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Accuracy of Bioelectrical Impedance Analysis in Estimated Longitudinal Fat-Free Mass Changes in Male Army Cadets

Accuracy of Bioelectrical Impedance Analysis in Estimated Longitudinal Fat-Free Mass Changes in... Abstract Introduction Bioelectrical impedance analysis (BIA) is a practical and rapid method for making a longitudinal analysis of changes in body composition. However, most BIA validation studies have been performed in a clinical population and only at one moment, or point in time (cross-sectional study). The aim of this study is to investigate the accuracy of predictive equations based on BIA with regard to the changes in fat-free mass (FFM) in Brazilian male army cadets after 7 mo of military training. The values used were determined using dual-energy X-ray absorptiometry (DXA) as a reference method. Materials and Methods The study included 310 male Brazilian Army cadets (aged 17–24 yr). FFM was measured using eight general predictive BIA equations, with one equation specifically applied to this population sample, and the values were compared with results obtained using DXA. The student’s t-test, adjusted coefficient of determination (R2), standard error of estimation (SEE), Lin’s approach, and the Bland–Altman test were used to determine the accuracy of the predictive BIA equations used to estimate FFM in this population and between the two moments (pre- and post-moment). Results The FFM measured using the nine predictive BIA equations, and determined using DXA at the post-moment, showed a significant increase when compared with the pre-moment (p < 0.05). All nine predictive BIA equations were able to detect FFM changes in the army cadets between the two moments in a very similar way to the reference method (DXA). However, only the one BIA equation specific to this population showed no significant differences in the FFM estimation between DXA at pre- and post-moment of military routine. All predictive BIA equations showed large limits of agreement using the Bland–Altman approach. Conclusion The eight general predictive BIA equations used in this study were not found to be valid for analyzing the FFM changes in the Brazilian male army cadets, after a period of approximately 7 mo of military training. Although the BIA equation specific to this population is dependent on the amount of FFM, it appears to be a good alternative to DXA for assessing FFM in Brazilian male army cadets. INTRODUCTION Approximately 500 students annually, from all regions of Brazil, enter the “Preparatory School of Army Cadets” (EsPCEx) with public tender in Campinas. Army cadets are required to have adequate levels of body composition and physical fitness as soon as they enter into a military career. Because of this, young military cadets undergo a rigorous physical training program1,2 in order to efficiently perform the tasks required in their military career. As a result of this military training, these individuals can demonstrate an increase in their fat-free mass (FFM), a maintain or decrease in fat mass (FM), and an improvement in physical performance.3,4 Observation of changes in body composition is fundamental to monitor physical health and to adequately prescribe proper nutrition, caloric intake, and physical training interventions. Dual-energy X-ray absorptiometry (DXA) has been used over the years, in several investigations, to evaluate FM and FFM because of the relative speed of scanning, low radiation exposure, and high accuracy and reproducibility of the measurements. However, problems with the use of DXA include the high expense of the test, and the facts that it is strictly dependent on operator experience, that the assessment can only be conducted in laboratory or clinical facilities, and that it is extremely difficult, if not impossible, to perform repeated measurements in a small amount of time. These issues make it difficult to implement the process with large samples.5 The body composition assessment for the army cadets is usually performed using less accurate methods such as body mass index and skinfold thickness.6,7 Therefore, effective and practical methods are necessary to accurately monitor these changes, which will allow for assessing larger numbers of cadets in a short time. Bioelectrical impedance analysis (BIA) is a widely used method to assess body composition in different clinical settings.8 It is also used in the field, mostly because it is a practical, quick, and relatively inexpensive method, when compared with other laboratory techniques.9 BIA provides resistance and reactance parameters that can be applied to specific equations.8 There are many BIA equations cited in the literature for different populations based on demographics (i.e., age and gender,10 disease and ethnicity,11 and elderly subjects12). Due to the electrical conduction of body water, and depending on the blood concentration of electrolytes,13 these equations are influenced by the specific characteristics of the populations for which they were developed, such as age, gender, disease, ethnicity, and elderly subject populations. Furthermore, the electrical conduction of the body water depends on the quantity of electrolytes; assuming that 73% of the FFM is water, the ratio between FM and FFM can influence the results.13 Therefore, it is preferable to apply the BIA equations when they have been specifically developed for the population being studied, or at least to populations with similar characteristics.14 Only one equation developed specifically for army cadets was found in the literature,15 and this equation was never longitudinally tested to verify the changes in body components. The BIA method should be able to detect changes that have occurred over a fixed period, especially when an intervention occurs (i.e., nutritional and/or physical exercise), in order to monitor the behavior of the body components,16 relative to the increase or decrease in both the FM and the FFM. The aim of this study is to investigate the accuracy of nine predictive equations based on BIA (one of these equations is specific for this population) with respect to the FFM changes in Brazilian male army cadets. In this study, we evaluated specifically cadets aged from 17 to 24 yr, after approximately 7 mo of military training, and using values determined by performing DXA as a reference. MATERIALS AND METHODS Study Subjects For these subjects to enter into their military careers in EsPCEx, they must first pass an admission process that consists of three phases: (1) an intellectual test, (2) a medical inspection, in which the candidate must send an authorization signed by the responsible doctor, along with a report of several medical examinations (i.e., exercise tests, complete blood count, electroencephalogram, and radiography of the lungs and head), and (3) a physical test, which includes exercises for muscular strength, endurance, and aerobic fitness. Data were collected in two cohorts (2013 and 2014) and two moments in time, defined by the beginning (March/April) and the end (October/November) of the military training year. The sample consisted of male volunteers aged between 17 and 24 yr. The eligibility and selection of the participants were performed according to the inclusion criteria described in Figure 1. Figure 1. View largeDownload slide Sample selection flowchart of the study. Note. EsPCEx, Preparatory School of Army Cadets. Figure 1. View largeDownload slide Sample selection flowchart of the study. Note. EsPCEx, Preparatory School of Army Cadets. Military Physical Training Military physical training was performed according to the manual “Military physical training manual EB20-MC-10.350,”2 where subjects exercised for 90 min/d, 5 d/wk. The military physical training consisted of continuous or interval running (two to three sessions per week), calisthenics exercises (one session per week), circuit resistance training (one to two sessions per week), swimming (one session per week), and sports training (two sessions per week). Study Design and Ethics A longitudinal approach was followed in which all assessments were performed at the Pediatric Research Center (CIPED) of the School of Medical Sciences at the University of Campinas (FCM-UNICAMP). The subjects performed the individual evaluations (anthropometry, BIA, and DXA) on the same day, both at the baseline (M1) and after military training (M2). In addition, they each answered a questionnaire related to health history, physical activity level, and nutritional aspects. All subjects were adequately informed about the research proposal and the procedures to which they would be subjected. They were informed that participation was voluntary and that anonymity would be preserved. All subjects who agreed to participate in the study signed an informed consent form. The research was approved by the Ethics Committee of the School of Medical Sciences, University of Campinas. All procedures followed Resolution No. 466 of 2012 of the National Health Council of the Ministry of Health of Brazil and conducted in accordance with the Declaration of Helsinki.17 Anthropometric Measurements Body weight (kg) was measured to the nearest 0.1 kg using a digital scale and height (cm) measurements to the nearest 0.1 cm were obtained using a vertical stadiometer, following the recommended protocols.18 Body mass index (BMI; kg/m2) was also calculated. Reference Method: Dual-Energy X-ray Absorptiometry (DXA) Body composition was determined using an iDXA (GE Healthcare Lunar, Madison, WI, USA) and enCore version 13.6 2011 software (GE Healthcare Lunar). Total body measurements were performed to determine FM, bone