BODY COMPOSITION

Whole body imaging was performed using 40 transaxial spin echo image slices (TE40, TR500, field of view 45 x 45 cm, slice thickness 10mm), covering the whole body from wrists to ankles in subjects lying prone with arms outstretched. The image slices were non-contiguous, with a gap of 40mm between slices such that the leading edge of each slice was 50mm from the next. To reduce respiratory motion artefact, signal averaging was performed, with 4 averages for the abdominal region and 2 for the pelvic region (L4/5 and below).

4.2.2.1 Adipose tissue segmentation

Segmentation of adipose tissue was performed using an automated technique developed specifically for this purpose. Adipose tissue can be differentiated by its high signal relative to other tissues on spin echo (T1-weighted) images, with minimal overlap of tissue intensities, and thus a threshold technique can be employed for segmenting this tissue from others. However, signal intensity varies on different acquisitions and also across a single image due to a non-linearly varying bias field and

field estimation and c o r r e c t i o n . T h e automated analysis was designed to overcome these variations in signal intensity without the use of a phantom. It uses a mosaic of multiple overlapping regions (tiles) within each image, in which the adipose tissue threshold is determined using the pixel intensity histogram for each tile. The tile with the greatest absolute difference in intensity (greatest contrast) is used as the reference tile and the four adjacent tiles are compared to this using the overlapping regions. If a neighbouring tile has a conflicting segmentation result with the reference tile within the overlapped region, the threshold for the neighbouring tile is reclassified to concur with the reference. This process is systematically propagated to all the tiles using the overlapping regions to ensure consistent segmentation across the whole image (figure 4.1). Once segmented, the total area of adipose tissue in each image is measured by summing the number of voxels identified as adipose tissue, with the voxel size calculated from the known field of view (45 x 45 cm) and matrix size (256 x 256).

Adipose tissue was measured in each image by this automated technique, with manual inspection to ensure correct segmentation. Separation into visceral and subcutaneous compartments was performed using a manual tracer to delineate the peritoneal cavity (figure 4.2). The high signal intensity from trabecular bone, due to the lipid content of bone marrow, resulted in the need for manual delineation and exclusion of bone from the analysis. The total volumes of subcutaneous and visceral fat mass were calculated independently, using the Cavalieri technique of averaging the area of adipose tissue between two images, using a truncated pyramid formula.^^ The volume was multiplied by the density of adipose tissue (0.95 g/cm^)^^ to obtain the mass.

4.2.2.2 Reproducibilitv of adipose tissue segmentation

The reproducibility of the technique has been examined in 10 volunteers (body mass index 19.2 to 29.2 kg/m^ who had MR scans of the abdominal region (diaphragm to perineum) performed twice on the same day. Adipose tissue quantification was determined and the paired sets from each subject compared (test-retest reliability). The mean values and mean of the absolute differences between repeated scans are shown in table 4.1.

Figure 4.1

The processing results of a transverse image acquired at the upper thigh level of one of the subjects studied, (a) The original acquired image; (b),(c) segmentation results based on manual thresholding; (d) Bias corrected image using overlapping mosaics; (e) initial three-class segmentation result; (f) final segmented adipose tissue distribution.

Figure 4.2

a) Abdominal magnetic resonance image with b) segmentation of adipose tissue by automated image analysis, delineated into visceral (white) and subcutaneous (black) compartments.

Table 4.1

Adipose tissue mass measurements from repeated abdominal MR scans for 10 subjects. Values are in kg. AT = adipose tissue.

1®‘ scan 2"' scan Mean absolute difference

% difference

Total AT 6.65 6.60 0.17 2 8 %

Subcutaneous AT 5.16 5.13 0.11 2.7 %

Visceral AT 1.48 1.47 0.09 8.0 %

The repeated values were highly correlated (r>0.99; p<0.0001 for all three measurements), with Bland-Altman plots showing 95% confidence limits for total AT: - 0.44 to +0.35, subcutaneous AT: -0.29 to + 0.22 and visceral AT: -0.22 to + 0.20 kg (figure 4.3). These results show excellent reproducibility of this technique, with differences of < 0.2 kg for total AT and <0.1 kg for visceral AT between scans.

Figure 4.3

Bland-Altman plots showing 95% limits of agreement for repeated scans, a) total, b) subcutaneous and c) visceral adipose tissue.

0.8 - S’ 0.6 - 0.8- » 0.6 - Total AT g »■“ -______________ _________________ «.35 ^ 0.2 - • •

I OO-___t . •___________ MS'

-0.05 0) , -0.2 e •

s

I - 0 4 - ---• --- -045 1 -0.6- o -0 . 8 1---1---1---1---1---1---1---1---1---1 0 2 4 6 8 10 12 14 16 18 Mean value (kg) 08 - Subcutaneous AT 0.6 -

n

° ’ 495% Ç 02- - “ 2 I 0 .0- ^ -0.03

i«- •••■• .

! « - • ... I -0.6 - 5 -0.8 T---1--- 1---1---1--- 1--- 1 0 2 4 6 8 10 12 Mean value (kg) Visceral AT

!

0

.

