| Title: | Metabolic score for visceral fat as a predictor of new-onset cardiovascular disease in patients with metabolic syndrome: a validation study in two prospective cohorts (UK Biobank and CHARLS) |
| Journal: | BMC Cardiovascular Disorders |
| Published: | 1 Aug 2026 |
| DOI: | https://doi.org/10.1186/s12872-026-06299-2 |
Publication 21238
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Abstract
BackgroundMetabolic syndrome (MetS) substantially increases cardiovascular disease (CVD) risk. The metabolism score for visceral fat (METS-VF) is a novel assessment tool that may substitute visceral adipose tissue measurement. This study investigated the association between METS-VF and new-onset CVD in MetS patients.MethodsThe data from two prospective cohorts: UK Biobank (UKB) and the China Health and Retirement Longitudinal Study (CHARLS). Multivariate Cox regression and restricted cubic spline (RCS) analyses assessed the association between METS-VF and new-onset CVD. The interaction between METS-VF and the CVD polygenic risk score (PRS) was analyzed in the UKB to assess genetic contributions. Receiver operating characteristic (ROC) curves, net reclassification improvement (NRI), and integrated discrimination improvement (IDI) were used to evaluate the discriminative capability of METS-VF for new-onset CVD. Subgroup analyses were performed to confirm the robustness of the results.ResultsA total of 101,292 individuals from the UK Biobank and 1,680 individuals from CHARLS were included. The median follow-up periods were 14.6 years in UKB and 5.0 years in CHARLS. High METS-VF was significantly associated with an increased risk of new-onset CVD in both UKB (HR = 1.63, 95% CI: 1.56-1.71) and CHARLS (HR = 2.11, 95% CI: 1.52-2.94) compared with low METS-VF. Individuals with the highest METS-VF and high genetic risk exhibited the highest risk of new-onset CVD (HR = 2.36, 95% CI: 2.14-2.61).ConclusionsMETS-VF is a valuable indicator for predicting new-onset CVD in individuals with MetS, providing new insights into the prevention and management of CVD in high-risk populations.Trial registrationNot applicable.</p>
8 Authors
- Zhen Tan
- Yijun Liu
- Lei Liu
- Mao Ye
- Xinrui Xue
- Shuang Li
- Xiaoping Li
- Hongqiang Ren
1 Application
| Application ID | Title |
|---|---|
| 106027 | Building a prediction and early warning system of cardiovascular disease based on artificial intelligence |
Enabling scientific discoveries that improve human health