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Open Access Research article

A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context

Ioannis K Valavanis1*, Stavroula G Mougiakakou123, Keith A Grimaldi4 and Konstantina S Nikita1

Author Affiliations

1 School of Electrical and Computer Engineering, National Technical University of Athens, 9 Iroon Polytechniou Str., 15780, Zografos, Athens, Greece

2 University of Bern, Medical School, ARTORG Center for Biomedical Engineering Research, Stauffacherstrasse 78, 3014 Bern, Switzerland

3 University Hospital - Inselspital - University of Bern, Division of Endocrinology, Diabetes and Clinical Nutrition, 3010 Bern, Switzerland

4 Sciona, Inc., 12635 E. Montview Blvd. #217, Aurora, CO 80045-7337, USA

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BMC Bioinformatics 2010, 11:453  doi:10.1186/1471-2105-11-453

Published: 8 September 2010

Abstract

Background

Obesity is a multifactorial trait, which comprises an independent risk factor for cardiovascular disease (CVD). The aim of the current work is to study the complex etiology beneath obesity and identify genetic variations and/or factors related to nutrition that contribute to its variability. To this end, a set of more than 2300 white subjects who participated in a nutrigenetics study was used. For each subject a total of 63 factors describing genetic variants related to CVD (24 in total), gender, and nutrition (38 in total), e.g. average daily intake in calories and cholesterol, were measured. Each subject was categorized according to body mass index (BMI) as normal (BMI ≤ 25) or overweight (BMI > 25). Two artificial neural network (ANN) based methods were designed and used towards the analysis of the available data. These corresponded to i) a multi-layer feed-forward ANN combined with a parameter decreasing method (PDM-ANN), and ii) a multi-layer feed-forward ANN trained by a hybrid method (GA-ANN) which combines genetic algorithms and the popular back-propagation training algorithm.

Results

PDM-ANN and GA-ANN were comparatively assessed in terms of their ability to identify the most important factors among the initial 63 variables describing genetic variations, nutrition and gender, able to classify a subject into one of the BMI related classes: normal and overweight. The methods were designed and evaluated using appropriate training and testing sets provided by 3-fold Cross Validation (3-CV) resampling. Classification accuracy, sensitivity, specificity and area under receiver operating characteristics curve were utilized to evaluate the resulted predictive ANN models. The most parsimonious set of factors was obtained by the GA-ANN method and included gender, six genetic variations and 18 nutrition-related variables. The corresponding predictive model was characterized by a mean accuracy equal of 61.46% in the 3-CV testing sets.

Conclusions

The ANN based methods revealed factors that interactively contribute to obesity trait and provided predictive models with a promising generalization ability. In general, results showed that ANNs and their hybrids can provide useful tools for the study of complex traits in the context of nutrigenetics.