Genetic susceptibility, glycaemic control and risk of dementia
Dementia is a growing health concern, as the number of people living with dementia worldwide more than doubled in the last decades, and pharmaceutical preventive strategies are largely missing. Obesity and diabetes are major risk factors for cognitive impairment and dementia. Hypothesis-generating findings from clinical trials in participants with diabetes suggest that glucagon-like peptide-1 (GLP-1) receptor agonist therapy, a class of glucose-lowering and weight loss medicines, may reduce the risk of cognitive impairment and dementia. Genetic epidemiological and Mendelian Randomization (MR) techniques can be used to assess genetic predisposition to dementia, including the associations of polymorphisms in GLP1R (as a proxy of GLP-1 receptor agonist therapy) with cognition, dementia risk and blood-based biomarkers for neurodegeneration (NfL) and neuro-inflammation (GFAP). We further can expand the pathway to other proteins and genes that are functionally related to GLP-1(R). This projects aims to assess the associations of GLP1R and related proteins in the UK Biobank, a prospective cohort of 0.5 million adults followed-up for over 10 years, with a wealth of information on lifestyle, medical history, biochemistry and metabolomics assays, genetics, brain MRI, and cause-specific morbidity.
The specific aims of the project include:
- Assessment of associations of GLP1R polymorphisms with cognition, characteristics of brain imaging, blood-based biomarkers and incident dementia
- Compare the findings to that for other anti-diabetic medication
- Asses the association of other genes and proteins of the GLP1(R) pathway to the outcomes above
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The student will gain experience in genetic epidemiology and analysis of large-scale prospective data. They will develop skills in conducting systematic literature reviews, analytical techniques, research planning, statistical programming, data analysis, and presentation skills. The student will be supported to publish peer-reviewed papers during their DPhil.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
Training in advanced statistics, genetic epidemiology, statistical programming, and scientific writing will be provided. Attendance at seminars, workshops and courses provided by the Department and University will also be encouraged. The candidate will have the opportunity to present their research work at relevant international/national conferences.
Candidates should have a Master degree in genetic epidemiology, clinical medicine, or medical statistics. Previous experience in conducting analyses with R, STATA, or Python is essential.