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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
Am J Clin Nutr. Author manuscript; available in PMC Aug 16, 2011.
Published in final edited form as:
PMCID: PMC3156610
NIHMSID: NIHMS204694
Comparison of DXA and MRI-measured adipose tissue depots in HIV-infected and control subjects
Rebecca Scherzer, PhD, Wei Shen, MD, Peter Bacchetti, PhD, Donald Kotler, MD, Cora E. Lewis, MD, Michael G. Shlipak, MD, MPH, Mark Punyanitya, Steven B. Heymsfield, MD, Carl Grunfeld, MD, PhD, and Study of Fat Redistribution and Metabolic Change in HIV Infection (FRAM)
Rebecca Scherzer, University of California, San Francisco;
Address Correspondence to: Office of the Principal Investigator, The FRAM Study, Carl Grunfeld, MD, PhD, University of California, San Francisco, Veterans Affairs Medical Center, Metabolism Section 111F, 4150 Clement Street, San Francisco, CA 94121, Phone: 415-750-2005, Fax: 415-750-6927, carl.grunfeld/at/ucsf.edu
Dr. Scherzer contributed to the analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, and statistical analysis.
Dr. Heymsfield, Shen, and Punyanitya contributed to the conception and design, analysis and interpretation of data, critical revision of the manuscript for important intellectual content, and obtaining of funding.
Drs. Bacchetti, Kotler, Lewis, and Shlipak contributed to the conception and design, analysis and interpretation of data, critical revision of the manuscript for important intellectual content, and statistical analysis.
Dr. Grunfeld contributed to the conception and design, analysis and interpretation of data, drafting of the manuscript, critical revision of the manuscript for important intellectual content, statistical analysis, obtaining of funding, and administrative support and supervision
Background
Studies in persons without HIV infection have compared dual energy X-ray absorptiometry (DXA) and magnetic resonance imaging (MRI) measured adipose tissue (AT), but no such study has been conducted in HIV+ subjects, who have a high prevalence of regional fat loss.
Objective
We compared DXA with MRI-measured trunk, leg, arm, and total fat in HIV+ and control subjects.
Design
Cross-sectional analysis in 877 HIV+ and 260 controls in FRAM (Fat Redistribution and Metabolic Change in HIV Infection), stratified by sex and HIV status.
Results
Univariate associations of DXA with MRI were strongest for total and trunk fat (r≥0.92), and slightly weaker in leg (r≥0.87) and arm (r≥0.71). Estimated limb fat averaged substantially higher for DXA than MRI for HIV+ and control, men and women (all p<0.0001). Trunk showed much less difference between DXA and MRI, but was still statistically significant (p<0.0001). Bland-Altman plots showed increasing differences and variability; higher average limb fat in controls and HIV+ (both p<0.0001) was associated with greater DXA vs. MRI difference. As controls have more limb fat than HIV+, the bias leads to even higher fat measured by DXA than by MRI when controls are compared to HIV+; more HIV+ subjects had leg fat in the bottom decile of controls by DXA than by MRI (p<0.0001).
Conclusions
Although DXA and MRI-measured AT depots correlate strongly in HIV+ subjects and controls, differences increase as average fat increases, particularly for limb fat. DXA may estimate a higher peripheral lipoatrophy prevalence than MRI in HIV+ subjects.
Keywords: DXA, MRI, adipose tissue depots, lipoatrophy, HIV infection
Both DXA and MRI have been used in previous studies to measure regional and total adiposity, but each method relies on assumptions that are not always recognized or appropriate. Only MRI and CT can directly measure adipose tissue (1) and measure visceral and subcutaneous adipose tissue separately. The major limitations include cost and availability for MRI and radiation exposure for CT. Most studies use DXA because of its availability and cost. DXA uses proprietary equations to infer the amount of fat based on relative density. DXA quantifies fat, rather than adipose tissue. According to the five-level body composition classification system(2), fat is a molecular level component and adipose tissue is a tissue level component. Eighty percent of adipose tissue is composed of fat (3, 4). On the other hand, fat can be distributed in tissue other than adipose tissue such as liver and muscle(3). There is a high correlation between DXA measured fat and MRI or CT measured adipose tissue. Compared to MRI and CT, DXA cannot separate visceral fat from subcutaneous trunk fat, and cannot discern organ fatty infiltration.
