Abstract
Objective and Method
To analyse data from the nationally representative National Diet and Nutrition Survey (NDNS) collected in 2000/2001 and to investigate how the BMI and two proxy indicators of central fat distribution, namely the waist circumference and the waist to height ratio (WHtR), are associated with each other and with cardiovascular disease (CVD) risk factors.
Results
Screening health risk by BMI alone would ‘miss’ 35% of men and 14% of women who are within the normal BMI range (18.5–25 kg/m2) but have central fat distribution, defined by WHtR > 0.5. In the total population this equates to 17% of all men and 6% of all women who would be inadequately screened by BMI alone. Compared to BMI, WHtR was more closely associated with CVD risk factors among both men and women. Furthermore, in a combined analysis of men and women, central fat distribution with a normal BMI was associated with higher levels of CVD risk factors than being overweight without central fat distribution.
Conclusion
WHtR is a simple and effective, non-invasive screening tool for CVD risk factors. Our proposed boundary value of 0.5 translates into a simple public health message: ‘Keep your waist circumference to less than half your height’.
Key Words: Central obesity; Central fat distribution; Waist to height ratio; Waist circumference, BMI; Cardiovascular CVD risk factors
Introduction
Obesity, particularly excess visceral fat, is associated with an increased risk of cardiovascular disease and mortality [1]. BMI has become the most widely accepted index of obesity and a proxy for total body fatness, but it does not differentiate between the over-muscled and the over-fat or distinguish between individuals with different types of fat distribution [2,3]. Jean Vague [4,5] first pointed out in the mid 20th century that people with a ‘central’ type of fat distribution (android shape) were at greater health risk than those whose fat was deposited ‘peripherally’ (gynoid shape). Only in the last 2 decades has there been a consensus view that health risks (predominantly cardiovascular disease (CVD) and diabetes) can be determined as much by the relative distribution of the excess fat as by its total amount. The use of imaging techniques such as computed tomography (CT) [6] and magnetic resonance imaging (MRI) [7] has subsequently indicated that central obesity (‘apple’ shape) is associated with a preferential deposition of fat in the internal, visceral fat depots rather than the external, subcutaneous fat depots (‘pear’ shape).
Relative fat distribution, as measured by the ratio of waist circumference to hip circumference (WHR), was popular for many years and is a good predictor of health risk [8]. However, although very useful for risk assessment, WHR is not always helpful in a risk management because both waist and hip can decrease with weight reduction and so the ratio of WHR can sometimes change very little. Another problem is that WHR requires measurements of 2 circumferences. Although our dataset allowed calculation of WHR, this index was not considered further in our study, since we do not believe that WHR is useful in a public health context as its use does not always motivate risk reduction.
Much attention has subsequently been given to the use of waist circumference for risk assessment and management [9,10], as this is more strongly correlated with visceral fat than WHR. The widely used cut-points [11], namely 102 cm for men and 88 cm for women, were originally intended as a simpler alternative to BMI cut-offs indicating a need for weight reduction. Different thresholds may however be needed for men and women, for different ages, and for ethnic groups [12]. Furthermore, a report from Japan showed a difference in metabolic risks between people of similar waist circumference with different heights [13].
In 1995 and 1996, another anthropometric index, waist to height ratio (WHtR), was shown to be better associated with metabolic risk factors. Researchers in Japan [14,15] and the UK [16,17] suggested that the same boundary value for risk (WHtR 0.5) might be used for both men and women. A boundary value of WHtR 0.6 was also proposed to indicate central obesity or more severe risk [18]. Collaborative work between these authors has continued to promote the use of WHtR for adults of all ethnic groups and for children [19]. Further, a recent meta-analysis [20] comparing the area under the curve from receiver-operating characteristics (ROC) analyses of various anthropometric indices and CVD risk factors showed that WHtR was the best discriminator for hypertension, diabetes, and dyslipidaemia in both sexes (i.e. better than BMI, waist circumference, and WHR). These authors have supported the previously suggested boundary value of 0.5.
We investigated the strength of association between various proposed indices of obesity, central obesity, and CVD risk factors using original data from the National Diet and Nutrition Survey (NDNS) of adults aged 19–64 years [21]. The fact that this survey is nationally representative makes it acceptable to generalise the results to the total population.
Measurements of weight, height, and waist circumference allowed us to investigate two proxy indicators of central fat distribution and central obesity, namely waist circumference and WHtR, as well as BMI, and to relate these to CVD risk factors, namely systolic blood pressure (SBP) and diastolic blood pressure (DBP), total cholesterol (TC), plasma non-HDL cholesterol (non-HDL-C), and plasma HDL cholesterol (HDL-C).
Methods
Measurements of Anthropometric Characteristics
In the NDNS, anthropometric data (weight, height, and waist circumference) were collected on 1,776 individuals who were representative of British adults aged between 19 and 64 years.
Measurements of standing height, weight, waist, and hip circumferences were each taken twice by interviewers trained in accurate measurement techniques [21]. Subjects were asked to remove their shoes and socks and to wear only light clothing. Weight was taken using Soehnle Quantratronic scales, (100 gram units) calibrated prior to the start of field-work. Height was measured using the Leicester Height Measure. Waist circumference measurement was taken at the midway point between the iliac crest and the lower rib at the end of a normal expiration.
