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1Department of Medicine, Obesity Research Center, St. Luke's-Roosevelt Hospital, and 2Institute of Human Nutrition, Columbia University, New York, New York; 3Laboratory of Epidemiology, Demography and Biometry, Geriatric Epidemiology Section, National Institute on Aging, Bethesda, Maryland; and 4Endocrinology Center for Gynecology, Beijing Obstetrics and Gynecology Hospital, Capital University of Medical Sciences, Beijing, China
Submitted 10 March 2006 ; accepted in final form 17 October 2006
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ABSTRACT |
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 TOP ABSTRACT METHODSRESULTSDISCUSSIONGRANTSDISCLOSURESACKNOWLEDGMENTSREFERENCES |
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race; body composition; fat distribution; muscle fat; imaging
Our laboratory recently described an IMAT depot located between muscle bundles, as measured on whole body MRI (7, 31). IMAT is defined as the adipose tissue visible on MRI images between muscle groups and beneath the muscle fascia. Subsequently, Albu et al. (1) found, in premenopausal African-American women who had significantly higher insulin resistance and acute insulin response to glucose than did their white counterparts, that whole body IMAT, but not VAT or SAT, was an important independent correlate of insulin resistance. Previously, IMAT in a single midthigh slice measured by computed tomography (CT) showed that insulin resistance was associated with increased subfascial adipose tissue in obese adults (10) and thinner older persons (11). Goodpaster and colleagues (10) also found that adipose tissue located beneath the fascia lata and, therefore, adjacent to skeletal muscle (SM), was significantly negatively correlated with insulin resistance, whereas adipose tissue located above the fascia (i.e., SAT) and removed from SM was not (10). Therefore, this depot is potentially important in understanding metabolic disease.
A comprehensive evaluation of whole body IMAT requires a whole body MRI or CT scan. Since acquiring a whole body scan demands considerable resources, including time required of subject in the scanner, costs of acquisition and image analyses, and for CT the issue of large radiation dose, a question arises as to whether it might be preferable or more cost effective to use one or more cross-sectional slices or combination of slices to estimate whole body IMAT in lieu of a whole body IMAT scan. Such a decision requires information on the predictive value of a subset of slices. The primary aim of this study was to determine which slice location, or combination of slice locations, provides the best estimates of whole body IMAT and to quantify the predictive value of these slice locations.
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METHODS |
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Subjects were independent, community-dwelling African-American, Asian, and Caucasian women and men (
18 yr) who had participated in one of five studies at St. Luke's-Roosevelt Hospital's Body Composition Unit between 1996 and 2002. Recruitment occurred through advertisements in newspapers and flyers posted in the local community. Inclusion criteria for all studies required that subjects be ambulatory, weight stable (±2 kg over past 6 mo), nonexercising based on self-report of no participation in vigorous routine or structured exercise, and nonsmoking. Race was determined by self-report according to the following criteria. All parents and grandparents were required to be of the same race: non-Hispanic African-American and non-Hispanic Caucasian, for African-American and Caucasian subjects, respectively. Asians were required to report all parents and grandparents as being of Eastern Asian origin.
Each subject completed a medical examination, and the majority of subjects had screening blood tests after an overnight fast that included a standard hematology and blood chemistry panel. Subjects with untreated diabetes mellitus, malignant/catabolic conditions, missing limb, who had had joint replacement, those currently taking estrogen replacement therapy, and those taking medications that could potentially influence body composition were excluded from the study. Menopausal status was determined by self-report of menses. Women who had not menstruated within the past year were considered postmenopausal. All studies were approved by the Institutional Review Board, and all subjects gave written consent to participate.
Body Composition
Subjects reported in the morning in a fasted state to the Body Composition Laboratory. With the subject wearing a hospital gown and foam slippers, body weight and height were measured to the nearest 0.1 kg (Weight Tronix, New York, NY) and 0.5 cm (Holtain Stadiometer, Crosswell, UK), respectively (8).
MRI
Acquisition.
Subjects were placed on the 1.5-T scanner (General Electric, 6X Horizon, Milwaukee, WI) platform with their arms extended above their heads. The protocol involved the acquisition of 
40 axial images, 10-mm thickness, and at 40-mm intervals across the whole body (26). The scanning protocol commenced at the L4–L5 intervertebral space in all subjects, and the lower body was scanned first. Thereafter, the L4–L5 position was relocated, and the upper body was scanned with arms outstretched above the head. Total body SM and TAT mass, including total SAT, VAT, and IMAT, was measured from the whole body multislice MRI. Individual slices located closest to the widest calf, midthigh, widest buttock, waist (L4–L5 interspace), shoulders, mid-upper arm, and mid-forearm were extracted from the whole body scan, and IMAT, SAT, and SM areas (cm2) within these slices were determined.