In the setting of HIV infection, the introduction of combination antiretroviral therapy (ARV) was followed by the observation of changes in fat distribution and metabolic abnormalities (5). In the era of ARV, HIV infection has been associated with a syndrome of lipoatrophy which is characterized by loss of subcutaneous adipose tissue (SAT), particularly in the limbs and lower trunk, without loss of visceral adipose tissue (VAT) (6, 7). It is therefore of interest whether DXA and MRI are comparable in measuring fat in patients with HIV infection, and whether earlier conclusions based on MRI can be extrapolated to DXA. It is also of interest whether the measurement choice affects the estimated prevalence of lipoatrophy.
To date, only small studies in HIV-infected subjects (8) and controls (9-11) have been conducted comparing DXA measurement of regional adipose tissue depots with MRI measures. A primary aim of the study of Fat Redistribution and Metabolic Change in HIV infection (FRAM) was to compare regional adipose tissue quantified by both DXA and MRI, in a large, nationally representative, multi-ethnic cohort of HIV-infected subjects and controls.
Protocol and Subjects
The study of Fat Redistribution and Metabolic Change in HIV Infection (FRAM) enrolled 1175 HIV-infected and 297 control men and women between 2000 and 2002. FRAM was designed to evaluate the prevalence and correlates of changes in fat distribution, insulin resistance, and dyslipidemia in a representative sample of HIV-infected subjects and controls in the United States. The methods have been described in detail previously (12). HIV-infected subjects were recruited from 16 HIV or infectious disease clinics or cohorts in 1999. Control subjects were recruited from two centers of the Coronary Artery Risk Development in Young Adults (CARDIA) study (13). CARDIA subjects were originally recruited as a sample of healthy 18- to 30-year old Caucasian and African American men and women from 4 cities in 1985-6 for a longitudinal study of cardiovascular risk factors, with population-based recruitment in three cities and recruitment from the membership of a prepaid health care program in the fourth city. CARDIA subjects from the year 15 exam were recruited for the FRAM cohort. The protocol was approved by institutional review boards at all sites.
MRI and DXA measurements were available in 78% of FRAM participants. The majority of subjects received DXA and MRI on the same day (76%), within the same week (9%) or same month (9%). Only 6% of subjects received DXA and MRI more than one month apart. Subjects were asked to fast prior to MRI and DXA scanning. Subjects were excluded if they had contraindications to MRI or DXA such as metal implants, claustrophobia, or weight greater than 136 kg and height greater than 6′ 5″ as per specifications of the scan manufacturers. For validity of comparison, we ensured that all models under comparison contained exactly the same observations. Therefore, we report here on the subset of 260 controls and 877 HIV-infected subjects who had available measurements of adipose tissue depots by both MRI and DXA.
Body Composition
Anthropometric Measurements
All staff were centrally trained and certified for measurements. Subjects wore light clothing or hospital gown and no shoes. Height was measured to the nearest 0.1 inch or cm and weight was measured to the nearest 0.1 pound or kg on calibrated stadiometers and scales. Body mass index (BMI) was calculated as weight/height2.
Magnetic Resonance Imaging
Whole body MRI was performed to quantify regional and total adipose tissue (14). Body composition was measured by MRI with subjects in the supine position, arms extended over head, and analyzed as described in detail elsewhere (6, 7, 12, 14). In brief, using the inter-vertebral space between the fourth and fifth lumbar vertebrae as origin, transverse images (10 mm slice thickness) were obtained every 40 mm from hand to foot. MRI scans were segmented using image analysis software (Tomovision Inc., Montreal, Canada). All scans were read at a single image reading center (IRC) at the Obesity Research Center, St. Luke’s Roosevelt Hospital, New York, NY. The MRI scan acquisition protocol was standardized across sites, and the IRC performed site visits to ensure protocol adherence. Imaging techniques and anatomical sites (based on bone landmarks) were identical between HIV-infected patients and controls. Tissue areas (cm2) were calculated by summing specific tissue pixels, then multiplying by individual pixel surface area. Volume per slice (cm3) of each tissue was calculated by multiplying area by thickness. Volume of each tissue for the space between two consecutive slices was calculated via a mathematical algorithm (15). When a single limb was outside the field of view, volume in the measured limb was doubled to obtain total limb volume. For comparison with DXA, adipose tissue volumes from MRI were multiplied by 0.9 kg/L to convert to fat mass, since adipose tissue has a density of 0.9 g/cm3 (16). Anatomic sites considered in this analysis were: trunk (defined as upper trunk plus lower trunk plus visceral adipose tissue), arm, leg and total adipose tissue.
