Key Laboratory of Growth Regulation and Translational Research of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, China.
Guangdong Provincial Key Laboratory of Food, Nutrition and Health, Department of Epidemiology, School of Public Health, Sun Yat-sen University, Guangzhou, China.
Diabetes Care. 2021 Feb;44(2):358-366. doi: 10.2337/dc20-1536. Epub 2020 Dec 7.
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 ( = 1,832, 270 cases of type 2 diabetes) and two validation cohorts (cohort 1: = 203, 48 cases; cohort 2: = 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 ( = 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 ( = 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 ( < 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.
确定与 2 型糖尿病风险相关的核心肠道微生物特征，以及与这些特征相关的潜在人口统计学、肥胖和饮食因素。
我们使用可解释的机器学习框架，在三个中国队列的横断面分析中确定与 2 型糖尿病相关的肠道微生物组特征：一个发现队列（n=1832，270 例 2 型糖尿病）和两个验证队列（队列 1：n=203，48 例；队列 2：n=7009，608 例）。我们构建了一个具有鉴定特征的微生物组风险评分（MRS）。我们检测了 MRS 与 249 例无 2 型糖尿病参与者血糖增量的前瞻性关联，并评估了 MRS 与宿主血液代谢物之间的相关性（n=1016）。我们将具有不同 MRS 水平的人类粪便样本转移到无菌小鼠中，以确认 MRS 与 2 型糖尿病的关系。然后，我们检测了人口统计学、肥胖和饮食因素与 MRS 的前瞻性关联（n=1832）。
MRS（包括 14 个微生物特征）与 2 型糖尿病一致相关，MRS 每增加 1 单位，风险比为 1.28（95%CI 1.23-1.33）、1.23（1.13-1.34）和 1.12（1.06-1.18），跨越三个队列。MRS 与未来血糖增量呈正相关（<0.05），并与多种肠道微生物群衍生的血液代谢物相关。动物研究进一步证实了 MRS 与 2 型糖尿病的关系。身体脂肪分布被发现是调节肠道微生物组与 2 型糖尿病关系的关键因素。
我们的研究结果揭示了一组与 2 型糖尿病风险和未来血糖增量相关的核心肠道微生物特征。
