Prenatal vitamin C and fish oil supplement use are associated with human milk microbiota composition in the Canadian CHILD Cohort Study
Bibliographic record
Abstract
Abstract Maternal diet may modulate human milk microbiota, but the effects of nutritional supplements are unknown. We examined the associations of prenatal diet and supplement use with milk microbiota composition. Mothers reported prenatal diet intake and supplement use using self-administered food frequency and standardised questionnaires, respectively. The milk microbiota was profiled using 16S rRNA gene sequencing. Associations of prenatal diet quality, dietary patterns, and supplement use with milk microbiota diversity and taxonomic structure were examined using Wilcoxon signed-rank tests and multivariable models adjusting for relevant confounders. A subset of 645 mothers participating in the CHILD Cohort Study (originally known as the Canadian Healthy Infant Longitudinal Development Study) provided one milk sample between 2 and 6 months postpartum and used prenatal multivitamin supplements ≥4 times a week. After adjusting for confounders, vitamin C supplement use was positively associated with milk bacterial Shannon diversity (β = 0.18, 95% CI = 0.05, 0.31) and Veillonella and Granulicatella relative abundance (β = 0.54; 95% CI = 0.05, 1.03 and β = 0.44; 95% CI = 0.04, 0.84, respectively), and negatively associated with Finegoldia relative abundance (β = –0.31; 95% CI = –0.63, –0.01). Fish oil supplement use was positively associated with Streptococcus relative abundance (β = 0.26; 95% CI = 0.03, 0.50). Prenatal diet quality and dietary patterns were not associated with milk microbiota composition. Prenatal vitamin C and fish oil supplement use were associated with differences in the milk microbiota composition. Future studies are needed to confirm our findings and elucidate mechanisms linking maternal supplement use to milk microbiota and child 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 source (direct Gemma or distilled Codex), 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".