During the past two decades, a major outgrowth of efforts by our research group at St. Luke’s-Roosevelt Hospital is the development of body composition models that include cellular level models, models based on body component ratios, total body potassium models, multi-component models, and resting energy expenditure-body composition models. This review summarizes these models with emphasis on component ratios that we believe are fundamental to understanding human body composition during growth and development and in response to disease and treatments. In-vivo measurements reveal that in healthy adults some component ratios show minimal variability and are relatively ‘stable’, for example total body water/fat-free mass and fat-free mass density. These ratios can be effectively applied for developing body composition methods. In contrast, other ratios, such as total body potassium/fat-free mass, are highly variable in vivo and therefore are less useful for developing body composition models. In order to understand the mechanisms governing the variability of these component ratios, we have developed eight cellular level ratio models and from them we derived simplified models that share as a major determining factor the ratio of extracellular to intracellular water ratio (E/I). The E/I value varies widely among adults. Model analysis reveals that the magnitude and variability of each body component ratio can be predicted by correlating the cellular level model with the E/I value. Our approach thus provides new insights into and improved understanding of body composition ratios in adults.

Background

Loss of subcutaneous (SAT) with sparing of visceral (VAT) adipose tissue (AT) has been documented in HIV + men and women. Intermuscular AT (IMAT) rivals VAT in independent associations with cardiovascular risk.

Objective

To determine whether the size and distribution of IMAT differs in HIV+ vs. HIV- men and/or women.

Design

We used whole-body MRI to measure VAT, IMAT and four SAT compartments and compared them by HIV status using whole-body skeletal muscle (SM) or total AT (TAT) as co-variates in multi-ethnic groups of healthy HIV- (n=86) and stable HIV+ (n=76) men and women.

Results

The sizes of AT depots (adjusting for SM) did not differ by HIV status, except for smaller gluteal SAT (lower trunk, between L4-L5 to greater trochanter) in both sexes (P<0.05). The AT distribution (adjusting for TAT) was significantly different, with larger VAT (P<0.05) and smaller gluteal and limb SAT (P<0.05) in both HIV+ sexes; IMAT increased more with TAT in HIV+ vs. HIV- men (P<0.05 for slope interaction) but there were no significant differences in women. There were significant race by HIV interactions in AT distribution with more pronounced VAT differences in non-Hispanic white men and larger trunk SAT in African Americans HIV+ vs. HIV-.

Conclusion

The AT distribution differed markedly in HIV+ vs. HIV- with limb and lower body SAT representing a smaller proportion of TAT in HIV+ in both sexes and IMAT representing a larger proportion of TAT in HIV+ vs. HIV- men.

The purpose of this study was to assess the agreement of the Lunar DPX-L with the newer Prodigy dual-energy X-ray absorptiometer (DXA) for determining total-body and regional (arms, legs, trunk) bone mineral density (BMD), bone mineral content (BMC), fat mass (FM), lean tissue mass (LTM), total body mass (BM) and percent fat. A total of 106 apparently healthy males (n=34) and females (n=72) between the ages of 8–72 years were scanned consecutively on the DPX-L (software version 1.35) and Prodigy DXA (enCORE v. 3.6 software). Paired t-tests indicated significantly higher measures by Prodigy for BM (percent difference= 1.1%) and total-body BMD (2.2%), BMC (2.9%), FM (3.5%), and percent fat (2.8%; P<0.001), but not LTM (−0.2%). Regional estimates of FM and bone tended to be overestimated by Prodigy relative to DPX-L. The percent difference was most pronounced for FM in the arms (14.2%) and trunk (8.5%), BMD in the legs (4.9%), LTM in arms (5.6%), and BMC in the trunk (5.9%); but all total-body and regional measures were strongly and significantly correlated (P<0.001). The method of Bland and Altman indicated that the Prodigy overestimated DPX-L for BM (r=0.343; P<0.001), and total-body measures of BMD (r=0.460; P<0.001), and BMC (r=0.321; P<0.001) at higher values, as indicated by the significant, positive association between difference (Prodigy−DPX-L) versus mean ((Prodigy+DPX-L)/2). Regionally, Prodigy overestimated DPX-L for BMD in the legs, BMC in the legs and trunk, and FM in the arms at higher values (P<0.001). In contrast, FM in the legs was underestimated by Prodigy relative to DPX-L at higher values (P<0.001), and no regional bias was observed for LTM. In conclusion, we recommend that correction equations be used for comparing BM, total-body BMD and BMC, and regionally for BMD in the legs, BMC in the legs and trunk, and FM in the arms and legs. The use of correction equations for other estimates is not required for making direct comparisons.