mineral content (BMC), and lean soft tissue (LST). FFM was calculated as the sum of the BMC and LST (FFM = BMC + LST) values. Reproducibility of the variables estimated using DXA was determined based on coefficients of variation (CV) and technical errors of measurement (TEM). These values were based on the test–retest realized with 23 subjects out of the study population. The CV in our laboratory were 0.74%, 0.28%, and 0.26% for FM, BMC, and LST, respectively, and TEMs were 0.25 kg, 0.02 kg, and 0.25 kg for FM, BMC, and LST, respectively. Bioelectrical Impedance Analysis (BIA) BIA measurements were performed according to the protocol recommended by Kyle.8 We used a Quantum II, single-frequency (50 kHz), tetrapolar device (RJL Systems, Detroit, MI, USA). BIA provides resistance (R) and reactance (Xc) values in Ohms (Ω). Reproducibility was calculated for a subgroup of this study population (23 subjects). The CV results obtained were 0.35% and 0.33% for R and Xc, respectively, and the TEM results were 3.54 Ω and 0.49 Ω for R and Xc, respectively. Selection of Predictive BIA Equations Nine predictive BIA equations were selected according to these criteria: (1) subjects whose ages were compatible to the sample in this study, (2) samples with male subjects only, and (3) samples using the same manufacturer and frequency (50 kHz) of BIA equipment. We selected nine predictive BIA equations published in the literature to estimate FFM (Table I). Table I. The Nine Predictive BIA Equations to Estimate Fat-Free Mass in the Male Army Cadets Reference Predictive BIA Equations of Fat-Free Mass Lukaski et al19 FFM = 0.734*(S2/R) + 0.116*Wt + 0.096*Xc + 0.878*Sex − 4.03 Chumlea et al20 FFM = 0.87*(S2/Z) + 3.50 Segal et al21 FFM = 0.00132*S2 − 0.04394*R + 0.3052*Wt − 0.1676*Age + 22.66827 Deurenberg et al12 FFM = 0.438*(S2/Z) + 0.308*Wt + 1.6*Sex + 7.04*S − 8.50 Deurenberg et al10 FFM = 0.34*(S2/Z) − 0.127*Age + 0.273*Wt + 4.56*Sex + 15.34*S − 12.44 Lohman22 FFM = 0.485*(S2/R) + 0.338*Wt + 5.32 Kotler et al11 FFM = 0.50*(S1.48/Z0.55)*(1.0/1.21) + 0.42*Wt + 0.49 Sun et al23 FFM = 0.65*(S2/R) + 0.26*Wt + 0.02*R − 10.68 Langer et al15 FFM = 0.508*Wt + 39.234*(S2/R)Log10 − 48.263 Reference Predictive BIA Equations of Fat-Free Mass Lukaski et al19 FFM = 0.734*(S2/R) + 0.116*Wt + 0.096*Xc + 0.878*Sex − 4.03 Chumlea et al20 FFM = 0.87*(S2/Z) + 3.50 Segal et al21 FFM = 0.00132*S2 − 0.04394*R + 0.3052*Wt − 0.1676*Age + 22.66827 Deurenberg et al12 FFM = 0.438*(S2/Z) + 0.308*Wt + 1.6*Sex + 7.04*S − 8.50 Deurenberg et al10 FFM = 0.34*(S2/Z) − 0.127*Age + 0.273*Wt + 4.56*Sex + 15.34*S − 12.44 Lohman22 FFM = 0.485*(S2/R) + 0.338*Wt + 5.32 Kotler et al11 FFM = 0.50*(S1.48/Z0.55)*(1.0/1.21) + 0.42*Wt + 0.49 Sun et al23 FFM = 0.65*(S2/R) + 0.26*Wt + 0.02*R − 10.68 Langer et al15 FFM = 0.508*Wt + 39.234*(S2/R)Log10 − 48.263 S, stature; R, resistance; Wt, weight; Xc, reactance; Z, impedance. Table I. The Nine Predictive BIA Equations to Estimate Fat-Free Mass in the Male Army Cadets Reference Predictive BIA Equations of Fat-Free Mass Lukaski et al19 FFM = 0.734*(S2/R) + 0.116*Wt + 0.096*Xc + 0.878*Sex − 4.03 Chumlea et al20 FFM = 0.87*(S2/Z) + 3.50 Segal et al21 FFM = 0.00132*S2 − 0.04394*R + 0.3052*Wt − 0.1676*Age + 22.66827 Deurenberg et al12 FFM = 0.438*(S2/Z) + 0.308*Wt + 1.6*Sex + 7.04*S − 8.50 Deurenberg et al10 FFM = 0.34*(S2/Z) − 0.127*Age + 0.273*Wt + 4.56*Sex + 15.34*S − 12.44 Lohman22 FFM = 0.485*(S2/R) + 0.338*Wt + 5.32 Kotler et al11 FFM = 0.50*(S1.48/Z0.55)*(1.0/1.21) + 0.42*Wt + 0.49 Sun et al23 FFM = 0.65*(S2/R) + 0.26*Wt + 0.02*R − 10.68 Langer et al15 FFM = 0.508*Wt + 39.234*(S2/R)Log10 − 48.263 Reference Predictive BIA Equations of Fat-Free Mass Lukaski et al19 FFM = 0.734*(S2/R) + 0.116*Wt + 0.096*Xc + 0.878*Sex − 4.03 Chumlea et al20 FFM = 0.87*(S2/Z) + 3.50 Segal et al21 FFM = 0.00132*S2 − 0.04394*R + 0.3052*Wt − 0.1676*Age + 22.66827 Deurenberg et al12 FFM = 0.438*(S2/Z) + 0.308*Wt + 1.6*Sex + 7.04*S − 8.50 Deurenberg et al10 FFM = 0.34*(S2/Z) − 0.127*Age + 0.273*Wt + 4.56*Sex + 15.34*S − 12.44 Lohman22 FFM = 0.485*(S2/R) + 0.338*Wt + 5.32 Kotler et al11 FFM = 0.50*(S1.48/Z0.55)*(1.0/1.21) + 0.42*Wt + 0.49 Sun et al23 FFM = 0.65*(S2/R) + 0.26*Wt + 0.02*R − 10.68 Langer et al15 FFM = 0.508*Wt + 39.234*(S2/R)Log10 − 48.263 S, stature; R, resistance; Wt, weight; Xc, reactance; Z, impedance. Statistical Analysis Data were analyzed using IBM SPSS Statistics version 21.0 (IBM, Chicago, IL, USA). The Shapiro–Wilk test was used to verify the normality of the variables. Logarithmic transformation (Log10) was adopted if the data did not present in a normal distribution. The paired Student’s t-test for the paired samples was used in the comparison between the FFM estimated using the predictive BIA equations, and the FFM as determined using DXA. The adjusted coefficient of determination (R2) and the standard error of estimate (SEE) were obtained using simple linear regression. Lin’s approach24 for the concordance correlation coefficient (CCC) was calculated using MedCalc Statistical Software. v.11.1.0, 2009 (Mariakerke, Belgium). The Lin’s approach allowed us to verify the accuracy (Cb) and precision (ρ) of the FFM estimated for the nine predictive BIA equations, against the FFM determined using DXA. The Bland–Altman method25 was used to verify the agreement between the FFM estimated for the nine predictive BIA equations versus those determined using DXA. Bivariate Pearson’s correlation (r) was conducted to determine the association of the BIA measures estimated and determined using DXA. The statistically significant level adopted was p < 0.05. RESULTS The length of military training was 6.8 ± 0.9 mo (2013 = 7.6 ± 0.2 mo and 2014 = 5.9 ± 0.2 mo; t = 64.8; p > 0.001), with minimum and maximum training times of 5.3 and 8.3 mo, respectively. Table II presents the general characteristics of the total sample of Brazilian male Army cadets before (M1) and after (M2) military physical training and the differences between the two time points. With the exception of the fat mass percentage (FM%) determined using DXA, all other variables (age, weight, height, BMI, BMC, FM (kg), and LST) at M2 showed a significant increase when compared with the M1 (p < 0.05). Table II. Military Characteristics at Pre- (M1), Post- (M2) and differences (∆ M2−M1) Between the Moments Variables (N = 310) M1 M2 Differences (∆ M2−M1) Mean ± SD Mean ± SD Mean ± SD Age (yr) 19.11 ± 1.13 19.68 ± 1.11* 0.57 ± 0.08 Weight (kg) 69.80 ± 8.56 71.63 ± 8.23* 1.82 ± 2.14 Height (cm) 175.73 ± 6.35 176.08 ± 6.31* 0.35 ± 0.77 BMI (kg/m2) 22.57 ± 2.23 23.08 ± 2.10* 0.51 ± 0.72 Dual-energy X-ray absorptiometry variables BMC (kg) 2.99 ± 0.38 3.04 ± 0.38* 0.05 ± 0.04 FM (kg) 12.04 ± 3.45 12.44 ± 3.16* 0.39 ± 1.77 LST (kg) 55.18 ± 6.28 56.58 ± 6.09* 1.40 ± 1.49 FM (%) 16.99 ± 3.64 17.13 ± 3.21 0.14 ± 2.12 Variables (N = 310) M1 M2 Differences (∆ M2−M1) Mean ± SD Mean ± SD Mean ± SD Age (yr) 19.11 ± 1.13 19.68 ± 1.11* 0.57 ± 0.08 Weight (kg) 69.80 ± 8.56 71.63 ± 8.23* 1.82 ± 2.14 Height (cm) 175.73 ± 6.35 176.08 ± 6.31* 0.35 ± 0.77 BMI (kg/m2) 22.57 ± 2.23 23.08 ± 2.10* 0.51 ± 0.72 Dual-energy X-ray absorptiometry variables BMC (kg) 2.99 ± 0.38 3.04 ± 0.38* 0.05 ± 0.04 FM (kg) 12.04 ± 3.45 12.44 ± 3.16* 0.39 ± 1.77 LST (kg) 55.18 ± 6.28 56.58 ± 6.09* 1.40 ± 1.49 FM (%) 16.99 ± 3.64 17.13 ± 3.21 0.14 ± 2.12 BMI, body mass index; BMC, bone mineral content; FM, fat mass (kg, kilograms; %, percent); LST, lean soft tissue. *Significant difference from M1, p < 0.05. Table II. Military Characteristics at Pre- (M1), Post- (M2) and differences (∆ M2−M1) Between the Moments Variables (N = 310) M1 M2 Differences (∆ M2−M1) Mean ± SD Mean ± SD Mean ± SD Age (yr) 19.11 ± 1.13 19.68 ± 1.11* 0.57 ± 0.08 Weight (kg) 69.80 ± 8.56 71.63 ± 8.23* 1.82 ± 2.14 Height (cm) 175.73 ± 6.35 176.08 ± 6.31* 0.35 ± 0.77 BMI (kg/m2) 22.57 ± 2.23 23.08 ± 2.10* 0.51 ± 0.72 Dual-energy X-ray absorptiometry variables BMC (kg) 2.99 ± 0.38 3.04 ± 0.38* 0.05 ± 0.04 FM (kg) 12.04 ± 3.45 12.44 ± 3.16* 0.39 ± 1.77 LST (kg) 55.18 ± 6.28 56.58 ± 6.09* 1.40 ± 1.49 FM (%) 16.99 ± 3.64 17.13 ± 3.21 0.14 ± 2.12 Variables (N = 310) M1 M2 Differences (∆ M2−M1) Mean ± SD Mean ± SD Mean ± SD Age (yr) 19.11 ± 1.13 19.68 ± 1.11* 0.57 ± 0.08 Weight (kg) 69.80 ± 8.56 71.63 ± 8.23* 1.82 ± 2.14 Height (cm) 175.73 ± 6.35 176.08 ± 6.31* 0.35 ± 0.77 BMI (kg/m2) 22.57 ± 2.23 23.08 ± 2.10* 0.51 ± 0.72 Dual-energy X-ray absorptiometry variables BMC (kg) 2.99 ± 0.38 3.04 ± 0.38* 0.05 ± 0.04 FM (kg) 12.04 ± 3.45 12.44 ± 3.16* 0.39 ± 1.77 LST (kg) 55.18 ± 6.28 56.58 ± 6.09* 1.40 ± 1.49 FM (%) 16.99 ± 3.64 17.13 ± 3.21 0.14 ± 2.12 BMI, body mass index; BMC, bone mineral content; FM, fat mass (kg, kilograms; %, percent); LST, lean soft tissue. *Significant difference from M1, p < 0.05. Table III shows the linear regression analysis of the FFM estimated using the nine predictive BIA equations and determined using DXA at M1 and M2 and also the differences between the two moments. With the exception of the equation by Langer et al,15 all other BIA equations showed a significant difference in FFM estimation as compared with the DXA results, both at M1 and M2 military training (p < 0.05). Table III. Linear Regression Analysis in M1, M2, and Differences (∆ M2−M1) Between the Moments M1 M2 Differences (∆ M2−M1) Fat-Free Mass (kg) Mean ± SD Linear Regression Mean ± SD Linear Regression Mean ± SD Intercept Slope R2 SEE (kg) Intercept Slope R2 SEE (kg) DXA 58.17 ± 6.60 — — — — 59.62 ± 6.41a — — — — 1.45 ± 1.49 Lukaski et al19* 58.83 ± 6.41b −158.24 122.47 0.76 3.24 61.31 ± 6.37a,b 4.18 0.90 0.81 2.82 2.48 ± 3.07 Chumlea et al20* 59.78 ± 6.95b −132.75 107.64 0.67 3.79 61.93 ± 6.94a,b 11.34 0.78 0.71 3.44 2.15 ± 