This statistic depicts the average body mass index (BMI) of U.S. adults aged 20 years and over as of 2016, by gender. According to the data, the average male BMI has increased from 27.8 in 1999-2000 to 29.1 as of 2015-2016.

This statistic depicts the average body mass index (BMI) of U.S. females aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average female BMI for those that identified as white was **** in 1999-2000 and increased to **** as of 2015-2016.

United States Prevalence of Overweight: % of Adults data was reported at 67.900 % in 2016. This records an increase from the previous number of 67.400 % for 2015. United States Prevalence of Overweight: % of Adults data is updated yearly, averaging 55.200 % from Dec 1975 (Median) to 2016, with 42 observations. The data reached an all-time high of 67.900 % in 2016 and a record low of 41.000 % in 1975. United States Prevalence of Overweight: % of Adults data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Prevalence of overweight adults is the percentage of adults ages 18 and over whose Body Mass Index (BMI) is more than 25 kg/m2. Body Mass Index (BMI) is a simple index of weight-for-height, or the weight in kilograms divided by the square of the height in meters.;World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).;;

Surveys in which U.S. adults report their current weight have shown that the share of those reporting they weigh 200 pounds or more has increased over the past few decades. In 2024, around 28 percent of respondents reported their weight as 200 pounds or more, compared to 15 percent in 1990. However, the same surveys show the share of respondents who report they are overweight has decreased compared to figures from 1990. What percentage of the U.S. population is obese? Obesity is an increasing problem in the United States that is expected to become worse in the coming decades. As of 2023, around one third of adults in the United States were considered obese. Obesity is slightly more prevalent among women in the United States, and rates of obesity differ greatly by region and state. For example, in West Virginia, around 41 percent of adults are obese, compared to 25 percent in Colorado. However, although Colorado is the state with the lowest prevalence of obesity among adults, a quarter of the adult population being obese is still shockingly high. The health impacts of being obese Obesity increases the risk of developing a number of health conditions including high blood pressure, heart disease, type 2 diabetes, and certain types of cancer. It is no coincidence that the states with the highest rates of hypertension are also among the states with the highest prevalence of obesity. West Virginia currently has the third highest rate of hypertension in the U.S. with 45 percent of adults with the condition. It is also no coincidence that as rates of obesity in the United States have increased so have rates of diabetes. As of 2022, around 8.4 percent of adults in the United States had been diagnosed with diabetes, compared to six percent in the year 2000. Obesity can be prevented through a healthy diet and regular exercise, which also increases overall health and longevity.

NA: Not applicable, for cells where the zero percent of the population fell into that category.(1) Prevalences and standard errors are calculated using the survey weights from the 5-year visit provided with the dataset. These adjust for unequal probability of selection and response. Survey and subclass estimation commands were used to account for complex sample design.(2) Overweight/obesity is defined as body mass index (BMI) z-score >2 standard deviations (SD) above age- and sex- specific WHO Childhood Growth Standard reference mean at all time points except birth, where we define overweight/obesity as weight-for-age z-score >2 SD above age- and sex- specific WHO Childhood Growth Standard reference mean.(3) To represent socioeconomic status, we used a composite index to capture multiple of the social dimensions of socioeconomic status. This composite index was provided in the ECLS-B data that incorporates information about maternal and paternal education, occupations, and household income to create a variable representing family socioeconomic status on several domains. The variable was created using principal components analysis to create a score for family socioeconomic status, which was then normalized by taking the difference between each score and the mean score and dividing by the standard deviation. If data needed for the composite socioeconomic status score were missing, they were imputed by the ECLS-B analysts [9].(4) We created a 5-category race/ethnicity variable (American Indian/Alaska Native, African American, Hispanic, Asian, white) from the mothers' report of child's race/ethnicity, which originally came 25 race/ethnic categories. To have adequate sample size in race/ethnic categories, we assigned a single race/ethnic category for children reporting more than one race, using an ordered, stepwise approach similar to previously published work using ECLS-B (3). First, any child reporting at least one of his/her race/ethnicities as American Indian/Alaska Native (AIAN) was categorized as AIAN. Next, among remaining respondents, any child reporting at least one of his/her ethnicities as African American was categorized as African American. The same procedure was followed for Hispanic, Asian, and white, in that order. This order was chosen with the goal of preserving the highest numbers of children in the American Indian/Alaska Native group and other non-white ethnic groups in order to estimate relationships within ethnic groups, which is often not feasible due to low numbers.