Definitions of Anthropometric Characteristics
In this paper, we have used the terms, definitions, and boundary values shown in table 1.
Measurements of Blood Pressure and CVD Risk Factors
Methods for blood pressure measurement and measurements of blood analytes (TC, non-HDL-C, HDL-C) are given in the published NDNS report [21].
Statistical Data Analysis
Data files were obtained from the Data Archive (www.data-archive.ac.uk) and relevant variables extracted. All analyses were conducted using SPSS version 16.0 and 17.0 (SPSS UK, Surrey, UK).
Normally distributed continuous variables have been summarised by means and standard deviations. Categorical variables are presented as percentages and compared with a chi-square test. Partial correlation coefficients were calculated between each anthropometric index and SBP, DBP, TC, HDL-C, and non-HDL-C after controlling for age and sex (for the whole sample) and age only (for each gender). Analysis of covariance (adjusted for age (years) and sex) was used to compare the 4 groups classified using dual criteria (BMI and WHtR). Pair-wise contrasts were performed with ‘Bonferroni’ correction for multiple comparisons. P-values of <0.05 (2-tailed test) were regarded as significant, although actual p-values are also quoted.
Results
Details of Subjects
Table 2 shows the mean values for anthropometric variables. All analyses are based on the sample of 1,776 adults who provided data for all 3 variables of relevance to this study (weight, height, and waist circumference). Their mean age (42 years, SD = 12 years) was identical to that of the total sample (n = 2,251).
Relationship of Anthropometric Indices with Each Other
BMI, waist circumference, and WHtR were all strongly correlated with each other (p < 0.0001) (age- and sex-adjusted coefficients: 0.844 (BMI vs. waist), 0.86 (BMI vs. WHtR), 0.95 (waist vs. WHtR)). Correlations for men and women separately are shown in table 3.
Relationship of Anthropometric Indices with CVD Risk Factors
Table 3 shows that, for all CVD risk factors, correlation coefficients were higher for WHtR than for BMI in both men and women. Correlations were fair (r > 0.25) for HDL-C and SBP, weaker for non-HDL-C, and weakest for TC.
Identification of the Most ‘at Risk’ Subjects by Combining Anthropometric Indices
For this part of the study, we focussed on two anthropometric indices: BMI and WHtR. To increase the power to detect differences, we combined data for men and women and adjusted for sex and age as covariates. The adults were subdivided into 4 groups according to BMI (boundary value > 25 kg/m2) and WHtR (boundary value for ‘apples’ > 0.5). The groups were defined as follows (% study population in brackets):
i) non-overweight ‘pears’ (BMI ≤ 25 and WHtR ≤ 0.5) (32%),
ii) overweight ‘pears’ (BMI > 25 and WHtR ≤ 0.5) (6%),
iii) non-overweight ‘apples’ (BMI ≤ 25 and WHtR > 0.5) (9%),
iv) overweight ‘apples’ (BMI > 25 and WHtR > 0.5) (53%). The mean values for CVD risk factors in these 4 groups, adjusted for age and sex, are shown in table 4.
Contrast tests indicated greater differences according to shape than relative weight. Thus, ‘apples’ (groups 3 and 4) had higher levels of all risk factors (higher TC, non-HDL-C, SBP, DBP, but lower HDL-C) than ‘pears’ (groups 1 and 2). By contrast, the differences attributable to weight (group 1 vs. 3; group 2 vs. 4) were smaller; ‘pears’ (groups 1 and 2) had similar levels of risk factors irrespective of whether they were overweight or not (i.e. BMI status).
Most interesting of all, non-overweight ‘apples’ (group 3) appeared to be at higher risk than overweight ‘pears’ (group 2). Thus, non-HDL-C was 0.3 mmol/l (7%) higher, while SBP and DBP were 4 mm Hg and 3 mm Hg higher in group 3 compared with group 2. These differences are clinically significant.
How Many People with Central Fat Distribution Are Not Screened as ‘at Risk’ by Measuring BMI Alone?
It has been traditional to screen individuals for health risk based solely on their weight and height, rather than their fat distribution. Because health risk is correlated better with central fat distribution, we wanted to compare screening efficiency based on central fat distribution as well as BMI. In this instance we used WHtR as our proxy for central fat distribution. Since it is appropriate to use the lower boundary values of anthropometric indices rather than those indicating overt risk, cross tabulations of NDNS data were performed using boundary values of BMI > 25 kg/m2 (overweight) and WHtR > 0.5 (central fat distribution).
Table 5 shows that, of those men who are not classed as overweight (BMI ≤ 25 kg/m2), 35% have WHtR of >0.5 and therefore have central fat distribution without being overweight. Similarly, of those women who are not classed as overweight, 14% have central fat distribution. In the total population this equates to 17% of all men and 6% of all women who would be ‘missed’ if screening was based on BMI alone.
Discussion
Importance of Using WHtR for Prediction of CVD Risk Factors
From our analysis, it is clear from the correlation coefficients that the proxy indicators of central fat distribution, namely waist circumference and especially WHtR, are better at predicting metabolic risk factors than BMI.
In the last few years, there has been an exponential increase in evidence from investigators around the world showing the superiority of WHtR over other anthropometric indices in their association with metabolic risks, hypertension, and stroke and chronic kidney disease.
