ORIGINAL ARTICLE. JR Moon 1,2, JR Stout 3, AE Smith-Ryan 4, KL Kendall 5, DH Fukuda 6, JT Cramer 7 and SE Moon 8

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1 European Journal of Clinical Nutrition (2013) 67, S40 S46 All rights reserved /13 ORIGINAL ARTICLE Tracking fat-free mass changes in elderly men and women using single-frequency bioimpedance and dual-energy X-ray absorptiometry: a four-compartment model comparison JR Moon 1,2, JR Stout 3, AE Smith-Ryan 4, KL Kendall 5, DH Fukuda 6, JT Cramer 7 and SE Moon 8 BACKGROUND/OBJECTIVES: To compare single estimations of fat-free mass (FFM) and to track FFM using single-frequency bioelectrical impedance analysis (BIA) and dual-energy X-ray absorptiometry (DXA) compared with a four-compartment (4C) model in healthy elderly Americans. SUBJECTS/METHODS: Thirty-four men and thirty-eight women (Caucasian, X65 years) were included in the study. Subjects participated in either the control group or the exercise group. All testing and training took place during the 21-week investigation. Body composition assessments using nine BIA equations, DXA and a 4C model were performed during weeks 1, 12 and 24 of the study. RESULTS: Single estimations for DXA and BIA produced high r values ( ) and low standard error of estimate values ( kg), producing subjective ratings of ideal for men and excellent for women. Both DXA and two BIA equations revealed the same significance when comparing groups and times with the 4C model. Individual accuracy for tracking changes was similar among BIA equations and DXA compared with the 4C model, with a total agreement of 25% for BIA and 27% for DXA compared with the 4C model. CONCLUSIONS: The current data in combination with the reliability errors for both BIA and DXA FFM estimations suggest that individual results should be interpreted with caution if FFM changes are o5 kg. However, DXA and BIA are both valid methods that can be used interchangeably to estimate FFM at a single time point or for tracking changes in FFM in small groups (15 22) of healthy American older adults. European Journal of Clinical Nutrition (2013) 67, S40 S46; doi: /ejcn Keywords: body composition; ageing; hypertrophy; sarcopenia INTRODUCTION In ageing adults, frailty and other chronic health conditions can be attributed to a decline across multiple physiological systems, resulting in a reduction of one s ability to complete tasks of everyday life. 1 Strength decrements can lead to sarcopenia (muscle loss), which increases the possibility of accidental falls in older adults, leading to potential hip fractures and other injuries. 2,3 Therefore, there exists a need for methods that identify the early onset of sarcopenia, as well as a guide to reduce the further development of sarcopenia at its earliest occurrence. Currently, a gold standard for total body fat-free mass (FFM) and fat mass (FM) assessment is the four-compartment (4C) model, which includes a measurement of bone, body water, body volume (BV)/density and body fat. However, the high cost and technical skill required to use this model reduces its practicality. Nevertheless, this method can accurately identify FFM and accurately track changes in body composition and has been suggested for use when tracking changes because of its validity in several populations. 4 8 More importantly, because of individual variations in FFM hydration/density and changes in the extracellular waterto-intracellular water ratio, a multiple-4c model that includes a total body water estimation is required to accurately predict or track changes in both FM and FFM in older men and women. 6 8 Another laboratory technique, dual-energy X-ray absorptiometry (DXA), has been used to assess FFM in the past, but the validity of DXA to accurately estimate body composition in varying populations has been questioned. 4,9 13 Bioelectrical impedance analysis (BIA) has become a popular field technique, because in its simplest form it only requires someone to remove his or her shoes to get an estimate of FM and FFM. In addition, an investigation by Janssen et al. 14 suggested classifying sarcopenia using a BIA total body muscle mass equation. Therefore, the purpose of the current investigation was to compare BIA FFM equations with a criterion 4C model in elderly men and women and to track changes in FFM during an exercise intervention designed to specifically increase FFM. In addition, this investigation sought to compare DXA FFM estimates with the 4C model. It was hypothesised that both BIA FFM equations and DXA would produce similar validity and accuracy for predicting and tracking FFM in the elderly. 