AI approaches towards personalised medicine and Intergenerational life-course evolution of diabetes
| Reference number | |
| Coordinator | Lunds universitet - Lunds Universitet CRC |
| Funding from Vinnova | SEK 2 796 129 |
| Project duration | July 2024 - September 2027 |
| Status | Ongoing |
| Venture | Swedish-Indian cooperation within innovation in the area of health and AI |
| Call | Cooperation with India within Health focusing on AI-based Digitalisation, Biodesign or Circular Economy |
Purpose and goal
Diabetes, a global health crisis, is projected to affect 783 million people by 2045. We aim to apply AI-ML methods to refine diabetes sub-classification in Swedish and Indian populations, identifying those needing intensive treatment to prevent complications. Our objective is to then identify T2D and subtype specific biomarkers to improve treatment preferences. We then aim to modeling life-course trajectories to help uncover pre-clinical pathways towards primordial prevention.
Expected effects and result
We will identify individuals at highest risk of diabetes and comorbidities. We will determine the applicability of Swedish study-derived coordinates for diabetes subgrouping in Indians. By comparing T2D and subtype-specific biomarkers, we’ll gain insights into distinct etiologies and treatment preferences across these diverse populations. Modeling life-course trajectories will provide invaluable insights towards primordial prevention.
Planned approach and implementation
Polygenic scores pertaining to diabetes traits and comorbidities and birth parameters will be assessed and compared in both populations. Novel clustering approaches using clinical measures and genetics will be explored. -Omics biomarkers in T2D and subgroups will be testes to better understand pathophysiology and possible implications for treatment. Life course modeling will be performed for promoting primordial-primary prevention strategies.