The Association of Serum Total Bile Acids With Bone Mineral Density in Chinese Adults Aged 20-59: A Retrospective Cross-Sectional Study
Abstract
OBJECTIVE: According to a recent study, serum total bile acids (TBA) may preserve lumbar bone mineral density (BMD) in Cushing syndrome patients, and BMD is directly linked to bone health. We were interested in examining the association between TBA and in Chinese adults aged 20-59 years.
METHODS: We retrospectively analyzed the physical examination results of 2,490 general healthy subjects in Hainan West Central Hospital. Femoral neck BMD and TBA were measured, and the relationship between TBA and femoral neck BMD was evaluated by curve fitting, a generalized additive model, and multiple linear regression analysis.
RESULTS: The fitted smooth curve and generalized additive model showed a nonlinear relationship between TBA and femoral neck BMD, and a positive correlation between TBA and femoral neck BMD was found after we made adjustments for the potential confounders.
CONCLUSION: TBA is positively associated with femoral neck BMD in Chinese adults aged 20-59 years.
How strong is the evidence?
Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause. Reported size: 2,490 participants or records.
Read the paper
Related studies
- Development of a crushable foam model for human trabecular bone
- Data-driven motion compensation in HR-pQCT.
- Modeling of structure, quality, and function in the orthodontic patient
- Exploring the mechanism of Heidihuang Pill in the treatment of osteoporosis based on network pharmacology, molecular docking, and experimental validation
- Effect of fabric on the accuracy of computed tomography-based finite element analyses of the vertebra
- Use of interventions involving virtual reality tasks during the climacteric: a systematic review
Go deeper in Evidence Hub
Evidence Hub adds a study data sheet (design, sample size, evidence level, key numbers), an AI summary and a chat grounded in the paper's text.