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Record W4417124206 · doi:10.1016/j.metdis.2025.100001

GLP-1 medicine and non-arteritic anterior ischemic optic neuropathy: Literature review and perspectives

2025· article· en· W4417124206 on OpenAlexafffund
Jia Nuo Feng, Yanming Chen, Tianru Jin

Bibliographic record

VenueMetabolism and Diseases · 2025
Typearticle
Languageen
FieldMedicine
TopicIntraoperative Neuromonitoring and Anesthetic Effects
Canadian institutionsDiabetes CanadaUniversity of TorontoUniversity Health Network
FundersCanadian Institutes of Health Research
KeywordsSemaglutideAnterior ischemic optic neuropathyDiabetes mellitusClinical trialNeuroprotectionAgonistObesityWeight loss

Abstract

fetched live from OpenAlex

GLP-1 therapeutics (defined as GLP-1 medicine hereafter), including GLP-1 receptor (GLP-1R) agonists (i.e. semaglutide) and glucose-dependent insulinotropic polypeptide (GIP)/GLP-1 receptor dual agonist (i.e. tirzepatide), were initially developed for diabetes treatment. Semaglutide and tirzepatide were then approved for treating obesity and body weight management. Beyond their capability in improving glucose disposal and lowering body weight, clinical trials are undertaking to assess their beneficial effect in other metabolic disorders. However, several recent retrospective studies indicated that semaglutide treatment increased the risk of non-arteritic anterior ischemic optic neuropathy (NAION). A few other studies, however, reported a lack of such association, or that the increased risk was very moderate. As an untreatable disorder, NAION mainly affects middle-aged and elderly people, associated with hyperglycemia and hyperlipidemia. Here, we highlighted additional beneficial effects of GLP-1 medicine, reviewed literature on the above association, and presented our view on future investigations. Based on literature controversy, the intrinsic limitations of retrospective studies, and the existence of neuroprotective effect of GLP-1 in neural systems, future meta-analyses are essential to quantitatively determine the potential association. Animal models may be utilized to explore the underlying mechanisms, if the association indeed exists.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.831
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

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

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 head0.007
GPT teacher head0.277
Teacher spread0.270 · 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 teacher head, not a consensus.

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

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

Citations0
Published2025
Admission routes2
Has abstractyes

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