abstract: Chronic consumption of diets high in resistant starch (RS) leads to reduced fat cell size compared to diets high in digestible starch (DS) in rats and increases total and meal fat oxidation in humans. The aim of the present study was to examine the rate of lipogenesis in key lipogenic organs following a high RS or DS meal. Following an overnight fast, male Wistar rats ingested a meal with an RS content of 2% or 30% of total carbohydrate and were then administered an i.p bolus of 50 μCi ^3^H~2~O either immediately or 1 hour post-meal. One hour following tracer administration, rats were sacrificed, a blood sample collected, and the liver, white adipose tissue (WAT), and gastrocnemius muscle excised and frozen until assayed for total ^3^H-lipid and ^3^H-glycogen content. Plasma triglyceride and NEFA concentrations and ^3^H-glycogen content did not differ between groups. In all tissues, except the liver, there was a trend for the rate of lipogenesis to be higher in the DS group than the RS group which reached significance only in WAT at 1 h (p \< 0.01). On a whole body level, this attenuation of fat deposition in WAT in response to a RS diet could be significant for the prevention of weight gain in the long-term.
author: Janine A Higgins; Marc A Brown; Leonard H Storlien
date: 2006
institute: 1University of Colorado Health Sciences Center, Denver, Colorado 80262, USA; 2Metabolic Research Centre, Faculty of Health & Behavioural Sciences, University of Wollongong, NSW 2522, Australia; 3AstraZeneca International, Mölndal, Sweden
references:
title: Consumption of resistant starch decreases postprandial lipogenesis in white adipose tissue of the rat

# Findings

It has been reported that chronic resistant starch (RS) feeding in rats causes a decrease in adipocyte cell size, a decrease in fatty acid synthase expression, and reduced whole-body weight gain relative to digestible starch (DS) feeding \[1,2\]. Additionally, in healthy adults, a single RS meal caused a substantial elevation in total and meal fat oxidation compared to a DS meal \[3\]. These data suggest that RS intake may influence postprandial lipid metabolism. The aim of the present study was to examine the rate of lipogenesis in key lipogenic organs acutely following a RS or DS meal.

Male Wistar rats (*Rattus norvegicus*) were obtained from the Animal Resource Center (Murdoch, Western Australia) and were housed in groups of three at the University of Wollongong, Animal House. The rats were maintained at 22°C on a 12-h light/dark cycle (light cycle from 0700–1900 h), with free access to a standard laboratory chow (Young Stock Feed, Young, Australia) and water. The study was conducted according to the National Health and Medical Research Council (NH&MRC; Australia) code of practice for the care and use of animals for scientific purposes. Test Diets were prepared as previously described \[5\]. As a percentage of total energy, all diets contained 67% carbohydrate (57% starch; 10% sucrose), 22% protein, and 11% fat. All diets were identical in composition except for the percentage of RS and DS included in the starch component. For the RS diet, the starch used was a natural high amylose starch, Hi-Maize 957™ (National Starch and Chemical Co), which is 60% amylose/40% amylopectin, versus waxy cornstarch, which is 0% amylose/100% amylopectin, for the DS diet. Diets were presented to the animals in an unprocessed, unpelleted form so the starches were not subjected to cooking or extrusion. The dietary fiber level of the starches was determined using the Association of Analytical Chemistry (AOAC) enzymatic-gravimetric method. Total dietary fiber (dry solids) was lower (approximately 1%) for the DS starch than the RS starch which contained 19.3% fiber.

Two weeks prior to a meal test, rats were presented with test diet ad libatum overnight to familiarize them with this diet and prevent neophobia on the test day. Twice over the next week, rats were starved for 5 h then placed in testing cages and presented with a meal of similar size to that which they would receive during the meal test (approximately 0.4 g of diet). Rats had no exposure to test diet or disruption of normal eating habits for seven days prior to the meal test. For all tests, rats were starved overnight (12–15 hours). The following morning, rats were placed in 25 × 25-cm wire testing cages and allowed 30 min to acclimate before testing commenced. The rat was then presented with a test meal of 1.0 g carbohydrate/kg body weight (1.49 g diet/kg body weight). Rats were allowed 15 minutes to consume all food presented to them. Rats were injected with 50 μCi ^3^H~2~O either immediately following food consumption (1 h groups) or 1 h post-ingestion (2 h groups). One hour after ^3^H~2~O administration, rats were sacrificed by i.p. injection of 250 mg/kg pentobarbitone sodium. 400 μl blood was collected and the gastrocnemius muscle, liver, epidydimal white adipose tissue (WAT), and interscapular brown adipose tissue (BAT) were excised and immediately freeze-clamped. All tissues were stored at -80°C until assayed for lipid and glycogen content. Plasma was pipetted into fresh tubes and stored at -20°C for subsequent analysis of triglyceride and non-esterified fatty acid (NEFA) concentrations.

All plasma samples were assayed in duplicate. Plasma triglycerides were measured using a colorimetric assay kit supplied by Boehringer Mannheim (Germany), according to the manufacturer's instructions. Plasma NEFAs were estimated using a NEFA C kit (Wako Pure Chemicals Inc., Japan). All colorimetric assays were analyzed using a BioRad (model 550) microplate reader (Hercules, CA).

Estimation of total lipogenesis and glycogenesis was conducted as previously described \[4\]. Briefly, 100–200 mg of tissue was weighed out and completely digested in 1 M KOH by heating at 70°C for 40 min. Half of this tissue digest was spotted onto filter paper, air dried, and glycogen precipitated using ethanol. The remainder of the tissue digest was added to a chloroform/methanol mixture, agitated and the lipid extracted using three hexane washes. Pooled hexane layers from each tissue were dried under nitrogen, suspended in scintillant and counted. Note that glycogen measurements were not performed on adipose tissue samples due to their very low total glycogen content.

Results are expressed as means ± SEM using Statistics for Windows (Analytical Software, Tallahassee, FL), unless otherwise indicated. Statistical differences between diet groups were determined using an ANOVA with repeated measures and protected least significant difference (PLSD) test for comparison of means.

There was no difference in body weight between the groups used (330 ± 18 g and 328 ± 20 g for the DS and RS groups, reapectively). All animals ate at least 87% of the diet presented to them. There was no difference in total test meal consumption between groups. Animals consumed 93% (0.376 g), 95% (0.384 g), 92% (0.372 g), and 96% (0.388 g) of food presented for the RS 1 h, DS 1 h, RS 2 h, DS 2 h groups, respectively.

Plasma triglyceride and NEFA concentrations did not differ between groups (Figure 1). Triglyceride concentrations at baseline, 1 h, and 2 h were 0.39 ± 0.04, 0.63 ± 0.11, and 0.54 ± 0.11 mmol/L for the DS group vs 0.38 ± 0.05, 0.65 ± 0.07, and 0.67 ± 0.09 mmol/L for the RS group. NEFA data showed similar trends.

In all tissues, except the liver, there was a trend for the rate of lipogenesis to be higher in the DS group than the RS group. This trend reached significance only in WAT at 1 h (p \< 0.01; Figure 2). There was no significant difference in the rate of glycogensis in the liver or gastrocnemius in response to a DS or RS meal at any time point investigated (data not shown).

The amount of carbohydrate fed per meal in this study is comparable to that used in oral glucose tolerance tests by other investigators (eg. \[4\]). The average meal size received during this study was 0.38 g. The small size of this meal facilitated relatively uniform and complete consumption of the meal by all animals. In addition, a small meal size reflects the nibbling nature of a rat's natural eating pattern and is therefore physiologically appropriate for use in a rodent model.

Over the 2 h postprandial period, there was approximately a 30% reduction in the amount of lipogenesis, exclusively in white adipose tissue, in response to a RS diet relative to a DS diet. This number is reflective of data in humans which shows a 20–30% increase in postprandial fat oxidation following a RS meal \[3\]. As rats are 'nibblers', they will conceivably consume a meal of the size fed in this study many times over the course of a day. There fore, the observed 30% reduction in lipid synthesis in response to a RS diet could reduce adipose tissue mass in the long-term.

Further studies need to be conducted to elucidate the mechanism/s behind the observed decrease in lipogenesis in WAT in response to RS consumption. As no differences in circulating plasma triglyceride or NEFA concentrations was observed, it is possible that differences in circulating insulin concentration play a role in this effect. We have previously shown that RS consumption, at the level fed in the present study, decreases circulating plasma insulin without changing plasma glucose concentration in rats \[6\]. It could be argued that this attenuation in insulin concentration due to RS consumption is effecting WAT lipogenic enzyme activity as it has previously been shown that these enzymes are more insulin responsive in WAT than liver which could explain the tissue specificity of the observed response.

Kabir et al \[2\] have previously reported that long-term feeding of a high RS diet reduces fat cell size relative to a high DS diet. Data presented here indicate that a high RS meal reduces total fat synthesis in white adipose tissue following a meal relative to a high DS meal. This decrease in lipid synthesis could indeed contribute to control of fat cell size in RS fed rats in the long-term. Decreased lipogenesis selectively in WAT and a reduction in adipocyte cell size in response to RS feeding have broad implications for the treatment and prevention of obesity. Furthermore, increasing the RS intake of a typical Western diet would be easy to achieve through the consumption of natural foods with a high RS content and/or the consumption of commercially produced foods which are fortified with high RS starches, such as breads, pasta, and cereals. RS can even be easily incorporated into foods such as candy and pizza dough, making increased consumption an achievable possibility.

# Abbreviations

RS, resistant starch

DS, digestible starch

WAT, white adipose tissue

BAT, brown adipose tissue

NEFA, non-esterified fatty acid

# Competing interests

The author(s) declare that they have no competing interests.

# Authors' contributions

JH conceived of the study design and was responsible for overall study coordination, conducting rat meal tests/sacrifice, data analysis, and manuscript preparation. MB was responsible for conducting rat meal tests/sacrifice, statistical analyses, and contributed to manuscript preparation. LS conducted rat tissue extractions and contributed to the study design and manuscript preparation.

