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Clinical Pharmacokinetics

, Volume 44, Issue 10, pp 1051–1065 | Cite as

Quantification of Lean Bodyweight

  • Sarayut Janmahasatian
  • Stephen B. Duffull
  • Susan Ash
  • Leigh C. Ward
  • Nuala M. Byrne
  • Bruce Green
Original Research Article

Abstract

Background: Lean bodyweight (LBW) has been recommended for scaling drug doses. However, the current methods for predicting LBW are inconsistent at extremes of size and could be misleading with respect to interpreting weight-based regimens.

Objective: The objective of the present study was to develop a semi-mechanistic model to predict fat-free mass (FFM) from subject characteristics in a population that includes extremes of size. FFM is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW.

Patients and methods: A total of 373 patients (168 male, 205 female) were included in the study. These data arose from two populations. Population A (index dataset) contained anthropometric characteristics, FFM estimated by dual-energy x-ray absorptiometry (DXA — a reference method) and bioelectrical impedance analysis (BIA) data. Population B (test dataset) contained the same anthropometric measures and FFM data as population A, but excluded BIA data. The patients in population A had a wide range of age (18–82 years), bodyweight (40.7–216.5kg) and BMI values (17.1–69.9 kg/m2). Patients in population B had BMI values of 18.7–38.4 kg/m2. A two-stage semi-mechanistic model to predict FFM was developed from the demographics from population A. For stage 1 a model was developed to predict impedance and for stage 2 a model that incorporated predicted impedance was used to predict FFM. These two models were combined to provide an overall model to predict FFM from patient characteristics. The developed model for FFM was externally evaluated by predicting into population B.

Results: The semi-mechanistic model to predict impedance incorporated sex, height and bodyweight. The developed model provides a good predictor of impedance for both males and females (r2 = 0.78, mean error [ME] = 2.30 × 10−3, root mean square error [RMSE] = 51.56 [approximately 10% of mean]). The final model for FFM incorporated sex, height and bodyweight. The developed model for FFM provided good predictive performance for both males and females (r2 = 0.93, ME = −0.77, RMSE = 3.33 [approximately 6% of mean]). In addition, the model accurately predicted the FFM of subjects in population B (r2 = 0.85, ME = −0.04, RMSE = 4.39 [approximately 7% of mean]).

Conclusions: A semi-mechanistic model has been developed to predict FFM (and therefore LBW) from easily accessible patient characteristics. This model has been prospectively evaluated and shown to have good predictive performance.

Keywords

Root Mean Square Error Predictive Performance Bioelectrical Impedance Analysis Lean Tissue Mass Good Predictive Performance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

We would like to acknowledge Christine Staatz, PhD, for her assistance in conducting the study. S. Janmahasatian was funded by a scholarship from the Australian Agency for International Development (AusAID). The authors have no potential conflicts of interest that are directly relevant to the contents of this study.

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Copyright information

© Adis Data Information BV 2005

Authors and Affiliations

  • Sarayut Janmahasatian
    • 1
  • Stephen B. Duffull
    • 1
    • 2
  • Susan Ash
    • 3
  • Leigh C. Ward
    • 4
  • Nuala M. Byrne
    • 5
  • Bruce Green
    • 1
    • 2
  1. 1.School of PharmacyUniversity of QueenslandSt LuciaAustralia
  2. 2.Center for Drug Development ScienceUniversity of California San FranciscoUSA
  3. 3.School of Public HealthQueensland University of TechnologyBrisbaneAustralia
  4. 4.Department of Biochemistry and Molecular BiologyUniversity of QueenslandBrisbaneAustralia
  5. 5.School of Human Movement StudiesQueensland University of TechnologyBrisbaneAustralia

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