MétaCan
Menu
Back to cohort
Record W4389304454 · doi:10.1016/j.isci.2023.108628

Amylin receptor agonism enhances the effects of liraglutide in protecting against the acute metabolic side effects of olanzapine

2023· article· en· W4389304454 on OpenAlexafffund
Kyle D. Medak, Stewart Jeromson, Annalaura Bellucci, Meagan Arbeau, David C. Wright

Bibliographic record

VenueiScience · 2023
Typearticle
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsBC Children's HospitalUniversity of British ColumbiaUniversity of Guelph
FundersCanadian Institutes of Health Research
KeywordsLiraglutideOlanzapineAmylinDyslipidemiaEndocrinologyMedicineGlucagonInternal medicineInsulin resistanceGlucose homeostasisPharmacologyInsulinDiabetes mellitusType 2 diabetesSchizophrenia (object-oriented programming)

Abstract

fetched live from OpenAlex

Olanzapine is a second-generation antipsychotic (AP) used in the management of schizophrenia. Although effective at reducing psychoses, APs cause rapid hyperglycemia, insulin resistance, and dyslipidemia, an effect mediated in part by glucagon. We tested if amylin, a hormone that reduces glucagon, or the amylin receptor agonist pramlintide would protect against acute olanzapine-induced impairments in glucose and lipid homeostasis alone or in combination with other glucose-lowering agents such as liraglutide. We demonstrated that pramlintide lowered olanzapine-induced increases in glucagon:insulin ratio with a trend to protect against excursions in blood glucose. There was an additive effect of pramlintide and liraglutide in protecting against olanzapine-induced hyperglycemia, which was mirrored by reductions in glucagon and attenuated markers of dyslipidemia. Our findings provide evidence that pramlintide, although moderately protective against some aspects of olanzapine-induced metabolic dysfunction, can be used to enhance the protective effects of other interventions against acute olanzapine-induced metabolic dysfunction.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.106
Threshold uncertainty score0.251

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.009
GPT teacher head0.263
Teacher spread0.254 · 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 designBench or experimental
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

Citations3
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueiScienceSame topicDiabetes Treatment and ManagementFrench-language works237,207