Dynamic Changes in Metabolic Syndrome Severity Score and Its Components with Risk of Type 2 Diabetes Mellitus:...

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.

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.

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.

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.

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.