The unrevealed links: periodontal health, human milk composition, and infant gut microbiome dynamics
Bibliographic record
Abstract
AIM: This review aims to identify the mechanistic relationships related to periodontal diseases and its possible association with changes in human milk composition and the composition and function of infants' gut microbiome. BACKGROUND: known whether maternal oral inflammatory diseases, including periodontal diseases, deleteriously affect human milk composition. METHODS: A narrative review was conducted according to SANRA, the Scale for the Assessment of Narrative Review Articles, guidelines. PubMed, Google Scholar, and Cochrane database of systematic reviews were searched from September 2019 up to December 2023 using keywords such as breast/human milk, maternal health/infections, and periodontal diseases. Reference lists of relevant articles were also screened. Our primary outcome of interest was human milk composition (i.e., any changes in macronutrients, immunological components, etc.). Secondary outcomes included changes in human milk microbiome and subsequent changes in the infant gut microbiome. Outcomes were synthesized using a narrative approach where the existing evidence and current literature were summarized. No risk of bias assessment of the studies was performed in this review. FINDINGS: The search yielded no studies investigating the relationship between periodontal diseases in nursing mothers and changes in human milk composition. However, a dose-response relationship exists between the severity of periodontal diseases and the risk of adverse pregnancy outcomes such as preterm birth. Mastitis and diabetes affected milk lipids. Immunoglobulin A (sIgA) was increased in mastitis, whereas reduced concentrations were reported in diabetes. Potential biological pathways through which periodontal diseases can negatively affect human milk composition include the systemic dissemination of inflammatory cytokines like IL-6, PGE2, and tumor necrosis factor (TNF)-β that can be up-regulated by bacterial by-products. This biological plausibility needs to be investigated, given the potentially negative impact on the quality of human milk that could be caused by periodontal inflammation.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".