Multivariate Analysis of Risk Factors for Cerebral Infarction Based on Specific Health Checkups in Japan
1
Department of Health and Welfare Services, National Institute of Public Health, 2-3-6 Minami, Wako 351-0197, Japan
2
Department of Ophthalmology, Juntendo University School of Medicine, 3-1-3 Hongo, Bunkyo-ku, Tokyo 113-8431, Japan
3
The University of Fukuchiyama, 3370, Aza Hori, Fukuchiyama shi 620-0886, Japan
*
Author to whom correspondence should be addressed.
J. Ageing Longev. 2022, 2(4), 277-292; https://doi.org/10.3390/jal2040023
Submission received: 21 September 2022
/
Revised: 1 November 2022
/
Accepted: 7 November 2022
/
Published: 12 November 2022
Abstract
:Stroke is a progressive disease with remissions and exacerbations; it significantly reduces the quality of life of patients and their family and caregivers. Primary prevention is necessary to reduce the growing incidence of stroke globally. In this study, we determined the risk factors for cerebral infarction in elderly Japanese residents and proposed a primary care strategy to prevent cerebral infarction. We investigated the relationship between the incidence of cerebral infarction and the results of checkups 10 years ago. Multivariate logistic regression analysis was performed to determine the variables related to the occurrence of cerebral infarction in biochemical tests and questionnaires administered ten years ago. Hypertension and abnormal creatinine levels were related to increased risk of cerebral infarction based on our findings of the health checkups conducted 10 years previously. Furthermore, weight gain or loss of >3 kg over the last year and habit of eating an evening meal within 2 h before going to bed were associated with an increased risk of cerebral infarction based on the questionnaire results from the specific health checkups. Long-term, large-scale prospective studies are required to determine the specific health items related to increased risk of cerebral infarction.
1. Introduction
In the 2019 Global Burden of Disease study, stroke was found to be the second-leading cause of death and the third-leading cause of death and disability combined globally [1]. Between 1990 and 2019, the number of affected individuals, prevalence, deaths, and disability-adjusted life-years of stroke increased by 70.0%, 85.0%, 43.0% and 32.0%, respectively [1]. Ischemic stroke, intracerebral hemorrhage, and subarachnoid hemorrhage accounted for 62.4%, 27.9% and 9.7% of all strokes in 2019, respectively. Stroke is a progressive disease with remissions and exacerbations; it significantly reduces the quality of life of patients and their family and caregivers. Primary prevention is necessary to reduce the growing incidence of stroke globally.
In Japan, the leading cause of death is malignant neoplasm (cancer), followed by heart disease and cerebrovascular disease [2]. Circulatory system diseases, including stroke, cause slightly fewer deaths than cancer in all age groups [2]. However, among individuals aged ≥ 75 years, stroke is the leading cause of death and causes almost 40,000 more deaths annually than cancer [2]. Considering that the number of elderly people is expected to increase in the future, primary prevention of stroke is essential to extend the average life expectancy. In addition, the major diseases requiring nursing care in Japan are stroke (16.1%) and heart disease (4.5%), which together account for one-fifth of the total diseases [3]. The next most common disease is dementia (17.6%); almost 30% of patients with dementia aged ≥ 65 years have vascular dementia due to cerebrovascular diseases [4]. In 2017, 1.11 million patients in Japan received continuous medical care for cerebrovascular diseases, and the annual medical cost for cerebrovascular disease was JPY 1.8 trillion (almost USD 22 billion). Furthermore, the rate of cognitive impairment and the nursing care burden increase after stroke. In particular, the risk factors of ischemic stroke, which accounts for 60% of all strokes, in elderly individuals should be identified and primary prevention strategies should be implemented to reduce the long-term medical and nursing care costs.
The Framingham Stroke Risk Profile (FSRP) is most commonly used for stroke risk assessment [5]. The FSRP study used Cox proportional hazards regression modeling to determine the 10-year risk factors of stroke based on the Framingham study. This study identified age, systolic blood pressure, use of antihypertensives, diabetes mellitus, current smoking, history of cardiovascular disease (coronary heart disease, heart failure, or intermittent claudication), history of atrial fibrillation, and left ventricular hypertrophy as risk factors of stroke [6,7,8]. However, the prevalence and predictors of stroke vary by race, country, temperature, and economic level, which requires the identification of stroke risks factors in Japanese people [1,5]. In addition, although many cohort studies have evaluated the risk factors for all strokes, few cohort studies have explored the risk factors for cerebral infarction only.
In 2008, the Japanese Ministry of Health, Labor and Welfare introduced the Japanese health checkup/guidance program to detect individuals with risk factors for metabolic syndrome. This program was designed to reduce the prevalence of metabolic syndrome and associated medical costs using medium- to long-term lifestyle changes [9]. The Specified Health Checkups in Japan contains 29 items based on an annual physical examination for the assessment of risk factors for metabolic syndrome, as well as a 23-item questionnaire. Since 1961, Japan has provided a universal health insurance system. Anonymized data from The Specified Health Checkups and information issued by medical institutions to the National Health Insurance are stored in the respective databases [6]. Analysis of these databases are permitted for academic research on medical cost optimization and improvement of the quality of medical services [10]. In this study, we conducted a new study to analyze the risk factor by combining the results of past Specific Health Checkup and the receipt data 10 years later.
We explored the association of incidence of cerebral infarction with findings of the Specific Health Checkups in Japan. We determined the risk factors for cerebral infarction in elderly Japanese residents and proposed a primary care strategy to prevent cerebral infarct.
