Current waist circumference (WC) and waist-to-hip ratio (WHR) cutoffs have been identified from studies of predominantly European-derived populations. However, these cutoffs may not be appropriate for other ethnic groups. This paper reviews the literature regarding ethnic differences in body composition and the appropriateness of ethnic-specific WC and WHR cutoffs in various ethnic groups. Studies investigating ethnic-specific cutoffs were identified among Aboriginal, Asian, African (Sub-Saharan), African-American, Hispanic, Middle Eastern, Pacific Islander and South American populations. Abstracts that recommended WC and/or WHR cutoffs (or rejected the use of cutoffs) were included with their supporting literature. The evidence for ethnic-specific WC and/or WHR cutoffs was then rated as either convincing, probable, possible or insufficient. The majority of studies recommending ethnic-specific cutoffs was for Asian populations. Few studies recommended cutoffs in Aboriginal, African (Sub-Saharan), Pacific Islanders and South American populations. All studies were cross-sectional, and the overwhelming majority of studies used receiver operating characteristic curves. The studies used a number of methods for assessing WC and WHR, and a variety of outcome measures, making cross-study comparison difficult. There is possible evidence that Asians should have a lower WC cutoff than Europeans. The evidence is insufficient for specific cutoffs for African-American, Hispanic and Middle Eastern populations but some studies indicate current cutoffs for Europeans may be appropriate, whereas there is insufficient evidence for the other ethnic groups. Future studies are needed to address the methodological limitations of the current literature.
Abdominal obesity is generally assessed by either the waist circumference (WC) or waist-to-hip ratio (WHR) measures. Prospective and case–control studies indicate that even with a ‘normal’ body mass index (BMI), those with an elevated WC or WHR can have a two- to threefold increase in cardiovascular disease (CVD) risk and premature death (Rexrode et al., 1998; Yusuf et al., 2005; Pischon et al., 2008). It is believed that abdominal obesity reflects an increased amount of intra-abdominal fat including visceral adipose tissue (VAT). Measures of VAT are strongly correlated with numerous CVD risk factors (Pouliot et al., 1994; Lemieux et al., 2001; Johnson et al., 2002), CVD (Fujimoto et al., 1999; Nicklas et al., 2004) and all-cause mortality (Kuk et al., 2006). More recently, the importance of hepatic fat has been identified with respect to increased metabolic risk (McKimmie et al., 2008). Thus, an increase in WC and WHR likely reflects increased VAT and hepatic fat, and in turn, increased risk.
The most commonly used cutoffs among Caucasians for WC are 102 cm for men and 88 cm for women (Lean et al., 1995, 1998; National Cholesterol Education Program, 2001). The WHR cutoff is 0.95 for men and 0.80 for women (U.S. Department of Agriculture, U.S. Department of Health and Human Services, 1990). However, these cutoffs have been based on studies in populations of European origin. A number of commentaries have raised the issue that these anthropometric cutoffs may not be appropriate for non-Europeans (World Health Organization, 2000; Misra, 2003; Stevens, 2003; Shiwaku et al., 2004; Wildman et al., 2004; Reaven, 2005). Indeed, a number of national and international organizations have put forward their own ethnic-specific guidelines in recent years (Table 1). This background paper aims to report, where literature is available, on the variations in disease risk that may occur at the same WC and/or WHR level among adults in different ethnic groups and the appropriateness of ethnicity-specific WC and/or WHR cutoffs in adults.
Methodological considerations for literature review
This review is focused on ethnic populations not represented in the earlier studies which identified WC/WHR cutoffs in Europeans. It must be recognized, that the term ethnicity is often very poorly defined, and individuals may also be associated with multiple ethnic groups depending on their heritage and where they currently reside. For this review, the following population groups were identified: Asians (Chinese, East Asian and South Asian), African (Sub-Saharan), Middle Eastern, and South American. These ethnic groups are consistent with those identified by the International Diabetes Federation in their worldwide definition of the metabolic syndrome (International Diabetes Federation, 2006). We have also included the following additional groups: Aboriginal, African-American, Hispanic and Pacific Islanders. The allocation of studies to these ethnic groups was based on how the study authors self-described their participants.
Although it may be argued that African-American and African populations be grouped together, there are substantially more data available for African-American populations than those in Africa, and substantial differences in environments and culture also exist. This was also considered with respect to the Hispanic and South American populations, the former tending to refer to a population living in the United States of Puerto Rican, Mexican, Central American, South American or Spanish origin. Lastly, Pacific Islanders present with high BMI and low body fat, but have a high prevalence of diabetes, presenting a phenotype not found in other ethnic groups (Sundborn et al., 2008).
Studies investigating ethnic-specific cutoffs were identified using the following parameters: the authors’ knowledge of existing literature, the use of the PubMed database using the above-mentioned ethnic groups cross-referenced with the term ‘waist’ and identification of appropriate references from those papers selected from the first two methods. Additional search terms of bone, skeletal muscle, osteoporosis and cancer were used in the PubMed search to focus on these areas, which may not be as predominant in the literature. On review of the selected abstracts (conducted by SAL), those articles that provided a recommendation regarding WC and/or WHR cutoffs (or recommendation of absence of support for cutoffs) were included. Further studies relevant to ethnic associations between WC and/or WHR with disease risk and outcomes, and different indices of body composition were reviewed. Searches for a number of health outcomes associated with WC and/or WHR, such as osteoporosis and cancer, showed that most studies related to metabolic risk factors for diabetes and CVD. The focus here on only CVD in certain ethnic groups simply reflects the absence of other data and should not be interpreted to indicate a lack of relationship, as the evidence is not currently available. Unless otherwise specified, receiver operating characteristic (ROC) curves were used to identify the recommended WC and/or WHR cutoffs. The evidence regarding ethnic-specific WC and/or WHR cutoffs was rated as either convincing, probable, possible or insufficient as defined in the Diet, nutrition and the prevention of chronic diseases: report of the joint WHO/FAO expert consultation (Table 2) (World Health Organization, 2003).
Although Aboriginal populations worldwide have diverse cultures, and most likely diverse genetic backgrounds, these populations when ‘Westernized’ usually have higher obesity rates than other populations in their vicinity (Vanasse et al., 2006; Steele et al., 2008; Kondalsamy-Chennakesavan et al., 2008b). In the cited Aboriginal populations, WC and/or WHR are positively associated with adverse lipids, blood pressure, C-reactive protein, measures of insulin resistance, diabetes risk, carotid intima-media thickness and CVD risk (Delisle and Ekoe, 1993; O’Dea et al., 1993; Daniel et al., 1999; Hu et al., 2000; Connelly et al., 2003; Tavares et al., 2003; McDonald et al., 2004; Bradshaw et al., 2007; Gracey et al., 2007; Shemesh et al., 2007; Wang et al., 2007; Wang and Hoy, 2004b; Kondalsamy-Chennakesavan et al., 2008b).
In North America, comparisons of Aboriginals and Europeans have reported no difference in the relationships between VAT and BMI (Gautier et al., 1999), total body fat (Lear et al., 2007c) or WC (Lear et al., 2007a). Some studies show differences in metabolic risk factors, whereas others show either no difference or clear increases in risk factors across a range of BMI and WC values (Young, 1996; Razak et al., 2005; Lear et al., 2007c; Razak et al., 2007). Australian Aboriginals living in a remote area were reported to have higher WHRs with lower BMIs than urban European Australians (Piers et al., 2003). In addition, Australian Aboriginals have higher rates of diabetes than Europeans of a similar body size (Kondalsamy-Chennakesavan et al., 2008b). To our knowledge, no studies have recommended specific WC and/or WHR cutoffs in Aboriginal populations. Therefore, there is insufficient evidence to suggest specific cutoffs for Aboriginal populations.
A number of studies have analyzed Asians as a homogeneous population. These studies found a higher percentage of body fat, increased prevalence of risk factors at lower BMIs (Deurenberg-Yap et al., 2000, 2001) and increased prevalence of abdominal obesity in Asians compared with those in Caucasians (Wu et al., 2007). Koster et al. (2008) found increased mortality to be associated with lower WCs in Asians compared with those in African-Americans and Europeans. Two meta-analyses conducted by the Obesity in Asia Collaboration recommended WC cutoffs of 85 and 80 cm for men and women, respectively, for the detection of both diabetes and hypertension risk (Table 3) (Huxley et al., 2007; Obesity in Asia Collaboration, 2008). However, they noted that the recommended WHR cutoffs of 0.90 for men and 0.80 for women were similar to those determined for Caucasians.
Chinese populations (mainland China, Hong Kong, Taiwan)
Substantial evidence indicates that increased WC and/or WHR is associated with health risks, for example, hypertension, dyslipidemia, impaired fasting glucose, diabetes (Folsom et al., 1994; Huang et al., 2002; Hu et al., 2007), lower testosterone (Chinese men) (Chu et al., 2008), CVD (Zhang et al., 2004) and mortality in Chinese populations (Zhang et al., 2007; Koster et al., 2008). However, evidence for WC and/or WHR linked to bone mineral density (Chu et al., 2008) or low back pain (Yip et al., 2001) is inconclusive.
Early evidence suggested that Chinese men and women have higher percentage body fat at the same BMIs as Europeans (Wang et al., 1994; Deurenberg et al., 1998, 1999). However, studies are inconsistent (Deurenberg et al., 1997; Lear et al., 2007b) and may reflect differences among Chinese populations such as the larger body build and lower percent body fat in Northern compared to Southern Chinese individuals (Deurenberg et al., 1999). Chinese men and women also display a greater amount of VAT for a given WC (Lear et al., 2007c) and body fat mass (Lear et al., 2007b) than Europeans. Consistent with these studies, Chinese men and women also have higher levels of metabolic risk factors at a given WC and/or WHR (Unwin et al., 1997; Lear et al., 2002; Razak et al., 2005).
