Bioavailability of magnesium from inorganic and organic compounds is similar in rats fed a high phytic acid diet
Bibliographic record
Abstract
A large section of the North American population is not meeting recommended intakes for magnesium (Mg). Supplementation and consumption of Mg-fortified foods are ways to increase intake. Currently, information on Mg bioavailability from different compounds and their efficacy in improving Mg status is scant. This study compared the relative ability of inorganic and organic Mg compounds to preserve the Mg status of rats when fed at amounts insufficient to retain optimal Mg status. Male Sprague-Dawley rats (n=12/diet group) were fed one of eight test diets supplemented with phytic acid (5 g/kg diet) and low levels of Mg (155 mg elemental Mg/kg diet) from Mg oxide, Mg sulphate, Mg chloride, Mg citrate, Mg gluconate, Mg orotate, Mg malate or ethylenediaminetetraacetic acid disodium Mg salt for five weeks. Rats were also fed three control diets that did not contain added phytic acid but were supplemented with 500 (NMgO, normal), 155 (LMgO, low) or 80 (DMgO, deficient) mg of Mg per kg diet as Mg oxide. Mg concentrations in femur, serum and urine showed a graded decrease in rats fed the control diets with lower Mg. Mg concentrations did not differ (P≥0.05) between rats fed the different test diets. Addition of phytic acid to the diet did not affect the Mg status of the rats. The results indicate that any differences in the Mg bioavailability of the compounds were small and physiologically irrelevant.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".