ArticleJournal of diabetes investigation2018
Development and validation of risk models to predict the 7-year risk of type 2 diabetes: The Japan Epidemiology Collaboration on Occupational Health Study.
Article in Journal of diabetes investigation, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 2 of them syntheses that pooled it.
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Who cites it
13 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Machine learning and artificial intelligence in type 2 diabetes prediction: a comprehensive 33-year bibliometric and literature analysis.Frontiers in digital health · 2025Pooled it
- Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study.Cardiovascular diabetology · 2022Pooled it
- Characterization of individuals in whom body weight loss precedes diabetes onset: a retrospective, observational, longitudinal cohort study based on health checkup in Japan.Endocrine journal · 2026Observational
- Performance of diabetes risk prediction models: a systematic review and meta-analysis.Endocrine connections · 2025Article
- Article
- Energy landscape analysis of health checkup data clarified multiple pathways to diabetes development in obese and non-obese subjects.Frontiers in endocrinology · 2025Observational
- Comprehensive Health Assessment Using Risk Prediction for Multiple Diseases Based on Health Checkup Data.AJPM focus · 2024Article
- Should insulin resistance (HOMA-IR), insulin secretion (HOMA-β), and visceral fat area be considered for improving the performance of diabetes risk prediction models.BMJ open diabetes research & care · 2024Article
- Prediction Models for Type 2 Diabetes Risk in the General Population: A Systematic Review of Observational Studies.International journal of endocrinology and metabolism · 2021Article
- Article
- Smoking cessation after long-term sick leave due to cancer in comparison with cardiovascular disease: Japan Epidemiology Collaboration on Occupational Health Study.Industrial health · 2020Article
- Risk Prediction of Dyslipidemia for Chinese Han Adults Using Random Forest Survival Model.Clinical epidemiology · 2019Article
- Development and validation of risk models to predict the 7-year risk of type 2 diabetes: The Japan Epidemiology Collaboration on Occupational Health Study.Journal of diabetes investigation · 2018Article
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28 authors.
Funding
Abstract
AIMS/
introductionWe 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
methodsThe 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.
resultsThe 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.
conclusionsThe 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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