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J Epidemiol. 2017 Mar;27(3S):S9-S21. doi: 10.1016/j.je.2016.12.003. Epub 2017 Feb 9.

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

Author information

1
Laboratory of Genome Technology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
2
Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
3
Department of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
4
Hisayama Research Institute for Lifestyle Diseases, Fukuoka, Japan.
5
Department of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
6
Department of Public Health, Hokkaido University Graduate School of Medicine, Sapporo, Japan.
7
Department of Health Sciences, University of Yamanashi, Yamanashi, Japan.
8
RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
9
Laboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
10
Division of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
11
Project Division of International Advanced Medical Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
12
Division of Clinical Genome Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
13
Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Division of Genetics, National Cancer Center Research Institute, Tokyo, Japan.
14
SNP Research Center, RIKEN Yokohama Institute, Yokohama, Japan; Department of Human Genetics and Disease Diversity, Graduate School of Medical and Dental Sciences, Tokyo Medical and Dental University, Tokyo, Japan; Bioresource Research Center, Tokyo Medical and Dental University, Tokyo, Japan.
15
SNP Research Center, RIKEN Yokohama Institute, Yokohama, Japan; Shinko Clinic, Medical Corporation Shinkokai, Tokyo, Japan.
16
Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Section of Hematology/Oncology, Department of Medicine, The University of Chicago, Chicago, USA.
17
Laboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Laboratory of Clinical Genome Sequencing, Graduate School of Frontier Sciences, The University of Tokyo, Tokyo, Japan. Electronic address: kmatsuda@k.u-tokyo.ac.jp.
18
Tokushukai Hospitals, Japan.
19
Nippon Medical School, Japan.
20
Juntendo University, Japan.
21
Nihon University, Japan.
22
Iwate Medical University, Japan.
23
Tokyo Metropolitan Institute of Gerontology, Japan.
24
The Cancer Institute Hospital of JFCR, Japan.
25
Aso Iizuka Hospital, Japan.
26
Osaka Medical Center for Cancer and Cardiovascular Diseases, Japan.
27
Shiga University of Medical Science, Japan.
28
National Hospital Organization, Osaka National Hospital, Japan.
29
Fukujuji Hospital, Japan.

Abstract

BACKGROUND:

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.

METHODS:

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.

RESULTS:

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.

CONCLUSIONS:

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.

KEYWORDS:

BioBank Japan Project; Biobank; Clinical information; Common disease; Family history

PMID:
28190657
PMCID:
PMC5363792
DOI:
10.1016/j.je.2016.12.003
[Indexed for MEDLINE]
Free PMC Article

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