2. Materials and Methods
2.1. Study Participants
We enrolled 33,824 individuals with insurance from a total of 106,978 residents of Mishima City, Japan. These 33,824 residents were treated at clinics and hospitals in this city in 2019 using the National Health Insurance. In 2009, 7438 residents aged > 40 years underwent a specific health checkup based on 29 physical examinations, laboratory tests, and a 23-item questionnaire. Of them, we selected 5909 individuals (2140 men and 3769 women; mean age: 75.0 ± 6.69 years, 49–84 years) who had received both medical treatment at a medical institution using the National Health Insurance in 2019 as well as a standard health examination in 2009. Individuals with history of stroke in 2009 were excluded. We investigated the relationship between the incidence of cerebral infarction in 2019 and the results of checkups 10 years ago.
2.2. Statistical Analysis
Logistic regression analysis (LRA) was performed to identify factors related to cerebral infarction. To exclude confounding factors among explanatory variables, multiple LRA was performed and calculate adjusted odds ratios (OR). All items on the questionnaire or biochemical tests were entered simultaneously as explanatory variables. Participants were categorized according to the presence or absence of cerebral infarction using the receipt data from the 2019 National Health Insurance. Cerebral infarction was defined as ICD10 classification of I630-639.
For the multivariate LRA, the dependent and independent variables were selected as incidence of cerebral infarction (existence/nonexistence) and results of the Specific Health Checkup plus questionnaire, respectively. Multivariate LRAs were conducted individually for biochemical tests and questionnaires. Potential common confounders (age, sex, drug intake, outpatient medical expenditures in 2009, and medical history) were included as explanatory variables in both multivariate LRAs. Data were analyzed using SPSS v. 27 and Modeler v. 18.3 (IBM Corp., Armonk, NY, USA). The National Institute of Public Health (NIPH-IBRA #12386) and the ethics committee municipal assembly of Mishima provided permission for this study. The study was performed in accordance with the International Ethical Guidelines for Epidemiology [11], Guidelines for the utilization of the Database for National Health Insurance Claim, Specific Medical Checkup/Health Guidance [12], and Guidelines of Security for Health Information Systems [13]. Participant data were anonymized by the local administration.
3. Results
We cross-tabulated the biochemical tests in 2009 and incidence of cerebral infarction in 2019 (Table 1); p-value was calculated by chi-square test and four items were identified as significantly different: “creatinine”, “urinary acid”, “leucocyte”, and “HbA1C”.
Table 2 presents the findings from the cross-tabulation of questionnaires administered in 2009 and incidence of cerebral infarction in 2019, which showed significant differences in seven items: “a medicine to lower blood pressure”, “insulin injections or a medicine to lower blood glucose”, “a medicine to lower cholesterol”, “heart disease history”, “current regular smoker”, “weight gain or loss of >3 kg over the last year”, “skip breakfast 3 days or more per week”.
LRA was performed to determine the variables related to the occurrence of cerebral infarction in biochemical tests (Table 3).
The crude ORs showed statistically significant associations for 13 items. However, to eliminate potential confounding factors, all explanatory variables possibly related to cerebral infarction were entered into the multivariate LRA, irrespective of the results of univariate LRA. Significant OR was identified for the incidence of cerebral infarction in four items, namely “age”, “systolic blood pressure”, “creatinine” and “outpatient medical expenditures in 2009” (Table 3).
LRA was performed to investigate the variables related to the incidence of cerebral infarction in questionnaires administered in 2009 (Table 4).
The crude ORs showed statistically significant associations for seven items (Table 4). All items, irrespective of the results of the univariate analysis, were entered into the multivariate LRA to identify those associated with the incidence of cerebral infarction. Significant ORs were observed for the incidence of cerebral infarction and six items, namely, “sex”, “a medicine to lower blood pressure”, “insulin injections or a medicine to lower blood glucose”, “weight gain or loss of >3 kg over the last year”, “evening meal within 2 h before going to bed” and “outpatient medical expenditures in 2009”.
4. Discussion
A recent research study reported that each USD 1 spent on cerebrovascular and cardiovascular disease prevention yields a return on investment of USD 10.9 [14]. Global and regional risk factors for cerebral infarction need to be considered for evidence-based healthcare planning, priority setting, primary prevention, and research [1]. Increased prevalence of several major stroke risk factors between 1990 and 2019 suggests that the existing primary stroke prevention strategies and countermeasures are inadequate and need to be strengthened worldwide [15,16]. The World Health Organization (WHO) recommends that efforts should be made to prevent stroke by appropriately managing hypertension, elevated lipids, diabetes, smoking, reduced physical activity, unhealthy diet, and abdominal obesity [17].
In this research, multivariate LRA demonstrated that hypertension (high systolic blood pressure) was related to a higher risk of incidence of cerebral infarction. The results are consistent with the WHO prevention strategies and FSRP risk factors of stroke. A point-based prediction model for stroke risk was developed and validated in a Japanese cohort study of healthy individuals in 2013. In this model, the group with blood pressure of ≥140 mmHg was associated with a hazard ratio of ≥3 compared to the normotensive group [18]. Antihypertensive drug use was also a predictor of stroke in the FSRP study [5]. In this study, there was a significant multivariate-adjusted OR for use of antihypertensives medicine to lower the blood pressure in the questionnaire, but a multivariate LRA that included blood pressure as an explanatory variable in biochemical tests did not show a significant OR (p = 0.07).
