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      Sixty-Five Common Genetic Variants and Prediction of Type 2 Diabetes

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          Abstract

          We developed a 65 type 2 diabetes (T2D) variant–weighted gene score to examine the impact on T2D risk assessment in a U.K.-based consortium of prospective studies, with subjects initially free from T2D ( N = 13,294; 37.3% women; mean age 58.5 [38–99] years). We compared the performance of the gene score with the phenotypically derived Framingham Offspring Study T2D risk model and then the two in combination. Over the median 10 years of follow-up, 804 participants developed T2D. The odds ratio for T2D (top vs. bottom quintiles of gene score) was 2.70 (95% CI 2.12–3.43). With a 10% false-positive rate, the genetic score alone detected 19.9% incident cases, the Framingham risk model 30.7%, and together 37.3%. The respective area under the receiver operator characteristic curves were 0.60 (95% CI 0.58–0.62), 0.75 (95% CI 0.73 to 0.77), and 0.76 (95% CI 0.75 to 0.78). The combined risk score net reclassification improvement (NRI) was 8.1% (5.0 to 11.2; P = 3.31 × 10 −7). While BMI stratification into tertiles influenced the NRI (BMI ≤24.5 kg/m 2, 27.6% [95% CI 17.7–37.5], P = 4.82 × 10 −8; 24.5–27.5 kg/m 2, 11.6% [95% CI 5.8–17.4], P = 9.88 × 10 −5; >27.5 kg/m 2, 2.6% [95% CI −1.4 to 6.6], P = 0.20), age categories did not. The addition of the gene score to a phenotypic risk model leads to a potentially clinically important improvement in discrimination of incident T2D.

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          Large-scale association analysis provides insights into the genetic architecture and pathophysiology of type 2 diabetes

          To extend understanding of the genetic architecture and molecular basis of type 2 diabetes (T2D), we conducted a meta-analysis of genetic variants on the Metabochip involving 34,840 cases and 114,981 controls, overwhelmingly of European descent. We identified ten previously unreported T2D susceptibility loci, including two demonstrating sex-differentiated association. Genome-wide analyses of these data are consistent with a long tail of further common variant loci explaining much of the variation in susceptibility to T2D. Exploration of the enlarged set of susceptibility loci implicates several processes, including CREBBP-related transcription, adipocytokine signalling and cell cycle regulation, in diabetes pathogenesis.
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            Is Open Access

            Risk models and scores for type 2 diabetes: systematic review

            Objective To evaluate current risk models and scores for type 2 diabetes and inform selection and implementation of these in practice. Design Systematic review using standard (quantitative) and realist (mainly qualitative) methodology. Inclusion criteria Papers in any language describing the development or external validation, or both, of models and scores to predict the risk of an adult developing type 2 diabetes. Data sources Medline, PreMedline, Embase, and Cochrane databases were searched. Included studies were citation tracked in Google Scholar to identify follow-on studies of usability or impact. Data extraction Data were extracted on statistical properties of models, details of internal or external validation, and use of risk scores beyond the studies that developed them. Quantitative data were tabulated to compare model components and statistical properties. Qualitative data were analysed thematically to identify mechanisms by which use of the risk model or score might improve patient outcomes. Results 8864 titles were scanned, 115 full text papers considered, and 43 papers included in the final sample. These described the prospective development or validation, or both, of 145 risk prediction models and scores, 94 of which were studied in detail here. They had been tested on 6.88 million participants followed for up to 28 years. Heterogeneity of primary studies precluded meta-analysis. Some but not all risk models or scores had robust statistical properties (for example, good discrimination and calibration) and had been externally validated on a different population. Genetic markers added nothing to models over clinical and sociodemographic factors. Most authors described their score as “simple” or “easily implemented,” although few were specific about the intended users and under what circumstances. Ten mechanisms were identified by which measuring diabetes risk might improve outcomes. Follow-on studies that applied a risk score as part of an intervention aimed at reducing actual risk in people were sparse. Conclusion Much work has been done to develop diabetes risk models and scores, but most are rarely used because they require tests not routinely available or they were developed without a specific user or clear use in mind. Encouragingly, recent research has begun to tackle usability and the impact of diabetes risk scores. Two promising areas for further research are interventions that prompt lay people to check their own diabetes risk and use of risk scores on population datasets to identify high risk “hotspots” for targeted public health interventions.
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              Translating the A1C Assay Into Estimated Average Glucose Values

