after chronic exposure to xenobiotics in the rat.
We acknowledge J. Stadler and B. Shertz for their help in study design and technical support, respectively.
Figure 1 Hepatic metabolism parameters: mean ± SD of 5–10 measurements from individual animals. Liver weight is expressed as percent body weight. 5′-DI activity is expressed as nmol [125I]rT3 deiodinated/hr/mg protein. UDPGT activity is expressed as nmol/min/mg protein/10.
*Significant by least significant difference. **Significant by least significant difference and Dunnett’s test. #Significant by Dunnett/Tamhane-Dunnett test.
Figure 2 Bar graph of dose-dependent NIS (A) and GST-pi (B) gene expression. Values represent mean ± SD of three measurements from individual animals.
Significantly different at *p < 0.05 from water and **p < 0.05 from vehicle controls by regularized t-test.
Figure 3 Agglomerative clustering of treated animals (r602–r604, NaI 100 mg/kg; r622–r625, PB 100 mg/kg; r642–r645, PTU 10 mg/kg; n = 3/group) based on individual expression values of Wnt signaling genes. Values represent the log2 of the fold change (each animal value divided by the control mean). Log2 values were uploaded into EPCLUST [Expression Profiler, European Bioinformatics Institute (http://www.ebi.ac.uk/expressionprofiler)] and clustered by Euclidean distance. Numerical values are encoded by colors: red and green are used to represent positive and negative values, respectively. Individual log2 values ranged from –2.47 to 3.26.
Figure 4 Increased circulating TSH activates protein kinase A (PKA) via the TSH receptor and the second-messenger cAMP in thyroid follicular cells. Cyto c, cytochrome c. We propose that decreased circulating thyroid hormone (T3), either by increasing T4 glucoronidation (PB) or decreasing peripheral conversion of T4 (PTU), modulates PKCζ activity and increases the transcription of Wnt target genes. Reactive oxygen species (ROS) generated by intracellular I2 after NaI or organic iodide metabolism may also increase protein kinase activity or cell death dependent on the magnitude of the oxidative stress and/or other extracellular signals.
Table 1 Thyroid hormone parameters.
Treatment (mg/kg/day) T3 (ng/mL) T4 (ng/mL) TSH (ng/dL) rT3 (ng/mL)
Water 73.6 ± 12.5 4.23 ± 0.83 12.2 ± 6.1 0.111 ± 0.015
NaI (0.1) 75.6 ± 12.4 4.37 ± 0.92 10.7 ± 3.6 0.118 ± 0.018
NaI (1) 69.0 ± 10.5 4.40 ± 1.01 11.5 ± 4.2 0.114 ± 0.022
NaI (10) 68.9 ± 10.4 4.38 ± 1.29 15.1 ± 7.8 0.106 ± 0.023
NaI (100) 73.3 ± 14.4 4.58 ± 1.14 13.1 ± 5.0 0.106 ± 0.020
PB (100) 56.5 ± 10.4* 2.52 ± 0.84* 22.4 ± 10.2* 0.080 ± 0.024*
PTU (10) 14.5 ± 7.3* 0.04 ± 0.10* 51.9 ± 12.4* 0.049 ± 0.018*
Values represent mean ± SD of 16 or more measurements from individual animals in each group.
*Statistical significance from control as determined by Jonckheere-Terpstra trend test, p < 0.05.
Table 2 Thyroid gland pathology.
Treatment (mg/kg/day) Thyroid (g) Colloid Hypertrophy Hyperplasia
Water 0.006 ± 0.001 – – —
NaI (100) 0.007 ± 0.002 – – —
PB (100) 0.007 ± 0.001 + + —
PTU (10) 0.018 ± 0.003* ++ ++ ++
Abbreviations: –, no lesions observed; +, lesions observed; ++, severe lesions observed. Values represent mean ± SD of 9–11 measurements from individual animals in each group.
*Statistical significance from control as determined by least significant difference and Dunnett’s test, p < 0.05.
Table 3 Principal component analysis.
Principal component Eigenvalue Proportion Cumulative
1 2.53220382 0.4220 0.4220
2 1.36004986 0.2267 0.6487
3 0.69452426 0.1158 0.7645
4 0.51077061 0.0851 0.8496
5 0.49305017 0.0822 0.9318
6 0.40940129 0.0682 1.0000
Table 4 Principal component values based on treatment group.
