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Development and validation of risk models to predict the 7-year risk of type 2 diabetes: The Japan Epidemiology Collaboration on Occupational Health Study.
Hu, Huanhuan; Nakagawa, Tohru; Yamamoto, Shuichiro; Honda, Toru; Okazaki, Hiroko; Uehara, Akihiko; Yamamoto, Makoto; Miyamoto, Toshiaki; Kochi, Takeshi; Eguchi, Masafumi; Murakami, Taizo; Shimizu, Makiko; Tomita, Kentaro; Nagahama, Satsue; Imai, Teppei; Nishihara, Akiko; Sasaki, Naoko; Ogasawara, Takayuki; Hori, Ai; Nanri, Akiko; Akter, Shamima; Kuwahara, Keisuke; Kashino, Ikuko; Kabe, Isamu; Mizoue, Tetsuya; Sone, Tomofumi; Dohi, Seitaro.
Affiliation
  • Hu H; Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
  • Nakagawa T; Hitachi, Ltd., Ibaraki, Japan.
  • Yamamoto S; Hitachi, Ltd., Ibaraki, Japan.
  • Honda T; Hitachi, Ltd., Ibaraki, Japan.
  • Okazaki H; Mitsui Chemicals, Inc., Tokyo, Japan.
  • Uehara A; Seijinkai Shizunai Hospital, Hokkaido, Japan.
  • Yamamoto M; Yamaha Corporation, Shizuoka, Japan.
  • Miyamoto T; Nippon Steel & Sumitomo Metal Corporation Kimitsu Works, Chiba, Japan.
  • Kochi T; Furukawa Electric Co., Ltd., Tokyo, Japan.
  • Eguchi M; Furukawa Electric Co., Ltd., Tokyo, Japan.
  • Murakami T; Mizue Medical Clinic, Keihin Occupational Health Center, Kanagawa, Japan.
  • Shimizu M; Mizue Medical Clinic, Keihin Occupational Health Center, Kanagawa, Japan.
  • Tomita K; Mitsubishi Plastics, Inc., Tokyo, Japan.
  • Nagahama S; All Japan Labor Welfare Foundation, Tokyo, Japan.
  • Imai T; Azbil Corporation, Tokyo, Japan.
  • Nishihara A; Azbil Corporation, Tokyo, Japan.
  • Sasaki N; Mitsubishi Fuso Truck and Bus Corporation, Kanagawa, Japan.
  • Ogasawara T; Mitsubishi Fuso Truck and Bus Corporation, Kanagawa, Japan.
  • Hori A; Department of Global Public Health, University of Tsukuba, Ibaraki, Japan.
  • Nanri A; Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
  • Akter S; Department of Food and Health Sciences, Fukuoka Women's University, Fukuoka, Japan.
  • Kuwahara K; Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
  • Kashino I; Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
  • Kabe I; Teikyo University Graduate School of Public Health, Tokyo, Japan.
  • Mizoue T; Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
  • Sone T; Furukawa Electric Co., Ltd., Tokyo, Japan.
  • Dohi S; Department of Epidemiology and Prevention, National Center for Global Health and Medicine, Tokyo, Japan.
J Diabetes Investig ; 9(5): 1052-1059, 2018 Sep.
Article in En | MEDLINE | ID: mdl-29380553
AIMS/INTRODUCTION: We previously developed a 3-year diabetes risk score in the working population. The objective of the present study was to develop and validate flexible risk models that can predict the risk of diabetes for any arbitrary time-point during 7 years. MATERIALS AND METHODS: The participants were 46,198 Japanese employees aged 30-59 years, without diabetes at baseline and with a maximum follow-up period of 8 years. Incident diabetes was defined according to the American Diabetes Association criteria. With routine health checkup data (age, sex, abdominal obesity, body mass index, smoking status, hypertension status, dyslipidemia, glycated hemoglobin and fasting plasma glucose), we developed non-invasive and invasive risk models based on the Cox proportional hazards regression model among a random two-thirds of the participants, and used another one-third for validation. RESULTS: The range of the area under the receiver operating characteristic curve increased from 0.73 (95% confidence interval 0.72-0.74) for the non-invasive prediction model to 0.89 (95% confidence interval 0.89-0.90) for the invasive prediction model containing dyslipidemia, glycated hemoglobin and fasting plasma glucose. The invasive models showed improved integrated discrimination and reclassification performance, as compared with the non-invasive model. Calibration appeared good between the predicted and observed risks. These models performed well in the validation cohort. CONCLUSIONS: The present non-invasive and invasive models for the prediction of diabetes risk up to 7 years showed fair and excellent performance, respectively. The invasive models can be used to identify high-risk individuals, who would benefit greatly from lifestyle modification for the prevention or delay of diabetes.
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Full text: 1 Database: MEDLINE Main subject: Biomarkers / Occupational Health / Diabetes Mellitus, Type 2 Type of study: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limits: Adult / Female / Humans / Male / Middle aged Country/Region as subject: Asia Language: En Journal: J Diabetes Investig Year: 2018 Type: Article Affiliation country: Japan

Full text: 1 Database: MEDLINE Main subject: Biomarkers / Occupational Health / Diabetes Mellitus, Type 2 Type of study: Etiology_studies / Incidence_studies / Observational_studies / Prognostic_studies / Risk_factors_studies / Screening_studies Limits: Adult / Female / Humans / Male / Middle aged Country/Region as subject: Asia Language: En Journal: J Diabetes Investig Year: 2018 Type: Article Affiliation country: Japan