Thao P Ho-Le1,2, Hanh M Pham2,3, Jacqueline R Center2,3, John A Eisman2,3,4, Hung T Nguyen1, Tuan V Nguyen5,6,7,8,9. 1. School of Biomedical Engineering, University of Technology Sydney (UTS), Ultimo, NSW, 2007, Australia. 2. Bone Biology Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia. 3. St Vincent Clinical School, UNSW Australia, Sydney, NSW, 2052, Australia. 4. School of Medicine, Notre Dame University, Fremantle, WA, 6160, Australia. 5. School of Biomedical Engineering, University of Technology Sydney (UTS), Ultimo, NSW, 2007, Australia. t.nguyen@garvan.org.au. 6. Bone Biology Division, Garvan Institute of Medical Research, 384 Victoria Street, Darlinghurst, NSW, 2010, Australia. t.nguyen@garvan.org.au. 7. St Vincent Clinical School, UNSW Australia, Sydney, NSW, 2052, Australia. t.nguyen@garvan.org.au. 8. School of Medicine, Notre Dame University, Fremantle, WA, 6160, Australia. t.nguyen@garvan.org.au. 9. School of Public Health and Community Medicine, UNSW Australia, Sydney, NSW, 2052, Australia. t.nguyen@garvan.org.au.
Abstract
The contribution of genetic variants to longitudinal bone loss has not been well documented. We constructed an "osteogenomic profile" based on 62 BMD-associated genetic variants and showed that the profile was significantly associated with bone loss, independently from baseline BMD and age. The osteogenomic profile can help predict bone loss in an individual. INTRODUCTION: The rate of longitudinal bone loss (ΔBMD) is a risk factor for fracture. The variation in ΔBMD is partly determined by genetic factors. This study sought to define the association between an osteogenomic profile and ΔBMD. METHODS: The osteogenomic profile was created from 62 BMD-associated SNPs from genome-wide association studies (GWAS) that were genotyped in 1384 elderly men and women aged 60+ years. Weighted genetic risk scores (GRS) were constructed for each individual by summing the products of the number of risk alleles and the sex-specific regression coefficients [associated with BMD from GWAS]. ΔBMD, expressed as annual percent change-in-BMD, was determined by linear regression analysis for each individual who had had at least two femoral neck BMD measurements. RESULTS: The mean ΔBMD was - 0.65% (SD 1.64%) for women and - 0.57% (SD 1.40%) for men, and this difference was not statistically significant (P = 0.32). In women, each unit increase in GRS was associated with 0.21% (SE 0.10) higher ΔBMD at the femoral neck (P = 0.036), and this association was independent of baseline BMD and age. In logistic regression analysis, each unit increase of GRS was associated with 41% odds (95%CI: 1.07-1.87) of rapid bone loss (ΔBMD ≤ - 1.2%/year; mean of rapid loss group = - 2.2%/year). There was no statistically significant association between ΔBMD and GRS in men. CONCLUSIONS: We conclude that the osteogenomic profile constructed from BMD-associated genetic variants is modestly associated with long-term changes in femoral neck BMD in women, but not in men.
The contribution of genetic variants to longitudinal bone loss has not been well documented. We constructed an "osteogenomic profile" based on 62 BMD-associated genetic variants and showed that the profile was significantly associated with bone loss, independently from baseline BMD and age. The osteogenomic profile can help predict bone loss in an individual. INTRODUCTION: The rate of longitudinal bone loss (ΔBMD) is a risk factor for fracture. The variation in ΔBMD is partly determined by genetic factors. This study sought to define the association between an osteogenomic profile and ΔBMD. METHODS: The osteogenomic profile was created from 62 BMD-associated SNPs from genome-wide association studies (GWAS) that were genotyped in 1384 elderly men and women aged 60+ years. Weighted genetic risk scores (GRS) were constructed for each individual by summing the products of the number of risk alleles and the sex-specific regression coefficients [associated with BMD from GWAS]. ΔBMD, expressed as annual percent change-in-BMD, was determined by linear regression analysis for each individual who had had at least two femoral neck BMD measurements. RESULTS: The mean ΔBMD was - 0.65% (SD 1.64%) for women and - 0.57% (SD 1.40%) for men, and this difference was not statistically significant (P = 0.32). In women, each unit increase in GRS was associated with 0.21% (SE 0.10) higher ΔBMD at the femoral neck (P = 0.036), and this association was independent of baseline BMD and age. In logistic regression analysis, each unit increase of GRS was associated with 41% odds (95%CI: 1.07-1.87) of rapid bone loss (ΔBMD ≤ - 1.2%/year; mean of rapid loss group = - 2.2%/year). There was no statistically significant association between ΔBMD and GRS in men. CONCLUSIONS: We conclude that the osteogenomic profile constructed from BMD-associated genetic variants is modestly associated with long-term changes in femoral neck BMD in women, but not in men.
Entities:
Keywords:
Bone loss; Bone mineral density; Genetic profiling; Genetic variant; Osteoporosis