Variable Pcomp1 Pcomp2 Pcomp3 Pcomp4 Pcomp5 Pcomp6
Lnsigrat3 0.491944 –0.079240 0.067459 0.816707 –0.153903 –0.237625
Lnsigrat5 0.502641 –0.050801 0.241487 –0.396262 0.480074 –0.546773
Lnsigrat7 0.494279 0.023657 0.208137 –0.391877 –0.721315 0.194792
Lnsigrat9 0.498959 –0.063955 –0.394606 –0.007669 0.406380 0.652712
Lnsigrat11 0.109047 0.688894 –0.627580 –0.063206 –0.133869 –0.312667
Lnsigrat13 0.013435 0.715482 0.586721 0.135445 0.206111 0.287812
Abbreviations: Lnsigrat#, natural logarithm of the transcription signal ratio for treatment number; Pcomp#, principal component number.
Table 5 Treatment-related effects on rhodopsin-like G-protein–coupled receptor and oxidative stress–related gene expression.
Gene group/namea Gene symbola GenBank accession no.a NaI PB PTU
Rhodopsin-like GPCRs
Alpha-1D adrenergic receptor Adra1d M60654 —c 1.6 3.0
Alpha-1B adrenergic receptor Adra1b M60655 2.0 2.1 3.4
Alpha-1C adrenergic receptor Adra1c U13368 –2.1 —c —c
Alpha-2A adrenergic receptor Adra2a U79031 1.9 — —
Beta-2 adrenergic receptor Adrb2 J03024 2.9 — —
Beta-3 adrenergic receptor Adrb3 S56481 –2.2 — –2.1
Serotonin receptor 6 Htr6 S62043 –1.6 — —
Serotonin receptor 4 Htr4 U20907 — — 4.1
Serotonin receptor 1F Htr1f L05596 — 7.7 —
Serotonin receptor 7 Htr7 L22558 — — 2.5
Dopaminergic receptor D-3 Drd3 A17753 — 3.4 —
Opioid receptor mu-1 Oprm1 D16349 — –2.7 —
Cholinergic receptor muscarinic 3 Chrm3 M16407 — — –1.7
Cholinergic receptor muscarinic 5 Chrm5 M22926 –1.9 — —
Neuropeptide Y5 receptor Npy5r U66274 2.1 — —
Panceatic polypeptide receptor Ppyr1 U42388 –2.1 — —
Interleukin 8 receptor beta Il8rb U70988 — — –2.0
Chemokine receptor 4 Cxcr4 U90610 — — 1.9
Chemokine receptor 1 Cx3cr1 U04808 — — 2.1
A3 adenosine receptor —b X93219 –2.8 — —
Angiotensin II receptor — M90065 — — –1.8
Angiotensin II receptor (AT1B) — X64052 — –3.7 —
Thyroid-stimulating hormone receptor Tshr M34842 — –1.6 –1.7
Chemokine orphan receptor 1 Cmkor1 AJ010828 1.5 — —
Endothelin receptor type B Ednrb X57764 — — 1.6
Prostaglandin E receptor 4 Ptger4 D28860 — — 3.0
Platelet-activating factor receptor Ptafr U04740 — — 2.6
Bradykinin receptor B1 Bdkrb1 AJ132230 –1.4 — —
GABAB receptor 1 Gabbr1 AB016161 –1.6 — —
Olfactory receptor pseudogene Olr1469 AF091570 –1.3 — —
Olfactory receptor 1696 Olr1696 AF034896 –1.7 — —
Olfactory receptor 1699 Olr1699 AF034899 –2.9 — —
Olfactory receptor 226 Olr226 M64386 — — –1.9
Olfactory receptor 1361 Olr1361 M64377 –2.0 — —
Olfactory receptor 1370 Olr1370 AF091577 –2.2 — —
Olfactory receptor 1493 Olr1493 AF091572 –2.4 — —
Olfactory receptor 1346 Olr1346 AF091578 –4.2 — —
Olfactory receptor 1687 Olr1687 AF091563 –5.3 –3.6 —
Oxidative stress–related genes
NAD(P)H dehydrogenase quinone Nqo1 J02679 2.2 1.5 2.0
Heme oxygenase 1 Hmox1 J02722 1.9 2.0 —
Glutamate-cysteine ligase Gclc J05181 1.5 1.5 —
Thioredoxin reductase 1 Txnrd1 U63923 1.5 — —
Glutathione peroxidase 1 Gpx1 X07365 1.4 — 1.4
Glutathione peroxidase 2 Gpx2 AA800587 2.4 — —
Glutathione reductase Gpr U73174 — 1.6 1.6
GST-pi Gstp2 X02904 2.5 2.0 2.6
Superoxide dismutase 3 Sod3 X68041 –1.4 — 1.7
Superoxide dismutase 2 Sod2 Y00497 1.6 — —
Superoxide dismutase 1 Sod1 M25157 –2.0 — —
Metallothionein-1a Mt1a M11794 –1.7 –1.9 –2.1
Peroxiredoxin 1 Prdx1 AI010083 1.2 — —
Nitric oxide synthase 2 Nos2 U16359 2.2 — —
Cytochrome b-245 alpha Cyba U18729 1.7 — 1.6
Monoamine oxidase A — S45812 — — –1.4
Values represent statistically significant (p < 0.01 by regularized t-test) mean fold change from control (n = 3) for 100 mg/kg/day NaI and PB and 10 mg/kg/day PTU.