Seven studies investigated Chinese-specific WC and/or WHR targets (Table 3) (Ko et al., 1999; Deurenberg-Yap et al., 2001; Bei-Fan, 2002; Lin et al., 2002; Diaz et al., 2007; Ko and Tang, 2007; Bao et al., 2008; Li et al., 2008b). Recommendations for WC ranged from 80.5 to 95.1 cm for men and 71.5 to 83.7 cm for women. Two of these studies recommended WHRs ranging from 0.85 to 0.90 for men and 0.76 to 0.80 for women (Ko et al., 1999; Deurenberg-Yap et al., 2001; Lin et al., 2002).
East Asian populations (Korea, Japan)
Among Korean and Japanese men and women, there is a clear association between either the WC or WHR and metabolic risk factors (Iso et al., 1991; Masuda et al., 1993; Sung et al., 2007), carotid intima-media thickness (Takami et al., 2001), CVD (Huang et al., 1997) and all-cause mortality in Japanese men living in the United States (Kalmijn et al., 1999). Those from Eastern Asia have a higher percentage of body fat than Caucasians, across a range of WC values (Kagawa et al., 2007). In addition, Japanese men have been reported to have more VAT at a given WC than Caucasian men (Kadowaki et al., 2006). A total of 10 studies, ranging in size from 349 to 12 725 participants, have investigated specific WC cutoffs in these populations (Table 3) (Hara et al., 2006; Kim et al., 2006; Hayashi et al., 2007; Lee et al., 2007; Han et al., 2008; Hyun et al., 2008; Matoba et al., 2008; Narisawa et al., 2008; Oka et al., 2008; Sato et al., 2008). Recommended WC cutoffs ranged from 85 to 90 cm for men and 78 to 86 cm for women, but no studies reported cutoffs for WHR.
South Asian populations (Bangladesh, India, Nepal, Pakistan, Sri Lanka)
Abdominal obesity is associated with a variety of risk factors among South Asians including insulin action and insulin resistance (McKeigue et al., 1992; Banerji et al., 1999; Rush et al., 2007b), lipids, markers of inflammation, the metabolic syndrome, carotid intima-media thickness, angiographically determined atherosclerosis and risk for myocardial infarction (Pais et al., 1996; Chambers et al., 2001; Venkatramana and Reddy, 2002; Valsamakis et al., 2004; Chow et al., 2008; Wierzbicki et al., 2008). South Asians have increased abdominal adiposity at a given BMI (Lean et al., 2001; Orr-Walker et al., 2005) and a higher percentage of body fat (Banerji et al., 1999; Deurenberg-Yap et al., 2000; Lear et al., 2007b; Rush et al., 2007a) than Europeans. Various studies indicated that South Asians seem to have a greater VAT, increased lipid or insulin levels than Caucasians at the same BMI or WC/WHR (Chowdhury et al., 1996; Raji et al., 2001; Lear et al., 2007b, 2007c). This is consistent with South Asians having increased lipid and insulin levels compared with Europeans at the same WC and/or WHR (Chandalia et al., 1999; Patel et al., 1999; Lear et al., 2003; Vikram et al., 2003).
Two studies reported appropriate WC and WHR cutoffs for South Asians. In a population representative study of men and women in Chennai, recommended WC values for detecting two or more of the following: diabetes, pre-diabetes, hypertension and dyslipidemia, were 87 cm for men and 82 cm for women (Mohan et al., 2007). A second study recommended WC values for identifying diabetes in men ⩾40 years of age of Bangladeshi, Indian and Pakistani origin as 95.8, 97.2 and 95.8 cm, respectively, and 87.5, 88.7 and 101.3 cm for women, respectively (Diaz et al., 2007).
Taken together, this evidence suggests that Asians have an increased metabolic risk at lower WC and WHR than Europeans, probably because of higher body fat and VAT. Those studies with European or Caucasian comparisons indicated a lower WC and/or WHR for Asians (Diaz et al., 2007; Huxley et al., 2007; Obesity in Asia Collaboration, 2008) We conclude that there is possible evidence for Asians to have lower WC and WHR cutoffs, and WC values of 85 and 80 cm, and WHR values of 0.90 and 0.80 for men and women, respectively, may be the most appropriate (Huxley et al., 2007; Obesity in Asia Collaboration, 2008).
African populations (Sub-Sahara Africa)
In Sub-Saharan African populations, WC is associated with blood pressure and hypertension, increased glucose, triglycerides and osteoporosis (Blaauw et al., 1994; Luke et al., 1997; Okosun et al., 1998; Okosun et al., 1999b; Mufunda et al., 2000; Olatunbosun et al., 2000; Snijder et al., 2004), whereas WHR has been reported to be associated with an increased risk of breast cancer in Nigerian women (Adebamowo et al., 2003; Okobia et al., 2006), and Nigerians with CVD have an increased WHR (Ebesunun et al., 2008). In addition, the WC cutoffs of 94 and 80 cm and the WHR cutoffs of 0.90 and 0.80, in men and women, respectively, were associated with an increased risk for dyslipidemia in the Seychelles (Paccaud et al., 2000).
Black women in South Africa not only have a slightly lower BMI at a given percentage body fat, but also less abdominal adipose tissue (determined by dual X-ray absorptiometry) at the same WC than European women (Rush et al., 2007a). A few small studies report African women having less VAT, better free fatty acid metabolism and TC:HDL-C ratio but increased insulin resistance compared with white women (van der Merwe et al., 2000; Punyadeera et al., 2001a, 2001b). Only one study reported on WC cutoffs in Africans (none investigating WHR cutoffs), and recommended 75.6 and 80.5 cm for men and 71.5 and 81.5 cm for women of Nigerian and Cameroon origin, respectively, for the identification of hypertension (Okosun et al., 2000b). This evidence is insufficient for recommending specific cutoffs for Sub-Saharan Africans.
In African-American men and women, WC and/or WHR have been associated with increased metabolic risk factors, increased bone mineral content, diabetes, cancer, ischemic stroke and all-cause mortality (Haffner et al., 1987; Okosun et al., 1998; Okosun et al., 1999a; Sidney et al., 1999; Suk et al., 2003; Shen et al., 2006; Kristal et al., 2007; Koster et al., 2008; Perry et al., 2008; Travison et al., 2008). The 94 and 80 cm WC cutoffs for European men and women, respectively, have been associated with a 1.5- and 2.0-fold increased risk in hypertension and a 3.9- and 1.6-fold increase in diabetes in men and women, respectively (Okosun et al., 1998).
African-Americans have been reported to have less body fat at a given BMI, and less VAT at a given total body fat mass, WC and/or WHR than Caucasians (Deurenberg et al., 1998). African-American women were reported to have higher WHR, appendicular muscle mass, total bone density and total body bone mineral content compared with white women matched for age, weight and height (Gasperino et al., 1995). Abdominal adiposity, as determined by dual X-ray absorptiometry, was also lower in African-American men, but not women, compared with Asian men and women, at a given total body fat, adjusted for age (Wu et al., 2007). In addition, African-American women may have higher subcutaneous abdominal adipose tissue than white women at a given fat mass (Lovejoy et al., 2001).
These studies suggest that African-Americans may be at a decreased risk for CVD compared with Europeans at a similar WC and/or WHR. However, Haffner et al. (1996) reported lower insulin sensitivity in African-Americans compared with that in whites even after accounting for differences in body fat distribution. African-American men and women also have an increased risk for hypertension at a given WC (Harris et al., 2000; Okosun et al., 2001, 2006), and have higher ApoB/ApoA1 values associated with the WC cutoffs of 94 and 80 cm (Okosun et al., 1999c) than white men and women.
Two studies provided WC cutoffs in African-Americans (Table 4) (Zhu et al., 2005; Diaz et al., 2007). The recommendations for WC cutoffs ranged from 89 to 108.9 cm for men and 83 and 104.6 cm for women. One additional study recommended that further investigation is needed (Okosun et al., 2000a), whereas another reported that African-Americans did not require WC cutoffs separate from whites (Okosun et al., 2000c). Unlike data in other ethnic groups, the finding that African-Americans may have less VAT than Europeans is inconsistent with findings of increased risks for CVD due to higher blood pressure and lipids at a given WC. These studies either suggest similar cutoffs to that of Europeans (possible evidence) or indicate insufficient evidence for specific cutoffs for African-Americans.
Although not specifically searched, we identified two studies investigating populations living in the Caribbean (Sargeant et al., 2002; Okosun et al., 2000b). These studies suggested WC cutoffs ranging from 80 to 88 cm for men and 84 to 88 cm for women. One of these studies reported WHR cutoffs of 0.87 and 0.80 for men and women, respectively (Sargeant et al., 2002).
For people of Hispanic American background, WC and/or WHR are associated with increased rates of metabolic risk factors, increased bone mineral content, diabetes, cancer, ischemic stroke and all-cause mortality (Haffner et al., 1987; Edelstein et al., 1997; Wei et al., 1997; Han et al., 2002; Suk et al., 2003; Afghani et al., 2004; Kristal et al., 2007; Koster et al., 2008; Perry et al., 2008). One study reported that VAT at a given WC was not appreciably different from that of whites (Carroll et al., 2008) and that insulin sensitivity was similar in Hispanics after adjusting for body composition (Haffner et al., 1996; Nelson et al., 2008). In contrast, it has been reported that Hispanics have a higher risk for hypertension than non-Hispanic whites at a similar WC (Okosun et al., 2006).