Our results of multivariate LRA showed that abnormal creatinine levels were related to an increased occurrence of cerebral infarction. Since creatinine is filtered by the kidneys and excreted in the urine, elevated blood creatinine levels indicate impaired kidney function. A previous study reported that chronic kidney disease was related to increased risks of stroke, asymptomatic cerebrovascular abnormalities, and cognitive impairment [19,20,21,22]. In Japan, patients with cerebral infarction patients and CKD have anemia, hypercoagulability, and inflammation. Furthermore, cardiogenic cerebral embolism is the most common clinical type [23]. In addition, renal failure was independently associated with cardiogenic cerebral embolism and subsequent poor outcomes [24]. In this study, most participants with abnormal creatinine levels had no history of renal disease (data not shown). Therefore, the onset of cerebral infarction may be prevented by early treatment.
Diabetes is associated with increased risk of stroke. Our results showed significant multivariate-adjusted ORs, insulin injections or use of antidiabetic drugs in the questionnaire, and significant crude ORs were obtained for biochemical blood glucose levels and HbA1C. However, the multivariate LRA did not show significant ORs of biochemical tests [5,17].
The relationship between weight change and cerebrovascular disease is not well-known [25,26]. Our findings from the multivariate LRA showed that “weight gain or loss of >3 kg over the last year” in the questionnaire was related to increased occurrence of cerebral infarction. In Japan, Kisanuki et al. reported that weight gain during middle age was related to high risk of stroke in women and high risk of coronary heart disease in men, and weight loss was related to high risks of stroke in men as well as women [27]. Although the previous study enrolled middle-aged participants, a similar risk was observed in the elderly participants in the present study. Furthermore, although the above study focused on alterations in body weight over a period of 5 years, our results of changes in body weight over a period of 1 year also increased the risk of cerebral infarction.
Our findings from the multivariate LRA showed that “evening meal within 2 h before going to bed” in the questionnaire was related to an increased risk of incidence of cerebral infarction. Regarding dietary risk, although a diet high in sodium, red meat, and alcohol, and low in fruits, vegetables, and whole grains is associated with stroke risk [5], there are few reports on the rhythm of meals. The item “evening meal within 2 h before going to bed” could be associated with high caloric intake. According to a WHO report, elevated lipids, diabetes, and abdominal obesity are reported to be risk factors [5] and eating before going to bed may be a background factor for these. The questionnaire used in this study did not include questions on caloric intake. More detailed research is needed in the future about the relationship with meals.
Future studies should investigate the risk of lifestyle and biochemical tests on the incidence of cerebral infarction to establish a more accurate screening method. It has been reported that specific medical health checkups in Japan are useful for screening for dementia [28,29]. It would be very efficient if specific health checkups, which screen for metabolic syndrome, could also be used for screening for cerebral infarction or dementia. Because this research was performed retrospectively, a large-scale prospective study is required to identify specific health checkup items associated with stroke risk.
5. Conclusions
Hypertension and abnormal creatinine levels were related to increased risk of cerebral infarction based on our findings of health checkups conducted 10 years previously. Furthermore, weight gain or loss of >3 kg over the last year and habit of eating evening meal within 2 h before going to bed were associated with an increased risk of cerebral infarction based on the questionnaire results from the specific health checkups. Long-term, large-scale prospective studies are required to determine the specific health items related to increased risk of cerebral infarction.
Author Contributions
Conceptualization, Y.T. and T.K.; methodology, Y.T. and Y.H.; investigation, Y.T.; formal analysis, Y.T.; writing original draft preparation, Y.T., Y.H. and T.K.; writing, review, and editing, Y.T., Y.H. and T.K.; supervision, T.K. All authors have read and agreed to the published version of the manuscript.
Funding
This work was supported by grants from MEXT/JSPS KAKENHI (JP21K02001).
Institutional Review Board Statement
This study was approved by the Institutional Review Board (NIPH-IBRA # 12386) of the National Institute of Public Health in Japan and the Mishima City Council.
Informed Consent Statement
The data used in this study was anonymous data with personal information removed by the municipality. In Japan, the national data of medical receipts and specific medical examinations can be used for academic research with high public interest for purposes other than the original purpose without the consent of the residents. (December 24, 2010 Minister of Health, Labor and Welfare Notification No. 424).
Data Availability Statement
To protect the participants’ anonymity, data will not be shared unless requested through an administrative procedure.
Conflicts of Interest
All authors report no conflict of interest related to this work.
References
- GBD 2019 Stroke Collaborators. Global, regional, and national burden of stroke and its risk factors, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet Neurol. 2021, 20, 795–820. [Google Scholar] [CrossRef]
- Ministry of Health, Labor and Welfare. Dynamic Statistics of Population 2019 (Japan). Available online: https://www.mhlw.go.jp/toukei/saikin/hw/jinkou/kakutei19/ (accessed on 12 September 2022).
- Ministry of Health, Labor and Welfare. National Livelihood Survey 2019 (Japan). Available online: https://www.mhlw.go.jp/toukei/list/20-21.html (accessed on 12 September 2022).
- Ministry of Health, Labor and Welfare. Current Status of Measures for Sementia 2014 (Japan). Available online: https://www.mhlw.go.jp/file/05-Shingikai-11901000-Koyoukintoujidoukateikyoku-Soumuka/0000069443.pdf (accessed on 12 September 2022).
