Completed Pregnancy, Children & Inherited Conditions Mental Health

Multimorbidity Mechanism and Therapeutics Research Collaborative

In plain English

AI plain-English summary

One in four people in the UK now live with two or more long-term health conditions at the same time, and this project will mine NHS patient records, genetic data, and clinical trial results to map how those conditions cluster and interact. The core problem is that medicine is largely designed to treat single diseases in isolation. When a patient has, say, diabetes, heart disease, and depression, the drugs for one condition can worsen another, or the treatments simply pile up without coordination. This project will systematically measure how often such “treatment competition” occurs and identify where existing safe drugs could be repurposed to tackle multiple conditions at once. If successful, the work could shift clinical practice away from single-disease guidelines toward treatment bundles that work for the whole patient. It may also reveal which combinations of conditions are most likely to emerge over time, allowing earlier intervention. The researchers plan to establish a national Multimorbidity Special Interest Group to embed these findings into NHS care. Two patient co-applicants and a public advisory group will help ensure the results reach the people who need them.

View original technical description
As people live to a greater age there is an increased risk of suffering more than one health condition at a time. Known as multimorbidity this has a serious effect on the daily lives of patients, their families and their carers. The project will examine the sequence and patterns of multimorbidity and the evidence obtained will aid both the prediction and treatment of patients with multiple health conditions. We will also seek to address the problem of coordinating treatments so that treatment for one condition does not cause difficulties in the treatment of another condition suffered by the same patient. The frequency of this "competition" between health conditions will be established and solutions identified. In doing this we hope to identify medicines that are able to treat more than one condition and investigate the potential of new uses for existing safe medicines. Our research will utilise anonymous patient data recorded by the NHS as well as the findings from existing genetic studies and clinical trials. We will look across the different types of evidence to check consistency to ensure our recommendations are sound. Where uncertainties remain we will recommend new clinical trials or genetic studies. In this way we hope to improve the outlook for patients, regardless of their particular combination of health conditions, by maximising the benefits from effective treatments. Two patient participants are co-applicants on this project. A Patient and Public Advisory Group will be established which will also assist in ensuring that the findings from the project are widely disseminated. During the course of our work we will liaise with organisations such as the Coalition for Collaborative Care with a view to the establishment of a national Multimorbidity Special Interest Group.

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Researchers

Adam Butterworth (Co-Investigator)Amanda Roberts (Co-Investigator)Aroon Hingorani (Principal Investigator)Ayath Ullah (Co-Investigator)Daniel Alexander (Co-Investigator)Debbie Lawlor (Co-Investigator)Harry Hemingway (Co-Investigator)Munir Pirmohamed (Co-Investigator)Nishi Chaturvedi (Co-Investigator)Reecha Sofat (Co-Investigator)Simon Ball (Co-Investigator)Spiros Denaxas (Co-Investigator)

Related Research

Grants with similar aims, by meaning.

MLTC-M Community of Practice in Statistical Methods for Multimorbidity Research
COMPUTE - complex multimorbidity phenotypes, trends and endpoints
GEroscience and Multi-Morbidity: identifying targets for intervention (GEMM)
Understanding early life determinants and mechanisms to preventing life course multimorbidity
Northern Ireland Multimorbidity Research and Discovery (NIMRAD) Consortium

Original classification

Research Grant

Plain English summaries and category classifications on this site are generated by AI and may not perfectly reflect the original research.