A total of five studies investigated Hispanic-specific WC cutoffs (Table 4) (Berber et al., 2001; Sanchez-Castillo et al., 2003; Zhu et al., 2005; Okosun et al., 2000a, 2000c). One study recommended a WC of 90 cm for men and 85 cm for women, and a WHR of 0.90–0.91 for men and 0.84–0.86 for men and women, respectively (Berber et al., 2001). Another study identified a range of WC cutoffs for detecting diabetes and hypertension using ROC curves, but then suggested that the ideal WC be at a point of identifying 80% of those with diabetes and/or hypertension, resulting in a recommendation of 90 cm for both men and women (Sanchez-Castillo et al., 2003). Two other studies recommended WC cutoffs based on BMI values (Okosun et al., 2000c; Zhu et al., 2005). The fourth study suggested that the current WC cutoffs based on Europeans provided low sensitivity with respect to metabolic risk factors (Okosun et al., 2000a). This evidence is insufficient to support specific WC or WHR cutoffs in Hispanic populations.
Middle Eastern populations (including Northeast Africa)
Studies investigating Middle Eastern populations have found that WC and/or WHR are associated with metabolic risk factors such as hypertension, diabetes, elevated glucose and insulin, insulin resistance, and TG (Emara et al., 1989; Onat et al., 1999; Al-Shayji and Akanji, 2004; Chehrei et al., 2007; Abolfotouh et al., 2008; Shahraki et al., 2008). In Turkish women, WHR has been reported to be associated with CVD (Onat et al., 1999). Five studies investigated WC cutoffs in populations residing in the Middle East (Mirmiran et al., 2004; Bouguerra et al., 2007; Mansour et al., 2007; Mansour and Al-Jazairi, 2007; Esteghamati et al., 2008; Al-Lawati and Jousilahti, 2008), whereas three reported on WHR cutoffs (Table 4) (Azizi et al., 2005; Mansour et al., 2007; Al-Lawati and Jousilahti, 2008). The recommended WC ranged from 80 to 97 cm in men and from 79 to 99 cm in women with those assessing WHR ranged from 0.86 to 0.97 for men and 0.78–0.92 for women. On the basis of these studies, there is insufficient evidence to suggest that those of Middle Eastern background have different WC or WHR cutoffs.
Pacific Islander populations
The Pacific Islander populations originate from the islands in the Pacific Ocean and may have similar genetic and cultural origins to one another. In these populations, WC/WHR is associated with adverse lipid and insulin levels, more glucose intolerance and some cancers (Goodman et al., 1997; Hodge et al., 1997; Grandinetti et al., 1998; Lindeberg et al., 1999; Bell et al., 2001; Novotny et al., 2007). Some studies have reported that Pacific Islanders have larger muscle masses and lower percentage body fat than Europeans at similar BMIs (Rush et al., 2004, 2009), and in women this has also been reported for similar WCs and WHRs (Rush et al., 2007a). Despite having less body fat and higher muscle mass, Pacific Islanders have a higher burden of CVD risk factors and prevalence of diabetes than New Zealand Europeans (Sundborn et al., 2008). Although no studies have investigated specific WC and/or WHR cutoffs, the commonly used cutoff of 88 cm was able to predict lipid and glucose levels associated with disease in Samoan women (Novotny et al., 2007). Given these findings, there is insufficient evidence to recommend specific WC and/or WHR cutoffs in Pacific Islanders.
South American populations
In populations within South America, WC and/or WHR have been reported to be associated with lipids, blood pressure, fasting glucose, insulin resistance and diabetes (Lemos-Santos et al., 2004; Olinto et al., 2004; Feldstein et al., 2005; Florencio et al., 2007; Perry et al., 2008). In addition, those with CVD have a higher prevalence of abdominal obesity (Oviedo et al., 2006). Six studies investigated WC and/or WHR cutoffs (Table 4) and recommended WC cutoffs between 88 and 90 cm for men and 83 and 84 cm for women (Pereira et al., 1999; Velasquez-Melendez et al., 2002; Perez et al., 2003; Pitanga and Lessa, 2005; Barbosa et al., 2006). The three studies reporting on WHR provided recommendations ranging from 0.85 to 0.95 in men and 0.80 to 1.18 for women (Pereira et al., 1999; Perez et al., 2003; Pitanga and Lessa, 2005). These studies suggest that WC cutoffs should be lower than, but WHR cutoffs similar to, those for Europeans. Nevertheless, the limited number of studies provides insufficient evidence to direct any recommendations.
Across the ethnic groups, there are varying levels of evidence linking WC and/or WHR to metabolic risk, body composition and various recommended ethnic-specific cutoffs for these measures. Most investigations relate to Asians, with much less information available for Middle Eastern, South American and Sub-Saharan African populations. Evidence indicates that WC and/or WHR are associated with increased metabolic risk related to diabetes and CVD among all ethnic groups but little information relates to other morbidities such as osteoporosis and cancer in any ethnic group. In addition, detailed investigations on body composition also vary among these ethnic groups. In general, there is possible evidence to support Asians having lower cutoffs than Europeans, and for African-American and Hispanics to have similar cutoffs to Europeans, and insufficient evidence for the other ethnic groups to guide recommendations for ethnic-specific cutoffs. On the basis of these levels of evidence, Table 5 outlines WC and WHR cutoffs, and/or ranges suggested from the literature cited in this review. For Asians, the suggested WC and WHR cutoffs are predominantly based on the two meta-analyses of over 100 000 men and women (Huxley et al., 2007; Obesity in Asia Collaboration, 2008), which are consistent with the other investigations (of which some are included in the meta-analyses). For the African-American, Hispanic and Middle Eastern ethnic groups, although evidence supporting ethnic-specific cutoffs is insufficient, some studies indicated that WC and/or WHR cutoffs for Europeans may be appropriate to use pending further comprehensive research. For the other ethnic groups, we conclude that there is insufficient evidence to suggest any cutoffs whether similar to or different from those for Europeans.
The relationship between WC and/or WHR with disease risk is continuous (Yusuf et al., 2005; Zhang et al., 2007). However, for practical clinical management and population health promotion strategies, established cutoffs for these measures are needed and it is important to develop cutoffs based on the identification of similar risk levels across populations. The most commonly used cutoffs throughout the world are those defined by Lean et al. (1995), derived from a cross-sectional population from the Netherlands of predominantly European origin. These cutoffs were based on the WC values corresponding with average BMIs of 25 and 30 kg/m2, and were later shown to relate to increases in risk factors (Lean et al., 1998). Since then, a number of organizations have adopted their use in guiding clinical management (The Scottish Intercollegiate Guidelines Network, 1996; National Cholesterol Education Program, 2001; Balkau et al., 2007). However, given the strength of evidence suggesting that WC and/or WHR is associated with increased risk for a number of diseases, independent of BMI (Kabat et al., 2008; Koster et al., 2008; Masala et al., 2008; Wang et al., 2008), cutoffs should be identified through direct analyses of their prediction of risk markers and/or preferably for disease outcomes (Molarius et al., 1999). In the reviewed studies, most have identified cutoffs related to metabolic risk. This is important, as it cannot be assumed that the relationship between disease risk and BMI is the same for WC and/or WHR.
The benefits of establishing ethnic-specific WC and WHR cutoffs need to be weighed against the practicality of their implementation. Defining ‘ethnicity’ among and within populations may prove to be challenging. In areas with significant representation of several ethnic groups and even sub-ethnic groups with different body composition and health characteristics such as Asian and South Asian sub-groups, this could mean the use of multiple cutoffs related to each individuals’ purported ethnicity. This may be a time-consuming task as well as a potentially sensitive issue for some. With individuals of mixed ethnic background, there are no analyses allowing the definition of specific cutoffs. However, in areas of ethnic homogeneity, the use of ethnic-specific cutoffs is likely to be more feasible.
None of the cited analyses of ethnic-specific WC and/or WHR cutoffs address the issue of why these ethnic variations occur. Genetic differences may affect body composition, metabolic risk factors and/or protection or predisposition to disease differences but genetic diversity between supposedly ‘distinct’ ethnic groups may not be as great as once thought (Li et al., 2008a). Therefore, a strong environmental influence may prove to be equally if not more important. It is recognized that the stature of two populations who share a similar genetic background can differ because of environmental exposure and particularly nutritional differences. One component of these modifiers is different fetal and early childhood nutrition exposures, which can influence offspring growth and the predisposition to obesity (Hales and Barker, 1992; Kaati et al., 2002). This is proposed as a possible contributor to the Asian Indian obesity phenotype of small body size but high body fat (Yajnik, 2004) and may be implicated in other observed inter-ethnic differences.
Limitations common to most of the reviewed studies include their cross-sectional nature, unrepresentative populations and the use of ROC curves, which depend on such basic characteristics as the prevalence of the exposure and its association to the outcome. Therefore, changes in either factor can result in different cutoffs. In addition, ROC curves provide a so-called ‘optimal’ cutoff for a given outcome by maximizing sensitivity and specificity, but this may not be the ideal management or public health policy approach to maximizing treatment or preventive strategies (James, 2005). In addition, the overwhelming majority of studies failed to provide a definition of ethnicity.
Those studies that defined their measurement technique revealed six different methods for WC and five for hip circumference. The predominant method for assessing WC involved measuring midway between the bottom of the lower rib and top of the iliac crest. For hip circumference, measurements were mostly taken at the point of largest gluteal protuberance. In addition, these studies used varying outcomes of metabolic measures and definitions at which ‘risk factors’ are defined. Most studies targeted metabolic risk factors associated with diabetes and CVD but some studies did indicate that WC and/or WHR were related to other diseases such as osteoporosis and cancer, as well as to all-cause mortality (Orozco and Nolla, 1997; Friedenreich, 2001; Koster et al., 2008). Given these limitations, a consensus is needed on the appropriate approach to defining ethnic-specific WC and/or WHR cutoffs.
