Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways

Yu Lin; Zhilu Xu; Yun Kit Yeoh; Hein Min Tun; Wenli Huang; Wei Jiang; Francis Ka Leung Chan; Siew Chien Ng

doi:10.1016/j.isci.2023.106476

Combing fecal microbial community data to identify consistent obesity-specific microbial signatures and shared metabolic pathways

iScience. 2023 Mar 22;26(4):106476. doi: 10.1016/j.isci.2023.106476. eCollection 2023 Apr 21.

Authors

Yu Lin^{1

2

3}, Zhilu Xu^{1

2

3}, Yun Kit Yeoh^{1

2

4}, Hein Min Tun^{1

5}, Wenli Huang^{1

2

3}, Wei Jiang^{1

2

3}, Francis Ka Leung Chan^{1

2

3}, Siew Chien Ng^{1

2

3}

Affiliations

¹ Microbiota I-Center (MagIC), Hong Kong SAR, China.
² Center for Gut Microbiota Research, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China.
³ 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.
⁴ Department of Microbiology, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China.
⁵ Jockey Club School of Public Health and Primary Care, Faculty of Medicine, the Chinese University of Hong Kong, Hong Kong SAR, China.

Abstract

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.

Keywords: Health sciences; Machine learning; Microbiome.