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Record W4406339339 · doi:10.1017/s109285292400244x

Are glucagon-like peptide-1 receptor agonists anti-consummatory drugs?

2024· review· en· W4406339339 on OpenAlexaff
Rodrigo B. Mansur, Joshua D. Di Vincenzo, Sebastian Badulescu, Hartej Gill, Aniqa Tabassum, Joshua D. Rosenblat, Roger S. McIntyre

Bibliographic record

VenueCNS Spectrums · 2024
Typereview
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsLiraglutideSemaglutideIncretinGlucagon-like peptide-1Glucose homeostasisEnergy homeostasisFood intakePsychologyReceptorNeuroscienceMedicinePharmacologyObesityInternal medicineType 2 diabetesEndocrinologyDiabetes mellitusInsulin resistance

Abstract

fetched live from OpenAlex

Incretin-based treatments, such as glucagon-like peptide-1 receptor (GLP-1R) agonists (eg liraglutide and semaglutide), have rapidly transformed obesity treatment. The well-documented weight loss effect from these agents is considered to be primarily a result of their actions on food intake, but frequent anecdotal reports from varied sources have suggested that they might also broadly affect consummatory behavior, including alcohol and drugs of abuse, suggesting a potential modulatory effect on reward behavior. Herein, we critically review the extant literature on the behavioral effects of GLP-1R agonists in humans, including their impact on feeding behavior, alcohol/drug intake, and overall reward response. We also consider the physiological and neurobiological underpinnings of GLP-1 actions, with a focus on its distinct central and peripheral roles, as well as its relationships with the broader energy homeostasis network. We conclude with a discussion on the implications of this line of research on how behavior is conceptualized, and the potential future directions for research.

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), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.638
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.022

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 head0.053
GPT teacher head0.320
Teacher spread0.267 · 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; both teacher heads agree on what is shown here.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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