Measurement of visceral adipose tissue by DXA ...

International Journal of Obesity (2000) 24, 263±270 ß 2000 Macmillan Publishers Ltd All rights reserved 0307±0565/00 $15.00 www.nature.com/ijo

Measurement of visceral adipose tissue by DXA combined with anthropometry in obese humans E Bertin,1*, C Marcus,1, J-C Ruiz,2, J-P Eschard1 and M Leutenegger1 1

Departments of Diabetology, Nutrition, and Metabolism, of Radiology, and of Internal Medicine, University Hospital, 51092 Reims Cedex, France, and 2Department of Rheumatology, Cochin University Hospital, 75014 Paris, France

OBJECTIVE: To get accurate measurements of visceral adipose tissue (VAT) using dual energy X-ray absorptiometry (DXA). DESIGN: DXA and anthropometric data and their combinations were compared to the VAT area calculated from a computed tomography (CT) single scan. SUBJECTS: 71 overweight subjects (44 women, 27 men), age: 16 ± 70 y, BMI: 27 ± 52 kg=m2. MEASUREMENTS: Total body and segmental tissue composition, and new parameters obtained from DXA, in addition to waist and hip cicumferences and abdominal sagittal diameter measurements. RESULTS: The ratio measured at the umbilical level (sagittal diameter ÿ subcutaneous fat width) (transverse internal 0.94 for women and 0.88 for men). It gave the most predictive diameter)=(height) was closely related to VAT (r equation for VAT: y 79.6x (s.e. 3.9) ÿ 149 cm2 for the whole population (r20.86, P < 0.0001, root mean square 38.2 cm2. An independent relationship between lean mass or its index (r 0.52 and 0.72, P < 0.001) and VAT was error also found in women. CONCLUSION: This study demonstrates the potential usefulness of DXA to supply accurate measurements of VAT in addition to total body composition determination in obese subjects. International Journal of Obesity (2000) 24, 263±270 Keywords: dual-energy X-ray absorptiometry; computed tomography; body fat; fat distribution; visceral fat

Glossary AFM: CT: DXA: TFM: FM: FMI: LM: LMI: SAT: SD: SFW: TED: TID: VAT: WC: WHR:

Abdominal fat mass Computed tomography Dual-energy X-ray absorptiometry Thigh fat mass Fat Mass Fat mass index Lean mass Lean mass index Subcutaneous adipose tissue Sagittal diameter Subcutaneous fat width (at the umbilical level) Transverse external diameter Transverse internal diameter Visceral adipose tissue Waist circumference Waist-to-hip circumference ratio

Introduction Prospective studies have demonstrated in both sexes that upper body or abdominal obesity estimated by *Correspondence: Dr. Eric Bertin, CHR de Reims, 140 rue des Capucins, 51100 Reims, France. E-mail: [email protected] Received 21 July 1998; revised 12 July 1999; accepted 30 September 1999

WHR was more closely related to metabolic disturbances and cardiovascular diseases than obesity per se. 1±3 Thereafter, CT and magnetic resonance imaging showed that intra-abdominal and especially visceral adipose tissue accumulation was implicated in these diseases, as an independent correlate of alterations in plasma glucose, insulin, lipid and lipoprotein concentrations.4 ± 7 However, these technologies are not always available for clinical use and have their own limits, such as imprecision of magnetic resonance imaging due to a long acquisition time, or high ionizing radiations of CT.8 Therefore, numerous authors have searched for other simple anthropometric measurements, such as abdominal diameters, and proposed several equations to get a better health risk prediction than by WHR.9 ± 13 Their ability to assess the amount of deep abdominal adipose tissue seemed, however, limited,9 and not useful in different populations because of the interdependence within the selected parameters (multicolinearity). In fact, assessment of amount of visceral fat by these means could have been improved by subcutaneous fat layer exclusion from abdominal diameters.14 Unfortunately, skinfold thickness measurements are inaccurate in obese subjects,15,16 and therefore new technical developments are needed for abdominal subcutaneous fat layer assessment. Interestingly, visceral adipose tissue area as determined by a single CT scan between L3 and L5 level was shown to be constant and greatly correlated to abdominal visceral fat mass obtained with a multiscan technique.11,17,18

