ABSTRACT
Objective:To develop a nomogram model for screening of type 2 diabetes mellitus in a community population.Methods:From October to December, 2020, 6 028 community residents who participated in the " national health physical examination" in Karamay community with complete physical examination data and met the inclusion and exclusion criteria were selected. The physical examination data included medical history, physical examination, laboratory, and ultrasound reports. Random segmentation sampling was used to divide the population into modeling and validation cohorts, and LASSO regression analysis was used to screen for independent factors associated with diabetes diagnosis. The independent influencing factors were furthor incorporated into the multi-factor logistic regression, and the RMS software package was used to construct the column chart. The area under receiver operating characteristic(ROC) curve was used to measure the differentiation of the model. The calibration curve can directly reflect the calibration degree of the model.Results:In the modeling group, multivariate logistic regression analysis showed that age, gender(female), history of hypertension, history of hyperlipidemia, HbA 1C, urinary microalbumin, homeostasis model assessment for insulin resistance, and triglycerides and glucose index were independently associated with diabetes. OR were 1.053(95% CI 1.038-1.069), 0.681(95% CI 0.512-0.906), 1.802(95% CI 1.227-2.626), 1.789(95% CI 1.303-2.448), 10.973(95% CI 8.318-14.745), 1.002(95% CI 1.001-1.004), 2.914(95% CI 2.248-3.799), and 2.673(95% CI 2.03-3.536), respectively. The areas under ROC curves of the training set and the validation set were 0.945 and 0.955, respectively. The optimal critical value in the ROC curve was 0.178(sensitivity 0.930, specificity 0.839) in the training set and 0.201(sensitivity 0.945, specificity 0.848) in the validation set. Conclusion:The screening model of type 2 diabetes developed in this study has good accuracy, which can be used as a screening tool for high-risk population of type 2 diabetes.