Variability exists in CKD biomarker studies, impacts findings
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Applying different assessment measures impacted the results of two studies on the progression of chronic kidney disease in children. Alternate evaluation produced variability that altered the CKD conclusions, according to research.
“This real-world comparison of two published suPAR biomarker studies demonstrated that substantial differences in biomarker levels can arise when measuring the same biomarker in the same samples using different assays,” Alison G. Abraham, PhD, MS, MHS, associate professor of epidemiology at Colorado University, and colleagues wrote. “The somewhat disheartening but eye-opening conclusion is that these differences are large enough that they can lead to divergent study conclusions.”
Abraham and colleagues compared two prospective cohort studies to determine variability. The comparison included 541 pediatric patients in the CKD in Children study, with a median age of 12 years and median glomerular filtration rate of 54 mL/min/1.73 m². The primary outcome involved a 50% decline in GFR from baseline or incident end-stage kidney disease. Researchers measured the urokinase-type plasminogen activator receptor (suPAR) using Quantikine ELISA immunoassay in the first study and Meso Scale Discovery platform in the second study. In an overlaying subset of data, Abraham and colleagues used suPAR information from both assessments and conducted an analysis.
Switching evaluation methods resulted in a 38% to 66% variability. Moreover, study results varied an additional 8% to 40% due to covariate and modeling choices, according to researchers. The overall variability led to different conclusions despite looking at similar samples of patients.
“This biomarker study comparison highlights the variability that may exist in the current CKD biomarker literature and the need for care in the interpretation of results from novel CKD biomarker studies. Ideally, all studies would include a validation component that would require some replication of associations,” Abraham and colleagues wrote. “To improve efforts to rapidly evaluate novel biomarkers, new studies should consider providing results in metrics that allow for cross-comparison to other studies so the degree of uncertainty regarding the value of a new biomarker is more transparent.”
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