Body mass index and cardiovascular disease in the Asia-Pacific Region: an overview of 33 cohorts involving 310 000 participants

Asia Pacific Cohort Studies Collaboration1

Correspondence to: Asia Pacific Cohort Studies Collaboration, Clinical Trials Research Unit, Faculty of Medicine and Health Sciences, University of Auckland, Private Bag 92019, Auckland, New Zealand. E-mail: c.nimhurchu{at}ctru.auckland.ac.nz


    Abstract
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Background Few prospective data from the Asia-Pacific region are available relating body mass index (BMI) to the risks of stroke and ischaemic heart disease (IHD). Our objective was to assess the age-, sex-, and region-specific associations of BMI with cardiovascular disease using individual participant data from prospective studies in the Asia-Pacific region.

Methods Studies were identified from literature searches, proceedings of meetings, and personal communication. All studies had at least 5000 person-years of follow-up. Hazard ratios were calculated from Cox models, stratified by sex and cohort, and adjusted for age at risk and smoking. The first 3 years of follow-up were excluded in order to reduce confounding due to disease at baseline.

Results A total of 33 cohort studies, including 310 283 participants, contributed 2 148 354 person-years of follow-up, during which 3332 stroke and 2073 IHD events were observed. There were continuous positive associations between baseline BMI and the risks of ischaemic stroke, haemorrhagic stroke, and IHD, with each 2 kg/m2 lower BMI associated a 12% (95% CI: 9, 15%) lower risk of ischaemic stroke, 8% (95% CI: 4, 12%) lower risk in haemorrhagic stroke, and 11% (95% CI: 9, 13%) lower risk of IHD. The strengths of all associations were strongly age dependent, and there was no significant difference between Asian and Australasian cohorts.

Conclusions This overview provides the most reliable estimates to date of the associations between BMI and cardiovascular disease in the Asia-Pacific region, and the first direct comparisons within the region. Continuous relationships of approximately equal strength are evident in both Asian and Australasian populations. These results indicate considerable potential for cardiovascular disease reduction with population-wide lowering of BMI.


Keywords Obesity, body mass index, cardiovascular disease, overview, cohort study, Asia

Accepted 6 February 2004


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Overweight and obesity are increasingly common health conditions globally. Obesity may be defined as the degree of fat storage associated with clearly elevated health risks. However, because fat mass is difficult to measure, the pragmatic definition of obesity is based upon body mass index (BMI). The WHO guidelines define a BMI of 18.5–24.9 kg/m2 as normal, 25–29.9 kg/m2 as Grade 1 overweight and ≥30 kg/m2 as Grade 2 overweight.1 Overweight is increasingly prevalent in developed and developing populations.2–4 The 1997 China Health and Nutrition Survey5 revealed that 14% of adults, aged 20–45 years, had a BMI >25 kg/m2, with prevalence much higher in urban compared with rural areas. Other results give the prevalence of overweight in South Korea6 as 24% in 1998, and in Japan7 as 21% in the early 1990s. In New Zealand and Australia, over half the population has a BMI exceeding 25 kg/m2,8,9

Secular trends indicate that mean BMI levels are increasing rapidly throughout the Asia-Pacific region probably as a result of increasing motorization resulting in reduced physical activity and increased availability of processed foods.5,10,11 Between 1982 and 1997 in China, the prevalence of overweight increased fourfold from 3.5% to 14.1%,5 an increase of almost 1% per year, which is equal to an additional 12 million overweight people per year. In Japan, there has also been a two- to four-fold increase in the number of overweight men, which has been most marked in rural areas.12 About half of the world's population live in the Asia-Pacific region and they account for about half of the global burden of cardiovascular disease.13 The implications of region-wide increases in BMI are therefore of considerable public health significance.

