Short-term green tea extract supplementation attenuates the postprandial blood glucose and insulin response following exercise in overweight men
Bibliographic record
Abstract
Green tea extract (GTE) ingestion improves glucose homeostasis in healthy and diabetic humans, but the interactive effect of GTE and exercise is unknown. The present study examined the effect of short-term GTE supplementation on the glycemic response to an oral glucose load at rest and following an acute bout of exercise, as well as substrate oxidation during exercise. Eleven sedentary, overweight men with fasting plasma glucose (FPG) ≥5.6 mmol·L −1 (age, 34 ± 13 years; body mass index = 32 ± 5 kg·m −2 ; FPG = 6.8 ± 1.0; mean ± SD) ingested GTE (3× per day, 1050 mg·day –1 total) or placebo (PLA) for 7 days in a double-blind, crossover design. The effects of a 75-g glucose drink were assessed on 4 occasions during both GTE and PLA treatments: On days 1 and 5 at rest, and again following an acute bout of exercise on days 3 and 8. The glycemic response was assessed via an indwelling continuous glucose monitor (CGM) and venous blood draws. At rest, 1-h CGM glucose area under the curve was not different (P > 0.05), but the postexercise response was lower after GTE versus PLA (330 ± 53 and 393 ± 65 mmol·L −1 ·min −1 , main effect of treatment, P < 0.05). The 1-h postprandial peaks in venous blood glucose (8.6 ± 1.6 and 9.8 ± 2.2 mmol·L −1 ) and insulin (96 ± 59 and 124 ± 68 μIU·ml −1 ) were also lower postexercise with GTE versus PLA (time × treatment interactions, P < 0.05). In conclusion, short-term GTE supplementation did not affect postprandial glucose at rest; however, GTE was associated with an attenuated glycemic response following a postexercise oral glucose load. These data suggest that GTE might alter skeletal muscle glucose uptake in humans.
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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 | 0.000 |
| 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 | 0.001 | 0.001 |
| 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".