Development of a Predictive Model for Type 2 Diabetes Mellitus Using Genetic and Clinical Data

Abstract

ObjectivesRecent genetic association studies have provided convincing evidence that several novel loci and single nucleotide polymorphisms (SNPs) are associated with the risk of developing type 2 diabetes mellitus (T2DM). The aims of this study were: 1) to develop a predictive model of T2DM using genetic and clinical data; and 2) to compare misclassification rates of different models.MethodsWe selected 212 individuals with newly diagnosed T2DM and 472 controls aged in their 60s from the Korean Genome and Epidemiology Study. A total of 499 known SNPs from 87 T2DM-related genes were genotyped using germline DNA. SNPs were analyzed for significant association with T2DM using various classification algorithms including Quest (Quick, Unbiased, Efficient, Statistical tree), Support Vector Machine, C4.5, logistic regression, and K-nearest neighbor.ResultsWe tested these models using the complete Korean Genome and Epidemiology Study cohort (n = 10,038) and computed the T2DM misclassification rates for each model. Average misclassification rates ranged at 28.2–52.7%. The misclassification rates for the logistic and machine-learning algorithms were lower than the statistical tree algorithms. Using 1-to-1 matched data, the misclassification rate of the statistical tree QUEST algorithm using body mass index and SNP variables was the lowest, but overall the logistic regression performed best.ConclusionsThe K-nearest neighbor method exhibited more robust results than other algorithms. For clinical and genetic data, our “multistage adjustment” model outperformed other models in yielding lower rates of misclassification. To improve the performance of these models, further studies using warranted, strategies to estimate better classifiers for the quantification of SNPs need to be developed.

Stacked classifiers for individualized prediction of glycemic control following initiation of metformin therapy in type 2 diabetes

“…A prototypical approach can be found in [28], where a cohort is divided into subgroups based on glycemic control status and regression analysis is used to identify significant independent predictors. More generally, algorithmic approaches have been applied to various other aspects of diabetes and diabetes care, including the diagnosis of diabetes [29–32], prediction of complications [33–37], genetic background and environment [38–41], and large scale factors such as healthcare spending on diabetes and diabetes-related subjects, managing health care systems, and enterprise-scale identification of patients with type 2 diabetes [42–46]. An extensive and well written review of the efforts mentioned above as well as many others can be found in [47].…”

Machine Learning and Data Mining Methods in Diabetes Research

“…On the other hand, Lee et al used various classification algorithms, such as SVM and logistic regression, to predict T2D by employing 499 known SNPs from 87 T2D-related genes [133]. Finally, in [134], authors used support vector machines to predict tyrosine kinase ligand-receptor pairs from their amino acid sequences.…”

Survey on clinical prediction models for diabetes prediction

“…As per the survey of above papers we can find many gaps that are to be filled, which are usage of larger dataset [23,34], outlier detection [35], improving prediction model [34], integration of optimization techniques to hybrid prediction model [33], implementation of prediction models for other diseases on android mobile [31], development of prediction model that include type 1 treatment plans with more attributes [30], usage of datasets of multiple classes [4].…”

“…A multi stage adjustment model with low misclassification rate which predicts which persons are most likely to develop diabetes is built by using KoGES dataset [23]. A physiological model which can predict the blood glucose level 30 min in advance was developed using five patients data by training SVR with physiological features.…”