Cohort studies in Europe and North America have shown that the risk of cardiovascular disease increases continuously with increasing BMI.14–16 However, few comparable prospective data have been available for the Asia-Pacific region. Cross-sectional studies in this region indicate that, at a given BMI, the prevalence of related conditions such as diabetes and hypertension is comparatively high in Asian populations.17–20 These results, together with findings that Asian populations tend to have higher percentage body fat at any given BMI,21,22 have led to the suggestion that lower cut-offs should be used to define overweight and obesity in Asian populations.12,23 However, few prospective data are available to provide reliable evidence to support this proposal, and no direct comparisons have been made of the strength of the association of BMI with disease endpoints in the different regions.


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The aim of these analyses was to investigate the risks of cardiovascular mortality and morbidity associated with BMI in the Asia-Pacific region, and to determine if the strengths of these associations were different in Asian and Australasian (predominantly Caucasian) populations.


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Identification of studies and collection of data
The Asia Pacific Cohort Studies Collaboration is an individual participant data overview. Methods of study identification, and the characteristics of studies included have been reported elsewhere.24 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) ≥5000 person-years of follow-up recorded; (4) date of birth or age, sex, and blood pressure recorded at baseline; (5) date of death or age at death recorded during follow-up. Studies were identified by literature searches (MEDLINE and EMBASE), scrutiny of abstracts from proceedings of meetings, and enquiry among collaborators and colleagues. There were no language restrictions.

In addition to the above variables, data sought on individual participants included date of baseline survey, ethnicity, history of cardiovascular disease, total blood cholesterol, diabetes, height, weight, smoking habit, and alcohol consumption. Height and weight were not criteria for inclusion in the collaboration so not all studies involved could contribute to the analyses of BMI reported here. Outcome data included non-fatal stroke, non-fatal myocardial infarction (MI), and cause-specific death. Non-fatal events were defined as those that did not result in death within 28 days. All data provided to the secretariat were checked for completeness and consistency and re-coded where necessary to maximize comparability across cohorts. Summary reports were referred back to principal investigators of each collaborating study for review and confirmation.

Statistical analysis
Outcomes examined included total ischaemic heart disease (IHD) events (non-fatal MI and all fatal IHD events combined), total stroke events (non-fatal and fatal combined), and haemorrhagic and ischaemic stroke subtypes (non-fatal and fatal combined). Stratified time-dependent Cox proportional-hazards analyses25 were used to regress time until first event against baseline BMI using individual participant data collected on all cohorts. All analyses were stratified by sex and cohort, and age at risk (age at the time of the event) was treated as an external time-dependent covariate. When examining differences across regions age-standardized analyses were used to account for differences in age distributions across regions. As well as examining BMI on a continuous scale, participants were divided into five groups according to baseline BMI and relative risks were plotted against mean BMI. The 95% CI for each exposure group were estimated using the ‘floating absolute risk’ method which avoids the use of an arbitrary reference group.26,27 Inverse variance weighting was used to pool results across cohorts.28 Statistical heterogeneity between studies was explored by regressing relative risk estimates computed from each study against summary covariate data (age, sex, region) obtained from each study.29 The amount of inter-study variation explained by covariates was estimated using the R2 statistic obtained from these meta-regressions. Fifty per cent of the variation in stroke risk and 37% of the variation in IHD risk was explained by age, sex, and region. Adjusted region-level estimates were also obtained by the inclusion of an interaction term (between BMI and region) in the Cox regression models.

BMI was calculated as weight (kg) divided by the square of height (m). Five categories of BMI were examined: <18.5, 18.5–21.9, 22.0–24.9, 25.0–29.9, and ≥30 kg/m2. These categories mirror WHO categories1 except that the WHO normal category (18.5–24.9 kg/m2) was divided into two because the majority of the study population and events fell into that particular category. Data from participants with a reported BMI of <12 kg/m2 or >60 kg/m2 were excluded from analysis (n = 14).

A key issue in analyses of BMI and disease is the possibility that prevalent disease may cause low BMI at baseline and also cause death early in follow-up. There was empirical evidence of this phenomenon in this overview, with the associations between BMI and all cardiovascular outcomes being U-shaped when the first 3 years of follow-up were included, indicating prevalent disease at baseline. Therefore events within 3 years of follow-up were excluded from analyses for all outcomes.

