Effect of black tea consumption on brachial artery flow-mediated dilation and ischaemia–reperfusion in humans
Bibliographic record
Abstract
Tea consumption is associated with reduced cardiovascular risk. Previous studies found that tea flavonoids work through direct effects on the vasculature, leading to dose-dependent improvements in endothelial function. Cardioprotective effects of regular tea consumption may relate to the prevention of endothelial ischaemia-reperfusion (IR) injury. Therefore, we examined the effect of black tea consumption on endothelial function and the ability of tea to prevent IR injury. In a randomized, crossover study, 20 healthy subjects underwent 7 days of tea consumption (3 cups per day) or abstinence from tea. We examined brachial artery (BA) endothelial function via flow-mediated dilation (FMD), using high resolution echo-Doppler, before and 90 min after tea or hot water consumption. Subsequently, we followed a 20-min ischaemia and 20-min reperfusion protocol of the BA after which we measured FMD to examine the potential of tea consumption to protect against IR injury. Tea consumption resulted in an immediate increase in FMD% (pre-consumption: 5.8 ± 2.5; post-consumption: 7.2 ± 3.2; p < 0.01), whilst no such change occurred after ingestion of hot water. The IR protocol resulted in a significant decrease in FMD (p < 0.005), which was also present after tea consumption (p < 0.001). This decline was accompanied by an increase in the post-IR baseline diameter. In conclusion, these data indicate that tea ingestion improves BA FMD. However, the impact of the IR protocol on FMD was not influenced by tea consumption. Therefore, the cardioprotective association of tea ingestion relates to a direct effect of tea on the endothelium in humans in vivo.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".