The Critical Role of Inflammation in Osteoporosis Prediction Unveiled by a Machine Learning Framework Integrating Multi-Source Data

BO LIU^1*Hongli Chang²Peipei Li²Hongguang Chang³Xuenan Wang²Wubing He^1,4,5

• ¹Shengli Clinical Medical College, Fujian Medical University, Fuzhou, China
• ²Institute of Brain Science and Brain-inspired Research, Shandong First Medical University, Jinan, China
• ³School of Continuing Education, Shandong First Medical University, Jinan, China
• ⁴Department of Emergency Surgery, Fujian Provincial Hospital, fuzhou, China
• ⁵Affiliated Provincial Hospital, Fuzhou University, Fuzhou, China

Objective: Osteoporosis poses a major global public health challenge. The limitations of current diagnostic methods, primarily diagnostic delays in bone density testing, are compounded by the insufficient exploration of inflammatory factors in predictive models for the disease's pathogenesis. This study aims to leverage multi-source data and machine learning to explore the value of inflammatory markers for osteoporosis prediction, establishing a high-precision model for early screening and precise prevention. Methods: A multi-center, multi-level research design was employed, integrating four independent datasets: the National Health and Nutrition Examination Survey (NHANES) database (12,988 adult women), a Chinese postmenopausal women specialized cohort (CPW-BMI) (312 participants), the Osteoporosis Phenotype Validation Cohort (OP-VC) (60 participants), and animal experimental data (40 C57BL/6J mice). A predictive indicator system comprising 22 clinical features and inflammatory markers was constructed. Various machine learning algorithms (including RUSBoosted Trees, Bagged Trees, Support Vector Machines, Gaussian Process Regression, etc.) were used to establish classification and regression prediction models, and model performance was evaluated through rigorous five-fold cross-validation and external validation. Results: Machine learning models based on inflammatory markers exhibited excellent predictive performance across different bone sites. At the femoral neck, the RUSBoosted Trees model achieved an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.9643 and an accuracy of 90.55%; at the lumbar spine, the Efficient Logistic Regression model achieved an AUC of 0.9685 and an accuracy of 91.79%. External validation demonstrated good generalization ability: in the Chinese population cohort, the Fine Gaussian Support Vector Machine model had a prediction error (Root Mean Square Error, RMSE) of 0.681; in the clinical cohort, serum levels of Interleukin-6 (IL-6), Tumor Necrosis Factor-alpha (TNF-α), and Interleukin-1 beta (IL-1β) were significantly elevated in the osteoporosis group; in animal experiments, a Linear Discriminant Analysis model based on three core inflammatory factors achieved 97.5% accuracy (AUC=0.9574). These results confirm the value of inflammatory markers in osteoporosis risk assessment. Conclusion: Using inflammation markers and machine learning, we created accurate models to predict osteoporosis. This work confirms inflammation's key role in the disease, providing new insights for early detection and targeted intervention.