Protein biomarkers for predicting incident carotid plaque: a nested case–control study in the ChinaHEART cohort with transportability assessment in the UK Biobank
Bolin Jin, Chunying Lin, Chaoqun Wu, Bowang Chen, Yi Han, Xiaoyan Zhang, Xueke Bai, Yang Yang, Jianlan Cui, Wei Xu, Lijuan Song, Hao Yang, Wenyan He, Yan Zhang, Yan Gao, Xi LiObjectives
Carotid plaque is an early manifestation of atherosclerosis and is closely associated with the risk of myocardial ischaemia, ischaemic stroke and other atherosclerotic cardiovascular diseases (ASCVDs). To identify new protein biomarkers associated with carotid plaque, which will enhance early warning of ASCVDs
Design
Nested case–control study within a prospective cohort, with external assessment of transportability in an independent population-based cohort.
Participants and data sources
The development cohort is from China, which is a nationwide, community-based cohort and the external validation cohort is from the UK Biobank in the UK. In the development cohort, 292 participants without carotid plaque at baseline were included, comprising 145 incident cases and 147 matched controls selected based on age, sex, low-density lipoprotein cholesterol, systolic blood pressure, body mass index, smoking status and diabetes history. In the validation dataset, 30 800 participants without baseline cardiovascular disease were included from a large population-based cohort.
Results
A total of 11 biomarkers, including thrombomodulin, intercellular cell adhesion molecule-3, P-selectin, growth differentiation factor-15, adiponectin, monocyte chemoattractant protein-1, IL-10, placental growth factor, tyrosine kinase receptor-2, vascular endothelial growth factor-D and vascular cell adhesion molecule-1, were selected by least absolute shrinkage and selection operator regression and used to construct a prediction model for the carotid plaque. The area under the receiver operator characteristic curve (AUC) of the eventual model is 0.761, and it showed good calibration capability graphically with a Brier score of 0.192. In the UK Biobank cohort, when these biomarkers were added to a traditional predictive model, a better predictive power was generated, with an AUC improvement of 0.021 (p<0.001, Delong test), Brier score of 0.093, a continuous net reclassification improvement of 0.259 (0.223–0.294, p<0.001) and an integrated discrimination improvement of 0.017 (0.015–0.019, p<0.001) relative to the traditional model.
Conclusions
The identified protein biomarkers were used to predict carotid plaque and may provide modest incremental value for risk stratification beyond traditional risk factors. However, the findings should be interpreted with caution and further studies are needed to evaluate their clinical utility.