Effects of green tea extracts on non-shivering thermogenesis during mild cold exposure in young men
Bibliographic record
Abstract
The effects of epigallocatechin-3-gallate (EGCG) and caffeine on non-shivering thermogenesis (NST) during cold exposure is unknown. The purpose of the present study was to quantify the effects of co-ingesting EGCG and caffeine on the thermogenic responses of a 3 h cold exposure. A total of eight healthy males were exposed to mild cold, using a liquid-conditioned suit perfused with 158C water, on two occasions and consumed a placebo or an extract of 1600 mg of EGCG and 600 mg of caffeine (Green tea). Thermic, metabolic and electromyographic measurements were monitored at baseline and during the cold exposure. Results showed that the AUC of shivering intensity over the cold exposure period was reduced by approximately 20% in the Green tea (266 (SEM 6)% maximal voluntary contraction (MVC) x min) compared with the Placebo (332 (SEM 69)%MVC x min) (P=0·01) treatments. In contrast, the total AUC for energy expenditure (EE) was approximately 10% higher in the Green tea (23·5 (SEM 1·4) kJ/kg x 180 min) compared with the Placebo (327 (SEM 74) kJ/kg 180 min) (P=0·007) treatments. The decrease in shivering activity combined with an increase in EE, following the ingestion of EGCG and caffeine during the cold exposure, indicates that NST pathways can be significantly stimulated in adult human subjects. The present study provides an experimental approach for human investigations into the potential role of diet and bioactive food ingredients in modulating NST during cold exposure. Stimulating NST pathways in such a manner may also provide important targets in the search of targets for the management of obesity and diabetes.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".