abstract: With increasing dietary cholesterol intake the liver switches from a mainly resilient to a predominantly inflammatory state, which is associated with early lesion formation.
author: Robert Kleemann; Lars Verschuren; Marjan J van Erk; Yuri Nikolsky; Nicole HP Cnubben; Elwin R Verheij; Age K Smilde; Henk FJ Hendriks; Susanne Zadelaar; Graham J Smith; Valery Kaznacheev; Tatiana Nikolskaya; Anton Melnikov; Eva Hurt-Camejo; Jan van der Greef; Ben van Ommen; Teake Kooistra
date: 2007
institute: 1Department of Vascular and Metabolic Diseases, TNO-Quality of Life, BioSciences, Gaubius Laboratory, Zernikedreef 9, 2333 CK Leiden, The Netherlands; 2Department of Vascular Surgery, Leiden University Medical Center, Albinusdreef 2, 2300 RC Leiden, The Netherlands; 3Department of Physiological Genomics, TNO-Quality of Life, BioSciences, Utrechtseweg 48, 3704 HE Zeist, The Netherlands; 4GeneGo Inc., Renaissance Drive, St Joseph, MI 49085, USA; 5Department of Analytical Research, TNO-Quality of Life, Quality and Safety, Utrechtseweg 48, 3704 HE Zeist, The Netherlands; 6AstraZeneca, CV&GI Research, Silk Road Business Park, Macclesfield, Cheshire SK10 2NA, UK; 7Vavilov Institute for General Genetics, Russian Academy of Science, Gubkin Street 3, 117809 Moscow, Russia; 8AstraZeneca CV&GI Research, 43183 Mölndal, Sweden
references:
title: Atherosclerosis and liver inflammation induced by increased dietary cholesterol intake: a combined transcriptomics and metabolomics analysis

# Background

Atherosclerosis is a multifactorial disease of the large arteries and the leading cause of morbidity and mortality in industrialized countries \[1\]. There is ample evidence that hypercholesterolemia (that is, elevated plasma levels of low-density lipoprotein (LDL) and very low-density lipoprotein (VLDL)) induced by genetic modification or enhanced intake of dietary lipids is a major causative factor in atherogenesis \[2,3\]. It is equally clear that from the very beginning of lesion formation, atherogenesis requires an inflammatory component, which is thought to drive the progression of the disease \[4,5\]. Indeed, some of the variation in the rate of lesion progression in different individuals may relate to variations in their basal inflammatory state \[6,7\]. However, while the inflammatory processes in the complex evolution of the lesion from the early fatty streak to a fibrous plaque are considered self-perpetuating phenomena, the initial trigger and origin of the inflammatory component in hypercholesterolemia remains enigmatic \[6,8\].

Recent observations by us and others suggest that the liver plays a key role in the inflammatory response evoked by dietary constituents (reviewed in \[8,9\]). For example, liver-derived inflammation markers such as C-reactive protein (CRP) and serum amyloid A (SAA) increase rapidly (within days) after consumption of an excess amount of dietary lipids \[8,10\], and thus by far precede the onset of early aortic lesion formation \[8\]. These findings suggest that nutritional cholesterol itself may contribute to the evolution of the inflammatory component of atherogenesis. We postulate that pro-atherogenic inflammatory factors originate at least partly from the liver. We also hypothesize that these factors come into play at high dietary cholesterol doses because of the exponential rather than linear nature of the relationship between cholesterol intake (measured as cholesterol plasma levels) and atherosclerotic lesion size \[11,12\] as specified in more detail in Additional data file 1.

In this study we sought evidence for the hypothesis that inflammation and hypercholesterolemia are not separate factors, but closely related features of the same trigger, dietary cholesterol. In particular, using a variety of newly developed functional bioinformatics tools, we addressed the question of how the liver responds to increasing dietary cholesterol loads at the gene transcription level and analyzed how hepatic cholesterol metabolism is linked to the hepatic inflammatory response, including underlying regulatory mechanisms. Notably, all analyses were performed at a very early stage of the atherogenic process (that is, after 10 weeks of cholesterol feeding) to limit potential feedback reactions from the vessel wall.

An established model for cholesterol-induced atherosclerosis, ApoE\*3Leiden transgenic (E3L) mice, allowed the application of experimental conditions that mimic the human situation: E3L mice display a lipoprotein profile similar to that of humans suffering from dysbetalipoproteinemia and develop atherosclerotic lesions that resemble human lesions with regard to morphology and cellular composition \[13,14\]. E3L mice were exposed to increasing doses of dietary cholesterol (as the only dietary variable modulated), and liver genome and metabolome datasets were analyzed in a unique context, that is, at the time point of first lesion development. Advanced (functional) bioinformatical analysis allowed us to merge metabolome and transcriptome datasets and to analyze pathways and biochemical processes comprehensively. Recent advances in systems biology (for example, new biological process software for network building and data mining) have enabled us to discover significant relationships and to identify transcriptional master regulators that control gene alteration and are ultimately responsible for effects at the process level.

# Results

## Effect of dietary cholesterol load on plasma lipids and early atherosclerosis

Treatment of female E3L mice with a cholesterol-free (Con), a low-cholesterol (LC) or a high-cholesterol (HC) diet resulted in total plasma cholesterol concentrations that stabilized at 5.9 ± 0.3 mM, 13.3 ± 1.9 mM and 17.9 ± 2.4 mM, respectively. The increase in plasma cholesterol in the LC and HC groups was confined to the pro-atherogenic lipoprotein particles VLDL and LDL (Figure 1a). High-density lipoprotein (HDL) levels and plasma triglyceride levels were comparable between the groups (Figure 1a and Table 1). The plasma levels of alanine aminotransferase (ALAT) and aspartate aminotransferase (ASAT), two markers of liver function, were comparable in the Con and LC groups and slightly elevated in the HC group.

Effects of dietary cholesterol on plasma lipids and inflammation markers

|                           | Con        | LC           | HC              |
|---------------------------|------------|--------------|-----------------|
| Body weight (start) (g)   | 20.3 ± 1.5 | 20.8 ± 1.5   | 20.6 ± 0.9      |
| Body weight gain (g)      | 0.4 ± 0.7  | 0.7 ± 0.8    | 0.6 ± 0.5       |
| Food intake (g/day)       | 2.6 ± 0.2  | 2.9 ± 0.3\*  | 2.5 ± 0.2^†^    |
| Plasma cholesterol (mM)   | 5.9 ± 0.3  | 13.3 ± 1.9\* | 17.9 ± 2.4\*^†^ |
| Plasma triglyceride (mM)  | 1.7 ± 0.4  | 2.3 ± 0.3    | 2.1 ± 0.7       |
| Plasma E-selectin (μg/ml) | 44.3 ± 2.3 | 44.3 ± 6.3   | 55.1 ± 8.5\*^†^ |
| Plasma SAA (μg/ml)        | 2.8 ± 0.6  | 4.7 ± 1.7    | 8.3 ± 2.7\*^†^  |
| Plasma ALAT (U/mL)        | 48 ± 44    | 45 ± 22      | 75 ± 23         |
| Plasma ASAT (U/mL)        | 260 ± 123  | 237 ± 57     | 569 ± 221\*^†^  |

Three groups of female E3L mice were fed either a cholesterol-free (Con) diet or the same diet supplemented with 0.25% (LC) or 1.0% (HC) w/w cholesterol. Listed are the average body weight at the start (t = 0) of the experimental period together with the body weight gain, the average daily food intake and the average plasma levels of cholesterol, triglycerides, E-selectin, serum amyloid A (SAA), alanine aminotransferase (ALAT) and aspartate aminotransferase (ASAT). All data are mean ± standard deviation. \**P* \< 0.05 versus Con; ^†^*P* \< 0.05 versus LC (ANOVA, least significant difference *post hoc* test).

After 10 weeks of dietary treatment, the animals were euthanized to score early atherosclerosis. Longitudinally opened aortas of the Con and LC groups were essentially lesion-free (*en face* oil red O-staining), while aortas of the HC group already contained lesions (not shown). Consistent with the presence of atherosclerosis in the HC group, the vascular inflammation marker E-selectin was elevated only in this group (Table 1). Early onset of atherosclerosis was analyzed in more detail in the valve area of the aortic root (Figure 1b), a region in which lesions develop first \[15\]. The total cross-sectional lesion area under basal conditions was 1,900 ± 900 μm^2^ (Con group; Figure 1c). Compared to the Con group, the lesion area was relatively moderately increased in the LC group (4.2-fold; *P* \< 0.05) and strongly increased in HC (19.5-fold; *P* \< 0.05), confirming the exponential rather than linear relationship between total plasma cholesterol levels and the lesion area.

Next, lesions in the aortic root were graded according to the classification of the American Heart Association. Under control conditions (Con); only about 10% of the aortic segments contained lesions, all of which were very mild type I lesions not identified by *en face* staining (Figure 1d). In the LC group, more (40%) aortic segments showed lesions, of which 38% were mild type I-III lesions and 2% were severe type IV lesions. In the HC group, 81% of the aortic segments displayed lesions, most of which were mild lesions (76% type I-III lesions; 5% type IV). The predominance of mild-type lesions confirms an early stage of atherosclerotic disease in all groups. Notably, a positive association was observed between the cross-sectional lesion area and the plasma levels of SAA, an inflammation marker formed in liver. SAA was significantly elevated in the HC group, pointing to a hepatic inflammatory response to cholesterol feeding (Table 1) that is associated with early atherosclerotic lesion formation.

## Analysis of the hepatic gene response to increasing doses of dietary cholesterol

To get insight into the complex traits underlying the (patho)physiological response of the liver to dietary cholesterol, whole-genome and metabolome measurements were made. Compared to the Con group, a relatively small number of genes (551) significantly changed with LC treatment (Figure 2). HC treatment modulated most (440 out of 551) of these genes and, additionally, affected 1,896 other genes. The individual gene expression profiles within a treatment group were very similar and formed clusters as confirmed by hierarchical clustering analysis (not shown). Differences in gene expression between the treatment groups were validated and confirmed for a selected group of genes by RT-PCR (Additional data file 2).

