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 to data over a long time period of time, developing several new computing approaches that our team has been working on and will now implement in this study.

This study is funded by the National Institute for Health and Care Research (NIHR) [Programme Grants for Applied Research (NIHR205461)]. The views expressed are those of the author(s) and not necessarily those of the NIHR or the Department of Health and Social Care.