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Looker, H.C.* ; Colombo, M.* ; Agakov, F.V.* ; Zeller, T.* ; Groop, L.C.* ; Thorand, B. ; Palmer, C.N.* ; Hamsten, A.* ; dé Fairé, U.H.* ; Nogoceke, E.* ; Livingstone, S.J.* ; Salomaa, V.V.* ; Leander, K.* ; Barbarini, N.N.* ; Bellazzi, R.* ; van Zuydam, N.* ; McKeigue, P.M.* ; Colhoun, H.M.* ; SUMMIT Investigators (*)

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

Diabetologia 58, 1363-1371 (2015)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Cardiovascular Diseases ; Epidemiology ; Protein Biomarkers ; Risk Factors ; Type 2 Diabetes Mellitus; Coronary-heart-disease; Apolipoprotein-c-iii; Risk Prediction; Elevated Levels; Atherosclerosis; Lipoproteins; Mortality; Plaques; Model
Sprache englisch
Veröffentlichungsjahr 2015
HGF-Berichtsjahr 2015
ISSN (print) / ISBN 0012-186X
e-ISSN 1432-0428
Zeitschrift Diabetologia
Quellenangaben Band: 58, Heft: 6, Seiten: 1363-1371 Artikelnummer: , Supplement: ,
Verlag Springer
Verlagsort Berlin ; Heidelberg [u.a.]
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Epidemiology (EPI)
POF Topic(s) 30202 - Environmental Health
Forschungsfeld(er) Genetics and Epidemiology
PSP-Element(e) G-504000-002
DOI 10.1007/s00125-015-3535-6
WOS ID WOS:000353893000027
Scopus ID 84939940579
Scopus ID 84924113562
Erfassungsdatum 2015-03-16
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