The EVA Study: Early Vascular Aging in Women With History of Preeclampsia
Bibliographic record
Abstract
Background Early vascular aging (EVA) is associated with higher risk of adverse cardiovascular events and can be estimated noninvasively by assessing arterial hemodynamics. Women with a history of preeclampsia have increased risk of cardiovascular disease, but underlying mechanisms are incompletely understood. We hypothesized that women with a history of preeclampsia display persistent arterial abnormalities and EVA in the postpartum period. Methods and Results We performed a comprehensive, noninvasive arterial hemodynamic evaluation in women with a history of preeclampsia (n=40) and age‐matched controls with previous normotensive pregnancies (n=40). We used validated methods integrating applanation tonometry with transthoracic echocardiography to obtain measures of aortic stiffness, steady and pulsatile arterial load, central blood pressure, and arterial wave reflections. Presence of EVA was defined as aortic stiffness higher than that predicted from reference values based on the participant's age and blood pressure. The association of preeclampsia with arterial hemodynamic variables was assessed with multivariable linear regression, and the association of severe preeclampsia with EVA was assessed with multivariable logistic regression, adjusted for confounders. We found that women with a history of preeclampsia had greater aortic stiffness, steady arterial load, central blood pressure, and arterial wave reflections when compared with controls. We observed a dose–response relationship, with the greatest abnormalities observed in subgroups with severe, preterm, or recurrent preeclampsia. Women with severe preeclampsia had 9.23 times greater odds of having EVA as compared with controls (95% CI, 1.67–51.06, P =0.011) and 7.87 greater odds of EVA as compared with women with nonsevere preeclampsia (95% CI, 1.29–47.77, P =0.025). Conclusions Our study comprehensively characterizes arterial hemodynamic abnormalities after preeclampsia and suggests that specific subgroups of women with a history of preeclampsia exhibit greater alterations in arterial hemodynamics related to arterial health. Our findings have important implications for understanding potential links between preeclampsia and cardiovascular events, and suggest women with severe, preterm, or recurrent preeclampsia as subgroups who may deserve intensification of efforts for prevention and early detection 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".