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Patterns of Cardiovascular Risk Factors in the Years Before Pregnancy in Nulliparous Women With and Without Preterm Birth and Small-for-Gestational-Age Delivery.

ABSTRACT: Background Women with either preterm or small-for-gestational-age (SGA) delivery have an elevated lifetime risk of cardiovascular disease that has been attributed to the accrual of vascular risk factors over time. We sought to determine whether an adverse cardiovascular risk factor profile develops in the years before pregnancies complicated by preterm delivery or SGA. Methods and Results Using administrative databases, we identified all 156 278 nulliparous women in Ontario, Canada, who had singleton pregnancies between January 2011 and December 2018 and ≥2 measurements of the following analytes between January 2008 and the start of pregnancy: glycosylated hemoglobin, glucose, lipids, and alanine aminotransferase. There were 11 078 women with preterm delivery and 19 367 with SGA. The 2 most recent pregravid tests were performed at median 0.6 (interquartile range, 0.3-1.4) and 1.9 (interquartile range, 1.1-3.3) years before pregnancy, respectively. Women with preterm delivery had higher pregravid glycosylated hemoglobin, glucose, low-density lipoprotein cholesterol, triglycerides, and alanine aminotransferase, and lower high-density lipoprotein cholesterol, than those without preterm delivery. In contrast, women with SGA had lower pregravid fasting glucose, random glucose, and triglycerides than those without SGA. In the years before pregnancy, women with preterm delivery had higher annual increases than their peers in glycosylated hemoglobin (0.7-times higher), triglycerides (7.9-times higher), and alanine aminotransferase (2.2-times higher). During this time, fasting glucose increased in women who developed preterm delivery but decreased in their peers. Conclusions An adverse cardiovascular risk factor profile evolves over time in the years before pregnancy complicated by preterm delivery, but does not necessarily precede SGA.

SUBMITTER: Retnakaran R

PROVIDER: S-EPMC8477886 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Objectives: Gestational diabetes mellitus (GDM) is a common complication in pregnancy, affecting around 14% of all pregnancies each year. It will likely further increase, as obesity becomes more prevalent. The impact of GDM on cardiovascular changes in pregnant women and her child is still unclear. The aim of the study was to measure the effects of physical activity before pregnancy on the cardiovascular system in patients with GDM in pregnancy. Methods: Two hundred and six pregnant women were included in this observational study. All participants were recruited at the tertiary level teaching University Hospital "Klinikum rechts der Isar" between 28 and 32 weeks gestation. Questionnaires dealing with pre-pregnancy daily and physical activity (PA) were evaluated. The cardiovascular status of the mothers included measurements of the intima-media thickness (IMT) of the carotid arteries. PA level was performed with a standardized 6-min-walking-test. Results: Ninety-nine women with GDM with a mean age of 33.84 (± 4.7) years were examined. One hundred seven healthy pregnant women aged 32.6 (± 4.2) years served as controls. The mean weight in the study group was 73.0 (± 20.3) kg and 61.7 (± 9.5) kg in the control group. Based on the higher weight in the study group, the Body Mass Index (BMI) was also significantly higher than in the control group (26.3 ± 7.1 vs. 21.6 ± 3; p < 0.001). The frequency of PA was significantly higher in the control group (p < 0.001). The objective fitness level was worse in pregnant women with GDM compared to healthy controls (472 vs. 523 m, p < 0.001). PA before and during pregnancy was less performed in the study group (86 vs. 64.5%, p = 0.002; 69 vs. 45.7%, p = 0.003). Women who were physically inactive before pregnancy had a 3-times higher risk to develop GDM compared to active women (OR = 2.67). The IMT was significantly thicker in the study group (0.48 ± 0.042 mm vs. 0.45 ± 0.042) mm; p = 0.006). Conclusion: Physical activity before pregnancy and a lower initial weight reduces the risk of developing GDM and cardiovascular risk factors in pregnancy. The development of prevention programs is certainly necessary.