a From GenBank (http://www.ncbi.nlm.nih.gov).
b —, Gene symbol unknown.
c —, Not statistically significant.
Table 6 Treatment-related effects on Wnt signaling and ribosomal protein gene expression.
Gene group/namesa Gene symbola GenBank accession no.a NaI PB PTU
Wnt signaling genes
APC fragment 2 of 6 —b L19304c —d –1.6 —d
APC fragment 4 of 6 — L19306 — –2.2 –2.6
APC protein Apc D38629 — — –2.0
c-jun oncogene Jun X17163 — 2.9 2.3
Cyclin D1 Ccnd1 D14014 — 1.4 2.6
Cyclin D1 partial Ccnd1 X75207c — 1.3 2.3
Cyclin D3 Ccnd3 D16309c — 1.4 1.7
Cyclin D3 partial Ccnd3 U49935c — 1.6 1.7
Frizzled homolog 1 Fzd1 L02529 1.5 — —
DNA damage inducible transcript Gadd45a L32591 — –1.7 –2.0
Insulin-like factor binding protein Igfbp2 M91595 — 1.7 5.2
Low-density lipoprotein receptor Ldlr X13722 — — 2.1
p38 MAP kinase Mapk14 U91847 — 1.6 —
p38 MAP kinase 2 Mapk14 U73142 — — 1.4
Protein kinase C, alpha Prkca X07286 — — 3.7
Protein kinase C, zeta Prkcz M18332 — 1.6 1.4
Plasminogen activator, urokinase Plau X63434 — — –1.8
VEGF receptor 2 Kdr U93306 — 1.9 2.8
Ribosomal protein genes
Ribosomal protein S5 Rps5 X58465 — — –1.4
Ribosomal protein S4 Rps4 X14210 — –1.4 –1.4
Ribosomal protein S6 Rps6 M29358 — –1.5 –1.2
Ribosomal protein S9 Rps9 X66370 — — –1.3
Ribosomal protein S8 Rps8 X06423 — –1.3 –1.3
Ribosomal protein S7 Rps7 X53377 — — –1.3
Ribosomal protein S3 Rps3 X51536 — — –1.3
40 kDa ribosomal protein Lamr1 D25224 — — –1.3
v-fos transformation effector Rps3a M84716 — –1.6 –1.2
Ribosomal protein L3 Rpl3 X62166 — — –1.4
Ribosomal protein L5 Rpl5 M17419 — –1.5 —
Ribosomal protein L9 Rpl9 X51706 — — –1.4
Ribosomal protein L10 Rpl10 X93352 — — –1.4
Ribosomal protein L11 Rpl11 X62146 — — –1.4
Ribosomal protein L12 Rpl12 X53504 — — –1.4
Ribosomal protein L14 Rpl14 X94242 — — –1.3
Ribosomal protein L17 Rpl17 X58389 — — –1.4
Ribosomal protein S25 Rps25 X62482 — — –1.4
Ribosomal protein L7 Rpl7a X15013 — — –1.3
Ribosomal protein L18 Rpl18a X14181 –1.4 — —
Ribosomal protein L21 Rpl21 M27905 — –1.4 –1.4
Ribosomal protein L22 Rpl22 X60212 –2.1 — –1.8
Ribosomal protein L23 Rpl23 X65228 — — –1.5
Ribosomal protein L26 Rpl26 X14671 — — –1.4
Ribosomal protein L28 Rpl28 X52619 — — –1.3
Ribosomal protein L37 Rpl37 X66369 — — —
Values represent statistically significant (p < 0.01 by regularized t-test) mean fold change from control (n = 3) for 100 mg/kg/day NaI and PB and 10 mg/kg/day PTU.
a From GenBank (http://www.ncbi.nlm.nih.gov).
b —, Gene symbol not known.
c p ≤ 0.03 by regularized t-test.
d —, Not statistically significant.
