Low Phytate Peas (Pisum sativum L.) Improve Iron Status, Gut Microbiome, and Brush Border Membrane Functionality In Vivo (Gallus gallus)
Bibliographic record
Abstract
The inclusion of pulses in traditional wheat-based food products is increasing as the food industry and consumers are recognizing the nutritional benefits due to the high protein, antioxidant activity, and good source of dietary fiber of pulses. Iron deficiency is a significant global health challenge, affecting approximately 30% of the world’s population. Dietary iron deficiency is the foremost cause of anemia, a condition that harms cognitive development and increases maternal and infant mortality. This study intended to demonstrate the potential efficacy of low-phytate biofortified pea varieties on dietary iron (Fe) bioavailability, as well as on intestinal microbiome, energetic status, and brush border membrane (BBM) functionality in vivo (Gallus gallus). We hypothesized that the low-phytate biofortified peas would significantly improve Fe bioavailability, BBM functionality, and the prevalence of beneficial bacterial populations. A six-week efficacy feeding (n = 12) was conducted to compare four low-phytate biofortified pea diets with control pea diet (CDC Bronco), as well as a no-pea diet. During the feeding trial, hemoglobin (Hb), body-Hb Fe, feed intake, and body weight were monitored. Upon the completion of the study, hepatic Fe and ferritin, pectoral glycogen, duodenal gene expression, and cecum bacterial population analyses were conducted. The results indicated that certain low-phytate pea varieties provided greater Fe bioavailability and moderately improved Fe status, while they also had significant effects on gut microbiota and duodenal brush border membrane functionality. Our findings provide further evidence that the low-phytate pea varieties appear to improve Fe physiological status and gut microbiota in vivo, and they highlight the likelihood that this strategy can further improve the efficacy and safety of the crop biofortification and mineral bioavailability approach.
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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 | 0.000 |
| 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 | 0.001 |
| 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".