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). Here, TNF is linked to osteoporosis.