Bioimpedance in 7-Year-Old Children: Validation by Dual X-Ray Absorptiometry Part 2: Assessment of Segmental Composition

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1 Original Paper Ann Nutr Metab 214;64: Received: June 18, 213 Accepted after revision: April 27, 214 Published online: August 9, 214 Bioimpedance in 7-Year-Old Children: Validation by Dual X-Ray Absorptiometry Part 2: Assessment of Segmental Composition Veronica Luque a, c Joaquin Escribano a, b Marta Zaragoza-Jordana a Carmen Rubio-Torrents a Natalia Ferré a Mariona Gispert-Llaurado a Ricardo Closa-Monasterolo a, c for the European Childhood Obesity Project Group a Universitat Rovira i Virgili, Institut d'investigació Sanitària Pere Virgili, and b Hospital Universitari Sant Joan de Reus, Reus, and c Hospital Universitari de Tarragona Joan XXIII, Tarragona, Spain Key Words Bioimpedance Body composition Children Dual X-ray absorptiometry Obesity Abstract Aim: Segmental body composition in children was assessed using the bioimpedance analyzer (BIA) TANITA BC-418 and compared with dual-energy X-ray absorptiometry (DXA) values. Methods: A cross-sectional validation study in which 7-year-old children from the Spanish subsample of the EU Childhood Obesity Project were assessed through anthropometry, BIA and DXA. Main outcome measures were fat and lean masses of the trunk, left arm and left leg (in kg) assessed through BIA direct outputs (BIA outputs ) and DXA. Predictive equations for the composition of each segment were derived from raw impedance and anthropometric measurements; results obtained from these predictive equations (BIA regressions ) were also compared to DXA. Results: One hundred seventy-one (84 boys) 7-year-old children were studied. BIA outputs and DXA results showed small differences for leg lean mass (6.5%) and high differences for trunk fat and trunk lean masses (>3%). BIA regressions results showed differences of about 2% for trunk fat mass, 1.5% for trunk lean mass and 3.7% for leg lean mass compared to DXA. Conclusions: Segmental body composition measures predicted by internal karger@karger.com S. Karger AG, Basel /14/ $39.5/ algorithms of the TANITA BC-418 were not valid for clinical or epidemiological use, except for leg lean mass. The assessment of segmental composition was improved using our own predictive equations combining segmental-specific anthropometric measurements with segmental impedances. Introduction 214 S. Karger AG, Basel Body composition assessment is of importance to understand its implications, for example as cause or consequence of different aspects of health and pathology [1, 2]. For instance, trunk fat mass has been related with a higher V.L. and J.E. contributed equally to this work. The European Childhood Obesity Project Group: Beyer J, Fritsch M, Grote V, Haile G, Handel U, Hannibal I, Koletzko B, Kreichauf S, Pawellek I, Schiess S, Verwied-Jorky S, von Kries R, Weber M (Children s University Hospital, University of Munich Medical Center, Munich, Germany); Dobrzańska A, Gruszfeld D, Janas R, Wierzbicka A, Socha P, Stolarczyk A, Socha J (Children s Memorial Health Institute, Warsaw, Poland); Carlier C, Dain E, Goyens P, Van Hees JN, Hoyos J, Langhendries JP, Martin F, Poncelet P, Xhonneux A (ULB Bruxelles and CHC St Vincent Liège, Belgium); Perrin E (Nutricia Research, Utrecht, The Netherlands), and Agostoni C, Giovannini M, Re Dionigi A, Riva E, Scaglioni S, Vecchi F, Verducci E (University of Milan, Italy). Dr. Ricardo Closa-Monasterolo Pediatric Research Unit, Faculty of Medicine and Health Sciences C/ Sant Llorenç 21 ES 4321 Reus (Spain) urv.cat

2 Segmental Bioimpedance in Children Ann Nutr Metab 214;64: cardiometabolic risk since childhood [3 5]. As another example, HIV-infected children under antiretroviral treatment may show lipodystrophy characterized by loss of peripheral adipose tissue and its accumulation in the abdomen [6, 7]. Assessment of segmental body composition may help to monitor lipodystrophy evolution and adapt the treatment to newer antiretroviral drugs that may result in lower metabolic disturbances [8, 9]. These examples show how important the assessment of segmental body composition can be for the understanding of several pathologies and the effect that dietary and physical activity interventions have on them. However, precise methods to assess segmental body compositions, such as magnetic resonance imaging or dual-energy X-ray absorptiometry (DXA), are expensive and not affordable for most of the health centers or epidemiological studies with this purpose [2]. Therefore, suitable methods to approach segmental body composition in clinical and epidemiological settings should be validated in children to spread the assessment of the distribution of different body compartments as a regular practice. Bioelectrical impedance (bioimpedance) is a fast and low-cost method used to predict body composition. Basically, an alternating current is applied to the human body that generates an electrical response as a result of its passage through the tissues containing water (conductive). Body composition is estimated by prediction models that assume the body to be a cylinder through which the current flows [1]. Considering that the human body is not a single cylinder, but can be drawn as a composite of four cylinders (limbs) connected with the fifth (cylinder trunk), bioimpedance allows estimating the composition of different segments (cylinders) by placing the electrodes at the desired anatomical locations [11]. The most modern bioimpedance segmental analyzers