Can creatinine-based eGFR equations misestimate kidney filtration?

Creatinine-based estimated glomerular filtration rate (eGFR) equations serve as the primary tool for assessing kidney health, yet their accuracy is limited by the non-renal variables that govern serum creatinine production. Because creatinine is a metabolic byproduct of muscle creatine, baseline levels fluctuate independently of kidney function.

Clinical impacts of non-renal variation

• Underestimation in high muscle mass: Individuals with high muscle mass generate more baseline creatinine, which falsely depresses eGFR calculations.
• Overestimation in muscle wasting: Conversely, older age, female sex, and conditions like sarcopenia or cachexia reduce baseline creatinine production. In these populations, eGFR equations can overestimate true kidney filtration by 10 to 20 mL/min/1.73 m², potentially masking advanced chronic kidney disease.

Biological and genetic mechanisms

• Creatine metabolism: Baseline serum creatinine is directly modulated by muscle mass and demographic factors rather than kidney filtration alone.
• GATM genetic variants: Genetic variation in the GATM gene can increase creatine synthesis, raising baseline serum creatinine levels and falsely lowering eGFR without any actual change in kidney filtration.

Clinical strategies for improved accuracy

• Combined biomarkers: Utilizing combined creatinine-cystatin C equations (eGFRcr-cys) integrates a muscle-mass independent biomarker, significantly reducing the misestimation of kidney filtration.
• Direct clearance: In cases of extreme demographics or altered body composition, guidelines support using direct exogenous clearance measurements to obtain an accurate GFR.

Bottom line

• Creatinine-based eGFR equations can significantly misestimate true kidney filtration due to non-renal confounders like age, sex, muscle mass, and GATM genetic variations; utilizing combined creatinine-cystatin C equations (eGFRcr-cys) or direct clearance measurements offers a highly reliable alternative for clinical decision-making.