Variability in performance of genetic-enhanced DXA-BMD prediction models across diverse ethnic and geographic populations: A risk prediction study

作者：Y.X. Liu, Xiang‐He Meng, Chong Wu, Kuan‐Jui Su, Anqi Liu, Qing Tian, Lan‐Juan Zhao, Chuan Qiu, Zhe Luo, Martha Isabel González-Ramírez, Hui Shen, Hong‐Mei Xiao, Hong‐Wen Deng · 发表于：PLoS Medicine · 年份：2024 · DOI：10.1371/journal.pmed.1004451 · 被引用次数：12 · 研究领域：Bone health and osteoporosis research、Genetic Associations and Epidemiology、Parathyroid Disorders and Treatments

BACKGROUND: Osteoporosis 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 FINDINGS: We 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 mod...