Chlorogenic acid differentially affects postprandial glucose and glucose-dependent insulinotropic polypeptide response in rats
Bibliographic record
Abstract
Regular coffee consumption significantly lowers the risk of type 2 diabetes (T2D). Coffee contains thousands of compounds; however, the specific component(s) responsible for this reduced risk is unknown. Chlorogenic acids (CGA) found in brewed coffee inhibit intestinal glucose uptake in vitro. The objective of this study was to elucidate the mechanisms by which CGA acts to mediate blood glucose response in vivo. Conscious, unrestrained, male Sprague-Dawley rats were chronically catheterized and gavage-fed a standardized meal (59% carbohydrate, 25% fat, 12% protein), administered with or without CGA (120 mg·kg(-1)), in a randomized crossover design separated by a 3-day washout period. Acetaminophen was co-administered to assess the effects of CGA on gastric emptying. The incretins glucagon-like peptide-1 (GLP-1) and glucose-dependent insulinotropic peptide (GIP) were measured. GLP-1 response in the presence of glucose and CGA was further examined, using the human colon cell line NCI-H716. Total area under the curve (AUC) for blood glucose was significantly attenuated in rats fed CGA (p < 0.05). Despite this, no differences in plasma insulin or nonesterified fatty acids were observed, and gastric emptying was not altered. Plasma GIP response was blunted in rats fed CGA, with a lower peak concentration and AUC up to 180 min postprandially (p < 0.05). There were no changes in GLP-1 secretion in either the in vivo or in vitro study. In conclusion, CGA treatment resulted in beneficial effects on blood glucose response, with alterations seen in GIP concentrations. Given the widespread consumption and availability of coffee, CGA may be a viable prevention tool for T2D.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".