Research
Our research seeks to uncover the causal pathways linking immune and metabolic factors to severe mental illness (SMI). By leveraging big data analytics and inter-disciplinary science, we aim to identify novel biomarkers and treatments that will help improve the mental and physical health of people living with SMI; namely schizophrenia, bipolar, and other disorders.
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Key questions
Exactly which immune and metabolic pathways are most important in causing symptoms of severe mental illness?
We call this the challenge of target triangulation or prioritisation.
What are the best ways of measuring the causal chains - from immune cells in the body, through changes in the brain, to biases of cognition and emotion - that link physical health to mental health?
We call this the challenge of biomarker innovation.
Which drugs or lifestyle interventions are most likely to benefit people living with severe mental illness, now and in the future, by addressing the immune or metabolic causes of their psychological symptoms?
We call this the challenge of therapeutic personalisation.
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Work packages
Inflammatory protein targets
Golam Khandaker & Tom Gaunt
Bristol
By using genetic screening, we will identify causal relationships between immune proteins and severe mental illness (SMI) outcomes. Focusing on inflammatory proteins, we will analyse large datasets, such as the UK Biobank and ALSPAC cohorts, and apply Mendelian randomisation to prioritise genetically causal immune proteins linked to disorders like schizophrenia and bipolar disorder. Further, we will refine our findings using multi-trait mapping, co-localisation, and protein-protein network analysis to understand how these proteins influence SMI and identify potential therapeutic targets for patient benefit.
Metabolic protein targets
Xueyi Shen & Andrew McIntosh
Edinburgh
Our research aims to uncover causal effects of metabolic proteins and metabolites on severe mental illness (SMI). Using advanced genetic tools and data from large biobanks like the UK Biobank and Generation Scotland, we will prioritise candidate proteins and metabolites with a likely causal role in SMI. We will then validate these targets using longitudinal cohort studies and perform multi-level analyses to understand how metabolic factors contribute to mental health, including the impact of polygenic risk scores on clinical outcomes. This work will also assess broader health effects through phenome-wide association studies (PheWAS).
Immune cell targets
Mary-Ellen Lynall
Cambridge
We aim to understand how immune cells contribute to severe mental illness (SMI) by examining genetic variants linked to disorders like schizophrenia. Previous research has shown that these genetic variants are active in immune cells, and we plan to explore this further. By analysing genetic data from large studies and combining it with immune cell data, we will identify which immune cells are most involved in SMI. We will use advanced techniques to pinpoint specific immune cell types and gene expression changes that could be causing SMI. Additionally, we will study immune cell data from patients with bipolar disorder and schizophrenia to understand how these genetic pathways relate to disease severity and symptoms. This research will help uncover new targets for treating SMI with direct patient benefit.
Our research is organised into 3 challenge areas: targets, biomarkers, and interventions.
Each challenge area is composed of three distinct work packages designed to help us answer our key research questions.
Immunophenotyping of early psychosis
Mary-Ellen Lynall & Graham Murray
Cambridge
Currently, there is limited detailed data on gene expression and molecular features in blood immune cells from people with severe mental illness (SMI). To fill this gap, we will collect blood samples from patients with psychotic disorders at the start of their treatment and analyse them using advanced techniques like single-cell gene sequencing and metabolomics. This will help us better understand the immune and metabolic processes involved in SMI. We will use this data to identify specific genetic and molecular markers linked to psychotic disorders, explore how these markers relate to symptoms and treatment response, and potentially guide the development of new treatments. This project will provide valuable insights and create a comprehensive resource for SMI research which will ultimately benefit patients directly.
Brain network biomarkers
Varun Warrier & Ed Bullmore
Cambridge
This research aims to improve how we measure inflammation and immune effects in the brain, which are linked to severe mental illness (SMI). First, we will develop new imaging techniques using MRI to study brain networks and identify how immune and metabolic factors affect brain structure and function. We will analyse genetic data to understand how brain connectivity relates to SMI. Additionally, we will make use of PET scans to measure inflammation in immune compartments around the brain, like the meninges and venous sinuses, which may influence neuropsychiatric disorders. By combining MRI and PET scans with deep learning algorithms, we aim to create better tools to study these areas, ultimately improving our understanding of how immune processes contribute to SMI and leading to better and more targeted treatments for patients.
Digital gameplay for cognitive testing
Paul Fletcher, with PWLE Co-Leads
Cambridge
This research focuses on using gameplay to better understand the cognitive and behavioral effects of immune and metabolic factors in severe mental illness (SMI). Traditional cognitive tests don't capture the underlying computational processes in the brain that drive symptoms, so we will create new, engaging gameplay tasks that measure key aspects like reward sensitivity and motivation. Using video games, which are more engaging and adaptive than traditional tests, we can gather detailed behavioural data that reflects the cognitive issues patients experience. We will apply this approach to patients with early psychosis and look for links between their gameplay performance and immune/metabolic factors. This will help identify new ways to measure and understand the cognitive changes associated with SMI and its treatments and will directly benefit patients.
Computational repurposing of immune and metabolic drugs for psychiatry
Naomi Wray
Oxford
Our goal is to explore the possibility of repurposing existing immune or metabolic drugs to treat serious mental illnesses (SMI) by targeting the immune and metabolic factors that contribute to symptoms. This approach uses a computational method to find drugs that can reverse the gene expression changes caused by the disorder, similar to how some drugs have been repurposed for other diseases like cancer and cardiovascular conditions.
We will match drug-related gene expression changes in immune cells with the changes seen in blood immune cells of people with SMI. To do this, we will gather and analyse existing gene expression data from both SMI patients and drug studies, focusing on immune cells like T and B cells. We will then identify drugs that could reverse the SMI-related gene expression and test their potential to treat SMI in patients.
Lifestyle management for severe mental illness
Yuri Milaneschi & Brenda Penninx, with PWLE Co-Leads
Amsterdam
This project aims to provide better guidance on lifestyle changes (diet, sleep, exercise) for people with severe mental illness (SMI). While lifestyle factors can impact mental and physical health, evidence of their effectiveness is often weak or unclear, making it hard for patients to decide if these changes are worthwhile.
To address this, the project has two main parts:
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Academic Research – Scientists will analyse large datasets to understand how lifestyle habits affect immune and metabolic pathways linked to SMI. They will use biomarkers, wearable devices, and past clinical trial data to assess causal relationships.
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Patient-Led Research – A team of people with lived experience will explore how acceptable and accessible lifestyle changes are for SMI patients. They will conduct surveys and focus groups to identify barriers and the best ways to communicate guidance.
The teams will collaborate to co-produce clear, evidence-based lifestyle recommendations for people withh lived experience, caregivers, and clinicians.
Predicting adverse physical health effects of anti-psychotic medication
Ben Perry, with Sarah Markham & Richard Mandunya
Birmingham & Cambridge
This project focuses on improving the prediction and management of physical and mental health conditions that often occur together (co-morbidity), particularly in people with severe mental illness (SMI). These conditions significantly reduce life expectancy, with cardiometabolic diseases being a major cause of early death.
The project will build on existing tools (PsyMetRiC and MOZART) that predict metabolic risks in those living with SMI. Researchers will analyse large health datasets, including genomic and clinical data, to enhance these predictions. Two co-leads with lived experience will work in partnership with scientists to ensure the tools are relevant, accessible, and useful for patients and healthcare providers.
The final goal is to create accurate, patient-centred risk prediction tools that can be integrated into routine healthcare, helping individuals manage their health more effectively and improve their quality of life.
