Analysis of lifestyle and metabolic predictors of visceral obesity with Bayesian Networks

BMC Bioinformatics

Research Article

André Tchernof¹  Sophie Rome²  Alex Aussem³  Sérgio Rodrigues de Morais³ 

[1] Endocrinology and Genomics, Laval University Medical Center and Department of Nutrition, Laval University, Quebec, Canada;RMND INSERM U870; INRA 1235, University of Lyon 1, 69622, Villeurbanne, France;University of Lyon, F-69000, Lyon, France;LIESP Laboratory, University of Lyon 1, 69622, Villeurbanne, France;

关键词: Bayesian Network;    Directed Acyclic Graph;    Visceral Obesity;    Bayesian Network Structure;    Markov Blanket;   

DOI  :  10.1186/1471-2105-11-487

 received in 2009-10-15, accepted in 2010-09-28,  发布年份 2010

来源: Springer

【 摘 要 】

BackgroundThe aim of this study was to provide a framework for the analysis of visceral obesity and its determinants in women, where complex inter-relationships are observed among lifestyle, nutritional and metabolic predictors. Thirty-four predictors related to lifestyle, adiposity, body fat distribution, blood lipids and adipocyte sizes have been considered as potential correlates of visceral obesity in women. To properly address the difficulties in managing such interactions given our limited sample of 150 women, bootstrapped Bayesian networks were constructed based on novel constraint-based learning methods that appeared recently in the statistical learning community. Statistical significance of edge strengths was evaluated and the less reliable edges were pruned to increase the network robustness. To allow accessible interpretation and integrate biological knowledge into the final network, several undirected edges were afterwards directed with physiological expertise according to relevant literature.ResultsExtensive experiments on synthetic data sampled from a known Bayesian network show that the algorithm, called Recursive Hybrid Parents and Children (RHPC), outperforms state-of-the-art algorithms that appeared in the recent literature. Regarding biological plausibility, we found that the inference results obtained with the proposed method were in excellent agreement with biological knowledge. For example, these analyses indicated that visceral adipose tissue accumulation is strongly related to blood lipid alterations independent of overall obesity level.ConclusionsBayesian Networks are a useful tool for investigating and summarizing evidence when complex relationships exist among predictors, in particular, as in the case of multifactorial conditions like visceral obesity, when there is a concurrent incidence for several variables, interacting in a complex manner. The source code and the data sets used for the empirical tests are available at http://www710.univ-lyon1.fr/~aaussem/Software.html.

【 授权许可】

CC BY   
© Aussem et al; licensee BioMed Central Ltd. 2010