Abdominal visceral fat assessment by DXA E Bertin et al

264

Thus CT use was usually restricted to a single scan for deep abdominal adipose tissue assessment from absolute visceral fat area.19 Recently, DXA was shown to give reliable measurements of abdominal fat mass vs multiscan CT.20 This widespread, non-invasive, and accurate technique allowed the determination of total as well as regional body composition especially at the abdominal level,21 ± 25 but was unable to calculate the intra-abdominal adipose tissue content independently from the subcutaneous one. In spite of this limitation, DXA is now largely used in nutrition and metabolic studies. The aim of this study was therefore to identify and validate news DXA parameters (alone or combined with anthropometry) able to accurately predict the amount of visceral adipose tissue (VAT) both in men and in women.

Methods Subjects

Seventy-one patients, 44 women and 27 men, aged 16 ± 70 y (respective means: 42 14 and 51 12 y) with BMI > 27 kg=m2 were recruited from the Department of Nutrition, Diabetology, and Metabolism in our hospital. Subjects with umbilical hernia, abdominal disembowelling, oedema, cardiac insuf®ciency, hepatic or renal impairment, and dehydration (clinical or biological) were excluded. All subjects provided written informed consent in accord with the Helsinki Declaration of 1975 as revised in 1983. DXA

DXA measurements were performed between 16.00 and 19.00 h with a total body scanner (QDR 2000=W HOLOGIC, Waltham, MA) using a single rectilinear scanning mode, in subjects wearing only a cotton brief with empty bladder. This equipment uses a switched-pulsed stable dual-energy radiation with two peak kilovoltages of 70 and 140 keV. Radiation dose is 4.6 mSv. The thin X-ray beam coming from an upper arm at 86.5 cm beyond the table top, passes through a calibration disk, and scans the whole body longitudinally from head to toe. A detector passing simultaneously under the patient feeds a computer with the absorption data recorded as pixel by pixel. For each pixel corresponding to a surface of 1.2 cm length1.9 mm width, the DXA device measures the attenuation of the two energy X-ray beams crossing the tissue. The mineral bone mass is determined from the skeleton-containing pixels, using an internal wheel. Simultaneously with the measurement of the skeleton, the percentage of fat is determined in the other pixels from the ratio of attenuation of the lower energy to that of the higher energy of the beam. The attenuation is in reference to internal standards of variable thickness simulating various International Journal of Obesity

fat mass percentages. Their attenuation is in reference to internal standards of variable thickness simulating various fat mass percentages. Their attenuation coef®cient is standardized with those of a stearate (standard of acrylic resin) and of a water ± stearate mixture (standard of acrylic resin with aluminum overlapping). This is calculated for all nonskeleton pixels scanned and extrapolated over the skeleton-containing pixels where beam attenuation is too high.26 The sum of all pixel values gives whole body composition in terms of FM, soft-tissue fat free mass, and mineral bone mass. Various regions of interest can also be considered, depending on the pixels selected. Data were analysed using a software version 5.67. Total body FM and soft-tissue fat free mass as lean mass (LM) were calculated, using the standard option of software. Their indices were expressed as: FMI ( FM=height2 (kg=m2)) and LMI ( LM=height2). In standard software readings, absolute and relative values of fat mass for arm=leg=trunk regions could be measured using standard cut lines.27 Furthermore, we analysed fat mass distribution at a higher precision level, by de®ning manually many other regions from skeletal landmarks. We thus selected the more discriminating ones in term of fat mass distribution: the abdominal region delineated by an upper horizontal border located at half of the distance between acromions and external end of iliac crests, a lower border determined by the external end of iliac crests and laterally to any trunk soft tissue; the thigh region between the upper part of the greater trochanters and the lower horizontal border de®ned at a distance equal to the height of the abdominal region and laterally to any leg soft tissue (Figure 1). The absolute fat mass of abdominal and thigh regions were therefore easily calculated. Using the protocol version of the software, we also de®ned two abdominal transverse diameters on the DXA scans: the transverse external diameter (TED) corresponding to the abdominal width, measured three pixels (3.6 cm) above the upper end of the iliac crests (umbilical level), and the transverse internal diameter (TID) which was the lean core of the abdomen stripped of the subcutaneous adipose tissue at the same level than TED. TID corresponded therefore to TED without the subcutaneous fat width (SFW) present on each side of the abdomen. SFW was equal to half of the difference between these two diameters. The limits of TED and TID were de®ned on the DXA scan image of each subject at a standardized level of brightness and contrast found to supply highly preproducible results independently of sex, BMI, and body FM distribution. The levels of brightness and contrast chosen were respectively 36 and 24. In these conditions of analysis, the reproducibility of TID and TED measurements was tested in the whole population by comparing the results obtained from two operators blinded to the results of the other one. The


