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Record W1775022724 · doi:10.1139/apnm-2012-0471

Caffeine and glucose homeostasis during rest and exercise in diabetes mellitus

2013· review· en· W1775022724 on OpenAlexaffvenue
Dessi P. Zaharieva, Michael C. Riddell

Bibliographic record

VenueApplied Physiology Nutrition and Metabolism · 2013
Typereview
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsYork University
Fundersnot available
KeywordsCaffeineGlucose homeostasisEndocrinologyInternal medicineMedicineHypoglycemiaDiabetes mellitusCarbohydrate metabolismHomeostasisInsulinInsulin resistance

Abstract

fetched live from OpenAlex

Caffeine is a substance that has been used in our society for generations, primarily for its effects on the central nervous system that causes wakefulness. Caffeine supplementation has become increasingly more popular as an ergogenic aid for athletes and considerable scientific evidence supports its effectiveness. Because of their potential to alter energy metabolism, the effects of coffee and caffeine on glucose metabolism in diabetes have also been studied both epidemiologically and experimentally. Predominantly targeting the adenosine receptors, caffeine causes alterations in glucose homeostasis by decreasing glucose uptake into skeletal muscle, thereby causing elevations in blood glucose concentration. Caffeine intake has also been proposed to increase symptomatic warning signs of hypoglycemia in patients with type 1 diabetes and elevate blood glucose levels in patients with type 2 diabetes. Other effects include potential increases in glucose counterregulatory hormones such as epinephrine, which can also decrease peripheral glucose disposal. Despite these established physiological effects, increased coffee intake has been associated with reduced risk of developing type 2 diabetes in large-scale epidemiological studies. This review paper highlights the known effects of caffeine on glucose homeostasis and diabetes metabolism during rest and exercise.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.963
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.029
GPT teacher head0.307
Teacher spread0.278 · 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.

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

Citations25
Published2013
Admission routes2
Has abstractyes

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