2

-

0.20 I 0 .0 - ; ^ . Me4°-ft01

1

- . , -0.22 I Û - 0 . 6 - - 95%

4.2.3 STATISTICAL ANALYSIS

All statistical analyses were performed using SPSS for Windows 6.0 (Tulsa, Oklahoma) with the critical alpha set at 0.05. The relationships between body size and composition variables and LV mass were initially checked for linearity (with zero intercept) using Tanner’s ‘special circumstance’ calculation.^^ In this procedure the correlation coefficient, r, for each combination of body size and composition variable and LV mass was compared with the ratio of the coefficient of variations for the same two variables

cvX/cvY. Where r is equal, or roughly equivalent (arbitrarily chosen as ±0.05) to

cvX/cvY, a linear relationship with a zero intercept is evident. Conversely if these two terms are not similar then either a linear relationship does not exist or the linear relationship has a positive or negative y intercept - a theoretically and practically implausible situation.

Subsequently, allometric procedures were investigated to describe the relationship between each combination of LV mass with a body composition or size variable. The general allometric equation Y = aX** can be linearised by taking natural logarithms of both sides of the equation: logY = loga + blogX+ loge, where e is the multiplicative residual error term. This makes the data amenable to simple linear regression (log-log linear regression) where b is the slope of the log-log plot. Regression analysis utilised the ordinary least-squares technique. This approach, despite being the most commonly adopted in cardiac research does have an assumption that error and/or biological variation between subjects is only associated with the dependent variable, making accurate methods of assessment for LV mass all the more important.

4.3 RESULTS

The mean, standard deviation and range of values for LV mass and body composition and size variables are represented in Table 4.1. The computations of Tanner’s ‘special circumstance’ data are reported in Table 4.2. Close similarity between the ratio of coefficient of variation data and the correlation coefficient was noted between LV mass and LBM. The use of ±0.05 as a comparison cut-off figure is arbitrary and individual comparisons should be subjectively interpreted for their degree of similarity. However, it is noticeable that the majority of other relationships produced very divergent ratios that would suggest either a non-linear relationship or a positive or negative y intercept. The ratio that came closest to, but outside, the arbitrary 0.05 cut-off was that for LV mass and height (0.09). This is supported by the data in column 3 where, although the

b exponent for height is 1.41, the confidence intervals are broad (±0.60) and thus include 1.0. Despite this, the scatter-plot of LV mass and height would not suggest a best-fit line that is linear and passes through the origin whilst optimising the reduction in residual error (see Figure 4.4a).

Table 4.1

Left ventricular mass (LVM) and body composition/size variables in the whole cohort. S/C = subcutaneous; SO = standard deviation.

Mean ± SD Range

LV mass (g) 183 ±27 122-255

Height (m) 1.75 ±0.07 1.59-1.91

Body mass (kg) 70.6 ±9.0 45.2-97.2

Body surface area (m^) 1.85 ±0.14 1.48-2.22

Lean body mass (kg) 58.8 ±6.3 41.7-76.4

Fat mass (kg) 11.8 ±4.1 3.5-24.5

Visceral fat mass (kg) 0.97 ±0.22 0.34-2.95

S/C fat mass (kg) 10.86 ±3.80 2.92-22.68

Leg fat mass (kg) 3.81 ±1.33 1.15-7.78

Arm fat mass (kg) 1.11 ±0.15 0.35-2.58

Torso fat mass (kg) 6.90 ±2.58 1.90-14.56

Table 4.2

The relationships between left ventricular mass (LVM) and body size & composition variables. Column 2: A comparison of the correlation coefficient and the ratio of coefficient of variation (r :

cvXIcvY)-, ratios are BOLD if the difference is <0.05. Column 3: The b exponents for the relationships (Data are mean ± 95% confidence intervals with r^ values in brackets).

LVM vs: r : c v X Ic v Y b exponent ± 95% Cl

Height (m) 0.34:0.25 1.41 ±0.60 (0.36)

Body mass (kg) 0.67:0.86 0.80 ±0.13 (0.66)

Body surface area (m^) 0.62:0.50 1.30 ±0.24 (0.64)

Lean body mass (kg) 0.65: 0.70 0.90 ±0.15 (0.66)

Fat mass (kg) 0.46 : 2.39 0.20 ±0.06 (0.48)

Visceral fat mass (kg) 0.30 : 3.34 0.12 ±0.05 (0.35)

The log-log linear regression analyses produced a range of b exponents describing the slope of the best-fit lines (Table 4.2). Most b exponents were significantly different from

1.0 and therefore represented curvi-linear relationships. Scatter-plots for LV mass against LBM and height are presented in Figures 4.4a and 4.4b, respectively. The figures visually demonstrate an essentially linear relationship between LV mass and LBM

(b=0.90±0.16). This is compared to a curvi-linear relationship between LV mass and height (b=1.41±0.60), that has already been alluded to. A range of r^ scores is represented in table 4.2 suggesting a degree of variability in the strength of association between relationships. The lowest r^ was 0.36 for LV mass-height, suggesting greater residual variability in this relationship.

Figure 4.4

Scatter plots for left ventricular mass (LVM) against height (a) and lean body mass (b)

Figure 1a. LVM plotted against HT Figure 1b. LVM plotted against LBM

300 300 250 250 200 200 150 S 150 100 100 100 150 200 250 0 10 20 30 40 50 60 70 80 90 HI (cm) LBM (kg)

For adipose tissue, the relationship between LV mass and a range of FM variables is consistently low (b range - 0.12 to 0.20). The r^ scores were generally lower for the fat variables than weight, BSA and LBM, again suggesting a greater degree of dispersion of data points from the best-fit line.

In document Exercise physiology and the renin-angiotensin system: Role of the ACE gene insertion/deletion polymorphism in cardiac growth and endurance exercise. (Page 70-77)