265

CT scan

Computed tomography was performed on a Siemens Somatom Plus 4 System (Erlangen, Germany) using the procedure described by SjoÈstroÈm et al.28 The subjects were examined in the supine position with both arms over their chest. A single scan of 10 mm thickness was performed at the fourth lumbar vertebrae (L4) level. Abdominal adipose tissue analysis was performed with the software program included in the above equipment. The total adipose tissue area was measured by delineating manually the outer abdominal wall and then computing the adipose tissue surface using an attenuation range of ÿ190 to ÿ30 Houns®eld units. These limits were found to be the more appropriate from a number of in-vivo experiments and from a review of the research literature.17 The visceral adipose tissue area was obtained by circumscribing it and with analysis by the computer which would de®ne if the attenuation condition of adipose tissue is within the limits ÿ190 to ÿ30 HU. The abdominal subcutaneous adipose tissue area (SAT) was calculated by subtracting the abdominal visceral adipose tissue area from that of the total adipose tissue.

Statistical analysis

Figure 1 Manually determined regions and diameters. R1, abdominal; R2, thigh; TID, transverse internal diameter.

coef®cients of variation (CV) for the TED and TID measurements were respectively 0.8% and 0.9% (maximal difference in TID measurement: 0.76 cm). All of the DXA data further reported were collected by one investigator. Anthropometry

Subjects' body weight in underclothes was measured to the nearest 100 g with a precision scale (type DR, HerveÂ-Pesage, France) and height measurement to the nearest 5 mm with a wall-mounted stadiometer. Body mass index (BMI) was calculated as weight divided by height squared (kg=m2). Waist circumference (WC) was measured midway between the costal arch and the iliac crest as palpated laterally with a ¯exible plastic tape measure in resting end-tidal position. Hip circumference was measured as the maximal circumference over the buttocks at the level of the trochanters. The sagittal diameter (SD) was determined in the supine position at the umbilical level. All measurements were collected by the same investigator just before DXA scanning. The CVs for waist circumference, hip circumference, and sagittal diameter were lower than 1.2%

Data are presented as means s.d. The Mann ± Whitney test was performed to analyse differences between men and women. Pearson product ± moment correlation coef®cients were used to analyse the relationships of DXA data alone or combined between themselves or with anthropometric measurements, to CT scan data. Then, the predictor variables of VAT were selected by using a backward stepwise multiple regression analysis (P value for F-to enter < 0.05) combined with a forward selection regression procedure (aimed to limit the number of potential predictor variables and thus formulate a one-predictor variable equation). Variables with the best independent relationships to VAT, while providing disappearance of relationships between age and isolated anthropometric measurements (and BMI) to VAT, were preferentially selected as predictor variables to reduce the risk of multicollinearity. Accuracy analysis was performed using CT data as the independent variable and DXA=anthropometric data as the dependent variables in a linear regression model. Residual variance was obtained from the coef®cient of determination, r2. The same procedure was used for gravimetric weight and DXA weight. The approach proposed by Bland and Altman,29 as the root mean square error (RMSE) and its CV,30 were used to calculate the degree of agreement between the two techniques and the precision of predictive equations. All statistics were done with a computer program software (StatviewTM SE, V1.03; Abacus Concepts, Berkeley, CA). International Journal of Obesity


