Unraveling the interconnectedness: A network-based study of type 2 diabetes and its comorbidities.

BACKGROUND: As the global prevalence of Type 2 Diabetes (T2DM) continues to rise, it becomes increasingly critical to delve into the systemic understanding of the disease. T2DM is widely acknowledged as a multifaceted condition characterized by numerous pre-existing and subsequent comorbidities. These complications, spanning both pre- and post-diabetic phases, are interconnected through shared genes or variants, often indicating underlying genetic correlations. While various studies have touched upon individual complications in relation to T2DM, there remains a gap in comprehensive research that systematically analyzes the evidence supporting the association of all these complications with T2DM.

METHODOLOGY: In this study, we employed a network-based system biology approach to build a Disease-Disease Association (DDA) network specific to T2DM. This network identifies numerous comorbidities based on gene sharing among various diseases. Subsequently, these linked diseases were categorized into different disease classes and further into pre- and post-diabetes complications for detailed investigation. We then conducted functional enrichment analysis on a core set of genes associated with diabetes to identify underlying domain activities, including biological processes, molecular functions, and pathways linked to the disease. Additionally, we analyzed variants within this core gene group to gain deeper insights into the genetic landscape of diabetes.

RESULTS: We identified 2140 diseases directly associated with T2DM, each sharing at least 27 common genes, by applying the Jaccard Index for Genes (JIg) filter with a threshold of 0.01. Among these, 244 most prominent diseases with JIg ≥0.1 were classified into 18 disease classes - including cardiovascular, neurological, metabolic, and inflammatory, etc - based on MeSH terms, and further linked to pre- and post-diabetic complications. Obesity, Hypertensive Disease, Metabolic Syndrome X, and Hyperglycemia were the top four among 27 prediabetic conditions, while Cardiovascular Disease, Atherosclerosis, Arteriosclerosis, Alzheimer, and Coronary Artery Disease led the 85 post-diabetic complications, all showing strong associations with T2DM. Furthermore, Enrichment analysis of the core genes revealed critical pathways, including PI3K-Akt signaling, along with biological processes associated with different stimulus response and regulation activities, and molecular functions involving different signaling receptors and binding activities, as well as other significant domain entities-highlighting their pivotal roles in T2DM development and progression.

CONCLUSION: Our study presents a significant advancement in understanding T2DM and its comorbidities. Employing a network-based methodology, we identified diseases linked to T2DM based on genetic similarities, subsequently classifying them into pre- and post-diabetes complications. Functional enrichment analysis of diabetes-associated genes elucidates key biological processes and pathways, offering insights into potential therapeutic targets. Our findings facilitate personalized treatment strategies and early interventions, contributing to addressing the global burden of T2DM and improving patient outcomes.