Association of cardiorenal biomarkers with mortality in metabolic syndrome patients: A prospective cohort study from NHANES
- VernacularTitle:Association of cardiorenal biomarkers with mortality in metabolic syndrome patients: A prospective cohort study from NHANES
- Author:
Qianyi GAO
1
;
Shuanglong JIA
;
Xingbo MO
;
Huan ZHANG
Author Information
- Publication Type:Journal Article
- Keywords: cardiovascular disease; machine learning; metabolic syndrome; mortality; NHANES
- From: Chronic Diseases and Translational Medicine 2024;10(4):327-339
- CountryChina
- Language:English
- Abstract: 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, and β2M 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, and β2M emerging as crucial indicators. Further research is needed to explore intervention strategies targeting these biomarkers to improve clinical outcomes.