We have previously validated the use of dual-energy X-ray absorptiometry (DXA) for measuring body composition of mice using the GE-Lunar PIXImus and software version 1.42 [1]. Since that report, newer versions of the software have been released. The purpose of the present study was to compare results from our original study with results analyzed using two newer versions of software (versions 1.44 and 1.45). Body composition data (lean tissue mass [LTM], fat mass [FM], bone mineral content [BMC], and bone mineral density [BMD]) were obtained from DXA scans of twenty-five, anesthetized male C57Bl/6J mice (6-11 weeks old; 19 to 29g). Relative to version 1.42, versions 1.44 and 1.45 significantly (P<0.001) overestimated LTM and BMD and underestimated FM and BMC. However, compared to carcass analysis, versions 1.44 and 1.45 significantly overestimated both FM and LTM and underestimated BMC. Results from 1.44 and 1.45 were highly correlated with carcass values for all body composition parameters. Prediction equations were developed for the two new software versions. Applying the prediction equation from 1.42, to the data obtained from 1.44 and 1.45 resulted in FM and LTM that were worse than if no equation was used. However, using their own developed equations resulted in data that were not significantly different than that from carcass analysis. These data suggest that software-specific equations are necessary for comparing DXA-derived data to that of chemical analysis.

Peripheral quantitative computed tomography (pQCT) was used to determine percent body fat in mice, and relative liver fat in lemmings fasted for 0, 6, 12 or 18 hours to induce a wide range of liver fat content. Accuracy of the pQCT was determined by comparing pQCT-derived fat to that from chemical extraction using 30 male mice (whole body) and 26 female lemmings (liver only). To determine whether pQCT could measure changes in liver fat (%) in live animals, two groups of lemmings were scanned on 4 consecutive days under anesthesia. Controls (n = 3) had ad libitum access to food, whereas the fasted group (n = 5) was deprived of food for 18 hr before being measured on day 2 and then refed. The coefficient of variation (CV) for determining percent body fat in mice using the pQCT was 3.9% (±1.8 SD). Percent body fat determined by pQCT significantly overestimated percent fat as measured by chemical extraction (14.5 ± 3.2 vs 12.3 ± 2.9% respectively, P < 0.01, mean ± SD). However, percent body fat by pQCT was highly related to chemical extraction percent fat (r = 0.95, P < 0.001). The liver attenuation values from pQCT were highly related to percent liver fat (r=0.98, P<0.001) in lemmings. The technique showed excellent precision with a CV of 0.3 ± 0.1%. The two groups (control vs fasted) did not differ in their percent liver fat on day 1 (5.4% vs 5.8%). On day 2 the fasted group had a significantly higher percent liver fat than controls (5.9% vs 17.3%; p<0.05). Following refeeding, there were no significant group differences in percent liver fat on days 3 and 4. Our data indicate that pQCT has good accuracy and precision for determining percent body fat, and liver fat in small animals and can be used to track changes in liver fat over time.

