Effect of green tea supplementation on blood pressure among overweight and obese adults
Bibliographic record
Abstract
BACKGROUND: Emerging randomized controlled trials (RCTs) investigating the effect of green tea or green tea extract (GTE) supplementation on blood pressure (BP) among overweight and obese adults reported inconsistent findings. OBJECTIVE: To conduct a systematic review and meta-analysis to clarify the efficacy of green tea or GTE on BP among overweight and obese adults. METHODS: Electronic databases, conference proceedings and gray literature were searched systematically to include parallel and cross-over RCTs examining the efficacy of green tea or GTE on BP compared with placebo. Data were meta-analyzed using a random-effects model, to compare the mean difference of the change in BP from baseline in the intervention and the placebo groups. RESULTS: Fourteen RCTs with 971 participants (47% women) were pooled for analysis. Green tea or GTE produced a significant effect on both SBP (mean difference -1.42 mmHg, 95% confidence interval -2.47 to -0.36, P = 0.008; I = 52%, P = 0.01 for heterogeneity) and DBP (mean difference -1.25 mmHg, 95% confidence interval -2.32 to -0.19, P = 0.02; I = 74%, P < 0.001 for heterogeneity), compared with placebo. The quality of evidence across studies was low. Similar results were found in subgroup and sensitivity analyses. CONCLUSION: Among overweight and obese adults, green tea or GTE supplementation is found to cause a small but significant reduction in BP. More high-quality RCTs with large sample sizes are needed to further confirm the efficacy on BP and make strong recommendations for green tea or GTE supplementation among the overweight and obese adults.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".