Skip to navigation Skip to main content Skip to footer

Approved Research

Study of eye and brain involvements in glaucoma and association with genetic and metabolomic factors using statistical models and AI learning approaches

Principal Investigator: Dr Kevin Chan
Approved Research ID: 91781
Approval date: December 5th 2022

Lay summary

Glaucoma is the leading cause of irreversible blindness globally. It is defined as a multifactorial disease, while intraocular pressure is currently the only clinically modifiable risk factor for glaucoma. However, glaucoma can progress even with a controlled intraocular pressure, indicating other major factors than intraocular pressure in contributing to the disease. In addition, since glaucoma is a slowly progressing disease, less than half of glaucoma patients are aware of the disease until reaching the late stages. Thus, there is an unmet need to identify early markers for the disease. Our lab and others have discovered that glaucoma may initially affect both the eyes and the brain. Recent studies have also found that glaucoma may share common metabolic pathways with diseases such as Alzheimer's. These initial findings suggest that characterizing the eye-brain pathways in glaucoma and their relationships with other diseases may help identify novel strategies for detecting and managing glaucoma. The eye and brain imaging data, as well as other measurements obtained from the UK Biobank can be used for identifying and confirming such characteristics in glaucoma. Moreover, the UK Biobank contains intraocular pressure measurements for over 100,000 patients, so analysis of their intraocular pressure data in concurrence with glaucomatous damage in the same subjects would provide better insights about their interactions. As a result, we will be better able to understand how to better detect or target glaucoma via and beyond controlling eye pressure.

The purpose of this research is to discover earlier glaucoma detection and better understanding of glaucoma. To do this, we will include comprehensive sets of imaging, genetics, metabolomics and epidemiologic data in addition to traditional clinical assessments for detecting glaucoma. A rolling 3-year period of project duration is proposed for the generation of hypotheses with annual updates. Throughout this period, we will use statistical models and machine learning to detect changes in human samples across disease stages to provide more options for investigating glaucoma detection and treatment. We expect that the results of this project will help reduce the prevalence of this irreversible but preventable disease.