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; on behalf of the SUMMIT Investigators

doi:10.1007/s00125-015-3535-6

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

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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 language	English
Pages (from-to)	1363-1371
Number of pages	9
Journal	Diabetologia
Volume	58
Issue number	6
Early online date	5 Mar 2015
DOIs	https://doi.org/10.1007/s00125-015-3535-6
Publication status	Published - Jun 2015

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Looker, H. C., Colombo, M., Agakov, F., Zeller, T., Groop, L., Thorand, B., Palmer, C. N., Hamsten, A., de Faire, U., Nogoceke, E., Livingstone, S. J., Salomaa, V., Leander, K., Barbarini, N., Bellazzi, R., van Zuydam, N., McKeigue, P. M., Colhoun, H. M., & on behalf of the SUMMIT Investigators (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

@article{cb6d300cdf5a4632ab04daaa30d7003d,

title = "Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes",

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.",

author = "Looker, {Helen C.} and Marco Colombo and Felix Agakov and Tanja Zeller and Leif Groop and Barbara Thorand and Palmer, {Colin N.} and Anders Hamsten and {de Faire}, Ulf and Everson Nogoceke and Livingstone, {Shona J.} and Veikko Salomaa and Karin Leander and Nicola Barbarini and Riccardo Bellazzi and {van Zuydam}, Natalie and McKeigue, {Paul M.} and Colhoun, {Helen M.} and {on behalf of the SUMMIT Investigators}",

year = "2015",

month = jun,

doi = "10.1007/s00125-015-3535-6",

language = "English",

volume = "58",

pages = "1363--1371",

journal = "Diabetologia",

issn = "0012-186X",

publisher = "Springer Science and Business Media Deutschland GmbH",

number = "6",

}

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 & on behalf of the SUMMIT Investigators 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

TY - JOUR

T1 - Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes

AU - Looker, Helen C.

AU - Colombo, Marco

AU - Agakov, Felix

AU - Zeller, Tanja

AU - Groop, Leif

AU - Thorand, Barbara

AU - Palmer, Colin N.

AU - Hamsten, Anders

AU - de Faire, Ulf

AU - Nogoceke, Everson

AU - Livingstone, Shona J.

AU - Salomaa, Veikko

AU - Leander, Karin

AU - Barbarini, Nicola

AU - Bellazzi, Riccardo

AU - van Zuydam, Natalie

AU - McKeigue, Paul M.

AU - Colhoun, Helen M.

AU - on behalf of the SUMMIT Investigators

PY - 2015/6

Y1 - 2015/6

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.

AB - 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.

U2 - 10.1007/s00125-015-3535-6

DO - 10.1007/s00125-015-3535-6

M3 - Article

C2 - 25740695

SN - 0012-186X

VL - 58

SP - 1363

EP - 1371

JO - Diabetologia

JF - Diabetologia

IS - 6

ER -

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

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