1 Department of Sports Exercise Science, United States Sports Academy, Daphne, AL, USA; 2 MusclePharm Sports Science Center Research Institute, Denver, CO, USA; 3 Sport and Exercise Science Program, University of Central Florida, Orlando, FL, USA; 4 Department of Exercise and Sport Science, University of North Carolina Chapel Hill, Chapel Hill, NC, USA; 5 Department of Health and Kinesiology, Georgia Southern University, Statesboro, GA, USA; 6 Department of Exercise Science, Creighton University, Omaha, NE, USA; 7 Department of Nutrition and Health Sciences, University of Nebraska Lincoln, Lincoln, NE, USA and 8 Department of Psychiatry, University of Kentucky College of Medicine, Lexington, KY, USA. Correspondence: Dr JR Moon, MusclePharm Sports Science Center Research Institute, 4721 Ironton St. Bldg A, Denver, CO 80239, USA. Jordan@musclepharm.com

2 MATERIALS AND METHODS Subjects Seventy-two (34 men and 38 women) healthy Caucasian older adults (X65 years) participated in this investigation. Descriptive characteristics of the subjects are presented in Table 1. An Institutional Review Board for Human Subjects approved the investigation, and all participants completed a written informed consent. All participants were ambulatory and not using a pacemaker and were considered healthy by evaluating a self-reported health history questionnaire. Eligibility criteria included men and women aged 65 years or older, a Geriatric Nutritional Risk Index X92 and a body mass index between 18 and 32. Subjects were excluded from the investigation if they had undergone a major surgery o4 weeks before enrollment, had current active malignant disease, had stated an immunodeficiency disorder, had a history of diabetes, had a partial or artificial limb, had kidney disease, had a history of uncontrollable hypertension, had a myocardial infarction within the past 3 months, had recently used antibiotics, had untreated clinically significant ascites, pleural effusion or edema as determined by a physician, had known dementia, brain metastases, eating disorders, a history of neurological or psychiatric disorders or were currently pursuing weight loss. Research design Subjects chose to participate in the control group or the exercise group. Control subjects maintained their current activity level, whereas the exercise group participated in a structured exercise program focusing on increasing muscle mass. All testing and training took place during the 21-week investigation. Body composition assessments were performed during weeks 1, 12 and 24 of the study. All body composition assessments were performed on the same day in no particular order following a 12-h fast (ad libitum water intake was allowed up to 1 h before testing). Participants were instructed to avoid exercise for at least 24 h before testing. Hydration status was determined using specific gravity via handheld refractometry (Model CLX-1; VEE GEE Scientific, Inc. Kirkland, WA, USA) before all body composition measurements. Specific gravity values indicated that all subjects were properly hydrated (41.004, o1.029). 15,16 Training protocol Before beginning the exercise program, subjects in the exercise group had their one-repetition maximum estimated based on a five-repetition maximum using bilateral leg press, leg extension and chest press. Subjects reported to the research lab for training 3 days per week with at least 48 h between sessions. By using standardised order principles of resistance exercise prescription, subjects completed three sets of 8 12 repetitions (until volitional fatigue) at approximately 80% of their pre-determined maximum. Exercises included the following: hack squat, bilateral leg press, leg extension, chest press and lateral pull down. Resistance was adjusted accordingly if a subject could not complete at least 8 12 repetitions. Each exercise was separated by a 2 5-min recovery period. Weight increased using the standard 2 2 principle. If subjects completed 12 repetitions for the last set of an exercise for two consecutive lifting sessions, weight was increased by at least % depending on the exercise. A periodisation protocol was used to optimise muscle mass increases: Week 1 Before testing Weeks 2 and 3 one set per exercise Weeks 3 and 4 two sets per exercise Weeks 5 through 10 three sets per exercise Week 11 download week Day 1 two sets per exercise Day 2 one set per exercise Day 3 two sets per exercise Week 12 Mid testing Weeks 13 through 22 three sets per exercise Week 23 Same as week 11 Week 24 After testing Bioelectrical impedance analysis BIA was used to measure total body resistance (R) and reactance (Xc) following the procedures recommended by the manufacturer (Imp DF50; ImpediMed Limited, San Diego, CA, USA) as reported by Moon et al. 17,18 Total body FFM was estimated using several prediction equations (Table 2). Previous test retest scans of 11 men and women measured h apart resulted in an single measurement error (SEM) of 8.91 and intra-class correlation coefficient (ICC) of 0.99 for R; an SEM of 2.55 and ICC of 0.74 for Xc; and FFM equations produced an SEM of 0.51±0.10 kg, ICC of 0.997±0.002 and minimum differences needed to be considered real (MD ¼ SEM(1.96)(O2)) ¼ 1.413±0.276 kg (range: kg). Dual-energy X-ray absorptiometry DXA (software version ; Lunar Prodigy Advance, Madison, WI, USA) was used to estimate total body bone mineral content (BMC) and FFM. BMC was converted to total body bone mineral (Mo) using the following equation: Mo ¼ total body BMC FFM values for DXA were calculated from DXA total mass estimates minus total fat estimates. Previous test retest scans of 11 men and women measured h apart resulted in an SEM of 0.05 kg and ICC for Mo, and an SEM of kg, ICC and an MD of 1.73 kg for FFM. Four-compartment model Criterion FFM was estimated using the 4C model described by Wang et al. 20 The equation includes measurements of BV, total body water, Mo and body mass. The equations for FFM and FFM density are as follows: 20,21 FFM ðkgþ¼bm ð2:748 ðbvþ 0:699ðTBWÞþ1:129ðMoÞ 2:051ðBWÞÞ FFM density ¼ 1/ððTBW/0:9937Þ þðmo/2:982þ þðresidual/1:404þþ Residual ¼ BM BF Mo TBW BV was determined from air-displacement plethysmography using the BOD POD (BP) following standard procedures that were previously reported. 22,23 All BV measurements were performed by a BOD PODcertified investigator who had previously demonstrated an SEM of 0.36 l with an ICC for BV in 11 men and women measured h apart. The BOD POD was selected over hydrostatic weighing for the determination of BV because of ease of use for an elderly population. In addition, an investigation by Yee et al. 24 indicated no differences between methods for estimating body composition in elderly subjects when used in multiplecompartment models. Criterion total body water estimations were conducted using D 2 O (99.8% D 2 O; Cambridge Isotope Laboratories, Inc., S41 Table 1. BIA prediction equations Number Equations Reference 1 FFM ¼ HT(cm) 2 / R þ BM þ Xc Sex 31 2 FFM ¼ (HT 2 /R) þ BM age þ 3.5 sex þ HT FFM ¼ (HT 2 /R) age þ BM þ 4.5 sex þ HT FFM ¼ HT(m) 2 /R) þ 4.5 sex þ BM 20 T (m) þ FFM ¼ 0.28 (HT 2 /R) þ 0.27 BM þ 4.5 sex þ 0.31 T (cm) FFM ¼ 0.58 (HT 2 /R) þ BM þ Xc þ sex FFM ¼ þ (0.499 HT 2 /R) þ (0.134 BM) þ (3.449 sex) 36 8 Men FFM ¼ 0.54 (HT 2 /R) þ 0.13 BM þ 0.13 Xc 0.11 age þ Women FFM ¼ 0.37 (HT 2 /R) þ 0.16 BM þ Men FFM ¼ (HT 2 /R) þ BM þ Xc þ Women FFM ¼ (HT 2 /R) þ BM þ Xc þ Abbreviations: age, in years; BM, body mass (kg); FFM, fat-free mass; HT, height (cm), equation 4 (m); sex, men, 1, women, 0; T, thigh circumference (cm); R, resistance from the DF50 BIA; Xc, reactance from the DF50 BIA.

3 S42 Table 2. Descriptive characteristics of subjects Group Women control Women exercise Men control Men exercise Reference body n Age (years) 73 (6) 70 (5) 70 (4) 73 (6) HT (cm) (5.5) (5.0) (5.5) (5.0) Pre BM (kg) (7.31) (7.54) (8.04) (9.34) FFM (kg) (3.18) a (3.24) a (4.65) a (7.37) a FFM density (gcm 3 ) (0.007) * (0.007) * (0.007) (0.007) * Water/FFM (%) 73.5 (2.1) 73.9 (1.8) 73.9 (1.7) a 73.3 (1.5) a 73.8 Mineral/FFM (%) 6.7 (1.3) 6.1 (0.5) * 6.0 (0.5) *,a 6.1 (0.5) *,a 6.8 Protein/FFM (%) 19.8 (2.9) 19.9 (1.9) 20.0 (1.6) a 20.7 (1.4) *,a 19.4 Mid BM (kg) (7.54) (7.56) (8.02) (8.89) FFM (kg) (3.27) (3.39) (4.78) (7.00) FFM density (gcm 3 ) (0.010) * (0.009) (0.007) (0.007) * Water/FFM (%) 73.5 (2.3) 74.2 (2.1) 73.8 (1.8) 72.4 (1.6) * 73.8 Mineral/FFM (%) 6.5 (0.8) 6.1 (0.9) * 6.1 (0.6) *,d 6.0 (0.6) *,d 6.8 Protein/FFM (%) 20.0 (2.3) 19.7 (2.0) 20.1 (1.8) 21.6 (1.5) *,b 19.4 Post BM (kg) (7.44) (7.52) (7.54) (9.06) FFM (kg) (3.32) e,f (3.78) d,e,f (4.36) f (6.72) f FFM density (gcm 3 ) (0.007) * (0.007) (0.006) (0.007) *,b Water/FFM (%) 73.4 (1.5) 73.4 (1.7) 74.1 (1.5) f 72.2 (1.5) *,b,f 73.8 Mineral/FFM (%) 6.4 (0.8) 5.8 (0.6) *,b 6.1 (0.5) *,b,f 6.0 (0.6) *,f 6.8 Protein/FFM (%) 20.2 (1.6) 20.8 (1.7) * 19.8 (1.4) f 21.7 (1.6) *,b,f 19.4 Abbreviations: BM, body mass; FFM, fat-free mass; HT, height. *Significantly different from reference body (Po0.017). a Significant interaction (Po0.05). b Significantly different from pre (Po0.05). c Significantly different from mid (Po0.05). d Delta significantly different from pre to mid between groups (Po0.05). e Delta significantly different from mid to post between groups (Po0.05). f Delta significantly different from pre to post between groups (Po0.05). Andover, MA, USA) following standard procedures that were previously reported. 