Accuracy and precision of dual-energy X-ray absorptiometry for body composition measurements in rhesus monkeys

Accuracy of body composition measurements by dual-energy X-ray absorptiometry (DXA) was compared with direct chemical analysis in 10 adult rhesus monkeys. DXA was highly correlated (r-values > 0.95) with direct analyses of body fat mass (FM), lean mass (LM) and lumbar spine bone mineral content (BMC). DXA measurements of total body BMC were not as strongly correlated (r-value = 0.58) with total carcass ash content. DXA measurements of body FM, LM and lumbar spine BMC were not different from data obtained by direct analyses (P-values > 0.30). In contrast, DXA determinations of total BMC (TBMC) averaged 15%, less than total carcass ash measurements (P = 0.002). In conclusion, this study confirms the accurate measurement of fat and lean tissue mass by DXA in rhesus monkeys. DXA also accurately measured lumbar spine BMC but underestimated total body BMC as compared with carcass ash determinations.     

Body Composition in a Seasonal Model of Obesity: Longitudinal Measures and Validation of DXA

Objective: Collared lemmings, Dicrostonyx groenlandicus, show rapid changes in body mass on a seasonal basis. The objective of this study was to measure longitudinal changes in body composition in animals undergoing photoperiod-induced weight gain and loss using DXA. Research Methods and Procedures: Adult, female collared lemmings exposed to either long (LD; 22 hours light/2 hours dark) or short (SD; 8 hours light/16 hours dark) photoperiods were anesthetized, and DXA was used to determine fat mass, lean tissue mass (LTM), total-body bone mineral content, and total-bone mineral density. After a baseline scan, one-half of the animals were transferred to the alternate photoperiod (SD-LD, weight loss; LD-SD, weight gain) and one-half remained on the same photoperiod (controls; SD-SD, LD-LD). Body composition was determined by DXA after 4 and 8 weeks. Animals were killed, and body composition was determined by carcass analysis. DXA-derived data were validated by comparing with carcass analysis. Results: Body composition by DXA was highly related to body composition measured by chemical analysis, thereby justifying the use of DXA. Lemmings in the SD-LD group lost weight, and this was reflected in measurable losses of fat and LTM. Lemmings in the LD-SD group gained weight, which was shown by measurable increases in fat, LTM and total-body bone mineral content. Discussion: Comparison of body composition determined by DXA to that by chemical extraction revealed that DXA is useful for measuring body composition. The longitudinal analysis revealed that collared lemmings undergo rapid changes in body composition when exposed to changes in photoperiod.     

Association of Lean Tissue and Fat Mass with Bone Mineral Content in Children and Adolescents

Objectives: There has been uncertainty in the adult body composition literature about whether fat mass (FM) or fat free mass is a better predictor of bone mineral content and bone mineral density. This issue has recently also been raised in the pediatric literature. Based on suggested skeletal muscle–bone relationships, this study tested the hypothesis that in children and adolescents lean tissue mass (LTM) is a better predictor of total bone mineral content (TBMC) than is FM. Research Methods and Procedures: Subjects were 133 Italian children and adolescents, 5 to 17 years of age, undergoing a routine medical screen. FM (kilograms), LTM (kilograms), and TBMC (kilograms) were measured by DXA. Multiple regression analyses tested the independent association of FM and LTM with bone mineral content. Results: Regression analyses, adjusting for pubertal status and other covariates, showed that FM and LTM were independently associated with TBMC. These associations were similar for boys and girls. TBMC was more strongly associated with LTM than FM. Discussion: These observations support the hypothesis that in children and adolescents a close association exists between LTM, a measure of skeletal muscle, and skeletal characteristics.     

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.     

