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      Development of Risk Score for Predicting 3-Year Incidence of Type 2 Diabetes: Japan Epidemiology Collaboration on Occupational Health Study

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      1 , * , 2 , 1 , 3 , 2 , 2 , 4 , 5 , 5 , 6 , 7 , 7 , 8 , 8 , 8 , 9 , 10 , 11 , 11 , 12 , 13 , 14 , 13 , 15 , 16 , 17 , 1 , 1 , 1 , 7 , 1 , 18 , 4 , for the Japan Epidemiology Collaboration on Occupational Health Study Group
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          Abstract

          Objective

          Risk models and scores have been developed to predict incidence of type 2 diabetes in Western populations, but their performance may differ when applied to non-Western populations. We developed and validated a risk score for predicting 3-year incidence of type 2 diabetes in a Japanese population.

          Methods

          Participants were 37,416 men and women, aged 30 or older, who received periodic health checkup in 2008–2009 in eight companies. Diabetes was defined as fasting plasma glucose (FPG) ≥126 mg/dl, random plasma glucose ≥200 mg/dl, glycated hemoglobin (HbA1c) ≥6.5%, or receiving medical treatment for diabetes. Risk scores on non-invasive and invasive models including FPG and HbA1c were developed using logistic regression in a derivation cohort and validated in the remaining cohort.

          Results

          The area under the curve (AUC) for the non-invasive model including age, sex, body mass index, waist circumference, hypertension, and smoking status was 0.717 (95% CI, 0.703–0.731). In the invasive model in which both FPG and HbA1c were added to the non-invasive model, AUC was increased to 0.893 (95% CI, 0.883–0.902). When the risk scores were applied to the validation cohort, AUCs (95% CI) for the non-invasive and invasive model were 0.734 (0.715–0.753) and 0.882 (0.868–0.895), respectively. Participants with a non-invasive score of ≥15 and invasive score of ≥19 were projected to have >20% and >50% risk, respectively, of developing type 2 diabetes within 3 years.

          Conclusions

          The simple risk score of the non-invasive model might be useful for predicting incident type 2 diabetes, and its predictive performance may be markedly improved by incorporating FPG and HbA1c.

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          Most cited references18

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          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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            Global estimates of undiagnosed diabetes in adults.

            The prevalence of diabetes is rapidly increasing worldwide. Type 2 diabetes may remain undetected for many years, leading to severe complications and healthcare costs. This paper provides estimates of the prevalence of undiagnosed diabetes mellitus (UDM), using available data from high quality representative population-based sources.
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              A risk score for predicting incident diabetes in the Thai population.

              The objective of this study was to develop and evaluate a risk score to predict people at high risk of diabetes in Thailand. A Thai cohort of 2,677 individuals, aged 35-55 years, without diabetes at baseline, was resurveyed after 12 years. Logistic regression models were used to identify baseline risk factors that predicted the incidence of diabetes; a simple model that included only those risk factors as significant (P < 0.05) when adjusted for each other was developed. The coefficients from this model were transformed into components of a diabetes score. This score was tested in a Thai validation cohort of a different 2,420 individuals. A total of 361 individuals developed type 2 diabetes in the exploratory cohort during the follow-up period. The significant predictive variables in the simple model were age, BMI, waist circumference, hypertension, and history of diabetes in parents or siblings A cutoff score of 6 of 17 produced the optimal sum of sensitivity (77%) and specificity (60%). The area under the receiver-operating characteristic curve (AUC) was 0.74. Adding impaired fasting glucose or impaired glucose tolerance status to the model slightly increased the AUC to 0.78; adding low HDL cholesterol and/or high triglycerides barely improved the model. The validation cohort demonstrated similar results. A simple diabetes risk score, based on a set of variables not requiring laboratory tests, can be used for early intervention to delay or prevent the disease in Thailand. Adding impaired fasting glucose or impaired glucose tolerance or triglyceride and HDL cholesterol status to this model only modestly improves the predictive ability.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                11 November 2015
                2015
                : 10
                : 11
                : e0142779
                Affiliations
                [1 ]Department of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan
                [2 ]Hitachi, Ltd., Ibaraki, Japan
                [3 ]Teikyo University, Graduate School of Public Health, Tokyo, Japan
                [4 ]Mitsui Chemicals, Inc., Tokyo, Japan
                [5 ]YAMAHA CORPORATION, Shizuoka, Japan
                [6 ]Nippon Steel & Sumitomo Metal Corporation Kimitsu Works, Chiba, Japan
                [7 ]Furukawa Electric Co., Ltd., Tokyo, Japan
                [8 ]Mizue Medical Clinic, Keihin Occupational Health Center, Kanagawa, Japan
                [9 ]Mitsubishi Plastics, Inc., Tokyo, Japan
                [10 ]All Japan Labour Welfare Foundation, Tokyo, Japan
                [11 ]Azbil Corporation, Tokyo, Japan
                [12 ]Mitsubishi Fuso Truck and Bus Corporation, Kanagawa, Japan
                [13 ]Department of Safety and Health, Tokyo Gas Co., Ltd., Tokyo, Japan
                [14 ]Health Design Inc., Tokyo, Japan
                [15 ]Mizuho Health Insurance Society, Tokyo, Japan
                [16 ]Fuji Electric Co., Ltd., Kanagawa, Japan
                [17 ]ADVANTAGE Risk Management Co., Ltd., Tokyo, Japan
                [18 ]National Institute of Public Health, Saitama, Japan
                University of Louisville, UNITED STATES
                Author notes

