Colleges
Min Gao
PhD
Researcher
- MSc, DPhil supervisor
- Fellow of Wolfson College
Metabolic Health and Depression, Data Science, Health Behaviours Intervention
Min Gao is an Epidemiologist and Health Behaviour Scientist focused on understanding the link between metabolic health and depression through data science and exploring interventions that can help break this connection through modifiable behavioural strategies.
Currently, Min is leading
1) BRC-funded clinical trial (DIME study), a ketogenic diet for patients with treatment-resistant depression.
2) ARC-funded observational and genetic research to explore the associations and mechanisms linking obesity and cardiometabolic disorders with depression, using UK Biobank, Qresearch and CPRD datasets.
3) the use of antidiabetic medications for depression and developing improved treatment strategies for individuals with comorbid type 2 diabetes and depression.
Min welcomes world-wide collaborations in these research areas and accepts requests for supervision of Oxford DPhil and MSc students in relevant areas.
Recent publications
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Association between ketone metabolism and the risk of depression: An observational and Mendelian randomization study.
Journal article
Dong X. et al, (2025), J Affect Disord
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Investigating the association between recorded smoking cessation interventions and smoking cessation in people living with cardiovascular disease using UK general practice data.
Journal article
Wu AD. et al, (2025), BMC Prim Care, 26
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Effects of Prebiotics and Probiotics on Symptoms of Depression and Anxiety in Clinically Diagnosed Samples: Systematic Review and Meta-analysis of Randomized Controlled Trials.
Journal article
Asad A. et al, (2024), Nutr Rev
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Strategies to improve the implementation of preventive care in primary care: a systematic review and meta-analysis.
Journal article
Heath L. et al, (2024), BMC Med, 22
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The Association Among Social Support, Self-Efficacy, Use of Mobile Apps, and Physical Activity: Structural Equation Models With Mediating Effects (Preprint)
Preprint
Wang T. et al, (2018)
