A genotyping and machine learning approach towards personalised nutrition
Principal Investigator: Mr Corentin Molitor
Approved Research ID: 55079
Approval date: June 12th 2020
The goals of this project are to develop a list of DNA mutations linked to diabetes and obesity, which will be used to develop a personalised nutrition platform. Indeed, with the list of mutations, we can assess the potential risk a certain person has to develop these particular diseases. This risk scoring will be combined with clinical data as part of clinical studies to assess the effect of diet personalisation on young people with diabetes and obesity. The UK biobank data will be used to refine the list of DNA mutations and to develop the risk score models prior to the clinical studies. The expected impact from this project is to motivate healthy dietary choices. This will be done by providing personalised nutritional advice, based on each person own information (DNA, biomarkers, clinical data!). Recent studies are showing that two persons can have a very different response to the same diet. This could explain why public health campaigns have not been effective in halting the rise in nutrition-related non-communicable diseases (NCDs) around the world. Notably because they rely on the 'one size fits all' approach, which fails to take into account how each person will respond to a given diet. We expect personalised nutrition to provide a solution to this issue.