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Food anticipatory hormonal responses: A systematic review of animal and human studies

2021· review· en· W3145541898 on OpenAlexaff
Aleksandrina Skvortsova, Dieuwke S. Veldhuijzen, Iris E.M. Kloosterman, Gustavo Pacheco‐López, Andrea W.M. Evers

Bibliographic record

VenueNeuroscience & Biobehavioral Reviews · 2021
Typereview
Languageen
FieldNeuroscience
TopicRegulation of Appetite and Obesity
Canadian institutionsMcGill University
FundersH2020 European Research CouncilEuropean Research Council
KeywordsGhrelinHormonePancreatic polypeptideAnimal studiesHuman studiesIngestionPhysiologyEndocrinologyGlucose homeostasisGlucagonInsulinInternal medicineBiologyMedicineInsulin resistance

Abstract

fetched live from OpenAlex

Food anticipatory hormonal responses (cephalic responses) are proactive physiological processes, that allow animals to prepare for food ingestion by modulating their hormonal levels in response to food cues. This process is important for digesting food, metabolizing nutrients and maintaining glucose levels within homeostasis. In this systematic review, we summarize the evidence from animal and human research on cephalic responses. Thirty-six animal and fifty-three human studies were included. The majority (88 %) of studies demonstrated that hormonal levels are changed in response to cues previously associated with food intake, such as feeding time, smell, and sight of food. Most evidence comes from studies on insulin, ghrelin, pancreatic polypeptide, glucagon, and c-peptide. Moreover, impaired cephalic responses were found in disorders related to metabolism and food intake such as diabetes, pancreatic insufficiency, obesity, and eating disorders, which opens discussions about the etiological mechanisms of these disorders as well as on potential therapeutic opportunities.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.009
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0040.003
Bibliometrics0.0080.008
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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 head0.394
GPT teacher head0.482
Teacher spread0.088 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSystematic review
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

Citations42
Published2021
Admission routes1
Has abstractyes

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