- Dufouil, C.; Beiser, A.; McLure, L.A.; Wolf, P.A.; Tzourio, C.; Howard, V.J.; Westwood, A.J.; Himali, J.J.; Sullivan, L.; Aparicio, H.J.; et al. Revised Framingham stroke risk profile to reflect temporal trends. Circulation 2017, 135, 1145–1159. [Google Scholar] [CrossRef] [PubMed]
- Salaycik, K.J.; Kelly-Hayes, M.; Beiser, A.; Nguyen, A.H.; Brady, S.M.; Kase, C.S.; Wolf, P.A. Depressive symptoms and risk of stroke: The Framingham Study. Stroke 2007, 38, 16–21. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Bineau, S.; Dufouil, C.; Helmer, C.; Ritchie, K.; Empana, J.P.; Ducimetière, P.; Alpérovitch, A.; Bousser, M.G.; Tzourio, C. Framingham stroke risk function in a large population-based cohort of elderly people: The 3C study. Stroke 2009, 40, 1564–1570. [Google Scholar] [CrossRef] [Green Version]
- Beer, C.; Alfonso, H.; Flicker, L.; Norman, P.E.; Hankey, G.J.; Almeida, O.P. Traditional risk factors for incident cardiovascular events have limited importance in later life compared with the health in men study cardiovascular risk score. Stroke 2011, 42, 952–959. [Google Scholar] [CrossRef] [Green Version]
- Tamaki, Y.; Okamoto, E.; Hiratsuka, Y.; Kumakawa, T. Influence of Specific Health Guidance on the Consultation Rate of Metabolic-Related Diseases. Adv. Public Health 2019, 2019, 9735127. [Google Scholar] [CrossRef]
- Ministry of Health, Labor and Welfare. NDB Open Data (Japan). Available online: https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000177182.html (accessed on 12 September 2022).
- Japanese Ministry of Health, Labour and Welfare. Ethical Guidelines for Epidemiological Research. Ministry of Education, Culture, Sports, Science and Technology. Available online: http://www.lifescience.mext.go.jp/files/pdf/n796_01.pdf (accessed on 12 September 2022).
- Japanese Ministry of Health, Labour and Welfare. Guideline for Provision of Database for National Health Insurance Claim and the Specific Medical Checkup and Specific Health Guidance. Available online: http://www.mhlw.go.jp/file/05-Shingikai-12401000-Hokenkyoku-Soumuka/0000064238_3.pdf (accessed on 12 October 2021).
- Japanese Ministry of Health, Labour and Welfare. Security Guidelines for Health Information Systems. Available online: http://www.mhlw.go.jp/file/05-Shingikai-12601000-Seisakutoukatsukan-Sanjikanshitsu_Shakaihoshoutantou/0000166260.pdf (accessed on 12 September 2022).
- Bertram, M.Y.; Sweeny, K.; Lauer, J.A.; Chisholm, D.; Sheehan, P.; Rasmussen, B.; Upreti, S.R.; Dixit, L.P.; George, K.; Deane, S. Investing in non-communicable diseases: An estimation of the return on investment for prevention and treatment services. Lancet 2018, 391, 2071–2078. [Google Scholar] [CrossRef]
- Murray, C.J.L.; Aravkin, A.Y.; Zheng, P.; Abbafati, C.; Abbas, K.M.; Abbasi-Kangevari, M.; Abd-Allah, F.; Abdelalim, A.; Abdollahi, M.; Abdollahpour, I. Global burden of 87 risk factors in 204 countries and territories, 1990–2019: A systematic analysis for the Global Burden of Disease Study 2019. Lancet 2020, 396, 1223–1249. [Google Scholar] [CrossRef]
- Murray, C.J.L.; Abbafati, C.; Abbas, K.M.; Abbasi, M.; Abbasi-Kangevari, M.; Abd-Allah, F.; Abdollahi, M.; Abedi, P.; Abedi, A.; Abolhassani, H.; et al. Five insights from the Global Burden of Disease Study 2019. Lancet 2020, 396, 1135–1159. [Google Scholar] [CrossRef]
- Johnson, W.; Onuma, O.; Owolabi, M.; Sachdev, S. Stroke: A global response is needed. Bull. World Health Organ. 2016, 94, 634A. [Google Scholar] [CrossRef]
- Yatsuya, H.; Iso, H.; Yamagishi, K.; Kokubo, Y.; Saito, I.; Suzuki, K.; Sawada, N.; Inoue, M.; Tsugane, S. Development of a point-based prediction model for the incidence of total stroke: Japan public health center study. Stroke 2013, 44, 1295–1302. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Miglinas, M.; Cesniene, U.; Janusaite, M.M.; Vinikovas, A. Cerebrovascular Disease and Cognition in Chronic Kidney Disease Patients. Front. Cardiovasc. Med. 2020, 7, 96. [Google Scholar] [CrossRef] [PubMed]
- Dad, T.; Weiner, D.E. Stroke and chronic kidney disease: Epidemiology, pathogenesis, and management across kidney disease stages. Semin. Nephrol. 2015, 35, 311–322. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Toyoda, K.; Ninomiya, T. Stroke and cerebrovascular diseases in patients with chronic kidney disease. Lancet Neurol. 2014, 13, 823–833. [Google Scholar] [CrossRef]
- Arnold, J.; Sims, D.; Ferro, C.J. Modulation of stroke risk in chronic kidney disease. Clin Kidney J. 2016, 9, 29–38. [Google Scholar] [CrossRef] [PubMed] [Green Version]
- Kamouchi, M. Stroke features and management in patients with chronic kidney disease. Contrib. Nephrol. 2013, 179, 92–99. [Google Scholar] [CrossRef] [Green Version]
- Miwa, K.; Koga, M.; Nakai, M.; Yoshimura, S.; Sasahara, Y.; Koge, J.; Sonoda, K.; Ishigami, A.; Iwanaga, Y.; Miyamoto, Y.; et al. Etiology and Outcome of Ischemic Stroke in Patients with Renal Impairment Including Chronic Kidney Disease: Japan Stroke Data Bank. Neurology 2022, 98, e1738–e1747. [Google Scholar] [CrossRef]
- Stevens, J.; Erber, E.; Truesdale, K.P.; Wang, C.H.; Cai, J. Long- and short-term weight change and incident coronary heart disease and ischemic stroke: The Atherosclerosis Risk in Communities Study. Am. J. Epidemiol. 2013, 178, 239–248. [Google Scholar] [CrossRef] [Green Version]
- Chen, C.; Ye, Y.; Zhang, Y.; Pan, X.F.; Pan, A. Weight change across adulthood in relation to all cause and cause specific mortality: Prospective cohort study. BMJ 2019, 367, l5584. [Google Scholar] [CrossRef] [Green Version]
- Kisanuki, K.; Muraki, I.; Yamagishi, K.; Kokubo, Y.; Saito, I.; Yatsuya, H.; Sawada, N.; Iso, H.; Tsugane, S.; JPHC Study Group. Weight change during middle age and risk of stroke and coronary heart disease: The Japan Public Health Center-based Prospective Study. Atherosclerosis 2021, 322, 67–73. [Google Scholar] [CrossRef]
- Tamaki, Y.; Hiratsuka, Y.; Kumakawa, T. Risk Factors for Dementia Incidence Based on Previous Results of the Specific Health Checkups in Japan. Healthcare 2020, 8, 491. [Google Scholar] [CrossRef] [PubMed]
- Tamaki, Y.; Hiratsuka, Y.; Kumakawa, T. Evaluation of Risk Factors for Dementia Incidence Based on Previous Questionnaire Results of Specific Health Checkups in Japan. J. Ageing Longev. 2021, 1, 48–59. [Google Scholar] [CrossRef]
Table 1.
