Does green tea extract enhance the anti‐inflammatory effects of exercise on fat loss?
Bibliographic record
Abstract
AIMS: Green tea extract (GTE) can exert antiobesity and anti-inflammatory effects. Our study determined whether the benefits of GTE are summative with exercise-induced changes in anthropometric indices, and the levels of inflammatory cytokines, adiponectin and irisin in inactive overweight women. METHODS: Thirty overweight female participants were randomized to 3 groups: endurance training + placebo (ET + P); endurance training + GTE (ET + GTE); and Control (no exercise) + placebo (Control, n = 10). The exercise intervention consisted of an 8-week endurance-training programme of 3 sessions per week (aerobics, aerobic circuit training, and fast walking or jogging at a moderate intensity of 40-59% of the heart rate reserve). The dose of GTE used was 500 mg/day in the form of a green tea capsule. RESULTS: Body weight, body mass index, waist to hip ratio and body fat percentage were decreased in both ET + P and ET + GTE interventions (P < .001 for both interventions). The reduction of anthropometric values in the ET + GTE group was significantly higher than ET + P interventions (P < .001). Both exercise interventions also significantly (P < .001) increased adiponectin (ET + GTE = 5.28 mg/mL [95% confidence interval {CI}, 4.48 to 6.08] and ET + P = 3.34 mg/mL [95% CI, 2.76 to 3.92]) and decreased high-sensitivity C-reactive protein (hs-CRP; ET + GTE = -0.95 mg/L [95% CI, -1.15 to -0.75] and ET + P = -0.35 mg/L [95% CI, -0.46 to -0.24]). Changes in adiponectin and hs-CRP were greater (P < .05) in ET + GTE compared to ET + P. There were no significant differences in irisin, interleukin-6 or tumour necrosis factor-α between the 3 groups (P > .05). CONCLUSIONS: GTE improves exercise-induced body composition by further decreasing exercise-induced changes in weight, body mass index, waist to hip ratio and body fat percentage. The combination of GTE and exercise also produced greater changes in anti-inflammatory (increases in adiponectin) and metabolic (decreases in hs-CRP) markers than exercise alone.
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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) | 0.000 | 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) | 0.003 | 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".