Multiple gene polymorphisms can improve prediction of nonvertebral fracture in postmenopausal women

Authors

  • Seung Hun Lee,

    1. Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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    • SHL and SWL are joint first authors; J-MK and CK are joint senior authors.
  • Seon Woo Lee,

    1. Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
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    • SHL and SWL are joint first authors; J-MK and CK are joint senior authors.
  • Seong Hee Ahn,

    1. Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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  • Taehyeung Kim,

    1. Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
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  • Kyeong-Hye Lim,

    1. Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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  • Beom-Jun Kim,

    1. Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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  • Eun-Hee Cho,

    1. Department of Internal Medicine, Kangwon National University College of Medicine, Chuncheon, Korea
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  • Sang-Wook Kim,

    1. Department of Internal Medicine, Kangwon National University College of Medicine, Chuncheon, Korea
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  • Tae-Ho Kim,

    1. Skeletal Diseases Genome Research Center and Department of Orthopedic Surgery, Kyungpook National University School of Medicine, Daegu, Korea
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  • Ghi Su Kim,

    1. Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
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  • Shin-Yoon Kim,

    1. Skeletal Diseases Genome Research Center and Department of Orthopedic Surgery, Kyungpook National University School of Medicine, Daegu, Korea
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  • Jung-Min Koh,

    Corresponding author
    1. Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea
    • Address correspondence to: Changwon Kang, PhD, Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea. E-mail: ckang@kaist.ac.kr

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    • SHL and SWL are joint first authors; J-MK and CK are joint senior authors.
  • Changwon Kang

    Corresponding author
    1. Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea
    • Address correspondence to: Changwon Kang, PhD, Department of Biological Sciences, Korea Advanced Institute of Science and Technology, Daejeon, Korea. E-mail: ckang@kaist.ac.kr

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    • SHL and SWL are joint first authors; J-MK and CK are joint senior authors.

ABSTRACT

Clinical risk factors (CRFs), with or without bone mineral density (BMD), are used to determine the risk of osteoporotic fracture (OF), which has a heritable component. In this study we investigated whether genetic profiling can additionally improve the ability to predict OF. Using 1229 unrelated Korean postmenopausal women, 39 single-nucleotide polymorphisms (SNPs) in 30 human genomic loci were tested for association with osteoporosis-related traits, such as BMD, osteoporosis, vertebral fracture (VF), nonvertebral fracture (NVF), and any fracture. To estimate the effects of genetic profiling, the genetic risk score (GRS) was calculated using five prediction models: (Model I) GRSs only; (Model II) BMD only; (Model III) CRFs only; (Model IV) CRFs and BMD; and (Model V) CRFs, BMD, and GRS. A total of 21 SNPs within 19 genes associated with one or more osteoporosis-related traits and were included for GRS calculation. GRS associated with BMD before and after adjustment for CRFs (p ranging from <0.001 to 0.018). GRS associated with NVF before and after adjustment for CRFs and BMD (p ranging from 0.017 to 0.045), and with any fracture after adjustment for CRFs and femur neck BMD (p = 0.049). In terms of predicting NVF, the area under the receiver operating characteristic curve (AUC) for Model I was 0.55, which was lower than the AUCs of Models II (0.60), III (0.64), and IV (0.65). Adding GRS to Model IV (in Model V) increased the AUC to 0.67, and improved the accuracy of NVF classification by 11.5% (p = 0.014). In terms of predicting any fracture, the AUC of Model V (0.68) was similar to that of Model IV (0.68), and Model V did not significantly improve the accuracy of any fracture classification (p = 0.39). Thus, genetic profiling may enhance the accuracy of NVF predictions and help to delineate the intervention threshold. © 2013 American Society for Bone and Mineral Research.

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