Nam Yonghyun, Lee Dong-Gi, Woerner Jakob, Lee Se-Hwan, Lee Min Ji, Jo Sung-Han, Jung Jaeun, Heo Su Chin, Jo Chris Hyunchul, Kim Dokyoon
Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104 USA.
McKay Orthopaedic Research Laboratory, Department of Orthopaedic Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA.
medRxiv. 2025 Apr 22:2025.04.21.25326169. doi: 10.1101/2025.04.21.25326169.
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.
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.
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.
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.
附着点病和附着点炎，包括肩袖疾病和其他肌腱疾病，是一组病因复杂的异质性肌肉骨骼疾病。了解全身健康状况如何影响其发病仍然是肌肉骨骼医学中的一项关键挑战。
我们使用来自432757名英国生物银行参与者的纵向电子健康记录（EHR）进行了一项大规模的全表型共病分析。将外周附着点病的发病病例与434种基线疾病表型的对照进行比较。构建了一个定向自我网络，使用基于比值比的关联将显著相关的共病与目标疾病联系起来。通过UMAP和DBSCAN进行无监督聚类，识别出数据驱动的共病聚类，这些聚类被整合为统一的内型——解释为导致疾病风险的不同全身概况。此外，应用基于元路径的轨迹分析来揭示导致疾病发病的时间结构多重共病链。
我们确定了183种与附着点病未来发展显著相关的基线疾病（FDR<0.05）。网络聚类揭示了8个共病聚类，这些聚类被整合为4种统一的内型：代谢-身心型、炎症-多系统型、机械损伤驱动型和衰老-干预相关型。元路径分析揭示了常见的三步疾病轨迹，如代谢-感染-肌肉骨骼和炎症性皮肤到关节的进展，突出了潜在的机制途径。这些内型表现出不同的临床特征，但具有生物学一致性，表明不同的全身健康概况可以汇聚以驱动肌腱相关疾病。
本研究引入了一个可扩展的框架，用于使用全表型共病网络识别附着点病和附着点炎背后的全身多重共病模式。通过整合网络聚类和元路径分析，我们发现了可解释的数据驱动内型，这些内型可能为个性化风险评估和靶向治疗策略提供信息。这些发现有助于生物银行规模疾病建模这一不断发展的领域，并为肌肉骨骼医学中的精准方法提供了基础。
