{"doi":"10.1101/2020.01.03.20016444","title":"Performance of FRAX in predicting fracture in the US postmenopausal women with varied race and genetic profiles","abstract":"Abstract Background Whether the Fracture Risk Assessment Tool (FRAX) performed differently in estimating the 10-year fracture probability in women of different genetic profiling and race remained unclear. Methods The genomic data in the Women’s Health Initiative study was analyzed (n=23,981). the genetic risk score (GRS) was calculated from 14 fracture-associated single nucleotide polymorphisms (SNPs) for each participant. FRAX without bone mineral density (BMD) was used to estimate fracture probability. Results FRAX significantly overestimated the risk of major osteoporotic fracture (MOF) in the WHI study. The most enormous overestimation was observed in women with low GRS (predicted/observed ratio [POR]: 1.61, 95% CI: 1.45-1.79), in Asian women (POR: 3.5, 95% CI 2.48-4.81), and in African American women (POR: 2.59, 95% CI: 2.33-2.87). Compared to the low GRS group, the 10-year probability of MOF adjusted for the FRAX score was 21% and 30% higher in median GRS group and high GRS group, respectively. Asian, African American, and Hispanic women respectively had a 78%, 76%, and 56% lower hazard than Caucasian women after the FRAX score was adjusted for. The results were similar when for hip fractures. Conclusions Our study suggested the FRAX performance varies significantly by both genetic profiling and race in postmenopausal women.","journal":"medRxiv","year":2020,"id":124520,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":2,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.6856,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":566183,"name":"Xiangxue Xiao","orcid":"0000-0002-9527-2548","position":1,"is_corresponding":false},{"id":566184,"name":"Yingke Xu","orcid":"0000-0003-0686-5104","position":2,"is_corresponding":false},{"id":566182,"name":"Qing Wu","orcid":"0000-0003-4679-8903","position":0,"is_corresponding":true}],"reference_count":39,"raw_metadata":null,"created_at":"2026-07-18T23:15:07.789881Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}