Burden, causes and consequence of carotid atherosclerosis in Chinese adults
Atherosclerosis is the key underlying cause of cardiovascular disease (CVD), and can be assessed by non-invasive ultrasound examination of carotid artery using intima-media thickness (cIMT) and plaques. In the China Kadoorie Biobank (CKB), data on cIMT and carotid plaques were collected among ~25,000 participants at two time points (2013-14 and 2020-21), along with a wide range of lifestyle (e.g. diet, smoking), physical characteristic (e.g. blood pressure, adiposity), blood biochemistry (e.g. blood glucose, lipids) and genome-wide genotyping data. These, together with fatal and non-fatal health outcome data (e.g. stroke, IHD), will enable comprehensive investigation of the burden, progression, determinants, and consequence of atherosclerosis in Chinese adults.
RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING
The specific DPhil project will be developed in discussion with the students and, depending on their interests and aptitude, may include some of the following objectives:
to develop and validate, using conventional and machine learning methods, an automated algorithm to characterise and quantify atherosclerosis (e.g. plaque number / size / density / stability) based on carotid images;
- to examine the associations of CVD risk factors with measures of carotid atherosclerosis and its progression in individuals with and without prior CVD;
- to undertake genome-wide association analyses to identify genetic risk factors for carotid atherosclerosis and its progression;
- to assess the associations of measures of carotid atherosclerosis with incident CVD (e.g. IHD, stroke) and non-CVD (e.g. chronic renal diseases);
- to develop and validate predictive algorithms for “vascular age”, by integrating conventional CVD risk factors and measures of carotid atherosclerosis and to assess their utility in clinical applications.
The student will work within a multi-disciplinary team and have in-house training in epidemiology, statistical programming, computational genetics, and attendance of relevant courses. By the end of the DPhil, the student will be competent to plan, undertake and interpret analyses of large datasets, and to report research findings in peer-reviewed journal and conference.
FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING
The project will be based within the CKB group in the Big Data Institute. There are excellent facilities and a world-class community of population health, data science, imaging process, and genomic medicine researchers. There will be opportunities to work with external research institutions.
The candidate should have (1) a good degree in medicine or biological sciences; (2) an MSc in epidemiology, statistics or data sciences; and (3) relevant experience in medical imaging.