Maternal Serum Prolactin and Prediction of Postpartum β-Cell Function and Risk of Prediabetes/Diabetes
Bibliographic record
Abstract
OBJECTIVE: The insulin resistance of mid- to late pregnancy poses a physiologic stress test for the pancreatic β-cells, which must respond by markedly increasing their secretion of insulin. This response is achieved through an expansion of β-cell mass induced by the hormones prolactin and human placental lactogen (HPL). Conversely, the furan fatty acid metabolite 3-carboxy-4-methyl-5-propyl-2-furanpropanoic acid (CMPF) has recently emerged as a negative regulator of β-cell function in pregnancy. Given their respective roles in the β-cell response to the stress test of gestation, we hypothesized that antepartum prolactin, HPL, and CMPF may relate to a woman's underlying glucoregulatory physiology and hence to her metabolic status after pregnancy. RESEARCH DESIGN AND METHODS: Three hundred and sixty-seven women underwent measurement of fasting serum prolactin, HPL, and CMPF in the late-2nd/early-3rd trimester, followed by an oral glucose tolerance test (OGTT) at 3 months postpartum that enabled assessment of glucose tolerance, insulin sensitivity/resistance, and β-cell function (Insulin Secretion-Sensitivity Index-2 [ISSI-2]). RESULTS: The postpartum OGTT identified 301 women with normal glucose tolerance (NGT) and 66 with prediabetes or diabetes. Serum prolactin in pregnancy was higher in women with postpartum NGT compared with those with postpartum prediabetes/diabetes (mean 98.2 vs. 80.2 ng/mL, P = 0.0003), whereas HPL and CMPF did not differ between the groups. On multiple linear regression analyses, antepartum prolactin was an independent determinant of postpartum ISSI-2 (β = 0.0016, t = 2.96, P = 0.003). Furthermore, higher serum prolactin in pregnancy independently predicted a lower risk of postpartum prediabetes/diabetes (odds ratio 0.50, 95% CI 0.35-0.72, P = 0.0002). CONCLUSIONS: Serum prolactin in pregnancy predicts postpartum β-cell function and risk of prediabetes/diabetes.
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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".