Supporting evidence has come from cross-sectional studies in adults from, among others, Taiwan [22–24], Greece [25], Jamaica [26], Hong Kong [27], Korea [28], Bangladesh [29], Singapore [30], China [31], Iran [32], Japan [33], Germany [34,35], Thailand [36,37], Pakistan [38], Australia [39], USA [40], Iraq [41], Korea [42], Brazil [43], and India [44].
A recent meta-analysis [20] comparing pooled data from 10 studies using the area under the curve from ROC analyses of various anthropometric indices and CVD risk in adults has shown that WHtR is better than BMI, waist circumference, and WHR. These authors have lent support to the previously proposed boundary value of WHtR 0.5 [15,45,46].
Some authors [e.g. 47] have argued that waist circumference is a more convenient measure than WHtR because of its simplicity. To an extent this is true, but concerns have been expressed that one set of boundary values for waist circumference (developed on Caucasian subjects) does not suit all ethnic groups [48] and that risk can differ for people with the same waist circumference, but different heights [13].
The most compelling argument for further consideration of WHtR has come from several studies which have confirmed its usefulness in children. In growing children, both height and waist circumference are continually changing, and it would be impossible to devise a set of boundary values for waist circumference. A boundary value of WHtR 0.5 for children is gaining support, too [46, 49–55]. To quote from a recent paper [55]: ‘The WHtR has several advantages; it is easy to calculate, does not require sex- and age-specific centiles, and, as has been previously suggested, it is a simple message, easily understood by clinicians and families, to ‘keep your waist circumference to less than half your height”.’
Prospective Studies
It is intriguing that the very first mention of WHtR came from the prospective Framingham study [56]. Prospective data from the UK Health and Lifestyle Survey [57,58] were originally used to support the use of WHtR to predict hypertension when the index was first proposed, and data from Sweden confirmed its use in the prediction of stroke. [59]. However, very recent analysis of 1,505 CVD cases from 16,332 men in the Physicians’ Health Study (mean age 61 years in 1991) and 414 cases from the 32,700 women in the Women's Health Study (mean age 61 years in 1999) have shown that WHtR demonstrated statistically the best model fit and strongest associations with CVD [60]. Prospective evidence such as these add particular support to our suggestion that WHtR is considered seriously.
Greater Risk Attached to Having Central Fat Distribution even at Normal Weight
The practical significance of this becomes apparent when the subjects are split into 4 groups according to their BMI and WHtR. The most ‘at risk’ group overall (highest SBP and DBP, TC, and non-HDL-C, and lowest HDL-C) is group 4 (overweight apples) which shows high BMI and high WHtR.
However, the most interesting comparison is between groups 2 and 3, which shows that people with high WHtR and normal BMI exhibit higher CVD risk factor levels than those who have low WHtR. This demonstrates that greater risk is attached to having central fat distribution even at normal weight and confirms previous observations in Japanese people [33].
Concern about Use of BMI Giving False Re-Assurance of Low Health Risk
This new analysis of nationally representative data from Great Britain shows that more than 1 in 3 normal weight men and 1 in 7 normal weight women may be at increased health risk on account of their central fat distribution. In the total population this equates to 17% of all men and 6% of all women who would be inadequately screened by BMI alone.
Limitations of This Study
As a preliminary investigation of the validity of using WHtR instead of BMI, we acknowledge that this study has some limitations in scope and depth of analysis. The modest sample size in the NDNS limits the power to demonstrate statistical significance when the data are stratified by sex or age group. However, when performed separately for men and women, the four-group contrast tests (ANCOVA, with adjustment for age) showed the same trends (data not shown).
No adjustment could be made for the impact of concurrent drug use, on which limited information was available. Approximately 7% of respondents were taking antihypertensives, but as their blood pressure was not significantly different from the remainder, their exclusion would not have materially affected the results. Furthermore, given that CVD medication is more likely in older people, those who are more overweight and/or have metabolic syndrome, the net effect of including all subjects is likely to be in the direction of underestimation of the true differences attributable to body weight or shape.
Further analysis is planned to explore the sensitivity and specificity of WHtR, compared with BMI in identifying subjects ‘at risk’ of CVD, but larger surveys may be better powered for this purpose. In addition, the absence of data on fasting triglycerides and glucose in the NDNS precludes an assessment of overall risk based on combined indicators.
It should be stated that no anthropometric measurement or index is a good predictor of abnormal blood lipids and blood pressure, and certainly not a surrogate for investigation of risk factors. However, WHtR may be more representative of body fat distribution than BMI and appears to be at least as efficient in identifying adults who may require follow-up.
Screening for Health Risk Should Use WHtR rather than BMI
It is vitally important that any screening scheme to help the public to minimise health risks encompasses an assessment of fat distribution (preferably using the WHtR) instead of the BMI.
These results support the suggestion from previous English [16] and Japanese [15,45,61,62] studies that an action point based on WHtR 0.5 could be a simpler and more effective tool for health promotion [45] than any tools based on BMI or waist circumference (e.g. the WHO public health ‘action point’ based on BMI [1] or the National Institute For Clinical Excellence (NICE) guidelines [63] which promote action levels based on waist circumference). Indeed, several investigators are now using a WHtR of 0.5 as a boundary value to analyse their results.
