ArticleClinical kidney journal2024
Integrating multiple kidney function markers to predict all-cause and cardiovascular disease mortality: prospective analysis of 366 758 UK Biobank participants.
Article in Clinical kidney journal, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Different eGFR markers and prediction of cardiovascular risk.Journal of internal medicine · 2026Trial
- Combined effects of diet and genetic predisposition on kidney function outcomes.Clinical kidney journal · 2026Article
- Cardiac Biomarkers Predict Major Adverse Cardiac Events (MACE) in Incident Haemodialysis Patients: Results from a Global Federated Database.Biomedicines · 2025Article
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Authors and funding
6 authors.
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Abstract
Background: 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. Methods: We conducted an exploratory factor analysis (EFA) of serum creatinine- and cystatin C-based estimated glomerular filtration rate [eGFRcre and eGFRcys; derived by the 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 in the UK Biobank without a history of kidney failure. Fitting Cox proportional hazards models, we compared the 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 (CHD) and cerebrovascular disease. Results: During 12.5 years of follow-up, 26 327 participants died from any cause, 5376 died from CVD, 2908 died from CHD and 1116 died from cerebrovascular disease. We identified two latent factors, EFA1 and EFA2, both representing kidney function variations. When using the CKD-EPI equation, 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 equation, eGFRcys performed better than EFA1 while all other results remaining similar. Conclusions: 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.
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