Comparison of Two Software Versions for Assessment of Body‐Composition Analysis by DXA

Objective: To compare two software versions provided by Lunar Co. for assessment of body composition analysis by DXA. Research Methods and Procedures: Soft‐tissue phantoms for lean tissue (water) and fat tissue (methanol) were repeatedly scanned using DXA machines (DPX‐L; Lunar Co., Madison, WI) and analyzed using software version 1.33 and the updated year 2000‐compatible version 1.35. For the intersoftware comparison, the phantoms were scanned 10 times (each scan was analyzed once) with both software versions using all three scanning modes (slow, medium, and fast) for a total of 60 scans and analyses. For the intermachine comparison, the same phantoms were scanned three times (each scan was analyzed once) with a second machine from the same manufacturer using all three scanning modes and version 1.35 only. Percentage of fat was the variable of interest. Results: For version 1.33, fat was 9.9 ± 0.4%, 10.0 ± 0.5%, and 11.0 ± 0.5% (mean ± SD) for the lean‐tissue phantom and 50.8 ± 0.3%, 50.9 ± 0.5%, and 51.1 ± 0.6% for the fat‐tissue phantom using the slow, medium, and fast scanning modes, respectively. For version 1.35, the respective fat values were 9.8 ± 0.7%, 9.9 ± 0.4%, and 10.3 ± 0.7%, and 50.6 ± 0.5%, 50.9 ± 0.6%, and 50.8 ± 0.8%, respectively. For the lean‐tissue phantom, the estimation of percentage of fat was significantly (p < 0.05) affected by scanning mode but not by software version. For the fat‐tissue phantom, the estimation of percentage of fat was not affected by either scanning mode or software version. The use of version 1.35 did not effect intermachine variability. Discussion: Versions 1.33 and 1.35 of the Lunar body composition software appear to be comparable. Soft‐tissue phantoms, such as the ones described in this paper, may be useful in monitoring the reproducibility of body composition analyses within and between DXA machines, particularly in longitudinal studies.     

Body Composition Measured by Dual‐energy X‐ray Absorptiometry Half‐body Scans in Obese Adults

Dual‐energy X‐ray absorptiometry (DXA) has become a common measurement of human body composition. However, obese subjects have been understudied largely due to weight and scan area restrictions. Newer DXA instruments allow for heavier subjects to be supported by the DXA scanner, but the imaging area is still smaller than the body size of some obese subjects. In this study, we determined the validity of an automated half‐scan methodology by comparing to the standard whole‐body scans in a cohort of obese volunteers. Fifty‐two subjects whose BMI >30 kg/m² completed whole‐body iDXA (GE Lunar) scans. The resulting scans were analyzed in three ways: the standard whole‐body scan, total body estimated from the left side, and from the right side. Fat mass, nonbone lean mass, bone mineral content (BMC), and percent fat derived from each half scan were compared to the whole‐body scans. Total fat mass, nonbone lean mass, or percent fat was comparable for the whole‐body scans, left, and right side scans (>97% within individuals and >99.9% for the group). The BMC estimate using the right side scan was slightly but statistically higher than the whole‐body BMC (～30 g or 1%, P < 0.001), while the left side scan BMC estimate was lower than the whole‐body BMC by the same magnitude. No significant magnitude bias was found for any of the composition variables. We conclude that the new iDXA half‐body analysis in obese subjects appears to be closely comparable to whole‐body analysis for fat mass, nonbone lean mass, and percent fat.     

Precision of a new tool to measure visceral adipose tissue (VAT) using dual‐energy X‐Ray absorptiometry (DXA)

Objective:

A new tool to quantify visceral adipose tissue (VAT) over the android region of a total body dual‐energy x‐ray absorptiometry (DXA) scan has recently been reported. The measurement, CoreScan, is currently available on Lunar iDXA densitometers. The purpose of the study was to determine the precision of the CoreScan VAT measurement, which is critical for understanding the utility of this measure in longitudinal trials.

Design and Methods:

VAT precision was characterized in both an anthropomorphic imaging phantom (measured on 10 Lunar iDXA systems) and a clinical population consisting of obese women (n = 32).

