PS-C29-10: WEIGHT GAIN IS ASSOCIATED WITH INCREASED ARTERIAL STIFFNESS IN HIGH-RISK PREGNANT WOMEN
Bibliographic record
Abstract
Objective: There have been increasing efforts to establish effective screening tools for preeclampsia in early pregnancy. Arterial stiffness is a composite indicator of vascular health, reflects endothelial dysfunction, and has been shown to be predictive of cardiovascular events in the general population, while it has been noted to be increased at the time for preeclampsia. Pregnancy is characterized by acute weight gain. However, whether pregnancy weight gain can affect arterial stiffness is not known. Therefore, we aimed to investigate the impact of pregnancy weight gain on arterial stiffness in high-risk pregnant women. Design and method: In this prospective longitudinal cohort study, pregnant women (n = 177) in their first trimester (10–13 weeks gestation) who met established clinical criteria for high-risk of preeclampsia were recruited from obstetrics clinics. Arterial stiffness measurements and weight were recorded every 4 weeks throughout pregnancy. Arterial stiffness was measured non-invasively using the validated tonometry-based SphygmoCor System. Results: Continuous variables were analyzed using linear. Analysis adjusted for maternal age showed that an increase in total pregnancy weight gain was significantly associated with increased arterial stiffness during pregnancy (0.213 [95%CI: 0.007, 0.068], p < 0.02). More specifically, an increase in late pregnancy weight gain, from second to third trimester, was significantly associated with an increase in arterial stiffness during pregnancy (adjusted beta: 0.052 [CI: 0.002, 0.102], p < 0.05). Conclusion: An increase in total pregnancy weight gain and more specifically late pregnancy weight gain is associated with an increase in arterial stiffness. This is important as arterial stiffness could be a predictive tool for preeclampsia in clinical practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".