are the 8-electrode devices on which the subject stands up on pairs of electrodes and holds hand grips with other pairs of electrodes in each. Sequential measurements between pairs of electrodes are performed to assess the impedance of the different body segments [11]. The TANITA BC-418 provides segmental values of impedance and body composition (through internal fixed prediction models), and, if validated, could be a useful method to estimate the composition of the children s abdomen and limb in clinical and epidemiological settings. Currently, several studies are using this bioimpedance analyzer (BIA) to estimate segmental body composition in children; however, it has not been validated to date. One of the ongoing studies using bioimpedance to estimate segmental body composition in children is the EU Childhood Obesity Program, a multicenter study carried out in five European countries. The EU Childhood Obesity Program aims to assess the effect of protein intake during the 1st year of life on later obesity risk (details of the project have been published elsewhere [12 14] ). Within NUTRIMENTHE, a research project funded by the European Commission through its 7th Framework Program [15], the EU Childhood Obesity Program assessed children s body composition using the BIA TANITA BC-418, which assesses segmental body composition. The aim of the present work was to validate the TANI- TA BC-418 to assess segmental body composition in children compared to DXA. Methods and Procedures Design A cross-sectional validation study within the EU Childhood Obesity Project was performed to assess possible differences between bioimpedance and DXA regarding segmental body composition in 7-year-old children. Subjects Four hundred fifty-two infants were recruited at birth from the Hospital Universitari de Tarragona Joan XXIII and the Hospital Universitari Sant Joan de Reus between October 22 and March 24 to take part in the Spanish subsample from the EU Childhood Obesity Project (explained in more detail elsewhere [13] ). From those 452, 231 were still under study after 7 years, and 188 attended the 7-year follow-up. From those 188, 185 had valid bioimpedance assessment, and 173 agreed to DXA assessment. Finally, both measures (bioimpedance and DXA) were available for 171. Anthropometry Anthropometric measurements were performed in duplicate (except skinfolds, which were measured in triplicate) by trained personnel following standard operating procedures based on the manual of Lohman et al. [16]. In detail, weight (in kg) was measured using a SECA 71 scale (precision ±1 g; SECA, Hamburg, Germany). Height (in cm) and sitting height (in cm) were measured using a digital infrared SECA 242 stadiometer (precision 1 mm). For sitting height, the movable piece of the stadiometer was placed on a wooden bench (approx. 5 cm high and 3 cm wide), where the stadiometer was tared (calibrated to zero). After this, the child was instructed to sit down on the wooden bench so that the buttocks were in contact with the backboard of the stadiometer as erect as possible, with the head in the Frankfort horizontal plane. The anthropometrist and the adult in charge of the child helped the child to keep the right position exerting a gentle pressure simultaneously on the abdomen and on the superior part of the sternum. The subischial length of the legs (cm) was calculated by subtracting the sitting height from the total height. Chest and waist circumferences (in cm) were measured with a flexible inelastic tape at the end of a normal expiration. Chest circumference was measured over the nipples, and waist at the level of the midpoint between the last rib margin and the iliac crest. Mid-upper arm and mid-thigh circumferences (in cm) were measured with a precision SECA insertion tape (precision 1 mm) at the midpoint between the 145

3 acromion and the olecranon process and midpoint between the inguinal crease and the patella, respectively. Tricipital and midthigh skinfolds (in mm) were measured with a Holtain caliper (precision.2 mm; Holtain Ltd, Dyfed, UK) at the midpoints commented above. Body mass index (BMI) was calculated. Dual-Energy X-Ray Absorptiometry All DXA measurements were assessed with the child wearing underwear by the same technician with a General Electric Lunar Prodigy Advance (Madison, Wisc., USA), which was daily calibrated with standard equipment at the Image Diagnostic Institute, Hospital Universitari de Tarragona Joan XXIII. The segments were automatically chosen by the software and regions of interest were adjusted by the technician to obtain higher precision [17]. The analyses were performed with EnCore 25, v (GE Lunar Corporation, Madison, Wisc., USA). Outcome measures were trunk fat mass, trunk lean mass, left arm fat mass, left arm lean mass, left leg fat mass and left leg lean mass (all in kg). Bioimpedance Segmental bioelectrical impedance with a high-frequency constant current (5 khz, 5 μa) was assessed in duplicate with the 8-electrode BIA Tanita BC-418 segmental body composition analyzer (Tanita Corporation, Tokyo, Japan). These 8 electrodes are located as follows: 2 in each handgrip (which are in contact with fingers and the thenar side of the hand) and 2 in each foot platform to stand up (which are in contact with toes and heels). With this BIA, current is supplied from the tips of the toes of both feet and the fingertips of both hands, and the voltage is measured on the heel of both feet and the thenar side of both hands. By switching the part of the body in which the current is flowing and