Relationship between lactation duration and insulin and glucose response among women with prior gestational diabetes
Bibliographic record
Abstract
BACKGROUND: Few studies have investigated whether favorable effects of lactation persist after weaning and protect women with prior gestational diabetes mellitus (GDM) against later development of insulin resistance and insulin secretion defects. OBJECTIVE: To investigate the impact of lactation duration on insulin and glucose response among women with prior GDM. DESIGN/METHODS: The study group comprised 144 women with a history of GDM between 2003 and 2010. Plasma insulin and glucose concentrations were obtained from a 75 g oral glucose tolerance test (OGTT). Total lactation duration (exclusive breastfeeding and breast and bottle-feeding) for all infants was self-reported in months. RESULTS: Mean age was 36.5±5.0 years. Time between delivery and metabolic testing was 4.0±1.9 years. Women breastfed for an average of 13.9±16.8 months. Most women (80.6%) reported a history of lactation. Women who lactated had higher homeostasis model assessment for insulin sensitivity (HOMA-IS) and Matsuda indices and lower fasting and 2-h post-OGTT insulin concentrations as well as area under the curve (AUC) for insulin (P≤0.01 for all). Compared with women who lactated for <10 months, women who lactated for ≥10 months had improved insulin sensitivity-secretion index, higher HOMA-IS and Matsuda indices, lower fasting and 2-h post-OGTT insulin concentrations as well as AUC for insulin, and lower incidence of impaired glucose intolerance (P≤0.05 for all). In multiple linear regression analyses, lactation duration emerged as an independent predictor of fasting insulin concentrations (β=-0.02) and insulin sensitivity indices (β=0.02) (P≤0.05 for all). CONCLUSIONS: These results suggest that longer duration of lactation is associated with improved insulin and glucose response among women with prior GDM.
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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 | 0.004 |
| 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".