Bioimpedance in children and adolescents between 5 and 18 years of age and its association with overweight and obesity

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Its use in children is controversial, as little is known about the accuracy of the measurements (resistance, reactance, and the resulting FFM and FM). Subjects/Methods We derived FM and FFM in 2,954 visits from 1,547 children and adolescents between 5 and 21 years of age using BIA measurements and skinfold-based equations. We used Bland-Altman analyses to compare the resulting estimates. Furthermore, we estimated sex- and age-specific percentile curves for height-normalized resistance (Rz/H) and reactance (Xc/H). We used the overall concordance correlation coefficient (OCCC), concordance correlation coefficient (CCC), and intraclass correlation coefficient (ICC) to estimate agreement between the different segments. Bland-Altman analyses were also used to compare Rz/H and Xc/H SDS. Results The FM estimates from BIA were lower than estimates from the skinfold-based calculations, especially for boys. The estimated FFM and FM showed weight-status-dependent discrepancies between the estimation methods. The ICC showed "excellent" (ICC > 0.9) agreement between all segmental measurements. The OCCC and CCC showed varying degrees of concordance. The Rz/H and Xc/H percentile curves showed a similar age progression for all body segments. Rz/H (SDS) and Xc/H (SDS) were lower in participants with obesity. Conclusion Both measures –- Rz/H and Xc/H – were highly dependent on age and BMI-SDS. Our results suggest that current estimation techniques may be inadequate for children and adolescents with severe obesity, highlighting the requirement for specialized approaches explicitly designed for this population. Health sciences/Medical research/Epidemiology Physical sciences/Physics/Techniques and instrumentation Scientific community and society/Scientific community/Education bioimpedance analysis fat mass fat free mass children adolescents obesity overweight resistance reactance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction With over 400 million (20%) affected, childhood overweight and obesity are a worldwide public health issue (1). In 2018, 15.4% of children and adolescents in Germany were overweight or obese (obesity prevalence: 5.9%) (2). As childhood obesity develops in early childhood (3) and likely persists into adulthood (4), implying a wide range of severe comorbidities, it is a serious issue for the affected individuals as well as for the public good. As BMI cannot accurately measure excess fat mass, precise obesity diagnostics are needed, especially in childhood (5,6). Bioimpedance analysis (BIA) is an affordable, non-invasive and safe method for evaluating body composition. It can be used as a screening tool (7) or at the bedside (8). In adolescents and adults, BIA-based estimations of fat and fat-free mass correlate strongly with dual-energy X-ray absorptiometry (DEXA) (9). However, its use in pediatrics is not without controversy. BIA is currently not recommended for children under 24 months of age (10), its accuracy is questionable, and prediction equations are tissue-, population-, and device-specific (9,11), thus leading to disparate calculation formulas that are often based on data from a few hundred participants. The models assume chemical composition and body shape are constant, but this isn't valid, especially during childhood and adolescence (8,12). BIA underestimates fat mass in patients with overweight or obesity due to factors like an increase in line surface area or differing water distributions. (12–14). Researchers are actively exploring the topic of body composition, utilizing various approaches. The findings are inconsistent. It would be beneficial to test methods that are straightforward to implement and cost-effective in a clinical setting. Here, we aimed to compare the estimates of fat-free mass (FFM) and fat mass (FM) using different BIA calculation algorithms and skinfold thickness. Additionally, we aimed to describe the physiological age trends of the BIA measures (resistance and reactance) as percentiles for a large cohort of German children and adolescents. We then evaluated how obesity affected these measures and the above-mentioned agreement measures. Subjects and Methods Study population The data for this study were collected as part of the LIFE Child Study, which focuses on the development of lifestyle diseases in a large pediatric cohort and, has been described elsewhere (15,16). There were 1,615 subjects (804 girls, 811 boys) between 4 and 21 years of age with at least one bioimpedance measurement and triceps and subscapular skinfold measurements. We excluded duplicated measurements (n = 6) and those of children under 5 years of age (n = 19) because of their limited representation. Furthermore, we excluded all subjects suffering from diseases that influence body composition, especially water balance (heart, kidney, liver, and cancer diseases, n = 87), subjects taking medications for such diseases (diuretics, antidiuretics, thyroid hormones, antiepileptic drugs, or antidepressants, n = 218), and subjects with implausibly low BMI-SDS (BMI-SDS 10° (n = 5), phase angle difference left/right > 1.5° (n = 9)). The final sample included n = 2,954 measurements from 1,547 subjects between 5 and 21 years of age. Measurement methods Body height was measured with a “Dr. Keller I” stadiometer (Längenmesstechnik GmbH Limbach, Limbach-Oberfrohna, Germany) with an accuracy of 0.1 cm. Weight was measured with children wearing light underclothes and without shoes using a “Seca 704” electronic scale (Seca GmbH and Co. KG, Hamburg, Germany) with an accuracy of 50 g up to 150 kg and an accuracy of 100 g for weight measuring up to 300 kg (n = 7). Trained and certified observers assessed pubertal development according to Tanner (17,18). Tanner stage (TS) 1 was considered pre-pubertal, TS 2-4 pubertal, and TS 5 post-pubertal. The subscapular and triceps skinfold was measured 3 times on the right body side in a standing position using a “Holtain” (Holtain, Crosswell) or “Harpenden Skinfold” (Baty International, Burgess Hill) caliper, both with an accuracy of 0.2 mm, and averaged. The details are described elsewhere (19). The BIA measurements were conducted between 2012 and 2022 using the "BIACORPUS RX 4000" (MEDI CAL HealthCare GmbH, Karlsruhe, Germany) and the accompanying proprietary software "BodyComp Professional." The BIACORPUS RX 400 measured resistance and reactance segmentally for the trunk and all four limbs at a frequency of 50 kHz. To ensure that bodily fluids were distributed evenly, individuals had to lie horizontally for at least five minutes before the measurement. No mandatory fasting period was imposed. There was no contact between the thighs and the upper arms or upper body. If necessary, the extremities of people with overweight were fanned out using pillows or sheets. The inner electrodes were attached between the bony prominences in the center of the wrists. The outside electrodes were positioned near the fingertips, at least 3 cm away from the core electrode. To guarantee the minimum distance required for younger children, the outer electrode was adhered to the palm of the hand. The electrodes were placed in a similar manner on the feet. The 4 electrode positions resulted in measurements for 6 segments: RARF, RALA, RFLF, RALF, and LARF, LALF, where "R" and "L" stand for right and left and "A" and "F" for arm and foot, respectively. We hypothesized the symmetrical axes RARF/LALF and RARF/LARF as comparable measurement segments. Statistical Analysis Using German reference data relevant to age and sex, BMI was computed and converted to BMI Standard Deviation Scores (BMI-SDS) (20). Weight classes were defined as follows: extreme underweight: BMI-SDS < - 1.88, underweight: - 1.88 < BMI-SDS < - 1.28, normal weight: -1.28 < BMI-SDS < 1.28, overweight: 1.28 < BMI-SDS < 1.88, obese: 1.88 < BMI-SDS < 2.58, and severely obese: BMI-SDS ³ 2.58 (20). The fat mass was calculated considering age, sex, and Tanner stage according to Slaughter (21). The respective formulas are listed in the supplement. The subjects’ Rz and Xc raw values were incorporated into the calculation formulas proposed by Gätjens et al., along with other requisite variables, to facilitate a comparative analysis of FFM and FM between the two BIA devices (22). Resistance and reactance were standardized for height in cm (H). Bland-Altman analyses and the calculations of the overall concordance correlation coefficient (OCCC), concordance correlation coefficient (CCC), and intraclass correlation coefficient (ICC) were applied to estimate the agreement in resistance/reactance between body halves and body diagonals for validation purposes (23–26). The agreement categories used for the OCCC and CCC are based on the Pearson product-moment correlation coefficient: ≥ 0.9 ("excellent"), < 0.9 and ≥ 0.7 ("good"), < 0.7 and ≥ 0.5 ("moderate"), and < 0.5 ("low"). We used the inter-rater agreement measures by Koo and Li to interpret the ICC (27). After standardizing resistance and reactance for body height, we estimated age- and sex-specific reference limits by applying generalized additive models for location, shape, and scale (28), excluding subjects with underweight or obesity. 