Prediction model for low bone mass mineral density in type 2 diabetes: an observational cross-sectional study

• Published in: Endocrine Vol. 86; no. 1; pp. 369 - 379
• Main Authors: Ji, Cheng, Ma, Jie, Sun, Lingjun, Sun, Xu, Liu, Lijuan, Wang, Lijun, Ge, Weihong, Bi, Yan
• Format: Journal Article
• Language: English
• Published: New York Springer US 09.05.2024; Springer Nature B.V
• Subjects: Alkaline phosphatase; Body mass index; Bone mass; Bone mineral density; Cholesterol; Collagen (type I); Diabetes; Diabetes mellitus (non-insulin dependent); Endocrinology; Glomerular filtration rate; High density lipoprotein; Humanities and Social Sciences; Internal Medicine; Medicine; Medicine & Public Health; multidisciplinary; Nomograms; Original Article; Osteoporosis; Peptides; Prediction models; Regression analysis; Risk assessment; Risk factors; Science; Type 2 diabetes; Osteoporosis; Bone; Nomogram
• ISSN: 1559-0100; 1355-008X; 1559-0100
• DOI: 10.1007/s12020-023-03500-w

Purpose Considering the prevalence of type 2 diabetes (T2D), osteoporosis should be considered a serious complication. However, an effective tool for the assessment of low bone mass mineral density (BMD) in T2D patients is not currently available. Therefore, the aim of our study was to establish a simple-to-use risk assessment tool by exploring risk factors for low BMD in T2D patients. Methods This study included 436 patients with a low BMD and 381 patients with a normal BMD. Multiple logistic regression analysis was performed to evaluate risk factors for low BMD in T2D patients. A nomogram was then developed from these results. A receiver operating characteristic (ROC) curve, calibration plot, and goodness-of-fit test were used to validate the nomogram. The clinical utility of the nomogram was also assessed. Results Multivariate logistic regression indicated that age, sex, education, body mass index (BMI), fasting C-peptide, high-density cholesterol (HDL), alkaline phosphatase (ALP), estimated glomerular filtration rate (eGFR), and type I collagen carboxy terminal peptide (S-CTX) were independent predictors for low BMD in T2D patients. The nomogram was developed from these variables using both the unadjusted area under the curve (AUC) and the bootstrap-corrected AUC (0.828). Calibration plots and the goodness-of-fit test demonstrated that the nomogram was well calibrated. Conclusions The nomogram-illustrated model can be used by clinicians to easily predict the risk of low BMD in T2D patients. Our study also revealed that common factors are independent predictors of low BMD risk. Our results provide a new strategy for the prediction, investigation, and facilitation of low BMD in T2D patients.