Bayesian Genome-wide Polygenic Score Integration with FRAX for Enhanced Fracture Risk Prediction in Postmenopausal Women

Importance Current fracture risk prediction tools, including the Fracture Risk Assessment Tool (FRAX), do not incorporate genetic risk factors limiting accuracy and contributing to misclassification and suboptimal care.

To develop and validate a novel genome-informed fracture risk assessment tool (Bayes-FRAX) integrating Bayesian genome-wide polygenic scores (GPS) into the established FRAX to enhance major osteoporotic fracture (MOF) prediction. Design, Setting, and Participants This retrospective cohort study analyzed clinical and genetic data from 6,932 postmenopausal women enrolled in the Women’s Health Initiative (1993-1998). Exposures Integration of GPS derived using Polygenic Risk Score Continuous Shrinkage (PRS-CS) and Summary data-based Bayesian regression (SBayesR) into FRAX. Main Outcomes and Measures Primary outcomes included the incidence of MOF. Predictive performance metrics assessed were area under the receiver operating characteristic curve (AUROC), area under the precision-recall curve (AUPRC), calibration slopes, Hosmer–Lemeshow goodness-of-fit tests, net reclassification improvement (NRI), diagnostic sensitivity, decision curve analysis (DCA), and external validation metrics.

Of 6,932 women, 513 (7.4%) experienced MOF. Bayes-FRAX significantly improved prediction over standard FRAX based solely on clinical risk factors, increasing AUROC from 0.662 to 0.680 for both PRS-CS and SBayesR. AUPRC improved from 0.120 (FRAX) to 0.140 (PRS-CS) and 0.138 (SBayesR). Calibration slopes were ideal (GPS-PRS-CS: 1.00 [95% CI: 0.8569–1.1431]; GPS-SBayesR: 1.00 [95% CI: 0.8560–1.1437]). Bayes-FRAX reclassified 3.5% of women, 34% near the intervention threshold. NRI improved by 4.59% (SBayesR) and 4.34% (PRS-CS), largely from better classification of women who fractured (5.85% and 5.65%). Decision curve analyses demonstrated greater net clinical benefit at clinically relevant thresholds, notably at the 20% threshold. External validation in 852 independent White postmenopausal women confirmed robust generalizability, with GPS significantly associated with fracture risk (PRS-CS OR = 0.148, 95% CI: 0.052–0.411; SBayesR OR = 0.116, 95% CI: 0.040–0.324). Likelihood ratio tests also supported improved model fit after GPS inclusion (PRS-CS: P < 0.001; SBayesR: P <0.001). Sensitivity analysis without BMD demonstrated stable AUROC (0.74).

and Relevance Integrating GPS into FRAX using Bayesian methods improved fracture risk prediction, reclassification, and decision-making. Bayes-FRAX provides a generalizable tool for personalized osteoporosis care, especially for women near treatment thresholds. Question Does incorporating genome-wide polygenic scores using Bayesian methods improve fracture risk prediction beyond the traditional FRAX? Findings In this cohort study of 6,932 postmenopausal women, adding Bayesian polygenic scores significantly improved prediction accuracy, calibration, and clinical reclassification, particularly among women older than 65 years, with robust external validation. Meaning Bayes-FRAX provides a personalized, more accurate fracture risk assessment, potentially improving clinical decision-making for postmenopausal women near treatment thresholds. Competing Interest Statement The authors have declared no competing interest. Funding Statement This study was supported by the National Institute on Minority Health and Health Disparities (R21MD013681, awarded to Dr. Q. Wu) and the National Institute on Aging (R01AG080017, awarded to Dr. Q. Wu). The funding sources had no role in the design and conduct of the study; collection, management, analysis, or interpretation of the data; preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication. Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Ethics committee/Institutional Review Board of The Ohio State University gave ethical approval for this work (IRB approval number: 2022H0420). I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data Availability The individual-level data used in this study are available through controlled access from the database of Genotypes and Phenotypes (dbGaP) at https://www.ncbi.nlm.nih.gov/projects/ gap/cgi-bin/study.cgi?study_id=phs000200.v12.p3. The genome-wide association summary statistics were obtained from the GEnetic Factors for Osteoporosis (GEFOS) Consortium and are publicly available at http://www.gefos.org/?q=content/data-release-2018