Machine learning approaches for predicting bisphosphonate-related osteonecrosis in women with osteoporosis using vegfa gene polymorphisms

Jin Woo Kim, Jeong Yee, Sang Hyeon Oh, Sun Hyun Kim, Sun Jong Kim, Jee Eun Chung, Hye Sun Gwak

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

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.

Original languageEnglish
Article number541
JournalJournal of Personalized Medicine
Volume11
Issue number6
DOIs
StatePublished - Jun 2021

Keywords

  • Bisphosphonate-related osteonecrosis
  • Gene polymorphism
  • Machine learning
  • VEGFA

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