The Association between Maternal Oral Inflammation and the Composition of Breast Milk: A Cohort Study
Bibliographic record
Abstract
Introduction: Maternal health conditions such as diabetes and obesity have been shown to be associated with altered breast milk composition. Periodontal diseases are among the common oral health conditions affecting mothers during pregnancy and postpartum. However, it is not known whether periodontal diseases affect breast milk composition (including immune cells, fatty acids, cytokines, etc.). Objectives: This study aimed to investigate the impact of maternal oral inflammatory load (OIL) on breast milk composition including neutrophil counts and activation state, and fatty acids concentrations. This study also aimed to identify the impact of maternal OIL on the oral health-related quality of life (OHRQoL) and the dietary intakes and quality of postpartum women. The association between maternal OIL and infant outcomes at birth were also investigated. Methods: This is a prospective cohort study where fifty breastfeeding mothers were recruited from St. Michael’s hospital and followed up from 2-4 weeks until 4-months postpartum. Oral rinse and breast milk samples were collected from the participants. Participants also completed an OHRQoL and 24-hr dietary recall questionnaires as part of the study. OIL, as expressed by the absolute oral neutrophil counts was used to assess the periodontal and oral inflammatory status of mothers. Mothers’ oral health state was categorized into “Healthy”, “Moderate” and “Severe” groups based on the oral neutrophil counts. Results: Mothers with moderate to severe OIL had a statistically significant decrease in the expression of activation biomarkers on breast milk neutrophils and decrease in the poly-unsaturated fatty acids at follow-up compared to baseline (p<0.01). Our study also showed a positive correlation between the maternal dietary inflammatory potential and OIL at follow-up. Infants of mothers with severe OIL had significantly lower birthweight z-scores compared to infants of mothers with moderate OIL (p=0.04). Conclusion: This study suggests that maternal OIL can affect breast milk composition. Future studies are needed to investigate the impact of the alterations in breast milk composition on infant health outcomes on the short and long term. This study represents a starting point for a new line of research aiming at filling the gap in the literature regarding this possible and clinically important association.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".