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

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

Research output: Contribution to journalArticle

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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
DOIs
Publication statusPublished - Jun 1 2015

Fingerprint

Type 2 Diabetes Mellitus
Cardiovascular Diseases
Biomarkers
Proteins
ROC Curve
Logistic Models
Apolipoprotein C-III
Interleukin-15
Troponin T
Brain Natriuretic Peptide
Interleukin-6
Insulin
Kidney

Keywords

  • Cardiovascular diseases
  • Epidemiology
  • Protein biomarkers
  • Risk factors
  • Type 2 diabetes mellitus

ASJC Scopus subject areas

  • Internal Medicine
  • Endocrinology, Diabetes and Metabolism

Cite this

Looker, H. C., Colombo, M., Agakov, F., Zeller, T., Groop, L., Thorand, B., ... Colhoun, H. M. (2015). Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes. Diabetologia, 58(6), 1363-1371. https://doi.org/10.1007/s00125-015-3535-6

Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes. / Looker, Helen C.; Colombo, Marco; Agakov, Felix; Zeller, Tanja; Groop, Leif; Thorand, Barbara; Palmer, Colin N.; Hamsten, Anders; de Faire, Ulf; Nogoceke, Everson; Livingstone, Shona J.; Salomaa, Veikko; Leander, Karin; Barbarini, Nicola; Bellazzi, Riccardo; van Zuydam, Natalie; McKeigue, Paul M.; Colhoun, Helen M.

In: Diabetologia, Vol. 58, No. 6, 01.06.2015, p. 1363-1371.

Research output: Contribution to journalArticle

Looker, HC, Colombo, M, Agakov, F, Zeller, T, Groop, L, Thorand, B, Palmer, CN, Hamsten, A, de Faire, U, Nogoceke, E, Livingstone, SJ, Salomaa, V, Leander, K, Barbarini, N, Bellazzi, R, van Zuydam, N, McKeigue, PM & Colhoun, HM 2015, 'Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes', Diabetologia, vol. 58, no. 6, pp. 1363-1371. https://doi.org/10.1007/s00125-015-3535-6
Looker HC, Colombo M, Agakov F, Zeller T, Groop L, Thorand B et al. Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes. Diabetologia. 2015 Jun 1;58(6):1363-1371. https://doi.org/10.1007/s00125-015-3535-6
Looker, Helen C. ; Colombo, Marco ; Agakov, Felix ; Zeller, Tanja ; Groop, Leif ; Thorand, Barbara ; Palmer, Colin N. ; Hamsten, Anders ; de Faire, Ulf ; Nogoceke, Everson ; Livingstone, Shona J. ; Salomaa, Veikko ; Leander, Karin ; Barbarini, Nicola ; Bellazzi, Riccardo ; van Zuydam, Natalie ; McKeigue, Paul M. ; Colhoun, Helen M. / Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes. In: Diabetologia. 2015 ; Vol. 58, No. 6. pp. 1363-1371.
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AU - Thorand, Barbara

AU - Palmer, Colin N.

AU - Hamsten, Anders

AU - de Faire, Ulf

AU - Nogoceke, Everson

AU - Livingstone, Shona J.

AU - Salomaa, Veikko

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AU - Barbarini, Nicola

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AU - van Zuydam, Natalie

AU - McKeigue, Paul M.

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N2 - 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.

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