The most important confounders of the association between BMI and cardiovascular disease are generally thought to be age, sex, and smoking history. Therefore all analyses were stratified by sex and cohort and adjusted for age at risk, and smoking was included as a covariate. Participants were categorized as smokers if they indicated that they were current smokers, ex-smokers, or ever smokers. Non-smokers were participants who indicated that they had never smoked. Seven cohorts included a smoking category entitled ‘Not current’. Participants in this category were excluded from the analyses since it was not possible to determine if they were ex-smokers or never smokers (n = 28 604). Serum cholesterol and blood pressure levels are intermediate risk factors on the causal pathway between BMI and cardiovascular disease and typically it is inappropriate to adjust for such variables.30,31 The potential for alcohol to act as a confounder is uncertain since alcohol may not be independently associated with BMI.32 Therefore serum cholesterol, blood pressure levels, and alcohol intake were not adjusted for in the analyses although the effect these factors had on the reported associations was examined separately and is reported.


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Data availability and study population characteristics
The analyses were based on data from 33 cohorts: 12 studies from Japan, 11 from mainland China, 2 from Singapore, 2 from Taiwan; 1 from Hong Kong, 1 from South Korea, 1 from New Zealand, and 3 from Australia (Table 1). In total, 310 283 participants contributed 2 148 354 person-years of follow-up and the mean duration of follow-up was 6.9 years. The mean age of the participants at baseline was 47 years and 41% were female. Overall, 10% of participants were from Japan, 15% from mainland China, 52% from South Korea, 4% from elsewhere in Asia (Singapore, Taiwan, and Hong Kong), and 20% were from ANZ (Australia & New Zealand). Although the South Korean cohort dominates the overview in terms of numbers of participants, the cohort contributed relatively few events to the analyses due to the low mean age of participants (Table 1). Regional differences were apparent with many more cases of IHD than stroke in ANZ cohorts, and more strokes than IHD in Asian cohorts. The overall mean baseline BMI was 23.6 kg/m2, with mean BMI 22.9 kg/m2 for Asian populations and 26.4 kg/m2 for ANZ populations (Figure 1).


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Table 1 Characteristics of cohorts, excluding the first 3 years of follow-up

 


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Figure 1 Kernal density estimates of body mass index distribution in Asia and Australia & New Zealand (ANZ)

 
BMI and stroke
A total of 3332 people suffered a stroke during follow-up. Of all strokes a total of 1334 (40%) were classified as ischaemic, 851 (26%) were classified as haemorrhagic, and the sub-type of the remainder was not recorded. Fifteen cohorts recording 1459 strokes provided data on diagnostic method and 629 (43%) of strokes within these cohorts were confirmed by CT or MRI scanning. A continuous relationship between increasing BMI and risk of both ischaemic and haemorrhagic stroke was apparent, but the association with haemorrhagic stroke was weak except at higher BMI levels (Figure 2). On average, a 2 kg/m2 reduction in BMI was associated with a 12% (95% CI: 9%, 15%) lower risk of ischaemic stroke compared with a 8% (95% CI: 4%, 12%) reduction in risk in haemorrhagic stroke (P for homogeneity = 0.04).



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Figure 2 Hazard ratios (floating absolute risk and 95% CI) for stroke and ischaemic heart disease by category of body mass index. Analyses adjusted for age at risk, cohort, and smoking

 
These associations were consistent for both men and women e.g. each 2 kg/m2 lower BMI was associated with a decrease in total (fatal and non-fatal combined) stroke risk of 11% (95% CI: 8%, 13%) for men and 8% (95% CI: 6%, 11%) for women. Adjustment for systolic blood pressure (SBP) attenuated associations by approximately two-thirds (a 2 kg/m2 reduction in BMI was associated with a 9% [95% CI: 6%, 11%] reduction in risk unadjusted compared with a 3% [95% CI: 1%, 5%] reduction when adjusted for SBP). Relative risks remained unchanged when analyses were adjusted for cholesterol and alcohol intake. However, none of the variables was included in the reported analyses (Figures 2 and 3).