Dual Energy X-ray Absorptiometry (DXA)
Total and regional body fat contents were measured with DXA scanners manufactured by GE Lunar (Madison, WI) or Hologic, Inc. (Bedford, MA). Lunar models used in this study included Prodigy, DPX, DPX-IQ, and DPX-L. Hologic models included QDR 2000 (pencil beam) and 4500 (fan beam) machines. To assist standardization of the values obtained on DXA scanning, a whole body phantom was sent to all sites for scanning (Bio Imaging Technologies, Inc(17)). DXA scans were analyzed centrally at the Obesity Research Center, St. Luke’s Roosevelt Hospital, New York, NY, using image analysis software provided by the respective scanner manufacturers. DXA scans from GE Lunar (Madison, WI) were analyzed using DPX Software, Version 4.7E, and Prodigy Software, Version 12.1. DXA scans from Hologic, Inc. (Bedford, MA) were analyzed using QDR Software, Version 11.1. From the DXA scans, total body fat and three regions were evaluated: trunk, leg, and arm. The arm region is separated from the trunk region at the glenohumeral joint, while the leg region is separated from the pelvic region at an angle perpendicular to the femoral neck. The superior end of the trunk region is constrained at a level just below the chin. Fat mass was calculated as total mass minus bone mineral content minus lean soft tissue. The coefficient of variation for all DXA instruments was 3.3% for fat.
Spearman correlation coefficients were calculated to examine the relationship of each DXA-measured adipose region with the corresponding MRI-measured region (trunk, leg, arm, and total), because many measures were found to be non-normally distributed. For each region, differences between DXA and MRI were compared using the Wilcoxon signed-rank test. Percent difference between DXA and MRI was calculated as: (DXA – MRI) / MRI × 100. Separate analyses were conducted for HIV-infected men, HIV-infected women, control men, and control women. Analyses were also stratified by ethnicity and by DXA machine type.
The Bland-Altman (18) method was used to assess the agreement between DXA and MRI. For each subject, we calculated the mean of the fat estimates from the two methods, and then calculated their difference. A graph of the difference between methods against the mean was plotted. The limits of agreement for the two methods, essentially the 95% Confidence Interval (CI) for the prediction of the difference based on the average, were calculated, as was the precision of those limits. In addition, the correlation (rho) between the average and difference and the standard error of the estimate (SEE) were calculated. This latter value represents the average expected error, as opposed to the maximum error, represented by the limits of agreement.
For purposes of comparing DXA to MRI, we defined lipoatrophy as having leg SAT below the 10th percentile of controls, with men and women done separately, as in previous analyses (19). Leg SAT distributions were displayed as histograms overlaid with smoothed curves from kernel density estimates, and the prevalence of lipoatrophy by DXA and MRI was compared using McNemar’s test.
Multivariable regression equations were calculated with the difference between DXA and MRI-measured trunk, leg, arm, or total fat as the dependent variable. Models were constructed for each outcome using HIV status, demographics (gender, age and race), and DXA machine type (Hologic vs. Lunar) as predictor variables. Interactions between HIV status, gender, ethnicity, and age were also assessed and included if they reached statistical significance. The linearity assumption was tested for continuous measures by adding quadratic terms to the models and by examining generalized additive models (20). To account for possible differences between study sites, likelihood ratio testing was used to determine whether a random site effect should be added to the model. Confidence intervals were determined using the bias-corrected accelerated bootstrap method (21), with p-values defined as the one minus the highest confidence level that still excluded zero; this was necessary because the error residuals appeared to be non-Gaussian.
All analyses were conducted using the SAS system, version 9.1 (SAS Institute, Inc., Cary, NC).
Subjects
Body composition by MRI and DXA were available on 1137 subjects whose characteristics are presented in Table 1. Compared with HIV-infected subjects, controls were slightly taller and weighed more (p≤0.001), and had greater amounts of fat and adipose tissue as measured by DXA and MRI (Table 2).
