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Prediction of Fat-Free Mass in Children

Abstract

Background

Fat-free mass (FFM) is an important covariate for predicting drug clearance. Models for predicting FFM have been developed in adults but there is currently a paucity of mechanism-based models developed to predict FFM in children.

Objective

The aim of this study was to develop and evaluate a model to predict FFM in children.

Methods

A large dataset (496 females and 515 males) was available for model building. Subjects had a relatively wide range of age (3–29 years) and body mass index values (12–44.9 kg/m2). Two types of models (M1 and M2) were developed to describe FFM in children. M1 was fully empirical and based on a linear model that contained all statistically significant covariates and their interactions. M2 was a simpler model that incorporated a maturation process. M1 was developed to provide the best possible description of the data (i.e. a positive control). In addition, a published adult model (M3) was applied directly as a reference description of the data. The predictive performances of the three models were assessed by visual predictive checks and by using mean error (ME) and root mean squared error (RMSE). A test dataset (90 females and 86 males) was available for external evaluation.

Results

M1 consisted of nine terms with up to second-level interactions. M2 was a sigmoid hyperbolic model based on postnatal age with an asymptote at the adult prediction (M3). For the index dataset, the ME and 95 % CI for M1, M2 and M3 were 0.09 (0.03–0.16), 0.24 (0.14–0.33) and 0.29 (0.06–0.51) kg, respectively, and RMSEs were 1.12 (1.03–1.23), 1.58 (1.46–1.72) and 3.76 (3.54–3.97) kg.

Conclusions

A maturation model that asymptoted to an established adult model was developed for prediction of FFM in children. This model was found to perform well in both male and female children; however, the adult model performed similarly to the maturation model for females. The ability to predict FFM in children from simple demographic measurements is expected to improve understanding of human body structure and function with direct application to pharmacokinetics.

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References

  1. Shen W, St-Onge M, Wang Z, Heymsfield SB. Study of body composition: an overview. In: Heymsfield SB, Lohman TG, Wang Z, Going SB, editors. Human body composition. 2nd ed. Champaign: Human Kinetics; 2005.

    Google Scholar 

  2. Green B, Duffull SB. What is the best size descriptor to use for pharmacokinetic studies in the obese? Br J Clin Pharmacol. 2004;58(2):119–33.

    PubMed Central  Article  PubMed  Google Scholar 

  3. Han PY, Duffull SB, Kirkpatrick CM, Green B. Dosing in obesity: a simple solution to a big problem. Clin Pharmacol Ther. 2007;82(5):505–8.

    CAS  Article  PubMed  Google Scholar 

  4. Cortinez LI, Anderson BJ, Penna A, Olivares L, Munoz HR, Holford NH, et al. Influence of obesity on propofol pharmacokinetics: derivation of a pharmacokinetic model. Br J Anaesth. 2010;105(4):448–56.

    CAS  Article  PubMed  Google Scholar 

  5. Roubenoff R, Kehayias JJ. The meaning and measurement of lean body mass. Nutr Rev. 1991;49(6):163–75.

    CAS  Article  PubMed  Google Scholar 

  6. Moore FD. Energy and the maintenance of the body cell mass. JPEN J Parenter Enteral Nutr. 1980;4(3):228–60.

    CAS  Article  PubMed  Google Scholar 

  7. Janmahasatian S, Duffull SB, Ash S, Ward LC, Byrne NM, Green B. Quantification of lean bodyweight. Clin Pharmacokinet. 2005;44(10):1051–65.

    Article  PubMed  Google Scholar 

  8. Lohman TG. Applicability of body composition techniques and constants for children and youths. Exerc Sport Sci Rev. 1986;14:325–57.

    CAS  Article  PubMed  Google Scholar 

  9. Cronk CE, Roche AF, Kent R, Berkey C, Reed RB, Valadian I, et al. Longitudinal trends and continuity in weight/stature from 3 months to 18 years. Hum Biol. 1982;54(4):729–49.

    CAS  PubMed  Google Scholar 

  10. Rhodin MM, Anderson BJ, Peters AM, Coulthard MG, Wilkins B, Cole M, et al. Human renal function maturation: a quantitative description using weight and postmenstrual age. Pediatr Nephrol. 2009;24(1):67–76.

