Association of cardiorenal biomarkers with mortality in metabolic syndrome patients:A prospective cohort study from NHANES  

作　　者：Qianyi Gao Shuanglong Jia Xingbo Mo Huan Zhang 

机构地区：[1]Department of Epidemiology,Jiangsu Key Laboratory of Preventive and Translational Medicine for Geriatric Diseases,MOE Key Laboratory of Geriatric Diseases and Immunology,School of Public Health,Suzhou Medical College of Soochow University,Suzhou,Jiangsu,China [2]Center for Genetic Epidemiology and Genomics,School of Public Health,Suzhou Medical College of Soochow University,Suzhou,Jiangsu,China

出　　处：《Chronic Diseases and Translational Medicine》2024年第4期327-339,共13页慢性疾病与转化医学（英文版）

基　　金：Project of the Priority Academic Program Development of Jiangsu Higher Education Institutions;Startup Fund from Soochow University(Grant/Award Numbers:Q413900313,Q413900412);National Natural Science Foundation of China(Grant/Award Numbers:82073636,82173597)。

摘　　要：Objectives:Approximately 20%-25%of the global adult population is affected by metabolic syndrome(MetS),highlighting its status as a major public health concern.This study aims to investigate the predictive value of cardiorenal biomarkers on mortality among patients with MetS,thus optimizing treatment strategies.Methods:Utilizing data from the National Health and Nutrition Examination Survey(NHANES)cycles between 1999 and 2004,we conducted a prospective cohort study involving 2369 participants diagnosed with MetS.We evaluated the association of cardiac and renal biomarkers with all-cause and cardiovascular disease(CVD)mortality,employing weighted Cox proportional hazards models.Furthermore,machine learning models were used to predict mortality outcomes based on these biomarkers.Results:Among 2369 participants in the study cohort,over a median follow-up period of 17.1 years,774(32.67%)participants died,including 260(10.98%)from CVD.The highest quartiles of cardiac biomarkers(N-terminal pro-B-type natriuretic peptide[NT-proBNP])and renal biomarkers(beta-2 microglobulin,[β2M])were significantly associated with increased risks of all-cause mortality(hazard ratios[HRs]ranging from 1.94 to 2.06)and CVD mortality(HRs up to 2.86),after adjusting for confounders.Additionally,a U-shaped association was observed between high-sensitivity cardiac troponin T(Hs-cTnT),creatinine(Cr),and all-cause mortality in patients with MetS.Machine learning analyses identified Hs-cTnT,NT-proBNP,andβ2M as important predictors of mortality,with the CatBoost model showing superior performance(area under the curve[AUC]=0.904).Conclusion:Cardiac and renal biomarkers are significant predictors of mortality in MetS patients,with Hs-cTnT,NT-proBNP,andβ2M emerging as crucial indicators.Further research is needed to explore intervention strategies targeting these biomarkers to improve clinical outcomes.

关 键 词：cardiovascular disease machine learning metabolic syndrome MORTALITY NHANES 

分 类 号：R589[医药卫生—内分泌]