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Glucagon-Like Peptide-1 Receptor Agonists and Risk of Neovascular Age-Related Macular Degeneration

2025· letter· en· W4411046438 on OpenAlexaffabout
Reut Shor, Andrew Mihalache, Atefeh Noori, R Shor, Radha P. Kohly, Marko M. Popovic, Rajeev H. Muni

Bibliographic record

VenueJAMA Ophthalmology · 2025
Typeletter
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsSt. Michael's HospitalToronto Western HospitalUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsMedicineDiabetes mellitusContext (archaeology)CohortMacular degenerationRetrospective cohort studyGlucagon-like peptide 1 receptorIncidence (geometry)PopulationInternal medicinePediatricsCohort studyEnvironmental healthOphthalmologyEndocrinology

Abstract

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Importance: Glucagon-like peptide-1 receptor agonists (GLP-1 RAs) are extensively used in treating diabetes and obesity, yet little is known about the long-term ocular effects of systemic prolonged exposure. Objective: To evaluate the risk of developing neovascular age-related macular degeneration (nAMD) associated with the use of GLP-1 RAs in patients with diabetes. Design, Setting, and Participants: This population-based, retrospective cohort study was conducted from January 2020 to November 2023, with a follow-up period of 3 years. Data analysis was performed from August 2024 to October 2024. The investigators used comprehensive administrative health and demographic data from patients in Ontario, Canada, which were collected by the Institute for Clinical Evaluative Sciences in the context of a universal public health care system. Inclusion criteria were patients aged 66 years or older with a diagnosis of diabetes and a minimum follow-up period of 12 months following initial diabetes diagnosis. Patients with incomplete Ontario Health Insurance Plan or Ontario Drug Benefit data or patients exposed to GLP-1 RA for less than 6 months were excluded. Of 1 119 517 eligible patients, a 1:2 matched cohort of 139 002 patients was created, including 46 334 patients who were exposed to GLP-1 RAs and 92 668 unexposed matched patients. Systemic comorbidities that were associated with any kind of AMD and socioeconomic status were used to calculate propensity scores. Exposure: GLP-1 RA use for 6 months or longer. Main Outcomes and Measures: The primary outcome was the incidence and time to event of nAMD during the follow-up period. Results: Among 139 002 matched patients, mean (SD) patient age was 66.2 (7.5) years, and 64 775 patients (46.6%) were women. The incidence of nAMD was higher among the exposed cohort than among the unexposed cohort. Cox proportional hazard models, both unadjusted (crude) and adjusted, estimated hazard ratios for nAMD development of greater than 2.0 among patients exposed to GLP-1 RAs (exposed, 0.2% vs unexposed, 0.1%; difference, 0.1%; crude: HR, 2.11; 95% CI, 1.58-2.82; adjusted: HR, 2.21; 95% CI, 1.65-2.96). Conclusions and Relevance: In this cohort study, the use of GLP-1 RAs among patients with diabetes was associated with a 2-fold higher risk of incident nAMD development than among similar patients with diabetes who did not receive a GLP-1 RA. Further research is needed to elucidate the exact pathophysiological mechanisms involved and to understand the trade-offs between the benefits and risks of GLP-1 RAs.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.013
GPT teacher head0.269
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations48
Published2025
Admission routes2
Has abstractyes

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