Cross-sectional analysis of BioBank Japan clinical data: A large cohort of 200,000 patients with 47 common diseases

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Author(s)

    • Hirata Makoto
    • Laboratory of Genome Technology, Institute of Medical Science, The University of Tokyo
    • Mushiroda Taisei
    • Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences
    • Murakami Yoshinori
    • Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo
    • Yuji Koichiro
    • Project Division of International Advanced Medical Research, Institute of Medical Science, The University of Tokyo
    • Furukawa Yoichi
    • Division of Clinical Genome Research, Institute of Medical Science, The University of Tokyo
    • Zembutsu Hitoshi
    • Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo|Division of Genetics, National Cancer Center Research Institute
    • Tanaka Toshihiro
    • SNP Research Center, RIKEN Yokohama Institute|Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University|Bioresource Research Center, Tokyo Medical and Dental University
    • Ohnishi Yozo
    • SNP Research Center, RIKEN Yokohama Institute|Shinko Clinic, Medical Corporation Shinkokai
    • Nakamura Yusuke
    • Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo|Section of Hematology/Oncology, Department of Medicine, The University of Chicago
    • Matsuda Koichi
    • Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo|Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo
    • Kamatani Yoichiro
    • Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences
    • Nagai Akiko
    • Department of Public Policy, Institute of Medical Science, The University of Tokyo
    • Ninomiya Toshiharu
    • Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University
    • Tamakoshi Akiko
    • Department of Public Health, Hokkaido University Graduate School of Medicine
    • Muto Kaori
    • Department of Public Policy, Institute of Medical Science, The University of Tokyo

Abstract

<p><i>Background:</i> To implement personalized medicine, we established a large-scale patient cohort, BioBank Japan, in 2003. BioBank Japan contains DNA, serum, and clinical information derived from approximately 200,000 patients with 47 diseases. Serum and clinical information were collected annually until 2012.</p><p><i>Methods:</i> We analyzed clinical information of participants at enrollment, including age, sex, body mass index, hypertension, and smoking and drinking status, across 47 diseases, and compared the results with the Japanese database on Patient Survey and National Health and Nutrition Survey. We conducted multivariate logistic regression analysis, adjusting for sex and age, to assess the association between family history and disease development.</p><p><i>Results:</i> Distribution of age at enrollment reflected the typical age of disease onset. Analysis of the clinical information revealed strong associations between smoking and chronic obstructive pulmonary disease, drinking and esophageal cancer, high body mass index and metabolic disease, and hypertension and cardiovascular disease. Logistic regression analysis showed that individuals with a family history of keloid exhibited a higher odds ratio than those without a family history, highlighting the strong impact of host genetic factor(s) on disease onset.</p><p><i>Conclusions:</i> Cross-sectional analysis of the clinical information of participants at enrollment revealed characteristics of the present cohort. Analysis of family history revealed the impact of host genetic factors on each disease. BioBank Japan, by publicly distributing DNA, serum, and clinical information, could be a fundamental infrastructure for the implementation of personalized medicine.</p>

Journal

  • Journal of Epidemiology

    Journal of Epidemiology 27(Supplement_III), S9-S21, 2017

    Japan Epidemiological Association

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