Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways
Summary: Obesity is associated with altered gut microbiome composition but data across different populations remain inconsistent. We meta-analyzed publicly available 16S-rRNA sequence datasets from 18 different studies and identified differentially abundant taxa and functional pathways of the obese...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-04-01
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| Series: | iScience |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223005539 |
| _version_ | 1797851733280423936 |
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| author | Yu Lin Zhilu Xu Yun Kit Yeoh Hein Min Tun Wenli Huang Wei Jiang Francis Ka Leung Chan Siew Chien Ng |
| author_facet | Yu Lin Zhilu Xu Yun Kit Yeoh Hein Min Tun Wenli Huang Wei Jiang Francis Ka Leung Chan Siew Chien Ng |
| author_sort | Yu Lin |
| collection | DOAJ |
| description | Summary: Obesity is associated with altered gut microbiome composition but data across different populations remain inconsistent. We meta-analyzed publicly available 16S-rRNA sequence datasets from 18 different studies and identified differentially abundant taxa and functional pathways of the obese gut microbiome. Most differentially abundant genera (Odoribacter, Oscillospira, Akkermansia, Alistipes, and Bacteroides) were depleted in obesity, indicating a deficiency of commensal microbes in the obese gut microbiome. From microbiome functional pathways, elevated lipid biosynthesis and depleted carbohydrate and protein degradation suggested metabolic adaptation to high-fat, low-carbohydrate, and low-protein diets in obese individuals. Machine learning models trained on the 18 studies were modest in predicting obesity with a median AUC of 0.608 using 10-fold cross-validation. The median AUC increased to 0.771 when models were trained in eight studies designed for investigating obesity-microbiome association. By meta-analyzing obesity-associated microbiota signatures, we identified obesity-associated depleted taxa that may be exploited to mitigate obesity and related metabolic diseases. |
| first_indexed | 2024-04-09T19:22:36Z |
| format | Article |
| id | doaj.art-ac7acb42437f496b9f0069362afc3eff |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-04-09T19:22:36Z |
| publishDate | 2023-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-ac7acb42437f496b9f0069362afc3eff2023-04-05T08:30:10ZengElsevieriScience2589-00422023-04-01264106476Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathwaysYu Lin0Zhilu Xu1Yun Kit Yeoh2Hein Min Tun3Wenli Huang4Wei Jiang5Francis Ka Leung Chan6Siew Chien Ng7Microbiota I-Center (MagIC), Hong Kong SAR, China; Center for Gut Microbiota Research, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China; Department of Medicine and Therapeutics, Institute of Digestive Disease, Li Ka Shing Institute of Health Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, ChinaMicrobiota I-Center (MagIC), Hong Kong SAR, China; Center for Gut Microbiota Research, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China; Department of Medicine and Therapeutics, Institute of Digestive Disease, Li Ka Shing Institute of Health Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, ChinaMicrobiota I-Center (MagIC), Hong Kong SAR, China; Center for Gut Microbiota Research, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China; Department of Microbiology, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, ChinaMicrobiota I-Center (MagIC), Hong Kong SAR, China; Jockey Club School of Public Health and Primary Care, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, ChinaMicrobiota I-Center (MagIC), Hong Kong SAR, China; Center for Gut Microbiota Research, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China; Department of Medicine and Therapeutics, Institute of Digestive Disease, Li Ka Shing Institute of Health Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, ChinaMicrobiota I-Center (MagIC), Hong Kong SAR, China; Center for Gut Microbiota Research, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China; Department of Medicine and Therapeutics, Institute of Digestive Disease, Li Ka Shing Institute of Health Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, ChinaMicrobiota I-Center (MagIC), Hong Kong SAR, China; Center for Gut Microbiota Research, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China; Department of Medicine and Therapeutics, Institute of Digestive Disease, Li Ka Shing Institute of Health Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, ChinaMicrobiota I-Center (MagIC), Hong Kong SAR, China; Center for Gut Microbiota Research, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China; Department of Medicine and Therapeutics, Institute of Digestive Disease, Li Ka Shing Institute of Health Sciences, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China; Corresponding authorSummary: Obesity is associated with altered gut microbiome composition but data across different populations remain inconsistent. We meta-analyzed publicly available 16S-rRNA sequence datasets from 18 different studies and identified differentially abundant taxa and functional pathways of the obese gut microbiome. Most differentially abundant genera (Odoribacter, Oscillospira, Akkermansia, Alistipes, and Bacteroides) were depleted in obesity, indicating a deficiency of commensal microbes in the obese gut microbiome. From microbiome functional pathways, elevated lipid biosynthesis and depleted carbohydrate and protein degradation suggested metabolic adaptation to high-fat, low-carbohydrate, and low-protein diets in obese individuals. Machine learning models trained on the 18 studies were modest in predicting obesity with a median AUC of 0.608 using 10-fold cross-validation. The median AUC increased to 0.771 when models were trained in eight studies designed for investigating obesity-microbiome association. By meta-analyzing obesity-associated microbiota signatures, we identified obesity-associated depleted taxa that may be exploited to mitigate obesity and related metabolic diseases.http://www.sciencedirect.com/science/article/pii/S2589004223005539Health sciencesMicrobiomeMachine learning |
| spellingShingle | Yu Lin Zhilu Xu Yun Kit Yeoh Hein Min Tun Wenli Huang Wei Jiang Francis Ka Leung Chan Siew Chien Ng Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways iScience Health sciences Microbiome Machine learning |
| title | Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways |
| title_full | Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways |
| title_fullStr | Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways |
| title_full_unstemmed | Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways |
| title_short | Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways |
| title_sort | combing fecal microbial community data to identify consistent obesity specific microbial signatures and shared metabolic pathways |
| topic | Health sciences Microbiome Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223005539 |
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