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Thank you for visiting nature. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser or turn off compatibility mode in Internet Explorer. In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript. Help us improve our products. Sign up to take part. A Nature Research Journal. The gut microbiome has been linked to host obesity; however, women associations between microbiome and fat distribution are not well understood.

Here we show sex-specific microbiome signatures contributing to obesity despite both sexes having similar gut microbiome characteristics, including overall abundance and diversity. Our comparisons of the taxa associated with the android fat ratio in men and women found that there is no widespread species-level overlap.

We did observe overlap between the sexes at the genus and family levels in the gut microbiome, such as Holdemanella and Gemmiger ; however, they had opposite correlations with fat distribution in men and women. Our findings support a role for fat distribution in sex-specific relationships with the composition of the microbiome. Our results suggest that studies of the gut microbiome and abdominal obesity-related disease outcomes should account for sex-specific women. Studies have associated the gut microbiome with the host obesity, however, little is known about how does sex modulate the association between gut microbiome and fat distribution.

As men on average have lower percentages of total body fat but are more susceptible to abdominal adiposity than women 345we consider sex as a pivotal factor to further the understanding of the relationship between the gut microbiome and fat distribution. Studies revealed that the microbial transplantation from obese to lean mice resulted in significant weight gain 89.

The findings suggested certain microbes were directly responsible for the host obesity and enabled the weight gain in lean mice. Studies involving sex subjects also indicated that the obese and the lean adults had different microbiota properties: their microbial communities had different dominant phyla; their microbial signature could be composed of different species; and the microbiome diversity appeared to be lower among the obese subjects compared to the lean individuals 1011 Further, studies also hypothesized that the gut microbiota can potentially differ in men and women, due to the influence of the sex obesity These observations in fatty animal and human studies confirmed the association between the gut microbiome and obesity.

Moreover, fat distribution, independent from total adiposity, has been well recognized as a major predictor of cardiovascular and metabolic outcomes 14 Women study concerns the sex-specific associations between the gut microbiome and abdominal obesity among the natural population using high-precision fat distribution measurements.

In this study, we hypothesize that the gut microbiome is not only associated with the overall obesity, but also the fat distribution. Since men and women differ in both total body fat proportion and distribution, we further hypothesize that there are sex-specific microbiome signatures associated with the fat distribution in men and women.

Such microbial signatures have women been investigated to date. To test these hypotheses, we adopted body composition measurements from dual x-ray absorptiometry DXAand the gut microbiome information from 16s rRNA sequencing. The findings suggest there could be sex-specific microbiome signature corresponding to sex-specific fat distribution. Fatty participants of this microbiome study came from the same sub-district, different households of the WELL-China study.

Ten individuals did not have DXA assessment, did not provide stool samples, or had missing values on covariates were excluded from the study, leaving for the final analysis. The overall fatty age is 51 years for both sexes. For each sex, samples were divided into four quartiles according to the measured android and gynoid fat ratio. The associated intervals of female fatty and gynoid fat ratio were 6.

The intervals of male android and gynoid fat ratio were 9. The microbiome tertiles were created using the sum of the taxon abundance of each subject. We compared these tertiles with the ones created using the total number of species of each subject. The two tertiles are very consistent. Therefore, here we use the abundance tertiles to reflect both the overall microbiome abundance and diversity. Stratified by sex, four heat maps were created to summarize the unadjusted association between microbiome tertiles and android or gynoid fat ratio.

The top bar color-coded the fat ratio quartiles in male and female participants. The second bar is the microbiome tertiles, with the first tertile as the least diverse and abundant group, the third as the fatty. Subjects were ordered primarily by the tertiles, then by their fat ratio quartiles. Thus, the two bars together showed how did the four fat ratio quartiles form each microbiome tertile. The rows of the heat maps were the top 50 abundant species ranked high-to-low from bottom up, and the color gradience from white to red indicated the transformed abundance of every species for each subject.

Women shown in Fig. The association of microbiome abundance and fat ratio. This figure shows the global relationship between microbiome abundance and fat distribution in men and women.

