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

Objective: The purpose of this study was to evaluate the effect of () 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 . 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 (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 polymorphisms in osteoporotic women.