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BackgroundCurrently, over 500 million adults get annual health checkup in China. However, few models have been developed for medical checkup population to predict the risk of incident diabetes in future years.AimsThe objective of this study was to establish a risk prediction model for 1-year incident diabetes in Chinese adults, based on 1,532,841 participants with remeasured health checkup data.MethodsWe included participants from Meinian health checkup population who had two measurements of fasting plasma glucose in 2017 and 2018 surveys. All included participants were non-diabetic adults at initial measurement, and the second visit took place at least six months later. We excluded participants with age 18 or u226580 years; alanine aminotransferase 200 U/L; estimated glomerular filtration rate 30 mL/min/1.73m2; proteinuria; systolic blood pressure 180 or diastolic blood pressure >115 mmHg; body mass index 18.5 or 35.0 kg/m2; total cholesterol >6.47 mmol/L, or triglyceride >5.65 mmol/L. Diabetes defined as fasting plasma glucose u2265 7.00 mmol/L, or self-reported diagnosis of diabetes, or use of anti-diabetic medications. Risk model was created using 10% of the participants (selected randomly) via stepwise regression model, and then validated using data from the remaining 10% of participants. Logistic regression models were created to estimate the 1-year risk of diabetes, using clinical measurements easily obtained in most health checkup settings in China. Discrimination and calibration was evaluated. Participants(n = 18,893) from MJ cohorts were used as validation cohorts to evaluate the performance of the equations.ResultsThe median follow-up period was 12 months (interquartile range, 11-13 months). 25,821(1.71%) participants in the derivation cohort and 731(3.9%) participants in the validation cohort developed diabetes at the second visit. Age, gender, body mass index, systolic blood pressure, fasting plasma glucose, total cholesterol, triglyceride, alanine aminotransferase, fatty liver were statistically significant predictors of 1-yearu2019s diabetes risk. Men had a numerically higher C-statistic than men (0.898 versus 0.891). Calibration u03c72 values were 15.3 (P=0.08) for men and 6.11 (P=0.73) for women, which indicated excellent goodness of fit for our equations. In the external validation, Sex specific equations had C-statistics of 0.928 (95% confidence interval: 0.913-0.942) for men and 0.889 (95% confidence interval: 0.891-0.916) for women. The calibration u03c72 was125 for men and 23.3 for women. DiscussionA risk model applied commonly available data in health checkup settings can be used to predict 1-year incidence of diabetes. To our knowledge, this is the first study to estimate the 1-year risk of diabetes in such settings in China. This model demonstrated an appropriate performance, and will aid in the identification of individuals who are at risk of developing incident diabetes within the next year in health checkup settings. This is important for timely intervention and to avoid chronic complications associated with diabetes.
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