Human milk immune factors, maternal nutritional status, and infant sex: The <scp>INSPIRE</scp> study
Bibliographic record
Abstract
OBJECTIVES: Breastfeeding is an energetically costly and intense form of human parental investment, providing sole-source nutrition in early infancy and bioactive components, including immune factors. Given the energetic cost of lactation, milk factors may be subject to tradeoffs, and variation in concentrations have been explored utilizing the Trivers-Willard hypothesis. As human milk immune factors are critical to developing immune system and protect infants against pathogens, we tested whether concentrations of milk immune factors (IgA, IgM, IgG, EGF, TGFβ2, and IL-10) vary in response to infant sex and maternal condition (proxied by maternal diet diversity [DD] and body mass index [BMI]) as posited in the Trivers-Willard hypothesis and consider the application of the hypothesis to milk composition. METHODS: We analyzed concentrations of immune factors in 358 milk samples collected from women residing in 10 international sites using linear mixed-effects models to test for an interaction between maternal condition, including population as a random effect and infant age and maternal age as fixed effects. RESULTS: IgG concentrations were significantly lower in milk produced by women consuming diets with low diversity with male infants than those with female infants. No other significant associations were identified. CONCLUSIONS: IgG concentrations were related to infant sex and maternal diet diversity, providing minimal support for the hypothesis. Given the lack of associations across other select immune factors, results suggest that the Trivers-Willard hypothesis may not be broadly applied to human milk immune factors as a measure of maternal investment, which are likely buffered against perturbations in maternal condition.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".