Dietary fruit and vegetable intake, gut microbiota, and type 2 diabetes: results from two large human cohort studies

Abstract

Background: Little is known about the inter-relationship among fruit and vegetable intake, gut microbiota and metabolites, and type 2 diabetes (T2D) in human prospective cohort study. The aim of the present study was to investigate the prospective association of fruit and vegetable intake with human gut microbiota and to examine the relationship between fruit and vegetable-related gut microbiota and their related metabolites with type 2 diabetes (T2D) risk.

Methods: This study included 1879 middle-age elderly Chinese adults from Guangzhou Nutrition and Health Study (GNHS). Baseline dietary information was collected using a validated food frequency questionnaire (2008-2013). Fecal samples were collected at follow-up (2015-2019) and analyzed for 16S rRNA sequencing and targeted fecal metabolomics. Blood samples were collected and analyzed for glucose, insulin, and glycated hemoglobin. We used multivariable linear regression and logistic regression models to investigate the prospective associations of fruit and vegetable intake with gut microbiota and the association of the identified gut microbiota (fruit/vegetable-microbiota index) and their related fecal metabolites with T2D risk, respectively. Replications were performed in an independent cohort involving 6626 participants.

Results: In the GNHS, dietary fruit intake, but not vegetable, was prospectively associated with gut microbiota diversity and composition. The fruit-microbiota index (FMI, created from 31 identified microbial features) was positively associated with fruit intake (p < 0.001) and inversely associated with T2D risk (odds ratio (OR) 0.83, 95%CI 0.71-0.97). The FMI-fruit association (p = 0.003) and the FMI-T2D association (OR 0.90, 95%CI 0.84-0.97) were both successfully replicated in the independent cohort. The FMI-positive associated metabolite sebacic acid was inversely associated with T2D risk (OR 0.67, 95%CI 0.51-0.86). The FMI-negative associated metabolites cholic acid (OR 1.35, 95%CI 1.13-1.62), 3-dehydrocholic acid (OR 1.30, 95%CI 1.09-1.54), oleylcarnitine (OR 1.77, 95%CI 1.45-2.20), linoleylcarnitine (OR 1.66, 95%CI 1.37-2.05), palmitoylcarnitine (OR 1.62, 95%CI 1.33-2.02), and 2-hydroglutaric acid (OR 1.47, 95%CI 1.25-1.72) were positively associated with T2D risk.

Conclusions: Higher fruit intake-associated gut microbiota and metabolic alteration were associated with a lower risk of T2D, supporting the public dietary recommendation of adopting high fruit intake for the T2D prevention.

Keywords: Cohort; Fruit and vegetable; Gut microbiota; Metabolites; Type 2 diabetes.

Fig. 1

The prospective association of fruit intake with the overall human gut microbiota in the Guangzhou Nutrition and Health Study. a–c Results of different α-diversity matrix. a Observed species. b Chao 1’s diversity parameter. c Shannon’s diversity parameter. Multivariable linear regression was used to estimate the difference in α-diversity comparing extreme quartiles (quartile 4 versus quartile 1) of fruit intake, adjusted for Bristol stool score, sequencing run, sequencing depth, age, sex, BMI, physical activity, education, income, smoking status, alcohol status, drug use (medications for hypertension, hyperlipidemia and T2D), T2D status, total energy intake, dietary intakes of vegetable, red and processed meat, fish and dairy products. d β-diversity: principal coordinate analysis (PCoA) plot based on Bray-Cutis distance at operational taxonomic unit (OTU) level. Permutational ANOVA (PERMANOVA) (999 permutations) was used to identify the variation of β-diversity in human gut microbiota structure comparing extreme quartiles of fruit intake, adjusted for the same covariates. e MaAsLin was used to identify the gut microbial biomarkers for fruit intake comparing extreme quartiles of fruit intake, adjusted for the same covariates. The Benjamini-Hochberg method was used to adjust p values for multiple testing. Value with asterisk is significantly different (*p < 0.05, ** p < 0.01, ***p < 0.001)

Fig. 2

Relationships among the fruit intake, fruit-gut microbiota index, and type 2 diabetes. a Multivariable linear regression was used to estimate the associations of fruit intake with fruit-microbiota index (FMI) in all participants in the Guangzhou Nutrition and Health Study (GNHS), and the Guangdong Gut Microbiome Project (GGMP). b Multivariable linear regression was used to estimate the associations of fruit intake with FMI in non-T2D participants in the GNHS and GGMP. c Multivariable logistic regression was used to estimate the association of FMI (per standardized unit increase) with type 2 diabetes (T2D) risk in the GNHS and GGMP respectively. The effect estimates from GNHS and GGMP were pooled using random effects meta-analysis for each of the above analyses

Fig. 3

Association of the fruit-microbiota index-related fecal metabolites and type 2 diabetes. Multivariable logistic regression was used to examine the association of the fruit-microbiota index (FMI)-related fecal metabolites (per standardized unit increase) with type 2 diabetes (T2D) risk in the Guangzhou Nutrition and Health Study (133 cases/1017 participants), adjusted for Bristol stool score, sequencing run, sequencing depth, age, sex, BMI, physical activity, education, income, smoking status, alcohol status, drug use (medications for hypertension, hyperlipidemia, and T2D), total energy intake, dietary intakes of vegetable, red and processed meat, fish, and dairy products. “FMI-positive” and “FMI-negative” represented that fecal metabolites had positive and negative association with FMI, respectively. The Benjamini-Hochberg method was used to control the false discovery rate due to multiple testing. Adjusted p value < 0.05 is significantly different