Frontiers in Diabetic Macular Edema: New Ways to Address Barriers and Health Disparities for Better Vision Outcomes

Diabetic macular edema and diabetic retinopathy are the 2 most common visual complications of diabetes.¹ Furthermore, diabetic retinopathy is the leading cause of vision loss among working-age adults.¹ We know that screening works and effective treatments are available, so why do we continue to see high rates of vision loss?

Racial and Social Influences on Diabetic Eye Disease

First, we know the prevalence of diabetes is higher among minority populations and those with lower socioeconomic status.^2,3 Data from the National Health and Nutritional Evaluation Survey 2005-2008 also show that diabetic retinopathy is 190% higher in non-Hispanic Blacks compared with Whites.⁴ Social determinants of health and glycemic control as well as barriers to care can influence the progression of diabetic retinopathy and diabetic macular edema (Figure 1).^5,6

A recent study highlights how socioeconomic barriers specifically affect the vision of patients with diabetes. Queries of the American Academy of Ophthalmology’s Intelligent Research in Sight (IRIS) database showed that patients with diabetic macular edema who are Black or Hispanic presented with worse baseline visual acuity compared with their White and non-Hispanic counterparts (Figure 2).⁷

In addition, insurance stratification was associated with differences in baseline visual acuity. Patients with private insurance were more likely to have good baseline visual acuity (£20/40) compared with patients who had Medicare or Medicaid (Figure 3).

The difference between 20/40 vision and 20/50 vision is the ability to drive versus reliance on others to attend healthcare appointments, which can further compound the social determinants of health. Unfortunately, data show that Hispanic and Black patients are at a high risk of non-improvement despite treatment with anti-vascular endothelial growth factor (VEGF) drugs (Figure 4).  

Screening Advances to Improve Early Detection

Screening is essential to detect early problems with vision because treatments are most effective when started early; however, there are not enough eye doctors to screen every patient. Non-mydriatic fundus imaging, telemedicine, and reading centers are currently used to improve screening in primary care and endocrinology clinics. New technologies include artificial intelligence (AI) to recognize whether patients require further care by an eye doctor.^8-10 AI could potentially be coupled with smartphone-based imaging to give patients an indication of when they need to be seen by an eye health professional.¹¹ These advances in screening are important because the vast majority of patients do not have visual symptoms until very late in the disease state. In essence, patients are unknowingly sitting on a ticking time bomb.

In 2020, the first AI algorithm in all of medicine was approved for detecting diabetic retinopathy.¹² The device is used with a traditional non-mydriatic camera. Images are captured, sent, and automatically read over a short period of time. The system then provides referrals with 87% sensitivity and 90% specificity for more than mild diabetic retinopathy.¹³

In addition to AI, the use of ultrawide field imaging is likely to become more common in practice. Traditional imaging methods take a 30° or 40° field of view, which can be equated to looking only at Iowa on a map of the United States while trying to figure out what is happening in California. Ultrawide field imaging provides a picture of the entire United States in one photograph which allows for more reliable detection of diabetic retinopathy.

Importance of Interdisciplinary Care and Advocacy Within Underserved Communities

Interdisciplinary teams consisting of patients, caregivers, and community leaders are also important to overcoming health disparities. Community leaders can help educate others on the social determinants of health and dispel rumors. Minority communities should know that diabetes and diabetic eye disease are conditions that can be screened for and that are treatable. The use of anti-VEGF agents can reduce diabetic eye disease progression significantly.

Improving Anti-VEGF Durability to Reduce Treatment Burden

One of the drawbacks of current anti-VEGF therapies is the need for frequent injections in patients with diabetic eye disease. The frequency of these injections is a significant burden for patients as well as providers. New treatments with greater durability are currently being studied. One strategy is in the form of an implant which can release an anti-VEGF agent over a long period of time.¹⁴ Another drug is taking a new approach by concurrently inhibiting angiopoietin-2 and VEGF to improve vessel integrity and increase treatment durability.¹⁵ Finally, brolucizumab is a high-dose drug with a higher potency than current anti-VEGF agents that is also being tested to see if durability is increased over time.¹⁶

Take-Home Messages

Diabetes and diabetic eye disease are commonplace. As such, it is important for patients with diabetes to understand that yearly vision screening is very important because symptoms are not present early in the disease state. Initiating treatment before symptoms are present can prevent poor vision outcomes over the long term. Patients with diabetes should also understand that a vast majority of people will not lose vision as long as they see an ophthalmologist or retina specialist for routine eye examinations and undergo treatment when necessary. All of these are good reasons to be hopeful for the future.

