A new AI-powered tool will scan millions of scientific papers to map exactly how specific foods trigger chronic diseases like heart disease, diabetes, and cancer. The problem is that current dietary advice is built on shaky ground. Researchers lack accurate tools to measure what people actually eat, don't fully understand the biological mechanisms linking diet to disease, and have little data on vulnerable groups who suffer most from diet-related illness. CoDiet aims to fix this by building a comprehensive surveillance system. It will use machine learning to analyse global literature on diet-disease links, develop better dietary assessment methods (solving a long-standing weakness in nutrition science), and create a tool that can track how population-level dietary changes affect NCD rates over time. If successful, the project could transform public health policy. Governments would gain a dynamic interface to monitor diet-related disease risk in real time and test which interventions—such as sugar taxes or food labelling changes—actually work. The tool would also enable personalised nutrition advice by accounting for individual variation in response to diet. This is applied research with a clear practical endpoint: giving policymakers and clinicians the evidence they need to reduce the burden of chronic disease at population scale.
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Our current understanding of the relationship between diet and the development of non-communicable disease (NCD) is limited by a number of factors. These include a lack of understanding of dietary mechanisms that drive NCD, inaccurate tools to collect dietary information, a nascent understanding of the role of personalised nutrition, and the lack of data in vulnerable groups where NCDs are often over-represented. The overarching aim of CoDiet is to develop a series of tools (through eight work packages) which will address the current gaps in our knowledge and lead to the development of a tool that will assess dietary-induced NCD risk. We will achieve this through the six objectives which will answer the challenges of the work programme 1: Development of AI-driven literature searching tools - bring clear understanding of large global literature in the field of physiological and metabolic links between diet and NCD 2: Enhance the understanding of NCD risk factors - we will bring a series of beyond the state of the technics to gain mechanistic insight 3: Understanding of the importance individual variation in response to diet to risk of NCD - this will give insight into the targeting of dietary NCD advice 4: Develop an enhanced method of dietary assessment using machine learning technologies - solving a fundamental problem in nutrition of lack of an accurate dietary tool 5: Develop an enhance diet-NCD monitoring tool - enabling change in NCD in response to diet to be monitored at the population level 6: Develop a dynamic interface between diet and NCD risk factor monitoring and policy - Ensuring CoDiet is applicable at a population level The investigation of these objectives and the answers they provide will open a pathway to enhancing the uptake of NCD protective diet at a population level.
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