the places were voltage is measured, the impedance in different regions of the body is calculated. To assess the impedance of the whole body, the signal is passed from both left and right feet to both left and right hands simultaneously. To assess the impedance of the left leg, the signal is passed from the right leg and both left and right hands simultaneously to the left leg. To measure the impedance of the left arm, the signal is passed from both left and right legs and right arm simultaneously to the left arm. The inverse current flows and voltage measurements are performed for the right leg and arm. The equations to calculate the impedance of each segment are company confidential (information provided by Tanita Corporation). The assessment was performed by trained nutritionists, who invited the children to step on the scale barefoot and dressed in underwear, and instructed the child to find the right position to get in contact with the 4 foot electrodes. The child was then told to hold the handles in a way in order to get in contact with the 4 hand electrodes, separating the arms from the trunk to prevent contact with other parts of the body. Results of segmental body composition were obtained through the algorithms included in the TANI- TA BC-418 (as direct outputs). These algorithms were constructed using as reference the data acquired through DXA in Japanese and Western subjects and performing repeated regression analyses, including height, weight, age and impedance between the right hand and foot as variables (information provided by the manufacturer). Outcomes were the mean values of repeated measures of total body, trunk, arm and leg impedances (in Ω), and trunk fat mass, trunk lean mass, left arm fat mass, left arm lean mass, left leg fat mass and left leg lean mass (all in kg). Thereafter, this group of outcomes will be named as BIA direct outputs (BIA outputs ). 146 Ann Nutr Metab 214;64: We constructed linear regression models with raw impedance values and segmental specific anthropometric measurements (considering DXA results as the dependent variable). The new predictive equations obtained using raw impedance values and anthropometric measurements were then used to calculate new body composition results for individual children. These new calculated outcomes will be named thereafter as body composition predicted through own linear regression models (BIA regressions ), and were trunk fat mass, trunk lean mass, left arm fat mass, left arm lean mass, left leg fat mass and left leg lean mass (all in kg). We calculated differences between DXA body composition parameters with BIA outputs and BIA regressions to determine percent differences. As there were no validity cutoff points for body composition, and considering the cost-effective and noninvasive benefits of BIA application, we considered a difference <1% as being precise enough for epidemiological use, a difference between 1 and 2% as moderate, and a difference >2% as imprecise. On other hand, for clinical use, we considered a difference <5% as precise, a difference between 5 and 1% as moderate, and a difference >1% as imprecise. Although both bioimpedance and DXA provides the composition for both arms and legs, we present and use only the composition of the left extremities to simplify the presentation of results. Statistics Power calculation was not performed prior to the present study, since bias between body composition techniques are usually detectable with small study samples (<3 participants/group) [18]. Therefore, we performed an a posteriori calculation of statistical power with our results. Descriptive results and differences (in %) were expressed as medians and interquartile ranges (IQR: 25th and 75th percentiles). Bias and limits of agreement were expressed as means ± 2 SD of the mean difference between methods, respectively. To assess differences between genders, Student s t test was used, and to assess differences between methods, Student s t test for repeated measures or Wilcoxon s test was applied as appropriate. Linear regression models were employed to quantify the degree of variation that may be attributable to bioimpedance and anthropometric measures. Predictive equations for the composition of trunk, left arm and left leg were derived from raw impedance results and anthropometric measures entered in the linear regression models. Results obtained from impedance predictive equations (BIA regressions ) were compared to results obtained through DXA to validate the new predictive equations, too. Predictive equations were internally validated with 1, bootstrap resamples (171 subjects were extracted from and replaced to the original sample, and this was repeated 1, times; therefore, 1, bootstrap samples of size n = 171 were randomly taken from the original sample with replacements). Bias and confidence intervals were calculated using the BCA method (bias-corrected and accelerated method) [19]. Cronbach s α was calculated to assess the consistency of BIA outputs and BIA regressions compared to DXA, and intraclass correlation coefficients (ICC) were calculated to assess reliability. The agreement between methods was assessed through Bland and Altman [2] plots. Data management and analyses were performed with IBM SPSS 19. (Chicago, Ill., USA). Ethical Issues The study protocol was in agreement with the Declaration of Helsinki [21] and was accepted by the Ethical Committees of both Hospitals. All families were informed about all procedures and signed written consent to participate in the study. Luque et al.

