One path, two solutions: Network-based analysis identifies targetable pathways for the treatment of comorbid type II diabetes and neuropsychiatric disorders

Abstract

Comorbid diseases complicate patient outcomes and escalate healthcare costs, necessitating the need for a deeper mechanistic understanding. Neuropsychiatric disorders (NPDs) such as Neurotic Disorder, Major Depression, Bipolar Disorder, Anxiety Disorder, and Schizophrenia significantly exacerbate Type 2 Diabetes Mellitus (DM2), often leading to suboptimal treatment outcomes. The neurobiological mechanisms underlying this comorbidity remain poorly understood. To address this gap, we developed a novel pathway-based network computational framework to identify critical shared disease mechanisms between DM2 and these five prevalent comorbid NPDs. Our approach involves reconstructing an integrated DM2 ∩ NPDs KEGG pathway-pathway network and employs two complementary analytical methods, including the ''minimum path to comorbidity'' method to identify the shortest path fostering comorbid development. This analysis uncovered shared pathways like the PI3K-Akt signaling pathway and highlighted key nodes such as calcium signaling, MAPK, estrogen signaling, and apoptosis pathways. Dysregulation of these pathways likely contributes to the development of DM2-NPDs comorbidity. These findings have significant clinical implications, as they identify promising therapeutic targets that could lead to more effective treatments addressing both DM2 and NPDs simultaneously. Our model not only elucidates the intricate molecular interactions driving this comorbidity but also identifies promising therapeutic targets, paving the way for innovative treatment strategies. Additionally, the framework developed in this study can be adapted to study other complex comorbid conditions, advancing personalized medicine for comorbidities and improving patient care.

Keywords

• Bioinformatics
• Type II diabetes
• Neuropsychiatric disorders
• Comorbid diseases
• Network analysis