Prediction of medication-related osteonecrosis of the jaws using machine learning methods from estrogen receptor 1 polymorphisms and clinical information

利用雌激素受体1多态性和临床信息,通过机器学习方法预测药物相关性颌骨坏死

阅读:1

Abstract

OBJECTIVE: The purpose of this study was to evaluate the effect of estrogen receptor 1 (ESR1) polymorphisms on the development of medication-related osteonecrosis of the jaws (MRONJ) in women with osteoporosis. METHODS: A total of 125 patients taking bisphosphonates was evaluated the relationship between MRONJ occurrence and single nucleotide polymorphisms (SNPs) of ESR1. Clinical information was collected, including current age, treatment duration, and comorbidity. Univariate and Multivariable regression analyzes were performed to evaluate the independent predictive factors for MRONJ occurrence. Predictive models were constructed using machine learning methods such as Lasso regression, Random forest (RF), and Support vector machine (SVM). The area under the receiver-operating curve (AUROC) was used to evaluate the performance of a binary classifier. RESULT: Two SNPs of ESR1 (rs4870056 and rs78177662) were significantly associated with MRONJ development. Patients with variant allele (A) of rs4870056 showed 2.45 times (95% CI, 1.03-5.87) the odds of MRONJ occurrence compared to those with wild-type homozygote (GG) after adjusting covariates. Additionally, carriers with variant allele (T) of rs78177662 had higher odds than those with wild-type homozygote (CC) (adjusted odds ratio (aOR), 2.64, 95% CI, 1.00-6.94). Among demographic variables, age ≥ 72 years (aOR, 3.98, 95% CI, 1.60-9.87) and bisphosphonate exposure ≥48 months (aOR, 3.16, 95% CI, 1.26-7.93) were also significant risk factors for MRONJ occurrence. AUROC values of machine learning methods ranged between 0.756-0.806 in the study. CONCLUSION: Our study showed that the MRONJ occurrence was associated with ESR1 polymorphisms in osteoporotic women.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。