Abstract
Reduced kidney function is a risk factor of cardiovascular and all-cause mortality. This association was demonstrated for several kidney function markers, but it is unclear whether integrating multiple measured markers may improve mortality risk prediction.We conducted an exploratory factor analysis (EFA) of serum creatinine- and cystatin C-based estimated glomerular filtration rate (eGFRcre and eGFRcys, derived by Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) and European Kidney Function Consortium (EKFC) equations), blood urea nitrogen (BUN), uric acid, and serum albumin among 366 758 participants of the UK Biobank without history of kidney failure. Fitting Cox-proportional hazard models, we compared ability of the identified latent factors to predict overall mortality and mortality by cardiovascular disease (CVD), also considering CVD-specific causes like coronary heart disease and cerebrovascular disease.During 12.5 years of follow-up, 26 327 deceased from any cause, 5 376 died from CVD, 2 908 from CHD, and 1 116 from cerebrovascular disease. We identified two latent factors, EFA1 and EFA2 both representing kidney function variations. When using the CKD-EPI equations, EFA1 performed like eGFRcys, with EFA1 showing slightly larger hazard ratios for overall and CVD-related mortality. At 10-years of follow-up, EFA1 and eGFRcys showed moderate discrimination performance for CVD-related mortality, outperforming all other kidney indices. eGFRcre was the least predictive marker across all outcomes. When using the EKFC equations, eGFRcys performed better than EFA1, all other results remaining similar.While EFA is an attractive approach to capture the complex effects of kidney function, eGFRcys remains the most practical and effective measurement for all-cause and CVD mortality risk prediction.