Analysis. SliceOmatic 4.2 image analysis software (Tomovision, Montreal, Canada) was used to analyze images on a PC workstation (Gateway, Madison, WI) at the Image Reading Center (New York, New York). IMAT in our laboratory is defined as IMAT that is visible between muscle groups and beneath the muscle fascia, as previously described (31) (Fig. 1). MRI volume estimates were converted to mass using the assumed density of 1.04 kg/l for SM and 0.92 kg/l for adipose tissue (30). All scans were read by the same analyst (P. Kuznia). The technical errors for four repeated readings of the same four whole body scans by the same observer (P. Kuznia) of MRI-derived SM, SAT, VAT, and IMAT volumes in our laboratory are 1.4, 1.7, 2.3, and 5.9%, respectively.
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Descriptive statistics were calculated for the study sample. Data are presented as means ± SD with ranges in the text, unless otherwise stated. Differences among the race/sex groups were determined using an analysis of variance. Homogeneity of residual variances was established by Levene's test. If variances were unequal, then the Brown-Forsythe F was used to test equality of means and post hoc comparisons used Dunnett's C test. Otherwise, equality of means was tested with the usual F ratio, and subsequent comparisons of means used the REGW-F range test. Pearson correlation coefficients were used to assess the bivariate linear relationships of all the single slices with age, weight, height, IMAT, and TAT. Linear regression analysis was used to assess the relation between independent variables (single slice from calf, thigh, buttocks, waist, shoulders, upper arm, and forearm) and the dependent variable (whole body IMAT volume). Regression equations were cross-validated using the method of deleted residuals (6). Data were split by sex and race because significant interactions between racial group and sex were found. Adjusted R2 was used as a measure of the strength of the relationship in multiple regressions. Exploratory analyses were conducted to investigate whether optimal slice location differed by menopausal status and degree of obesity [median split on body mass index (BMI)] by testing the significance of differences between correlated correlations using the method of Steiger (32). In all analyses, a two-tailed 
-level of 0.05 was used. Data were analyzed using SPSS version 13.0 (SPSS Institute, Chicago, IL).
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RESULTS |
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The descriptive characteristics of the sample are summarized in Table 1. Among the female groups, the African-American women had the highest mean BMI (kg/m2), weight, SM, TAT, and IMAT; the Asian women had the lowest values; and the Caucasian women were intermediate. Compared with all women, the Caucasian men were significantly taller, heavier, and had more SM mass.
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To determine which slice best predicts total body IMAT, the correlations between the single-slice IMAT areas and total body IMAT volume were examined. They ranged between r = 0.44 and 0.86 (African-American women), r = 0.21 and 0.72 (Asian women), r = 0.37 and 0.81 (Caucasian women), and r = 0.35 and 0.92 (Caucasian men), and are summarized in Table 2. The highest correlations between a single-slice IMAT area and IMAT volume were located at the midthigh (African-American women, r = 0.86, Caucasian women, r = 0.86, and Caucasian men, r = 0.86) and midcalf (Asian women, r = 0.72), respectively (Fig. 2). The highest correlations between a single-slice IMAT area and TAT volume were also found at the same slice locations as for total IMAT: midthigh slice (African-American women r = 0.67, Caucasian women r = 0.60, and Caucasian men r = 0.72) and midcalf (Asian women r = 0.76). The relationship of total body IMAT to a single midthigh slice area was found to differ by group (interaction P = 0.04); therefore, all subsequent analyses were performed separately for each sex/race group.
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Prediction Models for Total Body IMAT Volume
The single-slice IMAT areas (in cm2) of the calf, thigh, buttocks, waist, shoulders, upper arm, and forearm were included in linear regression models as independent variables, with total body IMAT volume (in liters) as the dependent variable. The coefficients for the developed regression equations using one, two, and three independent variables are shown in Table 3. The standard error of the estimate for a single slice or combined slices derived from the stepwise regression models ranged from 0.15 to 0.36 liter. In every case, the addition of a second and third slice reduced the standard error of the estimate and increased the amount of variance accounted for, making a statistically significant improvement in the prediction. The adjusted R2 ranged from 0.49 to 0.92. Although midthigh was not the best single-slice location for Asian women, we have included in Table 3, for comparability with the other groups, the coefficients for the regression of total body IMAT on midthigh slice as the first variable.
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DISCUSSION |
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As the prevalence of obesity increases world wide, it is well recognized that excess adiposity is associated with heart disease, stroke, hypertension, and Type 2 diabetes (4, 5, 11, 14, 21, 28, 35). Several studies have reported an association between IMAT and metabolic abnormalities (12, 16, 26, 27, 31, 33). One potential mechanism of action linking IMAT with insulin signaling is through triacylglycerol metabolites interfering with insulin signaling transduction, thereby altering whole body glucose and lipid metabolism. Therefore, for researchers investigating the relation of health risk with fat deposits, it may be of interest to estimate total body IMAT or to identify a good surrogate measure. Since the measurement of total body IMAT is costly in terms of time and resources, estimates made from one or more slices using the regression equations of Table 3 may serve the investigator's purpose.
Most previous studies of IMAT have utilized a single midthigh slice by CT (9, 10, 27). Although this methodology also quantifies IMAT cross-sectional area in the midthigh slice, the physical principals on which MRI and CT are based are very different; thus there is no assurance that the IMAT areas identified are equivalent (23). There are no studies showing how well total body IMAT is captured by a single CT slice. A first step toward assessing the interchangeability of these methods would be to evaluate the degree of agreement in IMAT areas from single CT and MRI midthigh slices.
