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日本生物银行临床数据的横断面分析:20万名患有47种常见疾病的患者的大型队列研究。

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

作者信息

Hirata Makoto, Kamatani Yoichiro, Nagai Akiko, Kiyohara Yutaka, Ninomiya Toshiharu, Tamakoshi Akiko, Yamagata Zentaro, Kubo Michiaki, Muto Kaori, Mushiroda Taisei, Murakami Yoshinori, Yuji Koichiro, Furukawa Yoichi, Zembutsu Hitoshi, Tanaka Toshihiro, Ohnishi Yozo, Nakamura Yusuke, Matsuda Koichi

机构信息

Laboratory of Genome Technology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.

Laboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.

出版信息

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

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.

摘要

背景

为实施个性化医疗,我们于2003年建立了一个大规模患者队列——日本生物银行。日本生物银行包含来自约20万名患有47种疾病的患者的DNA、血清和临床信息。血清和临床信息一直收集到2012年。

方法

我们分析了47种疾病参与者入组时的临床信息,包括年龄、性别、体重指数、高血压以及吸烟和饮酒状况,并将结果与日本患者调查和国民健康与营养调查数据库进行了比较。我们进行了多因素逻辑回归分析,对性别和年龄进行了调整,以评估家族史与疾病发生之间的关联。

结果

入组时的年龄分布反映了疾病发病的典型年龄。对临床信息的分析显示,吸烟与慢性阻塞性肺疾病、饮酒与食管癌、高体重指数与代谢性疾病、高血压与心血管疾病之间存在密切关联。逻辑回归分析表明,有瘢痕疙瘩家族史的个体比没有家族史的个体具有更高的优势比,突出了宿主遗传因素对疾病发病的强烈影响。

结论

对入组参与者临床信息的横断面分析揭示了当前队列的特征。对家族史的分析揭示了宿主遗传因素对每种疾病的影响。通过公开分发DNA、血清和临床信息,日本生物银行可能成为实施个性化医疗的基础基础设施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2620/5363792/15555f2b3eec/gr1.jpg

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