Association of gut microbiota with glycaemic traits and incident type 2 diabetes, and modulation by habitual diet: a population-based longitudinal cohort study in Chinese adults

Abstract

Aims/hypothesis The gut microbiome is mainly shaped by diet, and varies across geographical regions. Little is known about the longitudinal association of gut microbiota with glycaemic control. We aimed to identify gut microbiota prospectively associated with glycaemic traits and type 2 diabetes in a geographically diverse population, and examined the cross-sectional association of dietary or lifestyle factors with the identified gut microbiota. Methods The China Health and Nutrition Survey is a population-based longitudinal cohort covering 15 provinces/megacities across China. Of the participants in that study, 2772 diabetes-free participants with a gut microbiota profile based on 16S rRNA analysis were included in the present study (age 50.8 ± 12.7 years, mean ± SD). Using a multivariable-adjusted linear mixed-effects model, we examined the prospective association of gut microbiota with glycaemic traits (fasting glucose, fasting insulin, HbA1c and HOMA-IR). We constructed a healthy microbiome index (HMI), and used Poisson regression to examine the relationship between the HMI and incident type 2 diabetes. We evaluated the association of dietary or lifestyle factors with the glycaemic trait-related gut microbiota using a multivariable-adjusted linear regression model. Results After follow-up for 3 years, 123 incident type 2 diabetes cases were identified. We identified 25 gut microbial genera positively or inversely associated with glycaemic traits. The newly created HMI (per SD unit) was inversely associated with incident type 2 diabetes (risk ratio 0.69, 95% CI 0.58, 0.84). Furthermore, we found that several microbial genera that were favourable for the glycaemic trait were consistently associated with healthy dietary habits (higher consumption of vegetable, fruit, fish and nuts). Conclusions/interpretation Our results revealed multiple gut microbiota prospectively associated with glycaemic traits and type 2 diabetes in a geographically diverse population, and highlighted the potential of gut microbiota-based diagnosis or therapy for type 2 diabetes. Data availability The code for data analysis associated with the current study is available at https://github.com/wenutrition/Microbiota-T2D-CHNS Graphical abstract

Uncovering the link between gut microbiome, highly processed food consumption and diet quality through bioinformatics methods

“…In particular, and considering the links among GM, diet and T2D [37], [76], special focus was laid on glycated haemoglobin (HbA1c), a glycaemic trait that, along with fasting glucose (FC), is used to diagnose diabetes [77]. FC, however, was not considered in our work given that the vast majority of participants had normal glucose levels, at least regarding the first visit of this study.…”

Potential of gut microbiota for lipopolysaccharide biosynthesis in European women with type 2 diabetes based on metagenome

“…Moreover, the intestinal taxonomic features at the species level were found to be predictive for incident T2D over the long-term follow-up in Finnish adults ( Ruuskanen et al, 2022 ). Most recently, a population-based longitudinal cohort study in Chinese adults further confirmed the association of gut microbiota with glycemic control and incident T2D, and highlighted the potential of multiple gut microbiota in the diagnosis or therapy for T2D ( Wang et al, 2022 ).…”

Extremely small and incredibly close: Gut microbes as modulators of inflammation and targets for therapeutic intervention

“…Consistently, a study on fecal samples from healthy United States and Spanish participants found a significant difference in GM diversity overall and of specific bacterial genera in particular between the two nationalities ( Allali et al, 2015 ). Even within the same country, GM composition was found to be the number one predictor of whether a person came from Northern or Southern China, with geography having a far larger influence on the GM than ethnicity ( Lu et al, 2021 ; Wang et al, 2022 ). While the diets of different populations in different parts of the world undoubtedly contribute to these differences, these studies still beg the question: what is a healthy GM?…”