Dynamic Changes in Metabolic Syndrome Severity Score and Its Components with Risk of Type 2 Diabetes Mellitus: Insights from Joint Longitudinal and Survival Modeling.

Abstract

Background: Type 2 diabetes mellitus (T2DM) poses a significant global health burden with rising prevalence and costs. Metabolic syndrome is a major risk factor; however, the current binary classification limits risk stratification. Understanding dynamic changes in syndrome severity could improve prediction and prevention.

Objective: To evaluate the association between longitudinal changes of continuous metabolic syndrome severity score (cMetS-S) and its components with incident T2DM risk using joint longitudinal and survival models over an extended follow-up.

Methods: In this prospective cohort study within the Tehran Lipid and Glucose Study, 4990 adults aged 20-60 years without T2DM at baseline were followed for approximately 18 years with repeated metabolic assessments. We examined the association between longitudinal changes in the cMetS-S and its components with the incidence of T2DM, using joint longitudinal and survival models while adjusting for relevant covariates.

Results: During follow-up, 871 participants developed T2DM. Each 1-standard deviation increase in cMetS-S was linked to a 2.56-fold higher risk of T2DM (95% confidence interval [CI]: 2.29-2.86). Among individuals with normal baseline glycemia, the risk increased by 2.94 times (95% CI, 2.56-3.39), while those with prediabetes had a doubled risk (hazard ratio [HR] = 2.94, 95% CI: 1.72-2.34). Regarding individual metabolic components, longitudinal increases in fasting blood glucose (FBS) (HR = 4.62; 95% CI: 3.03-5.93), systolic blood pressure (SBP) (HR = 1.86, 95% CI: 1.10-3.73), triglycerides (TG) (HR = 1.20, 95% CI: 1.08-1.34), and waist circumference (WC) (HR = 1.32, 95% CI: 1.15-1.52) were independently associated with increased T2DM risk.

Conclusions and relevance: Longitudinal increases in the cMetS-S and its components, including FBS, SBP, WC, and TG, significantly elevate the individual risk of developing T2DM. The application of joint multivariate longitudinal and survival modeling provides a refined analytic approach that captures the temporal complexity of metabolic risk and identifies key modifiable determinants of T2DM onset.