Effects of chronic coffee consumption on glucose kinetics in the conscious rat
Bibliographic record
Abstract
Epidemiological studies indicate that regular coffee consumption reduces the risk of developing type 2 diabetes. Despite these findings, the biological mechanisms by which coffee consumption exerts these effects are unknown. The aim of this study was twofold: to develop a rat model that would further delineate the effects of regular coffee consumption on glucose kinetics, and to determine whether coffee, with or without caffeine, alters the actions of insulin on glucose kinetics in vivo. Male Sprague-Dawley rats were fed a high-fat diet for 4 weeks in combination with one of the following: (i) drinking water as placebo (PL), (ii) decaffeinated coffee (2 g/100 mL) (DC), or (iii) alkaloid caffeine (20 mg/100 mL) added to decaffeinated coffee (2 g/100 mL) (CAF). Catheters were chronically implanted in a carotid artery and jugular vein for sampling and infusions, respectively. Recovered animals (5 days postoperative) were fasted for 5 h before hyperinsulinemic-euglycemic clamps (2 mU x kg(-1) x min(-1)). Glucose was clamped at 6 mmol/L and isotopes (2-deoxy-[(14)C]glucose and [3-(3)H]glucose) were administered to obtain indices of whole-body and tissue-specific glucose kinetics. Glucose infusion rates and measures of whole-body metabolic clearance were greater in DC than in PL or CAF, indicating increased whole-body insulin sensitivity. As the only difference between DC and CAF was the addition of alkaloid caffeine, it can be concluded that caffeine antagonizes the beneficial effects of DC. Given these findings, decaffeinated coffee may represent a nutritional means of combating insulin resistance.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".