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

Juyoung Lee, Bhumsuk Keam, Eun Jung Jang, Mi Sun Park, Ji Young Lee, Dan Bi Kim, Chang Hoon Lee, Tak Kim, Bermseok Oh, Heon Jin Park, Kyu Bum Kwack, Chaeshin Chu, Hyung Lae Kim

Research output: Contribution to journalArticlepeer-review

17 Scopus citations


Objectives: Recent 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. Methods: We 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. Results: We 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. Conclusions: The 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.

Original languageEnglish
Pages (from-to)75-82
Number of pages8
JournalOsong Public Health and Research Perspectives
Issue number2
StatePublished - Sep 2011

Bibliographical note

Funding Information:
We thank the staff in the Division of Epidemiology and Health Index who contributed to gathering the clinical and epidemiological data. We also thank Dr. Thaler, English language editor at Bioedit, for her assistance. This study was supported by an intramural grant ( 2910-212-207; 2006-N72003-00 ) from Korea National Institute of Health .


  • Classification
  • Early predictive model
  • Single nucleotide polymorphism (SNP)
  • Type 2 diabetes mellitus (T2DM)


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