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: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:Background: Environmental enteric dysfunction (EED) is a major impediment to the Sustainable Development Goals of improved childhood survival and healthy growth worldwide. Few studies have longitudinally and endoscopically examined EED in children, limiting the development of effective interventions. Methods: The Study of Environmental Enteropathy and Malnutrition (SEEM, Pakistan) followed 416 at-risk children prospectively from birth to 24 months of age in a rural district of Pakistan with a high prevalence of undernutrition. The duodenal genome-wide methylome and transcriptome was determined in 52 undernourished SEEM participants refractory to nutritional interventions and 42 North American pediatric controls and coeliac disease patients. Biomarkers were measured at 9 months and tested for association with growth at 24 months. Findings: The EED core transcriptome exhibited suppression of genes regulating antioxidant and detoxification functions and lipid metabolism, and induction of genes encoding anti-microbial responses, interferon signaling, and lymphocyte activation. Relative to coeliac disease, suppression of genes encoding antioxidant and detoxification functions and induction of anti-microbial responses were EED-specific. At the epigenetic level, EED was notable for hyper-methylation of genes involved in epithelial metabolism and barrier function, and hypo-methylation of genes involved in immune responses and cell proliferation. Duodenal co-expression modules revealed associations between genes regulating lymphocyte proliferation and epithelial metabolism with histologic severity, faecal energy loss, and wasting (weight-for-height Z<-2.0), with data supporting epigenetic regulation. Immune response genes were attenuated by Giardia colonization. After accounting for growth at study entry, IGF-1 and ferritin predicted linear growth, whereas leptin correlated with expression of epithelial carbohydrate metabolism and stem cell renewal genes, and weight gain. Interpretation: Data suggest immune and metabolic pathways which may inform more effective interventions to improve outcomes in EED. A validated model may be used to identify infants at greatest risk for future poor growth.

Dataset Information

Long-term Neural Embedding of Childhood Adversity in a Population-Representative Birth Cohort Followed for 5 Decades.

Background

Methods

Results

Conclusions

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets