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Record W4401598356 · doi:10.1016/j.eclinm.2024.102789

GLP-1-based therapies for the treatment of resistant hypertension in individuals with overweight or obesity: a review

2024· review· en· W4401598356 on OpenAlexaff
Candace Jarade, Tetiana Zolotarova, Areesha Moiz, Mark J. Eisenberg

Bibliographic record

VenueEClinicalMedicine · 2024
Typereview
Languageen
FieldMedicine
TopicDiabetes Treatment and Management
Canadian institutionsMcGill UniversityJewish General Hospital
Fundersnot available
KeywordsMedicineLiraglutideOverweightWeight lossSemaglutideObesityBlood pressureInternal medicineGlucagon-like peptide 1 receptorPopulationAgonistType 2 diabetesEndocrinologyDiabetes mellitusReceptorEnvironmental health

Abstract

fetched live from OpenAlex

Despite the availability of a wide range of antihypertensive agents, a significant proportion of individuals with resistant hypertension (RHTN) struggle to achieve blood pressure (BP) control. Obesity ranks among the most significant modifiable risk factors for RHTN, with 56-91% of patients with RHTN classified as overweight or obese. Glucagon-like peptide-1 receptor agonist (GLP-1 RAs) are a class of anti-obesity medications that have recently demonstrated efficacy in reducing BP and improving cardiovascular (CV) outcomes in individuals with overweight or obesity. Among the available GLP-1-based therapies, liraglutide, semaglutide, and tirzepatide have been approved for chronic weight management in this population. Tirzepatide, a dual GLP-1 and glucose-dependent insulinotropic polypeptide receptor agonist, has the greatest effect on weight loss and BP reduction compared to GLP-1 RAs alone. To our knowledge, no trials have directly evaluated the effect of GLP-1 RAs or dual GLP-1/GIP receptor agonists on RHTN management. In this review article, we propose that targeting weight loss through GLP-1-based therapies should be explored as a treatment option for individuals with RHTN who are overweight or obese.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.863
Threshold uncertainty score0.910

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0050.001
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 head0.123
GPT teacher head0.402
Teacher spread0.279 · 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 designOther design
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

Citations24
Published2024
Admission routes1
Has abstractyes

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