For colleagues who are in endocrinology and primary care, it is important to keep abreast of new developments for the treatment of diabetic eye disease. As emerging treatments become available, they will be incorporated within the overall care plan for patients.

Ensure that patients, particularly those in socioeconomically challenged patient populations, are screened by using new technologies as they become available. Appropriate action needs to be taken early to achieve the level of outcomes that are seen in clinical trials. Ophthalmologists can do a lot for patients, but outcomes can only be improved if patients are educated about the importance of screening and treatment.

References

1. Centers for Disease Control and Prevention. Common eye disorders and diseases. Reviewed June 3, 2020. Accessed May 6, 2021. https://www.cdc.gov/visionhealth/basics/ced/index.html
2. Centers for Disease Control and Prevention. Prevalence of both diagnosed and undiagnosed diabetes. Reviewed June 24, 2020. Accessed May 6, 2021. https://www.cdc.gov/diabetes/data/statistics-report/diagnosed-undiagnosed-diabetes.html
3. Agardh E, Allebeck P, Hallqvist J, Moradi T, Sidorchuk A. Type 2 diabetes incidence and socio-economic position: a systematic review and meta-analysis. Int J Epidemiol. 2011;40(3):804-818.
4. Zhang X, Saaddine JB, Chou CF, et al. Prevalence of diabetic retinopathy in the United States, 2005-2008. JAMA. 2010;304(6):649-656.
5. McBrien KA, Naugler C, Ivers N, et al. Barriers to care in patients with diabetes and poor glycemic control-A cross-sectional survey. PLoS One. 2017;12(5):e0176135.
6. Hill-Briggs F, Adler NE, Berkowitz SA, et al. Social determinants of health and diabetes: a scientific review. Diabetes Care. 2020;44(1):258-279.
7. Malhotra NA, Greenlee TE, Iyer AI, Conti TF, Chen AX, Singh RP. Racial, ethnic, and insurance-based disparities upon initiation of anti-vascular endothelial growth factor therapy for diabetic macular edema in the US. Ophthalmology. Published online March 11, 2021. doi:10.1016/j.ophtha.2021.03.010
8. Wong TY, Bressler NM. Artificial intelligence with deep learning technology looks into diabetic retinopathy screening. JAMA. 2016;316(22):2366-2367.
9. Ting DSW, Cheung CY, Lim G, et al. Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA. 2017;318(22):2211-2223.
10. Gulshan V, Rajan RP, Widner K, et al. Performance of a deep-learning algorithm vs manual grading for detecting diabetic retinopathy in India. JAMA Ophthalmol. 2019;137(9):987-993.
11. Natarajan S, Jain A, Krishnan R, Rogye A, Sivaprasad S. Diagnostic accuracy of community-based diabetic retinopathy screening with an offline artificial intelligence system on a smartphone. JAMA Ophthalmol. 2019;137(10):1182-1188.
12. Leonard C. Ophthalmologists in the machine: the AI era. Review of Ophthalmology. November 5, 2020. Accessed May 6, 2021. https://www.reviewofophthalmology.com/article/ophthalmologists-in-the-machine-the-ai-era.
13. Abràmoff MD, Lavin PT, Birch M, Shah N, Folk JC. Pivotal trial of an autonomous AI-based diagnostic system for detection of diabetic retinopathy in primary care offices. NPJ Digit Med. 2018;1:39.  
14. Campochiaro PA, Marcus DM, Awh CC, et al. The port delivery system with ranibizumab for neovascular age-related macular degeneration: results from the randomized phase 2 ladder clinical trial. Ophthalmology. 2019;126(8):1141-1154.
15. Sahni J, Patel SS, Dugel PU, et al. Simultaneous inhibition of angiopoietin-2 and vascular endothelial growth factor-A with faricimab in diabetic macular edema: BOULEVARD phase 2 randomized trial. Ophthalmology. 2019;126(8):1155-1170.
16. Tadayoni R, Sararols L, Weissgerber G, Verma R, Clemens A, Holz FG. Brolucizumab: a newly developed anti-VEGF molecule for the treatment of neovascular age-related macular degeneration. Ophthalmologica. 2021;244(2):93-101.