Supplementary Information files for: Contribution of 20-year body mass index and waist circumference history to poor cardiometabolic health in overweight/obese and normal weight adults: a cohort studyBackground and Aims: We investigated the associations of 20-year body mass index (BMI) and waist circumference (WC) histories with risk of being 1) metabolically unhealthy overweight/obese (MUOO) vs metabolically healthy overweight/obese (MHOO) and 2) metabolically unhealthy normal weight (MUNW) vs metabolically healthy normal weight (MHNW). Methods and Results: Participants comprised 3,018 adults (2,280 males; 738 females) with BMI and WC measured, every ~5 years, in 1991-1994, 1997-1999, 2002-2004, 2007-2009, and 2012-2013. Mean age in 2012-2013 was 69.3 years, with a range of 59.7-82.2 years. Duration was defined as the number of times a person was overweight/obese (or centrally obese) across the 5 visits, severity as each person’s mean BMI (or WC), and variability as the within-person standard deviation of BMI (or WC). At the 2013-2013 visit, participants were categorised based on their weight (overweight/obese or normal weight; body mass index (BMI) ≥25 kg/m2 ) and health status (healthy or unhealthy; two or more of hypertension, low high-density lipoprotein cholesterol, high triglycerides, high glucose, and high homeostatic model assessment of insulin resistance). Logistic regression was used to estimate associations with the risk of being MUNW (reference MHNW) and MUOO (reference MHOO) at the last visit. BMI and WC severity were each related to increased risk of being unhealthy, with estimates being stronger among normal weight than overweight/obese adults. The estimates for variability exposures became null upon adjustment for severity. Individuals who were overweight/obese at all 5 time points had a 1.60 (0.96-2.67) times higher risk of being MUOO than MHOO compared to those who were only overweight/obese at one (i.e., the last) time point. The corresponding estimate for central obesity was 4.20 (2.88-6.12). Greater duration was also related to higher risk of MUNW than MHNW. Conclusion: Being overweight/obese yet healthy seems to be partially attributable to lower exposure to adiposity across 20 years of adulthood. The results highlight the importance of maintaining optimum and stable BMI and WC, both in adults who become and do not become overweight/obese.

In 2024, around 40 percent of U.S. men reported weighing 200 pounds or more. This statistic shows the average self-reported weight among U.S. men from 1990 to 2024.

BMI, body mass index; N, unweighted number; IQR, interquartile range.

Higher body mass index (BMI) is a well-established risk factor for type 2 diabetes, and rates of obesity and type 2 diabetes are substantially higher among Mexican-Americans relative to non-Hispanic European Americans. Mexican-Americans are genetically diverse, with a highly variable distribution of Native American, European, and African ancestries. Here, we evaluate the role of Native American ancestry on BMI and diabetes risk in a well-defined Mexican-American population. Participants were randomly selected among individuals residing in the Houston area who are enrolled in the Mexican-American Cohort study. Using a custom Illumina GoldenGate Panel, we genotyped DNA from 4,662 cohort participants for 87 Ancestry-Informative Markers. On average, the participants were of 50.2% Native American ancestry, 42.7% European ancestry and 7.1% African ancestry. Using multivariate linear regression, we found BMI and Native American ancestry were inversely correlated; individuals with 80% Native American ancestry. Furthermore, we demonstrated an interaction between BMI and Native American ancestry in diabetes risk among women; Native American ancestry was a strong risk factor for diabetes only among overweight and obese women (OR = 1.190 for each 10% increase in Native American ancestry). This study offers new insight into the complex relationship between obesity, genetic ancestry, and their respective effects on diabetes risk. Findings from this study may improve the diabetes risk prediction among Mexican-American individuals thereby facilitating targeted prevention strategies.