TABLE 1
TABLE 1
Subject characteristics for HIV-infected and control men and women
TABLE 2
TABLE 2
Comparison of DXA and MRI-measured adipose tissue mass1 by HIV status and Gender
Univariate comparisons between DXA and MRI
DXA estimated fat was consistently larger than MRI estimated adipose tissue (p<0.0001) in every depot and subgroup, with the exception of trunk in women, where MRI was larger than DXA (Table 2). Despite these large differences, all DXA-measured fat depots were strongly correlated with their corresponding MRI measures (r=0.71 to 0.96, all p<0.0001), but correlations tended to be slightly weaker for arm than for leg, trunk and total fat (Table 2).
The percent difference between DXA and MRI was greater for leg and arm, and less for trunk (Figure 1). In control subjects, the median percent difference in DXA-estimated fat compared to MRI estimated adipose tissue was up to 69% higher for leg and up to 120% higher for arm, while in HIV-infected subjects, the median percent difference in DXA-estimated fat compared to MRI was up to 30% higher for leg and up to 75% higher for arm. Trunk fat showed much less difference, with the median for DXA being 5% higher in men and 9% lower in women than MRI, for both control and HIV-infected subjects. Similarly, median percent differences were larger for total fat in controls compared with HIV-infected subjects (up to 20% higher in HIV and up to 35% higher in controls, p<0.0001).
Figure 1
Figure 1
Percent difference in adipose tissue estimated by DXA versus MRI. (A) Men, (B) Women. Closed bars: HIV+. Open bars: Control. Results are median +/− 95%CI. P-values are for HIV+ vs. Control. Tests for HIV+ vs. Control from Wilcoxon rank-sum test. (more ...)
Bland-Altman analysis was used to assess the agreement between DXA and MRI by plotting the difference (DXA minus MRI) against the amount of fat using the average of the two methods. The largest relative differences between DXA and MRI were found for limb fat, while trunk fat showed the least difference (Figure 2). More bias was seen in Control than in HIV for leg, arm, and total fat (all p< 0.01 for HIV vs. Control test of difference in rho). As measured by SEE, precision appeared similar in HIV and Control.
Figure 2
Figure 2
Figure 2
Figure 2
Bland-Altman plots comparing fat by HIV status. (A) Leg, (B) Arm, (C) Total, (D) Trunk in Men, (E) Trunk in Women. Closed symbols: HIV+. Open symbols: Control. Note that y-axes are different in panels A and B.
The amount of bias increased and the precision decreased as the average amount of limb fat increased. For example, at 5 kg of average leg fat, the estimated bias (mean±SD) was 1.3±1.7 in HIV and 1.6±1.3 in controls. By contrast, at 15 kg of leg fat, estimated bias was 3.9±5.2 in HIV and 4.8±3.9 in controls.
Bias in trunk fat was weaker and showed gender differences. A weak positive bias was seen in men, indicating that DXA tended to estimate higher amounts of trunk fat compared with MRI adipose tissue mass as the average trunk fat increased, whereas a negative bias was seen in women. In men, more trunk fat bias was seen in controls than in HIV (p=0.0003), while in women, more bias was seen in HIV (p<0.0001).
An examination of ethnic differences found that correlations between DXA and MRI tended to be slightly stronger in African-Americans than in Caucasians, regardless of HIV status (differences in r=0.04 to 0.11, p ≤ 0.003). Bland-Altman analysis revealed similar bias in African-Americans and Caucasians; an exception was seen in trunk fat, where Caucasian control women showed a slight positive bias (in contrast to the negative bias seen for other women), and where African-American HIV-infected men showed almost no bias (in contrast to the positive bias seen for other men).
Prevalence of Lipoatrophy by DXA and MRI
Because leg SAT is the depot most affected by HIV lipoatrophy, we compared the distributions of leg fat by DXA and adipose tissue mass by MRI (Figure 3). HIV-infected subjects demonstrated a dramatically lower distribution of leg fat than controls in both DXA and MRI (p<0.0001 in both men and women). However, a more pronounced upward shift in the distribution of DXA-estimated leg fat in controls was seen compared with MRI. The prevalence of lipoatrophy in HIV by leg fat (defined as being in the lowest decile of controls by DXA and MRI) was higher using DXA than when MRI was used in both men (69% of HIV-infected men for DXA vs. 50% of HIV-infected men for MRI, p<0.0001) and women (47% of HIV-infected women vs. 33% of HIV-infected women, p<0.0001).
Figure 3
Figure 3
Figure 3
Density plots comparing leg fat in HIV+ and Control men and women. (A) MRI in men. (B) DXA in men. (C) MRI in women. (D) DXA in women.
