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Interpretable Machine Learning Framework Reveals Robust Gut Microbiome Features Associated With Type 2 Diabetes.

Authors
  • Gou, Wanglong1
  • Ling, Chu-Wen2
  • He, Yan3
  • Jiang, Zengliang1, 4
  • Fu, Yuanqing1, 4
  • Xu, Fengzhe1
  • Miao, Zelei1
  • Sun, Ting-Yu2
  • Lin, Jie-Sheng2
  • Zhu, Hui-Lian2
  • Zhou, Hongwei3, 5
  • Chen, Yu-Ming6
  • Zheng, Ju-Sheng7, 4, 8
  • 1 Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China. , (China)
  • 2 Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China. , (China)
  • 3 Microbiome Medicine Center, Division of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, China. , (China)
  • 4 Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, China. , (China)
  • 5 State Key Laboratory of Organ Failure Research, Southern Medical University, Guangzhou, China. , (China)
  • 6 Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China [email protected] [email protected] , (China)
  • 7 Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China [email protected] [email protected] , (China)
  • 8 Westlake Laboratory of Life Sciences and Biomedicine, Hangzhou, China. , (China)
Type
Published Article
Journal
Diabetes care
Publication Date
Dec 07, 2020
Identifiers
DOI: 10.2337/dc20-1536
PMID: 33288652
Source
Medline
Language
English
License
Unknown

Abstract

To identify the core gut microbial features associated with type 2 diabetes risk and potential demographic, adiposity, and dietary factors associated with these features. We used an interpretable machine learning framework to identify the type 2 diabetes-related gut microbiome features in the cross-sectional analyses of three Chinese cohorts: one discovery cohort (n = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1: n = 203, 48 cases; cohort 2: n = 7,009, 608 cases). We constructed a microbiome risk score (MRS) with the identified features. We examined the prospective association of the MRS with glucose increment in 249 participants without type 2 diabetes and assessed the correlation between the MRS and host blood metabolites (n = 1,016). We transferred human fecal samples with different MRS levels to germ-free mice to confirm the MRS-type 2 diabetes relationship. We then examined the prospective association of demographic, adiposity, and dietary factors with the MRS (n = 1,832). The MRS (including 14 microbial features) consistently associated with type 2 diabetes, with risk ratio for per 1-unit change in MRS 1.28 (95% CI 1.23-1.33), 1.23 (1.13-1.34), and 1.12 (1.06-1.18) across three cohorts. The MRS was positively associated with future glucose increment (P < 0.05) and was correlated with a variety of gut microbiota-derived blood metabolites. Animal study further confirmed the MRS-type 2 diabetes relationship. Body fat distribution was found to be a key factor modulating the gut microbiome-type 2 diabetes relationship. Our results reveal a core set of gut microbiome features associated with type 2 diabetes risk and future glucose increment. © 2020 by the American Diabetes Association.

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