Objective

We have previously shown that surrounding fat causes an increase of up to 21% in bone mineral density (BMD) measured by Lunar ‘Intelligent DXA’ (iDXA), one of the latest generation dual energy X-ray absorptiometry (DXA) scanners [1]. The purpose of our study was to see if it was possible to avoid this artifact when measuring the BMD of metacarpals II, III, and IV by digital X-ray radiogrammetry (DXR).

Methods

We took X-rays of the bones of a cadaveric left hand which were immobilized in a wooden cradle to preserve an approximate in vivo configuration. The X-rays were digitized into Digital Imaging and Communications in Medicine (DICOM) files which were analyzed using dxr-online (dxr-online, Sectra, Sweden) which uses the same DXR-BMD algorithm previously used by Pronosco X-posure v2 and Sectra Osteoporosis package. The X-rays were repeated four times. We then surrounded the bones with a layer of lard, and again X-rayed four times. This process was repeated with the bones were covered by two layers, and then three layers of lard.

Results

The measured DXR-BMD increased by a maximum of 0.44% when the metacarpals were covered by either two or three layers of lard compared with when the metacarpals were not covered by lard.

Conclusion

The measurement of metacarpal BMD measured by DXR is minimally affected by surrounding lard. The measurement of metacarpal BMD by DXR seems to be a way of avoiding the artifactual change in BMD caused by fat, when it is measured by DXA.

Objective

To determine if increasing fatness interferes with the measurement of fat and bone mineral density (BMD) by dual-energy X-ray absorptiometry (Lunar iDXA).

Methods

We performed measurements of BMD and fat on a section of a beef femur defatted by prolonged boiling in detergent, completely surrounded by increasing thicknesses of lard. Initially the bone was placed in the marked spine area, overlying a 6L plastic bottle which was placed in the marked trunk area of the iDXA. The plastic bottle was then removed and further measurements were carried out with increasing thicknesses of lard surrounding the bone. Measurements were repeated 4 times.

Results

The reported measurement of BMD progressively increased with each increased layer of lard surrounding the bone. All the iDXA BMD measurements were significantly (P<0.01) different from one another. When surrounded by 3 layers of lard the reported BMD was 20.5% greater than the reported BMD when the bone was not surrounded by any lard. The differences between the actual amount of fat measured by chemical analysis and weighing, and the reported measurement of fat by iDXA were significant with all 3 thicknesses of lard (P<0.01); the percentage difference between the fat measured by iDXA and that measured chemically decreased as the number of layers of lard increased.

Conclusion

We found that iDXA overestimated fat by up to 11.1%. The percentage overestimation of fat diminished as the amount of fat increased. BMD was overestimated by 20.5% when surrounded by 3 layers of fat compared to when there was no surrounding fat. In contrast to fat, the percentage overestimation of BMD increased as increasing amounts of fat surrounding the bone Using earlier generation DXAs, others have reported that measurements were ± 20–50% inaccurate and differed according to the configuration of the phantoms. The measurement of BMD and fat is the main clinical purpose of iDXA; the present experiment has shown that there are substantial inaccuracies in the measurement of BMD and fat. It is not known how these inaccuracies compare with those of earlier generations of DXA machines.

Objective

The purpose of this study was to compare Tanita tetrapolar foot-to-foot bioelectrical impedance analysis (Model TBF-310, Tanita Corporation of America, Inc, Arlington Heights, IL; Tanita-BIA) and fan beam dual-energy X-ray absorptiometry (Hologic Discovery A v12.6, Waltham, MA; DXA) in diabetic patients.

Methods

Seventy Hispanic diabetic participants (23 male, 47 female; mean age: 53.03 ± 10.32 yrs; mean weight: 81.45 ± 17.65 kg; and mean body mass index: 31.40 ± 6.80 kg/m2) were selected from the Loma Linda University En Balance culturally-sensitive Spanish diabetes education program using the baseline data.

