Key Points Studies on GFR assessment in people with cancer are needed. In this study, the creatinine–cystatin C equations performed better than other equations, and this was observed in solid and hematological cancers. Our findings give support to the preferential use of creatinine and cystatin C–based equations in people with cancer. Background GFR assessment is important in clinical practice with implications for diagnosis, prognostication, and drug dosing. People with cancer are at risk of imprecision in GFR estimation. This cross-sectional study evaluated the performance of various creatinine and cystatin C–based equations in comparison with measured GFR (mGFR) in people with cancer. Methods We retrieved data for all adult patients who had mGFR by urinary iothalamate clearance between 2011 and 2023 at Mayo Clinic and use of an electronic health record diagnosis code for cancer within 2 years before mGFR. The CKD Epidemiology Collaboration (CKD-EPI), European Kidney Function Consortium, and Cockcroft–Gault equations were computed, along with performance metrics (bias, precision, and root mean square error [RMSE]. Confidence intervals were generated by bootstrapping, and analysis were stratified by solid and hematological cancers. Results From all adults with cancer and mGFR, 1145 had both creatinine and cystatin C available within 7 days of mGFR. Among all equations, the creatinine–cystatin C CKD-EPI equation provided the best performance, with small bias (median, 3.0; 95% confidence interval, 2.3 to 3.8) and higher precision (RMSE, 14.5) compared with creatinine-only or cystatin C–only equations (RMSE varying from 16.6 to 20), and this was also true in solid and hematological cancers. The creatinine–cystatin European Kidney Function Consortium equation had a similar performance to CKD-EPI, Cockcroft–Gault showed worst precision (30% of people with errors above 30%), and cystatin C CKD-EPI equation was the most biased, prone to underestimation of mGFR. Conclusions In our cohort of patients with mGFR and cancer, the CKD-EPI creatinine–cystatin C equation performed best for GFR assessment, and this was true for both solid and hematological cancers. Our findings give support for the preferential use of creatinine and cystatin C–based equations instead of creatinine-only or cystatin C–only equations in people with cancer. Podcast This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2025_03_25_CJASNMar20.3.3252025.mp3
Biomarker-guided infliximab therapy for immune checkpoint inhibitor-induced acute interstitial nephritis
Nuclear Magnetic Resonance-Based Glomerular Filtration Rate Estimation Is More Closely Associated with Serum Vancomycin Target Trough Achievement vs. Serum Creatinine-Based Equations
Correction: Stämmler et al. Estimating Glomerular Filtration Rate from Serum Myo-Inositol, Valine, Creatinine and Cystatin C. Diagnostics 2021, 11, 2291
Key Points Common kidney stones are unlikely to be an independent and direct cause of CKD in the general population. CKD may protect against kidney stones because of changes in key urinary factors critical for stone formation. Background Kidney stones and CKD are common disorders with a substantial interaction. Although observational studies have suggested a potential for enhanced CKD risk after prior kidney stones, the exact relationship remains ambiguous. Methods Shared comorbidities between two diseases were identified using unbiased screening. Genome-wide association study summary statistics were obtained from the UK Biobank (UKBB), FinnGen, and CKDGen, followed by genetic association analyses across various traits. Bidirectional Mendelian randomization (MR) analyses were performed to define causal links, complemented by multivariable MR that included the shared comorbidities including hypertension, diabetes, and obesity. Observational analyses were undertaken using cohorts from the Mayo Clinic and a UKBB subset. Results Despite identifying a total of 123 conditions as shared comorbidities, there was no significant genetic correlation between kidney stones and CKD. Unadjusted MR analysis revealed no significant association between kidney stones and CKD risk (UKBB [exposure]/FinnGen [outcome]: odds ratio [OR]=0.97, 95% confidence interval [CI], 0.88 to 1.06; FinnGen/UKBB: OR=1.17, 95% CI, 0.98 to 1.39). Kidney stones did significantly associate with a higher urinary albumin-creatinine ratio ( β =0.014, 95% CI, 0.002 to –0.025), but this association disappeared in the multivariable MR model ( β =0.009, 95% CI, −0.003 to 0.020). Furthermore, in a cross-sectional analysis limited to the UKBB cohort, a robust regression model did not detect an independent association between kidney stones and urinary albumin-creatinine ratio ( β =0.16, 95% CI, −0.04 to 0.35) or eGFR ( β =0.10, 95% CI, −0.07 to 0.28). Conversely, CKD associated with a diminished risk of kidney stones in multivariable MR models (UKBB/FinnGen: OR=0.77, 95% CI, 0.69 to 0.87; FinnGen/UKBB: OR=0.73, 95% CI, 0.66 to 0.81). Furthermore, in the Mayo Clinic cohort with available urinary biochemistries, lower eGFR was associated with lower urinary calcium excretion and urinary calcium oxalate/phosphate supersaturation. Conclusions In this study, kidney stones were not independently associated with CKD. Conversely, CKD was associated with a lower risk of calcium kidney stones likely via changes in key urinary traits, including lower calcium excretion.