Standardisation of Terminology and Measurement
As the importance of WHtR for health screening becomes more popular, we believe it would be useful to standardise terminology. Thus, waist to stature ratio (WSR), waist/height (W/Ht), waist:height ratio, waist circumference to height ratio (WC/Ht), and waist-to-height ratio (WHTR) could all be rephrased as waist to height ratio and abbreviated to WHtR.
Standardisation of the measurement of waist circumference will become even more important, and several studies have already addressed this issue [64,65]. It is particularly important that this standardisation includes population groups such as the elderly and very obese.
Conclusion
In 2006, in a Lancet editorial [66], Franzosi asked the question: ‘Should we continue to use BMI as a CVD risk factor?’
Our answer is a firm ‘no’. WHtR is a much better, simple, non-invasive tool for screening for CVD risk. The boundary value of 0.5 translates into a simple public health message: ‘Keep your waist circumference to less than half your height’. Considering the acknowledged health costs of central fat distribution and obesity [67], this simple message has the potential for significant cost savings.
Disclosure
The authors declared no conflict of interest.
Table 1.
Definitions and boundary value used
| Measurements | Term | Definition |
|---|---|---|
| Weight and height | overweight | BMI > 25 |
| Weight and height | total obesity | BMI > 30 |
| Waist circumference | central fat distribution | waist circumference > action level 1* |
| Waist circumference | central obesity | waist circumference > action level 2* |
| Waist circumference and height | non-central fat distribution – ‘pears’ | WHtR ≤ 0.5 |
| Waist circumference and height | central fat distribution – ‘apples’ | WHtR > 0.5 |
| Waist circumference and height | central obesity | WHtR > 0.6 |
Waist circumference action level 1 = >80 cm (women) or >94 cm (men); waist circumference action level 2 = >88 cm (women) or >102 cm (men).
NICE guidelines [63].
Table 2.
Subject characteristics – anthropometry means and standard deviations (SD)
| Male, n = 806 |
Female, n = 970 |
|||
|---|---|---|---|---|
| mean | SD | mean | SD | |
| Age | 42 | 12 | 42 | 12 |
| Height, cm | 176 | 7 | 162 | 6 |
| Weight, kg | 84 | 15 | 69 | 15 |
| BMI, kg/m2 | 27.2 | 4.5 | 26.5 | 5.6 |
| Waist circumference, cm | 96 | 12 | 83 | 12 |
| WHtR | 0.55 | 0.07 | 0.51 | 0.08 |
Table 3.
Partial correlation of anthropometric indices with each other and with individual CVD risk factors (adjusted for age)
| Men, n = 566 |
Women, n = 670 |
|||||
|---|---|---|---|---|---|---|
| BMI | waist | WHtR | BMI | waist | WHtR | |
| BMI | – | 0.875 | 0.890 | – | 0.830 | 0.848 |
| Waist | 0.875 | – | 0.935 | 0.830 | – | 0.962 |
| WHtR | 0.890 | 0.935 | – | 0.848 | 0.962 | – |
| Plasma TC, mmol/l | 0.098* | 0.076 | 0.119** | 0.053 | 0.076* | 0.088* |
| Plasma non-HDL-C, mmol/l | 0.157 | 0.148 | 0.185 | 0.143 | 0.180 | 0.194 |
| Plasma HDL-C, mmol/l | –0.218 | –0.261 | –0.247 | –0.258 | –0.297 | –0.303 |
| SBP | 0.214 | 0.230 | 0.223 | 0.250 | 0.267 | 0.289 |
| DBP | 0.168 | 0.171 | 0.179 | 0.170 | 0.177 | 0.201 |
All correlations significant at p < 0.0001; except for plasma TC where p is indicated as follows:
p < 0.05
p < 0.01.
Table 4.
CVD risk factors for groups defined by BMI and WHtR
| BMI and WHtR groups (adjusted* means for CVD risk factors, n = 1,776) |
|||||
|---|---|---|---|---|---|
| non-overweight ‘pears’ group 1 | overweight ‘pears’ group 2 | non-overweight ‘apples’ group 3 | overweight ‘apples’ group 4 | ||
| Number of subjects in groups | 562 | 114 | 154 | 946 | |
| Mean age (years) prior to adjustment | 38 | 38 | 45 | 45 | |
| Men/women, % | 30/70 | 22/78 | 60/40 | 55/45 | |
| TC, mmol/l (n = 1,249) | 5.08a | 5.13a | 5.41b | 5.40b | |
| Non-HDL-C, mmol/l (n = 1,249) | 3.8a | 3.9a | 4.2b | 4.3b | |
| HDL-C, mmol/l (n = 1,248) | 1.32a | 1.24a, b | 1.20b | 1.11c | |
| SBP (n = 1,707) | 121a | 121a | 125b | 128c | |
| DBP (n = 1,707) | 68a | 68a | 71b | 72b | |
Estimated means were adjusted for age (mean age = 42 years) and sex.
For each risk factor, values sharing the same superscript are not significantly different (p > 0.05). See text for definitions of groups 1–4.
For each risk factor, values sharing the same superscript are not significantly different (p > 0.05). See text for definitions of groups 1–4.
