Risk Factors That Most Accurately Predict Coronary Artery Disease Based on the Duration of Follow-up　― NIPPON DATA80 ―

Abstract

Background: This study assessed sex-specific time-associated changes in the impact of risk factors on coronary artery disease (CAD) mortality in a general population over long-term follow-up.

Methods and Results: A prospective longitudinal cohort study was conducted on representative Japanese populations followed up for 29 years. Data from 8,396 participants (3,745 men, 4,651 women) were analyzed. The sex-specific multivariable adjusted hazard ratios (HRs) and 95% confidence intervals (CIs) of 4 risk factors (smoking, diabetes, serum total cholesterol [TC], and systolic blood pressure [SBP]) for CAD mortality were calculated at baseline and at 10, 15, 20, 25, and 29 years of follow-up. In men, smoking (HR 3.23; 95% CI 1.16–9.02) and a 1-SD increase in TC (HR 1.82; 95% CI 1.29–2.57) were strongly associated with a higher risk of CAD in the first 10 years, but this association decreased over time. Diabetes (HR 2.30; 95% CI 1.37–3.85) and a 1-SD increase in SBP (HR 1.23; 95% CI 1.00–1.50) were strongly correlated with a higher risk of CAD after 29 years). In women, diabetes was correlated with CAD after 20 years (HR 2.53; 95% CI 1.19–5.36) and this correlation persisted until after 29 years (HR 2.47; 95% CI 1.40–4.35).

Conclusions: The duration of follow-up needed for the accurate assessment of risk factors for CAD mortality varies according to risk factor and sex.

Established risk factors for coronary artery disease (CAD) are dyslipidemia, hypertension, smoking, and diabetes.^1–5 The risk assessment chart for CAD was developed based on a 10-year probability of mortality in Japan.¹ The National Integrated Project for Prospective Observation of Non-communicable Disease And its Trends in the Aged (NIPPON DATA) has continued from then, and now provides a unique opportunity to study the risk assessment of CAD mortality based on a 29-year follow-up in a representative Japanese population. However, increases in the duration of follow-up periods will influence outcomes because of the aging of individuals and changes in the era (e.g., the introduction of new drugs and the spread of non-smoking policies), which must be interpreted in a multidimensional view. Because participants with these risk factors at baseline may start medication and modify their lifestyle during follow-up in compliance with health checkups and education received at hospitals or clinics, in addition to health information obtained through the mass media, particularly in recent years, it is intuitively appealing to hypothesize that CAD risks may not maintain proportionality in long-term follow-up.⁶ However, changes in the effects of CAD risk factors during long-term follow-up and how these changes may differ between risk factors have not yet been investigated. Each risk factor may have an appropriate follow-up duration for the assessment of CAD risk. Therefore, the follow-up duration needed to accurately assess risk factors identified at baseline for CAD mortality is of interest. Moreover, the risk factors for CAD mortality may differ between men and women.^1,7 Therefore, the aim of the present study was to assess sex-specific time-associated changes in the impact of risk factors on CAD mortality over the long-term follow-up of a general Japanese population through a 29-year prospective study. We examined the long-term effects of serum total cholesterol (TC) concentrations, systolic blood pressure (SBP), smoking behavior, and diabetes status used to calculate the 10-year probability of mortality in the NIPPON DATA80 risk assessment chart.¹

Methods

Study Participants

NIPPON DATA80 was a prospective, longitudinal cohort study on 300 randomly selected districts throughout Japan that combined the data of participants in the National Survey on Circulatory Disorders and dietary estimates from the National Nutrition Survey (NNS) conducted in 1980. The methods of these studies have been described in detail elsewhere.^1,8

In all, 10,546 (4,639 men, 5,907 women) community-dwelling adults aged ≥30 years were eligible and invited to participate in the present study. Individuals with an implausible energy intake (<500 or >5,000 kcal/day), missing information in the baseline survey, a history of CAD, or lost to follow-up were excluded, leaving 8,396 participants (3,745 men, 4,651 women).

