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Journal of Epidemiology logoLink to Journal of Epidemiology
. 2017 Feb 9;27(3 Suppl):S9–S21. doi: 10.1016/j.je.2016.12.003

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

Makoto Hirata a, Yoichiro Kamatani b, Akiko Nagai c, Yutaka Kiyohara d, Toshiharu Ninomiya e, Akiko Tamakoshi f, Zentaro Yamagata g, Michiaki Kubo h, Kaori Muto c, Taisei Mushiroda i, Yoshinori Murakami j, Koichiro Yuji k, Yoichi Furukawa l, Hitoshi Zembutsu m,n, Toshihiro Tanaka o,p,q, Yozo Ohnishi o,r, Yusuke Nakamura m,s; BioBank Japan Cooperative Hospital Groupv,w,x,y,z,aa,ab,ac,ad,ae,af,ag,u, Koichi Matsuda m,t,∗
PMCID: PMC5363792  PMID: 28190657

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, Common disease, Clinical information, Family history

Highlights

  • •

    The BioBank Japan Project (BBJ) annually collected clinical information.

  • •

    Analysis of the clinical information at enrollment characterized the BBJ cohort.

  • •

    Analysis of family history revealed impacts of host genetic factors on the diseases.

Introduction

BioBank Japan (BBJ) was established with the cooperation of 12 medical institutes, consisting of over 60 hospitals, as a leading project of the Ministry of Education, Culture, Sports, Science and Technology in 2003.1, 2 As a disease-oriented biobank, BBJ collected DNA and serum samples from approximately 200,000 patients with 47 diseases. BBJ annually updates clinical information, which is another essential element of biobanks.3 The clinical information associated with the biospecimens was utilized in previous studies to select or stratify the participant group. Samples and their clinical information were used for over 200 studies.4 However, so far, a comprehensive analysis of the clinical information of the BBJ cohorts has not been conducted. Here, we analyzed clinical information including age, sex, body mass index (BMI), hypertension, smoking, and drinking status across 47 diseases, and compared the results with the Japanese database. In addition, we assessed the association between target diseases and positive family history.

Materials and methods

Study design

In the present cohort, we focused on 47 common diseases (Table 1). Patients diagnosed with any one of the 47 diseases were recruited from 66 hospitals affiliated with 12 medical institutes between fiscal year of 2003 and 2007. The detailed protocol of the recruitment process has been described elsewhere.2 Written informed consent was obtained from all participants. The study protocol was reviewed and approved by the Ethics Committees of all participating institutions, including the Institute of Medical Science, the University of Tokyo, and the Center for Integrative Medical Sciences, RIKEN.

Table 1.

Baseline characteristics of participants with 47 diseases in the present cohort.