Comparison of three bioelectrical impedance methods with DXA in overweight and obese men

Objective: To compare bioelectrical impedance analysis (BIA) of body composition using three different methods against DXA in overweight and obese men. Research Methods and Procedures: Forty‐three healthy overweight or obese men (ages 25 to 60 years; BMI, 28 to 43 kg/m²) underwent BIA assessment of body composition using the ImpediMed SFB7 (version 6; ImpediMed, Ltd., Eight Mile Plains, Queensland, Australia) in multifrequency mode (Imp‐MF) and DF50 single‐frequency mode (Imp‐SF) and the Tanita UltimateScale (Tanita Corp., Tokyo, Japan). Validity was assessed by comparison against DXA using linear regression and limits of agreement analysis. Results: All three BIA methods showed good relative agreement with DXA [Imp‐MF: fat mass (FM), r² = 0.81; fat‐free mass (FFM), r² = 0.81; percentage body fat (BF%), r² = 0.69; Imp‐SF: FM, r² = 0.65; FFM, r² = 0.76; BF%, r² = 0.40; Tanita: BF%, r² = 0.44; all p < 0.001]. Absolute agreement between DXA and Imp‐MF was poor, as indicated by a large bias and wide limits of agreement (bias, ±1.96 standard deviation; FM, ?6.6 ± 7.7 kg; FFM, 8.0 ± 7.1 kg; BF%, ?7.0 ± 6.6%). Imp‐SF and Tanita exhibited a smaller bias but wide limits of agreement (Imp‐SF: FM, ?1.1 ± 8.5 kg; FFM, 2.5 ± 7.9 kg; BF%, ?1.7 ± 7.3% Tanita: BF%, 1.2 ± 9.5%). Discussion: Compared with DXA, Imp‐MF produced large bias and wide limits of agreement, and its accuracy estimating body composition in overweight or obese men was poor. Imp‐SF and Tanita demonstrated little bias and may be useful for group comparisons, but their utility for assessment of body composition in individuals is limited.     

Calcium supplementation increases bone mass in GH-deficient prepubertal children during GH replacement

BACKGROUND/AIMS: Since GH plays an important role in bone mineralization, and several studies demonstrated the positive influence of a higher calcium intake on bone mass, we studied the effect of calcium supplementation in GHD children during GH therapy. METHODS: 28 prepubertal GHD children, 5.0-9.9 years old, were assigned to two groups: group A (n = 14; 7 females) treated with GH, and group B (n = 14; 7 females) treated with GH + calcium gluconolactate and carbonate (1 g calcium/day per os). Auxological parameters, total bone mineral content (TBMC) and density (TBMD), leg BMC and BMD, lumbar BMD, fat mass (FM) and lean tissue mass (LTM), blood 25-hydroxyvitamin D (25-OHD), parathyroid hormone (PTH), osteocalcin (OC) and urinary N-terminal telopeptide of type I collagen (NTx) were determined at the start of therapy and after 1 and 2 years of treatment. RESULTS: During the 2 years of the study, TBMC, TBMD, leg BMC and BMD (but not lumbar BMD) increased in both groups of patients, however after 2 years of treatment they were significantly higher in the calcium-supplemented group B than in group A (p < 0.05, for all parameters). At the start of therapy, in both groups of patients percentage FM was higher and total and leg LTM lower than in controls (p < 0.05 for each parameter). Thereafter, FM decreased and LTM increased and after 2 years they were both different from baseline (p < 0.05). After 2 years of treatment, leg BMC and BMD were more positively correlated with regional leg LTM in patients of group B (r = 0.834 and r = 0.827, respectively; p < 0.001) than in patients of group A (r = 0.617 and r = 0.637, respectively; p < 0.05). 25-OHD and PTH levels were in the normal range in all patients at the start and during treatment. OC levels were lower and urinary NTx levels higher in patients than in controls (p < 0.05 for both parameters), either at the start and after 1 year of treatment. After 2 years of treatment, OC levels were significantly higher than at the start of the study (p < 0.05) in both groups of patients, but they were higher in group B than in group A (p < 0.05); on the contrary, urinary Ntx levels were lower in group B than in group A (p < 0.05). CONCLUSION: In GHD children, treated with GH, calcium supplementation improved bone mass; it may aid in reaching better peak bone mass and in protecting weight-bearing bones, usually completed in childhood to maximum levels, from risk of osteoporosis and fractures later in life.     

