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Record W4392640816 · doi:10.3389/fnut.2024.1341777

Human milk microbiome: associations with maternal diet and infant growth

2024· article· en· W4392640816 on OpenAlexafffund
Tamara T. Ajeeb, Emmanuel González, Noel W. Solomons, Marieke Vossenaar, Kristine G. Koski

Bibliographic record

VenueFrontiers in Nutrition · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGut microbiota and health
Canadian institutionsMcGill Genome CentreMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsLactationUnderweightBreastfeedingMedicineBiologyPediatricsPregnancyBody mass indexInternal medicineOverweight

Abstract

fetched live from OpenAlex

Introduction: Ingestion of human milk (HM) is identified as a significant factor associated with early infant gut microbial colonization, which has been associated with infant health and development. Maternal diet has been associated with the HM microbiome (HMM). However, a few studies have explored the associations among maternal diet, HMM, and infant growth during the first 6 months of lactation. Methods: = 35). Recruited mothers had vaginally delivered singleton births, had no subclinical mastitis or antibiotic treatments, and breastfed their infants. Data collected at both stages of lactation included two 24-h recalls, milk samples, and infant growth status indicators: head-circumference-for-age-z-score (HCAZ), length-for-age-z-score (LAZ), and weight-for-age-z-score (WAZ). Infants were divided into subgroups: normal weight (WAZ ≥ -1SD) and mildly underweight (WAZ < -1SD), non-stunted (LAZ ≥ -1.5SD) and mildly stunted (LAZ < -1.5SD), and normal head-circumference (HCAZ ≥ -1SD) and smaller head-circumference (HCAZ < -1SD). HMM was identified using 16S rRNA gene sequencing; amplicon analysis was performed with the high-resolution ANCHOR pipeline, and DESeq2 identified the differentially abundant (DA) HMM at the species-level between infant growth groups (FDR < 0.05) in both early and late lactation. Results: Using both cluster and univariate analyses, we identified (a) positive correlations between infant growth clusters and maternal dietary clusters, (b) both positive and negative associations among maternal macronutrient and micronutrient intakes with the HMM at the species level and (c) distinct correlations between HMM DA taxa with maternal nutrient intakes and infant z-scores that differed between breast-fed infants experiencing growth faltering and normal growth in early and late lactation. Conclusion: Collectively, these findings provide important evidence of the potential influence of maternal diet on the early-life growth of breastfed infants via modulation of the HMM.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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.

Opus teacher head0.004
GPT teacher head0.233
Teacher spread0.229 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2024
Admission routes2
Has abstractyes

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