Excessive hypercholesterolaemia during pregnancy as a risk factor for endothelial dysfunction in pre‐eclampsia
Bibliographic record
Abstract
Pregnancy induces significant changes in the maternal cardiovascular system, and insufficient vascular endothelial adaptations to pregnancy contribute to the development of pregnancy complications such as pre-eclampsia. Pre-eclampsia is not only a major cause of maternal morbidity and mortality, but also a significant risk factor for the development of later-life cardiovascular disease. However, the specific mechanisms underlying the pathophysiology of pre-eclampsia, as well as the mechanisms for an increased susceptibility to cardiovascular disease later in life, are not fully characterized. In this review, we discuss the concept that excessive pregnancy-specific dyslipidaemia, particularly hypercholesterolaemia, is a significant risk factor for the development of pre-eclampsia. We further outline novel potential mechanisms (i.e. oxidized low-density lipoprotein receptor 1 and toll-like receptor 4) underlying endothelial dysfunction induced by excessively high cholesterol levels during pregnancy (in the context of pre-eclampsia), in addition to discussing the overall implications of having had a pregnancy complicated by pre-eclampsia on later-life maternal vascular health. Determining the mechanisms by which excessive, pregnancy-specific dyslipidaemia/hypercholesterolaemia impact maternal endothelial health in pregnancy, and later in life, will create a window of opportunity to diagnose and develop targeted therapy for a susceptible population of women, aiming to ultimately reduce the societal burden of cardiovascular disease.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".