Path analysis to identify factors influencing osteoporosis: A cross-sectional study

Background: Osteoporosis is characterized by low bone mass and deterioration of bone tissue, which is influenced by both environmental factors and nutritional metabolism. The relationship between biochemical indicators and bone mineral density (BMD) is intricate and involves complex mechanisms. Path analysis, a statistical method that investigates causal relationships and the strength of associations among multiple factors, can be valuable in elucidating the connection between biochemical indicators and BMD. Methods: In this study, we employed advanced statistical techniques, specifically structural equation modeling (SEM) to investigate the intricate interrelationships among a myriad of factors that exert influence on BMD. This analytical approach facilitated not only the identification of the direct relationships between specific variables and BMD but also the exploration of the intricate of indirect pathway through which other variables contribute to the oval impact on BMD. By delving into the direct and indirect effects, we aimed to unravel the complex influences that collectively shape the state of bone health, providing a nuanced understanding of the multifaceted nature of the factors affecting BMD. Results: Our findings revealed that lipid levels had a significant indirect influence on BMD, which was mediated by body mass index (BMI). BMI exhibited both direct and indirect effects on BMD. Uric acid (UA) exerted a significant direct and indirect influence on BMD, with glomerular filtration rate (GFR) acting as the mediator. However, the total effect of UA on BMD was not significant due to the cancellation of positive effect UA on BMD but negative indirect effects of UA through GFR. For females, albumin had a significant direct effect on BMD, whereas this effect was not observed in males. The path analysis models generated results that demonstrated an acceptable fit for both female data (chi 2 = 9.63, df = 7, p = 0.21, comparative fit index (CFI) = 0.98, root mean square error of approximation (RMSEA) = 0.05) and male data (chi 2 = 6.26, df = 4, p = 0.18, CFI = 0.97, RMSEA = 0.06). Conclusions: Nutritional metabolism plays a crucial role in maintaining BMD in elderly females and males.