Skip to main content
SpringerLink
Log in

Evaluating the predictive factors of resting energy expenditure and validating predictive equations for Chinese obese children

  • Original Article
  • Published:
World Journal of Pediatrics Aims and scope Submit manuscript

Abstract

Background

To study the predictive factors of resting energy expenditure (REE) and evaluate the accuracy of predicted equations with indirect calorimeter (IC) in Chinese school-age children, particularly for the obese population.

Methods

Recruited children were from the department of child healthcare in Nanjing children’s hospital during July 2014–September 2015. Anthropometric parameters and body composition were measured by bioelectrical impedance. Measured REE was assessed by IC. Predicted REE was estimated using ten published equations.

Results

248 children aged 7–13 years were recruited, including 148 obese [body mass index standard deviation score (BMISDS) = 2.48 ± 0.91] and 100 non-obese (BMISDS = − 0.96 ± 1.08). The unit mass of REE (REE/kg) in obese group (29.06 ± 5.74) was lower than that in non-obese group (37.51 ± 6.56). The stepwise regression showed that age, BMISDS and fat-free mass (FFM) had a major impact on REE/kg as the regression equation: Y = 54.41 − 1.36 × X1 − 2.25 × X2 − 0.16 × X3 (Y REE/kg, X1 age, X2 BMISDS, X3 FFM; R = 0.633, R2 = 0.401, P < 0.01). The accuracy of predicted REE in obese subjects was 62.16% by the new predictive equations.

Conclusions

The REE/kg in obese children was lower and closely correlated with age, BMISDS and FFM. It is necessary to validate the new predictive equation in a larger sample to estimate energy requirements, particularly for children with obesity.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1

Similar content being viewed by others

References

  1. Song Y, Wang HJ, Ma J, Wang Z. Secular trends of obesity prevalence in urban Chinese children from 1985 to 2010: gender disparity. PLoS ONE. 2013;8:e53069.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Lu X, Shi P, Luo CY, Zhou YF, Yu HT, Guo CY, et al. Prevalence of hypertension in overweight and obese children from a large school-based population in Shanghai, China. BMC Public Health. 2013;13:24.

    Article  PubMed  PubMed Central  Google Scholar 

  3. Kantomaa MT, Stamatakis E, Kankaanpaa A, Kaakinen M, Rodriguez A, Taanila A, et al. Physical activity and obesity mediate the association between childhood motor function and adolescents’ academic achievement. Proc Natl Acad Sci U S A. 2013;110:1917–22.

    Article  CAS  PubMed  Google Scholar 

  4. He F, Liu J. Prevalence of obesity among primary students from 2009 to 2014 in China: an update meta-analysis. Int J Clin Exp Med. 2014;7:5348–52.

    PubMed  PubMed Central  Google Scholar 

  5. Hodson L, Harnden K, Banerjee R, Real B, Marinou K, Karpe F, et al. Lower resting and total energy expenditure in postmenopausal compared with premenopausal women matched for abdominal obesity. J Nutr Sci. 2014;3:e3.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Liang Y, Hou D, Zhao X, Wang L, Hu Y, Liu J, et al. Childhood obesity affects adult metabolic syndrome and diabetes. Endocrine. 2015;50:87–92.

    Article  CAS  PubMed  Google Scholar 

  7. Hopkins M, Blundell JE. Energy balance, body composition, sedentariness and appetite regulation: pathways to obesity. Clin Sci (Lond). 2016;130:1615–28.

    Article  CAS  PubMed  Google Scholar 

  8. Carpenter A, Pencharz P, Mouzaki M. Accurate estimation of energy requirements of young patients. J Pediatr Gastroenterol Nutr. 2015;60:4–10.

    Article  CAS  PubMed  Google Scholar 

  9. Molnar D, Jeges S, Erhardt E, Schutz Y. Measured and predicted resting metabolic rate in obese and nonobese adolescents. J Pediatr. 1995;127:571–7.

    Article  CAS  PubMed  Google Scholar 

  10. Owen OE, Kavle E, Owen RS, Polansky M, Caprio S, Mozzoli MA, et al. A reappraisal of caloric requirements in healthy women. Am J Clin Nutr. 1986;44:1–19.

    Article  CAS  PubMed  Google Scholar 

  11. Marra M, Montagnese C, Sammarco R, Amato V, Della VE, Franzese A, et al. Accuracy of predictive equations for estimating resting energy expenditure in obese adolescents. J Pediatr. 2015;166:1390–6.e1.

