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Gestational diabetes mellitus (GDM) is a pregnancy disorder associated with short‐ and long‐term adverse outcomes in both mothers and infants. The current clinical test of blood glucose levels late in the second trimester is inadequate for early detection of GDM. Here we show the utility of Raman spectroscopy (RS) for rapid and highly sensitive maternal metabolome screening for GDM in the first trimester. Key metabolites, including phospholipids, carbohydrates, and major amino acids, were identified with RS and validated with mass spectrometry, enabling insights into associated metabolic pathway enrichment. Using classical machine learning (ML) approaches, we showed the performance of the RS metabolic model (cross‐validation AUC 0.97) surpassed that achieved with patients' clinical data alone (cross‐validation AUC 0.59) or prior studies with single biomarkers. Further, we analyzed novel proteins and identified fetuin‐A as a promising candidate for early GDM prediction. A correlation analysis showed a moderate to strong correlation between multiple metabolites and proteins, suggesting a combined protein‐metabolic analysis integrated with ML would enable a powerful screening platform for first trimester diagnosis. Our study underscores RS metabolic profiling as a cost‐effective tool that can be integrated into the current clinical workflow for accurate risk stratification of GDM and to improve both maternal and neonatal outcomes. [ABSTRACT FROM AUTHOR]

Hypertensive disorders of pregnancy and other adverse pregnancy outcomes (APOs) are associated with an increased risk of future maternal cardiovascular disease. Physical activity during pregnancy reduces the risk of these APOs, yet few meet physical activity guidelines during pregnancy. Little is known about the role of sedentary behavior or sleep in APOs, a critical gap in knowledge given these behaviors comprise the majority of a 24-hour day. To address this knowledge gap, the Pregnancy 24/7 cohort study (2020–2025) uses 2 devices for 24-hour activity assessment in each trimester of pregnancy to examine associations of sedentary behavior, sleep, and the 24-hour activity cycle (composition of sedentary behavior, physical activity, and sleep) with hypertensive disorders and other APOs. Participants (n  = 500) are recruited from the University of Iowa, University of Pittsburgh, and West Virginia University in early pregnancy and followed through delivery. The activPAL3 micro and Actiwatch Spectrum Plus are worn in each trimester for 7 days of 24-hour wear to assess the 24-hour activity cycle. APOs are abstracted from medical charts. This study will provide critical data to fuel future research examining how modifying the 24-hour activity cycle in pregnancy can improve maternal health. [ABSTRACT FROM AUTHOR]

This document provides a list of references to various studies and articles related to adverse childhood experiences (ACEs) and their impact on various health outcomes. The studies cover a wide range of topics, including the relationship between ACEs and frequent headaches in adolescents, the contribution of different ACEs to overall ACE scores, the detection and validity of depression severity measures, the role of body mass index in the relationship between ACEs and mental health, the co-occurrence of depression and cardiometabolic disease after childhood maltreatment, the link between ACEs, depression, physical exercise, and cardiovascular health, racial differences in the association between ACEs and health outcomes, and more. These studies provide valuable insights into the complex relationship between ACEs and various health conditions, highlighting the need for further research in this area. [Extracted from the article]

Preeclampsia is a life‐threatening pregnancy disorder. Current clinical assays cannot predict the onset of preeclampsia until the late 2nd trimester, which often leads to poor maternal and neonatal outcomes. Here we show that Raman spectroscopy combined with machine learning in pregnant patient plasma enables rapid, highly sensitive maternal metabolome screening that predicts preeclampsia as early as the 1st trimester with >82% accuracy. We identified 12, 15 and 17 statistically significant metabolites in the 1st, 2nd and 3rd trimesters, respectively. Metabolic pathway analysis shows multiple pathways corresponding to amino acids, fatty acids, retinol, and sugars are enriched in the preeclamptic cohort relative to a healthy pregnancy. Leveraging Pearson's correlation analysis, we show for the first time with Raman Spectroscopy that metabolites are associated with several clinical factors, including patients' body mass index, gestational age at delivery, history of preeclampsia, and severity of preeclampsia. We also show that protein quantification alone of proinflammatory cytokines and clinically relevant angiogenic markers are inadequate in identifying at‐risk patients. Our findings demonstrate that Raman spectroscopy is a powerful tool that may complement current clinical assays in early diagnosis and in the prognosis of the severity of preeclampsia to ultimately enable comprehensive prenatal care for all patients. [ABSTRACT FROM AUTHOR]