47 Diseases Number of Subjects Mean (SD) age at registration (y)
 % of male subjects % of male patients (Patient survey)
Male Female
Whole cohort 199,982 62.66 14.66 61.55 16.02 53.05 N/A
Lung cancer 3779 67.64 9.54 66.07 9.81 64.25 50.51
Esophageal cancer 1291 65.66 8.06 65.56 10.44 86.29 84.00
Gastric cancer 6322 67.01 9.90 65.18 11.77 73.39 66.27
Colorectal cancer 6759 67.10 9.95 66.42 10.86 62.76 55.54
Liver cancer 1924 67.37 8.47 69.97 8.15 75.68 68.18
Pancreatic cancer 392 66.02 9.80 66.21 11.02 64.54 50.85
Gallbladder/cholangiocarcinoma 392 67.71 9.22 68.75 9.05 62.50 51.02
Prostate cancer 5066 72.60 7.46 N/A 100.00 100.00
Breast cancer 6336 63.74 11.21 57.67 11.98 0.73 1.33
Uterine cervical cancer 1218 N/A 51.83 13.33 0.00 0.00
Uterine corpus cancer 1026 N/A 58.93 10.65 0.00 0.00
Ovarian cancer 888 N/A 56.39 11.91 0.00 0.00
Hematological cancer 1307 60.99 15.08 60.26 16.65 54.32 53.97
Cerebral infarction 16,534 68.82 9.90 71.68 10.60 62.27 44.37
Cerebral aneurysm 2710 60.52 11.51 62.84 10.78 35.24 N/A
Epilepsy 2303 46.56 21.75 43.31 21.98 57.27 54.42
Bronchial asthma 8700 51.89 23.11 53.54 20.68 49.32 51.51
Pulmonary tuberculosis 863 62.14 16.82 62.43 19.34 71.38 64.10
Chronic obstructive pulmonary disease 2774 72.33 8.57 72.71 9.82 86.81 68.28
Interstitial lung disease/pulmonary fibrosis 808 68.74 11.41 68.11 11.97 58.04 55.32
Myocardial infarction 13,272 65.92 10.37 71.19 9.90 80.98 64.32
Unstable angina 4330 66.76 9.71 71.26 9.15 73.70 55.20
Stable angina 14,807 67.86 9.81 71.05 9.71 69.39 55.20
Arrhythmia 15,912 67.03 11.67 69.27 12.52 64.38 52.24
Heart failure 7610 66.01 12.63 71.46 12.72 61.81 38.18
Peripheral arterial diseases 2683 70.84 9.02 71.70 9.97 78.12 61.97
Chronic hepatitis B 1346 54.57 13.21 55.62 14.97 62.63 62.50
Chronic hepatitis C 5819 63.37 11.84 64.64 11.92 53.70 52.92
Liver cirrhosis 2519 62.74 11.50 65.38 14.17 62.29 49.52
Nephrotic syndrome 1056 47.45 22.88 48.23 21.74 60.32 58.06
Urolithiasis 6307 53.02 13.72 56.90 14.42 75.60 67.42
Osteoporosis 6743 72.28 12.89 73.77 9.57 7.59 7.23
Diabetes mellitus 39,697 63.31 11.33 65.80 12.00 63.23 52.74
Dyslipidemia 43,812 62.15 11.97 66.26 10.79 50.76 33.55
Graves' disease 2323 49.86 14.23 49.04 15.75 27.85 22.22
Rheumatoid arthritis 4139 64.05 12.12 62.39 12.29 20.25 18.87
Hay fever 5658 46.39 17.63 44.94 15.84 42.93 46.74
Drug eruption 585 60.53 16.17 54.82 17.46 45.81 N/A
Atopic dermatitis 2938 29.98 14.85 29.74 13.54 53.13 51.61
Keloid 809 48.53 19.97 43.31 19.60 38.94 N/A
Uterine fibroid 5904 N/A 44.69 9.49 0.00 0.00
Endometriosis 1843 N/A 38.93 8.22 0.00 0.00
Febrile seizure 333 4.16 3.57 4.35 5.09 60.96 N/A
Glaucoma 4755 66.87 12.43 70.03 10.95 46.79 41.98
Cataract 20,002 70.43 10.31 72.91 9.52 44.81 36.83
Periodontitis 3898 58.20 15.92 56.59 16.00 43.69 41.03
Amyotrophic lateral sclerosis 782 60.86 10.21 61.03 10.76 64.32 N/A
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We included patients who had been diagnosed with the diseases by physicians at the cooperating hospitals (eTable 1). As this project registered not only patients with newly developed diseases but also patients who were diagnosed and treated before starting the project, some participants were enrolled several years after disease onset or diagnosis.2 We excluded patients who had received a bone marrow transplant and those who were not of East Asian descent.

Clinical information

Clinical information including common clinical variables, disease-specific variables, prescriptions, and drug side-effect information, was collected from each participant. The detailed methods of the collection of clinical information has been described elsewhere.2 The clinical database was updated every year until 2012. After a thorough review and data-cleansing of clinical variables,2 clinical information of 199,982 participants with 47 diseases at enrollment was established on March 31 2015 and used in the current study.

Japanese database

The Ministry of Health, Labour and Welfare in Japan conducts a Patient Survey every three years and a National Health and Nutrition Survey every year. We obtained the results of the Patient Survey of 20055 and those of the National Health and Nutrition Survey of 2006.6 Table 65 in the Patient Survey was used to estimate Japanese patient numbers, stratified by sex and age for each disease. Distributions of BMI categories, hypertension prevalence, smoking history, and alcohol intake history in the general Japanese population were calculated from Tables 23, 49-2, 97, and 91 of the National Health and Nutrition Survey, respectively.

Analysis of clinical information

The distributions of BMI, hypertension prevalence, smoking history, and alcohol intake history in the BBJ cohort were adjusted for sex and age group for each table in the national public survey when we compared the distributions among the 47 diseases and Japanese database. BMI category and hypertension were defined according to World Health Organization (WHO) criteria as follows: BMI < 18.5 was defined as underweight, 18.5 ≤ BMI < 25 as normal, 25 ≤ BMI < 30 as overweight, and 30 ≤ BMI as obese7; hypertension was defined as systolic blood pressure ≥140 mmHg and diastolic blood pressure ≥90 mmHg or when participants were prescribed antihypertensive drugs. Multivariate logistic regression analyses were performed to assess the association between each target disease and positive family history associated with the target disease, adjusted for sex and age. SAS 9.4 software was used for the data analysis. A p-value of <0.05 was considered statistically significant.

Results

Basic characteristics (Age and sex)

We characterized the BioBank Japan cohort at enrollment by analyzing common clinical variables of age and sex across the target diseases. Mean age at enrollment, across the entire cohort or for each disease, was comparable between both sexes, but varied among the diseases (Table 1). The highest mean age was observed in men with prostate cancer and in women with osteoporosis (72.60 and 73.77 years, respectively), while the youngest mean age was observed in men and women with febrile seizures (4.16 and 4.35 years, respectively), reflecting the typical age of onset of each disease. A greater number of men were registered in the BBJ cohort compared to women (53.05% vs. 46.95%), while sex ratios varied according to the diseases (Table 1).

