title: Fat-free mass predictions through a Bayesian Network enable body composition comparisons in various populations.
abstract: The respective contribution of fat-free mass (FFM) and fat mass to body weight (Wgt) is a relevant indicator of risk for major public health issues. In an earlier study, a Bayesian Network (BN) was designed to predict FFM from a DXA database (1999-2004 NHANES, n = 10,402) with easily accessible variables [sex, age, Wgt, and height (Hgt)]. The objective of the present study was to assess the robustness of these BN predictions in different population contexts (age, BMI, ethnicity, etc.) when covariables were stochastically deduced from population-based distributions. BN covariables were adjusted to 82 published distributions for age, Wgt, and Hgt from 16 studies assessing body composition. Anthropometric adjustments required a surrogate database (n = 23,411) to get the missing correlation between published Wgt and Hgt distributions. Published BMI distributions and their predicted BN counterparts were correlated (R(2) = 0.99; P < 0.001). Predicted FFM distributions were closely adjusted to their published counterparts for both sexes between 20 and 79 y old, with some discrepancies for Asian populations. In addition, BN predictions revealed a very good agreement between FFM assessed in different population contexts. The mean difference between published FFM values (61.1 ± 3.44 and 42.7 ± 3.32 kg for men and women, respectively) and BN predictions (61.6 ± 3.11 and 42.4 ± 2.76 kg for men and women, respectively) was <1% when FFM was assessed by DXA; the difference rose to 3.6% when FFM was assessed by bioelectric impedance analysis or by densitometry methods. These results suggest that it is possible, within certain anthropometric limitations, to use BN predictions as a complementary body composition analysis for large populations.
authors.affiliation: Centre d'Examen de Sante Bretagne Ouest, 22000 Saint Brieuc, France. laurence.mioche@clermont.inra.fr
authors.forename: Laurence
authors.initials: L
authors.lastname: Mioche
authors.forename: Alain
authors.initials: A
authors.lastname: Brigand
authors.forename: Caroline
authors.initials: C
authors.lastname: Bidot
authors.forename: Jean-Baptiste
authors.initials: JB
authors.lastname: Denis
mesh_terms: D015502:Absorptiometry, Photon; D000273:Adipose Tissue; D001499:Bayes Theorem; D001823:Body Composition; D006801:Humans