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      Use of Net Reclassification Improvement (NRI) Method Confirms The Utility of Combined Genetic Risk Score to Predict Type 2 Diabetes

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          Abstract

          Background

          Recent genome-wide association studies (GWAS) identified more than 70 novel loci for type 2 diabetes (T2D), some of which have been widely replicated in Asian populations. In this study, we investigated their individual and combined effects on T2D in a Chinese population.

          Methodology

          We selected 14 single nucleotide polymorphisms (SNPs) in T2D genes relating to beta-cell function validated in Asian populations and genotyped them in 5882 Chinese T2D patients and 2569 healthy controls. A combined genetic score (CGS) was calculated by summing up the number of risk alleles or weighted by the effect size for each SNP under an additive genetic model. We tested for associations by either logistic or linear regression analysis for T2D and quantitative traits, respectively. The contribution of the CGS for predicting T2D risk was evaluated by receiver operating characteristic (ROC) analysis and net reclassification improvement (NRI).

          Results

          We observed consistent and significant associations of IGF2BP2, WFS1, CDKAL1, SLC30A8, CDKN2A/B, HHEX, TCF7L2 and KCNQ1 (8.5×10 −18< P<8.5×10 −3), as well as nominal associations of NOTCH2, JAZF1, KCNJ11 and HNF1B (0.05< P<0.1) with T2D risk, which yielded odds ratios ranging from 1.07 to 2.09. The 8 significant SNPs exhibited joint effect on increasing T2D risk, fasting plasma glucose and use of insulin therapy as well as reducing HOMA-β, BMI, waist circumference and younger age of diagnosis of T2D. The addition of CGS marginally increased AUC (2%) but significantly improved the predictive ability on T2D risk by 11.2% and 11.3% for unweighted and weighted CGS, respectively using the NRI approach ( P<0.001).

          Conclusion

          In a Chinese population, the use of a CGS of 8 SNPs modestly but significantly improved its discriminative ability to predict T2D above and beyond that attributed to clinical risk factors (sex, age and BMI).

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          Most cited references 66

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          PLINK: a tool set for whole-genome association and population-based linkage analyses.

          Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
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            Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.

            The steady-state basal plasma glucose and insulin concentrations are determined by their interaction in a feedback loop. A computer-solved model has been used to predict the homeostatic concentrations which arise from varying degrees beta-cell deficiency and insulin resistance. Comparison of a patient's fasting values with the model's predictions allows a quantitative assessment of the contributions of insulin resistance and deficient beta-cell function to the fasting hyperglycaemia (homeostasis model assessment, HOMA). The accuracy and precision of the estimate have been determined by comparison with independent measures of insulin resistance and beta-cell function using hyperglycaemic and euglycaemic clamps and an intravenous glucose tolerance test. The estimate of insulin resistance obtained by homeostasis model assessment correlated with estimates obtained by use of the euglycaemic clamp (Rs = 0.88, p less than 0.0001), the fasting insulin concentration (Rs = 0.81, p less than 0.0001), and the hyperglycaemic clamp, (Rs = 0.69, p less than 0.01). There was no correlation with any aspect of insulin-receptor binding. The estimate of deficient beta-cell function obtained by homeostasis model assessment correlated with that derived using the hyperglycaemic clamp (Rs = 0.61, p less than 0.01) and with the estimate from the intravenous glucose tolerance test (Rs = 0.64, p less than 0.05). The low precision of the estimates from the model (coefficients of variation: 31% for insulin resistance and 32% for beta-cell deficit) limits its use, but the correlation of the model's estimates with patient data accords with the hypothesis that basal glucose and insulin interactions are largely determined by a simple feed back loop.
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              Global estimates of the prevalence of diabetes for 2010 and 2030.

              We estimated the number of people worldwide with diabetes for the years 2010 and 2030. Studies from 91 countries were used to calculate age- and sex-specific diabetes prevalences, which were applied to national population estimates, to determine national diabetes prevalences for all 216 countries for 2010 and 2030. Studies were identified using Medline, and contact with all national and regional International Diabetes Federation offices. Studies were included if diabetes prevalence was assessed using a population-based methodology, and was based on World Health Organization or American Diabetes Association diagnostic criteria for at least three separate age-groups within the 20-79 year range. Self-report or registry data were used if blood glucose assessment was not available. The world prevalence of diabetes among adults (aged 20-79 years) will be 6.4%, affecting 285 million adults, in 2010, and will increase to 7.7%, and 439 million adults by 2030. Between 2010 and 2030, there will be a 69% increase in numbers of adults with diabetes in developing countries and a 20% increase in developed countries. These predictions, based on a larger number of studies than previous estimates, indicate a growing burden of diabetes, particularly in developing countries. Copyright 2009 Elsevier Ireland Ltd. All rights reserved.
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                Author and article information

                Affiliations
                [1 ]Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
                [2 ]Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
                [3 ]Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
                [4 ]Department of Statistics, The Chinese University of Hong Kong, Hong Kong SAR, China
                [5 ]School of Biomedical Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
                [6 ]Center for Genomics and Personalized Medicine Research, Center for Diabetes Research, Wake Forest School of Medicine, Winston-Salem, North Carolina, United States of America
                Innsbruck Medical University, Austria
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Conceived and designed the experiments: SKWT MCYN WYS JCNC RCWM. Performed the experiments: VKL. Analyzed the data: CHTT JSKH YW HML GJ ESHL XF. Contributed reagents/materials/analysis tools: APSK SKWT MCYN WYS JCNC RCWM. Wrote the paper: CHTT JCNC RCWM. Recruitment of patients: APSK JLFW YW MCYN WYS JCNC RCWM.

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, USA )
                1932-6203
                2013
                20 December 2013
                : 8
                : 12
                24376643 3869744 PONE-D-13-08467 10.1371/journal.pone.0083093

                This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                Counts
                Pages: 10
                Funding
                This study was supported by the Hong Kong Foundation for Research and Development in Diabetes established under the auspices of the Chinese University of Hong Kong, the Liao Wun Yuk Diabetes Research Memorial Fund, the Hong Kong Governments Research Grant Committee Central Allocation Scheme (CUHK 1/04C), Research Grants Council Earmarked Research Grant (CUHK4727/0M), the Innovation and Technology Fund (ITS/088/08 and ITS/487/09FP), Focused Investment Fund of the Chinese University, a Chinese University Direct Grant, the Research Fund of the Department of Medicine and Therapeutics, the Diabetes and Endocrine Research Fund of the Chinese University of Hong Kong, and National Institutes of Health Grant NIH-RFA DK-085545-01 (from the National Institutes of Diabetes and Digestive and Kidney Diseases). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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