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Record W1544134631 · doi:10.1113/jphysiol.2007.130526

Caffeine's impairment of insulin‐mediated glucose disposal cannot be solely attributed to adrenaline in humans

2007· article· en· W1544134631 on OpenAlexaff
Danielle S. Battram, Terry E. Graham, Flemming Dela

Bibliographic record

VenueThe Journal of Physiology · 2007
Typearticle
Languageen
FieldMedicine
TopicCoffee research and impacts
Canadian institutionsUniversity of Guelph
FundersLundbeckfonden
KeywordsCaffeineSalineMedicinePlaceboInternal medicineEndocrinologyPharmacology

Abstract

fetched live from OpenAlex

Caffeine (CAF) impedes insulin-mediated glucose disposal (IMGD) and increases plasma adrenaline concentrations ([ADR]; 0.6 nm). While the antagonism of ADR abolishes the CAF effect, infusion of ADR (0.75 nm) has no effect on IMGD. We have now examined CAF and ADR in concert to determine whether or not they elicit an additive response on IMGD. We hypothesized that CAF + ADR would elicit a greater effect than either CAF or ADR alone (i.e. that CAF effects would not be solely attributed to ADR). Subjects (n = 8) completed four trials in a randomized manner. An isoglycaemic-hyperinsulinaemic clamp was performed 30 min after the following treatments were administered: (1) placebo capsules and saline infusion ([ADR] = 0.29 nm) (PL trial), (2) CAF capsules (dose = 5 mg kg(-1)) and saline infusion ([ADR] = 0.62 nm) (CAF trial), (3) PL capsules and ADR infusion ([ADR] = 1.19 nm) (ADR trial), and (4) CAF capsules (dose = 5 mg kg(-1)) and ADR infusion ([ADR] = 0.93 nm) (CAF + ADR trial). As expected, CAF, ADR and CAF + ADR decreased (P <or= 0.05) IMGD compared to PL. CAF + ADR resulted in a more pronounced decrease in IMGD versus PL (42%) compared to CAF (26%) or ADR (24%) alone; however, the effect was not fully additive (P = 0.08). Furthermore, CAF decreased IMGD to a similar magnitude as ADR despite a 50% lower [ADR]. In summary, while ADR contributes to the CAF-induced impairment in IMGD, it is not solely responsible for caffeine's effects.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.886
Threshold uncertainty score0.325

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
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 head0.029
GPT teacher head0.338
Teacher spread0.309 · 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 designBench or experimental
Domainnot available
GenreEmpirical

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
Published2007
Admission routes1
Has abstractyes

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