==== Refs
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Environ Health PerspectEnviron. Health PerspectEnvironmental Health Perspectives0091-67651552-9924National Institute of Environmental Health Sciences 10.1289/ehp.7487ehp0113-00136216203247ResearchRisk of Congenital Anomalies after the Opening of Landfill Sites Palmer Stephen R. 1Dunstan Frank D.J. 1Fielder Hilary 1Fone David L. 1Higgs Gary 2Senior Martyn L. 31 Department of Epidemiology, Statistics and Public Health, Wales College of Medicine, Cardiff, Wales2 GIS Research Centre, School of Computing, University of Glamorgan, Pontypridd, Wales3 Department of City and Regional Planning, Cardiff University, Cardiff, WalesAddress correspondence to S. Palmer, Department of Epidemiology, Statistics and Public Health, Wales College of Medicine, Heath Park, Cardiff, CF14 4XN. Telephone: 029-20-742321. Fax: 029-20-742898. E-mail:
[email protected] authors declare they have no competing financial interests.
10 2005 14 6 2005 113 10 1362 1365 12 8 2004 14 6 2005 2005Publication of EHP lies in the public domain and is therefore without copyright. All text from EHP may be reprinted freely. Use of materials published in EHP should be acknowledged (for example, ?Reproduced with permission from Environmental Health Perspectives?); pertinent reference information should be provided for the article from which the material was reproduced. Articles from EHP, especially the News section, may contain photographs or illustrations copyrighted by other commercial organizations or individuals that may not be used without obtaining prior approval from the holder of the copyright. Concern that living near a particular landfill site in Wales caused increased risk of births with congenital malformations led us to examine whether residents living close to 24 landfill sites in Wales experienced increased rates of congenital anomalies after the landfills opened compared with before they opened. We carried out a small-area study in which expected rates of congenital anomalies in births to mothers living within 2 km of the sites, before and after opening of the sites, were estimated from a logistic regression model fitted to all births in residents living at least 4 km away from these sites and hence not likely to be subject to contamination from a landfill, adjusting for hospital catchment area, year of birth, sex, maternal age, and socioeconomic deprivation score. We investigated all births from 1983 through 1997 with at least one recorded congenital anomaly [International Classification of Diseases, Ninth Revision (ICD-9), codes 7400–7599; International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10), codes Q000–Q999]. The ratio of the observed to expected rates of congenital anomalies before landfills opened was 0.87 [95% confidence interval (CI), 0.75–1.00], and this increased to 1.21 (95% CI, 1.04–1.40) after opening, giving a standardized risk ratio of 1.39 (95% CI, 1.12–1.72). Enhanced congenital malformation surveillance data collected from 1998 through 2000 showed a standardized risk ratio of 1.04 (95% CI, 0.88–1.21). Causal inferences are difficult because of possible biases from incomplete case ascertainment, lack of data on individual-level exposures, and other socioeconomic and lifestyle factors that may confound a relationship with area of residence. However, the increase in risk after the sites opened requires continued enhanced surveillance of congenital anomalies, and site-specific chemical exposure studies.
congenital malformationsepidemiologylandfillsmall-area health statistics
==== Body
The possibility of adverse health effects of living near landfills has become a major public health issue (Dolk et al. 1998; Elliott et al. 2001; Fielder et al. 2000a, 2000b). The EUROHAZ-CON study of hazardous waste landfill sites in five European countries found a 33% increase in the risk of congenital abnormalities in infants born to mothers living within 3 km of the sites (Dolk et al. 1998). The Small Area Health Statistics Unit study of more than 19,000 landfill sites in Great Britain (Elliott et al. 2001) showed a statistically significant but very small (1%) increase in congenital anomalies in babies born to women living within 2 km of any landfill site. The authors acknowledged that this U.K. national study suffered from problems in the accuracy of environmental data and from the possible bias of differential underreporting of congenital anomalies by hospitals.
Our present study arose from health concerns expressed by residents that chemicals emitted from a single landfill site in the Rhondda Valley, Wales, were the cause of congenital anomalies. These fears led to a major public outcry, direct action, a public inquiry (Purchon 2001), and an international review by the Agency for Toxic Substances and Disease Registry (2003). In preliminary epidemiologic studies we found that rates of notified congenital anomalies in the local government areas where complaints of smells occurred were significantly higher than rates in socioeconomically matched control areas, but this was the case before as well as after the landfill opened (Fielder et al. 2000b). We therefore carried out a new study to test the null hypothesis that the opening of new land-fills in Wales was not associated with increased rates of congenital anomalies in nearby residents by comparing rates before and after sites opened. Validation of environmental data parameters was undertaken with the help of local government personnel and the government-funded Environment Agency. We also adjusted for the potential bias caused by differential underreporting of congenital anomalies by different hospitals.