### Acknowledgements

Starches were donated by National Starch and Chemical Company, Australia. Vitamin and mineral mixes were a gift from Millmaster Feeds, Enfield, Australia.


abstract: # OBJECTIVE
  .
  To forecast the number of U.S. individuals aged \<20 years with type 1 diabetes mellitus (T1DM) or type 2 diabetes mellitus (T2DM) through 2050, accounting for changing demography and diabetes incidence.
  .
  # RESEARCH DESIGN AND METHODS
  .
  We used Markov modeling framework to generate yearly forecasts of the number of individuals in each of three states (diabetes, no diabetes, and death). We used 2001 prevalence and 2002 incidence of T1DM and T2DM from the SEARCH for Diabetes in Youth study and U.S. Census Bureau population demographic projections. Two scenarios were considered for T1DM and T2DM incidence: *1*) constant incidence over time; *2*) for T1DM yearly percentage increases of 3.5, 2.2, 1.8, and 2.1% by age-groups 0–4 years, 5–9 years, 10–14 years, and 15–19 years, respectively, and for T2DM a yearly 2.3% increase across all ages.
  .
  # RESULTS
  .
  Under scenario 1, the projected number of youth with T1DM rises from 166,018 to 203,382 and with T2DM from 20,203 to 30,111, respectively, in 2010 and 2050. Under scenario 2, the number of youth with T1DM nearly triples from 179,388 in 2010 to 587,488 in 2050 (prevalence 2.13/1,000 and 5.20/1,000 \[+144% increase\]), with the greatest increase in youth of minority racial/ethnic groups. The number of youth with T2DM almost quadruples from 22,820 in 2010 to 84,131 in 2050; prevalence increases from 0.27/1,000 to 0.75/1,000 (+178% increase).
  .
  # CONCLUSIONS
  .
  A linear increase in diabetes incidence could result in a substantial increase in the number of youth with T1DM and T2DM over the next 40 years, especially those of minority race/ethnicity.
author: Giuseppina Imperatore; James P. Boyle; Theodore J. Thompson; Doug Case; Dana Dabelea; Richard F. Hamman; Jean M. Lawrence; Angela D. Liese; Lenna L. Liu; Elizabeth J. Mayer-Davis; Beatriz L. Rodriguez; Debra Standiford; Corresponding author: Giuseppina Imperatore, <firstname.lastname@example.com>.Received 6 April 2012 and accepted 4 September 2012.
date: 2012-12
references:
subtitle: : Dynamic modeling of incidence, mortality, and population growth
title: Projections of Type 1 and Type 2 Diabetes Burden in the U.S. Population Aged \<20 Years Through 2050

Diabetes is one of the most common and costly chronic pediatric diseases (1). The SEARCH for Diabetes in Youth study (SEARCH) estimated that in 2001 about 154,000 individuals in the U.S. aged \<20 years were living with diabetes and that each year approximately 15,000 youth aged \<20 years are being diagnosed with type 1 diabetes mellitus (T1DM) and 3,700 with type 2 diabetes mellitus (T2DM) (2,3). Assessing the future burden of diabetes in youth by diabetes type is crucial for implementing public health primary and secondary prevention programs and planning health care delivery services.

A number of studies have estimated the burden of diagnosed diabetes through 2050 in the U.S (4,5). A limitation is that they were not able to separate the contribution of T1DM from T2DM to the projected diabetes burden. Although the majority of adults with diabetes have T2DM, the majority of youth with diabetes currently have T1DM. On the other hand, T2DM may be becoming more common in adolescents, especially among minority youth (2,3).

There is substantial variation in the incidence of T1DM and T2DM across the major racial/ethnic groups in the U.S. The incidence of T1DM is highest among non-Hispanic whites (NHWs) and lowest in American Indians (3). In contrast, T2DM disproportionally affects individuals from all racial/ethnic minority groups (3). Therefore, changes in the race/ethnicity distribution of the U.S. population will substantially impact the absolute number of individuals living with T1DM or T2DM. This makes even more compelling the need for diabetes type–specific projections.

To overcome the limitations of previous studies and provide contemporary estimates of the national type-specific burden of diabetes in youth, we constructed a system of dynamic equations that incorporate diabetes prevalence and incidence, as well as birth, migration, and mortality estimates. These equations model the future burden of diabetes in U.S. youth aged \<20 years through 2050. In addition, we perform sensitivity analyses to assess the impact of increases in the incidence and/or changes in the risk of mortality separately for T1DM and T2DM.

# RESEARCH DESIGN AND METHODS

## Data sources

The data sources for this study include the U.S. Census Bureau (6) and the SEARCH study (2,3,7–11). Census data include estimates of the 2001 U.S. population by ages (0, 1, 2,…,19 years), race/ethnicity (NHW, non-Hispanic black \[NHB\], Hispanic, Asian and Pacific Islander \[API\], American Indian/Alaska Native \[AIAN\]), and sex, as well as projection estimates of births, deaths, and net migration by the same dimensions for the years 2002–2050. For each of these components of population change—fertility, mortality and net migration—the census applied three different assumptions to forecast the future population size. We used the series using the middle assumption for each of these components, also designated as the middle series. SEARCH data include diabetes prevalence in 2001 and incidence from 2002 to 2007 collected from geographically defined populations in Ohio, Colorado, South Carolina, and Washington, as well as Indian Health Service beneficiaries from four American Indian populations, and enrollees in managed health care plans in California and Hawaii. SEARCH is a multicenter study that began in 2001 and is conducting population-based ascertainment of youth aged \<20 years with clinically diagnosed, nongestational diabetes. Institutional review board(s) for each site approved the study protocol. A detailed description of the SEARCH study has been published elsewhere (2,3).

## Statistical analysis

### 2001 prevalence estimation.

We used Poisson regression to estimate T1DM and T2DM prevalence as a function of age (0,1,…,19 years), race/ethnicity (NHW, NHB, Hispanic, API, AIAN), and sex. The Bayesian information criterion was used to select the best fitting models (12). The Bayesian information criterion selects a model from a collection of possible nonnested models by maximizing the likelihood but with a penalty for higher dimensional models. Posterior predictive checking was used to assess model consistency with the data (13). We used the deviance function as a test measure and calculated the "Bayesian *P* value." Bayesian *P* values do not work like the more common frequentist *P* values; values between 0.1 and 0.9 indicate good model fit (14). Models were fit using Bayesian methods in WinBUGS (15). The final models included a cubic spline for age (knots at 0, 9, and 19 years), race/ethnicity, and sex. Posterior predictive checking yielded Bayesian *P* values equal to 0.39 and 0.22, indicating good model fit for T1DM and T2DM, respectively. Our estimates of the 2001 T1DM and T2DM prevalence were the means of the posterior distributions obtained from fitting the final models.

### 2002 incidence rate estimation.

From a population size of 30,549,412 person-years in 2002 through 2007, SEARCH identified 6,164 incident cases of T1DM and 1,534 incident cases of T2DM. We used Poisson regression to estimate T1DM and T2DM incidence as a function of age (0,1,…,19 years), race/ethnicity (NHW, NHB, Hispanic, API, AIAN), sex, and calendar year. We were primarily interested in incidence estimates for the year 2002. Including 6 years of data with random effects by year improved the year 2002 estimates by "borrowing strength" from the other years. The final models included a cubic spline for age (knots at 0, 9, and 19 years), race, sex, age by sex interactions, and random effects of calendar year. Posterior predictive checking yielded Bayesian *P* values equal to 0.29 and 0.12, indicating good model fit for T1DM and T2DM, respectively. Our estimates of the 2002 T1DM and T2DM incidence rates were the means of the posterior distributions obtained from fitting the final models.

### Projection model.

We constructed dynamic models consisting of systems of difference equations similar to models described previously (16,17). In these models, the U.S. population aged 0 to 19 years is modeled at 1-year intervals starting at year 2001 and ending at year 2050. Specifically, we defined numbers of individuals in various disease states (diabetes, no diabetes, and death). The mathematical details are presented in [Supplementary Appendix 1](http://care.diabetesjournals.org/lookup/suppl/doi:10.2337/dc12-0669/-/DC1).

### Sensitivity analyses.

We conducted sensitivity analyses by varying both relative risks of death in youth with diabetes versus nondiabetic youth and incidence rate projections for T1DM and T2DM. Two scenarios were considered for relative risks of death: *1*) the relative risks of death for youth in the age-group \<20 years with T1DM or T2DM are equal to one, i.e., death rates by age, race/ethnicity, and sex for persons with and without diabetes are equal to the census projected death rates; *2*) death rates of youth with diabetes are higher than those without diabetes with a relative risk equal to 1.5. Two scenarios for T1DM incidence rates were considered: *1*) constant incidence over time at 2002 levels (baseline scenario); *2*) yearly percentage increases of 3.5, 2.2, 1.8, and 2.1% by age-groups 0–4 years, 5–9 years, 10–14 years, and 15–19 years (as seen in a previous study from Colorado \[18\]), respectively, were applied to all 10 race-sex combinations. Correspondingly, two scenarios for T2DM incidence rates were considered: *1*) constant incidence over time at 2002 levels (baseline scenario); *2*) a yearly 2.3% increase applied uniformly to all ages (0–19 years) for each of the 10 race-sex combinations. This increase represents the overall increase of T1DM registered in the Colorado youth population (18).

# RESULTS

Our estimates of the 2002 T1DM and T2DM incidence rates by age and race/ethnicity from fitting the final models are presented in Fig. 1. The incidence of T1DM peaks around the age of 10 years and is highest among NHWs followed by NHBs, Hispanics, APIs, and AIANs (Fig. 1A). The incidence of T2DM peaks around 14 years of age. AIAN youth have the highest incidence rate, followed by NHBs, Hispanics, APIs, and NHWs (Fig. 1B).

We used these incidence rates in the projection model for each year from 2002 through 2050 in the baseline scenario, where we assume constant incidence rates over time.

## Baseline scenario

Table 1 shows the projected number of youth with T1DM and T2DM by race/ethnicity for selected years. The model forecasts that the number of youth with T1DM will increase by 23%, from 166,018 in 2010 to 203,385 in 2050. Due to the absolute increases in the numbers of minority youths in the population as projected by the U.S. Census, this increase is primarily driven by these youths. In 2010, NHWs represented 71% of all youth with T1DM, but by 2050 this proportion will decrease to 55%. Over the 40-year period, the number of Hispanic youth with T1DM is projected to increase 2.5-fold. The overall prevalence of T1DM remains largely unchanged from 1.97/1,000 in 2010 to 1.80/1,000 in 2050, a decrease of 9%, reflecting the lower incidence of T1DM among youth from minority groups compared with NHW youth.

