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Leif Groop

Leif Groop

Principal investigator

Leif Groop

Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes

Author

  • Helen C. Looker
  • Marco Colombo
  • Felix Agakov
  • Tanja Zeller
  • Leif Groop
  • Barbara Thorand
  • Colin N. Palmer
  • Anders Hamsten
  • Ulf de Faire
  • Everson Nogoceke
  • Shona J. Livingstone
  • Veikko Salomaa
  • Karin Leander
  • Nicola Barbarini
  • Riccardo Bellazzi
  • Natalie van Zuydam
  • Paul M. McKeigue
  • Helen M. Colhoun

Summary, in English

Aims/hypothesis We selected the most informative protein biomarkers for the prediction of incident cardiovascular disease (CVD) in people with type 2 diabetes. Methods In this nested case-control study we measured 42 candidate CVD biomarkers in 1,123 incident CVD cases and 1,187 controls with type 2 diabetes selected from five European centres. Combinations of biomarkers were selected using cross-validated logistic regression models. Model prediction was assessed using the area under the receiver operating characteristic curve (AUROC). Results Sixteen biomarkers showed univariate associations with incident CVD. The most predictive subset selected by forward selection methods contained six biomarkers: N-terminal pro-B-type natriuretic peptide (OR 1.69 per 1 SD, 95% CI 1.47, 1.95), high-sensitivity troponin T (OR 1.29, 95% CI 1.11, 1.51), IL-6 (OR 1.13, 95% CI 1.02, 1.25), IL-15 (OR 1.15, 95% CI 1.01, 1.31), apolipoprotein C-III (OR 0.79, 95% CI 0.70, 0.88) and soluble receptor for AGE (OR 0.84, 95% CI 0.76, 0.94). The prediction of CVD beyond clinical covariates improved from an AUROC of 0.66 to 0.72 (AUROC for Framingham Risk Score covariates 0.59). In addition to the biomarkers, the most important clinical covariates for improving prediction beyond the Framingham covariates were estimated GFR, insulin therapy and HbA(1c). Conclusions/interpretation We identified six protein biomarkers that in combination with clinical covariates improved the prediction of our model beyond the Framingham Score covariates. Biomarkers can contribute to improved prediction of CVD in diabetes but clinical data including measures of renal function and diabetes-specific factors not included in the Framingham Risk Score are also needed.

Department/s

  • Genomics, Diabetes and Endocrinology
  • EXODIAB: Excellence of Diabetes Research in Sweden

Publishing year

2015

Language

English

Pages

1363-1371

Publication/Series

Diabetologia

Volume

58

Issue

6

Document type

Journal article

Publisher

Springer

Topic

  • Cardiac and Cardiovascular Systems
  • Endocrinology and Diabetes

Keywords

  • Cardiovascular diseases
  • Epidemiology
  • Protein biomarkers
  • Risk factors
  • Type 2 diabetesmellitus

Status

Published

Research group

  • Genomics, Diabetes and Endocrinology

ISBN/ISSN/Other

  • ISSN: 1432-0428