RRR-α-Tocopherol Is the Predominant Stereoisomer of α-Tocopherol in Human Milk
Bibliographic record
Abstract
The naturally occurring α-tocopherol (α-T) stereoisomer, RRR-α-tocopherol (RRR-α-T), is known to be more bioactive than all-rac-α-tocopherol (all-rac-α-T), a synthetic racemic mixture of 8 stereoisomers. There is widespread use of all-rac-α-T in maternal supplements. The aim of the study was to thoroughly describe the α-T stereoisomer profile of human milk. We measured the α-T stereoisomer profile in milk from 2 cohorts of women: a cohort of 121 women who provided milk on days 30 and 60 of lactation (study 1) and a separate cohort of 51 women who provided milk on days 10, 21, 71, and 120 of lactation (study 2). RRR-α-T was the predominant stereoisomer (P < 0.0001) in all samples in both studies despite a large intrasubject range in total α-T (0.7–22 μg/mL). On average, RRR-α-T comprised 73–76% of total α-T, but average values for the synthetic stereoisomers were RRS, 8–14%; RSR, 6–8%; RSS, 5–6%; and the sum of 2S stereoisomers (Σ2S), 3–5%. Despite the predominance of RRR-α-T, the sum of the synthetic stereoisomers comprised as much as 48% of total α-T. We calculated the ratio of RRR to the sum of the synthetic 2R (RRS + RSR + RSS) stereoisomers (s2R) to assess the degree to which RRR is favored in milk. Consistent with discrimination among 2R stereoisomers in mammary tissue, RRR/s2R values ranged from 2.8 to 3.6, as opposed to the expected ratio of 0.33 if there was no discrimination. However, the RRR to s2R ratio did not correlate with milk α-T concentration, but both components of the ratio did. RRR-α-T is the predominant stereoisomer in human milk, concentrations of synthetic 2R stereoisomers were notable, and the relation between milk total α-T and stereoisomer profile is complex. Due to the wide range found in milk α-T stereoisomer profile, investigation into its impact on α-T status and functional outcomes in breastfed infants is warranted.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".