Machine Learning Insights into Age-Related Sex Differences in Blood Pressure: Influence of Diabetes and Body Mass Index.

INTRODUCTION

The age-related dynamics of blood pressure (BP) arise from complex, often concurrent interactions among multiple factors (e.g., sex, type-2 diabetes mellitus [T2DM], body mass index [BMI]), making it challenging to isolate individual variable effects. Disentangling factor's contribution to BP trajectories across the lifespan remains a challenge.

AIM: Machine learning (ML) algorithms were applied to data from 219 individuals from a publicly available dataset to model age-related trends in systolic and diastolic BP, using age, sex, BMI, heart rate, and T2DM as predictors.

METHODS

Five regression models (linear regression, random forests, support vector machines, gaussian process regression [GPR], and neural networks) were tested. The best-fitting models capturing complex predictor-target relationships were used to simulate systolic and diastolic BP trajectories under customized scenarios, independently varying sex, BMI, and T2DM to assess isolated effects.

RESULTS

The squared exponential GPR yielded the best predictions for systolic BP, while the Matern 5/2 kernel performed best for diastolic BP. Systolic BP increased with age, with steeper trends at higher BMI. Women had lower systolic BP in early and mid-adulthood, but values surpassed men's in older age, especially with T2DM. Diastolic BP rose until midlife, then declined in both sexes. Women showed a similar crossover pattern, attenuated by T2DM, particularly at higher BMI.

CONCLUSION

ML simulations from a static dataset assessed individual factors' contributions to BP trajectories, producing results consistent with empirical evidence (e.g., greater T2DM impact on BP dynamics and faster age-related BP rise in women than men) and highlighting the potential for counterfactual analyses.