Abstract
Understanding the biochemical pathways and interorgan cross talk underlying type 2 diabetes (T2D) is essential for elucidating its pathophysiology. These pathways provide a mechanistic framework linking molecular dysfunction to clinical phenotypes, enabling patient stratification based on dominant metabolic disturbances. Advances in multi-omics, including genomics, transcriptomics, proteomics, microbiomics, and metabolomics, offer a systems-level view connecting genetic variants and regulatory elements to disease traits. Single-cell technologies further refine this perspective by identifying cell-type-specific drivers of β-cell failure, hepatic glucose dysregulation, and adipose inflammation. AI-driven analytics and machine learning integrate these high-dimensional datasets, uncovering molecular signatures and regulatory networks involved in insulin signaling, lipid metabolism, mitochondrial function, and immune-metabolic cross talk. This review synthesizes current evidence on T2D's molecular architecture, emphasizing key pathways such as PI3K-Akt, AMPK, mTOR, JNK, and sirtuins. It also explores the role of gut microbiota in modulating host metabolism and inflammation. Adopting a pathway-centric systems biology approach moves beyond statistical associations toward mechanistic insight. Integrating multi-omics with AI-based modeling represents a transformative strategy for stratifying patients and guiding precision therapies in diabetes care. Impact statement This review translates complex biochemical pathways into therapeutic direction for type 2 diabetes, addressing a critical gap between molecular research and clinical care. By integrating multi-omics, AI, and systems biology, it empowers the scientific community to develop targeted interventions that reduce the global burden of this escalating metabolic disease.