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Kidney Int. 2019 Aug 30. pii: S0085-2538(19)30841-5. doi: 10.1016/j.kint.2019.07.025. [Epub ahead of print]

Integrative analysis of prognostic biomarkers derived from multiomics panels helps discrimination of chronic kidney disease trajectories in people with type 2 diabetes.

Author information

1
Department of Nephrology, Medical University of Vienna, Vienna, Austria; Center for Medical Statistics, Informatics and Intelligent Systems, Section for Clinical Biometrics, Medical University of Vienna, Vienna, Austria.
2
Department of Nephrology, Medical University of Vienna, Vienna, Austria.
3
Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, Indiana, USA.
4
Department of Internal Medicine IV (Nephrology and Hypertension), Medical University of Innsbruck, Innsbruck, Austria.
5
Lipotype GmbH, Tatzberg, Dresden, Germany.
6
Center for Medical Statistics, Informatics and Intelligent Systems, Section for Clinical Biometrics, Medical University of Vienna, Vienna, Austria.
7
International Nephrology Research and Training Centre, Institute of Pathophysiology, Semmelweis University, Budapest, Hungary.
8
Institute of Cardiovascular and Medical Sciences, University of Glasgow, Glasgow, UK.
9
Department of Internal Medicine, Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, Michigan, USA.
10
Wellcome Trust Centre for Human Genetics, University of Oxford, Oxford, UK; Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
11
Clinical Pharmacy and Pharmacology, Faculty of Medical Sciences, University Medical Center Groningen, Groningen, The Netherlands.
12
Department of Nephrology, Transplantation and Internal Medicine, Medical University of Silesia, Katowice, Poland.
13
Department of Clinical Sciences in Malmö, Lund University Diabetes Centre, Lund, Sweden.
14
Department of Nephrology, Medical University of Vienna, Vienna, Austria. Electronic address: rainer.oberbauer@meduniwien.ac.at.

Abstract

Clinical risk factors explain only a fraction of the variability of estimated glomerular filtration rate (eGFR) decline in people with type 2 diabetes. Cross-omics technologies by virtue of a wide spectrum screening of plasma samples have the potential to identify biomarkers for the refinement of prognosis in addition to clinical variables. Here we utilized proteomics, metabolomics and lipidomics panel assay measurements in baseline plasma samples from the multinational PROVALID study (PROspective cohort study in patients with type 2 diabetes mellitus for VALIDation of biomarkers) of patients with incident or early chronic kidney disease (median follow-up 35 months, median baseline eGFR 84 mL/min/1.73 m2, urine albumin-to-creatinine ratio 8.1 mg/g). In an accelerated case-control study, 258 individuals with a stable eGFR course (median eGFR change 0.1 mL/min/year) were compared to 223 individuals with a rapid eGFR decline (median eGFR decline -6.75 mL/min/year) using Bayesian multivariable logistic regression models to assess the discrimination of eGFR trajectories. The analysis included 402 candidate predictors and showed two protein markers (KIM-1, NTproBNP) to be relevant predictors of the eGFR trajectory with baseline eGFR being an important clinical covariate. The inclusion of metabolomic and lipidomic platforms did not improve discrimination substantially. Predictions using all available variables were statistically indistinguishable from predictions using only KIM-1 and baseline eGFR (area under the receiver operating characteristic curve 0.63). Thus, the discrimination of eGFR trajectories in patients with incident or early diabetic kidney disease and maintained baseline eGFR was modest and the protein marker KIM-1 was the most important predictor.

KEYWORDS:

biomarkers; chronic kidney disease; integrative analysis; multiomics; prognosis; type 2 diabetes

PMID:
31679767
DOI:
10.1016/j.kint.2019.07.025

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