For each risk factor, values sharing the same superscript are not significantly different (p > 0.05). See text for definitions of groups 1–4.
Table 5.
Adults in NDNS classified by BMI and WHtR to show those with central fat distribution who would be ‘missed’ by BMI screening
| WHtR ≤ 0.5 | WHtR > 0.5 | Total | |
|---|---|---|---|
| Men | |||
| Not overweight | |||
| Number | 170 | 92 | 262 |
| % | 64.9 | 35.1 | 100.0 |
| Overweight, BMI > 25 kg/m2 | |||
| Number | 25 | 519 | 544 |
| % | 4.6 | 95.4 | 100.0 |
| Total | |||
| Number | 195 | 611 | 806 |
| % | 24.2 | 75.8 | 100.0 |
| Women | |||
| Not overweight | |||
| Number | 392 | 62 | 454 |
| % | 86.3 | 13.7 | 100.0 |
| Overweight, BMI > 25 kg/m2 | |||
| Number | 89 | 427 | 516 |
| % | 17.2 | 82.8 | 100.0 |
| Total | |||
| Number | 481 | 489 | 970 |
| % | 49.6 | 50.4 | 100.0 |
Acknowledgments
The writing of this paper was supported by an educational grant from the Lipton Institute of Tea, part of Unilever PLC.
References
- 1.World Health Organisation (ed): Obesity Preventing and managing the global epidemic. Report of a WHO Consultation on Obesity, Geneva 3–5 June 1997. World Health Organ Tech Rep. 2000;894:i–xii–1–253. [PubMed] [Google Scholar]
- 2.Ashwell MA, McCall SA, Cole TJ, Dixon AK, Fat distribution and its metabolic complications: interpretations . Human Body Composition and Fat Distribution: a Report of an EC Workshop, London 10–12, 1985. In: Norgan NG, editor. Euronut Report. No 8. Wageningen: Foundation Dutch Institute for Nutrition; 1985. pp. pp 227–242. [Google Scholar]
- 3.Ashwell M. The fruit bowl approach to the treatment of obesity. BNF Nutr Bull. 1994;19:170–177. [Google Scholar]
- 4.Vague J. Le Traitment des obesities. Marseille Med. 1946;83:210–225. [Google Scholar]
- 5.Vague J. The degree of masculine differentiation of obesities: a factor determining predisposition to diabetes, atherosclerosis, gout, and uric calculous disease. Am J Clin Nutr. 1956;4:20. doi: 10.1093/ajcn/4.1.20. [DOI] [PubMed] [Google Scholar]
- 6.Ashwell MA, Cole TJ, Dixon AK. Obesity: new insight into anthropometric classification of fat distribution shown by computed tomography. Br Med J. 1985;290:1692–1694. doi: 10.1136/bmj.290.6483.1692. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Seidell JC, Bakker CJ, van der Kooy K. Imaging techniques for measuring adipose-tissue distribution - a comparison between computed tomography and 1.5-T magnetic resonance. Am J Clin Nutr. 1990;51:953–957. doi: 10.1093/ajcn/51.6.953. [DOI] [PubMed] [Google Scholar]
- 8.Björntorp P. The associations between obesity, adipose tissue distribution and disease. Acta Med Scand. 1988;723:121–134. doi: 10.1111/j.0954-6820.1987.tb05935.x. [DOI] [PubMed] [Google Scholar]
- 9.Després JP. Health consequences of visceral obesity. Ann Med. 2001;33:534–541. doi: 10.3109/07853890108995963. [DOI] [PubMed] [Google Scholar]
- 10.Després J, Moorjani S, Lupien PJ, Tremblay A, Nadeau A, Bouchard C. Regional distribution of body fat, plasma lipoproteins, and cardiovascular disease. Arteriosclerosis. 1990;10:497–511. doi: 10.1161/01.atv.10.4.497. [DOI] [PubMed] [Google Scholar]
- 11.Lean MEJ, Han TS, Morrison CE. Waist circumference as a measure for indicating need for weight management. BMJ. 1995;311:158–161. doi: 10.1136/bmj.311.6998.158. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Zhu S, Heymsfield SB, Toyoshima H, Wang Z, Pietrobelli A, Heshka S. Race-ethnicity-specific waist circumference cutoffs for identifying cardiovascular disease risk factors. Am J Clin Nutr. 2005;81:409–415. doi: 10.1093/ajcn.81.2.409. [DOI] [PubMed] [Google Scholar]
- 13.Hsieh S, Yoshinaga H. Do people with similar waist circumference share similar health risks irrespective of height? Tohoku J Exp Med. 1999;188:55–60. doi: 10.1620/tjem.188.55. [DOI] [PubMed] [Google Scholar]
- 14.Hsieh SD, Yoshinaga H. Abdominal fat distribution and coronary heart disease risk factors in men - waist/height ratio as a simple and useful predictor. Int J Obes Relat Metab Disord. 1995;19:585–589. [PubMed] [Google Scholar]
- 15.Hsieh SD, Yoshinaga H. Waist/height ratio as a simple and useful predictor of coronary heart disease risk factors in women. Intern Med. 1995;34:1147–1152. doi: 10.2169/internalmedicine.34.1147. [DOI] [PubMed] [Google Scholar]
