Jonathan Y Huang1,2, David S Siscovick3, Hagit Hochner4, Yechiel Friedlander4, Daniel A Enquobahrie1. 1. Department of Epidemiology, University of Washington, Seattle, WA, USA. 2. Department of Epidemiology, Biostatistics and Occupational Health; Institute for Health & Social Policy; McGill University, Montreal, QC, Canada. 3. New York Academy of Medicine, New York, NY, USA. 4. Braun School of Public Health, Hebrew University-Hadassah Medical Center, Jerusalem, Israel.
Abstract
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.
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) womenparticipants 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.
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