              OBJECTIVE The A1C assay, expressed as the percent of hemoglobin that is glycated, measures chronic glycemia and is widely used to judge the adequacy of diabetes treatment and adjust therapy. Day-to-day management is guided by self-monitoring of capillary glucose concentrations (milligrams per deciliter or millimoles per liter). We sought to define the mathematical relationship between A1C and average glucose (AG) levels and determine whether A1C could be expressed and reported as AG in the same units as used in self-monitoring. RESEARCH DESIGN AND METHODS A total of 507 subjects, including 268 patients with type 1 diabetes, 159 with type 2 diabetes, and 80 nondiabetic subjects from 10 international centers, was included in the analyses. A1C levels obtained at the end of 3 months and measured in a central laboratory were compared with the AG levels during the previous 3 months. AG was calculated by combining weighted results from at least 2 days of continuous glucose monitoring performed four times, with seven-point daily self-monitoring of capillary (fingerstick) glucose performed at least 3 days per week. RESULTS Approximately 2,700 glucose values were obtained by each subject during 3 months. Linear regression analysis between the A1C and AG values provided the tightest correlations (AGmg/dl = 28.7 × A1C − 46.7, R 2 = 0.84, P < 0.0001), allowing calculation of an estimated average glucose (eAG) for A1C values. The linear regression equations did not differ significantly across subgroups based on age, sex, diabetes type, race/ethnicity, or smoking status. CONCLUSIONS A1C levels can be expressed as eAG for most patients with type 1 and type 2 diabetes.
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                Author and article information

                Journal
                Diabetes
                Diabetes
                diabetes
                diabetes
                Diabetes
                Diabetes
                American Diabetes Association
                0012-1797
                1939-327X
                May 2015
                04 December 2014
                : 64
                : 5
                : 1830-1840
                Affiliations
                [1] 1Centre for Cardiovascular Genetics, Institute of Cardiovascular Science, University College London, London, U.K.
                [2] 2Department of Primary Care and Population Health, University College London, Royal Free Campus, London, U.K.
                [3] 3Department of Non-Communicable Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, U.K.
                [4] 4Department of Epidemiology and Public Health, University College London Institute of Epidemiology and Health Care, University College London, London, U.K.
                [5] 5University College London Genetics Institute, Department of Genetics, Environment and Evolution, London, U.K.
                [6] 6Centre for Population Health Sciences, University of Edinburgh, Edinburgh, U.K.
                [7] 7Medical Research Council Unit for Lifelong Health and Ageing at University College London, London, U.K.
                [8] 8Medical Research Council Epidemiology Unit, Institute of Metabolic Science, Addenbrooke’s Hospital, Cambridge, U.K.
                [9] 9School of Social and Community Medicine, University of Bristol, Bristol, U.K.
                [10] 10Medical Research Council Integrative Epidemiology Unit, University of Bristol, Bristol, U.K.
                [11] 11Division of Transplant Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
                [12] 12Metabolic Unit, Western General Hospital, Edinburgh, U.K.
                [13] 13Genetics Division, Research and Development, GlaxoSmithKline, Harlow, U.K.
                [14] 14Centre for Clinical Pharmacology, University College London, London, U.K.
                Author notes
                Corresponding author: Philippa J. Talmud, p.talmud@ 123456ucl.ac.uk .
                Article
                1504
                10.2337/db14-1504
                4407866
                25475436
                5342ee56-8b20-4f98-b100-7b6965a0f264
                © 2015 by the American Diabetes Association. Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered.
                History
                : 09 October 2014
                : 27 November 2014
                Page count
                Pages: 11
                Categories
                Genetics/Genomes/Proteomics/Metabolomics

                Endocrinology & Diabetes
                Endocrinology & Diabetes

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