Project description:Background Cardiovascular risk in young adulthood is an important determinant of lifetime cardiovascular disease risk. Women with adverse pregnancy outcomes (APOs) have increased cardiovascular risk, but the relationship of other factors is unknown. Methods and Results Among 4471 primiparous women, we related first-trimester atherogenic markers to risk of APO (hypertensive disorders of pregnancy, preterm birth, small for gestational age), gestational diabetes mellitus (GDM) and hypertension (130/80 mm Hg or antihypertensive use) 2 to 7 years after delivery. Women with an APO/GDM (n=1102) had more atherogenic characteristics (obesity [34.2 versus 19.5%], higher blood pressure [systolic blood pressure 112.2 versus 108.4, diastolic blood pressure 69.2 versus 66.6 mm Hg], glucose [5.0 versus 4.8 mmol/L], insulin [77.6 versus 60.1 pmol/L], triglycerides [1.4 versus 1.3 mmol/L], and high-sensitivity C-reactive protein [5.6 versus 4.0 nmol/L], and lower high-density lipoprotein cholesterol [1.8 versus 1.9 mmol/L]; P<0.05) than women without an APO/GDM. They were also more likely to develop hypertension after delivery (32.8% versus 18.1%, P<0.05). Accounting for confounders and factors routinely assessed antepartum, higher glucose (relative risk [RR] 1.03 [95% CI, 1.00-1.06] per 0.6 mmol/L), high-sensitivity C-reactive protein (RR, 1.06 [95% CI, 1.02-1.11] per 2-fold higher), and triglycerides (RR, 1.27 [95% CI, 1.14-1.41] per 2-fold higher) were associated with later hypertension. Higher physical activity was protective (RR, 0.93 [95% CI, 0.87-0.99] per 3 h/week). When evaluated as latent profiles, the nonobese group with higher lipids, high-sensitivity C-reactive protein, and insulin values (6.9% of the cohort) had increased risk of an APO/GDM and later hypertension. Among these factors, 7% to 15% of excess RR was related to APO/GDM. Conclusions Individual and combined first-trimester atherogenic characteristics are associated with APO/GDM occurrence and hypertension 2 to 7 years later. Registration URL: https://www.clinicaltrials.gov; Unique identifier: NCT02231398.

Project description:Preeclampsia and gestational hypertension are common complications of pregnancy associated with significant maternal and infant morbidity. Despite extensive research evaluating risk factors during pregnancy, most women who develop a hypertensive disorder of pregnancy are not considered high-risk and strategies for prevention remain elusive. We evaluated preconception blood pressure and its change into early pregnancy as novel risk markers for development of a hypertensive disorder of pregnancy. The EAGeR (Effects of Aspirin in Gestation and Reproduction) trial (2007-2011) randomized 1228 healthy women with a history of pregnancy loss to preconception-initiated low-dose aspirin versus placebo and followed participants for up to 6 menstrual cycles attempting pregnancy and throughout pregnancy if they became pregnant. Blood pressure was measured during preconception and throughout early gestation. The primary outcomes, preterm preeclampsia, term preeclampsia, and gestational hypertension, were abstracted from medical records. Among 586 women with a pregnancy >20 weeks' gestation, preconception blood pressure levels were higher for preterm preeclampsia (87.3±6.7 mm Hg mean arterial pressure), term preeclampsia (88.3±9.8 mm Hg), and gestational hypertension (87.9±9.1 mm Hg) as compared with no hypertensive disorder of pregnancy (83.9±8.6 mm Hg). Change in blood pressure from preconception into very early pregnancy was associated with development of preeclampsia (relative risk, 1.13 [95% CI, 1.02-1.25] per 2 mm Hg increase in mean arterial pressure at 4 weeks' gestation), particularly preterm preeclampsia (relative risk, 1.21 [95% CI, 1.01-1.45]). Randomization to aspirin did not alter blood pressure trajectory or risk of hypertension in pregnancy. Preconception blood pressure and longitudinal changes during early pregnancy are underexplored but crucial windows in the detection and prevention of hypertensive disorders of pregnancy. Registration- URL: http://www.clinicaltrials.gov. Unique identifier: NCT00467363.