3.70 Segal et al21 60.55 ± 5.84b −5.02 1.04 0.85 2.52 61.91 ± 5.68a,b −6.14 1.06 0.89 2.18 1.36 ± 1.71 Deurenberg et al12 55.31 ± 6.06b 2.51 1.00 0.85 2.52 56.98 ± 5.95a,b 1.94 1.01 0.88 2.19 1.67 ± 2.20 Deurenberg et al10 57.70 ± 5.39b −7.24 1.13 0.86 2.49 59.02 ± 5.30a,b −7.72 1.14 0.89 2.14 1.32 ± 1.76 Lohman22* 60.56 ± 6.42b −176.79 132.02 0.84 2.63 62.44 ± 6.31a,b 0.30 0.95 0.88 2.27 1.88 ± 2.46 Kotler et al26 58.95 ± 5.52b −8.21 1.13 0.89 2.22 60.35 ± 5.37a,b −9.25 1.14 0.91 1.90 1.40 ± 1.62 Sun et al23* 53.59 ± 5.71b −169.77 132.02 0.85 2.58 55.19 ± 5.67a,b 1.06 1.06 0.88 2.23 1.60 ± 2.11 Langer et al15 58.26 ± 6.11 −0.88 1.01 0.88 2.29 59.87 ± 5.90a −2.33 1.04 0.91 1.97 1.60 ± 1.82 M1 M2 Differences (∆ M2−M1) Fat-Free Mass (kg) Mean ± SD Linear Regression Mean ± SD Linear Regression Mean ± SD Intercept Slope R2 SEE (kg) Intercept Slope R2 SEE (kg) DXA 58.17 ± 6.60 — — — — 59.62 ± 6.41a — — — — 1.45 ± 1.49 Lukaski et al19* 58.83 ± 6.41b −158.24 122.47 0.76 3.24 61.31 ± 6.37a,b 4.18 0.90 0.81 2.82 2.48 ± 3.07 Chumlea et al20* 59.78 ± 6.95b −132.75 107.64 0.67 3.79 61.93 ± 6.94a,b 11.34 0.78 0.71 3.44 2.15 ± 3.70 Segal et al21 60.55 ± 5.84b −5.02 1.04 0.85 2.52 61.91 ± 5.68a,b −6.14 1.06 0.89 2.18 1.36 ± 1.71 Deurenberg et al12 55.31 ± 6.06b 2.51 1.00 0.85 2.52 56.98 ± 5.95a,b 1.94 1.01 0.88 2.19 1.67 ± 2.20 Deurenberg et al10 57.70 ± 5.39b −7.24 1.13 0.86 2.49 59.02 ± 5.30a,b −7.72 1.14 0.89 2.14 1.32 ± 1.76 Lohman22* 60.56 ± 6.42b −176.79 132.02 0.84 2.63 62.44 ± 6.31a,b 0.30 0.95 0.88 2.27 1.88 ± 2.46 Kotler et al26 58.95 ± 5.52b −8.21 1.13 0.89 2.22 60.35 ± 5.37a,b −9.25 1.14 0.91 1.90 1.40 ± 1.62 Sun et al23* 53.59 ± 5.71b −169.77 132.02 0.85 2.58 55.19 ± 5.67a,b 1.06 1.06 0.88 2.23 1.60 ± 2.11 Langer et al15 58.26 ± 6.11 −0.88 1.01 0.88 2.29 59.87 ± 5.90a −2.33 1.04 0.91 1.97 1.60 ± 1.82 DXA, dual-energy X-ray absorptiometry; SEE, standard error of estimated. *Logarithmic transformation. aSignificant difference from M1, p < 0.05. bSignificant difference from DXA, p < 0.05. Table III. Linear Regression Analysis in M1, M2, and Differences (∆ M2−M1) Between the Moments M1 M2 Differences (∆ M2−M1) Fat-Free Mass (kg) Mean ± SD Linear Regression Mean ± SD Linear Regression Mean ± SD Intercept Slope R2 SEE (kg) Intercept Slope R2 SEE (kg) DXA 58.17 ± 6.60 — — — — 59.62 ± 6.41a — — — — 1.45 ± 1.49 Lukaski et al19* 58.83 ± 6.41b −158.24 122.47 0.76 3.24 61.31 ± 6.37a,b 4.18 0.90 0.81 2.82 2.48 ± 3.07 Chumlea et al20* 59.78 ± 6.95b −132.75 107.64 0.67 3.79 61.93 ± 6.94a,b 11.34 0.78 0.71 3.44 2.15 ± 3.70 Segal et al21 60.55 ± 5.84b −5.02 1.04 0.85 2.52 61.91 ± 5.68a,b −6.14 1.06 0.89 2.18 1.36 ± 1.71 Deurenberg et al12 55.31 ± 6.06b 2.51 1.00 0.85 2.52 56.98 ± 5.95a,b 1.94 1.01 0.88 2.19 1.67 ± 2.20 Deurenberg et al10 57.70 ± 5.39b −7.24 1.13 0.86 2.49 59.02 ± 5.30a,b −7.72 1.14 0.89 2.14 1.32 ± 1.76 Lohman22* 60.56 ± 6.42b −176.79 132.02 0.84 2.63 62.44 ± 6.31a,b 0.30 0.95 0.88 2.27 1.88 ± 2.46 Kotler et al26 58.95 ± 5.52b −8.21 1.13 0.89 2.22 60.35 ± 5.37a,b −9.25 1.14 0.91 1.90 1.40 ± 1.62 Sun et al23* 53.59 ± 5.71b −169.77 132.02 0.85 2.58 55.19 ± 5.67a,b 1.06 1.06 0.88 2.23 1.60 ± 2.11 Langer et al15 58.26 ± 6.11 −0.88 1.01 0.88 2.29 59.87 ± 5.90a −2.33 1.04 0.91 1.97 1.60 ± 1.82 M1 M2 Differences (∆ M2−M1) Fat-Free Mass (kg) Mean ± SD Linear Regression Mean ± SD Linear Regression Mean ± SD Intercept Slope R2 SEE (kg) Intercept Slope R2 SEE (kg) DXA 58.17 ± 6.60 — — — — 59.62 ± 6.41a — — — — 1.45 ± 1.49 Lukaski et al19* 58.83 ± 6.41b −158.24 122.47 0.76 3.24 61.31 ± 6.37a,b 4.18 0.90 0.81 2.82 2.48 ± 3.07 Chumlea et al20* 59.78 ± 6.95b −132.75 107.64 0.67 3.79 61.93 ± 6.94a,b 11.34 0.78 0.71 3.44 2.15 ± 3.70 Segal et al21 60.55 ± 5.84b −5.02 1.04 0.85 2.52 61.91 ± 5.68a,b −6.14 1.06 0.89 2.18 1.36 ± 1.71 Deurenberg et al12 55.31 ± 6.06b 2.51 1.00 0.85 2.52 56.98 ± 5.95a,b 1.94 1.01 0.88 2.19 1.67 ± 2.20 Deurenberg et al10 57.70 ± 5.39b −7.24 1.13 0.86 2.49 59.02 ± 5.30a,b −7.72 1.14 0.89 2.14 1.32 ± 1.76 Lohman22* 60.56 ± 6.42b −176.79 132.02 0.84 2.63 62.44 ± 6.31a,b 0.30 0.95 0.88 2.27 1.88 ± 2.46 Kotler et al26 58.95 ± 5.52b −8.21 1.13 0.89 2.22 60.35 ± 5.37a,b −9.25 1.14 0.91 1.90 1.40 ± 1.62 Sun et al23* 53.59 ± 5.71b −169.77 132.02 0.85 2.58 55.19 ± 5.67a,b 1.06 1.06 0.88 2.23 1.60 ± 2.11 Langer et al15 58.26 ± 6.11 −0.88 1.01 0.88 2.29 59.87 ± 5.90a −2.33 1.04 0.91 1.97 1.60 ± 1.82 DXA, dual-energy X-ray absorptiometry; SEE, standard error of estimated. *Logarithmic transformation. aSignificant difference from M1, p < 0.05. bSignificant difference from DXA, p < 0.05. A significant correlation in FFM was seen between all BIA equations and the DXA result at both moments of evaluation (M1 and M2). As normality was not present in the FFM estimated based on BIA in the equations of Lukaski et al,19 Chumlea et al,20 Lohman,22 and Sun et al,23 a logarithmic transformation was used (Log10). The value of SEE of the nine predictive BIA equations in FFM estimates decreased from M1 to M2 (Table III). The coefficient of determination in the M1 varied between R2 = 0.67 and R2 = 0.89, and in the M2 varied between R2 = 0.71 and R2 = 0.91. Table IV and Figure 2 show the agreement between the FFM estimated using the nine predictive BIA equations and the value of FFM as determined by performing DXA, using the Bland–Altman plots25 and Lin’s concordance correlation coefficient (CCC).24 Three predictive BIA equations (Deurenberg et al,12 Deurenberg et al,10 and Sun et al23) overestimated FFM in comparison with DXA at both the M1 and the M2. The nine predictive BIA equations showed large limits of agreement at both the M1 and the M2. Regarding CCC (Table IV), the equations of Segal et al,21 Deurenberg et al,12 Deurenberg et al,10 Kotler et al,26 Sun et al,23 and Langer et al15 showed an increase in CCC from M1 to M2, whereas the equations of Chumlea et al20 and Lohman22 showed a decrease in CCC; the equation of Lukaski et al19 remained equal in at both the moments. Table IV. Concordance Analysis Between FFM Determined Using the Methods (DXA and BIA) in M1 and M2 Equations M1 M2 CCC Analysis CCC Analysis CCC ρ Cb CCC ρ Cb Lukaski et al19 0.87 0.8725 0.9949 0.87 0.8984 0.9662 Chumlea et al20 0.80 0.8226 0.9714 0.79 0.8440 0.9404 Segal et al21 0.85 0.9218 0.9264 0.87 0.9408 0.9261 Deurenberg et al12 0.83 0.9226 0.9014 0.86 0.9401 0.9135 Deurenberg et al10 0.90 0.9244 0.9771 0.92 0.9428 0.9770 Lohman22 0.86 0.9170 0.9372 0.85 0.9355 0.9100 Kotler et al26 0.92 0.9403 0.9761 0.93 0.9551 0.9769 Sun et al23 0.71 0.9184 0.7744 0.73 0.9380 0.7817 Langer et al15 0.93 0.9361 0.9970 0.95 0.9519 0.9957 Equations M1 M2 CCC Analysis CCC Analysis CCC ρ Cb CCC ρ Cb Lukaski et al19 0.87 0.8725 0.9949 0.87 0.8984 0.9662 Chumlea et al20 0.80 0.8226 0.9714 0.79 0.8440 0.9404 Segal et al21 0.85 0.9218 0.9264 0.87 0.9408 0.9261 Deurenberg et al12 0.83 0.9226 0.9014 0.86 0.9401 0.9135 Deurenberg et al10 0.90 0.9244 0.9771 0.92 0.9428 0.9770 Lohman22 0.86 0.9170 0.9372 0.85 0.9355 0.9100 Kotler et al26 0.92 0.9403 0.9761 0.93 0.9551 0.9769 Sun et al23 0.71 0.9184 0.7744 0.73 0.9380 0.7817 Langer et al15 0.93 0.9361 0.9970 0.95 0.9519 0.9957 CCC, concordance correlation coefficient; ρ, precision; Cb, accuracy. Table IV. Concordance Analysis Between FFM Determined Using the Methods (DXA and BIA) in M1 and M2 Equations M1 M2 CCC Analysis CCC Analysis CCC ρ Cb CCC ρ Cb Lukaski et al19 0.87 0.8725 0.9949 0.87 0.8984 0.9662 Chumlea et al20 0.80 0.8226 0.9714 0.79 0.8440 0.9404 Segal et al21 0.85 0.9218 0.9264 0.87 0.9408 0.9261 Deurenberg et al12 0.83 0.9226 0.9014 0.86 0.9401 0.9135 Deurenberg et al10 0.90 0.9244 0.9771 0.92 0.9428 0.9770 Lohman22 0.86 0.9170 0.9372 0.85 0.9355 0.9100 Kotler et al26 0.92 0.9403 0.9761 0.93 0.9551 0.9769 Sun et al23 0.71 0.9184 0.7744 0.73 0.9380 0.7817 Langer et al15 0.93 0.9361 0.9970 0.95 0.9519 0.9957 Equations M1 M2 CCC Analysis CCC Analysis CCC ρ Cb CCC ρ Cb Lukaski et al19 0.87 0.8725 0.9949 0.87 0.8984 0.9662 Chumlea et al20 0.80 0.8226 0.9714 0.79 0.8440 0.9404 Segal et al21 0.85 0.9218 0.9264 0.87 0.9408 0.9261 Deurenberg et al12 0.83 0.9226 0.9014 0.86 0.9401 0.9135 Deurenberg et al10 0.90 0.9244 0.9771 0.92 0.9428 0.9770 Lohman22 0.86 0.9170 0.9372 0.85 0.9355 0.9100 Kotler et al26 0.92 0.9403 0.9761 0.93 0.9551 0.9769 Sun et al23 0.71 0.9184 0.7744 0.73 0.9380 0.7817 Langer et al15 0.93 0.9361 0.9970 0.95 0.9519 0.9957 CCC, concordance correlation coefficient; ρ, precision; Cb, accuracy. Figure 2. View largeDownload slide View largeDownload slide Bland–Altman plots of the agreement. Note. FFM, fat-free mass determined using DXA and BIA equations. (A) Lukaski et al;19 (B) Chumlea et al;20 (C) Segal et al;21 (D) Deurenberg et al;12 (E) Deurenberg et al;10 (F) Lohman;22 (G) Kotler et al;26 (H) Sun et al;23 and (I) Langer et al15 at difference moments (Δ M2−M1). Solid black line, mean of the differences; dashed line, limits of agreement of 95%; continuous black line, correlation (r) between the average and the differences of the methods. Figure 2. View largeDownload slide View largeDownload slide Bland–Altman plots of the agreement. Note. FFM, fat-free mass determined using DXA and BIA equations. (A) Lukaski et al;19 (B) Chumlea et al;20 (C) Segal et al;21 (D) Deurenberg et al;12 (E) Deurenberg et al;10 (F) Lohman;22 (G) Kotler et al;26 (H) Sun et al;23 and (I) Langer et al15 at difference moments (Δ M2−M1). Solid black line, mean of the differences; dashed line, limits of agreement of 95%; continuous black line, correlation (r) between the average and the differences of the methods. Figure 2 shows the agreement of methods (DXA and BIA), using the Bland–Altman plots,25 for estimating FFM changes after about 7 mo of military physical training. With the