266

Results As shown in Table 1 for anthropometric and CT scan measurements, and in Table 2 for DXA data, there were large signi®cant differences for all variables between men and women, except for WC, and SD. AFM and especially thigh FM were lower in men than in women. Compared to women, men showed a more pronounced abdominal distribution of FM (expressed by AFM=thigh FM and AFM=total FM), with much more intra-abdominal fatness (higher VAT and lower SAT area in spite of a lower total abdominal adipose tissue area). Likewise, TED and SFW were lower, while TID was higher in men. Body composition assessment by DXA in this very obese population (20% of women with BMI > 40 kg=m2) demonstrated a strictly linear relationship between measured body weight and reconstituted weight from the sum of all DXA pixels (r 2 0.999, RMSE 0.068 kg, P < 0.0001). The mean relative difference between the two measures was equal to 0.5% in favour of gravimetry and was not correlated with body weight. Moreover, BMI and FM were linearly related in each gender group (r 0.90 for women and 0.81 for men, P < 0.0001). Thus the technique of measurement is valid up to extreme weights, with the only limit being the transverse diameter of the abdomen ( < 65 cm imposed by the width of the beam arm amplitude). TID was accurately assessed by DXA when compared to the same measurement obtained from the image generated by the CT scan, despite some unavoidable difference in patient positioning. Thus, the relative difference between DXA and CT (as a reference) values for TID was 5.7 3.8% (mean absolute difference in DXA vs CT value:ÿ1.9 cm). The degree of agreement between the two techniques for TID measurement did not depend on the CT TID value or any other body composition parameter. The same conclusion was found when analysing SD obtained by anthropometry and by the CT scan image examination (relative difference: 3.1 3.3% and absolute difference: ÿ0.88 cm).

There was a positive relationship between VAT and BMI in each gender, essentially dependent on the proportion of lean mass and thus LMI, as shown in Table 3. The correlation between VAT and LMI was particularly high in women (r 0.72, P < 0.0001), and was persistent by multiple regression analysis when adjusting for FMI. Among anthropometric parameters classically used for assessing fat mass distribution (WC, WHR, SD), SD showed the best correlation Table 2 Dual energy X-ray absorptiometry measurements Women (n 44)

Lean Mass (kg) Fat Mass (kg) LMI (kg=m2) FMI (kg=m2) AFM (kg) TFM (kg) AFM=TFM AFM=Fat Mass TED (cm) TID (cm) SFW (cm)

Men (n 27)

Mean

s.d.

Mean

s.d.

49.8 42.9 19.2 16.6 10.4 8.7 1.24 0.24 40.4 29.7 5.4

7.2 11.4 2.4 4.6 3.7 2.6 0.35 0.04 4.2 3.4 1.7

72.1 28.0 23.3 9.1 7.7 4.8 1.61 0.27 38.0 33.8 2.1

10.2 8.2 2.5 2.8 2.5 1.4 0.32 0.03 3.2 3.4 1.2

P < 0.01 for all variables between two genders using Mann ± Whitney test. LMI Lean mass index; FMI Fat mass index; AFM Abdominal fat mass; TFM Thigh fat mass; TED Transverse external diameter; TID Transverse internal diameter; SFW Subcutaneous fat width (umbilical level). Table 3 Correlation coef®cients between CT scan data (VAT or SAT) and anthropometry or total body composition data in 44 women and 27 men VAT

2

BMI (kg=m ) Lean Mass (kg) Fat Mass (kg) LMI (kg=m2) FMI (kg=m2) WC (cm) WHR SD (cm)

SAT

Women

Men

Women

Men

0.62§ 0.52{ 0.37{ 0.72§ 0.43{ 0.68§ 0.66§ 0.74§

0.42{ 0.16 0.22 0.45{ 0.31 0.63{ 0.66{ 0.67§

0.80§ 0.18 0.91§ 0.23 0.89§ 0.72§ 0.32* 0.68§

0.77§ 0.18 0.94§ 0.34 0.92§ 0.71§ 0.03 0.61{

*P < 0.05, {P < 0.02, {P < 0.001, §P < 0.0001. LMI Lean mass index; FMI Fat mass index; WC Waist circumference: WHR Waist-to-hip circumference ratio; SD Sagittal diameter.

Table 1 Anthropometric and CT scan measurements Women (n 44)

Weight (kg)* BMI (kg=m2)** WC (cm) WHR{ SD (cm) VAT (cm2){ SAT (cm2){

Men (n 27)

Mean

s.d.