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Figure 3 Estimated relative risk reduction in stroke and ischaemic heart disease associated with a 2 kg/m2 reduction in body mass index, by age and region

 
Age-specific associations of BMI and risk of haemorrhagic and ischaemic stroke were also estimated (Figure 3), showing stronger proportional associations in younger age groups. In absolute terms, the difference in stroke risk between the highest and lowest BMI groups was about four times as large in those aged ≥70 years than in those aged <60 years.

In these cohorts, the mean age at stroke was considerably lower in Asia (63 years) than in ANZ (74 years). This would tend to produce apparently stronger overall associations between BMI and stroke in Asian populations, since associations are steeper in the young (Figure 3). Age-standardized analyses demonstrated no significant differences between Asia and ANZ in the size or shape of the association between BMI and total stroke. In Asian cohorts, a 2 kg/m2 reduction in BMI was associated with a 9% (95% CI: 6%, 12%) reduction in risk of total stroke and in ANZ cohorts the same reduction in BMI was associated with 8% (95% CI: –3%, 18%) reduction in risk, (P for homogeneity = 0.9).

BMI and IHD
A total of 2073 people suffered from an IHD event during follow-up, of which 1224 (59%) were fatal and 849 (41%) were non-fatal. There were no differences in the association of BMI with fatal and non-fatal IHD, so fatal and non-fatal outcomes were combined for all reported analyses. A direct and continuous relationship between increasing BMI and risk of IHD was evident (Figure 2). Steeper associations were also apparent in younger age groups (Figure 3). In the age groups <60, 60–69, and ≥70 years, a 2 kg/m2 lower BMI was associated with 21% (95% CI: 17%, 24%), 10% (95% CI: 5%, 14%), and 6% (95% CI: 3%, 9%) lower IHD risk respectively. The absolute difference in IHD risk between the highest and lowest BMI groups was about 11 times as large in those aged ≥70 years than in those aged <60 years. These associations were consistent for both men and women for a 2 kg/m2 reduction: 12% (95% CI: 10%, 15%) and 10% (95% CI: 6%, 13%) respectively and estimates of relative risk remained unchanged when analyses were adjusted for total cholesterol and alcohol intake. However, adjustment for SBP attenuated the association by approximately one-third (a 2 kg/m2 reduction in BMI was associated with an 11% [95% CI: 9%, 13%] reduction in risk unadjusted compared with an 8% [95% CI: 6%, 10%] reduction when adjusted for SBP).

Again, differences between Asia and ANZ were evident in mean age at which IHD events occurred. In Asia, mean age at IHD event was 64 years while in ANZ the mean age of onset was 72 years. Age-standardized analyses demonstrated no significant differences between the regions: in Asian cohorts, a 2 kg/m2 reduction in BMI was associated with a 14% (95% CI: 10%, 19%) lower risk of IHD while in ANZ cohorts the same reduction in BMI was associated with a 10% (95% CI: 2%, 17%) lower risk (P for homogeneity = 0.3).


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This overview of prospective cohort studies provides the most reliable estimates to date of the associations between BMI, stroke and IHD in the Asia-Pacific region. The main findings were continuous positive associations between BMI and risks of stroke and IHD that continued down to a baseline BMI of approximately 20 kg/m2 and were similar in strength between Asian and ANZ cohorts. The meta-analysis has several strengths; it involves a large number of participants, utilizes individual participant data, and weight and height were measured rather than self-reported. The lack of CT-scan confirmed stroke subtypes will, however, have underestimated any differences between associations with ischaemic and haemorrhagic stroke since some diagnosed haemorrhagic strokes would have been ischaemic and vice versa. In addition, when adjusting for smoking the inclusion of ‘current’, ‘ex’ and ‘ever’ smokers in one ‘smoking’ category may have biased estimates since sensitivity analyses indicated that the risk is higher in former than in current smokers. For example, in current smokers a 2 kg/m2 lower BMI was associated with a 13% (95% CI: 10%, 17%) lower risk of IHD whereas in ex-smokers a 2 kg/m2 lower BMI was associated with a 7% (95% CI: 1%, 13%) lower risk.