Cross-tabulation results of the biochemical tests in 2009 and incidence of cerebral infarction in 2019.
Table 1.
Cross-tabulation results of the biochemical tests in 2009 and incidence of cerebral infarction in 2019.
| Incidence of Cerebral Infarction | |||||
|---|---|---|---|---|---|
| Item | Category | Nonexistence | Existence | Total | p-Value |
| Uric protein | Normal | 4736 | 825 | 5561 | 0.592 |
| 85% | 15% | 100% | |||
| Follow-up | 9 | 3 | 12 | ||
| 75% | 25% | 100% | |||
| Requires further testing | 223 | 35 | 258 | ||
| 86% | 14% | 100% | |||
| Requires treatment | 64 | 14 | 78 | ||
| 82% | 18% | 100% | |||
| Urinary sugar | Normal | 4935 | 854 | 5789 | 0.083 |
| 85% | 15% | 100% | |||
| Follow-up | 6 | 0 | 6 | ||
| 100% | 0% | 100% | |||
| Requires further testing | 44 | 7 | 51 | ||
| 86% | 14% | 100% | |||
| Requires treatment | 47 | 16 | 63 | ||
| 75% | 25% | 100% | |||
| Uric blood | Normal | 4162 | 734 | 4896 | 0.589 |
| 85% | 15% | 100% | |||
| Follow-up | 51 | 11 | 62 | ||
| 82% | 18% | 100% | |||
| Requires further testing | 706 | 110 | 816 | ||
| 87% | 13% | 100% | |||
| Requires treatment | 113 | 22 | 135 | ||
| 84% | 16% | 100% | |||
| Creatinine | Normal | 4733 | 791 | 5524 | <0.001 |
| 86% | 14% | 100% | |||
| Follow-up | 299 | 86 | 385 | ||
| 78% | 22% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Urea nitrogen | Normal | 4863 | 845 | 5708 | 0.662 |
| 85% | 15% | 100% | |||
| Follow-up | 169 | 32 | 201 | ||
| 84% | 16% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Urinary acid | Normal | 4661 | 793 | 5454 | 0.024 |
| 85% | 15% | 100% | |||
| Follow-up | 371 | 84 | 455 | ||
| 82% | 18% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Leucocyte | Normal | 4739 | 844 | 5583 | 0.014 |
| 85% | 15% | 100% | |||
| Follow-up | 293 | 33 | 326 | ||
| 90% | 10% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Erythrocyte | Normal | 3409 | 607 | 4016 | 0.690 |
| 85% | 15% | 100% | |||
| Follow-up | 1451 | 241 | 1692 | ||
| 86% | 14% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 172 | 29 | 201 | ||
| 86% | 14% | 100% | |||
| Hemoglobin | Normal | 4477 | 776 | 5253 | 0.906 |
| 85% | 15% | 100% | |||
| Follow-up | 459 | 83 | 542 | ||
| 85% | 15% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 96 | 18 | 114 | ||
| 84% | 16% | 100% | |||
| Hematocrit | Normal | 4734 | 824 | 5558 | 0.950 |
| 85% | 15% | 100% | |||
| Follow-up | 266 | 48 | 314 | ||
| 85% | 15% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 32 | 5 | 37 | ||
| 86% | 14% | 100% | |||
| Platelet | Normal | 4887 | 857 | 5744 | 0.319 |
| 85% | 15% | 100% | |||
| Follow-up | 145 | 20 | 165 | ||
| 88% | 12% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| AST(GOT) | Normal | 4443 | 766 | 5209 | 0.689 |
| 85% | 15% | 100% | |||
| Follow-up | 536 | 102 | 638 | ||
| 84% | 16% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 53 | 9 | 62 | ||
| 85% | 15% | 100% | |||
| ALT(GPT) | Normal | 4365 | 754 | 5119 | 0.826 |
| 85% | 15% | 100% | |||
| Follow-up | 547 | 101 | 648 | ||
| 84% | 16% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 120 | 22 | 142 | ||
| 85% | 15% | 100% | |||
| γGTP | Normal | 4357 | 751 | 5108 | 0.563 |
| 85% | 15% | 100% | |||
| Follow-up | 493 | 88 | 581 | ||
| 85% | 15% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 182 | 38 | 220 | ||
| 83% | 17% | 100% | |||
| Amylase | Normal | 4753 | 830 | 5583 | 0.824 |
| 85% | 15% | 100% | |||
| Follow-up | 279 | 47 | 326 | ||
| 86% | 14% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| ALP | Normal | 4775 | 830 | 5605 | 0.755 |
| 85% | 15% | 100% | |||
| Follow-up | 257 | 47 | 304 | ||
| 85% | 15% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| LDL-cholesterol | Normal | 1817 | 312 | 2129 | 0.762 |
| 85% | 15% | 100% | |||
| Follow-up | 3215 | 565 | 3780 | ||
| 85% | 15% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Total protein | Normal | 4876 | 853 | 5729 | 0.563 |