4 Table 1. Description of anthropometric and impedance measurements of the study sample Whole study sample (n = 171) Boys (n = 84) Girls (n = 87) Weight, kg 25.1 ( ) 25.7 ( ) 25.1 ( ) Height, cm ( ) ( ) ( ) Sitting height, cm 66.3 ( ) 66.8 ( ) 65.3 ( ) Leg subischial length, cm 56.6 ( ) 56.6 ( ) 56.6 ( ) Mid-upper arm circumference, cm 18.9 ( ) 18.5 ( ) 19.4 ( ) Chest circumference, cm 62.3 ( ) 62.8 ( ) 61.6 ( ) Waist circumference, cm 57.3 ( ) 57.3 ( ) 57.3 ( ) Mid-thigh circumference, cm 34.8 ( ) 34.4 ( ) 35.1 ( ) Tricipital skinfold, mm 1.3 ( ) 9. ( ) 12.3 ( ) Mid-thigh skinfold, mm 16.4 ( ) 13.2 ( ) 19.1 ( ) BMI ( ) ( ) ( ) Total body impedance, Ω ( ) ( ) 797. ( ) Left arm impedance, Ω ( ) 43.7 ( ) ( ) Left leg impedance, Ω 36.5 ( ) ( ) ( ) Values are presented as median (IQR). p <.1, p <.1 vs. boys. Table 2. Description of body composition results using BIA outputs and DXA DXA BIA outputs Difference, % Mean bias 95% limits of agreement Trunk fat mass, kg 1.92 ( ) 2.4 ( ) 3.4 (6.11 to 66.67).47 ±1.18 Trunk lean mass, kg 8.95 ( ) ( ) 41.7 (32.54 to 46.74) 3.55 ±1.53 Left arm fat mass, kg.28 (.16.47).4 (.3.5) ( 3.85 to 96.8).6 ±.27 Left arm lean mass, kg.88 (.78.98).7 (.6.8) ( to 12.6).17 ±.17 Left leg fat mass, kg 1.8 ( ) 1.2 (1. 1.5) (1.97 to 43.41).18 ±.42 Left leg lean mass, kg 2.95 ( ) 2.75 ( ) 6.5 ( 12.7 to.29).19 ±.55 Values are presented as median (IQR) unless indicated otherwise. Comparisons between methods (Student s t test for repeated measures or Wilcoxon s test as appropriate), differences, bias and limits of agreement (n = 171). Differences between methods (%) were calculated as [(BIA outputs DXA) 1]/DXA. Bias was calculated as BIA outputs DXA. 95% limits of agreement were calculated as ±2 SD of the difference between techniques. p <.1 vs. DXA. Results Results from 171 (84 boys and 87 girls) 7-year-old Caucasian children (±1 month) were obtained. Table 1 shows the anthropometric characteristics and raw impedance results of the study participants. Descriptive results of body composition obtained through DXA and BIA outputs are shown in table 2. Differences between techniques were very high for segmental analyses of the trunk (3% for trunk fat mass and 41% for trunk lean mass) and high for most other parameters (between 18 and 32%). The most precise outcome was the leg Segmental Bioimpedance in Children Ann Nutr Metab 214;64: lean mass, which showed an underestimation of 6.5% compared to DXA. Table 3 shows linear regression analyses in which segmental body composition measures assessed through DXA were the dependent variables. In this table, the use of BIA outputs is compared to the use of our own regression models (using raw impedance outputs and anthropometric measures). In all body segments, our regression models explained higher variability of body composition assessed with DXA compared to BIA outputs. The degree of the increase in R 2 varied from 2.3 to 37.9%. 147