Dual-energy X-ray absorptiometry (DXA) can accurately and nondestructively measure the body composition of small, free-living rodents

Dual-energy x-ray absorptiometry (DXA) is a nondestructive technique that can potentially measure specific components of whole-body composition in free-living and lab-raised animals. Our aim was to test the ability of DXA to measure the composition of a common arvicoline rodent, the northern red-backed vole (Clethrionomys rutilus). We used a DXA apparatus to obtain measurements of fat mass (FM), lean mass (LM),bone mineral content, bone mineral density, and fat-free mass(FFM) in carcasses of free-living and lab-raised voles. We then used chemical carcass analysis to derive predictive algorithms for actual values of FM, total body water, total protein, total mineral, LM, and FFM. Unexplained error in the equations for all voles grouped collectively ranged from R(2) = 0.82 to R(2) = 0.98. The DXA FM measurement had the highest coefficient of variation, and it was higher for free-living voles than for lab-raised voles. However, FM can be determined by difference with excellent precision by using the FFM equation (R(2) = 0.98). We also derived corrective terms for passive integrated transponder-tagged animals. Thus, DXA is a nonlethal, nondestructive tool capable of precisely and accurately measuring many specific parameters of whole-body composition in small free-living and lab-raised rodents.     

Prediction of Fatness by Standing 8‐Electrode Bioimpedance: A Multiethnic Adolescent Population

The objective of this study was to validate an 8‐electrode bioimpedance analysis (BIA₈) device (BC‐418; Tanita, Tokyo, Japan) for use in populations of European, Maori, Pacific Island, and Asian adolescents. Healthy adolescents (215 M, 216 F; 129 Pacific Island, 120 Asian, 91 Maori, and 91 European; age range 12–19 years) were recruited by purposive sampling of high schools in Auckland, New Zealand. Weight, height, sitting height, leg length, waist circumference, and whole‐body impedance were measured. Fat mass (FM) and fat‐free mass (FFM) derived from the BIA₈ manufacturer's equations were compared with measurements by dual‐energy X‐ray absorptiometry (DXA). DXA‐measured FFM was used as the reference to develop prediction equations based on impedance. A double cross‐validation technique was applied. BIA₈ underestimated FM by 2.06 kg (P < 0.0001) and percent body fat (%BF) by 2.84% (P < 0.0001), on average. However, BIA₈ tended to overestimate FM and %BF in lean and underestimate FM and %BF in fat individuals. Sex‐specific equations developed showed acceptable accuracy on cross‐validation. In the total sample, the best prediction equations were, for boys: FFM (kg) = 0.607 height (cm)²/impedance (Ω) + 1.542 age (y) + 0.220 height (cm) + 0.096 weight (kg) + 1.836 ethnicity (0 = European or Asian, 1 = Maori or Pacific) ? 47.547, R² = 0.93, standard error of estimate (SEE) = 3.09 kg; and, for girls: FFM (kg) = 0.531 height (cm)²/impedance (Ω) + 0.182 height (cm) + 0.096 weight (kg) + 1.562 ethnicity (0 = non‐Pacific, 1 = Pacific) ? 15.782, R² = 0.91, SEE = 2.19 kg. In conclusion, equations for fatness estimation using BIA₈ developed for our sample perform better than reliance on the manufacturer's estimates. The relationship between BIA and body composition in adolescents is ethnicity dependent.     

Accuracy of body composition measurements by dual energy x-ray absorptiometry in underweight patients with chronic intestinal disease and in lean subjects

