Human milk sIgA antibody in relation to maternal nutrition and infant vulnerability in northern Kenya
Bibliographic record
Abstract
BACKGROUND: The maternal buffering hypothesis posits that human lactation biology can buffer milk against the mild-to-moderate malnutrition that occurred routinely in evolutionary history through the mobilization of maternal body reserves. This perspective may provide insights for understanding human milk immune content variation, such as milk sIgA, which protects infants' intestines from microbial colonization and prevents diarrheal disease. OBJECTIVE: To investigate how maternal delivery of sIgA to milk may vary in a way that can buffer milk against maternal malnutrition, while taking into consideration infants' varying needs for immune protection across age or by sex. METHODOLOGY: A cross-sectional study analyzed archived milk specimens from breastfeeding mothers in Ariaal communities of northern Kenya surveyed during the 2006 Horn-of-Africa drought. Multiple regression models for ln-transformed sIgA were constructed using maternal nutrition, infant age/sex and their interactions as predictors. Maternal nutrition variables included iron-deficiency anemia (IDA), vitamin A deficiency (VAD) and mid-upper arm circumference (MUAC). Infant vulnerability was considered high in young age and/or male sex. RESULTS AND IMPLICATIONS: = 202). Significant interactions were observed between infant age and maternal VAD and between infant sex and maternal MUAC, such that milk sIgA content was low for younger infants particularly among VAD mothers, while among mothers with low MUAC, sIgA was lower for male infants. Results imply that mothers' ability to deliver/buffer milk sIgA may be lowered when nutritional stress is combined with high infant vulnerability to infection. LAY SUMMARY: Human milk sIgA antibody content was low for younger infants among vitamin A deficient mothers. Among mothers with small arm-circumference, milk sIgA was lower for sons. Double burden of raising young or male infants with high needs for immune protection and being malnourished, might lower maternal sIgA delivery to milk.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".