Multivariable Associations with DXA-MRI difference
To further explore the findings of differences in measurement of leg and trunk fat, we conducted multivariable analyses to examine the associations of HIV status, demographics, and DXA machine with the DXA-MRI difference (Table 3). Trunk fat estimates showed differences between DXA and MRI for women vs. men (−1.24 kg, p<0.0001), for African-Americans vs. Caucasians (−0.76 kg, p<0.0001), for use of the DXA Hologic machine vs. Lunar (−1.13 kg, p=0.003) and with increasing age (+0.15 kg per decade, p=0.039), but little difference in trunk fat was seen in HIV vs. Control (−0.96 kg, p=0.084) after adjustment.
TABLE 3
TABLE 3
Multivariable associations of HIV-status, Demographics and DXA Machine with DXA - MRI difference
Unlike the findings with trunk fat, the largest difference for leg fat by DXA vs. MRI was in HIV status, where the difference was larger in controls than in HIV-infected (2.2 kg, p<0.0001). The difference in DXA vs. MRI was larger in women than in men (0.94 kg, p<0.0001), and in African-Americans than in Caucasians (0.46 kg, p<0.0001).
Although DXA-measured fat is strongly correlated with MRI measured adipose tissue, our main finding was that associations were biased in both HIV-infected and control populations. As the average amount of fat increases, the difference between DXA and MRI tends to increase, with DXA giving larger estimates of fat, particularly for limb fat. Since controls have more limb fat than HIV-infected subjects and controls showed a greater upward shift in DXA fat, DXA estimated a higher prevalence of peripheral lipoatrophy compared with MRI in HIV-infected subjects. Although there is no accepted cutoff that defines HIV-associated lipoatrophy, we compared the prevalence of subjects having leg SAT below the 10th percentile of controls, finding a higher prevalence of this definition of lipoatrophy by DXA compared with MRI. In contrast, differences in DXA and MRI trunk fat estimates were much smaller in all subgroups, and there were gender- and race-related differences.
DXA and MRI measure distinct, but overlapping compartments. DXA estimates fat content by tissue density, while MRI measures adipose tissue volume. In addition to cellular lipid, adipose tissue contains extracellular water (~12% of total volume in analyses of excised specimens(22)), a small amount of intracellular water, other types of cells besides adipocytes, and extracellular solids. While these relationships may be affected as a result of fat depletion and composition changes related to lipoatrophy, this is not the complete answer, since bias was also seen in controls. Furthermore, if the inclusion of the non-lipid component in MRI analysis was the cause of the difference, one would expect MRI to give higher results than DXA, while the opposite was true.
Our finding that DXA and MRI are highly correlated but have important biases is supported by previous work in smaller studies of HIV-uninfected subjects. A positive bias was found comparing limb fat in DXA to MRI in a small study of 16 healthy men and women(11). A study of 13 healthy, premenopausal women found high correlation but poor agreement between DXA, MRI and underwater-measured adiposity, with differences between DXA and MRI atributed to fat calibration errors (9). Investigators concluded that no method can yet be regarded as a satisfactory reference technique.
Our finding that bias is proportional to the average amount of fat is similar to findings in the general population of more error in DXA in healthy men with higher adiposity and body thickness (23). They felt DXA trunk fat precision and accuracy may be decreased by several factors, such as observer error in delineating specific regions due to X-ray beam inability to detect the small amount of soft tissue mass.
For trunk, we found positive bias in men, but negative bias in women. This may be due to the fact that DXA estimates do not differentiate between intra-abdominal and subcutaneous fat, and the women in our study have less VAT but more upper and lower trunk SAT compared with men, both in HIV and controls (6, 7). A small 12-16 week study of HIV-infected subjects(8) found that DXA and MRI estimates of changes in SAT and VAT were strongly associated (R2=0.70, p<0.001), although DXA estimated larger changes in total body fat compared with MRI.
A possible contributor to these differences is that DXA measured fat also includes fat that MRI measured adipose tissue cannot detect. For example, in the trunk region, fat in the liver, intestine and all other viscera are not included in MRI-measured AT. Likewise, intra-muscular fat cannot be detected by MRI. In addition, small adipose tissue depots below the resolution of MRI are not included in MRI. These small adipose tissue depots include some of the VAT and inter-muscular adipose tissue depot in both trunk and limb regions. These differences may partially explain why DXA measured fat is higher than MRI measured adipose tissue. Additionally, in our MRI and DXA protocol, the cut-off between limbs and trunk in MRI and DXA are not identical. DXA limb fat may include more hip fat than MRI measured limb adipose tissue. The bias identified in the Bland-Altman analysis may also be due to more fat in the hip region in heavier subjects and women have more fat in the hip region than men do. However, these latter issues do not apply to arm fat, which shows similar trends.