The value of WC and WHR measurements is that they require only a tape measure, and can provide information regarding an individual's or population's risk for future health problems. Thus cutoffs should be developed consistently to identify populations and individuals at a pre-defined level of risk. Studies over the past two decades indicate that risks are greater for a given WC and/or WHR in different ethnic groups; therefore, different cutoffs may be needed. However, currently the evidence is too limited except in those populations of Asian origin who possibly do have evidence of lower WC and WHR cutoffs than in Europeans at equivalent risk. The evidence is less robust for other ethnic groups; current cutoffs may be appropriate for African-American, Hispanic and Middle Eastern populations, but there is insufficient evidence for populations of Aboriginal, Sub-Saharan Africa, Pacific Islander and South America origin. The many methodological differences between studies limit direct comparisons, and the variety of chosen health outcome and measurement techniques adds a further difficulty. Future studies should be prospective in nature, use representative populations, use common health outcomes, use standardized methods for assessing WC and WHR, and use analytical approaches that are not dependent on the prevalence of excess weight gain and are defined clearly in pre-specified different ethnic groups.
Conflict of interest
The authors declare no conflict of interest.
Abolfotouh MA, Soliman LA, Mansour E, Farghaly M, El-Dawaiaty AA (2008). Central obesity among adults in Egypt: prevalence and associated morbidity. East Mediterr Health J 14, 57–68.
Adebamowo CA, Ogundiran TO, Adenipekun AA, Oyesegun RA, Campbell OB, Akang EE et al. (2003). Waist-hip ratio and breast cancer risk in urbanized Nigerian women. Breast Cancer Res 5, R18–R24.
Afghani A, Abbott AV, Wiswell RA, Jaque SV, Gleckner C, Schroeder ET et al. (2004). Bone mineral density in Hispanic women: role of aerobic capacity, fat-free mass, and adiposity. Int J Sports Med 25, 384–390.
Al-Lawati JA, Jousilahti P (2008). Body mass index, waist circumference and waist-to-hip ratio cut-off points for categorisation of obesity among Omani Arabs. Public Health Nutr 11, 102–108.
Al-Shayji IA, Akanji AO (2004). Obesity indices and major components of metabolic syndrome in young adult Arab subjects. Ann Nutr Metab 48, 1–7.
Azizi F, Esmaillzadeh A, Mirmiran P, Ainy E (2005). Is there an independent association between waist-to-hip ratio and cardiovascular risk factors in overweight and obese women? Int J Cardiol 101, 39–46.
Balkau B, Deanfield JE, Despres JP, Bassand JP, Fox KA, Smith Jr SC et al. (2007). International Day for the Evaluation of Abdominal Obesity (IDEA): a study of waist circumference, cardiovascular disease, and diabetes mellitus in 168 000 primary care patients in 63 countries. Circulation 116, 1942–1951.
Banerji MA, Faridi N, Atluri R, Chaiken RL, Lebovitz HE (1999). Body composition, visceral fat, leptin, and insulin resistance in Asian Indian men. J Clin Endocrinol Metab 84, 137–144.
Bao Y, Lu J, Wang C, Yang M, Li H, Zhang X et al. (2008). Optimal waist circumference cutoffs for abdominal obesity in Chinese. Atherosclerosis 201, 378–384.
Barbosa PJ, Lessa I, de Almeida Filho N, Magalhaes LB, Araujo J (2006). Criteria for central obesity in a Brazilian population: impact on metabolic syndrome. Arq Bras Cardiol 87, 407–414.
Bei-Fan Z (2002). Predictive values of body mass index and waist circumference for risk factors of certain related diseases in Chinese adults: study on optimal cut-off points of body mass index and waist circumference in Chinese adults. Asia Pac J Clin Nutr 11 (Suppl 8), S685–S693.
Bell AC, Swinburn BA, Simmons D, Wang W, Amosa H, Gatland B (2001). Heart disease and diabetes risk factors in Pacific Islands communities and associations with measures of body fat. NZ Med J 114, 208–213.
Berber A, Gomez-Santos R, Fanghanel G, Sanchez-Reyes L (2001). Anthropometric indexes in the prediction of type 2 diabetes mellitus, hypertension and dyslipidaemia in a Mexican population. Int J Obes Relat Metab Disord 25, 1794–1799.
Blaauw R, Albertse EC, Beneke T, Lombard CJ, Laubscher R, Hough FS (1994). Risk factors for the development of osteoporosis in a South African population. A prospective analysis. S Afr Med J 84, 328–332.
Bouguerra R, Alberti H, Smida H, Salem LB, Rayana CB, El Atti J et al. (2007). Waist circumference cut-off points for identification of abdominal obesity among the tunisian adult population. Diabetes Obes Metab 9, 859–868.
Bradshaw PJ, Wilkes ET, Thompson PL (2007). Determinants of carotid intima-medial thickness in an urban Australian Aboriginal population. Atherosclerosis 192, 218–223.
Carroll JF, Chiapa AL, Rodriquez M, Phelps DR, Cardarelli KM, Vishwanatha JK et al. (2008). Visceral fat, waist circumference, and BMI: impact of race/ethnicity. Obesity (Silver Spring) 16, 600–607.
Chambers JC, Eda S, Bassett P, Karim Y, Thompson SG, Gallimore JR et al. (2001). C-reactive protein, insulin resistance, central obesity, and coronary heart disease risk in Indian Asians from the United Kingdom compared with European whites. Circulation 104, 145–150.
Chandalia M, Abate N, Garg A, Stray-Gundersen J, Grundy SM (1999). Relationship between generalized and upper body obesity to insulin resistance in Asian Indian men. J Clin Endocrinol Metab 84, 2329–2335.
Chehrei A, Sadrnia S, Keshteli AH, Daneshmand MA, Rezaei J (2007). Correlation of dyslipidemia with waist to height ratio, waist circumference, and body mass index in Iranian adults. Asia Pac J Clin Nutr 16, 248–253.
Chow CK, McQuillan B, Raju PK, Iyengar S, Raju R, Harmer JA et al. (2008). Greater adverse effects of cholesterol and diabetes on carotid intima-media thickness in South Asian Indians: comparison of risk factor-IMT associations in two population-based surveys. Atherosclerosis 199, 116–122.
Chowdhury B, Lantz H, Sjostrom L (1996). Computed tomography-determined body composition in relation to cardiovascular risk factors in Indian and matched Swedish males. Metabolism 45, 634–644.
Chu LW, Tam S, Kung AW, Lo S, Fan S, Wong RL et al. (2008). Serum total and bioavailable testosterone levels, central obesity, and muscle strength changes with aging in healthy Chinese men. J Am Geriatr Soc 56, 1286–1291.
Connelly PW, Hanley AJ, Harris SB, Hegele RA, Zinman B (2003). Relation of waist circumference and glycemic status to C-reactive protein in the Sandy Lake Oji-Cree. Int J Obes Relat Metab Disord 27, 347–354.
Daniel M, Marion SA, Sheps SB, Hertzman C, Gamble D (1999). Variation by body mass index and age in waist-to-hip ratio associations with glycemic status in an aboriginal population at risk for type 2 diabetes in British Columbia, Canada. Am J Clin Nutr 69, 455–460.
Delisle HF, Ekoe JM (1993). Prevalence of non-insulin-dependent diabetes mellitus and impaired glucose tolerance in two Algonquin communities in Quebec. CMAJ 148, 41–47.
Deurenberg P, Deurenberg Yap M, Wang J, Lin FP, Schmidt G (1999). The impact of body build on the relationship between body mass index and percent body fat. Int J Obes Relat Metab Disord 23, 537–542.
Deurenberg P, Ge K, Hautvast JG, Wang J (1997). Body mass index as predictor for body fat: comparison between Chinese and Dutch adult subjects. Asia Pac J Clin Nutr 6, 102–105.
Deurenberg P, Yap M, vanStaveren WA (1998). Body mass index and percent body fat: a meta-analysis among different ethnic groups. Int J Obes Relat Metab Disord 22, 1164–1171.
Deurenberg-Yap M, Chew SK, Lin VF, Tan BY, van Staveren WA, Deurenberg P (2001). Relationships between indices of obesity and its co-morbidities in multi-ethnic Singapore. Int J Obes Relat Metab Disord 25, 1554–1562.
Deurenberg-Yap M, Schmidt G, Wvan Staveren A, Deurenberg P (2000). The paradox of low body mass index and high body fat percentage among Chinese, Malays and Indians in Singapore. Int J Obes Relat Metab Disord 24, 1011–1017.
Diaz VA, Mainous III AG, Baker R, Carnemolla M, Majeed A (2007). How does ethnicity affect the association between obesity and diabetes? Diabet Med 24, 1199–1204.
Ebesunun MO, Agbedana EO, Taylor GO, Oladapo OO (2008). Plasma lipoprotein (a), homocysteine, and other cardiovascular disease (CVD) risk factors in Nigerians with CVD. Appl Physiol Nutr Metab 33, 282–289.
Edelstein SL, Knowler WC, Bain RP, Andres R, Barrett-Connor EL, Dowse GK et al. (1997). Predictors of progression from impaired glucose tolerance to NIDDM: an analysis of six prospective studies. Diabetes 46, 701–710.
Emara MK, Saadah A, Hassan M, Moussa M, Hourani H (1989). Pattern of obesity and insulin, glucagon, sex hormone binding globulin and lipids in obese Arab women. Diabetes Res 10, 175–181.
Esteghamati A, Ashraf H, Rashidi A, Meysamie A (2008). Waist circumference cut-off points for the diagnosis of metabolic syndrome in Iranian adults. Diabetes Res Clin Pract 82, 104–107.