5 Table 3. Linear regression models on body composition assessed by DXA as dependent variables: degree of variation explained by regressions of BIA outputs and our own constructed regressions using raw impedance results and anthropometry (each model separated by a line) Regressions from BIA outputs Regressions using raw impedance results from BIA and anthropometry dependent variables independent variables Β (95% CI) p value R 2, % independen t variables Β (95% CI) bias p value R 2, % Trunk fat mass (kg) assessed with DXA Trunk lean assessed with DXA Trunk fat (1.168, 1.314) < Total body impedance, Ω.5 (.3,.6) Weight, kg.333 (.279,.415).5.1 from BIA outputs Height, cm.91 (.115,.74).2.1 Waist circumference, cm.43 (.1,.69).2.11 Gender.36 (.25,.489).6.1 Trunk lean.649 (.576,.721) < Total body impedance, Ω.4 (.5,.3) Height, cm.129 (.19,.146) from BIA outputs Chest circumference, cm.31 (.13,.5) Gender.559 (.78,.45).1.1 Left arm fat assessed with DXA Left arm lean assessed with DXA Left leg fat assessed with DXA Left leg lean assessed with DXA Left arm fat (1.418, 1.627) <.1 83 Left arm impedance, Ω.1 (.1,.1) Weight, kg.59 (.46,.69).1.1 from BIA outputs Height, cm.18 (.23,.11) Tricipital skinfold, mm.11 (.,.22) Gender.42 (.1,.8).1.15 Left arm lean from BIA outputs.63 (.519,.742) < Left arm impedance, Ω.1 (.1, ) Weight, kg.24 (.17,.29) Height, cm.8 (.4,.11) Tricipital skinfold, mm.8 (.14,.2) Gender.29 (.53,.6) Left leg fat 1.99 (1.35, 1.163) < Left leg impedance, Ω.3 (.2,.4) Weight, kg.13 (.85,.121) from BIA outputs Height, cm.22 (.3,.15) Mid-thigh circumference, cm.13 (.3,.31).1.25 Mid-thigh skinfold, mm.21 (.14,.28) Gender.12 (.55,.179).1.1 Left leg lean.68 (.619,.742) < Left leg impedance, Ω.3 (.4,.2) Weight, kg.51 (.32,.64).2.1 from BIA outputs Height, cm.32 (.23,.41) Mid-thigh circumference, mm.26 (.18,.48).2.1 Mid-thigh skinfold, mm.23 (.3,.16) New predictive equations were generated from these linear regression models: trunk fat = (.5 impedance) + (.333 weight) (.91 height) + (.36 gender) + (.43 waist circumference); trunk lean = (.4 impedance) + (.129 height) (.559 gender) + (.31 chest circumference); arm fat mass (kg) = (.1 impedance) + (.59 weight) (.18 height) + (.42 gender) + (.11 triceps skinfold); arm lean =.173 (.1 impedance) + (.24 weight) + (.8 height) (.29 gender) (.8 triceps skinfold); leg fat = (.3 impedance) + (.13 weight) (.22 height) + (.12 gender) + (.13 mid-thigh circumference) + (.21 mid-thigh skinfold); leg lean = (.3 impedance) + (.51 weight) + (.32 height) + (.26 mid-thigh circumference) (.23 mid-thigh skinfold), where gender was 1 for males and 2 for females. We used our regression models to calculate body composition for individual children (BIA regressions ). These new body composition outcomes predicted through our own linear regression models are shown in table 4. Besides segment-specific impedance, weight, height and gender, segment-specific anthropometric measures had a significant effect on the composition of the segments: waist circumference for trunk fat mass, chest circumference for trunk lean 148 Ann Nutr Metab 214;64: mass, tricipital skinfold for fat and lean mass of the arm, and mid-thigh circumference and skinfold for fat and lean mass of the leg. Substituting height by leg subischial length did not improve the models of the leg s composition. Similarly, substituting height by sitting height in trunk models did not improve the results. In summary, weight and height were the major predictors of segmental composition (as shown by regression models). The use of impedance increased R 2 Luque et al.

