| Title: | The clinical utility of the BMD-related comprehensive genome-wide polygenic score in identifying individuals with a high risk of osteoporotic fractures |
| Journal: | Osteoporosis International |
| Published: | 9 Jan 2023 |
| Pubmed: | https://pubmed.ncbi.nlm.nih.gov/36622390/ |
| DOI: | https://doi.org/10.1007/s00198-022-06654-x |
| URL: | https://doi.org/10.1101/2022.11.16.22282416 |
| Citations: | 4 (4 in last 2 years) as of 8 Aug 2024 |
Publication 10946
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Abstract
The potential of bone mineral density (BMD)-related genome-wide polygenic score (PGS) in identifying individuals with a high risk of fractures remains unclear. This study suggests that an efficient PGS enables the identification of strata with up to a 1.5-fold difference in fracture incidence. Incorporating PGS into clinical diagnosis is anticipated to increase the population-level screening benefits.PurposeThis 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.MethodsGenome-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.ResultsFor each unit decrease in PGSs, the genome-wide PGSs were associated with up to 1.17-fold increased fracture risk. Out of four studied PGSs, PGS_TBBMD81$${\varvec{P}}{\varvec{G}}{\varvec{S}}\_{{\varvec{T}}{\varvec{B}}{\varvec{B}}{\varvec{M}}{\varvec{D}}}_{81}$$ (HR: 1.03; 95%CI 1.01-1.05, p = 0.001) had the weakest and the PGS_TBBMDldpred$${\varvec{P}}{\varvec{G}}{\varvec{S}}\_{{\varvec{T}}{\varvec{B}}{\varvec{B}}{\varvec{M}}{\varvec{D}}}_{{\varvec{l}}{\varvec{d}}{\varvec{p}}{\varvec{r}}{\varvec{e}}{\varvec{d}}}$$ (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_FNBMD63$${\varvec{P}}{\varvec{G}}{\varvec{S}}\_{{\varvec{F}}{\varvec{N}}{\varvec{B}}{\varvec{M}}{\varvec{D}}}_{63}$$, PGS_TBBMD81$${\varvec{P}}{\varvec{G}}{\varvec{S}}\_{{\varvec{T}}{\varvec{B}}{\varvec{B}}{\varvec{M}}{\varvec{D}}}_{81}$$, PGS_FNBMDldpred$${\varvec{P}}{\varvec{G}}{\varvec{S}}\_{{\varvec{F}}{\varvec{N}}{\varvec{B}}{\varvec{M}}{\varvec{D}}}_{{\varvec{l}}{\varvec{d}}{\varvec{p}}{\varvec{r}}{\varvec{e}}{\varvec{d}}}$$, and PGS_TBBMDldpred$${\varvec{P}}{\varvec{G}}{\varvec{S}}\_{{\varvec{T}}{\varvec{B}}{\varvec{B}}{\varvec{M}}{\varvec{D}}}_{{\varvec{l}}{\varvec{d}}{\varvec{p}}{\varvec{r}}{\varvec{e}}{\varvec{d}}}$$ improved the reclassification of fracture by 1.2% (95% CI, 1.0 to 1.3%), 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.ConclusionsOur findings suggested that an efficient PGS estimate enables the identification of strata with up to a 1.7-fold difference in fracture incidence. Incorporating PGS information into clinical diagnosis is anticipated to increase the benefits of screening programs at the population level.</p>
2 Authors
- Xiangxue Xiao
- Qing Wu
1 Application
| Application ID | Title |
|---|---|
| 58122 | Machine Learning Analysis for prediction of Osteoporosis using Multimodal Data |
Enabling scientific discoveries that improve human health