- 16.Ashwell M, Lejeune S, McPherson K. Ratio of waist circumference to height may be better indicator of need for weight management. BMJ. 1996;312:377. doi: 10.1136/bmj.312.7027.377. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Ashwell M, Cole T, Dixon A. Ratio of waist circumference to height is strong predictor of intraabdominal fat. BMJ. 1996;313:559–560. doi: 10.1136/bmj.313.7056.559d. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Ashwell M. The Ashwell Shape Chart - a public health approach to the metabolic risks of obesity. Int J Obes. 1998;22((suppl 3)):S213. [Google Scholar]
- 19.Ashwell M, Hsieh SD. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56:303–307. doi: 10.1080/09637480500195066. [DOI] [PubMed] [Google Scholar]
- 20.Lee CM, Huxley RR, Wildman RP, Woodward M. Indices of abdominal obesity are better discriminators of cardiovascular risk factors than BMI: a meta-analysis. J Clin Epidemiol. 2008;61:646–653. doi: 10.1016/j.jclinepi.2007.08.012. [DOI] [PubMed] [Google Scholar]
- 21.Ruston D, Hoare J, Henderson L, Gregory J, Bates CJ, Prentice A, Birch M, Swan G, Farron M, The National Diet and Nutrition Survey: Adults Aged 19–64 Years . Volume 4. London: The Stationery Office; 2004. Nutritional Status (Anthropometry and Blood Analytes), Blood Pressure and Physical Activity. [Google Scholar]
- 22.Lin WY, Lee LT, Chen CY, Lo H, Hsia HH, Liu IL, Lin RS, Shau WY, Huang KC. Optimal cut-off values for obesity: using simple anthropometric indices to predict cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord. 2002;26:1232–1238. doi: 10.1038/sj.ijo.0802040. [DOI] [PubMed] [Google Scholar]
- 23.Huang KC, Lin WY, Lee LT, Chen CY, Lo H, Hsia HH, Liu IL, Shau WY, Lin RS. Four anthropometric indices and cardiovascular risk factors in Taiwan. Int J Obes Relat Metab Disord. 2002;26:1060–1068. doi: 10.1038/sj.ijo.0802047. [DOI] [PubMed] [Google Scholar]
- 24.Huang HW, Tan SQ, Fu X, Peng DX, Liu XQ, Lin RJ, Wu SH, Huang JX. Study on the correlation between hypertension and the indexes of vascular endothelial function among people living in the community (in Chinese) Zhonghua Liu Xing Bing Xue Za Zhi. 2007;28:798–801. [PubMed] [Google Scholar]
- 25.Bertsias G, Mammas I, Linardakis M, Kafatos A. Overweight and obesity in relation to cardiovascular disease risk factors among medical students in Crete, Greece. BMC Public Health. 2003;3:3. doi: 10.1186/1471-2458-3-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 26.Sargeant LA, Bennett FI, Forrester TE, Cooper RS, Wilks RJ. Predicting incident diabetes in Jamaica: the role of anthropometry. Obes Res. 2002;10:792–798. doi: 10.1038/oby.2002.107. [DOI] [PubMed] [Google Scholar]
- 27.Ho SY, Lam TH, Janus ED. Waist to stature ratio is more strongly associated with cardiovascular risk factors than other simple anthropometric indices. Ann Epidemiol. 2003;13:683–691. doi: 10.1016/s1047-2797(03)00067-x. [DOI] [PubMed] [Google Scholar]
- 28.Jeong SK, Seo MW, Kim YH, Kweon SS, Nam HS. Does waist indicate dyslipidemia better than BMI in Korean adult population? J Korean Med Sci. 2005;20:7–12. doi: 10.3346/jkms.2005.20.1.7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Sayeed MA, Mahtab H, Latif ZA, Khanam PA, Ahsan KA, Banu A, Azad Khan AK. Waist-to-height ratio is a better obesity index than body mass index and waist-to-hip ratio for predicting diabetes, hypertension and lipidemia. Bangladesh Med Res Counc Bull. 2003;29:1–10. [PubMed] [Google Scholar]
- 30.Pua YH, Ong PH. Anthropometric indices as screening tools for cardiovascular risk factors in Singaporean women. Asia Pac J Clin Nutr. 2005;14:74–79. [PubMed] [Google Scholar]
- 31.Wu AH, Yu MC, Tseng CC, Pike MC. Body size, hormone therapy and risk of breast cancer in Asian-American women. Int J Cancer. 2007;120:844–852. doi: 10.1002/ijc.22387. [DOI] [PubMed] [Google Scholar]
- 32.Hadaegh F, Zabetian A, Harati H, Azizi F. Waist/height ratio as a better predictor of type 2 diabetes compared to body mass index in Tehranian adult men - a 3.6-year prospective study. Exp Clin Endocrinol Diabetes. 2006;114:310–315. doi: 10.1055/s-2006-924123. [DOI] [PubMed] [Google Scholar]
- 33.Hsieh SD, Muto T. The superiority of waist-to-height ratio as an anthropometric index to evaluate clustering of coronary risk factors among non-obese men and women. Prev Med. 2005;40:216–220. doi: 10.1016/j.ypmed.2004.05.025. [DOI] [PubMed] [Google Scholar]