Project description:Clinical guidelines recommend several risk factors to identify women in early pregnancy at high risk of developing pregnancy-associated hypertension. However, these variables result in low predictive accuracy. Here, we developed a prediction model for pregnancy-associated hypertension using graph-based semi-supervised learning. This is a secondary analysis of a prospective study of healthy pregnant women. To develop the prediction model, we compared the prediction performances across five machine learning methods (semi-supervised learning with both labeled and unlabeled data, semi-supervised learning with labeled data only, logistic regression, support vector machine, and random forest) using three different variable sets: [a] variables from clinical guidelines, [b] selected important variables from the feature selection, and [c] all routine variables. Additionally, the proposed prediction model was compared with placental growth factor, a predictive biomarker for pregnancy-associated hypertension. The study population consisted of 1404 women, including 1347 women with complete follow-up (labeled data) and 57 women with incomplete follow-up (unlabeled data). Among the 1347 with complete follow-up, 2.4% (33/1347) developed pregnancy-associated HTN. Graph-based semi-supervised learning using top 11 variables achieved the best average prediction performance (mean area under the curve (AUC) of 0.89 in training set and 0.81 in test set), with higher sensitivity (72.7% vs 45.5% in test set) and similar specificity (80.0% vs 80.5% in test set) compared to risk factors from clinical guidelines. In addition, our proposed model with graph-based SSL had a higher performance than that of placental growth factor for total study population (AUC, 0.71 vs. 0.80, p < 0.001). In conclusion, we could accurately predict the development pregnancy-associated hypertension in early pregnancy through the use of routine clinical variables with the help of graph-based SSL.

Project description:Low fertility remains a leading cause of poor productivity in dairy cattle. In this context, there is significant interest in developing novel tools for accurate early diagnosis of pregnancy. MicroRNAs (miRNAs) are short RNA molecules which are critically involved in regulating gene expression during both health and disease. MiRNAs have been shown to regulate ovarian function, uterine receptivity, embryonic development and placental function. Circulating miRNAs can provide useful biomarkers of tissue function and disease; importantly, differential miRNA profiles have been linked to pregnancy and preeclampsia in humans. This study sought to establish the potential of circulating miRNAs as biomarkers of early pregnancy in cattle. We applied Illumina small-RNA sequencing to profile miRNAs in plasma samples collected from eight non-pregnant heifers on Days 0, 8 and 16 of the oestrous cycle and 11 heifers on Days 16 and 24 of pregnancy. We sequenced a total of 46 samples and generated 9.2 million miRNA reads per sample. There were no differences in miRNA read abundance between any of the pregnant and non-pregnant time-points (FDRâ>â0.1). As a complementary approach, we analysed sample pools (3-4 samples/pool) corresponding to Days 0, 8 and 16 of the oestrous cycle and Day 24 of pregnancy (nâ=â3 pools/group) using Qiagen PCR arrays. A total of 16 miRNAs were differentially expressed (FDRâ<â0.1) in plasma between pregnant and non-pregnant animals. RT-qPCR validation using the same plasma samples confirmed that miR-26a was differentially upregulated on Day 16 pregnant relative to non-pregnant heifers (1.7-fold; Pâ=â0.043), whereas miR-1249 tended to be upregulated in Day 16 pregnant heifers (1.6-fold; Pâ=â0.081). Further validation in an independent group of heifers confirmed an increase in plasma miR-26a levels during early pregnancy, which was significant only on Day 24 (2.0-fold; Pâ=â0.027). Through genome-wide analyses we have successfully profiled plasma miRNA populations associated with early pregnancy in cattle. We have identified miR-26a as a potential circulating biomarker of early pregnancy. Sequencing of three sequencial samples from each of eight cycling animals (Days 0, 8 and 16, total 24 samples) and two samples from each of 11 pregnant animals (Days 16 and 24, total 22 samples). Main comparisons were between non-pregnant and pregnant groups

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Hypertension presenting early in pregnancy.

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