Systems Biology-Derived Biomarkers to Predict Progression of Renal Function Decline in Type 2 Diabetes.
| Autor: | Mayer G; Department of Internal Medicine IV (Nephrology and Hypertension), Medical University of Innsbruck, Innsbruck, Austria., Heerspink HJ; Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands., Aschauer C; Department of Nephrology, Medical University of Vienna, Vienna, Austria., Heinzel A; emergentec biodevelopment GmbH, Vienna, Austria., Heinze G; Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Section for Clinical Biometrics, Medical University of Vienna, Vienna, Austria., Kainz A; Department of Nephrology, Medical University of Vienna, Vienna, Austria., Sunzenauer J; Department of Nephrology, Medical University of Vienna, Vienna, Austria., Perco P; emergentec biodevelopment GmbH, Vienna, Austria., de Zeeuw D; Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands., Rossing P; Steno Diabetes Center, Gentofte, University of Copenhagen, Copenhagen, Denmark., Pena M; Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands., Oberbauer R; Department of Nephrology, Medical University of Vienna, Vienna, Austria rainer.oberbauer@meduniwien.ac.at. |
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| Jazyk: | angličtina |
| Zdroj: | Diabetes care [Diabetes Care] 2017 Mar; Vol. 40 (3), pp. 391-397. Date of Electronic Publication: 2017 Jan 11. |
| DOI: | 10.2337/dc16-2202 |
| Abstrakt: | Objective: Chronic kidney disease (CKD) in diabetes has a complex molecular and likely multifaceted pathophysiology. We aimed to validate a panel of biomarkers identified using a systems biology approach to predict the individual decline of estimated glomerular filtration rate (eGFR) in a large group of patients with type 2 diabetes and CKD at various stages. Research Design and Methods: We used publicly available "omics" data to develop a molecular process model of CKD in diabetes and identified a representative parsimonious set of nine molecular biomarkers: chitinase 3-like protein 1, growth hormone 1, hepatocyte growth factor, matrix metalloproteinase (MMP) 2, MMP7, MMP8, MMP13, tyrosine kinase, and tumor necrosis factor receptor-1. These biomarkers were measured in baseline serum samples from 1,765 patients recruited into two large clinical trials. eGFR decline was predicted based on molecular markers, clinical risk factors (including baseline eGFR and albuminuria), and both combined, and these predictions were evaluated using mixed linear regression models for longitudinal data. Results: The variability of annual eGFR loss explained by the biomarkers, indicated by the adjusted R 2 value, was 15% and 34% for patients with eGFR ≥60 and <60 mL/min/1.73 m 2 , respectively; variability explained by clinical predictors was 20% and 31%, respectively. A combination of molecular and clinical predictors increased the adjusted R 2 to 35% and 64%, respectively. Calibration analysis of marker models showed significant (all P < 0.0001) but largely irrelevant deviations from optimal calibration (calibration-in-the-large: -1.125 and 0.95; calibration slopes: 1.07 and 1.13 in the two groups, respectively). Conclusions: A small set of serum protein biomarkers identified using a systems biology approach, combined with clinical variables, enhances the prediction of renal function loss over a wide range of baseline eGFR values in patients with type 2 diabetes and CKD. (© 2017 by the American Diabetes Association.) |
| Databáze: | MEDLINE |
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