exception of the equation from panel B (Chumlea et al20), all of the predictive BIA equations showed a significant trend (p < 0.05) in the FFM changes. The mean of the differences ranged from −1.0 kg to 0.2 kg, and the equations from panels A, B, D, F, H, and I underestimated these differences, and the equations from panel C, E, and G overestimated the differences between the two methods (DXA and BIA) in the changes of FFM. All nine predictive BIA equations showed wide limits of agreement; however, the equation from panel C presented the largest amplitude in the limits of agreement, both superior and inferior (5.9 kg to −7.3 kg). DISCUSSION In our study, we observed that the FFM values of the male army cadets increased significantly after approximately 7 mo of military training (Table III). We used the same eight predictive BIA equations that were used in a previous study, in addition to a specific equation validated for this population.15 All nine predictive BIA equations were able to detect the FFM changes of the cadets between the 2 moments in a very similar way to the reference method (DXA). However, only the equation of Langer et al15 in the FFM estimation showed no significant differences between DXA at the M1 and M2 of military physical training. Regarding the individual analysis, the Bland–Altman plots25 showed wide limits of agreement in all equations. Nevertheless, a decrease in the limits of agreement in the Langer et al15 equation of ~1 kg and 0.5 kg was observed in M1 and M2, respectively. When Lin’s approach for strength of the agreement was applied,24 only three equations (Deurenberg et al,10 Kotler et al,26 and Langer et al15) showed strong correlation between CCC values and DXA (above 0.90), in both M1 and M2. Taken together, these results suggest that the equation of Langer et al15 seems to be a better equation for assessing body composition in the Brazilian male army cadets, which reinforces the findings of another investigation.15 DXA is widely used as criterion to verify the accuracy of BIA in the FFM and FM estimates.27–29 In our study, the results indicated a good relationship between both methods; however, at the individual level, we observed wide limits of agreement using the Bland–Altman plots.25 This large variation among individuals was also observed in other studies wherein new BIA equations were developed, using DXA as a reference in healthy and/or HIV-infected children and adolescents,30 obese children and adolescents,31 and boys from 12 yr to 19 yr of different ethnicities.32 One hypothesis to explain these results is that single-frequency BIA is a method used to evaluate total body water,33 without making a distinction between the amount of intracellular and extracellular water. Intracellular and extracellular water volumes may be a source of measurement errors, as an individual’s hydration level influences the variables provided by BIA. After total body water (TBW) measurement, an internal and unknown FFM quantification is performed, thereby influencing the results in two ways: (1) FFM is not directly measured and (2) it depends on the hydration level of the subject. Nevertheless, DXA does not take into account the hydration state of the individual in its assessments of the body components and is therefore not influenced by this variable.34 Several factors can affect the results that the BIA provides, such as non-standardization of body position, previous physical exercise, and food or fluid intake,33,35,36 which were all controlled for in our study. Another source of error may be the reference method used in the development of the equations utilized.33 The recommendation is that the validation of BIA equations should be performed against reference methods that include the four-compartment (4 C) model,37,38 hydrostatic weighing, DXA,38 or deuterium isotope dilution.38 Each of these reference methods are not without error and have their limitations;37 nonetheless, they are considered state of the art regarding body composition analysis. The equations tested in our study were used with different reference methods. Therefore, discrepancies in the results may be related to greater or lesser validity of the reference methods in which the equations for a specific population have been developed, and which may or may not be comparable with other reference methods.33 For this reason, the development and validation of specific BIA equations from a population with the same characteristics must be used to reduce bias, therefore providing greater accuracy and precision of the results. A limiting factor of this study was the lack of an indicator for more rigorous control of the evaluated subjects’ hydration level, which may have influenced the variables that BIA provides.19–21 Although all procedures were adopted and controlled in the subjects, it was not possible to verify the hydration level using appropriate methods.39 We believe that because we used a large sample having the same characteristics (homogeneous sample in relation to gender, age, physical activity level, and food control), these results may have good external and internal validity and may contribute to future research in individuals with similar characteristics to the subjects in the present study. CONCLUSION The eight general predictive BIA equations used in this study did not appear to be valid for analyzing the FFM changes in the Brazilian male army cadets between ages of 17 yr and 24 yr after the period about 7 mo of military training, using DXA as a reference method. Although the specific BIA equation is dependent on the amount of FFM, it appears to be a good alternative to DXA and is an equation that can be used in samples with the same characteristics as the cadets in our sample. Acknowledgments The authors are grateful to the officers and the cadets of the “Preparatory School Cadets Army” (EsPCEx) of Campinas-SP for their authorization and collaboration in this study. Funding Coordination for the Improvement of Higher Education Personnel – CAPES (process no. 23001.000422/98-30). REFERENCES 1 O’Connor JS , Bahrke MS , Tetu RG : 1988 active Army physical fitness survey . Mil Med 1990 ; 155 : 579 – 85 . Google Scholar CrossRef Search ADS PubMed 2 Manual de Campanha: Treinamento Físico Militar. 4ed ed. MINISTÉRIO DA DEFESA EXÉRCITO BRASILEIRO ESTADO-MAIOR DO EXÉRCITO: Brasília, 2015. Available at www.ipcfex.ensino.eb.br/downloads/eb20-mc-10.350novomanualdetfm.pdf; accessed March 12, 2017. 3 Friedl KE : Body composition and military performance – many things to many people . J Strength Cond Res 2012 ; 26 ( Suppl 2 ): S87 – 100 . Google Scholar CrossRef Search ADS PubMed 4 Friedl KE : Can you be large and not obese? The distinction between body weight, body fat, and abdominal fat in occupational standards . Diabetes Technol Ther 2004 ; 6 : 732 – 49 . Google Scholar CrossRef Search ADS PubMed 5 Mazess RB , Barden HS , Bisek JP , Hanson J : Dual-energy X-ray absorptiometry for total-body and regional bone-mineral and soft-tissue composition . Am J Clin Nutr 1990 ; 51 : 1106 – 12 . Google Scholar CrossRef Search ADS PubMed 6 Avila JA , Avila RA , Gonçalves EM , Barbeta VJO , Morcillo AM , Guerra-Junior G : Secular trends of height, weight and BMI in young adult Brazilian military students in the 20th century . Ann Hum Biol 2013 ; 40 : 554 – 6 . 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Google Scholar CrossRef Search ADS PubMed 10 Deurenberg P , van der Kooy K , Leenen R , Weststrate JA , Seidell JC : Sex and age specific prediction formulas for estimating body composition from bioelectrical impedance: a cross-validation study . Int J Obes 1991 ; 15 : 17 – 25 . Google Scholar PubMed 11 Kotler DP , Burastero S , Wang J , Pierson RN : Prediction of body cell mass, fat-free mass, and total body water with bioelectrical impedance analysis: effects of race, sex, and disease . Am J Clin Nutr 1996 ; 64 : 489S – 97S . Google Scholar CrossRef Search ADS PubMed 12 Deurenberg P , Kusters CS , Smit HE : Assessment of body composition by bioelectrical impedance in children and young adults is strongly age-dependent . Eur J Clin Nutr 1990 ; 44 : 261 – 8 . Google Scholar PubMed 13 Böhm A , Heitmann BL : The use of bioelectrical impedance analysis for body composition in epidemiological studies . Eur J Clin Nutr 2013 ; 67 ( Suppl 1 ): S79 – 85 . Google Scholar CrossRef Search ADS PubMed 14 Matias CN , Santos DA , Júdice PB , et al. : Estimation of total body water and extracellular water with bioimpedance in athletes: a need for athlete-specific prediction models . Clin Nutr 2016 ; 35 : 468 – 74 . Google Scholar CrossRef Search ADS PubMed 15 Langer RD , Borges JH , Pascoa MA , Cirolini VX , Guerra-Júnior G , Gonçalves EM : Validity of bioelectrical impedance analysis to estimation fat-free mass in the Army cadets . Nutrients 2016 ; 8 : 121 . Google Scholar CrossRef Search ADS PubMed 16 Siervogel RM , Wisemandle W , Maynard LM , et al. : Serial changes in body composition throughout adulthood and their relationships to changes in lipid and lipoprotein levels. The Fels Longitudinal Study . Arterioscler, Thromb, Vasc Biol 1998 ; 18 : 1759 – 64 . Google Scholar CrossRef Search ADS 17 WMA Declaration of Helsinki - Ethical Principles for Medical Research Involving Human Subjects. 2013 . Available at http://www.wma.net/en/30publications/10policies/b3/; accessed November 18, 2015). 18 Lohman TG , Roche AF : Anthropometric Standardization Reference Manual . Champaing, IL , Human Kinetics Books , 1988 . 19 Lukaski HC , Bolonchuk WW : Theory and validation of tetrapolar bioelectrical impedance method to assess human body composition. In: In Vivo Body Composition Studies , pp 410 – 4 . Edited by Ellis KJ , Yasumura S , Morgan WD , New York , Institute of Physical Sciences in Medicine, 1987 . 