Multi-Omics Technologies, Integrated with AI and Machine Learning, to Radically Transform Treatment of Type-2 Diabetes
Study by Researchers at Khalifa University with Harvard and Tulane University Addresses Critical Gap between Molecular Research and Clinical Care
Artificial Intelligence (AI), combined with machine learning (ML), and multi-omics technologies may radically change the treatment of Type-2 diabetes (T2D) and transform the way the disease is diagnosed, monitored, and managed, according to a study by Khalifa University researchers Professor Pierre Zalloua and Dr. Siobh谩n O鈥� Sullivan in collaboration with Prof Lu Qi School of Public Health,Tulane University Obesity Research Centre and Tulane Personalised Health Institute (US).听
Addressing a critical translational gap between molecular research and clinical practice, the study found that integrating multi-omics technologies with AI and ML can facilitate the development of targeted patient-specific interventions, helping to reduce the global burden of this escalating metabolic disease. Current estimates indicate that 463 million adults are living with Type-2 Diabetes, a figure projected to reach 700 million by 2045, with the most significant increases expected in the Middle East, North Africa and Asia.
The researchers emphasize that understanding the complex interplay between organs and biochemical pathways is essential for elucidating how Type-2 diabetes develops and progresses. These pathways help explain how problems at the molecular level lead to clinical symptoms, and they allow doctors to group patients based on their dominant metabolic disturbances.
Researchers point out new emerging technologies like multi-omics which integrate genes, proteins, metabolites, and gut microbes, offer more clarity on how genetic and regulatory changes drive disease pathogenesis. Single-cell tools further refine this understanding by pinpointing which specific cell types are responsible for issues like insulin production failure, liver glucose dysregulation, and inflammation in fat tissue.
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鈥淭his study explores the complex molecular architecture of Type 2 Diabetes, and the important role of multi-omics integration and systems biology. This is a step forward in bridging the gap between molecular insight and clinical translation.鈥�
鈥� Professor Siobhan O’sullivan, Khalifa University.
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AI and machine learning enable the integration and interrogation of these complex datasets revealing molecular patterns and regulatory networks involved in insulin signaling, fat metabolism, energy production, and immune system interactions. Such multi-omics analyses can enable precision diabetology, where therapies are tailored to the patient鈥檚 molecular profile rather than relying on a one-size-fits-all treatment approach.听 Predictive AI models can also generate personalized risk scores even before clinical symptoms appear, allowing for early intervention, the study finds.
Dr. Siobh谩n O’ Sullivan said: 鈥淭his study explores the complex molecular architecture of Type 2 Diabetes, and the important role of multi-omics integration and systems biology.听 This is a step forward in bridging the gap between molecular insight and clinical translation.鈥�
Overall, by adopting a systems biology approach, the study moves beyond conventional statistical associations, advancing towards a deeper mechanistic understanding of the disease, paving the way for more personalized treatments tailored to each patient鈥檚 unique metabolic profile, says the study.
Clarence Michael
English Editor – Specialist