Standard Gene Ontology (GO) biological process annotation allowed categorization of 52% of the differentially expressed genes based on their biological function (Table 2). LC treatment predominantly affected genes belonging to lipid and lipoprotein metabolism, protein metabolism, carbohydrate metabolism, energy metabolism and transport. HC treatment affected the same GO groups but, additionally, also genes relevant to immune and inflammatory responses, cell proliferation, apoptosis, cell adhesion and cytoskeleton integrity (Table 2 and Additional data file 3).

Overview of genes that are differentially expressed in response to cholesterol

|  | LC |  |  | HC |  |  |
|----|----|----|----|----|----|----|
|  |  |  |  |  |  |  |
| GO category | Up | Down | Total | Up | Down | Total |
| Lipid and lipoprotein metabolism (includes cholesterol and steroid metabolism) | 8 | 50 | 58 | 37 | 114 | 151 |
| Protein metabolism (includes protein folding and breakdown) | 34 | 14 | 48 | 143 | 98 | 241 |
| Other metabolism (includes carbohydrate metabolism) | 32 | 19 | 51 | 122 | 130 | 252 |
| Generation of precursor metabolites and energy | 10 | 15 | 25 | 24 | 47 | 71 |
| Transport | 31 | 15 | 46 | 125 | 77 | 202 |
| Immune and stress response/inflammation | 19 | 7 | 26 | 99 | 49 | 148 |
| Cell proliferation/apoptosis | 9 | 3 | 12 | 37 | 18 | 55 |
| Cell adhesion/cytoskeleton | 10 | 1 | 11 | 76 | 8 | 84 |

Differentially expressed genes of LC and HC groups (ANOVA FDR \< 0.05 and *t*-test compared to Con group *P* \< 0.01) were analyzed according to standard GO biological process annotation and grouped into functional categories.

To refine the liver transcriptome data analysis and to define which biological processes are switched on/off with increasing dietary cholesterol loads, we performed gene enrichment analysis in four different functional ontologies: biological processes, canonical pathway maps, cellular processes and disease categories using MetaCore™. This allowed us to analyze functionally related genes (for example, genes belonging to a specific biochemical process) as a whole. Table 3 summarizes the significantly changed biological processes for the LC and HC groups. Four key ('master') process categories were affected by cholesterol feeding: lipid metabolism; carbohydrate and amino acid metabolism; transport; immune and inflammatory responses.

Analysis of processes that are changed significantly upon treatment with dietary cholesterol

|  |  |  | Differentially expressed (%) |  |
|----|----|----|----|----|
|  |  |  |  |  |
| Master process | Subprocess (child terms) | Number of genes measured | LC | HC |
| Lipid metabolism |  | 264 | 8.7\* | 24.2\* |
|  | Fatty acid metabolism, fatty acid beta-oxidation | 8 | 0.0 | 50.0\* |
|  | Triacylglycerol metabolism | 7 | 0.0 | 57.1\* |
|  | Cholesterol metabolism | 27 | 33.3\* | 33.3\* |
|  | Cholesterol biosynthesis | 7 | 71.4\* | 57.1\* |
|  | Lipoprotein metabolism | 18 | 16.7\* | 44.4\* |
|  | Lipid biosynthesis | 105 | 11.4\* | 23.8\* |
|  |  |  |  |  |
| Immune response |  | 297 | 3.0 | 12.1\* |
|  | Antigen presentation, exogenous antigen | 10 | 10.0 | 70.0\* |
|  | Antigen processing | 17 | 5.9 | 35.3\* |
|  | Acute-phase response | 11 | 9.1 | 36.4\* |
|  |  |  |  |  |
| General metabolism |  | 3,600 | 3.3 | 13.1\* |
|  | Cellular polysaccharide metabolism | 19 | 5.3 | 26.3\* |
|  | Polysaccharide biosynthesis | 9 | 0.0 | 33.3\* |
|  | Cofactor metabolism | 116 | 5.2 | 21.6\* |
|  | Regulation of translational initiation | 9 | 0.0 | 44.4\* |
|  | Amino acid metabolism | 103 | 2.9 | 20.4\* |
|  |  |  |  |  |
| Transport |  | 1,119 | 2.9 | 14.3\* |
|  | Intracellular protein transport | 161 | 3.7 | 19.9\* |
|  | Golgi vesicle transport | 16 | 6.3 | 37.5\* |
|  | Mitochondrial transport | 11 | 18.2\* | 54.5\* |

Master processes and their subprocesses (child terms) are listed together with the number of genes measured (third column). Percentages reflect the fraction of genes differentially expressed (within a specific process or pathway) in the LC and HC groups compared to the Con group. Relevant biological processes were identified in GenMAPP by comparison of the set of differentially expressed genes (ANOVA; *P* \< 0.01 and FDR \< 0.05) with all genes present on the array. \*Biological processes with a Z-score \>2 and a PermuteP \< 0.05.

In the LC group, most significant effects occurred within the master process of lipid metabolism. Important subprocesses (that is, processes in which more than 10% of process-related genes changed significantly) were lipid biosynthesis, lipoprotein metabolism, cholesterol metabolism and cholesterol biosynthesis (Table 3). The overall functional effect of LC treatment can be summarized as a substantial down-regulation of cholesterol and lipid metabolism. This adaptive response of the liver indicates metabolic liver resilience up to doses of 0.25% (w/w) cholesterol.

A further increase of dietary cholesterol (1% w/w; HC group) intensified the changes in gene expression seen with LC treatment, indicating further metabolic adaptation. For example, all individual genes of the cholesterol biosynthesis pathway were down-regulated to a greater extent by HC than by LC treatment (see pathway map in Additional data file 4a): the gene of the rate-limiting enzyme of this pathway, *HMG CoA reductase* (*HDMH*), was down-regulated 2.8-fold and 10.6-fold by LC and HC treatments, respectively. Similarly, genes relevant for lipid and lipoprotein metabolism, *LDL receptor* (LC group, 1.3-fold down-regulated; HC group, 1.9-fold down-regulated) and *lipoprotein lipase* (LC group, 1.8-fold up-regulated; HC group, 5.5-fold up-regulated), were dose-dependently modulated.

Besides marked effects on 'lipid metabolism', HC treatment induced significant changes in the master processes: 'general metabolism', 'transport' and 'immune and inflammatory response' (Table 3). In particular, HC treatment enhanced the subprocesses involved in translational initiation, Golgi vesicle transport, mitochondrial transport, antigen presentation, antigen processing and acute phase response by affecting the expression of more than 35% of the genes in these subprocesses.

Significantly, HC but not LC dietary stress activated specific inflammatory pathways (that is, the platelet-derived growth factor (PDGF), interferon-γ (IFNγ), interleukin-1 (IL-1) and tumor necrosis factor-α (TNFα) signaling pathways; Figure 3). Activation of these inflammatory pathways with HC treatment leads to a significant up-regulation of MAP kinases, complement factors and acute phase proteins such as SAA. HC treatment significantly up-regulates, for example, all four *SAA* isotype genes (Figure 4a), which is consistent with the observed changes in SAA protein concentrations in plasma (compare Table 1).

More generally, HC treatment induced many genes, the gene products of which reportedly or putatively initiate or mediate inflammatory events (Additional data file 3), including genes encoding proteases, complement components, chemokines and their receptors, heat shock proteins, adhesion molecules and integrins, acute phase proteins, and inflammatory transcription factors, altogether indicating a profound reprogramming of the liver towards an inflammatory state not observed with LC treatment.

In separate experiments using female E3L mice, the hepatic inflammatory response to cholesterol-feeding was analyzed in more detail, including dose-dependency and time course. Plasma SAA served as a marker and readout of liver inflammation. Feeding of cholesterol at doses up to 0.50% w/w did not alter plasma SAA levels (Figure 4b). At higher cholesterol doses (≥0.75% w/w), plasma SAA levels increased markedly. A time course study with 1% w/w cholesterol (HC diet) showed that plasma SAA levels started to increase after 2 weeks and that plasma SAA levels continued to increase over time (Figure 4c). Together, these refined analyses indicate that the liver is resilient up to a cholesterol dose of about 0.50% w/w (adaptive response) and that the inflammatory response evoked with higher cholesterol concentrations starts within two weeks of starting the HC diet.

Enrichment analysis with disease categories confirmed the activation of many signaling and effector pathways relevant for inflammation and immunity by HC, but not by LC, treatment. The most affected (that is, activated at the gene expression level) disease categories with HC treatment were interrelated cardiovascular disorders and (auto) immune diseases, including cerebral and intracranial arterial diseases, cerebral amyloid angiopathy, hepatocellular carcinoma, and hepatitis (Additional data file 4b).

Altogether, this global analysis shows that the liver responds to a low load of dietary cholesterol mainly by adapting its metabolic program, whereas at a high cholesterol load the liver is much more extensively reprogrammed, and, in addition to metabolic adaptations, expresses genes involved in inflammatory stress.

## Analysis of diet-dependent metabolic changes in liver and plasma

To verify whether the switch from metabolic adaptation (with LC treatment) to hepatic inflammatory stress (with HC treatment) is also reflected at the metabolite level, we performed a comprehensive HPLC/MS-based lipidome analysis (measurement in total of about 300 identified di- and triglycerides, phosphatidylcholines, lysophosphatidylcholines, cholesterol esters) on liver homogenates of Con, LC and HC groups, and corresponding plasma samples.

The individual metabolite fingerprints within a treatment group were similar and formed clusters as assessed by principal component analysis (PCA; Figure 5). The clusters of the Con and LC groups overlapped partly, demonstrating that the Con and LC groups have a similar intrahepatic lipid pattern. This indicates that the metabolic adjustments on the gene level in the LC group were efficacious and enabled the liver to cope with moderate dietary stress. The HC cluster did not overlap with the clusters of the Con group, demonstrating that the switch to a proinflammatory liver gene expression profile is accompanied by development of a new metabolic hepatic state, which differs significantly from the metabolic state at baseline (Con group).

## Identification of transcriptional regulators that control the hepatic response to cholesterol

To identify the transcription factors and underlying regulatory mechanisms that govern the hepatic response to LC and HC stress, we performed a combined analysis of the liver transcriptome and metabolome dataset. Functional networks allowed the identification of transcriptional key ('master') regulators relevant for liver resilience and liver inflammation.

