Association of lifestyle with deep learning predicted electrocardiographic age

Frontiers in Cardiovascular Medicine

Cardiovascular Medicine

Cuili Zhang¹  Antônio H. Ribeiro²  Robert J. Thomas³  Honghuang Lin⁴  Biqi Wang⁴  Antonio L. P. Ribeiro⁵  Luisa C. C. Brant⁵  Xiao Miao⁶ 

[1] Department of Cardiology, The First Affiliated Hospital of Harbin Medical University, Harbin, China;Department of Information Technology, Uppsala University, Uppsala, Sweden;Department of Medicine, Division of Pulmonary, Critical Care & Sleep Medicine, Beth Israel Deaconess, Medical Center, Boston, MA, United States;Department of Medicine, University of Massachusetts Chan Medical School, Worcester, MA, United States;Faculty of Medicine and Telehealth Center, Hospital das Clínicas, Universidade Federal de Minas Gerais, Belo Horizonte, Brazil;Innovation Research Institute of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China;

关键词: biological age;    deep learning;    lifestyle;    epidemiology—analytic (risk factors);    electrocardiogram;   

DOI  :  10.3389/fcvm.2023.1160091

 received in 2023-02-06, accepted in 2023-04-04,  发布年份 2023

来源: Frontiers

【 摘 要 】

BackgroundPeople age at different rates. Biological age is a risk factor for many chronic diseases independent of chronological age. A good lifestyle is known to improve overall health, but its association with biological age is unclear.MethodsThis study included participants from the UK Biobank who had undergone 12-lead resting electrocardiography (ECG). Biological age was estimated by a deep learning model (defined as ECG-age), and the difference between ECG-age and chronological age was defined as Δage. Participants were further categorized into an ideal (score 4), intermediate (scores 2 and 3) or unfavorable lifestyle (score 0 or 1). Four lifestyle factors were investigated, including diet, alcohol consumption, physical activity, and smoking. Linear regression models were used to examine the association between lifestyle factors and Δage, and the models were adjusted for sex and chronological age.ResultsThis study included 44,094 individuals (mean age 64 ± 8, 51.4% females). A significant correlation was observed between predicted biological age and chronological age (correlation coefficient = 0.54, P < 0.001) and the mean Δage (absolute error of biological age and chronological age) was 9.8 ± 7.4 years. Δage was significantly associated with all of the four lifestyle factors, with the effect size ranging from 0.41 ± 0.11 for the healthy diet to 2.37 ± 0.30 for non-smoking. Compared with an ideal lifestyle, an unfavorable lifestyle was associated with an average of 2.50 ± 0.29 years of older predicted ECG-age.ConclusionIn this large contemporary population, a strong association was observed between all four studied healthy lifestyle factors and deaccelerated aging. Our study underscores the importance of a healthy lifestyle to reduce the burden of aging-related diseases.

【 授权许可】

Unknown   
© 2023 Zhang, Miao, Wang, Thomas, Ribeiro, Brant, Ribeiro and Lin.