Clinical Medicine, Molecular and Cellular Biology, Genomics/Genetics and Epigenetics

Feto-Maternal Medical Science

Establishment of Personalized OB/GYN Care by Integrative Analysis of Gene-Environment Interaction

Medical Sciences Course

  • Master / Doctoral Degree



Professor, MD, PhD

Research Theme

  • Omics analysis for perinatal diseases (e.g. pregnancy induced hypertension)
  • Early prediction by Bioinformatics for Pregnancy related disorders
  • DOHaD(Developmental Origins of Health and Disease)
Research Keywords:

Hypertensive Disorders of Pregnancy, Genomics, Omics, Obstetrics and gynecology, Community medicine

Technical Keywords:

Genomics, Genome Wide Association Study (GWAS), Metabolomics, Transcriptomics, Epidemiological analysis

Laboratory Introduction

1. ToMMo Birth and Three Generation Cohort Study
Though this cohort project, we have started multi-omics analysis to clarify pathophysiology of perinatal disorders. Particularly, genetic analysis with family tree genomic information would be powerful tools to elucidate undetermined mechanisms of maternal-fetal disorders.
2. Early Prediction by Bioinformatics for Pregnancy Related Disorders
In the MLOG (Maternity Life Log) project, participants will collect daily health parameters, such as body weight, duration of sleep, activity amount, blood pressure and body temperature using health care devices. With the multilayer integration of these life log information, lifestyle habits and omics analysis, we try to establish mobile healthcare programs for the prediction of pregnancy related disorders, including pregnancy induced hypertension, gestational diabetes and premature labor. MLOG project is the collaborative research with NTT DOCOMO with a widely accepted commercial service of mobile health service in Japan.
3. Establishment of Biomarkers for the diagnosis of feto-maternal disorders.
Using newly developed in silico technology, variety of omics analysis by the project team with multiple laboratories are in progress. These studies would reveal new biomarkers for the early prediction of perinatal disease (e.g. preeclampsia). Development of clinical diagnostic systems for personalized prevention would be strongly expected.

Figure 1. laboratory introduction

Figure 1. laboratory introduction

Recent Publications

  • Nishizawa A, et al. Analysis of HLA-G long-read genomic sequences in mother-offspring pairs with preeclampsia. Sci Rep. 2020 Nov 18;10(1):20027. doi: 10.1038/s41598-020-77081-3.
  • Nagaoka S, et al. Estimation of the carrier frequencies and proportions of potential patients by detecting causative gene variants associated with autosomal recessive bone dysplasia using a whole-genome reference panel of Japanese individuals. Hum Genome Var. 2021 Jan 15;8(1):2. doi: 10.1038/s41439-020-00133-7.
  • Sugawara J, et al. Maternal Baseline Characteristics and Perinatal Outcomes: the Tohoku Medical Megabank Project Birth and Three-Generation Cohort Study. J Epidemiol. 2020 Oct 10. doi: 10.2188/jea.JE20200338.
  • Sugawara J, et al. Maternity Log study: a longitudinal lifelog monitoring and multiomics analysis for the early prediction of complicated pregnancy. BMJ Open. 2019 Feb 19;9(2):e025939. doi: 10.1136/bmjopen-2018-025939.
  • Sugawara J, et al. Regional Birth Outcomes after the 2011 Great East Japan Earthquake and Tsunami in Miyagi Prefecture. Prehosp Disaster Med. 2018 Apr;33(2):215-219. doi: 10.1017/S1049023X18000183.