Prolactin Receptor Is Required for Normal Glucose Homeostasis and Modulation of β-Cell Mass during Pregnancy
Bibliographic record
Abstract
Increased islet mass is an adaptive mechanism that occurs to combat insulin resistance during pregnancy. Prolactin (PRL) can enhance beta-cell proliferation and insulin secretion in vitro, yet whether it is PRL or other pregnancy-related factors that mediate these adaptive changes during pregnancy is unknown. The objective of this study was to determine whether prolactin receptor (Prlr) is required for normal maternal glucose homeostasis during pregnancy. An ip glucose tolerance test was performed on timed-pregnant Prlr(+/+) and heterozygous null Prlr(+/-) mice on d 0, 15, and 18 of pregnancy. Compared with Prlr(+/+) mice, Prlr(+/-) mice had impaired glucose clearance, decreased glucose-stimulated insulin release, higher nonfasted blood glucose, and lower insulin levels during but not before pregnancy. There was no difference in their insulin tolerance. Prlr(+/+) mice show a significant incremental increase in islet density and beta-cell number and mass throughout pregnancy, which was attenuated in the Prlr(+/-) mice. Prlr(+/+) mice also had a more robust beta-cell proliferation rate during pregnancy, whereas there was no difference in apoptosis rate between the Prlr(+/+) and Prlr(+/-) mice before, during, or after pregnancy. Interestingly, genotype of the mothers had a significant impact on the offspring's phenotype, such that daughters derived from Prlr(+/-) mothers had a more severe phenotype than those derived from Prlr(+/+) mothers. In conclusion, this is the first in vivo demonstration that the action of pregnancy hormones, acting through Prlr, is required for normal maternal glucose tolerance during pregnancy by increasing beta-cell mass.
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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 | 0.000 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".