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the Clinical Trials Research Unit (V.F., V.P., C.M.M.L., A.R.), Department of Medicine and School of Population Health, Faculty of Medicine and Health Sciences, University of Auckland, New Zealand
the Department of Preventive Medicine (I.S.), Yonsei University College of Medicine, Seoul, Korea
The George Institute for International Health (M.W.), University of Sydney, Australia
the School of Population Health (K.J.), University of Queensland, Herston, Australia
the Department of Health Science (H.U.), Shiga University of Medical Science, Shiga, Japan.
Abstract
Background and Purpose— The cause of subarachnoid hemorrhage (SAH) is poorly understood and there are few large cohort studies of risk factors for SAH. We investigated the risk of SAH mortality and morbidity associated with common cardiovascular risk factors in the Asia-Pacific region and examined whether the strengths of these associations were different in Asian and Australasian (predominantly white) populations.
Methods— Cohort studies were identified from Internet electronic databases, searches of proceedings of meetings, and personal communication. Hazard ratios (HRs) for systolic blood pressure (SBP), current smoking, total serum cholesterol, body mass index (BMI), and alcohol drinking were calculated from Cox models that were stratified by sex and cohort and adjusted for age at risk.
Results— Individual participant data from 26 prospective cohort studies (total number of participants 306 620) that reported incident cases of SAH (fatal and/or nonfatal) were available for analysis. During the median follow-up period of 8.2 years, a total of 236 incident cases of SAH were observed. Current smoking (HR, 2.4; 95% CI, 1.8 to 3.4) and SBP >140 mm Hg (HR, 2.0; 95% CI, 1.5 to 2.7) were significant and independent risk factors for SAH. Attributable risks of SAH associated with current smoking and elevated SBP (140 mm Hg) were 29% and 19%, respectively. There were no significant associations between the risk of SAH and cholesterol, BMI, or drinking alcohol. The strength of the associations of the common cardiovascular risk factors with the risk of SAH did not differ much between Asian and Australasian regions.
Conclusions— Cigarette smoking and SBP are the most important risk factors for SAH in the Asia-Pacific region.
Key Words: meta-analysis risk factors subarachnoid hemorrhage
Introduction
Subarachnoid hemorrhage (SAH) constitutes 4% to 7% of all strokes and, because of its high morbidity/mortality,1–3 is one of the most devastating subtypes of stroke.4 Although previous studies have consistently indicated that cigarette smoking is the most important modifiable risk factor for SAH,5 the role of other common cardiovascular factors (eg, levels of blood pressure, serum cholesterol, body mass index , and alcohol intake) in the cause of SAH is poorly defined and the existing findings are controversial.6–8 The lack of knowledge on cause of SAH9 hampers its effective prevention. Stroke registry studies in the Asia-Pacific region indicate that the incidence of SAH is comparatively high in Maori/Pacific10 and Japanese11,12 people but very low in China13 and India,14 suggesting that risk factors (or their prevalence and/or significance) for SAH in these populations may be different from those in other regions. However, few prospective data are available to provide reliable evidence to examine this hypothesis, and no direct comparisons have been made of the strength of the association of common cardiovascular risk factors with SAH endpoints in the different regions. Such information is crucial to estimating the burden of SAH attributable to common cardiovascular risk factors and, more importantly, the burden that is potentially avoidable with the control of these risk factors at the population level. These estimates may also contribute to understanding why the incidence rates of SAH in various countries are relatively steady1,15 despite changes observed in the prevalence of some common cardiovascular risk factors. In addition, international comparisons are not possible within individual cohort studies. Overviews, or meta-analyses, of cohort studies can overcome these issues.
We sought to estimate the mortality and morbidity from SAH associated with common cardiovascular risk factors in the Asia-Pacific region, and to determine if the strength and shapes of these associations with age and sex were different in Asian and Australasian (predominantly white; Australia and New Zealand) populations.
Participants and Methods
Identification of Studies and Collection of Data
The Asia Pacific Cohort Studies Collaboration (APCSC) is an individual participant data overview (meta-analysis) of cohort studies in the Asia-Pacific region. Methods of study identification and the characteristics of studies included have been reported elsewhere.16 In brief, studies were eligible for inclusion in the project if they satisfied the following criteria: (1) a study population from the Asia-Pacific region; (2) prospective cohort study design; (3) at least 5000 person-years of follow-up recorded; (4) date of birth or age, sex, and blood pressure recorded at baseline; and (5) date of death or age at death recorded during follow-up.
