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Isaevska, E.

Publications and source records attributed to Isaevska, E..

2 recordsLinked to original sources

The prenatal exposome and genome in predictive modelling of DNA methylation

IntroductionFetal development represents a critical window during which genetic and environmental influences shape lifelong health. DNA methylation (DNAm) is a candidate underlying mechanism. While individual prenatal exposures have been related to DNAm, no studies have investigated the broader prenatal exposome, nor incorporated genetics with the exposome. Here, we integrated the prenatal exposome and genetics as predictors of DNAm at birth. MethodsWe used data from the Dutch Generation R (n=2282) and English Avon Longitudinal Study of Parents and Children (ALSPAC; n=809) cohorts. We performed epigenome-wide elastic net regression, using Generation R for model development/internal validation and ALSPAC for external validation, to predict DNAm at each CpG site. We used three models: Model 1 included 42 prenatal exposures, Model 2 additionally included child sex, gestational age and birth weight, and Model 3 further included meQTLs. ResultsIn Model 1, the prenatal exposome explained on average 0.7% of DNAm variation across 347 validated CpGs (0.1% of tested CpGs). This increased to 40,044 CpGs (10.2%) with 1.3% of variation explained in Model 2, and 91,305 CpGs (23.2%) with 3.0% of variation explained in Model 3. In Model 1, prenatal smoking was the largest predictor, followed by delivery characteristics, among which meconium-stained amniotic fluid was a novel finding. In Model 3, typically both SNPs and multiple prenatal exposures were selected. DiscussionWe find that genomic associations with cord blood DNAm are stronger and more widespread than prenatal exposures, although typically, the prenatal exposome explains additional variation in DNAm beyond genetic influences.

genomics↗

Inflammatory markers in pregnancy - surprisingly stable. Mapping trajectories and drivers in four large cohorts.

Adaptations of the immune system throughout gestation have been proposed as important mechanisms regulating successful pregnancy. Dysregulation of the maternal immune system has been associated with adverse maternal and fetal outcomes. To translate findings from mechanistic preclinical studies to human pregnancies, studies of serum immune markers are the mainstay. The design and interpretation of human biomarker studies require additional insights in the trajectories and drivers of peripheral immune markers. The current study mapped maternal inflammatory markers (C-reactive protein (CRP), interleukin (IL)-1{beta}, IL-6, IL-17A, IL-23, interferon-{gamma}) during pregnancy and investigated the impact of demographic, environmental and genetic drivers on maternal inflammatory marker levels in four multi-ethnic and socio-economically diverse population-based cohorts with more than 12,000 pregnant participants. Additionally, pregnancy inflammatory markers were compared to pre-pregnancy levels. Cytokines showed a high correlation with each other, but not with CRP. Inflammatory marker levels showed high variability between individuals, yet high concordance within an individual over time during and pre-pregnancy. Pre-pregnancy body mass index (BMI) explained more than 9.6% of the variance in CRP, but less than 1% of the variance in cytokines. The polygenic score of CRP was the best predictor of variance in CRP (>14.1%). Gestational age and previously identified inflammation drivers, including tobacco use and parity, explained less than 1% of variance in both cytokines and CRP. Our findings corroborate differential underlying regulatory mechanisms of CRP and cytokines and are suggestive of an individual inflammatory marker baseline which is, in part, genetically driven. While prior research has mainly focused on immune marker changes throughout pregnancy, our study suggests that this field could benefit from a focus on intra-individual factors, including metabolic and genetic components.

immunology↗