Average BMI by genotype for the top candidate SNP in each MESA ethnic group. (XLSX 41 kb)

Sample characteristicsof non-frail older Mexican Americans by BMI categories at baseline (N = 1,648).

Maternal obesity has been associated with a higher risk of pregnancy-related complications in mothers and offspring; however, effective interventions have not yet been developed. We tested two common interventions, calorie restriction and pravastatin administration, during pregnancy in a rhesus macaque model with the hypothesis that these interventions would normalize metabolic dysregulation in pregnant mothers leading to an improvement in infant metabolic and cognitive/social development. A total of 19 obese mothers were assigned to either one of the two intervention groups (n=5 for calorie restriction; n=7 for pravastatin) or an obese control group (n=7) with no intervention, and maternal gestational samples and postnatal infant samples were compared with lean control mothers (n=6). Gestational calorie restriction normalized one-carbon metabolism dysregulation in obese mothers but altered energy metabolism in their offspring. Although administration of pravastatin during pregnancy tended to normalize blood cholesterol in the mothers, it potentially impacted the gut microbiome and kidney function of their offspring. In the offspring, both calorie restriction and pravastatin administration during pregnancy tended to normalize the activity of AMPK in the brain at 6 months, and while results of the Visual Paired-Comparison test, which measures infant recognition memory, were not significantly impacted by either of the interventions, gestational pravastatin administration, but not calorie restriction, tended to normalize anxiety assessed by the Human Intruder test. Although the two interventions tested in a non-human primate model led to some improvements in metabolism and/or infant brain development, negative impacts were also found in both mothers and infants. Our study emphasizes the importance of assessing gestational interventions for maternal obesity on both maternal and offspring long-term outcomes. Methods Study population Pregnant female rhesus macaques (Macaca mulatta) from an indoor breeding colony at the California National Primate Research Center with appropriate social behavior and previous successful pregnancies were enrolled. Animal handling was approved by the UC Davis Institutional Animal Care and Use Committee (IACUC) (#19299). A qualitative real-time PCR assay (Jimenez & Tarantal, 2003) was used to identify mothers with male fetuses to include in this study. Since obesity is defined as subjects with body fat above 30% for women, according to guidelines from the American Society of Bariatric Physicians, American Medical Association, and in some publications (Okorodudu et al., 2010; Shah & Braverman, 2012), a Body Condition Score (BCS) of 3.5 (32.8 % body fat on average (Summers et al., 2012)) was used as the cutoff. Therefore, mothers with BCS of 3.5 and above were categorized as obese. Obese mothers were randomly assigned to the Obese Control (OC) group, OR group (received calorie Restriction), or OP group (received Pravastatin). Mothers with BCS of 2.5 and below were assigned to the Lean Control (LC) group. The unbalanced sample size was because some mothers were removed from the analyses due to fetal deaths for unknown reasons, misidentification of a female fetus, different timing for study enrollment, or technical issues upon collecting samples. The number of animals was six for the LC, seven for the OC, five for the OR, and seven for the OP groups. Feeding, rearing, and interventions Adult female animals were provided monkey diet (High Protein Primate Diet Jumbo #5047; LabDiet, St. Louis, MO, USA) twice a day between 6–9 am and 1–3 pm. The calories were provided as 56% from carbohydrates, 30% from protein, and 13% from. Mothers in the LC, OC, and OP groups were fed nine biscuits twice a day once pregnancy was confirmed. Mothers in the OR group received a restricted supply of food once the pregnancy was detected and was maintained throughout pregnancy. The food restriction was set such that the average total weight increase would be 8% body weight from the last day before conception because the recommended total weight gain in the 2nd and 3rd trimesters is 5-9 kg for the average US woman with obesity who weighs 80 kg and is 1.6 m in height (Body Mass Index of 30), according to the Institute of Medicine 2009 guidelines (Institute of Medicine and National Research Council, 2009). During nursing of infants older than 4 months, all mothers were provided twelve biscuits. Fresh produce was provided biweekly, and water was provided ad libitum for all mothers. Mothers in the OP group were given pravastatin sodium (ApexBio Technology, Houston, TX, USA) at 20 mg/kg body weight prepared in a neutralized syrup (20 mg/mL sodium bicarbonate dissolved in a fruit-flavored syrup (Torani, San Leandro, CA, USA)) once a day from the time pregnancy was confirmed until delivery. The caloric value of