Results:

The intrascanner precision for the VAT phantom across 9 quantities of VAT mass (0–1,800 g) ranged from 28.4 to 38.0 g. The interscanner precision ranged from 24.7 to 38.4 g. There was no statistical dependence on the quantity of VAT for either the inter‐ or intrascanner precision result (p = 0.670). Combining inter‐ and intrascanner precision yielded a total phantom precision estimate of 47.6 g for VAT mass, which corresponds to a 4.8% coefficient of variance (CV) for a 1 kg VAT mass. Our clinical population, who completed replicate total body scans with repositioning between scans, showed a precision of 56.8 g on an average VAT mass of 1110.4 g. This corresponds to a 5.1% CV. Hence, the in vivo precision result was similar to the phantom precision result.

Conclusions:

The study suggests that CoreScan has a relatively low precision error in both phantoms and obese women and therefore may be a useful addition to clinical trials where interventions are targeted towards changes in visceral adiposity.     

Body Composition at 6 months of Life: Comparison Of Air Displacement Plethysmography and Dual‐Energy X‐Ray Absorptiometry

Body composition assessment during infancy is important because it is a critical period for obesity risk development, thus valid tools are needed to accurately, precisely, and quickly determine both fat and fat‐free mass. The purpose of this study was to compare body composition estimates using dual‐energy x‐ray absorptiometry (DXA) and air displacement plethysmography (ADP) at 6 months old. We assessed the agreement between whole body composition using DXA and ADP in 84 full‐term average‐for‐gestational‐age boys and girls using DXA (Lunar iDXA v11–30.062; Infant whole body analysis enCore 2007 software, GE, Fairfield, CT) and ADP (Infant Body Composition System v3.1.0, COSMED USA, Concord, CA). Although the correlations between DXA and ADP for %fat (r = 0.925), absolute fat mass (r = 0.969), and absolute fat‐free mass (r = 0.945) were all significant, body composition estimates by DXA were greater for both %fat (31.1 ± 3.6% vs. 26.7 ± 4.7%; P < 0.001) and absolute fat mass (2,284 ± 449 vs. 1,921 ± 492 g; P < 0.001), and lower for fat‐free mass (5,022 ± 532 vs. 5,188 ± 508 g; P < 0.001) vs. ADP. Inter‐method differences in %fat decreased with increasing adiposity and differences in fat‐free mass decreased with increasing infant age. Estimates of body composition determined by DXA and ADP at 6 months of age were highly correlated, but did differ significantly. Additional work is required to identify the technical basis for these rather large inter‐method differences in infant body composition.     

Dual‐energy X‐ray Absorptiometry and Anthropometric Estimates of Visceral Fat in Black and White South African Women

Visceral adipose tissue (VAT) is associated with increased risk for cardiovascular disease, and therefore, accurate methods to estimate VAT have been investigated. Computerized tomography (CT) is the gold standard measure of VAT, but its use is limited. We therefore compared waist measures and two dual‐energy X‐ray absorptiometry (DXA) methods (Ley and Lunar) that quantify abdominal regions of interest (ROIs) to CT‐derived VAT in 166 black and 143 white South African women. Anthropometry, DXA ROI, and VAT (CT at L4–L5) were measured. Black women were younger (P < 0.001), shorter (P < 0.001), and had higher body fat (P < 0.05) than white women. There were no ethnic differences in waist (89.7 ± 18.2 cm vs. 90.1 ± 15.6 cm), waist:height ratio (WHtR, 0.56 ± 0.12 vs. 0.54 ± 0.09), or DXA ROI (Ley: 2.2 ± 1.5 vs. 2.1 ± 1.4; Lunar: 2.3 ± 1.4 vs. 2.3 ± 1.5), but black women had less VAT, after adjusting for age, height, weight, and fat mass (76 ± 34 cm² vs. 98 ± 35 cm²; P < 0.001). Ley ROI and Lunar ROI were correlated in black (r = 0.983) and white (r = 0.988) women. VAT correlated with DXA ROI (Ley: r = 0.729 and r = 0.838, P < 0.01; Lunar: r = 0.739 and r = 0.847, P < 0.01) in black and white women, but with increasing ROI android fatness, black women had less VAT. Similarly, VAT was associated with waist (r = 0.732 and r = 0.836, P < 0.01) and WHtR (r = 0.721 and r = 0.824, P < 0.01) in black and white women. In conclusion, although DXA‐derived ROIs correlate well with VAT as measured by CT, they are no better than waist or WHtR. Neither DXA nor anthropometric measures are able to accurately distinguish between high and low levels of VAT between population groups.     

