Establishment of a Nomogram Model for Predicting Abnormal Bone Mass in Diabetic Patients

Hui LIU; Xinyi PENG; Tonger LIANG; Yong ZHAO

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Hui LIU, Xinyi PENG, Tonger LIANG, Yong ZHAO. Establishment of a Nomogram Model for Predicting Abnormal Bone Mass in Diabetic Patients[J]. Journal of Kunming Medical University.

Citation:

Hui LIU, Xinyi PENG, Tonger LIANG, Yong ZHAO. Establishment of a Nomogram Model for Predicting Abnormal Bone Mass in Diabetic Patients[J]. Journal of Kunming Medical University.

Hui LIU, Xinyi PENG, Tonger LIANG, Yong ZHAO. Establishment of a Nomogram Model for Predicting Abnormal Bone Mass in Diabetic Patients[J]. Journal of Kunming Medical University.

Citation:

Hui LIU, Xinyi PENG, Tonger LIANG, Yong ZHAO. Establishment of a Nomogram Model for Predicting Abnormal Bone Mass in Diabetic Patients[J]. Journal of Kunming Medical University.

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Establishment of a Nomogram Model for Predicting Abnormal Bone Mass in Diabetic Patients

Department of Health Management Center，Chengdu Third People’s Hospital/Affiliated Hospital of Southwest Jiaotong University，Chengdu Sichuan 610031，China

Received Date: 2025-09-25
Available Online: 2026-01-03

Abstract

Abstract

Objective To establish a nomogram model for predicting abnormal bone mass in diabetic patients. Methods A total of 944 diabetic patients who underwent health checkups at the Affiliated Hospital of Southwest Jiaotong University （Chengdu Third People's Hospital） from January 2024 to January 2025, were selected. The patients were divided into a training set （n = 661） and a validation set （n = 283） in a 7∶3 ratio. The training group was further categorized into normal and abnormal bone mass groups based on T-scores/Z-scores. Various indicators were compared between the two groups, and potential factors were screened using LASSO regression. Logistic regression was then performed to establish a nomogram, which was subsequently validated. Results Among the 661 patients in the training set, 323 （48.87%） exhibited abnormal bone mass. Logistic regression analysis based on LASSO regression results showed that male sex, age, glycated hemoglobin, uric acid, and type I procollagen N-terminal propeptide （PINP） were the independent risk factors for abnormal bone mass in diabetic patients, while BMI served as a protective factor （P < 0.05）. The AUC for predicting abnormal bone mass in diabetic patients was 0.804 （95%CI: 0.771～0.837） in the training set, with a sensitivity of 73.1% and a specificity of 74.9%. In the validation set, the AUC was 0.774 （95%CI: 0.739～0.810）, with a sensitivity of 75.9% and a specificity of 65.1%. The prediction curve closely matched the standard curve, and the decision curve analysis indicated that the patient benefit rate was highest when the model-predicted probability ranged from 0.15 to 0.95. Conclusion Abnormal bone mass in diabetic patients is primarily influenced by factors such as sex, age, and BMI. The nomogram model established based on these factors can effectively predict the risk of abnormal bone mass in diabetic patients.
- Diabetes,
- Abnormal bone mass,
- Multivariate analysis,
- Nomogram

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References(26)

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