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45 An alternative way of measuring hand-to-foot single frequency bioempedance
I.F. Freitas Jr; E.C. Rush, G. Kolt, A. Luke
46 Ability of bioelectrical impedance to predict percentage fat mass in children of two different ethnic origins
VP. Wickramasinghe, GJ Cleghorn, KA Edmiston, AJ Murphy, RA Abbott and PSW Davies
47 A bioelectrical impedance analysis equation for predicting total body water and fat-free mass in children with Human Immunodeficiency Virus-1 in the pre-HAART and HAART eras
TH Joffe, S Welle, R Roubenoff, SL Gorbach, GA Weinberg, C Duggan, L Furuta, J Nicchitta, TM Lipinczyk and TL Miller
48 Comparison of the Lunar DPX-L and Prodigy dual-energy X-ray absorptiometers for assessing total and regional body composition
DM Huffman, NM Landy, E Potter, TR Nagy and BA Gower
49 Body composition estimation using leg-to-leg bioelectrical impedance: a six-site international crossvalidation
study
A. Boulier, W.C. Chumlea, A. De Lorenzo, M. Deurenberg-Yap, S.S. Sun, L. Léger and Y. Schutz
International Journal of Body Composition Research 2005, Vol. 3 No. 1: 3-4
I.F. Freitas Jr1; E.C. Rush2, G. Kolt2, A. Luke3
1UNESP – São Paulo State University, Department of Physical Education, Campus Presidente Prudente, Sao
Paulo, Brazil; 2Faculty of Health and Environmental Sciences, Auckland University of Technology, Auckland,
New Zealand; 3Department of Preventive Medicine & Epidemiology, Loyola University Medical School,
Maywood, IL, USA
This short communication presents the findings of a substudy of a larger group comparing the effect of change of position on bioelectrical resistance measurements.
International Journal of Body Composition Research 2005, Vol. 3 No. 1: 5-14
VP. Wickramasinghe1, GJ Cleghorn1, KA Edmiston1, AJ Murphy1, RA Abbott2 and PSW Davies1
1Children’s Nutrition Research Centre, Department of Paediatrics and Child Health, University of Queensland,
Brisbane, Australia. 2School of Human Movement Studies, Queensland University of Technology, Australia
Detailed analysis of body composition in children has helped to understand changes that occur in growth and disease. Bioelectrical impedance analysis (BIA) has gained popularity as a simple, non-invasive and inexpensive tool of body composition assessment. Being an indirect technique, prediction equations have to be used in the assessment of body composition. There are many prediction equations available in the literature for the assessment of body composition from BIA. This study aims to cross-validate some of those prediction equations to determine the suitability of their use on Australian children of white Caucasian and Sri Lankan origins. Height, weight and BIA were measured. Total body water was measured using the isotope dilution method (D2O). Fat-mass (FM) and %FM were estimated from BIA using ten prediction equations described in the literature. Five to 14.99-year-old healthy, 96 white Caucasians and 42 Sri Lankan children were studied. The equation of Schaefer et al was the most suitable prediction equation for this group with the lowest mean bias for %FM assessment in both Caucasian (–1.0±9.6%) and Sri Lankan (1.6±5.2%) children and the fat content of the individuals did not influence the predictions by this equation. Impedance index (height2/impedance) explained for 80% of TBW in white Caucasians and 93% in Sri Lankans and figures were similar for the prediction of FFM. We conclude that BIA can be used effectively in the assessment of body composition in children. However, for the assessment of body composition using BIA, either prediction equations should be derived to suit the local populations or existing equations should be cross-validated to determine their suitability before their application.