What is the significance of these differences between DXA and MRI? Both DXA and MRI have been used in previous studies to estimate regional and total adiposity in HIV infection, but results from studies utilizing DXA may not be able to be directly extrapolated to studies in which MRI or other methods are used. Consequently, comparisons of HIV and controls and the prevalence or amount of lipoatrophy will differ depending on which method is used to quantify regional adipose tissue as well as how lipoatrophy is defined. However, when certain guidelines are followed (24), DXA has been found to have adequate internal validity for measuring body composition changes.
One limitation of our study was the lack of an absolute reference standard for estimating regional fat quantities. Another limitation was that several different DXA machine models were utilized in this study. Prior work (25) has found that although fan- and pencil-beam models are highly correlated, small but significant differences exist between the instruments. However, sensitivity analysis including only Hologic machines, admittedly including both fan- and pencil-beam models, did not change our key finding that DXA is more likely than MRI to find lipoatrophy in HIV-infected subjects. DXA and MRI estimates of regional fat also differ because the cuts are slightly different: DXA cuts are at an angle perpendicular to the femoral neck, while MRI cuts are perpendicular to the longitudinal axis of the body. Although the vast majority of subjects had DXA and MRI scans performed on the same day, we also examined the association of time between scans with difference between DXA and MRI measured fat, finding little association (rho ≈ −0.02 or less, p>0.70). Finally, direct comparison is limited by the fact that DXA measures fat by attenuation of x-ray, while MRI directly measures AT volume (3).
Further study in other populations is required to characterize the differences between DXA and MRI measurements of adipose tissue, including those testing differences in clinical outcomes between the two techniques. Comparison of DXA and MRI should also be made among HIV-infected subjects with other methods such as CT, since small studies of HIV-uninfected subjects have found important differences in variability and accuracy between these three methods (26, 27). For example, the slice traditionally used in CT studies of visceral adipose tissue is not the best marker of visceral adiposity compared to MRI measures (28). In the current study, we found that although DXA-measured adipose tissue correlates strongly with MRI-measures in both HIV-infected subjects and controls, the difference between MRI and DXA increases as the average fat increases, particularly for limb fat. DXA may therefore estimate a higher peripheral lipoatrophy prevalence in HIV-infected subjects. Both leg and arm fat were higher for DXA, but the DXA-MRI differences vary among important subgroups such as leg fat in HIV vs. controls and trunk fat in men vs. women. Therefore, caution must be used when comparing study results using different methods of fat measurement.
Acknowledgments
Supported by NIH grants RO1- DK57508, HL74814, and HL 53359, and NIH GCRC grants M01- RR00036, RR00051, RR00052, RR00054, RR00083, RR0636, and RR00865. The funding agency had no role in the collection or analysis of the data.
Appendix
Conflicts of Interest
None. Drs. Scherzer, Shen, Bacchetti, Kotler, Lewis, Shlipak, Punyanitya, Heymsfield, and Grunfeld received funding from the supporting grants.
Role of the Funder
The funder played no role in the conduct of the study, collection of the data, management of the study, analysis of data, interpretation of the data or preparation of the manuscript. A representative of the funding agent participated in planning the protocol. As part of the standard operating procedures of CARDIA, the manuscript was reviewed at the NHLBI, but no revisions were requested.
