Bioavailability of folic acid and L‐5‐methyltetrahydrofolic acid in fortified bread: a randomized placebo‐controlled trial
Bibliographic record
Abstract
Folic acid fortification has been introduced in several countries to reduce the incidence of neural tube defects. However, it could mask the hematologic signs of vitamin B 12 deficiency. L‐5‐methyltetrahydrofolic acid (MTHF), which is unlikely to mask vitamin B 12 deficiency, may be a safer fortificant, but is not stable in most food matrices. Microencapsulation of MTHF with antioxidant sodium ascorbate is an effective means of preventing loss during baking and storage. The aim of the study was to investigate the bioavailability of microencapsulated MTHF in bread in a 16‐wk, double‐blind, randomized placebo trial. Healthy volunteers 18–45 y (n=45) were randomly assigned to bread containing MTHF (452 μg), folic acid (400 μg) or placebo. Fasting blood was analyzed for red cell and plasma folate concentrations at baseline, 8, and 16 wks. At 16 wk, after adjustment for baseline concentrations, mean (95% CI) red cell folate was 572 (341, 804) and 428 (200, 656) nmol/L higher in the MTHF ( P <0.001) and folic acid ( P =0.002) groups, respectively, than in the placebo group. Mean plasma folate was 28 (15, 41) and 26 (13, 39) nmol/L higher in the MTHF ( P <0.001) and folic acid ( P =0.01) groups, respectively, than in the placebo group. In conclusion, bread fortified with MTHF was at least as effective as folic acid in raising red cell and plasma folate concentrations in this population. (Supported by the Advanced Food Materials Network)
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".