Associations between antibiotic exposure during pregnancy, birth weight and aberrant methylation at imprinted genes among offspring

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2013

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Abstract

Objectives: Low birth weight (LBW) has been associated with common adult-onset chronic diseases, including obesity, cardiovascular disease, type II diabetes and some cancers. The etiology of LBW is multi-factorial. However, recent evidence suggests exposure to antibiotics may also increase the risk of LBW. The mechanisms underlying this association are unknown, although epigenetic mechanisms are hypothesized. In this study, we evaluated the association between maternal antibiotic use and LBW and examined the potential role of altered DNA methylation that controls growth regulatory imprinted genes in these associations. Methods: Between 2009-2011, 397 pregnant women were enrolled and followed until delivery. Prenatal antibiotic use was ascertained through maternal self-report. Imprinted genes methylation levels were measured at differentially methylated regions (DMRs) using bisulfite pyrosequencing. Generalized linear models were used to examine associations among antibiotic use, birth weight and DMR methylation fractions. Results: After adjusting for infant gender, race/ethnicity, maternal body mass index, delivery route, gestational weight gain, gestational age at delivery, folic acid intake, physical activity, maternal smoking and parity, antibiotic use during pregnancy was associated with 138 g lower birth weight compared with non-antibiotic use (β-coefficient=-132.99, s.e.=50.70, P=0.008). These associations were strongest in newborns of women who reported antibiotic use other than penicillins (β-coefficient=-135.57, s.e.=57.38, P=0.02). Methylation at five DMRs, IGF2 (P=0.05), H19 (P=0.15), PLAGL1 (P=0.01), MEG3 (P=0.006) and PEG3 (P=0.08), was associated with maternal antibiotic use; among these, only methylation at the PLAGL1 DMR was also associated with birth weight. Conclusion: We report an inverse association between in utero exposure to antibiotics and lower infant birth weight and provide the first empirical evidence supporting imprinted gene plasticity in these associations. © 2013 Macmillan Publishers Limited.

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10.1038/ijo.2013.47

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Vidal, AC, SK Murphy, AP Murtha, JM Schildkraut, A Soubry, Z Huang, SEB Neelon, B Fuemmeler, et al. (2013). Associations between antibiotic exposure during pregnancy, birth weight and aberrant methylation at imprinted genes among offspring. International Journal of Obesity, 37(7). pp. 907–913. 10.1038/ijo.2013.47 Retrieved from https://hdl.handle.net/10161/24664.

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Scholars@Duke

Murphy

Susan Kay Murphy

Associate Professor in Obstetrics and Gynecology

Dr. Murphy is a tenured Associate Professor in the Department of Obstetrics and Gynecology and serves as Chief of the Division of Reproductive Sciences. As a molecular biologist with training in human epigenetics, her research interests are largely centered around the role of epigenetic modifications in health and disease. 

Dr. Murphy has ongoing projects on gynecologic malignancies, including approaches to eradicate ovarian cancer cells that survive chemotherapy and later give rise to recurrent disease. Dr. Murphy is actively involved in many collaborative projects relating to the Developmental Origins of Health and Disease (DOHaD).

Her lab is currently working on preconception environmental exposures in males, particularly on the impact of cannabis on the sperm epigenome and the potential heritability of these effects. They are also studying the epigenetic and health effects of in utero exposures, with primary focus on children from the Newborn Epigenetics STudy (NEST), a pregnancy cohort she co-founded who were recruited from central North Carolina between 2005 and 2011. Dr. Murphy and her colleagues continue to follow NEST children to determine relationships between prenatal exposures and later health outcomes.

Iversen

Edwin Severin Iversen

Research Professor of Statistical Science

Bayesian statistical modeling with application to problems in genetic
epidemiology and cancer research; models for epidemiological risk
assessment, including hierarchical methods for combining related
epidemiological studies; ascertainment corrections for high risk
family data; analysis of high-throughput genomic data sets.


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