Background: To determine the impact of the COVID-19 pandemic on prenatal and postpartum depressive symptoms in rural versus urban populations., Methods: A retrospective cohort study was conducted among 24,227 cisgender women who gave birth from 2010 to 2021 at an academic medical center located in a rural midwestern state. Exclusion criteria were <18 years old, incarcerated, or without a documented zip code. The Patient Health Questionnaire-9 (PHQ-9) was administered during pregnancy and the Edinburgh Postnatal Depression Scale (EPDS) during postpartum. A sub-cohort also completed a COVID-related questionnaire. Rurality was defined as living in a county with <50,000 people. The COVID-19 era was defined as 1/1/2020 to 9/25/2021. Chi-square and Fisher exact tests were used as appropriate. Significance was set at α < 0.05., Results: Rural participants were more likely (p ≤ 0.001) to exhibit clinical depression symptoms before the pandemic in both the prenatal (8.63 % of rural participants vs. 6.49 % of urban participants) and postpartum periods (11.19 % rural vs. 9.28 % urban). During the pandemic, urban participants had increased postpartum depression. Rural participants endorsed more financial and labor concerns, whereas urban participants expressed support system concerns., Limitations: Study data were gathered from participants who gave birth at a single, midwestern hospital. Results may not be widely generalizable given the homogeneity of participants., Conclusions: Rural women experienced higher rates of prenatal and postpartum depressive symptoms compared to their urban counterparts. The COVID-19 pandemic was a significant stressor, revealing specific mental health vulnerabilities among birthing people., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Donna Santillan reports financial support was provided by National Institutes of Health. Stephen K. Hunter reports financial support was provided by Health Resources and Services Administration. Stephen K. Hunter reports financial support was provided by Iowa Department of Health and Human Services. Mark K. Santillan reports financial support was provided by National Institutes of Health. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Introduction : Preeclampsia, a hypertensive disorder of pregnancy, results in increased lifetime cardiovascular disease (CVD) risk. Total aortic stiffness, a robust risk factor for CVD, is composed of load-dependent (blood pressure load on arterial wall) and structural (intrinsic changes in arterial wall) mechanisms. Total aortic stiffness is also associated with reduced cardiovagal baroreflex sensitivity (BRS). We sought to determine 1) whether elevated total aortic stiffness among women with a history of preeclampsia (hxPE) is attributed to load-dependent or structural stiffness, and 2) whether either mechanism is associated with lower BRS. Methods : Total aortic stiffness (carotid-femoral pulse wave velocity) and spontaneous cardiovagal BRS (sequence technique) were measured among women 1-5 years postpartum (n=115; age 34 ±4yrs; hxPE n=51; controls n=64). Structural aortic stiffness was calculated from participant-specific exponential models, standardizing aortic stiffness to a 'reference' blood pressure. Load-dependent stiffness was calculated as total minus structural stiffness. Results : Total (+0.8 m/sec, 95% CI (-0.99, -0.23), p=0.002) and load-dependent (+0.4 m/sec, 95% CI (-0.56, -0.22), P<0.001), but not structural (95% CI (-0.52, 0.08), p=0.16), aortic stiffness were higher among women with hxPE compared with controls. Women with a hxPE had lower BRS (p=0.042) that was negatively associated with total (B =-3.24 ms/mmHg, 95% CI (-6.35, -0.13), p=0.042) and load-dependent (B =-5.91ms/mmHg, 95% CI (-11.31, -0.51), p=0.033) aortic stiffness. Conclusion : Load-dependent, not structural, aortic stiffness mechanisms contribute to higher total aortic stiffness among women with hxPE and was associated with lower cardiovagal BRS. Postpartum BP monitoring is critical to reduce increased CVD risk in preeclampsia.

The maternal cardiovascular system undergoes functional and structural adaptations during pregnancy and postpartum to support increased metabolic demands of offspring and placental growth, labor, and delivery, as well as recovery from childbirth. Thus, pregnancy imposes physiological stress upon the maternal cardiovascular system, and in the absence of an appropriate response it imparts potential risks for cardiovascular complications and adverse outcomes. The proportion of pregnancy-related maternal deaths from cardiovascular events has been steadily increasing, contributing to high rates of maternal mortality. Despite advances in cardiovascular physiology research, there is still no comprehensive understanding of maternal cardiovascular adaptations in healthy pregnancies. Furthermore, current approaches for the prognosis of cardiovascular complications during pregnancy are limited. Machine learning (ML) offers new and effective tools for investigating mechanisms involved in pregnancy-related cardiovascular complications as well as the development of potential therapies. The main goal of this review is to summarize existing research that uses ML to understand mechanisms of cardiovascular physiology during pregnancy and develop prediction models for clinical application in pregnant patients. We also provide an overview of ML platforms that can be used to comprehensively understand cardiovascular adaptations to pregnancy and discuss the interpretability of ML outcomes, the consequences of model bias, and the importance of ethical consideration in ML use.