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

Yong Liu, Xiang-He Meng, Chong Wu, Kuan-Jui Su, Anqi Liu, Qing Tian, Lan-Juan Zhao, Chuan Qiu, Zhe Luo, Martha I Gonzalez-Ramirez, Hui Shen, Hong-Mei Xiao and Hong-Wen Deng

PLOS Medicine, 2024, vol. 21, issue 8, 1-27

Abstract: 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. Conclusions: In 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. Yong Liu and co-workers compareWhy was this study done?: What did the researchers do and find?: What do these findings mean?:

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pmed00:1004451

DOI: 10.1371/journal.pmed.1004451

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