Disease Progression Modeling of Estimated Glomerular Filtration Rate (eGFR): A Pharmacometrics Approach

Abstract

Background

Estimated glomerular filtration rate (eGFR) is a key clinical marker for assessing kidney complications in type 2 diabetes mellitus (T2DM). This study aimed to develop and validate a disease progression model of eGFR in Malaysian T2DM patients, with and without diabetic nephropathy (DN).

Methods

Retrospective data from 251 patients (3241 observations) were analyzed using NONMEM software. Baseline eGFR was assessed without covariates, and both linear and non-linear models were tested. Model selection was based on the likelihood ratio test (5% significance level), objective function value (OFV), visual predictive check (VPC), relative standard error, and scientific plausibility. External validation was performed using data from 109 patients.

Results

A linear model best described disease progression, with a baseline eGFR of 84.6 mL/min/1.73 m² and a decline rate of −0.0041 mL/min/1.73 m²/year. Cardiovascular disease (CVD) reduced eGFR by 1.05 mL/min/1.73 m²/year, while fasting blood sugar (FBS) above 7.4 mmol/L correlated with an additional decline of 0.043 mL/min/1.73 m²/year. Angiotensin receptor blockers (ARBs) improved eGFR by 0.4 mL/min/1.73 m²/year, whereas statins and metformin contributed improvements of 0.34 and 0.32 mL/min/1.73 m²/year, respectively. External validation confirmed model consistency with observed data.

Conclusion

Glycaemic control and CVD significantly impact eGFR decline. ARBs, statins, and metformin help preserve kidney function. Effective glycaemic management is crucial in slowing kidney deterioration, especially in T2DM patients at risk for DN.