Prediction of Bone Mineral Density and Fragility Fracture by Genetic Profiling

Ho-Le, Thao P.; Center, Jacqueline R; Eisman, John A.; Nguyen, Hang Thi; Nguyen, Tuan V.

doi:10.1002/jbmr.2998

Cited by 49 publications

(42 citation statements)

References 46 publications

(81 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…In postmenopausal women of Korean background, a genetic profiling of 39 SNPs in 30 human genomic loci increased the precision of nonvertebral fracture prediction and help to define the risk threshold [ 72 ], while a profiling of 35 risk alleles was significantly associated the risk of vertebral fracture [ 72 , 73 ] in patients on bisphosphonate. Recently, we have shown that the incorporation of an “osteogenomic profile” of 62 BMD-associated SNPs into existing Garvan Fracture Risk Calculator could modestly improve the predictive accuracy of fracture [ 74 ], and this finding was consistent with a previous observation from MrOS study [ 75 ]. Taken together, these latest results studies suggest that genetic profiling could help improve the accuracy of fracture prediction over and above that of clinical risk factors.…”

Section: Room For Improvementsupporting

confidence: 84%

Individualized fracture risk assessment: State-of-the-art and room for improvement

Nguyen

2018

Osteoporosis and Sarcopenia

View full text Add to dashboard Cite

Fragility fracture is a serious clinical event, because it is associated with increased risk of mortality and reduced quality of life. The risk of fracture is determined by multiple risk factors, and their effects may be interactional. Over the past 10 years, a number of predictive models (e.g., FRAX, Garvan Fracture Risk Calculator, and Qfracture) have been developed for individualized assessment of fracture risk. These models use different risk profiles to estimate the probability of fracture over 5- and 10-year period. The ability of these models to discriminate between those individuals who will and will not have a fracture (i.e., area under the receiver operating characteristic curve [AUC]) is generally acceptable-to-good (AUC, 0.6 to 0.8), and is highly variable between populations. The calibration of existing models is poor, particularly in Asian populations. There is a strong need for the development and validation of new prediction models based on Asian data for Asian populations. We propose approaches to improve the accuracy of existing predictive models by incorporating new markers such as genetic factors, bone turnover markers, trabecular bone score, and time-variant factors. New and more refined models for individualized fracture risk assessment will help identify those most likely to sustain a fracture, those most likely to benefit from treatment, and encouraging them to modify their risk profile to decrease risk.

show abstract

Section: Room For Improvementsupporting

confidence: 84%

Individualized fracture risk assessment: State-of-the-art and room for improvement

Nguyen

2018

Osteoporosis and Sarcopenia

View full text Add to dashboard Cite

show abstract

“…(10,11) Although these individual SNPs have modest effect size on fracture risk, GRS, as summarized from individual risk SNPs, can improve AUC and accuracy of fracture W u e t a l -1 6 -prediction. (31) In the present study, we found that GRS ranked the 7 th most important variable in the optimal MOF prediction model of gradient boosting, where the model includes major conventional risk factors as predictors. The contributions of GRS to fracture prediction we observed in this study were consistent with previous studies, (31) which used 63 associated SNPs identified 8 years ago.…”

Section: Assessment Of Variable Importancementioning

confidence: 51%

Machine learning approaches for fracture risk assessment: a comparative analysis of genomic and phenotypic data in 5,130 older men

Nasoz

Jung

et al. 2020

Preprint

View full text Add to dashboard Cite

The study aims were to develop fracture prediction models by using machine learning approaches and genomic data, as well as to identify the best modeling approach for fracture prediction. The genomic data of Osteoporotic Fractures in Men, cohort Study (n=5,130), was analyzed. After a comprehensive genotype imputation, genetic risk score (GRS) was calculated from 1,103 associated SNPs for each participant. Data were normalized and split into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and logistic regression were used to develop prediction models for major osteoporotic fractures separately, with GRS, bone density and other risk factors as predictors. For model training, the synthetic minority over-sampling technique was used to account for low fracture rate, and 10-fold cross-validation was employed for hyperparameters optimization. In the testing set, the area under the ROC curve (AUC) and accuracy were used to assess the model performance. The McNemar test was employed for pairwise comparisons to examine the accuracy difference between models. The results showed that the prediction performance of gradient boosting was the best, with AUC of 0.71 and an accuracy of 0.88, and the GRS ranked as the 7th most important variable in the model. The performance of random forest and neural network were also better than that of logistic regression. Pairwise comparisons showed that the accuracy difference between models was significant. This study suggested that improving fracture prediction can be achieved by incorporating genetic profiling and by utilizing the gradient boosting approach.

show abstract

“…For example, GRS of 39 SNPs increased the precision of non-vertebral fracture prediction in postmenopausal Korean women [31]. Additionally, GRS based on 63 SNPs improved the accuracy of non-trauma fracture prediction [26]. One of our recent studies on older US men also found that GRS is one of the most important variables in MOF prediction models developed by the gradient boosting approach [32].…”

Section: Discussionmentioning

confidence: 97%

“…As the allelic frequency of these discovered SNPs featured high variability in the population, and each SNP is associated with small effect size, the contribution of any single SNP to fracture susceptibility is expected to be minimal [25]. The cumulative effects of many associated genetic variants possibly cause osteoporotic fracture [26,27]. Thus polygenic scores summarized from risk alleles at each locus have commonly been employed to quantify the overall genetic effect contributing to fracture risk [28].…”

Section: Introductionmentioning

confidence: 99%

Performance of FRAX in Predicting Fractures in US Postmenopausal Women with Varied Race and Genetic Profiles

Xiao

2020

JCM

View full text Add to dashboard Cite

Background: Whether the Fracture Risk Assessment Tool (FRAX) performed differently in estimating the 10-year fracture probability in women of different genetic profiling and race remained unclear. Methods: The genomic data in the Women’s Health Initiative (WHI) study was analyzed (n = 23,981). The genetic risk score (GRS) was calculated from 14 fracture-associated single nucleotide polymorphisms (SNPs) for each participant. FRAX without bone mineral density (BMD) was used to estimate fracture probability. Results: FRAX significantly overestimated the risk of major osteoporotic fracture (MOF) in the WHI study. The most significant overestimation was observed in women with low GRS (predicted/observed ratio (POR): 1.61, 95% CI: 1.45–1.79) specifically Asian women (POR: 3.5, 95% CI 2.48–4.81) and in African American women (POR: 2.59, 95% CI: 2.33–2.87). Compared to the low GRS group, the 10-year probability of MOF adjusted for the FRAX score was 21% and 30% higher in the median GRS group and high GRS group, respectively. Asian, African American, and Hispanic women respectively had a 78%, 76%, and 56% lower hazard than Caucasian women after the FRAX score was adjusted. The results were similar for hip fractures. Conclusions: Our study suggested the FRAX performance varies significantly by both genetic profile and race in postmenopausal women.

show abstract

Prediction of Bone Mineral Density and Fragility Fracture by Genetic Profiling

Cited by 49 publications

References 46 publications

Individualized fracture risk assessment: State-of-the-art and room for improvement

Individualized fracture risk assessment: State-of-the-art and room for improvement

Machine learning approaches for fracture risk assessment: a comparative analysis of genomic and phenotypic data in 5,130 older men

Performance of FRAX in Predicting Fractures in US Postmenopausal Women with Varied Race and Genetic Profiles

Contact Info

Product

Resources

About