Q J Med 2003; 96: 441-447
© 2003 Association of Physicians
Waist circumference, waist-to-hip ratio and body mass index as predictors of adipose tissue compartments in men
From the Lipoprotein Research and Biomathematics Units, School of Medicine and Pharmacology, University of Western Australia and the Western Australian Institute for Medical Research, Perth, Australia
Received 26 November 2002 and in revised form 28 March 2003
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Background: The accumulation of fat in visceral and posterior subcutaneous adipose tissue compartments is highly correlated with the metabolic abnormalities that contribute to increased risk of diabetes mellitus and cardiovascular disease.
Aim: To determine which of waist circumference (WC), waist-to-hip ratio (WHR) and body mass index (BMI) was the best predictor of intraperitoneal and posterior subcutaneous abdominal adipose tissue mass in men.
Methods: We studied 59 free-living men with a wide range of BMI. WC, WHR and BMI were determined using standard methods. Intraperitoneal, retroperitoneal, anterior subcutaneous and posterior subcutaneous abdominal adipose tissue masses (IPATM, RPATM, ASAATM and PSAATM, respectively) were quantified using magnetic resonance imaging.
Results: In univariate regression analysis, WC, WHR and BMI were all significantly and positively correlated (all p < 0.05) with IPATM, RPATM, ASAATM and PSAATM. To assess the relative strength of these associations, we used non-nested regression models. There was no significant difference between WC and WHR in predicting IPATM and RPATM; WC was a stronger predictor of ASAATM (p < 0.001) and PSAATM (p < 0.001) than WHR; WC was also a stronger predictor of IPATM (p = 0.042) and RPATM (p = 0.045) than BMI, but the relative strengths of WC and BMI in predicting ASSATM and PSAATM did not different significantly (p > 0.05); there was no significant difference between BMI and WHR in predicting IPATM and RPATM (p>0.05), but BMI was a stronger predictor of ASAATM (p = 0.036) and PSAATM (p < 0.001) than WHR.
Discussion: In men WC is the anthropometric index that most uniformly predicts the distribution of adipose tissue among several fat compartments in the abdominal region, there apparently being little value in measuring WHR or BMI.
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Introduction |
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Obesity is a rapidly growing health problem in both developed and developing countries.1 Visceral obesity, the most clinical important topographical form, is typically seen in overweight and obese men. It is closely linked with insulin resistance, hypertension and dyslipidaemia, and is causally related to increased risk of type 2 diabetes and cardiovascular disease.2,3 Difference in the regional accumulation of abdominal fat can account specifically for variations in the risk of diabetes and CVD among who are overweight or obese.4–6 This may relate to heterogeneity in the metabolic properties and anatomical location of adipocytes, and their consequences on insulin resistance and dyslipidaemia.7 Besides visceral fat, recent data also suggest that accumulation of subcutaneous fat in the posterior abdominal region may be significantly predictive of insulin resistance.8–11
Accurate quantification of body fat compartments requires imaging techniques such as magnetic resonance imaging (MRI) and computed tomography (CT).12,13 However, these techniques are relatively expensive and complex, and are impractical for routine clinical settings or large-scale studies. Simple clinical anthropometric measurements, such as waist circumference (WC), waist-to-hip ratio (WHR) and body mass index (BMI), may be conveniently used to assess regional adiposity,13 and some of these surrogate markers correlate reasonably well with laboratory-based measures of adiposity using MRI or CT.6,14–16 However, the relative abilities of WC, WHR and BMI to predict fat accumulation in intraperitoneal, retroperitoneal, anterior and posterior (deep) subcutaneous abdominal adipose tissue compartments still remain unclear.
The aim of the present study was to investigate the relationship between anthropometric measures of obesity (WC, WHR and BMI) and different abdominal adipose tissue compartments (measured with MRI) in free-living men with a wide range of BMI. We wished to determine whether these anthropometric measures could be used as surrogate markers to estimate adipose tissue mass in these depots, and specifically, to ascertain which clinical marker was the best predictor of intraperitoneal and posterior subcutaneous abdominal adipose tissue mass.
