Integrating Multimodal Data and Genomics for a Comprehensive Assessment of Cardiovascular Aging and Its Impact.
Authors
Affiliations (7)
Affiliations (7)
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou, Zhejiang, China.
- School of Mathematics and Statistics, Northeast Normal University, Changchun, China.
- School of Social Sciences, Tsinghua University, Beijing, China.
- China Population and Development Research Center, Beijing, China.
- Department of Cardiology, the Second Affiliated Hospital of Zhejiang University School of Medicine, State Key Laboratory of Transvascular Implantation Devices, Heart Regeneration and Repair Key Laboratory of Zhejiang Province, Transvascular Implantation Devices Research Institute, Hangzhou, China.
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Intelligent Preventive Medicine. Electronic address: [email protected].
- Center of Clinical Big Data and Analytics of The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China; Institute of Digital and Intelligent Health, Zhejiang University, Hangzhou, Zhejiang, China; Zhejiang Provincial Key Laboratory of Intelligent Preventive Medicine. Electronic address: [email protected].
Abstract
Age-related structural and functional remodeling of the heart and vessels increases cardiovascular disease (CVD) risk, yet comprehensive assessments using multimodal imaging and genetic characterization remains limited. We aimed to quantify cardiovascular aging using multimodal biomarkers and evaluate its genetic architecture, lifestyle determinants, and prognostic relevance. From the UK biobank, cardiovascular magnetic resonance (CMR), electrocardiogram, arterial stiffness, and carotid ultrasound biomarkers were integrated to establish a cardiovascular aging measure using machine learning (ML) models. Seven ML models were evaluated to predict cardiovascular age, and cardiovascular age gap (CardioAG) was calculated using the best-performing model. Associations between CardioAG and incident CVD outcomes were assessed, alongside modality-specific analyses to evaluate the incremental value of multimodal integration. Whole-genome sequencing (WGS) analyses were conducted to identify genetic variants associated with CardioAG, and linear regression models were applied to examine relationships between CardioAG and key lifestyle factors. Among 22,452 participants with complete multimodal data, 13,694 individuals free of baseline CVDs (median age 62.6 years [IQR 56.5-68.3], median follow-up 4.8 years [IQR 3.7-6.3]) were selected for model development. With 57 cardiovascular aging biomarkers, the CatBoost model performed best on the test dataset (Pearson r = 0.75; mean absolute error = 3.88 years). CardioAG was independently associated with hypertension, stroke, atrial fibrillation, coronary artery disease, and composite major adverse cardiovascular event (MACE, hazard ratio = 1.08, 95% CI, 1.06-1.10). Incremental and ablation analyses demonstrated complementary contributions across imaging and functional modalities, with multimodal integration enhancing predictive accuracy and providing independent prognostic value for MACE. WGS analysis identified novel common genetic variants associated with interindividual variability in CardioAG, including RN7SKP155, SVIL, and CBFA2T3, highlighting genetic contributions to vascular remodeling, electrophysiological and hemodynamic regulation, and cardiovascular functional reserve. Dietary factors, sleep duration, physical activity level, smoking, and alcohol intake were found to be significantly associated with CardioAG. This study developed a unified multimodal cardiovascular aging metric that integrates cardiac structure and function, vascular remodeling and stiffness, and electrophysiological features into a single biologically grounded, cumulative aging signal, providing incremental prognostic utility for CVD, and broadening the biological and genetic landscape underlying cardiovascular aging.