An evaluation of metabolic risks for coronary death in the Asia Pacific region

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Abstract

Aim

To investigate the generalizability of current definitions of the metabolic syndrome in Asia-Pacific populations, and whether information on metabolic risk factors could be better used to discriminate fatal coronary heart disease (CHD) risk.

Methods and Results

Analyses were performed on individual participant data from 26 cohorts involving 329, 166 participants from the Asia Pacific region. Sensitivity and specificity estimates for CHD death associated with cut-points as defined by the U.S. National Cholesterol Education Panel (NCEP) were determined for component risk factors of a modified NCEP-defined metabolic syndrome. Five cohorts (6437 subjects, 53 CHD deaths) measuring all five risk factors at baseline were used to evaluate the association between the metabolic syndrome and CHD, and to compare risk discrimination using a definition including each risk factor as a continuous variable.

Sensitivity and specificity estimates for risk factor cut-points varied considerably by region (Asia versus Australia/New Zealand) and moderately by sex. The adjusted hazard ratio for CHD death associated with the modified NCEP-defined metabolic syndrome was 2.05 (95%CI, 1.13–3.72). On receiver operator characteristic analysis, the area-under-the-curve for CHD death was 0.586 (95%CI: 0.439–0.732) for the modified NCEP-defined metabolic syndrome, and 0.733, 95%CI: 0.664–0.802) for a definition including each of the metabolic risk factors in their continuous form.

Conclusion

Specific cut-points for metabolic risk factors are not generalizable between populations. This finding is not restricted to measures of central obesity. A multivariable definition of the metabolic syndrome including all risk factors as continuous variables improves CHD risk discrimination substantially.

Introduction

The metabolic syndrome is associated with increased risk of developing coronary heart disease (CHD) [1], [2]. Most clinical definitions of the metabolic syndrome, including the definition most recently produced by the International Diabetes Federation [3], require dichotomizing and then counting individual risk factors. This also includes the US National Cholesterol Education Program—Adult Treatment Panel III (NCEP-ATPIII) definition [4], which requires at least three of the following five risk factors to be present—hypertension, hypertriglyceridaemia, low levels of high density lipoprotein (HDL) cholesterol, hyperglycaemia and abdominal obesity. Therefore, establishing a diagnosis of the metabolic syndrome in an individual requires establishing the presence or absence of each of these risk factors, based on NCEP-defined threshold values.

Estimating an individual's risk of developing CHD based on “counting” of traditional coronary risk factors has the potential for poor specificity and sensitivity [5]. Instead, methods based on multivariable equations (e.g. Framingham risk functions) to estimate CHD risk more accurately are now almost universally incorporated into therapeutic guidelines worldwide [4], [6]. The theoretical considerations underpinning this approach for traditional CHD risk factors apply also to “metabolic” risk factors. Blood pressure, triglycerides, HDL cholesterol, fasting blood glucose, and measures of obesity have each been shown to be associated independently with the risk of CHD [7], [8], [9], [10], [11]. These associations are described as continuous and log-linear, such that threshold values to define abnormality cannot be clearly identified. Even if categorization of continuous risk factors is considered clinically useful, the generalizability of threshold values to define abnormality is unclear. Physicians and researchers in many Asian countries frequently rely on the guidelines and clinical definitions primarily developed from North American populations [12], [13].

The Asia Pacific Cohort Studies Collaboration (APCSC) is an individual participant data meta-analysis of prospective studies conducted in the Asia-Pacific region. Using these data, we examine the generalizability of NCEP-ATPIII metabolic syndrome cut-points for discriminating CHD risk in populations in the Asia-Pacific region. We further compare different methods of defining metabolic risks for CHD in this region, using a variety of mathematical models.

Section snippets

Materials and methods

The design of APCSC, an individual participant data meta-analysis of cohort studies conducted in the Asia-Pacific region, has been reported elsewhere [14]. Studies were classified as “Asian” if participants were recruited from Mainland China, Hong Kong, Japan, Korea, Singapore, Taiwan or Thailand, or “ANZ” if participants were recruited from Australia or New Zealand. Each study reported deaths by underlying cause. The current analyses were restricted to fatal CHD, as classified according to the

Results

The cohorts included in the current analyses, and the characteristics of these studies at baseline are described in Table 1.

Discussion

These analyses confirm that individuals in the Asia-Pacific region with high levels of multiple “metabolic” risk factors are at increased risk of CHD death. However, using the modified NCEP-ATPIII definition as an example, we demonstrate that definitions of the metabolic syndrome requiring dichotomizing and “counting” of risk factors discriminate CHD risk substantially less well than those that utilize these risk factors as continuous variables. Even if cut-points are considered necessary for

Acknowledgements

Funding/Support: APCSC has received grants from the National Health and Medical Research Council of Australia and the Health Research Council of New Zealand, and an unrestricted educational grant from Pfizer Inc.

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    Writing Committee: A. Patel, F. Barzi, M. Woodard, C. Ni Mhurchu, T. Ohkubo, T.H. Lam, T. Welborn. Executive Committee: M. Woodward, X. Fang, D.F. Gu, Y. Imai, T.H. Lam, W.H. Pan, A. Rodgers, I. Suh, H.J. Sun, H. Ueshima. Statistical Analyses: F. Barzi, V. Parag, M. Woodward. Participating studies and principal collaborators in APCSC: Aito Town: A. Okayama, H. Ueshima, H. Maegawa; Akabane: M. Nakamura, N. Aoki; 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.

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