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Subjects
We studied 59 non-smoking Caucasian men selected from the community with a wide range of BMI. Subjects with a history of familial dyslipidaemia, medical disorders or drugs known to affect lipid metabolism were excluded. All subjects were consuming ad libitum, weight-maintenance diets and had been advised by a qualified dietitian to continue an isocaloric intake for 4 weeks. They were studied at the end of this period if their body weight, measured serially, varied by < 3%. Volunteers gave written consent and the study was approved by the ethics committee of the Royal Perth Hospital.
Protocols
Weight was measured in light clothing without shoes after emptying bladder. Height was measured as the distance from the top of the head to the bottom of the feet (no shoes) using a fixed stadiometer. BMI was calculated as the weight (kg) divided by the square of the height (m). Waist circumference (cm) was taken with a tape measure as the point midway between the costal margin and iliac crest in the mid-axillary line, with the subject standing and breathing normally. Hip circumference (cm) was measured at the widest point around the greater trochanter. The waist-to-hip ratio was calculated as the waist measurement divided by the hip measurement. All measurements in the metabolic ward were carried out after a 14-h fast in a temperature-controlled room. Arterial blood pressure was recorded after 3 min in the supine position using a Dinamap 1846 SX/P monitor (Critikon). Body composition was estimated at rest in the supine position after emptying bladder using a Holtain Body Composition Analyser (Holtain) from which total adipose tissue mass (TATM) and fat free mass (FFM) were derived; FFM was calculated using the formula: FFM = (0.85xH2/Z)+3.04, where H is the height (cm) of the subject and Z is the impedance.17 For this measure, subjects were asked to fast overnight and to refrain from alcoholic beverages for 24 h; they were then studied in the morning for 15 min after emptying their bladder and in a temperature-controlled room. The technical error for FFM was < 3%, calculated from three repeated measurements by the same operator. They were studied in a semi-recumbent position and allowed to drink only water. Venous blood was collected for measurement of biochemical analytes. Fasting plasma cholesterol, triglyceride, high-density-lipoprotein (HDL)–cholesterol and glucose were determined by standard enzymic methods.
MRI
MRI of eight transaxial segments (field of view, 40–48 cm; 10 mm thickness) at intervertebral disc levels from T11 to the S1 used a 1.0T Picker MR scanner (Picker International), and a T1-weighted fast-spin echo sequence with a high fat:water signal ratio.18 Subcutaneous abdominal adipose tissue (SAAT), intraperitoneal adipose tissue (IPAT) and retroperitoneal adipose tissue (RPAT) areas were calculated by summing the relevant adipose tissue pixel units with purpose-designed software. An in-house program written in C++ was used specifically for the estimation of regional adipose tissue compartments. Because in the T1-weighted MRI images the signal intensity of adipose tissue was higher than that of non-adipose tissue, we used a simple threshold method to separate adipose from non-adipose tissue. A threshold value was defined for each image by analysing the intensity histogram and choosing the value for the lowest point between two intensity peaks (i.e. one corresponding to adipose tissue and the other to non-adipose tissue). The anatomical segmentations were defined manually using a computer mouse. The landmark used for separating IPAT and RPAT in the MRI images was the posterior peritoneum, which overlies the pancreas and kidneys. In our experience, this landmark can be identified confidently down to the level of the pelvis. Fat anterior to the posterior peritoneum and anterior abdominal wall was defined as IPAT and fat posterior to be the posterior peritoneum was defined as RPAT. Corresponding adipose tissue volumes were derived by the method of Ross et al., from which SAAT mass (M), IPATM and RPATM were calculated by multiplying the density of adipose tissue (0.9196 kg/l).18 The imaging protocol has a technical error of < 10% and is highly correlated (R2 = 99%) with measurements obtained from imaging of the abdominal region using contiguous transaxial slices; this was confirmed using four subjects included in the present study.
