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BAckground

Globally, it is estimated that approximately 1.3 billion people live with some form of vision impairment and, with population growth and aging, this number is expected to significantly increase. Even mild vision impairment can have considerable economic costs and adverse influences on quality of life. On the other hand, 80% of vision impairment is either treatable or preventable. Therefore, it is crucial to understand the aetiology and risk factors of common eye diseases which are the major contributors to vision impairment, such as cataract, glaucoma, diabetic retinopathy, and age-related macular degeneration (AMD).

Glaucoma is a leading cause of irreversible blindness globally. In 2013, 64 million adults worldwide were estimated to have glaucoma, and the number of cases is expected to double by 2040. In China, >3% of the population has glaucoma, and primary angle-closure glaucoma (PACG) is the major subtype, in contrast with Western populations where primary open-angle glaucoma (POAG) predominates. The causes of glaucoma, particularly PACG, are not fully understood and most of the previous research has been constrained by small study size or retrospective study designs.

About one-third of individuals with diabetes are expected to develop diabetic retinopathy (DR), and the risk is substantially higher in many low- and middle-income countries, including China, where diabetes is poorly detected and managed.

The China Kadoorie Biobank (CKB) is a large prospective cohort study of >0.5 million adults recruited from 10 diverse areas across China during 2004-08. To date, >1800 glaucoma cases and >5000 cases of DR have been recorded among participants through electronic linkages to hospital records. During the 3rd resurvey which will be conducted in 2020, intraocular pressure (IOP) will be measured and retinal images will be collected history of vision and eye diseases. This highly enriched database will provide a great opportunity for comprehensive assessment of the patterns and risk factors of IOP and glaucoma, DR, and other retinal diseases in Chinese adults. 

RESEARCH EXPERIENCE, RESEARCH METHODS AND TRAINING

The specific DPhil project will be subject to further discussion and personal interest, but may focus on a specific disease area and include the following areas of work:

  1. Development of a machine learning and/or AI-based approach for diagnosing DR and other retinal diseases, and characterising macular, retinal vasculature and optic nerve abnormalities based on the retinal images captured among ~25,000 participants;
  2. To investigate the patterns and associations of environmental and lifestyle factors with risks of selected eye diseases (e.g. glaucoma and levels of IOP; RD and AMD);
  3. To identify genetic risk factors for selected eye diseases using genome-wide genotyping data.

The student will work within a multi-disciplinary team, and will gain research experience in systematic literature review, study design and planning, epidemiological and statistical methodology, statistical programming, data analysis and scientific writing. It is anticipated that the candidate will publish their results by the end of their DPhil.   

FIELD WORK, SECONDMENTS, INDUSTRY PLACEMENTS AND TRAINING 

The project will provide an extensive range of training opportunities through attending specific courses, meetings, workshops and seminars, along with regular supervisory meetings. There may be opportunities to work with external partners from industry and other research institutions. 

PROSPECTIVE CANDIDATE

Candidates should have a higher degree in vision sciences, medicine, epidemiology, or another related area. Previous postgraduate training or experience in epidemiology or statistics is necessary. Candidates should also have a strong interest in the epidemiology of eye disease.

Supervisors