Projections of the number of individuals aged \<20 years with T1DM or T2DM and prevalence estimates\* for selected years, by race/ethnicity under baseline scenario†

![](2515tbl1)

The model estimated that in 2010 20,203 youth in the U.S. had T2DM. Youth of minority groups represented the majority of T2DM cases, while 25% were NHW. The number of youth with T2DM is projected to increase to 30,111 by 2050. As for T1DM, this increase is driven largely by the increase in the population of youth of minority groups. By 2050 Hispanics are estimated to represent 50% of U.S. youth aged \<20 years with T2DM. Between 2010 and 2050, the overall prevalence of T2DM may increase by 13%, from 0.24/1,000 to 0.27/1,000.

## Increased incidence scenario

Table 2 presents the results of the sensitivity analyses in which the incidence rates of T1DM and T2DM increase while the relative risk of death is equal to 1.0. Under this scenario, the model projects the number of youth with T1DM will increase 3.3-fold, from 179,388 in 2010 to 587,488 in 2050. The number of youth with T1DM will increase 6.6-fold in Hispanics, 5.4-fold in APIs, 4.4-fold in AIANs, 3.0-fold in NHBs, and 2.5-fold in NHWs. The prevalence will increase by 144%, from 2.13/1,000 to 5.20/1,000, with the highest estimate still among NHW youth (7.04/1,000).

Projections of the number of individuals aged \<20 years with T1DM or T2DM and prevalence estimates\* for selected years, by race/ethnicity under increased incidence rate scenario†

![](2515tbl2)

Under the scenario of an annual increase of 2.3% in the incidence of T2DM, the model indicates that, in the U.S., in 2010 there were 22,820 youth aged \<20 years with T2DM. By 2050, our model predicts that this number will almost quadruple, to 84,131, with Hispanics representing 50% and NHBs 27% of all youth with T2DM. On the basis of our projections, the prevalence will rise from 0.27/1,000 in 2010 to 0.75/1,000 in 2050, an increase of 178%. The prevalence will be highest in NHBs (1.63/1,000) and lowest in NHWs (0.28/1,000).

For both T1DM and T2DM, the differences in the numbers of youth with diabetes from the baseline scenario are due to the increasing incidence rates over baseline.

Under the increasing incidence scenario and the baseline scenario, when we set the relative risk of death at 1.5 for youth with either T1DM or T2DM diabetes, results were virtually identical (data not shown).

# CONCLUSIONS

We have estimated the future burden of diabetes in youth by type in the major race/ethnic groups in the U.S., using the most recent population-based estimates of diabetes incidence and prevalence and taking into account demographic changes over time. Our model projected that over the next 40 years, at the current incidence rates, the number of youth with T1DM and T2DM may increase by 23% and 49%, respectively. However, if the incidence of T1DM or T2DM increases, there may be more than a threefold increase in the number of youth with T1DM and about a fourfold increase in the number of youth with T2DM, especially among minority youth.

Very little is known about effective prevention of T1DM, and more research is needed. However, T2DM can be prevented in high risk adults. Additional research is needed to examine the most effective methods for T2DM prevention in youth and should address strategies applicable to obesity prevention and control, as well as strategies for youth at high risk for T2DM. The projected increase in the prevalence of T2DM should serve as a call to action so that by 2050 the actual number of affected youth will fall markedly short of our projections.

Because of the early age of onset and longer diabetes duration, children and adolescents are at risk for developing diabetes-related complications at a younger age. As these youth age, this profoundly affects their productivity, quality of life, and life expectancy and increases health care costs. Even in childhood, the medical expenditures of youth with diabetes are approximately 6.2 times of those without diabetes (19). The health care system and society as a whole will need to plan and prepare for the delivery of quality health care to meet the needs of the growing number of youth with diabetes. This may need to include the training of additional health care professionals to treat and manage children and adolescents with T1DM and T2DM.

Strengths of the current study include the use of contemporary population-based estimates of the prevalence and incidence of T1DM and T2DM from the SEARCH study for the major race/ethnic groups in the U.S. This enabled us to quantify race/ethnicity–specific future diabetes burden. Prevalence and incidence estimates were based on physician's diagnosis of T1DM or T2DM, and case definitions met consistent eligibility criteria (2,3). Moreover, physician's diagnosis of diabetes type was in good agreement with the etiologic biochemical and clinical characteristics of the two major types of diabetes (20).

The projections have some limitations. First, the recent estimated increase in the incidence of T1DM is limited and only available in one U.S. study conducted in Colorado (18). The Colorado study found a slightly lower annual increase in T1DM incidence among youth than a large registry-based study conducted in 17 European countries (EURODIAB; overall yearly average increase 2.3% in Colorado vs. 3.9% in Europe) (18,21). However, the pattern of the increase in Colorado was similar to that observed in Europe, with children younger than 5 years old experiencing the greatest relative increase. If the actual rate of increase in the U.S. is more similar to that observed in Europe, then our projections may underestimate the future burden of T1DM in the U.S. However, it should be noted that EURODIAB included only children aged 0–15 years and the greatest relative increase in the incidence rate was observed in countries with low baseline incidence. Second, in our study we applied the same rate of increase across all race/ethnic groups. The Colorado study population included only NHWs and Hispanics, and the overall rate of increase in Hispanics was slightly lower than that of NHWs (1.6 vs. 2.7% per year, respectively). The U.S. Census projections indicate that the proportion of the youth population of NHW race/ethnicity will diminish from 62% in 2001 to 41% in 2050 (22). Because of this demographic shift and the possibility that youth of other races/ethnicities than NHW may experience a lower increase in the incidence, it is possible that the number of youth with T1DM could be lower than that estimated by our study under the increasing incidence scenario. Third, we assumed constant increases in T1DM incidence over time and did not account for the possible effect of yet to be identified primary prevention strategies that may influence our predicted number of youth with T1DM. Given our current knowledge, the increased incidence scenario should be taken with caution. However, we would like to point out that recent findings from Europe indicated a constant linear trend over a 15-year period (21) or, starting in the early 1990s, even a steeper increase (23).

Finally, because of the lack of population-based estimates of T2DM incidence trends in youth aged \<20 years, we used a yearly increase of 2.3% in our increasing incidence scenario, based on the overall increase of T1DM in the Colorado study (18). In the Pima Indians, a population at very high risk of developing T2DM (24), among youth aged 5–14 years, between 1965 and 2003 the incidence of T2DM increased almost sixfold (25). In Finnish adolescents and young adults (aged 15–39 years) during a 10-year period from 1992 to 2002, the incidence of T2DM increased on average by 4.3% per year, while that of T1DM increased by 3.9% per year (26). Obesity is a major risk factor for the development of T2DM. Since the 1980s, obesity prevalence among U.S. children and adolescents tripled; however, recent national data indicate that during the last decade obesity prevalence may be leveling off at 17% (27). If obesity remains stable for the next 40 years, it is plausible that the current T2DM incidence rate will remain steady. However, even under this scenario, the number of youth with T2DM may increase by 49%. On the other hand, implementation of interventions for the prevention of childhood obesity at the individual or population level may result in decreasing T2DM incidence over time (28,29).

In both scenarios in our study, increasing the relative risk of death to 1.5 did not affect our estimates. This might be partially explained by the very low number of diabetes-related deaths in this age-group (1.15 per million youths) (30).

Our projections suggest a shift in the proportional distribution of racial/ethnic groups among youth with T1DM. By 2050, about half of T1DM youths will be of minority race/ethnic groups. This change may influence potential trends in clinical presentation, treatment patterns, and quality of care. Minority youth are more likely to be overweight or obese (27) and this may lead to a misdiagnosis of T2DM. Among SEARCH study participants, minority youth with T1DM were significantly more likely to have poor glucose control (glycated hemoglobin \>9%) than NHW youth (31). Minority youth with T1DM are also more likely to live in households with low income and parental education (7–11). This in turn may affect their access to and quality of health care (32,33). Because of the changing demographics of the youth population with T1DM, health care policies and delivery systems need to assure that less advantaged youth receive appropriate care.

Our projections indicate a serious picture of the future national diabetes burden in youth. Even if the incidence remains at 2002 levels, because of the population growth projected by the U.S. Census the future numbers of youth with diabetes is projected to increase, resulting in increased health care needs and costs. Future planning should include strategies for implementing childhood obesity prevention programs and primary prevention programs for youth at risk for developing T2DM. Likewise, to prevent future human suffering and health care costs, effective interventions for the prevention of diabetes-related complications should be available to all youth with diabetes (34). At the same time, it is crucial to continuously monitor diabetes trends at the population level, as well as complications and quality of care among youth.

# Supplementary Material

###### Supplementary Data

###### News Release

###### Slide Set

## Acknowledgments

SEARCH for Diabetes in Youth is funded by the Centers for Disease Control and Prevention (PA 00097, DP-05-069, and DP-10-001) and supported by the National Institutes of Health, National Institute of Diabetes and Digestive and Kidney Diseases.

J.M.L. is employed by Kaiser Permanente Southern California. No other potential conflicts of interest relevant to this article were reported.

G.I. conceived the study, participated in study design and coordination, and wrote a first draft of the manuscript. J.P.B. developed and programmed the multistate dynamic models, participated in study design and coordination, and helped draft the manuscript. T.J.T. developed and programmed the incidence projection model, participated in study design and coordination, and helped draft the manuscript. D.C. participated in study design and coordination, assembled the SEARCH prevalence and incidence data for analysis, and commented on drafts of the manuscript. D.D., R.F.H., J.M.L., A.D.L., L.L.L., E.J.M.-D., and D.S. reviewed and edited the manuscript and contributed to discussion. B.L.R. reviewed the manuscript and contributed to discussion. G.I. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Parts of this study were presented in poster form at the 71st Scientific Sessions of the American Diabetes Association, San Diego, California, 24–28 June 2011.

The SEARCH for Diabetes in Youth study is indebted to the many youth and their families, and their health care providers, whose participation made this study possible.

# References


date: 2017
references:
title: Non-O blood groups associated with higher risk of heart attack

# Introduction

Having a non-O blood group is associated with a higher risk of heart attack, according to research presented recently at Heart Failure 2017 and the 4th World Congress on Acute Heart Failure.

