Effects of encapsulated green tea and Guarana extracts containing a mixture of epigallocatechin-3-gallate and caffeine on 24 h energy expenditure and fat oxidation in men
Bibliographic record
Abstract
It has been reported that green tea has a thermogenic effect, due to its caffeine content and probably also to the catechin, epigallocatechin-3-gallate (EGCG). The main aim of the present study was to compare the effect of a mixture of green tea and Guarana extracts containing a fixed dose of caffeine and variable doses of EGCG on 24 h energy expenditure and fat oxidation. Fourteen subjects took part to this randomized, placebo-controlled, double-blind, cross-over study. Each subject was tested five times in a metabolic chamber to measure 24 h energy expenditure, substrate oxidation and blood pressure. During each stay, the subjects ingested a capsule of placebo or capsules containing 200 mg caffeine and a variable dose of EGCG (90, 200, 300 or 400 mg) three times daily, 30 min before standardized meals. Twenty-four hour energy expenditure increased significantly by about 750 kJ with all EGCG-caffeine mixtures compared with placebo. No effect of the EGCG-caffeine mixture was observed for lipid oxidation. Systolic and diastolic blood pressure increased by about 7 and 5 mmHg, respectively, with the EGCG-caffeine mixtures compared with placebo. This increase was significant only for 24 h diastolic blood pressure. The main finding of the study was the increase in 24 h energy expenditure with the EGCG-caffeine mixtures. However, this increase was similar with all doses of EGCG in the mixtures.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".