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

Siobhán O'Sullivan1, Lu Qi2, Pierre Zalloua3,4

  • 1Department of Biological Sciences, College of Medicine and Health Sciences, Khalifa University, Abu Dhabi, UAE.

FEBS Letters
|July 17, 2025
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Summary

Understanding type 2 diabetes (T2D) requires exploring its biochemical pathways and interorgan communication. This review integrates multi-omics and AI to reveal molecular drivers for targeted T2D therapies.

Keywords:
artificial intelligenceclinical translationdigital twinsmulti‐omics integrationprecision medicinesystems biologytype 2 diabetes

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Area of Science:

  • Biochemistry
  • Systems Biology
  • Metabolic Disease Research

Background:

  • Type 2 Diabetes (T2D) pathophysiology is complex, involving intricate biochemical pathways and interorgan communication.
  • Understanding molecular dysfunction is key to linking metabolic disturbances with clinical T2D phenotypes.
  • Current research requires advanced methods to integrate diverse biological data for a holistic view.

Purpose of the Study:

  • To synthesize current evidence on the molecular architecture of T2D.
  • To highlight key biochemical pathways and cellular mechanisms driving T2D.
  • To explore the integration of multi-omics and AI for advancing T2D research and therapy.

Main Methods:

  • Review of multi-omics data (genomics, transcriptomics, proteomics, metabolomics, microbiomics).
  • Application of single-cell technologies to identify cell-type-specific T2D drivers.
  • Utilizing AI-driven analytics and machine learning for high-dimensional dataset integration.

Main Results:

  • Identified key molecular pathways (e.g., PI3K-Akt, AMPK, mTOR, JNK, sirtuins) implicated in T2D.
  • Highlighted the role of gut microbiota in modulating host metabolism and inflammation.
  • Demonstrated the power of AI in uncovering molecular signatures and regulatory networks in T2D.

Conclusions:

  • A pathway-centric systems biology approach offers mechanistic insight into T2D.
  • Integrating multi-omics and AI is crucial for patient stratification and precision diabetes care.
  • This approach bridges the gap between molecular research and clinical application for T2D interventions.