Modulation of Cholesterol Transport by Insulin-Treated Gestational Diabetes Mellitus in Human Full-Term Placenta1
Bibliographic record
Abstract
Gestational diabetes mellitus (GDM) is a common complication of pregnancy that is characterized by glucose intolerance, leads to dyslipidemia, and is aggravated by obesity. Cholesterol is taken up by the placenta as part of lipoproteins through the scavenger receptor class B type I receptor (SRBI), low-density lipoprotein receptor (LDLR), and very low density lipoprotein receptor (VLDLR), and its efflux is then mediated by ABCA1 and ABCG1. PCSK9 is involved in the degradation of LDLR and VLDLR. The goal of this study was to evaluate the impact of GDM and prepregnancy body mass index (BMI) on cholesterol transport through the modulation of the expression of several key players. Human full-term placenta, maternal, and venous cord blood samples were obtained at delivery from normal-weight women without GDM (n = 10), normal-weight women with GDM (n = 6), and overweight/obese women with GDM (n = 6). Lipids (total cholesterol, high-density lipoprotein, low-density lipoprotein, triglycerides, free fatty acids, apolipoprotein A1, apolipoprotein B100) levels were evaluated in blood samples. Messenger RNA and protein expression levels (LDLR, VLDLR, SRBI, ABCA1, ABCG1, proprotein convertase subtilisin/kexin type 9, liver x receptors, peroxisome proliferator-activated receptors) were assessed in human full-term placenta, respectively, by real-time RT-PCR and Western blots. Lipoprotein lipase activity was evaluated using a commercial kit on tissue homogenates. Overall, our study demonstrates that GDM affects the maternal and neonatal lipid profiles as well as different key players of placental cholesterol transfer from the maternal to the fetal circulation, depending on the maternal BMI. These changes could affect the fetal metabolism and predispose the fetus to future metabolic diseases.
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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 | 0.000 |
| 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".