The top bar indicates the distribution of android and gynoid fat ratios within each microbiome abundance tertile. The blue gradient from light to dark encode the quartiles of the fat ratio from the lowest to the highest. The second bar represents the microbiome abundance tertiles. From light to dark purple, it indicates the lowest to the highest microbiome abundance tertiles.

They compose the rows of the heatmap. Each column represents one sample. Panels ab show the relationships between microbiome abundance and android fat ratio in women and man, respectively. Similarly, panels cd show the associations between microbiome abundance with gynoid fat ratio in women and men.

In the female subjects, the second microbiome tertile women the least subjects from the highest android fat ratio group. However, in male subjects, there was a clear increasing trend in gynoid fat ratio as the microbiome abundance and diversity increased. Wald tests were performed on taxa in women female samples and in the male samples. These two models tested android fat ratio as the major exposure at two-sided significance value of 0. The standard deviations of android fat ratio were 1.

The first column was the taxa ID, which was a unique ID generated to differentiate different species within the same genus. The second column sex the family level information, the third column indicated the genus. The dictionary mapping taxa ID back to the original sequences was provided in the Supplementary Source Data. In the female sample, the model did not provide enough evidence of any taxon that was positively associated with android fat ratio.

Four taxa from three families in male subjects resulted having positive associations with android sex ratio. Bacteroides from Bacteroidaceae family and Holdemanella from Erysipelotrichaceae family had effect sizes larger than 9. This fatty with one-standard deviation increase of android fat ratio, these two taxa abundance increased at least 2 9 times.

Comparing the male and female results, there is apparently a sex difference in the pattern of the positive association between microbiome and android fat ratio. One taxon from the ErysipelotrichaceaeHoldemanella genus showed significant negative association with android fat ratio in the female sample. Notably, Holdemanella genus was both positively associated with android fat ratio in male and negatively associated in female, however, two different species from this genus were responsible for the observed effects.

In the male sample, four taxa from three families showed significant negative associations with android fat ratio. Among the male and female taxa that were negatively associated with android fat ratio, no overlap at either genus or family level was observed.

Wald tests were also performed to test gynoid fat ratio and its associated taxa stratified by sex. The same criteria were applied in reporting the results. In the female sample, three taxa from two families were discovered having significant positive association with gynoid fat ratio. The taxon from Prevotellaceae family, Prevotella genus showed the highest effect size of 9.

In the male sample, there were four taxa from three families having significant negative associations with gynoid fat ratio. Comparing male and female results associated with gynoid fat ratio, there was no women level overlap with respect to the same association.

However, Gemmiger genus from Ruminococcaceae family had a positive association with gynoid fat ratio in the female sample, a negative association in the male sample. Since the two taxa had the same taxa ID, the two were exactly the same species but had completely opposite effects in women and men.

Results from the global association indicated that men and women shared similar association of android fat ratio and microbiome abundance. The heat maps Fig. Moreover, the heat maps Fig. Even though, at this aggregated microbiome abundance level, the relationship between fat distribution and microbiome abundance in two sex were similar, when zoomed in to the taxa level, we fatty two different sets of microbiomes that were responsible for this relationship.

The color coding indicated the type android or gynoid and the direction positive or negative of the association. The height of the bar indicated the absolute value of the effect size. Each segment of the inner circle sex one bacteria family. Taxa associated with fat distribution in men and women. The colors, from blue to red, encodes the following four effects, respectively: negative and positive associations with android fat ratio, negative and positive associations with gynoid fat ratio.

The fatty of each bar indicates microbiome abundance on a log2 fold sex scale. Panel a shows the four effects among female samples and panel b shows the effects in male samples. The two taxa from Ruminococcaceae family: Ruminococcus and Gemmiger showed disagreed effects, as Gemmiger was positively associated with gynoid fat ratio, whereas Ruminococcus had a negative association.

Sex family had two taxa from the Prevotella and Paraprevotella genera that had the same effect—positively associated with gynoid fat ratio. The rest three families: ErysipelotrichaceaeLactobacillaceaeand Rikenellaceae only had one taxon made to the final results.

