Novel Role of the Renin–Angiotensin System in Preeclampsia Superimposed on Chronic Hypertension and the Effects of Exercise in a Mouse Model
Bibliographic record
Abstract
Gestational hypertensive disorders, such as preeclampsia, affect 6% to 8% of all pregnancies in North America, and they are the leading cause of maternal mortality in industrialized countries, accounting for 16% of deaths. Women with hypertension have an increased risk (15% to 25%) of developing preeclampsia. Our aim was to investigate the mechanisms implicated in preeclampsia superimposed on chronic hypertension and in the protective effects of exercise in a mouse model. Female mice overexpressing human angiotensinogen and human renin were used as a model of preeclampsia superimposed on chronic hypertension. In the trained group, mothers were placed in cages with access to a wheel before mating, and they remained within these throughout gestation. Blood pressure was measured by telemetry. We found that angiotensin II type I receptor was increased, whereas the Mas receptor was decreased in the placenta and the aorta of pregnant sedentary transgenic mice. This would produce a decrease in angiotensin-(1-7) effects in favor of angiotensin II. Supporting the functional contribution of this modulation, we found that the prevention of most pathological features in trained transgenic mice was associated with a normalization of placental angiotensin II type 1 and Mas receptors and an increase in aortic Mas receptor. We also found reduced circulating and placental soluble Fms-like tyrosine kinase-1 in trained transgenic mice compared with sedentary mice. This study demonstrates that modulation of the renin-angiotensin system is a key mechanism in the development of preeclampsia superimposed on chronic hypertension, which can be altered by exercise training to prevent disease features in an animal model.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".