From omics to AI-mapping the pathogenic pathways in type 2 diabetes.

IF 3 4区 生物学 Q1 Biochemistry, Genetics and Molecular Biology
Siobhán O'Sullivan, Lu Qi, Pierre Zalloua
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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.

从组学到人工智能绘制2型糖尿病的致病途径。
了解2型糖尿病(T2D)的生化途径和器官间串扰对阐明其病理生理学至关重要。这些途径提供了将分子功能障碍与临床表型联系起来的机制框架,使患者能够基于显性代谢紊乱进行分层。多组学的进展,包括基因组学、转录组学、蛋白质组学、微生物组学和代谢组学,提供了将遗传变异和调控元件与疾病特征联系起来的系统级观点。单细胞技术通过识别β细胞衰竭、肝糖调节异常和脂肪炎症的细胞类型特异性驱动因素,进一步完善了这一观点。人工智能驱动的分析和机器学习整合了这些高维数据集,揭示了胰岛素信号、脂质代谢、线粒体功能和免疫代谢串音中涉及的分子特征和调节网络。这篇综述综合了目前关于T2D分子结构的证据,强调了PI3K-Akt、AMPK、mTOR、JNK和sirtuins等关键通路。它还探讨了肠道微生物群在调节宿主代谢和炎症中的作用。采用以途径为中心的系统生物学方法超越了统计关联,走向了机制洞察。将多组学与基于人工智能的建模相结合,代表了糖尿病患者分层和指导精确治疗的变革策略。本综述将复杂的生化途径转化为2型糖尿病的治疗方向,解决了分子研究和临床护理之间的关键差距。通过整合多组学、人工智能和系统生物学,它使科学界能够制定有针对性的干预措施,减轻这种不断升级的代谢性疾病的全球负担。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
FEBS Letters
FEBS Letters 生物-生化与分子生物学
CiteScore
7.00
自引率
2.90%
发文量
303
审稿时长
1.0 months
期刊介绍: FEBS Letters is one of the world''s leading journals in molecular biology and is renowned both for its quality of content and speed of production. Bringing together the most important developments in the molecular biosciences, FEBS Letters provides an international forum for Minireviews, Research Letters and Hypotheses that merit urgent publication.
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