Association of the newly proposed dietary index for gut microbiota and all-cause and cardiovascular mortality among individuals with diabetes and prediabetes

Abstract

Background: The Gut Microbiota Dietary Index (DI-GM) is a newly developed assessment tool that quantitatively evaluates the nutritional modulation of intestinal microbial communities through systematic characterization of diet-microbiome interactions. The relationship between DI-GM and the risk of death has not been elucidated in patients with diabetes or prediabetes. The present cohort study examined the longitudinal relationship between DI-GM scores and both overall mortality and mortality from cardiovascular disease in this clinically vulnerable population.

Method: The investigation used data from the National Health and Nutrition Examination Survey (NHANES) 2007-2018. Analytical approaches, including multivariable-adjusted Cox proportional hazards regression, restricted cubic spline (RCS) modeling, stratified subgroup evaluations, and sensitivity assessments, were employed to examine the relationships linking DI-GM scores to mortality outcomes among individuals with diabetes or prediabetes.

Result: During an average monitoring duration of 77.39 months within the cohort of 8,409 participants, 1,430 fatalities from all causes were documented, including 381 cases attributed to cardiovascular events. Multivariable-adjusted Cox regression analyses showed a negative correlation, with a 1-unit increase in DI-GM corresponding to an 8% lower all-cause mortality risk (HR = 0.92, 95% CI: 0.89-0.95; p < 0.001) and an 11% reduction in cardiovascular-specific mortality (HR = 0.89, 95% CI: 0.83-0.95; p < 0.001). When comparing extreme quartiles of DI-GM distribution, participants in the highest quartile exhibited 26% lower all-cause mortality (HR = 0.74, 95% CI: 0.63-0.87; p < 0.001; trend p < 0.05) and 30% lower cardiovascular mortality (HR = 0.70, 95% CI: 0.52-0.96; p = 0.025; trend p < 0.05) than those in the lowest quartile. Subgroup analyses confirmed the consistency of the results in most categories. Restricted cubic splines demonstrated negative correlations between DI-GM and both mortality outcomes. The Beneficial Gut Microbiota Score (BGMS) exhibited inverse associations with mortality risks, while the Unfavorable Gut Microbiota Score (UGMS) showed no significant relationship. In the sensitive analysis, the robustness of multiple interpolation results was verified by deleting missing data.

Conclusion: Among patients with diabetes or prediabetes, elevated DI-GM levels were negatively correlated with all-cause mortality and cardiovascular mortality risks.

Keywords: all-cause mortality; cardiovascular mortality; diabetes; dietary index of gut microbiota (DI-GM); prediabetes.

Figure 1

Screening process of participants.

Figure 2

The restricted cubic spline (RCS) curves for all-cause mortality (A) and cardiovascular mortality (B) in patients with diabetes or prediabetes, adjusted for age, gender, race, educational attainment, marital status, BMI, PIR, hypertension, drinking, smoking, physical activity, coronary heart disease, stroke, cancer, insulin use status and total cholesterol.

Figure 3

The restricted cubic spline (RCS) curves for all-cause mortality (A) and cardiovascular mortality (B) among patients with diabetes or prediabetes, comparing Beneficial Gut Microbiota Score (BGMS) and Unfavorable Gut Microbiota Score (UGMS). The model was adjusted for age, gender, race, educational attainment, marital status, BMI, PIR, hypertension, drinking, smoking, physical activity, coronary heart disease, stroke, cancer, insulin use status and total cholesterol.

Figure 4

Stratified analysis of mortality from diabetes or prediabetes with DI-GM. Stratified multivariable analyses were performed using Model 3, with all stratification variables systematically excluded from covariate adjustment to avoid over-adjustment bias. This methodological approach ensured that variables used for subgroup categorization were not included as confounders in respective stratified models.