Phenome-wide comorbidity network analysis reveals clinical risk patterns in enthesopathy and enthesitis
Listed in
This article is not in any list yet, why not save it to one of your lists.Abstract
Background
Enthesopathy and enthesitis, including rotator cuff disease and other tendon disorders, represent a heterogeneous group of musculoskeletal conditions with complex etiologies. Understanding how systemic health profiles influence their onset remains a critical challenge in musculoskeletal medicine.
Methods
We conducted a large-scale, phenome-wide comorbidity analysis using longitudinal electronic health records (EHR) from 432,757 UK Biobank participants. Incident cases of peripheral enthesopathies were compared to controls across 434 baseline disease phenotypes. A directed ego network was constructed to link significantly associated comorbidities to the target condition using odds ratio-based associations. Unsupervised clustering via UMAP and DBSCAN identified data-driven comorbidity clusters, which were consolidated into unified endotypes-interpreted as distinct systemic profiles contributing to disease risk. Additionally, metapath-based trajectory analysis was applied to uncover temporally structured multimorbidity chains leading to disease onset.
Results
We identified 183 baseline conditions significantly associated with the future development of enthesopathy (FDR < 0.05). Network clustering revealed eight comorbidity clusters, which were consolidated into four unified endotypes: Metabolic-Psychosomatic, Inflammatory-Multisystem, Mechanical-Injury-driven, and Aging-Intervention-related. Metapath analysis uncovered common three-step disease trajectories, such as metabolic-infectious-musculoskeletal and inflammatory skin-to-joint progressions, highlighting potential mechanistic pathways. These endotypes showed diverse clinical features but shared biological coherence, suggesting that different systemic health profiles can converge to drive tendon-related disease.
Conclusions
This study introduces a scalable framework for identifying systemic multimorbidity patterns underlying enthesopathy and enthesitis using phenome-wide comorbidity networks. By integrating network clustering and metapath analysis, we uncover interpretable, data-driven endotypes that may inform individualized risk assessment and targeted care strategies. These findings contribute to the growing field of biobank-scale disease modeling and offer a foundation for precision approaches in musculoskeletal medicine.
