Diabetes, Metabolic Syndrome and Obesity (Jun 2021)
Nomogram Predicting the Risk of Progression from Prediabetes to Diabetes After a 3-Year Follow-Up in Chinese Adults
Abstract
Kai Liang,1– 4 Xinghong Guo,1– 4 Chuan Wang,1– 4 Fei Yan,1– 4 Lingshu Wang,1– 4 Jinbo Liu,1– 4 Xinguo Hou,1– 4 Wenjuan Li,1– 4 Li Chen1– 4 1Department of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China; 2Institute of Endocrine and Metabolic Diseases of Shandong University, Jinan, 250012, People’s Republic of China; 3Key Laboratory of Endocrine and Metabolic Diseases, Shandong Province Medicine & Health, Jinan, 250012, People’s Republic of China; 4Jinan Clinical Research Center for Endocrine and Metabolic Diseases, Jinan, 250012, People’s Republic of ChinaCorrespondence: Li Chen; Wenjuan LiDepartment Of Endocrinology, Qilu Hospital, Cheeloo College of Medicine, Shandong University, Jinan, 250012, People’s Republic of China, Tel +86 18560083989; +86 18560080331Fax +860531-82169323Email [email protected]; [email protected]: To develop a nomogram for predicting the risk of progression from prediabetes to diabetes and provide a quantitative predictive tool for early clinical screening of high-risk populations of diabetes.Materials and Methods: This study was a retrospective cohort study and part of the investigation conducted for the Risk Evaluation of cAncers in Chinese diabeTic Individuals: a lONgitudinal (REACTION) study. A total of 1857 prediabetic participants at baseline underwent oral glucose tolerance test and hemoglobin A1c (HbA1c) testing after 3 years. The areas under the receiver operating characteristic curves (AUCs) were adopted to measure the predictive value of progression to diabetes, using baseline fasting plasma glucose (FPG), 2-hr postprandial plasma glucose (2hPG), HbA1c or combined models. Decision curve analysis determined the model with the best discriminative ability. A nomogram was formulated and internally validated, providing an individualized predictive tool by calculating total scores.Results: After 3 years, 145 participants developed diabetes, and the annual incidence was estimated to be 2.60%. Among the three single indicators and four combined models, model 4 combined of FPG, 2hPG, and HbA1c showed the best performance in risk predication, with an AUC of 0.742. The nomogram constructed via model 4 was validated and demonstrated good prediction for the risk of diabetes. The nomogram score/predicted probability was a numeric value representing the prediction model score of individual patients. Notably, all nomogram scores showed relatively high negative predictive values.Conclusion: The nomogram constructed in this study effectively predicts and quantifies the risk of progression from prediabetes to diabetes after a 3-year follow-up and could be adopted to identify Chinese patients at high risk for diabetes in order to provide timely interventions.Keywords: nomogram, diabetes, prediabetes, predictive value