the administration was made so as not to influence body weight or skew nutritional value of the diet among all treatment groups. Both interventions were applied only during gestation. Although most mothers were allowed to deliver naturally, cesarean delivery was performed for fetal indications when recommended by veterinarians (2 for each of the LC and OC groups, and 1 for the OP group). These mothers did not accept their infant following birth, so foster mothers were provided. Sample Collection and pre-processing prior to sample storage The animal caretakers and researchers who collected samples were blinded for group assignment by coding all animals by IDs. The collected biological samples were randomized by using random numbers and the group assignment was blinded during the data collection. Both mothers (during pregnancy) and infants were weighed every week. One day before sample collection, food was removed 30 min after the afternoon feeding, and biological samples were collected prior to the morning feeding. To collect biological samples, animals were anesthetized using 5–30 mg/kg ketamine or 5–8 mg/kg telazol. Both maternal and infant blood was collected using 5 mL lavender top (EDTA) tubes (Monoject, Cardinal Health, Dublin, OH, USA) and urine was collected from the bladder by ultrasound-guided transabdominal cystotomy using a 22-gauge needle and stored in a 15 mL Falcon tube. A placental sample was collected at GD150 transabdominally under ultrasound guidance using an 18-gauge needle attached to a sterile syringe. Sample processing was as previously described in (Hasegawa et al., 2022). Necropsy was conducted between 9:30 am–1:30 pm. First, infants at the age of PD180 were fasted and anesthetized with ketamine, and plasma and urine were collected. Then, euthanasia was performed with 120 mg/kg pentobarbital, followed by heparin injection, clamping of the descending aorta, and flushing with saline until clear. The kidney and brain (amygdala, hippocampus, hypothalamus, and prefrontal cortex) were collected, weighed, and immediately frozen on dry ice or liquid nitrogen to store at -80 °C until further analyses. Metabolite extraction and analysis by 1H NMR, and measurement of insulin, cholesterol, cytokine, and cortisol Detailed procedures were previously described (Hasegawa et al., 2022). Briefly, plasma and urine samples were filtered using Amicon Ultra Centrifugal Filter (3k molecular weight cutoff; Millipore, Billerica, MA, USA), and the supernatant was used for analysis. For both the placental and brain tissue samples, polar metabolites were extracted using our previously reported method (Hasegawa et al., 2020). A total of 180 μL of sample (tissue extract or filtered urine or serum) was transferred to 3 mm Bruker NMR tubes (Bruker, Billerica, MA, USA). Within 24 h of sample preparation, all 1H NMR spectra were acquired using the noesypr1d pulse sequence on a Bruker Avance 600 MHz NMR spectrometer (Bruker, Billerica, MA, USA) (O’Sullivan et al., 2013). Chenomx NMRSuite (version 8.1, Chenomx Inc., Edmonton, Canada) (Weljie et al., 2006) was used to identify and quantify metabolites. Heparin-treated plasma samples were used to measure insulin and 17 cytokines and chemokines (hs-CRP, Granulocyte-macrophage colony-stimulating factor, IFN-γ, TNF-α, transforming growth factor-α, monocyte chemoattractant protein-1, macrophage inflammatory protein-1β (MIP-1β), and interleukin (IL)-1β, IL-1 receptor antagonist (IL-1ra), IL-2, IL-6, IL-8, IL-10, IL-12/23 p40, IL-13, IL-15, and IL-17A) using a multiplex Bead-Based Kit (Millipore) on a Bio-Plex 100 (Bio-rad, Hercules, CA) following the manufacturer’s protocol. For each sample, a minimum of fifty beads per region were collected and analyzed with Bio-Plex Manager software using a 5-point standard curve with immune marker quantities extrapolated based on the standard curve. Two samples were removed for analysis of TNF-α and IL-1ra as technical errors (both from Animal ID 1132103: 895.2 and 1115.1 pg/mL at gestational days (GD) 90; 510.8 and 617.2 pg/mL at GD120, respectively). Plasma cholesterol level was measured by Clinical Laboratory Diagnostic Product (OSR6116) on Beckman Coulter AU480 (Beckman Coulter, Brea, CA). Infant plasma cortisol level at PD110 was assessed as previously described (Vandeleest et al., 2019; Walker et al., 2018). In short, infants were transferred to a test room at 9 am and blood was drawn at 11 am (Sample 1), followed by another blood collection at 4 pm (Sample 2) and intramuscular injection of 500 μg/kg dexamethasone (Dex) (American Regent Laboratories, Inc., Shirley, NY). On the next day, a blood sample was collected at 8:30 am (Sample 3), and then 2.5 IU of adrenocorticotropic hormone (Amphastar Pharmaceuticals, Inc., Rancho Cucamonga, CA) was injected intramuscularly. The last blood was collected (Sample 4) 30 min after adrenocorticotropic hormone injection. The collected blood samples were processed and stored, and cortisol concentration was assessed by a chemiluminescent assay on the ADVIA Centaur CP platform