2,435 BIA measurements from 1,255 children and adolescents (624 girls, 631 boys) were finally used to estimate age- and sex-adjusted reference percentiles for Xc/H and Rz/H. We compared the different FM estimates by applying Bland-Altman statistics (23). Also, we explored the associations between weight status, respective FM (estimated by Slaughter equations), and the Bland-Altman results. Linear regression models were used to estimate associations between BMI-SDS changes and changes in resistance/reactance-SDS between consecutive visits. The measurement pairs were between 10 and 24 months apart. Subjects with a ∆BMI-SDS/year of more than 1.5 were excluded. The significance level was set to ⍺ = 0.05. Data analyzed with R (Version 4.42) (29). All visual representations were created using the ggplot2 (30). Results Table 1 shows the basic participant characteristics. Participant inclusion and exclusion are visualized in Supplementary Fig. 1. Measurement of agreement between the BIA segments In general, both Rz and Xc reached “good” OCCCs (OCCC Rz/H = 0.899, OCCC Xc/H = 0.898). However, especially for Rz/H, the horizontal derivatives RFLF and RALA showed markedly lower CCC values. The OCCC Rz/H increased to 0.986 when RFLF and RALA were omitted, and the OCCC Xc/H increased to 0.92. “Excellent” CCCs were reached between the arm-to-leg-derivatives representing the body halves (RARF/LALF: CCC Rz/H = 0.96, CCC Xc/H = 0.99). Interestingly, for Xc, the CCC between the LARF derivative and the other arm-to-leg derivatives was considerably weaker (Supplementary Fig. 2).The ICC also showed “excellent” agreement (ICC3 All-Rz/H: 0.97; All-Xc/H: 0.94). Looking at the RARF/LALF and RARF/LARF segments also showed “excellent” agreement (ICC3 RARF/LALF Rz/H: 0.99, Xc/H: 0.96; ICC3 RALF/LARF: Rz/H: 0.98, Xc/H: 0.93). Bland-Altman analyses of Rz/H and the symmetric measurement sections revealed that there were no systematic or proportionate biases between RALF and LARF. However, there was, a slight systemic bias in the Xc/H-SDS for the left-and-right-body-side segments (t-test Xc/H-SDS RARF vs. LALF d = 0.02, p = 0.03). Systemic biases also existed for the Xc/H-SDS values for girls (t-test Xc/H-SDS RALF vs. LARF: d = -0.05, p = 0.00; Xc/H-SDS RARF vs. LALF: d = 0.02, p = 0.03). See Fig. 1 and Supplementary Fig. 3. Percentile Curves Rz/H and Xc/H Age progression was similar across the six measurement segments. For example, Fig. 2 displays the percentile curves for Rz/H and Xc/H for the RARF derivative. Both metrics showed a downward trend from the age of 5 across all percentiles. For boys, the decrease became less steep from the age of 15 but continued until the end of the observation period for Rz/H. Xc/H plateaued from the same age, even suggesting a slight increase. For girls, the decline ended in a plateau from the age of 12 for both measures. In general, resistance and reactance were similar in boys and girls until the age of 12, but, afterwards, values were higher in girls. See Supplementary Tables 1–12 for reference values. Associations with BMI-SDS In general, Rz/H and Xc/H values were lower for the group with obesity and severe obesity compared with the normal weight reference. Most values were below the corresponding age- and sex-specific medians, especially for Rz/H (Figs. 3 and Supplementary Fig. 4). There were strong inverse associations between the change in Rz/H-SDS and change in BMI-SDS (adjusted for BMI-SDS at t0) with effect sizes between ß = -0.5 and ß=-0.6 (all ps < 0.001). Therefore, consistent with the results above, an increase in BMI-SDS was associated with a decrease in Rz/H (see Fig. 4 ). The effects for Xc/H-SDS were inverse as well but considerably weaker, with effect sizes between ß=-0.2 SDS and ß=-0.3 SDS (all ps < 0.001). For RFLF and LARF no significant association with Xc/H-SDS was found. Comparison of different estimation methods for determining body composition We found substantial differences in FFM (kg), FFM%, FM (kg), and FM% between the estimates of the BIACORPUS RX 4000 and Gätjens et al.’s formulas ( 22 ). FFM (kg) showed significantly higher values for the BIACORPUS algorithm for both sexes (t-test girls d = 0.11, p = 0.03; boys: d = 1.50, p < 0.001). FFM (%) showed similar results (t-test girls d = 0.01, p < 0.001; boys: d = 0.03, p < 0.001). However, the agreement depended on the mean estimates (see Supplementary Fig. 5). The stratified Bland-Altman plot (see Supplementary Fig. 6) shows different biases for different FFM intervals. Consistently, for FM (kg) and FM (%), significantly lower estimates were found for the BIACORPUS calculation formulas compared with Gätjens et al. (t-test FMkg girls d = -0.129, p = 0.01; boys: d = -1.50, p < 0.001 and t-test FM% girls d = − 0.01, p < 0.001; boys: d = − 0.03, p < 0.001; see Supplementary Fig. 7). For boys, the Slaughter formulae estimated a higher FM (%) than the BIA (mean difference: 3.49 (-11.23, 18.21)), indicating a systematic bias. This bias was particularly true for boys whose skinfold sum was greater than 35 mm. In boys, the variance and bias increased with increasing mean estimated FM. For girls, there was a similar tendency. However, the effect sizes were much weaker, with no discernible proportionate or systematic biases (see Fig. 5 ). Between 25% and 30% mean FM%, the calculation equation for girls switched to the one for girls with skinfolds > 35 mm. Boys with mean FM%s higher than 23% usually had skinfolds > 35 mm. Similar results were found when stratifying the sample according to weight group (See Supplementary Fig. 8). Discussion Measurement of agreement between the BIA segments The OCCCs, CCCs, and ICCs showed “good” to “excellent” agreement between the various measurement sections. The agreement was improved considerably by excluding derivatives RALA and RFLF. Symmetric segments showed no systematic or proportional biases in the Bland-Altman analyses for Rz/H (SDS). Hence, averaging the respective derivatives could reduce the influence of measurement errors and produce more stable results. Significant differences were seen between the Xc/H-SDS values for girls and the RARF/LALF-Xc/H-SDS values for boys. Depending on the BIA device and measurement protocol, there were contradictory statements regarding the comparability of the segments, for example, for the left and right sides of the body. Lafontant et al. recently reported differences between the body halves for Rz and Xc, among others ( 31 ). Percentile Curves Rz/Xc Boys' Rz/H levels dropped more during puberty than girls'. From then, the boys´ percentiles were below those of girls. This difference could be due to greater muscle growth during puberty ( 32 ). Due to the high water content of muscular tissue ( 33 ), resistance falls as muscle mass and fat-free mass increase. Hence, the plateau in Rz/H values in girls may suggest that the increase in fat-free mass has stalled ( 34 ). A similar plateau could be observed in the Xc/H percentiles. The Xc values are usually associated with cell mass and cell-bound water ( 35 ). Wells et al. found comparable patterns in a smaller study sample ( 36 ). The use of the raw BIA measures for clinical use is a current subject of research ( 37 ). Associations with BMI-SDS We found lower Rz/H and Xc/H SDS in children and adolescents with obesity. Furthermore, our longitudinal analysis showed that an increase in BMI-SDS was associated with decreasing Rz/H-SDS and Xc/H-SDS. Lower Rz and Xc values in subjects with obesity have been found in some studies ( 23 , 24 ). Numerous factors, such as increased FM, increased height or muscle mass, and possible hyperhydration in children with obesity may cause the differences. However, animal studies have demonstrated that obesity does not necessarily result in increased muscle mass ( 38 ). Plus, the faster growth of children with obesity is offset by slower pubertal growth, which results in near equal terminal heights for adults ( 39 ). This raises doubts about an explanation based solely on height. Possible reasons for hyperhydration include hormonal changes (antidiuretic hormone and aldosterone) and inflammation of the adipose tissue ( 40 , 41 ). Comparison of different estimation methods for determining body composition There was insufficient agreement in the FM estimates between the proprietary algorithm of applied BIACORPUS RX 4000 and the Gätjens equation. Thus, a critical perspective should be applied when comparing the outputs of various BIA devices ( 42 ). To combat this issue, a recent study suggested that it might be possible to develop conversion formulas, providing comparable values for, e.g., resistance measured by disparate BIA devices ( 43 ). When comparing the BIA-based and skinfold-based (Slaughter