To highlight sex and age characteristics of the BBJ cohort, we further compared the sex and age distribution for each disease with the Patient Survey. We included participants with 42 out of the 47 diseases for the comparison, as we obtained the relevant clinical data from the Patient Survey (eTable 2). Almost all diseases displayed equivalent age distributions, while lower proportions of participants <20 years of age were observed in three diseases (bronchial asthma, atopic dermatitis and hay fever), which are likely to occur in younger populations (eFigs. 1.1–1.5 and eTable 3). The proportion of male participants with dyslipidemia was considerably higher in the BBJ cohort (50.76%) than in the Patient Survey (33.55%), although both age distributions appeared equivalent. The low proportion of female patients with heart failure aged ≥80 years resulted in a lower proportion of female participants in the BBJ cohort. We also observed a low proportion of elderly female participants with cerebral infarction, chronic obstructive pulmonary disease (COPD), peripheral arterial diseases (PAD), unstable angina, stable angina, and myocardial infarction in the BBJ cohort. Varied distributions between the BBJ cohort and Patient Survey were observed in pulmonary tuberculosis and nephrotic syndrome.

Basic characteristics (Lifestyle and physical status)

We also evaluated life style including smoking and alcohol intake history, and physical status including BMI and blood pressure, at enrollment in the BBJ cohort. We included participants ≥20 years of age in this analysis because the frequency of smoking, alcohol intake, and hypertension among individuals under 20 years of age is quite low, and the criteria for underweight or obesity according to BMI in children and teenagers are different from those applied to adults.8 Furthermore, we compared the BBJ cohort and the National Health and Nutrition Survey 2006 for physical and life style, after adjusting for sex and age, because sex- and age-distribution varied among diseases.

Smoking history at enrollment (including subjects both with and without information on current smoking status) was positive in 74.98% of male subjects and 21.24% of female subjects in the BBJ cohort, while current smokers accounted for 27.78% of male subjects and 10.45% of female subjects (Table 2). The highest frequency of positive smoking history in both sexes was observed in COPD, followed by PAD in male subjects, and esophageal cancer in female subjects (Table 2). The highest proportion of ex-smokers for both sexes was observed in participants with lung cancer, esophageal cancer and COPD (71.45%, 64.88% and 64.68% in male subjects, and 21.75%, 30.86% and 39.44% in female subjects, respectively), while the highest proportion of current smokers for both sexes was observed in participants with Graves' disease (49.84% in male subjects and 24.92% in female subjects) (Table 2). We then compared age-adjusted smoking history among the 47 diseases. The frequency of smokers was highest among participants with COPD, esophageal cancer, interstitial lung diseases/pulmonary fibrosis, pancreatic cancer, and cardiovascular diseases, in which smoking was shown to be a critical risk factor (Fig. 1 and eTable 4).

Table 2.

Baseline smoking status of participants with 47 diseases in the present cohort.

