Tianyuan Lu1,2, Vincenzo Forgetta1, Julyan Keller-Baruch1, Maria Nethander3,4, Derrick Bennett5,6, Marie Forest1, Sahir Bhatnagar7,8, Robin G Walters5,9, Kuang Lin5, Zhengming Chen5,9, Liming Li10, Magnus Karlsson11,12, Dan Mellström3, Eric Orwoll13,14, Eugene V McCloskey15, John A Kanis16,17, William D Leslie18, Robert J Clarke5, Claes Ohlsson3,19, Celia M T Greenwood1,7,20,21, J Brent Richards22,23,24,25. 1. Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, H3T 1E2, Canada. 2. Quantitative Life Sciences Program, McGill University, Montreal, Canada. 3. Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 4. Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden. 5. Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK. 6. National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. 7. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada. 8. Department of Diagnostic Radiology, McGill University, Montreal, Canada. 9. MRC Population Health Research Unit, University of Oxford, Oxford, UK. 10. School of Public Health, Peking University Health Science Centre, Beijing, China. 11. Clinical and Molecular Osteoporosis Research Unit, Department of Orthopedics and Clinical Sciences, Lund University, Lund, Sweden. 12. Skåne University Hospital, Malmö, Sweden. 13. Bone & Mineral Unit, Oregon Health & Science University, Portland, USA. 14. Department of Medicine, Oregon Health & Science University, Portland, USA. 15. Mellanby Centre for Bone Research, Centre for Integrated Research in Musculoskeletal Ageing, University of Sheffield, Sheffield, UK. 16. Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, UK. 17. Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Australia. 18. Department of Medicine, University of Manitoba, Winnipeg, Canada. 19. Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden. 20. Department of Human Genetics, McGill University, Montreal, Canada. 21. Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada. 22. Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, H3T 1E2, Canada. brent.richards@mcgill.ca. 23. Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada. brent.richards@mcgill.ca. 24. Department of Human Genetics, McGill University, Montreal, Canada. brent.richards@mcgill.ca. 25. Department of Twin Research and Genetic Epidemiology, King's College London, London, UK. brent.richards@mcgill.ca.
Abstract
BACKGROUND: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. METHODS: We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. RESULTS: A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. CONCLUSIONS: We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.
BACKGROUND: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. METHODS: We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. RESULTS: A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. CONCLUSIONS: We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.
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