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Maria Gomez

Maria Gomez

Professor

Maria Gomez

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

Author

  • Michael Kammer
  • Andreas Heinzel
  • Jill A. Willency
  • Kevin L. Duffin
  • Gert Mayer
  • Kai Simons
  • Mathias J. Gerl
  • Christian Klose
  • Georg Heinze
  • Roman Reindl-Schwaighofer
  • Karin Hu
  • Paul Perco
  • Susanne Eder
  • Laszlo Rosivall
  • Patrick B. Mark
  • Wenjun Ju
  • Matthias Kretzler
  • Mark I. McCarthy
  • Hiddo L. Heerspink
  • Andrzej Wiecek
  • Maria F. Gomez
  • Rainer Oberbauer

Summary, in English

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.

Department/s

  • Diabetic Complications
  • EXODIAB: Excellence in Diabetes Research in Sweden

Publishing year

2019-08-30

Language

English

Pages

1381-1388

Publication/Series

Kidney International

Volume

96

Issue

6

Document type

Journal article

Publisher

Nature Publishing Group

Topic

  • Urology and Nephrology
  • Endocrinology and Diabetes

Keywords

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

Status

Published

Research group

  • Diabetic Complications

ISBN/ISSN/Other

  • ISSN: 0085-2538