The adaptation of hepatic lipid metabolism to LC stress was mainly controlled by retinoid X receptor (RXR), SP-1, peroxisome proliferator activated receptor-α (PPARα), sterol regulatory element binding protein (SREBP)1 and SREBP2 (networks not shown), which are established positive regulators of genes involved in cholesterol biosynthesis \[16\]. Combined analysis of genome and metabolite datasets revealed that the intrahepatic level of eicosapentaenoic acid, a suppressor of SREBP1 \[17\], was increased, providing a molecular explanation for the observed down-regulation of genes involved in cholesterol biosynthesis (Additional data file 5).

A subsequent network analysis of HC-modulated genes allowed the identification of transcription factors that mediate the evolution of hepatic inflammation and are ultimately responsible for the effects on the process level. HC-evoked changes require specific transcriptional master regulators, some of which are established in this context (nuclear factor kappa B (NF-κB), activator protein-1 (AP-1), CAAT/enhancer-binding protein (C/EBP)β, p53), and others that are new (CREB-binding protein (CBP), hepatocyte nuclear factor-4α(HNF4α), SP-1, signal transducer and activator of transcription-3/-5 (STAT-3/-5), Yin Yang-1 (YY1); Figure 6 and Additional data file 6).

Consistent with this, the identified transcription factors control the expression of genes encoding acute phase response proteins, complement factors, growth factors, proteases, chemokine receptors and factors stimulating cell adhesion, as confirmed by data mining. Most importantly, HC treatment induced genes whose products can act extracellularly (Additional data file 7) and possess reportedly pro-atherogenic properties. Examples include complement components (C1qb, C1qR, C3aR1, C9), chemoattractant factors (ccl6, ccl12, ccl19), chemoattractant receptors (CCR2, CCR5), cytokines inducing impaired endothelial barrier function (IFN-γ), adhesion regulators (integrin β2, integrin β5, CD164 antigen/sialomucin, junction adhesion molecule-2), growth factors (PDGF, vascular endothelial growth factor (VEGF)-C, transforming growth factor (TGF)-β), proteases involved in matrix remodeling during atherogenesis (cathepsins B, L, S and Z; matrix metalloprotease-12), and cardiovascular risk factors/inflammation markers (haptoglobin, orosomucoid 2, fibrinogen-like protein 2, α1-microglobulin). This upregulation of pro-atherogenic candidate genes in the HC group is consistent with the observed enhanced early atherosclerosis found in this group.

Expansion of the lipid and inflammatory networks revealed that hepatic lipid metabolism is linked to the hepatic inflammatory response via specific transcriptional regulators that control both processes. Among these dual regulators were CBP, C/EBPs, PPARα and SP-1 (Table 4). The presence of molecular links between lipid metabolism and inflammation raises the possibility that specific intervention with an anti-inflammatory compound may, in turn, affect plasma cholesterol levels. In a first attempt to test this possibility, female E3L mice were fed a HC diet to increase plasma cholesterol levels (from 5.3 mM to 19.3 mM) and systemic inflammation (SAA from 1.7 μg/ml to 9.2 μg/ml). Then, animals were treated with the same HC diet supplemented with salicylate, an inhibitor of NF-κB signaling, or vehicle. While plasma cholesterol and SAA levels remained elevated in the vehicle-treated group, the salicylate-treated group displayed significantly lower plasma SAA levels (7.7 μg/ml; *P* \< 0.05) and significantly reduced cholesterol levels (9.9 mM; *P* \< 0.05) demonstrating that specific intervention into the inflammatory component does indeed affect plasma cholesterol.

Identified master regulators that control inflammatory reprogramming of the liver

| Transcription factor | Regulator of/node point for | Example of downstream effects |
|----|----|----|
| AP-1 (c-jun/c-fos) | Inflammation | Mmp-12, col1a1, hsp27 |
| CREP binding protein (CBP) | Lipid, inflammation, immune response, cell proliferation | Very broadly acting coactivator (can bind to SREBPs) |
| C/EBPs | Lipid, inflammation, energy metabolism | Acute phase genes (for SAA, CRP, fibrinogen), hepatic gluconeogenesis and lipid homeostasis, energy metabolism (PEPCK, FAS), TGF-β signaling |
| Forkhead transcription factor FOXO1 | Lipid, inflammation/proliferation | Glycolysis, pentose phosphate shunt, and lipogenic and sterol synthetic pathways, LPL (via SHP) |
| NF-κB | Inflammation | SAA, CD83, CD86, CCR5, VEGF-C |
| PPARα/RXRα | Lipid, inflammation | LPL, ABCA1, macrophage activation, glucose homeostasis |
| p53 | Inflammation | HSP27, HSPA4, IFI16, IBP3, RBBP4 |
| SMAD3 | Inflammation | Proteases and growth factors (via TGF-β signaling) |
| SP-1 | Lipid, inflammation | ABCA1, ICAM-1, cellular matrix genes COL1A1, COL1A2 |
| SREBP-1/-2 | Lipid, inflammation | Sterol biosynthesis genes, LDLR, link to C/EBPα |
| STAT1/3/5 | Inflammation | Acute phase genes |
| YY1 | Inflammation/proliferation | Inflammatory response genes (SAA, vWF, CCR5), cellular matrix genes |

Biological networks were generated using MetaCore™ software and transcriptional master regulators were identified in significant gene networks (*P* \< 0.05).

# Discussion

Development of atherosclerotic lesions requires a lipid component (hypercholesterolemia) and an inflammatory component. In the present study, we demonstrate that high dose dietary cholesterol (HC diet) strongly induces early atherosclerotic lesion formation in a humanized model for atherosclerosis, E3L mice. This is not, or is only slightly, the case with a cholesterol-free (Con) or low dose cholesterol (LC) diet. Importantly, Con, LC, and HC diets dose-dependently increase plasma cholesterol levels, but only HC treatment induces a marked systemic inflammatory response, which precedes lesion formation and is related to liver inflammation. We employed newly developed (functional) systems biology technologies to unravel how increasing the dose of dietary cholesterol affects liver homeostasis and evokes hepatic inflammation. The following important findings were made. The liver absorbs escalating doses of dietary cholesterol primarily by adjusting the expression level of genes involved in lipid metabolism, as revealed by advanced gene expression analysis. This metabolic resilience is confirmed by analysis of metabolites in liver. At high doses of dietary cholesterol, the liver also develops an inflammatory stress response, which is characterized by up-regulation of pro-atherogenic candidate genes and activation of (at least four distinct) inflammatory pathways. The evolution of hepatic inflammation involves specific transcriptional regulators, several of which have been newly identified in this study. Interestingly, some of these transcription factors have a dual role and control both hepatic lipid metabolism and hepatic inflammation, indicating that the same regulatory mechanisms underlie these processes and thereby link the two processes.

The present study delineates, to our knowledge for the first time, the genome-wide response of the liver to increasing doses of dietary cholesterol, with specific attention to inflammatory processes, and in relation to early atherosclerotic lesion formation. The liver responds to moderate elevations in dietary cholesterol (LC diet) by adjusting major metabolic processes related to lipid metabolism. For example, the expression of genes involved in endogenous cholesterol synthesis (for example, *HMG-CoA reductase*) and cholesterol uptake from plasma (for example, *LDLR*) is diminished. At high loads of dietary cholesterol (HC diet), the liver strives for homeostasis by intensifying the changes in gene expression observed with the LC diet. Similar dose-dependent effects of dietary cholesterol have been reported by others \[18\] but the number of studies that assess dose-dependent effects of dietary cholesterol is relatively small and analyses are restricted to a limited number of genes. Our genome-wide approach is comprehensive and demonstrates that metabolic processes as a whole are adjusted at the level of gene expression.

Importantly, the adjustment at the gene level is efficacious only up to a certain degree of cholesterol stress: while low loads of cholesterol are fully absorbed (consider the comparable intrahepatic lipidome fingerprints for the LC and Con groups), exposure to high loads of dietary cholesterol in the HC group significantly altered the liver lipidome, despite further intensified adjustment of gene expression. Our combined analysis of genes and functional readouts (lipid metabolites) clearly demonstrates that a dose of 1% w/w of cholesterol, which is typically used to induce experimental dyslipidemia and atherosclerosis in mice \[13,19,20\], is an extreme condition because the metabolic resilience of the liver is already overstretched.

Concomitant with the adjustment of metabolic genes to HC dietary stress, HC treatment also evokes a hepatic inflammatory response. The development of an inflammatory gene expression profile upon feeding of a diet containing cholesterol has also been reported by others. For example, Tous *et al*. \[21\] showed that atherosclerosis-prone apoE-/- mice receiving a high fat/high cholesterol diet develop an impairment of liver histology consisting of fat accumulation, macrophage proliferation, and inflammation, and that there is a chronological and quantitative relationship between liver impairment and the formation of atheromatous lesions. Vergnes *et al*. \[22\] showed that cholesterol and cholate components of the atherogenic diet have distinct pro-atherogenic effects on gene expression and particularly that cholesterol is required for induction of genes involved in acute inflammation in C57BL/6J mice. Recinos *et al*. \[23\] reported that liver gene expression in LDLR-/- mice is associated with diet and lesion development and demonstrated the induction of components of the alternative component pathway. Zabalawi *et al*. \[24\] showed the induction of fatal inflammation in LDLR-/- and ApoAI-/-LDLR-/- mice fed dietary fat and cholesterol. However, the exact molecular inflammatory pathways switched on/off by dietary cholesterol have remained unknown. While some of the above studies employing microarray analysis have examined some of the individual components of the inflammatory response to cholesterol, we have set out to generate a holistic profile of the complex, interrelated nature of the response of the liver to cholesterol. Advanced pathway analysis combined with functional network building enabled us to unravel four key inflammatory pathways (IFNγ, TNFα, IL-1, and PDGF pathways) that play central roles in the evolution of cholesterol-induced inflammation in the liver. Further research is necessary to resolve the sequence of events over time (for example, which pathway is switched on first). Remarkably, these pathways are also critical for lesion development in the vessel wall, suggesting that the inflammatory response to cholesterol stress described herein for the liver may involve similar routes in other tissues and, as such, has more general significance.

