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Record W2026034043 · doi:10.1159/000108341

Histamine and the Regulation of Body Weight

2007· review· en· W2026034043 on OpenAlexaff
Emilie A. Jørgensen, Ulrich Knigge, Jørgen Warberg, Andreas Kjær

Bibliographic record

VenueNeuroendocrinology · 2007
Typereview
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsInnovation Cluster (Canada)
Fundersnot available
KeywordsHistaminergicHistamineEndocrinologyAppetiteInternal medicineHistamine receptorHistamine H3 receptorLeptinHistamine H1 receptorNeurotransmitterStimulationReceptorBiologyChemistryMedicineObesityAntagonist

Abstract

fetched live from OpenAlex

Energy intake and expenditure is regulated by a complex interplay between peripheral and central factors. An exhaustive list of peptides and neurotransmitters taking part in this complex regulation of body weight exists. Among these is histamine, which acts as a central neurotransmitter. In the present article we review current evidence pointing at an important role of histamine in the regulation of appetite and metabolism. Studies using both knockout mouse models as well as pharmacological studies have revealed that histamine acts as an anorexigenic agent via stimulation of histamine H(1) receptors. One effect of histamine in the regulation of appetite is to act as a mediator of the inhibitory effect of leptin on appetite. It seems that histamine may attenuate and delay the development of leptin resistance in high-fat-diet-induced obesity. Furthermore, histamine may also act to accelerate lipolysis. Based on the current evidence of the involvement of histamine in the regulation of body weight, the histaminergic system is an obvious target for the development of pharmacological agents to control obesity. At present, H(3) receptor antagonists that stimulate the histaminergic system may be the most promising histaminergic drugs for antiobesity therapy.

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 score0.996
Threshold uncertainty score0.645

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.054
GPT teacher head0.322
Teacher spread0.268 · 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

Citations65
Published2007
Admission routes1
Has abstractyes

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