13 Sep 2024 — In a £4.8 million (US$6.3 million) eight-year program, UK researchers and institutions will use artificial intelligence (AI) to examine the link between nutrition, health inequality and the development of multiple long-term conditions. The project will investigate inflammation as a critical biological driver that underlies various chronic health conditions, such as diabetes, arthritis and high blood pressure.
Moreover, inflammation could help to explain how nutrition and diet are linked to these long-term health conditions and why they are more common in specific social and ethnic groups.
The University of East Anglia (UEA), UK, will lead the project. It is supported by the country’s National Institute for Health and Care Research with funding and the engagement of an extensive patient network. Other UK-based partner institutions include the University of Exeter and the Quadram Institute.
“About one in four of the UK population have multiple long-term conditions,” says lead professor Alex Macgregor of UEA’s Norwich Medical School, lead researcher of the project. “It is one of the greatest challenges facing individuals and health services, both now and for the coming decades and is associated with a reduction in quality of life, increases in use of health services and reduced life expectancy.”
“We have a multi-disciplinary team of scientists with expertise in clinical research, nutrition and data science who will use advanced computing to examine why some people are prone to developing multiple long-term conditions.”
InflAIM program
The project “Inflammation, nutrition and the evolution of multiple long-term conditions — an AI-based analysis of intersectionality in longitudinal health data,” or the InflAIM program, will apply cutting-edge analytical methods to large-scale national and international datasets.
Specifically, it aims to identify new ways to slow the progression of multiple long-term health problems in people most at risk. The researchers note that variation in nutrition and malnutrition could help explain the social gradient in multimorbidity — when two or more long-term conditions occur together.
The researchers explain that multimorbidity is impacted by various factors, such as biological, social, behavioral and environmental aspects, and can be seen as a series of disease clusters. “Improving the characterization of these clusters with AI and machine learning could have significant benefits to health and social care.”
The InflAIM program will apply analytical methods to large-scale national and international datasets.The study, which kicked off in February, is a national collaboration between epidemiologists, computer scientists, statisticians, nutritionists, clinicians, social scientists and policymakers. It is supported by a wide range of institutions, including the national health and well-being platform Evergreen Life and Richmond Group of Charities — a coalition of 13 voluntary health and social care organizations.
UK health priority
Preventing long-term condition onset and progression is a priority area for the UK’s Department of Health.
Health minister Karin Smyth highlights: “Long-term health conditions are one of the many challenges facing our National Health Service (NHS), and I am determined we harness AI to tackle them.”
“This groundbreaking research will help identify patients most at risk and the most appropriate treatments, ensuring they receive the highest quality care. We can only fix our broken NHS by building a healthy society, helping people live well for longer.”
Role of nutrition
According to the researchers involved in the InflAIM project, nutrition’s role in multiple long-term conditions has “received little attention to date and has significant potential for intervention at the population scale.”
The Quadram Institute’s Food and Nutrition National Bioscience Research Infrastructure will contribute its expertise in nutrients and dietary bioactives.
Dr. Maria Traka, head of this center, details: “We are working to assess the relationships between dietary factors and multimorbidity, focusing on the role of nutrients and dietary bioactives.”
“Bioactives are derived from plants and have many beneficial health benefits, and many fruits and vegetables are rich sources. By combining our data in nutrition and bioactive composition with AI models (e.g., natural language processing), we expect to understand key drivers of disease and identify preventative strategies based on diet.”
Meanwhile, co-investigator Ailsa Welch, professor at UEA’s Norwich Medical School, highlights the program’s aims to be thorough and inclusive, “unlike current personalized nutrition plans that often focus on short-term results and a limited group of people.”
“By looking at long-term health and including a wide range of individuals, we aim to address everyone’s needs, not just those already health conscious.”