et al.) estimates of body fat in girls and normal-weight boys, there were no significant discrepancies. However, findings from Forte et al. showed that even in normal weight children, the lack of agreement between the two estimates means that they cannot replace one another ( 44 ). In boys, especially with skinfold sums above 35 mm or BMI-SDS > 1.881, we found a systematic bias, with lower BIA-based fat mass estimates. Furthermore, we found that the bias increased with increasing estimated FM (see Fig. 5 ). The underestimation of body fat mass by BIA for participants with obesity has previously been described in the literature ( 14 , 45 ). Possible hypotheses for the cause range from hyperhydration to simple physical explanatory models ( 13 , 14 , 46 , 47 ). BIA measurements showed good associations with anthropometric data and metabolic indices (e.g., liver enzymes, indicators of protein, carbohydrate, and lipid metabolism as well as inflammation) in a study involving adult participants with severe obesity. Their results suggest that BIA-based measurements carry a useful diagnostic value especially in combination with other methods ( 48 ). While the skinfold-based formulas proposed by Slaughter are widely recommended for estimating body fat in pediatric studies, they warrant a critical evaluation ( 45 ). Although we cannot determine the underlying source of the inaccuracies in the formulas, there are likely sources of biases. Slaughter et al. originally validated their results using mean values from the literature during the 80s, before the obesity epidemic ( 21 ). Thus, the measurements used to derive the formulas did not include enough cases of severe obesity. It has also been documented that the equations by Slaughter et al. overestimate or underestimate body fat ( 49 ). When looking at the estimates of the BIA devices, they may suffer from overfit to the original study cohorts or do not fit well for severe obesity as these cases were very rare just two decades ago, as already mentioned above (see Fig. 10). Strengths and limitations of this study Our study has several strengths, including a large number of participants, a standardized examination setting, a relatively wide age range, and the records of intra-individual changes over time. To date, only a limited number of studies have assessed the raw measurement values of bioimpedance devices, specifically Rz and Xc, and their associations with weight status in children and adolescents. Even fewer have considered segmental BIA measures, which are particularly effective for assessing the composition of individual body segments ( 50 ). However, there are several limitations. The manufacturer's proprietary computation formulas for fat-free mass in children and adolescents could not be assessed, and conclusions could only be drawn from the measurements and the resulting estimates. In addition, as the study population was almost exclusively Caucasian, generalizability is limited. Conclusion We found that the BIA-measured Rz/H and Xc/H were strongly dependent on age. Furthermore, both exhibited a considerable association with weight status. For normal weight girls and boys, the body fat BIA- and skinfold-based estimates showed good agreement. However, for obesity, a proportionate and consistent bias was discovered, especially for boys with obesity. Declarations Data Availability Statement The data set presented in this article cannot be shared publicly due to ethical and legal restrictions. The LIFE Child Study collects potentially sensitive information. Publishing the data is not covered by the informed consent provided by the study participants. Additionally, the LIFE Data Protection Concept requires all (external and internal) researchers who want to access the data to sign a project agreement. Researchers interested in accessing data from the LIFE Child Study may contact the study by writing to [email protected] . Acknowledgments We give our sincerest thanks to the children and adolescents who took part in the LIFE Child Study as well as their families. We would also like to thank the study outpatient team who carried out the examinations. The authors also thank Jane Zagorski for her excellent language editing and editorial support (as always)! Author Contribution Statement The authors confirm contributions to the paper as follows: study conception and design: Wieland Kiess, Klara Böker, and Mandy Vogel; analysis and interpretation of results: Klara Böker and Mandy Vogel; manuscript preparation: Klara Böker, Mandy Vogel, Wieland Kiess, and Annelie Grundmann. All authors reviewed the results and approved the final version of the manuscript. Funding The authors gratefully acknowledge all the participants and their families for their cooperation and enthusiastic participation in the LIFE Child Study. Furthermore, they appreciate the dedicated contributions of the LIFE Child Study team. This publication was supported by LIFE—Leipzig Research Center for Civilization Diseases, University of Leipzig. LIFE was funded by means of the European Union, by means of the European Social Fund (ESF), by the European Regional Development Fund (ERDF), and by means of the Free State of Saxony within the framework of the excellence initiative. Furthermore, LIFE Child is supported by the Free State of Saxony as per the budget approved by the state parliament and Leipzig University's Medical Faculty. In addition, the project was funded by the Federal Ministry of Education and Research (rarfBMBF) as part of the German Center for Child and Adolescent Health (DZKJ) under the funding code 01GL2405A. The authors have declared that they have no competing or potential conflicts of interest. Ethical Approval The LIFE Child Study was designed in accordance with the Declaration of Helsinki. Approval from the Ethics Committee of the University of Leipzig took place in 2010 (reference number: Reg. No. 264–10–19042010). Fully informed and written consent was obtained from each participant and their parents at each visit to the study outpatient clinic. Competing Interests The authors declare no conflict of interest. References Obesity and overweight [Internet]. [cited 2022 Mar 8]. Available from: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight Robert Koch-Institut. 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Clinical Nutrition. 2013 Oct 1;32(5):824–9. de Sousa LGO, Marshall AG, Norman JE, Fuqua JD, Lira VA, Rutledge JC, et al. The effects of diet composition and chronic obesity on muscle growth and function. J Appl Physiol (1985). 2021 Jan 1;130(1):124–38. Shalitin S, Kiess W. Putative Effects of Obesity on Linear Growth and Puberty. Horm Res Paediatr. 2017;88(1):101–10. Enhörning S, Struck J, Wirfält E, Hedblad B, Morgenthaler NG, Melander O. Plasma Copeptin, A Unifying Factor behind the Metabolic Syndrome. The Journal of Clinical Endocrinology & Metabolism. 2011 Jul 1;96(7):E1065–72. Hall JE, Mouton AJ, Da Silva AA, Omoto ACM, Wang Z, Li X, et al. Obesity, kidney dysfunction, and inflammation: interactions in hypertension. Cardiovascular Research. 2021 Jul 7;117(8):1859–76. Sheean P, Gonzalez MC, Prado CM, McKeever L, Hall AM, Braunschweig CA. American Society for Parenteral and Enteral Nutrition Clinical Guidelines: The Validity of Body Composition Assessment in Clinical Populations. Journal of Parenteral and Enteral Nutrition. 2020;44(1):12–43. Rudnev S, Burns JS, Williams PL, Lee MM, Korrick SA, Denisova T, et al. Comparison of bioimpedance body composition in young adults in the Russian Children’s Study. Clinical Nutrition ESPEN. 2020 Feb;35:153–61. Forte GC, Rodrigues CAS, Mundstock E, Santos TSD, Filho AD, Noal J, et al. Can skinfold thickness equations be substituted for bioimpedance analysis in children? Jornal de Pediatria. 2021 Jan;97(1):75–9. Silva DRP, Ribeiro AS, Pavão FH, Ronque ERV, Avelar A, Silva AM, et al. Validity of the methods to assess body fat in children and adolescents using multi-compartment models as the reference method: a systematic reviewq. REV ASSOC MED BRAS. Oliveira Filho JMD, Bernardes PS, Serpa GHC, Siqueira GDDJ, Noll M, Venâncio PEM, et al. BIOELECTRICAL VECTOR ANALYSIS IN OBESE ADOLESCENTS. Rev paul pediatr. 2020;38:e2019017. Brunani A, Perna S, Soranna D, Rondanelli M, Zambon A, Bertoli S, et al. Body composition assessment using bioelectrical impedance analysis (BIA) in a wide cohort of patients affected with mild to severe obesity. Clinical Nutrition. 2021 Jun 1;40(6):3973–81. Leal AAD, Faintuch J, Morais ÁAC, Noe JAB, Bertollo DM, Morais RC, et al. Bioimpedance analysis: Should it be used in morbid obesity? American Journal of Human Biology. 2011;23(3):420–2. Freedman DS, Ogden CL, Kit BK. Interrelationships between BMI, skinfold thicknesses, percent body fat, and cardiovascular disease risk factors among U.S. children and adolescents. BMC Pediatr. 2015 Dec;15(1):188. Ward LC. Segmental bioelectrical impedance analysis: an update. Current Opinion in Clinical Nutrition & Metabolic Care. 2012 Sep;15(5):424. Table Table 1 is available in the Supplementary Files section. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files SupplementEJCN.docx Supplementary material Table1ParticipantsXXXbasiccharacteristicsandbodycomposition.xlsx Table 1 Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: revise 29 Sep, 2025 Review # 2 received at journal 28 Sep, 2025 Reviewer # 2 agreed at journal 28 Sep, 2025 Review # 1 received at journal 25 Aug, 2025 Reviewer # 1 agreed at journal 11 Aug, 2025 Reviewers invited by journal 30 Jul, 2025 Editor assigned by journal 17 Jul, 2025 Submission checks completed at journal 17 Jul, 2025 First submitted to journal 16 Jul, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7143279","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":493283778,"identity":"8af2331d-eb14-461f-9685-03b3b6bea597","order_by":0,"name":"Klara Böker","email":"data:image/png;base64,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","orcid":"","institution":"Medical Faculty Leipzig University","correspondingAuthor":true,"prefix":"","firstName":"Klara","middleName":"","lastName":"Böker","suffix":""},{"id":493283779,"identity":"6694bd8a-d9f5-47c0-b015-a8eb78b802de","order_by":1,"name":"Mandy Vogel","email":"","orcid":"","institution":"Medical Faculty Leipzig University","correspondingAuthor":false,"prefix":"","firstName":"Mandy","middleName":"","lastName":"Vogel","suffix":""},{"id":493283780,"identity":"c503e75d-e343-4981-a858-65a9ae947359","order_by":2,"name":"Annelie Grundmann","email":"","orcid":"","institution":"Medical Faculty Leipzig University","correspondingAuthor":false,"prefix":"","firstName":"Annelie","middleName":"","lastName":"Grundmann","suffix":""},{"id":493283781,"identity":"a8712c96-e526-47f7-ab87-f2465a1042e6","order_by":3,"name":"Wieland Kiess","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Wieland","middleName":"","lastName":"Kiess","suffix":""}],"badges":[],"createdAt":"2025-07-16 21:05:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7143279/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7143279/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":88339498,"identity":"f1a98d3d-20bb-48df-9f40-6b7395ad5e2c","added_by":"auto","created_at":"2025-08-05 12:25:06","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":108586,"visible":true,"origin":"","legend":"\u003cp\u003eResults of Bland-Altman analyses comparing the Rz/H-SDS and Xc/H-SDS for the derivatives RARF and LALF. No significant systematic differences were found for Rz/H (bias girls: - 0.018, bias boys: 0.002). Slight systemic bias in the Xc/H-SDS could be found for both sexes (bias girls: 0.027, bias boys: 0.024).\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143279/v1/30a05fa6b9f41a3054a88dda.jpg"},{"id":88340017,"identity":"24745a60-1d34-4801-bf9c-804cfde714ab","added_by":"auto","created_at":"2025-08-05 12:33:06","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":105731,"visible":true,"origin":"","legend":"\u003cp\u003eThe 2.5\u003csup\u003eth\u003c/sup\u003e, 50\u003csup\u003eth\u003c/sup\u003e and 97.5\u003csup\u003eth\u003c/sup\u003e percentile curves for Rz/H and Xc/H shown for the derivative RARF as an example. The percentile curves for Rz/H and Xc/H showed similar patterns for boys and girls. Generally, resistance (Rz/H) and reactance (Xc/H) were similar in boys and girls until the age of 12. After that, girls showed higher values than boys.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143279/v1/154ebd3d0ca965269440d749.jpg"},{"id":88339501,"identity":"e4b4d140-da73-4ec2-87c0-68d4a95d4228","added_by":"auto","created_at":"2025-08-05 12:25:06","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114072,"visible":true,"origin":"","legend":"\u003cp\u003eCompared with the reference ranges derived from a non-obese study population, Rz/H values were lower for children and adolescents with obesity or severe obesity, with most values below the 50\u003csup\u003eth\u003c/sup\u003e percentile. The other derivatives showed similar patterns.\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143279/v1/32436ada1febe97e07869057.jpg"},{"id":88339505,"identity":"7e4e9619-6924-4442-b707-280b1ef9d900","added_by":"auto","created_at":"2025-08-05 12:25:06","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":110357,"visible":true,"origin":"","legend":"\u003cp\u003eAssociations between changes in Rz/H-SDS and Xc/H-SDS and changes in BMI-SDS. We found strong inverse associations between changes in Rz/H-SDS and changes in BMI-SDS, even after adjusting for BMI-SDS at baseline. Similar associations were seen to a lesser extent for Xc/H.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143279/v1/96ee95b05e7091cbf13c7778.jpg"},{"id":88341309,"identity":"5f610645-3e57-4741-87d2-883a18b3902e","added_by":"auto","created_at":"2025-08-05 12:41:06","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":116483,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman analyses for FM (%) between skinfold-based Slaughter et al. equations and BIACORPUS RX 4000. The density graph at the top shows the distribution of measurements. Points are colored according to the estimated FM derived from equations by Slaughter et al. Boys with a skinfold sum \u0026gt; 35 mm showed a considerable bias to higher skinfold-based estimates when compared with the BIA-based estimates (bias: 4.14). No such difference could be observed for girls (bias: 0.11).\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7143279/v1/f5fa71428b29588ef4609949.jpg"},{"id":88341582,"identity":"c46d73db-6d8b-4c35-9726-353a25a1f46f","added_by":"auto","created_at":"2025-08-05 12:49:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1199760,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7143279/v1/0a864695-fa4c-4bba-9fb5-43ad60af8076.pdf"},{"id":88340018,"identity":"18f2e4d2-68ce-4601-9825-3e49374b1b8e","added_by":"auto","created_at":"2025-08-05 12:33:06","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2405914,"visible":true,"origin":"","legend":"Supplementary material","description":"","filename":"SupplementEJCN.docx","url":"https://assets-eu.researchsquare.com/files/rs-7143279/v1/da0d99a6ccc0f06cd487c812.docx"},{"id":88340015,"identity":"3ef6a9eb-12d9-48cc-bc6c-23ba7bf78be6","added_by":"auto","created_at":"2025-08-05 12:33:06","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":10816,"visible":true,"origin":"","legend":"Table 1","description":"","filename":"Table1ParticipantsXXXbasiccharacteristicsandbodycomposition.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7143279/v1/2b817fba2a762ae2ac4503d1.xlsx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Bioimpedance in children and adolescents between 5 and 18 years of age and its association with overweight and obesity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eWith over 400 million (20%) affected, childhood overweight and obesity are a worldwide public health issue (1). In 2018, 15.4% of children and adolescents in Germany were overweight or obese (obesity prevalence: 5.9%)\u0026nbsp;(2). As childhood obesity develops in early childhood (3) and likely persists into adulthood (4), implying a wide range of severe comorbidities, it is a serious issue for the affected individuals as well as for the public good. As BMI cannot accurately measure excess fat mass, precise obesity diagnostics are needed, especially in childhood (5,6).\u003c/p\u003e\n\u003cp\u003eBioimpedance analysis (BIA) is an affordable, non-invasive and safe method for evaluating body composition. It can be used as a screening tool (7) or at the bedside (8). In adolescents and adults, BIA-based estimations of fat and fat-free mass correlate strongly with \u0026nbsp;dual-energy X-ray absorptiometry (DEXA) (9). However, its use in pediatrics is not without controversy. BIA is currently not recommended for children under 24 months of age (10), its accuracy is questionable, and prediction equations are tissue-, population-, and device-specific (9,11), thus leading to disparate calculation formulas that are often based on data from a few hundred participants. The models assume chemical composition and body shape are constant, but this isn\u0026apos;t valid, especially during childhood and adolescence (8,12). BIA underestimates fat mass in patients with overweight or obesity due to factors like an increase in line surface area or differing water distributions.\u0026nbsp;(12\u0026ndash;14).\u003c/p\u003e\n\u003cp\u003eResearchers are actively exploring the topic of body composition, utilizing various approaches. The findings are inconsistent. It would be beneficial to test methods that are straightforward to implement and cost-effective in a clinical setting. Here, we aimed to compare the estimates of fat-free mass (FFM) and fat mass (FM) using different BIA calculation algorithms and skinfold thickness. Additionally, we aimed to describe the physiological age trends of the BIA measures (resistance and reactance) as percentiles for a large cohort of German children and adolescents. We then evaluated how obesity affected these measures and the above-mentioned agreement measures.