Lead author Tessa Kole, a Master's degree student at the University Medical Centre Groningen, the Netherlands, said: 'It has been suggested that people with non-O blood groups (A, B, AB) are at higher risk for heart attacks and overall cardiovascular mortality, but this suggestion comes from case–control studies, which have a low level of evidence. If this was confirmed, it could have important implications for personalised medicine.'

The current study was a meta-analysis of prospective studies reporting on O and non-O blood groups, and incident cardiovascular events, including myocardial infarction (heart attack), coronary artery disease, ischaemic heart disease, heart failure, cardiovascular events and cardiovascular mortality.

The study included 1 362 569 subjects from 11 prospective cohorts, described in nine articles. There were a total of 23 154 cardiovascular events. The researchers analysed the association between blood group and all coronary events, combined cardiovascular events and fatal coronary events.

The analysis of all coronary events included 771 113 people with a non-O blood group and 519 743 people with an O blood group, of whom 11 437(1.5%) and 7 220 (1.4%) suffered a coronary event, respectively. The odds ratio (OR) for all coronary events was significantly higher in carriers of a non-O blood group, at 1.09 \[95% confidence interval (CI) of 1.06–1.13\].

The analysis of combined cardiovascular events included 708 276 people with a non-O blood group and 476 868 people with an O blood group, of whom 17 449 (2.5%) and 10 916 (2.3%) had an event, respectively. The OR for combined cardiovascular events was significantly higher in non-O blood group carriers, at 1.09 (95% CI 1.06–1.11).

The analysis of fatal coronary events did not show a significant difference between people with O and non-O blood groups.

'We demonstrate that having a non-O blood group is associated with a 9% increased risk of coronary events and a 9% increased risk of cardiovascular events, especially myocardial infarction', said Ms Kole.

The mechanisms that might explain this risk are under study. The higher risk for cardiovascular events in non-O blood group carriers may be due to having greater concentrations of von Willebrand factor, a blood clotting protein which has been associated with thrombotic events. Further, non-O blood group carriers, specifically those with an A blood group, are known to have higher cholesterol. And galectin-3, which is linked to inflammation and worse outcomes in heart failure patients, is also higher in those with a non-O blood group.

Ms Kole said: 'More research is needed to identify the cause of the apparent increased cardiovascular risk in people with a non-O blood group. Obtaining more information about risk in each non-O blood group (A, B and AB) might provide further explanations of the causes.'

She concluded: 'In future, blood group should be considered in risk assessment for cardiovascular prevention, together with cholesterol, age, sex and systolic blood pressure. It could be that people with an A blood group should have a lower treatment threshold for dyslipidaemia or hypertension, for example. We need further studies to validate if the excess cardiovascular risk in non-O blood group carriers may be amenable to treatment.'

# References


abstract: This study describes and validates a new method for metagenomic biomarker discovery by way of class comparison, tests of biological consistency and effect size estimation. This addresses the challenge of finding organisms, genes, or pathways that consistently explain the differences between two or more microbial communities, which is a central problem to the study of metagenomics. We extensively validate our method on several microbiomes and a convenient online interface for the method is provided at <http://huttenhower.sph.harvard.edu/lefse/>.
author: Nicola Segata; Jacques Izard; Levi Waldron; Dirk Gevers; Larisa Miropolsky; Wendy S Garrett; Curtis Huttenhower
date: 2011
institute: 1Department of Biostatistics, 677 Huntington Avenue, Harvard School of Public Health, Boston, MA 02115, USA; 2Department of Molecular Genetics, 245 First Street, The Forsyth Institute, Cambridge, MA 02142, USA; 3Department of Oral Medicine, Infection and Immunity, 188 Longwood Ave, Harvard School of Dental Medicine, Boston, MA 02115, USA; 4Microbial Sequencing Center, 7 Cambridge Center, The Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; 5Department of Immunology and Infectious Diseases, 665 Huntington Avenue, Harvard School of Public Health, Boston, MA 02115, USA; 6Department of Medicine, 75 Francis Street, Harvard Medical School, Boston, MA 02115, USA; 7Department of Medical Oncology, 44 Binney Street, Dana-Farber Cancer Institute, MA 02215, USA
references:
title: Metagenomic biomarker discovery and explanation

# Background

Biomarker discovery has proven to be one of the most broadly applicable and successful means of translating molecular and genomic data into clinical practice. Comparisons between healthy and diseased tissues have highlighted the importance of tasks such as class discovery (detecting novel subtypes of a disease) and class prediction (determining the subtype of a new sample) \[1-4\], and recent metagenomic assays have shown that human microbial communities can be used as biomarkers for host factors such as lifestyle \[5-7\] and disease \[7-10\]. As sequencing technology continues to develop and makes microbial biomarkers increasingly easily detected, this enables clinical diagnostic and microbiological applications through the comparison of microbial communities \[11,12\].

The human microbiome, consisting of the total microbial complement associated with human hosts, is an important emerging area for metagenomic biomarker discovery \[13,14\]. Changes in microbial abundances in the gut, oral cavity, and skin have been associated with disease states ranging from obesity \[15-17\] to psoriasis \[18\]. More generally, the metagenomic study of microbial communities is an effective approach for identifying the microorganisms or microbial metabolic characteristics of any uncultured sample \[19,20\]. Analyses of metagenomic data typically seek to identify the specific organisms, clades, operational taxonomic units, or pathways whose relative abundances differ between two or more groups of samples, and several features of microbial communities have been proposed as potential biomarkers for various disease states. For example, single pathogenic organisms can signal disease if present in a community \[21,22\], and increases and decreases in community complexity have been observed in bacterial vaginosis \[23\] and Crohn's disease \[8\]. Each of these different types of microbial biomarkers is correlated with disease phenotypes, but few bioinformatic methods exist to explain the class comparisons afforded by metagenomic data.

Identifying the most biologically informative features differentiating two or more phenotypes can be challenging in any genomics dataset, and this is particularly true for metagenomic biomarkers. Robust statistical tools are needed to ensure the reproducibility of conclusions drawn from metagenomic data, which is crucial for clinical application of the biological findings. Related challenges are associated with high-dimensional data regardless of the data type or experimental platform; the number of potential biomarkers, for example, is typically much higher than the number of samples \[24-26\]. Metagenomic analyses additionally present their own specific issues, including sequencing errors, chimeric reads \[27,28\], and complex underlying biology; many microbial communities have been found to show remarkably high inter-subject variability. For example, large differences are detected even among the gut microbiomes of twins \[29\], and both human microbiomes and environmental communities are thought to be characterized by the presence of a long tail of rare organisms \[30-32\]. Moreover, simply identifying potential biomarkers without elucidating their biological consistency and roles is only a precursor to understanding the underlying mechanisms of microbe-microbe or host-microbe interactions \[33\]. In many cases, it is necessary to explain not just how two biological samples differ, but why. This problem is referred to as class comparison: how can the differences between phenotypes such as tumor subtype or disease state be explained in terms of consistent biological pathways or molecular mechanisms?

A number of methods have been proposed for class discovery or comparison in metagenomic data. MEGAN \[34\] is a metagenomic analysis tool with recent additions for phylogenetic comparisons \[35\] and statistical analyses \[36\]. MEGAN, however, can only compare single pairs of metagenomes, as is also the case with STAMP \[37\], which does introduce a concept of 'biological relevance' in the form of confidence intervals. UniFrac \[38\] compares sets of metagenomes at a strictly taxonomic level using phylogenetic distance, while MG-RAST \[39\], ShotgunFunctionalizeR \[40\], mothur \[41\], and METAREP \[42\] all process metagenomic data using standard statistical tests (mainly *t*-tests with some modifications). Most methods for community analysis from an ecological perspective rely on unsupervised cluster analyses based on principal component analysis \[43\] or principal coordinate analysis \[44\]. These can successfully detect groups of related samples, but they fail to include prior knowledge of phenotypes or environmental conditions associated with the groups, and they generally do not identify the biological features responsible for group relationships. Metastats \[45\] is the only current method that explicitly couples statistical analysis (to assess whether metagenomes differ) with biomarker discovery (to detect features characterizing the differences) based on repeated *t* statistics and Fisher's tests on random permutations. However, none of these methods, even those offering nuanced analyses of metagenomic data, provide biological class explanations to establish statistical significance, biological consistency, and effect size estimation of predicted biomarkers.

In this work, we present the linear discriminant analysis (LDA) effect size (LEfSe) method to support high-dimensional class comparisons with a particular focus on metagenomic analyses. LEfSe determines the features (organisms, clades, operational taxonomic units, genes, or functions) most likely to explain differences between classes by coupling standard tests for statistical significance with additional tests encoding biological consistency and effect relevance. Class comparison methods typically predict biomarkers consisting of features that violate a null hypothesis of no difference between classes; we additionally detect the subset of features with abundance patterns compatible with an algorithmically encoded biological hypothesis and estimate the sizes of the significant variations. In particular, effect size provides an estimation of the magnitude of the observed phenomenon due to each characterizing feature and it is thus a valuable tool for ranking the relevance of different biological aspects and for addressing further investigations and analyses. The introduction of prior biological knowledge in the method contributes to constrain the analysis and thus to address the challenges traditionally connected with high-dimensional data mining. LEfSe thus aims to support biologists by suggesting biomarkers that explain most of the effect differentiating phenotypes of interest (two or more) in biomarker discovery comparative and hypothesis-driven investigations. The visualization of the discovered biomarkers on taxonomic trees provides an effective means for summarizing the results in a biologically meaningful way, as this both statistically and visually captures the hierarchical relationships inherent in 16S-based taxonomies/phylogenies or in ontologies of pathways and biomolecular functions.

We validated this approach using data from human microbiomes, a mouse model of ulcerative colitis, and environmental samples, in each case predicting groups of organisms or operational taxonomic units that concisely differentiate the classes being compared. We further evaluated LEfSe using synthetic data, observing that it achieves a substantially better false positive rate compared to standard statistical tests, at the price of a moderately increased false negative rate (that can be adjusted as needed by the user). An implementation of LEfSe including a convenient graphical interface incorporated in the Galaxy framework \[46,47\] is provided online at \[48\].

