Development and validation of a nomogram incorporating triglyceride-glucose index and aggregate index of systemic inflammation for assessing osteoporosis risk in type 2 diabetes mellitus: a dual-center retrospective study.

OBJECTIVES

In order to identify the factors related to osteoporosis in patients with type 2 diabetes, it is necessary to establish a visual risk classification model to evaluate the current risk and verify its effectiveness.

METHODS

This retrospective observational study included 2,265 patients with type 2 diabetes who were admitted to the First People's Hospital of Neijiang City between January 2021 and December 2025. All these patients underwent dual-energy X-ray absorptiometry (DXA) examinations during the same admission or physical examination visit. They were divided into the training group and the validation group in a ratio of 7:3. A predictive model was established using multivariate Logistic regression analysis and the Least Absolute Shrinkage and Selection Operator (Lasso) regression. A nomogram for clinical application was also established and its discrimination ability, calibration, and clinical practicability were verified.

RESULTS

Multivariate Logistic regression analysis revealed that age, gender, BMI, Triglyceride, Monocyte count, TyG and AISI were independent risk factors for osteoporosis in patients with type 2 diabetes. The nomogram model showed a reliable predictive effect, with an area under the curve (AUC) of 0.847 (95% CI, 0.821-0.874) in the training cohort and 0.833 (95% CI, 0.791-0.875) in the validation cohort. Decision curve analysis (DCA) confirmed that the nomogram model had strong clinical practical value.

CONCLUSION

The risk factor assessment chart for osteoporosis in patients with type 2 diabetes established in this study has good discriminative ability and provides an objective tool for clinical personnel to conduct early assessment.