    Article  PubMed  Google Scholar 

  11. Ejlerskov KT, Jensen SM, Christensen LB, Ritz C, Michaelsen KF, Mølgaard C. Prediction of fat-free body mass from bioelectrical impedance and anthropometry among 3-year-old children using DXA. Sci Rep. 2014;4:3889.

    PubMed Central  Article  PubMed  Google Scholar 

  12. Nielsen BM, Dencker M, Ward L, Linden C, Thorsson O, Karlsson MK, et al. Prediction of fat-free body mass from bioelectrical impedance among 9- to 11-year-old Swedish children. Diabetes Obes Metab. 2007;9(4):521–39.

    CAS  Article  PubMed  Google Scholar 

  13. Foster BJ, Platt RW, Zemel BS. Development and validation of a predictive equation for lean body mass in children and adolescents. Ann Hum Biol. 2012;39(3):171–82.

    Article  PubMed  Google Scholar 

  14. Anderson BJ, Holford NHG. Mechanistic basis of using body size and maturation to predict clearance in humans. Drug Metab Pharmacokinet. 2009;24(1):25–36.

    CAS  Article  PubMed  Google Scholar 

  15. Crawford JD, Terry ME, Rourke GM. Simplification of drug dosage calculation by application of the surface area principle. Pediatrics. 1950;5(5):783–90.

    CAS  PubMed  Google Scholar 

  16. Anderson BJ, Holford NH. Mechanism-based concepts of size and maturity in pharmacokinetics. Annu Rev Pharmacol Toxicol. 2008;48:303–32.

    CAS  Article  PubMed  Google Scholar 

  17. Bachrach LK. Dual energy X-ray absorptiometry (DEXA) measurements of bone density and body composition: promise and pitfalls. J Pediatr Endocrinol Metab. 2000;13(Suppl 2):983–8.

    PubMed  Google Scholar 

  18. Mattsson S, Thomas BJ. Development of methods for body composition studies. Phys Med Biol. 2006;51(13):R203–28.

    CAS  Article  PubMed  Google Scholar 

  19. Pietrobelli A, Peroni DG, Faith MS. Pediatric body composition in clinical studies: which methods in which situations? Acta Diabetol. 2003;40(Suppl 1):S270–3.

    Article  PubMed  Google Scholar 

  20. Lohman TG, Chen Z. Dual-energy x-ray absorptiometry. In: Heymsfield SB, Lohman TG, Wang Z, Going SB, editors. Human body composition. 2nd ed. Champaign: Human Kinetics; 2005.

    Google Scholar 

  21. Taylor RW, Grant AM, Williams SM, Goulding A. Sex differences in regional body fat distribution from pre- to postpuberty. Obesity (Silver Spring). 2010;18(7):1410–6.

    Article  PubMed  Google Scholar 

  22. Beal S, Sheiner LB, Boeckmann A, Bauer RJ. NONMEM user’s guides (1989–2011). Ellicott City: Icon Development Solutions; 2011.

    Google Scholar 

  23. Holford NH. Wings for NONMEM Version 720 for NONMEM 7.2; 2011. Available at: http://wfn.sourceforge.net.

  24. Holford NH. The visual predictive check: superiority to standard diagnostic (Rorschach) plots [abstract no. 738]. In: Population Approach Group in Europe (PAGE) 14th meeting. Pamplona; 2005.

  25. Sheiner LB, Beal SL. Some suggestions for measuring predictive performance. J Pharmacokinet Biopharm. 1981;9(4):503–12.

    CAS  Article  PubMed  Google Scholar 

  26. Tanner JM. Growth at adolescence. 2nd ed. Oxford: Blackwell Scientific Publications; 1962.

    Google Scholar 

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Acknowledgments

No external funds were used in the conduct of this study. None of the authors have any potential conflict of interest that might relevant to the contents of this manuscript.

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Correspondence to Hesham Saleh Al-Sallami.

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Al-Sallami, H.S., Goulding, A., Grant, A. et al. Prediction of Fat-Free Mass in Children. Clin Pharmacokinet 54, 1169–1178 (2015). https://doi.org/10.1007/s40262-015-0277-z

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  • DOI: https://doi.org/10.1007/s40262-015-0277-z

Keywords

  • Root Mean Square Error
  • Test Dataset
  • External Evaluation
  • Objective Function Value
  • Visual Predictive Check