6 Table 4. Description of body composition results using our own linear regression models with raw impedance results and anthropometry: compared to DXA BIA regressions Difference vs. DXA, % Mean bias 95% limits of agreement Trunk fat mass, kg 2.39 ( ) 2.79 (4.25 to 37.88).36 ±.75 Trunk lean mass, kg 9.8 ( ) 1.54 ( 1.63 to 4.47).12 ±.87 Left arm fat mass, kg.22 (.11.41) 2.83 ( to 4.72).6 ±.2 Left arm lean mass, kg.85 (.75.95) 3.15 ( 8.52 to 1.88).4 ±.14 Left leg fat mass, kg 1.7 ( ) 12.8 (3.15 to 22.16).11 ±.27 Left leg lean mass, kg 3.5 ( ) 3.69 (.58 to 7.24).97 ±.31 Values are presented as median (IQR) unless indicated otherwise. Student s t test for repeated measures or Wilcoxon s test as appropriate. Differences between methods were calculated as [(BIA regressions DXA) 1]/ DXA. Bias was calculated as BIA regressions DXA. 95% limits of agreement were calculated as ±2 SD of the difference between techniques. p <.1 vs. DXA. Table 5. Reliability assessment of body composition obtained through bioimpedance (TANITA BC-418 algorithms and from impedance predictive equations) compared to measures obtained through DXA BIA outputs BIA regressions Cronbach s α ICC (95% CI) Cronba ch s α ICC (95% CI) Trunk fat mass (.514,.927) (.658,.976) Trunk lean mass (.21,.443) (.856,.926) Left arm fat mass (.65,.85) (.761,.946) Left arm lean mass (.82,.88) (.692,.926) Left leg fat mass (.561,.946) (.84,.98) Left leg lean mass (.499,.882) (.778,.953) p < % for trunk fat mass, 6.2% for trunk lean mass, 1.6% for arm fat mass, 3.7% for arm lean mass, 1.2% for leg fat mass and 2.9% for leg lean mass. In general, segment-specific impedances contributed highly to the variability in the composition of the segment compared with segment-specific anthropometric measurements (in all cases except for leg lean mass). The differences between BIA regressions and DXA are lower than the differences between BIA outputs and DXA in all cases. The use of our own constructed linear regressions reduced the difference between methods by about 1% in trunk fat mass, almost 4% in trunk lean mass, more than 11% in arm fat mass, more than 16% in arm lean mass, almost 6% in leg fat mass and almost 3% in leg lean mass. The consistency and reliability analyses showed a high Cronbach s α and ICC for almost all results obtained through BIA outputs. However, the ICC was very low for trunk lean mass predictions using the TANITA BC-418 Segmental Bioimpedance in Children Ann Nutr Metab 214;64: (.141; table 5 ). In general, results predicted using our regressions with raw impedance values (BIA regressions ) showed higher internal consistency and reliability than the results directly obtained from the device (BIA outputs ; including predictions of trunk lean mass). Bland and Altman plots assessing agreement between measures of trunk fat mass showed a linear distribution of the bias of BIA outputs ( fig. 1 a). The use of BIA regressions to calculate trunk fat mass reduced the limits of agreement and avoided the linear distribution of the bias ( fig. 1 b). Bland and Altman plots showed a marked trend to overestimate trunk lean mass predicted by BIA outputs ( fig. 1 c), while the use of BIA regressions narrowed the bias range around the zero difference ( fig. 1 d). In the same way, arm and leg fat mass ( fig. 2 a and 3 a, respectively) were systematically biased when assessed through the device algorithms (BIA outputs ). There was a 149

7 2 2 Difference in trunk fat mass (BIA DXA) (kg) 1 1 Difference in trunk fat mass (PRED DXA) (kg) a Average trunk fat mass (BIA + DXA) (kg) b Average trunk fat mass (PRED + DXA) (kg) 7 7 Difference in trunk lean mass (BIA DXA) (kg) Difference in trunk lean mass (PRED DXA) (kg) c Average trunk lean mass (BIA + DXA) (kg) d Average trunk lean mass (PRED + DXA) (kg) Fig. 1. Bland and Altman plots of trunk fat mass ( a, b ) and trunk lean mass ( c, d ) assessed by TANITA BC-418 algorithms (BIA algorithms; a, c ) and by our own predictive equations (PRED; b, d ) compared to DXA. linear trend to overestimate in the lowest fat mass range and to underestimate in the highest fat mass range. Results from our own predictive equations showed a nonsystematic error of the arm and leg fat mass ( fig. 2 b and 3 b, respectively) and substantially improved the limits of agreement for the leg fat mass ( fig. 3 b vs. 3 a). The arm lean mass was systematically underestimated by the device algorithms (BIA outputs ; fig. 2 c) but corrected using our own predictive equation ( fig. 2 d). For both extremities, error assessing lean mass was reduced when estimated using our own predictive equations (about 3% mean error for arm and leg; fig. 2 d, 3 d vs. 2 c, 3 c). We classified children according to their trunk fat mass percentile measured by DXA into 2 groups: <9th 15 Ann Nutr Metab 214;64: percentile and 9th percentile. A total of 17 children were classified as being 9th percentile of trunk fat mass (assessed by DXA). From those, 3 children (17.6%) were not overweight or obese (had not a BMI 9th percentile of the reference population [22] ). The sensitivity and specificity to correctly classify children in the appropriate group was 76.5% (49.8, 92.2) and 97.4% (93., 99.2) for BIA outputs and 88.2% (62.2, 97.9) and 98.7% (94.9, 99.8) for BIA regressions, respectively. The a posteriori statistical power calculation showed that body composition assessed with internal TANITA algorithms and with our linear regression models had a power of 1%, with a confidence interval of 95% in all cases. Luque et al.

