Ting Xu,1,2 Yucong Zhang,1,2 Yi Zhou,3 Lixue Yin,3 Xinwen Min,4 Shouling Wu,5 Cuntai Zhang,1,2 Lei Ruan1,2 

1Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China; 2Key Laboratory of Vascular Aging, Ministry of Education, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China; 3Department of Cardiovascular Ultrasound and Non-Invasive Cardiology, Sichuan Provincial People’s Hospital, Sichuan Academy of Medical Sciences, University of Electronic Science and Technology of China, Chengdu, 610072, People’s Republic of China; 4Department of Cardiology, Sinopharm Dongfeng Central Hospital, Hubei University of Medicine, Shiyan, 442008, People’s Republic of China; 5Department of Cardiology, Kailuan Hospital, Hebei United University, Tangshan, 063000, People’s Republic of China

Correspondence: Lei Ruan, Department of Geriatrics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China, Tel +86-15972220677, Email ruanlei8863@sina.com

Purpose: This study aimed to investigate the clinical implications of vascular aging (VAg) phenotypes based on the difference between chronological age (CA) and vascular age (VA).
Patients and Methods: We defined VA as the predicted age in a multivariable linear regression model including structural and functional parameters of arteries and conventional risk factors, in a multicentric, cross-sectional cohort (n=15580). According to the 10th and 90th percentiles of Δ-age (CA minus VA), we then classified the status of VAg into 3 phenotypes: the early VAg (EVA), the Normal VAg and the supernormal VAg (SUPERNOVA). We used Cox survival analysis to investigate the association between VAg phenotypes and the risk for adverse clinical outcomes (including all-cause death and cardiovascular disease) in an independent, prospective cohort (n=5316).
Results: In the prospective cohort (11.07 years, 927 events), when compared to the Normal VAg phenotype, EVA had an increased risk (HR: 2.43; 95% CI: 1.80– 3.27) and SUPERNOVA had a decrease risk (HR: 0.75; 95% CI: 0.64– 0.90) of adverse clinical outcomes, in particular stroke events. EVA also showed a higher risk of myocardial infarction (HR: 3.21, 95% CI: 1.56– 6.62) and all-cause death (HR: 1.79, 95% CI: 1.12– 2.85). The associations were independent of the atherosclerotic cardiovascular disease risk score. Further, the C-statistics increased 0.010 (P < 0.001), 0.013 (P < 0.001) and 0.016 (P < 0.001) separately when adding baPWV, adding the combination of baPWV and CIMT, and adding the VAg phenotypes to a model of conventional risk factors in predicting cardiovascular events.
Conclusion: This is the first study to evaluate the clinical implications of VAg phenotypes using multicentric data and undergone external validation in China. Our results emphasized that the classification of VAg phenotypes may be a potential tool to identify individuals who were susceptible to or resilient to VAg.

Keywords: age-related disease, carotid intima-media thickness, pulse wave velocity, risk assessment, vascular age, vascular aging