| 85% | 15% | 100% | |||
| Follow-up | 156 | 24 | 180 | ||
| 87% | 13% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Total-cholesterol | Normal | 2335 | 414 | 2749 | 0.335 |
| 85% | 15% | 100% | |||
| Follow-up | 2093 | 373 | 2466 | ||
| 85% | 15% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 604 | 90 | 694 | ||
| 87% | 13% | 100% | |||
| HDL-cholesterol | Normal | 4859 | 845 | 5704 | 0.107 |
| 85% | 15% | 100% | |||
| Follow-up | 137 | 20 | 157 | ||
| 87% | 13% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 36 | 12 | 48 | ||
| 75% | 25% | 100% | |||
| Neutral fat | Normal | 4034 | 680 | 4714 | 0.110 |
| 86% | 14% | 100% | |||
| Follow-up | 864 | 176 | 1040 | ||
| 83% | 17% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 134 | 21 | 155 | ||
| 86% | 14% | 100% | |||
| Blood glucose level | Normal | 3481 | 574 | 4055 | 0.086 |
| 86% | 14% | 100% | |||
| Follow-up | 1194 | 231 | 1425 | ||
| 84% | 16% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 357 | 72 | 429 | ||
| 83% | 17% | 100% | |||
| HbA1C | Normal | 2634 | 400 | 3034 | 0.001 |
| 87% | 13% | 100% | |||
| Follow-up | 1974 | 384 | 2358 | ||
| 84% | 16% | 100% | |||
| Requires further testing | 0 | 0 | 0 | ||
| 0% | 0% | 0% | |||
| Requires treatment | 424 | 93 | 517 | ||
| 82% | 18% | 100% | |||
| Total | 5032 | 877 | 5909 | ||
| 85% | 15% | 100% | |||
p-Value: Chi-square test.
Table 2.
Cross-tabulation results of questionnaires conducted in 2009 and incidence of cerebral infarction in 2019.
Table 2.
Cross-tabulation results of questionnaires conducted in 2009 and incidence of cerebral infarction in 2019.
| Incidence of cerebral infarction | |||||
|---|---|---|---|---|---|
| Nonexistence | Existence | Total | p-Value | ||
| A medicine to lower blood pressure | Yes | 1776 | 410 | 2186 | <0.001 |
| 81.2% | 18.8% | 100.0% | |||
| No | 3256 | 467 | 3723 | ||
| 87.5% | 12.5% | 100.0% | |||
| Insulin injections or a medicine to lower blood glucose | Yes | 383 | 103 | 486 | <0.001 |
| 78.8% | 21.2% | 100.0% | |||
| No | 4649 | 774 | 5423 | ||
| 85.7% | 14.3% | 100.0% | |||
| A medicine to lower cholesterol | Yes | 1151 | 248 | 1399 | 0.001 |
| 82.3% | 17.7% | 100.0% | |||
| No | 3881 | 629 | 4510 | ||
| 86.1% | 13.9% | 100.0% | |||
| Heart disease history | Yes | 258 | 77 | 335 | <0.001 |
| 77.0% | 23.0% | 100.0% | |||
| No | 4774 | 800 | 5574 | ||
| 85.6% | 14.4% | 100.0% | |||
| Chronic renal failure history | Yes | 8 | 2 | 10 | 0.646 |
| 80.0% | 20.0% | 100.0% | |||
| No | 5024 | 875 | 5899 | ||
| 85.2% | 14.8% | 100.0% | |||
| Anemia history | Yes | 555 | 95 | 650 | 0.863 |
| 85.4% | 14.6% | 100.0% | |||
| No | 4477 | 782 | 5259 | ||
| 85.1% | 14.9% | 100.0% | |||
| Current regular smoker | Yes | 621 | 72 | 693 | <0.001 |
| 89.6% | 10.4% | 100.0% | |||
| No | 4411 | 805 | 5216 | ||
| 84.6% | 15.4% | 100.0% | |||
| Weight gained more than10kg since 20 years old | Yes | 1545 | 289 | 1834 | 0.184 |
| 84.2% | 15.8% | 100.0% | |||
| No | 3487 | 588 | 4075 | ||
| 85.6% | 14.4% | 100.0% | |||
| Exercising for 30 minutes or more, 2 days or more every week | Yes | 2256 | 404 | 2660 | 0.498 |
| 84.8% | 15.2% | 100.0% | |||
| No | 2776 | 473 | 3249 | ||
| 85.4% | 14.6% | 100.0% | |||
| Walking more than 1hour everyday | Yes | 2620 | 469 | 3089 | 0.443 |
| 84.8% | 15.2% | 100.0% | |||
| No | 2412 | 408 | 2820 | ||
| 85.5% | 14.5% | 100.0% | |||
| Walk faster than people of your age and sex | Yes | 2803 | 471 | 3274 | 0.272 |
| 85.6% | 14.4% | 100.0% | |||
| No | 2229 | 406 | 2635 | ||
| 84.6% | 15.4% | 100.0% | |||
| Weight gain or loss of more than 3kg over the last year | Yes | 911 | 185 | 1096 | 0.035 |
| 83.1% | 16.9% | 100.0% | |||
| No | 4121 | 692 | 4813 | ||
| 85.6% | 14.4% | 100.0% | |||
| Eating pace | Faster | 383 | 64 | 447 | 0.551 |
| 85.7% | 14.3% | 100.0% | |||
| Normal | 3505 | 599 | 4104 | ||
| 85.4% | 14.6% | 100.0% | |||
| Slower | 1144 | 214 | 1358 | ||
