Chronic coffee consumption alters gut microbiome: potential mechanism to explain the protective effects of coffee on type 2 diabetes?
Bibliographic record
Abstract
Cumulative epidemiological studies indicate a negative relationship between coffee consumption and type 2 diabetes (T2D). However, the specific mechanisms by which coffee reduces T2D is unknown but may involve the metabolism of polyphenols in coffee by the gut microbiome. To this end, we examined the impact of chronic coffee consumption on the gut microbiome in lean and diet‐induced obese rats. Male Sprague‐Dawley rats (n=40) were randomized into chow (CH, 12% kcal fat) or high fat (HF, 60% kcal fat) dietary groups. Each group was further divided into groups consuming water or coffee for 8wk, consuming food and fluid ad libitum (n=10/treatment). Coffee consumption was associated with lower weight gain and body fat percentage in both CH and HF (p<0.05). Oral glucose and insulin tolerance tests showed HF (p<0.05) but not coffee consumption to alter insulin action. Compared to CH, HF resulted in a reduction in Total Eubacteria levels in water but not coffee treatment (p<0.05). Lactobacillus was decreased in the HF‐water group but not HF‐coffee suggesting coffee had a protective effect. Alterations in the gut microbiome were supported by analysis of serum metabolites by proton nuclear magnetic resonance spectroscopy (1H NMR) and gas chromatography mass spectrometry (GC‐MS). This data indicate that gut microbiota‐host metabolic interactions may mediate the beneficial effects of coffee on the development of 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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".