Precision and Accuracy of Dual‐Energy X‐ray Absorptiometry for Determining in Vivo Body Composition of Mice

Objective: To evaluate the precision and accuracy of dual‐energy X‐ray absorptiometry (DXA) for the measurement of total‐bone mineral density (TBMD), total‐body bone mineral (TBBM), fat mass (FM), and bone‐free lean tissue mass (LTM) in mice. Research Methods and Procedures: Twenty‐five male C57BL/6J mice (6 to 11 weeks old; 19 to 29 g) were anesthetized and scanned three times (with repositioning between scans) using a peripheral densitometer (Lunar PIXImus). Gravimetric and chemical extraction techniques (Soxhlet) were used as the criterion method for the determination of body composition; ash content was determined by burning at 600°C for 8 hours. Results: The mean intraindividual coefficients of variation (CV) for the repeated DXA analyses were: TBMD, 0.84%; TBBM, 1.60%; FM, 2.20%; and LTM, 0.86%. Accuracy was determined by comparing the DXA‐derived data from the first scan with the chemical carcass analysis data. DXA accurately measured bone ash content (p = 0.942), underestimated LTM (0.59 ± 0.05g, p < 0.001), and overestimated FM (2.19 ± 0.06g, p < 0.001). Thus, DXA estimated 100% of bone ash content, 97% of carcass LTM, and 209% of carcass FM. DXA‐derived values were then used to predict chemical values of FM and LTM. Chemically extracted FM was best predicted by DXA FM and DXA LTM [FM = ?0.50 + 1.09(DXA FM) ? 0.11(DXA LTM), model r² = 0.86, root mean square error (RMSE) = 0.233 g] and chemically determined LTM by DXA LTM [LTM = ?0.14 + 1.04(DXA LTM), r² = 0.99, RMSE = 0.238 g]. Discussion: These data show that the precision of DXA for measuring TBMD, TBBM, FM, and LTM in mice ranges from a low of 0.84% to a high of 2.20% (CV). DXA accurately measured bone ash content but overestimated carcass FM and underestimated LTM. However, because of the close relationship between DXA‐derived data and chemical carcass analysis for FM and LTM, prediction equations can be derived to more accurately predict body composition.     

Comparison of gross body fat‐water magnetic resonance imaging at 3 Tesla to dual‐energy X‐ray absorptiometry in obese women

Objective:

Improved understanding of how depot‐specific adipose tissue mass predisposes to obesity‐related comorbidities could yield new insights into the pathogenesis and treatment of obesity as well as metabolic benefits of weight loss. We hypothesized that three‐dimensional (3D) contiguous “fat‐water” MR imaging (FWMRI) covering the majority of a whole‐body field of view (FOV) acquired at 3 Tesla (3T) and coupled with automated segmentation and quantification of amount, type, and distribution of adipose and lean soft tissue would show great promise in body composition methodology.

Design and Methods:

Precision of adipose and lean soft tissue measurements in body and trunk regions were assessed for 3T FWMRI and compared to dual‐energy X‐ray absorptiometry (DXA). Anthropometric, FWMRI, and DXA measurements were obtained in 12 women with BMI 30‐39.9 kg/m².

Results:

Test–retest results found coefficients of variation (CV) for FWMRI that were all under 3%: gross body adipose tissue (GBAT) 0.80%, total trunk adipose tissue (TTAT) 2.08%, visceral adipose tissue (VAT) 2.62%, subcutaneous adipose tissue (SAT) 2.11%, gross body lean soft tissue (GBLST) 0.60%, and total trunk lean soft tissue (TTLST) 2.43%. Concordance correlation coefficients between FWMRI and DXA were 0.978, 0.802, 0.629, and 0.400 for GBAT, TTAT, GBLST, and TTLST, respectively.