47 Diseases Smoking status
Male subjects
 Female subjects
Never smoker Ex-smoker Current smoker Smoker with unknown status Never smoker Ex-smoker Current smoker Smoker with unknown status
Whole cohort 25.02 43.75 27.78 3.45 78.76 9.37 10.45 1.42
Lung cancer 11.73 71.45 14.05 2.78 73.39 21.75 3.89 0.97
Esophageal cancer 10.92 64.88 18.74 5.46 56.57 30.86 9.14 3.43
Gastric cancer 18.53 55.01 22.72 3.74 76.16 15.33 7.36 1.15
Colorectal cancer 22.87 49.81 23.78 3.55 79.54 12.14 6.94 1.38
Liver cancer 20.39 45.13 26.84 7.64 77.29 10.48 9.17 3.06
Pancreatic cancer 15.60 55.60 28.00 0.80 66.91 16.55 14.39 2.16
Gallbladder/cholangiocarcinoma 26.34 48.15 21.40 4.12 80.69 10.34 6.90 2.07
Prostate cancer 31.88 46.54 17.19 4.39 N/A
Breast cancer 42.22 42.22 15.56 0.00 78.24 13.04 7.63 1.08
Uterine cervical cancer N/A 63.60 14.36 19.09 2.96
Uterine corpus cancer N/A 80.80 9.35 8.16 1.69
Ovarian cancer N/A 80.09 9.55 8.85 1.51
Hematologic cancer 27.99 45.60 20.20 6.20 81.41 10.15 6.02 2.41
Cerebral infarction 24.86 47.69 24.36 3.10 82.97 8.96 6.77 1.30
Cerebral aneurysm 19.06 48.99 27.80 4.15 69.68 15.69 12.59 2.04
Epilepsy 36.12 28.21 31.45 4.22 76.19 7.52 14.79 1.50
Bronchial asthma 26.44 40.08 30.96 2.53 65.90 13.48 19.24 1.39
Pulmonary tuberculosis 19.31 50.17 29.04 1.49 78.84 10.79 8.30 2.07
Chronic obstructive pulmonary disease 7.31 64.68 25.81 2.20 35.77 39.44 22.54 2.25
Interstitial lung disease/pulmonary fibrosis 13.07 62.96 20.04 3.92 72.59 18.07 8.73 0.60
Myocardial infarction 17.96 56.98 21.56 3.50 70.36 17.62 10.56 1.45
Unstable angina 21.60 55.61 20.26 2.52 76.59 13.85 8.58 0.98
Stable angina 21.70 54.36 21.95 1.99 79.56 11.64 8.07 0.74
Arrhythmia 25.61 50.01 21.42 2.95 83.20 9.58 6.50 0.72
Heart failure 23.68 49.63 23.05 3.63 79.32 12.06 7.53 1.09
Peripheral arterial disease 10.39 52.66 31.74 5.22 64.67 20.91 12.65 1.76
Chronic hepatitis B 28.69 31.11 34.87 5.33 77.30 8.79 12.47 1.43
Chronic hepatitis C 22.32 38.40 34.49 4.79 74.75 9.21 13.71 2.33
Liver cirrhosis 19.74 34.05 40.65 5.56 73.94 10.80 13.25 2.00
Nephrotic syndrome 26.97 39.89 30.52 2.62 69.25 14.40 12.74 3.60
Urolithiasis 29.17 28.68 38.46 3.69 76.98 5.49 15.61 1.92
Osteoporosis 32.60 38.83 25.15 3.42 87.79 5.27 5.99 0.95
Diabetes mellitus 23.08 41.24 32.45 3.24 78.49 9.26 11.11 1.15
Dyslipidemia 24.42 43.83 27.52 4.23 81.61 8.26 8.80 1.33
Graves' disease 20.20 26.06 49.84 3.91 60.68 12.90 24.92 1.50
Rheumatoid arthritis 17.30 41.84 35.46 5.40 78.09 8.53 11.41 1.97
Hay fever 42.40 28.30 24.43 4.88 77.01 8.91 12.18 1.89
Drug eruption 25.00 42.58 29.69 2.73 72.37 9.54 16.45 1.64
Atopic dermatitis 48.12 13.75 36.53 1.60 70.64 7.06 20.91 1.39
Keloid 38.08 35.43 25.17 1.32 72.69 7.96 18.06 1.29
Uterine fibroid N/A 73.66 9.34 14.68 2.31
Endometriosis N/A 70.96 8.86 16.67 3.52
Febrile seizure 100.00 0.00 0.00 0.00 100.00 0.00 0.00 0.00
Glaucoma 30.31 42.49 24.10 3.10 87.76 5.98 4.87 1.39
Cataract 28.69 43.61 24.22 3.47 86.66 6.23 5.95 1.17
Periodontitis 35.55 27.76 36.06 0.63 78.78 6.10 14.63 0.49
Amyotrophic lateral sclerosis 37.10 17.97 41.94 3.00 84.67 2.30 13.03 0.00
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Fig. 1.

Fig. 1

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Age-adjusted ratios of participants with a smoking history for each disease. The distributions of male (A) and female (B) participants with a smoking history in the BBJ cohort and in the National Health and Nutrition Survey (Japan, 2006) were compared. Age-adjustment was performed according to the age distribution of the National Health and Nutrition Survey (Japan, 2006).

A positive alcohol history at enrollment (including those with and without current drinking status) was found in 69.68% of male subjects and 28.20% of female subjects (Table 3). The proportion of current drinkers in the whole cohort was much higher than that of ex-drinkers in both sexes: 52.24% and 13.35% of male subjects and 21.70% and 3.99% of female subjects were current and ex-drinkers, respectively. Among the 47 diseases, the proportion of ex-drinkers was relatively high among participants with liver cirrhosis (34.05% in male subjects and 10.80% in female subjects), liver cancer (34.83% and 10.94%), pulmonary tuberculosis (33.17% and 11.20%), esophageal cancer (25.59% and 16%), and pancreatic cancer (29.72% and 10.14%) (Table 3). Age-adjusted alcohol intake history showed that the frequency of drinkers in esophageal cancer was remarkably higher than that in other diseases for male and female subjects (Fig. 2 and eTable 5). To highlight the smoking and drinking status in the BBJ cohort, the frequency of smokers or drinkers, stratified by sex and age group, was compared between the BBJ and the National Health and Nutrition Survey. The BBJ cohort had a higher frequency of smokers among female subjects across all age groups and among elderly male subjects, particularly among those >60 years of age; the frequency of drinkers was almost equivalent between the BBJ and the National Health and Nutrition Survey for both sexes and across all age groups (eFig. 2A and B and eTables 6 and 7).

Table 3.

Baseline alcohol intake status of participants with 47 diseases in the present cohort.

