Impact of maternal prenatal metabolic abnormalities on metabolic hormones in human milk
Bibliographic record
Abstract
Metabolic hormones are present in human milk, but no studies have investigated the impact of maternal metabolic status during pregnancy on milk insulin and adiponectin concentrations. We aimed to investigate the association of prenatal metabolic status with milk insulin and adiponectin. Participants (n=170) underwent a 3‐h oral glucose tolerance test at 30 (95% CI 25, 33) weeks gestation and donated milk in the first week (early milk) and at 3 months postpartum (mature milk). Prenatal metabolic abnormalities including higher pregravid BMI (beta±SE 0.053±0.014, p=0.0003), in addition to gravid hyperglycemia (0.218±0.087, p=0.01), insulin resistance (0.255±0.047, p<0.0001), lower insulin sensitivity (−0.521±0.108, p<0.0001), and higher serum adiponectin (0.116±0.029, p<0.0001) were associated with higher insulin in mature milk with multiple adjustment. Prenatal metabolic measures were not associated with milk adiponectin, but obstetrical measures including nulliparity (0.171±0.058, p=0.004), longer duration of gestation (0.546±0.146, p=0.0002), and unscheduled C‐section (0.387±0.162, p=0.02) were associated with higher adiponectin in early milk with multiple adjustment. In conclusion, maternal prenatal metabolic abnormalities were associated with high insulin concentrations in mature milk, while only obstetrical parameters were associated adiponectin concentrations in early milk. Grant Funding Source : CDA, CFDR, CIHR
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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 | 0.002 |
| 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".