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Glucagon-Like Peptide-1 Receptor Agonists and Risk for Ischemic Optic Neuropathy : A Target Trial Emulation
Kamika R Reynolds1, Kimberly M O'Malley1, Jason A Roy2
1Center for Pharmacoepidemiology and Treatment Science, Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, New Jersey, and Center for Health Outcomes, Policy, and Economics, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, New Jersey (K.R.R., K.M.O., C.V.D.).
Glucagon-like peptide-1 receptor agonists (GLP-1RAs) showed a slightly higher risk for ischemic optic neuropathy (ION) compared to SGLT2is and DPP4is in type 2 diabetes patients. The absolute risk for ION remained very low across all drug classes.
Area of Science:
- Ophthalmology
- Endocrinology
- Pharmacology
Background:
- Nonarteritic anterior ischemic optic neuropathy (NAION) is a leading cause of optic neuropathy in adults.
- Limited data exist on the association between glucagon-like peptide-1 receptor agonists (GLP-1RAs) and NAION.
Purpose of the Study:
- To compare the risk of ischemic optic neuropathy (ION) in patients using GLP-1RAs versus sodium-glucose cotransporter-2 inhibitors (SGLT2is) and dipeptidyl peptidase-4 inhibitors (DPP4is).
Main Methods:
- Observational study using a large U.S. commercial claims database (2017-2022).
- Emulated a target trial comparing type 2 diabetes patients initiating GLP-1RAs, SGLT2is, or DPP4is.
- Adjusted for over 80 covariates using inverse probability of treatment weighting.
Main Results:
- The 18-month risk for ION was higher with GLP-1RAs compared to SGLT2is (3.0 per 10,000) and DPP4is (3.6 per 10,000).
- Absolute risk for ION remained low across all groups.
- Higher risk differences were observed in men, older patients (>50 years), and those with cardiovascular disease or ophthalmic conditions.
Conclusions:
- GLP-1RA use was associated with a higher 18-month risk of ION compared to SGLT2is and DPP4is.
- The absolute risk of ION remains very low.
- Observed associations may be influenced by residual confounding due to data limitations.
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