In 2024, the mean average weight reported by men was 195 pounds, while the mean average weight for women was 164 pounds. This statistic shows the mean self-reported weight among U.S. adults from 1990 to 2024, by gender, in pounds.

In 2023, it was estimated that around 37 percent of adults with an annual income of less than 15,000 U.S. dollars were obese, compared to 29 percent of those with an annual income of 75,000 dollars or more. This statistic shows the percentage of U.S. adults who were obese in 2023, by income.

Mean difference for reported and measured height, weight, and BMI by sociodemographic characteristics.

The late Quaternary of North America was marked by prominent ecological changes, including the end-Pleistocene megafaunal extinction, the spread of human settlements, and the rise of agriculture. Here we examine the mechanistic reasons for temporal changes in mammal species association and body size during this time period. Building upon the co-occurrence results from Lyons et al. (2016) – wherein each species pair was classified as spatially aggregated, segregated, or random – we examined body mass differences (BMD) between each species pair for each association type and time period (Late Pleistocene: 40,000 14C - 11,700 14C ybp, Holocene: 11,700 14C - 50 ybp, and Modern: 50 - 0 yrs). In the Late Pleistocene and Holocene, the BMD of both aggregated and segregated species pairs was significantly smaller than the BMD of random pairs. These results are consistent with environmental filtering and competition as important drivers of community structure in both time periods. Modern assemblages showed a breakdown between BMD and co-occurrence patterns: the average BMD of aggregated, segregated, and random species pairs did not differ from each other. Collectively, these results indicate that the late Quaternary mammalian extinctions not only eliminated many large- bodied species but were followed by a re-organization of communities that altered patterns of species coexistence and associated differences in body size.

Methods Dataset

We analyzed a dataset of pairwise associations produced in Lyons et al. (2016). In this work by Lyons and colleagues, species associations were evaluated using species-by-site presence-absence matrices from 369 localities across North America. The dataset covers three different time periods: Late Pleistocene (40,000 14C ybp to 11,700 14C ybp), Holocene (11,700 14C ybp to 50 ybp) and Modern (50 – 0 ybp). Lyons et al. (2016) generated this co-occurrence data based on a collection of datasets known to include reliable data to minimize bias related to taxonomic resolution and taxa misidentification. The studied assemblages consisted of lists of species in a locality. Information about dataset collection and preparation for the analysis in Lyons et al. (2016) is fully detailed in Appendix 1.

For each species in Lyons et al. (2016), estimates of average body mass in grams were extracted from the MOM (Mass of Mammals) database (Smith et al. 2003) version 3. For extant species, estimates were averaged across sexes and species’ geographic ranges. For extinct mammals, the MOM database compiled body mass estimates from the primary and secondary literature, and from regressions using tooth measurements. Body mass data were log10-transformed prior to analyses. BMDs were then calculated between each pair of North American mammal species. While some species’ average body masses might have changed through time, our log-transformation makes it unlikely that results would be substantially affected; temporal changes in average body mass would need to change across an order of magnitude to impact our results.

