TY - JOUR
T1 - Maternal gestational weight gain and DNA methylation in young women
T2 - Application of life course mediation methods
AU - Huang, Jonathan Y.
AU - Siscovick, David S.
AU - Hochner, Hagit
AU - Friedlander, Yechiel
AU - Enquobahrie, Daniel A.
N1 - Publisher Copyright:
© 2017 Future Medicine Ltd.
PY - 2017/12
Y1 - 2017/12
N2 - Aim: To investigate the role of maternal gestational weight gain (GWG) and prepregnancy BMI on programming offspring DNA methylation. Methods: Among 589 adult (age = 32) women participants of the Jerusalem Perinatal Study, we quantified DNA methylation in five candidate genes. We used inverse probability-weighting and parametric g-formula to estimate direct effects of maternal prepregnancy BMI and GWG on methylation. Results: Higher maternal GWG, but not prepregnancy BMI, was inversely related to offspring ABCA1 methylation (β =-1.1% per quartile; 95% CI:-2.0,-0.3) after accounting for ancestry, parental and offspring exposures. Total and controlled direct effects were nearly identical suggesting included offspring exposures did not mediate this relationship. Results were robust to sensitivity analyses for missing data and model specification. Conclusion: We find some support for epigenetic programming and highlight strengths and limitations of these methods relative to other prevailing approaches.
AB - Aim: To investigate the role of maternal gestational weight gain (GWG) and prepregnancy BMI on programming offspring DNA methylation. Methods: Among 589 adult (age = 32) women participants of the Jerusalem Perinatal Study, we quantified DNA methylation in five candidate genes. We used inverse probability-weighting and parametric g-formula to estimate direct effects of maternal prepregnancy BMI and GWG on methylation. Results: Higher maternal GWG, but not prepregnancy BMI, was inversely related to offspring ABCA1 methylation (β =-1.1% per quartile; 95% CI:-2.0,-0.3) after accounting for ancestry, parental and offspring exposures. Total and controlled direct effects were nearly identical suggesting included offspring exposures did not mediate this relationship. Results were robust to sensitivity analyses for missing data and model specification. Conclusion: We find some support for epigenetic programming and highlight strengths and limitations of these methods relative to other prevailing approaches.
KW - ABCA1
KW - DNA methylation
KW - DOHaD
KW - candidate gene
KW - gestational weight gain
KW - intermediate confounding
KW - marginal structural model
KW - parametric g-formula
UR - http://www.scopus.com/inward/record.url?scp=85034615108&partnerID=8YFLogxK
U2 - 10.2217/epi-2017-0085
DO - 10.2217/epi-2017-0085
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C2 - 29106309
AN - SCOPUS:85034615108
SN - 1750-1911
VL - 9
SP - 1559
EP - 1571
JO - Epigenomics
JF - Epigenomics
IS - 12
ER -