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

Looker, Helen C.; Colombo, Marco; Agakov, Felix; Zeller, Tanja; Groop, Leif LU ; Thorand, Barbara; Palmer, Colin N.; Hamsten, Anders; de Faire, Ulf and Nogoceke, Everson, et al. (2015) In Diabetologia 58(6). p.1363-1371
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... (More)
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. (Less)
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Contribution to journal
publication status
published
subject
keywords
Cardiovascular diseases, Epidemiology, Protein biomarkers, Risk factors, Type 2 diabetesmellitus
in
Diabetologia
volume
58
issue
6
pages
1363 - 1371
publisher
Springer Verlag
external identifiers
  • wos:000353893000027
  • scopus:84939940579
ISSN
1432-0428
DOI
10.1007/s00125-015-3535-6
language
English
LU publication?
yes
id
9f1d94ff-778d-40c4-a929-c4b63b382bae (old id 7425064)
date added to LUP
2015-07-03 07:06:15
date last changed
2017-07-02 03:11:52
@article{9f1d94ff-778d-40c4-a929-c4b63b382bae,
  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 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.},
  author       = {Looker, Helen C. and Colombo, Marco and Agakov, Felix and Zeller, Tanja and Groop, Leif and Thorand, Barbara and Palmer, Colin N. and Hamsten, Anders and de Faire, Ulf and Nogoceke, Everson and Livingstone, Shona J. and Salomaa, Veikko and Leander, Karin and Barbarini, Nicola and Bellazzi, Riccardo and van Zuydam, Natalie and McKeigue, Paul M. and Colhoun, Helen M.},
  issn         = {1432-0428},
  keyword      = {Cardiovascular diseases,Epidemiology,Protein biomarkers,Risk factors,Type 2 diabetesmellitus},
  language     = {eng},
  number       = {6},
  pages        = {1363--1371},
  publisher    = {Springer Verlag},
  series       = {Diabetologia},
  title        = {Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes},
  url          = {http://dx.doi.org/10.1007/s00125-015-3535-6},
  volume       = {58},
  year         = {2015},
}