Results

DXA vs Tanita-BIA fat mass (FM), percent fat mass (%FM), and fat-free mass (FFM) were compared using Pearson’s (FM: 0.96, %FM: 0.91, and FFM: 0.95), and Spearman’s rank (FM: 0.94, %FM: 0.91, and FFM: 0.93) correlation coefficients. Bland-Altman analyses were also used to compare the difference (DXA – BIA) vs average of DXA and BIA results and showed general agreement between the two methods. When Tanita-BIA was regressed onto DXA, the adjusted R2 was: FM=0.91; %FM=0.83; FFM=0.90. Gender combined concordance correlations with 95% confidence intervals were calculated using a bootstrap re-sampling of the data and found high associations [FM: 0.93 (95% CI: 0.89, 0.96)], [%FM: 0.86 (95% CI: 0.79, 0.90)], and [FFM: 0.93 (95% CI: 0.89, 0.96)].

Conclusion

Tanita-BIA may provide valid measures of fat, percent body fat and fat-free mass in Hispanic diabetics, and could be a convenient and practical approach for assessment in community-based research.

Objectives

Diabetes mellitus and obesity are prevalent in the Hispanic community. This group has not benefited greatly from diabetes interventions due to cultural, language and financial constraints. We designed a prospective cohort study to determine the clinical impact on adiposity and glycemic control in Hispanics with type 2 diabetes.

Research design and methods

The program conducted in Spanish by a multidisciplinary team of health care providers focused on improving glycemic control and complications through cultural lifestyle changes. Outcomes were changes in glycemic control by fasting insulin, glucose and HbA1c, body composition and selected adipokines, adiponectin, leptin and ghrelin. Body composition was measured by dual energy x-ray absorptiometry. Changes from baseline at three months were compared using paired t-tests and with Spearman’s correlations.

Results

Glycemic control improved by HbA1c (7.9% ± 2.0% vs 7.1% ± 1.7%; P = <0.001), and fasting glucose (166.4 ± 66.0 mg/dl vs 143.2 ± 57.9 mg/dl; P = 0.003). Body weight (81.3 ± 17.9 kg vs 80.3 ± 18.0 kg; P = 0.002), waist circumference (101.6 ± 13.4 cm vs 99.1 ± 12.7 cm; P = 0.015), and truncal fat (16.5 ± 5.7 kg vs 15.9 ± 5.6 kg; P = 0.001) decreased. Only leptin (19.6 ± 15.0 ng/ml vs 16.3 ± 12.7 ng/ml; P = 0.002) was reduced and related to change in body weight (r = 0.392; P = 0.022).

Conclusions

Our program significantly improved glycemic control and decreased obesity in diabetic Hispanic subjects. The early benefits on glycemic control may be related to reductions in leptin through loss of adipose tissue. Success in impacting diabetes and related complications can occur in a culturally focused and multidisciplinary context.

Objective

To assess the effects of a language-sensitive diabetes education program on dietary changes and plasma lipid profiles.

Method

Hispanic participants (n=13 males and 18 females, mean age = 54.00 + 10.68 years) participated in a 3-month health education study. Spearman correlation coefficients were used to evaluate correlations between dietary intake and laboratory measurements.

Results

There were significant decreases in serum total cholesterol (−16.07 mg/dl, P= 0.035), HDL cholesterol (−3.23 mg/dl, P = 0.01), LDL cholesterol (−11.71 mg/dl, P = 0.013) and dietary cholesterol (−79.22 mg, P = 0.03). No significant mean change was observed in triglyceride and total cholesterol/HDL ratio. There was also a reduction in body mass index (BMI) (−0.15 kg/m2, P = 0.40), fasting glucose (−3.90 mg/dl, P = 0.43) and dual energy X-ray absorptiometry (DXA) total fat (−0.50, P = 0.97). Although not statistically significant, saturated fatty acids (−4.90 g, P = 0.19), polyunsaturated fatty acids (−3.31g, P = 0.11), and carbohydrate (−44.82 g, P = 0.22), decreased after three months.

Conclusion

There were significant improvements in dietary intake and serum lipids after a three-month culture-specific diabetes education program.