\u0026nbsp;\u003c/p\u003e"},{"header":"Subjects and Methods ","content":"\u003ch2\u003eStudy population\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eThe data for this study were collected as part of the LIFE Child Study, which focuses on the development of lifestyle diseases in a large pediatric cohort and, has been described elsewhere (15,16). There were 1,615 subjects (804 girls, 811 boys) between 4 and 21 years of age with at least one bioimpedance measurement and triceps and subscapular skinfold measurements. We excluded duplicated measurements (n = 6) and those of children under 5 years of age (n = 19) because of their limited representation. Furthermore, we excluded all subjects suffering from diseases that influence body composition, especially water balance (heart, kidney, liver, and cancer diseases, n = 87), subjects taking medications for such diseases (diuretics, antidiuretics, thyroid hormones, antiepileptic drugs, or antidepressants, n = 218), and subjects with implausibly low BMI-SDS (BMI-SDS \u0026lt; - 4: n = 2). Implausible bioimpedance measurements were excluded (phase angle \u0026gt; 10° (n = 5), phase angle difference left/right \u0026gt; 1.5° (n = 9)). The final sample included n = 2,954 measurements from 1,547 subjects between 5 and 21 years of age.\u0026nbsp;\u003c/p\u003e\n\u003ch2\u003eMeasurement methods\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eBody height was measured with a “Dr. Keller I” stadiometer (Längenmesstechnik GmbH Limbach, Limbach-Oberfrohna, Germany) with an accuracy of 0.1 cm. Weight was measured with children wearing light underclothes and without shoes using a “Seca 704” electronic scale (Seca GmbH and Co. KG, Hamburg, Germany) with an accuracy of 50 g up to 150 kg and an accuracy of 100 g for weight measuring up to 300 kg (n = 7). Trained and certified observers assessed pubertal development according to Tanner (17,18). Tanner stage (TS) 1 was considered pre-pubertal, TS 2-4 pubertal, and TS 5 post-pubertal. The subscapular and triceps skinfold was measured 3 times on the right body side in a standing position using a “Holtain” (Holtain, Crosswell) or “Harpenden Skinfold” (Baty International, Burgess Hill) caliper, both with an accuracy of 0.2 mm, and averaged. The details are described elsewhere (19).\u003c/p\u003e\n\u003cp\u003eThe BIA measurements were conducted between 2012 and 2022 using the \"BIACORPUS RX 4000\" (MEDI CAL HealthCare GmbH, Karlsruhe, Germany) and the accompanying proprietary software \"BodyComp Professional.\" The BIACORPUS RX 400 measured resistance and reactance segmentally for the trunk and all four limbs at a frequency of 50 kHz. To ensure that bodily fluids were distributed evenly, individuals had to lie horizontally for at least five minutes before the measurement. No mandatory fasting period was imposed. There was no contact between the thighs and the upper arms or upper body. If necessary, the extremities of people with overweight were fanned out using pillows or sheets. The inner electrodes were attached between the bony prominences in the center of the wrists. The outside electrodes were positioned near the fingertips, at least 3 cm away from the core electrode. To guarantee the minimum distance required for younger children, the outer electrode was adhered to the palm of the hand. The electrodes were placed in a similar manner on the feet. The 4 electrode positions resulted in measurements for 6 segments: RARF, RALA, RFLF, RALF, and LARF, LALF, where \"R\" and \"L\" stand for right and left and \"A\" and \"F\" for arm and foot, respectively. We hypothesized the symmetrical axes RARF/LALF and RARF/LARF as comparable measurement segments.\u003c/p\u003e\n\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\n\u003cp\u003eUsing German reference data relevant to age and sex, BMI was computed and converted to BMI Standard Deviation Scores (BMI-SDS) (20). Weight classes were defined as follows: extreme underweight: BMI-SDS \u0026lt; - 1.88, underweight: - 1.88 \u0026lt; BMI-SDS \u0026lt; - 1.28, normal weight: -1.28 \u0026lt; BMI-SDS \u0026lt; 1.28, overweight: 1.28 \u0026lt; BMI-SDS \u0026lt; 1.88, obese: 1.88 \u0026lt; BMI-SDS \u0026lt; 2.58, and severely obese: BMI-SDS\u0026nbsp;³\u0026nbsp;2.58\u0026nbsp;(20).\u0026nbsp;The fat mass was calculated considering age, sex, and Tanner stage according to Slaughter\u0026nbsp;(21). The respective formulas are listed in the supplement. The subjects’ Rz and Xc raw values were incorporated into the calculation formulas proposed by Gätjens et al., along with other requisite variables, to facilitate a comparative analysis of FFM and FM between the two BIA devices\u0026nbsp;(22). Resistance and reactance were standardized for height in cm (H).\u003c/p\u003e\n\u003cp\u003eBland-Altman analyses and the calculations of the overall concordance correlation coefficient (OCCC), concordance correlation coefficient (CCC), and intraclass correlation coefficient (ICC) were applied to estimate the agreement in resistance/reactance between body halves and body diagonals for validation purposes\u0026nbsp;(23–26).\u0026nbsp;The agreement categories used for the OCCC and CCC are based on the Pearson product-moment correlation coefficient: ≥ 0.9 (\"excellent\"), \u0026lt; 0.9 and ≥ 0.7 (\"good\"), \u0026lt; 0.7 and ≥ 0.5 (\"moderate\"), and \u0026lt; 0.5 (\"low\"). We used the inter-rater agreement measures\u0026nbsp;by Koo and Li to interpret the ICC (27). After standardizing resistance and reactance for body height, we estimated age- and sex-specific reference limits by applying generalized additive models for location, shape, and scale (28), excluding subjects with underweight or obesity.\u0026nbsp;2,435 BIA measurements from 1,255 children and adolescents (624 girls, 631 boys) were finally used to estimate age- and sex-adjusted reference percentiles for Xc/H and Rz/H. We compared the different FM estimates by applying Bland-Altman statistics\u0026nbsp;(23). Also, we explored the associations between weight status, respective FM (estimated by Slaughter equations), and the Bland-Altman results.\u003c/p\u003e\n\u003cp\u003eLinear regression models were used to estimate associations between BMI-SDS changes and changes in resistance/reactance-SDS between consecutive visits. The measurement pairs were between 10 and 24 months apart. Subjects with a ∆BMI-SDS/year of more than 1.5 were excluded.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe significance level was set to ⍺ = 0.05. Data analyzed with R (Version 4.42) (29). All visual representations were created using the ggplot2 (30).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eTable\u0026nbsp;1 shows the basic participant characteristics. Participant inclusion and exclusion are visualized in Supplementary Fig.\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMeasurement of agreement between the BIA segments\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn general, both Rz and Xc reached \u0026ldquo;good\u0026rdquo; OCCCs (OCCC\u003csub\u003eRz/H\u003c/sub\u003e = 0.899, OCCC\u003csub\u003eXc/H\u003c/sub\u003e = 0.898). However, especially for Rz/H, the horizontal derivatives RFLF and RALA showed markedly lower CCC values. The OCCC\u003csub\u003eRz/H\u003c/sub\u003e increased to 0.986 when RFLF and RALA were omitted, and the OCCC\u003csub\u003eXc/H\u003c/sub\u003e increased to 0.92. \u0026ldquo;Excellent\u0026rdquo; CCCs were reached between the arm-to-leg-derivatives representing the body halves (RARF/LALF: CCC\u003csub\u003eRz/H\u003c/sub\u003e = 0.96, CCC\u003csub\u003eXc/H\u003c/sub\u003e = 0.99). Interestingly, for Xc, the CCC between the LARF derivative and the other arm-to-leg derivatives was considerably weaker (Supplementary Fig.\u0026nbsp;2).The ICC also showed \u0026ldquo;excellent\u0026rdquo; agreement (ICC3 All-Rz/H: 0.97; All-Xc/H: 0.94). Looking at the RARF/LALF and RARF/LARF segments also showed \u0026ldquo;excellent\u0026rdquo; agreement (ICC3 RARF/LALF Rz/H: 0.99, Xc/H: 0.96; ICC3 RALF/LARF: Rz/H: 0.98, Xc/H: 0.93).\u003c/p\u003e\u003cp\u003eBland-Altman analyses of Rz/H and the symmetric measurement sections revealed that there were no systematic or proportionate biases between RALF and LARF. However, there was, a slight systemic bias in the Xc/H-SDS for the left-and-right-body-side segments (t-test Xc/H-SDS RARF vs. LALF d\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;0.03). Systemic biases also existed for the Xc/H-SDS values for girls (t-test Xc/H-SDS RALF vs. LARF: d = -0.05, p\u0026thinsp;=\u0026thinsp;0.00; Xc/H-SDS RARF vs. LALF: d\u0026thinsp;=\u0026thinsp;0.02, p\u0026thinsp;=\u0026thinsp;0.03). See Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Supplementary Fig.\u0026nbsp;3.