Project description:All subjects were recruited at Centennial Women?s Hospital and the Perinatal Research Center in Nashville, TN beginning in 2003. Pregnant women were enrolled during their first clinical visit after obtaining informed consent as described previously. Demographic and clinical data specific to the fetus was collected from clinical records. Gestational age of the neonate was determined by maternal reporting of the last menstrual period and corroboration by ultrasound dating. Accurate knowledge of gestational age (GA) is essential for proper monitoring and care of neonates. However, accurate GA measures are often not available. DNA methylation has previously been shown to associate with GA, and has been used to accurately predict chronological age in adults. In the current study, we examine whether DNA methylation in cord blood can be used to predict gestational age at birth. Results: We found that GA can be accurately predicted from DNA methylation of neonatal cord blood and blood spot samples (DNAm GA), using 148 CpG sites selected through elastic net regression in six training datasets (N=207). We evaluated predictive accuracy in six testing datasets (N=1,202), and found that the accuracy of DNAm GA meets or exceeds accuracy of gestational age estimates based on established methods. We also found an increased DNAm GA, relative to clinical GA, was associated with increased birthweight percentile (p=.00057), adjusting for GA, sex, and ancestry, suggesting that DNAm GA could represent developmental age more accurately than clinical estimates of GA. Conclusions: Further development of this predictor could provide a method of accurate neonatal estimation of GA for use in resource-limited populations, or in cases where GA cannot be estimated clinically. When clinical estimates are available, the predictor can be used to test hypotheses related to developmental age and other early life circumstances, and may provide increased accuracy beyond clinical estimates. 36 Umbilical cord blood samples were collected in EDTA tubes soon after placental delivery. Blood samples were centrifuged at 3,000 RPM to separate plasma, and buffy coats were aliquoted and stored at -80oC. DNA was extracted using the DNeasy Kit (Qiagen). DNA methylation was interrogated for each sample using the HumanMethylation450 BeadChip (Illumina).

Project description:All subjects were recruited at Centennial Women?s Hospital and the Perinatal Research Center in Nashville, TN beginning in 2003. Pregnant women were enrolled during their first clinical visit after obtaining informed consent as described previously. Demographic and clinical data specific to the fetus was collected from clinical records. Gestational age of the neonate was determined by maternal reporting of the last menstrual period and corroboration by ultrasound dating. Accurate knowledge of gestational age (GA) is essential for proper monitoring and care of neonates. However, accurate GA measures are often not available. DNA methylation has previously been shown to associate with GA, and has been used to accurately predict chronological age in adults. In the current study, we examine whether DNA methylation in cord blood can be used to predict gestational age at birth. Results: We found that GA can be accurately predicted from DNA methylation of neonatal cord blood and blood spot samples (DNAm GA), using 148 CpG sites selected through elastic net regression in six training datasets (N=207). We evaluated predictive accuracy in six testing datasets (N=1,202), and found that the accuracy of DNAm GA meets or exceeds accuracy of gestational age estimates based on established methods. We also found an increased DNAm GA, relative to clinical GA, was associated with increased birthweight percentile (p=.00057), adjusting for GA, sex, and ancestry, suggesting that DNAm GA could represent developmental age more accurately than clinical estimates of GA. Conclusions: Further development of this predictor could provide a method of accurate neonatal estimation of GA for use in resource-limited populations, or in cases where GA cannot be estimated clinically. When clinical estimates are available, the predictor can be used to test hypotheses related to developmental age and other early life circumstances, and may provide increased accuracy beyond clinical estimates.

Project description:Background: Preeclampsia (PE) is a placental disease characterized by hypertension and proteinuria in pregnant women, which is associated with a high maternal and infantile morbidity. However, circulating biomarkers able to predict the prognosis of PE are lacking. Methods: Thirty-eight women were included in the study. They consisted of 19 patients with PE (13 with severe PE and 6 women with non-severe PE) and 19 gestational age-matched normal pregnancy controls. We measured coagulation pathway, endothelial responses and microparticle release and circulating gene expression in PE patient groups and normotensive controls. Results: The measurement of markers associated with coagulation pathway, endothelial activation and circulating microparticles enabled to discriminate PE from normal pregnancy but were not sufficient to distinguish severe from non-severe PE. PE patients also exhibited a specific transcriptional program distinct from that of control women and subtle differences were observed between severe and non-severe PE. Functional annotation of the up-modulated signature in PE highlighted two main functions related to ribosome and complement. Importantly, we found that 8 genes were specifically up-modulated in severe preeclampsia. Among these genes, the expression of VSIG4 was significantly increased in patients with severe preeclampsia in comparison with controls and patients with non-severe preeclampsia. Conclusion: Using transcriptional signatures of blood samples, we identified the gene encoding the estrogen receptor as a potential diagnostic marker of severe preeclampsia. In addition, the determination of this gene may improve the prognostic assessment of severe preeclampsia. Thirty-eight women were included in the study: 19 patients with PE, including 6 women with non-severe PE and 13 with severe PE, and 19 women with normal pregnancy (NP) selected according to age, weight, smoking status, race, gestational age at the inclusion, and blood pH (Table 1 of manuscript). Women with NP had no history of medical illness or medication, and received routing prenatal care. The diagnostic of PE was based on a blood pressure of M-bM-^IM-% 140/90 mmHg taken twice, uricemia above normal laboratory range (120-420 M-BM-5mol/L), and proteinuria higher than 300 mg in a 24 hour-collection, occurring after 20 gestational weeks in previously normotensive women (Table 2). The criteria used to define severe PE included one of the following conditions: a blood pressure higher than 160/110 mmHg, a proteinuria higher than 1500 mg/24h), a multisystem disorder, maternal cerebral symptoms (seizures, stroke) or intrauterine growth restriction below the 3M-BM-0 percentile. Women with multiple gestations, fetal congenital malformations/chromosomal abnormalities, recent infection, antiphospholipid antibodies, trauma, drug or alcohol abuse during pregnancy, preexisting hypertension, thrombophilia with PE history, or women receiving anticoagulant or antiaggregation therapy were excluded from the study. Two microarrays (one non-severe PE and one normal) were discarded from the analysis for technical reasons. Thus, only 36 microarrays are included here.

Dataset Information

Circulating proteome profiling of Westlake Precision Birth Cohort

Publications

Genome-wide genotype-serum proteome mapping provides insights into the cross-ancestry differences in cardiometabolic disease susceptibility.

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