Diabetes mellitus continues to be a heavy burden on health and health resources throughout the world. In the USA the burden is borne disproportionately by ethnic minorities such as Hispanics. Therefore health education for Hispanics is important and it can help reduce the incidence of diabetes among Hispanics in the USA.

The measurement of human body composition allows for the estimation of body tissues, organs, and their distributions in living persons without inflicting harm. From a nutritional perspective, the interest in body composition has increased multi-fold with the global increase in the prevalence of obesity and its complications. The latter has driven in part the need for improved measurement methods with greater sensitivity and precision. There is no single gold standard for body-composition measurements in-vivo. All methods incorporate assumptions that do not apply in all individuals and the more accurate models are derived by using a combination of measurements, thereby reducing the importance of each assumption. This review will discuss why the measurement of body composition or human phenotyping is important; discuss new areas where the measurement of body composition (human phenotyping) is recognized as having important application; and will summarize recent advances made in new methodology. Reference will also be made to areas we cannot yet measure due to the lack of appropriate measurement methodologies, most especially measurements methods that provide information on kinetic states (not just static state) and metabolic function.

A study was conducted to appraise a new EchoMRI™ device for body composition analysis (BCA) of infants and to compare it with dual energy X-ray absorptiometry (DXA), using chemical analysis as a reference method.

The calibration part of the study included cross-validation comparisons between EchoMRI™ measurements of awake, anesthetized and dead piglets of the calibration set. It also included comparison of two different approaches to refining the calibration of EchoMRI™, by low- or by high-dimensional linear regressions. Only the low-dimensional approach was applied to DXA.

The validation part yielded EchoMRI™ accuracy of 27 g and 70 g for fat and total water, respectively, on piglets scanned while anesthetized, as compared with 24 g and 57 g, respectively, for DXA.

EchoMRI™ precision was found to be 4 g and 7 g for fat and total water, respectively, for anesthetized piglets, as compared to 16 g and 14 g, respectively, for DXA. The differences between fat measurements of awake, anesthetized and dead piglets can be statistically significant, but are comparable in magnitude to random errors.

To summarize: Characterization of random errors by CV, especially that of fat, is not suitable for BCA, whereas absolute errors or errors relative to total body weight can be applicable. Low- and high-dimensional regressions offer nearly the same accuracy improvements. Improved DXA and EchoMRI™ offer nearly the same accuracy, within 1% of weight in fat, while EchoMRI™ has better precision, within 0.2 % of weight in fat for anesthetized and dead piglets as compared to DXA's 0.5–0.6%.

Objective

To validate the use of quantitative magnetic resonance (QMR) to measure fat and lean mass in conscious rats.

Methods

Fifty Osborne-Mendel rats (249-770 g) were scanned using the Echo Medical 2 MHz body composition analyzer. Each rat was scanned under six settings (three acquisition times, with and without determination of total water). Precision was determined by the calculated coefficient of variation (CV) of three consecutive scans. Accuracy was determined by comparing the first scan to chemical carcass analysis and analyzed by paired t-tests and least-squares regression analyses. Twenty-five rats were used in the validation study, and 25 in the cross-validation study.

Results

The precision for fat, lean and water at all settings was <1%. QMR significantly overestimated fat (~5%; P<0.0001), and underestimated both lean (~12.5%; P<0.0001) and total water (~5.5%; P<0.0001). All QMR measures were significantly correlated with carcass measures (r2>0.99; P<0.0001). Using prediction equations from the validation study with the cross-validation rats, there were no significant differences between QMR fat and carcass fat at any setting (P>0.400). For four of the six QMR settings, there were no significant differences between QMR and carcass lean (P>0.05). For total water, all QMR settings were significantly different than carcass (P<0.05), but only by ~1%.

