Sarah J Schrauben1,2, Haochang Shou2, Xiaoming Zhang2, Amanda Hyre Anderson2,3, Joseph V Bonventre4, Jing Chen5, Steven Coca6, Susan L Furth7, Jason H Greenberg8, Orlando M Gutierrez9, Joachim H Ix10, James P Lash11, Chirag R Parikh12, Casey M Rebholz13, Venkata Sabbisetti4, Mark J Sarnak14, Michael G Shlipak15, Sushrut S Waikar16, Paul L Kimmel17, Ramachandran S Vasan18, Harold I Feldman19,2, Jeffrey R Schelling20. 1. Department of Medicine, Perelman School of Medicine, Center for Clinical Epidemiology and Biostatistics at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania Sarah.Schrauben@pennmedicine.upenn.edu. 2. Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania. 3. Department of Epidemiology, School of Public Health and Tropical Medicine, Tulane University, New Orleans, Louisiana. 4. Division of Renal Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts. 5. Department of Medicine, Tulane University School of Medicine, New Orleans, Louisiana. 6. Division of Nephrology, Department of Internal Medicine, Icahn School of Medicine at Mount Sinai, New York, New York. 7. Division of Nephrology, Department of Pediatrics, The Children's Hospital of Philadelphia, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania. 8. Section of Nephrology, Department of Pediatrics, Program of Applied Translational Research, Yale University School of Medicine, New Haven, Connecticut. 9. Departments of Medicine and Epidemiology, University at Alabama at Birmingham, Birmingham, Alabama. 10. Division of Nephrology-Hypertension, Department of Medicine, University of California San Diego School of Medicine, San Diego, California. 11. Division of Nephrology, Department of Medicine, University of Illinois at Chicago, Chicago, Illinois. 12. Section of Nephrology, Department of Internal Medicine, Johns Hopkins School of Medicine, Baltimore, New York. 13. Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland. 14. Division of Nephrology, Department of Medicine, Tufts Medical Center, Boston, Massachusetts. 15. Department of Medicine, University of California, San Francisco, San Francisco, California. 16. Section of Nephrology, Department of Medicine, Boston Medical Center, Boston, Massachusetts. 17. National Institute of Diabetes and Digestive and Kidney Diseases, Bethesda, Maryland. 18. Departments of Medicine and Epidemiology, Boston University Schools of Medicine and Public Health, Boston, Massachusetts. 19. Department of Medicine, Perelman School of Medicine, Center for Clinical Epidemiology and Biostatistics at the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania. 20. Division of Nephrology, Department of Internal Medicine, MetroHealth Campus, and Department of Physiology and Biophysics, Case Western Reserve University School of Medicine, Cleveland, Ohio.
Abstract
BACKGROUND: Although diabetic kidney disease is the leading cause of ESKD in the United States, identifying those patients who progress to ESKD is difficult. Efforts are under way to determine if plasma biomarkers can help identify these high-risk individuals. METHODS: In our case-cohort study of 894 Chronic Renal Insufficiency Cohort Study participants with diabetes and an eGFR of <60 ml/min per 1.73 m2 at baseline, participants were randomly selected for the subcohort; cases were those patients who developed progressive diabetic kidney disease (ESKD or 40% eGFR decline). Using a multiplex system, we assayed plasma biomarkers related to tubular injury, inflammation, and fibrosis (KIM-1, TNFR-1, TNFR-2, MCP-1, suPAR, and YKL-40). Weighted Cox regression models related biomarkers to progression of diabetic kidney disease, and mixed-effects models estimated biomarker relationships with rate of eGFR change. RESULTS: Median follow-up was 8.7 years. Higher concentrations of KIM-1, TNFR-1, TNFR-2, MCP-1, suPAR, and YKL-40 were each associated with a greater risk of progression of diabetic kidney disease, even after adjustment for established clinical risk factors. After accounting for competing biomarkers, KIM-1, TNFR-2, and YKL-40 remained associated with progression of diabetic kidney disease; TNFR-2 had the highest risk (adjusted hazard ratio, 1.61; 95% CI, 1.15 to 2.26). KIM-1, TNFR-1, TNFR-2, and YKL-40 were associated with rate of eGFR decline. CONCLUSIONS: Higher plasma levels of KIM-1, TNFR-1, TNFR-2, MCP-1, suPAR, and YKL-40 were associated with increased risk of progression of diabetic kidney disease; TNFR-2 had the highest risk after accounting for the other biomarkers. These findings validate previous literature on TNFR-1, TNFR-2, and KIM-1 in patients with prevalent CKD and provide new insights into the influence of suPAR and YKL-40 as plasma biomarkers that require validation.
BACKGROUND: Although diabetic kidney disease is the leading cause of ESKD in the United States, identifying those patients who progress to ESKD is difficult. Efforts are under way to determine if plasma biomarkers can help identify these high-risk individuals. METHODS: In our case-cohort study of 894 Chronic Renal Insufficiency Cohort Study participants with diabetes and an eGFR of <60 ml/min per 1.73 m2 at baseline, participants were randomly selected for the subcohort; cases were those patients who developed progressive diabetic kidney disease (ESKD or 40% eGFR decline). Using a multiplex system, we assayed plasma biomarkers related to tubular injury, inflammation, and fibrosis (KIM-1, TNFR-1, TNFR-2, MCP-1, suPAR, and YKL-40). Weighted Cox regression models related biomarkers to progression of diabetic kidney disease, and mixed-effects models estimated biomarker relationships with rate of eGFR change. RESULTS: Median follow-up was 8.7 years. Higher concentrations of KIM-1, TNFR-1, TNFR-2, MCP-1, suPAR, and YKL-40 were each associated with a greater risk of progression of diabetic kidney disease, even after adjustment for established clinical risk factors. After accounting for competing biomarkers, KIM-1, TNFR-2, and YKL-40 remained associated with progression of diabetic kidney disease; TNFR-2 had the highest risk (adjusted hazard ratio, 1.61; 95% CI, 1.15 to 2.26). KIM-1, TNFR-1, TNFR-2, and YKL-40 were associated with rate of eGFR decline. CONCLUSIONS: Higher plasma levels of KIM-1, TNFR-1, TNFR-2, MCP-1, suPAR, and YKL-40 were associated with increased risk of progression of diabetic kidney disease; TNFR-2 had the highest risk after accounting for the other biomarkers. These findings validate previous literature on TNFR-1, TNFR-2, and KIM-1 in patients with prevalent CKD and provide new insights into the influence of suPAR and YKL-40 as plasma biomarkers that require validation.
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