# Results and discussion

LEfSe is an algorithm for high-dimensional biomarker discovery and explanation that identifies genomic features (genes, pathways, or taxa) characterizing the differences between two or more biological conditions (or classes) (Figure 1). It emphasizes statistical significance, biological consistency and effect relevance, allowing researchers to identify differentially abundant features that are also consistent with biologically meaningful categories (subclasses; see Materials and methods). LEfSe first robustly identifies features that are statistically different among biological classes. It then performs additional tests to assess whether these differences are consistent with respect to expected biological behavior; for example, given some known population structure within a set of input samples, is a feature more abundant in all population subclasses or in just one? Specifically, we first use the non-parametric factorial Kruskal-Wallis (KW) sum-rank test \[49\] to detect features with significant differential abundance with respect to the class of interest; biological consistency is subsequently investigated using a set of pairwise tests among subclasses using the (unpaired) Wilcoxon rank-sum test \[50,51\]. As a last step, LEfSe uses LDA \[52\] to estimate the effect size of each differentially abundant feature and, if desired by the investigator, to perform dimension reduction.

We have specifically designed LEfSe for biomarker discovery in metagenomic data. We thus summarize our results here from applying the tool to 16S rRNA gene and whole genome shotgun datasets to detect bacterial organisms and functional characteristics differentially abundant between two or more microbial environments. These include body sites within human microbiomes (mucosal surfaces and aerobic/anaerobic environments), adult and infant microbiomes, inflammatory bowel disease status in a mouse model, bacterial and viral environmental communities, and synthetic data for quantitative computational evaluation.

## Taxa characterizing body sites within the human microbiome

Microbial community organization at multiple human body sites is an area of active current research, since both low- and high-throughput methods have shown both differences and overlaps among the microbiota of multiple body sites \[53,54\]. We examined these differences in the 16S-based phylometagenomic dataset from 24 individuals enrolled in the Human Microbiome Project \[13,55\]. A minimum of 5,000 16S rRNA gene sequences were obtained for 301 samples from 24 healthy subjects (12 male, 12 female) covering 18 body sites, including 6 main body site categories: the oral cavity (9 sub-sites sampled), the vagina (3 sub-sites sampled), the skin (2 sub-sites sampled), the retroauricular crease (2 sub-sites sampled), the nasal cavity (1 sample) and the gut (1 sample). We validated LEfSe by contrasting mucosal versus non-mucosal body site classes and by comparing three levels of aerobic environments (anaerobic, microaerobic, and aerobic). In both cases, the sub-sites within each class of body site were used as a biological subclass.

## Mucosal surfaces are colonized by diverse bacteria; non-mucosal microbiomes are strongly enriched for Actinobacteria

Our first analysis focused on differences in microbiota composition between mucosal and non-mucosal body sites. The oral cavity, gut, and vaginal sites were classified as sources of mucosal communities and the anterior fossa (skin), nasal cavity, and retroauricular crease as non-mucosal. Mucosal environments differ greatly from the other body sites, characterized primarily by interaction with the human immune system, oxidative challenge, and hydration \[56\].

LEfSe provides three main outputs (Figure 2), describing the effect sizes of differences observed among mucosal/non-mucosal communities, the phylogenetic distribution of these differences based on the Ribosomal Database Project (RDP) bacterial taxonomy \[57\], and the raw data driving these effects. LEfSe detected 15 bacterial clades showing statistically significant and biologically consistent differences in non-mucosal body sites (Figure 2a).

The most differentially abundant bacterial taxa in non-mucosal body sites belong to phyla with prevalent aerobic members: Actinobacteria, Firmicutes, and Proteobacteria, including environmental organisms from the Betaproteobacteria and Gammaproteobacteria clades. Non-mucosal overrepresented genera include *Propionibacterium*, *Staphylococcus* (found exclusively in non-mucosal samples), *Corynebacterium*, and *Pseudomonas*. Also of note is the relevant representation of plastids from plant organisms (chloroplasts), for which the distribution of associated taxa varies, as some are limited to non-mucosal surfaces (environmental exposure and potentially cosmetic products) and others to the digestive track (ingested food). No clades are consistently present in all mucosal body sites, demonstrating the β-diversity of these communities (that is, differences among their population structure), but many taxa within Actinobacteria, Bacillales, and several other clades are relatively abundant at all non-mucosal sites. The within-subject β-diversity at all phylogenetic levels is highlighted in Additional file 1, quantifying the extent to which distances among different mucosal body sites are larger than the equivalent distances among non-mucosal sites. This leads to a lack of taxa common to all mucosal body sites, and therefore no taxa are determined by LEfSe to be characteristic of the mucosa as a whole.

The Actinomycetales are usually the most abundant phylogenetic unit (order level) in non-mucosal communities, with percentages higher than 90% in several skin samples and at most 20% in the great majority of the oral mucosal samples and substantially lower in the vagina and gut (Figure 2c). From a quantitative viewpoint, the taxonomic order Actinomycetales makes up essentially all of the detected members of the phylum Actinobacteria, except in the vaginal site, which reported a substantial Bifidobacteriales presence. Bifidobacteriales themselves are not detected as differentially abundant between mucosal and non-mucosal body sites, since this is a feature only of the vaginal samples and not of all mucosal body sites. The contrast of many clades' abundance versus distribution is striking; for example, the genera *Alloscardovia*, *Parascardovia* and *Scardovia* are present in all body sites at very low abundances, while *Gardnerella* is overrepresented only in vaginal samples, with over three orders of magnitude difference in abundance. A similar commonality of distribution was found for the Bacillales at an even lower abundance. At the genus level, *Propionibacterium*, *Staphylococcus*, *Corynebacterium* and *Pseudomonas* are differentiated by both distribution and abundance. The *Staphylococcus* genus in particular is detected by LEfSe with a very high LDA score (more than five orders of magnitude), reflecting marked abundance in non-mucosal sites (mean 10%, 18% and 21% in the skin, retroauricular crease and anterior nares body sites, respectively) and consistently low abundance in mucosal sites (mean less than 0.001%).

## Classes with multiple levels: distinct aerobic, anaerobic, and microaerobic communities in the human microbiome

The roles of anaerobic metabolism in the commensal human microbiota have not yet been fully investigated due to the difficulty of studying these communities in culture. We thus further investigated the aerobicity characteristics of human microbial communities at a high level by grouping body sites into three classes with distinct levels of available molecular oxygen. The high-O~2~ exposure class includes body sites directly and permanently exposed to oxygen: skin, anterior nares and retroauricular crease. The mid-O~2~ exposure class includes the oral and vaginal body sites that can be directly, but not permanently, atmospherically exposed, and the low-O~2~ exposure class (the gut) is mainly anaerobic. The body sites included in the three classes may have other distinguishing features in addition to different oxygen exposure and, in general, these confounding factors can cause features unrelated with aerobiosis to be detected as biomarkers. However, the LEfSe biological consistency step assures that the detected biomarkers are characteristic of all the subclasses of a given class and with respect to all subclasses of the other classes. For example, the high-abundance of a bacterial clade in the mouth due to an oral-specific niche is not detected as a biomarker unless the same niche is also present in the vaginal samples (the other body site in the mid-O~2~ class) and not present in any high-O~2~ or low-O~2~ single body sites. So LEfSe will detect biomarkers more confidently connected with the aerobiosis characteristics than traditional methods that do not incorporate subclass information. Moreover, LEfSe is specifically able to analyze ordinal classes with multiple levels, and in agreement with established microbiology, we observed specific microbial clades ubiquitous within and characteristic to each of these three environments, detailed as follows (Figure 2d).

LEfSe allows ordinal classes with more than two levels to be analyzed in two different stringencies. The first requires significant taxa to differ between every pair of class values (that is, aerobicity in this example; see Materials and methods); the discovered biomarkers must accurately distinguish all individual classes (high-, mid-, and low-O~2~). In this example (Figure 2d; strict version), we detected 13 clades with LDA scores above 2, showing three distinct abundance levels. Alternatively, LEfSe can determine significant taxa differing in at least one (and possibly multiple) class value(s) (non-strict version); in other words, biomarkers that distinguish at least one individual class. Using this method (Figure 2e), we find 60 clades with LDA scores of at least 2.

Using either approach, each oxygen level is broadly characterized by a specific clade. The overall abundances of the Actinobacteria phylum are higher in body sites directly exposed to molecular oxygen with several members of the Actinomycetales order that colonize the skin. Actinomycetales includes the *Propionibacterium* genus, which is highly abundant on the skin, low in moderate-O~2~ environments, and absent from the gut. The Lactobacillales (primarily Bacilli) are specific to moderate O~2~ exposure levels, with conversely lower presences in the high-O~2~ exposure class, and are again absent from the gut. The Bacteroidaceae (particularly *Bacteroides*) are ubiquitous in anaerobic samples; interestingly, however, members of this family are more abundant in high oxygen availability conditions (particularly in skin and retroauricular crease) than in medium oxygen availability, showing the niche diversity within the phylogenetic branching. This is in agreement with observations that the microenvironment of many microbial consortia shows extreme biogeographical variation with respect to nutrients, metabolites, and oxygen availability \[58,59\].

## Bifidobacteria and additional clades are underrepresented in a mouse model of ulcerative colitis

Rodent models have been established to provide a uniquely accurate and tractable model for studying the gut microbiota, including the molecular and cellular mechanisms driving chronic intestinal inflammation \[60-63\]. In particular, mouse models of inflammatory bowel disease \[63\] facilitate a mechanistic evaluation of the contribution of the gut microbiota to the initiation and perpetuation of chronic intestinal inflammation, as occurs in human Crohn's disease and ulcerative colitis \[64\]. One host molecular mechanism known to maintain the balance between immune regulation and the commensal microflora is T-bet, a transcription factor expressed in many immune cell subsets. Its loss in the absence of an adaptive immune system results in a highly penetrant and aggressive form of ulcerative colitis \[65\] that is specifically dependent on and transmissible through the gut flora. We thus sought to investigate the characteristics of the fecal microbiota in a mouse model of spontaneous colitis that occurs in a colony of Balb/c *T-bet*^-/-^ × *Rag2*^-/-^ mice using 16S rRNA gene metagenomic data \[66,67\].

