The Role of Adiponectin during Pregnancy and Gestational Diabetes
Bibliographic record
Abstract
Pregnancy involves a range of metabolic adaptations to supply adequate energy for fetal growth and development. Gestational diabetes (GDM) is defined as hyperglycemia with first onset during pregnancy. GDM is a recognized risk factor for both pregnancy complications and long-term maternal and offspring risk of cardiometabolic disease development. While pregnancy changes maternal metabolism, GDM can be viewed as a maladaptation by maternal systems to pregnancy, which may include mechanisms such as insufficient insulin secretion, dysregulated hepatic glucose output, mitochondrial dysfunction and lipotoxicity. Adiponectin is an adipose-tissue-derived adipokine that circulates in the body and regulates a diverse range of physiologic mechanisms including energy metabolism and insulin sensitivity. In pregnant women, circulating adiponectin levels decrease correspondingly with insulin sensitivity, and adiponectin levels are low in GDM. In this review, we summarize the current state of knowledge about metabolic adaptations to pregnancy and the role of adiponectin in these processes, with a focus on GDM. Recent studies from rodent model systems have clarified that adiponectin deficiency during pregnancy contributes to GDM development. The upregulation of adiponectin alleviates hyperglycemia in pregnant mice, although much remains to be understood for adiponectin to be utilized clinically for 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".