Feldstein CA, Akopian M, Olivieri AO, Kramer AP, Nasi M, Garrido D (2005). A comparison of body mass index and waist-to-hip ratio as indicators of hypertension risk in an urban Argentine population: a hospital-based study. Nutr Metab Cardiovasc Dis 15, 310–315.
Florencio TT, Ferreira HS, Cavalcante JC, Stux GR, Sawaya AL (2007). Short stature, abdominal obesity, insulin resistance and alterations in lipid profile in very low-income women living in Maceio, north-eastern Brazil. Eur J Cardiovasc Prev Rehabil 14, 346–348.
Folsom AR, Eckfeldt JH, Weitzman S, Ma J, Chambless LE, Barnes RW et al. (1994). Relation of carotid artery wall thickness to diabetes mellitus, fasting glucose and insulin, body size, and physical activity. Atherosclerosis Risk in Communities (ARIC) Study Investigators. Stroke 25, 66–73.
Friedenreich CM (2001). Review of anthropometric factors and breast cancer risk. Eur J Cancer Prev 10, 15–32.
Fujimoto WY, Bergstrom RW, Boyko EJ, Chen KW, Leonetti DL, Newell-Morris L et al. (1999). Visceral adiposity and incident coronary heart disease in Japanese-American men. The 10-year follow-up results of the Seattle Japanese-American Community Diabetes Study. Diabetes Care 22, 1808–1812.
Garcia RG, Cifuentes AE, Caballero RS, Sanchez L, Lopez-Jaramillo P (2006). A proposal for an appropriate central obesity diagnosis in Latin American population. Int J Cardiol 110, 263–264.
Gasperino JA, Wang J, Pierson Jr RN, Heymsfield SB (1995). Age-related changes in musculoskeletal mass between black and white women. Metabolism 44, 30–34.
Gautier JF, Milner MR, Elam E, Chen K, Ravussin E, Pratley RE (1999). Visceral adipose tissue is not increased in Pima Indians compared with equally obese Caucasians and is not related to insulin action or secretion. Diabetologia 42, 28–34.
Goodman MT, Hankin JH, Wilkens LR, Lyu LC, McDuffie K, Liu LQ et al. (1997). Diet, body size, physical activity, and the risk of endometrial cancer. Cancer Res 57, 5077–5085.
Gracey M, Burke V, Martin DD, Johnston RJ, Jones T, Davis EA (2007). Assessment of risks of ‘lifestyle’ diseases including cardiovascular disease and type 2 diabetes by anthropometry in remote Australian Aborigines. Asia Pac J Clin Nutr 16, 688–697.
Grandinetti A, Chang HK, Mau MK, Curb JD, Kinney EK, Sagum R et al. (1998). Prevalence of glucose intolerance among Native Hawaiians in two rural communities. Native Hawaiian Health Research (NHHR) Project. Diabetes Care 21, 549–554.
Haffner SM, D’Agostino R, Saad MF, Rewers M, Mykkanen L, Selby J et al. (1996). Increased insulin resistance and insulin secretion in nondiabetic African-Americans and Hispanics compared with non-Hispanic whites. The Insulin Resistance Atherosclerosis Study. Diabetes 45, 742–748.
Haffner SM, Stern MP, Hazuda HP, Pugh J, Patterson JK (1987). Do upper-body and centralized adiposity measure different aspects of regional body-fat distribution? Relationship to non-insulin-dependent diabetes mellitus, lipids, and lipoproteins. Diabetes 36, 43–51.
Hales CN, Barker DJ (1992). Type 2 (non-insulin-dependent) diabetes mellitus: the thrifty phenotype hypothesis. Diabetologia 35, 595–601.
Han JH, Park HS, Kim SM, Lee SY, Kim DJ, Choi WH (2008). Visceral adipose tissue as a predictor for metabolic risk factors in the Korean population. Diabet Med 25, 106–110.
Han TS, Williams K, Sattar N, Hunt KJ, Lean ME, Haffner SM (2002). Analysis of obesity and hyperinsulinemia in the development of metabolic syndrome: San Antonio Heart Study. Obes Res 10, 923–931.
Hara K, Matsushita Y, Horikoshi M, Yoshiike N, Yokoyama T, Tanaka H et al. (2006). A proposal for the cutoff point of waist circumference for the diagnosis of metabolic syndrome in the Japanese population. Diabetes Care 29, 1123–1124.
Harris MM, Stevens J, Thomas N, Schreiner P, Folsom AR (2000). Associations of fat distribution and obesity with hypertension in a bi-ethnic population: the ARIC study. Atherosclerosis Risk in Communities Study. Obes Res 8, 516–524.
Hayashi T, Boyko EJ, McNeely MJ, Leonetti DL, Kahn SE, Fujimoto WY (2007). Minimum waist and visceral fat values for identifying Japanese Americans at risk for the metabolic syndrome. Diabetes Care 30, 120–127.
Hodge AM, Dowse GK, Toelupe P, Collins VR, Zimmet PZ (1997). The association of modernization with dyslipidaemia and changes in lipid levels in the Polynesian population of Western Samoa. Int J Epidemiol 26, 297–306.
Hu D, Hannah J, Gray RS, Jablonski KA, Henderson JA, Robbins DC et al. (2000). Effects of obesity and body fat distribution on lipids and lipoproteins in nondiabetic American Indians: The Strong Heart Study. Obes Res 8, 411–421.
Hu D, Xie J, Fu P, Zhou J, Yu D, Whelton PK et al. (2007). Central rather than overall obesity is related to diabetes in the Chinese population: the InterASIA study. Obesity (Silver Spring) 15, 2809–2816.
Huang B, Rodreiguez BL, Burchfiel CM, Chyou PH, Curb JD, Sharp DS (1997). Associations of adiposity with prevalent coronary heart disease among elderly men: the Honolulu Heart Program. Int J Obes Relat Metab Disord 21, 340–348.
Huang KC, Lin WY, Lee LT, Chen CY, Lo H, Hsia HH et al. (2002). Four anthropometric indices and cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord 26, 1060–1068.
Huxley R, Barzi F, Lee CM, Lear S, Shaw J, Lam TH et al. (2007). Waist circumference thresholds provide an accurate and widely applicable method for the discrimination of diabetes. Diabetes Care 30, 3116–3118.
Hyun YJ, Kim OY, Jang Y, Ha JW, Chae JS, Kim JY et al. (2008). Evaluation of metabolic syndrome risk in Korean premenopausal women: not waist circumference but visceral fat. Circ J 72, 1308–1315.
International Diabetes Federation (2006). International Diabetes Federation (IDF) Worldwide Definition of the Metabolic Syndrome. Brussels, Belgium.
Iso H, Kiyama M, Naito Y, Sato S, Kitamura A, Iida M et al. (1991). The relation of body fat distribution and body mass with haemoglobin A1c, blood pressure and blood lipids in urban Japanese men. Int J Epidemiol 20, 88–94.
James WP (2005). Assessing obesity: are ethnic differences in body mass index and waist classification criteria justified? Obes Rev 6, 179–181.
Johnson S, Martin D, Sarin C (2002). Diabetes Mellitus in the First Nations population of British Columbia, Canada. Int J Circumpolar Health 61, 260–263.
Kaati G, Bygren LO, Edvinsson S (2002). Cardiovascular and diabetes mortality determined by nutrition during parents’ and grandparents’ slow growth period. Eur J Hum Genet 10, 682–688.
Kabat GC, Kim M, Hunt JR, Chlebowski RT, Rohan TE (2008). Body mass index and waist circumference in relation to lung cancer risk in the Women's Health Initiative. Am J Epidemiol 168, 158–169.
Kadowaki T, Sekikawa A, Murata K, Maegawa H, Takamiya T, Okamura T et al. (2006). Japanese men have larger areas of visceral adipose tissue than Caucasian men in the same levels of waist circumference in a population-based study. Int J Obes (Lond) 30, 1163–1165.
Kagawa M, Binns CB, Hills AP (2007). Body composition and anthropometry in Japanese and Australian Caucasian males and Japanese females. Asia Pac J Clin Nutr 16 (Suppl 1), 31–36.
Kalmijn S, Curb JD, Rodriguez BL, Yano K, Abbott RD (1999). The association of body weight and anthropometry with mortality in elderly men: the Honolulu Heart Program. Int J Obes Relat Metab Disord 23, 395–402.
Kim JA, Choi CJ, Yum KS (2006). Cut-off values of visceral fat area and waist circumference: diagnostic criteria for abdominal obesity in a Korean population. J Korean Med Sci 21, 1048–1053.
Ko GT, Chan JC, Cockram CS, Woo J (1999). Prediction of hypertension, diabetes, dyslipidaemia or albuminuria using simple anthropometric indexes in Hong Kong Chinese. Int J Obes Relat Metab Disord 23, 1136–1142.
Ko GT, Tang JS (2007). Waist circumference and BMI cut-off based on 10-year cardiovascular risk: evidence for ‘central pre-obesity’. Obesity (Silver Spring) 15, 2832–2839.
Kondalsamy-Chennakesavan S, Hoy WE, Wang Z, Briganti E, Polkinghorne K, Chadban S et al. (2008a). Anthropometric measurements of Australian Aboriginal adults living in remote areas: comparison with nationally representative findings. Am J Hum Biol 20, 317–324.
Kondalsamy-Chennakesavan S, Hoy WE, Wang Z, Shaw J (2008b). Quantifying the excess risk of type 2 diabetes by body habitus measurements among Australian aborigines living in remote areas. Diabetes Care 31, 585–586.
Koster A, Leitzmann MF, Schatzkin A, Mouw T, Adams KF, van Eijk JT et al. (2008). Waist circumference and mortality. Am J Epidemiol 167, 1465–1475.
