Can EGCG Reduce Abdominal Fat in Obese Subjects?
Bibliographic record
Abstract
OBJECTIVE: To evaluate metabolic effects of epigallocatechin gallate (EGCG) supplementation when combined with a program of regular aerobic exercise in overweight/obese post-menopausal women. METHODS: Thirty-eight overweight or obese postmenopausal women exercised at moderate intensity, viz. walking three times per week for 45 min at 75% of age-predicted maximum heart rate (HR), and took a 150 mg capsule of EGCG (Teavigo) or placebo (lactose) twice daily for 12 weeks. Blood parameters (lipids, glucose and insulin), blood pressure, heart rate, arterial function and anthropometry were assessed at 0, 6 and 12 wk. At wk 0 and 12, body composition was assessed by dual energy X-ray absorptiometry (DXA) and abdominal fat was assessed by DXA and computed tomography (CT). RESULTS: Waist circumference (p < 0.01), total body fat (p < 0.02), abdominal fat (by DXA) (p < 0.01) and intra abdominal adipose tissue (by CT) (p < 0.01) were reduced in both treatment groups, with no difference between placebo and Teavigo. Teavigo significantly decreased resting HR (p < 0.01) and reduced plasma glucose in subjects with impaired glucose tolerance (p < 0.05). CONCLUSIONS: Moderate consumption of EGCG can improve the health status of overweight individuals undergoing regular exercise by reducing HR and plasma glucose concentrations. Loss of body fat, however, may require a higher intake of EGCG, other catechins or addition of metabolic stimulants.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".