25 Reliability measurements from 11 men and women for D 2 Oin one urine sample measured in triplicate resulted in an SEM value of 0.33 l with an ICC Previous test retest calculations of 11 men and women measured h apart using the above 4C model with bioimpedance spectroscopy total body water estimates in place of D2O resulted in an SEM of kg, ICC of and an MD of 1.65 kg for FFM. Although multi-compartment models are recommended over 2C models for assessing body composition, the potential propagation of errors due to the inherent measurement error of each device used to assess each variable may offset the improved accuracy of 4C model estimates of body composition. 26 However, the 4C model has demonstrated SEM values o1% fat in multiple laboratories. 4,22,27,28 Specifically, a propagated error of 0.7% fat as reported by Withers in 1999 would result in an MD of 1.77% fat, similar to the MD of 1.65 kg for FFM using the above 4C model. Therefore, it can be concluded that the MD for the 4C model used in this investigation may range from 1.41 to 1.77 kg for FFM. However, the conservative 1.77 kg for FFM was selected for use in this manuscript, as it surpassed the MD values for DXA and most BIA FFM equations. Statistical analysis Data were analysed using a custom-built LabVIEW Program version (National Instruments, Austin, TX, USA) and Microsoft Excel 2010 (Microsoft Corporation, Redmond, WA, USA). The validity and comparisons of BIA prediction equations and DXA was based upon the evaluation of predicted values versus the criterion or actual values from the 4C model by calculating the constant error (CE ¼ actual predicted), r value (Pearson s product moment correlation qffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi coefficient), standard error of estimate (SEE) and total error (TE ¼ ðpredicted actualþ 2 /n). 9 The mean differences (CEs) between criterion and predicted values were analysed using dependent t-tests with Bonferroni alpha adjustments. The method of Bland and Altman was used to identify the 95% limits of agreement (LOA) between the criterion and predicted values. 29 Differences between groups and times were analysed using repeatedmeasures analysis of variance. Post-hoc dependent t-tests were performed to compare differences between times for all variables. Post-hoc independent t-tests were performed to compare the differences between FFM estimation techniques (BIA, DXA and 4C). Post-hoc tests were performed regardless of significant interactions for all FFM estimation techniques. An alpha of 0.05 was used for significance for all between and within comparisons. Subject characteristics at pre, mid and post were compared with the reference bodies of Brozek et al. 30 using one-sample t-tests, and differences between time points were analysed using dependent t-tests. RESULTS Subject characteristic at pre, mid and post time points are depicted in Table 2. Results for tracking changes and the validity results comparing all methods and equations to the 4C model are depicted in Tables 3 and 4. Validity results for pre, mid and post time points were similar for the exercise and control groups, as well as between time points. Therefore, the pre, mid and post data and the control and exercise group data were pooled for all methods and equations. Delta values comparing all time points produced similar validity for both the exercise and control groups, and thus all delta values (pre vs mid, mid vs post and pre vs post) were combined. Both DXA and BIA (equations 5 and 6) revealed the same significance when comparing groups and times with the 4C model. Interactions, significant differences between groups and significant differences between times were found for both exercise and control women for equations 5 and 6, DXA and the 4C model (Table 3). Individual accuracy for tracking changes was similar among BIA equations and DXA compared with the 4C model (Figures 1 and 2). Total individual accuracy combining all groups for the BIA equations was 25%, and for the DXA was 27%,comparing all subjects who met the MD who agreed with subjects who met the MD for the 4C model.

