Population-specific reference intervals for cystatin C, creatinine and urea, and their impact on diagnostic classification and eGFR stratification in Nigerian adults.

OBJECTIVES

To establish population-specific reference intervals (RIs) for serum cystatin C, creatinine, and urea among healthy Nigerian adults and evaluate the potential diagnostic implications of applying manufacturer-derived RIs established in non-African populations.

DESIGN &

METHODS

A cross-sectional reference interval study was conducted among 243 apparently healthy adults (120 males and 123 females), aged 18-65 years, recruited in Abuja, Nigeria. Serum cystatin C, creatinine, and urea were measured using standardized laboratory methods. RIs were derived non-parametrically as the 2.5th-97.5th percentiles in accordance with CLSI C28-A3 recommendations. The need for sex-specific partitioning was assessed using the Lahti method. Diagnostic misclassification was evaluated by comparing classifications based on locally derived and manufacturer-provided RIs using McNemar's test. Estimated glomerular filtration rate (eGFR) was calculated using the CKD-EPI 2021 equations.

RESULTS

Creatinine and cystatin C required sex-specific partitioning, whereas urea did not. The combined RI for urea was 2.55-6.99 mmol/L. Creatinine RIs were 52.07-116.82 μmol/L in males and 44.47-96.23 μmol/L in females. Cystatin C RIs were 0.30-2.07 mg/L in males and 0.17-1.94 mg/L in females. Compared with locally derived RIs, manufacturer-derived intervals classified substantially more healthy individuals as abnormal for cystatin C (65.9% vs 5.8%; net reclassification +60.1%, p < 0.001) and female creatinine (52.5% vs 5.0%; +47.5%, p < 0.001), with minimal discordance for urea. Most participants (87.2%) were classified within KDIGO G1-G2 eGFR categories.

CONCLUSION

Locally derived kidney biomarker RIs differ substantially from manufacturer-provided intervals in this Nigerian population, particularly for cystatin C. Reliance on externally derived RIs may substantially overclassify kidney dysfunction and contribute to inappropriate clinical decision-making. Population-specific validation should be prioritized to improve diagnostic accuracy and CKD assessment in African populations.