Inflammation, nutrition and the evolution of multiple long-term conditions - an AI-based analysis of intersectionality in longitudinal health data (inflAIM)


The term multimorbidity describes the presence of two or more long term conditions occurring together; a common example is the presence of diabetes, arthritis and high blood pressure. About one in four of the UK population have multimorbidity. It is one of the greatest challenges facing individuals and health services, both now and for the coming decades.

This multimorbidity is associated with a reduction in quality of life, increased use of health services and reduced life expectancy. To date, multimorbidity has been seen as a random assortment of diseases, making it difficult to address. Variation in nutrition and malnutrition could provide an explanation for the social gradient in MLTC. The role of nutrition in MLTC has received little attention to date and has significant potential for intervention at population scale. With new understanding of the impact of various factors (including biological, social, behavioural, environmental and others), multimorbidity can be seen as a series of non-random clusters of disease. Improving the characterisation of these clusters with artificial intelligence and machine learning could have significant benefits to health and social care.

We put together a multi-disciplinary team of scientists with expertise in clinical research and data science to use advanced computing methodology to examine the reasons why some people are prone to developing multiple long-term conditions.

Our focus will be to look at statistical and computing methods that can be applied over a long time period. Members of our team have developed a number of new computing approaches that have shown some promise.

Funded by NIHR

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