  12. Henes ST, Cummings DM, Hickner RC, Houmard JA, Kolasa KM, Lazorick S, et al. Comparison of predictive equations and measured resting energy expenditure among obese youth attending a pediatric healthy weight clinic: one size does not fit all. Nutr Clin Pract. 2013;28:617–24.

    Article  PubMed  Google Scholar 

  13. Schusdziarra V, Wolfschlager K, Hausmann M, Wagenpfeil S, Erdmann J. Accuracy of resting energy expenditure calculations in unselected overweight and obese patients. Ann Nutr Metab. 2014;65:299–309.

    Article  CAS  PubMed  Google Scholar 

  14. Uemura A, Mexitalia M, Susanto JC, Yamauchi T. Validating predictive factors for resting energy expenditure of adolescents in Indonesia. Int J Food Sci Nutr. 2012;63:145–52.

    Article  PubMed  Google Scholar 

  15. Cameron JD, Sigal RJ, Kenny GP, Alberga AS, Prud’Homme D, Phillips P, 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;41:611–7.

    Article  CAS  PubMed  Google Scholar 

  16. Ahmadi S, Mirzaei K, Hosseinnezhad A, Keshavarz SA, Ahmadivand Z. Resting energy expenditure may predict the relationship between obesity and susceptibility to depression disorders. Minerva Med. 2013;104:207–13.

    CAS  PubMed  Google Scholar 

  17. Marković-Jovanović SR, Stolić RV, Jovanović AN. The reliability of body mass index in the diagnosis of obesity and metabolic risk in children. J Pediatr Endocrinol Metab. 2016;28:515–23.

    Google Scholar 

  18. Woo P, Murthy G, Wong C, Hursh B, Chanoine JP, Elango R. Assessing resting energy expenditure in overweight and obese adolescents in a clinical setting: validity of a handheld indirect calorimeter. Pediatr Res. 2017;81:51–6.

    Article  PubMed  Google Scholar 

  19. Dummler R, Zittermann A, Schafer M, Emmerich M. Postoperative assessment of daily energy expenditure. Comparison of two methods. Anaesthesist. 2013;62:20–6 (in German).

  20. Harris JA, Benedict FG. A biometric study of human basal metabolism. Proc Natl Acad Sci U S A. 1918;4:370–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Schofield WN. Predicting basal metabolic rate, new standards and review of previous work. Hum Nutr Clin Nutr. 1985;39(Suppl 1):5–41.

    PubMed  Google Scholar 

  22. Energy and protein requirements. Report of a joint FAO/WHO/UNU Expert Consultation. World Health Organ Tech Rep Ser. 1985;724:1–206.

    Google Scholar 

  23. Mifflin MD, St Jeor ST, Hill LA, Scott BJ, Daugherty SA, Koh YO. A new predictive equation for resting energy expenditure in healthy individuals. Am J Clin Nutr. 1990;51:241–7.

    Article  CAS  PubMed  Google Scholar 

  24. ten Haaf T, Weijs PJ. Resting energy expenditure prediction in recreational athletes of 18–35 years: confirmation of Cunningham equation and an improved weight-based alternative. PLoS ONE. 2014;9:e108460.

    Article  PubMed  PubMed Central  Google Scholar 

  25. Liu B, Woo J, Tang N, Ng K, Ip R, Yu A. Assessment of total energy expenditure in a Chinese population by a physical activity questionnaire: examination of validity. Int J Food Sci Nutr. 2001;52:269–82.

    Article  CAS  PubMed  Google Scholar 

  26. Jia H, Meng Q, Shan C. Study on energy expenditure in healthy adults. Chin J Clin Nutr. 1999;7:70–3.

    Google Scholar 

  27. Trumbo P, Schlicker S, Yates AA, Poos M, Food and Nutrition Board of the Institute of Medicine, The National Academies. Dietary reference intakes for energy, carbohydrate, fiber, fat, fatty acids, cholesterol, protein and amino acids. J Am Diet Assoc. 2002;102:1621–30.

    Article  PubMed  Google Scholar 

  28. Rao ZY, Wu XT, Liang BM, Wang MY, Hu W. Comparison of five equations for estimating resting energy expenditure in Chinese young, normal weight healthy adults. Eur J Med Res. 2012;17:26.

    Article  PubMed  PubMed Central  Google Scholar 

  29. Rosales-Velderrain A, Goldberg RF, Ames GE, Stone RL, Lynch SA, Bowers SP. Hypometabolizers: characteristics of obese patients with abnormally low resting energy expenditure. Am Surg. 2014;80:290–4.