Statistical analyses
All analyses used SPSS 10.1 (SPSS). The data were expressed as arithmetric means±SD or geometric means (95%CI). Associations were examined by Pearson univariate after logarithmic transformation of skewed variables where appropriate. Univariate regression models with anthropometric variables as predictors of the measurements of fat mass were used to avoid the problem of multicolinearity with highly correlated variables in multivariable models. The set of non-nested models were then compared using the t-distribution, as described by Andel,19 to determine the relative strength of the correlations between the anthropometric and MRI variables. Statistical significance was defined at the 5% level.
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Table 1 shows the anthropometric and biochemical characteristics of the 59 men. On average the subjects were middle-aged, normotensive and obese, with a wide range of BMI. Eleven were overweight and 40 were obese (BMI
30 kg/m2). The mean proportions of total adipose tissue as IPATM, RPATM, ASAATM and PSAATM were 10.0%, 1.3%, 4.6% and 8.0%, respectively. Compared with the non-obese group (n = 18), the obese group (n = 41) were on average dyslipidaemic and insulin-resistant (data not shown). Seven of the subjects had impaired fasting glucose (plasma glucose concentration 6.1–6.9 mmol/l) and two had diabetes mellitus (fasting glucose concentration 7.1 mmol/l).
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Table 2 shows the Pearson univariate correlation coefficients between the anthropometric measures of obesity and all adipose tissue compartments, with the corresponding scattergrams for IPATM and PSAATM in Figure 1. WC, WHR and BMI were all significantly and positively correlated (p < 0.05) with the mass of all adipose tissue compartments, the highest correlation being seen with PSAATM (Table 2). The associations between the anthropometric measures of obesity and MRI variables remained significance after adjusting for age (data not shown). Table 3 shows the comparison of the relative strength of these anthropometric measures in predicting adipose tissue masses. The t-values in Table 3 refer to the comparisons of the non-nested models and a p value < 0.05 indicates significant differences in the strength of the associations (Pearson’s correlation coefficients) shown in Table 2. Hence, there was no significant difference between WC and WHR in predicting IPATM and RPATM. However, WC was a stronger predictor of ASAATM (p < 0.001) and PSAATM (p < 0.001) than WHR. WC was also a stronger predictor of IPATM (p = 0.042) and RPATM (p = 0.045) than BMI, but the relative strength of WC and BMI in predicting ASSATM and PSAATM did not different significantly (p > 0.05). There was no significant difference between BMI and WHR in predicting IPATM and RPATM (p > 0.05). However, BMI was a stronger predictor of ASAATM (p = 0.036) and PSAATM (p < 0.001) than WHR.
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Discussion |
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This correlational analysis suggests that in men who are on average overweight-to-obese, waist circumference is a better predictor of the distribution of adipose tissue among several fat compartments in the abdominal region than are waist-to-hip ratio and body mass index. Specifically, waist circumference predicted intraperitoneal adipose tissue mass better than body mass index, and predicted posterior subcutaneous adipose tissue mass better than waist-to-hip ratio.
Several studies have examined the association of conventional anthropometric measures with regional abdominal adipose tissues in obesity.20–25 Using MRI, Kamel et al. found that in 22 obese women, WC and WHR were equally correlated with total intra-abdominal fat. These associations with WC or WHR were not, however, found in 18 obese men.20 Owens et al. reported that WHR was the strong predictor of total intra-abdominal fat in 76 healthy obese children.21 Using computed tomography, Ferland et al. reported that in 51 obese women, WHR was a good predictor of intra-abdominal adipose tissue.22 These studies did not examine IPATM or subcutaneous adipose tissue mass. Using MRI, Ross et al. found a strong association of WC with total subcutaneous adipose tissue in 15 obese women.23 However, they did not dissociate ASAATM from PSAATM. In another study by Ross et al., both BMI and WC predicted the distribution of abdominal subcutaneous and visceral fat in Caucasian men and women.24 However, they did not examine the association of WHR with these regional fat masses. The findings among these studies probably varied owing to difference in gender, sample sizes and imaging protocols. The present report extends the aforementioned observations by subdividing abdominal ATM into IPATM, RPATM, ASAATM and PSAATM, and explores the relationship between these anthropometric measures and adipose tissue masses in men with a wide range of BMI.