Analyses

PAIRS analyses

Lyons et al. (2016) performed the original evaluation of pairwise co-occurrence, whose results form the basis of our analysis. More details about their analysis can be found in Appendix 1, but the general logic of the method will be described here. Briefly, to determine whether each pair of species was aggregated, segregated, or randomly associated for a given time period, a co-occurrence metric (i.e., C-score) was calculated for each pair of species in a species-by-site presence-absence matrix. A null distribution of C-scores was then generated by shuffling matrix elements, while preserving row and column totals (i.e., the “fixed-fixed” algorithm). The observed C-score was then compared to the null distribution to determine whether a given species pair was significantly aggregated, segregated, or random. These analyses were conducted using the software application PAIRS version 1.0 (Ulrich 2008). Overall, there were 25,459 species pairs across the three time intervals (Late Pleistocene, Holocene and Modern). Table 2 summarizes the results in Lyons et al. (2016). Previous research (Lyons et al. 2016; Tóth et al. 2019) has shown that these types of co-occurrence analyses are robust to differences in collection mode, temporal grain, spatial or temporal extent, taphonomic bias, taxonomic resolution, and sampling biases.

Lyons, S. K., K. L. Amatangelo, A. K. Behrensmeyer, A. Bercovici, J. L. Blois, M. Davis, W. A. DiMichele, A. Du, J. T. Eronen, J. Tyler Faith, G. R. Graves, N. Jud, C. Labandeira, C. V. Looy, B. McGill, J. H. Miller, D. Patterson, S. Pineda-Munoz, R. Potts, B. Riddle, R. Terry, A. Tóth, W. Ulrich, A. Villaseñor, S. Wing, H. Anderson, J. Anderson, D. Waller, and N. J. Gotelli. 2016. Holocene shifts in the assembly of plant and animal communities implicate human impacts. Nature 529:80-83.

Smith, F. A., S. K. Lyons, S. K. M. Ernest, K. E. Jones, D. M. Kaufman, T. Dayan, P. A. Marquet, J. H. Brown, and J. P. Haskell. 2003. Body mass of late Quaternary mammals. Ecology 84:3403-3403.

Ulrich, W. 2008. Pairs—a FORTRAN program for studying pair-wise species associations in ecological matrices. Version 1.0. http://www.keib.umk.pl/pairs/?lang=en.

Average BMI by genotype for rs12255372 in MESA and WHI Hispanics. (XLSX 39 kb)

In 2023, Black adults had the highest obesity rates of any race or ethnicity in the United States, followed by American Indians/Alaska Natives and Hispanics. As of that time, around ** percent of all Black adults were obese. Asians/Pacific Islanders had by far the lowest obesity rates. Obesity in the United States Obesity is a present and growing problem in the United States. An astonishing ** percent of the adult population in the U.S. is now considered obese. Obesity rates can vary substantially by state, with around ** percent of the adult population in West Virginia reportedly obese, compared to ** percent of adults in Colorado. The states with the highest rates of obesity include West Virginia, Mississippi, and Arkansas. Diabetes Being overweight and obese can lead to a number of health problems, including heart disease, cancer, and diabetes. Being overweight or obese is one of the most common causes of type 2 diabetes, a condition in which the body does not use insulin properly, causing blood sugar levels to rise. It is estimated that just over ***** percent of adults in the U.S. have been diagnosed with diabetes. Diabetes is now the seventh leading cause of death in the United States, accounting for ***** percent of all deaths.