20 Chumlea WC , Baumgartner RN , Roche AF : Specific resistivity used to estimate fat-free mass from segmental body measures of bioelectric impedance . Am J Clin Nutr 1988 ; 48 : 7 – 15 . Google Scholar CrossRef Search ADS PubMed 21 Segal KR , Van Loan M , Fitzgerald PI , Hodgdon JA , Van Itallie TB : Lean body mass estimation by bioelectrical impedance analysis: a four-site cross-validation study . Am J Clin Nutr 1988 ; 47 : 7 – 14 . Google Scholar CrossRef Search ADS PubMed 22 Lohman TG : Prediction equations and skinfolds, bioelectric impedance, and body mass index. In: Advances in Body Composition Assessment , pp 37 – 56 . Edited by Human Kinetics, Champaign, IL , United States of America , 1992 . 23 Sun SS , Chumlea WC , Heymsfield SB , et al. : Development of bioelectrical impedance analysis prediction equations for body composition with the use of a multicomponent model for use in epidemiologic surveys . Am J Clin Nutr 2003 ; 77 : 331 – 40 . Google Scholar CrossRef Search ADS PubMed 24 Lin LI : A concordance correlation coefficient to evaluate reproducibility . Biometrics 1989 ; 45 : 255 – 68 . Google Scholar CrossRef Search ADS PubMed 25 Bland JM , Altman DG : Statistical methods for assessing agreement between two methods of clinical measurement . Lancet 1986 ; 1 : 307 – 10 . 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Google Scholar CrossRef Search ADS PubMed 30 Horlick M , Arpadi SM , Bethel J , et al. : Bioelectrical impedance analysis models for prediction of total body water and fat-free mass in healthy and HIV-infected children and adolescents . Am J Clin Nutr 2002 ; 76 : 991 – 9 . Google Scholar CrossRef Search ADS PubMed 31 Lazzer S , Bedogni G , Agosti F , De Col A , Mornati D , Sartorio A : Comparison of dual-energy X-ray absorptiometry, air displacement plethysmography and bioelectrical impedance analysis for the assessment of body composition in severely obese Caucasian children and adolescents . Br J Nutr 2008 ; 100 : 918 – 24 . Google Scholar CrossRef Search ADS PubMed 32 Sluyter JD , Schaaf D , Scragg RKR , Plank LD : Prediction of fatness by standing 8-electrode bioimpedance: a multiethnic adolescent population . Obesity (Silver Spring) 2010 ; 18 : 183 – 9 . Google Scholar CrossRef Search ADS PubMed 33 Kyle UG , Bosaeus I , De Lorenzo AD , et al. : Bioelectrical impedance analysis-part II: utilization in clinical practice . Clin Nutr 2004 ; 23 : 1430 – 53 . Google Scholar CrossRef Search ADS PubMed 34 Kohrt WM : Body composition by DXA: tried and true? Med Sci Sports Exerc 1995 ; 27 : 1349 – 53 . Google Scholar CrossRef Search ADS PubMed 35 Kushner RF , Schoeller DA , Fjeld CR , Danford L : Is the impedance index (ht2/R) significant in predicting total body water? Am J Clin Nutr 1992 ; 56 : 835 – 9 . Google Scholar CrossRef Search ADS PubMed 36 Schols AM , Dingemans AM , Soeters PB , Wouters EF : Within-day variation of bioelectrical resistance measurements in patients with chronic obstructive pulmonary disease . Clin Nutr 1990 ; 9 : 266 – 71 . 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Accuracy of Bioelectrical Impedance Analysis in Estimated Longitudinal Fat-Free Mass Changes in Male Army Cadets

Military Medicine , Volume Advance Article (7) – Jun 28, 2018
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Abstract

Abstract Introduction Bioelectrical impedance analysis (BIA) is a practical and rapid method for making a longitudinal analysis of changes in body composition. However, most BIA validation studies have been performed in a clinical population and only at one moment, or point in time (cross-sectional study). The aim of this study is to investigate the accuracy of predictive equations based on BIA with regard to the changes in fat-free mass (FFM) in Brazilian male army cadets after 7 mo of military training. The values used were determined using dual-energy X-ray absorptiometry (DXA) as a reference method. Materials and Methods The study included 310 male Brazilian Army cadets (aged 17–24 yr). FFM was measured using eight general predictive BIA equations, with one equation specifically applied to this population sample, and the values were compared with results obtained using DXA. The student’s t-test, adjusted coefficient of determination (R2), standard error of estimation (SEE), Lin’s approach, and the Bland–Altman test were used to determine the accuracy of the predictive BIA equations used to estimate FFM in this population and between the two moments (pre- and post-moment). Results The FFM measured using the nine predictive BIA equations, and determined using DXA at the post-moment, showed a significant increase when compared with the pre-moment (p < 0.05). All nine predictive BIA equations were able to detect FFM changes in the army cadets between the two moments in a very similar way to the reference method (DXA). However, only the one BIA equation specific to this population showed no significant differences in the FFM estimation between DXA at pre- and post-moment of military routine. All predictive BIA equations showed large limits of agreement using the Bland–Altman approach. Conclusion The eight general predictive BIA equations used in this study were not found to be valid for analyzing the FFM changes in the Brazilian male army cadets, after a period of approximately 7 mo of military training. Although the BIA equation specific to this population is dependent on the amount of FFM, it appears to be a good alternative to DXA for assessing FFM in Brazilian male army cadets. INTRODUCTION Approximately 500 students annually, from all regions of Brazil, enter the “Preparatory School of Army Cadets” (EsPCEx) with public tender in Campinas. Army cadets are required to have adequate levels of body composition and physical fitness as soon as they enter into a military career. Because of this, young military cadets undergo a rigorous physical training program1,2 in order to efficiently perform the tasks required in their military career. As a result of this military training, these individuals can demonstrate an increase in their fat-free mass (FFM), a maintain or decrease in fat mass (FM), and an improvement in physical performance.3,4 Observation of changes in body composition is fundamental to monitor physical health and to adequately prescribe proper nutrition, caloric intake, and physical training interventions. Dual-energy X-ray absorptiometry (DXA) has been used over the years, in several investigations, to evaluate FM and FFM because of the relative speed of scanning, low radiation exposure, and high accuracy and reproducibility of the measurements. However, problems with the use of DXA include the high expense of the test, and the facts that it is strictly dependent on operator experience, that the assessment can only be conducted in laboratory or clinical facilities, and that it is extremely difficult, if not impossible, to perform repeated measurements in a small amount of time. These issues make it difficult to implement the process with large samples.5 The body composition assessment for the army cadets is usually performed using less accurate methods such as body mass index and skinfold thickness.6,7 Therefore, effective and practical methods are necessary to accurately monitor these changes, which will allow for assessing larger numbers of cadets in a short time. Bioelectrical impedance analysis (BIA) is a widely used method to assess body composition in different clinical settings.8 It is also used in the field, mostly because it is a practical, quick, and relatively inexpensive method, when compared with other laboratory techniques.9 BIA provides resistance and reactance parameters that can be applied to specific equations.8 There are many BIA equations cited in the literature for different populations based on demographics (i.e., age and gender,10 disease and ethnicity,11 and elderly subjects12). Due to the electrical conduction of body water, and depending on the blood concentration of electrolytes,13 these equations are influenced by the specific characteristics of the populations for which they were developed, such as age, gender, disease, ethnicity, and elderly subject populations. Furthermore, the electrical conduction of the body water depends on the quantity of electrolytes; assuming that 73% of the FFM is water, the ratio between FM and FFM can influence the results.13 Therefore, it is preferable to apply the BIA equations when they have been specifically developed for the population being studied, or at least to populations with similar characteristics.14 Only one equation developed specifically for army cadets was found in the literature,15 and this equation was never longitudinally tested to verify the changes in body components. The BIA method should be able to detect changes that have occurred over a fixed period, especially when an intervention occurs (i.e., nutritional and/or physical exercise), in order to monitor the behavior of the body components,16 relative to the increase or decrease in both the FM and the FFM. The aim of this study is to investigate the accuracy of nine predictive equations based on BIA (one of these equations is specific for this population) with respect to the FFM changes in Brazilian male army cadets. In this study, we evaluated specifically cadets aged from 17 to 24 yr, after approximately 7 mo of military training, and using values determined by performing DXA as a reference. MATERIALS AND METHODS Study Subjects For these subjects to enter into their military careers in EsPCEx, they must first pass an admission process that