Bifidobacterium longum and microbiome maturation modify a nutrient intervention for stunting in Zimbabwean infants
Bibliographic record
Abstract
BACKGROUND: Small-quantity lipid-based nutrient supplements (SQ-LNS), which has been widely tested to reduce child stunting, has largely modest effects to date, but the mechanisms underlying these modest effects are unclear. Child stunting is a longstanding indicator of chronic undernutrition and it remains a prevalent public health problem. The infant gut microbiome may be a key contributor to stunting; and mother and infant fucosyltransferase (FUT) phenotypes are important determinants of infant microbiome composition. METHODS: We investigated whether mother-infant FUT status (n = 792) and infant gut microbiome composition (n = 354 fecal specimens from 172 infants) modified the impact of an infant and young child feeding (IYCF) intervention, that included SQ-LNS, on stunting at age 18 months in secondary analysis of a randomized trial in rural Zimbabwe. FINDINGS: We found that the impact of the IYCF intervention on stunting was modified by: (i) mother-infant FUT2+/FUT3- phenotype (difference-in-differences -32.6% [95% CI: -55.3%, -9.9%]); (ii) changes in species composition that reflected microbiome maturation (difference-in-differences -68.1% [95% CI: -99.0%, -28.5%); and (iii) greater relative abundance of B. longum (differences-in-differences 49.1% [95% CI: 26.6%, 73.6%]). The dominant strains of B. longum when the intervention started were most similar to the proficient milk oligosaccharide utilizer subspecies infantis, which decreased with infant age and differed by mother-infant FUT2+/FUT3- phenotypes. INTERPRETATION: These findings indicate that a persistently "younger" microbiome at initiation of the intervention reduced its benefits on stunting in areas with a high prevalence of growth restriction. FUNDING: Bill and Melinda Gates Foundation, UK DFID/Aid, Wellcome Trust, Swiss Agency for Development and Cooperation, US National Institutes of Health, UNICEF, and Nutricia Research Foundation.
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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 | 0.001 | 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".