Precision Prevention of Kidney Stone Disease: Epidemiology, Health Inequalities, and Incretin-Based Therapies
Supervisors
Dr Kevin Wing, School of Health and Wellbeing, University of Glasgow
Dr Catherine Lovegrove, School of Cancer Sciences, University of Glasgow
Dr Jocelyn Friday, School of Health and Wellbeing, University of Glasgow
Summary
Kidney stone disease (KSD) is common and recurrent, affecting ~10% of the population. It causes substantial morbidity and healthcare utilisation, including emergency hospital admissions and operations. KSD risk is heterogeneous, arising from genetic, dietary, behavioural, and socioeconomic factors. Current empirical medical and preventive strategies are generic and relatively ineffective. Identifying patients at greatest risk of KSD and developing tailored preventive therapies are key priorities for precision medicine and health equality. Obesity is a causal risk factor for KSD. Incretin-based therapies, including GLP-1 receptor agonists, are increasingly prescribed for obesity and type 2 diabetes; their effects on KSD risk are unknown. They may reduce risk through weight loss and improved metabolic health, but may influence urinary composition, diet, and hydration to increase lithogenic risk. This PhD integrates large-scale electronic health record analysis, advanced causal inference, and translational clinical research to determine how KSD risk varies by patient characteristics and how incretin-based therapies modify this risk. The project combines epidemiology, target trial emulation, and prospective assessment of biomarkers to classify patients into clinically-meaningful risk and treatment-response strata. The findings will identify modifiable risk factors and determine whether incretin-based therapies could contribute to targeted KSD prevention, or whether specific patients need greater monitoring for recurrence. By linking population-level evidence with causal and mechanistic research, the project will support more equitable, targeted approaches to prevention while providing interdisciplinary training in data science, epidemiology, causal inference, and clinical research.
Supervisor(s) for each project will be holding a 30-minute Q&A session during the first two weeks of December.
If you have any questions about a project, you are welcome to attend the relevant session.
If you would like to receive the webinar details, please submit a request via the Webinar Link Request Form.