4 Table 3. Comparison of methods for predicting FFM compared to 4C model in men S43 Group, method, equation no. Mean s.d. r SEE TE CE LOA Trend Exercise and control (n ¼ 102) 4C DXA * * w * w * w * w * * w * w * w Control delta (n ¼ 57) 4C 0.44 a,f 1.18 DXA 0.29 f w w w w Exercise delta (n ¼ 45) 4C 0.32 a,f 1.34 DXA 0.36 f w * Abbreviations: 4C, four-compartment model; CE, constant error/mean difference; DXA, dual-energy X-ray absorptiometry; FFM, fat-free mass; LOA, limits of agreement; r, Pearson s product moment correlation coefficient; SEE, standard error of estimate; TE, total error. *Significantly different from 4C (Po0.005). w Significant slope (Po0.05). a Significant interaction (Po0.05). b Significantly different from pre (Po0.05). c Significantly different from mid (Po0.05). d Delta significantly different from pre to mid between groups (Po0.05). e Delta significantly different from mid to post between groups (Po0.05). f Delta significantly different from pre to post between groups (Po0.05). DISCUSSION In agreement with our hypothesis, both DXA and BIA produced similar and accurate results compared with the 4C model when used for single predictions of FFM in elderly men and women. In addition, in agreement with past literature in obese women, 6 both DXA and BIA produced similar results compared with the 4C model when used to track changes in FFM in older adults. The results from this investigation suggest that both BIA and DXA can be used to predict and track changes in FFM in healthy elderly American adults with similar group and individual accuracy. However, not all BIA equations produced the same results, and only equations 5 and 6 resulted in similar accuracy compared with DXA. Bioimpedance analysis (BIA) FFM equations Single FFM estimations for all equations for both men and women produced high r values (40.78) and low SEE values (o3.3 kg), producing subjective ratings of good to ideal for the men and excellent to ideal for women. 9 However, several equations produced significant CE values (Po0.005) and large TE values ( kg). The most accurate equation for men (no. 1) was the only equation to produce nonsignificant (P40.005) CE values, as well as TE values (2.68 kg) considered excellent. 9 For women, the most accurate equation (no. 9) was one of only two equations to produce nonsignificant (P ) CE values, as well as TE values (1.68 kg) considered ideal. 9 All equations for women (± kg) had tighter LOAs than the same equations in men (± kg), although all equations produced acceptable SEE values for both men and women, suggesting that equations 1 9 could potentially be used in older men and women. Currently, only equation 9 for older men and women and equations 1 for men and 8 for women are suggested for use over more complicated FFM methods such as a 4C model, 2C model or DXA. Limited research is also available regarding the ability of any body composition method to track changes in ageing adults. However, BIA has shown promise for tracking changes in other populations. 6,21 In agreement with past literature, with the exception of equation 4 in the exercise group comparing pre to post (P ¼ 0.003), all equations produced nonsignificant CEs compared with the 4C model (P40.05). However, equations 5 and 6 produced more accurate estimations compared with all other equations. For men, equation 5 resulted in the lowest CE,

5 S44 Table 4. Comparison of methods for predicting FFM compared to 4C model in women Group, method, equation no. Mean s.d. r SEE TE CE LOA Trend Exercise and control (n ¼ 114) 4C DXA * * w * w * * * w * * w w Control delta (n ¼ 66) 4C 0.10 a,e,f 1.14 DXA 0.09 a,f w w w a,e,f w a,f w w Exercise delta (n ¼ 48) 4C 0.83 a,b,e,f 1.32 DXA 0.84 a,b,f w * a,b,c,e,f a,b,c,f w Abbreviations: 4C, four-compartment model; CE, constant error/mean difference; DXA, Dual-energy X-ray absorptiometry; FFM, fat-free mass; LOA, limits of agreement; r, Pearson s product moment correlation coefficient; SEE, standard error of estimate; TE, total error. *Significantly different from 4C (Po0.005). w Significant slope (Po0.05). a Significant interaction (Po0.05). b Significantly different from pre (Po0.05). c Significantly different from mid (Po0.05). d Delta significantly different from pre to mid between groups (Po0.05). e Delta significantly different from mid to post between groups (Po0.05). f Delta significantly different from pre to post between groups (Po0.05). Figure 1. Individual results from pre to post for all groups comparing the 4C model with DXA.

