A multivariate analysis of the impact of HIV infection on the risk of osteoporosis based on the NHANES database.

Osteoporosis is a common metabolic bone disease characterized by decreased bone density and increased fracture risk. Human immunodeficiency virus (HIV) infection is considered one of the independent risk factors for osteoporosis, but its specific mechanisms are not yet clear. This study aims to explore the relationship between HIV infection and osteoporosis based on the National Health and Nutrition Examination Survey database and to analyze the impact of related clinical factors on bone density. This study utilized National Health and Nutrition Examination Survey data from 2013 to 2018 to analyze bone density in individuals with and without HIV. The association between HIV infection and osteoporosis was assessed using multiple linear regression, Spearman correlation analysis, and logistic regression models. A neural network model was employed to predict the risk of osteoporosis. The study also analyzed the effects of factors such as age, gender, body mass index, calcium, and protein intake on bone density. In this study, the bone density of HIV-positive patients was significantly lower than that of HIV-negative patients (P < .001). Multivariate regression analysis showed that HIV infection is an independent risk factor for decreased bone density and is associated with clinical factors such as age, gender, and body mass index. Logistic regression analysis indicated that the risk of osteoporosis in HIV-positive individuals was significantly increased (odds ratios = 819.18, P < .001). The area under the curve value of the neural network model was 0.872, demonstrating high predictive accuracy. There is a significant correlation between HIV infection and osteoporosis, with HIV-positive patients having significantly lower bone density than HIV-negative individuals. Factors such as age, gender, calcium, and protein intake have important effects on changes in bone density. This study provides new directions for the screening and early intervention of osteoporosis in HIV-infected individuals, and the neural network model offers high predictive value, supporting clinical decision-making.