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Innovative Techniques
Abbreviations: BN, Bayesian network; CHU, Clermont-Ferrand University Hospital Centre; DXA, dual-energy X-ray absorptiometry; FFM, fat-free mass; FM, fat mass; FM%, percentage of fat mass; NHANES, National Health and Nutrition Examination Survey; SEP, standard error of prediction
The relative contribution of fat-free mass (FFM) and fat mass (FM) to body weight is a relevant indicator for major public health issues(1,2). Due to the accumulation of excess fat tissue, the worldwide increase in the prevalence of obesity contributes to a high risk of metabolic disorders such as CVD and type 2 diabetes(3). In addition, FFM loss in ageing populations and its progression towards sarcopenia increase morbidity and mortality(4-6). Accurate measurements of body composition can be obtained from reference methods, such as underwater weighing, dilution techniques and dual-energy X-ray absorptiometry (DXA). Although the use of such methods is widespread, their application is time consuming and expensive, and as a result, they are not relevant for use as a part of routine clinical examinations or population studies. Bioelectrical impedance has often been considered to be a convenient tool for body composition analysis. However, the recorded bioelectrical values (resistance and reactance) must be used in equations that are body shape specific, and accurate FFM and FM assessments require adjustments with a gold standard method(7-10).
In contrast, simple anthropological measurements, such as body weight or BMI, cannot give a reliable quantification of body composition, although they are of predictive interest at a population level(3,11-13). Models that provide reliable predictions of body composition from very simple covariables are still needed. Because increasingly more information on body composition analysis is available, either directly from large surveys (e.g. National Health and Nutrition Examination Survey; NHANES) or indirectly through scientific publications, we investigated the potential of a non-parametric model derived from a Bayesian network (BN) to predict body composition. A BN provides a global joint probability distribution over a set of random variables(14,15). The ability to combine variables and the growing capabilities of computer calculations have made these models increasingly popular....