Abstract
PURPOSE: Metabolic syndrome (MetS) is an increasingly prevalent issue in China's public health landscape. Few studies have investigated the metabolic syndrome (MetS) in overweight people. We proposed to analyze and contrast the occurrence of MetS in normal-weight and overweight individuals and identify potential indicators for forecasting MetS in adults in Zhejiang Province. METHODS: This cohort study included 359 adults aged 40-65 years and followed up for five years in Zhejiang Province. The study assessed the predictive capabilities of five indicators linked to obesity and lipid levels, namely body mass index (BMI), waist-to-height ratio (WHtR), triglyceride-glucose index (TyGi), and their combined indices (TyG-BMI, TyG-WHtR). The evaluation was done employing the area under the Receiver Operating Characteristic (ROC) Curve (AUC). DeLong test was applied to compare area under different ROC curves.We evaluated the relationships between five variables and MetS using multivariate logistic regression. RESULTS: In normal-weight individuals, the five-year cumulative incidence of MetS was 21.85%, but in overweight people, it was 60.33%. After adjusting for confounding factors, BMI, WHtR, TyGi, TyG-BMI, and TyG-WHtR were independently linked to MetS in normal-weight individuals, while BMI, TyGi, TyG-BMI, and TyG-WHtR were independently linked to MetS in overweight individuals. In normal-weight individuals, the WHtR (AUC=0.738 and optimal threshold value =0.469) and TyG-WHtR (AUC=0.731 and optimal threshold value =4.121) had the larger AUC, which was significantly greater than that of the different three indicators. The TyG-BMI (AUC=0.769 and optimal threshold value = 211.099) was the best predictor of MetS in overweight individuals. CONCLUSION: The five-year cumulative incidence of MetS in overweight people was approximately triple that of normal-weight people in Zhejiang Province. In the overweight population, the TyG-BMI performed better than the other indices in predicting MetS. WHtR and TyG-WHtR outperformed BMI, TyGi, and TyG-BMI in anticipating MetS in a normal-weight population.