Evidence map›Paper›PMID 28190660›Full record

ReviewJournal of epidemiology2017

Overview of BioBank Japan follow-up data in 32 diseases.

Makoto Hirata, Akiko Nagai, Yoichiro Kamatani, Toshiharu Ninomiya, Akiko Tamakoshi, Zentaro Yamagata, Michiaki Kubo, Kaori Muto, Yutaka Kiyohara, Taisei Mushiroda and 9 more

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in Journal of epidemiology, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers, 4 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
39citing papers in PubMed, 4 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

39 citing papers in PubMed, 4 syntheses or guidelines pooled it.

  1. Pooled it
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  5. Polygenic Prediction of Nongoal Response to Statin Therapy.Circulation. Genomic and precision medicine · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

19 authors.

Makoto HirataLaboratory of Genome Technology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Akiko NagaiDepartment of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Yoichiro KamataniLaboratory for Statistical Analysis, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Toshiharu NinomiyaDepartment of Epidemiology and Public Health, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan.
Akiko TamakoshiDepartment of Public Health, Hokkaido University Graduate School of Medicine, Sapporo, Japan.
Zentaro YamagataDepartment of Health Sciences, University of Yamanashi, Yamanashi, Japan.
Michiaki KuboRIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Kaori MutoDepartment of Public Policy, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Yutaka KiyoharaHisayama Research Institute for Lifestyle Diseases, Fukuoka, Japan.
Taisei MushirodaLaboratory for Pharmacogenomics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan.
Yoshinori MurakamiDivision of Molecular Pathology, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Koichiro YujiProject Division of International Advanced Medical Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Yoichi FurukawaDivision of Clinical Genome Research, Institute of Medical Science, The University of Tokyo, Tokyo, Japan.
Hitoshi ZembutsuLaboratory of Molecular Medicine, Institute of Medical Science, The University of Tokyo, Tokyo, Japan; Division of Genetics, National Cancer Center Research Institute, Tokyo, Japan.
Toshihiro TanakaSNP 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.
Yozo OhnishiSNP Research Center, RIKEN Yokohama Institute, Yokohama, Japan; Shinko Clinic, Medical Corporation Shinkokai, Tokyo, Japan.
Yusuke NakamuraLaboratory 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.
BioBank Japan Cooperative Hospital Group
Koichi MatsudaLaboratory 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.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWe established a patient-oriented biobank, BioBank Japan, with information on approximately 200,000 patients, suffering from any of 47 common diseases. This follow-up survey focused on 32 diseases, potentially associated with poor vital prognosis, and collected patient survival information, including cause of death. We performed a survival analysis for all subjects to get an overview of BioBank Japan follow-up data.

methodsA total of 141,612 participants were included. The survival data were last updated in 2014. Kaplan-Meier survival analysis was performed after categorizing subjects according to sex, age group, and disease status. Relative survival rates were estimated using a survival-rate table of the Japanese general population.

resultsOf 141,612 subjects (56.48% male) with 1,087,434 person-years and a 97.0% follow-up rate, 35,482 patients died during follow-up. Mean age at enrollment was 64.24 years for male subjects and 63.98 years for female subjects. The 5-year and 10-year relative survival rates for all subjects were 0.944 and 0.911, respectively, with a median follow-up duration of 8.40 years. Patients with pancreatic cancer had the least favorable prognosis (10-year relative survival: 0.184) and patients with dyslipidemia had the most favorable prognosis (1.013). The most common cause of death was malignant neoplasms. A number of subjects died from diseases other than their registered disease(s).

conclusionsThis is the first report to perform follow-up survival analysis across various common diseases. Further studies should use detailed clinical and genomic information to identify predictors of mortality in patients with common diseases, contributing to the implementation of personalized medicine.

Indexed as

Biological Specimen BanksDiseaseCause of DeathFollow-Up StudiesHealth SurveysHumansJapanSurvival AnalysisBiobankBioBank Japan projectClinical informationCommon diseaseFollow-up survey

Identifiers

PMID28190660
PMCPMC5363789

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.