Birthweight, Maternal Weight Trajectories and Global DNA Methylation of LINE-1 Repetitive Elements

Abstract

Low birthweight, premature birth, intrauterine growth retardation, and maternal malnutrition have been related to an increased risk of cardiovascular disease, type 2 diabetes mellitus, obesity, and neuropsychiatric disorders later in life. Conversely, high birthweight has been linked to future risk of cancer. Global DNA methylation estimated by the methylation of repetitive sequences in the genome is an indicator of susceptibility to chronic diseases. We used data and biospecimens from an epigenetic birth cohort to explore the association between trajectories of fetal and maternal weight and LINE-1 methylation in 319 mother-child dyads. Newborns with low or high birthweight had significantly lower LINE-1 methylation levels in their cord blood compared to normal weight infants after adjusting for gestational age, sex of the child, maternal age at delivery, and maternal smoking during pregnancy (p = 0.007 and p = 0.036, respectively), but the magnitude of the difference was small. Infants born prematurely also had lower LINE-1 methylation levels in cord blood compared to term infants, and this difference, though small, was statistically significant (p = 0.004). We did not find important associations between maternal prepregnancy BMI or gestational weight gain and global methylation of the cord blood or fetal placental tissue. In conclusion, we found significant differences in cord blood LINE-1 methylation among newborns with low and high birthweight as well as among prematurely born infants. Future studies may elucidate whether chromosomal instabilities or other functional consequences of these changes contribute to the increased risk of chronic diseases among individuals with these characteristics.

Dietary factors, epigenetic modifications and obesity outcomes: Progresses and perspectives

“…Thus, a recent report has evidenced that the methylation of different gene promoters (RXRA, eNOS) at birth is associated with child's later adiposity (Godfrey et al, 2011), suggesting that prenatal developmental may alter the epigenetic regulation of key components of metabolic disease risk and that a perinatal epigenetic analysis might identify subjects at high risk of developing later obesity. Another interesting result has been recently found when comparing cord blood global Long Interspersed Nucleotide Element-1 (LINE-1) methylation among newborns with low and high birth weight as well as among prematurely born infants, which might contribute to the increased risk of chronic diseases among individuals with these characteristics (Michels et al, 2011).…”

Genome-wide DNA methylation in neonates exposed to maternal depression, anxiety, or SSRI medication during pregnancy

“…Details of the data and biospecimen collection have been described elsewhere. 38 In brief, pregnant women were asked to complete a 2-page questionnaire and asked permission to abstract information from their pregnancy charts and to collect samples from umbilical cord and placenta after detachment for research purposes. The questionnaire elicited information about race and ethnicity, height, age, smoking habits, and alcohol consumption, among other pregnancy attributes and behaviors.…”

Maternal BMI at the start of pregnancy and offspring epigenome-wide DNA methylation: Findings from the Pregnancy and Childhood Epigenetics (PACE) consortium

“…Epigenetic modifications, such as DNA methylation, might partly mediate associations between maternal and offspring phenotypes by causing changes to gene expression that are mitotically heritable (6,(13)(14)(15). Differential DNA methylation has been reported when assessing offspring exposed in utero to extreme maternal undernutrition (16)(17)(18)(19), maternal morbid obesity (20) and less extreme maternal underweight and maternal obesity (21), in comparison to those not exposed; yet weak or no evidence has been found for associations between continuous maternal BMI and offspring DNA methylation, whether globally (22,23), at specific loci identified in array (21,24,25) or at candidate genes (26). However, individual studies were limited in sample size and thus underpowered to detect differential methylation.…”