Biomarkers of Fruit and Vegetable Intake in Men and Women
Bibliographic record
Abstract
A high fruit and vegetable (FAV) intake is associated with a lower prevalence of chronic diseases but identifying the optimal number of daily FAV servings (DFAVS) needed to reduce chronic disease risk is difficult due to the biases of common self‐report dietary assessment tools. In this regard, plasma carotenoid levels have been suggested to be more accurate markers of FAV consumption than self‐reported intakes. The aim of the present study was to examine the associations between DFAVS and plasma carotenoid levels in a group of 155 men and 110 women enrolled in 6 full‐feeding dietary interventions we previously conducted. We measured and compared fasting plasma carotenoids (α‐carotene, β‐carotene, β‐cryptoxanthin, lutein, lycopene, zeaxanthin) and retinol levels with regards to the number of DFAVS provided and consumed by participants. Plasma β‐cryptoxanthin, lutein and zeaxanthin levels were positively associated with consumed DFAVS (p<0.005). However, consumed DFAVS were negatively associated with plasma α‐carotene (p<0.0005) and lycopene (p<0.0001) levels while no association was noted with plasma β‐carotene and retinol. When men and women were analyzed separately, we found that for any given number of DFAVS that were consumed, women had higher circulating lutein levels compared to men (p=0.0110). Significant sex*DFAVS (p=0.0001) and sex*dietary β‐cryptoxanthin (p=0.0001) interactions were also noted favoring higher plasma β‐cryptoxanthin levels in women than in men. Results for these feeding trials reinforce the use of plasma β‐cryptoxanthin and lutein levels as biomarkers of FAV consumption but suggest the existence of potential sex differences influencing circulating β‐cryptoxanthin and lutein levels following FAV consumption.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".