UNDERSTANDING DISEASE COMPLEXITY IN GRAVES’ DISEASE: A POPULATION-BASED NETWORK ANALYSIS OF COMORBIDITIES
Author(s)
Minseol Jang, PharmD1, Hae Sun Suh, MA, MS, PhD2;
1Kyung Hee University, Department of Regulatory Science, Graduate School, Seoul, Korea, Republic of, 2Kyung Hee University, College of Pharmacy, Seoul, Korea, Republic of
1Kyung Hee University, Department of Regulatory Science, Graduate School, Seoul, Korea, Republic of, 2Kyung Hee University, College of Pharmacy, Seoul, Korea, Republic of
OBJECTIVES: Patients with Graves’ disease (GD) often experience multiple comorbidities due to underlying hormonal imbalances. While there is increasing need of individualized care, there is limited understanding of how specific comorbid conditions co-occur and evolve over time in patients with GD. This study aimed to explore the structure and evolution of disease comorbidities in patients with GD.
METHODS: Using the Health Insurance Review and Assessment database, we identified patients newly diagnosed with GD between January 2019 and January 2021. Disease co-occurrence networks were constructed using ICD-10 diagnosis codes. Nodes represented individual diseases, and edges denoted co-occurrence. We assessed key network metrics—including degree centrality, which quantifies the number of direct connections of a node and reflects disease prominence within the network; betweenness centrality, which measures the extent to which a disease lies on the shortest paths between other diseases, indicating its potential role as a bridge in comorbidity progression; and clustering coefficient, which captures the degree of interconnectivity among a node’s neighbors—to characterize disease influence and network cohesion. Community detection analysis was used to identify clusters of tightly linked diseases by utilizing Louvain algorithm. Networks were stratified by age, sex, and time since diagnosis.
RESULTS: Out of 1,336,375 patients, 88,997 GD patients (mean age: 43.3 [SD: 15.9]; 69.6% female), the disease network included 160 nodes and 1,807 edges (average degree: 22.7). Comorbidities increased over time, reflecting the progressive accumulation of chronic and age-related diseases. Acute bronchitis, gastritis, and hypertension were most frequent diseases with high centrality. Sex-specific root diseases included dry eye syndrome and spondylopathies in females, and gastro-esophageal reflux disease, thyroid nodules, back pain, and dry eye syndrome in males.
CONCLUSIONS: This study reveals evolving, sex-specific comorbidity networks in GD patients. Network-based insights may inform targeted screening and tailored care strategies, supporting precision medicine approaches in endocrine disorders.
METHODS: Using the Health Insurance Review and Assessment database, we identified patients newly diagnosed with GD between January 2019 and January 2021. Disease co-occurrence networks were constructed using ICD-10 diagnosis codes. Nodes represented individual diseases, and edges denoted co-occurrence. We assessed key network metrics—including degree centrality, which quantifies the number of direct connections of a node and reflects disease prominence within the network; betweenness centrality, which measures the extent to which a disease lies on the shortest paths between other diseases, indicating its potential role as a bridge in comorbidity progression; and clustering coefficient, which captures the degree of interconnectivity among a node’s neighbors—to characterize disease influence and network cohesion. Community detection analysis was used to identify clusters of tightly linked diseases by utilizing Louvain algorithm. Networks were stratified by age, sex, and time since diagnosis.
RESULTS: Out of 1,336,375 patients, 88,997 GD patients (mean age: 43.3 [SD: 15.9]; 69.6% female), the disease network included 160 nodes and 1,807 edges (average degree: 22.7). Comorbidities increased over time, reflecting the progressive accumulation of chronic and age-related diseases. Acute bronchitis, gastritis, and hypertension were most frequent diseases with high centrality. Sex-specific root diseases included dry eye syndrome and spondylopathies in females, and gastro-esophageal reflux disease, thyroid nodules, back pain, and dry eye syndrome in males.
CONCLUSIONS: This study reveals evolving, sex-specific comorbidity networks in GD patients. Network-based insights may inform targeted screening and tailored care strategies, supporting precision medicine approaches in endocrine disorders.
Conference/Value in Health Info
2026-05, ISPOR 2026, Philadelphia, PA, USA
Value in Health, Volume 29, Issue S6
Code
EPH52
Topic
Epidemiology & Public Health
Disease
SDC: Diabetes/Endocrine/Metabolic Disorders (including obesity), SDC: Systemic Disorders/Conditions (Anesthesia, Auto-Immune Disorders (n.e.c.), Hematological Disorders (non-oncologic), Pain)