Materials and Methods
Birth data.
Cases of statutorily notifiable congenital anomalies were obtained from the U.K. Office of National Statistics for 1983 to 1997. All births with at least one recorded anomaly with International Classification of Diseases, Ninth Revision [ICD-9; World Health Organization (WHO) 1979] and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10; WHO 1994] codes in the range of 7400–7599 for ICD-9 and Q000–Q999 for ICD-10 were included. Subgroup analysis was carried out for two major groups where numbers of cases were reasonably large, namely, chromosomal anomalies (ICD-9 codes 7580–7589, ICD-10 codes Q900–Q999) and cardiovascular defects (ICD-9 codes 7450–7479, ICD-10 codes Q210–Q289) and also for abdominal wall defects (ICD-9 codes 7567, ICD-10 codes Q79.2–Q79.3) that were of special concern in Wales (Fielder et al. 2000b).
The denominator was taken from the computerized Child Health System (Andrews et al. 1996) register of live births in Wales from 1983 through 1997, which recorded sex, gestation, and birth weight of each child and demographic details of the mother. We used a geographic information system point-in-polygon technique to assign area-based socioeconomic deprivation scores (Townsend et al. 1988) to each birth, using the score of the census enumeration district containing the postal code of maternal residence. Cases of anomalies were linked to birth records if there were an identical date of birth and at least two of the following four matching variables: birth weight, postal code, mother’s date of birth, and mother’s age. We also linked unique pairs where date of birth differed but three or more of the other four variables matched. This method resulted in matches being found for 6,780 of the 7,233 (94%) birth defects records; the remaining 453 (6%) could not be linked. Unmatched births with congenital anomalies were not more likely to reside near landfill sites. Hospital of birth was not available to us for all individual births. We therefore input hospital of birth on the basis of postal code of residence within catchment areas derived from a subset of the Child Health System database.
For the period 1998 through 2000, we obtained congenital anomalies data from the newly established Wales Congenital Anomaly Register and Information Service (CARIS) (Botting 2000) that became operational in 1998 and that has substantially increased reporting rates in Wales (Greenacre et al. 2000). Only live births were included to match to the Child Health System. Of the 2,633 congenital anomalies in live births reported, 2,534 (96%) were matched. There was no statistical association between unmatched cases and distance from landfill.
Landfill data.
We sought information from the Environment Agency to identify new landfill sites in Wales opening from 1985 and licensed to accept commercial, industrial, and household waste; the dates on which they became operational; details of the site capacity; and types of waste accepted as described by the categories of the National Waste Classification Scheme 1999 (Environment Agency 1999). We identified landfills that were licensed to take chemical waste (types 28, 29) and those that subsequently introduced containment (removal of landfill from one part to new specially lined sections), and/or gas venting. We checked these data with the respective local government departments and made substantial corrections, especially to the dates when sites began to receive waste. The Environment Agency identified 20 sites that opened during the period, and four additional sites where there was major expansion or significant change of use during the study period.
We calculated the distance between the maternal residence at the time of birth and the grid reference of the centroid of the site as determined by the Environment Agency. Where sites were situated in proximity, we allocated the birth to the nearest site. Elliott et al. (2001) defined exposure as births within 2 km of the centroid of sites, whereas Dolk et al. (1998) used 3 km as the definition. Results were similar for the 2 km and 3 km distances; unless otherwise stated, we report only the 2 km findings.
Statistical analysis.
Because congenital anomalies registration rates changed over the 15-year period and the ascertainment rate varied considerably between hospitals, we calculated “expected” rates from a logistic regression model fitted to the set of all births at least 4 km away from any of the major sites. This model incorporated maternal age, hospital of birth, year of birth, deprivation (quintiles of the distribution of Townsend scores), and the sex of the baby. We derived a predicted probability for each birth, and by summing these over a given area we calculated the expected number of congenital anomalies. We then compared this with the observed number to calculate a standardized risk ratio. We derived 95% confidence intervals (CIs) for the ratio (Breslow and Day 1994).
To examine the appropriateness of concentric circles to define exposure, we estimated the spatial distribution of risk by calculating observed and expected frequencies for squares with 250-m sides. To reduce the influence of random variation, we calculated standardized values (observed–expected/square root of expected) and applied a kernel smoothing method (Diggle 1990) to these as a descriptive tool to visualize risk patterns.