47 Diseases Alcohol intake
Male subjects
 Female subjects
Never drinker Ex-drinker Current drinker Drinker with unknown status Never drinker Ex-drinker Current drinker Drinker with unknown status
Whole cohort 30.32 13.35 52.24 4.09 71.80 3.99 21.70 2.52
Lung cancer 26.73 15.85 54.21 3.21 69.94 6.37 21.81 1.87
Esophageal cancer 8.29 25.59 61.38 4.74 47.43 16.00 32.00 4.57
Gastric cancer 25.19 18.96 51.49 4.36 73.12 7.57 17.62 1.69
Colorectal cancer 23.53 15.88 56.15 4.45 73.11 5.83 18.99 2.08
Liver cancer 23.90 34.83 33.01 8.27 76.59 10.94 9.41 3.06
Pancreatic cancer 25.70 29.72 42.97 1.61 65.22 10.14 23.91 0.72
Gallbladder/cholangiocarcinoma 30.58 27.27 38.43 3.72 84.83 2.07 13.10 0.00
Prostate cancer 29.33 13.05 51.47 6.16 N/A
Breast cancer 28.89 11.11 60.00 0.00 63.67 5.21 29.02 2.10
Uterine cervical cancer N/A 58.90 5.57 30.80 4.73
Uterine corpus cancer N/A 71.02 3.59 22.51 2.89
Ovarian cancer N/A 68.91 3.83 24.13 3.13
Hematologic cancer 29.05 12.28 50.72 7.95 72.76 5.69 18.10 3.45
Cerebral infarction 28.51 18.33 49.38 3.78 79.64 4.97 13.67 1.73
Cerebral aneurysm 23.72 15.81 55.34 5.13 66.86 6.69 24.05 2.40
Epilepsy 37.35 16.11 42.03 4.50 66.54 4.67 25.88 2.90
Bronchial asthma 34.16 9.44 53.53 2.87 69.26 3.83 25.29 1.62
Pulmonary tuberculosis 30.69 33.17 34.32 1.82 74.69 11.20 12.03 2.07
Chronic obstructive pulmonary disease 38.48 17.86 42.01 1.66 74.50 6.80 17.00 1.70
Interstitial lung disease 32.02 17.32 46.71 3.95 76.74 4.53 16.62 2.11
Myocardial infarction 41.68 13.70 41.29 3.32 79.48 5.42 13.48 1.62
Unstable angina 39.27 13.59 44.10 3.04 78.80 4.56 14.76 1.88
Stable angina 34.04 13.74 49.55 2.67 78.71 4.68 15.27 1.35
Arrhythmia 26.37 14.17 56.05 3.42 75.69 4.33 18.60 1.37
Heart failure 32.31 17.08 46.34 4.27 79.82 5.78 13.30 1.09
PAD 31.01 19.94 43.80 5.24 77.46 8.10 11.62 2.82
Chronic hepatitis B 27.18 18.08 47.45 7.28 68.43 5.70 24.03 1.83
Chronic hepatitis C 30.61 26.60 37.50 5.29 72.35 8.90 15.51 3.25
Liver cirrhosis 19.23 35.20 38.92 6.65 70.86 11.35 14.24 3.56
Nephrotic syndrome 39.70 14.42 41.95 3.93 67.59 6.37 23.82 2.22
Urolithiasis 32.49 5.44 56.80 5.27 72.96 2.72 22.00 2.32
Osteoporosis 39.63 14.43 42.89 3.05 82.07 2.47 13.70 1.76
Diabetes mellitus 31.47 15.17 49.88 3.49 79.10 5.29 14.02 1.59
Dyslipidemia 30.78 10.71 54.08 4.44 76.03 3.40 18.52 2.06
Graves' disease 38.24 8.82 48.37 4.58 66.73 5.02 25.93 2.32
Rheumatoid arthritis 35.47 13.79 44.95 5.79 75.46 4.11 17.89 2.54
Hay fever 31.18 4.31 58.22 6.29 59.31 2.80 32.50 5.40
Drug eruption 30.98 15.29 49.02 4.71 71.57 2.68 21.74 4.01
Atopic dermatitis 45.44 3.68 47.04 3.84 57.37 2.44 36.62 3.57
Keloid 39.33 7.33 50.67 2.67 63.71 1.51 33.69 1.08
Uterine fibroid N/A 53.52 2.23 38.57 5.67
Endometriosis N/A 55.51 2.26 34.56 7.66
Febrile seizure 100.00 0.00 0.00 0.00 66.67 0.00 33.33 0.00
Glaucoma 27.42 12.46 56.14 3.98 77.61 2.98 17.16 2.25
Cataract 31.05 14.42 50.58 3.95 81.00 2.95 14.49 1.57
Periodontitis 29.82 7.20 61.53 1.45 63.54 2.75 32.14 1.57
Amyotrophic lateral sclerosis 28.57 0.00 57.14 14.29 100.00 0.00 0.00 0.00
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Fig. 2.

Fig. 2

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Age-adjusted ratio of participants with alcohol history in each disease. The distributions of male (A) and female (B) participants with a drinking history in the BBJ cohort and in the National Health and Nutrition Survey (Japan, 2006) were compared. Age-adjustment was performed according to the age distribution of the National Health and Nutrition Survey (Japan, 2006).

