Gut microbiome dysbiosis as a predictive marker for metabolic syndrome onset in overweight young adults: a prospective observational study

Abstract

Background: Metabolic syndrome (Met-S) is a significant worldwide health burden, and it has been observed to increase prevalence among overweight youth adults.It is posited that emerging evidence indicates that a state of gut microbiome dysbiosis can be the antecedent to the clinical expression of the abnormal metabolism states; nevertheless, longitudinal prospective evidence on younger cohorts is scarce.

Objective: The goal of this study was to assess the ability of baseline gut microbiome composition as a predictive biomarker of Met-S onset during a 24-month follow-up period in overweight young adults.

Methods: A potentially observational pre-post cohort consisting of 210 obese adults (18-30 years) without Met-S at baseline was identified.At baseline, gut microbiome was profiled by 16S rRNA gene sequencing (V37V4hypervariable areas).Fasting blood glucose, lipid and blood pressure, waist and HOMA-IR were evaluated at baseline, 12 months and 24 months.The main result was incident Met-S with an International Diabetes Federation definition.Multivariate logistic regression and receiver operating characteristic (ROC) analysis and a multifaceted Combined Dysbiosis Index (CDI) was constructed and verified.

Results: A total of 58/24 months (27.6% Met-S developed) had significantly less alpha diversity (Shannon Index: 3.41 vs. 4.18, p<0.001), a higher Firmicutes/Bacteroidetes (F/B) ratio (3.82 vs. 2.21, p<0.001), and fewer AkkermansCDI, which combines various microbiome parameters, had an AUC of 0.88 (95% CI: 0.82 0.93) predicting Met-S, better than that of single clinical markers.Longitudinal findings demonstrated that there was gradual changes in microbial composition and metabolic parameters between groups.

Conclusion: Baseline dysbiosis of the gut microbiome is a strong independent predictor of the development of Met-S in overweight young adults. The CDI provides clinically translatable solution to early risk stratify, which can be used to integrate microbiome profiling in preventive metabolic healthcare.