A Predictive Model of Risk Factors for Conversion From Major Depressive Disorder to Bipolar Disorder Based on Clinical Characteristics and Circadian Rhythm Gene Polymorphisms

• Published in: Frontiers in psychiatry Vol. 13; p. 843400
• Main Authors: Xu, Zhi, Chen, Lei, Hu, Yunyun, Shen, Tian, Chen, Zimu, Tan, Tingting, Gao, Chenjie, Chen, Suzhen, Chen, Wenji, Chen, Bingwei, Yuan, Yonggui, Zhang, Zhijun
• Format: Journal Article
• Language: English
• Published: Frontiers Media S.A 11.07.2022
• Subjects: bipolar disorder; circadian rhythm; conversion; machine learning; predictive model; Psychiatry
• ISSN: 1664-0640; 1664-0640
• DOI: 10.3389/fpsyt.2022.843400

Background Bipolar disorder (BD) is easy to be misdiagnosed as major depressive disorder (MDD), which may contribute to a delay in treatment and affect prognosis. Circadian rhythm dysfunction is significantly associated with conversion from MDD to BD. So far, there has been no study that has revealed a relationship between circadian rhythm gene polymorphism and MDD-to-BD conversion. Furthermore, the prediction of MDD-to-BD conversion has not been made by integrating multidimensional data. The study combined clinical and genetic factors to establish a predictive model through machine learning (ML) for MDD-to-BD conversion. Method By following up for 5 years, 70 patients with MDD and 68 patients with BD were included in this study at last. Single nucleotide polymorphisms (SNPs) of the circadian rhythm genes were selected for detection. The R software was used to operate feature screening and establish a predictive model. The predictive model was established by logistic regression, which was performed by four evaluation methods. Results It was found that age of onset was a risk factor for MDD-to-BD conversion. The younger the age of onset, the higher the risk of BD. Furthermore, suicide attempts and the number of hospitalizations were associated with MDD-to-BD conversion. Eleven circadian rhythm gene polymorphisms were associated with MDD-to-BD conversion by feature screening. These factors were used to establish two models, and 4 evaluation methods proved that the model with clinical characteristics and SNPs had the better predictive ability. Conclusion The risk factors for MDD-to-BD conversion have been found, and a predictive model has been established, with a specific guiding significance for clinical diagnosis.