LEfSe was applied to the microbiota data of 20 *T-bet*^-/-^ × *Rag2*^-/-^ (case) and 10 *Rag2*^-/-^ (control) mice (dataset provided in Additional File 10), finding 19 differentially abundant taxonomic clades (α = 0.01) with an LDA score higher than 2.0 (Figure 3). These differentially abundant clades were consonant with both our prior 16S rRNA-based sequence analysis using complete linkage hierarchical clustering and quantitative real time PCR-based experiments performed on the same fecal DNA samples \[67\]. More specifically, the marked loss in Bifidobacteriaceae and *Bifidobacterium* associated with *T-bet*^-/-^ × *Rag2*^-/-^ we observed here may explain the positive responsiveness of this colitis to a *Bifidobacterium animalis* subsp. *lactis* fermented milk product validated with low-throughput approaches \[67\].

At the family level, the *Rag2*^-/-^ enrichment of Bifidobacteriaceae, Porphyromonadaceae, Staphylococcaceae and the *T-bet*^-/-^ × *Rag2*^-/-^ enrichment of Lachnospiraceae confirm our reports in \[68\] using culture-based and quantitative real time PCR techniques. LEfSe's LDA score more informatively reorders these taxa relative to the *P*-values found for these families in our previous work, highlighting the Bifidobacteria and, interestingly, several clades within the Clostridia. These include the *Rag2*^-/-^-specific *Roseburia* and *Papillibacter* genera belonging to *T-bet*^-/-^ × *Rag2*^-/-^-specific families (Lachnospiraceae and Ruminococcaceae). The significant presence of *Metascardovia* (Bifidobacteriaceae) in *Rag2*^-/-^ mice is also interesting, as it may have a role similar to *Bifidobacterium* and because *Metascardovia* has been previously observed primarily in the oral cavity \[68\]. This analysis both highlights the agreement of LEfSe's effect size estimation with respect to low-throughput confirmations and suggests additional clades to be further investigated experimentally.

## A comparison with current metagenomic analysis tools using viral and microbial pathways from environmental data

We applied LEfSe to the environmental data of \[69\], a dataset with the goal of characterizing the functional roles of viromes (that is, viral metagenomes) versus microbiomes (that is, bacterial metagenomes). This task was used in \[45\] to characterize the Metastats algorithm on the same raw data. Among the 29 high-level functional roles (including unclassified roles) in the subsystem hierarchy of the SEED \[70\] and NMPDR \[71\] frameworks, LEfSe identifies only the 'Nucleosides and nucleotides' subsystem to be strictly differentially abundant among all environmental subclasses, specifically with higher levels in viromes than microbiomes. This is an accurate characterization of exactly the protein function most commonly encoded in viral genomes, whereas bacterial genomes of course encode a wide range of less specifically enriched functionality. When LEfSe is relaxed to detect significant variations consistent for at least one, rather than all, environmental subclasses, we additionally determine the 'Respiration' subsystem to be significantly enriched in microbiomes with respect to viromes, likely reflecting the uniformly aerobic bacterial metabolism captured by these data.

In addition to the Nucleosides and nucleotides and Respiration subsystems, Metastats \[45\] reports five other high-level functional roles as differentially abundant (*P* = 0.001). However, when taking the subclass structure into account across the sampled environments, these additional differences show much less consistent variation. This is demonstrated in Figure 4, which reports histograms of raw data for these cases and the different results of LEfSe, Metastats and the KW test alone. Moreover, since the subsystem framework is hierarchical (three levels), LEfSe's results include a cladogram showing the significant differences on each level (see Figure 4 for a two-level cladogram, and Additional file 2 for a three-level cladogram).

Considering all three levels of SEED functional specificity, LEfSe reports 59 subsystems to be more abundant in microbial metagenomes and only 7 that are more abundant in viral metagenomes (Additional file 3). Bacterial genomes encode a much greater quantity and diversity of biomolecular functionality than most viral genomes, and these differences are thus to be expected. However, they also highlight a consideration specific to most metagenomic (and, more generally, ecological) analyses, which typically analyze relative abundances. A few very common subsystems in viromes (that is, Nucleosides and nucleotides) will force the relative abundance of all other subsystems to decrease, resulting in apparent under-abundance. The subsystems detected to be virus-specific may thus show this trend in part due to the normalization of abundances in each sample. This issue is specific to neither LEfSe nor Metastats, however, and must be taken into account during interpretation of any relative abundance data, metagenomic or otherwise \[72\].

## Functional activity within the infant and adult microbiota indicates post-weaning microbial specialization

Just as LEfSe can determine whether organisms or pathways are differentially abundant among several metagenomic samples, it can also focus on individual enzymes or orthologous groups. Kurokawa *et al*. \[73\] analyzed 13 gut metagenomes from nine adults and four unweaned infants in terms of the functions of orthologous gene families. They originally did this by comparing the COGs \[74,75\] found in each metagenome to a reference database; later, White *et al*. \[45\] applied the Metastats algorithm to directly detect differences between infant and adult microbiomes. Using significance α values of 0.01 due to the low cardinality of the classes (in particular the infant class), LEfSe detected 366 COGs to be enriched in either adult or infant metagenomes, 17 of which have a LDA score higher than 3 (Additional file 4).

Among the 17 COG profiles with LEfSe scores higher than 3, 11 are also detected by Metastats. The six COGs not detected by Metastats (Additional file 5) are Outer membrane protein (COG1538) and Na^+^-driven multidrug efflux pump (COG0534), enriched in adults, and Transposase and inactivated derivatives (COG2801, COG2963), Transcriptional regulator/sugar kinase (COG1940) and Transcriptional regulator (COG1309), enriched in infants. All six COGs possess abundance profiles that are completely non-overlapping between infant and adult individuals (apart from COG1538, in which the lowest level in adults is slightly lower than the highest in infants) and are thus nominally quite discriminative. On the other hand, among the 192 COGs found by Metastats, 9 are not detected by LEfSe even at the lowest LDA score threshold (Additional file 6). All possess overlapping abundance values between infant and adult classes (at least two, and often more, of the highest samples in the less abundant class overlap the putatively more abundant class). This lack of discriminatory power precludes LEfSe from highlighting the differences as significant between adults and infants, particularly given the low number of infant samples.

Intriguingly, LEfSe's distinct list of functional activities in the core infant and adult microbiomes is suggestive of 'generalist' microbial activity during early life and specialization over time \[76\]. In fact, inspecting the five differentially abundant COGs with the highest effect sizes for each class, we find for infants very high-level functional groups related to broad transcriptional regulation (COG1609, COG1940, COG1309 and COG3711). In adults, all five represent more specialized orthologous groups, including COG1629 (Outer membrane receptor proteins, mostly Fe transport), COG1595 (DNA-directed RNA polymerase specialized sigma subunit, sigma24 homolog), and COG4771 (Outer membrane receptor for ferrienterochelin and colicins). Since the number of differentially abundant COGs is very high (366), this observation was only highlighted at the top of the candidate biomarker list due to LEfSe's effect size quantification, which allows the most characteristic differences among classes to emerge. For the same reason, we can easily confirm that sugar metabolism plays a crucial role in the infant gut and iron metabolism in adults, as already stated in \[45,73\]; the COGs with the highest LDA scores indeed possess sugar and glucose functional activities for infants and iron-related functionality for adults.

## LEfSe achieves a very low false positive rate in synthetic data

We further investigated the ability of LEfSe to detect biomarkers using synthetic high-dimensional data (see Materials and methods for the description of the dataset) in comparison with the KW test alone (a non-parametric adaptation of the analysis of variance (ANOVA)) and with Metastats \[45\]. The LDA effect size step of LEfSe is not considered here for simplicity, and the artificial data are detailed in Figure 5.

Theoretically, the settings of the first two experiments (Figure 5a,b) exactly match the application conditions for the KW test. The false positive rate (mean 2.5%, regardless of the distance between feature means and of the standard deviation of the normal distribution) is in fact consistent with the α value of 0.05, given that the negative features are half of the total. LEfSe behaved qualitatively very similar to KW, but with a considerably lower false positive rate (less than 0.5% in the great majority of the cases against a mean value of 2.5%) and a higher false negative rate. In biology, false positives are often perceived as more dramatic than false negatives \[77-79\]; this is often attributable to the fact that it is undesirable to invest in expensive experimental follow-up of false positives, whereas in high-throughput settings, a few true positives outweigh the false negatives that are left uninvestigated. With this motivation for minimizing false positives, we conclude that LEfSe performs at least as well as KW when no meaningful subclass structure is available. On the other hand, when subclasses can be identified internally to the classes and some of them do not agree with the trend among classes, LEfSe performs qualitatively and quantitatively much better than KW (Figure 5c). The false positives are in fact always substantially lower than KW, whereas the false negatives are higher only for very noisy features. Metastats \[45\] seems to achieve results very similar to KW (Additional file 7) with the same disadvantages with respect to LEfSe.

# Conclusions

Gaining insight into the structure, organization, and function of microbial communities has been proposed as one of the major research challenges of the current decade \[80\], and it will be enabled by both experimental and computational metagenomic analyses. To this end, we have developed the LEfSe algorithm for comparative metagenomic studies, permitting the characterization of microbial taxa specific to an experimental or environmental condition, the detection of pathways and biological mechanisms over- or under-represented in different communities, and the identification of metagenomic biomarkers in mammalian microbiomes. LEfSe is shown here to be effective in detecting differentially abundant features in the human microbiome (characteristically mucosal or aerobic taxa) and in a mouse model of colitis. A comparison with existing statistical methods and state-of-the-art metagenomic analyses of environmental, infant gut microbiome, and synthetic data shows that LEfSe consistently provides lower false positive rates and can effectively aid in explaining the biology underlying differences in microbial communities.