Many studies have demonstrated the independent contributions of regional adiposity to metabolic abnormalities of obesity.8–11,26,27 Accumulation of fat in the intraperitoneal or subcutaneous abdominal regions have been shown to be strongly linked with insulin resistance and dyslipidaemia. Although accurate quantification of body fat compartments with imaging techniques can predict metabolic abnormalities, it is impractical for routine clinical practice or larger scale studies. Our results suggest that measurement of WC could be used as a better overall surrogate index of IPATM and PSAATM than WHR or BMI.
BMI has been conventionally used to define and classify overweight and obesity. However, BMI does not account for the wide variation in body fat distribution, and has considerable limitations in predicting intra-abdominal fat accumulation.13 Consistent with this, we found that BMI had a weaker association with IPATM and RPATM than WC or WHR (Table 2). The WHR is also a practical index of regional adipose tissue distribution and has been widely used to investigate the relations between regional adipose tissue distribution and metabolic profile.13 As seen in Table 2, WHR was reasonably well correlated with the mass of all adipose tissue. However, the WHR value does not account for large variations in the level of total fat and abdominal visceral adipose tissues.28 Moreover, it requires two measurements, waist and hip circumference, which may contribute to summative measurement error. On the other hand, waist circumference is a convenient and simple index that determines the accumulation of abdominal adipose tissue.13 Accordingly, WC has been shown to be a preferred index over the WHR to estimate the amount of abdominal adipose tissues,22,28 consistent with the present findings.
Since the univariate approach used in the present study to examine association between variables produced a set of non-nested models, simple comparison of values of R2 was not valid. To avoid the problems of multicolinearity with highly correlated anthropometric variables in multivariate models, we used non-nested models to compare the relative strength of the anthropometric indices in associating with regional adipose tissue masses.19 These non-nested models were compared using t-tests that accounted for residual sums of squares for the model and for correlation between the independent variables.
Our study does have limitations. The relatively small sample size of the present study might have been underpowered to demonstrate the true strength of the associations between the anthropometric and MRI variables. Only about 60% of the regional adipose tissue mass could be accounted for by the anthropometric indices employed in our study, reflecting the inherent limitations of these indices as predictor variables. It might therefore have been useful to employ other simple techniques to assess fat mass, such as skinfold thickness and dual energy absorptiometry. However, these techniques do not also allow detailed assessment of the all individual adipose tissue compartments under investigation.
In conclusion, our results confirm the importance of the waist circumference as a surrogate marker of the distribution of adiposity in the abdominal region in men. Accordingly, we propose that waist circumference is probably the most convenient and reliable clinical measure of abdominal fat compartments. Our study does not suggest any clinical value in measuring the waist-to-hip ratio or body mass index in this group of subjects. Whether our conclusions also apply to women, younger age groups and other racial groups with different body habitus, merits further investigation.
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Acknowledgments |
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This study was supported by research grants from the Raine Medical Research Foundation, Royal Perth Hospital Medical Research Foundation, National Heart Foundation of Australia and the National Health and Medical Research Council. PHRB is a Career Development Fellow of the National Heart Foundation. DC was in receipts of a research scholarship from NHMRC Clinical Centres of Excellence at the Royal Perth Hospital. We are grateful for the assistance of Drs F. Riches, S. Song and J. Hua in data collection and handling.
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Footnotes |
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Address correspondence to A/Professor Gerald F Watts, School of Medicine and Pharmacology, University of Western Australia, Royal Perth Hospital, GPO Box X2213, Perth, WA 6847, Australia. e-mail: gfwatts{at}cyllene.uwa.edu.au
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