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Record W4238318726 · doi:10.21203/rs.3.rs-17184/v1

Examining the relationship between maternal body size, gestational glucose tolerance status, mode of delivery and ethnicity on mother’s milk microbiota at three months post-partum

2020· preprint· en· W4238318726 on OpenAlexafffund
Lauren LeMay‐Nedjelski, James Butcher, Sylvia H. Ley, Michelle R. Asbury, Anthony J. Hanley, Alex Kiss, Sharon Unger, Julia K. Copeland, Pauline W. Wang, Bernard Zinman, Alain Stintzi, Deborah L. O’Connor

Bibliographic record

VenueResearch Square · 2020
Typepreprint
Languageen
FieldMedicine
TopicBreastfeeding Practices and Influences
Canadian institutionsUniversity of OttawaUniversity of Toronto
FundersCanadian Institutes of Health Research
KeywordsPost partumEthnic groupObstetricsMedicineGestational diabetesGestationPregnancyDemographyBiologyGeneticsSociology

Abstract

fetched live from OpenAlex

Abstract Background: Few studies have examined how maternal body mass index (BMI), mode of delivery and ethnicity affect the microbial composition of human milk and none have examined associations with maternal metabolic status. Given the high prevalence of maternal adiposity and impaired glucose metabolism, and the importance of human milk in the colonization of the infant gut, we systematically investigated the associations between these maternal factors and milk microbial composition and functionality. Methods: Women ≥20 years were recruited during pregnancy and milk samples were collected at 3 months post-partum (NCT01405547). Demographic data, weight, height, and a 3-hour oral glucose tolerance test were conducted at 30 (95% CI: 25-33) weeks gestation. Metagenomic DNA extraction and 16S ribosomal RNA gene sequencing of the V4 hypervariable region (Illumina MiSeq) was carried out on 113 milk samples. Results: Multivariable linear regression analyses demonstrated no significant associations between maternal characteristics (maternal BMI [pre-pregnancy, 3 months post-partum], glucose tolerance, mode of delivery and ethnicity) and microbiota alpha-diversity; however, pre-pregnancy BMI was associated with human milk beta-diversity (Bray-Curtis p=0.040). Women with a pre-pregnancy BMI >30 kg/m2 (obese) had a greater incidence of Bacteroidetes (incidence rate ratio [IRR]: 3.70 [95% CI: 1.61-8.48]) and a reduced incidence of Proteobacteria (0.62 [0.43-0.90]), compared to overweight women (BMI 25.0-29.9 kg/m2) as assessed by multivariable Poisson regression. Increased incidence of Gemella was observed among overweight (versus healthy) mothers with gestational diabetes (5.96 [1.85-19.21]) and obese (versus healthy) mothers with impaired glucose tolerance (4.04 [1.63-10.01]). An increased incidence of Brevundimonas (16.70 [5.99-46.57]) was found in the milk of women who underwent an unscheduled C-section versus vaginal delivery. Lastly, functional gene inference demonstrated that obesity was associated with increased abundance of genes encoding for the biosynthesis of secondary metabolites in milk (coefficient=0.00028, p=0.0070). Conclusions: Mother’s milk has a diverse microbiota of which its diversity and differential abundance appear associated with maternal body size, glucose tolerance status, mode of delivery, and ethnicity. Further research is warranted to determine whether this variability in the milk microbiota impacts colonization of the infant gut.

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)0.003
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 score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.142
GPT teacher head0.399
Teacher spread0.257 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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