- 34.Bosy-Westphal A, Geisler C, Onur S, Korth O, Selberg O, Schrezenmeir J, Muller MJ. Value of body fat mass vs anthropometric obesity indices in the assessment of metabolic risk factors. Int J Obes (Lond) 2006;30:475–483. doi: 10.1038/sj.ijo.0803144. [DOI] [PubMed] [Google Scholar]
- 35.Schneider HJ, Glaesmer H, Klotsche J, Bohler S, Lehnert H, Zeiher AM, Marz W, Pittrow D, Stalla GK, Wittchen HU. Accuracy of anthropometric indicators of obesity to predict cardiovascular risk. J Clin Endocrinol Metab. 2007;92:589–594. doi: 10.1210/jc.2006-0254. [DOI] [PubMed] [Google Scholar]
- 36.Aekplakorn W, Kosulwat V, Suriyawongpaisal P. Obesity indices and cardiovascular risk factors in Thai adults. Int J Obes (Lond) 2006;30:1782–1790. doi: 10.1038/sj.ijo.0803346. [DOI] [PubMed] [Google Scholar]
- 37.Aekplakorn W, Pakpeankitwatana V, Lee CM, Woodward M, Barzi F, Yamwong S, Unkurapinun N, Sritara P. Abdominal obesity and coronary heart disease in Thai men. Obesity (Silver Spring) 2007;15:1036–1042. doi: 10.1038/oby.2007.604. [DOI] [PubMed] [Google Scholar]
- 38.Khan A, Haq FU, Pervez MB, Saleheen D, Frossard PM, Ishaq M, Hakeem A, Sheikh HT, Ahmad U. Anthropometric correlates of blood pressure in normotensive Pakistani subjects. Int J Cardiol. 2008;124:259–262. doi: 10.1016/j.ijcard.2006.12.040. [DOI] [PubMed] [Google Scholar]
- 39.Neville KA, Cohn RJ, Steinbeck KS, Johnston K, Walker JL. Hyperinsulinemia, impaired glucose tolerance, and diabetes mellitus in survivors of childhood cancer: prevalence and risk factors. J Clin Endocrinol Metab. 2006;91:4401–4407. doi: 10.1210/jc.2006-0128. [DOI] [PubMed] [Google Scholar]
- 40.Diaz VA, Mainous AG 3rd, Baker R, Carnemolla M, Majeed A. How does ethnicity affect the association between obesity and diabetes? Diabet Med. 2007;24:1199–1204. doi: 10.1111/j.1464-5491.2007.02244.x. [DOI] [PubMed] [Google Scholar]
- 41.Mansour AA, Al-Jazairi MI. Cut-off values for anthropometric variables that confer increased risk of type 2 diabetes mellitus and hypertension in Iraq. Arch Med Res. 2007;38:253–258. doi: 10.1016/j.arcmed.2006.09.014. [DOI] [PubMed] [Google Scholar]
- 42.Lee K, Song YM, Sung J. Which obesity indicators are better predictors of metabolic risk?: healthy twin study. Obesity (Silver Spring) 2008;16:834–840. doi: 10.1038/oby.2007.109. [DOI] [PubMed] [Google Scholar]
- 43.Pitanga FJ, Lessa I. Waist-to-height ratio as a coronary risk predictor among adults (in Portuguese) Rev Assoc Med Bras. 2006;52:157–161. doi: 10.1590/s0104-42302006000300016. [DOI] [PubMed] [Google Scholar]
- 44.Joshi PP. Is waist to height ratio a better and more practical measure of obesity to assess cardiovascular or diabetes risk in indians? J Assoc Physicians India. 2008;56:202–3. author reply 203–204. [PubMed] [Google Scholar]
- 45.Ashwell M, Hsieh SD. Six reasons why the waist to height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56:303–307. doi: 10.1080/09637480500195066. [DOI] [PubMed] [Google Scholar]
- 46.McCarthy H, Ashwell M. Waist:height ratios in British children aged 5–16 years: a suggestion for a simple public health message - 'keep your waist circumference to less than half your height'. Proc Nutr Soc. 2002;61:116A. [Google Scholar]
- 47.Kato M, Takahashi Y, Inoue M, Tsugane S, Kadowaki T, Noda M. Comparisons between anthropometric indices for predicting the metabolic syndrome in Japanese. Asia Pac J Clin Nutr. 2008;17:223–228. [PubMed] [Google Scholar]
- 48.Ashwell M, Hsieh S. Six reasons why the waist-to-height ratio is a rapid and effective global indicator for health risks of obesity and how its use could simplify the international public health message on obesity. Int J Food Sci Nutr. 2005;56:303–307. doi: 10.1080/09637480500195066. [DOI] [PubMed] [Google Scholar]
- 49.Weili Y, He B, Yao H, Dai J, Cui J, Ge D, Zheng Y, Li L, Guo Y, Xiao K, Fu X, Ma D. Waist-to-height ratio is an accurate and easier index for evaluating obesity in children and adolescents. Obesity (Silver Spring) 2007;15:748–752. doi: 10.1038/oby.2007.601. [DOI] [PubMed] [Google Scholar]
- 50.Hara M, Saitou E, Iwata F, Okada T, Harada K. Waist-to-height ratio is the best predictor of cardiovascular disease risk factors in Japanese schoolchildren. J Atheroscler Thromb. 2002;9:127–132. doi: 10.5551/jat.9.127. [DOI] [PubMed] [Google Scholar]