In addition, data sought on individual participants included total blood cholesterol, height, weight, cigarette smoking habit, and alcohol consumption. However, because these variables were not inclusion criteria for the collaboration, not all studies provided such data. Outcome data for this report included first-ever-in-a-lifetime SAH events (classified according to the ICD-9 code 430), whether fatal or nonfatal, that occurred during the follow-up period. Nonfatal events were defined as those that did not result in death within 28 days. In 7 studies (235 083 participants) that provided information, the diagnosis of SAH was based on CT/MRI scanning, brain autopsy, or cerebrospinal fluid examination in 84% of cases.
Statistical Analysis
Only those cohorts that provided data on baseline systolic blood pressure (SBP), blood cholesterol, BMI, smoking habit, and alcohol drinking were included in the analyses. All analyses were further restricted to participants aged 20 years or older. BMI was calculated as weight (kg) divided by the square of height (m). The available data only permitted analysis of smoking and alcohol drinking habits as categorical variables: current versus not current (includes former and never). For the Melbourne cohort, current drinkers included ever-drinkers. Analyses were undertaken for total (fatal and nonfatal) SAH events, and sensitivity analyses examined fatal SAH events only.
Stratified Cox proportional hazards analyses17 were used to regress time until first event against baseline cardiovascular risk factors. All analyses were stratified by sex and cohort to control for confounding and reduce statistical heterogeneity. Age at risk (age at the time of the event) was treated as an external time-dependent covariate18 to assess change in hazards as an individual’s age increased. The hazard ratios and corresponding 95% CI for individual risk factors were adjusted for all other risk factors analyzed. Usual associations for SBP and cholesterol were estimated by adjusting for regression dilution bias.19 Repeat measurements of SBP and serum cholesterol available from 7 cohorts were used to estimate regression dilution factors (SBP=1.8 and cholesterol=1.7) using mixed models that took into account the varying time intervals between measurements.19
As well as examining SBP, cholesterol, and BMI on a continuous scale, these risk factors were divided into the following dichotomized groups (based on both sample sizes and clinical usefulness of the levels); SBP, <140 and 140 mm Hg; cholesterol, <4.5 and 4.5 mmol/L; and BMI, <22 and 22 kg/m2. Attributable risks, adjusted for other risk factors, were calculated using population-attributable fractions20–22 for these categories and for current smoking and drinking. To assess the shape of the associations, additional analyses were conducted dividing baseline SBP, cholesterol, and BMI into the following quartiles: SBP (<115, 115 to 129, 130 to 144, 145 mm Hg), cholesterol (<4.0, 4.0 to 4.9, 5.0 to 5.9, 6.0 mmol/L), and BMI (<22.0, 22.0 to 23.9, 24.0 to 25.9, 26.0 kg/m2). Ninety-five percent CIs for each quartile were estimated using the "floating absolute risk" method, which avoids the use of an arbitrary reference group.21,22
Age-specific analyses included age at risk categories younger than 55 and 55 years or older, and analyses were also conducted by sex and region (Asia versus Australasia). Effect modification was assessed with the use of statistical interaction terms for age, sex, and region in the Cox model. Further sensitivity analyses investigated the impact of excluding the Korea Medical Insurance Corporation (KMIC) cohort study from the analyses, because it contributed the largest number of SAH events in this report, and cases diagnosed on clinical findings only.
Results
The analyses were based on 26 cohorts from APCSC that provided data on nonfatal and/or fatal SAH events and baseline SBP, cholesterol, BMI, smoking, and alcohol drinking habits (Table 1). In total, 306 620 participants contributed 1 898 565 person-years of follow-up. The Asian cohorts tended to have lower means than the Australasian cohorts for SBP, cholesterol, and BMI (Table 2). No significant differences in mean diastolic blood pressure levels were found between the Asian (78.9 SD 10.9 mm Hg) and Australasian (77.4 SD 12.1 mm Hg) cohorts. Proportionately more Asian participants were current smokers (except Asian women; sex-specific data not shown in Table 2) and fewer drank alcohol compared with Australasian participants.
