Estimation of Physiological Metrics from Resting ECGs Using Deep Learning in the UK Biobank, Including submaximal exercise derived V̇O ₂ max, Body Fat Percentage, and Grip Strength

Maximal oxygen consumption is the gold standard for cardiorespiratory fitness but requires resource-intensive physical testing. Recent reports show that machine learning models can extract additional information from ECGs, yet the potential of ECG as a source of physiological metrics remains underutilized. While routinely collected resting electrocardiograms (ECG) provide an opportunistic window into cardiorespiratory fitness, current deep learning models often struggle with cross-cohort transferability or remain dependent on active exercise data. We developed population specific models using the UK Biobank to estimate submaximal exercise derived ( N = 8,540) and a panel of other physiological metrics (sample sizes up to N = 78,265) from resting 12-lead ECGs using Patient Contrastive Learning of Representations (PCLR), an AI based tool that converts ECG into a set of 320 features (ECG-PCLR). Data were split 80%:20% (training:test) and models were evaluated on a set-aside test subset. We demonstrate that ECG-PCLR embeddings alone can estimate submaximal and body fat percentage with Pearson correlations ( r ) of 0.61 and 0.65, respectively. They also estimate systolic blood pressure, forced expiratory volume in 1 second (FEV1), and grip strength with r values from 0.31 to 0.55. Adding ECG embeddings to basic predictors (age, sex and BMI) improves submaximal prediction by an absolute Δ R 2 of 8% and by 1% to 13% for other physiologic parameters.