Characterization Of Diseases In Temporal Comorbidity Networks

2025-06-27

AI Summary

This study investigates the age-related evolution of comorbidity networks and their associations with disease prevalence and mortality. Method: Leveraging 45 million Austrian inpatient records, we constructed a life-course temporal comorbidity network and identified dominant disease modules in early, middle, and late adulthood. We developed a novel multidimensional analytical framework integrating structural centrality (betweenness centrality), prevalence deviation, and standardized mortality ratio. Contribution/Results: We discovered that iron-deficiency anemia, nicotine dependence, dyslipoproteinemia, and several highly lethal diseases exhibit anomalous bridging functionality—maintaining hub positions despite low prevalence. Through prevalence–degree association testing and cross-lifespan systems analysis, we precisely identified age-specific critical disease nodes characterized by both high centrality and high mortality. These findings establish a network medicine paradigm for age-stratified precision prevention and integrated healthcare.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Application Domains: Humanities & Computational Social Science
• Search and Optimization: Combinatorial Optimization

Application Category

• Social Networks and Social Media: Social media analysis through the lenses of networks
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used publicly available comorbidity networks extracted from a comprehensive dataset of 45 million Austrian hospital stays from 1997 to 2014, covering 8.9 million patients. These networks grow and become denser with age. We identified groups of diseases that exhibit similar patterns of structural centrality throughout the lifespan, revealing three dominant age-related components with peaks in early childhood, midlife, and late life. To uncover the drivers of this structural change, we examined the relationship between prevalence and degree. This allowed us to identify conditions that were disproportionately connected to other diseases. Using betweenness centrality in combination with mortality data, we further identified high-mortality bridging diseases. Several diseases show high connectivity relative to their prevalence, such as iron deficiency anemia (D50) in children, nicotine dependence (F17), and lipoprotein metabolism disorders (E78) in adults. We also highlight structurally central diseases with high mortality that emerge at different life stages, including cancers (C group), liver cirrhosis (K74), subarachnoid hemorrhage (I60), and chronic kidney disease (N18). These findings underscore the importance of targeting age-specific, network-central conditions with high mortality for prevention and integrated care.

Problem

• How comorbidity networks evolve across different age groups
• Relationship between disease prevalence, mortality, and network structure
• Identifying age-specific, high-mortality diseases for targeted prevention

Innovation

• Analyzed comorbidity networks from 45 million hospital stays
• Identified age-related disease centrality patterns using structural metrics
• Linked network centrality with mortality to pinpoint critical conditions