Supplementary Tables. Table S1. OTUs showing at least moderate (A >0.2) heritability in the extended TwinsUK 16S dataset. Each variance component, additive genetics (A, or heritability), shared environment (C) and unique environment (E) shown along with upper and lower 95Â % confidence intervals. Average relative abundance of the OTUs in the overall dataset is also shown. Table S2. 149 Bonferroni-significant OTU-adiposity associations. OTU-adiposity results are obtained from linear mixed effects regression models and include the original results (OTU-Adiposity) and results after adjustment for BMI (OTU-Adiposity BMI-adjusted). We also provide the OTU heritability and average OTU abundance in the cohort. The last two columns show the OTU results from the within MZ twin-pair difference analyses (MZ Diff Beta), and whether the MZ results were concordant with the linear mixed effects results. Reported phenotypes are visceral fat (VFM), subcutaneous fat (SFM), android/gynoid ratio (AGR), BMI, waist/hip ratio (WHR) and % trunk fat (pTF). Table S3. Genus-level associations with adiposity, where the collapsed taxonomy OTU genus was significantly associated with adiposity. Reported phenotypes are visceral fat (VFM), subcutaneous fat (SFM), android/gynoid ratio (AGR) and BMI. Table S4. Replication of TwinsUK (TUK-D) results in three independent cohorts, the American Gut (AG), the Flemish Gut Flora Project (FGFP) and the expanded TwinsUK dataset (TUK-R). The table lists the 97 OTUs forming 149 significant adiposity associations in TwinsUK and their association with BMI in both the replication and discovery samples. In addition, the table outlines the results of two meta-analyses that were performed across the studies, the first of just the independent cohorts, and the second including the discovery cohort. Finally, the last section of the table shows which OTUs were replicated in at least one of the replication cohorts. Table S5. Human genomic analyses at adiposity-related candidate host genes FHIT, TDRG1 and ELAVL4. The table shows significant genetic associations between the adiposity-related candidate host genetic variants and adiposity-associated OTUs. The final three columns summarize the results of the association between the host genetic variants and DNA methylation at CpG sites targeted by the Illumina 450Â k array. DNA methylation association was performed at the lead OTU-associated SNP in each locus; therefore, rs1433722 was not tested (NA). Table S6. List of the 97 BMI-associated loci reported in Locke et al. [8] that were used for analysis in this study. The last column denotes the BMI GWAS P values from Locke et al. [8]. Table S7. Demographics of the TwinsUK microbiome discovery sample and the American Gut replication sample. Table S8. ENA accession IDs for samples in this study currently available online and basic metadata. (XLSX 252 kb)

The purpose of this data set was to compile body mass information for all mammals on Earth so that we could investigate the patterns of body mass seen across geographic and taxonomic space and evolutionary time. We were interested in the heritability of body size across taxonomic groups (How conserved is body mass within a genus, family, and order?), in the overall pattern of body mass across continents (Do the moments and other descriptive statistics remain the same across geographic space?), and over evolutionary time (How quickly did body mass patterns iterate on the patterns seen today? Were the Pleistocene extinctions size specific on each continent, and did these events coincide with the arrival of man?). These data are also part of a larger project that seeks to integrate body mass patterns across very diverse taxa (NCEAS Working Group on Body size in ecology and paleoecology: linking pattern and process across space, time and taxonomic scales). We began with the updated version of Wilson and Reeder's (1993) taxonomic list of all known Recent mammals of the world (N = 4629 species) to which we added status, distribution, and body mass estimates compiled from the primary and secondary literature. Whenever possible, we used an average of male and female body mass, which was in turn averaged over multiple localities to arrive at our species body mass values. The sources are line referenced in the main data set, with the actual references appearing in a table within the metadata. Mammals have individual records for each continent they occur on. Please note that our data set is more than an amalgamation of smaller compilations. Although we relied heavily a data set for Chiroptera by K. E. Jones (N = 905), the CRC handbook of Mammalian Body Mass (N = 688), and a data set compiled for South America by P. Marquet (N = 505), these total less than half the records in the current database. The remainder are derived from more than 150 other sources (see reference table). Furthermore, we include a comprehensive late Pleistocene species assemblage for Africa, North and South America, and Australia (an additional 230 species). 'Late Pleistocene' is defined as approximately 11 ka for Africa, North and South America, and as 50 ka for Australia, because these times predate anthropogenic impacts on mammalian fauna. Estimates contained within this data set represent a generalized species value, averaged across gender and geographic space. Consequently, these data are not appropriate for asking population-level questions where the integration of body mass with specific environmental conditions is important. All extant orders of mammals are included, as well as several archaic groups (N = 4859 species). Because some species are found on more than one continent (particularly Chiroptera), there are 5731 entries.