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003ePercentile Curves Rz/H and Xc/H\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAge progression was similar across the six measurement segments. For example, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the percentile curves for Rz/H and Xc/H for the RARF derivative. Both metrics showed a downward trend from the age of 5 across all percentiles. For boys, the decrease became less steep from the age of 15 but continued until the end of the observation period for Rz/H. Xc/H plateaued from the same age, even suggesting a slight increase. For girls, the decline ended in a plateau from the age of 12 for both measures. In general, resistance and reactance were similar in boys and girls until the age of 12, but, afterwards, values were higher in girls. See Supplementary Tables\u0026nbsp;1\u0026ndash;12 for reference values.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociations with BMI-SDS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn general, Rz/H and Xc/H values were lower for the group with obesity and severe obesity compared with the normal weight reference. Most values were below the corresponding age- and sex-specific medians, especially for Rz/H (Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Supplementary Fig.\u0026nbsp;4). There were strong inverse associations between the change in Rz/H-SDS and change in BMI-SDS (adjusted for BMI-SDS at t0) with effect sizes between \u0026szlig; = -0.5 and \u0026szlig;=-0.6 (all ps\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Therefore, consistent with the results above, an increase in BMI-SDS was associated with a decrease in Rz/H (see Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The effects for Xc/H-SDS were inverse as well but considerably weaker, with effect sizes between \u0026szlig;=-0.2 SDS and \u0026szlig;=-0.3 SDS (all ps\u0026thinsp;\u0026lt;\u0026thinsp;0.001). For RFLF and LARF no significant association with Xc/H-SDS was found.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of different estimation methods for determining body composition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe found substantial differences in FFM (kg), FFM%, FM (kg), and FM% between the estimates of the BIACORPUS RX 4000 and G\u0026auml;tjens et al.\u0026rsquo;s formulas (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). FFM (kg) showed significantly higher values for the BIACORPUS algorithm for both sexes (t-test girls d\u0026thinsp;=\u0026thinsp;0.11, p\u0026thinsp;=\u0026thinsp;0.03; boys: d\u0026thinsp;=\u0026thinsp;1.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). FFM (%) showed similar results (t-test girls d\u0026thinsp;=\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; boys: d\u0026thinsp;=\u0026thinsp;0.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). However, the agreement depended on the mean estimates (see Supplementary Fig.\u0026nbsp;5). The stratified Bland-Altman plot (see Supplementary Fig.\u0026nbsp;6) shows different biases for different FFM intervals.\u003c/p\u003e\u003cp\u003eConsistently, for FM (kg) and FM (%), significantly lower estimates were found for the BIACORPUS calculation formulas compared with G\u0026auml;tjens et al. (t-test FMkg girls d = -0.129, p\u0026thinsp;=\u0026thinsp;0.01; boys: d = -1.50, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and t-test FM% girls d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.01, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; boys: d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.03, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; see Supplementary Fig.\u0026nbsp;7).\u003c/p\u003e\u003cp\u003eFor boys, the Slaughter formulae estimated a higher FM (%) than the BIA (mean difference: 3.49 (-11.23, 18.21)), indicating a systematic bias. This bias was particularly true for boys whose skinfold sum was greater than 35 mm. In boys, the variance and bias increased with increasing mean estimated FM. For girls, there was a similar tendency. However, the effect sizes were much weaker, with no discernible proportionate or systematic biases (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Between 25% and 30% mean FM%, the calculation equation for girls switched to the one for girls with skinfolds\u0026thinsp;\u0026gt;\u0026thinsp;35 mm. Boys with mean FM%s higher than 23% usually had skinfolds\u0026thinsp;\u0026gt;\u0026thinsp;35 mm. Similar results were found when stratifying the sample according to weight group (See Supplementary Fig.\u0026nbsp;8).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003e\u003cb\u003eMeasurement of agreement between the BIA segments\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe OCCCs, CCCs, and ICCs showed \u0026ldquo;good\u0026rdquo; to \u0026ldquo;excellent\u0026rdquo; agreement between the various measurement sections. The agreement was improved considerably by excluding derivatives RALA and RFLF. Symmetric segments showed no systematic or proportional biases in the Bland-Altman analyses for Rz/H (SDS). Hence, averaging the respective derivatives could reduce the influence of measurement errors and produce more stable results. Significant differences were seen between the Xc/H-SDS values for girls and the RARF/LALF-Xc/H-SDS values for boys. Depending on the BIA device and measurement protocol, there were contradictory statements regarding the comparability of the segments, for example, for the left and right sides of the body. Lafontant et al. recently reported differences between the body halves for Rz and Xc, among others (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003ePercentile Curves Rz/Xc\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBoys' Rz/H levels dropped more during puberty than girls'. From then, the boys\u0026acute; percentiles were below those of girls. This difference could be due to greater muscle growth during puberty (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Due to the high water content of muscular tissue (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), resistance falls as muscle mass and fat-free mass increase. Hence, the plateau in Rz/H values in girls may suggest that the increase in fat-free mass has stalled (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). A similar plateau could be observed in the Xc/H percentiles. The Xc values are usually associated with cell mass and cell-bound water (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). Wells et al. found comparable patterns in a smaller study sample (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). The use of the raw BIA measures for clinical use is a current subject of research (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eAssociations with BMI-SDS\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe found lower Rz/H and Xc/H SDS in children and adolescents with obesity. Furthermore, our longitudinal analysis showed that an increase in BMI-SDS was associated with decreasing Rz/H-SDS and Xc/H-SDS.\u003c/p\u003e\u003cp\u003eLower Rz and Xc values in subjects with obesity have been found in some studies (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Numerous factors, such as increased FM, increased height or muscle mass, and possible hyperhydration in children with obesity may cause the differences. However, animal studies have demonstrated that obesity does not necessarily result in increased muscle mass (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Plus, the faster growth of children with obesity is offset by slower pubertal growth, which results in near equal terminal heights for adults (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This raises doubts about an explanation based solely on height. Possible reasons for hyperhydration include hormonal changes (antidiuretic hormone and aldosterone) and inflammation of the adipose tissue (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003eComparison of different estimation methods for determining body composition\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThere was insufficient agreement in the FM estimates between the proprietary algorithm of applied BIACORPUS RX 4000 and the G\u0026auml;tjens equation. Thus, a critical perspective should be applied when comparing the outputs of various BIA devices (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e). To combat this issue, a recent study suggested that it might be possible to develop conversion formulas, providing comparable values for, e.g., resistance measured by disparate BIA devices (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eWhen comparing the BIA-based and skinfold-based (Slaughter et al.) estimates of body fat in girls and normal-weight boys, there were no significant discrepancies. However, findings from Forte et al. showed that even in normal weight children, the lack of agreement between the two estimates means that they cannot replace one another (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e). In boys, especially with skinfold sums above 35 mm or BMI-SDS\u0026thinsp;\u0026gt;\u0026thinsp;1.881, we found a systematic bias, with lower BIA-based fat mass estimates. Furthermore, we found that the bias increased with increasing estimated FM (see Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The underestimation of body fat mass by BIA for participants with obesity has previously been described in the literature (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Possible hypotheses for the cause range from hyperhydration to simple physical explanatory models (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). BIA measurements showed good associations with anthropometric data and metabolic indices (e.g., liver enzymes, indicators of protein, carbohydrate, and lipid metabolism as well as inflammation) in a study involving adult participants with severe obesity. Their results suggest that BIA-based measurements carry a useful diagnostic value especially in combination with other methods (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e). While the skinfold-based formulas proposed by Slaughter are widely recommended for estimating body fat in pediatric studies, they warrant a critical evaluation (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e). Although we cannot determine the underlying source of the inaccuracies in the formulas, there are likely sources of biases.