Range

Mean

s.d.

Range

95.8 36.9 113 0.94 26.5 157 527

15.2 5.9 15 0.1 2.9 78 131

66 ± 123 27.5 ± 51.6 87 ± 143 0.74 ± 1.18 22 ± 32 48 ± 324 264 ± 864

103.3 33.5 117 1.04 27.6 260 306

15.3 4.5 11 0.07 3.4 99 119

73 ± 134 27.0 ± 44.3 100 ± 139 0.9 ± 1.18 23 ± 37 114 ± 498 114 ± 600

*P < 0.05, **P < 0.01, {P < 0.0001 (P degree of signi®cance between men and women using Mann ± Whitney test.) WC Waist circumference: WHR Waist-to-hip circumference ratio; SD Sagittal diameter; VAT Visceral adipose tissue; SAT Subcutaneous adipose tissue. International Journal of Obesity


with VAT in women (r 0.74, P < 0.0001), and in men (r 0.67, P < 0.0001). No segmental fat mass value (nor any percentage referring to FM) obtained with DXA standard software readings could predict VAT over anthropometry. Table 4 shows the relationships of VAT and SAT to some relevant parameters selected among numerous DXA data alone, or combined between themselves or with anthropometric measurements. Linear regression analysis of simple DXA data alone or combined between themselves to VAT, demonstrated that TID was the best predictor of VAT (r 0.90 in women and 0.79 in men, P < 0.0001), whereas TED was poorly contributive. Moreover, TID (as VAT) was not signi®cantly related to SAT, in each gender. Therefore, TID and SFW were of great interest for the construction of anthropometry ± DXA combinations. Among all the new parameters empirically de®ned, the ratio (SDÿSFW)(TID)=height showed the highest correlation with VAT (r 0.94 and (r2 0.87 in women, r 0.88 and r2 0.77 in men, P < 0.0001 in both sexes) and no signi®cant correlation with SAT. Subtracting other SFW multiples than one from SD in the above ratio did not improve these results. This ratio was selected in both sexes using stepwise multiple regression models against all other variables (data not shown), and no other predictor variable appeared besides this ratio. As presented in Figure 2, the regression lines in men and in women were very similar and followed a parallel direction, but were not signi®cantly different. This observation allowed us to pool the values of both gender in order to get a unique predictive equation. The equation thus obtained from DXA and anthropometry, for VAT prediction (in cm2), was: 79.6(s.e. 3.97) (SDÿ SFW)(TID)=height (values in cm)ÿ149, for the whole population (r 0.93 and r2 0.86, P < 0.0001). RMSE, which is a measure of the precision

267

Figure 2 Relationships between VAT area measured by CT and that estimated from the ratio: (SDÿSFW) (TID at the umbilical level)=(height), in 44 women and 27 men. Predictive equations for VAT area: y 74.5 ( 4.4 s.e.)xÿ137 (r 2 0.87, root mean square error 27.6 cm2, P < 0.0001) in women and y 74.8 ( 8.1 s.e.)xÿ111 (r 2 0.77, root mean square error 48.4 cm2, P < 0.0001) in men.

of any predictive equation, was 38.2 cm2 and the CV standardizing the RMSE value on the mean CT measurement VAT area was 19.4%. Another way to analyse the precision of the agreement between the response variable and the predictor variable for the VAT area involved plotting the difference between these data against their mean, as reported by Bland and Altman (Figure 3).29 The mean VAT area was equal to 195 95 cm2 and the s.d. for the difference between the response variable (CT measurement) and the predictor variable was 38 cm2 (values ranging from ÿ67 to 140 cm2). Ten out of the 71 patients presented a difference higher than 50 cm2. Moreover, the precision was better in women with only ®ve out of 44 (11%) presenting a difference higher than 40 cm2 (s.d. of the difference

Table 4 Correlation coef®cients between CT scan data (VAT or SAT) and dual energy X-ray absorptiometry data alone, combined between themselves or with anthropometric measurements, in overweight subjects (44 women and 27 men) VAT

