The Utility of Genetic Risk Score to Improve Performance of FRAX for Fracture Prediction in US Postmenopausal Women

Xiao, Xiangxue; Wu, Qing

doi:10.1007/s00223-021-00809-4

Cited by 15 publications

(6 citation statements)

References 37 publications

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“…Prior studies have demonstrated the potential use of BMD-decreasing PGS in predicting fracture risk; however, they provided only limited predictive power [19][20][21][22]. A PGS based on 62 femur neckrelated SNPs revealed a hazard ratio (HR) of 1.20 for incident fracture per one standard deviation (SD) increase [23].…”

Section: Introductionmentioning

confidence: 99%

The Clinical Utility of the BMD-related comprehensive Genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures

Xiao

2022

Preprint

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Purpose: This study sought to construct genome-wide polygenic scores for femoral neck and total body BMD and to estimate their potential in identifying individuals with a high risk of osteoporotic fractures. Methods: Genome-wide polygenic scores were developed and validated for femoral neck and total body BMD. We externally tested the PGSs, both by themselves and in combination with available clinical risk factors, in 455,663 European ancestry individuals from the UK Biobank. The predictive accuracy of the developed genome-wide PGS was also compared with previously published restricted PGS employed in fracture risk assessment. Results: For each unit decrease in PGSs, the genome-wide PGSs were associated with up to a 1.17-fold increased fracture risk. Out of four studied PGSs, PGS_TBBMD_81 (HR: 1.03; 95%CI 1.01-1.05, p=0.001) had the weakest and the PGS_TBBMD_ldpred (HR: 1.17; 95%CI 1.15-1.19, p<0.0001) had the strongest association with an incident fracture. In the reclassification analysis, compared to the FRAX base model, the models with, PGS_FNBMD_63, PGS_TBBMD_81, PGS_FNBMD_ldpred, and PGS_TBBMD_ldpred improved the reclassification of fracture by 2% (95% CI, 1.5% to 2.4%), 0.2% (95% CI, 0.1% to 0.3%), 1.4% (95% CI, 1.3% to 1.5%), and 2.2% (95% CI, 2.1% to 2.4%), respectively. Conclusions: Our findings suggested that an efficient PGS estimate enables the identification of strata with up to a 1.5-fold difference in fracture incidence. Incorporating PGS information into clinical diagnosis is anticipated to increase the benefits of screening programs at the population level.

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Section: Introductionmentioning

confidence: 99%

The Clinical Utility of the BMD-related comprehensive Genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures

Xiao

2022

Preprint

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“…We evaluated the prediction models' performance in terms of the concordance index (C-index), which can be regarded as the fractures of all pairs of individuals whose predicted survival times were correctly ordered. This metric is based on the Harrell C statistic, as described in previous studies [24][25][26]. However, although the C-index is easily implementable using available statistical packages and algorithms, it has an inherent limitation in its unclear validity/reliability [27] in datasets with censored data, as in our study, due to the possibility of inflated type 1 error [27].…”

Section: Discussionmentioning

confidence: 99%

Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm

Kong

Lee²,

Bae³

et al. 2022

Endocrinol Metab

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Background: Since image-based fracture prediction models using deep learning are lacking, we aimed to develop an X-ray-based fracture prediction model using deep learning with longitudinal data. Methods: This study included 1,595 participants aged 50 to 75 years with at least two lumbosacral radiographs without baseline fractures from 2010 to 2015 at Seoul National University Hospital. Positive and negative cases were defined according to whether vertebral fractures developed during follow-up. The cases were divided into training (n=1,416) and test (n=179) sets. A convolutional neural network (CNN)-based prediction algorithm, DeepSurv, was trained with images and baseline clinical information (age, sex, body mass index, glucocorticoid use, and secondary osteoporosis). The concordance index (C-index) was used to compare performance between DeepSurv and the Fracture Risk Assessment Tool (FRAX) and Cox proportional hazard (CoxPH) models. Results: Of the total participants, 1,188 (74.4%) were women, and the mean age was 60.5 years. During a mean follow-up period of 40.7 months, vertebral fractures occurred in 7.5% (120/1,595) of participants. In the test set, when DeepSurv learned with images and clinical features, it showed higher performance than FRAX and CoxPH in terms of C-index values (DeepSurv, 0.612; 95% confidence interval [CI], 0.571 to 0.653; FRAX, 0.547; CoxPH, 0.594; 95% CI, 0.552 to 0.555). Notably, the DeepSurv method without clinical features had a higher C-index (0.614; 95% CI, 0.572 to 0.656) than that of FRAX in women. Conclusion: DeepSurv, a CNN-based prediction algorithm using baseline image and clinical information, outperformed the FRAX and CoxPH models in predicting osteoporotic fracture from spine radiographs in a longitudinal cohort.

