Research questions:
1.Despite the existing cardiovascular (CV) risk prediction score, but many more risk factors remain to be found. What novel risk factors can improve the predictive ability of current CV risk prediction models?
2.How do trajectories of these risk factors correlate with CV health?
3.How to integrate novel risk factors and their change trajectories into the CV risk prediction models to realize dynamic and accurate assessment of CV risk?
Objectives and scientific rationale:
Identification of novel risk factors for cardiovascular disease (CVD) is a clinical imperative given the public health burden attributable to CVDs, including coronary heart disease (CAD), structural heart disease, arrhythmia, and so on. By identifying novel factors and their trajectories, precise CVD risks can be estimated, personalized prevention strategies can be developed, improving CVD health and reducing the burden of CVD on global health. Metabolic risk factors (hypertension, diabetes, abdominal obesity, serum lipids, et.al), behavioral risk factors (sleep disorder, tobacco, alcohol, diet quality, physical activity, mental health, et.al), social determinants of health, geriatric syndrome (frailty and sarcopenia), some blood biomarkers, and electrocardiogram derived biomarkers are associated with the development and progression of CVD, but novel risk factors for CVD need to be explored. Besides, most previous studies focused on baseline assessment of risk factors, not taking into consideration the changes in CVD risk factors during follow-up. In comparison with a single assessment at baseline, studying changes in CVD risk factors can reflect more comprehensive biological associations and determine the necessity of intervention on these risk factors. In current study, we aimed to explore novel risk factors for CVD, investigate the associations of changes in risk factors with the development and progression of CVD, and establish novel prediction models for CVDs.