Results
1983–1997.
Between 1983 and 1997, 542,682 births were identified on the Child Health System in Wales, of which 6,148 (1.1%) had at least one congenital anomaly within the codes defined in ICD-9 (WHO 1979) and ICD-10 (WHO 1994). This included 97 abdominal wall defects, 416 chromosomal anomalies, and 544 cardiovascular defects. Proportions of congenital anomalies did not vary significantly by maternal age or deprivation scores except for chromosomal abnormalities, where the proportion increased significantly with maternal age.
For the pooled data from the 20 sites opening during the period, the ratio of observed to expected congenital anomalies within 2 km before opening was 0.87 (95% CI, 0.75–1.00), and this increased to 1.21 (95% CI, 1.04–1.40) after opening, with a standardized risk ratio of 1.39 (95% CI, 1.12–1.72) (Table 1). In the 15 sites that introduced new containment units after opening, there was a small increase in the rate ratio, but the 95% CI included unity. Gas control was introduced in 10 sites, and in these a 25% fall in rate ratio was observed, but again, the 95% CI included unity.
Seven sites introduced both containment and gas control. In the 8 sites that introduced only containment, the rate ratio was 1.47 (95% CI, 0.45–7.56). For the three sites that introduced only gas control, the numbers of cases were too small to allow the calculation of a CI. Using a definition of exposure of 3 km, with consequent larger numbers of exposed births, the rate ratio was 3.93 (95% CI, 1.43–16.95) for containment only and 0.16 (95% CI, 0.04–0.45) for gas control only.
Six sites that opened during the study period were licensed to accept chemical waste. In these sites the rate before opening was higher than for all sites, but the standardized risk ratios after opening were very similar to the overall risk ratios. In the analysis of all pooled data for the prespecified subcategories of congenital anomalies, the risk ratio for cardiovascular defects (n = 544) within 2 km before and after opening was 1.74 (95% CI, 0.83–3.78). The risk ratios were 2.33 (95% CI, 0.53–14.0) for abdominal defects (n = 97) and 1.27 (95% CI, 0.64–2.58) for chromosomal abnormalities (n = 416).
There was substantial variation in risk ratios between sites, and the small numbers of births around many of the sites gave wide CIs. For 16 sites there were sufficient data to calculate site-specific risk ratios; in 10 sites, the point estimate was above unity, and in 2 of these, the lower limit of the 95% CI of the risk ratio was above unity, giving a significant increased risk. For 6 sites, the point estimate was below unity, but in none was the upper limit of the 95% CI below unity, so there was not strong evidence for an increased risk.
For illustrative purposes the distribution of smoothed risk estimates around two sites, Nantygwyddon and Trecatti, is shown in Figure 1. The population distribution followed the contours of the valleys. In the first case, an area of increased relative risk became apparent in the period after the site opened immediately adjacent to the site. In the second case, areas of increased relative risk were apparent before and after opening in the same location to the west of the landfill.
1998–2000.
There were 97,292 births identified on the Child Health System, and CARIS identified 2,633 congenital anomalies in live births, of which 2,534 (2.6%) were matched to these births. No data from this system were available for the periods before the landfills opened. Consequently, only the relation between risk of congenital anomalies and distance from the landfills after opening could be studied. Overall, within 2 km of the 20 landfills studied for 1998–2000, the standardized risk ratio was 1.04 (95% CI, 0.88–1.21) (Table 2). For the sites licensed to take chemicals, the standardized risk ratio was 1.08 (95% CI, 0.75–1.41). For those introducing containment, it was 1.19 (95% CI, 0.90–1.48), and for those that introduced gas control, it was 1.14 (95% CI, 0.88–1.41). These results include the 7 sites where both containment and gas control were introduced. When these 7 sites were excluded from analysis, the standardized risk ratio for containment only was 1.19 (95% CI, 0.90–1.48); for gas control only, the numbers were insufficient to allow an accurate calculation of a CI. If the definition of exposure is changed to living within 3 km, as for the pre-1997 data, the standardized risk ratio for containment only was 1.04 (95% CI, 0.72–1.36) and for gas control only was 1.77 (95% CI, 1.20–2.34).
