T P Ho-Le1,2,3, H T T Tran3, J R Center1,4, J A Eisman1,4,5, H T Nguyen2, T V Nguyen6,7,8,9. 1. Healthy Ageing Theme, Garvan Institute of Medical Research, Sydney, Australia. 2. Faculty of Science, Engineering and Technology, Swinburne University of Technology, Melbourne, Australia. 3. Faculty of Engineering and Information Technology, Hatinh University, Hatinh, Vietnam. 4. St Vincent Clinical School, UNSW Sydney, Sydney, Australia. 5. School of Medicine Sydney, University of Notre Dame Australia, Sydney, Australia. 6. Healthy Ageing Theme, Garvan Institute of Medical Research, Sydney, Australia. t.nguyen@garvan.org.au. 7. St Vincent Clinical School, UNSW Sydney, Sydney, Australia. t.nguyen@garvan.org.au. 8. School of Medicine Sydney, University of Notre Dame Australia, Sydney, Australia. t.nguyen@garvan.org.au. 9. School of Biomedical Engineering, University of Technology, Sydney, Australia. t.nguyen@garvan.org.au.
Abstract
Using decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD. INTRODUCTION: Genetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction. METHODS: The study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds. RESULTS: In both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models. CONCLUSIONS: The addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction.
Using decision curve analysis on 2188 women and 1324 men, we found that an osteogenomic profile constructed from 62 genetic variants improved the clinical net benefit of fracture risk prediction over and above that of clinical risk factors and BMD. INTRODUCTION: Genetic profiling is a promising tool for assessing fracture risk. This study sought to use the decision curve analysis (DCA), a novel approach to determine the impact of genetic profiling on fracture risk prediction. METHODS: The study involved 2188 women and 1324 men, aged 60 years and above, who were followed for up to 23 years. Bone mineral density (BMD) and clinical risk factors were obtained at baseline. The incidence of fracture and mortality were recorded. A weighted individual genetic risk score (GRS) was constructed from 62 BMD-associated genetic variants. Four models were considered: CRF (clinical risk factors); CRF + GRS; Garvan model (GFRC) including CRF and femoral neck BMD; and GFRC + GRS. The DCA was used to evaluate the clinical net benefit of predictive models at a range of clinically reasonable risk thresholds. RESULTS: In both women and men, the full model GFRC + GRS achieved the highest net benefits. For 10-year risk threshold > 18% for women and > 15% for men, the GRS provided net benefit above those of the CRF models. At 20% risk threshold, adding the GRS could help to avoid 1 additional treatment per 81 women or 1 per 24 men compared with the Garvan model. At lower risk thresholds, there was no significant difference between the four models. CONCLUSIONS: The addition of genetic profiling into the clinical risk factors can improve the net clinical benefit at higher risk thresholds of fracture. Although the contribution of genetic profiling was modest in the presence of BMD + CRF, it appeared to be able to replace BMD for fracture prediction.
Authors: Thao Phuong Ho-Le; Thach S Tran; Dana Bliuc; Hanh M Pham; Steven A Frost; Jacqueline R Center; John A Eisman; Tuan V Nguyen Journal: Elife Date: 2021-02-09 Impact factor: 8.140