Our results suggest that hepatic inflammatory response may be causatively related to lesion initiation in the aorta, because pro-atherogenic candidate genes (that is, genes encoding candidate inflammatory components reportedly or putatively involved in early lesion formation) were found to be upregulated specifically in the HC group but not, or only slightly, in the LC group. The presence of a 'hepatic source' for inflammatory factors in HC stress may also explain the observed exponential (rather than linear) increase in lesion formation seen with increasing dietary cholesterol loads \[11,12\]. Consistent with this notion is the view that the inflammatory arm of atherogenesis is a principle driving force of lesion development.

An inflammatory reprogramming of the liver has also been observed in C57BL/6J mice treated with a 1.25% w/w cholesterol diet resulting in total plasma cholesterol concentrations of 3.6 mM \[22\], that is, a level comparable to the Con group in our study. Unlike that in E3L mice, the total plasma cholesterol in C57BL/6J mice is mainly confined to HDL, an anti-atherogenic, anti-inflammatory lipoprotein facilitating transport of cholesterol from the periphery back to the liver. The fact that mice with a strongly different lipoprotein profile (E3L, LDLR-/- and C57BL/6) show a similar hepatic inflammatory response to cholesterol feeding indicates that the observed inflammatory effect of dietary cholesterol is a general phenomenon and not restricted to the model of dysbetalipoproteinemia used herein. Also, it suggests that the influx of dietary cholesterol into the liver (via chylomicrons) rather than plasma cholesterol is key to the inflammatory response of the liver. This supposition would also be in accord with the rapidity of the effect: in a time-resolved analysis of plasma SAA during atherogenesis, we report here a strong elevation of plasma SAA within two weeks of cholesterol-feeding in female E3L mice. This is also in line with the inflammatory reprogramming of C57BL6/J mice within three weeks \[22\] and clearly demonstrates that the hepatic inflammatory response precedes the formation of atherosclerotic lesions, suggesting that dietary cholesterol can be an important trigger and a possible source of the inflammatory component of atherosclerotic disease. In the present study, the liver function markers ALAT and ASAT remained within the normal levels stipulated for the function criteria for donor livers \[25\], indicating normal liver function under the experimental conditions applied in this study. Our results do not exclude the possibility, however, that sterols may oxidize and become toxic and that the oxidized sterols contribute, at least partly, to the inflammatory effects observed by us and others.

Inflammation may also arise from established risk factors other than high plasma cholesterol (for example, hypertension, diabetes/hyperglycemia). Dietary glucose can modulate the mRNA expression and serum concentrations of immune parameters but these alterations rapidly normalize in normoglycemic subjects \[26\]. In the case of an impaired metabolic state, however, postprandial hyperglycemia increases the magnitude and duration of systemic inflammatory responses, which probably promotes the development of cardiovascular disease.

Our results show that the evolution of hepatic inflammation is controlled by specific transcriptional regulators, some of which are well known in the context of cholesterol-inducible inflammation (SREBPs, NF-κB, AP-1, C/EBPs), while others have been newly identified in the present study (CBP, HNF4α, SP-1, STAT-3/-5, YY1). Interestingly, some of these factors may also represent molecular links between lipid/cholesterol metabolism and inflammation. Supportive evidence for an interrelationship between liver metabolism and inflammation also comes from pharmacological intervention studies. On the one hand, cholesterol-lowering drugs reduce the general inflammatory status and the expression of liver-derived inflammation markers in E3L mice (compare to the pleiotropic effects of statins) \[7,14,27\] The anti-inflammatory IKKβ-inhibiting compound salicylate \[28,29\] reduces plasma cholesterol in the same mouse model (this paper) indicating that modulation of cholesterol levels via inflammation may be possible as well. A hypocholesterolemic effect of salicylate has also been reported in catfish \[30\] and salicylate was found to inhibit hepatic lipogenesis in isolated rat hepatocytes *in vitro* \[31\]. Prigge and Gebhard \[32\] showed that acetylsalicylate (aspirin), a classical inhibitor of COX1 and COX2 \[29\], induces biliary cholesterol secretion in the rat, an effect that may contribute to the cholesterol-lowering effect seen with compounds of the salicylate category: in diabetic human subjects, very high doses of aspirin (around 7 g/d) were associated with a 15% reduction of total plasma cholesterol and CRP \[29\].

# Conclusion

We demonstrate that dietary cholesterol is not only a lipid risk factor but also a trigger of hepatic inflammation and, as such, also involved in the evolution of the inflammatory arm of atherosclerotic disease. A certain degree of genetic resilience and elasticity allows the liver to cope with moderate cholesterol stress, but high loads of cholesterol result in an inflammatory pro-atherogenic response (involving specific pathways and transcriptional regulators), which enhances early lesion formation. Our findings that cholesterol and inflammation are closely linked via specific transcriptional master regulators might lead to new strategies for future therapeutic intervention.

# Materials and methods

## Animals and diets

Female E3L mice were used at the age of 12 weeks for all experiments. Animal experiments were approved by the Institutional Animal Care and Use Committee of The Netherlands Organization for Applied Scientific Research (TNO) and were in compliance with European Community specifications regarding the use of laboratory animals.

A group of E3L mice (*n* = 17) was treated with a cholesterol-free diet (diet T; Hope Farms, Woerden, The Netherlands) for 10 weeks (Con group). The major ingredients of diet T (all w/w) were cacao butter (15%), corn oil (1%), sucrose (40.5%), casein (20%), corn starch (10%) and cellulose (6%). Two other groups (*n* = 17 each) received the same diet but supplemented with either 0.25% w/w cholesterol (LC group) or 1.0% w/w cholesterol (HC group). After ten weeks of diet feeding, animals were euthanized under anesthesia to collect livers, hearts and aortas. Tissues were snap-frozen in liquid nitrogen and stored at -80°C until use.

To assess the effect of salicylate on plasma levels of inflammation markers and cholesterol, two groups of female E3L mice (*n* = 10; 12 weeks old) were treated with the HC diet for 3 weeks. Then, HC dietary treatment was either continued (vehicle control group) or animals were fed HC supplemented with 0.12% w/w salicylate (equaling a dose of 145 mg/kg/day) for 8 weeks. Plasma samples were obtained by tail bleeding without fixation of the test animals to minimize stress.

## Analyses of plasma lipids and proteins

Total plasma cholesterol and triglyceride levels were measured after 4 h of fasting, using kits No.1489437 (Roche Diagnostics, Almere, The Netherlands) and No.337-B (Sigma, Aldrich Chemie BV, Zwijndrecht, The Netherlands) \[33\]. For lipoprotein profiles, pooled plasma was fractionated using an ẢKTA FPLC system (Pharmacia, Roosendaal, The Netherlands) \[9\]. The plasma levels of SAA were determined by ELISA as reported \[14\]. Plasma ALAT and ASAT levels were determined spectrophotometrically using a Reflotron system (Roche Diagnostics) \[9\].

For lipiodomics analysis of liver homogenates and plasma samples, electrospray liquid chromatography mass spectroscopy (LC-MS) analysis was applied \[34\]. Briefly, samples (5 μl) were incubated with 200 μl isopropanol and a mixture of internal standards (heptadecanoyl-lysophosphatidylcholine, di-lauroyl-phosphatidylcholine, heptadecanoyl-cholesterol and tri-heptadecanoyl-glycerol; Sigma, St Louis, MO, USA)). After vortexing, the lipids were extracted and isolated by centrifugation (lipids in isopropanol phase). Electrospray LC-MS lipid analysis was performed on a Thermo LTQ apparatus equipped with a Surveyor HPLC system (Thermo Electron, San Jose, CA, USA). The samples were measured in fully randomized sequences. Quality control samples, prepared from a single pool of E3L mouse reference tissue, were analyzed at regular intervals (bracketing 10 samples). The LC-MS raw data files were processed using a software developed by TNO (IMPRESS) to generate comprehensive peak tables (m/z value, retention time and peak area). Data were then subjected to retention time alignment of peaks, internal standard correction of peak areas and quality control resulting in a final lipidomics dataset.

The obtained lipidomics dataset was analyzed and visualized by PCA essentially as described \[35\]. Prior to analysis, the data were mean-centered and auto-scaled to ensure an equal contribution of all lipid measurements to the PCA-model.

## Analyses of atherosclerosis

Hearts were fixed and embedded in paraffin to prepare serial cross sections (5 μm thick) throughout the entire aortic valve area for (immuno) histological analysis. Cross sections were stained with hematoxylin-phloxine-saffron, and atherosclerosis was analyzed blindly in four cross-sections of each specimen (at intervals of 30 μm) as reported \[14,36\]. QWin-software (Leica) was used for morphometric computer-assisted analysis of lesion number, lesion area, and lesion severity as described in detail elsewhere \[7\]. Significance of difference was calculated by one-way analysis of variance (ANOVA) test followed by a least significant difference *post hoc* analysis using SPSS 11.5 for Windows (SPSS, Chicago, IL, USA). The level of statistical significance was set at α \< 0.05.

## Nucleic acid extraction and gene expression analysis

Total RNA was extracted from individual livers (*n* = 5 per group) using RNAzol (Campro Scientific, Veenendaal, The Netherlands) and glass beads according to the manufacturer's instructions. The integrity of each RNA sample obtained was examined by Agilent Lab-on-a-chip technology using the RNA 6000 Nano LabChip kit and a bioanalyzer 2100 (both Agilent Technologies, Amstelveen, The Netherlands). The quality control procedure is described in Additional data file 8. The One-Cycle Target Labeling and Control Reagent kit (Affymetrix \#900493) and the protocols optimized by Affymetrix were used to prepare biotinylated cRNA (from 5 μg of total RNA) for microarray hybridization (*n* = 5 per group). The quality of intermediate products (that is, biotin-labeled cRNA and fragmented cRNA) was again controlled using the RNA 6000 Nano Lab-on-a-chip and bioanalyzer 2100. Microarray analysis was carried out using an Affymetrix technology platform and Affymetrix GeneChip^®^ mouse genome 430 2.0 arrays (45,037 probe sets; 34,000 well-characterized mouse genes). Briefly, fragmented cRNA was mixed with spiked controls, applied to Affymetrix Test chips, and good quality samples were then used to hybridize with murine GeneChip^®^ 430 2.0 arrays. The hybridization, probe array washing and staining, and washing procedures were executed as described in the Affymetrix protocols, and probe arrays were scanned with a Hewlett-Packard Gene Array Scanner (Leiden Genome Technology Center, Leiden, The Netherlands).

