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Front Genet. 2015 Mar 17;6:75. doi: 10.3389/fgene.2015.00075. eCollection 2015.

Integrated genomic and BMI analysis for type 2 diabetes risk assessment.

Author information

1
Institute of Mathematics, School of Science and Technology, Universidad Metropolitana San Juan, Puerto Rico.
2
Department of Health Behavior, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA.
3
Department of Statistics, Colegio de Postgraduados Texcoco, México.
4
Division of Epidemiology and Biostatistics, Mel and Enid Zuckerman College of Public Health, University of Arizona Tucson, AZ, USA.
5
Department of Biostatistics, School of Public Health, University of Alabama at Birmingham Birmingham, AL, USA.

Abstract

Type 2 Diabetes (T2D) is a chronic disease arising from the development of insulin absence or resistance within the body, and a complex interplay of environmental and genetic factors. The incidence of T2D has increased throughout the last few decades, together with the occurrence of the obesity epidemic. The consideration of variants identified by Genome Wide Association Studies (GWAS) into risk assessment models for T2D could aid in the identification of at-risk patients who could benefit from preventive medicine. In this study, we build several risk assessment models, evaluated with two different classification approaches (Logistic Regression and Neural Networks), to measure the effect of including genetic information in the prediction of T2D. We used data from to the Original and the Offspring cohorts of the Framingham Heart Study, which provides phenotypic and genetic information for 5245 subjects (4306 controls and 939 cases). Models were built by using several covariates: gender, exposure time, cohort, body mass index (BMI), and 65 SNPs associated to T2D. We fitted Logistic Regressions and Bayesian Regularized Neural Networks and then assessed their predictive ability by using a ten-fold cross validation. We found that the inclusion of genetic information into the risk assessment models increased the predictive ability by 2%, when compared to the baseline model. Furthermore, the models that included BMI at the onset of diabetes as a possible effector, gave an improvement of 6% in the area under the curve derived from the ROC analysis. The highest AUC achieved (0.75) belonged to the model that included BMI, and a genetic score based on the 65 established T2D-associated SNPs. Finally, the inclusion of SNPs and BMI raised predictive ability in all models as expected; however, results from the AUC in Neural Networks and Logistic Regression did not differ significantly in their prediction accuracy.

KEYWORDS:

Logistic Regression; Neural Network; genetic score; risk assessment; type 2 diabetes

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