Diabetic kidney disease (DKD) is the leading cause of end-stage kidney disease (ESKD). Prognostic biomarkers reflective of underlying molecular mechanisms are critically needed for effective management of DKD. A three-marker panel was derived from a proteomics analysis of plasma samples by an unbiased machine learning approach from participants (N = 58) in the Clinical Phenotyping and Resource Biobank study. When combined with standard clinical parameters, this panel improved prediction of the composite outcome of ESKD or a 40% decline in glomerular filtration rate (GFR). The panel was validated in an independent group (N=68), which also had kidney transcriptomic profiles. One marker, plasma angiopoietin 2 (ANGPT2), was significantly associated with outcomes in cohorts from the American Cardiovascular Health Study (N=3183) and the Chinese Cohort Study of Chronic Kidney Disease (N=210). Glomerular transcriptional Angiopoietin/Tie (ANG-TIE) pathway scores, derived from the expression of 154 ANG-TIE signaling mediators, correlated positively with plasma ANGPT2 levels and kidney outcomes. Higher receptor expression in glomeruli and higher ANG-TIE pathway scores in endothelial cells corroborated potential functional effects in the kidney from elevated plasma ANGPT2 levels. Our work suggests that ANGPT2 is a promising prognostic endothelial biomarker with likely functional impact on glomerular pathogenesis in DKD.
Funding
This work was supported by a grant from the University of Michigan Health System and Peking University Health Sciences Center Joint Institute for Translational and Clinical Research. JL was supported by the China Scholarship Council (201906370288) while visiting the University of Michigan. Dr. Vasquez is supported through funds from the National Institutes of Health, 5UH3DK114870-05. This study was also supported, in part, by funding from the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) through the George M. O’Brien Michigan Kidney Translational Core Center, grant 2P30-DK-081943; the Integrated Systems Biology Approach to Diabetic Microvascular Complications grant, R24DK082841; P30DK89503; and JDRF Center for Excellence (5-COE-2019-861-S-B). The Kidney Precision Medicine Project (KPMP), UH3-DK-114907, is a multi-year project funded by NIDDK with the purpose of understanding and finding new ways to treat chronic kidney disease (CKD) and acute kidney injury (AKI). See Supplemental Acknowledgments for consortium details. C-STRIDE is supported by a grant from China International Medical Foundation-Renal Anemia Fund, the grants from the National Natural Science Found (No. 82070748, 82090020 and 82090021). CHS is supported by contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006, and grants U01HL080295 and U01HL130114 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by R01AG023629 from the National Institute on Aging (NIA).