## Gene expression data analysis

Raw signal intensities were normalized using the GCRMA algorithm (Affylm package in R). Datasets are freely accessible online through ArrayExpress \[37\]. Normalized signal intensities below 10 were replaced by 10. Probe sets with an absent call in all arrays were removed before further analysis of the data. RT-PCR was performed essentially as described \[27,38\] to validate and confirm differences in gene expression between the treatment groups.

Statistical analysis was performed in BRB ArrayTools (Dr Richard Simon and Amy Peng Lam \[39\]). Con, LC and HC groups were tested for differentially expressed genes using class comparisons with multiple testing corrections by estimation of false discovery rate (FDR). Differentially expressed genes were identified at a threshold for significance of α \< 0.01 and a FDR \< 5%. Within the set of differentially expressed genes, a Student's *t*-test was carried out to analyze differential expression of individual genes between the cholesterol-fed groups and the Con group. Differences of *P* \< 0.01 versus Con were considered significant.

For biological interpretation of the differentially expressed genes, software tools GenMAPP and MetaCore™ (GeneGo Inc., St Joseph, MI, USA) were used. Enrichment of biological processes (GO annotation) was analyzed in GenMAPP, biological processes in GenMAPP with a Z-score \>2 and PermuteP \< 0.05 were considered as significantly changed. In MetaCore™, enrichment analysis \[40\] of four independent ontologies was performed. In addition to biological process gene ontology, data were also analyzed in canonical pathway maps, GeneGo-cellular processes and disease categories.

Distribution by canonical pathway maps reveals the most significant signaling and/or metabolic pathways. Experimental data are visualized as red/blue thermometers pointing up/down, and signifying up/down-regulation of the map objects. Distribution by GeneGo processes provides the most significant functional process categories enriched with experimental data. GeneGo processes represent comprehensive pre-built process-specific networks, in which all objects are interconnected by experimentally validated interactions. The up- and down-regulated genes are visualized as red or blue circles, respectively. The disease categories represent sets of genes associated with certain diseases. Gene enrichment analysis shows the relative enrichment of the up- and down-regulated genes with the genes from different disease categories. As in the case of process enrichment, this procedure is carried out by p value distribution.

The biological networks were assembled from manually curated protein-protein, protein-DNA and protein-ligand (metabolite) interactions, which are accumulated in the MetaCore™ database. Each edge or link on the network is based on small-experiment data referenced in the corresponding literature. The legend for MetaCore™ Networks from the MetaCore™ guideline is provided in Additional data file 6d. For the generation of functional networks, transcriptome and metabolome datasets were merged, allowing combined analysis. Networks were generated using the Shortest path (SP) algorithm, which links the nodes from experimental datasets by the shortest directed graphs, allowing up to two additional steps using interactions and nodes from the MetaCore™ database. To present most of the relevant network data on the same figure, we used the add/expand function and the Merge Networks feature. The resulting networks provide links based on the known interaction data not only between the nodes from the query data set(s), but also between the nodes that regulate the given genes or metabolites. Network nodes with available experimental data are distinguished with red or blue circles, representing up- or down-regulation, respectively.

# Abbreviations

ALAT, alanine aminotransferase; AP, activator protein; ASAT, aspartate aminotransferase; CBP, CREB-binding protein; C/EBP, CAAT/enhancer-binding protein; Con, cholesterol-free; CRP, C-reactive protein; E3L, ApoE\*3Leiden transgenic; FDR, false discovery rate; GO, Gene Ontology; HC, high-cholesterol; HDL, high-density lipoprotein; HNF, hepatocyte nuclear factor; IFN, interferon; IL, interleukin; LC, low-cholesterol; LC-MS, liquid chromatography mass spectroscopy; LDL, low-density lipoprotein; NF-κB, nuclear factor kappa B; PCA, principal component analysis; PDGF, platelet-derived growth factor; PPAR, peroxisome proliferator activated receptor; RXR, retinoid X receptor; SAA, serum amyloid A; SREBP, sterol regulatory element binding protein; STAT, signal transducer and activator of transcription; TGF, transforming growth factor; TNF, tumor necrosis factor; VLDL, very low-density lipoprotein; YY, Yin Yang.

# Authors' contributions

RK provided the conceptual background to the analysis, interpreted the results and wrote the manuscript. LV did the *in vivo* atherosclerosis studies, performed the assays and interpreted the data. MvE performed the computational analysis including biological processes and assisted with manuscript writing. YN coordinated the software development and the multidimensional analysis of biological processes using (pathway) networks. NC supervised the work and coordinated the lipidomics and genomics analyses. EV developed the lipidomics methodology and performed the lipidomics measurements. AS coordinated the multivariate statistical analysis and drafted the manuscript. HH helped with data interpretation and evaluated the manuscript. SZ performed animal experiments and quantified plasma inflammation markers. GS participated in designing the experiment and manuscript writing. VK developed the tools for multidimensional data analyses and performed computations. TN assisted with the preparation of the figures of networks and pathways. AM assisted in data interpretation and bioinformatical techniques for gene ontology analyses. EH participated in designing the study and manuscript preparation. JG coordinated the development of the metabolomics technologies and critically evaluated the manuscript. BO led the bioinformatical analyses, developed the concepts for integrated data analysis and drafted the manuscript. TK initiated the study, interpreted the data and helped with manuscript writing. All authors read and approved the final manuscript.

# Additional data files

The following additional data are available with the online version of this paper. Additional data file 1 shows the exponential positive correlation between atherosclerotic lesion area and total plasma cholesterol in female E3L mice. Additional data file 2 shows the validation and confirmation of Affymetrix microarray gene expression data by RT-PCR analysis. Additional data file 3 is a table of the genes (including GenBank identification number and the gene symbol) that are differentially expressed with increasing doses of dietary cholesterol. Additional data file 4 shows the canonical pathway analysis for cholesterol metabolism and analysis of the gene expression data based on GO annotation with disease categories (MetaCore™ software, GeneGO). Additional data file 5 shows the comprehensive network analysis (functional OMICs analysis) by merging gene expression datasets with the metabolite datasets using MetaCore™ network software. Additional data file 6 shows the biological networks of differentially expressed genes in the HC group allowing the identification of transcriptional master regulators. Additional data file 7 is a table lisitng cholesterol-induced factors with reported extracellular function. Additional data file 8 describes the quality control analysis steps for RNA samples prior to hybridization on Affymetrix microarrays usig Agilent Lab-on-a-chip technology.

# Supplementary Material

###### Additional data file 1

Exponential positive correlation between atherosclerotic lesion area and total plasma cholesterol in female E3L mice.

Click here for file

###### Additional data file 2

Validation and confirmation of Affymetrix microarray gene expression data by RT-PCR analysis.

Click here for file

###### Additional data file 3

Genes (including GenBank identification number and the gene symbol) that are differentially expressed with increasing doses of dietary cholesterol.

Click here for file

###### Additional data file 4

Canonical pathway analysis for cholesterol metabolism and analysis of the gene expression data based on GO annotation with disease categories (MetaCore™ software, GeneGO).

Click here for file

###### Additional data file 5

Comprehensive network analysis (functional OMICs analysis) by merging gene expression datasets with the metabolite datasets using MetaCore™ network software.

Click here for file

###### Additional data file 6

Biological networks of differentially expressed genes in the HC group allowing the identification of transcriptional master regulators.

Click here for file

###### Additional data file 7

Cholesterol-induced factors with reported extracellular function.

Click here for file

###### Additional data file 8

Quality control analysis steps for RNA samples prior to hybridization on Affymetrix microarrays usig Agilent Lab-on-a-chip technology.

Click here for file

### Acknowledgements

This study was supported by the Dutch Organization for Scientific Research (NWO; grant VENI 016. 036.061 to RK), the Dutch Heart Foundation (NHS; grant 2002B102 to LV), and the TNO research program NISB (to RK, ME, NC, EV, AS, JG and TK). We are grateful to Maren White for helpful discussions and critical reading of the manuscript. We thank AstraZeneca for supporting this study. We thank Ally Perlina, Wilbert Heijne, Robert-Jan Lamers, Annie Jie and Karin Toet for excellent bioinformatical and analytical help. The authors gratefully acknowledge grant support from The European Nutrigenomics Organisation (NuGO, CT-2004-505944; Focus Team 'Metabolic Stress and Disease to RK, TK and SZ).


author: Etzel Cardeña\*Correspondence: <firstname.lastname@example.com>[^1][^2][^3]
date: 2014-01-27
institute: Department of Psychology, Lund UniversityLund, Sweden
references:
title: A call for an open, informed study of all aspects of consciousness

Science thrives when there is an open, informed discussion of all evidence, and recognition that scientific knowledge is provisional and subject to revision. This attitude is in stark contrast with reaching conclusions based solely on a previous set of beliefs or on the assertions of authority figures. Indeed, the search for knowledge wherever it may lead inspired a group of notable scientists and philosophers to found in 1882 the Society for Psychical Research in London. Its purpose was "to investigate that large body of debatable phenomena… without prejudice or prepossession of any kind, and in the same spirit of exact and unimpassioned inquiry which has enabled Science to solve so many problems." Some of the areas in consciousness they investigated such as psychological dissociation, hypnosis, and preconscious cognition are now well integrated into mainstream science. That has not been the case with research on phenomena such as purported telepathy or precognition, which some scientists (a clear minority according to the surveys conducted <http://en.wikademia.org/Surveys_of_academic_opinion_regarding_parapsychology>) dis-miss *a priori* as pseudoscience or illegitimate. Contrary to the negative impression given by some critics, we would like to stress the following:

1.  Research on parapsychological phenomena (psi) is being carried out in various accredited universities and research centers throughout the world by academics in different disciplines trained in the scientific method (e.g., circa 80 Ph.D.s have been awarded in psi-related topics in the UK in recent years). This research has continued for over a century despite the taboo against investigating the topic, almost complete lack of funding, and professional and personal attacks (Cardeña, 201). The Parapsychological Association has been an affiliate of the AAAS since 1969, and more than 20 Nobel prizewinners and many other eminent scientists have supported the study of psi or even conducted research themselves (Cardeña, 2013).