Biochemical and Baseline Examinations

The survey in local health centers consisted of a physical examination, blood tests, and a self-administered questionnaire. In the lifestyle questionnaire, participants were asked about their alcohol drinking status (non-drinker, ex-drinker, current drinker), smoking status (non-smoker, ex-smoker, current smoker), history of diabetes and cardiovascular disease (CVD), family history of heart disease (father and/or mother), time since the last meal, and medication history. Dietary intakes were assessed in each household using a 3-day weighing dietary record method. The nutrient intake of each household member was estimated by dividing household intake data in the NNS in 1980 proportionally with average intake as categorized by sex and age groups. The detailed procedures and validation results for these estimations have been reported elsewhere.⁸

Non-fasting blood samples were obtained at baseline. Serum was separated and centrifuged soon after blood coagulation. Samples were shipped to a single laboratory for blood chemistry measurements. Serum TC concentrations were measured enzymatically and standardized by the Centers for Disease Control and Prevention/National Heart, Lung, and Blood Institute Lipids Standardization Program and the US Cholesterol Reference Method Laboratory Network.^9,10 Blood glucose concentrations were also measured at this site using the cupric-neocuproine method.² Diabetes was defined as a casual blood glucose level ≥200 mg/dL or as a history of diabetes.

Height in stockinged feet and weight in light clothing were measured. Body mass index (BMI) was calculated as weight (kg) divided by height squared (m²). Nurses measured baseline blood pressure using a standard mercury sphygmomanometer on the right arm of participants in a sitting position after a 5-min rest.

Follow-up Survey

Participants were followed up for 29 years. The vital status of participants was confirmed every 5 years, and the last year of confirmed survival was treated as censored. The underlying causes of mortality listed in the National Vital Statistics were coded according to the 9th revision of the International Statistical Classification of Diseases (ICD-9) through to the end of 1994 and according to the 10th revision of the International Statistical Classification of Diseases (ICD-10) from the start of 1995 through to the end of 2010. Mortality from CAD was defined as ICD-9 codes 410–414 and ICD-10 codes I20–25. The details of these classifications have been described elsewhere.¹¹ Permission to use the National Vital Statistics was obtained from the Management and Coordination Agency, Government of Japan.

This study was approved by the Institutional Review Board of Shiga University of Medical Science for Ethical Issues (No. 12-18, 2000; No. 17-21-1, 2010).

Statistical Analyses

Participant characteristics for variables related to CAD are given as the mean±SD or as percentages according to sex and were compared between the sexes using unpaired t-tests or Chi-squared tests. Hazard ratios (HRs) of CAD were calculated using Cox’s proportional hazards regression analysis for each sex separately. HR estimates and 95% confidence intervals (CIs) are presented for 4 risk factors for CAD mortality: 1-SD increases in serum TC (mg/dL), 1-SD increases in SBP (mmHg), diabetes vs. non-diabetes, and current smoking behavior as a reference for non- and ex-smokers, based on a 10-year probability of mortality in the NIPPON DATA80 risk assessment chart.¹ All models were adjusted for age. Model 1 was further adjusted for each risk factor (e.g., in the case of SBP, Model 1 was adjusted for serum TC, diabetes, and smoking). Model 2 was further adjusted for BMI and drinking status (non-, ex-, or current drinker) in addition to the risk factors adjusted for in Model 1. Cox’s proportional hazards regression analysis was performed between 4 risk factors at baseline (serum TC, SBP, diabetes, and current smoking behavior) and CAD mortality, and at the 10-, 15-, 20-, 25-, and 29-year follow-up time points. The same analyses were conducted among women who were stratified by age into 2 groups, namely <50 and ≥50 years of age.

Because most sudden cardiac mortalities are described in Japanese mortality certificates as “coronary artery disease” or “heart failure” and “unknown cause”,¹² we included “heart failure” as an outcome in the sensitivity analysis.

All analyses were performed using SAS software version 9.4 (SAS Institute, Cary, NC, USA).

