Variations in Human Milk Metabolites After Gestational Diabetes: Associations with Infant Growth
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Gestational diabetes mellitus (GDM) is a condition characterized by hyperglycemia and is associated with increased risk of obesity and diabetes in exposed children. Differences in human milk composition between women with (GDM+) and without GDM (GDM-) suggest that GDM could impact milk production and composition, potentially influencing infant growth. However, this association remains poorly understood. The objective was to study the association between GDM and human milk composition and its influence on infant growth, focusing on metabolites and bioactive molecules involved in energy metabolism. METHODS: Using a cross-sectional design, 24 metabolites were measured by GC-MS in human milk obtained at 2 months postpartum from 20 GDM+ women and 29 GDM- women. Anthropometric measures, as well as lipid and glycemic profiles, were collected. Infant weight and length data were obtained from health records. RESULTS: < 0.05). Specific human milk metabolites showed distinct correlations with maternal glycemic as well as infant growth, depending on GDM status. While maternal glycemia was associated with succinate and malate in all groups, maternal glycemia was specifically correlated with valine and glutamate in GDM+ mothers. Additionally, in GDM+ women, α-ketoglutarate and glycine were negatively correlated with infant growth. CONCLUSIONS: The results of this study suggest that GDM can influence the mother's health beyond delivery, impacting the mammary gland biology with effects on the human milk composition. Further, correlations with infant growth suggest that GDM-dependent variations in milk composition potentially influence infant growth and metabolism.
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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 | 0.000 | 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".