MétaCan
Menu
Back to cohort
Record W4410397471 · doi:10.4088/pcc.24nr03855

The Role of Glucagon-Like Peptide-1 Receptor Agonists in Alcohol Use Disorder

2025· review· en· W4410397471 on OpenAlexaff
Sadaf Khan, Pavani Sayana, Olu-Lawal Oluwanifesimi, Garima Yadav, Zeeshan Mansuri, Shailesh Jain

Bibliographic record

VenueThe Primary Care Companion For CNS Disorders · 2025
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMcMaster UniversityHamilton Health Sciences
Fundersnot available
KeywordsAlcohol use disorderGlucagon-like peptide-1AlcoholReceptorGlucagon-like peptide 1 receptorGlucagonAgonistEndocrinologyInternal medicineChemistryMedicinePharmacologyBiochemistryHormoneDiabetes mellitus

Abstract

fetched live from OpenAlex

Alcohol use disorder (AUD) is a critical health condition that increases the risk of a variety of social and physical health impairments. Glucagon-like peptide-1 (GLP-1) receptor agonists are potentially effective in reward system-related disorders. The use of GLP-1 receptor agonists has been shown to decrease overall consumption of alcohol in AUD in addition to managing obesity and weight loss. The main objective of this narrative review was to examine the potential benefits, dosages, and mechanisms of GLP-1 receptor agonists on alcohol consumption and how they can potentially modify alcohol-seeking behavior. The principal observation included the effect of GLP-1 receptor agonists on the mesolimbic pathways in the central nervous system, the central amygdala, and the GABAergic neurons in the central nervous system. Current research also focuses on the use of GLP-1 receptor agonists in improving glycemic control and reduction of obesity. and in those with AUD who have coexisting diabetes mellitus. As decreasing glucose levels and alcohol-seeking behavior are 2 dual effects of the GLP-1 receptor agonists, the dosage can be adjusted accordingly to achieve the desired benefits while reducing the potential side effects of the drug class. .

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.021
GPT teacher head0.294
Teacher spread0.273 · 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.

Study designNot applicable
Domainnot available
GenreReview

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 routes1
Has abstractyes

Explore more

Same venueThe Primary Care Companion For CNS DisordersSame topicDiabetes Treatment and ManagementFrench-language works237,207