Dual-energy X-ray absorptiometry measurements of the body composition of pigs of 90- to 130-kilograms body weight

Temperatures in Pigs During 3 T MRI Temperatures, Heart Rates, and Breathing Rates of Pigs During RF Power Deposition in a 3 T (128 MHz) Body Coil

Exposure to radiofrequency (RF) power deposition during magnetic resonance imaging (MRI) induces elevated body-tissue temperatures and may cause changes in heart and breathing rates, disturbing thermoregulation. Eleven temperature sensors were placed in muscle tissue and one sensor in the rectum (measured in 10 cm depth) of 20 free-breathing anesthetized pigs to verify temperature curves during RF exposure. Tissue temperatures and heart and breathing rates were measured before, during, and after RF exposure. Pigs were placed into a 60-cm diameter whole-body resonator of a 3 T MRI system. Nineteen anesthetized pigs were divided into four RF exposure groups: sham (0 W/kg), low-exposure (2.7 W/kg, mean exposure time 56 min), moderate-exposure (4.8 W/kg, mean exposure time 31 min), and high-exposure (4.4 W/kg, mean exposure time 61 min). One pig was exposed to a whole-body specific absorption rate (wbSAR) of 11.4 W/kg (extreme-exposure). Hotspot temperatures, measured by sensor 2, increased by mean 5.0 ± 0.9°C, min 3.9; max 6.3 (low), 7.0 ± 2.3°C, min 4.6; max 9.9 (moderate), and 9.2 ± 4.4°C, min 6.1, max 17.9 (high) compared with 0.3 ± 0.3°C in the sham-exposure group (min 0.1, max 0.6). Four time-temperature curves were identified: sinusoidal, parabolic, plateau, and linear. These curve shapes did not correlate with RF intensity, rectal temperature, breathing rate, or heart rate. In all pigs, rectal temperatures increased (2.1 ± 0.9°C) during and even after RF exposure, while hotspot temperatures decreased after exposure. When rectal temperature increased by 1°C, hotspot temperature increased up to 42.8°C within 37 min (low-exposure) or up to 43.8°C within 24 min (high-exposure). Global wbSAR did not correlate with maximum hotspot. Bioelectromagnetics. 2021;42:37-50. © 2020 The Authors. Bioelectromagnetics published by Wiley Periodicals LLC on behalf of Bioelectromagnetics Society.

Evaluation of DXA against the four-component model of body composition in obese children and adolescents aged 5-21 years

BACKGROUND

Body composition is increasingly measured in pediatric obese patients. Although dual-energy X-ray absorptiometry (DXA) is widely available, and is precise, its accuracy for body composition assessment in obese children remains untested.

OBJECTIVE

We aimed to evaluate DXA against the four-component (4C) model in obese children and adolescents in both cross-sectional and longitudinal contexts.

DESIGN

Body composition was measured by DXA (Lunar Prodigy) and the 4C model in 174 obese individuals aged 5-21 years, of whom 66 had a second measurement within 1.4 years. The Bland-Altman method was used to assess agreement between techniques for baseline body composition and change therein.

RESULTS

A significant minority of individuals (n=21) could not be scanned successfully due to their large size. At baseline, in 153 individuals with complete data, DXA significantly overestimated fat mass (FM; Delta=0.9, s.d. 2.1 kg, P<0.0001) and underestimated lean mass (LM; Delta=-1.0, s.d. 2.1 kg, P<0.0001). Multiple regression analysis showed that gender, puberty status, LM and FM were associated with the magnitude of the bias. In the longitudinal study of 51 individuals, the mean bias in change in fat or LM did not differ significantly from zero (FM: Delta=-0.02, P=0.9; LM: Delta=0.04, P=0.8), however limits of agreement were wide (FM: +/-3.2 kg; LM: +/-3.0 kg). The proportion of variance in the reference values explained by DXA was 76% for change in FM and 43% for change in LM.

CONCLUSIONS

There are limitations to the accuracy of DXA using Lunar Prodigy for assessing body composition or changes therein in obese children. The causes of differential bias include variability in the magnitude of tissue masses, and stage of pubertal development. Further work is required to evaluate this scenario for other DXA models and manufacturers.

Bone mass in young women is dependent on lean body mass

Relationships between bone mineral density (BMD) and body mass, height, fat mass, and lean mass have been reported. This study examined the relationship between body size and composition on bone density in young premenopausal women. In this study, a cross-sectional design was used. Seventy-one healthy women aged between 24 and 36 yr selected to have a wide range of boy habitus (mean body mass index, 22.7+/-3.0) underwent a dual-energy X-ray absorptiometry (DXA) whole-body bone density scan (Hologic QDR 2000). Their bone density and soft tissue body composition and anthropometric parameters (skinfolds, girths, limb lengths, bone breadths, height, and body mass) were analyzed, and their body composition was assessed by underwater weighing (UWW). Bone-free lean mass (BFLM) determined by DXA was correlated with both bone mineral content (BMC) and BMD (r=0.74, p<0.001; r=0.48, p<0.001, respectively). In addition, fat-free mass (FFM) determined by UWW was correlated with BMC and BMD (r=0.80, p<0.001; r=0.48, p<0.001, respectively). Controlling for height in the model removed most of the correlations with whole-body BMD, with the exception of FFM, BFLM, and shoulder breadth (r=0.39, p<0.001; r=0.37, p<0.01; and r=0.34, p<0.01, respectively). No correlation was found between fat mass by DXA, UWW, and sum of skinfolds and BMD. These results indicate that bone mass in premenopausal women is dependent on lean body mass.