Conclusions:

While Bland–Altman plots demonstrated agreement between FWMRI and DXA for GBAT and TTAT, a negative bias existed for GBLST and TTLST measurements. Differences may be explained by the FWMRI FOV length and potential for DXA to overestimate lean soft tissue. While more development is necessary, the described 3T FWMRI method combined with fully‐automated segmentation is fast (<30‐min total scan and post‐processing time), noninvasive, repeatable, and cost‐effective.     

Comparison of Hologic QDR‐1000/W and 4500W DXA Scanners in 13‐ to 18‐Year Olds

Objective: Body composition measurements made using Hologic QDR‐1000/W pencil‐beam and QDR‐4500W fan‐beam scanners (Bedford, MA) were compared in a sample of 13‐ to 18‐year‐old white and black youth (n = 219). Research Methods and Procedures: Total fat (FAT), fat‐free soft tissue (FFST), bone mineral content (BMC), bone mineral density (BMD), and percent body fat (%BF) were compared between repeated measurements using the QDR‐4500 and between the two scanners using mixed model ANOVA. Intraclass correlation coefficients and Bland‐Altman limits of agreement were used to evaluate inter‐ and intrascanner reliability. Results: Intraclass correlation coefficients for repeated measurements using the QDR‐4500 ranged from 0.997 to 0.999 for FAT, %BF, FFST, and BMC and 0.987 for BMD. Mean measurements made using the two scanners differed significantly for FAT, %BF, BMC, and BMD (p < 0.0001), and scan by sex interactions were significant (all p < 0.0005). There were no significant differences in mean measurements between repeat scans using the QDR‐4500 (all p > 0.19). Limits of agreement for measurements of FAT, FFST, and %BF made using the two scanners were approximately three times as wide as those for two measurements using the QDR‐4500. For lower values of FAT and %BF, the QDR‐4500 gave higher measurements than the QDR‐1000, whereas at higher values, this relationship was reversed. The QDR‐1000 tended to give higher BMC measurements, with larger differences for higher values. Discussion: Using different models of DXA scanners within a study may reduce precision of body composition measurement. This issue needs to be considered in the design of longitudinal studies.     

Hispanic and Asian pubertal girls have higher android/gynoid fat ratio than whites

Objective: To examine differences in body size, composition, and distribution of body fat among Hispanic, white, and Asian adolescents. Research Methods and Procedures: This included cross‐sectional data from the baseline sample of the Adequate Calcium Today trial. Participants included 180 Asian, 234 Hispanic, and 325 white girls 11.8 ± 0.05 years of age from Arizona, California, Hawaii, Indiana, Ohio, and Nevada. Anthropometric and DXA measurements (Lunar Prodigy) were standardized across sites. Tanner pubertal stage was self‐selected from line drawings. Physical activity was assessed by a validated questionnaire. Comparisons between ethnic groups were examined using contrasts in the context of a general linear model. Results: Controlling for pubertal stage and study site only, Asians weighed less than Hispanics and were shorter than Hispanics and whites. Controlling for pubertal stage, height, weight, and study site, Asians had shorter leg lengths, smaller waist circumference, longer trunk lengths, more lean mass, less total fat mass, and less gynoid fat mass than Hispanics and whites; Asians had larger bitrochanteric width than whites; Asians had smaller DXA‐derived android fat mass than Hispanics; and whites had smaller mean android/gynoid fat ratio than Hispanics. However, whites had a smaller android/gynoid fat ratio than both Asians and Hispanics in a model that adjusted for ethnicity, pubertal stage, bitrochanteric width, waist circumference, trunk length, log of physical activity, and study site, which explained 77% of the variation. Discussion: Ethnic differences in fat distribution are partially explained by differences in skeletal dimensions.     

