| Title: | Finding biomarkers for moderate depression using blood nuclear magnetic resonance metabolomics |
| Lead Institution: | University of Oxford |
| Principal investigator: | Dr Fay Probert |
Application 72185
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About
Depression is a common and debilitating illness that affects people of all ages and demographics, largely independent of economic factors. Depression can be treated with antidepressant drugs, which are effective in some people but not others. The precise reason for this discrepancy remains unknown, but probably reflect the fact that people with depression have all had different social, cultural, and physiological trajectories leading up to the point that the diagnosis was made. For example, some people may have developed depression where dietary factors and obesity may greatly contribute to their psychological symptoms. Others diagnosed with depression have normal weight and diet, but experienced psychological trauma, or are burdened with another medical disease like cancer, which contributes to their symptoms of depression. In addition to this, the brain likes to keep secrets. It is harder to see what is going on in the brain than any other organ in the body, so understanding the precise mechanisms of how brain function is different between depressed and non-depressed people is difficult. Using a very large sample population, we can group individuals who have self-reported depression based on shared characteristics, such as age, sex, body mass index, level of systemic inflammation, and nutrition. By classifying people with depression in this way, we aim to see whether the presence of depression can be predicted by the relative abundance of small chemical compounds, known as metabolites, in the blood. Metabolomics, the study of metabolites, can be used as a tool for defining the presence or progress of disease, and identifying biological targets for new therapeutics. We aim to determine whether a person can be predicted as having depression based on the concentration of a combination of their blood metabolites, and we expect the accuracy of a prediction is likely to vary if we narrow our focus based on specific characteristics. We expect that the study will take approximately 12 months to complete, as we will be exploring how depression is associated with lifestyle, demographic, and blood chemistry factors in many different combinations. In addition, our current methods for classifying disease based on metabolomics must be adapted to the UK Biobank's much larger cohort. Depending on how accurately we can predict depression, this study has the potential to influence the way depression in the community is diagnosed and treated.1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 11561 | A multivariate blood metabolite algorithm stably predicts risk and resilience to major depressive disorder in the general population | Daniel E. Radford-Smith (+6) | 2023 | EBioMedicine |
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