Bioelectrical impedance underestimates total and truncal fatness in abdominally obese women

OBJECTIVE

To compare estimates of total and truncal fatness from eight-electrode bioelectrical impedance analysis equipment (BIA(8)) with those from DXA in centrally obese women. The secondary aim was to examine BMI and waist circumference (WC) as proxy measures for percentage total body fat (%TBF) and truncal body fat percentage (tr%BF).

RESEARCH METHODS AND PROCEDURES

This was a cross-sectional study of 136 women (age, 48.1 +/- 7.7 years; BMI, 30.4 +/- 2.9 kg/m(2); %TBF(DXA), 46.0 +/- 3.7%; WC, 104 +/- 8 cm). Fatness was measured by DXA and Tanita BC-418 equipment (Tanita Corp., Tokyo, Japan). Agreement among methods was assessed by Bland-Altman plots, and regression analysis was used to evaluate anthropometric measures as proxies for total and abdominal fatness.

RESULTS

The percentage of overweight subjects was 41.9%, whereas 55.9% of the subjects were obese, as defined by BMI, and all subjects had a WC exceeding the World Health Organization cut-off point for abdominal obesity. Compared with DXA, the BIA(8) equipment significantly underestimated total %BF (-5.0; -3.6 to -8.5 [mean; 95% confidence interval]), fat mass (-3.6; -3.9 to -3.2), and tr%BF (-8.5; -9.1 to -7.9). The discrepancies between the methods increased with increasing adiposity for both %TBF and tr%BF (both p < 0.001). Variation in BMI explained 28% of the variation in %TBF(DXA) and 51% of %TBF(BIA8). Using WC as a proxy for truncal adiposity, it explained only 18% of tr%BF(DXA) variance and 27% of tr%BF(BIA8) variance. The corresponding figures for truncal fat mass were 49% and 35%, respectively. No significant age effects were observed in any of the regressions.

DISCUSSION

BIA(8) underestimated both total and truncal fatness, compared with DXA, with higher dispersion for tr%BF than %TBF. The discrepancies increased with degree of adiposity, suggesting that the accuracy of BIA is negatively affected by obesity.

Evaluation of Lunar Prodigy dual-energy X-ray absorptiometry for assessing body composition in healthy persons and patients by comparison with the criterion 4-component model

BACKGROUND

Dual-energy X-ray absorptiometry (DXA) is widely used to assess body composition in research and clinical practice. Several studies have evaluated its accuracy in healthy persons; however, little attention has been directed to the same issue in patients.

OBJECTIVE

The objective was to compare the accuracy of the Lunar Prodigy DXA for body-composition analysis with that of the reference 4-component (4C) model in healthy subjects and in patients with 1 of 3 disease states.

DESIGN

A total of 215 subjects aged 5.0-21.3 y (n = 122 healthy nonobese subjects, n = 55 obese patients, n = 26 cystic fibrosis patients, and n = 12 patients with glycogen storage disease). Fat mass (FM), fat-free mass (FFM), and weight were measured by DXA and the 4C model.

RESULTS

The accuracy of DXA-measured body-composition outcomes differed significantly between groups. Factors independently predicting bias in weight, FM, FFM, and percentage body fat in multivariate models included age, sex, size, and disease state. Biases in FFM were not mirrored by equivalent opposite biases in FM because of confounding biases in weight.

CONCLUSIONS

The bias of DXA varies according to the sex, size, fatness, and disease state of the subjects, which indicates that DXA is unreliable for patient case-control studies and for longitudinal studies of persons who undergo significant changes in nutritional status between measurements. A single correction factor cannot adjust for inconsistent biases.

Resting energy expenditure in obese children aged 4 to 15 years: measured versus predicted data

AIM

To measure the relationship of resting energy expenditure (REE) and body composition, and to compare REE data calculated from anthropometric parameters using published equations with measurements obtained by indirect calorimetry (IC) in a population of obese paediatric patients.

METHODS

The study included 82 healthy obese paediatric subjects (49 boys, 33 girls; body mass index 29.6 +/- 5.0 kg/m , age 1 1.4 +/- 2.6 y, weight 72.4 +/- 20.9 kg, height 155 +/- 14 cm). REE was measured by IC, body composition was determined by dual energy X-ray absorptiometry (DXA). Bootstrap analysis was performed to validate the step-down linear regression analysis results.

RESULTS

Lean body mass (LBM) and weight were identified as the most significant determinants of REE. LBM was the best single predictor (r = 0.78; p < 0.001) for REE. Regression equations are given in the text. Prediction of REE on the basis of published anthropometric formulas was strongly dependent from the equation used. Some equations tend to underestimate REE in the population studied with a considerable systematic error.

CONCLUSION

In the present paper we show that (1) the published equations to predict REE in obese subjects yield scattered data and some are even biased by a systematic error, and that (2) the inclusion of DXA-derived LBM improves accuracy and precision of predicted REE in boys and girls aged from 4 to 10 y and in boys from 11 to 15 y.

Overview of horse body composition and muscle architecture: implications for performance

Locomotion requires skeletal muscle to sustain and generate force. A muscle's force potential is proportional to its weight. Since the larger the muscle the larger its potential power output, a better understanding of the proportion of skeletal muscle a horse possesses may lead to a better understanding of horse performance. Several techniques exist to assess body composition, which include dual energy X-ray absorption, underwater (hydrostatic) weighing, derivation from total body water, bio-electric impedance, air displacement, body condition scoring, cadaver dissection and ultrasound. The relevance of each method to the equine industry will be discussed as will the practical information that the existing horse body composition studies have provided. Attention will be given to the data regarding the implications of body composition on the performance horse. The limited number of studies discussing different varieties of muscle architectures and the functional importance of these muscles will also be addressed. These body composition data may provide a better understanding of important issues in horse care that can lead to more optimal horse care techniques and a healthier and safer environment for horses.