Among the 5 risk factors analyzed, SBP and smoking were the only significant risk factors for total SAH events (Figure 1). Overall, the hazard ratio for SBP 140 mm Hg was 2.0 (95% CI, 1.5 to 2.7), and that for current smoking was 2.4 (95% CI, 1.8 to 3.4). The significance of elevated SBP was more pronounced in younger subjects and in females compared with males. However, these differences were not statistically significant. The association between SBP and risk of SAH was not significantly different between Asian and Australasian subjects. The risk of total SAH increased steeply with level of SBP (Figure 2). Overall, a 10-mm Hg difference in SBP was associated with a 31% (95% CI, 23 to 38) difference in risk of total SAH. The hazardous effect of current smoking on the risk of total SAH occurrence was not dependent on age, sex, or region. The attributable risks associated with current smoking and elevated SBP (140 mm Hg) were 29% (95% CI, 21% to 35%) and 19% (95% CI, 13% to 24%), respectively.
No significant associations were found between cholesterol, BMI, or alcohol drinking with the risk of total SAH events (Figures 1 and 2 ). Overall, the hazard ratio for cholesterol 4.5 mmol/L compared with <4.5 mmol/L was 0.9 (95% CI, 0.7 to 1.3), BMI 22 kg/m2 compared with <22 kg/m2 was 1.0 (95% CI, 0.7 to 1.3), and current drinking compared with not drinking was 1.0 (95% CI 0.7 to 1.4). Confining analyses to fatal SAH events or to events with objective confirmation of hemorrhage, or excluding the KMIC cohort study (either from the overall data or just from the Asian data), or excluding studies with no SAH events did not statistically significantly alter our results.
Discussion
To our knowledge, this project is the first large-scale individual participant SAH data overview of risk factors for SAH in cohorts from the Asia-Pacific region. Overall, Asians tended to have lower means for SBP, cholesterol, and BMI, and fewer were alcohol drinkers, but there were more current male smokers compared with Australians and New Zealanders. This research extends findings from other epidemiological studies of SAH. First, we found that cigarette smoking and elevated SBP are the most important risk factors, each of which doubles the risk of SAH in the Asia-Pacific region. Whereas the hazardous effect of cigarette smoking was strikingly consistent across all strata defined by age, sex, and region, the association with elevated SBP was more pronounced in younger subjects (younger than 55 years) and in women. Second, we detected no associations between the risk of SAH and cholesterol level, BMI, or alcohol drinking. Third, this is the first study to show that the associations of the common cardiovascular risk factors with the risk of SAH did not differ much between Asian and Australasian regions.
Major strengths and limitations of the APCSC study have been described elsewhere.23,24 In brief, the APCSC provides a large and unique set of individual participant database involving Asian cohorts and both men and women. The combination of data from a number of Asian and Australasian cohorts results in a relatively large number of SAH events, thus providing precise estimates of risk factor–SAH associations by region (Asia versus Australasia). Analyzing SBP, cholesterol, and BMI levels as categorical and continuous variables adjusted for each other and for regression dilution bias is a particular methodological strength of this study. Another important advantage is the high proportion of cases with objective confirmation of SAH, thus minimizing misclassification bias. However, we were not able to evaluate ethnic-specific associations of risk factors with SAH or to examine the association between different levels of cigarette smoking and alcohol drinking on the risk of SAH.
The absence of substantial gender differences in the significance of risk factors for SAH, the attenuation with age of the association with SBP, and the lack of a significant association between cholesterol levels and SAH are in line with our previous analyses on risk factors for intracerebral hemorrhage,23,24 suggesting some similarities in the pathogenesis of the 2 major subtypes of hemorrhagic stroke. The strong and positive association between baseline level of SBP and the risk of SAH concur with those found between "usual" level of SBP and the risk of ischemic stroke and intracerebral hemorrhage in the Asian-Pacific region.23 Unlike previous findings of continuous positive relationships between baseline BMI and the risk of both ischemic stroke and intracerebral hemorrhage in the Asia–Pacific region,25 there were no such associations with SAH. Our findings did not support some other observations that lean BMI5 and low total cholesterol8,26 are associated with greater risk of SAH. Although it is possible that the low cutoff points used in our analyses may have contributed to the lack of the associations between BMI, total cholesterol, and SAH, sensitivity analyses using different cutoff points consistently found no associations. Strong positive associations between occurrence of SAH and smoking and elevated blood pressure but no significant associations with BMI, alcohol consumption, and total serum cholesterol in men were also found in the KMIC Study.7
Our results indicated that up to 19% of cases of SAH in the region were attributable to systolic blood pressure >140 mm Hg and 29% of cases of SAH were attributable to smoking. This suggests that a substantial proportion of SAH events could potentially be prevented by reducing blood pressure and smoking at a population level. Overall, each 10 mm Hg decrease in mean SBP is expected to result in a reduction in SAH of 31%. That the risk of SAH associated with SBP varies with age and gender has emphasized the importance of blood pressure control programs in young subjects (younger than 55 years) and in women. More generally, our findings of the significance of current smoking and elevated blood pressure as risk factors for SAH concur with results from other investigations in the Asia–Pacific region7,23,27,28 and elsewhere.6 Exposure to these risk factors individually and/or in combination promotes formation, growth, and rupture of intracranial aneurysm(s)29–31— a major cause of SAH. The consistency of the data across studies involving different designs and populations suggests that cigarette smoking and elevated blood pressure are causally related to SAH.