\u003c/p\u003e\u003cp\u003eSlaughter et al. originally validated their results using mean values from the literature during the 80s, before the obesity epidemic (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Thus, the measurements used to derive the formulas did not include enough cases of severe obesity. It has also been documented that the equations by Slaughter et al. overestimate or underestimate body fat (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e). When looking at the estimates of the BIA devices, they may suffer from overfit to the original study cohorts or do not fit well for severe obesity as these cases were very rare just two decades ago, as already mentioned above (see Fig.\u0026nbsp;10).\u003c/p\u003e\u003cp\u003e\u003cb\u003eStrengths and limitations of this study\u003c/b\u003e\u003c/p\u003e\u003cp\u003e Our study has several strengths, including a large number of participants, a standardized examination setting, a relatively wide age range, and the records of intra-individual changes over time. To date, only a limited number of studies have assessed the raw measurement values of bioimpedance devices, specifically Rz and Xc, and their associations with weight status in children and adolescents. Even fewer have considered segmental BIA measures, which are particularly effective for assessing the composition of individual body segments (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, there are several limitations. The manufacturer's proprietary computation formulas for fat-free mass in children and adolescents could not be assessed, and conclusions could only be drawn from the measurements and the resulting estimates. In addition, as the study population was almost exclusively Caucasian, generalizability is limited.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe found that the BIA-measured Rz/H and Xc/H were strongly dependent on age. Furthermore, both exhibited a considerable association with weight status. For normal weight girls and boys, the body fat BIA- and skinfold-based estimates showed good agreement. However, for obesity, a proportionate and consistent bias was discovered, especially for boys with obesity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data set presented in this article cannot be shared publicly due to ethical and legal restrictions. The LIFE Child Study collects potentially sensitive information. Publishing the data is not covered by the informed consent provided by the study participants. Additionally, the LIFE Data Protection Concept requires all (external and internal) researchers who want to access the data to sign a project agreement. Researchers interested in accessing data from the LIFE Child Study may contact the study by writing to [email protected].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;We give our sincerest thanks to the children and adolescents who took part in the LIFE Child Study as well as their families. We would also like to thank the study outpatient team who carried out the examinations. The authors also thank Jane Zagorski for her excellent language editing and editorial support (as always)!\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm contributions to the paper as follows: study conception and design: Wieland Kiess, Klara B\u0026ouml;ker, and Mandy Vogel; analysis and interpretation of results: Klara B\u0026ouml;ker and Mandy Vogel; manuscript preparation: Klara B\u0026ouml;ker, Mandy Vogel, Wieland Kiess, and Annelie Grundmann. All authors reviewed the results and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge all the participants and their families for their cooperation and enthusiastic participation in the LIFE Child Study. Furthermore, they appreciate the dedicated contributions of the LIFE Child Study team. This publication was supported by LIFE\u0026mdash;Leipzig Research Center for Civilization Diseases, University of Leipzig. LIFE was funded by means of the European Union, by means of the European Social Fund (ESF), by the European Regional Development Fund (ERDF), and by means of the Free State of Saxony within the framework of the excellence initiative. Furthermore, LIFE Child is supported by the Free State of Saxony as per the budget approved by the state parliament and Leipzig University\u0026apos;s Medical Faculty. In addition, the project was funded by the Federal Ministry of Education and Research (rarfBMBF) as part of the German Center for Child and Adolescent Health (DZKJ) under the funding code 01GL2405A. The authors have declared that they have no competing or potential conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe LIFE Child Study was designed in accordance with the Declaration of Helsinki. \u0026nbsp;Approval from the Ethics Committee of the University of Leipzig took place in 2010 (reference number: Reg. No. 264\u0026ndash;10\u0026ndash;19042010). Fully informed and written consent was obtained from each participant and their parents at each visit to the study outpatient clinic.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eObesity and overweight [Internet]. [cited 2022 Mar 8]. 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Arbeitsgemeinschaft Adipositas im Kindes und Jugendalter (AGA); 2019. Available from: https://www.awmf.org/leitlinien/detail/ll/050-002.html\u003c/li\u003e\n\u003cli\u003eSlaughter MH, Lohman TG, Boileau RA, Horswill CA. Skinfold Equations for Estimation of Body Fatness in Children and Youth. 1988; \u003c/li\u003e\n\u003cli\u003eG\u0026auml;tjens I, Schmidt SCE, Plachta-Danielzik S, Bosy-Westphal A, M\u0026uuml;ller MJ. Body Composition Characteristics of a Load-Capacity Model: Age-Dependent and Sex-Specific Percentiles in 5- to 17-Year-Old Children. Obesity Facts. 2021 Oct 5;14(6):593. \u003c/li\u003e\n\u003cli\u003eBland JM, Altman DG. STATISTICAL METHODS FOR ASSESSING AGREEMENT BETWEEN TWO METHODS OF CLINICAL MEASUREMENT. \u003c/li\u003e\n\u003cli\u003eShrout PE, Fleiss JL. Intraclass correlations: uses in assessing rater reliability. Psychol Bull. 1979 Mar;86(2):420\u0026ndash;8. \u003c/li\u003e\n\u003cli\u003eLin LIK. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics. 1989;45(1):255\u0026ndash;68. \u003c/li\u003e\n\u003cli\u003eBarnhart HX, Haber M, Song J. Overall Concordance Correlation Coefficient for Evaluating Agreement Among Multiple Observers. Biometrics. 2002 Dec;58(4):1020\u0026ndash;7. \u003c/li\u003e\n\u003cli\u003eKoo TK, Li MY. A Guideline of Selecting and Reporting Intraclass Correlation Coefficients for Reliability Research. Journal of Chiropractic Medicine. 2016 Jun;15(2):155\u0026ndash;63. \u003c/li\u003e\n\u003cli\u003eRigby RA, Stasinopoulos DM. Generalized Additive Models for Location, Scale and Shape. Journal of the Royal Statistical Society Series C: Applied Statistics. 2005 Jun 1;54(3):507\u0026ndash;54. \u003c/li\u003e\n\u003cli\u003eR: The R Project for Statistical Computing [Internet]. [cited 2024 Mar 21]. Available from: https://www.r-project.org/\u003c/li\u003e\n\u003cli\u003eWickham H. ggplot2: elegant graphics for data analysis [Internet]. Springer; 2016 [cited 2017 Jul 27]. Available from: https://books.google.de/books?hl=en\u0026amp;lr=\u0026amp;id=XgFkDAAAQBAJ\u0026amp;oi=fnd\u0026amp;pg=PR8\u0026amp;dq=hadley+wickham+ggplot2\u0026amp;ots=soY06Qe-7Q\u0026amp;sig=hFEDsqcnYlO_4Jfl7gvJLERpL1E\u003c/li\u003e\n\u003cli\u003eLafontant K, Sterner DA, Fukuda DH, Stout JR, Park JH, Thiamwong L. Comparing Device-Generated and Calculated Bioimpedance Variables in Community-Dwelling Older Adults. Sensors. 2024 Aug 30;24(17):5626. \u003c/li\u003e\n\u003cli\u003eWebber CE, Barr RD. Age‐ and gender‐dependent values of skeletal muscle mass in healthy children and adolescents. J cachexia sarcopenia muscle. 2012 Mar;3(1):25\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eMitchell HH, Hamilton TS, Steggerda FR, Bean HW. THE CHEMICAL COMPOSITION OF THE ADULT HUMAN BODY AND ITS BEARING ON THE BIOCHEMISTRY OF GROWTH. Journal of Biological Chemistry. 