Prolactin and Maternal Metabolism in Women With a Recent GDM Pregnancy and Links to Future T2D: The SWIFT Study
Bibliographic record
Abstract
CONTEXT: Prolactin is a multifaceted hormone known to regulate lactation. In women with gestational diabetes mellitus (GDM) history, intensive lactation has been associated with lower relative risk of future type 2 diabetes (T2D). However, the role of prolactin in T2D development and maternal metabolism in women with a recent GDM pregnancy has not been ascertained. OBJECTIVE: We examined the relationships among prolactin, future T2D risk, and key clinical and metabolic parameters. METHODS: We utilized a prospective GDM research cohort (the SWIFT study) and followed T2D onset by performing 2-hour 75-g research oral glucose tolerance test (OGTT) at study baseline (6-9 weeks postpartum) and again annually for 2 years, and also by retrieving clinical diagnoses of T2D from 2 years through 10 years of follow up from electronic medical records. Targeted metabolomics and lipidomics were applied on fasting plasma samples collected at study baseline from 2-hour 75-g research OGTTs in a nested case-control study (100 future incident T2D cases vs 100 no T2D controls). RESULTS: Decreasing prolactin quartiles were associated with increased future T2D risk (adjusted odds ratio 2.48; 95% CI, 0.81-7.58; P = 0.05). In women who maintained normoglycemia during the 10-year follow-up period, higher prolactin at baseline was associated with higher insulin sensitivity (P = 0.038) and HDL-cholesterol (P = 0.01), but lower BMI (P = 0.001) and leptin (P = 0.002). Remarkably, among women who developed future T2D, prolactin was not correlated with a favorable metabolic status (all P > 0.05). Metabolomics and lipidomics showed that lower circulating prolactin strongly correlated with a T2D-high risk lipid profile, with elevated circulating neutral lipids and lower concentrations of specific phospholipids/sphingolipids. CONCLUSION: In women with recent GDM pregnancy, low circulating prolactin is associated with specific clinical and metabolic parameters and lipid metabolites linked to a high risk of developing T2D.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".