Mean BMI at enrollment in the BBJ cohort was 23.51 in male subjects and 22.94 in female subjects. Analysis of BMI in each disease revealed that underweight participants (BMI<18.5) had an increased association of various cancers, while overweight or obese participants (BMI ≥ 25) had an increased association of metabolic and cardiovascular diseases (Table 4, Fig. 3 and eTable 8). When comparing the National Health and Nutrition Survey and the BBJ, there was a greater proportion of participants with overweight or obesity in the BBJ, among male and female subjects and across all age-groups; conversely, similar distribution patterns were found when comparing the BBJ cohort and the Survey, by sex and age-group (eFig. 2C and eTables 6 and 7). In contrast, in the BBJ cohort, there were fewer underweight participants in their twenties (for both sexes) but more underweight participants >60 years (among male subjects) and >50 years (among female subjects) (eFig. 2D and eTables 6 and 7).

Table 4.

Baseline BMI and hypertension of participants with 47 diseases in the present cohort.

47 Diseases BMI
 %Hypertension
Male subjects
 Female subjects
 Male subjects Female subjects
Mean (SD) Mean (SD)
Whole cohort 23.51 3.47 22.94 3.89 51.52 41.11
Lung cancer 22.29 3.05 22.05 3.37 36.74 33.83
Esophageal cancer 20.53 2.96 19.77 3.25 27.29 24.86
Gastric cancer 21.25 3.04 20.34 3.26 30.91 24.26
Colorectal cancer 22.66 3.17 22.00 3.51 38.00 30.31
Liver cancer 22.68 3.27 22.82 3.96 44.64 45.51
Pancreatic cancer 20.44 3.19 19.90 3.03 30.83 29.50
Gallbladder/cholangiocarcinoma 21.46 3.29 22.20 3.89 33.47 31.29
Prostate cancer 23.28 2.86 N/A 38.00 N/A
Breast cancer 23.87 3.75 22.74 3.60 52.17 22.82
Cervical cancer N/A 21.93 3.29 N/A 19.23
Uterine cancer N/A 23.74 4.37 N/A 25.83
Ovarian cancer N/A 22.04 3.38 N/A 19.21
Hematopoietic tumor 23.11 3.23 21.87 3.33 30.94 26.71
Cerebral infarction 23.53 3.19 23.39 3.86 67.12 65.34
Cerebral aneurysm 23.86 3.36 23.11 3.64 65.45 59.52
Epilepsy 23.47 3.84 22.70 4.19 36.36 25.64
Bronchial asthma 23.79 3.71 23.78 4.55 41.19 35.14
Pulmonary tuberculosis 20.82 3.28 20.26 3.26 32.31 37.80
Chronic obstructive pulmonary disease 21.30 3.37 20.33 4.08 46.05 44.66
Interstitial lung disease/pulmonary fibrosis 23.02 3.21 22.62 3.75 42.37 40.65
Myocardial infarction 24.04 3.23 23.40 3.74 73.11 77.01
Unstable angina 24.02 3.21 23.74 3.74 74.33 74.80
Stable angina 23.85 3.16 23.63 3.66 77.17 77.45
Arrhythmia 23.53 3.30 22.90 3.80 66.75 65.33
Heart failure 23.50 3.89 22.64 4.31 78.70 78.10
Peripheral arterial diseases 22.52 3.25 22.44 3.80 70.21 69.17
Chronic hepatitis B 23.32 3.11 22.55 3.51 40.67 33.60
Chronic hepatitis C 22.86 3.15 22.54 3.65 46.19 40.71
Liver cirrhosis 22.88 3.51 23.03 4.05 52.18 49.41
Nephrotic syndrome 23.00 3.34 22.50 3.94 62.32 50.68
Urolithiasis 24.43 3.39 23.59 4.16 37.27 35.61
Osteoporosis 21.96 3.52 22.26 3.62 49.22 44.46
Diabetes mellitus 24.03 3.72 24.57 4.41 60.32 62.82
Dyslipidemia 24.78 3.45 24.11 3.88 64.47 59.48
Graves' disease 23.55 3.63 22.35 3.61 41.03 32.00
Rheumatoid arthritis 22.49 3.29 21.85 3.69 40.57 33.56
Hay fever 23.66 3.17 22.06 3.51 24.70 14.77
Drug eruption 23.27 3.39 22.62 4.07 49.81 30.87
Atopic dermatitis 23.01 3.51 21.48 3.70 13.24 5.75
Keloid 23.95 3.29 22.71 4.11 27.60 18.82
Uterine fibroid N/A 22.29 3.51 N/A 14.09
Endometriosis N/A 21.43 3.25 N/A 7.93
Febrile seizure 28.73 0.00 21.26 4.80 N/A N/A
Glaucoma 23.05 3.18 22.90 3.66 43.87 41.08
Cataract 23.08 3.15 23.05 3.85 49.53 45.68
Periodontitis 23.27 3.14 22.25 3.37 27.33 15.90
Amyotrophic lateral sclerosis 21.14 1.68 28.00 3.14 N/A N/A
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Fig. 3.

