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Discovery of BMI-Associated Gut Microbiome Signatures in the AWI-Gen 2 Cohort
Obesity is increasing across sub-Saharan Africa amid rapid dietary, environmental, and urbanization transitions, yet the gut microbiome features associated with adiposity in African populations remain under-characterized. This study investigated whether species-level gut microbiome composition is as...
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2026
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| _version_ | 1869483685505400832 |
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| access_status_str | Open Access |
| author | Yagoubi, Meriem |
| author_browse | Yagoubi, Meriem |
| author_facet | Yagoubi, Meriem |
| author_sort | Yagoubi, Meriem |
| collection | Thesis |
| description | Obesity is increasing across sub-Saharan Africa amid rapid dietary, environmental, and urbanization transitions, yet the gut microbiome features associated with adiposity in African populations remain under-characterized. This study investigated whether species-level gut microbiome composition is associated with body mass index (BMI) in the AWI-Gen 2 cohort, and whether BMI-associated microbial signals remain detectable after accounting for the dominant geographic structure of the microbiome. Gut microbiome community structure and diversity were characterized across 1,665 participants, and differential abundance analysis was used to identify taxa associated with BMI after controlling for study site. Machine learning models were then trained to assess how well microbiome profiles predict continuous BMI, and SHAP analysis was used to identify the features driving predictions. Geographic site explained substantially more compositional variation than BMI (6.1% versus 0.4%), confirming that BMI-associated patterns are embedded within a stronger background of site-related structure. Shannon diversity was significantly lower in overweight and obese participants than in normal-weight participants. Differential abundance analysis identified 33 BMI-associated microbial species, including taxa related to Lachnospiraceae, Oscillospiraceae, Akkermansia muciniphila, Ruthenibacterium lactatiformans, Methanosphaera stadtmanae, and Prevotella-related species-level genome bins. XGBoost achieved the highest cross-validated performance (R² = 0.461 ± 0.045), with SHAP analysis highlighting Lachnospiraceae-related features as primary contributors. These results support a context-specific model of microbiome–BMI relationships in African populations: BMI-associated taxa are detectable as candidate biomarkers but arise within a dominant background of site, diet, and urbanization. This study prioritizes candidate taxa for future functional validation. |
| format | Thesis |
| id | oai:fount.aucegypt.edu:etds-3879 |
| institution | American University in Cairo (Egypt) |
| last_indexed | 2026-07-01T04:02:55.806Z |
| license_str | Not specified — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from AUC Knowledge Fountain — bepress |
| publishDate | 2026 |
| publishDateRange | 2026 |
| publishDateSort | 2026 |
| publisher | AUC Knowledge Fountain |
| publisherStr | AUC Knowledge Fountain |
| record_format | dspace |
| source_str | AUC Knowledge Fountain — bepress |
| spelling | oai:fount.aucegypt.edu:etds-3879 Discovery of BMI-Associated Gut Microbiome Signatures in the AWI-Gen 2 Cohort Yagoubi, Meriem Obesity is increasing across sub-Saharan Africa amid rapid dietary, environmental, and urbanization transitions, yet the gut microbiome features associated with adiposity in African populations remain under-characterized. This study investigated whether species-level gut microbiome composition is associated with body mass index (BMI) in the AWI-Gen 2 cohort, and whether BMI-associated microbial signals remain detectable after accounting for the dominant geographic structure of the microbiome. Gut microbiome community structure and diversity were characterized across 1,665 participants, and differential abundance analysis was used to identify taxa associated with BMI after controlling for study site. Machine learning models were then trained to assess how well microbiome profiles predict continuous BMI, and SHAP analysis was used to identify the features driving predictions. Geographic site explained substantially more compositional variation than BMI (6.1% versus 0.4%), confirming that BMI-associated patterns are embedded within a stronger background of site-related structure. Shannon diversity was significantly lower in overweight and obese participants than in normal-weight participants. Differential abundance analysis identified 33 BMI-associated microbial species, including taxa related to Lachnospiraceae, Oscillospiraceae, Akkermansia muciniphila, Ruthenibacterium lactatiformans, Methanosphaera stadtmanae, and Prevotella-related species-level genome bins. XGBoost achieved the highest cross-validated performance (R² = 0.461 ± 0.045), with SHAP analysis highlighting Lachnospiraceae-related features as primary contributors. These results support a context-specific model of microbiome–BMI relationships in African populations: BMI-associated taxa are detectable as candidate biomarkers but arise within a dominant background of site, diet, and urbanization. This study prioritizes candidate taxa for future functional validation. 2026-06-05T07:00:00Z thesis application/pdf https://fount.aucegypt.edu/etds/2818 https://fount.aucegypt.edu/context/etds/article/3879/viewcontent/Final_Thesis___Meriem_Yagoubi.pdf Theses and Dissertations AUC Knowledge Fountain gut microbiome Body Mass Index (BMI) AWI-Gen 2 cohort African populations MetaPhlAn taxonomic profiling Machine learning SHAP feature importance Differential abundance analysis Geographic microbiome variation Compositional data analysis Bioinformatics Biotechnology Environmental Microbiology and Microbial Ecology |
| spellingShingle | gut microbiome Body Mass Index (BMI) AWI-Gen 2 cohort African populations MetaPhlAn taxonomic profiling Machine learning SHAP feature importance Differential abundance analysis Geographic microbiome variation Compositional data analysis Bioinformatics Biotechnology Environmental Microbiology and Microbial Ecology Yagoubi, Meriem Discovery of BMI-Associated Gut Microbiome Signatures in the AWI-Gen 2 Cohort |
| title | Discovery of BMI-Associated Gut Microbiome Signatures in the AWI-Gen 2 Cohort |
| title_full | Discovery of BMI-Associated Gut Microbiome Signatures in the AWI-Gen 2 Cohort |
| title_fullStr | Discovery of BMI-Associated Gut Microbiome Signatures in the AWI-Gen 2 Cohort |
| title_full_unstemmed | Discovery of BMI-Associated Gut Microbiome Signatures in the AWI-Gen 2 Cohort |
| title_short | Discovery of BMI-Associated Gut Microbiome Signatures in the AWI-Gen 2 Cohort |
| title_sort | discovery of bmi associated gut microbiome signatures in the awi gen 2 cohort |
| topic | gut microbiome Body Mass Index (BMI) AWI-Gen 2 cohort African populations MetaPhlAn taxonomic profiling Machine learning SHAP feature importance Differential abundance analysis Geographic microbiome variation Compositional data analysis Bioinformatics Biotechnology Environmental Microbiology and Microbial Ecology |
| url | https://fount.aucegypt.edu/etds/2818 https://fount.aucegypt.edu/context/etds/article/3879/viewcontent/Final_Thesis___Meriem_Yagoubi.pdf |
| work_keys_str_mv | AT yagoubimeriem discoveryofbmiassociatedgutmicrobiomesignaturesintheawigen2cohort |