Participant characteristics by data set.

BackgroundOsteoporosis is a major global health issue, weakening bones and increasing fracture risk. Dual-energy X-ray absorptiometry (DXA) is the standard for measuring bone mineral density (BMD) and diagnosing osteoporosis, but its costliness and complexity impede widespread screening adoption. Predictive modeling using genetic and clinical data offers a cost-effective alternative for assessing osteoporosis and fracture risk. This study aims to develop BMD prediction models using data from the UK Biobank (UKBB) and test their performance across different ethnic and geographical populations.Methods and findingsWe developed BMD prediction models for the femoral neck (FNK) and lumbar spine (SPN) using both genetic variants and clinical factors (such as sex, age, height, and weight), within 17,964 British white individuals from UKBB. Models based on regression with least absolute shrinkage and selection operator (LASSO), selected based on the coefficient of determination (R2) from a model selection subset of 5,973 individuals from British white population. These models were tested on 5 UKBB test sets and 12 independent cohorts of diverse ancestries, totaling over 15,000 individuals. Furthermore, we assessed the correlation of predicted BMDs with fragility fractures risk in 10 years in a case-control set of 287,183 European white participants without DXA-BMDs in the UKBB.With single-nucleotide polymorphism (SNP) inclusion thresholds at 5×10−6 and 5×10−7, the prediction models for FNK-BMD and SPN-BMD achieved the highest R2 of 27.70% with a 95% confidence interval (CI) of [27.56%, 27.84%] and 48.28% (95% CI [48.23%, 48.34%]), respectively. Adding genetic factors improved predictions slightly, explaining an additional 2.3% variation for FNK-BMD and 3% for SPN-BMD over clinical factors alone. Survival analysis revealed that the predicted FNK-BMD and SPN-BMD were significantly associated with fragility fracture risk in the European white population (P ConclusionsIn this study, we observed that combining genetic and clinical factors improves BMD prediction compared to clinical factors alone. Adjusting inclusion thresholds for genetic variants (e.g., 5×10−6 or 5×10−7) rather than solely considering genome-wide association study (GWAS)-significant variants can enhance the model’s explanatory power. The study highlights the need for training models on diverse populations to improve predictive performance across various ethnic and geographical groups.

背景 骨质疏松症（Osteoporosis）是全球性重大健康问题，会削弱骨骼强度并提升骨折风险。双能X线吸收测定法（Dual-energy X-ray absorptiometry, DXA）是测量骨密度（bone mineral density, BMD）及诊断骨质疏松症的金标准，但该方法成本高昂、操作复杂，阻碍了大规模筛查的普及。利用遗传与临床数据开展预测建模，可为骨质疏松症及骨折风险评估提供一种经济高效的替代方案。本研究旨在基于英国生物库（UK Biobank, UKBB）数据开发骨密度预测模型，并在不同族裔与地理人群中测试其性能。

研究方法与结果 我们从英国生物库中纳入17964名英国白人个体，基于遗传变异与临床因素（包括性别、年龄、身高、体重），分别构建股骨颈（femoral neck, FNK）与腰椎（lumbar spine, SPN）的骨密度预测模型。模型构建采用最小绝对收缩和选择算子（least absolute shrinkage and selection operator, LASSO）回归，模型选择依据来自5973名英国白人个体组成的模型选择子集的决定系数（R²）。随后，我们在5个英国生物库测试集与12个不同祖先背景的独立队列（总样本量超15000人）中对上述模型进行测试。此外，我们在英国生物库中287183名未接受DXA骨密度检测的欧洲白人参与者组成的病例对照队列中，评估了预测骨密度与10年脆性骨折风险的相关性。

当单核苷酸多态性（single-nucleotide polymorphism, SNP）的纳入阈值设为5×10^−6与5×10^−7时，股骨颈骨密度与腰椎骨密度预测模型的最高决定系数分别达到27.70%（95%置信区间[CI]：27.56%~27.84%）与48.28%（95%CI：48.23%~48.34%）。相较于仅使用临床因素的模型，加入遗传因素可小幅提升预测性能，为股骨颈骨密度的变异解释率额外提升2.3%，为腰椎骨密度额外提升3%。生存分析显示，在欧洲白人人群中，预测的股骨颈骨密度与腰椎骨密度均与脆性骨折风险显著相关（P < 0.001）。结论 本研究发现，相较于仅使用临床因素的模型，结合遗传与临床因素可提升骨密度预测性能。调整遗传变异的纳入阈值（例如5×10^−6或5×10^−7）而非仅考虑全基因组关联研究（genome-wide association study, GWAS）显著变异，可增强模型的解释能力。本研究强调，需在多样化人群中开展模型训练，以提升不同族裔与地理群体的预测性能。