Few prospective cohort studies have examined the association between BMI and cardiovascular disease in the Asia Pacific region. A large cohort study of Korean men, some of whom were included in this meta-analysis, examined BMI and mortality over a 12-year follow-up period,33 and also found a positive association between BMI and coronary mortality down to a BMI of <18 kg/m2. Results from other smaller individual cohorts are compatible with these findings.34

Most data, to date, on BMI and stroke subtypes were provided by the US Nurses study followed over a 16-year period.35 A 1 kg/m2 increase in BMI was associated with a 5% (95% CI: 3%, 7%) increased risk of ischaemic stroke but there was no clear association with haemorrhagic stroke. The current overview is consistent with these findings for ischaemic stroke, and suggests positive associations for both stroke subtypes, but a steeper association overall for ischaemic stroke, and an association with haemorrhagic stroke mainly at high BMI levels. The reasons for different associations with stroke subtypes remain unclear, especially considering that some effects of BMI are mediated through blood pressure, which is associated at least as strongly with haemorrhagic as with ischaemic stroke.36 This finding could reflect an intermediate role of cholesterol fractions, since cholesterol tends to have qualitatively different associations with stroke subtypes (although exploratory adjustment for total cholesterol had no clear effect in these analyses). Other possibilities include the effects of body fat on insulin sensitivity or circulating levels of oestrogen and other hormones.37,38

The associations seen here between BMI and IHD were very similar in size and shape to those seen with ischaemic stroke. They were also of similar strength to those observed in Western cohort studies.39 The attenuation in proportional risk of cardiovascular disease with increasing age seen here extends that noted in other studies.40 It has been suggested that that this age-related pattern may be due to the inter-relation of several factors including the pathophysiology of ageing, detection and diagnosis of disease in the elderly, comorbidity, competing risks, selective survival, ceiling effects, and methods of analysis in ageing populations.41 In the analyses reported here, proportional associations between BMI and risk of stroke and IHD were about one-third as steep in participants aged >70 years compared with those aged <60 years. However, the absolute risk difference associated with a given difference in BMI was at least four times greater in those aged >70 years.

BMI can be considered to provide the most useful, albeit crude, population-level measure of obesity. Although it can be assumed that individuals with a BMI of ≥25 kg/m2 have an excess fat mass in their body, BMI does not distinguish between weight associated with muscle and weight associated with fat. As a result the relationship between BMI and body fat content varies according to body build and across populations, and age and gender also have an influence on percentage body fat mass.4 Therefore, cross-sectional comparisons of BMI should be interpreted with caution.

Controversy still exists regarding the exact shape of the association between body weight and mortality. Several studies have shown a U-shaped relationship,14,21,22 in which risk of mortality is increased among the very obese and the very lean. However, some studies that have excluded smokers and those with existing disease at baseline (who tend to be leaner) have suggested that death rates increase linearly with increasing adiposity, with no excess risk among the very lean.40,42 Additionally, risks of non-fatal events, including incidence of diabetes mellitus, high blood pressure, and IHD begin to increase from well below a BMI of 25 kg/m2. 15,16,43 The current analyses extend these findings and demonstrate that risk of cardiovascular disease increases linearly from BMI levels as low as 18 kg/m2. These data therefore provide no clear epidemiological basis for specific cutpoints to define overweight, in either Asian or Australasian populations. Categorization of overweight suggests that those in the ‘normal’ BMI category do not have any increased risk of disease, whereas several analyses to date, including the current study, have shown that a considerable proportion of BMI-attributable events occur below 25 kg/m2 (or even 23 kg/m2). As generally seen, many more events arise from the ‘moderate’ middle of the distribution than the ‘high-risk’ tail.44 One argument for varying cutpoints has been prevalence of related disease, such as diabetes, at a given BMI level. However, this would logically lead to cutpoints varying substantially by many different factors, such as age and sex, as well as by ethnicity. The use of actual BMI values combined quantitatively with other risk factor data (including ethnicity), will lead to better estimates of absolute risk.45