Exogenous pulmonary surfactant prevents the development of intra‐abdominal adhesions in rats

Intra‐abdominal adhesions are major post‐operative complications for which no effective means of prevention is available. We aimed to evaluate the efficacy of exogenous pulmonary surfactant administration in the prevention of post‐operative abdominal adhesions. Rats were randomly assigned to undergo laparotomy (L) or gastroenterostomy (GE) and then treated with surfactant (groups L‐S and GE‐S, respectively). Intra‐abdominal adhesions, collagen fibre content, metalloproteinase (MMP)‐9, expression of growth factors (TGF‐β, KGF and VEGF), type III procollagen (PCIII) and pro‐caspase 3, as well as isolectin B4 and ED1‐positive cells expressing MMP‐9, were evaluated. Groups treated with surfactant (GE‐S and L‐S) exhibited fewer adhesions. A significant reduction in collagen fibre content was observed in GE‐S compared to GE animals (P < 0.001). In situ and gelatin zymography analysis showed higher MMP‐9 expression and activity in the GE‐S group compared to the GE group (P < 0.05). ED1‐positive cell counts were significantly higher in the GE‐S group (P < 0.001) than in the GE group. Virtually all cells positive for ED1 were MMP‐9+. Double‐labelling of MMP‐9 with IB4 showed no significant differences between GE‐S and GE groups. TGF‐β, KGF, PCIII and pro‐caspase‐3 mRNA expression decreased significantly in GE‐S compared to GE animals (P < 0.05). Surfactant administration also reduced apoptosis in the GE‐S group. These findings suggest that surfactant reduces the intra‐abdominal adhesions triggered by laparotomy and gastrointestinal anastomosis, thus preventing fibrosis formation at the peritoneal surfaces. This preclinical study suggests an innovative treatment strategy for intra‐abdominal adhesions with surfactant and to endorse its putative mechanism of action.     

Regional Body Composition: Cross‐calibration of DXA Scanners—QDR4500W and Discovery Wi

Differences exist in body composition assessed by dual‐energy X‐ray absorptiometers (DXAs) between devices produced by different manufacturers and different models from the same manufacturer. Cross‐calibration is needed to allow body composition results to be compared in multicenter trials or when scanners are replaced. The aim was to determine reproducibility and extent of agreement between two fan‐beam DXA scanners (QDR4500W, Discovery Wi) for body composition of regional sites. The sample was: 39 women 50.6 ± 9.6 years old with BMI 26.8 ± 5.5 kg/m², body fat 33 ± 7%. Four whole body scans (two on each device) were performed over 3 weeks. Major variables were fat mass, nonosseous lean mass, and bone mineral content (BMC) for the truncal and appendicular regions. Extent of agreement was assessed using Bland and Altman plots. Both devices demonstrated good precision with mean test–retest differences close to zero for fat mass, nonosseous lean mass, and BMC of the truncal and appendicular regions. Evaluation of interdevice agreement revealed significant differences for truncal and appendicular BMC, nonosseous lean mass, and fat mass. The greatest interdevice difference was for truncal fat mass (0.69 ± 0.60 kg). Differences in truncal and appendicular fat mass increased in magnitude at higher mean values. Furthermore, differences in truncal and appendicular fat mass were strongly related to BMI (R = ?0.61, R = ?0.55, respectively). In conclusion, in vivo cross‐calibration is important to ensure comparability of regional body composition data between scanners, especially for truncal fat mass and for subjects with higher BMI.     

Apolipoprotein A‐II Polymorphism and Visceral Adiposity in African‐American and White Women

To determine the association between the ?265 T to C substitution in the apolipoprotein A‐II (APOA‐II) gene and levels of visceral adipose tissue (VAT) in a group of premenopausal African‐American and white women, we genotyped 237 women (115 African‐American and 122 white) for this polymorphism. Body composition was assessed by DXA, and VAT was determined from a single computed tomography scan. In addition to VAT, we examined the association between the polymorphism and other phenotypes (total body fat, total abdominal adipose tissue, and subcutaneous abdominal adipose tissue). The mutant C allele in the APOA‐II gene was less frequent in African‐American compared with white women, 23% vs. 36%, respectively (p < 0.01). VAT was significantly higher in carriers of the C allele compared with noncarriers after adjustment for total body fat (p < 0.05). When separate analyses by ethnic group were conducted, the association between the polymorphism and VAT was observed in white (p < 0.05) but not African‐American (p = 0.57) women. There was no association between the polymorphism and the other phenotypes. These results indicate a significant association between the T265C APOA‐II polymorphism and levels of VAT in premenopausal women. This association is present in white but not African‐American women.     

Evaluation of a new body composition phantom for quality control and cross-calibration of DXA devices.

