Active Mental Health Psychology & Behaviour

The South Wales and South West England Mental Health Platform Hub

In plain English

AI plain-English summary

A team of UK researchers is building a detailed biological and psychological profile of 600 people with severe mental illnesses to finally understand what causes schizophrenia, bipolar disorder, and schizoaffective disorder. For decades, no genuinely new treatments have emerged for these conditions, which cost the UK £118 billion annually and shorten lives as much as serious chronic diseases. The core problem is that doctors still diagnose based on symptoms alone—like treating cancer by its location rather than its genetic profile. This project will combine clinical data, brain scans, genetics, immune markers, and developmental history from over 12,000 existing patients, then intensively study 600 representative individuals. The team will also access the Welsh SAIL Databank for environmental data and use advanced epigenetics to explore how life experiences alter gene activity. If successful, this work could replace symptom-based labels with a causally informed classification system, enabling precision treatments tailored to each patient’s biology. The openly shared dataset will also serve as a global resource for other researchers developing machine-learning models. This is fundamental science aimed at breaking a decades-long therapeutic deadlock, with the potential to transform how severe mental illness is diagnosed and treated.

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The Challenge: Schizophrenia (SZ), bipolar disorder (BD), and schizoaffective disorder (SAD) are core severe mental illnesses (SMIs) and place an enormous burden on individuals, their carers and wider society. They account for more than a quarter of the ~£118Bn annual UK costs of mental illness and are associated with a reduction in life expectancy similar to some of the most serious chronic illnesses in medicine. Yet, unlike other areas of medicine, no truly novel treatments have been developed for SMIs in decades and consequently real-world outcomes and recovery rates are little changed since the mid-twentieth century. The fundamental cause for this stasis is a lack of causal understanding. Consequently, we are unable to classify patients according to the biological, psychological or social causes of their symptoms, or in any way that is informed by why they have developed their condition which hampers the discovery of new mechanistically informed treatments. Instead, we rely on symptom-based diagnoses that show poor correspondence to underlying physiology. This approach has been unfavourably compared to cancer treatment prior to precision treatments where non-specific, modestly effective treatments, with severe side effects, were selected based largely on the tissue origin rather than patient-specific cancer profiling. There must me another way. Our vision is that by combining rich phenotypic data acquired at scale across multiple domains (e.g. clinical, cognitive, developmental, immune and metabolic, genomic and brain imaging) with sophisticated analysis, we will be able to move towards a new causally and mechanistically informed diagnostic approach for SMIs that will advance psychiatry and improve the lives of people with these disorders. To achieve this, we have created an outstanding interdisciplinary network of early-, mid- and senior-career researchers and people with lived experience from South Wales (Cardiff Swansea) and South-West England (Bath, Bristol, Exeter). Drawing on our world-leading cohorts of more than 12,000 patients with BD-SAD-SZ our SW2 hub will build an unprecedentedly detailed cohort of 600 representative participants. We will also access the Welsh SAIL Databank to incorporate rich environmental and developmental data, interrogate the biological correlates of these factors with our world-leading expertise in advanced epigenetic. Drawing on our expertise in machine-learning and big data analysis, we will then apply advanced analyses improve how we group patients across the SZ-BD spectrum. Underpinned by the application of cutting-edge genomics we will access the dark genome for the first time at scale in severe mental illness. Sharing of data and availability of replication cohorts has been critical to the success of psychiatric genetics and will be essential to the wider application of machine-learning models. The diverse range of phenotypic data generated and curated by the SW2 Hub will be made openly available to the academic community providing a unique data resource of exceptional value to international researchers to develop new methods, for replication of results and combining of datasets for more powerful outputs that will inform better ways of diagnosing people. These programs of work will create a rich resource for the field that will support training and the delivery of excellent research as a key facet of the UKRI Mental Health Research Platform but more importantly will offer a route to advancing psychiatry and improving the lives of people with some of our most serious mental illnesses.

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Researchers

Anita Thapar (Co-Investigator)Ann John (Co-Investigator)Antonio Fernández Pardiñas (Co-Investigator)Carolyn McNabb (Co-Investigator)Chloe Harrison (Co-Investigator)Golam Khandaker (Co-Investigator)Ian Jones (Co-Investigator)James Walters (Principal Investigator)Jeremy Hall (Co-Investigator)Joanna Martin (Co-Investigator)Jonathan Mill (Co-Investigator)Neil Harrison (Co-Investigator)Peter Holmans (Co-Investigator)Robert Andrews (Co-Investigator)Tania Gergel (Co-Investigator)Thomas Lancaster (Co-Investigator)

Related Research

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Original classification

Research and Innovation

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