These findings demonstrate that a concept of class explanation including both statistical and biological significance is highly beneficial in tackling the statistical challenges associated with high-dimensional biomarker discovery \[28,81,82\]. Specifically, LEfSe determines features potentially able to explain the differences among conditions rather than the features that simply possess uneven distributions among classes. This is distinct from most current statistical approaches \[45\] and akin to the incorporation of biological prior knowledge that has proven highly successful in recent genome-wide association studies \[83-85\]. Moreover, particularly in (often noisy) metagenomic datasets, effect size can serve as an orthogonal measure to complement ranking biomarkers based on *P*-values alone. Differences between classes can be very statistically significant (low *P*-value) but so small that they are unlikely to be biologically responsible for phenotypic differences. On the other hand, a biomarker with a relatively large *P*-value (for example, 0.01) may correspond to a large effect size, with statistical significance diminished by technical noise. LEfSe investigates both aspects computationally by testing both the consistency and the effect size of differences in feature abundance among classes with respect to the structure of the problem. This is performed subsequently to standard statistical significance tests and is integrated in LEfSe by assessing biologically meaningful groups of samples among subclasses within each condition. This coupling of statistical approaches with biological consistency and effect size estimation alleviates possible artifacts or statistical inhomogeneity known to be common in metagenomic data, for example, extreme variability among subjects or the presence of a long tail of rare organisms \[32,86\]. Similarly, while multiple hypothesis corrected statistical significance speaks to the potential reproducibility of a result, estimation of effect size in high-dimensional settings is crucial for addressing biological consistency and interpretability.

The biology highlighted by these investigations speaks to the potential of metagenomics for both microbial ecology and translational applications. For example, certain bacterial clades are frequently detected as biomarkers even in diverse environments, suggesting that some species can adapt in surprisingly condition-specific manners. *Staphylococcus* and the Bacillales, for example, are discriminative for mucosal tissues, aerobic conditions, and murine colitis, whereas no Proteobacteria consistently characterize any of these conditions, even though they always represent a substantial portion of the communities. These observations may reflect extensive microenvironmental heterogeneity and the coexistence of generalist and specialist bacteria \[87-89\].

In addition to these insights into microbiology, metagenomic biomarkers, including the abundances of specific organisms, abundances of entire clades, or the presence/absence of specific organisms, can serve to describe host phenotypes, lifestyle, diet, and disease as well \[5-10\]. If the depletion of *Bifidobacterium* species in ulcerative colitis proves to occur early in human disease etiology, this and comparable shifts in the microbiota have potential applications in the detection of human disorders \[90,91\], especially as shifts in some bacterial consortia can be detected easily and inexpensively. Oral microbial biomarkers, for example, can be easily acquired and analyzed with microarray chips targeted for bacterial profiling \[92\]. These appear particularly promising for clinical applications \[11\], as the microbial communities in the saliva seem to represent one potential proxy for other human microbiota \[93\]. Other important clinical applications of metagenomic analyses include probiotic treatments \[94,95\] and microbiome transplantation \[96-99\] for gastrointestinal diseases.

LEfSe, the computational approach to biomarker class comparisons detailed here, thus contributes to the understanding of microbial communities and guides biologists in detecting novel metagenomic biomarkers. The algorithm's effectiveness on real and synthetic data has been highlighted by several experiments in which we successfully characterized both host-associated microbiota and environmental microbiomes in multiple contexts. To support ongoing metagenomic analyses, we have implemented LEfSe as a user-friendly web application that can provide both raw data and publication-ready graphical results, including reports of detected microbial variation on taxonomic trees for visual and biological summarization. LEfSe is freely available online in the Galaxy workflow framework \[46,47\] at the following link \[48\].

# Materials and methods

The LEfSe algorithm is introduced in overview in the Results section, and Figure 6 illustrates in detail the format of the input (a matrix with *n* rows and *m* columns) and the three steps performed by the computational tool: the KW rank sum test \[49\] on classes, the pairwise Wilcoxon test \[50,51\] between subclasses of different classes, and the LDA \[52\] on the relevant features.

Each of the *n* features is represented with a positive-valued vector containing its abundances in the *m* samples, and each sample is associated with values describing its class and, optionally, subclass and/or originating subject. The factorial KW rank sum test is applied to each feature with respect to the class factor; the subclass and subject information are used as stratifying subgroups when present. Features that, according to the KW rank sum test, do not violate the null hypothesis of identical value distribution among classes (with default *P*-value, α = 0.05) are not further analyzed. The pairwise Wilcoxon test is applied to retained features belonging to subclasses of different classes. For each feature, the pairwise Wilcoxon test is not satisfied if at least one comparison between subclasses has a *P*-value higher than the chosen α or if the sign of variation is not equal among all comparisons. For example, if a feature appears in samples from two classes with three subclasses each, all nine comparisons between subclasses in different classes must violate the null hypothesis, and all signs of the differences between medians must be consistent. The features that pass the pairwise Wilcoxon test are considered successful biomarkers. An LDA model is finally built with the class as dependent variable and the remaining feature values, subclass, and subject values as independent variables. This model is used to estimate their effect sizes, which are obtained by averaging the differences between class means (using unmodified feature values) with the differences between class means along the first linear discriminant axis, which equally weights features' variability and discriminatory power. The LDA score for each biomarker is obtained computing the logarithm (base 10) of this value after being scaled in the \[1,10^6^\] interval and, regardless of the absolute values of the LDA score, it induces the ranking of biomarker relevance. For robustness, LDA is additionally supported by bootstrapping (default 30-fold) and subsequent averaging.

LEfSe's first two steps employ non-parametric tests because of the nature of metagenomic data. Relative abundances will, in most cases, violate the main assumption of typical parametric tests (normal population in each class), whereas non-parametric tests are much more robust to the underlying distribution of the data since they are distribution-free approaches. The only assumption of the Wilcoxon and KW tests is that the distributions in each class are identically shaped with possible differences in the medians. For example, the bimodal or multimodal abundance distribution of an organism violates the assumptions of parametric tests but not those of non-parametric tests, unless the number of peaks in the distribution (or, more generally, the shape of the distribution) also changes among classes. LDA is used for effect size estimation as our experiments determined it to more accurately estimate biological consistency compared to approaches like differences in group means/medians or support vector machines (SVMs) \[100\]. A comparison between LDA and SVM approaches for effect size estimation on the murine model of ulcerative colitis (for which low-throughput biological validations of biomarkers are available in \[67\]) is reported in our supplemental material (Additional files 8 and 9) and shows the advantages of LDA with respect to upranking features of potential biological interest. Theoretically, this is motivated by LDA's ability to find the axis of highest variance and SVM's focus on features' combined predictive power rather than single feature relevance. Note that as we are performing class comparison rather than class prediction, it is worth specifying that the effect size estimation accuracy of an algorithm is not directly connected with its predictive ability (for example, SVM approaches are generally considered more accurate than LDA for prediction).

## Multiclass strategies

Comparisons with more than two classes require special strategies for applying the Wilcoxon and LDA steps, whereas the factorial KW test is already appropriate for this setting. Our multiclass strategy for the Wilcoxon test depends on the problem-specific strategy chosen by the user to define features differentially distributed among the *n* classes. In the most stringent strategy, we require that all *n* abundance profiles of a feature are statistically significantly distinct among all *n* classes. This strategy, called 'strict', is implemented by requiring that all Wilcoxon tests between classes are significant. A more permissive strategy, called 'non-strict', considers a feature as a biomarker if at least one class is significantly different from all others. The more permissive strategy thus needs to satisfy only a subset of the Wilcoxon tests. Regardless of the strategy, the LDA step always reports the highest score detected among all pairwise class comparisons.

## Subclass structure variants encoding different biological hypotheses

Different interpretations of the biomarker class comparison problem are implemented in LEfSe by modifying the requirements for pairwise Wilcoxon comparisons among subclasses. If classes contain subclasses that represent distinct strata, we test only comparisons within each identical subclass (Figure 4). For example, to assess the effect of a treatment on two sub-types of the same disease, we compare pre- and post-treatment levels within each subclass and require that the trend observed at the class level is significant independently for both subclasses. To implement this variant, LEfSe performs the Wilcoxon step only comparing subclasses with the same name. Alternatively, subclasses may represent covariates within which feature levels may vary but for which the problem does not dictate explicit stratification (Figure 2). In both settings, we explicitly require all the pairwise comparison to reject the null hypothesis for detecting the biomarker; thus, no multiple testing corrections are needed.

## Subclasses containing few samples

When few samples are available, non-parametric tests like the Wilcoxon have reduced power to detect differences. This can affect LEfSe when subclasses are very small, preventing the overall test from even rejecting the null hypothesis. For this reason, small subclasses should be avoided when possible, for example, by excluding them from the problem or by grouping together all subclasses with small cardinalities. For cases in which removing or grouping subclasses is not possible or disrupts the biological consistency of the analysis, LEfSe substitutes the Wilcoxon test with a test to compare whether subclass medians differ with the expected sign. The user can choose the subclass cardinality threshold at which this median comparison is substituted for the Wilcoxon test.

## Parameter settings

Except as stated otherwise in Results, all experiments in this study were run with LEfSe's α parameter for pairwise tests set to 0.05 for both class normality and subclass tests, and the threshold on the logarithmic score of LDA analysis was set to 2.0. The stringency of these parameters is easily tunable (also through the web interface) and allows the user to detect biomarkers with lower *P*-values and/or higher effect size in order, for example, to prioritize additional biological experiments and validations. All LDA scores are determined by bootstrapping over 30 cycles, each sampling two-thirds of the data with replacement, with the maximum influence of the LDA coefficients in the LDA score of three orders of magnitude.

## Data description

Except as stated otherwise, taxonomic abundances for 16S samples were generated from filtered sequence reads using the RDP classifier \[101\], with confidences below 80% rebinned to 'uncertain'. For all the datasets described below, the final input for LEfSe is a matrix of relative abundances obtained from the read counts with per-sample normalization to sum to one. Witten-Bell smoothing \[102\] was used to accommodate rare types, but due to LEfSe's non-parametric approach, this has minimal effect on the discovered biomarkers and on the LDA score. This also allows our biomarker discovery method to avoid most effects of sequence quality issues as long as any sequencing biases are homogeneous among different conditions, as no specific assumptions on the statistical distribution and noise model are made by the algorithm as is standard for non-parametric approaches.