This study evaluated a new body composition phantom and its use for quality control and cross-calibration of dual-energy X-ray absorptiometry (DXA) instruments for measurements of body composition. We imaged the variable composition phantom (Lunar, Madison, WI) on eight different DXA devices. Deviations of up to 7% fat were observed when we compared the percent fat values measured by the different devices with the nominal values provided by the manufacturer. Absolute precision error of percent fat measurements for the phantom ranged from 0.6 to 0.8%. The phantom's percent fat values were also compared with whole body composition measurements from 130 female and male volunteers. The phantom detected differences in percent fat values that were similar to those found by comparing in vivo measurements with values from different DXA scanner models from the same manufacturer. When comparing different models of scanners from different manufacturers, such as the Hologic QDR-4500 and the Lunar DPX-IQ, the phantom showed a different relationship than was seen for patients. Therefore, corrections or comparisons based on the phantom data alone would be incorrect. In conclusion, the Lunar variable composition phantom is capable of accurately measuring the fat calibration of DXA devices and may be suitable for cross-sectional cross-calibration between scanners from the same manufacturer; however, for comparison of DXA scanners from different manufacturers, in vivo cross-calibration is still the only accurate method. The phantom may be used in longitudinal quality control to verify an instrument's temporal stability.     

Regional Intra‐Subject Variability in Abdominal Adiposity Limits Usefulness of Computed Tomography

Objective: Computed tomography (CT) and magnetic resonance imaging, the most accurate methods of abdominal fat measurement, have been applied using a number of protocols, ranging from single‐slice area determination to multiple‐slice volume calculation. The aim of this study was to assess the validity of single‐slice CT for abdominal fat area measurement by estimating the intra‐subject variability in abdominal fat areas and comparing the ranking of subjects across four contiguous abdominal levels. Research Methods and Procedures: Nineteen premenopausal women (age, 35.3 ± 1.4 years; mean ± SE) were studied. CT was used to measure intra‐abdominal fat (IAF) area, percentage of total intra‐abdominal area (%IAF), subcutaneous abdominal fat (SAF) area, and IAF/SAF at four adjacent cross‐sectional lumbar levels (L2–L4). Intra‐subject variability (percentage) was defined as SD/mean × 100. Total body fat was measured by DXA, which was further analyzed for central abdominal fat. Results: Mean body mass index was 24.9 ± 1.0 kg/m². The average (range) intra‐subject variability was 28% (8% to 61%) for IAF, 46% (19% to 124%) for %IAF, 26% (14% to 38%) for SAF area, and 19% (7% to 71%) for IAF/SAF. The pattern of this variability was not uniform between subjects, because their ranking by IAF area was markedly different at each CT level. Discussion: We demonstrated significant intra‐subject variability in CT‐measured adipose tissue areas across four predetermined sites. This resulted in a difference in the ordering of subjects by IAF at each of the four imaging sites, suggesting that the usefulness of single‐slice CT in the assessment of abdominal adiposity in premenopausal women may be limited, particularly when performed for the purpose of making comparisons between subjects based on abdominal fat area.     

Estimating abdominal adipose tissue with DXA and anthropometry

Objective: To identify an anatomically defined region of interest (ROI) from DXA assessment of body composition that when combined with anthropometry can be used to accurately predict intra‐abdominal adipose tissue (IAAT) in overweight/obese individuals. Research Methods and Procedures: Forty‐one postmenopausal women (age, 49 to 66 years; BMI, 26 to 37 kg/m²) underwent anthropometric and body composition assessments. ROI were defined as quadrilateral boxes extending 5 or 10 cm above the iliac crest and laterally to the edges of the abdominal soft tissue. A single‐slice computed tomography (CT) scan was measured at the L3 to L4 intervertebral space, and abdominal skinfolds were taken. Results: Forward step‐wise regression revealed the best predictor model of IAAT area measured by CT (r² = 0.68, standard error of estimate = 17%) to be: IAAT area (centimeters squared) = 51.844 + DXA 10‐cm ROI (grams) (0.031) + abdominal skinfold (millimeters) (1.342). Interobserver reliability for fat mass (r = 0.994; coefficient of variation, 2.60%) and lean mass (r = 0.986, coefficient of variation, 2.67%) in the DXA 10‐cm ROI was excellent. Discussion: This study has identified a DXA ROI that can be reliably measured using prominent anatomical landmarks, in this case, the iliac crest. Using this ROI, combined with an abdominal skinfold measurement, we have derived an equation to predict IAAT in overweight/obese postmenopausal women. This approach offers a simpler, safer, and more cost‐effective method than CT for assessing the efficacy of lifestyle interventions aimed at reducing IAAT. However, this warrants further investigation and validation with an independent cohort.     

