BMC Psychiatry (Jun 2019)

Methylation analysis for postpartum depression: a case control study

  • Yukako Nakamura,
  • Masahiro Nakatochi,
  • Shohko Kunimoto,
  • Takashi Okada,
  • Branko Aleksic,
  • Miho Toyama,
  • Tomoko Shiino,
  • Mako Morikawa,
  • Aya Yamauchi,
  • Akira Yoshimi,
  • Yoko Furukawa-Hibi,
  • Taku Nagai,
  • Masako Ohara,
  • Chika Kubota,
  • Kiyofumi Yamada,
  • Masahiko Ando,
  • Norio Ozaki

DOI
https://doi.org/10.1186/s12888-019-2172-x
Journal volume & issue
Vol. 19, no. 1
pp. 1 – 9

Abstract

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Abstract Background Postpartum depression (PPD) is a major depressive disorder that occurs after childbirth. Objective diagnostic and predictive methods for PPD are important for early detection and appropriate intervention. DNA methylation has been recognized as a potential biomarker for major depressive disorder. In this study, we used methylation analysis and peripheral blood to search for biomarkers that could to lead to the development a predictive method for PPD. Methods Study participants included 36 pregnant women (18 cases and 18 controls determined after childbirth). Genome-wide DNA methylation profiles were obtained by analysis with an Infinium Human Methylation 450BeadChip. The association of DNA methylation status at each DNA methylation site with PPD was assessed using linear regression analysis. We also conducted functional enrichment analysis of PPD using The Database for Annotation, Visualization and Integrated Discovery 6.8 to explore enriched functional-related gene groups for PPD. Results In the analysis with postpartum depressed state as an independent variable, the difference in methylation frequency between the postpartum non-depressed group and the postpartum depressed group was small, and sites with genome-wide significant differences were not confirmed. After analysis by The Database for Annotation, Visualization and Integrated Discovery 6.8, we revealed four gene ontology terms, including axon guidance, related to postpartum depression. Conclusions These findings may help with the development of an objective predictive method for PPD.

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