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Integrating Multimodal Data and Genomics for a Comprehensive Assessment of Cardiovascular Aging and Its Impact
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DOI：10.1016/j.jocmr.2026.102783.png)
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Abstract
En 中文
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.
Keywords:
CardioAG
Cardiovascular Age Gap
CVD
Cardiovascular Disease
ML
Machine Learning
DL
Deep Learning
CV
Cross Validation
MAE
Mean Absolute Error
CMR
Cardiovascular Magnetic Resonance
MACE
Major Adverse Cardiovascular Event
HR
Hazard Ratio
CI
Confidence Interval
IQR
Interquartile Range
ECG
Electrocardiogram
CIMT
Carotid Intima-Media Thickness
ASI
Arterial Stiffness Index
cf-PWV
carotid-femoral pulse wave velocity
WGS
Whole-Genome Sequencing
WES
whole-exome sequencing
RVs
Rare Variants
PC
Principal Component
LV
Left Ventricle
LA
Left Atrium
RV
Right Ventricle
RA
Right Atrium
DM
Diabetes Mellitus
BMI
Body Mass Index
BSA
Body Surface Area
HF
Heart Failure
AF
Atrial Fibrillation
CAD
Coronary Artery Disease
cardiovascular disease
biological age
aging
machine learning
whole-genome sequencing
cardiovascular magnetic resonance
prediction model
Journal
IF:
6.1
Papers:
2.3K
Citations:
6.9K
Organization
Cited Papers