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Record W3201649000 · doi:10.1093/jn/nxab270

Oligosaccharides and Microbiota in Human Milk Are Interrelated at 3 Months Postpartum in a Cohort of Women with a High Prevalence of Gestational Impaired Glucose Tolerance

2021· article· en· W3201649000 on OpenAlexafffund
Lauren LeMay‐Nedjelski, Chloe Yonemitsu, Michelle R. Asbury, James Butcher, Sylvia H. Ley, Anthony J. Hanley, Alex Kiss, Sharon Unger, Julia K. Copeland, Pauline W. Wang, Alain Stintzi, Lars Bode, Deborah L. O’Connor

Bibliographic record

VenueJournal of Nutrition · 2021
Typearticle
Languageen
FieldNursing
TopicInfant Nutrition and Health
Canadian institutionsLunenfeld-Tanenbaum Research InstituteSinai Health SystemSunnybrook Health Science CentreUniversity of OttawaHospital for Sick ChildrenUniversity of Toronto
FundersNational Institute of General Medical SciencesCanadian Institutes of Health ResearchCanadian Diabetes Association
KeywordsGestational diabetesBreast milkImpaired glucose toleranceCohortBacteriaBreastfeedingPregnancyBiologyMedicineFood sciencePhysiologyEndocrinologyInternal medicineGestationDiabetes mellitusType 2 diabetesBiochemistryPediatricsGenetics

Abstract

fetched live from OpenAlex

BACKGROUND: Human milk is a rich source of human milk oligosaccharides (HMOs) and bacteria. It is unclear how these components interact within the breast microenvironment. OBJECTIVES: The objectives were first, to investigate the association between maternal characteristics and HMOs, and second, to assess the association between HMOs and microbial community composition and predicted function in milk from women with high rates of gestational glucose intolerance. METHODS: This was an exploratory analysis of a previously completed prospective cohort study (NCT01405547) where milk samples (n = 107) were collected at 3 mo postpartum. Milk microbiota composition was analyzed by V4-16S ribosomal RNA gene sequencing and HMOs by rapid high-throughput HPLC. Data were stratified and analyzed by maternal secretor status phenotype and associations between HMOs and microbiota were determined using linear regression models (ɑ-diversity), Adonis (B-diversity), Poisson regression models (differential abundance), and general linear models (predicted microbial function). RESULTS: Prepregnancy BMI, race, and frequency of direct breastfeeding, but not gestational glucose intolerance, were found to be significantly associated with a number of HMOs among secretors and non-secretors. Fucosyllacto-N-hexaose was negatively associated with microbial richness (Chao1) among secretors [B-estimate (SE): -9.3 × 102 (3.4 × 102); P = 0.0082] and difucosyllacto-N-hexaose was negatively associated with microbiota diversity (Shannon index) [-1.7 (0.78); P = 0.029] among secretors. Lacto-N-neotetraose (LNnT) was associated with both microbial B-diversity (weighted UniFrac R2 = 0.040, P = 0.036) and KEGG ortholog B-diversity (Bray-Curtis R2 = 0.039, P = 0.043) in secretors. Additionally, difucosyllactose in secretors and disialyllacto-N-hexaose and LNnT in non-secretors were associated with enrichment of predicted microbial genes encoding for metabolism- and infection-related pathways (P-false discovery rate < 0.1). CONCLUSIONS: HMOs are associated with the microbial composition and predicted microbial functions in human milk at 3 mo postpartum. Further research is needed to investigate the role these relations play in maternal and infant health.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.255
Teacher spread0.246 · 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".

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Citations18
Published2021
Admission routes2
Has abstractno

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