Protein biomarkers for the prediction of cardiovascular disease in type 2 diabetes.
OBJECTIVE
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
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.
Diabetes Epidemiology Unit, University of Dundee, Mackenzie Building, Kirsty Semple Way, Dundee, DD2 4BF, UK, h.c.looker@dundee.ac.uk.
Helen C
HC
Looker
Marco
M
Colombo
Felix
F
Agakov
Tanja
T
Zeller
Leif
L
Groop
Barbara
B
Thorand
Colin N
CN
Palmer
Anders
A
Hamsten
Ulf
U
de Faire
Everson
E
Nogoceke
Shona J
SJ
Livingstone
Veikko
V
Salomaa
Karin
K
Leander
Nicola
N
Barbarini
Riccardo
R
Bellazzi
Natalie
N
van Zuydam
Paul M
PM
McKeigue
Helen M
HM
Colhoun
D000368:Aged; D053305:Apolipoprotein C-III / Q000097:blood; D019540:Area Under Curve; D015415:Biomarkers / Q000097:blood*; D002318:Cardiovascular Diseases / Q000150:complications* / Q000175:diagnosis; D016022:Case-Control Studies; D048909:Diabetes Complications; D003924:Diabetes Mellitus, Type 2 / Q000150:complications* / Q000175:diagnosis; D005060:Europe; D005260:Female; D005919:Glomerular Filtration Rate; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D007328:Insulin / Q000627:therapeutic use; D019409:Interleukin-15 / Q000097:blood; D015850:Interleukin-6 / Q000097:blood; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D020097:Natriuretic Peptide, Brain / Q000097:blood; D010446:Peptide Fragments / Q000097:blood; D012372:ROC Curve; D012307:Risk Factors; D020107:Troponin T / Q000097:blood
D053305:Apolipoprotein C-III; D015415:Biomarkers; D006442:Glycated Hemoglobin; D007328:Insulin; D019409:Interleukin-15; D015850:Interleukin-6; D020097:Natriuretic Peptide, Brain; D010446:Peptide Fragments; D020107:Troponin T; C517652:hemoglobin A1c protein, human; C109794:pro-brain natriuretic peptide (1-76)