The effect of caffeine on glucose kinetics in humans – influence of adrenaline
Bibliographic record
Abstract
While caffeine impedes insulin-mediated glucose disposal in humans, its effect on endo-genous glucose production (EGP) remains unknown. In addition, the mechanism involved in these effects is unclear, but may be due to the accompanying increase in adrenaline concentration. We studied the effect of caffeine on EGP and glucose infusion rates (GIR), and whether or not adrenaline can account for all of caffeine's effects. Subjects completed three isoglycaemic-hyperinsulinaemic clamps (with 3-[(3)H]glucose infusion) 30 min after ingesting: (1) placebo capsules (n= 12); (2) caffeine capsules (5 mg kg(-1)) (n= 12); and either (3) placebo plus a high-dose adrenaline infusion (HAdr; adrenaline concentration, 1.2 nM; n= 8) or (4) placebo plus a low-dose adrenaline infusion (LAdr; adrenaline concentration, 0.75 nM; n= 6). With caffeine, adrenaline increased to 0.6 nM but no effect on EGP was observed. While caffeine and HAdr decreased GIR by 13 (P < 0.05) and 34% (P < 0.05) versus the placebo, respectively, LAdr did not result in a significant reduction (5%) in GIR versus the placebo. Due to the fact that both caffeine and LAdr resulted in similar adrenaline concentrations, but resulted in different decreases in GIR, it is concluded that adrenaline alone does not account for the effects of caffeine and additional mechanisms must be involved.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".