                Competing Interests: T. Nakagawa, S. Yamamoto, and T. Honda belong to Hitachi, Ltd.; H. Okazaki and S. Dohi, Mitsui Chemicals, Inc.; A. Uehara and M. Yamamoto, YAMAHA CORPORATION; T. Miyamoto, Nippon Steel & Sumitomo Metal Corporation Kimitsu Works; T. Kochi, M. Eguchi, and I. Kabe, Furukawa Electric Co., Ltd.; T. Murakami, C. Shimizu, and M. Shimizu, Mizue Medical Clinic, Keihin Occupational Health Center; K. Tomita, Mitsubishi Plastics, Inc.; S. Nagahama, All Japan Labour Welfare Foundation; T. Imai and A. Nishihara, Azbil Corporation; N. Sasaki, Mitsubishi Fuso Truck and Bus Corporation; A. Hori and C. Nishiura, Tokyo Gas Co., Ltd.; N. Sakamoto, Health Design Inc.; T. Totsuzaki, Mizuho Health Insurance Society; N. Kato, Fuji Electric Co., Ltd.; K. Fukasawa, ADVANTAGE Risk Management Co., Ltd. T. Nakagawa, S. Yamamoto, T. Honda, H. Okazaki, S. Dohi, A. Uehara, M. Yamamoto, T. Miyamoto, T. Kochi, M. Eguchi, I. Kabe, T. Murakami, C. Shimizu, M. Shimizu, K. Tomita, S. Nagahama, T. Imai, A. Nishihara, N. Sasaki, A. Hori, C. Nishiura, N. Sakamoto, T. Totsuzaki, N. Kato, and K. Fukasawa are the health professionals in each participating company. All authors declare no conflict of interest, patents, products in development or marketed products etc. This does not alter the authors' adherence to PLOS ONE policies on sharing data and materials.

                Conceived and designed the experiments: SD T. Mizoue. Performed the experiments: A. Nanri K. Kuwahara K. Kurotani T. Mizoue. Analyzed the data: A. Nanri HH. Contributed reagents/materials/analysis tools: TN SY TH HO AU MY T. Miyamoto TK ME T. Murakami CS MS KT SN TI A. Nishihara N. Sasaki IK SD. Drafted the plan for the data analyses: A. Nanri K. Kuwahara SN AH N. Sakamoto CN TT NK KF HH SA K. Kurotani T. Mizoue TS SD. Interpretation of the results and revision of the manuscript: A. Nanri TN K. Kuwahara SY TH HO AU MY T. Miyamoto TK ME T. Murakami CS MS KT SN TI A. Nishihara N. Sasaki AH N. Sakamoto CN TT NK KF HH SA K. Kurotani IK T. Mizoue TS SD. Wrote the paper: A. Nanri. Conducted data collection: A. Nanri K. Kuwahara K. Kurotani T. Mizoue.

                ¶ Membership of the Japan Epidemiology Collaboration on Occupational Health Study Group is provided in the Acknowledgments.

                Article
                PONE-D-14-44442
                10.1371/journal.pone.0142779
                4641714
                26558900
                b68d0e13-b77d-4b6c-9b4b-57e293ebb641
                Copyright @ 2015

                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

                History
                : 24 November 2014
                : 27 October 2015
                Page count
                Figures: 2, Tables: 5, Pages: 16
                Funding
                This study was supported by a grant from the Industrial Health Foundation and Grant-in-Aid for Scientific Research (B) (25293146) from the Japan Society for the Promotion of Science.
                Categories
                Research Article
                Custom metadata
                The data are hosted in the National Center for Global Health and Medicine. Currently, the data cannot be widely shared because the research group has not obtained permission from participating companies to provide the data on request. However, the data can be requested by academic researchers for non-commercial research; inquiries and applications can be made to Department of Epidemiology and Prevention, Center for Clinical Sciences, National Center for Global Health and Medicine, Tokyo, Japan (Dr. Mizoue, mizoue@ 123456ri.ncgm.go.jp ).

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