Predictive Performance of Seven Clinical Surrogates of Visceral Adipose Tissue for Cardiovascular Mortality: A Sub‐Analysis of 102 385 Adults From the Mexico City Prospective Study

Background Visceral adipose tissue (VAT) has been associated with cardiovascular disease (CVD) mortality. However, the comparative performance of VAT‐related clinical surrogates remains poorly characterised. Objectives To evaluate the performance of seven VAT‐related clinical surrogates for predicting cause‐specific CVD mortality. Methods We analysed data from the Mexico City Prospective Cohort, a population‐based prospective cohort study, with baseline recruitment between 1998 and 2004 and ongoing mortality follow‐up. CVD mortality included deaths from cardiac, stroke‐related and other vascular causes. Seven VAT‐related surrogates (METS‐VF, CVAI, EVA, DAAT, LAP, VAI and DAI) were estimated using clinical, biochemical and anthropometric data at baseline. Associations with outcomes were evaluated using Cox regression models to estimate adjusted hazard ratios (aHR). Discrimination was assessed with Harrell's C‐statistic (Cs) and calibration with slope plots. Results In a subsample of 102 385 participants without diabetes (median age: 47 years; 67% female) followed through a median of 20.13 years, 4068 (3.97%) died from any CVD causes. An increase in 1‐SD unit of METS‐VF (Cs: 0.73; aHR: 1.23, 95% CI: 1.18–1.29), EVA (Cs: 0.73; aHR: 1.19, 1.15–1.24), CVAI (Cs: 0.71; aHR: 1.19, 1.15–1.23) and DAAT (Cs: 0.63; aHR: 1.18, 1.14–1.23) was associated with CVD mortality and showed the highest predictive capacity and good calibration among the surrogates. Adding METS‐VF to the Globorisk score among individuals classified as intermediate risk slightly improved discrimination for CVD mortality. Conclusions In this large cohort of Mexican adults, four VAT‐related clinical surrogates, particularly METS‐VF, showed good discriminatory performance for long‐term CVD mortality. Our results could support future studies that could incorporate VAT estimation to improve CVD risk stratification.