Acute Effects of White Button and Shiitake Mushroom Powder Supplementation on Postprandial Lipemia and Glycemia Following a High-Fat Meal
Bibliographic record
Abstract
Background To determine the acute effects on postprandial lipemia and glycemia by supplementing a high-fat meal with either white button (WB) or shiitake (SH) mushroom powder. Methods Nine healthy participants (4-male, 5-female, 23.3±1.3 years, 17.8±6% body fat, 56.2±11.4kg fat free mass) consumed a control hamburger. At one-week intervals, after consumption of a control meal, participants consumed hamburgers in random order, supplemented with 14g of either WB or SH mushroom powder. Peripheral blood for lipids (triglycerides, high-density lipoprotein HDL, low-density lipoprotein LDL), and glucose was obtained at baseline (t=0 hours) and postprandially every two hours for six hours. Data were analyzed using linear mixed effects models. Results Lower LDL levels were observed for both SH and WB burgers compared to the control burger (p=0.0007) over the six-hour period. Mushroom powder content did not alter triglyceride, HDL, or glucose levels. Gender affected triglyceride and HDL levels over the treatment course. Triglyceride levels were higher in males (p=0.0084), and HDL levels were lower in females (p=0.0005). Triglyceride and glucose levels were higher, (p< 0.001 and p< 0.0001 respectively), during the postprandial time course (t=0, 2, 4, 6 hours). Conclusions Supplementing SH or WB mushrooms during a high-fat meal may lower serum LDL levels.
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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) | 0.001 | 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".