Development and validation of nomogram prediction model for diabetic hearing impairment based on the levels of lncRNA MALAT1, miR-199b and AGEs in peripheral blood.

Objective: To develop a nomogram prediction model for diabetic hearing impairment (DHI) based on peripheral blood long noncoding RNA Methods: This cross-sectional study included 318 diabetic patients without pre-existing hearing loss, who were randomly divided into a training set (n=223) and a validation set (n=95) at a ratio of 7:3. Peripheral blood levels of LncRNA MALAT1 and miR-199b were quantified using real-time fluorescence quantitative PCR (RT-qPCR), and AGEs were measured by enzyme-linked immunosorbent assay (ELISA). Pure-tone audiometry (PTA) was performed to diagnose hearing impairment. Multivariate logistic regression identified independent influencing factors, and a prediction model in the form of nomogram is constructed accordingly. In order to evaluate the prediction accuracy of the model, the operating characteristic curve (ROC curve) and calibration curve of the subjects were further drawn. Decision curve analysis (DCA) was adopted to comprehensively evaluate the significance and value of the nomogram model. Results: There was no significant difference in baseline characteristics between training and validation sets (all Conclusion: The nomogram integrates clinical and biomarker data to predict DHI risk, offering a practical tool for early intervention.