| 84.2% | 15.8% | 100.0% | |||
| Evening meal within 2 hours before going to bed | Yes | 506 | 106 | 612 | 0.069 |
| 82.7% | 17.3% | 100.0% | |||
| No | 4526 | 771 | 5297 | ||
| 85.4% | 14.6% | 100.0% | |||
| Have snack after the evening meal | Yes | 498 | 75 | 573 | 0.214 |
| 86.9% | 13.1% | 100.0% | |||
| No | 4534 | 802 | 5336 | ||
| 85.0% | 15.0% | 100.0% | |||
| Skip breakfast 3 days or more per week | Yes | 295 | 32 | 327 | 0.008 |
| 90.2% | 9.8% | 100.0% | |||
| No | 4737 | 845 | 5582 | ||
| 84.9% | 15.1% | 100.0% | |||
| Drink alcohol | Rarely(can’t drink) | 972 | 155 | 1127 | 0.383 |
| 86.2% | 13.8% | 100.0% | |||
| Sometimes | 1096 | 205 | 1301 | ||
| 84.2% | 15.8% | 100.0% | |||
| Everyday | 2964 | 517 | 3481 | ||
| 85.1% | 14.9% | 100.0% | |||
| Feel refreshed after a night’s sleep | Yes | 3929 | 675 | 4604 | 0.463 |
| 85.3% | 14.7% | 100.0% | |||
| No | 1103 | 202 | 1305 | ||
| 84.5% | 15.5% | 100.0% | |||
| Start lifestyle modifications | no plan to improve | 1059 | 199 | 1258 | 0.83 |
| 84.2% | 15.8% | 100.0% | |||
| going to start in the future (within 6 months) | 413 | 73 | 486 | ||
| 85.0% | 15.0% | 100.0% | |||
| going to start soon (in a month) | 566 | 93 | 659 | ||
| 85.9% | 14.1% | 100.0% | |||
| already started (<6 months ago) | 1453 | 252 | 1705 | ||
| 85.2% | 14.8% | 100.0% | |||
| already started (≥6 months ago) | 1541 | 260 | 1801 | ||
| 85.6% | 14.4% | 100.0% | |||
| Willing to have Health Guidance | Yes | 2565 | 471 | 3036 | 0.135 |
| 84.5% | 15.5% | 100.0% | |||
| No | 2467 | 406 | 2873 | ||
| 85.9% | 14.1% | 100.0% | |||
| Total | 5032 | 877 | 5909 | ||
| 85.2% | 14.8% | 100.0% | |||
p-Value: Chi-square test.
Table 3.
Multivariate logistic regression analysis of biochemical tests in 2009.
| Item | Multivariate Adjusted Odds Ratio | 95% CI | p-Value | |
|---|---|---|---|---|
| Lower Limit | Upper Limit | |||
| Age | 1.081 | 1.064 | 1.098 | <0.001 |
| Sex(Women/Men) | 1.070 | 0.824 | 1.389 | 0.613 |
| Height (cm) | 0.996 | 0.979 | 1.012 | 0.590 |
| Weight (kg) | 0.999 | 0.982 | 1.016 | 0.881 |
| Abdominal circumference(cm) | 1.007 | 0.992 | 1.021 | 0.382 |
| A medicine to lower blood pressure(+/−) | 1.166 | 0.988 | 1.376 | 0.070 |
| Insulin injections or a medicine to lower blood glucose(+/−) | 1.294 | 0.995 | 1.682 | 0.055 |
| A medicine to lower cholesterol (Yes/No) | 1.044 | 0.876 | 1.245 | 0.628 |
| Systolic blood pressure(mmHg) | 1.009 | 1.003 | 1.016 | 0.005 |
| Diastolic blood pressure(mmHg) | 0.999 | 0.989 | 1.009 | 0.865 |
| Uric protein(+±/−) | 0.853 | 0.619 | 1.176 | 0.331 |
| Urinary sugar(+±/−) | 1.164 | 0.710 | 1.909 | 0.548 |
| Uric blood(+±/−) | 0.937 | 0.764 | 1.148 | 0.529 |
| Creatinine(+±/−) | 1.494 | 1.136 | 1.964 | 0.004 |
| Urea nitrogen(+±/−) | 0.966 | 0.643 | 1.451 | 0.866 |
| Urinary acid(+±/−) | 1.186 | 0.896 | 1.569 | 0.232 |
| Leucocyte(+±/−) | 0.784 | 0.537 | 1.143 | 0.206 |
| Erythrocyte(+±/−) | 0.919 | 0.773 | 1.092 | 0.338 |
| Hemoglobin(+±/−) | 0.967 | 0.753 | 1.243 | 0.796 |
| Hematocrit(+±/−) | 1.005 | 0.713 | 1.416 | 0.977 |
| Platelet(+±/−) | 0.755 | 0.463 | 1.231 | 0.260 |
| AST(GOT)(+±/−) | 0.985 | 0.737 | 1.316 | 0.918 |
| ALT(GPT)(+±/−) | 1.022 | 0.768 | 1.360 | 0.882 |
| γGPT(+±/−) | 1.092 | 0.858 | 1.390 | 0.473 |
| Amylase(+±/−) | 0.856 | 0.613 | 1.196 | 0.362 |
| ALP(+±/−) | 0.969 | 0.696 | 1.348 | 0.851 |
| LDL-cholesterol(+±/−) | 1.033 | 0.851 | 1.255 | 0.742 |
| Total protein(+±/−) | 0.779 | 0.494 | 1.231 | 0.285 |
| Total-cholesterol(+±/−) | 1.027 | 0.853 | 1.237 | 0.780 |
| HDL-cholesterol(+±/−) | 0.836 | 0.554 | 1.262 | 0.394 |
| Neutral fat (+±/−) | 1.049 | 0.867 | 1.270 | 0.622 |
| Blood glucose level (+±/−) | 0.929 | 0.774 | 1.115 | 0.428 |
| HbA1C (+±/−) | 1.104 | 0.935 | 1.303 | 0.243 |
| Outpatient Medical Expenditures in 2009 | 1.000 | 1.000 | 1.000 | <0.001 |
| _cons | 0.000 | 0.000 | ||
A Total of 34 items were entered simultaneously as the independent variables.
