Background: There are limited data on the relationship between antihypertensive medication use in early pregnancy and risk of birth defects.
Methods: Using data from the National Birth Defects Prevention Study, we examined associations between specific antihypertensive medication classes and 28 noncardiac birth defects. We analyzed self-reported data on 17,038 case and 11,477 control pregnancies with estimated delivery dates during 1997-2011. We used multivariable logistic regression to estimate odds ratios (ORs) and 95% confidence intervals, adjusted for maternal age, race/ethnicity, body mass index, parity, pregestational diabetes, and study site, for associations between individual birth defects and antihypertensive medication use during the first trimester of pregnancy. We compared risk among women reporting early pregnancy antihypertensive medication use to normotensive women.
Results: Hypertensive women who reported early pregnancy antihypertensive medication use were more likely to be at least 35 years old, non-Hispanic Black, obese, multiparous, and to report pregestational diabetes than normotensive women. Compared to normotensive women, early pregnancy antihypertensive medication use was associated with increased risk of small intestinal atresia (adjusted OR 2.4, 95% CI 1.2-4.7) and anencephaly (adjusted OR 1.9, 95% CI 1.0-3.5). Risk of these defects was not specific to any particular medication class.
Conclusions: Maternal antihypertensive medication use was not associated with the majority of birth defects we analyzed, but was associated with an increased risk for some birth defects. Because we cannot entirely rule out confounding by the underlying hypertension and most ORs were based on small numbers, the increased risks observed should be interpreted with caution.
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http://dx.doi.org/10.1002/bdr2.1372 | DOI Listing |
Diabetes Care
January 2025
Department of Epidemiology and Biostatistics, School of Public Health, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Objective: To estimate the incidence and identify risk factors for diagnosed type 2 diabetes (T2D) among young U.S. adults.
View Article and Find Full Text PDFJ Am Geriatr Soc
January 2025
Department of Epidemiology and Population Health, Stanford University, Stanford, California, USA.
Background: Deprescribing antihypertensives is of growing interest in geriatric medicine, yet the impact on functional status is unknown. We emulated a target trial of deprescribing antihypertensive medications compared with continued use on functional status measured by activities of daily living (ADL) in a long-term care population.
Methods: We included 12,238 Veteran Affairs long-term care residents age 65+ who had a stay ≥ 12 weeks between 2006 and 2019.
Cureus
December 2024
Medical Strategic Affairs, Torrent Pharmaceuticals Ltd., Ahmedabad, IND.
Introduction: Elevated central aortic pressure, cardiac output and peripheral vascular resistance contribute to high morbidity in relation to end organ dysfunction in obstructive and non-obstructive coronary artery disease (NOCAD) cases despite revascularization. Bisoprolol preempts further progression of left ventricular dysfunction in such cases due to anti-ischemic and anti-hypertensive effects, further extending its evaluation in local Indian settings.
Methods: Post-hoc analyses of NOCAD patients with epicardial stenosis (N=378, 30 to 70% stenosis) from cross-sectional analyses conducted across eighty centers in India.
Sci Rep
January 2025
Department of Plastic Surgery, Jiangxi Provincial Children's Hospital, 1666 Diezihu Avenue, Nanchang, China.
The objective of this study was to evaluate the efficacy and safety of propranolol hydrochloride tablets and oral solution in neonates with severe IHs. A retrospective cohort study included 184 consecutive neonates diagnosed with severe IHs and treated with propranolol from January 2016 to June 2023. Of these, 126 patients received propranolol tablets, and 58 received propranolol oral solution.
View Article and Find Full Text PDFSci Rep
January 2025
School of Pharmacy, Guilin Medical University, Guilin, 541199, China.
The hypotensive side effects caused by drugs during their use have been a vexing issue. Recent studies have found that deep learning can effectively predict the biological activity of compounds by mining patterns and rules in the data, providing a potential solution for identifying drug side effects. In this study, we established a deep learning-based predictive model, utilizing a data set comprised of compounds known to either elevate or lower blood pressure.
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