Fat Mass Measured by DXA Varies with Scan Velocity

Objective: To study the influence of scan velocities of DXA on the measured size of fat mass, lean body mass, bone mineral content and density, and total body weight. Research Methods and Procedures: The subjects were 71 healthy white adults, 38 women and 33 men. The mean age was 41.7 ± 13.5 years and body mass index was 28.6 ± 5.6 kg/m². The subjects were scanned consecutively in slow, medium, and fast scan mode by a Lunar DPX-IQ DXA scanner. Results: Throughout the body mass index and sagittal height ranges, scanned lean body mass significantly decreased with higher scan velocity and lean body mass was 2.7% lower in fast than in medium mode (p < 0.0001). In contrast, fat mass, percentage of body fat, and bone mineral contents were higher with increasing scan velocity. Areas not analyzed by the scanner, so called blue spots, increased with scan velocity and sagittal height, and their presence significantly enhanced the error. Body weight estimated by DXA in slow mode was −0.8% lower than scale weight in the women (p < 0.001) and −0.2% in men (not significant), and the difference was greater with increasing scan velocity. Discussion: Scan velocity significantly influences the measured fat mass size, lean body mass, bone mineral content, and body weight. To obtain the most accurate results, slow mode is preferable and fast scans should be avoided. Future studies should report and take scan velocity into consideration.     

Assessing Body Composition among 3‐ to 8‐Year‐Old Children: Anthropometry,BIA, and DXA

Objective: To examine the inter‐relationships of body composition variables derived from simple anthropometry [BMI and skinfolds (SFs)], bioelectrical impedance analysis (BIA), and dual energy x‐ray (DXA) in young children. Research Methods and Procedures: Seventy‐five children (41 girls, 34 boys) 3 to 8 years of age were assessed for body composition by the following methods: BMI, SF thickness, BIA, and DXA. DXA served as the criterion measure. Predicted percentage body fat (%BF), fat‐free mass (FFM; kilograms), and fat mass (FM; kilograms) were derived from SF equations [Slaughter (SL)1 and SL2, Deurenberg (D) and Dezenberg] and BIA. Indices of truncal fatness were also determined from anthropometry. Results: Repeated measures ANOVA showed significant differences among the methods for %BF, FFM, and FM. All methods, except the D equation (p = 0.08), significantly underestimated measured %BF (p < 0.05). In general, correlations between the BMI and estimated %BF were moderate (r = 0.61 to 0.75). Estimated %BF from the SL2 also showed a high correlation with DXA %BF (r = 0.82). In contrast, estimated %BF derived from SFs showed a low correlation with estimated %BF derived from BIA (r = 0.38); likewise, the correlation between DXA %BF and BIA %BF was low (r = 0.30). Correlations among indicators of truncal fatness ranged from 0.43 to 0.98. Discussion: The results suggest that BIA has limited utility in estimating body composition, whereas BMI and SFs seem to be more useful in estimating body composition during the adiposity rebound. However, all methods significantly underestimated body fatness as determined by DXA, and, overall, the various methods and prediction equations are not interchangeable.     

