Enhancing comorbidity network inference with risk-enriched health trajectories embedding

2026-07-06

AI Summary

Existing comorbidity network approaches often overlook temporal dynamics, suffer from confounding by shared risk factors, and struggle to distinguish direct from indirect disease associations, resulting in overly dense networks with limited clinical interpretability. This study proposes a novel framework for constructing comorbidity networks that integrates individual health trajectories, semantic similarity, and temporal co-occurrence information. For the first time, it jointly incorporates risk factor adjustment, temporal embedding, and a sparse Gaussian graphical model (GGM-Lasso) to infer direct disease–disease associations. Applied to UK Biobank data, the method identifies four clinically coherent comorbidity communities and key pathological hubs. Furthermore, it defines four distinct disease progression phenotypes based on network community structure, which exhibit significantly divergent long-term survival trajectories, offering a foundation for precision risk stratification and targeted intervention.

Abstract

Multimorbidity poses a growing challenge for individual health, reducing quality of life and increasing treatment burden, resulting in a multiplicative impact on healthcare system management and fragmented care trajectories. Comorbidity networks could provide crucial insight into characterising multimorbidity and disease relationships. However, existing approaches to comorbidity network construction face critical limitations: they overlook temporal information by relying on cross-sectional statistics, produce biased association estimates by ignoring confounding due to shared risk factors, and fail to distinguish between direct and indirect disease associations, thereby yielding fully connected networks. To address these limitations, we develop a methodological framework for population-level disease network inference that uses individual health trajectories to learn disease associations, capturing semantic similarity and temporal co-occurrence. Sparse network estimation is achieved via Gaussian Graphical Models with Lasso regularisation, informed by prior clinical knowledge on shared risk factors derived from a dedicated confounding evaluation step. Applied to UK Biobank data comprising 24 cardiometabolic diseases and 76 risk factors, the resulting network revealed clinically meaningful disease patterns. Topological analysis identifies key pathological hubs, reveals potential actionable targets for multimorbidity management, and identifies four distinct disease communities that align with the established cardiometabolic taxonomy. Building on this community structure, we derive community-based patient representations that capture disease progression dynamics. Clustering these representations reveals four progression phenotypes with significantly different long-term survival trajectories, highlighting the potential of the framework for risk stratification and personalised care.

Problem

• comorbidity network
• multimorbidity
• confounding
• temporal trajectories
• disease association

Innovation

• comorbidity network
• health trajectories embedding
• Gaussian Graphical Model
• confounding adjustment
• multimorbidity phenotypes