AFM (kg) TFM (kg) AFM=TFM AFM=SFW TED (cm) TID (cm) SFW (cm) (SD)(TID) (SD)(TID)=height (SD)(TID)=BMI (SDÿSFW) (SDÿSFW)(TID) (SDÿSFW)(TID)=height

SAT

Women

Men

Women

Men

0.57§ 0.06 0.75§ 0.83§ 0.54§ 0.90§ ÿ0.23 0.89§ 0.91§ 0.66§ 0.86§ 0.92§ 0.94§

0.51{ 0.27 0.61{ 0.80§ 0.52{ 0.79§ ÿ0.40{ 0.80§ 0.84§ 0.80§ 0.82§ 0.84§ 0.88§

0.89§ 0.72§ 0.33{ 0.07 0.86§ 0.21 0.83§ 0.47{ 0.47{ 0.22 0.17 0.20 0.20

0.82§ 0.88§ ÿ0.02 ÿ0.20 0.74§ 0.11 0.80§ 0.42{ 0.42{ ÿ0.12 0.32 0.25 0.26

*P < 0.05, {P < 0.03, {P < 0.001, §P < 0.0001. AFM Abdominal fat mass; TFM Thigh fat mass; SFW Subcutaneous fat width (at the umbilical level); SD Sagittal diameter; TED Transverse external diameter; TID Transverse internal diameter.

Figure 3 Difference between measured and predicted VAT value, plotted against mean VAT. The VAT area data was obtained by either CT or by the predictive equation from the ratio: (SDÿSFW)(TID at the umbilical level)=(height), in 71 subjects (44 women and 27 men). International Journal of Obesity


268

Figure 4 Relative difference in VAT area (CTÿpredictive equation value=CT value) plotted against VAT area measured by CT in the 71 subjects.

28 cm2). The analysis of the relative difference (measuredÿpredicted value=measured value) vs the average of measured and predicted values showed a standard error of 2.8% for the relative difference with a s.d. equal to 23.2%. A moderate positive relationship was found between this relative difference and the CT measurement, and can be explained by the VAT under 60 cm2 observed in four female cases. As reported in Figure 4, the relative difference was much higher in these four subjects (ÿ94 32%, s.e. 15.9%) and quite lower in the 67 others with VAT area beyond 60 cm2 (ÿ0.1 17.7%, s.e. 2.2%). A previous equation proposed by Treuth et al 31 for VAT assessment in women was based on SD, age, WC and trunk %fat. When it was applied to our female population, the actual VAT was signi®cantly different from the estimated one (mean difference s.e.: ÿ31 8.8 cm2, P < 0.01) with a weaker correlation between the measured and the predicted area (r2 0.57).

Discussion The present study focused on potential developments of the DXA technique to measure the amount of deep abdominal adipose tissue associated with susceptibility to non-insulin dependent diabetes mellitus and cardiovascular diseases, both in men and in women. A recent Hologic software version (5.67) was used for all body composition DXA measurements. For CT, the L4 level rather than the L4 ± L5 level was selected to be strictly at the umbilical level (where SD was measured) and to avoid a bias due to bone attenuation of the DXA beam by iliac crests. Previous studies have shown that the VAT area was constant between the L3 and L5 level.11,17,18 Anthropometric variables usually relevant to body fat distribution (SD, WC, WHR and TED) were only International Journal of Obesity

