Effect of decaffeinated coffee on the serum glucose and insulin responses elicited by oral glucose
Bibliographic record
Abstract
A large amount of epidemiologic evidence has emerged indicating an association of coffee consumption with decreased risk for type‐2 diabetes. However, pure caffeine may acutely decrease insulin sensitivity. We conducted a randomized controlled crossover study to determine the effect of an Arabica and Robusta blend decaffeinated coffee (DC), administered either before or concurrently with 75g oral glucose (OGTT), on serum glucose and insulin responses. Ten healthy subjects were studied on 4 separate occasions after overnight fasts. DC was consumed with an OGTT (DC0), or 30min (DC‐30) or 60min (DC‐60) before the OGTT, or not at all (Control). Water was consumed 30min before the OGTT in the control treatment. The incremental area under the curve (AUC) for glucose after DC‐60 was similar to control, but significantly less than that after DC0. There were no differences in insulin response between treatments. An insulin sensitivity index based on fasting glucose and insulin concentrations (HOMAr) at 0 min tended to be less (p=0.052) on DC‐60 compared to DC0. These results suggest that proximity to a meal may influence the effect of decaffeinated coffee on glycemic response. Additional research should examine whether Arabica and Robusta coffee sources possess similar efficacy.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".