Assessment of GFR Estimation Methods in Patients With Obesity

Area of Science:

• Nephrology
• Metabolic Disorders
• Biostatistics

Background:

• Obesity is a significant risk factor for chronic kidney disease (CKD).
• Accurate estimation of glomerular filtration rate (GFR) is essential for managing CKD.
• Limited data exist on the performance of the European Kidney Function Consortium (EKFC) equation in individuals with overweight or obesity.

Purpose of the Study:

• To evaluate the performance of the non-indexed EKFC equation in estimating GFR.
• To compare the EKFC equation's performance against the 2021 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation and machine learning (ML) models.
• To assess GFR estimation accuracy in a cohort of patients with overweight or obesity using measured GFR (mGFR) as a reference.

Main Methods:

• Calculated median bias (eGFR minus mGFR) for each GFR estimation method.
• Determined the concordance correlation coefficient for the EKFC equation, CKD-EPI 2021 equation, and ML models.
• Utilized plasma iohexol clearance for measuring GFR (mGFR) in the study cohort.

Main Results:

• The non-indexed EKFC equation demonstrated the highest median bias, particularly in patients with overweight and obesity.
• EKFC equation showed lower concordance with mGFR compared to CKD-EPI 2021 and ML models.
• EKFC was the least accurate for CKD stage classification in obese patients, especially those with hyperfiltration.

Conclusions:

• The EKFC equation performs worse than ML-based models and the CKD-EPI 2021 equation in estimating GFR for individuals with overweight or obesity.
• There is a need for improved GFR estimation methods in overweight and obese populations.
• Further validation of optimized GFR estimation models in larger, diverse cohorts is recommended.