Fig. 3

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Age-adjusted ratio of participants with overweight or underweight in each disease. The distributions of obese or underweight participants among male (A) and female (B) subjects in the BBJ cohort and in the National Health and Nutrition Survey (Japan, 2006) were compared. Age-adjustment was performed according to the age distribution of the National Health and Nutrition Survey (Japan, 2006). BMI ≥25 was defined as overweight and BMI less than 18.5 was defined as underweight.

Nearly half of the participants of the BBJ cohort had hypertension (51.52% of male subjects and 41.11% of female subjects, Table 4) at enrollment. The frequency of hypertension in cardiovascular diseases, particularly in coronary diseases, was higher than that in other diseases, while the frequency of hypertension among cancer participants tended to be low (Table 4, Fig. 4 and eTable 9). The frequency of hypertension increased with age, similarly to the increase observed in the Survey. However, the frequency of hypertension among subjects <50 years of age was higher and subjects >60 years of age was lower in the BBJ cohort than in the Survey (eFig. 2E and eTables 6 and 7).

Fig. 4.

Fig. 4

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Age-adjusted ratio of participants with hypertension in each disease. The distributions of male (A) and female (B) participants with hypertension in the BBJ cohort and in the National Health and Nutrition Survey (Japan, 2006) were compared. Age-adjustment was performed according to the age distribution of the National Health and Nutrition Survey (Japan, 2006). Participants with a systolic blood pressure ≥135-mmHg, a diastolic blood pressure ≥90-mmHg, or participants prescribed antihypertensive medication, were diagnosed with hypertension.

Family history

Finally, we performed multivariate logistic-regression analysis using age and sex status as covariates to assess the association between positive family history and disease risk. We were able to obtain the questionnaire-based information regarding family history of 45 diseases out of the 47 diseases (eTable 10). For all the diseases, except for PAD, there was a significant association with a positive family history, with an odds ratio of >1.7 (Fig. 5 and eTable 11). Notably, the odds ratios for keloid, chronic hepatitis B, and Grave's disease were relatively high (149.417, 53.474, and 23.751, respectively) indicating the strong impact of genetic and familial factors on disease onset.

Fig. 5.

Fig. 5

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Sex- and age-adjusted odds ratios in family history, related with the 47 diseases. Dots represent odds ratios and bars represent 95% CIs by logistic regression analysis. The list of family histories, associated with the 47 diseases, is set out in eTable 2.

Discussion

We analyzed common clinical variables at enrollment, across the whole BBJ cohort, as well as for each target disease, and we compared these results with those of the Japanese database to highlight the characteristics of the BBJ cohort. Statistical analyses were not conducted in this study, as the large-scale cohort sample in the BBJ would yield relatively low-p-values, even when absolute differences were very small. The distribution of age, life style, and physical status, showed that the characteristics of each disease group could generally be explained.

It is an established fact that smoking and/or alcohol intake are risk factors for various diseases including cancer, cardiovascular disease, hepatic disease, and respiratory disease.9, 10 In fact, these diseases showed a higher frequency of participants with a positive smoking or drinking history at enrollment in the BBJ cohort (Fig. 1, Fig. 2 and eTables 4 and 5). Although we cannot estimate the odds ratios of smoking and drinking status due to the lack of control data in the present cohort, age-adjusted distributions of the smoking and drinking histories of participants suggest that these lifestyle factors have a significant impact on disease onset.

Analysis of BMI at enrollment indicated that lower BMI was more prevalent among participants with malignant tumors, while higher BMI was common among participants with metabolic and cardiovascular disease (Fig. 3 and eTable 8). Obesity could be a risk factor for dyslipidemia, type 2 diabetes, coronary disease,11 while cancer can induce weight loss. Therefore, we need to be cautious in the interpretation of the association between diseases and lifestyle or physical factors.

To highlight the characteristics of the BBJ cohort, we compared the age and sex distributions of the BBJ cohort with those of the Patient Survey for each disease, and the distributions of smoking and drinking history, BMI and hypertension in the BBJ cohort with those of the National Health and Nutrition Survey. It is difficult to discuss the discrepancy or consistency between the BBJ cohort and the Japanese database, because backgrounds of the subjects and methods to determine the numbers of patients or the distributions of life style and physical status were different. However, the comparisons between the BBJ cohort and the Japanese database gave us better insight about the characteristics of the BBJ cohort, contributing to utmost utilization of the biobank samples.

