J Am Soc Nephrol. 2026 Jul 9. doi: 10.1681/ASN.0000001201. Online ahead of print.
ABSTRACT
Recognizing its clinical utility, the 2024 Kidney Disease Improving Global Outcomes (KDIGO) guideline recommends using equations that integrate serum creatinine and cystatin C for chronic kidney disease (CKD) diagnosis and staging. Cystatin C-inclusive equations generally outperform creatinine-based equations in bias and accuracy across diverse populations, including children and patients with comorbidities. Moreover, cystatin C-inclusive equations consistently demonstrate greater prognostic value than creatinine-only equations for clinical outcomes such as kidney failure with replacement therapy (KFRT), cardiovascular disease, and mortality, particularly in individuals with large differences between creatinine- and cystatin C-based estimates of glomerular filtration rate (eGFR). Among hospitalized patients and those undergoing significant changes in muscle mass, such as during weight loss or critical illness, cystatin C may improve GFR estimation and facilitate earlier detection of acute kidney injury. In clinical practice, cystatin C informs CKD staging, risk stratification, and medication dosing, particularly in scenarios where creatinine-based equations are less reliable. Although the integration of cystatin C into risk equations has shown mixed results, its inclusion enhances calibration in populations with frailty or large eGFR differences. Emerging evidence also supports the use of cystatin C in cancer care and kidney transplantation. Despite its promise, widespread clinical adoption of cystatin C faces several barriers, including in-house availability. To surmount these barriers, collaboration between nephrology and laboratory services is needed. As evidence grows, cystatin C-based approaches have the potential to transform CKD detection and management. This paper synthesizes recent evidence on cystatin C's role in GFR estimation, prognostic utility, and clinical applications, highlighting emerging areas and strategies for its implementation in healthcare systems.
PMID:42424121 | DOI:10.1681/ASN.0000001201