Abstract
Recent genome-wide association studies (GWAS) have identified several novel single nucleotide polymorphisms (SNPs) associated with type 2 diabetes (T2D). Various models using clinical and/or genetic risk factors have been developed for T2D risk prediction. However, analysis considering algorithms for genetic risk factor detection and regression methods for model construction in combination with interactions of risk factors has not been investigated. Here, using genotype data of 7,360 Japanese individuals, we investigated risk prediction models, considering the algorithms, regression methods and interactions. The best model identified was based on a Bayes factor approach and the lasso method. Using nine SNPs and clinical factors, this method achieved an area under a receiver operating characteristic curve (AUC) of 0.8057 on an independent test set. With the addition of a pair of interaction factors, the model was further improved (p-value 0.0011, AUC 0.8085). Application of our model to prospective cohort data showed significantly better outcome in disease-free survival, according to the log-rank trend test comparing Kaplan-Meier survival curves (). While the major contribution was from clinical factors rather than the genetic factors, consideration of genetic risk factors contributed to an observable, though small, increase in predictive ability. This is the first report to apply risk prediction models constructed from GWAS data to a T2D prospective cohort. Our study shows our model to be effective in prospective prediction and has the potential to contribute to practical clinical use in T2D.
Highlights
The prevalence of diabetes mellitus is increasing and it has become one of the major global diseases, the most common form worldwide being type 2 diabetes (T2D) [1]
Previous studies have shown that clinical risk factors such as age, gender, body mass index (BMI), family history of T2D, systolic blood pressure, highdensity lipoprotein cholesterol level, triglycerides level, insulin secretion and fasting plasma glucose are risk predictors for T2D [7], their predictive ability may have been influenced by study design and population [8]
On the basis of the average area under the curve (AUC), we determined the optimal number of single nucleotide polymorphisms (SNPs) for model construction for each combination of algorithms and methods
Summary
The prevalence of diabetes mellitus is increasing and it has become one of the major global diseases, the most common form worldwide being type 2 diabetes (T2D) [1]. The incidence of T2D has been increasing rapidly in many countries, including Japan, over the past few decades [2,3,4] and it is estimated that 500 million individuals will be affected by some form of diabetes by 2030 if no preventive strategies are implemented [5]. Multiple genetic and clinical risk factors are expected to contribute to the pathogenesis of T2D. Previous studies have shown that clinical risk factors such as age, gender, body mass index (BMI), family history of T2D, systolic blood pressure, highdensity lipoprotein cholesterol level, triglycerides level, insulin secretion and fasting plasma glucose are risk predictors for T2D [7], their predictive ability may have been influenced by study design and population [8]. KCNQ1, C2CD4A/4B, UBE2E2 and ANK1 have recently been reported as common susceptibility loci for T2D in the Japanese population [13,14,15]
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