consists of three phases: (1) an intellectual test, (2) a medical inspection, in which the candidate must send an authorization signed by the responsible doctor, along with a report of several medical examinations (i.e., exercise tests, complete blood count, electroencephalogram, and radiography of the lungs and head), and (3) a physical test, which includes exercises for muscular strength, endurance, and aerobic fitness. Data were collected in two cohorts (2013 and 2014) and two moments in time, defined by the beginning (March/April) and the end (October/November) of the military training year. The sample consisted of male volunteers aged between 17 and 24 yr. The eligibility and selection of the participants were performed according to the inclusion criteria described in Figure 1. Figure 1. View largeDownload slide Sample selection flowchart of the study. Note. EsPCEx, Preparatory School of Army Cadets. Figure 1. View largeDownload slide Sample selection flowchart of the study. Note. EsPCEx, Preparatory School of Army Cadets. Military Physical Training Military physical training was performed according to the manual “Military physical training manual EB20-MC-10.350,”2 where subjects exercised for 90 min/d, 5 d/wk. The military physical training consisted of continuous or interval running (two to three sessions per week), calisthenics exercises (one session per week), circuit resistance training (one to two sessions per week), swimming (one session per week), and sports training (two sessions per week). Study Design and Ethics A longitudinal approach was followed in which all assessments were performed at the Pediatric Research Center (CIPED) of the School of Medical Sciences at the University of Campinas (FCM-UNICAMP). The subjects performed the individual evaluations (anthropometry, BIA, and DXA) on the same day, both at the baseline (M1) and after military training (M2). In addition, they each answered a questionnaire related to health history, physical activity level, and nutritional aspects. All subjects were adequately informed about the research proposal and the procedures to which they would be subjected. They were informed that participation was voluntary and that anonymity would be preserved. All subjects who agreed to participate in the study signed an informed consent form. The research was approved by the Ethics Committee of the School of Medical Sciences, University of Campinas. All procedures followed Resolution No. 466 of 2012 of the National Health Council of the Ministry of Health of Brazil and conducted in accordance with the Declaration of Helsinki.17 Anthropometric Measurements Body weight (kg) was measured to the nearest 0.1 kg using a digital scale and height (cm) measurements to the nearest 0.1 cm were obtained using a vertical stadiometer, following the recommended protocols.18 Body mass index (BMI; kg/m2) was also calculated. Reference Method: Dual-Energy X-ray Absorptiometry (DXA) Body composition was determined using an iDXA (GE Healthcare Lunar, Madison, WI, USA) and enCore version 13.6 2011 software (GE Healthcare Lunar). Total body measurements were performed to determine FM, bone mineral content (BMC), and lean soft tissue (LST). FFM was calculated as the sum of the BMC and LST (FFM = BMC + LST) values. Reproducibility of the variables estimated using DXA was determined based on coefficients of variation (CV) and technical errors of measurement (TEM). These values were based on the test–retest realized with 23 subjects out of the study population. The CV in our laboratory were 0.74%, 0.28%, and 0.26% for FM, BMC, and LST, respectively, and TEMs were 0.25 kg, 0.02 kg, and 0.25 kg for FM, BMC, and LST, respectively. Bioelectrical Impedance Analysis (BIA) BIA measurements were performed according to the protocol recommended by Kyle.8 We used a Quantum II, single-frequency (50 kHz), tetrapolar device (RJL Systems, Detroit, MI, USA). BIA provides resistance (R) and reactance (Xc) values in Ohms (Ω). Reproducibility was calculated for a subgroup of this study population (23 subjects). The CV results obtained were 0.35% and 0.33% for R and Xc, respectively, and the TEM results were 3.54 Ω and 0.49 Ω for R and Xc, respectively. Selection of Predictive BIA Equations Nine predictive BIA equations were selected according to these criteria: (1) subjects whose ages were compatible to the sample in this study, (2) samples with male subjects only, and (3) samples using the same manufacturer and frequency (50 kHz) of BIA equipment. We selected nine predictive BIA equations published in the literature to estimate FFM (Table I). Table I. The Nine Predictive BIA Equations to Estimate Fat-Free Mass in the Male Army Cadets Reference Predictive BIA Equations of Fat-Free Mass Lukaski et al19 FFM = 0.734*(S2/R) + 0.116*Wt + 0.096*Xc + 0.878*Sex − 4.03 Chumlea et al20 FFM = 0.87*(S2/Z) + 3.50 Segal et al21 FFM = 0.00132*S2 − 0.04394*R + 0.3052*Wt − 0.1676*Age + 22.66827 Deurenberg et al12 FFM = 0.438*(S2/Z) + 0.308*Wt + 1.6*Sex + 7.04*S − 8.50 Deurenberg et al10 FFM = 0.34*(S2/Z) − 0.127*Age + 0.273*Wt + 4.56*Sex + 15.34*S − 12.44 Lohman22 FFM = 0.485*(S2/R) + 0.338*Wt + 5.32 Kotler et al11 FFM = 0.50*(S1.48/Z0.55)*(1.0/1.21) + 0.42*Wt + 0.49 Sun et al23 FFM = 0.65*(S2/R) + 0.26*Wt + 0.02*R − 10.68 Langer et al15 FFM = 0.508*Wt + 39.234*(S2/R)Log10 − 48.263 Reference Predictive BIA Equations of Fat-Free Mass Lukaski et al19 FFM = 0.734*(S2/R) + 0.116*Wt + 0.096*Xc + 0.878*Sex − 4.03 Chumlea et al20 FFM = 0.87*(S2/Z) + 3.50 Segal et al21 FFM = 0.00132*S2 − 0.04394*R + 0.3052*Wt − 0.1676*Age + 22.66827 Deurenberg et al12 FFM = 0.438*(S2/Z) + 0.308*Wt + 1.6*Sex + 7.04*S − 8.50 Deurenberg et al10 FFM = 0.34*(S2/Z) − 0.127*Age + 0.273*Wt + 4.56*Sex + 15.34*S − 12.44 Lohman22 FFM = 0.485*(S2/R) + 0.338*Wt + 5.32 Kotler et al11 FFM = 0.50*(S1.48/Z0.55)*(1.0/1.21) + 0.42*Wt + 0.49 Sun et al23 FFM = 0.65*(S2/R) + 0.26*Wt + 0.02*R − 10.68 Langer et al15 FFM = 0.508*Wt + 39.234*(S2/R)Log10 − 48.263 S, stature; R, resistance; Wt, weight; Xc, reactance; Z, impedance. Table I. The Nine Predictive BIA Equations to Estimate Fat-Free Mass in the Male Army Cadets Reference Predictive BIA Equations of Fat-Free Mass Lukaski et al19 FFM = 0.734*(S2/R) + 0.116*Wt + 0.096*Xc + 0.878*Sex − 4.03 Chumlea et al20 FFM = 0.87*(S2/Z) + 3.50 Segal et al21 FFM = 0.00132*S2 − 0.04394*R + 0.3052*Wt − 0.1676*Age + 22.66827 Deurenberg et al12 FFM = 0.438*(S2/Z) + 0.308*Wt + 1.6*Sex + 7.04*S − 8.50 Deurenberg et al10 FFM = 0.34*(S2/Z) − 0.127*Age + 0.273*Wt + 4.56*Sex + 15.34*S − 12.44 Lohman22 FFM = 0.485*(S2/R) + 0.338*Wt + 5.32 Kotler et al11 FFM = 0.50*(S1.48/Z0.55)*(1.0/1.21) + 0.42*Wt + 0.49 Sun et al23 FFM = 0.65*(S2/R) + 0.26*Wt + 0.02*R − 10.68 Langer et al15 FFM = 0.508*Wt + 39.234*(S2/R)Log10 − 48.263 Reference Predictive BIA Equations of Fat-Free Mass Lukaski et al19 FFM = 0.734*(S2/R) + 0.116*Wt + 0.096*Xc + 0.878*Sex − 4.03 Chumlea et al20 FFM = 0.87*(S2/Z) + 3.50 Segal et al21 FFM = 0.00132*S2 − 0.04394*R + 0.3052*Wt − 0.1676*Age + 22.66827 Deurenberg et al12 FFM = 0.438*(S2/Z) + 0.308*Wt + 1.6*Sex + 7.04*S − 8.50 Deurenberg et al10 FFM = 0.34*(S2/Z) − 0.127*Age + 0.273*Wt + 4.56*Sex + 15.34*S − 12.44 Lohman22 FFM = 0.485*(S2/R) + 0.338*Wt + 5.32 Kotler et al11 FFM = 0.50*(S1.48/Z0.55)*(1.0/1.21) + 0.42*Wt + 0.49 Sun et al23 FFM = 0.65*(S2/R) + 0.26*Wt + 0.02*R − 10.68 Langer et al15 FFM = 0.508*Wt + 39.234*(S2/R)Log10 − 48.263 S, stature; R, resistance; Wt, weight; Xc, reactance; Z, impedance. Statistical Analysis Data were analyzed using IBM SPSS Statistics version 21.0 (IBM, Chicago, IL, USA). The Shapiro–Wilk test was used to verify the normality of the variables. Logarithmic transformation (Log10) was adopted if the data did not present in a normal distribution. The paired Student’s t-test for the paired samples was used in the comparison between the FFM estimated using the predictive BIA equations, and the FFM as determined using DXA. The adjusted coefficient of determination (R2) and the standard error of estimate (SEE) were obtained using simple linear regression. Lin’s approach24 for the concordance correlation coefficient (CCC) was calculated using MedCalc Statistical Software. v.11.1.0, 2009 (Mariakerke, Belgium). The Lin’s approach allowed us to verify the accuracy (Cb) and precision (ρ) of the FFM estimated for the nine predictive BIA equations, against the FFM determined using DXA. The Bland–Altman method25 was used to verify the agreement between the FFM estimated for the nine predictive BIA equations versus those determined using DXA. Bivariate Pearson’s correlation (r) was conducted to determine the association of the BIA measures estimated and determined using DXA. The statistically significant level adopted was p < 0.05. RESULTS The length of military training was 6.8 ± 0.9 mo (2013 = 7.6 ± 0.2 mo and 2014 = 5.9 ± 0.2 mo; t = 64.8; p > 0.001), with minimum and maximum training times of 5.3 and 8.3 mo, respectively. Table II presents the general characteristics of the total sample of Brazilian male Army cadets before (M1) and after (M2) military physical training and the differences between the two time points. With the exception of the fat mass percentage (FM%) determined using DXA, all other variables (age, weight, height, BMI, BMC, FM (kg), and LST) at M2 showed a significant increase when compared with the M1 (p < 0.05). Table II. Military Characteristics at Pre- (M1), Post- (M2) and differences (∆ M2−M1) Between the Moments Variables (N = 310) M1 M2 Differences (∆ M2−M1) Mean ± SD Mean ± SD Mean ± SD Age (yr) 19.11 ± 1.13 19.68 ± 1.11* 0.57 ± 0.08 Weight (kg) 69.80 ± 8.56 71.63 ± 8.23* 1.82 ± 2.14 Height (cm) 175.73 ± 6.35 176.08 ± 6.31* 0.35 ± 