    PubMed  Google Scholar 

  30. Jia WP, Yang M, Shao XY, Bao YQ, Lu JX, Xiang KS. Resting energy expenditure and its relationship with patterns of obesity and visceral fat area in Chinese adults. Biomed Environ Sci. 2005;18:103–7.

    PubMed  Google Scholar 

  31. Rodríguez G, Moreno LA, Sarría A, Pineda I, Fleta J, Pérez-González JM, et al. Determinants of resting energy expenditure in obese and non-obese children and adolescents. J Physiol Biochem. 2002;58:9–16.

    Article  PubMed  Google Scholar 

  32. Siervo M, Oggioni C, Lara J, Celismorales C, Mathers JC, Battezzati A, et al. Age-related changes in resting energy expenditure in normal weight, overweight and obese men and women. Maturitas. 2015;80:406–13.

    Article  PubMed  Google Scholar 

  33. Geisler C, Muller MJ. Impact of Fat-Free Mass Quality and Detailed Body Composition on Changes of Resting Energy Expenditure with Age. Current Nurition Reports. 2017;6(2):111–21.

    Article  Google Scholar 

  34. Browning MG, Evans RK. The contribution of fat-free mass to resting energy expenditure: implications for weight loss strategies in the treatment of adolescent obesity. Int J Adolesc Med Health. 2015;27:241–6.

    Article  PubMed  Google Scholar 

  35. Hofsteenge GH, Chinapaw MJ, Delemarre-van de Waal HA, Weijs PJ. Validation of predictive equations for resting energy expenditure in obese adolescents. Am J Clin Nutr. 2010;91:1244–54.

    Article  CAS  PubMed  Google Scholar 

  36. de Oliveira EP, Orsatti FL, Teixeira O, Maesta N, Burini RC. Comparison of predictive equations for resting energy expenditure in overweight and obese adults. J Obes. 2011;2011:534714.

    Article  PubMed  PubMed Central  Google Scholar 

  37. de Oliveira FC, Alves RD, Zuconi CP, Ribeiro AQ, Bressan J. Agreement between different methods and predictive equations for resting energy expenditure in overweight and obese Brazilian men. J Acad Nutr Diet. 2012;112:1415–20.

    Article  PubMed  Google Scholar 

  38. Nightingale CM, Rudnicka AR, Owen CG, Donin AS, Newton SL, Furness CA, et al. Are ethnic and gender specific equations needed to derive fat free mass from bioelectrical impedance in children of South Asian, black African-Caribbean and white European origin? Results of the assessment of body composition in children study. PLoS ONE. 2013;8:e76426.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Henry CJ. Basal metabolic rate studies in humans: measurement and development of new equations. Public Health Nutr. 2005;8:1133–52.

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

The authors gratefully acknowledge the contributions of the doctors and nurses of the Nanjing Children Hospital and the participation of the adolescents and their parents.

Funding

This work was supported by grants from Jiangsu Province Research Project (BE2015607) and National Natural Science Foundation (81273064).

Author information

Authors and Affiliations

  1. Department of Children Health Care, Chilren’s hospital affiliated with Nanjing medical university, 72 Guangzhou Road, Nanjing, 210008, China

    Lin Zhang, Ran Chen, Rong Li, Meng-Ying Chen, Rong Huang & Xiao-Nan Li

Authors
  1. Lin Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Ran Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Meng-Ying Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiao-Nan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Li XN designed the research, collected clinical data, and wrote the manuscript. Zhang L collected clinical data, analyzed the data, and wrote the manuscript. Chen R, Li R, Chen MY and Huang R collected clinical data. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Xiao-Nan Li.

Ethics declarations

Ethical approval

The study “Evaluating the predictive factors of resting energy expenditure and validating predictive equations for Chinese obese children” was approved by the Research Ethics Committee of Children’s Hospital of Nanjing Medical University, China (No.201412004-1) and conducted according to the principles in the Declaration of Helsinki and its later amendments or comparable ethical clinical standards. All of the enrolled subjects and their parents were fully informed about the procedure and the purpose of this study and written consent was obtained from the parents of all children before inclusion.

Conflict of interest

No competing financial interests exist.

Informed consent

Additional informed consent was obtained from all individual participants for whom identifying information is included in this article.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, L., Chen, R., Li, R. et al. Evaluating the predictive factors of resting energy expenditure and validating predictive equations for Chinese obese children. World J Pediatr 14, 160–167 (2018). https://doi.org/10.1007/s12519-017-0111-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12519-017-0111-9

Keywords

Search

Navigation