Sex, Body Mass Index, and Dietary Fiber Intake Influence the Human Gut Microbiome
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{"title"=>"Sex, body mass index, and dietary fiber intake influence the human gut microbiome", "type"=>"journal", "authors"=>[{"first_name"=>"Christine", "last_name"=>"Dominianni", "scopus_author_id"=>"55803663200"}, {"first_name"=>"Rashmi", "last_name"=>"Sinha", "scopus_author_id"=>"7402857340"}, {"first_name"=>"James J.", "last_name"=>"Goedert", "scopus_author_id"=>"7103299277"}, {"first_name"=>"Zhiheng", "last_name"=>"Pei", "scopus_author_id"=>"7101715223"}, {"first_name"=>"Liying", "last_name"=>"Yang", "scopus_author_id"=>"55732940500"}, {"first_name"=>"Richard B.", "last_name"=>"Hayes", "scopus_author_id"=>"55537667100"}, {"first_name"=>"Jiyoung", "last_name"=>"Ahn", "scopus_author_id"=>"7403019290"}], "year"=>2015, "source"=>"PLoS ONE", "identifiers"=>{"pui"=>"604108145", "sgr"=>"84928902130", "pmid"=>"25874569", "scopus"=>"2-s2.0-84928902130", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "doi"=>"10.1371/journal.pone.0124599", "issn"=>"19326203"}, "id"=>"61f4c190-96cb-3bc1-ae2d-ec9acf22e6f2", "abstract"=>"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/m2 vs. <25 kg/m2) 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.", "link"=>"http://www.mendeley.com/research/sex-body-mass-index-dietary-fiber-intake-influence-human-gut-microbiome", "reader_count"=>165, "reader_count_by_academic_status"=>{"Unspecified"=>4, "Professor > Associate Professor"=>8, "Researcher"=>30, "Student > Doctoral Student"=>14, "Student > Ph. D. 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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/2022390"], "description"=>"<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>", "links"=>[], "tags"=>["discovery rate", "Animal models point", "gut microbiome composition", "body mass index", "454 FLX technology", "gut microbiome", "bmi", "fiber intake", "Human Gut Microbiome", "qiime", "relationship", "manova", "Dietary Fiber Intake Influence", "16 S rRNA genes"], "article_id"=>1380995, "categories"=>["Uncategorised"], "users"=>["Christine Dominianni", "Rashmi Sinha", "James J. Goedert", "Zhiheng Pei", "Liying Yang", "Richard B. Hayes", "Jiyoung Ahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0124599.t002", "stats"=>{"downloads"=>7, "page_views"=>38, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_PERMANOVA_1_analysis_of_personal_factors_with_the_unweighted_UniFrac_distance_matrix_/1380995", "title"=>"PERMANOVA<sup>1</sup> analysis of personal factors with the unweighted UniFrac distance matrix.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-15 03:34:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/2022375"], "description"=>"<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>", "links"=>[], "tags"=>["discovery rate", "Animal models point", "gut microbiome composition", "body mass index", "454 FLX technology", "gut microbiome", "bmi", "fiber intake", "Human Gut Microbiome", "qiime", "relationship", "manova", "Dietary Fiber Intake Influence", "16 S rRNA genes"], "article_id"=>1380980, "categories"=>["Uncategorised"], "users"=>["Christine Dominianni", "Rashmi Sinha", "James J. Goedert", "Zhiheng Pei", "Liying Yang", "Richard B. Hayes", "Jiyoung Ahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0124599.g001", "stats"=>{"downloads"=>2, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gut_microbiome_according_to_sex_/1380980", "title"=>"Gut microbiome according to sex.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-15 03:34:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/2022389"], "description"=>"<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>", "links"=>[], "tags"=>["discovery rate", "Animal models point", "gut microbiome composition", "body mass index", "454 FLX technology", "gut microbiome", "bmi", "fiber intake", "Human Gut Microbiome", "qiime", "relationship", "manova", "Dietary Fiber Intake Influence", "16 S rRNA genes"], "article_id"=>1380994, "categories"=>["Uncategorised"], "users"=>["Christine Dominianni", "Rashmi Sinha", "James J. Goedert", "Zhiheng Pei", "Liying Yang", "Richard B. Hayes", "Jiyoung Ahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0124599.t001", "stats"=>{"downloads"=>6, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Population_Characteristics_/1380994", "title"=>"Population Characteristics.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-15 03:34:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/2022383"], "description"=>"<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>", "links"=>[], "tags"=>["discovery rate", "Animal models point", "gut microbiome composition", "body mass index", "454 FLX technology", "gut microbiome", "bmi", "fiber intake", "Human Gut Microbiome", "qiime", "relationship", "manova", "Dietary Fiber Intake Influence", "16 S rRNA genes"], "article_id"=>1380988, "categories"=>["Uncategorised"], "users"=>["Christine Dominianni", "Rashmi Sinha", "James J. Goedert", "Zhiheng Pei", "Liying Yang", "Richard B. Hayes", "Jiyoung Ahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0124599.g003", "stats"=>{"downloads"=>2, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gut_microbiome_according_to_BMI_in_women_and_men_separately_/1380988", "title"=>"Gut microbiome according to BMI in women and men separately.