Kristal AR, Arnold KB, Schenk JM, Neuhouser ML, Weiss N, Goodman P et al. (2007). Race/ethnicity, obesity, health related behaviors and the risk of symptomatic benign prostatic hyperplasia: results from the prostate cancer prevention trial. J Urol 177, 1395–1400; quiz 1591.
Kuk JL, Katzmarzyk PT, Nichaman MZ, Church TS, Blair SN, Ross R (2006). Visceral fat is an independent predictor of all-cause mortality in men. Obesity (Silver Spring) 14, 336–341.
Lau DC, Douketis JD, Morrison KM, Hramiak IM, Sharma AM, Ur E (2007). 2006 Canadian clinical practice guidelines on the management and prevention of obesity in adults and children [summary]. CMAJ 176, S1–S13.
Lean ME, Han TS, Bush H, Anderson AS, Bradby H, Williams R (2001). Ethnic differences in anthropometric and lifestyle measures related to coronary heart disease risk between South Asian, Italian and general-population British women living in the west of Scotland. Int J Obes Relat Metab Disord 25, 1800–1805.
Lean ME, Han TS, Morrison CE (1995). Waist circumference as a measure for indicating need for weight management. Br Med J 311, 158–161.
Lean ME, Han TS, Seidell JC (1998). Impairment of health and quality of life in people with large waist circumference. Lancet 351, 853–856.
Lear SA, Chen MM, Frohlich JJ, Birmingham CL (2002). The relationship between waist circumference and metabolic risk factors: Cohorts of European and Chinese descent. Metabolism 51, 1427–1432.
Lear SA, Humphries KH, Frohlich JJ, Birmingham CL (2007a). Appropriateness of current thresholds for obesity-related measures among Aboriginal people. CMAJ 177, 1499–1505.
Lear SA, Humphries KH, Kohli S, Birmingham CL (2007b). The use of BMI and waist circumference as surrogates of body fat differs by ethnicity. Obesity (Silver Spring) 15, 2817–2824.
Lear SA, Humphries KH, Kohli S, Chockalingam A, Frohlich JJ, Birmingham CL (2007c). Visceral adipose tissue accumulation differs according to ethnic background: results of the Multicultural Community Health Assessment Trial (M-CHAT). Am J Clin Nutr 86, 353–359.
Lear SA, Toma M, Birmingham CL, Frohlich JJ (2003). Modification of the relationship between simple anthropometric indices and risk factors by ethnic background. Metabolism 52, 1295–1301.
Lee JS, Kawakubo K, Mori K, Akabayashi A (2007). Effective cut-off values of waist circumference to detect the clustering of cardiovascular risk factors of metabolic syndrome in Japanese men and women. Diab Vasc Dis Res 4, 340–345.
Lemieux I, Pascot A, Prud’homme D, Almeras N, Bogaty P, Nadeau A et al. (2001). Elevated C-reactive protein: another component of the atherothrombotic profile of abdominal obesity. Arterioscler Thromb Vasc Biol 21, 961–967.
Lemos-Santos MG, Valente JG, Goncalves-Silva RM, Sichieri R (2004). Waist circumference and waist-to-hip ratio as predictors of serum concentration of lipids in Brazilian men. Nutrition 20, 857–862.
Li JZ, Absher DM, Tang H, Southwick AM, Casto AM, Ramachandran S et al. (2008a). Worldwide human relationships inferred from genome-wide patterns of variation. Science 319, 1100–1104.
Li R, Lu W, Jia J, Zhang S, Shi L, Li Y et al. (2008b). Relationships between indices of obesity and its cardiovascular comorbidities in a Chinese population. Circ J 72, 973–978.
Lin WY, Lee LT, Chen CY, Lo H, Hsia HH, Liu IL et al. (2002). Optimal cut-off values for obesity: using simple anthropometric indices to predict cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord 26, 1232–1238.
Lindeberg S, Eliasson M, Lindahl B, Ahren B (1999). Low serum insulin in traditional Pacific Islanders—the Kitava Study. Metabolism 48, 1216–1219.
Lovejoy JC, Smith SR, Rood JC (2001). Comparison of regional fat distribution and health risk factors in middle-aged white and African American women: the Healthy Transitions Study. Obes Res 9, 10–16.
Luke A, Durazo-Arvizu R, Rotimi C, Prewitt TE, Forrester T, Wilks R et al. (1997). Relation between body mass index and body fat in black population samples from Nigeria, Jamaica, and the United States. Am J Epidemiol 145, 620–628.
Mansour AA, Al-Hassan AA, Al-Jazairi MI (2007). Cut-off values for waist circumference in rural Iraqi adults for the diagnosis of metabolic syndrome. Rural Remote Health 7, 765.
Mansour AA, Al-Jazairi MI (2007). Cut-off values for anthropometric variables that confer increased risk of type 2 diabetes mellitus and hypertension in Iraq. Arch Med Res 38, 253–258.
Masala G, Bendinelli B, Versari D, Saieva C, Ceroti M, Santagiuliana F et al. (2008). Anthropometric and dietary determinants of blood pressure in over 7000 Mediterranean women: the European Prospective Investigation into Cancer and Nutrition-Florence cohort. J Hypertens 26, 2112–2120.
Masuda T, Imai K, Komiya S (1993). Relationship of anthropometric indices of body fat to cardiovascular risk in Japanese women. Ann Physiol Anthropol 12, 135–144.
Matoba Y, Inoguchi T, Nasu S, Suzuki S, Yanase T, Nawata H et al. (2008). Optimal cut points of waist circumference for the clinical diagnosis of metabolic syndrome in the Japanese population. Diabetes Care 31, 590–592.
McDonald SP, Maguire GP, Duarte N, Wang XL, Hoy WE (2004). Carotid intima-media thickness, cardiovascular risk factors and albuminuria in a remote Australian Aboriginal community. Atherosclerosis 177, 423–431.
McKeigue PM, Pierpoint T, Ferrie JE, Marmot MG (1992). Relationship of glucose intolerance and hyperinsulinaemia to body fat pattern in south Asians and Europeans. Diabetologia 35, 785–791.
McKimmie RL, Daniel KR, Carr JJ, Bowden DW, Freedman BI, Register TC et al. (2008). Hepatic steatosis and subclinical cardiovascular disease in a cohort enriched for type 2 diabetes: the Diabetes Heart Study. Am J Gastroenterol 103, 3029–3035.
Mirmiran P, Esmaillzadeh A, Azizi F (2004). Detection of cardiovascular risk factors by anthropometric measures in Tehranian adults: receiver operating characteristic (ROC) curve analysis. Eur J Clin Nutr 58, 1110–1118.
Misra A (2003). Revisions of cutoffs of body mass index to define overweight and obesity are needed for the Asian-ethnic groups. Int J Obes Relat Metab Disord 27, 1294–1296.
Mohan V, Deepa M, Farooq S, Narayan KM, Datta M, Deepa R (2007). Anthropometric cut points for identification of cardiometabolic risk factors in an urban Asian Indian population. Metabolism 56, 961–968.
Molarius A, Seidell JC, Sans S, Tuomilehto J, Kuulasmaa K (1999). Varying sensitivity of waist action levels to identify subjects with overweight or obesity in 19 populations of the WHO MONICA Project. J Clin Epidemiol 52, 1213–1224.
Mufunda J, Scott LJ, Chifamba J, Matenga J, Sparks B, Cooper R et al. (2000). Correlates of blood pressure in an urban Zimbabwean population and comparison to other populations of African origin. J Hum Hypertens 14, 65–73.
Narisawa S, Nakamura K, Kato K, Yamada K, Sasaki J, Yamamoto M (2008). Appropriate waist circumference cutoff values for persons with multiple cardiovascular risk factors in Japan: a large cross-sectional study. J Epidemiol 18, 37–42.
National Cholesterol Education Program (2001). Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III). JAMA 285, 2486–2497.
Nelson TL, Bessesen DH, Marshall JA (2008). Relationship of abdominal obesity measured by DXA and waist circumference with insulin sensitivity in Hispanic and non-Hispanic white individuals: the San Luis Valley Diabetes Study. Diabetes Metab Res Rev 24, 33–40.
Nicklas BJ, Penninx BW, Cesari M, Kritchevsky SB, Newman AB, Kanaya AM et al. (2004). Association of visceral adipose tissue with incident myocardial infarction in older men and women: the Health, Aging and Body Composition Study. Am J Epidemiol 160, 741–749.
Novotny R, Nabokov V, Derauf C, Grove J, Vijayadeva V (2007). BMI and waist circumference as indicators of health among Samoan women. Obesity (Silver Spring) 15, 1913–1917.
Obesity Education Initiative Expert Panel (1998). Clinical Guidelines on the Identification, Evaluation and Treatment of Overweight and Obesity in Adults: The Evidence Report. National Institutes of Health: National Heart, Lung and Blood Institute: Bethesda.
Obesity in Asia Collaboration (2008). Is central obesity a better discriminator of the risk of hypertension than body mass index in ethnically diverse populations? J Hypertens 26, 169–177.
O’Dea K, Patel M, Kubisch D, Hopper J, Traianedes K (1993). Obesity, diabetes, and hyperlipidemia in a central Australian aboriginal community with a long history of acculturation. Diabetes Care 16, 1004–1010.
Oka R, Kobayashi J, Yagi K, Tanii H, Miyamoto S, Asano A et al. (2008). Reassessment of the cutoff values of waist circumference and visceral fat area for identifying Japanese subjects at risk for the metabolic syndrome. Diabetes Res Clin Pract 79, 474–481.
Okobia MN, Bunker CH, Zmuda JM, Osime U, Ezeome ER, Anyanwu SN et al. (2006). Anthropometry and breast cancer risk in Nigerian women. Breast J 12, 462–466.
