Liu Peng Fei, Ren Yan Xin, Wang Peng, Ma Xiu Mei, Geng Kang
China Aerospace Science & Industry Corporation 731 hospital, Beijing, People's Republic of China.
Chengdu First People's Hospital, Chengdu Integrated TCM and Western Medicine Hospital, Chengdu, Sichuan, People's Republic of China.
Diabetes Metab Syndr Obes. 2025 Sep 11;18:3447-3464. doi: 10.2147/DMSO.S475409. eCollection 2025.
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.
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.
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.
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.
为解决2型糖尿病（T2DM）常见并发症骨质疏松性骨折（OPF）的高致残率和高死亡率问题，本研究旨在为T2DM患者创建一个早期OPF风险预测模型。
对868例T2DM患者进行单中心回顾性研究，使用多维数据。数据集按8:2的比例分为训练集和验证集。通过逻辑回归分析，确定关键预测因素并将其纳入列线图预测模型。使用包括Hosmer-Lemeshow检验、受试者工作特征（ROC）曲线分析和决策曲线分析在内的各种统计方法评估模型的可靠性、有效性和可推广性。验证集用于测试模型。
女性（OR 2.681，95%CI 1.046 - 6.803，P = 0.04）、年龄（OR 1.068，95%CI 1.023 - 1.115，P =
