Infant Vitamin D Supplements, Fecal Microbiota and Their Metabolites at 3 Months of Age in the CHILD Study Cohort
Bibliographic record
Abstract
Infant vitamin D liquid formulations often contain non-medicinal excipients such as glycerin (ie. glycerol) and 1,2-propanediol (1,2-PD). We examined whether infant vitamin D supplementation is associated with fecal glycerol and 1,2-PD concentrations at 3 months of age and characterized associations between these two molecules, and gut microbiota and their metabolites. Fecal metabolites and microbiota were quantified using Nuclear Magnetic Resonance Spectroscopy and 16S rRNA sequencing, respectively, in 575 infants from the CHILD Study at 3 months of age. Vitamin D supplement use was determined using questionnaires. Vitamin D supplementation was associated with greater odds of high 1,2-PD (adjusted OR 1.65 95% CI: 1.06, 2.53) and with decreased odds of high fecal glycerol (adjusted OR: 0.62 95% CI: 0.42, 0.90) after adjustment for breastfeeding and other covariates. Our findings were confirmed in linear regression models; vitamin D supplementation was positively associated with fecal 1,2-PD and inversely associated with glycerol (aβ: 0.37, 95% CI 0.03, 0.71 & aβ: −0.23 95% CI −0.44, −0.03, respectively). Fecal 1,2-PD and glycerol concentrations were negatively correlated with each other. Positive correlations between fecal 1,2-PD, Bifidobacteriaceae, Lactobacillaceae, Enterobacteriaceae and acetate levels were observed. Our research demonstrates that infant vitamin D supplement administration may differentially and independently influence infant gut microbiota metabolites.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".