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Improved prediction of fracture risk leveraging a genome-wide polygenic risk score

BACKGROUND: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteopo...

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Autores principales: Lu, Tianyuan, Forgetta, Vincenzo, Keller-Baruch, Julyan, Nethander, Maria, Bennett, Derrick, Forest, Marie, Bhatnagar, Sahir, Walters, Robin G., Lin, Kuang, Chen, Zhengming, Li, Liming, Karlsson, Magnus, Mellström, Dan, Orwoll, Eric, McCloskey, Eugene V., Kanis, John A., Leslie, William D., Clarke, Robert J., Ohlsson, Claes, Greenwood, Celia M. T., Richards, J. Brent
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860212/
https://www.ncbi.nlm.nih.gov/pubmed/33536041
http://dx.doi.org/10.1186/s13073-021-00838-6
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author Lu, Tianyuan
Forgetta, Vincenzo
Keller-Baruch, Julyan
Nethander, Maria
Bennett, Derrick
Forest, Marie
Bhatnagar, Sahir
Walters, Robin G.
Lin, Kuang
Chen, Zhengming
Li, Liming
Karlsson, Magnus
Mellström, Dan
Orwoll, Eric
McCloskey, Eugene V.
Kanis, John A.
Leslie, William D.
Clarke, Robert J.
Ohlsson, Claes
Greenwood, Celia M. T.
Richards, J. Brent
author_facet Lu, Tianyuan
Forgetta, Vincenzo
Keller-Baruch, Julyan
Nethander, Maria
Bennett, Derrick
Forest, Marie
Bhatnagar, Sahir
Walters, Robin G.
Lin, Kuang
Chen, Zhengming
Li, Liming
Karlsson, Magnus
Mellström, Dan
Orwoll, Eric
McCloskey, Eugene V.
Kanis, John A.
Leslie, William D.
Clarke, Robert J.
Ohlsson, Claes
Greenwood, Celia M. T.
Richards, J. Brent
author_sort Lu, Tianyuan
collection PubMed
description BACKGROUND: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. METHODS: We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. RESULTS: A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13–1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727–0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791–0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. CONCLUSIONS: We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00838-6.
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spelling pubmed-78602122021-02-05 Improved prediction of fracture risk leveraging a genome-wide polygenic risk score Lu, Tianyuan Forgetta, Vincenzo Keller-Baruch, Julyan Nethander, Maria Bennett, Derrick Forest, Marie Bhatnagar, Sahir Walters, Robin G. Lin, Kuang Chen, Zhengming Li, Liming Karlsson, Magnus Mellström, Dan Orwoll, Eric McCloskey, Eugene V. Kanis, John A. Leslie, William D. Clarke, Robert J. Ohlsson, Claes Greenwood, Celia M. T. Richards, J. Brent Genome Med Research BACKGROUND: Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. METHODS: We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. RESULTS: A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13–1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727–0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791–0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. CONCLUSIONS: We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13073-021-00838-6. BioMed Central 2021-02-03 /pmc/articles/PMC7860212/ /pubmed/33536041 http://dx.doi.org/10.1186/s13073-021-00838-6 Text en © The Author(s) 2021 Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Lu, Tianyuan
Forgetta, Vincenzo
Keller-Baruch, Julyan
Nethander, Maria
Bennett, Derrick
Forest, Marie
Bhatnagar, Sahir
Walters, Robin G.
Lin, Kuang
Chen, Zhengming
Li, Liming
Karlsson, Magnus
Mellström, Dan
Orwoll, Eric
McCloskey, Eugene V.
Kanis, John A.
Leslie, William D.
Clarke, Robert J.
Ohlsson, Claes
Greenwood, Celia M. T.
Richards, J. Brent
Improved prediction of fracture risk leveraging a genome-wide polygenic risk score
title Improved prediction of fracture risk leveraging a genome-wide polygenic risk score
title_full Improved prediction of fracture risk leveraging a genome-wide polygenic risk score
title_fullStr Improved prediction of fracture risk leveraging a genome-wide polygenic risk score
title_full_unstemmed Improved prediction of fracture risk leveraging a genome-wide polygenic risk score
title_short Improved prediction of fracture risk leveraging a genome-wide polygenic risk score
title_sort improved prediction of fracture risk leveraging a genome-wide polygenic risk score
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7860212/
https://www.ncbi.nlm.nih.gov/pubmed/33536041
http://dx.doi.org/10.1186/s13073-021-00838-6
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