The Antioxidant Capacity of Breast Milk and Plasma of Women with or without Gestational Diabetes Mellitus
Bibliographic record
Abstract
Women with gestational diabetes (GD) have reduced antioxidant capacity; however, the relationship between maternal diet, maternal biochemical capacity, breast milk concentration, and infant intake has not been adequately explored in the literature. An exploration of underlying mechanism(s) is warranted, particularly for nutrient antioxidants impacted by maternal intake. These nutrients may provide a means for modifying maternal and infant antioxidant capacity. Oxygen radical absorbance capacity (ORAC), alpha-tocopherol, ascorbic acid, and beta-carotene concentrations were measured in breast milk of women with and without GD. Plasma, three-day diet records, and breast milk were collected at 6 to 8 weeks postpartum. Student’s t-test was used to compare breast milk ORAC, nutrient antioxidant concentration and plasma ORAC between women with and without GD. Pearson correlations were used to determine associations among antioxidant concentrations in breast milk and dietary antioxidant intake. Breast milk antioxidant concentrations were associated with maternal intake of beta-carotene (r = 0.629, p = 0.005). Breast milk and plasma ORAC and antioxidant vitamin concentrations were not significantly different between GD and NG women. Breast milk ORAC associated with breast milk alpha-tocopherol for NG (r = 0.763, p = 0.010), but not GD women (r = 0.385, p = 0.35), and with breast milk ascorbic acid for GD (r = 0.722, p = 0.043) but not NG women (r = 0.141, p = 0.70; interaction p = 0.041). In GD participants, breast milk ORAC was significantly associated with plasma ORAC (r = 0.780, p = 0.039). ORAC and antioxidant vitamin concentrations in breast milk in women with GD were comparable to women with NG; however, the relationships between breast milk ORAC and vitamin concentrations differed in GD versus NG women for alpha-tocopherol and ascorbic acid.
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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 | 0.002 |
| 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".