1945 May;158(3):625\u0026ndash;37. \u003c/li\u003e\n\u003cli\u003eLukaski HC, Bolonchuk WW, Hall CB, Siders WA. Validation of tetrapolar bioelectrical impedance method to assess human body composition. J Appl Physiol (1985). 1986 Apr;60(4):1327\u0026ndash;32. \u003c/li\u003e\n\u003cli\u003eLukaski HC, Talluri A. Phase angle as an index of physiological status: validating bioelectrical assessments of hydration and cell mass in health and disease. Rev Endocr Metab Disord. 2023 Jun 1;24(3):371\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eWells JCK, Williams JE, Quek RY, Fewtrell MS. Bio-electrical impedance vector analysis: testing Piccoli\u0026rsquo;s model against objective body composition data in children and adolescents. Eur J Clin Nutr. 2019 Jun;73(6):887\u0026ndash;95. \u003c/li\u003e\n\u003cli\u003eAzevedo ZMA, Moore DCBC, de Matos FAA, Fonseca VM, Peixoto MVM, Gaspar-Elsas MIC, et al. Bioelectrical impedance parameters in critically ill children: Importance of reactance and resistance. Clinical Nutrition. 2013 Oct 1;32(5):824\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003ede Sousa LGO, Marshall AG, Norman JE, Fuqua JD, Lira VA, Rutledge JC, et al. The effects of diet composition and chronic obesity on muscle growth and function. J Appl Physiol (1985). 2021 Jan 1;130(1):124\u0026ndash;38. \u003c/li\u003e\n\u003cli\u003eShalitin S, Kiess W. Putative Effects of Obesity on Linear Growth and Puberty. Horm Res Paediatr. 2017;88(1):101\u0026ndash;10. \u003c/li\u003e\n\u003cli\u003eEnh\u0026ouml;rning S, Struck J, Wirf\u0026auml;lt E, Hedblad B, Morgenthaler NG, Melander O. Plasma Copeptin, A Unifying Factor behind the Metabolic Syndrome. The Journal of Clinical Endocrinology \u0026amp; Metabolism. 2011 Jul 1;96(7):E1065\u0026ndash;72. \u003c/li\u003e\n\u003cli\u003eHall JE, Mouton AJ, Da Silva AA, Omoto ACM, Wang Z, Li X, et al. Obesity, kidney dysfunction, and inflammation: interactions in hypertension. Cardiovascular Research. 2021 Jul 7;117(8):1859\u0026ndash;76. \u003c/li\u003e\n\u003cli\u003eSheean P, Gonzalez MC, Prado CM, McKeever L, Hall AM, Braunschweig CA. American Society for Parenteral and Enteral Nutrition Clinical Guidelines: The Validity of Body Composition Assessment in Clinical Populations. Journal of Parenteral and Enteral Nutrition. 2020;44(1):12\u0026ndash;43. \u003c/li\u003e\n\u003cli\u003eRudnev S, Burns JS, Williams PL, Lee MM, Korrick SA, Denisova T, et al. Comparison of bioimpedance body composition in young adults in the Russian Children\u0026rsquo;s Study. Clinical Nutrition ESPEN. 2020 Feb;35:153\u0026ndash;61. \u003c/li\u003e\n\u003cli\u003eForte GC, Rodrigues CAS, Mundstock E, Santos TSD, Filho AD, Noal J, et al. Can skinfold thickness equations be substituted for bioimpedance analysis in children? Jornal de Pediatria. 2021 Jan;97(1):75\u0026ndash;9. \u003c/li\u003e\n\u003cli\u003eSilva DRP, Ribeiro AS, Pav\u0026atilde;o FH, Ronque ERV, Avelar A, Silva AM, et al. Validity of the methods to assess body fat in children and adolescents using multi-compartment models as the reference method: a systematic reviewq. REV ASSOC MED BRAS. \u003c/li\u003e\n\u003cli\u003eOliveira Filho JMD, Bernardes PS, Serpa GHC, Siqueira GDDJ, Noll M, Ven\u0026acirc;ncio PEM, et al. BIOELECTRICAL VECTOR ANALYSIS IN OBESE ADOLESCENTS. Rev paul pediatr. 2020;38:e2019017. \u003c/li\u003e\n\u003cli\u003eBrunani A, Perna S, Soranna D, Rondanelli M, Zambon A, Bertoli S, et al. Body composition assessment using bioelectrical impedance analysis (BIA) in a wide cohort of patients affected with mild to severe obesity. Clinical Nutrition. 2021 Jun 1;40(6):3973\u0026ndash;81. \u003c/li\u003e\n\u003cli\u003eLeal AAD, Faintuch J, Morais \u0026Aacute;AC, Noe JAB, Bertollo DM, Morais RC, et al. Bioimpedance analysis: Should it be used in morbid obesity? American Journal of Human Biology. 2011;23(3):420\u0026ndash;2. \u003c/li\u003e\n\u003cli\u003eFreedman DS, Ogden CL, Kit BK. Interrelationships between BMI, skinfold thicknesses, percent body fat, and cardiovascular disease risk factors among U.S. children and adolescents. BMC Pediatr. 2015 Dec;15(1):188. \u003c/li\u003e\n\u003cli\u003eWard LC. Segmental bioelectrical impedance analysis: an update. Current Opinion in Clinical Nutrition \u0026amp; Metabolic Care. 2012 Sep;15(5):424. \u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"bioimpedance analysis, fat mass, fat free mass, children, adolescents, obesity, overweight, resistance, reactance","lastPublishedDoi":"10.21203/rs.3.rs-7143279/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7143279/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground/Objectives\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBioimpedance analysis (BIA) is a non-invasive method for estimating fat-free mass (FFM) and fat mass (FM) using resistance and reactance. Its use in children is controversial, as little is known about the accuracy of the measurements (resistance, reactance, and the resulting FFM and FM).\u003c/p\u003e\u003cp\u003e\u003cb\u003eSubjects/Methods\u003c/b\u003e\u003c/p\u003e\u003cp\u003eWe derived FM and FFM in 2,954 visits from 1,547 children and adolescents between 5 and 21 years of age using BIA measurements and skinfold-based equations. We used Bland-Altman analyses to compare the resulting estimates. Furthermore, we estimated sex- and age-specific percentile curves for height-normalized resistance (Rz/H) and reactance (Xc/H). We used the overall concordance correlation coefficient (OCCC), concordance correlation coefficient (CCC), and intraclass correlation coefficient (ICC) to estimate agreement between the different segments. Bland-Altman analyses were also used to compare Rz/H and Xc/H SDS.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e\u003cp\u003eThe FM estimates from BIA were lower than estimates from the skinfold-based calculations, especially for boys. The estimated FFM and FM showed weight-status-dependent discrepancies between the estimation methods. The ICC showed \"excellent\" (ICC\u0026thinsp;\u0026gt;\u0026thinsp;0.9) agreement between all segmental measurements. The OCCC and CCC showed varying degrees of concordance. The Rz/H and Xc/H percentile curves showed a similar age progression for all body segments. Rz/H (SDS) and Xc/H (SDS) were lower in participants with obesity.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e\u003cp\u003eBoth measures \u0026ndash;- Rz/H and Xc/H \u0026ndash; were highly dependent on age and BMI-SDS. Our results suggest that current estimation techniques may be inadequate for children and adolescents with severe obesity, highlighting the requirement for specialized approaches explicitly designed for this population.\u003c/p\u003e","manuscriptTitle":"Bioimpedance in children and adolescents between 5 and 18 years of age and its association with overweight and obesity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-05 12:25:01","doi":"10.21203/rs.3.rs-7143279/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"revise","date":"2025-09-29T13:37:22+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-09-28T07:51:59+00:00","index":2,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-09-28T07:18:45+00:00","index":2,"fulltext":"This content is not available."},{"type":"editorInvitedReview","content":"This content is not available.","date":"2025-08-26T00:56:52+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewerAgreed","content":"This content is not available.","date":"2025-08-12T01:43:52+00:00","index":1,"fulltext":"This content is not available."},{"type":"reviewersInvited","content":"","date":"2025-07-30T15:26:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-17T13:12:47+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-17T11:51:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"European Journal of Clinical Nutrition","date":"2025-07-16T21:00:17+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"european-journal-of-clinical-nutrition","isNatureJournal":false,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"ejcn","sideBox":"Learn more about [European Journal of Clinical Nutrition](http://www.nature.com/ejcn/)","snPcode":"41430","submissionUrl":"https://mts-ejcn.nature.com/cgi-bin/main.plex","title":"European Journal of Clinical Nutrition","twitterHandle":"@ejcneditor","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"f87edb29-40af-4e9b-a7f1-a5aa8ef5bde7","owner":[],"postedDate":"August 5th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":52384255,"name":"Health sciences/Medical research/Epidemiology"},{"id":52384256,"name":"Physical sciences/Physics/Techniques and instrumentation"},{"id":52384257,"name":"Scientific community and society/Scientific community/Education"}],"tags":[],"updatedAt":"2026-04-18T19:35:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-05 12:25:01","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7143279","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7143279","identity":"rs-7143279","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}