PIXImus DXA with Different Software Needs Individual Calibration to Accurately Predict Fat Mass

Objective: To validate GE PIXImus2 DXA fat mass (FM) estimates by chemical analysis, to compare previously published correction equations with an equation from our machine, and to determine intermachine variation. Research Methods and Procedures: C57BL/6J (n = 16) and Aston (n = 14) mice (including ob/ob), Siberian hamsters (Phodopus sungorus) (n = 15), and bank voles (Clethrionomys glareolus) (n = 37) were DXA scanned postmortem, dried, then fat extracted using a Soxhlet apparatus. We compared extracted FM with DXA‐predicted FM corrected using an equation designed using wild‐type animals from split‐sample validation and multiple regression and two previously published equations. Sixteen animals were scanned on both a GE PIXImus2 DXA in France and a second machine in the United Kingdom. Results: DXA underestimated FM of obese C57BL/6J by 1.4 ± 0.19 grams but overestimated FM for wild‐type C57BL/6J (2.0 ± 0.11 grams), bank voles (1.1 ± 0.09 grams), and hamsters (1.1 ± 0.13 grams). DXA‐predicted FM corrected using our equation accurately predicted extracted FM (accuracy 0.02 grams), but the other equations did not (accuracy, ?1.3 and ?1.8 grams; paired Student's t test, p < 0.001). Two similar DXA instruments gave the same FM for obese mutant but not lean wild‐type animals. Discussion: DXA using the same software could use the same correction equation to accurately predict FM for obese mutant but not lean wild‐type animals. PIXImus machines purchased with new software need validating to accurately predict FM.     

Assessing body composition with DXA and bioimpedance: effects of obesity, physical activity, and age

Objective: This study evaluated to what extent dual‐energy X‐ray absorptiometry (DXA) and two types of bioimpedance analysis (BIA) yield similar results for body fat mass (FM) in men and women with different levels of obesity and physical activity (PA). Methods and Procedures: The study population consisted of 37–81‐year‐old Finnish people (82 men and 86 women). FM% was estimated using DXA (GE Lunar Prodigy) and two BIA devices (InBody (720) and Tanita BC 418 MA). Subjects were divided into normal, overweight, and obese groups on the basis of clinical cutoff points of BMI, and into low PA (LPA) and high PA (HPA) groups. Agreement between the devices was calculated by using the Bland–Altman analysis. Results: Compared to DXA, both BIA devices provided on average 2–6% lower values for FM% in normal BMI men, in women in all BMI categories, and in both genders in both HPA and LPA groups. In obese men, the differences were smaller. The two BIA devices provided similar means for groups. Differences between the two BIA devices with increasing FM% were a result of the InBody (720) not including age in their algorithm for estimating body composition. Discussion: BIA methods provided systematically lower values for FM than DXA. However, the differences depend on gender and body weight status pointing out the importance of considering these when identifying people with excess FM.     

Mice Deficient in Phosphofructokinase‐M Have Greatly Decreased Fat Stores

Synthesis of triacylglycerol requires the glucose‐derived glycerol component, and glucose uptake has been viewed as the rate‐limiting step in glucose metabolism in adipocytes. Furthermore, adipose tissue contains all three isoforms of the glycolytic enzyme phosphofructokinase (PFK). We here report that mice deficient in the muscle isoform PFK‐M have greatly reduced fat stores. Mice with disrupted activity of the PFK‐M distal promoter were obtained from Lexicon Pharmaceuticals, developed from OmniBank OST#56064. Intra‐abdominal fat was measured by magnetic resonance imaging of the methylene proton signal. Lipogenesis from labeled glucose was measured in isolated adipocytes. Lipolysis (glycerol and free fatty acid release) was measured in perifused adipocytes. Intra‐abdominal fat in PFK‐M–deficient female mice (5–10 months old) was 17 ± 3% of that of wild‐type littermates (n = 4; P < 0.02). Epididymal fat weight in 15 animals (7–9.5 months) was 34 ± 4% of control littermate (P < 0.002), with 10–30% lower body weight. Basal and insulin‐stimulated lipogenesis in PFK‐M–deficient epididymal adipocytes was 40% of the rates in cells from heterozygous littermates (n = 3; P < 0.05). The rate of isoproterenol‐stimulated lipolysis in wild‐type adipocytes declined ～10% after 1 h and 50% after 2 h; in PFK‐M–deficient cells it declined much more rapidly, 50% in 1 h and 90% in 2 h, and lipolytic oscillations appeared to be damped (n = 4). These results indicate an important role for PFK‐M in adipose metabolism. This may be related to the ability of this isoform to generate glycolytic oscillations, because such oscillations may enhance the production of the triacylglycerol precursor α‐glycerophosphate.