Andreas Heinzel1, Michael Kammer1,2, Gert Mayer3, Roman Reindl-Schwaighofer1, Karin Hu1, Paul Perco3, Susanne Eder3, Laszlo Rosivall4, Patrick B Mark5, Wenjun Ju6, Matthias Kretzler6, Peter Gilmour7, Jonathan M Wilson8, Kevin L Duffin8, Moustafa Abdalla9,10,11, Mark I McCarthy9,10,12, Georg Heinze2, Hiddo L Heerspink13, Andrzej Wiecek14, Maria F Gomez15, Rainer Oberbauer16. 1. Department of Nephrology, Medical University of Vienna, Vienna, Austria. 2. Section for Clinical Biometrics, Center for Medical Statistics, Informatics and Intelligent Systems (CeMSIIS), Medical University of Vienna, Vienna, Austria. 3. Department of Internal Medicine IV (Nephrology and Hypertension), Medical University of Innsbruck, Innsbruck, Austria. 4. International Nephrology Research and Training Centre, Institute of Pathophysiology, Semmelweis University, Budapest, Hungary. 5. Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, U.K. 6. Department of Internal Medicine and Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI. 7. Astellas Pharma Europe B.V., Leiden, the Netherlands. 8. Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, IN. 9. Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, U.K. 10. Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, U.K. 11. Computational Statistics and Machine Learning, Department of Statistics, University of Oxford, Oxford, U.K. 12. National Institute for Health Research Oxford Biomedical Research Centre, Churchill Hospital, Oxford, U.K. 13. Clinical Pharmacy and Pharmacology, Faculty of Medical Sciences, University Medical Center Groningen, Groningen, the Netherlands. 14. Department of Nephrology, Transplantation and Internal Medicine, Medical University of Silesia in Katowice, Katowice, Poland. 15. Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund, Sweden. 16. Department of Nephrology, Medical University of Vienna, Vienna, Austria rainer.oberbauer@meduniwien.ac.at.
Abstract
OBJECTIVE: The decline of estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes is variable, and early interventions would likely be cost-effective. We elucidated the contribution of 17 plasma biomarkers to the prediction of eGFR loss on top of clinical risk factors. RESEARCH DESIGN AND METHODS: We studied participants in PROVALID (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers), a prospective multinational cohort study of patients with type 2 diabetes and a follow-up of more than 24 months (n = 2,560; baseline median eGFR, 84 mL/min/1.73 m2; urine albumin-to-creatinine ratio, 8.1 mg/g). The 17 biomarkers were measured at baseline in 481 samples using Luminex and ELISA. The prediction of eGFR decline was evaluated by linear mixed modeling. RESULTS: In univariable analyses, 9 of the 17 markers showed significant differences in median concentration between stable and fast-progressing patients. A linear mixed model for eGFR obtained by variable selection exhibited an adjusted R2 of 62%. A panel of 12 biomarkers was selected by the procedure and accounted for 34% of the total explained variability, of which 32% was due to 5 markers. The individual contribution of each biomarker to the prediction of eGFR decline on top of clinical predictors was generally low. When included into the model, baseline eGFR exhibited the largest explained variability of eGFR decline (R2 of 79%), and the contribution of each biomarker dropped below 1%. CONCLUSIONS: In this longitudinal study of patients with type 2 diabetes and maintained eGFR at baseline, 12 of the 17 candidate biomarkers were associated with eGFR decline, but their predictive power was low.
OBJECTIVE: The decline of estimated glomerular filtration rate (eGFR) in patients with type 2 diabetes is variable, and early interventions would likely be cost-effective. We elucidated the contribution of 17 plasma biomarkers to the prediction of eGFR loss on top of clinical risk factors. RESEARCH DESIGN AND METHODS: We studied participants in PROVALID (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers), a prospective multinational cohort study of patients with type 2 diabetes and a follow-up of more than 24 months (n = 2,560; baseline median eGFR, 84 mL/min/1.73 m2; urine albumin-to-creatinine ratio, 8.1 mg/g). The 17 biomarkers were measured at baseline in 481 samples using Luminex and ELISA. The prediction of eGFR decline was evaluated by linear mixed modeling. RESULTS: In univariable analyses, 9 of the 17 markers showed significant differences in median concentration between stable and fast-progressing patients. A linear mixed model for eGFR obtained by variable selection exhibited an adjusted R2 of 62%. A panel of 12 biomarkers was selected by the procedure and accounted for 34% of the total explained variability, of which 32% was due to 5 markers. The individual contribution of each biomarker to the prediction of eGFR decline on top of clinical predictors was generally low. When included into the model, baseline eGFR exhibited the largest explained variability of eGFR decline (R2 of 79%), and the contribution of each biomarker dropped below 1%. CONCLUSIONS: In this longitudinal study of patients with type 2 diabetes and maintained eGFR at baseline, 12 of the 17 candidate biomarkers were associated with eGFR decline, but their predictive power was low.
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