Project description:The goal of the study was to establish an efficient first-trimester predictive model for any type of preterm birth before 37 gestational weeks (spontaneous preterm birth (PTB) or preterm prelabor rupture of membranes (PPROM)) in the absence of other pregnancy-related complications, such as gestational hypertension, preeclampsia, fetal growth restriction, or small for gestational age. The retrospective study was performed in the period from 11/2012 to 3/2020. Peripheral blood samples were collected from 6440 Caucasian individuals involving 41 PTB and 65 PPROM singleton pregnancies. A control group with 80 singleton term pregnancies was selected on the basis of equal sample-storage time. A combination of only six microRNAs (miR-16-5p, miR-21-5p, miR-24-3p, miR-133a-3p, miR-155-5p, and miR-210-3p; AUC 0.812, p < 0.001, 70.75% sensitivity, 78.75% specificity, cut-off > 0.652) could predict preterm delivery before 37 gestational weeks in early stages of gestation in 52.83% of pregnancies with a 10.0% FPR. This predictive model for preterm birth based on aberrant microRNA expression profile was further improved via implementation of maternal clinical characteristics (maternal age and BMI at early stages of gestation, infertility treatment with assisted reproductive technology, occurrence of preterm delivery before 37 gestational weeks in previous pregnancy(ies), and presence of any kind of autoimmune disease (rheumatoid arthritis, systemic lupus erythematosus, antiphospholipid syndrome, type 1 diabetes mellitus, or other autoimmune disease)). With this model, 69.81% of pregnancies destined to deliver before 37 gestational weeks were identified with a 10.0% FPR at early stages of gestation. When other clinical variables as well as those mentioned above—such as positive first-trimester screening for early preeclampsia with onset before 34 gestational weeks and/or fetal growth restriction with onset before 37 gestational weeks using the Fetal Medicine Foundation algorithm, as well as positive first-trimester screening for spontaneous preterm birth with onset before 34 gestational weeks using the Fetal Medicine Foundation algorithm—were added to the predictive model for preterm birth, the predictive power was even slightly increased to 71.70% with a 10.0% FPR. Nevertheless, we prefer to keep the first-trimester screening for any type of preterm birth occurring before 37 gestational weeks in the absence of other pregnancy-related complications as simple as possible.

Project description:ObjectiveTo predict preterm birth in nulliparous women using logistic regression and machine learning.DesignPopulation-based retrospective cohort.ParticipantsNulliparous women (N = 112,963) with a singleton gestation who gave birth between 20-42 weeks gestation in Ontario hospitals from April 1, 2012 to March 31, 2014.MethodsWe used data during the first and second trimesters to build logistic regression and machine learning models in a "training" sample to predict overall and spontaneous preterm birth. We assessed model performance using various measures of accuracy including sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) in an independent "validation" sample.ResultsDuring the first trimester, logistic regression identified 13 variables associated with preterm birth, of which the strongest predictors were diabetes (Type I: adjusted odds ratio (AOR): 4.21; 95% confidence interval (CI): 3.23-5.42; Type II: AOR: 2.68; 95% CI: 2.05-3.46) and abnormal pregnancy-associated plasma protein A concentration (AOR: 2.04; 95% CI: 1.80-2.30). During the first trimester, the maximum AUC was 60% (95% CI: 58-62%) with artificial neural networks in the validation sample. During the second trimester, 17 variables were significantly associated with preterm birth, among which complications during pregnancy had the highest AOR (13.03; 95% CI: 12.21-13.90). During the second trimester, the AUC increased to 65% (95% CI: 63-66%) with artificial neural networks in the validation sample. Including complications during the pregnancy yielded an AUC of 80% (95% CI: 79-81%) with artificial neural networks. All models yielded 94-97% negative predictive values for spontaneous PTB during the first and second trimesters.ConclusionAlthough artificial neural networks provided slightly higher AUC than logistic regression, prediction of preterm birth in the first trimester remained elusive. However, including data from the second trimester improved prediction to a moderate level by both logistic regression and machine learning approaches.

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Patterns of Cardiovascular Risk Factors in the Years Before Pregnancy in Nulliparous Women With and Without Preterm Birth and Small-for-Gestational-Age Delivery.

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