Table 4.
Multivariate logistic regression analysis of questionnaires administered in 2009.
| Item | Multivariate Adjusted Odds Ratio | 95% CI | p-Value | ||
|---|---|---|---|---|---|
| Lower Limit | Upper Limit | ||||
| Age | years | 1.066 | 0.886 | 1.281 | 0.499 |
| Sex | (Female/Male) | 1.086 | 1.070 | 1.103 | 0.000 |
| A medicine to lower blood pressure | (−/+) | 1.272 | 1.086 | 1.489 | 0.003 |
| Insulin injections or a medicine to lower blood glucose | (−/+) | 1.291 | 1.011 | 1.648 | 0.041 |
| A medicine to lower cholesterol | (−/+) | 1.052 | 0.886 | 1.248 | 0.566 |
| Heart disease history | (−/+) | 1.300 | 0.984 | 1.719 | 0.065 |
| Chronic renal failure history | (−/+) | 1.101 | 0.227 | 5.336 | 0.905 |
| Anemia history | (−/+) | 1.038 | 0.815 | 1.322 | 0.764 |
| Current regular smoker | (−/+) | 0.799 | 0.607 | 1.051 | 0.108 |
| Weight gained more than10kg since 20 years old | (−/+) | 0.971 | 0.821 | 1.149 | 0.734 |
| Exercising for 30 minutes or more, 2 days or more every week | (−/+) | 0.947 | 0.802 | 1.119 | 0.524 |
| Walking more than 1hour everyday | (−/+) | 0.977 | 0.829 | 1.152 | 0.784 |
| Walk faster than people of your age and sex | (−/+) | 0.923 | 0.790 | 1.079 | 0.314 |
| Weight gain or loss of more than 3kg over the last year | 1.232 | 1.019 | 1.489 | 0.031 | |
| Eating pace Normal | Reference Group | 0.239 | |||
| Faster | 1.160 | 0.970 | 1.388 | 0.103 | |
| Slower | 0.971 | 0.729 | 1.293 | 0.840 | |
| Evening meal within 2 hours before going to bed | (−/+) | 1.322 | 1.042 | 1.677 | 0.022 |
| Have snack after the evening meal | (−/+) | 0.962 | 0.736 | 1.256 | 0.774 |
| Skip breakfast 3 days or more per week | (−/+) | 0.788 | 0.532 | 1.166 | 0.233 |
| Drink alcohol Rarely (can’t drink) | Reference Group | 0.171 | |||
| Sometimes | 1.184 | 0.982 | 1.429 | 0.078 | |
| Everyday | 1.000 | 0.794 | 1.261 | 0.997 | |
| Feel refreshed after a night’s sleep | (−/+) | 0.897 | 0.751 | 1.073 | 0.234 |
| Start lifestyle modifications | 0.941 | ||||
| no plan to improve | Reference Group | ||||
| going to start in the future (within 6 months) | 0.949 | 0.704 | 1.280 | 0.733 | |
| going to start soon ( in a month) | 1.001 | 0.747 | 1.341 | 0.995 | |
| already started (<6 months ago) | 0.919 | 0.653 | 1.294 | 0.630 | |
| already started (≥6 months ago) | 1.013 | 0.748 | 1.372 | 0.932 | |
| Willing to have Health Guidance | (−/+) | 1.138 | 0.972 | 1.332 | 0.109 |
| Outpatient Medical Expenditures (2009) | 1.000 | 1.000 | 1.000 | 0.001 | |
| _cons | 0.001 | 0.000 | |||
A Total of 32 items were entered simultaneously as the independent variables.
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MDPI and ACS Style
Tamaki, Y.; Hiratsuka, Y.; Kumakawa, T. Multivariate Analysis of Risk Factors for Cerebral Infarction Based on Specific Health Checkups in Japan. J. Ageing Longev. 2022, 2, 277-292. https://doi.org/10.3390/jal2040023
AMA Style
Tamaki Y, Hiratsuka Y, Kumakawa T. Multivariate Analysis of Risk Factors for Cerebral Infarction Based on Specific Health Checkups in Japan. Journal of Ageing and Longevity. 2022; 2(4):277-292. https://doi.org/10.3390/jal2040023
Chicago/Turabian StyleTamaki, Yoh, Yoshimune Hiratsuka, and Toshiro Kumakawa. 2022. "Multivariate Analysis of Risk Factors for Cerebral Infarction Based on Specific Health Checkups in Japan" Journal of Ageing and Longevity 2, no. 4: 277-292. https://doi.org/10.3390/jal2040023