It is well known that the reliability of a measure affects the number of cases needed to detect an effect of a particular size, so it is of interest to estimate the number of cases with single- or multiple-slice IMAT data that would be needed to obtain statistical power equivalent to that which would have been obtained by measuring total body IMAT (22). The adjusted R2 from the single- and multiple-slice regressions may be interpreted as a reliability coefficient (R) in the measurement of total body IMAT in that it provides an estimate of how much between-subject variability is accounted for by the regressors. If N is the sample size necessary to detect an effect under perfect reliability (R = 1), then the necessary sample size (N') under reduced reliability (R < 1) is N/R (18). Thus, for example, if the multiple-slice whole body IMAT is taken as a measure of the true IMAT volume, and a single-slice R2 = 0.75, then equivalent power would be achieved by measuring single slices in one-third more cases. The attractiveness of this option will depend on the relative cost of enrolling additional subjects vs. that for acquiring and analyzing additional measurements. Investigators can easily make an approximate calculation of these costs for a planned study at a specific facility; those wishing to make more comprehensive and rigorous calculations can consult the literature on cost-effective research design (2, 20). Investigators who already have acquired single-slice MRI images at the appropriate locations in their physiological research might use our models to make estimates of total body IMAT, bearing in mind the limitations of the sampling method and the sample size.
The locations of the highest correlations between single-slice IMAT area and total body adipose tissue volumes were identical to those for total IMAT: midthigh for African American women, Caucasian women, and Caucasian men, and midcalf for Asian women.
MRI Measurement Issues
MRI depends on the density of hydrogen nuclei and the relaxation time of the tissue to separate different tissues of interest. The visual appearances of SM and adipose tissue in an image are strikingly different (33). IMAT is attempting to quantify the adipose tissue located between the muscle bundles. We adopted a standard protocol, where comparisons of MRI with corresponding cadaver analysis of leg and arm sections showed a high correlation (r = 0.92, P < 0.001) for IMAT (17). Use of this protocol, coupled with the advantage of a single reader analyst, reduced the variability in IMAT measurement.
Study Limitations
The exact location of each slice above or below the L4–L5 position is influenced by height of the subject, and each slice may not be anatomically identical across subjects. All components of body composition, including IMAT, are potentially influenced by dietary intake, levels of physical activity and/or inactivity, and exercise, for which no independent measures were acquired. This study used a convenience sample of urban-dwelling healthy African-American women, Asian women, and Caucasian women and men and cannot be considered representative of the general adult population. Although the relationships between variables (single slice-total IMAT) found in this sample should hold in other samples of healthy subjects, extrapolation of these findings to other samples should be made with caution. Also, the amount of variance accounted for by the regressions in this study may overstate their predictive value, since the slice locations were not specified a priori. The presence of unreported and undiagnosed medical conditions that could affect body composition cannot be ruled out. Race group was determined by self-report, which is reported to be a suitable proxy for genetic ancestry, especially when assessing disease risk (25), but does not take into account degrees of admixture. It is also possible, given the significant differences in BMI among the ethnic subgroups in our sample, that the variables in our models do not adequately take into account the differences in BMI.
In addition, further studies are needed to confirm our findings in patients with health-related disorders. Our developed models are based on healthy ambulatory adults, and therefore our models may not be accurate when applied to subjects with disproportional muscle growth or atrophy or following soon after significant weight change. Another limitation is that we had a relatively small number of Asian women, so that our findings for this group should be viewed cautiously.
Study Strengths
We had available for this study a sample of African-American, Asian, and Caucasian women and Caucasian men whose total body IMAT had been measured by MRI and were able to evaluate the representative value of different slice locations (from continuous scans across the whole body) rather than assuming that a single thigh slice, as used in previous studies, was the best location. One MRI reader analyzed all scans for IMAT, including a subset reread over time to establish intrareader variability. All scans were acquired in one center under high levels of quality control by well-trained examiners skilled in performing a protocol for body composition measurements.
In conclusion, single cross-sectional images at midthigh had the highest correlation of IMAT area with whole body IMAT volume in a sample of African American women, Caucasian women, and Caucasian men. In view of the relatively high correlations, for group studies it may be more cost-effective to estimate IMAT based on one or more slices than to acquire and segment numerous images necessary to quantify whole body IMAT. (3, 34)
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GRANTS |
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DISCLOSURES |
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ACKNOWLEDGMENTS |
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FOOTNOTES |
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The costs of publication of this article were defrayed in part by the payment of page charges. The article must therefore be hereby marked "advertisement" in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.
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, African-American women: IMAT = 0.53 + 10.4 x midthigh IMAT (SEE = 0.36 liter, adjusted R2 = 0.74, n = 39). 
, Asian women: IMAT = 0.67 + 160.2 x midcalf IMAT (SEE = 0.27 liter, adjusted R2 = 0.49, n = 21). 
, Caucasian men: IMAT = 0.27 + 13.8 x midthigh IMAT (SEE = 0.21 liter, adjusted R2 = 0.84, n = 39).