Coffee, glucose homeostasis, and insulin resistance: physiological mechanisms and mediators
Bibliographic record
Abstract
Epidemiological studies show coffee consumption to be correlated to large risk reductions in the prevalence of type 2 diabetes (T2D). Such correlations are seen with decaffeinated and caffeinated coffee, and occur regardless of gender, method of brewing, or geography. They also exist despite clear evidence showing that caffeine causes acute postprandial hyperglycemia and lower whole-body insulin sensitivity. As the beneficial effects of coffee consumption exist for both decaffeinated and caffeinated coffee, a component of coffee other than caffeine must be responsible. This review examines the specific coffee compounds responsible for coffee's effects on T2D, and their potential physiological mechanisms of action. Being plant-derived, coffee contains many beneficial compounds found in fruits and vegetables, including antioxidants. In fact, coffee is the largest source of dietary antioxidants in industrialized nations. When green coffee is roasted at high temperatures, Maillard reactions create a number of unique compounds. Roasting causes a portion of the antioxidant, chlorogenic acid, to be transformed into quinides, compounds known to alter blood glucose levels. Coffee consumption may also mediate levels of gut peptides (glucose-dependent insulinotropic polypeptide and glucagon-like peptide-1), hormones intimately involved in the regulation of satiety and insulin secretion. Finally, coffee may have prebiotic-like properties, altering gut flora and ultimately digestion. In summary, it is evident that a better understanding of the role of coffee in the development and prevention of T2D has the potential to uncover novel therapeutic targets and nutraceutical formulations for the disease.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".