Development and validation of risk models to predict the 7‐year risk of type 2 diabetes: The Japan Epidemiology Collaboration on Occupational Health Study. Issue 5 (6th March 2018)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Journal of diabetes investigation. Volume 9:Issue 5(2018)
• Page Start: 1052
• Page End: 1059
• Publication Date: 2018-03-06
• Subjects: Japanese -- Risk model -- Type 2 diabetes; Diabetes -- Periodicals; Diabetes -- Research -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1111/jdi.12809
• Languages: English
• ISSNs: 2040-1116

Abstract

Abstract: 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. Abstract : 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. 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.