ArticleTherapeutic advances in endocrinology and metabolism2026
Association and prognostic value of adiposity distribution indices for all-cause mortality in cardiovascular-kidney-metabolic syndrome stages 0-3: a nationwide study.
Article in Therapeutic advances in endocrinology and metabolism, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Cardiovascular-kidney-metabolic syndrome (CKM) is a major public health challenge. While obesity is a central driver of CKM, body mass index (BMI) fails to adequately reflect body fat distribution and its associated prognostic heterogeneity. Objectives: To evaluate the prognostic value of emerging anthropometric indices reflecting fat distribution in predicting all-cause mortality risk among individuals in the early and intermediate stages of CKM (stages 0-3). Design: A population-based retrospective cohort study utilizing data from the National Health and Nutrition Examination Survey (NHANES) 1999-2006, with mortality linkage follow-up through 2019. Methods: We analyzed 5907 adults with CKM stages 0-3. Eleven adiposity indices were evaluated using four machine learning algorithms (LASSO-Cox, RSF-Boruta, RSF, survival XGBoost) to identify key mortality predictors. Statistical analyses incorporated multivariable Cox proportional hazards regression, restricted cubic splines, false discovery rate (FDR) correction for multiple testing, and a sensitivity analysis across the entire CKM continuum (stages 0-4). Results: Four indices reflecting peripheral-to-central adiposity predominance (TCWR, CWR, AWR, SWR) were consistently identified as key predictors. Higher levels independently correlated with lower all-cause mortality (all FDR-adjusted Conclusion: Peripheral-to-central adiposity indices are strong predictors of mortality in CKM stages 0-3, providing prognostic information beyond BMI and serving as practical tools for early risk stratification. Future studies should evaluate their dynamic changes and validate their applicability across diverse populations.
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