Appendix Asia Pacific Cohort Studies Collaboration
Writing committee: V. Feigin, V. Parag, C.M.M. Lawes, A. Rodgers, I. Suh, M. Woodward, K. Jamrozik, H. Ueshima.
Statistical Analyses
V. Parag, R.B. Lin, S. Vander Hoorn, D.A. Bennett, F. Barzi, M. Woodward.
Executive Committee
D.F. Gu, T.H. Lam, C.M.M. Lawes, S. MacMahon, W.H. Pan, A. Rodgers, I. Suh, H. Ueshima, M. Woodward.
Participating Studies and Principal Collaborators
Aito Town: A. Okayama, H. Ueshima; H. Maegawa; Akabane: N. Aoki, M. Nakamura, N. Kubo, T. Yamada; Anzhen02: Z.S. Wu; Anzhen: C. H. Yao, Z. S. Wu; Australian Longitudinal Study of Aging: G. Andrews; Australian National Heart Foundation: T. A. Welborn; Beijing Aging: Z. Tang; Beijing Steelworkers: L. S. Liu, J. X. Xie; Blood Donors’ Health: R. Norton, S. Ameratunga, S. MacMahon, G. Whitlock; Busselton: M. W. Knuiman; Canberra-Queanbeyan: H. Christensen; Capital Iron and Steel Company: X. G. Wu; CISCH: J. Zhou, X. H. Yu; Civil Service Workers: A. Tamakoshi; CVDFACTS: W. H. Pan; East Beijing: Z. L. Wu, L. Q. Chen, G. L. Shan; Electricity Generating Authority of Thailand: P. Sritara; Fangshan: D. F. Gu, X. F. Duan; Fletcher Challenge: S. MacMahon, R. Norton, G. Whitlock, R. Jackson; Guangzhou: Y. H. Li; Guangzhou Occupational: T. H. Lam, C. Q. Jiang; Hisayama: M. Fujishima, Y. Kiyohara, H. Iwamoto; Hong Kong: J. Woo, S. C. Ho; Huashan: Z. Hong, M. S. Huang, B. Zhou; Kinmen: J. L. Fuh; Konan: H. Ueshima, Y. Kita, S. R. Choudhury; KMIC: I. Suh, S. H. Jee, I. S. Kim; Melbourne: G. G. Giles; Miyama: T. Hashimoto, K. Sakata; Newcastle: A. Dobson; Ohasama: Y. Imai, T. Ohkubo, A. Hozawa; Perth: K. Jamrozik, M. Hobbs, R. Broadhurst; Saitama: K. Nakachi; Seven Cities: X. H. Fang, S. C. Li, Q. D. Yang; Shanghai Factory Workers: Z. M. Chen; Shibata: H. Tanaka; Shigaraki Town: Y. Kita, A. Nozaki, H. Ueshima; Shirakawa: H. Horibe, Y. Matsutani, M. Kagaya; Singapore Heart: K. Hughes, J. Lee; Singapore NHS92: D. Heng, S. K. Chew; Six Cohorts: B. F. Zhou, H. Y. Zhang; Tanno/Soubetsu: K. Shimamoto, S. Saitoh; Tianjin: Z. Z. Li, H. Y. Zhang; Western Australia AAA Screenees: P. Norman, K. Jamrozik; Xi’an: Y. He, T. H. Lam; Yunnan: S. X. Yao. (The underlined studies provided data used in this report.)
Acknowledgments
This project has received grants from and the Health Research Council of New Zealand, the National Institute on Aging Grant PO1-AG17625, the National Health and Medical Research Council of Australia, and an unrestricted educational grant from Pfizer Inc. The commercial sponsor had no influence on design, analysis, or interpretation of results. C.M.M.L. is supported by the National Heart Foundation (New Zealand) Fellowship.
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