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Impact of Specific Metabolic Syndrome Combinations on Model-Estimated 10-Year Cardiovascular Risk in a Taiwanese
Tsung-Min Yeh1,2, Kuang-Chen Hung3,4,5,6, Chia-Lien Hung7
1Department of Family Medicine, Taichung Armed Forces General Hospital, No. 348, Sec. 2, Zhongshan Road, Taiping District, Taichung City 411, Taiwan.
None:
Background: Metabolic syndrome (MetS) affects over 30% of the global population and is closely linked to higher cardiovascular (CV) morbidity and mortality. Although MetS is recognized as a significant CV risk factor, limited studies have examined which specific combinations of MetS components are associated with long-term predicted CV risk. Furthermore, limited evidence exists using established 10-year CV risk-prediction models in Asian populations. Methods: We analyzed data from 111,695 Taiwanese adults aged 30-75 years who underwent health screenings from 2007 to 2022. Predicted CV risk was estimated using the Framingham Risk Score (FRS) and Atherosclerotic Cardiovascular Disease (ASCVD) Risk Estimator at baseline and at 5- and 10-year follow-ups. Cox regression models adjusted for clinical variables were applied to evaluate the association between different MetS patterns and progression in estimated 10-year CV risk. Results: Of the 111,695 participants, 4435 had persistent MetS with the same exact three components at both baseline and follow-up. Among the MetS combinations, the TFB pattern (elevated triglycerides, fasting glucose, and blood pressure) was consistently associated with greater progression in predicted 10-year CV risk over 5- and 10-years follow-up periods in both the FRS (HR = 1.189-1.204) and ASCVD (HR = 1.144-1.146) models (all p < 0.05). Although the effect sizes were modest, the associations were consistent across models and time points. Conclusions: The TFB pattern was consistently associated with greater progression in predicted 10-year cardiovascular risk across both the FRS and ASCVD models. These findings suggest that evaluating specific MetS patterns may provide additional value beyond the total number of components and may help clinicians prioritize high-risk individuals for targeted screening and early intervention.
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