2.  Despite a negative attitude by some editors and reviewers, results supporting the validity of psi phenomena continue to be published in peer-reviewed, academic journals in relevant fields, from psychology to neuroscience to physics e.g., (Storm et al., 2010; Bem, 2011; Hameroff, 2012; Radin et al., 2012).

3.  Increased experimental controls have not eliminated or even decreased significant support for the existence of psi phenomena, as suggested by various recent meta-analyses (Sherwood and Roe, 2003; Schmidt et al., 2004; Bösch et al., 2006; Radin et al., 2006; Storm et al., 2010, 2012, 2013; Tressoldi, 2011; Mossbridge et al., 2012; Schmidt, 2012).

4.  These meta-analyses and other studies (Blackmore, 1980)suggest that data supportive of psi phenomena cannot reasonably be accounted for by chance or by a "file drawer" effect. Indeed, contrary to most disciplines, parapsychology journals have for decades encouraged publication of null results and of papers critical of a psi explanation (Wiseman et al., 1996; Schönwetter et al., 2011). A psi trial registry has been established to improve research practice <http://www.koestler-parapsychology.psy.ed.ac.uk/TrialRegistryDetails.html>.

5.  The effect sizes reported in most meta-analyses are relatively small and the phenomena cannot be produced on demand, but this also characterizes various phenomena found in other disciplines that focus on complex human behavior and performance such as psychology and medicine (Utts, 1991; Richard and Bond, 2003).

6.  Although more conclusive explanations for psi phenomena await further theoretical and research developments, they do not *prima facie* violate known laws of nature given modern theories in physics that transcend classical restrictions of time and space, combined with growing evidence for quantum effects in biological systems (Sheehan, 2011; Lambert et al., 2013).

With respect to the proposal that "exceptional claims require exceptional evidence," the original intention of the phrase is typically misunderstood (Truzzi, 1978). Even in its inaccurate interpretation what counts as an "exceptional claim" is far from clear. For instance, many phenomena now accepted in science such as the existence of meteorites, the germ theory of disease, or, more recently, adult neurogenesis, were originally considered so exceptional that evidence for their existence was ignored or dismissed by contemporaneous scientists. It is also far from clear what would count as "exceptional evidence" or who would set that threshold. Dismissing empirical observations *a priori*, based solely on biases or theoretical assumptions, underlies a distrust of the ability of the scientific process to discuss and evaluate evidence on its own merits. The undersigned differ in the extent to which we are convinced that the case for psi phenomena has already been made, but not in our view of science as a non-dogmatic, open, critical but respectful process that requires thorough consideration of all evidence as well as skepticism toward both the assumptions we already hold and those that challenge them.

Daryl Bem, Professor Emeritus of Psychology, Cornell University, USA

Etzel Cardeña, Thorsen Professor of Psychology, Lund University, Sweden

Bernard Carr, Professor in Mathematics and Astronomy, University of London, UK

C. Robert Cloninger, Renard Professor of Psychiatry, Genetics, and Psychology, Washington University in St. Louis, USA

Robert G. Jahn, Past Dean of Engineering, Princeton University, USA

Brian Josephson, Emeritus Professor of Physics, University of Cambridge, UK (Nobel prizewinner in physics, 1973)

Menas C. Kafatos, Fletcher Jones Endowed Professor of Computational Physics, Chapman University, USA

Irving Kirsch, Professor of Psychology, University of Plymouth, Lecturer in Medicine, Harvard Medical School, USA, UK

Mark Leary, Professor of Psychology and Neuroscience, Duke University, USA

Dean Radin, Chief Scientist, Institute of Noetic Sciences, Adjunct Faculty in Psychology, Sonoma State University, USA

Robert Rosenthal, Distinguished Professor, University of California, Riverside, Edgar Pierce Professor Emeritus, Harvard University, USA

Lothar Schäfer, Distinguished Professor Emeritus of Physical Chemistry, University of Arkansas, USA

Raymond Tallis, Emeritus Professor of Geriatric Medicine, University of Manchester, UK

Charles T. Tart, Professor in Psychology Emeritus, University of California, Davis, USA

Simon Thorpe, Director of Research CNRS (Brain and Cognition), University of Toulouse, France

Patrizio Tressoldi, Researcher in Psychology, Università degli Studi di Padova, Italy

Jessica Utts, Professor and Chair of Statistics, University of California, Irvine, USA

Max Velmans, Professor Emeritus in Psychology, Goldsmiths, University of London, UK

Caroline Watt, Senior Lecturer in Psychology, Edinburgh University, UK

Phil Zimbardo, Professor in Psychology Emeritus, Stanford University, USA

And…

P. Baseilhac, Researcher in Theoretical Physics, University of Tours, France

Eberhard Bauer, Dept. Head, Institute of Border Areas of Psychology and Mental Hygiene, Freiburg, Germany

Julie Beischel, Adjunct Faculty in Psychology and Integrated Inquity, Saybrook University, USA

Hans Bengtsson, Professor of Psychology, Lund University, Sweden

Michael Bloch, Associate Professor of Psychology, University of San Francisco, USA

Stephen Braude, Professor of Philosophy Emeritus, University of Maryland Baltimore County, USA

Richard Broughton, Senior Lecturer, School of Social Sciences, University of Northampton, UK

Antonio Capafons, Professor of Psychology, University of Valencia, Spain

James C. Carpenter, Adjunct Professor of Psychiatry, University of North Carolina, Chapel Hill, USA

Allan Leslie Combs, Doshi Professor of Consciousness Studies, California Institute of Integral Studies, USA

Deborah Delanoy, Emeritus Professor of Psychology, University of Northampton, UK

Arnaud Delorme, Professor of Neuroscience, Paul Sabatier University, France

Vilfredo De Pascalis, Professor of General Psychology, "La Sapienza" University of Rome, Italy

Kurt Dressler, Professor in Molecular Spectroscopy Emeritus, Eidg. Techn. Hochschule Zürich, Switzerland

Hoyt Edge, Hugh H. and Jeannette G. McKean Professor of Philosophy, Rollins College, USA

Suitbert Ertel, Emeritus Professor of Psychology, University of Göttingen, Germany

Franco Fabbro, Professor in Child Neuropsychiatry, University of Udine, Italy

Enrico Facco, Professor of Anesthesia and Intensive Care, University of Padua, Italy

Wolfgang Fach, Researcher, Institute of Border Areas of Psychology and Mental Hygiene, Freiburg, Germany

Harris L. Friedman, Former Research Professor of Psychology, University of Florida, USA

Alan Gauld, Former Reader in Psychology, University of Nottingham, UK

Antoon Geels, Professor in the Psychology of Religion Emeritus, Lund University, Sweden

Bruce Greyson, Carlson Professor of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, USA

Erlendur Haraldsson, Professor Emeritus of Psychology, University of Iceland, Iceland

Richard Conn Henry, Academy Professor (Physics and Astronomy), The Johns Hopkins University, USA

David J. Hufford, University Professor Emeritus, Penn State College of Medicine, USA

Oscar Iborra, Researcher, Department of Experimental Psychology, Granada University, Spain

Harvey Irwin, former Associate Professor, University of New England, Australia

Graham Jamieson, Lecturer in Human Neuropsychology, University of New England, Australia

Erick Janssen, Adjunct Professor, Department of Psychology, Indiana University, USA

Per Johnsson, Head, Department of Psychology, Lund University, Sweden

Edward F. Kelly, Research Professor in the Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, USA

Emily Williams Kelly, Research Assistant Professor in the Department of Psychiatry and Neurobehavioral Sciences, University of Virginia, Charlottesville, USA

Hideyuki Kokubo, Researcher, Institute for Informatics of Consciousness, Meiji University, Japan

Jeffrey J. Kripal, J. Newton Rayzor Professor of Religious Studies, Rice University, USA

Stanley Krippner, Professor of Psychology and Integrated Inquiry, Saybrook University, USA

David Luke, Senior Lecturer, Department of Psychology and Counselling, University of Greenwich, UK

Fatima Regina Machado, Researcher, Universidade de São Paulo, Brasil

Markus Maier, Professor in Psychology, University of Munich, Germany

Gerhard Mayer, Researcher, Institute of Border Areas of Psychology and Mental Hygiene, Freiburg, Germany

Antonia Mills, Professor First Nations Studies, University of Northern British Columbia, Canada

Garret Moddel, Professor in Electrical, Computer, & Energy Engineering, University of Colorado, Boulder, USA

Alexander Moreira-Almeida, Professor of Psychiatry, Universidade Federal de Juiz de Fora, Brasil

Andrew Moskowitz, Professor in Psychology and Behavioral Sciences, Aarhus University, Denmark

Julia Mossbridge, Fellow in Psychology, Northwestern University, USA

Judi Neal, Professor Emeritus of Management, University of New Haven, USA

Roger Nelson, Retired Research Staff, Princeton University, USA

Fotini Pallikari, Professor of Physics, University of Athens, Greece

Alejandro Parra, Researcher in Psychology, Universidad Abierta Interamericana, Argentina

José Miguel Pérez Navarro, Lecturer in Education, International University of La Rioja, Spain

Gerald H. Pollack, Professor in Bioengineering. University of Washington, Seattle, USA

John Poynton, Professor Emeritus in Biology, University of KwaZulu-Natal, South Africa

David Presti, Senior Lecturer, Neurobiology and Cognitive Science, University of California, Berkeley, USA

Thomas Rabeyron, Lecturer in Clinical Psychology, Nantes University, France

Inmaculada Ramos Lerate, Researcher in Physics, Alba Synchrotron Light Source, Barcelona, Spain.

