Associated organisationsAmsterdam UMC · Charité - Universitätsmedizin Berlin · Default Community Account · Emory University · IND Marion Leboyer 31204 · King's College London · University of Amsterdam · University of Antwerp · University of Cape TownEurope PMC affiliations are not treated as award recipients or mapped locations.
Funding£5.2M
PeriodJun 2024 — May 2029
In plain English
AI plain-English summary
Depression treatments fail for many people because anti-inflammatory medications are given to everyone, not just those whose depression is driven by inflammation. This study aims to fix that mismatch. The problem is that clinical trials have tested anti-inflammatory drugs in people with depression without first checking whether those individuals actually have elevated inflammation. As a result, promising treatments may have been wrongly dismissed as ineffective. This project will comb through existing trial data to identify which clinical features, blood markers, and brain scans reliably predict who will respond to anti-inflammatories. The team will then build a machine-learning decision tool that doctors could use to match the right drug to the right patient early on. If the tool works, it could transform depression from a one-size-fits-all diagnosis into a condition where treatment is guided by biology. For the roughly one-third of people with depression who have elevated inflammation, this could mean faster relief and fewer failed medication trials. The study is co-designed with people who have lived experience of depression, ensuring the tool addresses what patients actually need and want.
View original technical description
There is a high risk that the mental health field is foregoing the opportunity to use anti-inflammatory medications in depression, because of the lack of studies that target the right people by stratifying them based on inflammatory markers. This study, co-designed with people with lived experience (PWLE) of depression, will: (1) Identify, in existing clinical trials of anti- inflammatories in major depressive disorder (MDD), a set of hypothesis-driven, inflammation-related, clinical, blood, and neuroimaging stratification markers that accurately predict depression response; (2) Use machine-learning to generate a decision tool that identifies depressed people who will respond to anti-inflammatories medications and thus could access these drugs as early intervention; (3) Assess the feasibility and acceptability of the tool in a proof-of-concept, open-labelled, stratified prospective study, delivered in Europe, the USA and South Africa; (4) Explore PWLE’s views regarding the role of inflammation in depression; and (5) Produce scientific publications, conference presentations and public engagement outputs (blogs, podcasts, social media campaigns) describing the outcomes and impact of the study. Our study, bringing together n=10 existing trials led by PIs/collaborators (n>1200 people with depression) and n=250 newly recruited individuals, will lead to innovative personalized treatments and early interventions for depression according to patients’ stratification markers.
Finding the right treatment, for the right people, at the right time for anxiety
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