Immune cell profiles and predictive modeling in osteoporotic vertebral fractures using XGBoost machine learning algorithms.

Abstract

Background: Osteoporosis significantly increases the risk of vertebral fractures, particularly among postmenopausal women, decreasing their quality of life. These fractures, often undiagnosed, can lead to severe health consequences and are influenced by bone mineral density and abnormal loads. Management strategies range from non-surgical interventions to surgical treatments. Moreover, the interaction between immune cells and bone cells plays a crucial role in bone repair processes, highlighting the importance of osteoimmunology in understanding and treating bone pathologies.

Methods: This study aims to investigate the xCell signature-based immune cell profiles in osteoporotic patients with and without vertebral fractures, utilizing advanced predictive modeling through the XGBoost algorithm.

Results: Our findings reveal an increased presence of CD4 + naïve T cells and central memory T cells in VF patients, indicating distinct adaptive immune responses. The XGBoost model identified Th1 cells, CD4 memory T cells, and hematopoietic stem cells as key predictors of VF. Notably, VF patients exhibited a reduction in Th1 cells and an enrichment of Th17 cells, which promote osteoclastogenesis and bone resorption. Gene expression analysis further highlighted an upregulation of osteoclast-related genes and a downregulation of osteoblast-related genes in VF patients, emphasizing the disrupted balance between bone formation and resorption. These findings underscore the critical role of immune cells in the pathogenesis of osteoporotic fractures and highlight the potential of XGBoost in identifying key biomarkers and therapeutic targets for mitigating fracture risk in osteoporotic patients.