Gestational Diabetes Mellitus, Human Milk Composition, and Infant Growth
Bibliographic record
Abstract
Background: Gestational diabetes mellitus (GDM) is known to affect human milk composition. Aims of this study were to compare macronutrient and energy content of human milk of women with (GDM + ) and without GDM (GDM − ), to assess the association between maternal health and human milk macronutrient and energy content and association between human milk macronutrient and energy content and infant growth. Study Design and Methods: Two months after delivery, hindmilk samples were collected. Triglyceride (TG), lactose, and protein content of human milk were measured. An oral glucose tolerance test was performed. Infant weight and length at birth and 2 months were collected. Weight-for-age (WAZ) and weight-for-length z -scores were calculated. Results: Twenty-four GDM + and 29 GDM − women were included. Protein, lactose, and energy content of human milk were similar between groups. TG concentration was higher in GDM + than in GDM − women (6.3 ± 2.0 versus 5.3 ± 1.2, p = 0.04). This difference was no longer significant after adjustment for maternal age and infant sex ( p = 0.23). Maternal age was associated with TG ( r = 0.28, p = 0.04) and lactose ( r = −0.30, p = 0.03), while fasting glucose was associated with proteins ( r = 0.30, p = 0.03) and tended to be associated with TG ( r = 0.27, p = 0.05) and energy ( r = 0.24, p = 0.08). TG levels in human milk were associated with weight (β: 0.26, 95% confidence interval [CI]: 0.02 to 0.50) and WAZ (β: 0.40, 95% CI: 0.05 to 0.75) at 2 months among children unexposed (GDM − ) to GDM, but not among children exposed (GDM + ) Conclusions: In conclusion, GDM status, maternal age, and fasting glucose level were associated with human milk composition. Finally, TG in human milk was associated with infant growth among GDM − children but not among GDM + children. ClinicalTrials.gov NCT02872402.
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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 | 0.000 |
| 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".