Ren Fail. 2026 Dec;48(1):2646035. doi: 10.1080/0886022X.2026.2646035. Epub 2026 Apr 22.
ABSTRACT
Individual cardiometabolic risk factors (CMRFs) are contributors to reduced kidney function, their cumulative impact and population-specific differences remain unclear. This cross-sectional study investigated the association between incremental CMRFs and estimated glomerular filtration rate (eGFR). We analyzed data from two nationally representative surveys, China Health and Retirement Longitudinal Study wave 3 (N = 10,043) and the U.S. Health and Retirement Study wave 13 (N = 9,373), comprising 19,416 participants aged ≥45 years. Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equations, integrating serum creatinine and cystatin C, were applied to calculate eGFRscr-cysc, categorized into normal (≥90 mL/min/1.73 m2), mildly decreased (60-89 mL/min/1.73 m2), and moderately/severely decreased (<60 mL/min/1.73 m2). Multinomial logistic regression was employed to assess the association between cumulative CMRFs (0-5) and eGFRscr-cysc categories, adjusting for covariables. Analyses were conducted on unweighted data without applying complex survey sampling weights. A graded association between the cumulative burden of CMRFs and eGFRscr-cysc decline was observed. Each additional CMRF was associated with an increased risk of mild eGFRscr-cysc decline in the fully adjusted model (relative risk ratio [RRR] = 1.141, 95% confidence interval [95% CI]: 1.108-1.175) and moderate/severe eGFRscr-cysc decline (RRR = 1.433, 95% CI: 1.364-1.505). Participants with five CMRFs had a significantly higher risk of moderate/severe eGFRscr-cysc decline compared to those without CMRFs (RRR = 6.178; 95% CI: 3.718-10.268). Predictive margins showed that eGFRscr-cysc decline was more prevalent in the American population compared to the Chinese population. These cross-sectional findings suggest that integrating routine screening and management of cardiometabolic risk factors into CKD prevention programs may help protect kidney health in aging populations.
PMID:42020337 | DOI:10.1080/0886022X.2026.2646035