6 research outputs found

    Sex, Body Mass Index, and Dietary Fiber Intake Influence the Human Gut Microbiome

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    <div><p>Increasing evidence suggests that the composition of the human gut microbiome is important in the etiology of human diseases; however, the personal factors that influence the gut microbiome composition are poorly characterized. Animal models point to sex hormone-related differentials in microbiome composition. In this study, we investigated the relationship of sex, body mass index (BMI) and dietary fiber intake with the gut microbiome in 82 humans. We sequenced fecal 16S rRNA genes by 454 FLX technology, then clustered and classified the reads to microbial genomes using the QIIME pipeline. Relationships of sex, BMI, and fiber intake with overall gut microbiome composition and specific taxon abundances were assessed by permutational MANOVA and multivariate logistic regression, respectively. We found that sex was associated with the gut microbiome composition overall (p=0.001). The gut microbiome in women was characterized by a lower abundance of Bacteroidetes (p=0.03). BMI (>25 kg/m<sup>2</sup><i>vs</i>. <25 kg/m<sup>2</sup>) was associated with the gut microbiome composition overall (p=0.05), and this relationship was strong in women (p=0.03) but not in men (p=0.29). Fiber from beans and from fruits and vegetables were associated, respectively, with greater abundance of Actinobacteria (p=0.006 and false discovery rate adjusted q=0.05) and Clostridia (p=0.009 and false discovery rate adjusted q=0.09). Our findings suggest that sex, BMI, and dietary fiber contribute to shaping the gut microbiome in humans. Better understanding of these relationships may have significant implications for gastrointestinal health and disease prevention.</p></div

    Gut microbiome according to BMI in women and men separately.

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    <p>Unweighted principal coordinate analysis plot of the first two principal coordinates categorized by BMI (<25 kg/m<sup>2</sup>, ≄25 kg/m<sup>2</sup>) in (A) women and (B) men. Ellipses were added to plots using the R package, latticeExtra (R version 2.15.3). Alpha rarefaction plots of Shannon diversity indices grouped by normal weight (<25 kg/m<sup>2</sup>; open circles) and overweight/obese (≄25 kg/m<sup>2</sup>; red circles) status for women (C) and for men (D). Statistical significance was assessed by non-parametric Monte Carlo permutations (QIIME). (E) Relative abundance of Firmicures and Bacteroidetes. Mann-Whitney-Wilcoxon test was used to test for overall differences using SAS software (version 9.3).</p

    Population Characteristics.

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    <p><sup>1</sup>All characteristics were compared by sex using either Chi square or Mann-Whitney-Wilcoxon tests. All analyses were carried out using SAS software (version 9.3).</p><p><sup>2</sup>Race was grouped as White and Other for Chi square test.</p><p>Population Characteristics.</p

    PERMANOVA<sup>1</sup> analysis of personal factors with the unweighted UniFrac distance matrix.

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    <p><sup>1</sup>Adonis, which uses permutational multivariate analysis of variance (PERMANOVA), was used to test statistical significances of association of overall composition with personal factors. All analyses were carried out using the QIIME pipeline.</p><p><sup>2</sup>BMI was categorized as normal weight (<25 kg/m<sup>2</sup>) versus overweight or obese (≄25 kg/m<sup>2</sup>).</p><p><sup>3</sup>Total and specific sources of dietary fiber were categorized as low (quartiles 1–3) versus high (quartile 4) intake.</p><p>PERMANOVA<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124599#t002fn001" target="_blank"><sup>1</sup></a> analysis of personal factors with the unweighted UniFrac distance matrix.</p

    Gut microbiome according to sex.

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    <p>(A) Unweighted principal coordinate analysis plot of the first two principal coordinates categorized by sex. Ellipses were added to plots using the R package, latticeExtra (R version 2.15.3). (B) Relative abundance of the three major phyla. Mann-Whitney-Wilcoxon test was used to test for overall differences using SAS software (version 9.3). Nominal p-values are listed below each phylum.</p

    Additional file 1: Figures S1-S3 and Tables S1-S9. of The gut microbiota in conventional and serrated precursors of colorectal cancer

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    Figure S1. Principal coordinate analysis (PCoA) of the unweighted and weighted UniFrac distances for quality control stool specimens. Figure S2. Rarefaction curves of richness and the Shannon index. Figure S3. Count heatmap of top 20 OTUs contributing the most to the Dirichlet components of the Dirichlet multinomial mixture model. Table S1. Quality control intra‐class correlation coefficients (ICCs) and 95% CIs for the Shannon index and normalized counts of selected phyla and genera. Table S2. Number of participants with polyp(s) in the specified colon locations, stratified by assignment into case type and polyp location groupings used in analysis. Table S3. Richness and Shannon diversity index by group. Table S4. Differentially abundant OTUs between controls and conventional adenoma cases, hyperplastic polyp cases, or SSA cases. Table S5. Differentially abundant taxa (phylum‐genus levels) between controls and conventional adenoma cases, hyperplastic polyp cases, or SSA cases. Table S6. Differentially abundant taxa (phylum‐OTU level) between controls and proximal or distal conventional adenoma cases. Table S7. Differentially abundant taxa (phylum‐OTU level) between controls and non‐advanced or advanced conventional adenoma cases. Table S8. Sensitivity analysis—excluding participants (n = 5) who collected their stool sample <2 weeks after their colonoscopy. Table S9. Sensitivity analysis—excluding participants (n = 19 from the NYU study) who had taken antibiotics within 30 days prior to sample collection (antibiotic usage information was not available in the CDC study). (PDF 631 kb
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