Conclusions

QMR showed excellent precision for the determination of fat, lean and water. Despite overestimating fat and underestimating lean and water, all were highly related to carcass values. When tested in the cross-validation group, QMR fat could be accurately predicted at all settings; however, lean mass (two settings) and water were still slightly different (less than 1%).

Objective and methods:

Given the profound weight loss after gastric banding and bypass we compared fat compartmentalization by whole body magnetic resonance imaging in women and men after these procedures to two groups of non-surgical controls who were either matched for age, weight and height or were of lower body mass index (BMI).

Result:

In women post-surgery (n=17; BMI 31.7 kg/m2) there was lower visceral adipose tissue (VAT) (1.4 vs 2.5 kg; P<0.01) compared with matched controls (n=59; BMI 32.1 kg/m2). In contrast, VAT (5.3 vs 5.4 kg) was nearly identical in men post-surgery (n=10; BMI 34.1 kg/m2) compared with matched controls (n=10; BMI 32.1 kg/m2) even though the degree of weight reduction was not significantly different from women (27.4 vs 32.6%). Furthermore, VAT when adjusted for total adipose tissue (TAT) was 43% less in women post-surgery (1.2 vs 2.1 kg; P=0.03) than in controls with lower BMI (25.1 kg/m2). After adjustment for TAT, subcutaneous adipose tissue in women post-surgery was significantly greater than matched controls (35.1 vs 34.2 kg; P=0.03). There was a significant negative correlation of VAT and the degree of weight loss in women (r=−0.57; P=0.018) but this relationship was not significant in men (r=−0.39; P=0.27). Skeletal muscle was lower in both sexes compared with matched controls (women, 21.8 vs 23.1 kg; men, 32.5 vs 35.5 kg).

Conclusion:

Prospective studies are necessary to confirm if there is a sexual dimorphism in the effects of bariatric surgery on body composition.

Objective

Jackson and Pollock’s (JP) ground-breaking research reporting generalized body density equations to estimate body fat was carried out in the late 1970s. Since then we have experienced an ‘obesity epidemic’. Our aim was to examine whether the original quadratic equations established by Jackson and co-workers are valid in the 21st century.

Methods

Reanalyzing the original JP data, an alternative, more biologically sound exponential power-function model for body density is proposed that declines monotonically, and hence predicts body fat to rise monotonically, with increasing skin-fold thicknesses. The model also remains positive irrespective of the subjects’ sum-of-skinfold thicknesses or age.

Results

Compared to the original quadratic model proposed by JP, our alternative exponential power-function model is theoretically and empirically more accurate when predicting body fat of obese subjects (sums of skinfolds >120mm). A cross-validation study on 14 obese subjects confirmed these observations, when the JP quadratic equations under estimated body fat predicted using dual energy x-ray absorptiometry (DXA) by 2.1% whereas our exponential power-function model was found to underestimate body fat by less than 1.0%. Otherwise, the agreement between the DXA fat (%) and the two models were found to be almost identical, with both coefficients of variation being 10.2%.

Conclusions

Caution should be exercised when predicting body fat using the JP quadratic equations for subjects with sums of skinfolds>120 mm. For these subjects, we recommend estimating body fat using the tables reported in the present manuscript, based on the more biologically sound and empirically valid exponential power-function model.

Objective

The aim of this study was to assess the precision and accuracy of a quantitative magnetic resonance (QMR) instrument for measuring body composition in live, non-anesthetized mice.

Methods

Forty-eight mice of varying strains, ages and body weights (15.3 to 50.2g) were scanned three times each in the QMR instrument. Animals were killed and chemical carcass analysis performed for comparison. Precision was assessed as the coefficient of variation (CV) for the triplicate scans and accuracy was determined by comparing the first QMR data with the chemical analysis. Prediction equations were generated by linear regression analysis and used in a cross-validation study in which 26 mice were scanned once each, killed, and chemical carcass analysis performed.

