IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v18y2021i10p5083-d552418.html
   My bibliography  Save this article

Epigenetic Alterations of Maternal Tobacco Smoking during Pregnancy: A Narrative Review

Author

Listed:
  • Aurélie Nakamura

    (Université Grenoble Alpes, Inserm, CNRS, IAB, 38000 Grenoble, France)

  • Olivier François

    (Université Grenoble Alpes, Laboratoire TIMC, CNRS UMR 5525, 38000 Grenoble, France)

  • Johanna Lepeule

    (Université Grenoble Alpes, Inserm, CNRS, IAB, 38000 Grenoble, France)

Abstract

In utero exposure to maternal tobacco smoking is the leading cause of birth complications in addition to being associated with later impairment in child’s development. Epigenetic alterations, such as DNA methylation (DNAm), miRNAs expression, and histone modifications, belong to possible underlying mechanisms linking maternal tobacco smoking during pregnancy and adverse birth outcomes and later child’s development. The aims of this review were to provide an update on (1) the main results of epidemiological studies on the impact of in utero exposure to maternal tobacco smoking on epigenetic mechanisms, and (2) the technical issues and methods used in such studies. In contrast with miRNA and histone modifications, DNAm has been the most extensively studied epigenetic mechanism with regard to in utero exposure to maternal tobacco smoking. Most studies relied on cord blood and children’s blood, but placenta is increasingly recognized as a powerful tool, especially for markers of pregnancy exposures. Some recent studies suggest reversibility in DNAm in certain genomic regions as well as memory of smoking exposure in DNAm in other regions, upon smoking cessation before or during pregnancy. Furthermore, reversibility could be more pronounced in miRNA expression compared to DNAm. Increasing evidence based on longitudinal data shows that maternal smoking-associated DNAm changes persist during childhood. In this review, we also discuss some issues related to cell heterogeneity as well as downstream statistical analyses used to relate maternal tobacco smoking during pregnancy and epigenetics. The epigenetic effects of maternal smoking during pregnancy have been among the most widely investigated in the epigenetic epidemiology field. However, there are still huge gaps to fill in, including on the impact on miRNA expression and histone modifications to get a better view of the whole epigenetic machinery. The consistency of maternal tobacco smoking effects across epigenetic marks and across tissues will also provide crucial information for future studies. Advancement in bioinformatic and biostatistics approaches is key to develop a comprehensive analysis of these biological systems.