8 .4.4 Differences in arm fat mass (BIA DXA) (kg) Difference in arm fat mass (PRED DXA) (kg) a Average arm fat mass (BIA + DXA) (kg) b Average arm fat mass (PRED + DXA) (kg) Difference in arm lean mass (BIA DXA) (kg).25 Difference in arm lean mass (PRED DXA) (kg).25 c Average arm lean mass (BIA + DXA) (kg) d Average arm lean mass (PRED + DXA) (kg) Fig. 2. Bland and Altman plots of arm fat mass ( a, b ) and arm lean mass ( c, d ) assessed by TANITA BC-418 algorithms (BIA algorithms; a, c ) and by our own predictive equations (PRED; b, d ) compared to DXA. Discussion Segmental Bioimpedance in Children Ann Nutr Metab 214;64: The present paper shows the comparison of segmental body composition assessed with a reference method (DXA) and body composition predicted with bioimpedance through two different methods: (1) body composition outputs obtained from TANITA BC-418 internal algorithms (BIA outputs ) and (2) body composition calculated using raw impedance values in linear regression models (BIA regressions ). We have analyzed bias, limits of agreement and reliability of the assessment of body composition with bioimpedance compared to DXA. This work shows a high bias on segmental composition outputs using TANITA BC-418 internal algorithms and provides new equations specific to estimate segmental composition in this population, which improved the precision of estimates. Segmental Body Composition Predicted by TANITA BC-418 Internal Algorithms We found wide differences in trunk fat mass and trunk lean mass when comparing results of TANITA internal algorithms against DXA results. Trunk fat mass measures seemed to be reliable but showed a systematic error, having a linear trend to overestimate fat mass in children with less abdominal fat and to underestimate fat mass in children with higher abdominal fat deposition. In the case of trunk lean mass measures, TANITA BC-418 internal 151

9 1. 1. Difference in leg fat mass (BIA DXA) (kg).5 Difference in leg fat mass (PRED DXA) (kg) a Average leg fat mass (BIA + DXA) (kg) b Average leg fat mass (PRED + DXA) (kg) 1 1 Difference in leg lean mass (BIA DXA) (kg) 1 Difference in leg lean mass (PRED DXA) (kg) c Average leg lean mass (BIA + DXA) (kg) d Average leg lean mass (PRED + DXA) (kg) Fig. 3. Bland and Altman plots of leg fat mass ( a, b ) and leg lean mass ( c, d ) assessed by TANITA BC-418 algorithms (BIA algorithms; a, c,) and by our own predictive equations (PRED; b, d ) compared to DXA. algorithms showed a very low reliability and a systematic trend to overestimate when compared to DXA measurements. The bias of the composition of extremities was also wide, except for the lean mass of the leg, which might be the only really reliable and consistent prediction of segmental composition, with a precision only acceptable for epidemiological use. The average bias of the segmental composition assessment was in agreement with the bias published by Fuller et al. [23], which was around 2% in most of the cases. In summary, predictions made by the BIA algorithms (BIA outputs ) for trunk and arm fat and lean masses, and leg fat mass cannot be considered precise in our study sample. 152 Ann Nutr Metab 214;64: Segmental Body Composition Predicted with Raw Impedance with Our Own Linear Regression Models Segmental lean mass (trunk, arm and leg) predicted through our own linear regression models, which included segmental impedance, weight, height, gender and segmental-specific anthropometric measures (BIA regressions ), showed a very low difference compared with the reference method; these measures were highly consistent and reliable. Theoretically, in segmental analyses, the length of the segment should be used instead of the total height, since the proportion between limbs and trunk may vary between individuals. However, when we tried to use the leg s length instead of the total height in the regression models, this did not improve the results, but slightly Luque et al.