moderately associated with VAT (r < 0.74). These data are in agreement with those of other studies conducted in obese patients9,32 and con®rm the limits of visceral fat assessment by anthropometry in overweight subjects. Total body and standard segmental tissue composition data calculated by DXA were weaker predictors of VAT than anthropometry. However, the positive correlation between LMI and VAT found in both genders and especially in women was noticeable, whereas FM was not related to LM in our population. This correlation might depend on muscle mass, since a positive relationship has been previously found between androgenicity and WHR in women.33 It could also be related to an increase in the hydration fraction of fat-free tissue. Indeed, an independent positive relationship between VAT and the hydration factor of LM was recently reported in women after statistically adjusting for variations in plasma insulin.34 These results could partly explain the high prevalence of arterial hypertension besides impaired insulin action in android obesity. However, these data must be further con®rmed by other studies. The two ratios previously described by Svendsen et al23 from the whole AFM determination (AFM=thigh FM and AFM=total FM) were not much better than anthopometry, and therefore not satisfactory in terms of accuracy (Table 4). Our exclusively overweight population, unlike that selected by Svendsen, could easily explain these results. The subcutaneous fat content, which impairs the ability of the abdominal fat variable to estimate the VAT, was indeed much greater in our population. We came to similar conclusions using the equations proposed by Treuth et al31 based on interdependent variables. Further, these two previous studies were only conducted in women. It is also possible that the equations proposed by these authors did not validate as strongly because of the use of another equipment in their studies (model DPX-L, software version 3.1 without correction for tissue thickness, LUNAR Radiation Corp., Madison, WI). These data demonstrate that other DXA parameters needed to be developed for VAT assessment besides total body composition determination in obese subjects. The possibility of measuring the width of the abdominal cavity (TID) in addition to SFW, was of great interest for VAT assessment, although DXA cannot provide the subcutaneous fat thickness involved in SD. Kvist et al11 previously found that TID, as directly measured on the CT image, was the best simple predictor variable of VAT area when compared to other CT-scan diameters. Thus, they proposed a predicted VAT area based on TID and sagittal internal diameter (SD-sagittal SFW), which was closely related to the measured VAT area (r > 0.99) once integrated in an ellipse equation. We report here for the ®rst time that DXA may provide an accurate measurement of TID and found that VAT was much more related to the simple


parameter TID than to TED. The precision in TID measurement was maintained in subjects with massive obesity (data not shown). This is particularly important since the DXA instrument may provide measurements in patients presenting a TED up to 65 cm against the more usual 50 cm for CT. The challenge was then to measure the intraabdominal cavity thickness. For this purpose, we have postulated that sagittal and transverse subcutaneous fat layer were correlated and then we subtracted different SFW multiples (0.5 to 2) from SD. Among them, (SDÿ1 SFW) showed the best relationship with VAT (r 0.86 for women and 0.82 for men, P < 0.0001) and greatly improved the results for SD alone. Then, (SDÿSFW)(TID) and some other combinations highly correlated to VAT (Table 4) were selected by stepwise multiple regression models. Finally, dividing (SDÿSFW)(TID) by various anthropometric and=or DXA indices of corpulence unrelated to VAT, showed that the ratio (SDÿSFW)(TID)=(height) presented the highest relationship with VAT both in men and in women. From this ratio, our newly developed equation was validated with a higher r2 and a lower RMSE value than the previous equations.31 The ellipse model constructed from this ratio, as suggested by Kvist et al,11 did not improve the precision of our predictive equation. The loss of precision of the equation found in some subjects could be due to some unavoidable difference in abdominal positioning or analysis level when using the two techniques. Moreover, CT measures the adipose tissue rather than the fat content, unlike DXA. Therefore, some apparent imprecision in our predictive equation could be explained by the different body composition level model analysed by these techniques.35 In the present study, we have therefore de®ned for the ®rst time a one-predictor variable of VAT constructed from independent variables and excluding other variables especially sex, age, BMI, and other `classical' predictor variables (WC, WHR, SD) dependent on subcutaneous fat thickness. This approach limits the risk of multicol linearity and our equation should be valid in various obese populations. Obtaining a unique equation usable in men as in women reinforces this hypothesis. We should also specify that TID and SFW were obtained with the single rectilinear scanning mode and not with the array mode which could give false values especially in obese subjects. As concerns the applicability of our equation in non-obese subjects, it is not secured and should be tested. Further developments would be expected from our approach: the direct determination of abdominal diameters, SD, TED, TID and then SFW by the DXA computer which could automatically calculate the VAT area; the direct measurement of these variables throughout the abdomen during the DXA scanning could also be performed and compared to a CT ± multiscan measurement of the total VAT mass.

We conclude that our results clearly demonstrate the usefulness of DXA in the measurement of VAT area from the determination of SFW and TID at the abdominal level. Direct combination of DXA data with simple anthropometric parameters (SD and height) provides the best predictive indices of VAT both in men and in women. Further, this study highlights a relationship between LMI and VAT especially in women.

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