6 S45 Figure 2. Individual results from pre to post for all groups comparing the 4C model to bioelectrical impedance (BIA) equation number five. SEE and TE values, and highest r values, whereas both equations 5 and 6 resulted in the most accurate results for women. Contrary to the group findings, individual accuracy for the BIA equations resulted in less accurate estimations of FFM compared with the 4C model. Similar to the group results, equations 5 and 6 also resulted in the most accurate individual results, with a 32% and 27% accuracy compared with the 4C model, respectively. Equation 5 produced the tightest LOAs for men (o±3.16 kg), and both equations 5 and 6 produced low LOAs for women (o±2.81 kg). FFM becomes more variable with age. 8 Furthermore, using BIA or DXA to classify an older adult as sarcopenic from a single FFM estimation is not recommended because of the large individual errors. The current data in combination with the reliability errors for both BIA and DXA FFM estimations suggest that results should be interpreted with caution if individual FFM changes are o5 kg; however, the magnitude of the change in FFM estimated by BIA and DXA may not be accurate. Nevertheless, DXA and BIA can be used interchangeably for tracking changes in FFM in small groups (15 22) of healthy older adults. Dual-energy X-ray absorptiometry Single FFM estimations for DXA for both men and women produced high r values (40.86) and low SEE values (o1.84 kg), producing subjective ratings of ideal for the men and excellent for women. 9 However, significant CE values were observed (Po0.005) for both men and women, indicating a trend for DXA to overestimate FFM in both men (1.75 kg) and women (1.12 kg). Nevertheless, correcting for these overestimations may allow DXA to accurately assess FFM in healthy older adults. The FFM hydration status of the subjects in the current investigation did not significantly deviate from the reference body (73.8), and thus the errors in DXA resulted in values similar to those found in other 2C models (TE o2.5 kg), with a subjective rating of excellent to ideal. 9 Contrary to past literature findings when tracking changes in overweight and obese women, DXA produced nonsignificant CEs compared with the 4C model (P40.05) in elderly adults (21; Evans 6, no. 291). The lower body mass index and body mass of the subjects in the current population compared with past investigations could have resulted in the differing results. However, in contrast to the group findings, individual accuracy for DXA resulted in less accurate estimations of FFM compared with the 4C model. A 27% total agreement with the 4C model suggests large individual errors when tracking FFM changes (Figures 1 and 2). In addition, LOAs were no better for DXA (o±3.29 kg) compared with the BIA equations (o±3.16 kg). In conclusion, both BIA and DXA can be used to estimate FFM at a single time point and be used to track changes in groups of older men and women. However, on the basis of the current results and because of the individual variations in FFM densities and the other FFM components (water, mineral and protein), it is suggested that a multiple-compartment model be used for a single assessment of FFM in elderly individuals, as the density of CONFLICT OF INTEREST JRM is Research Institute Director at MusclePharm Corporation, but the corporation was not involved in the submission. JRS has received consulting fees and grant support from Abbott Nutrition. JTC is a paid consultant for Abbott Nutrition, Vital Pharmaceuticals Inc., and has served as an expert witness for Vital Pharmaceuticals. JTC has also received lecture fees from General Nutrition Center and grant support from Rock Creek Pharmaceuticals, Abbott Nutrition and General Nutrition Center. JTC receives royalties from Holocomb Hathaway for a coauthored book. The remaining authors declare no conflict of interest. ACKNOWLEDGEMENTS We thank all the subjects who participated in this investigation. This project was funded by Abbott Nutrition. ImpediMed Limited supplied the electrodes and BIA device used in this investigation. Publication of this article was supported by a grant from seca Gmbh & Co. KG, Hamburg, Germany. REFERENCES 1 Hamerman D. Toward an understanding of frailty. Ann Intern Med 1999; 130: Census UBot. Special Tabulations on Aging-Extensive Data on Mobility and Self-Care. US Bureau of the Census: Washington, DC, Lipsitz LA, Nakajima I, Gagnon M, Hirayama T, Connelly CM, Izumo H et al. 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