0.77 BMI (kg/m2) 22.57 ± 2.23 23.08 ± 2.10* 0.51 ± 0.72 Dual-energy X-ray absorptiometry variables BMC (kg) 2.99 ± 0.38 3.04 ± 0.38* 0.05 ± 0.04 FM (kg) 12.04 ± 3.45 12.44 ± 3.16* 0.39 ± 1.77 LST (kg) 55.18 ± 6.28 56.58 ± 6.09* 1.40 ± 1.49 FM (%) 16.99 ± 3.64 17.13 ± 3.21 0.14 ± 2.12 Variables (N = 310) M1 M2 Differences (∆ M2−M1) Mean ± SD Mean ± SD Mean ± SD Age (yr) 19.11 ± 1.13 19.68 ± 1.11* 0.57 ± 0.08 Weight (kg) 69.80 ± 8.56 71.63 ± 8.23* 1.82 ± 2.14 Height (cm) 175.73 ± 6.35 176.08 ± 6.31* 0.35 ± 0.77 BMI (kg/m2) 22.57 ± 2.23 23.08 ± 2.10* 0.51 ± 0.72 Dual-energy X-ray absorptiometry variables BMC (kg) 2.99 ± 0.38 3.04 ± 0.38* 0.05 ± 0.04 FM (kg) 12.04 ± 3.45 12.44 ± 3.16* 0.39 ± 1.77 LST (kg) 55.18 ± 6.28 56.58 ± 6.09* 1.40 ± 1.49 FM (%) 16.99 ± 3.64 17.13 ± 3.21 0.14 ± 2.12 BMI, body mass index; BMC, bone mineral content; FM, fat mass (kg, kilograms; %, percent); LST, lean soft tissue. *Significant difference from M1, p < 0.05. Table II. Military Characteristics at Pre- (M1), Post- (M2) and differences (∆ M2−M1) Between the Moments Variables (N = 310) M1 M2 Differences (∆ M2−M1) Mean ± SD Mean ± SD Mean ± SD Age (yr) 19.11 ± 1.13 19.68 ± 1.11* 0.57 ± 0.08 Weight (kg) 69.80 ± 8.56 71.63 ± 8.23* 1.82 ± 2.14 Height (cm) 175.73 ± 6.35 176.08 ± 6.31* 0.35 ± 0.77 BMI (kg/m2) 22.57 ± 2.23 23.08 ± 2.10* 0.51 ± 0.72 Dual-energy X-ray absorptiometry variables BMC (kg) 2.99 ± 0.38 3.04 ± 0.38* 0.05 ± 0.04 FM (kg) 12.04 ± 3.45 12.44 ± 3.16* 0.39 ± 1.77 LST (kg) 55.18 ± 6.28 56.58 ± 6.09* 1.40 ± 1.49 FM (%) 16.99 ± 3.64 17.13 ± 3.21 0.14 ± 2.12 Variables (N = 310) M1 M2 Differences (∆ M2−M1) Mean ± SD Mean ± SD Mean ± SD Age (yr) 19.11 ± 1.13 19.68 ± 1.11* 0.57 ± 0.08 Weight (kg) 69.80 ± 8.56 71.63 ± 8.23* 1.82 ± 2.14 Height (cm) 175.73 ± 6.35 176.08 ± 6.31* 0.35 ± 0.77 BMI (kg/m2) 22.57 ± 2.23 23.08 ± 2.10* 0.51 ± 0.72 Dual-energy X-ray absorptiometry variables BMC (kg) 2.99 ± 0.38 3.04 ± 0.38* 0.05 ± 0.04 FM (kg) 12.04 ± 3.45 12.44 ± 3.16* 0.39 ± 1.77 LST (kg) 55.18 ± 6.28 56.58 ± 6.09* 1.40 ± 1.49 FM (%) 16.99 ± 3.64 17.13 ± 3.21 0.14 ± 2.12 BMI, body mass index; BMC, bone mineral content; FM, fat mass (kg, kilograms; %, percent); LST, lean soft tissue. *Significant difference from M1, p < 0.05. Table III shows the linear regression analysis of the FFM estimated using the nine predictive BIA equations and determined using DXA at M1 and M2 and also the differences between the two moments. With the exception of the equation by Langer et al,15 all other BIA equations showed a significant difference in FFM estimation as compared with the DXA results, both at M1 and M2 military training (p < 0.05). Table III. Linear Regression Analysis in M1, M2, and Differences (∆ M2−M1) Between the Moments M1 M2 Differences (∆ M2−M1) Fat-Free Mass (kg) Mean ± SD Linear Regression Mean ± SD Linear Regression Mean ± SD Intercept Slope R2 SEE (kg) Intercept Slope R2 SEE (kg) DXA 58.17 ± 6.60 — — — — 59.62 ± 6.41a — — — — 1.45 ± 1.49 Lukaski et al19* 58.83 ± 6.41b −158.24 122.47 0.76 3.24 61.31 ± 6.37a,b 4.18 0.90 0.81 2.82 2.48 ± 3.07 Chumlea et al20* 59.78 ± 6.95b −132.75 107.64 0.67 3.79 61.93 ± 6.94a,b 11.34 0.78 0.71 3.44 2.15 ± 3.70 Segal et al21 60.55 ± 5.84b −5.02 1.04 0.85 2.52 61.91 ± 5.68a,b −6.14 1.06 0.89 2.18 1.36 ± 1.71 Deurenberg et al12 55.31 ± 6.06b 2.51 1.00 0.85 2.52 56.98 ± 5.95a,b 1.94 1.01 0.88 2.19 1.67 ± 2.20 Deurenberg et al10 57.70 ± 5.39b −7.24 1.13 0.86 2.49 59.02 ± 5.30a,b −7.72 1.14 0.89 2.14 1.32 ± 1.76 Lohman22* 60.56 ± 6.42b −176.79 132.02 0.84 2.63 62.44 ± 6.31a,b 0.30 0.95 0.88 2.27 1.88 ± 2.46 Kotler et al26 58.95 ± 5.52b −8.21 1.13 0.89 2.22 60.35 ± 5.37a,b −9.25 1.14 0.91 1.90 1.40 ± 1.62 Sun et al23* 53.59 ± 5.71b −169.77 132.02 0.85 2.58 55.19 ± 5.67a,b 1.06 1.06 0.88 2.23 1.60 ± 2.11 Langer et al15 58.26 ± 6.11 −0.88 1.01 0.88 2.29 59.87 ± 5.90a −2.33 1.04 0.91 1.97 1.60 ± 1.82 M1 M2 Differences (∆ M2−M1) Fat-Free Mass (kg) Mean ± SD Linear Regression Mean ± SD Linear Regression Mean ± SD Intercept Slope R2 SEE (kg) Intercept Slope R2 SEE (kg) DXA 58.17 ± 6.60 — — — — 59.62 ± 6.41a — — — — 1.45 ± 1.49 Lukaski et al19* 58.83 ± 6.41b −158.24 122.47 0.76 3.24 61.31 ± 6.37a,b 4.18 0.90 0.81 2.82 2.48 ± 3.07 Chumlea et al20* 59.78 ± 6.95b −132.75 107.64 0.67 3.79 61.93 ± 6.94a,b 11.34 0.78 0.71 3.44 2.15 ± 3.70 Segal et al21 60.55 ± 5.84b −5.02 1.04 0.85 2.52 61.91 ± 5.68a,b −6.14 1.06 0.89 2.18 1.36 ± 1.71 Deurenberg et al12 55.31 ± 6.06b 2.51 1.00 0.85 2.52 56.98 ± 5.95a,b 1.94 1.01 0.88 2.19 1.67 ± 2.20 Deurenberg et al10 57.70 ± 5.39b −7.24 1.13 0.86 2.49 59.02 ± 5.30a,b −7.72 1.14 0.89 2.14 1.32 ± 1.76 Lohman22* 60.56 ± 6.42b −176.79 132.02 0.84 2.63 62.44 ± 6.31a,b 0.30 0.95 0.88 2.27 1.88 ± 2.46 Kotler et al26 58.95 ± 5.52b −8.21 1.13 0.89 2.22 60.35 ± 5.37a,b −9.25 1.14 0.91 1.90 1.40 ± 1.62 Sun et al23* 53.59 ± 5.71b −169.77 132.02 0.85 2.58 55.19 ± 5.67a,b 1.06 1.06 0.88 2.23 1.60 ± 2.11 Langer et al15 58.26 ± 6.11 −0.88 1.01 0.88 2.29 59.87 ± 5.90a −2.33 1.04 0.91 1.97 1.60 ± 1.82 DXA, dual-energy X-ray absorptiometry; SEE, standard error of estimated. *Logarithmic transformation. aSignificant difference from M1, p < 0.05. bSignificant difference from DXA, p < 0.05. Table III. Linear Regression Analysis in M1, M2, and Differences (∆ M2−M1) Between the Moments M1 M2 Differences (∆ M2−M1) Fat-Free Mass (kg) Mean ± SD Linear Regression Mean ± SD Linear Regression Mean ± SD Intercept Slope R2 SEE (kg) Intercept Slope R2 SEE (kg) DXA 58.17 ± 6.60 — — — — 59.62 ± 6.41a — — — — 1.45 ± 1.49 Lukaski et al19* 58.83 ± 6.41b −158.24 122.47 0.76 3.24 61.31 ± 6.37a,b 4.18 0.90 0.81 2.82 2.48 ± 3.07 Chumlea et al20* 59.78 ± 6.95b −132.75 107.64 0.67 3.79 61.93 ± 6.94a,b 11.34 0.78 0.71 3.44 2.15 ± 3.70 Segal et al21 60.55 ± 5.84b −5.02 1.04 0.85 2.52 61.91 ± 5.68a,b −6.14 1.06 0.89 2.18 1.36 ± 1.71 Deurenberg et al12 55.31 ± 6.06b 2.51 1.00 0.85 2.52 56.98 ± 5.95a,b 1.94 1.01 0.88 2.19 1.67 ± 2.20 Deurenberg et al10 57.70 ± 5.39b −7.24 1.13 0.86 2.49 59.02 ± 5.30a,b −7.72 1.14 0.89 2.14 1.32 ± 1.76 Lohman22* 60.56 ± 6.42b −176.79 132.02 0.84 2.63 62.44 ± 6.31a,b 0.30 0.95 0.88 2.27 1.88 ± 2.46 Kotler et al26 58.95 ± 5.52b −8.21 1.13 0.89 2.22 60.35 ± 5.37a,b −9.25 1.14 0.91 1.90 1.40 ± 1.62 Sun et al23* 53.59 ± 5.71b −169.77 132.02 0.85 2.58 55.19 ± 5.67a,b 1.06 1.06 0.88 2.23 1.60 ± 2.11 Langer et al15 58.26 ± 6.11 −0.88 1.01 0.88 2.29 59.87 ± 5.90a −2.33 1.04 0.91 1.97 1.60 ± 1.82 M1 M2 Differences (∆ M2−M1) Fat-Free Mass (kg) Mean ± SD Linear Regression Mean ± SD Linear Regression Mean ± SD Intercept Slope R2 SEE (kg) Intercept Slope R2 SEE (kg) DXA 58.17 ± 6.60 — — — — 59.62 ± 6.41a — — — — 1.45 ± 1.49 Lukaski et al19* 58.83 ± 6.41b −158.24 122.47 0.76 3.24 61.31 ± 6.37a,b 4.18 0.90 0.81 2.82 2.48 ± 3.07 Chumlea et al20* 59.78 ± 6.95b −132.75 107.64 0.67 3.79 61.93 ± 6.94a,b 11.34 0.78 0.71 3.44 2.15 ± 3.70 Segal et al21 60.55 ± 5.84b −5.02 1.04 0.85 2.52 61.91 ± 5.68a,b −6.14 1.06 0.89 2.18 1.36 ± 1.71 Deurenberg et al12 55.31 ± 6.06b 2.51 1.00 0.85 2.52 56.98 ± 5.95a,b 1.94 1.01 0.88 2.19 1.67 ± 2.20 Deurenberg et al10 57.70 ± 5.39b −7.24 1.13 0.86 2.49 59.02 ± 5.30a,b −7.72 1.14 0.89 2.14 1.32 ± 1.76 Lohman22* 60.56 ± 6.42b −176.79 132.02 0.84 2.63 62.44 ± 6.31a,b 0.30 0.95 0.88 2.27 1.88 ± 2.46 Kotler et al26 58.95 ± 5.52b −8.21 1.13 0.89 2.22 60.35 ± 5.37a,b −9.25 1.14 0.91 1.90 1.40 ± 1.62 Sun et al23* 53.59 ± 5.71b −169.77 132.02 0.85 2.58 55.19 ± 5.67a,b 1.06 1.06 0.88 2.23 1.60 ± 2.11 Langer et al15 58.26 ± 6.11 −0.88 1.01 0.88 2.29 59.87 ± 5.90a −2.33 1.04 0.91 1.97 1.60 ± 1.82 DXA, dual-energy X-ray absorptiometry; SEE, standard error of estimated. *Logarithmic transformation. aSignificant difference from M1, p < 0.05. bSignificant difference from DXA, p < 0.05. A significant correlation in FFM was seen between all BIA equations and the DXA result at both moments of evaluation (M1 and M2). As normality was not present in the FFM estimated based on BIA in the equations of Lukaski et al,19 Chumlea et al,20 Lohman,22 and Sun et al,23 a logarithmic transformation was used (Log10). The value of SEE of the nine predictive BIA equations in FFM estimates decreased from M1 to M2 (Table III). The coefficient of determination in the M1 varied between R2 = 0.67 and R2 = 0.89, and in the M2 varied between R2 = 0.71 and R2 = 0.91. Table IV and Figure 2 show the agreement between the FFM estimated using the nine predictive BIA equations and the value of FFM as determined by performing DXA, using the Bland–Altman plots25 and Lin’s concordance correlation coefficient (CCC).24 Three predictive BIA equations (Deurenberg et al,12 Deurenberg et al,10 and Sun et al23) overestimated FFM in comparison with DXA at both the M1 and the M2. The nine predictive BIA equations showed large limits of agreement at both the M1 and the M2. Regarding CCC (Table IV), the equations of Segal et al,21 Deurenberg et al,12 Deurenberg et al,10 Kotler et al,26 Sun et al,23 and Langer et al15 showed an increase in CCC from M1 to M2, whereas the equations of Chumlea et al20 and Lohman22 showed a decrease in CCC; the equation of Lukaski et al19 remained equal in at both the moments. Table IV. Concordance Analysis Between FFM Determined Using the Methods (DXA and BIA) in M1 and M2 Equations M1 M2 CCC Analysis CCC Analysis CCC ρ Cb CCC ρ Cb Lukaski et al19 0.87 0.8725 0.9949 0.87 0.8984 0.9662 Chumlea et al20 0.80 0.8226 0.9714 0.79 0.8440 0.9404 Segal et al21 0.85 0.9218 0.9264 0.87 0.9408 0.9261 Deurenberg et al12 0.83 0.9226 0.9014 0.86 0.9401 0.9135 Deurenberg et al10 0.90 0.9244 0.9771 0.92 0.9428 0.9770 Lohman22 0.86 0.9170 0.9372 0.85 0.9355 0.9100 Kotler et al26 0.92 0.9403 0.9761 0.93 0.9551 0.9769 Sun et al23 0.71 0.9184 0.7744 0.73 0.9380 0.7817 Langer et al15 0.93 