David S. Friedman, MD, PhD, MPH
Janey L. Wiggs, MD, PhD
See a list of Glaucoma faculty
The Glaucoma Center of Excellence was established with the explicit goal of shrinking the timeline in bringing sight-saving advances to our patients and people throughout the world.
Glaucoma encompasses several conditions that cause optic neuropathy, or damage to the optic nerve. Glaucoma affects an estimated 60 million people worldwide—making it the second leading cause of blindness worldwide, according to the World Health Organization.
Primary open-angle glaucoma (POAG) is the most common form of glaucoma; it is associated with increased intraocular pressure (IOP), also known as ocular hypertension, which may in turn, lead to retinal ganglion cell death and optic neuropathy. Secondary glaucoma occurs as a complication of eye surgeries, injuries, infections, or other ophthalmic conditions. Glaucoma may even occur without increased IOP in normal tension glaucoma. Many kinds of glaucoma have strong genetic and/or environmental risk factors, and any form of the disease can cause irreversible blindness if left untreated.
Major Research Breakthroughs
Research programs in glaucoma investigate risk factors for glaucoma, as well as methods for early disease detection and novel therapeutics. In the last 20 years, our investigators have:
- Demonstrated the use of spectral domain and 3D swept-source optical coherence tomography to detect retinal nerve fiber layer thinning, which can occur before clinically detectable, irreversible vision loss in glaucoma
- Identified structural remodeling of astrocytes as a potential new target for disease pathogenesis
- Identified over 100 novel genetic risk factors for glaucoma and related ocular traits
- Identified environmental risk factors for exfoliation glaucoma, including residence in northern latitudes
- Identified subtypes of glaucoma based on machine learning of visual field defects and specific optic nerve features
2020 Vision: Promising Areas For Future Research
Investigators aim to develop models for disease screening and risk prediction based on machine learning, fundus images, and genetic risk factors.