BACKGROUND: To assess the accuracy of Dual-energy X-ray absorptiometry (DXA) in underweight patients with chronic gastrointestinal disease, we investigated the ability of DXA to detect variations in body composition induced by infusion of parenteral nutrition (PN). Furthermore, the influence of a low body weight per se on the accuracy of DXA was studied by placing packets of lard on lean healthy subjects. METHODS: The hydration study included 11 patients with short bowel syndrome on long-term home parenteral nutrition (9 women and 2 men), and (mean +/- SD) 49.5 +/- 17.1 yr., 19.3 +/- 3.1 kg/m2. The lard study, where packets of lard were placed either over the thighs or the trunk region, was performed in 8 healthy lean male volunteers, 26.4 +/- 7.4 yr., and 21.0 + 0.9 kg/m2. Body composition, including measures of the total mass (TM), soft tissue mass (STM), lean tissue mass (LTM), fat mass (FM), and total body mineral content (TBBMC), was assessed by DXA. The fat fraction of the lard packets (3.49 kg), measured in triplicate by chemical fat extraction, was 52.2%. RESULTS: Hydration study; The increase in scale weight (BW) of approximately 0.90 kg due to infusion of PN correlated significantly to the increase in TM (R-square = 0.72, SEE 0.36 kg, p < 0.01), and the increase in STM (R-square = 0.69, SEE 0.38 kg, p < 0.01), however not with the increase LTM (R-square = 0.30, SEE 1.06 kg, p = 0.08). Mean changes in TM (0.88 kg), STM (0.88 kg), and LTM (0.81 kg) were not significantly different from changes in BW (p > 0.05). Lard study; Regardless of position, measurements of FM and LTM of the added lard were not significantly different from expected values. However, the composition of the lard packets into FM and LTM was more accurately detected when the packets were placed over the thighs than over the trunk region. The accuracy of DXA in individual subjects, expressed as the SD of the difference between expected and measured values, was 1.03 kg and 1.06 kg for the detection of changes in LTM and FM, respectively, and 0.18 kg for the detection of changes in STM and TM. CONCLUSIONS: On a group level, DXA provided sufficient accuracy to detect small changes in body composition in underweight patients with chronic gastrointestinal disease. However, the accuracy errors were higher than reported in normal weight subjects. The accuracy was not influenced by a low body weight per se.     

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.     

Usefulness of Anthropometry and DXA in Predicting Intra‐abdominal Fat in Obese Men and Women

Objective: To investigate the usefulness of anthropometry and DXA in predicting intra‐abdominal fat (IAF) in obese men and women. Research Methods and Procedures: Observational, cross sectional study of 22 women and 18 men with a body mass index of 30 or above. IAF from 20 cm above and 10 cm below the L4 to L5 intervertebral disc was measured by magnetic resonance imaging (MRI) as a reference method. Central abdominal fat was measured from the upper border of L2 to the lower border of L4 by DXA. Waist and hip circumferences were also measured. Results: In obese women DXA, waist circumference and waist‐hip ratio were equally well correlated with IAF (r = 0.74, 0.75, and 0.70, respectively). In obese men DXA was moderately correlated with IAF measured by MRI (r = 0.46), whereas waist circumference and waist‐hip ratio were not significantly correlated with IAF. Discussion: The prediction of IAF in obese subjects was highly dependent on sex more than in non‐obese persons. Anthropometry and DXA were equally useful in obese women, whereas anthropometry had no predictive power and DXA was the only acceptable predictor of IAF in obese men.     

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.     

Validation of BIA in Obese Children and Adolescents and Re‐evaluation in a Longitudinal Study

Decrease in fat mass (FM) is a one of the aims of pediatric obesity treatment; however, measurement techniques suitable for routine clinical assessment are lacking. The objective of this study was to validate whole‐body bioelectrical impedance analysis (BIA; TANITA BC‐418MA) against the three‐component (3C) model of body composition in obese children and adolescents, and to test the accuracy of our new equations in an independent sample studied longitudinally. A total of 77 white obese subjects (30 males) aged 5–22 years, BMI‐standard deviation score (SDS) 1.6–3.9, had measurements of weight, height (HT), body volume, total body water (TBW), and impedance (Z). FM and fat‐free mass (FFM) were calculated using the 3C model or predicted from TANITA. FFM was predicted from HT²/Z. This equation was then evaluated in 17 other obese children (5 males) aged 9–13 years. Compared to the 3C model, TANITA manufacturer's equations overestimated FFM by 2.7 kg (P < 0.001). We derived a new equation: FFM = ?2.211 + 1.115 (HT²/Z), with r² of 0.96, standard error of the estimate 2.3 kg. Use of this equation in the independent sample showed no significant bias in FM or FFM (mean bias 0.5 ± 2.4 kg; P = 0.4), and no significant bias in change in FM or FFM (mean bias 0.2 ± 1.8 kg; P = 0.7), accounting for 58% (P < 0.001) and 55% (P = 0.001) of the change in FM and FFM, respectively. Our derived BIA equation, shown to be reliable for longitudinal assessment in white obese children, will aid routine clinical monitoring of body composition in this population.     

