A semi-mechanistic model for quantification of lean body weight

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Introduction: Lean body weight (LBW) has been recommended to scale drug dose. The current estimate of LBW [1] however inconsistent at extremes of size [2] and could be misleading with respect to interpreting weight-based regimen.Aim: To develop a semi-mechanistic model to predict Fat Free Mass (FFM) from subject characteristics in a population that includes extremes of size. Fat free mass (FFM) is considered to closely approximate LBW. There are several reference methods for assessing FFM, whereas there are no reference standards for LBW.Methods: A total of 373 patients (168 men, 205 women) were available for study. These data arose from two data sets. Data set A [index data set] contained anthropometric characteristics, fat-free mass (FFM) estimated by dual-energy X-ray absorptiometry (DXA - a reference method) and bioelectrical impedance analysis (BIA) data. Data set B [test data set] contained the same anthropometric measures and FFM data as data set A, but excluded BIA data. The patients in data set A had a wide range of age (18-82 years), weights (41 – 196 kg) and BMI values (17.1-69.9 kg/m2). Patients in data set B had BMI values of 18.7-38.4 kg/m2. A two stage semi-mechanistic model to FFM was developed from the demographics from data set A. For stage one, a model was developed to predict impedance (Z). For stage two, 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 data set B.Results: The semi-mechanistic model to predict Z incorporated sex, height and weight. The developed models provide a good predictor of the impedance for both males and females (r2 = 0.78, ME = 2.30 × 10-3, RMSE = 51.56 [~ 10% of mean]). The final model for FFM incorporated sex, height and weight, where sex and BMI were the basis for prediction of Z and height was additionally required to predict FFM from Z. The developed model for FFM provided a good predictive performance for both males and females (r2 = 0.93, ME = -0.77, RMSE = 3.33 [~ 6% of mean]). In addition, the model predicted accurately the FFM of subjects in data set B (r2 = 0.85, ME = -0.04, RMSE = 4.39 [~ 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.

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