| Title |
Genetic risk score based on the lifetime prevalence of femoral fracture in 924 consecutive autopsies of Japanese males
|
|---|---|
| Published in |
Journal of Bone and Mineral Metabolism, October 2015
|
| DOI | 10.1007/s00774-015-0718-7 |
| Pubmed ID | |
| Authors |
Heying Zhou, Seijiro Mori, Tatsuro Ishizaki, Masashi Tanaka, Kumpei Tanisawa, Makiko Naka Mieno, Motoji Sawabe, Tomio Arai, Masaaki Muramatsu, Yoshiji Yamada, Hideki Ito |
| Abstract |
A genetic risk score (GRS) was developed for predicting fracture risk based on lifetime prevalence of femoral fractures in 924 consecutive autopsies of Japanese males. A total of 922 non-synonymous single nucleotide polymorphisms (SNPs) located in 62 osteoporosis susceptibility genes were genotyped and evaluated for their association with the prevalence of femoral fracture in autopsy cases. GRS values were calculated as the sum of risk allele counts (unweighted GRS) or the sum of weighted scores estimated from logistic regression coefficients (weighted GRS). Five SNPs (α-ʟ-iduronidase rs3755955, C7orf58 rs190543052, homeobox C4 rs75256744, G patch domain-containing gene 1 rs2287679, and Werner syndrome rs2230009) showed a significant association (P < 0.05) with the prevalence of femoral fracture in 924 male subjects. Both the unweighted and weighted GRS adequately predicted fracture prevalence; areas under receiver-operating characteristic curves were 0.750 [95 % confidence interval (CI) 0.660-0.840] and 0.770 (95 % CI 0.681-0.859), respectively. Multiple logistic regression analysis revealed that the odds ratio (OR) for the association between fracture prevalence and unweighted GRS ≥3 (n = 124) was 8.39 (95 % CI 4.22-16.69, P < 0.001) relative to a score <3 (n = 797). Likewise, the OR for a weighted GRS of 6-15 (n = 135) was 7.73 (95 % CI 3.89-15.36, P < 0.001) relative to scores of 0-5 (n = 786). The GRS based on risk allele profiles of the five SNPs could help identify at-risk individuals and enable implementation of preventive measures for femoral fracture. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 23% |
| Researcher | 4 | 15% |
| Other | 2 | 8% |
| Student > Master | 2 | 8% |
| Student > Bachelor | 2 | 8% |
| Other | 3 | 12% |
| Unknown | 7 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 35% |
| Biochemistry, Genetics and Molecular Biology | 6 | 23% |
| Agricultural and Biological Sciences | 3 | 12% |
| Sports and Recreations | 1 | 4% |
| Unknown | 7 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 October 2015.
All research outputs
#21,180,380
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Outputs from Journal of Bone and Mineral Metabolism
#563
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#236,490
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Outputs of similar age from Journal of Bone and Mineral Metabolism
#6
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