The relationship between vascular-metabolic disorders, cognitive impairment and dementia

Background

Vascular-metabolic disorders have become a major contributor to the development of chronic comorbidity conditions worldwide. These disorders are identified as potential risk factors for developing cognitive decline and dementia. Vascular-metabolic disorders may have a direct impact on altering brain structure as well as cognitive performance of affected individuals. There are 55 million people with dementia worldwide based on WHO 2023 data and dementia prevalence is on the rise.

This project aims to assess associations between vascular-metabolic disorders, cognitive decline and dementia in a large contemporary UK population. The UK Biobank is a prospective cohort of 0.5 million adults with a wealth of information on lifestyle, medical history, biochemistry, metabolomics and proteomic assays, genetics, morbidity and mortality. GP data on clinical events are available as well as brain imaging and cognitive function tests from thousands of individuals. Utilising multiparametric MRI techniques such as functional MRI, structural MRI and diffusion-weighted MRI will provide detailed insights into the atypical functional and structural properties of brain regions.

The aims of the project are:

1. To examine the association between conventional and novel vascular-metabolic risk factors with cognitive impairment and dementia types, independent from genetic predisposition to dementia.
2. To quantify the changes in the brain structure associated with different vascular-metabolic disorders and decline in cognition.
3. To quantify the changes in cognitive function associated with different vascular-metabolic disorders.
4. To develop a prediction model to assess the risk of developing cognitive decline/dementia among individuals with vascular-metabolic disorders by integrating both multiparametric neuroimaging and cognitive function tests.

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The student will gain experience in chronic disease epidemiology, population health 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, epidemiological methods, genetic epidemiology, statistical programming, and scientific writing might be provided as needed. 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 conferences. 

PROSPECTIVE  STUDENT

Candidates should have a medical degree or a Master's degree in genetic/epidemiology, medical statistics or a closely related discipline. Proficiency in conducting epidemiological analyses with R, Stata, Python or SAS is essential.