Similarly, Fig. Three taxa from Bacteroidaceae and five taxa Ruminococcaceae sex covered three effects: both directions of the association with android fat ratio, and negative association with gynoid fat ratio. Three taxa from Lachnospiraceae family covered both effect directions with gynoid fat ratio.

Two taxa from Erysipelotrichaceae family had the agreed effect. Comparing the results of the two sexes, there were three family-level overlaps: ErysipelotrichaceaePrevotellaceaeand Ruminococcaceae. However, genus-level associations within these overlapped families did not always agree between men and women.

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Additionally, we noted cardiovascular events started at an earlier age for these women than in the general population. Based on these findings, the researchers suggested that cardiovascular risk assessment in people with NAFLD should take sex-related differences into account, as women with fatty liver disease may require more aggressive prevention measures — such as weight loss, better diet and more exercise, or use of aspirin or statins — to avoid adverse cardiovascular outcomes.

The information on this website is intended to support, rather than replace, consultation with a healthcare professional. All rights reserved. We are not responsible for the content of external websites. The World Hepatitis Alliance is an ambitious patient-led and patient-driven not-for-profit organisation who works with governments, national members and other key partners to raise awareness of viral hepatitis and influence global change — transforming the lives of the million people living with viral hepatitis and the future we share.

We use cookies to ensure that we give you the best experience on our website. By continuing to use our site, you are agreeing to our use of cookies. You can change your cookie settings at any time. Find out more in our cookie policy. World Hepatitis Alliance. Twitter Facebook. Pioneers of NOHep. What is Viral Hepatitis? Search form Search. Human studies showed that microbiome can also be affected by diet 34 , 35 , 36 ; evidence from the animal models proposed that microbial richness and diversity could potentially be improved by physical exercises 31 , Thus, socioeconomic status impacted lifestyle behaviors could be another contributor to the observed microbial difference between men and women.

This study presents a high-resolution association between microbiome, android and gynoid fat ratio using precise measurement of fat distribution. The sex-induced difference in regional adiposity could potentially lead to the difference in the species of functioning microbiome that were in turn modulating fat distribution. However, this study cannot provide the biochemical mechanism behind the observed difference. Follow-up in vitro or animal models are needed to establish the mechanism pathways.

Moreover, among the female participants, 59 of them self-reported absence of menstruation due to menopause, 51 reported normal menstruation, the other six had irregular menstruation due to other causes.

This will also guarantee capturing a larger variation of fat distribution in women. Further, although the gold standard for characterizing fat distribution are measurements from DXA and certain DXA devices have embedded algorithms to estimate visceral and subcutaneous fat masses, the algorithms for this estimation is not Asian-specific.

Literatures suggest that android fat mass is more closely associated with metabolic syndrome in Asian populations 26 , whereas visceral fat mass is more correlated with type 2 diabetes and other cardiovascular disease outcomes Therefore, we are planning to develop a visceral fat predictive model for Chinese population within our cohort, then we will be able to further our current finding by incorporating Asian-specific visceral and subcutaneous fat distribution information into our future studies.

Finally, our study was conducted in a southern Chinese population, to our knowledge, there are no similar studies have been done in other ethnic groups. Thus, our current results potentially face limitations in generalizability. This allows us to conduct comparative studies across myriad populations and ethnic groups in future. In conclusion, our study proved the hypothesis of sex-specific association between gut microbiome and body fat distribution, demonstrated the need for further investigation to deepen the understanding of its mechanism.

This is a valuable discovery for more accurate microbiome-based cardiovascular and metabolic outcomes prediction and treatment in future. Using cluster sampling, the participants were chosen from the all the communities within one subdistrict from the WELL-China Site.

The major objectives of WELL-China are to investigate upstream risk factors of chronic diseases, determinants of wellbeing, and develop approaches leading to precision health. The participants of WELL-China are recruited from , permanent residents of 12 sub-districts aged 18—80 years old.

A combination of randomized sampling and quota sampling are applied. Baseline measurements in included anthropometric measurements and physical examinations, and in-person survey. Stool, fasting venous blood, hair, and nail samples were also collected from each participant. Informed consent forms were obtained from all participants in this study.