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“…For example, Thao et al reported that genetic profiling of 63 BMD-related genetic variants could enhance fracture prediction performance when compared to the Garvan fracture risk calculator [ 23 ]. Our prior work demonstrated that incorporating genetic information from 81 BMD-related genetic variants could improve fracture prediction performance beyond FRAX [ 24 ]. A more recent study generated and validated a genome-wide PGS for speed of sound (SOS) also reported a consistent association with fracture risk [ 25 ].…”

Section: Discussionmentioning

confidence: 99%

Validation of a genome-wide polygenic score in improving fracture risk assessment beyond the FRAX tool in the Women’s Health Initiative study

Xiao

2023

PLoS ONE

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Background Previous study has established two polygenic scores (PGSs) related to femoral neck bone mineral density (BMD) (PGS_FNBMDldpred) and total body BMD (PGS_TBBMDldpred) that are associated with fracture risk. However, these findings have not yet been externally validated in an independent cohort. Objectives This study aimed to validate the predictive performance of the two established PGSs and to investigate whether adding PGSs to the Fracture Risk Assessment Tool (FRAX) improves the predictive ability of FRAX in identifying women at high risk of major osteoporotic fracture (MOF) and hip fractures (HF). Methods The study used the Women’s Health Initiative (WHI) cohort of 9,000 postmenopausal women of European ancestry. Cox Proportional Hazard Models were used to assess the association between each PGS and MOF/HF risk. Four models were formulated to investigate the effect of adding PGSs to the FRAX risk factors: (1) Base model: FRAX risk factors; (2) Base model + PGS_FNBMDldpred; (3) Base model + PGS_TBBMDldpred; (4) Base model + metaPGS. The reclassification ability of models with PGS was further assessed using the Net Reclassification Improvement (NRI) and the Integrated discrimination improvement (IDI). Results The study found that the PGSs were not significantly associated with MOF or HF after adjusting for FRAX risk factors. The FRAX base model showed moderate discrimination of MOF and HF, with a C-index of 0.623 (95% CI, 0.609 to 0.641) and 0.702 (95% CI, 0.609 to 0.718), respectively. Adding PGSs to the base FRAX model did not improve the ability to discriminate MOF or HF. Reclassification analysis showed that compared to the model without PGS, the model with PGS_TBBMDldpred (1.2%, p = 0.04) and metaPGS (1.7%, p = 0.05) improve the reclassification of HF, but not MOF. Conclusions The findings suggested that incorporating genetic information into the FRAX tool has minimal improvement in predicting HF risk for elderly Caucasian women. These results highlight the need for further research to identify other factors that may contribute to fracture risk in elderly Caucasian women.

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The Utility of Genetic Risk Score to Improve Performance of FRAX for Fracture Prediction in US Postmenopausal Women

Cited by 15 publications

References 37 publications

The Clinical Utility of the BMD-related comprehensive Genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures

The Clinical Utility of the BMD-related comprehensive Genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures

Development of a Spine X-Ray-Based Fracture Prediction Model Using a Deep Learning Algorithm

Validation of a genome-wide polygenic score in improving fracture risk assessment beyond the FRAX tool in the Women’s Health Initiative study

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