Impact of the choice of biomarkers and equations to estimate kidney function on the epidemiology of chronic kidney disease.

Purpose of review: The CKD-EPI equations were updated in 2021 to remove the race variable from eGFR estimation. In the same year, the creatinine-based EKFC equation was published, subsequently supplemented by the cystatin C-based EKFC equation. Recent findings suggest that the prevalence of chronic kidney disease (CKD) can vary depending on the equation, the biomarker, and the population studied.

Recent findings: Using the CKD-EPI 2021 equation instead of the CKD-EPI 2009 equation results in an increased prevalence of CKD among Black individuals in the U.S. and a decreased prevalence among non-Blacks. The CKD-EPI equations may underestimate the prevalence of CKD in India and in some sub-Saharan African populations. This is corrected by using the EKFC equation and dedicated Q-values. In general, the prevalence of CKD is slightly higher with EKFC than with the CKD-EPI equations. The CKD-EPI cys equation generally leads to a higher CKD prevalence than the CKD-EPIcrea equations. Few epidemiological data are available for EKFC cys .

Summary: The choice of biomarkers and equations has an impact on the prevalence of CKD, with implications that also depend on the characteristics of the population being studied.