No influence of supplemental dietary calcium intake on the bioavailability of spinach carotenoids in humans
Bibliographic record
Abstract
Dietary carotenoid intake, especially from fruits and vegetables, has been associated with a reduced incidence of several chronic diseases. However, its bioavailability can vary, depending on the food matrix and host factors. Recently, it has been suggested that divalent minerals negatively impinge on carotenoid bioavailability by reducing bile-salt and non-esterified fatty-acid levels in the gut, which normally aid in emulsifying carotenoids. The aim of the present study was to investigate whether supplemental Ca would negatively influence carotenoid absorption in humans. A total of twenty-five healthy, non-obese men (age: 20-46 years, BMI<30 kg/m2) were recruited for this postprandial, randomised, crossover, double-blinded trial. Following a randomised block design, each participant received (after 2-week washout periods), on three occasions separated by 1 week, 270 g of spinach-based meals (8·61 (sd 1·08) mg carotenoids/100 g fresh weight), supplemented with 0, 500 or 1000 mg of Ca (as calcium carbonate), with each participant acting as his or her own control. Blood samples were collected at regular postprandial intervals for up to 10 h following test meal intake, and standardised lunches were served. TAG-rich lipoprotein fractions were separated and carotenoid concentrations determined. AUC for meals without supplemented Ca were 22·72 (sem 2·78) nmol×h/l (lutein), 0·19 (sem 3·90) nmol×h/l (β-carotene) and 2·80 (sem 1·75) nmol×h/l (β-cryptoxanthin). No significant influence of supplementation with either 500 or 1000 mg of supplemental Ca was found. In conclusion, Ca - the most abundant divalent mineral in the diet - given at high but physiological concentrations, does not appear to have repercussions on the bioavailability of carotenoids from a spinach-based meal.
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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) | 0.001 | 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 | 0.001 | 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".