Association of lifestyle with deep learning predicted electrocardiographic age.

ABSTRACT:

Background

People 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.

Methods

This 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

SUBMITTER: Zhang C 

PROVIDER: S-EPMC10165078 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature