Distribution and diagnostic modeling of osteoporosis and comorbidities across demographic factors: A cross-sectional study of 2224 female patients

Objective: This study investigates the distribution of osteoporosis (OP) and its associated comorbidities across different demographic factors. Furthermore, this study seeks to develop a statistically-based diagnostic model leveraging demographic and health indicators to provide personalized risk assessments for OP. Methods: A retrospective analysis was conducted on the demographic data, health profiles, and bone density measurements of 2224 female patients. Key variables associated with OP were identified using chi-square tests. Feature selection was refined through Lasso regression and recursive feature elimination (RFE), which guided the development of a logistic regression-based dynamic nomogram. This model was subsequently implemented on the Shiny platform for personalized online OP risk assessments. Results: Among 2224 female patients, 801 (36.0 %) were diagnosed with OP. Women aged 70 and older exhibited a significantly higher prevalence of OP compared to younger age groups (OR = 5.83, 95 % CI: 1.74-19.61, P = 0.004), and this remained significant in the multivariable analysis (OR = 5.18, 95 % CI: 1.19-22.52, P = 0.028). Later age at menarche was associated with increased OP risk (OR = 1.31, 95 % CI: 1.09-1.57, P = 0.004), persisting in multivariable analysis (OR = 1.25, 95 % CI: 1.03-1.52, P = 0.023). In rheumatoid arthritis (RA) patients, higher education reduced OP risk, with secondary education (OR = 0.09, P = 0.024) and college education (OR = 0.04, P = 0.009) showing protective effects. Diabetic patients who were unmarried or had nontraditional marital statuses showed increased OP risk (univariate OR = 2.73, P = 0.006; multivariate OR = 2.34, P = 0.029). Among nonalcoholic fatty liver disease (NAFLD) patients, age at menopause was significantly linked to OP risk (univariate OR = 1.04, P = 0.012). The prediction model showed strong performance (AUC = 0.720), and the dynamic nomogram on the Shiny platform provided effective personalized OP risk assessments. Conclusion: Age and age at menarche are significant risk factors for OP, with later menarche increasing the risk. In RA patients, higher education levels were associated with a lower risk of OP. In contrast, unmarried or nontraditional marital statuses increased OP risk among diabetic patients. Additionally, age at menopause was found to be a significant factor for OP risk in NAFLD patients. The prediction model developed in this study, with an AUC of 0.720, provides a reliable method for personalized OP risk assessment through a dynamic nomogram. These findings highlight the crucial role of demographic factors in predicting OP risk and underscore the importance of personalized treatment strategies for effective OP prevention and management.