As one of our main aims was to identify genetic factors causing susceptibility to diseases, we analyzed the association between positive family history and disease onset to evaluate the impact of host genetic factors. It has been reported that a positive family history is an important risk factors for many common chronic diseases,12, 13, 14, 15, 16, 17, 18, 19 and keloid, chronic hepatitis B, and Graves' disease showed the highest odds ratios for a positive family history (Fig. 5). While it is important to consider the possibility that perinatal transmission, a major route of hepatitis B virus transmission,20 resulted in the high odds ratio observed in chronic hepatitis B, several genome-wide association studies (GWAS), which identified some single nucleotide polymorphism loci significantly associated with these diseases in Japan,21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32 support the finding that genetic factors are associated with these diseases. However, the odds ratios, calculated in the previous genomic studies, were not as high as in the present analysis, suggesting the possibility that further genomic analysis could identify novel genomic loci. In addition, the fact that common clinical variables were consistently identified across the 47 diseases enabled us to evaluate and compare the risk significance of the positive family history on the diseases and to perform further genomic or other “omics” analyses based on these results.

This study has some limitations. We could not eliminate the possibility of reporting bias, causing significantly higher odds ratio of positive family history in almost all target diseases, as the information on family history was mainly based on participants' interviews, although this was completed by certified medical coordinators. Another limitation of this analysis is that the reference population for each logistic analysis was not the disease-free general population but the participants with the other diseases in the cohort. Therefore, again, we need to take into account selection bias.33

In conclusion, we have established a large biobank cohort, consisting of approximately 200,000 patients with 47 diseases. Analysis of the clinical dataset and comparisons between the present cohort and the Japanese database largely revealed consistent trends in common clinical variables, particularly among participants aged ≥40 years, suggesting that the sampling is representative for the general patient population in Japan. Further analysis, combined with various high-throughput ‘omics’ technologies, using their DNA and serum samples, will aid us to identify novel genomic variants or biomarkers associated with disease progression or drug efficacy, contributing to the implementation of personalized medicine.

Conflicts of interest

None declared.

Acknowledgements

We express our gratitude to all the participants in the BioBank Japan Project. We thank all the medical coordinators of the cooperating hospitals for collecting samples and clinical information, as well as Yasushi Yamashita and staff members of the BioBank Japan Project for administrative support. We also thank Dr. Kumao Toyoshima for his overall supervision of the BioBank Japan Project. This study was supported by funding from the Tailor-Made Medical Treatment with the BBJ Project from Japan Agency for Medical Research and Development, AMED (since April 2015), and the Ministry of Education, Culture, Sports, Science, and Technology (from April 2003 to March 2015).

Footnotes

Peer review under responsibility of the Japan Epidemiological Association.

Appendix A

Supplementary data related to this article can be found at http://dx.doi.org/10.1016/j.je.2016.12.003.

Contributor Information

Koichi Matsuda, Email: kmatsuda@k.u-tokyo.ac.jp.

BioBank Japan Cooperative Hospital Group:

Masaki Shiono, Kazuo Misumi, Reiji Kaieda, Hiromasa Harada, Shiro Minami, Mitsuru Emi, Naoya Emoto, Hajime Arai, Ken Yamaji, Yoshimune Hiratsuka, Satoshi Asai, Mitsuhiko Moriyama, Yasuo Takahashi, Tomoaki Fujioka, Wataru Obara, Seijiro Mori, Hideki Ito, Satoshi Nagayama, Yoshio Miki, Akihide Masumoto, Akira Yamada, Yasuko Nishizawa, Ken Kodama, Hiromu Kutsumi, Yoshihisa Sugimoto, Yukihiro Koretsune, Hideo Kusuoka, and Takashi Yoshiyama

Appendix A. Author list for the BioBank Japan Cooperative Hospital Group

Members of medical institutions cooperating on the BioBank Japan Project who coauthored this paper include Masaki Shiono, Kazuo Misumi, Reiji Kaieda, Hiromasa Harada (Tokushukai Hospitals); Shiro Minami, Mitsuru Emi, Naoya Emoto (Nippon Medical School), Hajime Arai, Ken Yamaji, Yoshimune Hiratsuka (Juntendo University), Satoshi Asai, Mitsuhiko Moriyama, Yasuo Takahashi (Nihon University), Tomoaki Fujioka, Wataru Obara (Iwate Medical University), Seijiro Mori, Hideki Ito (Tokyo Metropolitan Institute of Gerontology), Satoshi Nagayama, Yoshio Miki (The Cancer Institute Hospital of JFCR), Akihide Masumoto, Akira Yamada (Aso Iizuka Hospital), Yasuko Nishizawa, Ken Kodama (Osaka Medical Center for Cancer and Cardiovascular Diseases), Hiromu Kutsumi, Yoshihisa Sugimoto (Shiga University of Medical Science), Yukihiro Koretsune, Hideo Kusuoka (National Hospital Organization, Osaka National Hospital), and Takashi Yoshiyama (Fukujuji Hospital).

Appendix A. Supplementary data

The following are the supplementary data related to this article:

mmc1.pdf (215.9KB, pdf)
mmc2.pdf (247.4KB, pdf)

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Supplementary Materials

mmc1.pdf (215.9KB, pdf)
mmc2.pdf (247.4KB, pdf)

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