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Record W4214607566 · doi:10.3389/fendo.2022.838410

Recent Advances in Incretin-Based Pharmacotherapies for the Treatment of Obesity and Diabetes

2022· review· en· W4214607566 on OpenAlexafffund
Qiming Tan, Seun Akindehin, Camila E. Orsso, Richelle C. Waldner, Richard D. DiMarchi, Timo D. Müller, Andrea M. Haqq

Bibliographic record

VenueFrontiers in Endocrinology · 2022
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsUniversity of Alberta
FundersCanadian Institutes of Health Research
KeywordsIncretinMedicineType 2 diabetesDiabetes mellitusHormoneGlucagon-like peptide-1Internal medicineObesityExenatideEndocrinologyPharmacologyReceptorBioinformaticsBiology

Abstract

fetched live from OpenAlex

The incretin hormone glucagon-like peptide-1 (GLP-1) has received enormous attention during the past three decades as a therapeutic target for the treatment of obesity and type 2 diabetes. Continuous improvement of the pharmacokinetic profile of GLP-1R agonists, starting from native hormone with a half-life of ~2-3 min to the development of twice daily, daily and even once-weekly drugs highlight the pharmaceutical evolution of GLP-1-based medicines. In contrast to GLP-1, the incretin hormone glucose-dependent insulinotropic polypeptide (GIP) received little attention as a pharmacological target, because of conflicting observations that argue activation or inhibition of the GIP receptor (GIPR) provides beneficial effects on systemic metabolism. Interest in GIPR agonism for the treatment of obesity and diabetes was recently propelled by the clinical success of unimolecular dual-agonists targeting the receptors for GIP and GLP-1, with reported significantly improved body weight and glucose control in patients with obesity and type II diabetes. Here we review the biology and pharmacology of GLP-1 and GIP and discuss recent advances in incretin-based pharmacotherapies.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.993
Threshold uncertainty score0.810

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
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 head0.033
GPT teacher head0.328
Teacher spread0.295 · 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 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

Citations146
Published2022
Admission routes2
Has abstractyes

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