M4-HEALTH: digital twins for prevention and treatment of cardiometabolic diseases
Description: Cardiometabolic diseases span from obesity and diabetes to cardiovascular events, and form the most common cause of death in the world. These interlinked etiologies are highly complex, involving multiple organs and time-scales. To deal with this complexity, our previous VR-funded project "M4-health: a foundation for general AI in healthcare" developed hybrid, multi-organ, and multiscale digital twin models. These twins look like a person on the outside, and can simulate what happens with physiology, when you e.g. change your diet or take certain medications. However, corresponding multi-organ, multi-level, and time-resolved data are still missing to make critical improvements, to make a proper validation of the organ crosstalk, and to evaluate the twin technology’s impact on real patients. Therefore, in this new project, we will: 1) use a combination of multi-organoid ex vivo systems with multi-modal longitudinal clinical studies, to improve the description of how fat regulation spreads across organs, and leads to diseases; 2) co-design the improved digital twin eHealth platforms, with end-users in preventive care and in treatment of type 2 diabetes and fatty liver disease; 3) evaluate the impact the new tool has on patient understanding, motivation, health, and resulting health economy. The project results will directly scale to more applications, e.g. via a large EU-project coordinated by the applicant.
Funder: Vetenskapsrådet
Amount: SEK 3,802,000
Funding type: research
Funder scheme: Project grant
Start date: 2024-01-01
End date: 2027-12-31
Investigator: Gunnar Cedersund
Institution: Linköping University
Funded outputs count: 6
Funder award ID: 2023-05460
OpenAlex ID: G6016708184