Objective To discuss the risk factors for impaired fasting glucose(IFG)and construct and validate a predictive model based on column charts of the risk of IFG occurrence.Methods This retrospective study included 3 037 individuals who underwent routine physical examinations at a hospital in Shenyang between August and December 2022.The population was randomly divided into a training group(n=2 126)and a validation group(n=911)in a 7∶3 ratio,and physical examination data were collected.LASSO regression analy-sis was used to screen predictive variables and logistic regression analysis was used to further screen and construct a column chart pre-dictive model.The validation group was used to conduct an internal validation of the feasibility of the model,and the area under the curve(AUC)of receiver operator characteristic(ROC)and goodness of fit tests were used to evaluate the model effectiveness.Results Among the 3 037 included individuals,2 880 did not experience IFG and 157 did.The results showed that age(OR=1.04,95％CI:1.02-1.05),body mass index(BMI,OR=1.10,95％CI:1.05-1.17),systolic blood pressure(SBP,OR=1.01,95％CI:1.00-1.03),triglycerides(TG,OR=1.20,95％CI:0.99-1.51),and a history of hypertension(OR=1.28,95％CI:1.04-1.59)were independent risk factors for IFG occurrence in this population.Based on these variables,a column chart prediction model was constructed.In the training group,the model predicted an AUC of 0.722(95％CI:0.68-0.77)for IFG occurrence,while in the validation group,it predicted an AUC of 0.907(95％CI:0.87-0.94)for IFG occurrence.The results of the Hosmer-Lemeshow goodness of fit test showed that the models of the training and validation groups were not significantly different(P＞0.05);that is,the actual probability was consistent with the prediction probability of the model,and the models calibration was good.Conclusion A risk prediction model for IFG occurrence that included five variables:age,BMI,SBP,TG,and history of hypertension could be construted.This model might help to identify high-risk groups for IFG early and allow for inter-vention in a timely manner.

Objective To discuss the risk factors for impaired fasting glucose(IFG)and construct and validate a predictive model based on column charts of the risk of IFG occurrence.Methods This retrospective study included 3 037 individuals who underwent routine physical examinations at a hospital in Shenyang between August and December 2022.The population was randomly divided into a training group(n=2 126)and a validation group(n=911)in a 7∶3 ratio,and physical examination data were collected.LASSO regression analy-sis was used to screen predictive variables and logistic regression analysis was used to further screen and construct a column chart pre-dictive model.The validation group was used to conduct an internal validation of the feasibility of the model,and the area under the curve(AUC)of receiver operator characteristic(ROC)and goodness of fit tests were used to evaluate the model effectiveness.Results Among the 3 037 included individuals,2 880 did not experience IFG and 157 did.The results showed that age(OR=1.04,95％CI:1.02-1.05),body mass index(BMI,OR=1.10,95％CI:1.05-1.17),systolic blood pressure(SBP,OR=1.01,95％CI:1.00-1.03),triglycerides(TG,OR=1.20,95％CI:0.99-1.51),and a history of hypertension(OR=1.28,95％CI:1.04-1.59)were independent risk factors for IFG occurrence in this population.Based on these variables,a column chart prediction model was constructed.In the training group,the model predicted an AUC of 0.722(95％CI:0.68-0.77)for IFG occurrence,while in the validation group,it predicted an AUC of 0.907(95％CI:0.87-0.94)for IFG occurrence.The results of the Hosmer-Lemeshow goodness of fit test showed that the models of the training and validation groups were not significantly different(P＞0.05);that is,the actual probability was consistent with the prediction probability of the model,and the models calibration was good.Conclusion A risk prediction model for IFG occurrence that included five variables:age,BMI,SBP,TG,and history of hypertension could be construted.This model might help to identify high-risk groups for IFG early and allow for inter-vention in a timely manner.