Gert Mayer1, Hiddo J L Heerspink2, Constantin Aschauer3, Andreas Heinzel4, Georg Heinze5, Alexander Kainz3, Judith Sunzenauer3, Paul Perco4, Dick de Zeeuw2, Peter Rossing6, Michelle Pena2, Rainer Oberbauer7. 1. Department of Internal Medicine IV (Nephrology and Hypertension), Medical University of Innsbruck, Innsbruck, Austria. 2. Department of Clinical Pharmacy and Pharmacology, University of Groningen, University Medical Center Groningen, Groningen, the Netherlands. 3. Department of Nephrology, Medical University of Vienna, Vienna, Austria. 4. emergentec biodevelopment GmbH, Vienna, Austria. 5. Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Section for Clinical Biometrics, Medical University of Vienna, Vienna, Austria. 6. Steno Diabetes Center, Gentofte, University of Copenhagen, Copenhagen, Denmark. 7. Department of Nephrology, Medical University of Vienna, Vienna, Austria rainer.oberbauer@meduniwien.ac.at.
Abstract
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 R2 value, was 15% and 34% for patients with eGFR ≥60 and <60 mL/min/1.73 m2, respectively; variability explained by clinical predictors was 20% and 31%, respectively. A combination of molecular and clinical predictors increased the adjusted R2 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.
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 R2 value, was 15% and 34% for patients with eGFR ≥60 and <60 mL/min/1.73 m2, respectively; variability explained by clinical predictors was 20% and 31%, respectively. A combination of molecular and clinical predictors increased the adjusted R2 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.
Authors: Andreas Heinzel; Michael Kammer; Gert Mayer; Roman Reindl-Schwaighofer; Karin Hu; Paul Perco; Susanne Eder; Laszlo Rosivall; Patrick B Mark; Wenjun Ju; Matthias Kretzler; Peter Gilmour; Jonathan M Wilson; Kevin L Duffin; Moustafa Abdalla; Mark I McCarthy; Georg Heinze; Hiddo L Heerspink; Andrzej Wiecek; Maria F Gomez; Rainer Oberbauer Journal: Diabetes Care Date: 2018-07-06 Impact factor: 19.112
Authors: Paul Perco; Wenjun Ju; Julia Kerschbaum; Johannes Leierer; Rajasree Menon; Catherine Zhu; Matthias Kretzler; Gert Mayer; Michael Rudnicki Journal: JCI Insight Date: 2019-06-20
Authors: Christine P Limonte; Erkka Valo; Daniel Montemayor; Farsad Afshinnia; Tarunveer S Ahluwalia; Tina Costacou; Manjula Darshi; Carol Forsblom; Andrew N Hoofnagle; Per-Henrik Groop; Rachel G Miller; Trevor J Orchard; Subramaniam Pennathur; Peter Rossing; Niina Sandholm; Janet K Snell-Bergeon; Hongping Ye; Jing Zhang; Loki Natarajan; Ian H de Boer; Kumar Sharma Journal: Am J Nephrol Date: 2020-10-14 Impact factor: 3.754
Authors: Christian Reiterer; Karin Hu; Samir Sljivic; Markus Falkner von Sonnenburg; Edith Fleischmann; Alexander Kainz; Barbara Kabon Journal: BMC Nephrol Date: 2020-07-28 Impact factor: 2.388
Authors: Ahmed Alaini; Deepak Malhotra; Helbert Rondon-Berrios; Christos P Argyropoulos; Zeid J Khitan; Dominic S C Raj; Mark Rohrscheib; Joseph I Shapiro; Antonios H Tzamaloukas Journal: World J Methodol Date: 2017-09-26