Results

The baseline characteristics of participants are given in Table 1. Current smoking, current drinking, SBP, diastolic blood pressure, casual blood glucose level, diabetes, and diabetes history were higher in men than in women. In contrast, BMI and serum TC were higher in women than in men.

Table 1. Baseline Characteristics of Participants (NIPPON DATA80, 1980)

	Men (n=3,745)	Women (n=4,651)	P value
Age (years)	49.7±13.1	49.7±13.2	0.942
Body mass index (kg/m2)	22.5±2.8	22.8±3.3	<0.001
Smoking status			<0.001
Non-smoker	670 (17.9)	4,150 (89.2)
Ex-smoker	697 (18.6)	97 (2.1)
Current smoker	2,378 (63.5)	404 (8.7)
Drinking status			<0.001
Non-drinker	741 (19.8)	3,651 (78.5)
Ex-drinker	209 (5.6)	62 (1.3)
Current drinker	2,795 (74.6)	938 (20.2)
Systolic blood pressure (mmHg)	138.3±20.7	133.4±21.4	<0.001
Diastolic blood pressure (mmHg)	83.7±12.3	79.4±11.9	<0.001
Hypertension history	747 (20.0)	868 (18.7)	0.138
Hypertensive drugs	371 (9.9)	491 (10.6)	0.329
Casual blood glucose level (mg/dL)	130.9±37.4	129.0±33.9	0.022
Diabetes history	154 (4.1)	98 (2.1)	<0.001
Diabetes*	257 (6.9)	189 (4.1)	<0.001
Serum total cholesterol (mg/dL)	186.7±32.9	190.2±33.8	<0.001
Family history of heart disease	511 (13.6)	619 (13.3)	0.094

Unless indicated otherwise, values are presented as the mean±SD or as n (%). P values were obtained from unpaired t-tests or Chi-squared tests. *Diabetes was defined as a casual blood glucose concentration ≥200 mg/dL or a history of diabetes.

Table 2 shows the results of the Cox’s proportional hazards regression analysis with CAD mortality as a dependent variable and the 4 risk factors as independent variables, with the length of follow-up cut off at 10, 15, 20, 25, and 29 years.

Table 2. Cox’s Proportional HR for CAD Mortality According to Risk Factors at 10, 15, 20, 25, and 29 Years of Follow-up (NIPPON DATA80, 1980–2009)

