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Area of Science:

  • Genetics and Genomics
  • Machine Learning
  • Cardiovascular Health

Background:

  • Accurate prediction of blood pressure (BP) is crucial for cardiovascular disease prevention.
  • Current prediction models often rely on demographic and clinical factors, with limited integration of genetic information.
  • Polygenic risk scores (PRSs) offer a way to quantify genetic predisposition to complex traits like hypertension.

Purpose of the Study:

  • To develop and evaluate non-linear machine learning (ML) models for predicting systolic and diastolic blood pressure (SBP and DBP).
  • To assess the performance improvement gained by incorporating multiple PRSs into prediction models.
  • To compare the efficacy of linear versus non-linear models and analyze performance across different racial/ethnic groups.

Main Methods:

  • Construction of a two-model ensemble: a baseline model (demographic/clinical variables) and a genetic model (including PRSs).
  • Evaluation of both linear and non-linear ML approaches for BP prediction.
  • Utilized PRSs derived from genome-wide association studies (GWAS) for SBP and DBP.
  • Performance was quantified using percentage variance explained (PVE) on a held-out test dataset.

Main Results:

  • Non-linear baseline models significantly improved PVE compared to linear models (SBP: 30.1% vs. 28.1%; DBP: 17.4% vs. 14.3%).
  • Incorporating seven PRSs enhanced genetic model PVE (SBP: 5.1% vs. 4.8%; DBP: 5% vs. 4.7%) compared to a single PRS.
  • Adding 14 more PRSs further increased PVE (SBP: 6.3%; DBP: 5.7%).
  • Non-White populations showed greater benefit from the inclusion of additional PRSs.

Conclusions:

  • Non-linear ML models offer improved BP prediction accuracy over linear models.
  • The integration of multiple PRSs substantially enhances the predictive power of genetic models for BP.
  • These findings underscore the importance of genetic information and advanced ML techniques for personalized BP management, particularly in diverse populations.