Human milk fatty acid composition from nine countries varies most in DHA
Bibliographic record
Abstract
Many published studies of breast milk FA composition are limited to populations from one or two countries. We aimed to examine the degree to which FA compositions vary across a number of diverse populations. Because diet and maternal adipose stores influence breast milk FA composition, differences in FA composition between groups most likely reflect habitual dietary differences. Approximately 50 breast milk samples (full breast expression) were collected from women in Australia, Canada, Chile, China, Japan, Mexico, Philippines, the United Kingdom, and the United States. The proportion of saturated FA was relatively constant among countries, with the exception of the Philippines, where levels of lauric and myristic acids were elevated (means greater than two times the mean of most other countries). Monounsaturated FA also varied little, with the exception of low levels of oleic acid in the Philippines and high levels of erucic acid in China. Although arachidonic acid (C20:4n-6) levels were similar among all countries (means ranging from 0.36 wt % to 0.49 wt %), mean DHA (C22:6n-3) levels ranged from 0.17 to 0.99 wt %, with the highest levels in Japanese milk and the lowest levels in Canadian and U.S. samples. The results of this study demonstrate that the proportion of saturated and monounsaturated FA are relatively constant across a large number of countries, whereas the level of some of the PUFA, especially DHA, are highly variable.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".