Follow-up duration (years)	Model	Men (n=3,745)						Women (n=4,651)
		Person- years	CAD	HR (95% CI) for smoking*	HR (95% CI) for diabetes**	HR (95% CI) for TC***	HR (95% CI) for SBP***	Person- years	CAD	HR (95% CI) for smoking*	HR (95% CI) for diabetes**	HR (95% CI) for TC***	HR (95% CI) for SBP***
<10	Age adjusted	35,822	22 (0.6)	2.60 (0.95–7.11)	2.62 (0.96–7.19)	1.81 (1.30–2.50)	1.11 (0.74–1.65)	45,176	19 (0.4)	1.12 (0.26–4.85)	0.82 (0.11–6.12)	0.80 (0.49–1.31)	0.84 (0.53–1.33)
	Model 1			3.09 (1.11–8.60)	2.08 (0.76–5.71)	1.88 (1.36–2.62)	1.03 (0.69–1.55)			1.09 (0.25–4.74)	0.85 (0.11–6.41)	0.82 (0.50–1.35)	0.86 (0.54–1.37)
	Model 2			3.23 (1.16–9.02)	1.89 (0.68–5.24)	1.82 (1.29–2.57)	1.03 (0.67–1.58)			1.00 (0.22–4.51)	0.84 (0.11–6.33)	0.83 (0.50–1.36)	0.87 (0.54–1.39)
<15	Age adjusted	51,911	36 (1.0)	2.62 (1.19–5.78)	2.59 (1.17–5.74)	1.68 (1.28–2.20)	1.27 (0.93–1.72)	66,213	36 (0.8)	1.54 (0.60–3.96)	1.82 (0.65–5.16)	0.86 (0.60–1.23)	0.81 (0.58–1.14)
	Model 1			3.03 (1.36–6.78)	1.99 (0.89–4.43)	1.73 (1.32–2.27)	1.20 (0.88–1.63)			1.54 (0.60–3.98)	1.91 (0.68–5.43)	0.88 (0.61–1.26)	0.83 (0.59–1.16)
	Model 2			3.15 (1.40–7.06)	1.85 (0.82–4.17)	1.68 (1.26–2.24)	1.22 (0.88–1.70)			1.37 (0.51–3.64)	1.86 (0.65–5.29)	0.90 (0.62–1.30)	0.85 (0.60–1.20)
<20	Age adjusted	66,241	59 (1.6)	1.70 (0.97–2.96)	2.64 (1.42–4.93)	1.65 (1.33–2.05)	1.25 (0.98–1.59)	85,762	57 (1.2)	1.80 (0.88–3.66)	2.40 (1.14–5.07)	1.05 (0.80–1.38)	1.02 (0.79–1.33)
	Model 1			1.97 (1.11–3.48)	2.13 (1.14–4.00)	1.68 (1.35–2.08)	1.18 (0.92–1.51)			1.86 (0.91–3.80)	2.44 (1.15–5.17)	1.04 (0.79–1.37)	1.02 (0.79–1.33)
	Model 2			1.97 (1.11–3.49)	1.98 (1.05–3.72)	1.60 (1.28–2.01)	1.14 (0.87–1.47)			1.68 (0.80–3.50)	2.53 (1.19–5.36)	1.07 (0.81–1.41)	1.07 (0.82–1.39)
<25	Age adjusted	78,764	79 (2.1)	1.82 (1.12–2.98)	2.85 (1.65–4.91)	1.58 (1.31–1.91)	1.27 (1.03–1.58)	103,831	80 (1.7)	1.50 (0.79–2.83)	2.20 (1.13–4.26)	1.08 (0.86–1.36)	1.18 (0.95–1.46)
	Model 1			2.08 (1.26–3.42)	2.30 (1.33–3.98)	1.60 (1.32–1.93)	1.21 (0.98–1.51)			1.57 (0.83–2.97)	2.15 (1.11–4.18)	1.06 (0.84–1.34)	1.18 (0.95–1.46)
	Model 2			2.12 (1.29–3.51)	2.23 (1.28–3.87)	1.56 (1.28–1.90)	1.21 (0.97–1.52)			1.46 (0.76–2.80)	2.20 (1.13–4.28)	1.08 (0.85–1.36)	1.20 (0.97–1.50)
<29	Age adjusted	87,491	97 (2.6)	1.68 (1.09–2.60)	2.79 (1.68–4.64)	1.41 (1.18–1.68)	1.26 (1.04–1.53)	116,985	104 (2.2)	1.37 (0.77–2.46)	2.52 (1.43–4.43)	1.16 (0.96–1.42)	1.14 (0.94–1.38)
	Model 1			1.84 (1.19–2.87)	2.34 (1.40–3.91)	1.41 (1.18–1.69)	1.22 (1.00–1.48)			1.42 (0.79–2.55)	2.43 (1.38–4.28)	1.14 (0.94–1.39)	1.12 (0.92–1.36)
	Model 2			1.87 (1.20–2.92)	2.30 (1.37–3.85)	1.39 (1.16–1.68)	1.23 (1.00–1.50)			1.39 (0.77–2.52)	2.47 (1.40–4.35)	1.16 (0.95–1.42)	1.15 (0.94–1.40)

Coronary artery disease (CAD) data are given as n (%). Model 1 was adjusted for age, smoking status, diabetes, serum total cholesterol (TC), and systolic blood pressure (SBP; note, independent variables were excluded from each model). Model 2 was further adjusted for body mass index and drinking status. *Hazard ratios (HR) for current smokers vs. non- and ex-smokers. **HR for diabetes (casual blood glucose concentration ≥200 mg/dL or a history of diabetes) vs. non-diabetes. ***HR for a 1-SD increase in TC (mg/dL) or SBP (mmHg). CI, confidence interval.

