ABU DHABI, 14th November, 2026 (WAM) -- Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) is advancing research using artificial intelligence (AI) to improve early diabetes detection through innovative approaches to identifying risk factors and analysing health and genetic indicators. Marking World Diabetes Day, observed annually on 14th November, researchers are exploring how AI can identify early signs of diabetes risk using data that conventional screening may overlook, in line with this year's theme, "Early action on diabetes".

MBZUAI leads the Human Phenotype Project (HPP) in the UAE, a free, voluntary longitudinal study in Abu Dhabi that follows participants for up to 25 years. Open to UAE citizens and residents aged 18 to 70, it combines more than 40 layers of health data, including genetic information, blood biomarkers, medical imaging, continuous glucose monitoring and sleep tracking, and draws on a population of more than 200 nationalities to establish health reference standards that account for age and ethnicity. HPP findings revealed that 40 percent of individuals whose fasting glucose initially appeared normal later recorded readings within the prediabetes range during continuous monitoring.

HPP data is also supporting GluFormer, an AI foundation model created by MBZUAI researchers and collaborators to analyse daily glucose fluctuations rather than relying solely on average measurements such as the HbA1c test. Published in Nature in January 2026, GluFormer was trained on more than 10 million continuous glucose monitoring measurements from 10,812 HPP participants and can identify glucose patterns using just two weeks of data. A multimodal version incorporating dietary information can simulate individual glucose responses to specific foods.

Another initiative focuses on genetic susceptibility to type 2 diabetes through polygenic risk scores (PRS). The five-year project, running from 2025 to 2030, draws on the Emirati Genome Program's repository of 900,000 genomes and type 2 diabetes diagnoses recorded through Malaffi, Abu Dhabi's health information exchange, to develop and validate a population-specific PRS for Emiratis, addressing limitations of existing models largely built on European genetic data. The team will also conduct a genome-wide association study to help physicians identify higher-risk individuals earlier.

Research led by Professor Imran Razzak, Associate Professor of Computational Biology at MBZUAI, is uncovering links between oral microbes and metabolic changes associated with diabetes. AI models using selected oral microbial characteristics distinguished individuals with prediabetes more accurately than models based on complete microbial profiles. Related research found that fruit, vegetable and fibre intake, as well as Mediterranean-style and whole-food plant-based diets, are associated with particular microbial species and pathways.

Through the emerging field of oculomics, MBZUAI researchers are developing AI systems that detect signs of systemic disease in retinal images. During a research showcase with Cleveland Clinic Abu Dhabi in October 2025, MBZUAI demonstrated how eye scans could help identify early indicators associated with diabetes, hypertension, Alzheimer's disease and heart disease. Professor Jianing Qiu, Assistant Professor of Personalised Medicine, leads work on retinal biomarkers of metabolic and vascular dysfunction, while Professor Razzak's team is combining retinal vascular imaging with electrocardiogram (ECG) data to detect early signs of heart failure. These systems are designed to support clinicians rather than replace them.

The research reflects the UAE's broader investment in healthcare technology, with healthcare among the priority sectors of the UAE National AI Strategy 2031, while initiatives such as the Emirati Genome Program and Malaffi are strengthening the data foundations needed for earlier disease risk identification.

(Source: WAM.ae)