{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"institution":[{"name":"bioRxiv"}],"indexed":{"date-parts":[[2026,11,3]],"date-time":"2026-11-03T14:21:50Z","timestamp":1793715710579,"version":"4.6.1"},"posted":{"date-parts":[[2023,3,22]]},"group-title":"Microbiology","reference-count":132,"publisher":"openRxiv","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"accepted":{"date-parts":[[2023,3,22]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                <jats:p>\n                  The relationship between the gut microbiota, short chain fatty acid (SCFA) metabolism, and obesity remains unclear due to conflicting reports from studies with limited statistical power. Additionally, this association has rarely been explored in large scale diverse populations. Here, we investigated associations between fecal microbial composition, predicted metabolic potential, SCFA concentrations, and obesity in a large (\n                  <jats:italic>N<\/jats:italic>\n                  = 1,934) adult cohort of African-origin spanning the epidemiologic transition, from Ghana, South Africa, Jamaica, Seychelles, and the United States (US). The greatest gut microbiota diversity and total fecal SCFA concentration was found in the Ghanaian population, while the lowest levels were found in the US population, respectively representing the lowest and the highest end of the epidemiologic transition spectrum. Country-specific bacterial taxa and predicted-functional pathways were observed, including an increased prevalence of\n                  <jats:italic>Prevotella<\/jats:italic>\n                  ,\n                  <jats:italic>Butyrivibrio<\/jats:italic>\n                  ,\n                  <jats:italic>Weisella<\/jats:italic>\n                  and\n                  <jats:italic>Romboutsia<\/jats:italic>\n                  in Ghana and South Africa, while\n                  <jats:italic>Bacteroides<\/jats:italic>\n                  and\n                  <jats:italic>Parabacteroides<\/jats:italic>\n                  were enriched in Jamaican and the US populations. Importantly, \u2019VANISH\u2019 taxa, including\n                  <jats:italic>Butyricicoccus and Succinivibrio<\/jats:italic>\n                  , were significantly enriched in the Ghanaian cohort, reflecting the participants\u2019 traditional lifestyles. Obesity was significantly associated with lower SCFA concentrations, a decrease in microbial richness, and dissimilarities in community composition, and reduction in the proportion of SCFA synthesizing bacteria including\n                  <jats:italic>Oscillospira<\/jats:italic>\n                  ,\n                  <jats:italic>Christensenella<\/jats:italic>\n                  ,\n                  <jats:italic>Eubacterium<\/jats:italic>\n                  ,\n                  <jats:italic>Alistipes<\/jats:italic>\n                  ,\n                  <jats:italic>Clostridium<\/jats:italic>\n                  and\n                  <jats:italic>Odoribacter<\/jats:italic>\n                  . Further, the predicted proportions of genes in the lipopolysaccharide (LPS) synthesis pathway were enriched in obese individuals, while genes associated with butyrate synthesis via the dominant pyruvate pathway were significantly reduced in obese individuals. Using machine learning, we identified features predictive of metabolic state and country of origin. Country of origin could accurately be predicted by the fecal microbiota (AUC = 0.97), whereas obesity could not be predicted as accurately (AUC = 0.65). Participant sex (AUC = 0.75), diabetes status (AUC = 0.63), hypertensive status (AUC = 0.65), and glucose status (AUC = 0.66) could all be predicted with different success. Interestingly, within country, the predictive accuracy of the microbiota for obesity was inversely correlated to the epidemiological transition, being greatest in Ghana (AUC = 0.57). Collectively, our findings reveal profound variation in the gut microbiota, inferred functional pathways, and SCFA synthesis as a function of country of origin. While obesity could be predicted accurately from the microbiota, the variation in accuracy in parallel with the epidemiological transition suggests that differences in the microbiota between obesity and non-obesity may be larger in low-to-middle countries compared to high-income countries. Further examination of independent study populations using multi-omic approaches will be necessary to determine the factors that drive this association.\n                <\/jats:p>","DOI":"10.1101\/2023.03.21.533195","type":"posted-content","created":{"date-parts":[[2023,3,22]],"date-time":"2023-03-22T23:45:11Z","timestamp":1679528711000},"source":"Crossref","is-referenced-by-count":5,"title":["Gut microbiota and fecal short chain fatty acids differ with adiposity and country of origin: The METS-Microbiome Study"],"prefix":"10.64898","author":[{"given":"Gertrude","family":"Ecklu-Mensah","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Candice","family":"Choo-Kang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maria Gjerstad","family":"Maseng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sonya","family":"Donato","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Pascal","family":"Bovet","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kweku","family":"Bedu-Addo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jacob","family":"Plange-Rhule","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Terrence E.","family":"Forrester","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Estelle V.","family":"Lambert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dale","family":"Rae","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Amy","family":"Luke","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Brian T.","family":"Layden","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Stephen","family":"O\u2019Keefe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jack A.","family":"Gilbert","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0692-8585","authenticated-orcid":false,"given":"Lara R.","family":"Dugas","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"54368","reference":[{"issue":"6","key":"2023032504000601000_2023.03.21.533195v1.1","doi-asserted-by":"crossref","first-page":"e1002358","DOI":"10.1371\/journal.pcbi.1002358","article-title":"Metabolic Reconstruction for Metagenomic Data and Its Application to the Human Microbiome","volume":"8","year":"2012","journal-title":"PLoS Computational Biology"},{"key":"2023032504000601000_2023.03.21.533195v1.2","doi-asserted-by":"crossref","unstructured":"Agyemang, Charles , Sandra Boatemaa , Grace Agyemang Frempong , and Ama de-Graft Aikins . 2016. \u201cObesity in Sub-Saharan Africa.\u201d In Metabolic Syndrome: A Comprehensive Textbook, edited by Rexford S. 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