Sites and Investigators
University Hospitals of Cleveland (Barbara Gripshover, M.D.); Tufts University (Abby Shevitz, M.D. and Christine Wanke, M.D.); Stanford University (Andrew Zolopa, M.D. and Lisa Gooze, M.D.); University of Alabama at Birmingham (Michael Saag, M.D. and Barbara Smith, Ph.D.); John Hopkins University (Joseph Cofrancesco and Adrian Dobs); University of Colorado Heath Sciences Center (Constance Benson, M.D. and Lisa Kosmiski, M.D.); University of North Carolina at Chapel Hill (Charles van der Horst, M.D.); University of California at San Diego (W. Christopher Mathews, M.D. and Daniel Lee, M.D.); Washington University (William Powderly, M.D. and Kevin Yarasheski, Ph.D.); VA Medical Center, Atlanta (David Rimland, M.D.); University of California at Los Angeles (Judith Currier, M.D. and Matthew Leibowitz, M.D.); VA Medical Center, New York (Michael Simberkoff, M.D. and Juan Bandres, M.D.); VA Medical Center, Washington DC (Cynthia Gibert, M.D. and Fred Gordin, M.D.); St Luke’s-Roosevelt Hospital Center (Donald Kotler, M.D. and Ellen Engelson, Ph.D.); University of California at San Francisco (Morris Schambelan, M.D. and Kathleen Mulligan, Ph.D.); Indiana University (Michael Dube, M.D.); Kaiser Permanente, Oakland (Stephen Sidney, M.D.); University of Alabama at Birmingham (Cora E. Lewis, M.D.).
Data Coordinating Center
University of Alabama, Birmingham (O. Dale Williams, Ph.D, Heather McCreath, Ph.D, Charles Katholi, Ph.D, George Howard, Ph.D, Tekeda Ferguson, and Anthony Goudie)
Image Reading Center
St Luke’s-Roosevelt Hospital Center: (Steven Heymsfield, M.D., Jack Wang, M.S. and Mark Punyanitya).
Office of the Principal Investigator
University of California, San Francisco, Veterans Affairs Medical Center and the Northern California Institute for Research and Development: (Carl Grunfeld, M.D., Ph.D, Phyllis Tien, M.D., Peter Bacchetti, Ph.D, Dennis Osmond, Ph.D, Andrew Avins, M.D., Michael Shlipak, M.D., Rebecca Scherzer, Ph.D, Mae Pang, R.N., M.S.N., Heather Southwell, M.S., R.D., Erin Madden, MPH, and Yong Kyoo Chang, MS).
Contributor Information
Rebecca Scherzer, University of California, San Francisco.
Wei Shen, Obesity Research Center, St Luke’s-Roosevelt Hospital and Institute of Human Nutrition, Columbia University College of Physicians and Surgeons, New York.
Peter Bacchetti, University of California, San Francisco.
Donald Kotler, St. Luke’s-Roosevelt Hospital, Columbia University, New York.
Cora E. Lewis, Division of Preventive Medicine, University of Alabama at Birmingham.
Michael G. Shlipak, University of California, San Francisco and Veterans Affairs Medical Center.
Mark Punyanitya, St. Luke’s-Roosevelt Hospital, Columbia University, New York.
Steven B. Heymsfield, Merck & Co, Rahway, NJ.
Carl Grunfeld, University of California, San Francisco and Veterans Affairs Medical Center.
1. Despres JP, Ross R, Lemieux S. Imaging techniques applied to the measurement of human body composition. In: Roche AF, Heymsfield SB, Lohman TG, editors. Human body composition. Human Kinetics; Chicago, IL: 1996. pp. 149–166.
2. Wang ZM, Pierson RN, Jr., Heymsfield SB. The five-level model: a new approach to organizing body-composition research. Am J Clin Nutr. 1992;56:19–28. [PubMed]
3. Shen W, Wang Z, Punyanita M, et al. Adipose tissue quantification by imaging methods: a proposed classification. Obes Res. 2003;11:5–16. [PMC free article] [PubMed]
4. Snyder WS, Cook MJ, Nasset ES, Karhausen RL, Howells GP, Tipton IH. Report of the Task Group on Reference Man. Pergamon Press; Oxford, UK: 1975. pp. 40–5. ICRP Publication 23.
5. Safrin S, Grunfeld C. Fat distribution and metabolic changes in patients with HIV infection. Aids. 1999;13:2493–505. [PubMed]
6. Bacchetti P, Gripshover B, Grunfeld C, et al. Fat distribution in men with HIV infection. J Acquir Immune Defic Syndr. 2005;40:121–31. [PMC free article] [PubMed]
7. Bacchetti P, Cofrancesco J, Heymsfield S, et al. Fat distribution in women with HIV infection. J Acquir Immune Defic Syndr. 2006;42:562–71. [PMC free article] [PubMed]
8. Lan S, Engelson E, Agin D, et al. Validation of dual-energy X-ray absorptiometry in the assessment of change in fat compartments, compared to measurement by magnetic resonance imaging, in HIV-infected adults. International Journal of Body Composition. 2003;1:13–19.