International Journal of Body Composition Research 2005, Vol. 3 No. 1: 15-24
TH Joffe1, S Welle2, R Roubenoff3, SL Gorbach1, GA Weinberg4, C Duggan5, L Furuta5, J Nicchitta6, TM Lipinczyk6,7 and TL Miller6,7
1Department of Family Medicine and Community Health, Tufts School of Medicine, Boston, MA; 2University of
Rochester School of Medicine, General Clinical Research Center, Rochester, NY; 3Jean Mayer USDA Human
Nutrition Research Center on Aging, Tufts University, Boston, MA; 4University of Rochester School of Medicine,
Division of Pediatric Infectious Diseases, Rochester, NY; 5Children’s Hospital, Division of Gastroenterology and
Nutrition, Harvard Medical School, Boston, MA; 6University of Rochester School of Medicine, Division of
Pediatric Gastroenterology, Rochester, NY; 7Miller School of Medicine at the University of Miami, Division of
Pediatric Clinical Research, Miami FL, USA
Bioelectrical impedance analysis (BIA) is commonly used to measure body composition, however limited studies of its usefulness in children with the human immunodeficiency virus (HIV) -1 infection exist. The objective of the study was to provide a BIA equation for predicting body composition in outpatient pediatric HIV populations, to compare performance of our equation to published equations derived from both non-HIV and HIVpositive pediatric populations and to evaluate performance of our equation developed in the pre-highly active antiretroviral (HAART) era, in a separate HIV-positive pediatric population on HAART. Total body water (TBW) by deuterium dilution and BIA measures from 30 HIV-positive pediatric subjects in the pre-HAART era were used to develop an equation for estimating body composition. We evaluated 18 published pediatric BIA equations in our subjects using Bland Altman analysis, and the performance of our model in a separate HIV-positive pediatric population on HAART with dual energy X-ray absorptiometry (DXA) measures. Using multivariate techniques, we developed a predictive equation for TBW using height2 and resistance in children off HAART that correlated well (r=.95) with FFM measures obtained by DXA in children receiving HAART. A number of published BIA equations developed in healthy children also provided good estimates of TBW or FFM in our subjects. In conclusion: We provide a new BIA equation for estimating body composition in children on or off HAART. Thus BIA measures in HIV-infected children without clinically apparent lipodystrophy are not affected by HAART, although fat distribution cannot be well-defined by BIA. Published models derived from HIV populations do not always out-perform those derived from healthy subjects.
International Journal of Body Composition Research 2005, Vol. 3 No. 1: 25-30
DM Huffman, NM Landy, E Potter, TR Nagy and BA Gower
Department of Nutrition Sciences, Division of Physiology and Metabolism, University of Alabama at
Birmingham, AL, USA
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 percentage fat. A total of one hundred and six 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.
International Journal of Body Composition Research 2005, Vol. 3 No. 1: 31-39
A. Boulier1, W.C. Chumlea2, A. De Lorenzo3, M. Deurenberg-Yap4, S.S. Sun2, L. Léger5 and Y. Schutz6
1Inserm U.695, Hôpital Bichat, Paris, France; 2Lifespan Health Research Center, Wright State University, Dayton,
OH, USA; 3The Division of Human Physiology, University ‘Tor Vegata’, Roma, Italy; 4Health Promotion Board,
Singapore; 5Dept. Kinésiologie, Université de Montréal, Montréal, Canada; 6Dept. of Physiology, University of
Lausanne, Switzerland
Traditional body composition measurements using bioelectrical impedance are limited by maintaining a supine
position for a long time and by use of specific population equations. In order to evaluate a rapid bioelectrical
impedance method, an international, multiple centre and multiple ethnic group study was performed. 335 men
and 501 women of various BMI (range: 12–44 kg/m2) from seven countries were measured using Dual Energy
X-ray Absorptiometry (DXA), hydrostatic weighing, deuterium oxide dilution (D2O), skinfold thickness and
results were compared to a ‘leg-to-leg bio electrical impedance analysis method’ (LLBIA) which used a rectangular
impulse at 114 kHz. The lowest differences (D, mean ± SD) in Fat Mass (FM) and Fat Free Mass (FFM) were
found between LLBIA and DXA (DFM= -0.52 ± 3.67 kg, P<0.001 and DFFM = 0.65 ± 3.50 kg, P<0.001). Standard
errors of estimate (SEE) were close to those observed between the references (SEEFM = 3.26 kg and SEEFFM =
3.45 kg). The Bland and Altman method revealed a significant bias (P<0.001). Ethnicity explains 19% and 18% ofthe differences observed (P<0.001) for FM and FFM). The length of the legs does not explain this variance (P =0.99 for FFM). It is concluded that although specific equations for the heterogeneous populations studied (low
to high BMI’s) were not required for the LLBIA method, ethnic differences must still be taken into account. The
error involved can be tolerated for clinical and epidemiological studies, in particular when the body composition
of groups are studied.