Cystatin C- and Creatinine-based Estimated GFR Differences: Prevalence and Predictors in the UK Biobank.

ABSTRACT

Rationale & Objective: Large differences between estimated glomerular filtration rate (eGFR) based on cystatin C (eGFRcys) and creatinine (eGFRcr) occur commonly. A comprehensive evaluation of factors that contribute to these differences is needed to guide the interpretation of discrepant eGFR values. Study Design: Cohort study. Setting & Participants: 468,969 participants in the UK Biobank. Exposures Candidate sociodemographic, lifestyle factors, comorbidities, medication usage, and physical and laboratory predictors. Outcomes: eGFRdiff, defined as eGFRcys minus eGFRcr, categorized into 3 levels lower eGFRcys (eGFRdiff, less than -15 mL/min/1.73 m2), concordant eGFRcys and eGFRcr (eGFRdiff, -15 to < 15 mL/min/1.73 m2), and lower eGFRcr (eGFRdiff, ≥15 mL/min/1.73 m2). Analytical Approach: Multinomial logistic regression models were constructed to identify predictors of lower eGFRcys or lower eGFRcr. We developed 2 prediction models comprising 375,175 participants: (1) a clinical model using clinically available variables and (2) an enriched model additionally including lifestyle variables. The models were internally validated in an additional 93,794 participants. Results: Mean ± standard deviation of eGFRcys was 88 ± 16 mL/min/1.73 m2, and eGFRcr was 95 ± 13 mL/min/1.73 m2; 25% and 5% of participants were in the lower eGFRcys and lower eGFRcr groups, respectively. In the multivariable enriched model, strong predictors of lower eGFRcys were older age, male sex, South Asian ethnicity, current smoker (vs never smoker), history of thyroid dysfunction, chronic inflammatory disease, steroid use, higher waist circumference and body fat, and urinary albumin-creatinine ratio >300 mg/g. Odds ratio estimates for these predictors were largely inverse of those in the lower eGFRcr group. The model's area under the curve was 0.75 in the validation set, with good calibration (1.00). Limitations: Limited generalizability. Conclusions: This study highlights the multitude of demographic, lifestyle, and health characteristics that are associated with large eGFRdiff. The clinical model may identify individuals who are likely to have discrepant eGFR values and thus should be prioritized for cystatin C testing.

Estimated glomerular filtration rate (eGFR) based on cystatin C and creatinine may differ substantially within an individual. Although most clinicians are aware that creatinine is influenced by muscle mass, there are additional numerous lifestyle and health characteristics that may affect serum concentrations of either biomarker. Our analyses of 468,969 individuals in the UK Biobank identified independent predictors of large differences between eGFR based on cystatin C and eGFR based on creatinine, which may inform the interpretation of discrepant eGFR values within an individual. We developed models that may be implemented at a population level to help health systems identify individuals who are likely to have large differences between eGFR based on cystatin C and eGFR based on creatinine and thus should be prioritized for cystatin C testing.