Discussion
We tested the null hypothesis that the opening of landfill sites taking mainly domestic and commercial waste was not associated with an increased risk of congenital anomalies in nearby residents, although we did not have sufficient statistical power to explore relationships with subgroups of congenital anomalies with much precision. Data on actual exposures to chemicals that might be emitted from the landfills are not available within the United Kingdom. We have now found, when summing over all the landfills, that the rates of all congenital anomalies identified through statutory notifiable data increased significantly after sites opened until 1997, and this was the case whether we took 2- or 3-km distances as the definition of exposure. We were concerned to find out if this increase persisted after 1997 using more recent complete and accurate data collected, but we did not find an increased risk over the 3 years from 1998 through 2000.
Several other landfill studies have been carried out with varying findings (Barry and Bove 1997; Croen et al. 1997; Dolk et al. 1998; Elliott et al. 2001; Geschwind et al. 1992; Kharrazi et al. 1997; Marshall et al. 1997; Orr et al. 2002; Vrijheid et al. 2002a). The most recent study in the United States by Orr et al. (2002) was a case–control study of birth defects in racial or ethnic minority children born to mothers in California. They found an increased risk for all defects of 1.12 (95% CI, 0.98–1.27) in births to mothers living in the same census tract as a hazardous waste site. The EUROHAZCON study also looked at 21 landfill sites in Britain, France, Italy, Denmark, and Belgium that took hazardous waste in a case–control study of nonchromosomal (Dolk et al. 1998) and chromosomal (Vrijheid et al. 2002a) congenital anomalies. Living within 3 km was associated with a 33% increased risk compared with living 3–7 km away. Risk decreased with distance from the sites, and adjustment for area socioeconomic level did not remove the effect. Elliott et al. (2001) considered all landfill sites in Great Britain, most of which were small and held only domestic waste. They found that 80% of the British population lived within 2 km of such sites. There was a small excess risk when examining sites only after they opened, but a secondary analysis comparing before and after opening on about half the sites did not find an excess risk. However, their control population was essentially rural, and therefore, differences between the study and control groups could possibly account for these findings. Also, bias of differential reporting of congenital anomalies by hospitals may have occurred. In our study there was a 3-fold variation in rates around different hospitals, and this was considered in the analysis.
There are a number of well-recognized problems in interpreting ecologic studies of point sources of pollution (Vrijheid 2000). One potential source of bias is the use of surrogate measures of exposure in the absence of individual-level exposures. The proxy exposure measures of distance from site assumes that the toxic effect is airborne because smell is often the provoking event, but contamination of ground-water and percolation through soil must also be considered (Vrijheid et al. 2002b). Distance of residence from the landfill cannot take into account duration of residence or the proportion of time that individuals spent away from their residence. Nor is it possible to detect directional patterns using concentric circles. The radii of 2 km chosen by Elliott et al. (2001) were pragmatic, maximizing the power of comparisons while remaining within plausible estimates of the range of chemicals dispersed from a site; no sound evidence has yet been published to measure human exposures with distance from land-fills in the United Kingdom, but expert opinion suggests that small particles from landfills may be detectable up to 3 km away (WHO 2001). We are exploring alternatives to using concentric circles (James et al. 2003). In the two identified sites presented in this article, the distribution of increased risk is not uniform with distance, and the patterns are quite different. Pooling results across sites will mask these differences.
Another problem in studying single sites is that most have resident populations too small to give sufficient power to demonstrate a small but important public health effect. Aggregation of data around several sites is therefore necessary to obtain statistical power, but the chemical content of different sites may vary enormously and dilute any real associations with specific pollutants. Also, the influence of those sites with larger populations will predominate. It is also necessary to use broad groups of conditions to obtain sufficient numbers of cases; specific congenital anomalies may have different etiologies that cannot be identified in pooled studies (Elliott and Wakefield 2000). Data on what was disposed of in the landfills are not available, nor is there insufficient information about what chemicals come from specific sites to enable analyses of sensible subgroups of sites.
We have shown that the rate of congenital anomalies in residents living within 2 km of landfills was significantly higher after landfills opened, but these data cannot prove or disprove a causal link. The existence of other sources of pollution in the same temporal and geographic relationship to the population as the landfill sites is not a likely explanation. An alternative hypothesis is that opening of landfills triggered a change in the makeup of the population living nearby or triggered changes in social and other lifestyle or medical care characteristics of mothers living there (e.g., uptake of screening and terminations for congenital anomalies), although we have no data to suggest this happened. A single snapshot of area deprivation would not adjust for this. Furthermore, area-based deprivation measures may not accurately reflect socioeconomic status of the individual, and we did not have data on maternal smoking, nutrition, or other lifestyle factors that may confound the relationship between residence near landfills and congenital anomalies.
