Exploring Effects of Treatment with Anti-depressants on Insulin Resistance: A Real-world Electronic Medical Records Based Study
Depression (DP) and Diabetes Mellitus (DM) are leading causes of disability worldwide, and are major contributors to the overall global burden of disease. Compared to the general population, patients with DM are at increased risk of developing DP. At the same time, patients with DP are more likely to develop DM. Although a beneficial association of depression-improvement with glycaemic control in patients with DM has been observed, few studies have evaluated the potential association of anti-depressants on insulin resistance in patients with and without co-morbidities at population level. While epigenetic factors may activate common pathways that promote DM and DP, the molecular drivers of such bi-directional effects are poorly understood. Notably, it is not known whether treatment with some anti-depressants promotes insulin resistance more than treatment with other classes of anti-depressants.
This PhD project is designed to conduct a series of pharmaco-epidemiological studies with the ultimate goal to enhance understanding of DM and DP relationships at the population level. This project also creates a unique opportunity to use population-level “Big Data” to address this issue – combining nationally representative longitudinal Electronic Medical Records from primary and ambulatory care systems of UK and/or USA. Specific aims of the project are to explore:
1. longitudinal trends in anti-depressant prescribing patterns in patients with DP, and separately in patients with DM;
2. risk of development of DM by classes of anti-depressants in patients with DP;
3. association of glycaemic control and risk of development of DP in patients with DM;
4. association of treatment with anti-depressants and glycaemic outcomes in patients with DM.
There is no convincing real-world evidence on glycaemic effects of treatment with different classes of anti-depressants. If this project identifies an adverse signal, this will inform clinicians and patients on possible risks of treatment with particular classes of anti-depressants, and will provide evidence to design subsequent randomised clinical trials. Conversely, if no adverse signal is identified, clinicians and patients will be reassured of the safety of different classes of anti-depressants in terms of insulin resistance in a real-world population.
Clinical Epidemiology, Biostatistics, Big Data, Data Science, Electronic Medical Records, Longitudinal Study, Diabetes, Depression
• MSc in Statistics, Health Informatics, Clinical Epidemiology, or related disciplines.
• Strong statistical methodological background & communications skills.
• Excellent programming skills (SAS, R, Python, C++) and experience in working with relational databases (SQL).
• Interest to develop research career in clinical biostatistics / epidemiology, especially in chronic and mental diseases.
• Ability to work in collaborative environment with local and international researchers.
Desirable Criteria for International Scholarship:
1. At least 80% in the masters degree
2. At least 2 research publications in peer-reviewed indexed journals.