Dietary Phytic Acid, Dephytinization, and Phytase Supplementation Alter Trace Element Bioavailability—A Narrative Review of Human Interventions
Bibliographic record
Abstract
BACKGROUND: Phytic acid is abundant in plant-based diets and acts as a micronutrient inhibitor for humans and non-ruminant animals. Phytases are enzymes that break down phytic acid, releasing micronutrients and enhancing their bioavailability, particularly iron and zinc. Deficiencies in iron and zinc are significant public health problems, especially among populations with disease-associated malnutrition or those in developing countries consuming phytic acid-rich diets. This narrative review aimed to summarize findings from human intervention studies on the interactions between phytic acid, phytase, and micronutrient bioavailability. METHODS: An extensive PubMed search (1 January 1990 to 8 February 2024) was conducted using MeSH terms (phytic acid, phytase, IP6, "inositol hexaphosphate," micronutrient, magnesium, calcium, iron, zinc). Eligible studies included human intervention trials investigating the bioavailability of micronutrients following (a) phytase supplementation, (b) consumption of phytic acid-rich foods, or (c) consumption of dephytinized foods. In vitro, animal, cross-sectional, and non-English studies were excluded. RESULTS: 3055 articles were identified. After the title and full-text review, 40 articles were eligible. Another 2 were identified after cross-checking reference lists from included papers, resulting in 42 included articles. Most studies exploring the efficacy of exogenous phytase (9 of 11, 82%) or the efficacy of food dephytinization (11 of 14, 79%) demonstrated augmented iron and zinc bioavailability. Most phytic acid-rich food-feeding studies (13 of 17, 77%) showed compromised iron and zinc bioavailability. CONCLUSIONS: Strong evidence supports decreased iron and zinc bioavailability in phytic acid-rich diets and significant improvements with phytase interventions. Studies of longer periods and within larger populations are needed.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.004 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".