Frontiers in endocrinology · 2026
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.
СтатьяНаблюдательное исследованиеМногоцентровое исследованиеВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Luo Yuan, Wei Hongyan, Chen Li, Tang Zhongbao, Zhang Liyue
Аннотация
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.
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.