Results

The mean CV was 1.58% for fat mass (FM) and 0.78% for lean-tissue mass (LTM). QMR significantly (P<0.01) overestimated FM (7.76±5.93 vs. 6.03±5.17g) and underestimated LTM (20.73±6.19 vs. 22.48±6.75g) when compared with chemical carcass analysis. A strong relationship between QMR and chemical data (r2=0.99 and r2=0.97 for fat and LTM respectively; P<0.0001) allowed for the generation of correction equations that were applied to QMR data in the cross-validation study. There was no significant difference between data predicted from QMR and chemical carcass data for FM and LTM (P=0.15 and 0.10 respectively).

Conclusion

The QMR instrument showed excellent precision and data was highly correlated with chemical carcass analysis. This combined with QMR's speed for whole animal analysis (95 seconds) make it a highly feasible and useful method for the determination of body composition in live, non-anesthetized mice.

Objectives:

To examine the validity of body composition estimates obtained using foot-to-foot bio-electrical impedance analysis (BIA) in overweight and obese children by comparison to a reference four-compartment model (4-CM).

Subjects/Methods:

38 males: age (mean ± sd) 13.6 ± 1.3 years, body mass index 30.3 ± 6.0 kg.m−2 and 14 females: age 14.7 ± 2.2 years, body mass index 32.4 ± 5.7 kg.m−2 participated in the study. Estimates of fat-free mass (FFM), fat mass (FM) and percentage body fat (PBF) obtained using a Tanita model TBF-310 and a 4-CM (derived from body mass, body volume, total body water and total body bone mineral measurements) were compared using bias and 95% limits of agreement (Tanita minus 4-CM estimates).

Results:

Body composition estimates obtained with the Tanita TBF-310 were not significantly different from 4-CM assessments: for all subjects combined the bias was −0.7kg for FM, 0.7kg for FFM and −1.3% for PBF. However, the 95% limits of agreement were substantial for individual children: males, up to ±9.3kg for FFM and FM and ±11.0% for PBF; females, up to ±5.5kg for FFM and FM and ±6.5% for PBF.

Conclusions:

The Tanita TBF-310 foot-to-foot BIA body composition analyser with the manufacturer's prediction equations is not recommended for application to individual children who are overweight and obese although it may be of use for obtaining group mean values.

Objective

Bioelectrical impedance analysis (BIA) of hydration and body composition has made significant progress during the past 3 decades. With the development of Bioimpedance spectroscopy (BIS), bioimpedance has been expanded to reliably predict extracellular fluid (ECF) and total body water (TBW), allowing the calculation of fat-free mass (FFM) and fat mass (FM). In this study, a new BIS device (ImpediVet™), designed for body composition measurements in animals, was assessed for precision and accuracy in measuring TBW, FFM and FM in rats.

Methods

In a validation study, 25 rats were measured for body composition (TBW, FFM and FM) using BIS and chemical carcass analysis (CCA). BIS precision was assessed by the coefficient of variation using multiple BIS readings, while BIS accuracy was assessed by regression analysis of BIS and CCA values for each body compartment. In a cross-validation study, prediction equations generated from the validation group for TBW, FFM and FM were applied to an independent cohort of 25 rats that were measured by BIS and CCA. Linear regression analysis and paired t-tests were used to assess significance of relationships and measurement differences within groups.

Results

In the validation study, BIS was highly correlated with CCA for TBW (r2=0.988), FFM (r2=0.987) and FM (r2=0.966). Even so, BIS significantly underestimated TBW (mean: −31.07 g, −13.3%, p<0.001) and FFM (−50.69 g, −15.5%, p<0.001), while overestimating FM (+65.75 g, +63.5%, p<0.001). In the independent, cross-validation group of rats the prediction equations accurately predicted carcass values for TBW (−0.2%, p=0.350), FFM (−0.2%, p=0.457) and FM (+1.5%, p=0.508).

Conclusion

Based on these results, BIS provided a precise and accurate means to determine in vivo body composition in rats.