Effect of Green Tea Flavonoid Supplementation on Features of Metabolic Syndrome (MeS)
Bibliographic record
Abstract
Green tea, rich in flavonoids, has been shown to possess cardiovascular health benefits. This is a preliminary report on a randomized controlled trial investigating whether green tea beverage or extract supplementation improved the cardiovascular risk profile associated with MeS. Age and sex‐matched trios of participants with MeS were randomly assigned to control (4 cups water/day), green tea beverage (4 cups/day), or green tea supplement (2 capsules & 4 cups water/day) group for 8 weeks. Fasting blood samples, physical measurements were taken at screening, 4 & 8 weeks. Blood samples were analyzed for lipid, glucose, catechin and nitric oxide (NOx) levels. Body weight decreased in green tea (‐1.9 Kg) vs control (+0.1 Kg). There is an increasing trend in HDL cholesterol in green tea (+1.0 mg/dl) vs control. A decrease in ox‐LDL was found in green tea group vs baseline. Interestingly, no consistent effects were seen on glucose levels. No significant difference in the plasma catechin and serum NOx concentrations among the three groups. However, green tea beverage group showed a decreasing trend in serum NOx levels (p<0.1) compared to baseline, indicating anti‐ inflammatory effect. Green tea beverage or supplements may aid weight loss, raise HDL levels and may help in reducing ox‐LDL and NOx levels in MeS subjects. Thus, chronic green tea consumption may promote cardiovascular health by reducing oxidative stress and inflammation in at risk subjects. Funded by CHES, OSU
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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 | 0.001 |
| 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".