title: Multiple gene polymorphisms can improve prediction of nonvertebral fracture in postmenopausal women.
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.
authors.affiliation: Division of Endocrinology and Metabolism, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
authors.forename: Seung Hun
authors.initials: SH
authors.lastname: Lee
authors.forename: Seon Woo
authors.initials: SW
authors.lastname: Lee
authors.forename: Seong Hee
authors.initials: SH
authors.lastname: Ahn
authors.forename: Taehyeung
authors.initials: T
authors.lastname: Kim
authors.forename: Kyeong-Hye
authors.initials: KH
authors.lastname: Lim
authors.forename: Beom-Jun
authors.initials: BJ
authors.lastname: Kim
authors.forename: Eun-Hee
authors.initials: EH
authors.lastname: Cho
authors.forename: Sang-Wook
authors.initials: SW
authors.lastname: Kim
authors.forename: Tae-Ho
authors.initials: TH
authors.lastname: Kim
authors.forename: Ghi Su
authors.initials: GS
authors.lastname: Kim
authors.forename: Shin-Yoon
authors.initials: SY
authors.lastname: Kim
authors.forename: Jung-Min
authors.initials: JM
authors.lastname: Koh
authors.forename: Changwon
authors.initials: C
authors.lastname: Kang
mesh_terms: D019540:Area Under Curve; D015519:Bone Density / Q000235:genetics; D005260:Female; D050723:Fractures, Bone / Q000235:genetics* / Q000503:physiopathology; D020022:Genetic Predisposition to Disease; D006801:Humans; D008875:Middle Aged; D015663:Osteoporosis, Postmenopausal / Q000150:complications / Q000503:physiopathology; D020641:Polymorphism, Single Nucleotide / Q000235:genetics*; D017698:Postmenopause / Q000235:genetics*; D011379:Prognosis; D012307:Risk Factors
keywords: FRACTURE; GENETIC PROFILING; INDIVIDUALIZED PROGNOSIS; OSTEOPOROSIS; RECLASSIFICATION ANALYSIS