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Exercise and inactivity as modifiers of β cell function and type 2 diabetes risk

2023· review· en· W4319659193 on OpenAlexafffund
Liam G. Hall, John P. Thyfault, James D. Johnson

Bibliographic record

VenueJournal of Applied Physiology · 2023
Typereview
Languageen
FieldMedicine
TopicPancreatic function and diabetes
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesCanadian Institutes of Health ResearchNational Institutes of HealthGovernment of Canada
KeywordsDeconditioningType 2 diabetesMedicinePhysical exerciseDiabetes mellitusInsulinEndocrinologyIncretinInternal medicineDiseaseAutonomic nervous systemExercise physiologyBioinformaticsPhysical therapyBiologyBlood pressureHeart rate

Abstract

fetched live from OpenAlex

Exercise and regular physical activity are beneficial for the prevention and management of metabolic diseases such as obesity and type 2 diabetes, whereas exercise cessation, defined as deconditioning from regular exercise or physical activity that has lasted for a period of months to years, can lead to metabolic derangements that drive disease. Adaptations to the insulin-secreting pancreatic β-cells are an important benefit of exercise, whereas less is known about how exercise cessation affects these cells. Our aim is to review the impact that exercise and exercise cessation have on β-cell function, with a focus on the evidence from studies examining glucose-stimulated insulin secretion (GSIS) using gold-standard techniques. Potential mechanisms by which the β-cell adapts to exercise, including exerkine and incretin signaling, autonomic nervous system signaling, and changes in insulin clearance, will also be explored. We will highlight areas for future research.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.027
GPT teacher head0.287
Teacher spread0.260 · 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 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

Citations10
Published2023
Admission routes2
Has abstractyes

Explore more

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