Exploring comorbidity networks in mild traumatic brain injury subjects through graph theory: a traumatic brain injury model systems study

Abstract

Background: Traumatic brain injuries (TBIs) are characterized by myriad comorbidities that affect the functioning of the affected individuals. The comorbidities that TBI subjects experience span a wide range, ranging from psychiatric diseases to those that affect the various systems of the body. This is compounded by the fact that the problems that TBI subjects face could span over an extended period post-primary injury. Further, no drug exists to prevent the spread of secondary injuries after a primary impact.

Methods: In this study, we employed graph theory to understand the patterns of comorbidities after mild TBIs. Disease comorbidity networks were constructed for old and young subjects with mild TBIs and a novel clustering algorithm was applied to understand the comorbidity patterns.

Results: Upon application of network analysis and the clustering algorithm, we discovered interesting associations between comorbidities in young and old subjects with the condition. Specifically, bipolar disorder was seen as related to cardiovascular comorbidities, a pattern that was observed only in the young subjects. Similar associations between obsessive-compulsive disorder and rheumatoid arthritis were observed in young subjects. Psychiatric comorbidities exhibited differential associations with non-psychiatric comorbidities depending on the age of the cohort.

Conclusion: The study results could have implications for effective surveillance and the management of comorbidities post mild TBIs.

Keywords: Centrality; Comorbidities; Disease comorbidity network; Graph theory; Traumatic brain injuries.

Fig. 1

Schematic workflow of the analysis of disease comorbidity networks in mTBI subjects. Flow chart represents various steps in the construction of disease comorbidity network and subsequent clustering based on betweenness centrality

Fig. 2

Disease comorbidity network of all mTBI subjects at 5 years post injury. The nodes in the network represent the comorbidities, while the edges represent associations in the form of phi-correlation coefficient. Node size indicates prevalence, and edge thickness represents the strength of the association

Fig. 3

Distribution of three centrality measures constructed from the comorbidity network of all mTBI subjects. Panel A represents the distribution of betweenness centrality, panel B represents degree centrality, and panel C represent eigenvector centrality

Fig. 4

Application of betweenness centrality-based clustering to the comorbidity network of all mTBI subjects. Individual clusters encompass nodes and edges that represent the comorbidity and the association between them, respectively

Fig. 5

Disease comorbidity network of old mTBI subjects at 5 years post injury. The nodes in the network represent the comorbidities, while the edges represent associations in the form of phi-correlation coefficient. Node size indicates prevalence, and edge thickness represents the strength of the association

Fig. 6

Application of betweenness centrality-based clustering to the comorbidity network of old mTBI subjects. Individual clusters encompass nodes and edges that represent the comorbidity and the association between them, respectively

Fig. 7

Disease comorbidity network of young mTBI subjects at 5 years post injury. The nodes in the network represent the comorbidities, while the edges represent associations in the form of phi-correlation coefficient. Node size indicates prevalence, and edge thickness represents the strength of the association

Fig. 8

Application of betweenness centrality-based clustering to the comorbidity network of young mTBI subjects. Individual clusters encompass nodes and edges that represent the comorbidity and the association between them, respectively