Diabetol Metab Syndr. 2025 Jul 11;17(1):263. doi: 10.1186/s13098-025-01846-x.
ABSTRACT
BACKGROUND: Nutrient interactions with the gut microbiome modulate the development of metabolic dysfunction-associated steatotic liver disease (MASLD) and cardiovascular disease. The dietary index for gut microbiota (DI-GM) is an innovative and comprehensive diet index to assess quality for health of gut microbiota.
METHOD: This is a cohort study from the interview date to the date of death or the end of follow-up (December 31, 2019). Involving 13,390 participants in National Health and Nutrition Examination Survey (NHANES), including 3538 with MASLD and 9852 without MASLD. DI-GM was calculated using 14 foods and nutrients with clear positive or negative impacts on gut microbiota, and MASLD was assessed based on liver steatosis and cardiometabolic risk factors, with all-cause and cardiovascular mortality determined through probabilistic matching and death certificate review. Restricted Cubic Spline (RCS) analysis and Cox regression were palyed for the DI-GM-mortality correlation. Subgroup analyses to identify the interactive factors that influence their relationship in MASLD. Six sensitivity analyses reinforced findings.
RESULTS: MASLD participants exhibited lower DI-GM levels, which were statistically associated with higher mortality. Each DI-GM unit increase in MASLD was associated with a 13% lower all-cause mortality (HR = 0.87, 95% CI 0.78-0.98) and a 19.5% lower cardiovascular mortality (HR = 0.805, 95% CI 0.690-0.938). In advanced fibrosis MASLD, this increase was linked to a 20% lower cardiovascular mortality risk (HR = 0.800, 95% CI 0.691-0.927). Age and prediabetes significantly modified DI-GM’s effect on mortality risk.
CONCLUSIONS: The study revealed a significant inverse correlation between the DI-GM and all-cause/cardiovascular mortality in patients with MASLD, which provide dietary suggestions and guidance for MASLD patients in preventing early mortality. However, limitations such as the cross-sectional design, potential residual confounding, and population-specific generalizability should be considered when interpreting these findings.
PMID:40646653 | DOI:10.1186/s13098-025-01846-x