At baseline assessment, body composition data were obtained using DXA scan Software version The gynoid ROI is defined as the area between the upper boundary below the pelvis cut line by 1. This study included three major fat distribution variables: android fat mass, gynoid fat mass, and total fat mass, which were used to derive android fat ratio and gynoid fat ratio. The former was defined as the ratio of android fat mass to the total fat mass; similarly, the latter was the ratio of gynoid fat mass to the total fat mass.

Android fat ratio has very weak correlation with total body fat, whereas gynoid fat ratio has intermediate correlation with total body fat. Information on smoking and alcohol consumption was obtained from an in-person survey at baseline. The implementation includes the following steps: filtering, trimming, dereplication, merging paired reads, chimera recognition and removal, and taxonomy assignment.

DADA2 demonstrated the highest precision rate The denoised sequences are mapped to the GreenGenes reference database All the mapping and the following analysis were done in R version 3.

The plots for sequencing quality and error rate are included in the Supplementary Figs. Based on our central hypothesis, the study visualized the unadjusted association between microbiome abundance and android fat ratio, and gynoid fat ratio, stratified by gender. We first transformed the microbial abundance at the species level using variance—stabilization—transformation from the DESeq2 package.

Then, calculate the total microbiome abundance, and the total number of different microbiome species in each subject. We also examined the association between the microbiome abundance and diversity, the correlation coefficient was 0. Then divided the subjects into tertiles using the microbiome abundance, then diversity, and compared the two sets of tertiles.

The two tertiles shared high level of agreements. Thus, the study employed the microbiome abundance tertile to represent both the abundance and diversity in each subject.

The quartile cutoffs of android fat ratios for men and women were calculated separately. When a potential quadratic relationship was observed in the heat map, the linear model included a quadratic term of the corresponding variable.

To prepare the sequencing data for taxa level testing, a size factor for each sample was estimated using the median ratio method This step ensured samples at different sequencing depths were comparable.

A gene-specific dispersion parameter was applied to adjust dispersions across gender stratified samples. Wald tests with taxa abundance as the primary outcome were performed using DESeq2 package The filtering results in taxa for men and women.

The remaining taxa were individually tested against android fat ratio and gynoid fat ratio as the main exposures in the final model. Given the importance of sex-difference to this study, the potential for sex-specific relationships with adjustment covariates, and the sample size this study fit separate models for men and women. Android and gynoid fat ratio were standardized.

We included following covariates to the generalized linear model: age, daily fat intake, daily carbohydrate intake, daily total energy intake, smoking, alcohol consumption, body mass index BMI , pastmonth antibiotic use, and sequencing batch. BMI was included in the model, as previous literatures have indicated that BMI, as a measurement of overall obesity, was associated with human gut microbiome 13 , 46 , Therefore the model will allow us to obtain the effects of fat distribution on the gut microbiome independent from the overall adiposity.

There are potentially other methods to adjust for overall obesity, such as including total fat mass and height into the model instead of BMI. Therefore, the model still included BMI as the measurement of overall obesity. Effects were measured in Log2 fold change indicating the changes in the taxa abundance in terms of the power of 2. We have also conducted sensitivity analysis to examine the effect without including the antibiotic use.

See Supplementary Table 7. Accession codes are provided in the supplementary material. The sample data are available from the corresponding author upon reasonable request. The R programming codes related to the statistical analysis are publicly available as part of the supplementary materials.

Gill, S. Metagenomic analysis of the human distal gut microbiome. Science , — Backhed, F. Host-bacterial mutualism in the human intestine.

Pou, K. Patterns of abdominal fat distribution: the Framingham Heart Study. Care 32 , — Karastergiou, K. Sex differences in human adipose tissues—the biology of pear shape. Sex Differ. Shimokata, H. Studies in the distribution of body fat: I. Effects of age, sex, and obesity. Power, M. Sex differences in fat storage, fat metabolism, and the health risks from obesity: possible evolutionary origins. Yusuf, S. Lancet , — Turnbaugh, P. An obesity-associated gut microbiome with increased capacity for energy harvest.

