Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes
(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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https://lup.lub.lu.se/record/7425064
- author
- organization
- publishing date
- 2015
- type
- 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
- external identifiers
-
- wos:000353893000027
- scopus:84939940579
- pmid:25740695
- 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
- 2016-04-01 10:24:30
- date last changed
- 2024-04-07 07:55:06
@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}},
keywords = {{Cardiovascular diseases; Epidemiology; Protein biomarkers; Risk factors; Type 2 diabetesmellitus}},
language = {{eng}},
number = {{6}},
pages = {{1363--1371}},
publisher = {{Springer}},
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}},
doi = {{10.1007/s00125-015-3535-6}},
volume = {{58}},
year = {{2015}},
}