In men, the most influential risk factor for CAD mortality in the first 10 years was smoking, followed by serum TC. However, diabetes became the most significant factor after 20 years until the last follow-up. The HRs for smoking and serum TC on CAD mortality were the highest in the first 10 years and decreased over the duration of follow-up. In contrast, the positive relationships between diabetes, SBP, and CAD mortality were strengthened over the duration of the follow-up. The relationship between diabetes and CAD mortality became apparent after 20 years, and HRs continued to increase until after 29 years. A positive relationship between SBP and CAD mortality during follow-up was initially observed after 29 years. In women, diabetes at baseline was positively associated with CAD mortality after 20 years, and a strong relationship remained until the last follow-up. In women, no relationship was observed between the other 3 risk factors (serum TC, SBP, and smoking) and CAD mortality at any of the follow-up time points. Similar results were obtained when participants with “heart failure” as an outcome were included (data not shown).

The results of the subgroup analysis of Table 2 in which women were stratified by the age of 50 years are given in the Supplementary Table. Diabetes was associated with CAD mortality only among women ≥50 years old. The relationship between diabetes and CAD mortality was stronger in women ≥50 years of age because there were few women <50 years of age with CAD mortality.

Discussion

In men, smoking behavior and higher serum TC at baseline had the strongest correlation with a higher risk of CAD mortality in the first 10 years of follow-up, but this correlation weakened with increasing follow-up duration. In contrast, diabetes and higher SBP at baseline had the strongest association with CAD mortality after 29 years. Therefore, the harmful effects of smoking and higher serum TC on CAD mortality decreased, whereas the adverse effects of diabetes and SBP increased, with time. In women, diabetes was associated with higher CAD mortality after 20 years, and this association was persisted for up to 29 years of follow-up. Notably, the detrimental effects of diabetes on CAD mortality increased with time. Hence, the duration of follow-up needed to accurately assess risk factors for CAD mortality varied between risk factors and sex.

Smoking

In men, smoking was the strongest risk factor for CAD mortality in 15 years among the 4 risk factors examined. In Japan, the prevalence of smoking has decreased markedly in the past 40 years. According to the National Health and Nutrition Surveys in Japan,^13,14 the prevalence of current smoking in men was 63% in 1980, 55% in 1990, 46% in 2000, and 32% in 2010; the prevalence of previous smoking was 19% in 1980, 24% in 1990, 27% in 2000, and 31% in 2010. Therefore, the cessation of smoking during the follow-up period by those who smoked at baseline may have contributed the moderation of the HRs of smoking on CAD mortality.

The prevalence of smoking in women was only 9%, and this may explain why it was difficult to detect a relationship between smoking and CAD mortality.¹⁵

Diabetes

In men and women, the HRs of diabetes at baseline for CAD mortality increased with an increase in the follow-up period. The Health and Welfare Statistics Association and the National Health and Nutrition Survey reported that the number of individuals who are highly suspected to have diabetes and those with the possibility of having diabetes has been increasing.¹⁴ Increases in the incidence of diabetes with aging and the epidemic of diabetes in Japan may have resulted in diabetes being a stronger risk factor for CAD mortality during the follow-up period. In addition, the lack of a breakthrough in diabetes medications in the past 3 decades may have affected the results obtained.¹⁶ Early control of blood glucose levels may be needed, particularly if their effects persist later in life. Diabetes in women aged <50 years was not associated with CAD mortality based on the risk assessment chart for the 10-year probability of CAD death.¹ The results of the present study support diabetes in younger women increasing the risk of CAD mortality in later life (after 20 years) during the follow-up period. It is also important to note that there was a transition from ICD-9 to ICD-10 15 years into the follow-up; this may have affected the increase in HRs for diabetes in men and women after 15 years (<20 years) because CAD may have been included in “heart failure” in ICD-9, resulting in an underestimation of CAD mortality.

