Green tea catechin: does it lower blood cholesterol?
Bibliographic record
Abstract
Abstract Tea is one of the most popular beverages in the world, especially in Asian societies. Green, oolong and black tea are three main types of tea products. Catechin is the principal polyphenol compound in all tea products including four major subgroup compounds, epigallocatechin gallate (EGCG), epicatehin (EC), epigallocatechin (EGC) and epicatechin gallate (ECG). Green tea contains highest amount of catechin compared to oolong and black tea since fermentation process can significantly reduce the amount of catechin in tea product, which polyphenol oxidase can convert catechins to theaflavins and thearubigins during fermentation process. Therefore, green tea catechin is becoming more and more attractive to nutritionists since it can provide several health benefits to human body. Cholesterol lowering effect is one of the health benefits been studied and proposed over decade. There are well documented evidences that suggested green tea catechin, in particular EGCG has the potential to lower blood cholesterol concentrations. Since the pool bioavailability and absorption ability of catechin, researchers believed that green tea catechin may significantly inhibit lipids absorption in intestine. Mechanisms are including inhibition of pancreatic lipase activity, lipids hydrolysis, and emulsification in intestine and precipitation of micellar cholesterol. In vitro studies, animal studies as well as most of human RCT, consistent results been observed that dietary intake of green tea beverages or extracts could significantly lower circulating cholesterol concentration, in particular lower LDL-C and total cholesterol level. However, in 2010, European Food Safety Authority (EFSA) denied the health claim of cholesterol lowering benefits of dietary intake of green tea or green tea catechins. In this presentation, current scientific evidences and EFSA judgment will be reviewed and discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".