Peng Fei Liu, Yan Xin Ren, Peng Wang, Xiu Mei Ma, Kang Geng
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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.</p><p><strong>Results: </strong>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.</p><p><strong>Conclusion: </strong>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.</p>","PeriodicalId":11116,"journal":{"name":"Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy","volume":"18 ","pages":"3447-3464"},"PeriodicalIF":3.0000,"publicationDate":"2025-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12435361/pdf/","citationCount":"0","resultStr":"{\"title\":\"Early Prediction Model for Osteoporotic Fracture in Type 2 Diabetes Patients: A Nomogram Approach Based on a Single-Center Retrospective Study.\",\"authors\":\"Peng Fei Liu, Yan Xin Ren, Peng Wang, Xiu Mei Ma, Kang Geng\",\"doi\":\"10.2147/DMSO.S475409\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>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.</p><p><strong>Methods: </strong>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.</p><p><strong>Results: </strong>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. 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引用次数: 0
摘要
背景:为解决2型糖尿病(T2DM)常见并发症骨质疏松性骨折(OPF)致残率和死亡率高的问题,本研究旨在建立T2DM患者早期OPF风险预测模型。方法:采用多维数据对868例T2DM患者进行单中心回顾性研究。数据集以8:2的比例分成训练集和验证集。通过逻辑回归分析,确定关键预测因素,并将其纳入Nomogram预测模型。采用Hosmer-Lemeshow检验、ROC曲线分析、决策曲线分析等统计方法评估模型的信度、效度和可推广性。使用验证集对模型进行测试。结果: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=0.003)、体重指数(BMI) (OR 0.912, 95% CI 0.851 ~ 0.979, P=0.010)、血乳酸水平(OR 0.747, 95% CI 0.597 ~ 0.935, P=0.011)、腰椎t -评分(OR 0.644, 95% CI 0.499 ~ 0.833, P=0.001)、股骨颈t -评分(OR 0.412, 95% CI 0.292 ~ 0.602, P)。本研究综合性别、年龄、BMI、乳酸、腰椎、股骨颈t值等关键因素,构建预测T2DM患者OPF风险的Nomogram。该模型可以帮助医生准确评估T2DM患者发生OPF的风险,便于早期发现和及时治疗。具有重要的临床实用价值。
Early Prediction Model for Osteoporotic Fracture in Type 2 Diabetes Patients: A Nomogram Approach Based on a Single-Center Retrospective Study.
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.
期刊介绍:
An international, peer-reviewed, open access, online journal. The journal is committed to the rapid publication of the latest laboratory and clinical findings in the fields of diabetes, metabolic syndrome and obesity research. Original research, review, case reports, hypothesis formation, expert opinion and commentaries are all considered for publication.