Okosun IS, Boltri JM, Hepburn VA, Eriksen MP, Davis-Smith M (2006). Regional fat localizations and racial/ethnic variations in odds of hypertension in at-risk American adults. J Hum Hypertens 20, 362–371.
Okosun IS, Choi S, Dent MM, Jobin T, Dever GE (2001). Abdominal obesity defined as a larger than expected waist girth is associated with racial/ethnic differences in risk of hypertension. J Hum Hypertens 15, 307–312.
Okosun IS, Cooper RS, Prewitt TE, Rotimi CN (1999a). The relation of central adiposity to components of the insulin resistance syndrome in a biracial US population sample. Ethn Dis 9, 218–229.
Okosun IS, Cooper RS, Rotimi CN, Osotimehin B, Forrester T (1998). Association of waist circumference with risk of hypertension and type 2 diabetes in Nigerians, Jamaicans, and African-Americans. Diabetes Care 21, 1836–1842.
Okosun IS, Forrester TE, Rotimi CN, Osotimehin BO, Muna WF, Cooper RS (1999b). Abdominal adiposity in six populations of West African descent: prevalence and population attributable fraction of hypertension. Obes Res 7, 453–462.
Okosun IS, Liao Y, Rotimi CN, Choi S, Cooper RS (2000a). Predictive values of waist circumference for dyslipidemia, type 2 diabetes and hypertension in overweight White, Black, and Hispanic American adults. J Clin Epidemiol 53, 401–408.
Okosun IS, Prewitt TE, Liao Y, Cooper RS (1999c). Association of waist circumference with ApoB to ApoAI ratio in black and white Americans. Int J Obes Relat Metab Disord 23, 498–504.
Okosun IS, Rotimi CN, Forrester TE, Fraser H, Osotimehin B, Muna WF et al. (2000b). Predictive value of abdominal obesity cut-off points for hypertension in blacks from west African and Caribbean island nations. Int J Obes Relat Metab Disord 24, 180–186.
Okosun IS, Tedders SH, Choi S, Dever GE (2000c). Abdominal adiposity values associated with established body mass indexes in white, black and hispanic Americans. A study from the Third National Health and Nutrition Examination Survey. Int J Obes Relat Metab Disord 24, 1279–1285.
Olatunbosun ST, Kaufman JS, Cooper RS, Bella AF (2000). Hypertension in a black population: prevalence and biosocial determinants of high blood pressure in a group of urban Nigerians. J Hum Hypertens 14, 249–257.
Olinto MT, Nacul LC, Gigante DP, Costa JS, Menezes AM, Macedo S (2004). Waist circumference as a determinant of hypertension and diabetes in Brazilian women: a population-based study. Public Health Nutr 7, 629–635.
Onat A, Sansoy V, Uysal O (1999). Waist circumference and waist-to-hip ratio in Turkish adults: interrelation with other risk factors and association with cardiovascular disease. Int J Cardiol 70, 43–50.
Orozco P, Nolla JM (1997). Associations between body morphology and bone mineral density in premenopausal women. Eur J Epidemiol 13, 919–924.
Orr-Walker B, Evans MC, Reid IR, Cundy T (2005). Increased abdominal fat in young women of Indian origin. Asia Pac J Clin Nutr 14, 69–73.
Oviedo G, Moron de Salim M, Solano L (2006). [Obesity anthropometrics indicators and the association with coronary ischemic disease]. Nutr Hosp 21, 694–698.
Paccaud F, Schluter-Fasmeyer V, Wietlisbach V, Bovet P (2000). Dyslipidemia and abdominal obesity: an assessment in three general populations. J Clin Epidemiol 53, 393–400.
Pais P, Pogue J, Gerstein H, Zachariah E, Savitha D, Jayprakash S et al. (1996). Risk factors for acute myocardial infarction in Indians: a case-control study. Lancet 348, 358–363.
Patel S, Unwin N, Bhopal R, White M, Harland J, Ayis SA et al. (1999). A comparison of proxy measures of abdominal obesity in Chinese, European and South Asian adults. Diabet Med 16, 853–860.
Pereira RA, Sichieri R, Marins VM (1999). [Waist: hips girth ratio as a predictor of arterial hypertension]. Cad Saude Publica 15, 333–344.
Perez M, Casas JP, Cubillos-Garzon LA, Serrano NC, Silva F, Morillo CA et al. (2003). Using waist circumference as a screening tool to identify Colombian subjects at cardiovascular risk. Eur J Cardiovasc Prev Rehabil 10, 328–335.
Perry A, Wang X, Kuo YT (2008). Anthropometric correlates of metabolic syndrome components in a diverse sample of overweight/obese women. Ethn Dis 18, 163–168.
Piers LS, Rowley KG, Soares MJ, O’Dea K (2003). Relation of adiposity and body fat distribution to body mass index in Australians of Aboriginal and European ancestry. Eur J Clin Nutr 57, 956–963.
Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, Overvad K et al. (2008). General and abdominal adiposity and risk of death in Europe. N Engl J Med 359, 2105–2120.
Pitanga FJ, Lessa I (2005). [Anthropometric indexes of obesity as an instrument of screening for high coronary risk in adults in the city of Salvador—Bahia]. Arq Bras Cardiol 85, 26–31.
Pouliot MC, Despres JP, Lemieux S, Moorjani S, Bouchard C, Tremblay A et al. (1994). Waist circumference and abdominal sagittal diameter: best simple anthropometric indexes of abdominal visceral adipose tissue accumulation and related cardiovascular risk in men and women. Am J Cardiol 73, 460–468.
Punyadeera C, van der Merwe MT, Crowther NJ, Toman M, Immelman AR, Schlaphoff GP et al. (2001a). Weight-related differences in glucose metabolism and free fatty acid production in two South African population groups. Int J Obes Relat Metab Disord 25, 1196–1205.
Punyadeera C, van der Merwe MT, Crowther NJ, Toman M, Schlaphoff GP, Gray IP (2001b). Ethnic differences in lipid metabolism in two groups of obese South African women. J Lipid Res 42, 760–767.
Raji A, Seely EW, Arky RA, Simonson DC (2001). Body fat distribution and insulin resistance in healthy Asian Indians and Caucasians. J Clin Endocrinol Metab 86, 5366–5371.
Razak F, Anand S, Vuksan V, Davis B, Jacobs R, Teo KK et al. (2005). Ethnic differences in the relationships between obesity and glucose-metabolic abnormalities: a cross-sectional population-based study. Int J Obes Relat Metab Disord 29, 656–667.
Razak F, Anand SS, Shannon H, Vuksan V, Davis B, Jacobs R et al. (2007). Defining obesity cut points in a multiethnic population. Circulation 115, 2111–2118.
Reaven GM (2005). The metabolic syndrome: requiescat in pace. Clin Chem 51, 931–938.
Rexrode KM, Carey VJ, Hennekens CH, Walters EE, Colditz GA, Stampfer MJ et al. (1998). Abdominal adiposity and coronary heart disease in women. JAMA 280, 1843–1848.
Rush E, Plank L, Chandu V, Laulu M, Simmons D, Swinburn B et al. (2004). Body size, body composition, and fat distribution: a comparison of young New Zealand men of European, Pacific Island, and Asian Indian ethnicities. N Z Med J 117, U1203.
Rush EC, Freitas I, Plank LD (2009). Body size, body composition and fat distribution: comparative analysis of European, Maori, Pacific Island and Asian Indian adults. Br J Nutr 10, 1–10.
Rush EC, Goedecke JH, Jennings C, Micklesfield L, Dugas L, Lambert EV et al. (2007a). BMI, fat and muscle differences in urban women of five ethnicities from two countries. Int J Obes (Lond) 31, 1232–1239.
Rush EC, Plank LD, Yajnik CS (2007b). Interleukin-6, tumour necrosis factor-alpha and insulin relationships to body composition, metabolism and resting energy expenditure in a migrant Asian Indian population. Clin Endocrinol (Oxf) 66, 684–690.
Sanchez-Castillo CP, Velazquez-Monroy O, Berber A, Lara-Esqueda A, Tapia-Conyer R, James WP (2003). Anthropometric cutoff points for predicting chronic diseases in the Mexican National Health Survey 2000. Obes Res 11, 442–451.
Sargeant LA, Bennett FI, Forrester TE, Cooper RS, Wilks RJ (2002). Predicting incident diabetes in Jamaica: the role of anthropometry. Obes Res 10, 792–798.
Sato A, Asayama K, Ohkubo T, Kikuya M, Obara T, Metoki H et al. (2008). Optimal cutoff point of waist circumference and use of home blood pressure as a definition of metabolic syndrome: the Ohasama study. Am J Hypertens 21, 514–520.
Shahraki T, Shahraki M, Roudbari M, Gargari BP (2008). Determination of the leading central obesity index among cardiovascular risk factors in Iranian women. Food Nutr Bull 29, 43–48.
Shemesh T, Rowley KG, Jenkins A, Brimblecombe J, Best JD, O’Dea K (2007). Differential association of C-reactive protein with adiposity in men and women in an Aboriginal community in northeast Arnhem Land of Australia. Int J Obes (Lond) 31, 103–108.
Shen W, Punyanitya M, Chen J, Gallagher D, Albu J, Pi-Sunyer X et al. (2006). Waist circumference correlates with metabolic syndrome indicators better than percentage fat. Obesity (Silver Spring) 14, 727–736.
Shiwaku K, Anuurad E, Enkhmaa B, Kitajima K, Yamane Y (2004). Appropriate BMI for Asian populations. Lancet 363, 1077.
