Effects of Mayonnaise on Postprandial Serum Lutein/Zeaxanthin and β-Carotene Concentrations in Humans
Bibliographic record
Abstract
To clarify the effects of different physical forms of oil on postprandial serum lutein/zeaxanthin and beta-carotene concentrations, we performed a vegetable meal loading test. Eighteen healthy subjects participated in the test, which consisted of broccoli as a control (CON) meal, broccoli with oil (OIL), and broccoli with mayonnaise (MS), consumed in random order. After collection of fasting blood samples, subjects consumed one of the three test meals. Fasting and postprandial changes in serum carotenoids were assessed 2, 4, and 6 h after ingestion of each test meal. Serum lutein/zeaxanthin and beta-carotene concentrations were measured. Although no significant change was noted after the CON meal, the serum lutein/zeaxanthin concentration was higher at 4 h after consumption of the OIL meal, and at 2, 4 and 6 h after consumption of the MS meal, as compared with the fasting state. Serum beta-carotene concentrations did not change after ingestion of either the CON or the OIL meal but were elevated 2, 4, and 6 h after MS ingestion as compared with the fasting state. The incremental areas under the curves (IAUCs) of serum lutein/zeaxanthin and beta-carotene concentrations were higher after the MS meal than after the CON meal. IAUCs after the OIL meal exhibited no statistically significant differences from the CON and MS meals. We suggest that mayonnaise contributes to increase serum lutein/zeaxanthin and beta-carotene concentrations when consumed with vegetables rich in these carotenoids.
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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) | 0.000 | 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".