At a population level, these analyses suggest an appropriate focus is on mean BMI rather than on proportions of the population above arbitrary thresholds. Not only would this lead to improved comparability across region and time, which has been hampered by varying cut-points, but would help encourage a focus on population-wide prevention. The potential benefits at a population level are considerable, since non-optimal BMI is a leading cause of death and disability globally, causing about 2.5 million deaths and 33 million DALYs (disability-adjusted life years) each year.46 Of this BMI-attributable burden, about one-third occurs in the Asia Pacific region.

In summary, this individual participant meta-analysis indicates that increasing BMI is an important risk factor for cardiovascular disease in the Asia-Pacific region. Relative risk of cardiovascular disease associated with BMI begins increasing well below the cut-off point of 25 kg/m2 but no differences in size or shape of association were seen between Asian and ANZ populations. However, due to different BMI distributions, more BMI-attributable cardiovascular in individuals with BMI <25 kg/m2 will occur in Asia than in ANZ. These findings indicate the urgent need for effective strategies to prevent further increases in mean population weight in this region.


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Asia Pacific Cohort Studies Collaboration
Writing Committee: C Ni Mhurchu1, A Rodgers1, WH Pan2, DF Gu3, M Woodward4.

1Clinical Trials Research Unit, University of Auckland, New Zealand.

2Epidemiology and Public Health, Institute of Biomedical Sciences, Taipei, Taiwan.

3Fu Wai Hospital, Chinese Academy of Medical Sciences, Beijing, China.

4Institute for International Health, University of Sydney, Australia.

Statistical Analyses: V Parag, R Lin, DA Bennett, S Vander Hoorn, M Woodward, F Barzi.

Executive Committee: S MacMahon (Chair), DA Bennett, DF Gu, TH Lam, C Lawes, WH 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: ZS Wu; Anzhen: CH Yao, ZS Wu; Beijing Steelworkers: LS Liu, JX Xie; Busselton: MW Knuiman; Canberra-Queanbeyan: H. Christensen; Capital Iron and Steel Company: XG Wu; CISCH: J Zhou, XH Yu; Civil Service Workers: A Tamakoshi; East Beijing: ZL Wu, LQ Chen, GL Shan; Fangshan: DF Gu, XF Duan; Fletcher Challenge: S MacMahon, R Norton, G Whitlock, R Jackson; Hisayama: M Fujishima, Y Kiyohara, H Iwamoto; Hong Kong: J Woo, S Ho; Huashan: Z Hong, MS Huang, B Zhou; Kinmen: JL Fuh; Konan: H Ueshima, Y Kita, SR Choudhury; KMIC:I Suh, SH Jee, IS Kim; Melbourne: G Giles; Miyama: T Hashimoto, K Sakata; Ohasama: Y Imai, T Ohkubo, A Hozawa; Perth: K Jamrozik, M Hobbs, R Broadhurst; Saitama: K Nakachi; Seven Cities Cohorts: XH Fang, SC Li, QD Yang; Shanghai Factory Workers: ZM 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; Six Cohorts: BF Zhou, HY Zhang; Tanno/Soubetsu: K Shimamoto, S Saitoh; Tianjin: ZZ Li, HY Zhang; CVDFACTS: WH Pan; Xi'an: Y He, TH Lam; Yunnan: SX Yao. (The underlined studies provided data used in this paper).


    Acknowledgments
 
The Asia Pacific Cohort Studies Collaboration has been supported by grants from the Health Research Council of New Zealand, the National Health and the Medical Research Council of Australia, the US National Institutes of Health, and an unrestricted educational grant from Pfizer Inc. We thank Sarah Lewington and Gary Whitlock for helpful comments on an earlier version of this report, and Clarissa Gould-Thorpe for secretarial support. Cliona Ni Mhurchu and Anthony Rodgers held Research Fellowships from the National Heart Foundation of New Zealand during this project.


    Notes
 
1 Members listed in Appendix. Back


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