DXA-Derived Skeletal Phenotypes and Hip Fracture Risk: A Backdoor-Adjusted Causal Analysis

2026-05-29

AI Summary

This study aims to scientifically evaluate the causal effects of hip bone phenotypes derived from dual-energy X-ray absorptiometry (DXA) on hip fracture risk and to improve clinical risk stratification. Leveraging UK Biobank data, it systematically applies backdoor adjustment—a causal inference method guided by directed acyclic graphs (DAGs)—to control for confounding bias and estimate the average treatment effect (ATE) of 16 hip phenotypes. Results indicate that total femoral bone mineral content (BMC) and bone mineral density (BMD) exhibit the strongest protective effects, with each standard deviation increase associated with a reduction of 4.7 hip fractures per 1,000 person-years. Integrating the top 11 ATE-ranked phenotypes significantly enhances predictive model performance, achieving an AUC of 0.842, surpassing that of FRAX+BMD. This work establishes a novel, causality-driven paradigm for osteoporosis-related fracture risk assessment.

Abstract

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.

Problem

• hip fracture risk
• DXA-derived skeletal phenotypes
• risk stratification
• bone mineral density
• causal analysis

Innovation

• backdoor-adjusted causal inference
• DXA-derived skeletal phenotypes
• average treatment effect (ATE)
• hip fracture risk stratification
• directed acyclic graph (DAG)