Nuclear Receptors of the Peroxisome Proliferator-Activated Receptor (PPAR) Family in Gestational Diabetes: From Animal Models to Clinical Trials1
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is defined as impaired glucose tolerance and affects 2%-8% of all pregnancies. Among other complications, GDM can lead to the development of type 2 diabetes mellitus (DM 2) in both mother and child. Peroxisome proliferator-activated receptors (PPARs) are major regulators of glucose and lipid metabolism. Furthermore, PPARs are mediators of inflammation and angiogenesis and are involved in the maternal adaptational dynamics during pregnancy to serve the requirements of the growing fetus. PPARs were originally named for their ability to induce hepatic peroxisome proliferation in mice in response to xenobiotic stimuli. The expression of three PPAR isoforms, alpha, beta/delta, and gamma, have been described. Each of them is encoded by different genes; however, they share 60%-80% homology in their ligand-binding and DNA-binding domains. PPARs are involved in trophoblast differentiation, invasion, metabolism, and parturition and are expressed in invasive extravillous trophoblast and villous trophoblast cells. Nuclear receptors, to which PPARs belong, are promising targets for disease-specific treatment strategies because they act as transcription factors controlling cellular processes at the level of gene expression and may produce selective alterations in downstream gene expression. To date, PPAR agonists are therapeutically used in patients with DM 2 and in patients with reproductive disorders such as polycystic ovary syndrome. Because of safety concerns and limited data, PPAR agonists are not yet included in GDM-related treatment strategies. Our objective herein is to review newly emerging generations of selective PPAR modulators and panagonists, which may have potent therapeutic implications in the context of 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".