Peng Fei Liu,1 Yan Xin Ren,1 Peng Wang,2 Xiu Mei Ma,3 Kang Geng4,5 

1China Aerospace Science & Industry Corporation 731 hospital, Beijing, People’s Republic of China; 2Chengdu First People’s Hospital, Chengdu Integrated TCM and Western Medicine Hospital, Chengdu, Sichuan, People’s Republic of China; 3Key Laboratory for Human Disease Gene Study of Sichuan Province and Institute of Laboratory Medicine, Sichuan Provincial People’s Hospital, University of Electronic Science and Technology of China, Chengdu, Sichuan, People’s Republic of China; 4Department of Plastic and Burns Surgery, National Key Clinical Construction Specialty, The Affiliated Hospital of Southwest Medical University, Luzhou, Sichuan, People’s Republic of China; 5Metabolic Vascular Disease Key Laboratory of Sichuan Province, Sichuan Clinical Research Center for Nephropathy, Cardiovascular and Metabolic Diseases Key Laboratory of Luzhou, Luzhou, Sichuan, People’s Republic of China

Correspondence: Kang Geng, The Affiliated Hospital of Southwest Medical University, Metabolic Vascular Disease Key Laboratory of Sichuan Province, Luzhou, Sichuan, People’s Republic of China, Email gengkang2013@swmu.edu.cn

Background: To address the high disability and mortality rates of osteoporotic fracture (OPF), a common complication of type 2 diabetes mellitus (T2DM), this study seeks to create an early OPF risk prediction model for T2DM patients.
Methods: A single-center retrospective study was conducted on 868 T2DM patients using Multi-dimensional data. The dataset was split into training and validation sets at an 8:2 ratio. Through logistic regression analyses, key predictive factors were pinpointed and incorporated into a Nomogram prediction model. The model’s reliability, validity, and generalizability were assessed using various statistical methods, including the Hosmer-Lemeshow test, Receiver Operator Characteristic (ROC) curve analysis, and decision curve analysis. The validation set was used to test the model.
Results: Female gender (OR 2.681, 95% CI 1.046– 6.803, P=0.04), age (OR 1.068, 95% CI 1.023– 1.115, P=0.003), body mass index (BMI) (OR 0.912, 95% CI 0.851– 0.979, P=0.010), blood lactic acid level (OR 0.747, 95% CI 0.597– 0.935, P=0.011), lumbar T-score (OR 0.644, 95% CI 0.499– 0.833, P=0.001), and femoral neck T-score (OR 0.412, 95% CI 0.292– 0.602, P< 0.001) were identified as independent factors predicting OPF in T2DM patients. Based on these factors, a Nomogram model was constructed. The model showed a high degree of agreement with actual data (Hosmer-Lemeshow test, P=0.406), with an Area Under the Curve (AUC) value of 0.831. It demonstrated good clinical benefits across different thresholds and excellent generalization ability on the validation set.
Conclusion: This study integrated key factors such as gender, age, BMI, lactic acid, lumbar spine, and femoral neck T-values to construct a Nomogram for predicting the risk of OPF in T2DM patients. This model can assist doctors in accurately assessing the risk of OPF in T2DM patients, facilitating early detection and timely treatment. It has significant clinical practical value.

Keywords: type 2 diabetes mellitus, osteoporosis, bone fracture, nomogram, risk prediction