Effect of Protein Intake on Bone Mineralization during Weight Loss: A 6‐Month Trial

Objective: The long‐term effect of dietary protein on bone mineralization is not well understood. Research Methods and Procedures: Sixty‐five overweight (body mass index, 25 to 29.9 kg/m²) or obese (≥30 kg/m²) subjects were enrolled in a randomized, placebo‐controlled, 6‐month dietary‐intervention study comparing two controlled ad libitum diets with matched fat contents: high protein (HP) or low protein (LP). Body composition was assessed by DXA. Results: In the HP group, dietary‐protein intake increased from 91.4 g/d to a 6‐month intervention mean of 107.8 g/d (p < 0.05) and decreased in the LP group from 91.1 g/d to 70.4 g/d (p < 0.05). Total weight loss after 6 months was 8.9 kg in the HP group, 5.1 kg in the LP group, and none in the control group. After 6 months, bone mineral content (BMC) had declined by 111 ± 13 g (4%) in the HP group and by 85 ± 13 g (3%) in the LP group (not significant). Loss of BMC was more positively correlated with loss of body fat mass (r = 0.83; p < 0.0001) than with loss of body weight. Six‐month BMC loss, adjusted for differences in fat loss, was greater in the LP group than in the HP group [difference in LP vs. HP, 44.8 g (95% confidence interval, 16 to 73.8 g); p < 0.05]. Independent of change in body weight and composition during the intervention, highprotein intake was associated with a diminished loss of BMC (p < 0.01). Discussion: Body‐fat loss was the major determinant of loss of BMC, and we found no adverse effects of 6 months of high‐protein intake on BMC.     

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.     

Body Composition and Energy Metabolism Following Roux‐en‐Y Gastric Bypass Surgery

Roux‐en‐Y gastric bypass (RYGB) surgery has become an accepted treatment for excessive obesity. We conducted a longitudinal study to assess regional body composition, muscle proteolysis, and energy expenditure before RYGB, and 6 and 12 months after RYGB. Whole‐body and regional fat mass (FM) and lean mass (LM) were assessed via dual energy X‐ray absorptiometry (DXA), and myofibrillar protein degradation was estimated by urinary 3‐methylhistidine (3‐MeH) in 29 subjects. Energy expenditure and substrate oxidation were also determined using a whole‐room, indirect calorimeter in 12 of these subjects. LM loss constituted 27.8 ± 10.2% of total weight loss achieved 12 months postoperatively, with the majority of LM loss (18 ± 6% of initial LM) occurring in the first 6 months following RYGB. During this period, the trunk region contributed 66% of whole‐body LM loss. LM loss occurred in the first 6 months after RYGB despite decreased muscle protein breakdown, as indicated by a decrease in 3‐MeH concentrations and muscle fractional breakdown rates. Sleep energy expenditure (SEE) decreased from 2,092 ± 342 kcal/d at baseline to 1,495 ± 190 kcal/day at 6 months after RYGB (P < 0.0001). Changes in both LM and FM had an effect on the reduction in SEE (P < 0.001 and P = 0.005, respectively). These studies suggest that loss of LM after RYGB is significant and strategies to maintain LM after surgery should be explored.     

Comparison of Methods to Assess Body Composition Changes during a Period of Weight Loss

Objective: To assess the accuracy of body composition measurements by air displacement plethysmography and bioelectrical impedance analysis (BIA) compared with DXA during weight loss. Research Methods and Procedures: Fifty‐six healthy but overweight participants, 34 women and 22 men (age, 52 ± 8.6 years; weight, 92.2 ± 11.6 kg; BMI, 33.3 ± 2.9 kg/m²) were studied in an outpatient setting before and after 6 months of weight loss (weight loss, 5.6 ± 5.5 kg). Subjects were excluded if they had initiated a new drug therapy within 30 days of randomization, were in a weight loss program, or took a weight loss drug within 90 days of randomization. Subjects were randomly assigned either to a self‐help program, consisting of two 20‐minute sessions with a nutritionist and provision of printed materials and other self‐help resources, or to attendance at meetings of a commercial program (Weight Watchers). Body composition was examined by each of the methods before and after weight loss. Results: BIA (42.4 ± 5.8%) underestimated percentage fat, whereas the BodPod (Siri = 51.7 ± 6.9%; Brozek = 48.5 ± 6.5%) overestimated percentage fat compared with DXA (46.1 ± 7.9%) before weight loss. Correlation coefficients for detecting changes in body composition between DXA and the other methods were relatively high, with Brozek Δfat mass (FM; r² = 0.63), Siri FM (r² = 0.65), tetrapolar BIA percentage fat (r² = 0.57), and Tanita FM (r² = 0.61) being the highest. Discussion: In conclusion, all of the methods were relatively accurate for assessing body composition compared with DXA, although there were biases. Furthermore, each of the methods was sensitive enough to detect changes with weight loss.     

