Correlate factors and nomogram model establishment for women with low bone mineral density: a cross-sectional diagnostic prediction model study

Background The proportion of women suffering from osteopenia is high, and early diagnosis and treatment are crucial. However, clinical diagnosis requires special equipment and may involve radiation damage. The purpose of this study was to establish a prediction nomogram based on easily obtainable routine physical examination indicators and risk factors for the early prediction of low bone mineral density (LBMD) in women. Methods We conducted a two-center cross-sectional diagnostic prediction model study involving women who underwent medical examinations from January 1, 2019, to December 31, 2021. Included participants ( n = 3,121) from the physical examination center were randomly divided into a training set (2185) and an internal validation set (936) at a 7:3 ratio. Female outpatients ( n = 1,138) from another independent medical center within the same period were enrolled as the external validation set. Dual-energy X-ray absorptiometry (DXA) was used to measure bone mineral density (BMD) at the lumbar spine (L1–L4) and left femoral neck. Demographic characteristics, laboratory test results, and DXA measurements were retrospectively extracted from the hospital electronic health record system. Univariate analysis and multiple logistic regression analysis were used to screen for risk factors related to LBMD, and a predictive model for LBMD was constructed with the training set. Afterwards, a nomogram was developed and validated by receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and clinical decision curve analysis (DCA) in the internal validation set and the external validation set. Results A total of 4,259 people were included in this study. The detection rates of LBMD in the training set, internal validation set, and external validation set were 30.48% (666/2185), 31.08% (291/936), and 33.48% (381/1138), respectively. The important relevant factors for LBMD in women were age (OR = 1.204, 95% CI: 1.184–1.225, p < 0.001), weight (OR = 0.943, 95% CI: 0.927–0.960, p < 0.001), serum creatinine (OR = 0.984, 95% CI: 0.970–0.999, p = 0.034), serum phosphorus (OR = 0.698 per 0.1 mmol/L; 95% CI: 0.641–0.761, p < 0.001) and serum magnesium (OR = 0.600 per 0.1 mmol/L; 95% CI: 0.486–0.741, p < 0.001). A nomogram was established as a tool for predicting LBMD based on the above relevant factors. The area under the ROC curve (AUC) of the nomogram was 0.894 (95% CI, 0.877–0.912) based on the training set. The calibration curve and DCA both indicated a high degree of accuracy in the model’s predictions in the internal and external validation sets. Conclusion This novel nomogram can effectively predict LBMD in women, and the indicators are easy to obtain. This is helpful in screening high-risk populations for LBMD and early acceptance of personalized intervention measures.