Recent developments in high-throughput proteomic technologies enable the discovery of novel biomarkers of coronary atherosclerosis. The aims of this study were to test if plasma protein subsets could detect coronary artery calcifications (CAC) in asymptomatic individuals and if they add predictive value beyond traditional risk factors.
MethodsUsing proximity extension assays, 1,342 plasma proteins were measured in 1,827 individuals from the Impaired Glucose Tolerance and Microbiota (IGTM) study and 883 individuals from the Swedish Cardiopulmonary BioImage Study (SCAPIS) aged 50-64 years without history of ischaemic heart disease and with CAC assessed by computed tomography. After data-driven feature selection, extreme gradient boosting machine learning models were trained on the IGTM cohort to predict the presence of CAC using combinations of proteins and traditional risk factors. The trained models were validated in SCAPIS.
ResultsThe best plasma protein subset (44 proteins) predicted CAC with an area under the curve (AUC) of 0.691 in the validation cohort. However, this was not better than prediction by traditional risk factors alone (AUC = 0.710, P = .17). Adding proteins to traditional risk factors did not improve the predictions (AUC = 0.705, P = .6). Most of these 44 proteins were highly correlated with traditional risk factors.
ConclusionsA plasma protein subset that could predict the presence of subclinical CAC was identified but it did not outperform nor improve a model based on traditional risk factors. Thus, support for this targeted proteomics platform to predict subclinical CAC beyond traditional risk factors was not found.
AbbreviationsASCVDatherosclerotic cardiovascular disease
CACcoronary artery calcification
CACScoronary artery calcification score
FINDRISCFinnish diabetes risk score
HDLhigh-density lipoprotein
IGTM studyimpaired glucose tolerance and microbiota study
IHDischaemic heart disease
LASSOleast absolute shrinkage and selection operator
MRMRmaximum relevance minimum redundancy
NPXnormalized protein expression
OGTToral glucose tolerance test
ROCreceiver operating characteristic
SBPsystolic blood pressure
SCAPISSwedish cardiopulmonary bioimage study
XGBextreme gradient boosting
© 2024 The Author(s). Published by Elsevier Inc.
留言 (0)