0.9361 0.9970 0.95 0.9519 0.9957 Equations M1 M2 CCC Analysis CCC Analysis CCC ρ Cb CCC ρ Cb Lukaski et al19 0.87 0.8725 0.9949 0.87 0.8984 0.9662 Chumlea et al20 0.80 0.8226 0.9714 0.79 0.8440 0.9404 Segal et al21 0.85 0.9218 0.9264 0.87 0.9408 0.9261 Deurenberg et al12 0.83 0.9226 0.9014 0.86 0.9401 0.9135 Deurenberg et al10 0.90 0.9244 0.9771 0.92 0.9428 0.9770 Lohman22 0.86 0.9170 0.9372 0.85 0.9355 0.9100 Kotler et al26 0.92 0.9403 0.9761 0.93 0.9551 0.9769 Sun et al23 0.71 0.9184 0.7744 0.73 0.9380 0.7817 Langer et al15 0.93 0.9361 0.9970 0.95 0.9519 0.9957 CCC, concordance correlation coefficient; ρ, precision; Cb, accuracy. Table IV. Concordance Analysis Between FFM Determined Using the Methods (DXA and BIA) in M1 and M2 Equations M1 M2 CCC Analysis CCC Analysis CCC ρ Cb CCC ρ Cb Lukaski et al19 0.87 0.8725 0.9949 0.87 0.8984 0.9662 Chumlea et al20 0.80 0.8226 0.9714 0.79 0.8440 0.9404 Segal et al21 0.85 0.9218 0.9264 0.87 0.9408 0.9261 Deurenberg et al12 0.83 0.9226 0.9014 0.86 0.9401 0.9135 Deurenberg et al10 0.90 0.9244 0.9771 0.92 0.9428 0.9770 Lohman22 0.86 0.9170 0.9372 0.85 0.9355 0.9100 Kotler et al26 0.92 0.9403 0.9761 0.93 0.9551 0.9769 Sun et al23 0.71 0.9184 0.7744 0.73 0.9380 0.7817 Langer et al15 0.93 0.9361 0.9970 0.95 0.9519 0.9957 Equations M1 M2 CCC Analysis CCC Analysis CCC ρ Cb CCC ρ Cb Lukaski et al19 0.87 0.8725 0.9949 0.87 0.8984 0.9662 Chumlea et al20 0.80 0.8226 0.9714 0.79 0.8440 0.9404 Segal et al21 0.85 0.9218 0.9264 0.87 0.9408 0.9261 Deurenberg et al12 0.83 0.9226 0.9014 0.86 0.9401 0.9135 Deurenberg et al10 0.90 0.9244 0.9771 0.92 0.9428 0.9770 Lohman22 0.86 0.9170 0.9372 0.85 0.9355 0.9100 Kotler et al26 0.92 0.9403 0.9761 0.93 0.9551 0.9769 Sun et al23 0.71 0.9184 0.7744 0.73 0.9380 0.7817 Langer et al15 0.93 0.9361 0.9970 0.95 0.9519 0.9957 CCC, concordance correlation coefficient; ρ, precision; Cb, accuracy. Figure 2. View largeDownload slide View largeDownload slide Bland–Altman plots of the agreement. Note. FFM, fat-free mass determined using DXA and BIA equations. (A) Lukaski et al;19 (B) Chumlea et al;20 (C) Segal et al;21 (D) Deurenberg et al;12 (E) Deurenberg et al;10 (F) Lohman;22 (G) Kotler et al;26 (H) Sun et al;23 and (I) Langer et al15 at difference moments (Δ M2−M1). Solid black line, mean of the differences; dashed line, limits of agreement of 95%; continuous black line, correlation (r) between the average and the differences of the methods. Figure 2. View largeDownload slide View largeDownload slide Bland–Altman plots of the agreement. Note. FFM, fat-free mass determined using DXA and BIA equations. (A) Lukaski et al;19 (B) Chumlea et al;20 (C) Segal et al;21 (D) Deurenberg et al;12 (E) Deurenberg et al;10 (F) Lohman;22 (G) Kotler et al;26 (H) Sun et al;23 and (I) Langer et al15 at difference moments (Δ M2−M1). Solid black line, mean of the differences; dashed line, limits of agreement of 95%; continuous black line, correlation (r) between the average and the differences of the methods. Figure 2 shows the agreement of methods (DXA and BIA), using the Bland–Altman plots,25 for estimating FFM changes after about 7 mo of military physical training. With the exception of the equation from panel B (Chumlea et al20), all of the predictive BIA equations showed a significant trend (p < 0.05) in the FFM changes. The mean of the differences ranged from −1.0 kg to 0.2 kg, and the equations from panels A, B, D, F, H, and I underestimated these differences, and the equations from panel C, E, and G overestimated the differences between the two methods (DXA and BIA) in the changes of FFM. All nine predictive BIA equations showed wide limits of agreement; however, the equation from panel C presented the largest amplitude in the limits of agreement, both superior and inferior (5.9 kg to −7.3 kg). DISCUSSION In our study, we observed that the FFM values of the male army cadets increased significantly after approximately 7 mo of military training (Table III). We used the same eight predictive BIA equations that were used in a previous study, in addition to a specific equation validated for this population.15 All nine predictive BIA equations were able to detect the FFM changes of the cadets between the 2 moments in a very similar way to the reference method (DXA). However, only the equation of Langer et al15 in the FFM estimation showed no significant differences between DXA at the M1 and M2 of military physical training. Regarding the individual analysis, the Bland–Altman plots25 showed wide limits of agreement in all equations. Nevertheless, a decrease in the limits of agreement in the Langer et al15 equation of ~1 kg and 0.5 kg was observed in M1 and M2, respectively. When Lin’s approach for strength of the agreement was applied,24 only three equations (Deurenberg et al,10 Kotler et al,26 and Langer et al15) showed strong correlation between CCC values and DXA (above 0.90), in both M1 and M2. Taken together, these results suggest that the equation of Langer et al15 seems to be a better equation for assessing body composition in the Brazilian male army cadets, which reinforces the findings of another investigation.15 DXA is widely used as criterion to verify the accuracy of BIA in the FFM and FM estimates.27–29 In our study, the results indicated a good relationship between both methods; however, at the individual level, we observed wide limits of agreement using the Bland–Altman plots.25 This large variation among individuals was also observed in other studies wherein new BIA equations were developed, using DXA as a reference in healthy and/or HIV-infected children and adolescents,30 obese children and adolescents,31 and boys from 12 yr to 19 yr of different ethnicities.32 One hypothesis to explain these results is that single-frequency BIA is a method used to evaluate total body water,33 without making a distinction between the amount of intracellular and extracellular water. Intracellular and extracellular water volumes may be a source of measurement errors, as an individual’s hydration level influences the variables provided by BIA. After total body water (TBW) measurement, an internal and unknown FFM quantification is performed, thereby influencing the results in two ways: (1) FFM is not directly measured and (2) it depends on the hydration level of the subject. Nevertheless, DXA does not take into account the hydration state of the individual in its assessments of the body components and is therefore not influenced by this variable.34 Several factors can affect the results that the BIA provides, such as non-standardization of body position, previous physical exercise, and food or fluid intake,33,35,36 which were all controlled for in our study. Another source of error may be the reference method used in the development of the equations utilized.33 The recommendation is that the validation of BIA equations should be performed against reference methods that include the four-compartment (4 C) model,37,38 hydrostatic weighing, DXA,38 or deuterium isotope dilution.38 Each of these reference methods are not without error and have their limitations;37 nonetheless, they are considered state of the art regarding body composition analysis. The equations tested in our study were used with different reference methods. Therefore, discrepancies in the results may be related to greater or lesser validity of the reference methods in which the equations for a specific population have been developed, and which may or may not be comparable with other reference methods.33 For this reason, the development and validation of specific BIA equations from a population with the same characteristics must be used to reduce bias, therefore providing greater accuracy and precision of the results. A limiting factor of this study was the lack of an indicator for more rigorous control of the evaluated subjects’ hydration level, which may have influenced the variables that BIA provides.19–21 Although all procedures were adopted and controlled in the subjects, it was not possible to verify the hydration level using appropriate methods.39 We believe that because we used a large sample having the same characteristics (homogeneous sample in relation to gender, age, physical activity level, and food control), these results may have good external and internal validity and may contribute to future research in individuals with similar characteristics to the subjects in the present study. CONCLUSION The eight general predictive BIA equations used in this study did not appear to be valid for analyzing the FFM changes in the Brazilian male army cadets between ages of 17 yr and 24 yr after the period about 7 mo of military training, using DXA as a reference method. Although the specific BIA equation is dependent on the amount of FFM, it appears to be a good alternative to DXA and is an equation that can be used in samples with the same characteristics as the cadets in our sample. Acknowledgments The authors are grateful to the officers and the cadets of the “Preparatory School Cadets Army” (EsPCEx) of Campinas-SP for their authorization and collaboration in this study. Funding Coordination for the Improvement of Higher Education Personnel – CAPES (process no. 23001.000422/98-30). REFERENCES 1 O’Connor JS , Bahrke MS , Tetu RG : 1988 active Army physical fitness survey . Mil Med 1990 ; 155 : 579 – 85 . Google Scholar CrossRef Search ADS PubMed 2 Manual de Campanha: Treinamento Físico Militar. 4ed ed. MINISTÉRIO DA DEFESA EXÉRCITO BRASILEIRO ESTADO-MAIOR DO EXÉRCITO: Brasília, 2015. 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Military Medicine – Oxford University Press

Published: Jun 28, 2018

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