Machine Learning-Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study

Clinical Snapshot

Key Findings

• P Value: P=0.001

• Effect Size: Hazard ratio 0.695 for Phenotype 2 vs Phenotype 1

• Primary Outcome: Incident stroke over mean 14.7 years follow-up

• Nnt Or Sensitivity: 30% reduction in stroke risk for favorable phenotype (HR 0.695)

• Confidence Interval: 95% CI 0.559-0.864

Clinical Application

Limited by requirement for CMR imaging and complex phenotyping algorithms not readily available in routine practice Relevant to Australian cardiology and stroke prevention services, though CMR access varies. Could inform PBS-funded preventive strategies if validated and simplified for clinical use Middle-aged adults undergoing cardiac imaging assessment, particularly those with cardiovascular risk factors

Abstract

BACKGROUND: Cardiovascular magnetic resonance (CMR) is widely used across various cardiac conditions and systematically assesses cardiac anatomical structures and functional dynamics. Machine learning (ML) can accurately predict outcomes and understand the inherent features of clinical data. OBJECTIVE: This study aimed to derive CMR phenotypes related to cardiovascular aging, investigate the relationship between these phenotypes and stroke risk, and relearn these phenotypes using supervised ML. METHODS: We enrolled 36,467 participants without stroke and extracted CMR parameters from the UK Biobank, with follow-up data extending until September 30, 2023. Using the generative topographic mapping technique, we identified latent grid nodes among participants and then derived phenotypes through agglomerative hierarchical clustering. We used supervised ML models to predict cardiac function phenotypes and used Cox proportional hazards models to assess the association between these phenotypes and long-term stroke risk. RESULTS: We enrolled 36,467 participants in the study. The mean age was 54.9 (SD 7.5) years, with 17,442 (47.8%) male participants. During a mean follow-up time of 14.7 (SD 1.1) years, 500 (1.4%) participants developed stroke and 664 (1.8%) participants died, respectively. After generative topographic mapping modeling, we identified 2 distinct phenotypes: phenotype 1, characterized by adverse cardiac function and an accumulation of cardiovascular risk factors, reflecting cardiovascular aging; and phenotype 2, associated with a lower risk of stroke (hazard ratio 0.695, 95% CI 0.559-0.864; P=.001), which remained significant after accounting for competing mortality (hazard ratio 0.578, 95% CI 0.484-0.691; P<.001). We selected the random forest model as the optimal model for the phenotypes, demonstrating high accuracy (area under the curve 0.914, 95% CI 0.911-0.918 for training and 0.867, 95% CI 0.858-0.876 for validation) and calibration ability (Brier score 0.111, 95% CI 0.109-0.113 for training and 0.132, 95% CI 0.127-0.137 for validation). CONCLUSIONS: By integrating unsupervised and supervised ML methods, we identified cardiovascular aging-related phenotypes that demonstrate robust predictive ability for incident stroke, which may have the potential to improve preventive and therapeutic strategies for high-risk populations.

References

1. 1.Yuan, K., Kong, D., Zhong, J., Xie, M., Liu, R., Sun, W., & Liu, X. (2026). Machine Learning-Derived Cardiovascular Aging Phenotypes From Cardiac Function and Stroke Risk in the UK Biobank: Cohort Study. JMIR Aging. https://doi.org/10.2196/77017