9. Tothill P, Han TS, Avenell A, McNeill G, Reid DM. Comparisons between fat measurements by dual-energy X-ray absorptiometry, underwater weighing and magnetic resonance imaging in healthy women. Eur J Clin Nutr. 1996;50:747–52. [PubMed]
10. Tothill P, Han TS, Avenell A, McNeill G, Reid DM. Comparisons between fat measurements by dual-energy X-ray absorptiometry, magnetic resonance imaging and underwater weighing. Appl Radiat Isot. 1998;49:457–9. [PubMed]
11. Fuller NJ, Hardingham CR, Graves M, et al. Assessment of limb muscle and adipose tissue by dual-energy X-ray absorptiometry using magnetic resonance imaging for comparison. Int J Obes Relat Metab Disord. 1999;23:1295–302. [PubMed]
12. Tien PC, Benson C, Zolopa AR, Sidney S, Osmond D, Grunfeld C. The study of fat redistribution and metabolic change in HIV infection (FRAM): methods, design, and sample characteristics. Am J Epidemiol. 2006;163:860–9. [PMC free article] [PubMed]
13. Friedman GD, Cutter GR, Donahue RP, et al. CARDIA: study design, recruitment, and some characteristics of the examined subjects. J Clin Epidemiol. 1988;41:1105–16. [PubMed]
14. Gallagher D, Belmonte D, Deurenberg P, et al. Organ-tissue mass measurement allows modeling of REE and metabolically active tissue mass. Am J Physiol. 1998;275:E249–58. [PubMed]
15. Shen W, Wang Z, Tang H, et al. Volume estimates by imaging methods: model comparisons with visible woman as the reference. Obes Res. 2003;11:217–25. [PMC free article] [PubMed]
16. Siri WE. Body composition from fluid spaces and density analysis of methods. In: Brozeck J, Henschel A, editors. Techniques for Measuring Body Composition. National Academy of Sciences / National Research Council; Washington, DC: 1961. pp. 223–244.
17. Pearson D, Horton B, Green D, Miller C, Jackson S. In-vivo and Phantom Bone Mineral and Body Composition Calibration of DXA. American Society for Bone and Mineral Research; Seattle, WA: 2004.
18. Bland JM, Altman DG. Statistical methods for assessing agreement between two methods of clinical measurement. Lancet. 1986;1:307–10. [PubMed]
19. Kosmiski LA, Bacchetti P, Kotler DP, et al. Relationship of Fat Distribution with Adipokines in HIV Infection. J Clin Endocrinol Metab. 2007 [PubMed]
20. Hastie T, Tibshirani R. Generalized Additive Models. Chapman and Hall; New York: 1990.
21. Efron B, Tibshirani R. An Introduction to the Bootstrap. Chapman and Hall; London: 1993.
22. Wang J, Pierson RN., Jr. Disparate hydration of adipose and lean tissue require a new model for body water distribution in man. J Nutr. 1976;106:1687–93. [PubMed]
23. Park YW, Heymsfield SB, Gallagher D. Are dual-energy X-ray absorptiometry regional estimates associated with visceral adipose tissue mass? Int J Obes Relat Metab Disord. 2002;26:978–83. [PubMed]
24. Lohman TG, Harris M, Teixeira PJ, Weiss L. Assessing body composition and changes in body composition. Another look at dual-energy X-ray absorptiometry. Ann N Y Acad Sci. 2000;904:45–54. [PubMed]
25. Soriano JM, Ioannidou E, Wang J, et al. Pencil-beam vs fan-beam dual-energy X-ray absorptiometry comparisons across four systems: body composition and bone mineral. J Clin Densitom. 2004;7:281–9. [PubMed]
26. Greenfield JR, Samaras K, Chisholm DJ, Campbell LV. Regional intra-subject variability in abdominal adiposity limits usefulness of computed tomography. Obes Res. 2002;10:260–5. [PubMed]
27. Snijder MB, Visser M, Dekker JM, et al. The prediction of visceral fat by dual-energy X-ray absorptiometry in the elderly: a comparison with computed tomography and anthropometry. Int J Obes Relat Metab Disord. 2002;26:984–93. [PubMed]
28. Shen W, Punyanitya M, Chen J, et al. Visceral adipose tissue: relationships between single slice areas at different locations and obesity-related health risks. Int J Obes (Lond) 2007;31:763–9. [PMC free article] [PubMed]