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 Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a

Background: Genomics-based prediction could be useful since genome-wide genotyping costs less than many clinical tests. We tested whether machine learning methods could provide a clinically-relevant genomic prediction of quantitative ultrasound speed of sound (SOS)-a risk factor for osteoporotic fracture. Methods:We used 341,449 individuals from UK Biobank with SOS measures to develop genomically-predicted SOS (gSOS) using machine learning algorithms. We selected the optimal algorithm in 5,335 independent individuals and then validated it and its ability to predict incident fracture in an independent test dataset (N = 80,027). Finally, we explored whether genomic prescreening could complement a UK-based osteoporosis screening strategy, based on the validated tool FRAX.Results: gSOS explained 4.8-fold more variance in SOS than FRAX clinical risk factors (CRF) alone (r 2 = 23% vs. 4.8%). A standard deviation decrease in gSOS, adjusting for the CRF-FRAX score was associated with a higher increased odds of incident major osteoporotic fracture (1,491 cases / 78,536 controls, OR = 1.91 [1.70-2.14], P = 10 -28 ) than that for measured SOS (OR = 1.60 [1.50-1.69], P = 10 -52 ) and femoral neck bone mineral density (147 cases / 4,594 controls, OR = 1.53 [1.27-1.83], P = 10 -6 ). Individuals in the bottom decile of the gSOS distribution had a 3.25-fold increased risk of major osteoporotic fracture (P = 10 -18 ) compared to the top decile. A gSOS-based FRAX score, identified individuals at high risk for incident major osteoporotic fractures better than the CRF-FRAX score (P = 10 -14 ). Introducing a genomic prescreening step into osteoporosis screening in 4,741 individuals reduced the number of required clinical visits from 2,455 to 1,273 and the number of BMD tests from 1,013 to 473, while only reducing the sensitivity to identify individuals eligible for therapy from 99% to 95%.Interpretation: The use of genotypes in a machine learning algorithm resulted in a clinicallyrelevant prediction of SOS and fracture, with potential to impact healthcare resource utilization.

Vascular endothelial growth factor (VEGF) is important for bone formation and has been associated with osteoporosis in humans. Therefore, we conducted a two‐sample Mendelian randomization study to test whether genetically decreased circulating VEGF was associated with decreased bone mineral density (BMD) and increased risk of fracture. Summary statistics from a genomewide association study (GWAS) meta‐analysis of circulating VEGF level (n = 16,112) were used to identify 10 genetic variants explaining up to 52% of the variance in circulating VEGF levels. GWAS meta‐analyses on dual‐energy X‐ray absorptiometry (DXA)‐derived BMD of forearm, lumbar spine, and femoral neck (n = up to 32,735) and BMD estimated from heel calcaneus ultrasound (eBMD) (n = 426,824) were used to assess the effect of genetically lowered circulating VEGF levels on BMD. A GWAS meta‐analysis including a total of 76,549 cases and 470,164 controls was used to assess the effect of genetically lowered circulating VEGF levels on risk of fracture. A natural log‐transformed pg/mL decrease in circulating VEGF levels was not associated with a decrease in forearm BMD (0.02 standard deviation [SD], 95% confidence interval [CI] −0.024 to 0.064, p = 0.38), lumbar spine BMD (−0.005 SD, 95% CI −0.03 to 0.019, p = 0.67), femoral neck BMD (0.004 SD, 95% CI −0.017 to 0.026, p = 0.68), eBMD (−0.006 SD, 95% CI −0.012 to −0.001, p = 0.031) or risk of fracture (odds ratio = 0.99, 95% CI 0.98 to 1.0, p = 0.37) in inverse‐variance–weighted Mendelian randomization analyses. Sensitivity analyses did not provide evidence that our results were influenced by pleiotropy. Genetically lowered circulating VEGF was not associated with a decrease in BMD or increased risk of fracture, suggesting that efforts to influence circulating VEGF level are unlikely to have beneficial effects on osteoporosis outcomes and that previous observational associations of circulating VEGF with BMD were influenced by confounding or reverse causation. © 2019 American Society for Bone and Mineral Research.