- 51.Maffeis C, Banzato C, Talamini G. Waist-to-height ratio, a useful index to identify high metabolic risk in overweight children. J Pediatr. 2008;152:207–213. doi: 10.1016/j.jpeds.2007.09.021. [DOI] [PubMed] [Google Scholar]
- 52.McCarthy HD, Ashwell M. A study of central fatness using waist-to-height ratios in UK children and adolescents over two decades supports the simple message - 'keep your waist circumference to less than half your height'. Int J Obes (Lond) 2006;30:988–992. doi: 10.1038/sj.ijo.0803226. [DOI] [PubMed] [Google Scholar]
- 53.Savva SC, Tornaritis M, Savva ME, Kourides Y, Panagi A, Silikiotou N, Georgiou C, Kafatos A. Waist circumference and waist-to-height ratio are better predictors of cardiovascular disease risk factors in children than body mass index. Int J Obes Relat Metab Disord. 2000;24:1453–1458. doi: 10.1038/sj.ijo.0801401. [DOI] [PubMed] [Google Scholar]
- 54.Kahn HS, Imperatore G, Cheng YJ. A population-based comparison of BMI percentiles and waist-to-height ratio for identifying cardiovascular risk in youth. J Pediatr. 2005;146:482–488. doi: 10.1016/j.jpeds.2004.12.028. [DOI] [PubMed] [Google Scholar]
- 55.Garnett SP, Baur LA, Cowell CT. Waist-to-height ratio: a simple option for determining excess central adiposity in young people. Int J Obes (Lond) 2008;32:1028–1030. doi: 10.1038/ijo.2008.51. [DOI] [PubMed] [Google Scholar]
- 56.Kannel WB, Cupples LA, Ramaswami R, Stokes J, Kreger BE, Higgins M. Regional obesity and risk of cardiovascular disease: the Framingham study. J Clin Epidemiol. 1991;44:183–190. doi: 10.1016/0895-4356(91)90265-b. [DOI] [PubMed] [Google Scholar]
- 57.Cox B, Whichelow M. Ratio of waist circumference to height is better predictor of death than body mass index. BMJ. 1996;313:1487. doi: 10.1136/bmj.313.7070.1487. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 58.Cox B, Whichelow M, Prevost A. The development of cardiovascular disease in relation to anthropometric indices and hypertension in British adults. Int J Obes Relat Metab Disord. 1998;22:966–973. doi: 10.1038/sj.ijo.0800705. [DOI] [PubMed] [Google Scholar]
- 59.Lu M, Ye W, Adami HO, Weiderpass E. Prospective study of body size and risk for stroke amongst women below age 60. J Intern Med. 2006;260:442–450. doi: 10.1111/j.1365-2796.2006.01706.x. [DOI] [PubMed] [Google Scholar]
- 60.Gelber RP, Gaziano JM, Orav EJ, Manson JE, Buring JE, Kurth T. Measures of obesity and cardiovascular risk among men and women. J Am Coll Cardiol. 2008;52:605–615. doi: 10.1016/j.jacc.2008.03.066. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 61.Hsieh SD, Yoshinaga H, Muto T, Sakurai Y, Kosaka K. Health risks among Japanese men with moderate body mass index. Int J Obes Relat Metab Disord. 2000;24:358–362. doi: 10.1038/sj.ijo.0801157. [DOI] [PubMed] [Google Scholar]
- 62.Hsieh SD, Yoshinaga H, Muto T. Waist-to-height ratio, a simple and practical index for assessing central fat distribution and metabolic risk in Japanese men and women. Int J Obes Relat Metab Disord. 2003;27:610–616. doi: 10.1038/sj.ijo.0802259. [DOI] [PubMed] [Google Scholar]
- 63.National Institute for Health and Clinical Excellence (ed): Obesity: the prevention, identification, assessment and management of overweight and obesity in adults and children National Institute for Health and Clinical Excellence (NICE) Clinical Guideline. 2006;43:84. www.nice.org.uk/CG043. [PubMed] [Google Scholar]
- 64.Groeneveld IF, Solomons NW, Doak CM. Determination of central body fat by measuring natural waist and umbilical abdominal circumference in Guatemalan schoolchildren. Int J Pediatr Obes. 2007;2:114–121. doi: 10.1080/17477160601127426. [DOI] [PubMed] [Google Scholar]
- 65.Kagawa M, Byrne NM, Hills AP. Comparison of body fat estimation using waist:height ratio using different 'waist' measurements in Australian adults. Br J Nutr. 2008:1–7. doi: 10.1017/S0007114508966095. [DOI] [PubMed] [Google Scholar]
- 66.Franzosi MG. Should we continue to use BMI as a cardiovascular risk factor? Lancet. 2006;368:624–625. doi: 10.1016/S0140-6736(06)69222-2. [DOI] [PubMed] [Google Scholar]
- 67.HØjgaard B, Olsen KR, SØgaard J, SØrensen TIA, Gyrd-Hansen D. Economic costs of abdominal obesity. Obes Facts. 2008;1:146–154. doi: 10.1159/000137822. [DOI] [PMC free article] [PubMed] [Google Scholar]