Suggested Citation

  • Aurélie Nakamura & Olivier François & Johanna Lepeule, 2021. "Epigenetic Alterations of Maternal Tobacco Smoking during Pregnancy: A Narrative Review," IJERPH, MDPI, vol. 18(10), pages 1-19, May.
  • Handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5083-:d:552418
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/18/10/5083/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1660-4601/18/10/5083/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. World Health Organization, 2019. "WHO report on the global tobacco epidemic 2019: Offer help to quit tobacco use," University of California at San Francisco, Center for Tobacco Control Research and Education qt1g16k8b9, Center for Tobacco Control Research and Education, UC San Francisco.
    2. Allison A Appleton & David A Armstrong & Corina Lesseur & Joyce Lee & James F Padbury & Barry M Lester & Carmen J Marsit, 2013. "Patterning in Placental 11-B Hydroxysteroid Dehydrogenase Methylation According to Prenatal Socioeconomic Adversity," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-7, September.
    3. Sol Pía Juárez & Juan Merlo, 2013. "Revisiting the Effect of Maternal Smoking during Pregnancy on Offspring Birthweight: A Quasi-Experimental Sibling Analysis in Sweden," PLOS ONE, Public Library of Science, vol. 8(4), pages 1-7, April.
    4. Parnian Kheirkhah Rahimabad & Thilani M. Anthony & A. Daniel Jones & Shakiba Eslamimehr & Nandini Mukherjee & Susan Ewart & John W. Holloway & Hasan Arshad & Sarah Commodore & Wilfried Karmaus, 2020. "Nicotine and Its Downstream Metabolites in Maternal and Cord Sera: Biomarkers of Prenatal Smoking Exposure Associated with Offspring DNA Methylation," IJERPH, MDPI, vol. 17(24), pages 1-15, December.
    5. Kevin G. Murphy & Stephen R. Bloom, 2006. "Gut hormones and the regulation of energy homeostasis," Nature, Nature, vol. 444(7121), pages 854-859, December.
    6. John D. Storey, 2002. "A direct approach to false discovery rates," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 479-498, August.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Mikael O. Ekblad & Julie Blanc & Ivan Berlin, 2021. "Special Issue on the Effects of Prenatal Smoking/Nicotine Exposure on the Child’s Health," IJERPH, MDPI, vol. 18(10), pages 1-4, May.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Youngchao Ge & Sandrine Dudoit & Terence Speed, 2003. "Resampling-based multiple testing for microarray data analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 12(1), pages 1-77, June.
    2. Bajgrowicz, Pierre & Scaillet, Olivier, 2012. "Technical trading revisited: False discoveries, persistence tests, and transaction costs," Journal of Financial Economics, Elsevier, vol. 106(3), pages 473-491.
    3. Maria Neufeld & Carina Ferreira-Borges & Jürgen Rehm, 2020. "Implementing Health Warnings on Alcoholic Beverages: On the Leading Role of Countries of the Commonwealth of Independent States," IJERPH, MDPI, vol. 17(21), pages 1-20, November.
    4. Wen Shi & Xi Chen & Jennifer Shang, 2019. "An Efficient Morris Method-Based Framework for Simulation Factor Screening," INFORMS Journal on Computing, INFORMS, vol. 31(4), pages 745-770, October.
    5. Dørum Guro & Snipen Lars & Solheim Margrete & Saebo Solve, 2011. "Smoothing Gene Expression Data with Network Information Improves Consistency of Regulated Genes," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 10(1), pages 1-26, August.
    6. Jianqing Fan & Xu Han, 2017. "Estimation of the false discovery proportion with unknown dependence," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(4), pages 1143-1164, September.
    7. A Bottle & P Aylin, 2011. "Predicting the false alarm rate in multi-institution mortality monitoring," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 62(9), pages 1711-1718, September.
    8. Van Hanh Nguyen & Catherine Matias, 2014. "On Efficient Estimators of the Proportion of True Null Hypotheses in a Multiple Testing Setup," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 41(4), pages 1167-1194, December.
    9. Shigeyuki Matsui & Hisashi Noma, 2011. "Estimating Effect Sizes of Differentially Expressed Genes for Power and Sample-Size Assessments in Microarray Experiments," Biometrics, The International Biometric Society, vol. 67(4), pages 1225-1235, December.
    10. Lianming Wang & David B. Dunson, 2010. "Semiparametric Bayes Multiple Testing: Applications to Tumor Data," Biometrics, The International Biometric Society, vol. 66(2), pages 493-501, June.
    11. Ebrahimi, Nader, 2008. "Simultaneous control of false positives and false negatives in multiple hypotheses testing," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 437-450, March.
    12. B. Moerkerke & E. Goetghebeur & J. De Riek & I. Roldán‐Ruiz, 2006. "Significance and impotence: towards a balanced view of the null and the alternative hypotheses in marker selection for plant breeding," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 169(1), pages 61-79, January.
    13. Zaili Fang & Inyoung Kim & Jeesun Jung, 2018. "Semiparametric Kernel-Based Regression for Evaluating Interaction Between Pathway Effect and Covariate," Journal of Agricultural, Biological and Environmental Statistics, Springer;The International Biometric Society;American Statistical Association, vol. 23(1), pages 129-152, March.
    14. Mark Rempel, 2016. "Improving Overnight Loan Identification in Payments Systems," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 48(2-3), pages 549-564, March.
    15. Timothy B. Armstrong, 2014. "Adaptive Testing on a Regression Function at a Point," Cowles Foundation Discussion Papers 1957R, Cowles Foundation for Research in Economics, Yale University, revised Feb 2015.
    16. Nucera, Federico & Valente, Giorgio, 2013. "Carry trades and the performance of currency hedge funds," Journal of International Money and Finance, Elsevier, vol. 33(C), pages 407-425.
    17. Nickole Moon & Christopher P. Morgan & Ruth Marx-Rattner & Alyssa Jeng & Rachel L. Johnson & Ijeoma Chikezie & Carmen Mannella & Mary D. Sammel & C. Neill Epperson & Tracy L. Bale, 2024. "Stress increases sperm respiration and motility in mice and men," Nature Communications, Nature, vol. 15(1), pages 1-20, December.
    18. Axel Gandy & Georg Hahn, 2016. "A Framework for Monte Carlo based Multiple Testing," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 43(4), pages 1046-1063, December.
    19. Sinha, Sanjoy K. & Kaushal, Amit & Xiao, Wenzhong, 2014. "Inference for longitudinal data with nonignorable nonmonotone missing responses," Computational Statistics & Data Analysis, Elsevier, vol. 72(C), pages 77-91.
    20. Iain Melvin & Jason Weston & William Stafford Noble & Christina Leslie, 2011. "Detecting Remote Evolutionary Relationships among Proteins by Large-Scale Semantic Embedding," PLOS Computational Biology, Public Library of Science, vol. 7(1), pages 1-8, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jijerp:v:18:y:2021:i:10:p:5083-:d:552418. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.