Serum TC Concentrations

In men, serum TC were the second largest risk factor for CAD mortality in 15 years; however, this risk decreased during follow-up, and this may be attributed to the dissemination of statins and physiological reductions in serum TC in men. The use of statins for hypercholesterolemia became a major treatment in Japan from 1989.¹⁷ Serum TC biologically decrease with age in men, but increase in women.¹⁸ HRs between serum TC and CAD mortality increased slightly in women. Weaker or negative relationships have been reported between serum TC and CAD in women.¹⁹ Serum cholesterol levels increase after 50 years of age in women, and this may be attributed to changes associated with menopause.^18,20 In women, CAD mortality markedly increases after menopause, which is approximately 10 years later than the age at which in CAD mortality increases men.²¹ However, in the present study, a relationship was not observed between serum TC and CAD mortality in women in either age group (<50 and ≥50 years).

Mean serum TC have been gradually increasing in the Japanese general population in both men (from 186 mg/dL in 1980 to 199, 200, and 201 mg/dL in 1990, 2000, and 2010, respectively) and women (from 191 mg/dL in 1980 to 207, 208, and 209 mg/dL in 1990, 2000, and 2010, respectively). This increase has been attributed to the more abundant intake of fat and food from animal sources.²² The effects of Westernization of the diet on serum cholesterol levels also need to be considered for CAD mortality in future in Japan.

Systolic Blood Pressure

The harmful effects of SBP on CAD mortality increased after 29 years in men and slightly increased in women. However, the rate of treatment of hypertensive individuals increased with reductions in SBP in both men and women between 1980 and 2010,²³ which may postpone CAD mortality to a later period. However, the control rate among individuals receiving treatment for hypertension is lower in men than in women, and thus the prevalence of hypertension in men aged >50 years did not change between 1980 and 2010, whereas the prevalence of hypertension decreased in women of all ages.²³ These sex differences may contribute to the differences in the results seen in this study for men and women. Similar to blood glucose levels, early control of blood pressure is important because of its lasting effects until later life.

Strengths and Limitations

To the best of our knowledge, this is the first study to assess sex-specific time-associated changes in CAD risk factors (serum TC, SBP, smoking behavior, and diabetes) in a randomly selected representative Japanese population through a 29-year prospective study. However, the present study has some limitations. There was no information available on the history of hyperlipidemia, and it was not possible to obtain data on changes in risk factors, treatment status, and diets during the follow-up period. However, we assumed that the number of individuals receiving treatment for hyperlipidemia in 1980 was small because statins only became a top-selling drug in Japan from 1989. Another limitation is the transition from ICD-9 to ICD-10 during the study period, which occurred 15 years into the follow-up period. However, we considered the effect of this transition on the results, as well as the effect of the inclusion of “heart failure” in the analysis, and the results obtained were similar. Furthermore, although there was no information on excessive drinking, we adjusted for abstinence (ex-drinkers) because we speculated that many excessive drinkers stopped drinking over time.

Conclusions

In men, smoking behavior and higher serum TC at baseline showed the strongest correlation with a higher risk of CAD mortality in the first 10 years of follow-up; however, these relationships decreased over time. Diabetes and a higher SBP at baseline had the strongest associations with CAD mortality after 29 years. In women, diabetes was associated with CAD mortality after 20 years, and its harmful effects persisted until 29 years. The duration of the follow-up needed to accurately assess risk factors for CAD mortality varied between risk factors and sex.

Acknowledgments

The authors extend deep thanks to all members of the Japanese Association of Public Health Center Directors and all staff of the public health centers, as listed in the Appendix, for their cooperation with this study.

Author Contributions

Y.O., H.U., Y.N. designed the study; H.U., A.K., N.O., T. Ohkubo, N.M., T. Okamura, K.M., A.O. conducted the study and provided essential materials; H.U., Y.N., K.K., A.K., N.O., T. Ohkubo, N.M., T. Okamura, K.M., A.O. critically revised the manuscript for important intellectual content; Y.O. analyzed data; Y.O., H.U. wrote the manuscript; Y.O. had primary responsibility over the final content. All authors read and approved the final manuscript.