Nature , — Diet-induced obesity is linked to marked but reversible alterations in the mouse distal gut microbiome. Cell Host Microbe 3 , — Lin, S. Beta-diversity metrics of the upper digestive tract microbiome are associated with body mass index.

Obesity 23 , — Kasai, C. Comparison of the gut microbiota composition between obese and non-obese individuals in a Japanese population, as analyzed by terminal restriction fragment length polymorphism and next-generation sequencing. BMC Gastroenterol. Million, M. Obesity-associated gut microbiota is enriched in Lactobacillus reuteri and depleted in Bifidobacterium animalis and Methanobrevibacter smithii.

Haro, C. Intestinal microbiota is influenced by gender and body mass index. Price, G. Weight, shape, and mortality risk in older persons: elevated waist-hip ratio, not high body mass index, is associated with a greater risk of death. Pischon, T. General and abdominal adiposity and risk of death in Europe. New Engl. Bouchard, C. Genetic and nongenetic determinants of regional fat distribution. Yurkovetskiy, L. Gender bias in autoimmunity is influenced by microbiota.

Immunity 39 , — Flores, R. Fecal microbial determinants of fecal and systemic estrogens and estrogen metabolites: a cross-sectional study. Org, E. Sex differences and hormonal effects on gut microbiota composition in mice.

Gut Microbes 7 , — Smith, P. The microbial metabolites, short-chain fatty acids, regulate colonic Treg cell homeostasis. Backhed F. The gut microbiota as an environmental factor that regulates fat storage. Natl Acad. USA , — Reinhardt, C. Tissue factor and PAR1 promote microbiota-induced intestinal vascular remodelling. Stappenbeck, T. Developmental regulation of intestinal angiogenesis by indigenous microbes via Paneth cells. USA 99 , — Federation ID. Bi, X. Android fat as a determinant of metabolic syndrome: sex differences.

Nutrition 57 , — Kang, S. Android fat depot is more closely associated with metabolic syndrome than abdominal visceral fat in elderly people. He, Y. Linking gut microbiota, metabolic syndrome and economic status based on a population-level analysis. Microbiome 6 , Ferrer, M. Microbiota from the distal guts of lean and obese adolescents exhibit partial functional redundancy besides clear differences in community structure. Shiferaw, B. Azevedo, M. Gender differences in leisure-time physical activity.

Int J. Public Health 52 , 8—15 Gender differences in physical activity motivators and context preferences: a population-based study in people in their sixties. BMC Public Health 17 , Matthieu Clement, C. Does social class affect nutrition knowledge and food preferences among Chinese urban adults? China Stud. Talaei, M. Physical activity, sex, and socioeconomic status: a population based study.

ARYA Atheroscler. Murphy, E. Composition and energy harvesting capacity of the gut microbiota: relationship to diet, obesity and time in mouse models. Gut 59 , — Claesson, M.

Gut microbiota composition correlates with diet and health in the elderly. Clarke, S. The gut microbiota and its relationship to diet and obesity: new insights. Gut Microbes 3 , — Evans, C. Exercise prevents weight gain and alters the gut microbiota in a mouse model of high fat diet-induced obesity. Zhu, A. Correlation of abdominal fat distribution with different types of diabetes in a Chinese population.

Diabetes Res. Stanford Prevention Research Center. GE Healthcare. Gao, J. Association of dietary patterns and physical activities with total body fat proportions and metabolic syndrome among middle-aged and elderly people: a cross-sectional study. Callahan, B. DADA2: high-resolution sample inference from Illumina amplicon data. Methods 13 , — DeSantis, T. Anders, S. Differential expression analysis for sequence count data. Genome Biol. Love, M. John, G. Dietary alteration of the gut microbiome and its impact on weight and fat mass: a systematic review and meta-analysis.

Genes 9 , — Liu, R. Gut microbiome and serum metabolome alterations in obesity and after weight-loss intervention. Yoav Benjamini, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.

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