## Human microbiome data

The 16S rRNA-based phylometagenomic dataset of the normal (healthy) human microbiome was made available through the Human Microbiome Project \[13\], and consists of 454 FLX Titanium sequences spanning the V3 to V5 variable regions obtained for 301 samples from 24 healthy subjects (12 male, 12 female) enrolled at a single clinical site in Houston, TX. These samples cover 18 different body sites, including 6 main body site categories: the oral cavity (9 samples), the gut (1 sample), the vagina (3 samples), the retroauricular crease (2 samples), the nasal cavity (1 sample) and the skin (2 samples). Detailed protocols used for enrollment, sampling, DNA extraction, 16S amplification and sequencing are available on the Human Microbiome Project Data Analysis and Coordination Center website \[103\], and are also described elsewhere \[55,56\]. In brief, genomic DNA was isolated using the Mo Bio PowerSoil kit \[104\] and subjected to 16S amplifications using primers designed incorporating the FLX Titanium adapters and a sample barcode sequence, allowing directional sequencing covering variable regions V5 to partial V3 (primers: 357F 5'-CCTACGGGAGGCAGCAG-3' and 926R 5'-CCGTCAATTCMTTTRAGT-3'). Resulting sequences were processed using a data curation pipeline implemented in mothur \[41\], which reduces the sequencing error rate to less than 0.06% as validated on a mock community. As part of the pipeline parameters, to pass the initial quality control step, one unambiguous mismatch to the sample barcode and two mismatches to the PCR amplification primers were allowed. Sequences with an ambiguous base call or a homopolymer longer than eight nucleotides were removed from subsequent analyses, as suggested previously \[105\]. Based on the supplied quality scores, all sequences were trimmed when a base call with a score below 20 was encountered. All sequences were aligned using a NAST-based sequence aligner to a custom reference based on the SILVA alignment \[106,107\]. Sequences that were shorter than 200 bp or that did not align to the anticipated region of the reference alignment were removed from further analysis. Chimeric sequences were identified using the mothur implementation of the ChimeraSlayer algorithm \[108\]. Unique reads were classified with the MSU RDP classifier v2.2 \[58\] using the taxonomy proposed by \[109\], maintained at the RDP (RDP 10 database, version 6). The 16S rRNA reads are available in the Sequence Read Archive at \[110\].

## *T-bet^-/-^ × Rag2^-/-^* and *Rag2^-/-^* mouse data

*T-bet*^-/-^ × *Rag2*^-/-^ and *Rag2*^-/-^ mice, their husbandry, and their chow have been described in \[67\]. The animal studies and experiments were approved and carried out according to Harvard University's Standing Committee on Animals as well as National Institutes of Health guidelines. Collection, processing, and extraction of DNA from fecal samples were performed as described in \[67\]. The V5 and V6 regions of the 16S rRNA gene were targeted for amplification and multiplex pyrosequencing with error-correcting barcodes. Sequencing was performed using a Roche FLX Genome Sequencer at DNAVision (Charleroi, Belgium) and data were preprocessed to remove sequences with low-quality scores. There were 7,579 ± 2,379 high-quality 16S reads per sample with a mean read length of 278 bp.

## Viral and microbial environmental data

We retrieved from the online supplemental material of \[69\] the 80 available metagenomes (42 viromes, 38 microbiomes). We identified three environments containing at least seven samples and grouped them into coral, hyper-saline, and marine subclasses; the fourth subclass, other, groups all environments with few samples.

## Infant and adult microbiome data

The COG profiles of the nine adult and four unweaned infant microbiomes were obtained from the supplemental material of \[73\] and used unmodified in this study.

## Synthetic datasets

We built three collections of artificial datasets in order to compare LEfSe to KW and Metastats. All datasets have 1,000 features and 100 samples belonging evenly to two classes, and the values are sampled from a Gaussian normal distribution. The samples in the two classes are further organized in four subclasses (two per class) with equal cardinality. Of the 1,000 features, 500 features have different means across classes and should thus be detected as biomarkers (positive features), the other 500 features are evenly distributed among classes or among at least one subclass in both classes and should not be detected as discriminative (negative features). The methods are evaluated assessing the false positive rate (number of erroneously detected biomarkers with respect to the total number of features) and the false negative rate (number of correctly detected non-discriminant features with respect to the total number of features, that is, sensitivity). The three collections of datasets (graphically shown in Figure 5) differ in the distribution of values in the subclasses and in the mean/standard deviation of the normal distribution. (a) The subclasses in the same class have the same parameters (thus the subclass organization is meaningless). Negative features all have μ = 10,000 and σ = 100, whereas one class of the positive features has μ = 10,000 - t (σ = 100) and the other μ = 10,000 + t (σ = 100) where t is a parameter ranging from 1 to 150. The performances of all methods are assessed at regular steps of the t parameter. (b) Datasets in this collection are defined in the same way as collection (a) but with t = 1,000 for all datasets and σ ranging from 1,000 to 10,000. (c) The negative class in the third collection has different subclass distribution. In particular, the second subclass of the first class has the same mean of the first subclass of the second class. The other two subclasses have different means (μ = 10,000 - t and μ = 10,000 + t, t = 1,000), but the feature is not considered differential since the difference is not consistent between subclasses. The positive features are defined in the same way as dataset (b).

## Implementation and availability of the method

LEfSe is implemented in Python and makes use of R statistical functions in the coin \[111\] and MASS \[112\] libraries through the rpy2 library \[113\] and of the matplotlib \[114\] library for graphical output. LEfSe is provided with a graphical interface in the Galaxy framework \[46,47\], which allows the user to select parameters (the primary three stringency parameters, the multiclass setting, and other computational, statistical, and graphical preferences), to pipeline data between modules in a workflow framework, to generate publication-quality graphical outputs, and to combine these results with other statistical and metagenomic analyses. LEfSe is available at \[48\].

# Abbreviations

bp: base pair; KW: Kruskal-Wallis; LDA: linear discriminant analysis; LEfSe: linear discriminant analysis effect size; PCR: polymerase chain reaction; RDP: Ribosomal Database Project; SVM: support vector machines.

# Authors' contributions

NS and CH conceived the study; NS and LM implemented the methodology; NS: JI: LW: DG: WG: and CH analyzed the results; NS: JI: LW: DG: WG: and CH wrote the manuscript. All authors read and approved the manuscript in its final form.

# Supplementary Material

###### Additional file 1

**Supplementary Figure S6**. Histogram of within-subject β-diversity (community dissimilarity) between different mucosal (red) and non-mucosal (green) body sites.

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###### Additional file 2

**Supplementary Figure S1**. Cladogram representing the differences between viromes and microbiomes on the subsystem framework.

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###### Additional file 3

**Supplementary Figure S2**. Histogram of LDA logarithmic scores of biomarkers found by LEfSe comparing microbiomes and viromes within the subsystem framework.

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###### Additional file 4

**Supplementary Figure S3**. Histogram of LDA logarithmic scores of COG biomarkers found by LEfSe comparing adult and infant microbiomes.

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###### Additional file 5

**Supplementary Figure S4**. Functional features (COGs) that are discrimantive for the comparison between adult and infant microbiomes according to LEfSe but not detected by Metastats among the discriminant features with LDA score higher than 3. If we consider all the discriminant features without threhold on LDA score, LEfSe identifies 366 COGs in total, 185 of which are not discriminant for Metastats.

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###### Additional file 6

**Supplementary Figure S5**. Functional features (COGs) that are discrimantive for the comparison between adult and infant microbiomes according to Metastats but not detected by LEfSe. Even if median and variance suggest the differences to be discriminative, there are always some microbiomes (at least two) that are overlapping between classes. This is due to the stringent α-value (0.01) set for the KW test in LEfSe and to the fact that we use non-parametric statistics (differently from Metastats). Notice, however, that even using a low α-value LEfSe detects many more biomarkers than metastats (366 versus 192).

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###### Additional file 7

**Supplementary Figure S9**. Comparison between LEfSe and Metastats using the synthetic data described in Figure 5 and in the Materials and methods. LEfSe was applied as detailed in the paper; for Metastats we used the default settings (that is, α = 0.05 and N~permutations~ = 1,000) and, as for LEfSe and KW, we disabled the per-sample normalization as the features are independent. **(a,b)** Metastats has a higher false positive rate (average 5%) than LEfSe (average below 0.5%) and lower false negative rate. **(c)** When the subclass information is meaningful (see Figure 5 for the representation of the dataset), LEfSe performs substantially better than Metastats both in terms of false positive and false negatives. Overall, on these synthetic data, Metastats achieves very similar results compared to KW (Figure 5) and neither of them can make use of additional information regarding the within-class structure, thus achieving poor results compared to LEfSe when such kinds of information are available.

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###### Additional file 8

**Supplementary Figure S7**. SVM-based effect size estimation for the biomarkers found for the *Rag2^-/-^* versus *T-bet^-/-^xRag2^-/-^* comparison reported in Figure 3 of the manuscript. The LDA-based approach for assessing effect size (Figure 3) is closer to the biological follow-up experiments and is more visually consistent. The reason for LDA superiority over SVM approaches for effect size estimation is theoretically connected with the ability of LDA to find the axis with the highest variance, and the SVM effort on evaluating the combined feature predictive power rather than single feature relevance. It is worth specifying that the effect size estimation accuracy of an algorithm is not directly connected with its predictive ability (SVM approaches are usually considered more accurate than LDA for prediction).

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###### Additional file 9

**Supplementary Figure S8**. Comparison between the features with the highest SVM-based effect size (*Papillibacter*, on the left), the highest LDA-based effect size (*Bifidobacterium*, in the center), and the Actinobacteria phylum (on the right). From a visual analysis, *Bifidobacerium* shows a larger effect size, which is also evident looking at the ratios between class means, suggesting LDA as a better option for effect size estimation than SVM approaches. As detailed in the manuscript, the relevance of *Bifidobacterium* has been experimentally validated. Moreover, the large difference in the score given by the SVM approach to Actinobacteria compared to *Bifidobacterium* and *Papillibacter* is not consistent.

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###### Additional file 10

***T-bet*^-/-^ × *Rag2*^-/-^ - *Rag2*^-/-^ dataset**. Input LEfSe file for the analysis of the ulcerative colitis phenotype in mice.

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## Acknowledgements

We would like to thank the entire Human Microbiome Project consortium, including the four sequencing centers (the Broad Institute, Washington University, Baylor College of Medicine, and the J Craig Venter Institute), associated investigators from many additional institutions, and the NIH Office of the Director Roadmap Initiative. This work was supported in part by grant DE017106 from the National Institute of Dental and Craniofacial Research (JI), NIH grants AI078942 (WSG) and Burroughs Wellcome Fund (WSG), and was funded by NIH 1R01HG005969 to CH.