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

Helen C. Looker (Lead / Corresponding author), 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, on behalf of the SUMMIT Investigators

    Research output: Contribution to journalArticlepeer-review

    36 Citations (Scopus)

    Abstract

    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 HbA1c.

    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.

    Original languageEnglish
    Pages (from-to)1363-1371
    Number of pages9
    JournalDiabetologia
    Volume58
    Issue number6
    Early online date5 Mar 2015
    DOIs
    Publication statusPublished - Jun 2015

    Fingerprint Dive into the research topics of 'Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes'. Together they form a unique fingerprint.

    Cite this