Association of Multiple Plasma Biomarker Concentrations with Progression of Prevalent Diabetic Kidney Disease: Findings from the Chronic Renal Insufficiency Cohort (CRIC) Study

Sarah J Schrauben, Haochang Shou, Xiaoming Zhang, Amanda Hyre Anderson, Joseph V Bonventre, Jing Chen, Steven Coca, Susan L Furth, Jason H Greenberg, Orlando M Gutierrez, Joachim H Ix, James P Lash, Chirag R Parikh, Casey M Rebholz, Venkata Sabbisetti, Mark J Sarnak, Michael G Shlipak, Sushrut S Waikar, Paul L Kimmel, Ramachandran S Vasan, Harold I Feldman, Jeffrey R Schelling, CKD Biomarkers Consortium and the Chronic Renal Insufficiency Cohort (CRIC) Study Investigators, Lawrence J Appel, Alan S Go, Jiang He, Robert G Nelson, Panduranga S Rao, Mahboob Rahman, Vallabh O Shah, Raymond R Townsend, Mark L Unruh, Michelle Denburg, Bradley Warady, Josef Coresh, Morgan Grams, Alison Abraham, Eugene Rhee, Ruth Dubin, Tom Hostetter, Rajat Deo, Dawei Xie, Shawn Ballard, Krista Whitehead, Heather Collins, Peter Ganz, Sarah J Schrauben, Haochang Shou, Xiaoming Zhang, Amanda Hyre Anderson, Joseph V Bonventre, Jing Chen, Steven Coca, Susan L Furth, Jason H Greenberg, Orlando M Gutierrez, Joachim H Ix, James P Lash, Chirag R Parikh, Casey M Rebholz, Venkata Sabbisetti, Mark J Sarnak, Michael G Shlipak, Sushrut S Waikar, Paul L Kimmel, Ramachandran S Vasan, Harold I Feldman, Jeffrey R Schelling, CKD Biomarkers Consortium and the Chronic Renal Insufficiency Cohort (CRIC) Study Investigators, Lawrence J Appel, Alan S Go, Jiang He, Robert G Nelson, Panduranga S Rao, Mahboob Rahman, Vallabh O Shah, Raymond R Townsend, Mark L Unruh, Michelle Denburg, Bradley Warady, Josef Coresh, Morgan Grams, Alison Abraham, Eugene Rhee, Ruth Dubin, Tom Hostetter, Rajat Deo, Dawei Xie, Shawn Ballard, Krista Whitehead, Heather Collins, Peter Ganz

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.

Keywords: biomarker; chronic diabetic complications; chronic kidney disease; diabetes; diabetic kidney disease; diabetic nephropathy; end stage kidney disease; epidemiology and outcomes.

Copyright © 2021 by the American Society of Nephrology.

Figures

Graphical abstract
Graphical abstract
Figure 1.
Figure 1.
Scatterplot of plasma biomarker concentrations (pg/ml) on y axis were consistenty higher among those with lower baseline eGFR (ml/min per 1.73 m2) values on x axis. Top row (left to right): KIM-1, TNFR-1, and TNFR-2. Bottom row (left to right): MCP-1, suPAR, and YKL-40.
Figure 2.
Figure 2.
Higher plasma biomarker concetrations (per increment in log2 biomarker values) of KIM-1, TNFR-1, TNFR-2, MCP-1, suPAR, and YKL-40 were associated with a greater risk of DKD progression, even after adjustment for age, sex, race/ethnicity, education, clinical center, systolic BP, diastolic BP, BMI, hsCRP, hemoglobin A1c, antihypertensive medication use, smoking status, baseline eGFR (ml/min per 1.73 m2), and UPCR. All HRs are adjusted for age, sex, race/ethnicity, education, clinical center, systolic BP, diastolic BP, BMI, hsCRP, hemoglobin A1c, antihypertensive medication use, smoking status, baseline eGFR (ml/min per 1.73 m2), and UPCR.
Figure 3.
Figure 3.
Higher plasma biomarker concetrations (per increment in log2 biomarker values) of KIM-1, TNFR-2, and YKL-40 associated with an increased risk of DKD progression after backward selection of all biomarkers and adjustment for age, sex, race/ethnicity, education, clinical center, systolic BP, diastolic BP, BMI, hsCRP, hemoglobin A1c, antihypertensive medication use, smoking status, baseline eGFR (ml/min per 1.73 m2), and UPCR. All HRs are adjusted for age, sex, race/ethnicity, education, clinical center, systolic BP, diastolic BP, BMI, hsCRP, hemoglobin A1c, antihypertensive medication use, smoking status, baseline eGFR (ml/min per 1.73 m2), baseline UPCR, KIM-1, TNFR-2, MCP-1, and YKL-40. Biomarkers were selected to remain in model via backward selection (P<0.05).
Figure 4.
Figure 4.
Higher plasma biomarker concetrations (per increment in log2 biomarker values) of TNFR-1, TNFR-2, KIM-1, and YKL-40 were significantly associated with the annual rate of decline in eGFR (ml/min per 1.73 m2 per year) after adjustment for age, sex, race/ethnicity, education, clinical center, systolic BP, diastolic BP, BMI, hsCRP, hemoglobin A1c, antihypertensive medication use, smoking status, baseline eGFR (ml/min per 1.73 m2), and UPCR. All β-coefficients are adjusted for age, sex, race/ethnicity, education, clinical center, systolic BP, diastolic BP, BMI, hsCRP, hemoglobin A1c, antihypertensive medication use, smoking status, baseline eGFR (ml/min per 1.73 m2), and UPCR.

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