Body composition predicted with a Bayesian network from simple variables.

The relative contributions of fat-free mass (FFM) and fat mass (FM) to body weight are key indicators for several major public health issues. Predictive models could offer new insights into body composition analysis. A non-parametric equation derived from a probabilistic Bayesian network (BN) was established by including sex, age, body weight and height. We hypothesised that it would be possible to assess the body composition of any subject from easily accessible covariables by selecting an adjusted FFM value within a reference dual-energy X-ray absorptiometry (DXA) measurement database (1999-2004 National Health and Nutrition Examination Survey (NHANES), n 10 402). FM was directly calculated as body weight minus FFM. A French DXA database (n 1140) was used (1) to adjust the model parameters (n 380) and (2) to cross-validate the model responses (n 760). French subjects were significantly different from American NHANES subjects with respect to age, weight and FM. Despite this different population context, BN prediction was highly reliable. Correlations between BN predictions and DXA measurements were significant for FFM (R2 0·94, P < 0·001, standard error of prediction (SEP) 2·82 kg) and the percentage of FM (FM%) (R2 0·81, P < 0·001, SEP 3·73 %). Two previously published linear models were applied to the subjects of the French database and compared with BN predictions. BN predictions were more accurate for both FFM and FM than those obtained from linear models. In addition, BN prediction generated stochastic variability in the FM% expressed in terms of BMI. The use of such predictions in large populations could be of interest for many public health issues.