GEMINI

GEMINI: Genetic Evaluation of Multimorbidity towards INdividualisation of Interventions

Researching possible links between long term conditions that could help improve interventions and treatments

More than 50% of people over the age of 65 are living with more than one long term condition, also known as multimorbidity. Despite this, people with multimorbidity are often excluded from clinical trials and there has been limited research into identifying the causes of multimorbidity. For example, we often do not know if two common long-term conditions occur together by chance as we get older, whether one leads to the other, or if they share a risk factor. This problem is partly because health care professionals and researchers tend to focus on one condition at a time. For example, there has been a lot of research into the causes and consequences of osteoarthritis but not why people with osteoarthritis have a higher frequency of asthma, even when accounting for sex, age and obesity.

Aims

The aim of our research is to uncover new links between long term conditions that could lead to improved interventions including drug treatments or other more focused treatments. These new links could include a better understanding of which cells in the body are most critical to the presence of two conditions in the same patient.

Activity

To achieve our aims, we have formed a partnership called the GEMINI (Genetic Evaluation of Multimorbidity towards INdividualisation of Interventions) collaborative. This team includes two people with multimorbidity, health care professionals including those in primary care and experts in statistics and genetics. We will study the causes of multimorbidity with a new approach, using existing databases of DNA sequence information linked to diseases from 10,000s of people.

Using this genetic approach our initial research has identified many new and interesting links between conditions that were not previously well known. We will complement the genetic approach with data from millions of patients in primary care. These patients are representative of the UK as a whole and will allow us to study large numbers of people with combinations of conditions even if these combinations are quite rare.

Our research plans are divided into three parts:

 First, we will use three sources of data from patients in primary care (GPs) to define the conditions we will study. We will start from all conditions that are long term and present in more than 1% of the people over 65 years. We will then use millions of DNA sequence changes – the genetic information we inherit from our parents – to identify which conditions share broad biological mechanisms.
Second, we will use a smaller number of genetic variants to identify the specific mechanisms involved. These techniques are based on the principle that inherited DNA sequence changes are fixed for life and so provide us with a way of assessing the causal direction of associated risk factors and diseases. For example, we will use genetics to test whether one disease leads to a second disease, or whether a shared risk factor leads to both. These risk factors will include well known risks such as obesity and more detailed measures of biology, such as how genes are switched on and off in different cells and tissues.
Third, we will explore in greater depth patient data for the conditions highlighted in the first two steps using primary care databases. To support this we will hold workshops with patients and carers to understand in detail the most important outcomes of these conditions, for example is reduced lifespan more or less important than risk of frequent hospitalisation? We will then study patients with new combinations of conditions to see if they suffer from worse outcomes.

Outputs

The outputs are primarily an increased understanding of the biological pathways that contribute to multi-morbidity. Importantly GEMINI will help untangle which conditions are shared in patients due to chance or coincidental reasons such as ageing, or relative wealth and general health, and which are shared due to genuine shared biological mechanisms. Only when we know the mechanisms will we be able to design effective interventions.

Funding

This is one of 6 programmes supported by UKRI Medical Research Council Strategic Priorities Fund, an £830 million investment in multi- and interdisciplinary research. These projects are working in parallel with similar National Institute for Health and Social Care Research Artificial Intelligence in Multimorbidity (NIHR-AIM) programmes:

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