Machine learning approaches for predicting bisphosphonate-related osteonecrosis in women with osteoporosis using vegfa gene polymorphisms
- 주제(키워드) Bisphosphonate-related osteonecrosis , Gene polymorphism , Machine learning , VEGFA
- 등재 SCIE, SCOPUS
- 발행기관 MDPI AG
- 발행년도 2021
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000182028
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/jpm11060541
초록/요약
Objective: This nested case–control study aimed to investigate the effects of VEGFA poly-morphisms on the development of bisphosphonate-related osteonecrosis of the jaw (BRONJ) in women with osteoporosis. Methods: Eleven single nucleotide polymorphisms (SNPs) of the VEGFA were assessed in a total of 125 patients. Logistic regression was performed for multivariable analy-sis. Machine learning algorithms, namely, fivefold cross-validated multivariate logistic regression, elastic net, random forest, and support vector machine, were developed to predict risk factors for BRONJ occurrence. Area under the receiver-operating curve (AUROC) analysis was conducted to assess clinical performance. Results: The VEGFA rs881858 was significantly associated with BRONJ development. The odds of BRONJ development were 6.45 times (95% CI, 1.69–24.65) higher among carriers of the wild-type rs881858 allele compared with variant homozygote carriers after adjusting for covariates. Additionally, variant homozygote (GG) carriers of rs10434 had higher odds than those with wild-type allele (OR, 3.16). Age ≥ 65 years (OR, 16.05) and bisphosphonate exposure ≥ 36 months (OR, 3.67) were also significant risk factors for BRONJ occurrence. AUROC values were higher than 0.78 for all machine learning methods employed in this study. Conclusion: Our study showed that the BRONJ occurrence was associated with VEGFA polymorphisms in osteoporotic women. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
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