Postnatal signals for later cardiovascular morbidity after preterm pre-eclampsia
Leslie, A.; Maadh, S.; Lee, M.; Jones, O.; Priestner, L.; Duhig, K.; Farrant, J. P.; Hutchings, D. C.; Naish, J. H.; Miller, C. A.; Myers, J.; Ormesher, L.
Show abstract
IntroductionPreterm pre-eclampsia is associated with increased risk of later cardiovascular disease. This study examines cardiometabolic health 3-6 years post-preterm pre-eclampsia and explores whether early postnatal cardiovascular phenotypes relate to later cardiovascular morbidity. MethodsPICk-UP trial participants who experienced preterm pre-eclampsia underwent assessments including anthropometry, blood pressure (BP), arteriography, echocardiography, biomarkers and cardiac magnetic resonance (CMR) imaging 3-6 years postpartum. The primary outcome was hypertension prevalence, with secondary outcomes including cardiac fibrosis, remodelling, and function, obesity, and lipid abnormalities. Associations between baseline, pregnancy and postnatal characteristics with the primary and secondary outcomes were explored. ResultsForty-five women were included; 37 underwent echocardiography and 20 had CMR. At 3-6 years, 53% had hypertension, 32% developed de novo hypertension, 30% had adverse left ventricular (LV) remodelling, 49% had diastolic dysfunction, and 27% were obese. Myocardial fibrosis was detected in 35% of CMR participants. No cardiovascular measures changed from 6 months postpartum to 3-6 years. Women who developed hypertension demonstrated higher BP and LV mass index, from 6 weeks postpartum, with distinct postnatal BP trajectories. Women with myocardial fibrosis exhibited higher sFlt and CRP concentrations from 6 weeks postpartum, with sFlt correlating with native T1 at 3-6 years. DiscussionWomen with prior preterm pre-eclampsia show significant cardiometabolic morbidity 3-6 years postpartum. Early postnatal phenotypes indicate long-term cardiovascular risk. Persistent anti-angiogenic imbalance and inflammation may contribute to myocardial fibrosis. Early BP, weight, and biomarker measurement may help identify at-risk women, warranting further studies on optimising postnatal care to mitigate cardiovascular risk after preterm pre-eclampsia.
Matching journals
The top 2 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Contemporary Burden of Cardiovascular Disease in Pregnancy: Insights from a Real-World Pregnancy Electronic Health Record Cohort 94%
- Genetically Predicted IL-18 Inhibition and Risk of Cardiovascular Events: A Mendelian Randomization Study 94%
- Adverse pregnancy outcomes and coronary artery disease risk: A negative control Mendelian randomization study 93%
Similar papers in this journal
- Joint associations of pregnancy complications and postpartum maternal renal biomarkers with severe cardiovascular morbidities: A US racially diverse prospective birth cohort study 95%
- Preterm birth and cardiometabolic health trajectories from birth to adulthood: the Avon Longitudinal Study of Parents and Children 94%
- Blood pressure, cardiometabolic traits and cardiovascular events in women with uterine fibroids: a genetic correlation and Mendelian randomization study 94%
Similar papers in this journal
- Derivation of an Outcome-Driven Threshold for Aortic Pulse Wave Velocity: An Individual-Participant Meta-Analysis 94%
- Prediction of Preeclampsia from Clinical and Genetic Risk Factors in Early and Late Pregnancy Using Machine Learning and Polygenic Risk Scores 93%
- Hypertensive Disorders of Pregnancy and Primary Aldosteronism 93%
Similar papers in this journal
- Sex Differences and Role of Lysyl Oxidase Like 2 (LOXL2) in Angiotensin II-Induced Hypertension in Mice 93%
- Associations of circulating T-cell subsets in carotid artery stiffness: the Multi-Ethnic Study of Atherosclerosis 92%
- Hypertension drives microbial translocation and shifts in the fecal microbiome of non-human primates 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.