Integrating Polygenic Risk Improves Generative Forecasting of Disease Trajectories

Abstract

Predicting the longitudinal sequence of diseases an individual will develop over their lifetime is a central challenge in medicine. While recent AI models can process health histories, they have been limited by cohort size and the omission of genetic data. Here we introduce the Next Health Event (NHE) model, a generative transformer trained on the health trajectories of 7.1 million research participants. By using a transformer architecture to integrate demographic data, longitudinal BMI, and polygenic risk scores (PRS) for 297 traits with sequential health history, NHE significantly outperforms baseline models, including XGBoost with the same inputs, in predicting the next diagnosis across 129 conditions (Top-1 accuracy 25.5% vs. 22.3%). Systematic ablation studies reveal that both PRS and longitudinal BMI provide substantial, non-redundant predictive power, whereas self-reported lifestyle information offers limited additional value. The model's predictive accuracy is the same when forecasting prospectively reported incident outcomes vs. combined prospectively and retrospectively reported outcomes (AUROC 0.917), demonstrating its utility for real-world risk assessment. By uniting large-scale health histories with genetics, our work establishes a new framework for predictive health and demonstrates that generative models can effectively forecast individual disease pathways.

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