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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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 teacher head, 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".