Chris Roe, Professor of Psychology, University of Northampton, UK

Stefan Schmidt, Professor, Europa Universität Viadrina, Germany

Gary E. Schwartz, Professor of Psychology, Medicine, Neurology, Psychiatry, and Surgery, University of Arizona, USA

Daniel P. Sheehan, Professor of Physics, University of San Diego, USA

Simon Sherwood, Senior Lecturer in Psychology, University of Greenwich, UK

Christine Simmonds-Moore, Assistant Professor of Psychology, University of West Georgia, USA

Mário Simões, Professor in Psychiatry. University of Lisbon, Portugal

Huston Smith, Prof. of Philosophy Emeritus, Syracuse University, USA

Jerry Solfvin, Associate Professor in Indic Studies, University of Massachusetts, Dartmouth, USA

Lance Storm, Visiting Research Fellow, University of Adelaide, Australia

Jeffrey Allan Sugar, Assistant Professor of Clinical Psychiatry, University of Southern California, Los Angeles, USA

Neil Theise, Professor of Pathology and Medicine, The Icahn School of Medicine at Mount Sinai, USA

Jim Tucker, Bonner-Lowry Associate Professor of Psychiatry and Neurobehavioral Sciences, University of Virginia, USA

Yulia Ustinova, Associate Professor in History, Ben-Gurion University of the Negev, Israel

Walter von Lucadou, Senior Lecturer at the Furtwangen Technical University, Germany

Maurits van den Noort, Senior Researcher, Free University of Brussels, Belgium

David Vernon, Senior Lecturer in Psychology, Canterbury Christ Church University, UK

Harald Walach, Professor, Europa Universität Viadrina, Germany

Helmut Wautischer, Senior Lecturer in Philosophy, Sonoma State University, USA

Donald West, Emeritus Professor of Clinical Criminology, University of Cambridge, UK

N.C. Wickramasinghe, Professor in Astrobiology, Cardiff University, UK

Fred Alan Wolf, formerly Professor in physics at San Diego State University, the Universities of Paris, London, and the Hebrew University of Jerusalem

Robin Wooffitt, Professor of Sociology, University of York, UK

Wellington Zangari, Professor in Psychology, University of Sao Paulo, Brazil

Aldo Zucco, Professor, Dipartimento di Psicologia Generale, Università di Padova, Italy

Although for practical reasons only one author is listed, the call is a collective creation of the co-signatories.

# References

[^1]: This article was submitted to the journal Frontiers in Human Neuroscience.

[^2]: Edited by: Christian Agrillo, University of Padova, Italy

[^3]: Reviewed by: Imants Baruss, King's University College at The University of Western Ontario, Canada


abstract: This is a report on a 37-patient continuation study of the open ended, Omega-3 Fatty Acid (O-3FA) add-on study. Subjects consisted of the original 19 patients, along with 18 new patients recruited and followed in the same fashion as the first nineteen. Subjects carried a DSM-IV-TR diagnosis of Bipolar Disorder and were visiting a Mood Disorder Clinic regularly through the length of the study. At each visit, patients' clinical status was monitored using the Clinical Monitoring Form. Subjects reported on the frequency and severity of irritability experienced during the preceding ten days; frequency was measured by way of percentage of days in which subjects experienced irritability, while severity of that irritability was rated on a Likert scale of 1 – 4 (if present). The irritability component of Young Mania Rating Scale (YMRS) was also recorded quarterly on 13 of the 39 patients consistently. Patients had persistent irritability despite their ongoing pharmacologic and psychotherapy.
  .
  Omega-3 Fatty Acid intake helped with the irritability component of patients suffering from bipolar disorder with a significant presenting sign of irritability. Low dose (1 to 2 grams per day), add-on O-3FA may also help with the irritability component of different clinical conditions, such as schizophrenia, borderline personality disorder and other psychiatric conditions with a common presenting sign of irritability.
author: Kemal Sagduyu; Mehmet E Dokucu; Bruce A Eddy; Gerald Craigen; Claudia F Baldassano; Ayşegül Yıldız
date: 2005
institute: 1University of Missouri – Kansas City, Missouri, 8801 West 148^th^ Terrace, Overland Park, KS 66221, USA; 2Washington University, School of Medicine, Department of Psychiatry, Campus Box: 8134, 660 South Euclid Avenue, St. Louis, Missouri, 63110, USA; 3Department of Psychiatry, School of Medicine, University of Missouri-Kansas City, Resource Development Institute, 601 Walnut Street, Kansas City, MO 64106, USA; 4Mood Disorders Psychopharmacology Unit, University Health Network, Toronto Western Hospital, 399 Bathurst Street, ECW-3D-010, Toronto, Ontario M5T 2S8, Canada; 5Mood and Anxiety Disorders Clinic, Department of Psychiatry, University of Pennsylvania, 3535 Market Street, 2nd floor, Philadelphia, PA 19104, USA; 6Dokuz Eylül Medical School, Department of Psychiatry, İzmir, Turkey
references:
title: Omega-3 fatty acids decreased irritability of patients with bipolar disorder in an add-on, open label study

# Introduction

According to the United States National Institute of Mental Health (NIMH), Bipolar Disorder (BPD), also known as manic-depressive illness, is a serious medical illness that causes shifts in a person's mood, energy, and ability to function. Different from the normal ups and downs that everyone goes through, the symptoms of bipolar disorder are severe. Bipolar disorder is a complex, chronic condition associated with considerable morbidity and mortality, including a high rate of suicide. Bipolar disorder causes dramatic mood swings from overly "high" and/or irritable to sad and hopeless, and then back again, often with periods of normal mood in between. Severe changes in energy and behavior go along with these changes in mood. The periods of highs and lows are called episodes of mania and depression. Most people with bipolar disorder can achieve substantial stabilization of their mood swings and related symptoms over time with proper treatment. A strategy that combines medication and psychosocial treatment is optimal for managing the disorder over time.

# Background

Omega-3 fatty acids (0-3FA) may have a beneficial effect on irritable mood. Low O-3FA levels in red blood cell membranes of depressed patients hint that O-3FA may be helpful in treating mood disorders \[1\]. A recent article has given an excellent review of O-3FA and studies showing their effectiveness in depression, bipolar disorder and aggression \[2\]. In this article, two published studies are discussed that have reported on similar therapeutic effects of O-3FA \[2\]. One placebo-controlled study of 20 patients revealed that ethyl ester of eicosapentaenoic acid (E-EPA) was effective in stabilizing the moods of depressed patients \[3\]. Another report, a double-blind, placebo controlled study (N = 22/19), measured the effect of O-3FA docosahexaenoic acid (DHA) on the aggressive tendencies of college students. The O-3FA DHA group (1.5–1.8 g O-FA DHA/day) did not display any increase in aggressive tendencies when external stressors peaked, while the placebo group displayed a significant increase in their aggressive tendencies under similar circumstances \[4\]. In a recent study, 25% of 111 patients with bipolar-I disorder who met criteria for a DSM-IV major depressive episode also experienced substantial irritability in the absence of associated symptoms of mania. These findings suggest that abnormal irritability is not limited to mania or mixed states \[5\]. However, recent studies give caution that at a 6 gram per day average daily dose, as a single agent, omega 3 fatty acids may not be as effective as an antidepressant \[6-9\].

O-3FA may also help with the irritability component of different clinical conditions, such as depression, mania, schizophrenia, borderline personality disorder and other psychiatric conditions with a common presenting sign of irritability. Numerous other conditions have an irritability component, including Borderline Personality Disorder, Alzheimer's disease, Premenstrual Dysphoric Disorder, to name a few \[10-12\]. There is one report suggesting beneficial effect of Omega-3 Fatty acid treatment for Borderline Personality Disorder. This double-blind, placebo-controlled pilot study specifically showed that EPA may influence both aggression and depression \[12\]. Although attention-deficit/hyperactivity disorder (ADHD) also has an irritability component, recent publications bring doubt to the O3FA connection in ADHD \[13,14\].

A recent, open ended, O-3FA add-on study has shown beneficial effect of O-3FA on irritability in 19 patients with mood disorders \[15\]. These patients had already been receiving different combinations of pharmacotherapy and talk therapy. Despite their treatment, the irritability component of their illness was still causing social, occupational and other life disturbances. Hence, they were chosen for the O-3FA add-on component of the study. In the nineteen-patient phase of the study, bipolar patients of every subtype, ages 18 to 65 years, with significant irritability were studied. All patients received a systematic assessment battery at entry and were treated by a psychiatrist, trained to deliver care and measure outcomes in patients with bipolar disorder, consistent with expert recommendations. At every follow-up visit, the treating psychiatrist completed a standardized assessment and assigns a clinical status based on DSM-IV criteria. Patients had independent evaluations at regular intervals throughout the study and remain under the care of the same treating psychiatrist while receiving variable medications and talk therapy, depending on their need \[15\]. In the 19-patient study, a paired sample t-test revealed a large decrease in the percent of days irritable after O-3FA was administered. Before treatment, the mean irritability percentage was 81.05 (SD = 23.31) and after treatment the mean irritability percentage dropped to 30.00 (SD = 36.67). Despite the small number of patients in the study (n = 19), the difference between means was statistically significant (t (18) 4.512, p \< .001). Using a paired sample t-test, a significant difference was also found between the highest irritability score (mean = 2.79; SD = 0.92) and the last recorded irritability (mean = 0.79; SD = 0.85) while taking O-3FA (t(18) = 8.270; p \< .001) \[15\].

# Methods

This is a report on a 37-patient continuation phase of the open ended, O-3FA add-on study. Subjects consisted of the original 19 patients, in addition to the 18 new patients recruited and followed in the same fashion as the first nineteen \[15\]. Subjects carried a DSM-IV-TR \[16\] diagnosis of Bipolar Disorder and were visiting a Mood Disorder Clinic regularly throughout the length of the study. At each visit, patients' clinical status was monitored using the Clinical Monitoring Form \[17\]. Subjects reported on the frequency and severity of irritability experienced during the preceding ten days; frequency was measured by way of percentage of days in which subjects experienced irritability, while severity of that irritability was rated on a Likert scale of 1 – 4 (if present). The irritability component of Young Mania Rating Scale \[18\] (YMRS) was also recorded quarterly on 13 of the 39 patients consistently. The patients were asked about general dietary omega-3 intake before the fish oil was added on, and basic nutritional guidance was given to subjects at the clinic. Patients in general were not heavy fish/product consumers.

## Dosage

Starting dose and last maintenance dose were available for 37 subjects (Table 1). Subjects self-medicated, and therefore, the last maintenance dose of O-3FA was chosen by each subject. The mean starting dose was 1824.32 mg (SD 1075.07), and the mean for the last maintenance dose was considerably higher at 2878.38 mg (SD 2011.79). The increase was statistically significant using a paired sample t-test (t = -3.44, 36df, p = .001).