10 worsened it. Probably, this was due to the fact that our study sample was very homogeneous in age and ethnicity. Children and adolescents vary in length proportions of limbs and trunk to total height across different ages and different ethnicities. It is possible that studies in samples of a wider age range with different pubertal stages and different ethnicities would probably benefit from including segment length rather than total height, but it was not necessary in our study sample. In fact, this could be one of the main reasons devices provide so low precise estimations of segment compositions, since predictive equations have been generated from a relatively small population of a wide age range and do not take into account the different contributions of the different segments at different ages. Segmental fat mass from BIA regressions became more precise than BIA outputs but still remained with a moderate-to-high mean difference for trunk fat mass and arm fat mass (around 2%), and a moderate bias (around 12%) for the leg fat mass. However, although mean difference was still not as low as wanted, we narrowed the limits of agreement and avoided the systematic error produced by assessments made with the device algorithms. A limitation of this work could be the use of DXA as reference [2], since the gold standard would be magnetic resonance imaging. To our knowledge, there is still a need to validate this DXA device to assess regional body composition in children. However, it has been demonstrated to be an acceptable 2-component model method, which is highly reproducible in whole body and regional body composition measurements [17, 24 26]. An additional possible limitation of using DXA was that the daily calibration of the device did not include segmental-specific analyses, which would add value to the reference method. Finally, it is worth commenting that a different selection of the segments performed by DXA and BIA could be performed. While DXA assessment allows the investigator checking and adjusting the segment, bioimpedance does not allow any control of the segments, which could partially explain the limited agreement between these two methods. Use of Internal Algorithms versus Our Own Predicted Values from Direct Impedance Data In our study, all the predictions made with our linear regression models were better than the predictions made by the internal algorithms of the impedance analyzer: bias was reduced, internal consistency and reliability was increased, agreement with the reference method was improved, and systematic error was avoided. Segmental Bioimpedance in Children Ann Nutr Metab 214;64: The use of segmental-specific anthropometric measures (circumferences and skinfolds) combined with segmental impedances in linear regression models markedly reduced the bias of the method (from 2 to 4%, depending on the segment), validating its use for segmental composition in the epidemiological setting. Our study adds to the scientific knowledge the confirmation that anthropometry and bioimpedance can be combined for segmental body composition assessment. Several authors have reported the potential usefulness of measuring the resistance and the length of body segments to assess segmental fat-free mass and total body water [27 29]. Our results for the whole leg and use of mid-thigh circumference and mid-thigh skinfold established that percent differences between the methods were lower than those from Fuller et al. [23], who assessed the composition of the calf and thigh separately (using only segment length). The a posteriori statistical power calculation showed that the sample size and results had enough power for the conclusions drawn. With a sample of 17 children, we would have been able to obtain a statistical power of 8% with a 95% confidence interval. Application of the Technique Given the different distribution of body tissues along the different body segments, and according to the child s development or pathology, it would be worth to have a precise, low-cost and fast method to assess abdominal fat at clinical and epidemiological settings. We found that 17.6% of children with a high trunk fat mass had not been classified as overweight or obese. This fact indicates that one could disregard very important information if measures of abdominal fat are not taken into account in the epidemiology for associations between obesity and cardiovascular risk. In our study, we have shown that the segmental composition BIA outputs were not precise at individual level. However, we have demonstrated that the use of validated equations for specific populations could be highly sensitive and specific to detect children with a high trunk fat mass. It may be worth commenting that in regression models major contributors to segmental composition were weight and height. However, the use of segmental-specific impedances improved the prediction by a mean of 3%. In summary, segmental-specific impedances improved the predictions more than segmental-specific anthropometric measurements, but the difference between both techniques was probably slight. However, considering that anthropometric measurements usually have a high inter- and intra-anthropometrist variability, and consid- 153

11 ering that anthropometrists were trained in this study, bioimpedance may have an advantage over anthropometry since this technique is not influenced by the investigator or clinician using the current BIA. All these analyses suggest that segmental bioimpedance may provide valuable information and thus support nutritional status monitoring and management of pediatric patients. In summary, we found that segmental body composition measurements predicted by internal algorithms of TANITA BC-418 were not valid at individual level, neither for clinical nor for epidemiological use, except for leg lean mass. The assessment of segmental body composition was improved using segmental-specific anthropometric measurements, e.g. trunk fat and lean masses combined with waist and chest circumferences, respectively. Further research is needed to obtain reference values for segmental body composition at different ages and for both genders to be used in clinical settings while assessing segmental body composition with bioimpedance. Acknowledgments We are very grateful to the families taking enthusiastically part in the Childhood Obesity Project. We gratefully acknowledge Francisco Javier Villalba Rubio (from IDI, Hospital Universitari de Tarragona Joan XXIII) for his help in performing DXA measurements, Pol Solé Navais (URV) for his help with data input and Pilar Hernández (IISPV) for her advice and support regarding statistical analyses. The studies reported herein have been carried out with partial financial support from the European Union, within the 5th Framework Programme, research grants No. QLRT and QLK1-CT , the 6th. 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