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

Biobank Japan Cooperative Hospital Group

Research output: Contribution to journalArticle

38 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)S9-S21
JournalJournal of epidemiology
Volume27
Issue number3
DOIs
Publication statusPublished - Jan 1 2017

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Japan
Cross-Sectional Studies
Drinking
Body Mass Index
Logistic Models
Smoking
Regression Analysis
Hypertension
Keloid
Precision Medicine
Nutrition Surveys
Metabolic Diseases
Age Distribution
Esophageal Neoplasms
Health Surveys
Serum
Age of Onset
Chronic Obstructive Pulmonary Disease
Lung Neoplasms
Cardiovascular Diseases

All Science Journal Classification (ASJC) codes

  • Epidemiology

Cite this

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

In: Journal of epidemiology, Vol. 27, No. 3, 01.01.2017, p. S9-S21.

Research output: Contribution to journalArticle

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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.",
author = "{Biobank Japan Cooperative Hospital Group} and Makoto Hirata and Yoichiro Kamatani and Akiko Nagai and Yutaka Kiyohara and Toshiharu Ninomiya and Akiko Tamakoshi and Zentaro Yamagata and Michiaki Kubo and Kaori Muto and Taisei Mushiroda and Yoshinori Murakami and Koichiro Yuji and Yoichi Furukawa and Hitoshi Zembutsu and Toshihiro Tanaka and Yozo Ohnishi and Yusuke Nakamura and Koichi Matsuda and Masaki Shiono and Kazuo Misumi and Reiji Kaieda and Hiromasa Harada and Shiro Minami and Mitsuru Emi and Naoya Emoto and Hajime Arai and Ken Yamaji and Yoshimune Hiratsuka and Satoshi Asai and Mitsuhiko Moriyama and Yasuo Takahashi and Tomoaki Fujioka and Wataru Obara and Seijiro Mori and Hideki Ito and Satoshi Nagayama and Yoshio Miki and Akihide Masumoto and Akira Yamada and Yasuko Nishizawa and Ken Kodama and Hiromu Kutsumi and Yoshihisa Sugimoto and Yukihiro Koretsune and Hideo Kusuoka and Takashi Yoshiyama",
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AU - Biobank Japan Cooperative Hospital Group

AU - Hirata, Makoto

AU - Kamatani, Yoichiro

AU - Nagai, Akiko

AU - Kiyohara, Yutaka

AU - Ninomiya, Toshiharu

AU - Tamakoshi, Akiko

AU - Yamagata, Zentaro

AU - Kubo, Michiaki

AU - Muto, Kaori

AU - Mushiroda, Taisei

AU - Murakami, Yoshinori

AU - Yuji, Koichiro

AU - Furukawa, Yoichi

AU - Zembutsu, Hitoshi

AU - Tanaka, Toshihiro

AU - Ohnishi, Yozo

AU - Nakamura, Yusuke

AU - Matsuda, Koichi

AU - Shiono, Masaki

AU - Misumi, Kazuo

AU - Kaieda, Reiji

AU - Harada, Hiromasa

AU - Minami, Shiro

AU - Emi, Mitsuru

AU - Emoto, Naoya

AU - Arai, Hajime

AU - Yamaji, Ken

AU - Hiratsuka, Yoshimune

AU - Asai, Satoshi

AU - Moriyama, Mitsuhiko

AU - Takahashi, Yasuo

AU - Fujioka, Tomoaki

AU - Obara, Wataru

AU - Mori, Seijiro

AU - Ito, Hideki

AU - Nagayama, Satoshi

AU - Miki, Yoshio

AU - Masumoto, Akihide

AU - Yamada, Akira

AU - Nishizawa, Yasuko

AU - Kodama, Ken

AU - Kutsumi, Hiromu

AU - Sugimoto, Yoshihisa

AU - Koretsune, Yukihiro

AU - Kusuoka, Hideo

AU - Yoshiyama, Takashi

PY - 2017/1/1

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N2 - 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.

AB - 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.

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