Development and validation of a nomogram prediction model for thyroid dysfunction in patients with type 2 diabetes mellitus.

Research has shown that the concurrent presence of Diabetes Mellitus (DM) and Thyroid Dysfunction (TD) can exacerbate diabetes-related complications and impose a significant economic burden on healthcare systems. Therefore, this study aimed to develop a nomogram model for predicting the risk of TD in patients with Type 2 Diabetes Mellitus (T2DM) and to validate its predictive performance. A total of 1853 patients with T2DM diagnosed at the First Hospital of Hebei Medical University from 2019 to 2024 were included in the study. The dataset was randomly divided into a training set (n = 1297) and a validation set (n = 556) at a 7:3 ratio using the R software. Univariate and multivariate logistic regression analyses were conducted to identify predictors of TD, and these predictors were used to construct the nomogram model. The model was evaluated using the receiver operating characteristic (ROC) curve and the area under the curve (AUC), calibration curve, the Hosmer-Lemeshow test, and decision curve analysis (DCA). HDL-C, BUN, gender, GLU, Hypertension, Hyperuricemia, Coronary Heart Disease, and Liver disease were identified as predictors of TD. A nomogram model was constructed based on these eight factors. The model demonstrated good discrimination in both the training and validation sets. The calibration curves indicated a good fit of the model in both datasets. The decision curve analysis showed that the model had good clinical applicability. The nomogram developed in this study can predict the risk of developing TD in patients with T2DM. It enables clinicians to identify T2DM patients at high risk of concurrent TD, which may help facilitate the development of effective preventive measures and improve patient prognosis.