Correlates of the difference in plasma carotenoid concentrations between men and women
Bibliographic record
Abstract
Health professionals consider the evaluation of eating habits to be challenging, given the potential biases of dietary questionnaires based on self-reported data. Circulating carotenoid concentrations are reliable biomarkers of dietary carotenoid intake and could be useful in the validation of dietary assessment tools. However, there is a sex difference in circulating carotenoids, with women displaying higher concentrations compared with men independent of intake. The aim of the present study was to identify the correlates of plasma carotenoid concentrations among men (n 155) and women (n 110) enrolled in six fully controlled dietary interventions with varying dietary carotenoid intakes. We looked at the associations of post-intervention fasting plasma carotenoid concentrations (α-carotene, β-carotene, β-cryptoxanthin, lutein, lycopene and zeaxanthin) with physical and metabolic characteristics. We found that increased body weight (r -0·47, P<0·0001) and waist circumference (r -0·46, P<0·0001) were associated with lower plasma total carotenoid concentrations, while elevated plasma LDL-cholesterol (r 0·49, P<0·0001) and HDL-cholesterol (r 0·50, P<0·0001) concentrations were correlated with higher total carotenoids in plasma. Women had significantly higher plasma total carotenoid concentrations compared with men, despite significantly lower dietary carotenoid intake. Adjustment of circulating carotenoid concentrations for plasma HDL-cholesterol eliminated sex difference in plasma carotenoid concentrations. Our results suggest that physical characteristics as well as plasma lipids are associated with circulating carotenoid concentrations and that these variables should be taken into account when using plasma carotenoids as biomarkers for food intake in men and women.
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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) | 0.000 | 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) | 0.002 | 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".