Iron Bioavailability from Multiple Biofortified Foods Using an In Vitro Digestion, Caco-2 Assay for Optimizing a Cyclical Menu for a Randomized Efficacy Trial
Bibliographic record
Abstract
Inadequate nutritional status contributes to substantial losses in human health and productivity globally. A multiple biofortified food crop trial targeting iron, zinc, and vitamin A deficiencies among young children and their breastfeeding mothers is being conducted in India. We sought to determine the relative iron bioavailability from biofortified and conventional crops and crop combinations representative of a cyclical menu using crops targeted for inclusion in the feeding trial. Crops were procured from India, cooked, freeze-dried, and analyzed with an established in vitro digestion/Caco-2 iron bioavailability assay using a fixed sample weight. Crop proportions representative of meals planned for the human study were determined and combined such that samples included either all biofortified or all control crops. Crops were analyzed as single crops (n = 4) or crop combinations (n = 7) by variety (biofortified or control) in triplicate. The primary outcome was iron uptake measured by Caco-2 ferritin production normalized to total Caco-2 protein (nanograms of ferritin/milligrams of cell protein) analyzed for effects of crop variety and crop proportion using generalized linear models. Biofortified pearl millet alone demonstrated higher iron uptake than conventional varieties (5.01 ± 1.66 vs. 2.17 ± 0.96; P = 0.036). Addition of sweet potato or sweet potato + pulse improved iron uptake for all proportions tested in control varieties and select proportions for biofortified varieties (P ≤ 0.05). Two multiple crop combinations demonstrated modestly higher iron uptake from biofortified crops. Optimizing total iron delivery should consider matrix effects, processing, and promoters/inhibitors of iron absorption in addition to total iron concentration. Future directions include evaluating recipes as prepared for consumption and comparison against human iron bioavailability studies.
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".