The effect of lutein- and zeaxanthin-rich foods <i>v.</i> supplements on macular pigment level and serological markers of endothelial activation, inflammation and oxidation: pilot studies in healthy volunteers
Bibliographic record
Abstract
The aim of the present study was to compare the effect of lutein- and zeaxanthin-rich foods and supplements on macular pigment level (MPL) and serological markers of endothelial activation, inflammation and oxidation in healthy volunteers. We conducted two 8-week intervention studies. Study 1 (n 52) subjects were randomised to receive either carrot juice (a carotene-rich food) or spinach powder (a lutein- and zeaxanthin-rich food) for 8 weeks. Study 2 subjects (n 75) received supplements containing lutein and zeaxanthin, β-carotene, or placebo for 8 weeks in a randomised, double-blind, placebo-controlled trial. MPL, serum concentrations of lipid-soluble antioxidants, inter-cellular adhesion molecule 1, vascular cell adhesion molecule 1, C-reactive protein and F2-isoprostane levels were assessed at baseline and post-intervention in both studies. In these intervention studies, no effects on MPL or markers of endothelial activation, inflammation or oxidation were observed. However, the change in serum lutein and zeaxanthin was associated or tended to be associated with the change in MPL in those receiving lutein- and zeaxanthin-rich foods (lutein r 0.40, P = 0.05; zeaxanthin r 0.30, P = 0.14) or the lutein and zeaxanthin supplement (lutein r 0.43, P = 0.03; zeaxanthin r 0.22, P = 0.28). In both studies, the change in MPL was associated with baseline MPL (food study r - 0.54, P < 0.001; supplement study r - 0.40, P < 0.001). We conclude that this 8-week supplementation with lutein and zeaxanthin, whether as foods or as supplements, had no significant effect on MPL or serological markers of endothelial activation, inflammation and oxidation in healthy volunteers, but may improve MPL in the highest serum responders and in those with initially low MPL.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".