Nutritional, inflammatory, and ecological correlates of maternal retinol allocation to breast milk in agro‐pastoral Ariaal communities of northern Kenya
Bibliographic record
Abstract
OBJECTIVES: Vitamin A (VA) is an essential micronutrient required for a range of biological functions throughout life. VA deficiency (VAD) claims an estimated 1 million preschool children's lives annually. Human milk is enriched with VA (retinol) from the maternal blood, which originates from the hepatic reserve and dietary intake. Secreting retinol into milk will benefit the nursing infant through breast milk, but retaining retinol is also important for the maternal health. Previous studies found that the public health intervention of high-dose VA supplementation to lactating mothers did not significantly lower child mortality. The World Health Organization (WHO) recently acknowledged that our understanding about the principle of VA allocation within the maternal system and the secretion into milk is too incomplete to devise an effective intervention. METHODS: We present a secondary analysis of data collected among lactating mothers in VAD endemic northern Kenya (n = 171), examining nutritional, inflammatory, and ecological factors that might associate with maternal retinol allocation. Regression models were applied using the outcome milk-retinol allocation index: milk retinol/(milk retinol + serum retinol). RESULTS: Ten percent of the sample was identified as VAD. The average milk retinol concentration was 0.1 μmo/L, grossly below what is considered minimally necessary for an infant (1 μmol/L). VAD mothers and mothers with inflammation did not seem to compromise their milk retinol even though their serum retinol was lower than non-VAD and noninflammation mothers. Breast milk fat concentration positively correlated with milk retinol but not with serum retinol. CONCLUSIONS: This exploratory study contributes toward an understanding of maternal retinol allocation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".