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Abstract
Acute and chronic kidney disease continues to confer significant morbidity and mortality in the clinical setting. Despite high prevalence of these conditions, few validated biomarkers exist to predict kidney dysfunction. In this study, we utilized a novel kidney multiplex panel to measure 21 proteins in plasma and urine to characterize the spectrum of biomarker profiles in kidney disease. Blood and urine samples were obtained from age-/sex-matched healthy control subjects (HC), critically-ill COVID-19 patients with acute kidney injury (AKI), and patients with chronic or end-stage kidney disease (CKD/ESKD). Biomarkers were measured with a kidney multiplex panel, and results analyzed with conventional statistics and machine learning. Correlations were examined between biomarkers and patient clinical and laboratory variables. Median AKI subject age was 65.5 (IQR 58.5–73.0) and median CKD/ESKD age was 65.0 (IQR 50.0–71.5). Of the CKD/ESKD patients, 76.1% were on hemodialysis, 14.3% of patients had kidney transplant, and 9.5% had CKD without kidney replacement therapy. In plasma, 19 proteins were significantly different in titer between the HC versus AKI versus CKD/ESKD groups, while NAG and RBP4 were unchanged. TIMP-1 (PPV 1.0, NPV 1.0), best distinguished AKI from HC, and TFF3 (PPV 0.99, NPV 0.89) best distinguished CKD/ESKD from HC. In urine, 18 proteins were significantly different between groups except Calbindin, Osteopontin and TIMP-1. Osteoactivin (PPV 0.95, NPV 0.95) best distinguished AKI from HC, and β2-microglobulin (PPV 0.96, NPV 0.78) best distinguished CKD/ESKD from HC. A variety of correlations were noted between patient variables and either plasma or urine biomarkers. Using a novel kidney multiplex biomarker panel, together with conventional statistics and machine learning, we identified unique biomarker profiles in the plasma and urine of patients with AKI and CKD/ESKD. We demonstrated correlations between biomarker profiles and patient clinical variables. Our exploratory study provides biomarker data for future hypothesis driven research on kidney disease.
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1 Western University, Medicine, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884)
2 Western University, Pediatrics, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884)
3 Western University, Epidemiology and Biostatistics, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884)
4 Western University, Epidemiology and Biostatistics, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884); Western University, Computer Science, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884); The Vector Institute for Artificial Intelligence, Toronto, Canada (GRID:grid.494618.6) (ISNI:0000 0005 0272 1351); Lawson Health Research Institute, London, Canada (GRID:grid.415847.b) (ISNI:0000 0001 0556 2414)
5 Western University, Medicine, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884); Western University, Pediatrics, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884); Lawson Health Research Institute, London, Canada (GRID:grid.415847.b) (ISNI:0000 0001 0556 2414)
6 Thermo Fisher Scientific, Vienna, Austria (GRID:grid.415847.b)
7 Thermo Fisher Scientific, Rockford, USA (GRID:grid.418190.5) (ISNI:0000 0001 2187 0556)
8 Western University, Medicine, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884); Lawson Health Research Institute, London, Canada (GRID:grid.415847.b) (ISNI:0000 0001 0556 2414)
9 Western University, Pediatrics, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884); Lawson Health Research Institute, London, Canada (GRID:grid.415847.b) (ISNI:0000 0001 0556 2414); Western University, Clinical Neurological Sciences, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884); Western University, Physiology and Pharmacology, London, Canada (GRID:grid.39381.30) (ISNI:0000 0004 1936 8884); London Health Sciences Centre, London, Canada (GRID:grid.412745.1) (ISNI:0000 0000 9132 1600)