# Statistical Results

## Percentage of Irritable (Days)

The initial mean was 63.51 (SD 34.17), indicating that on average, subjects were irritable for about six of the previous ten days. The mean for the last recorded percentage was less than half of the initial score: 30.27 (SD 34.03). The decrease was found to be statistically significant using a paired sample t-test (t = 4.36, 36 df, p \< .001). The difference between the distributions was examined using the non-parametric sign test. The number of negative differences (25) significantly exceeded positive differences (7); there were five ties, and the pre/post distributions were significantly different (p \< .003).

## YMRS Irritability Sub-score

Thirty four subjects had initial and last recorded YMRS irritability sub-scores. As with the above means there was a sizable decrease. The initial mean score was 3.18 (SD 1.09). The mean for the last recorded percentage was 1.68 (SD 1.89). The decrease was found to be statistically significant using a paired sample t-test (t = 4.21, 33 df, p \< .001).

## YMRS Total Score

Starting and last recorded YMRS scores were available for 34 subjects. The mean starting score 10.71 (SD 6.77), and the mean for the last recorded score was 4.85 (SD 5.63). The decrease found to be statistically significant using a paired sample t-test (t = 4.14, 33 df, p \< .001).

## Severity

Thirty six subjects had initial and last recorded severity scores on the ADE. Again, a decrease was found. The initial mean score was 2.14 (SD 1.22). The mean for the last recorded score was 0.94 (SD 0.92). This decrease was found to be statistically significant using a paired sample t-test (t = 5.23, 35 df, p \< .001).

## Composite: Severity and Irritability

As an exploratory measure, a composite score was created by multiplying the ADE severity score, which has a maximum of 4 points, by the percentage of the ten days prior to measurement which the patient was rated as irritable. The initial mean on this composite was 159.72. As with other measures, there was wide variation: SD = 122.92. The mean for this measure on the last recorded scores was percentage was about one-fourth of the initial score: 43.89 (SD 64.38). The decrease was found to be statistically significant using a paired sample t-test (t = 5.00, 35 df, p \< .001).

## Last Recorded Maintenance Dose and Percentage of Irritability After

Because of apparent wide variation on these two measures and a concern that outliers may have affected some results, the last recorded irritability scores were plotted against the maintenance dose. This revealed a rather bimodal pattern, in which relatively lower irritability measures (≤ 50%) clustered in the quadrant with lower dosage levels (≤ 4,000 mg).

## Duration and YMRS Total

In response to a similar observation regarding wide variation in the last recorded values (84 days to 5.5 years) the values were also plotted. A clearly bimodal pattern appeared in which 11 subjects (about one-third of study participants) clustered in the quadrant representing short duration (\<500 days) and higher YMRS totals (\>7). The remaining two-thirds of subjects clustered in the quadrant representing short duration and lower YMRS totals (\<6).

## Subject Weight

The mean start weight was 176.97 lbs (SD 43.13), and the mean for the last weight recorded was slightly higher at 178.59 lbs (SD 43.24). The increase was not statistically significant.

# Follow-up Subjects

Follow-up information, recorded after the collection of the "last" scores for most of the above variables, was available for 13 of the 37 subjects. Final YMRS total or scale scores were not available for this sub-group.

## Omega 3 Duration

The final date recorded for the duration of O3 was derived based from an O3 start date and a "final" date recorded for O3. The time period ranged 84 days to 1995 days (5.46 years). The mean duration of O3 for this group was 439.62 days (SD = 487.46).

## Dosage

For these subjects, the mean starting dose was 1807.69 mg (SD 990.34), and the mean for the last maintenance dose was higher at 2615.38 mg (SD 1894.66). The increase was not significant.

## Percentage Irritable (Days)

The initial mean was 82.31 (SD 20.88). The mean for the last recorded percentage was dramatically lower: 25.38 (SD 32.04). The decrease was found to be statistically significant using a paired sample t-test (t = 6.52 12 df, p \< .001). The difference between the distributions was examined using a sign test. The number of negative differences (12) significantly exceeded positive differences (0); there was one tie, and the pre/post distributions were significantly different (p \< .001).

## Severity

The initial mean score for the 13 subjects with final scores was 2.69 (SD 0.95). The mean for the final score was 0.77 (SD 0.83). This decrease was found to be statistically significant using a paired sample t-test (t = 6.22, 12 df, p \< .001).

## Composite: Severity and Irritability

An exploratory composite score, described above, was also created for the subjects with final scores. For these subjects, the initial mean was higher than that of the total group, 223.08. Again, there was wide variation: SD = 104.19. The mean for this measure on the last recorded scores was percentage was much lower that the initial score: 33.08 (SD 39.87). The decrease was found to be statistically significant using a paired sample t-test (t = 6.70, 12 df, p \< .001).

## Weight

For these 13 subjects, the mean start weight was 166.23 lbs (SD 35.68), and the mean for the final weight recorded was also slightly higher at 168.23 lbs (33.62). As with the previous finding regarding weight, the increase was not statistically significant.

# Results

Omega-3 Fatty Acids added onto the existing treatment helped with the irritability component of a significant percentage of patients suffering from bipolar disorder with a persistent sign of irritability.

# Discussion

As seen from the standard deviations of several of the variables discussed here, measures ranged widely. This creates difficulty in using descriptive data, such as means, to adequately portray subject attributes and performance. Using data reduction techniques or grouping subjects according to high and low scores on various attributes may be one way to increase the descriptiveness, which would be possible and more reasonable with a larger pool of subjects.

A potential limitation or interpretive consideration merits discussion. For many of the variables discussed above, noticeable differences in measures were observed between the "starting" versus "last recorded" group (n = 37) and the "starting" versus "final" measures group (n = 13). Given these differences and the smaller number of subjects in the second set of comparisons, "starting" versus "final" comparisons should be interpreted with caution until differences inherent in this "final" subgroup (n = 13) are more clearly understood. This is clearly seen in the results of sign tests, in which the apparent magnitude of the "final" effects is pronounced.

Statistically significant within-subjects differences were found in several independent variables. This is especially notable given the small number of subjects. The preliminary findings suggest that a rigorously designed study tailored especially to the examination of the effects of O-3FA is warranted.

The majority of data were collected within an ongoing "best-practice, outpatient bipolar disorder study" that involved medications and talk therapy which we have not reported or discussed herein. Results must, therefore, be interpreted with caution.

There are several mechanisms through which O-3FA are theorized to help with mood, irritability, aggression etc. Suggested theories of mechanism converge on the theory of nerve cell membrane stabilization. A recent study has come closest to showing physical proof of effectiveness of O-3FA through indirect demonstration of greater membrane fluidity, as detected by reductions in Tesla-2 (T2) values in MRI scans \[19\]. The overlapping beneficial effects of antipsychotics, antidepressants, anticonvulsants, O-3FA, and nonpsychoactive cannabinoids, as they relate to pain, stroke, schizophrenia, psychoneuroimmunology, Alzheimer's disease, and stress, may be because of their common effects at protein kinases, thus affecting the structure and function of the cell membrane and the cell \[20\]. These changes should help the cell operate within an optimal level of excitation, which may be related to emerging evidence that these therapeutic agents have neuroprotective value \[20\]. A recent randomized placebo controlled double blind intervention study suggests an adaptogenic role for O-3FA in stress \[21\].

We would like to discuss briefly the issue of daily dosing of O-3FA for nutrition and medicinal purpose: Recent studies give caution that at a 6 gram per day average daily dose, as a single agent, omega 3 fatty acids may not be as effective as an antidepressant \[6-9\]. However, these studies may have given too high of a dose of O-3FA, above 6 grams daily, with possibly beyond a therapeutic window of effectiveness for O-3FA. Our scatter plots indicate that the optimum effective dose for irritability is at 1–2 gram of EPA plus DHA per day, which would be the dosing we suggest. A recent exploratory dose study of O-3FA for schizophrenic patients showed that 2 g/day EPA-treated patients had lower symptom scores, and needed less medication greatest. In this study, there was a positive relationship between improvement on rating scales and rise in red blood cell arachidonic acid concentration as well \[22\].

The United States (US) accounts for more than 51% of the 430.3 billion dollar expended on pharmaceutical products worldwide each year \[23\]. World healthcare society first needs access to low-cost, nontoxic, non-expert-dependent interventions to ensure basic health outcomes. Food may represent the most cost-effective means of promoting public health \[23\]. The American Heart Association recommends consumption of two servings of fish per week for persons with no history of coronary heart disease and at least one serving of fish daily for those with known coronary heart disease \[24\]. Approximately 1 g per day of EPA acid plus DHA acid is recommended for cardioprotection \[24\]. Higher dosages of omega-3 fatty acids are required to reduce elevated triglyceride levels (2 to 4 g per day) and to reduce morning stiffness and the number of tender joints in patients with rheumatoid arthritis (at least 3 g per day) \[24\].

We conclude that it is beneficial in many ways to establish a regular intake of 1–2 g per day EPA acid plus DHA, similar to daily intake of vitamins with minerals. Dietary interventions to remedy omega-3 deficiency is necessary \[23\]. It is time for more aggressive funding for research into medicinal foods, such as omega-three fatty acids \[23\].

## Figures and Tables

Initial, Last and Final Omega 3 Dosages (mg). summarizes dosage under three conditions. Figures for the Initial Dose include two subjects (n = 39) for whom no corresponding follow-up data were available.

| Initial, Last Recorded and Final Omega 3 Dosages (mg) |  |  |  |
|----|----|----|----|
|  | Initial | Last Recorded | Final^T1^ |
|  |  |  |  |
| n | 39 | 37 | 13 |
| Mean | 1833.33 | 2878.38 | 2615.38 |
| Mode | 1000^T2,\ T3^ | 1000 | 2000 |
| Median | 2000 | 2000 | 2000 |
| SD | 1071.91 | 2011.79 | 1894.66 |

T1 = Final group results (n = 13) are discussed below.

T2 = Multiple modes exist. The smallest value is shown.

T3 = One gram (1,000 miligram) of fish oil; of which about 180 milligrams is (eicosapentaenoic acid) EPA and 120 milligrams is DHA (docosahexaenoic acid), (for a total of 300 milligrams of omega 3's) in each clear capsule.