Screening of bacterial strains capable of converting biodiesel‐derived raw glycerol into 1,3‐propanediol, 2,3‐butanediol and ethanol

The ability of bacterial strains to assimilate glycerol derived from biodiesel facilities to produce metabolic compounds of importance for the food, textile and chemical industry, such as 1,3‐propanediol (PD), 2,3‐butanediol (BD) and ethanol (EtOH), was assessed. The screening of 84 bacterial strains was performed using glycerol as carbon source. After initial trials, 12 strains were identified capable of consuming raw glycerol under anaerobic conditions, whereas 5 strains consumed glycerol under aerobiosis. A plethora of metabolic compounds was synthesized; in anaerobic batch‐bioreactor cultures PD in quantities up to 11.3 g/L was produced by Clostridium butyricum NRRL B‐23495, while the respective value was 10.1 g/L for a newly isolated Citrobacter freundii. Adaptation of Cl. butyricum at higher initial glycerol concentration resulted in a PD_max concentration of ～32 g/L. BD was produced by a new Enterobacter aerogenes isolate in shake‐flask experiments, under fully aerobic conditions, with a maximum concentration of ～22 g/L which was achieved at an initial glycerol quantity of 55 g/L. A new Klebsiella oxytoca isolate converted waste glycerol into mixtures of PD, BD and EtOH at various ratios. Finally, another new C. freundii isolate converted waste glycerol into EtOH in anaerobic batch‐bioreactor cultures with constant pH, achieving a final EtOH concentration of 14.5 g/L, a conversion yield of 0.45 g/g and a volumetric productivity of ～0.7 g/L/h. As a conclusion, the current study confirmed the utilization of biodiesel‐derived raw glycerol as an appropriate substrate for the production of PD, BD and EtOH by several newly isolated bacterial strains under different experimental conditions.     

Effect of sarcopenia on cardiovascular disease risk factors in obese postmenopausal women

Objective: To compare sarcopenic‐obese and obese postmenopausal women for risk factors predisposing to cardiovascular disease (CVD) and determine whether there may be a relationship between muscle mass and metabolic risk in obese postmenopausal women. Research Methods and Procedures: In this cross‐sectional study, 22 healthy obese postmenopausal women (mean age, 66 ± 5 years; mean BMI, 27 ± 3 kg/m²) were divided into two groups matched for age (±2 years) and fat mass (FM) (±2%). Sarcopenia was defined as a muscle mass index of <14.30 kg fat‐free mass (FFM)/m² (which corresponds to 1 standard deviation below the values of a young reference population), and obesity was defined as an FM of >35% (which corresponds to the World Health Organization guidelines). FM, FFM (measured by DXA), daily energy expenditure (accelerometry), dietary intake (3‐day dietary record), and blood biochemical analyses (lipid profile, insulin, glucose, and C‐reactive protein) were obtained. Visceral fat mass (VFM) was calculated by the equation of Bertin, which estimates VFM from DXA measurements. Results: Obese women had more FFM (p = 0.006), abdominal FM (p = 0.047), and VFM (p = 0.041) and a worse lipid profile [p = 0.040 for triglycerides; p = 0.004 for high‐density lipoprotein (HDL); p = 0.026 for total cholesterol/HDL] than sarcopenic‐obese postmenopausal women. Obese women also ingested significantly more animal (p = 0.001) and less vegetal proteins (p = 0.013), although both groups had a similar total protein intake (p = 0.967). Discussion: Sarcopenia seems to be associated with lower risk factors predisposing to CVD in obese postmenopausal women. With the increase in the number of aging people, the health implications of being sarcopenic‐obese merit more attention.