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-15 03:34:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/2022378"], "description"=>"<p>(A) 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>). 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>", "links"=>[], "tags"=>["discovery rate", "Animal models point", "gut microbiome composition", "body mass index", "454 FLX technology", "gut microbiome", "bmi", "fiber intake", "Human Gut Microbiome", "qiime", "relationship", "manova", "Dietary Fiber Intake Influence", "16 S rRNA genes"], "article_id"=>1380983, "categories"=>["Uncategorised"], "users"=>["Christine Dominianni", "Rashmi Sinha", "James J. Goedert", "Zhiheng Pei", "Liying Yang", "Richard B. Hayes", "Jiyoung Ahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0124599.g002", "stats"=>{"downloads"=>2, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gut_microbiome_according_to_BMI_/1380983", "title"=>"Gut microbiome according to BMI.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-15 03:34:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/2022392"], "description"=>"<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>", "links"=>[], "tags"=>["discovery rate", "Animal models point", "gut microbiome composition", "body mass index", "454 FLX technology", "gut microbiome", "bmi", "fiber intake", "Human Gut Microbiome", "qiime", "relationship", "manova", "Dietary Fiber Intake Influence", "16 S rRNA genes"], "article_id"=>1380997, "categories"=>["Uncategorised"], "users"=>["Christine Dominianni", "Rashmi Sinha", "James J. Goedert", "Zhiheng Pei", "Liying Yang", "Richard B. Hayes", "Jiyoung Ahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0124599", "stats"=>{"downloads"=>5, "page_views"=>26, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Sex_Body_Mass_Index_and_Dietary_Fiber_Intake_Influence_the_Human_Gut_Microbiome_/1380997", "title"=>"Sex, Body Mass Index, and Dietary Fiber Intake Influence the Human Gut Microbiome", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-15 03:34:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/2022391"], "description"=>"<p><sup>1</sup> All estimates were computed using generalized linear regression models where principal coordinates were treated as outcomes and all personal factors treated as predictor variables. All analyses were carried out using SAS software (version 9.3).</p><p><sup>2</sup> Fiber from fruits and vegetables, sex, race, and age were included jointly in multivariate regression models.</p><p>Univariate and multivariate linear regressions<a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0124599#t003fn001\" target=\"_blank\"><sup>1</sup></a> for personal factors and unweighted principal coordinates (PC).</p>", "links"=>[], "tags"=>["discovery rate", "Animal models point", "gut microbiome composition", "body mass index", "454 FLX technology", "gut microbiome", "bmi", "fiber intake", "Human Gut Microbiome", "qiime", "relationship", "manova", "Dietary Fiber Intake Influence", "16 S rRNA genes"], "article_id"=>1380996, "categories"=>["Uncategorised"], "users"=>["Christine Dominianni", "Rashmi Sinha", "James J. Goedert", "Zhiheng Pei", "Liying Yang", "Richard B. Hayes", "Jiyoung Ahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0124599.t003", "stats"=>{"downloads"=>4, "page_views"=>16, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Univariate_and_multivariate_linear_regressions_1_for_personal_factors_and_unweighted_principal_coordinates_PC_/1380996", "title"=>"Univariate and multivariate linear regressions<sup>1</sup> for personal factors and unweighted principal coordinates (PC).", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2015-04-15 03:34:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/2022388"], "description"=>"<p>(A) Unweighted principal coordinate analysis plot of the first two principal coordinates categorized by fruit and vegetable fiber intake (Low: 1.6–11.7 g/day [equivalent to quartile 1–3], High: 11.7–21.9 g/day [quartile 4]). Ellipses were added to plots using the R package, latticeExtra (R version 2.15.3). (B) A heatmap based on unsupervised classification of Spearman correlations between the relative abundance of taxa (genus level) and three dietary fiber sources using the R package, gplots, (R version 2.15.3). For this analysis, only genera that were present in ≥15% of samples were included. Taxa belonging to Clostridia (Cluster 1, *) and Bifidobacteriales (Cluster 2, <b>°</b>) are marked.</p>", "links"=>[], "tags"=>["discovery rate", "Animal models point", "gut microbiome composition", "body mass index", "454 FLX technology", "gut microbiome", "bmi", "fiber intake", "Human Gut Microbiome", "qiime", "relationship", "manova", "Dietary Fiber Intake Influence", "16 S rRNA genes"], "article_id"=>1380993, "categories"=>["Uncategorised"], "users"=>["Christine Dominianni", "Rashmi Sinha", "James J. Goedert", "Zhiheng Pei", "Liying Yang", "Richard B. Hayes", "Jiyoung Ahn"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0124599.g004", "stats"=>{"downloads"=>3, "page_views"=>33, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Gut_microbiome_according_to_dietary_fiber_intake_/1380993", "title"=>"Gut microbiome according to dietary fiber intake.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2015-04-15 03:34:47"}

PMC Usage Stats | Further Information

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Relative Metric

{"start_date"=>"2015-01-01T00:00:00Z", "end_date"=>"2015-12-31T00:00:00Z", "subject_areas"=>[]}
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