Sources of Funding

This work was supported by a Grant-in-Aid from the Ministry of Health and Welfare under the auspices of the Japanese Association for Cerebro-cardiovascular Disease Control, a Research Grant for Cardiovascular Diseases (7A-2) from the Ministry of Health, Labour and Welfare, and a Health and Labor Sciences Research Grant, Japan (Comprehensive Research on Aging and Health, grant numbers H11-Chouju-046, H14-Chouju-003, H17-Chouju-012, H19-Chouju-Ippan014; and Comprehensive Research on Life-Style Related Diseases including Cardiovascular Diseases and Diabetes Mellitus, grant numbers H22-Junkankitou-Seishuu-Sitei-017, H25-Junkankitou-Seishuu-Sitei-022).

Disclosures

None declared.

IRB Information

This study was approved by the Institutional Review Board of Shiga University of Medical Science for Ethical Issues (No. 12-18, 2000; No. 17-21-1, 2010).

Appendix

The NIPPON DATA80/90 Research Group

Chairpersons: Hirotsugu Ueshima (Center for Epidemiologic Research in Asia, Shiga University of Medical Science, Otsu, Shiga), Akira Okayama (Research Institute of Strategy for Prevention, Tokyo), Katsuyuki Miura (Center for Epidemiologic Research in Asia, Department of Public Health, Shiga University of Medical Science, Otsu, Shiga) for the NIPPON DATA80; Hirotsugu Ueshima, Tomonori Okamura (Department of Preventive Medicine and Public Health, Keio University School of Medicine, Tokyo), Katsuyuki Miura for the NIPPON DATA90.

Research Members: Shigeyuki Saitoh (School of Health Sciences, Sapporo Medical University, Sapporo, Hokkaido), Kiyomi Sakata (Department of Hygiene and Preventive Medicine, Iwate Medical University, Morioka, Iwate), Atsushi Hozawa (Department of Preventive Medicine and Epidemiology, Tohoku Medical Megabank Organization, Tohoku University, Sendai, Miyagi), Yosikazu Nakamura (Department of Public Health, Jichi Medical University, Shimotsuke, Tochigi), Nobuo Nishi (Center for International Collaboration and Partnership, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo), Nagako Okuda (Department of Health and Nutrition, University of Human Arts and Sciences, Saitama), Takayoshi Ohkubo (Department of Hygiene and Public Health Teikyo University School of Medicine, Tokyo), Fumiyoshi Kasagi (Institute of Radiation Epidemiology, Radiation Effects Association, Tokyo), Yoshitaka Murakami (Department of Medical Statistics, Toho University, Tokyo), Tohru Izumi (Kitasato University, Sagamihara, Kanagawa), Yasuhiro Matsumura (Faculty of Health and Nutrition, Bunkyo University, Chigasaki, Kanagawa), Toshiyuki Ojima (Department of Community Health and Preventive Medicine, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka), Koji Tamakoshi (Department of Public Health and Health Information Dynamics, Nagoya University Graduate School of Medicine, Nagoya, Aichi), Hideaki Nakagawa (Medical Research Institute, Kanazawa Medical University, Kanazawa, Ishikawa), Yoshikuni Kita (Faculty of Nursing Science, Tsuruga Nursing University, Tsuruga, Fukui), Aya Kadota, Yasuyuki Nakamura (Shiga University of Medical Science, Otsu, Shiga), Naomi Miyamatsu (Department of Clinical Nursing, Shiga University of Medical Science, Otsu, Shiga), Takehito Hayakawa (Kinugasa Research Organization, Ritsumeikan University, Kyoto), Katsushi Yoshita (Osaka City University Graduate School of human life science, Osaka), Yoshihiro Miyamoto (Department of Preventive Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka), Akira Fujiyoshi (Department of Hygiene, Wakayama Medical University, Wakayama), Kazunori Kodama (Radiation Effects Research Foundation, Hiroshima) and Yutaka Kiyohara (Hisayama Research Institute for Lifestyle Discascs, Hisayama-cho, Fukuoka).

Supplementary Files

Please find supplementary file(s);

http://dx.doi.org/10.1253/circj.CJ-20-0739

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