The increased rates of congenital anomalies around landfill sites in Wales persisted until 1998. In the next 2 years, the rate ratios fell to close to 1. Is this simply due to less biased surveillance data, or has improved management of sites in recent years reduced exposure of the population to hazardous chemicals? Data quality of the congenital anomalies registers used up to 1998 is certainly an important issue because considerable underreporting is well documented. However, even if our adjustment for reporting hospitals did not fully compensate for possible differential reporting by distance of landfills, such bias would not explain the changes in rates with distance after opening of the landfills. Furthermore, such a fall in risk is not readily explained by the alternative hypothesis of social change. Intriguingly, using data up to 1997 in those sites that introduced gas control, which is likely to reduce exposure of the public to emissions, some time after opening, showed a fall in the risk ratio. In those sites where movement of landfill material would have increased because of the introduction of new containment cells, possibly temporarily increasing emissions, there was an increased risk ratio. This pattern was not evident in the enhanced data after 1998, although numbers of cases were very small.
Conclusions
In Wales, pooling data from populations living within 2 km of 24 landfill sites that opened from 1983 through 1997, we have found that the ratio of observed to expected rates of congenital anomalies increased by about 40%. This increase did not persist in data collected from 1998 through 2000. Causal inference is hampered by lack of data on individual-level exposures. Other socioeconomic and lifestyle factors should be considered in the etiology of higher area rates of congenital anomalies, but there is also an urgent need to characterize and minimize exposure of individuals (particularly pregnant women) to environmental chemicals that may be emitted from any industrial site of concern. Continued enhanced surveillance is also needed to overcome the bias for differential ascertainment of congenital anomalies.
The permission of the Directors of Public Health as Caldicott guardians in the former Welsh health authorities was obtained to use data stored on the Child Health System.
Figure 1 Spatial distribution of smoothed standardized (observed–expected/expected) risk ratios for congenital anomalies within squares with 250-m sides in two areas. Colored squares are quintiles of the ratio observed–expected/square root of expected in descending order: red, orange, light blue, dark blue, green. (A) Nantygwyddon before opening; (B) Nantygwyddon after opening; (C) Trecatti before opening; (D) Trecatti after opening.
Table 1 Births, congenital anomalies, and standardized risk ratios (95% CIs) within 2 km of the landfill sites, 1983–1997.
Before
After
Sites Congenital anomalies Total births Standardized risk ratio Congenital anomalies Total births Standardized risk ratio Ratio after:before
Before opening and after opening of sites 182 14,230 0.87 (0.75–1.00) 173 15,451 1.21 (1.04–1.40) 1.39 (1.21–1.72)
As above but including four sites with significant expansion 223 16,948 0.89 (0.78–1.01) 202 17,860 1.22 (1.06–1.39) 1.38 (1.13–1.67)
Fifteen sites introducing containment after opening 12 766 1.11 (0.64–1.81) 102 8,097 1.27 (1.05–1.53) 1.15 (0.63–2.29)
Eight sites with containment but no gas control 3 380 0.79 (0.29–1.90) 28 2,757 1.16 (0.81–1.63) 1.47 (0.45–7.56)
Ten sites introducing gas control after opening 64 3,617 1.39 (1.09–1.75) 20 2,310 1.03 (0.67–1.52) 0.74 (0.42–1.24)
Three sites with gas control but no containment 1 113 — 0 88 — —
Six sites that accept chemical waste 38 2,358 1.15 (0.84–1.55) 43 4,319 1.25 (0.93–1.65) 1.08 (0.68–1.72)
—, Numbers are too small to quote reliable CIs.
Table 2 Births, congenital anomalies, and standardized risk ratios (95% CIs) within 2 km of the landfill sites, 1998–2000.
Sites Congenital anomalies Total births Standardized risk ratio
Before opening and after opening of sites 178 5,297 1.04 (0.88–1.21)
As above but including four sites with significant expansion 204 6,134 1.04 (0.88–1.21)
Fifteen sites introducing containment after opening 124 3,827 1.11 (0.92–1.31)
Eight sites with containment but no gas control 70 2,129 1.19 (0.90–1.48)
Ten sites introducing gas control after opening 55 1,756 1.14 (0.88–1.41)
Three sites with gas control but no containment 1 58 1.03a
Six sites that accept chemical waste 42 1,216 1.08 (0.75–1.41)
a Numbers are too small to quote reliable CIs.
==== Refs
References
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Environ Health Perspect. 2005 Oct 14; 113(10):1362-1365
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Environ Health Perspect
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10.1289/ehp.7487
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