Analyzing Comorbidity Patterns in Patients With Thyroid Disease Using Large-Scale Electronic Medical Records:...

Area of Science:

• Endocrinology and Network Medicine
• Public Health and Epidemiology

Background:

• Thyroid disease (TD) is a significant endocrine disorder with unclear comorbidity patterns.
• Understanding these patterns is crucial for global health and patient management.

Purpose of the Study:

• To analyze TD comorbidity patterns using a network-based approach.
• To investigate how comorbidities differ across patient subgroups (sex, age, TD type).

Main Methods:

• Retrospective analysis of large-scale private and public health data.
• Utilized ICD-10 codes for comorbidity identification (prevalence >2%).
• Constructed phenotypic comorbidity networks (PCNs) and applied PageRank algorithm.

Main Results:

• Identified complex comorbidity patterns in 18,311 (private) and 50,242 (public) TD patients.
• Hypertension, diabetes, and lipoprotein disorders were most prevalent comorbidities.
• Comorbidity complexity increased with age; males and benign TD patients showed more diverse associations.

Conclusions:

• TD comorbidities are complex, notably involving cardiocerebrovascular diseases and diabetes.
• Comorbidity associations differ significantly across TD subgroups.
• Findings aid in understanding and improving integrated management of TD patients.