Comparison of the accuracy of resting energy expenditure assessment using bioimpedance analysis and indirect respiratory calorimetry in children with simple obesity

Background: Assessment of resting energy expenditure (REE) is necessary for the formation of a diet for obesity patients. The «gold standard» for assessment of resting energy expenditure (REE) is indirect respiratory calorimetry. Currently, bioimpedance analyzers are increasingly being used in clinical practice to assess energy consumption at rest, including in obese children. However, the accuracy of such an assessment remains unclear.

Aims: To determine the accuracy of the assessment of resting energy expenditure using bioimpedance analysis in children with simple obesity compared with indirect respiratory calorimetry.

Materials and methods: Resting energy expenditure was assessed by bioimpedance analysis, Harris-Benedict formula and indirect respiratory calorimetry in all obese children. Comparability of methods was assessed using the Bland-Altman analysis.

Results: The study included 320 children aged 7 to 17 years with simple obesity.Resting energy expenditure assessed by bioimpedance analysis was on average 232 kcal lower than the actual. A significant CI (-448 to 912 kcal) was revealed, as well as a large LOA from -514 to 979 kcal. REE calculated by the Harris-Benedict formula on average corresponded to the actual one, and CI varied from -38 to 27 kcal. However, large LOA from -514 to 979 kcal, indicating a high individual variability of resting energy consumption.

Conclusions: Bioimpedance analyzers underestimate REE in obese children compared to indirect respiratory calorimetry and the Harris-Benedict formula. Given the significant discrepancies in the accuracy of REE assessment, bioimpedance analysis cannot be considered as an alternative to indirect respiratory calorimetry to assess resting energy in children with simple obesity.

1. Seagle HM, Strain GW, Makris A, Reeves RS; American Dietetic Association. Position of the American Dietetic Association: weight management. J Am Diet Assoc. 2009 Feb;109(2):330-46. doi: https://doi.org/10.1016/j.jada.2008.11.041.

2. Fullmer S, Benson-Davies S, Earthman CP, et al. Evidence analysis library review of best practices for performing indirect calorimetry in healthy and non-critically ill individuals. J AcadNutr Diet. 2015;115(9):1417-1446.e2. doi: https://doi.org/10.1016/j.jand.2015.04.003.

3. Marra M, Cioffi I, Sammarco R, et al. Are Raw BIA Variables Useful for Predicting Resting Energy Expenditure in Adults with Obesity? Nutrients. 2019;11(2):216. doi: https://doi.org/10.3390/nu11020216.

4. Bland JM, Altman DG. Measuring agreement in method comparison studies. Stat Methods Med Res. 1999;8(2):135-160. doi: https://doi.org/10.1177/096228029900800204

5. Johnstone AM, Murison SD, Duncan JS, et al. Factors influencing variation in basal metabolic rate include fat-free mass, fat mass, age, and circulating thyroxine but not sex, circulating leptin, or triiodothyronine. Am J Clin Nutr. 2005;82(5):941-948. doi: https://doi.org/10.1093/ajcn/82.5.941

6. Okorokov PL, Vasyukova OV, Shiryaeva TY. Resting metabolic rate and factors of its variability in adolescents with obesity. Vopr Det Dietol. 2019;17(3):5-9. (In Russ.). doi: https://doi.org/10.20953/1727-5784-2019-3-5-9

7. Vybornaia KV, Sokolov AI, Kobel’kova IV, et al. Basal metabolic rate as an integral indicator of metabolism intensity. Vopr Det Dietol. 2017;86(5):5-10. (In Russ.). doi: https://doi.org/10.24411/0042-8833-2017-00069

8. Bedogni G, Bertoli S, De Amicis R, et al. External Validation of Equations to Estimate Resting Energy Expenditure in 2037 Children and Adolescents with and 389 without Obesity: A Cross-Sectional Study. Nutrients. 2020;12(5):1421. doi: https://doi.org/10.3390/nu12051421

9. Okorokov PL, Vasyukova OV. Features of body composition and basal metabolic rate in adolescents with morbid obesity. Pediatria n.a. G.N. Speransky. 2021;100(4):216-221. (In Russ.).

10. Olafsdottir AS, Torfadottir JE, Arngrimsson SA. Health Behavior and Metabolic Risk Factors Associated with Normal Weight Obesity in Adolescents. PLoS One. 2016;11(8):e0161451. doi: https://doi.org/10.1371/journal.pone.0161451

11. Wiklund P, Törmäkangas T, Shi Y, et al. Normal-weight obesity and cardiometabolic risk: A 7-year longitudinal study in girls from prepuberty to early adulthood. Obesity. 2017;25(6):1077-1082. doi: https://doi.org/10.1002/oby.21838

12. Cameron JD, Sigal RJ, Kenny GP, et al. Body composition and energy intake — skeletal muscle mass is the strongest predictor of food intake in obese adolescents: The HEARTY trial. Appl Physiol Nutr Metab. 2016;41(6):611-617. doi: https://doi.org/10.1139/apnm-2015-0479