Objective: The purpose of this study is to establish a predictive model of cognitive impairment in elderly people with diabetes. Methods: We analyzed a total of 878 elderly patients with diabetes who were part of the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2014. The data were randomly divided into training and validation cohorts at a ratio of 6:4. The least absolute shrinkage and selection operator (LASSO) logistic regression analysis to identify independent risk factors and construct a prediction nomogram for cognitive impairment. The performance of the nomogram was assessed using receiver operating characteristic (ROC) curve and calibration curve. Decision curve analysis (DCA) was performed to evaluate the clinical utility of the nomogram. Results: LASSO logistic regression was used to screen eight variables, age, race, education, poverty income ratio (PIR), aspartate aminotransferase (AST), blood urea nitrogen (BUN), serum uric acid (SUA), and heart failure (HF). A nomogram model was built based on these predictors. The ROC analysis of our training set yielded an area under the curve (AUC) of 0.786, while the validation set showed an AUC of 0.777. The calibration curve demonstrated a good fit between the two groups. Furthermore, the DCA indicated that the model has a favorable net benefit when the risk threshold exceeds 0.2. Conclusion: The newly developed nomogram has proved to be an important tool for accurately predicting cognitive impairment in elderly patients with diabetes, providing important information for targeted prevention and intervention measures.Copyright © 2024 Sensen Wu et al.