Sidney S, Lewis CE, Hill JO, Quesenberry Jr CP, Stamm ER, Scherzinger A et al. (1999). Association of total and central adiposity measures with fasting insulin in a biracial population of young adults with normal glucose tolerance: the CARDIA study. Obes Res 7, 265–272.
Snijder MB, Zimmet PZ, Visser M, Dekker JM, Seidell JC, Shaw JE (2004). Independent association of hip circumference with metabolic profile in different ethnic groups. Obes Res 12, 1370–1374.
Steele CB, Cardinez CJ, Richardson LC, Tom-Orme L, Shaw KM (2008). Surveillance for health behaviors of American Indians and Alaska Natives-findings from the behavioral risk factor surveillance system, 2000–2006. Cancer 113, 1131–1141.
Stevens J (2003). Ethnic-specific revisions of body mass index cutoffs to define overweight and obesity in Asians are not warranted. Int J Obes Relat Metab Disord 27, 1297–1299.
Suk SH, Sacco RL, Boden-Albala B, Cheun JF, Pittman JG, Elkind MS et al. (2003). Abdominal obesity and risk of ischemic stroke: the Northern Manhattan Stroke Study. Stroke 34, 1586–1592.
Sundborn G, Metcalf PA, Gentles D, Scragg RK, Schaaf D, Dyall L et al. (2008). Ethnic differences in cardiovascular disease risk factors and diabetes status for Pacific ethnic groups and Europeans in the Diabetes Heart and Health Survey (DHAH) 2002–2003, Auckland New Zealand. NZ Med J 121, 28–39.
Sung KC, Ryan MC, Kim BS, Cho YK, Kim BI, Reaven GM (2007). Relationships between estimates of adiposity, insulin resistance, and nonalcoholic fatty liver disease in a large group of nondiabetic Korean adults. Diabetes Care 30, 2113–2118.
Takami R, Takeda N, Hayashi M, Sasaki A, Kawachi S, Yoshino K et al. (2001). Body fatness and fat distribution as predictors of metabolic abnormalities and early carotid atherosclerosis. Diabetes Care 24, 1248–1252.
Tavares EF, Vieira-Filho JP, Andriolo A, Sanudo A, Gimeno SG, Franco LJ (2003). Metabolic profile and cardiovascular risk patterns of an Indian tribe living in the Amazon Region of Brazil. Hum Biol 75, 31–46.
The Scottish Intercollegiate Guidelines Network [SIGN] (1996). The Scottish Intercollegiate Guidelines Network [SIGN]: Obesity in Scotland: Integrating Prevention with Weight Management. Royal College of Physicians: Edinburgh, Scotland.
Travison TG, Araujo AB, Esche GR, McKinlay JB (2008). The relationship between body composition and bone mineral content: threshold effects in a racially and ethnically diverse group of men. Osteoporos Int 19, 29–38.
Unwin N, Harland J, White M, Bhopal R, Winocour P, Stephenson P et al. (1997). Body mass index, waist circumference, waist-hip ratio, and glucose intolerance in Chinese and Europid adults in Newcastle, UK. J Epidemiol Community Health 51, 160–166.
U.S. Department of Agriculture, U.S. Department of Health and Human Services (1990). Dietary Guidelines for Americans, 3rd edn, Government Printing Office: Washington, DC, US.
Valsamakis G, Chetty R, Anwar A, Banerjee AK, Barnett A, Kumar S (2004). Association of simple anthropometric measures of obesity with visceral fat and the metabolic syndrome in male Caucasian and Indo-Asian subjects. Diabet Med 21, 1339–1345.
van der Merwe MT, Crowther NJ, Schlaphoff GP, Gray IP, Joffe BI, Lonnroth PN (2000). Evidence for insulin resistance in black women from South Africa. Int J Obes Relat Metab Disord 24, 1340–1346.
Vanasse A, Demers M, Hemiari A, Courteau J (2006). Obesity in Canada: where and how many? Int J Obes (Lond) 30, 677–683.
Velasquez-Melendez G, Kac G, Valente JG, Tavares R, Silva CQ, Garcia ES (2002). Evaluation of waist circumference to predict general obesity and arterial hypertension in women in Greater Metropolitan Belo Horizonte, Brazil. Cad Saude Publica 18, 765–771.
Venkatramana P, Reddy PC (2002). Association of overall and abdominal obesity with coronary heart disease risk factors: comparison between urban and rural Indian men. Asia Pac J Clin Nutr 11, 66–71.
Vikram NK, Pandey RM, Misra A, Sharma R, Devi JR, Khanna N (2003). Non-obese (body mass index <25 kg/m2) Asian Indians with normal waist circumference have high cardiovascular risk. Nutrition 19, 503–509.
Wang J, Thornton JC, Russell M, Burastero S, Heymsfield S, Pierson Jr RN (1994). Asians have lower body mass index (BMI) but higher percent body fat than do whites: comparisons of anthropometric measurements. Am J Clin Nutr 60, 23–28.
Wang Y, Jacobs EJ, Patel AV, Rodriguez C, McCullough ML, Thun MJ et al. (2008). A prospective study of waist circumference and body mass index in relation to colorectal cancer incidence. Cancer Causes Control 19, 783–792.
Wang Z, Hoy WE (2004a). Body size measurements as predictors of type 2 diabetes in Aboriginal people. Int J Obes Relat Metab Disord 28, 1580–1584.
Wang Z, Hoy WE (2004b). Waist circumference, body mass index, hip circumference and waist-to-hip ratio as predictors of cardiovascular disease in Aboriginal people. Eur J Clin Nutr 58, 888–893.
Wang Z, Rowley K, Wang Z, Piers L, O’Dea K (2007). Anthropometric indices and their relationship with diabetes, hypertension and dyslipidemia in Australian Aboriginal people and Torres Strait Islanders. Eur J Cardiovasc Prev Rehabil 14, 172–178.
Wei M, Gaskill SP, Haffner SM, Stern MP (1997). Waist circumference as the best predictor of noninsulin dependent diabetes mellitus (NIDDM) compared to body mass index, waist/hip ratio and other anthropometric measurements in Mexican Americans—a 7-year prospective study. Obes Res 5, 16–23.
WHO/IASO/IOTF (2000). The Asia-Pacific Perspective: Redefining Obesity and its Treatment. Health Communications Australia: Melbourne.
Wierzbicki AS, Nishtar S, Lumb PJ, Lambert-Hammill M, Crook MA, Marber MS et al. (2008). Waist circumference, metabolic syndrome and coronary artery disease in a Pakistani cohort. Int J Cardiol 128, 77–82.
Wildman RP, Gu D, Reynolds K, Duan X, He J (2004). Appropriate body mass index and waist circumference cutoffs for categorization of overweight and central adiposity among Chinese adults. Am J Clin Nutr 80, 1129–1136.
World Health Organization (2000). The Asia-Pacific Perspective: Redefining Obesity and its Treatment. WHO: Geneva.
World Health Organization (2003). Diet, Nutrition and the Prevention of Chronic Diseases: Report of the joint WHO/FAO expert consultation. WHO Technical Report Series. World Health Organization: Geneva.
Wu CH, Heshka S, Wang J, Pierson Jr RN, Heymsfield SB, Laferrere B et al. (2007). Truncal fat in relation to total body fat: influences of age, sex, ethnicity and fatness. Int J Obes (Lond) 31, 1384–1391.
Yajnik CS (2004). Obesity epidemic in India: intrauterine origins? Proc Nutr Soc 63, 387–396.
Yip YB, Ho SC, Chan SG (2001). Tall stature, overweight and the prevalence of low back pain in Chinese middle-aged women. Int J Obes Relat Metab Disord 25, 887–892.
Young TK (1996). Obesity, central fat patterning, and their metabolic correlates among the inuit of the central Canadian Arctic. Hum Biol 68, 245–263.
Yusuf S, Hawken S, Ounpuu S, Bautista L, Franzosi MG, Commerford P et al. (2005). Obesity and the risk of myocardial infarction in 27,000 participants from 52 countries: a case-control study. Lancet 366, 1640–1649.
Zhang X, Shu XO, Gao YT, Yang G, Matthews CE, Li Q et al. (2004). Anthropometric predictors of coronary heart disease in Chinese women. Int J Obes Relat Metab Disord 28, 734–740.
Zhang X, Shu XO, Yang G, Li H, Cai H, Gao YT et al. (2007). Abdominal adiposity and mortality in Chinese women. Arch Intern Med 167, 886–892.
Zhu S, Heymsfield SB, Toyoshima H, Wang Z, Pietrobelli A, Heshka S (2005). Race-ethnicity-specific waist circumference cutoffs for identifying cardiovascular disease risk factors. Am J Clin Nutr 81, 409–415.
Dr Lear is a Canadian Institutes of Health Research New Investigator.
Contributors: All authors contributed to the identification of the literature, the articles included and the preparation of the paper.
About this article
Cite this article
Lear, S., James, P., Ko, G. et al. Appropriateness of waist circumference and waist-to-hip ratio cutoffs for different ethnic groups. Eur J Clin Nutr 64, 42–61 (2010). https://doi.org/10.1038/ejcn.2009.70
- waist circumference
- waist-to-hip ratio
Prevalence of obesity and an interrogation of the correlation between anthropometric indices and blood pressures in urban Lagos, Nigeria
Scientific Reports (2021)
The association between dietary inflammatory index, muscle strength, muscle endurance, and body composition in Iranian adults
Eating and Weight Disorders - Studies on Anorexia, Bulimia and Obesity (2021)
Nutrition Journal (2020)
Diagnosing metabolic syndrome in a multi-ethnic country: is an ethnic-specific cut-off point of waist circumference needed?
Nutrition & Diabetes (2020)
Socio-cultural norms of body size in Westerners and Polynesians affect heart rate variability and emotion during social interactions
Culture and Brain (2019)