用因果分析筛选出最能预测髋部骨折的骨密度指标
DXA-Derived Skeletal Phenotypes and Hip Fracture Risk: A Backdoor-Adjusted Causal Analysis
- 基于因果图调整混杂因素，量化16种骨骼表型对骨折风险的影响
- 股骨总骨量和骨密度每提高一标准差，可减少4.7例/千人骨折
- 结合临床数据+关键表型，预测准确率优于传统工具FRAX
目的：通过预设混杂因子调整，比较双能X线吸收法（DXA）-derived髋部骨骼表型与髋部骨折风险的关系，并评估按后门路径调整平均治疗效应（ATE）排序的表型是否提升风险分层。方法：分析21,098名英国生物样本库参与者，其数据包括健康记录、髋部DXA测量及预设协变量。评估16种涵盖骨矿物质含量（BMC）、骨密度（BMD）和髋部区域T值的表型。混杂因子选择依据预设有向无环图（DAG）。在绝对风险差异尺度上，估算每标准差（SD）增加的后门调整后ATE。评估总股骨BMD的效果异质性，并评估将按ATE大小排序的表型与临床变量结合后的下游预测表现。结果：21,098名参与者中，115人发生髋部骨折。所有16种表型均显示负的后门调整后ATE。最大效应出现在总股骨BMC和总股骨BMD，每增加1个标准差，风险差异为-0.0047，相当于每千人减少约4.7例骨折。总股骨BMD的条件效应在老年人和低体重者中更强。预测模型中，临床变量加上前11个ATE排名表型的AUC达0.842，优于含股骨颈BMD的FRAX（0.709），敏感度更高（0.748 vs. 0.443），特异度相近（0.793 vs. 0.777）。结论：不同DXA-derived髋部骨骼表型的后门调整后效应存在差异。表型层面的因果评估有助于识别对风险分层最有信息量的DXA指标。
原文摘要 · Abstract (English)
Purpose: To compare dual-energy X-ray absorptiometry (DXA)-derived hip skeletal phenotypes in relation to hip fracture risk using prespecified confounder adjustment and to assess whether phenotypes ranked by their backdoor-adjusted average treatment effects (ATEs) improve risk stratification. Methods: We analyzed 21,098 UK Biobank participants with linked health records, hip DXA-derived skeletal measures, and prespecified covariates. Sixteen phenotypes spanning bone mineral content (BMC), bone mineral density (BMD), and T-score across hip-related regions were evaluated. Confounder selection was guided by a prespecified directed acyclic graph (DAG). Backdoor-adjusted ATEs were estimated on the absolute risk-difference scale per standard deviation (SD) increase. Effect heterogeneity was evaluated for total femur BMD, and downstream prediction was assessed using clinical variables combined with phenotypes ranked by ATE magnitude. Results: Among 21,098 participants, 115 had hip fractures. All 16 phenotypes showed negative backdoor-adjusted ATEs per SD increase. The largest ATEs were observed for total femur BMC and total femur BMD, each with a risk difference of -0.0047, corresponding to approximately 4.7 fewer hip fractures per 1,000 participants per SD higher phenotype value. Conditional effects of total femur BMD were stronger among older participants and those with lower BMI. In prediction, clinical variables plus the top 11 ATE-ranked phenotypes achieved higher AUC than FRAX with femoral neck BMD (0.842 vs. 0.709), with higher sensitivity (0.748 vs. 0.443) and similar specificity (0.793 vs. 0.777). Conclusion: DXA-derived hip skeletal phenotypes differed in their backdoor-adjusted ATEs. Phenotype-level causal evaluation may help identify informative DXA measures for risk stratification.
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