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

Zhi Xu; Lei Chen; Yunyun Hu; Tian Shen; Zimu Chen; Tingting Tan; Chenjie Gao; Suzhen Chen; Wenji Chen; Bingwei Chen; Yonggui Yuan; Zhijun Zhang

doi:10.3389/fpsyt.2022.843400

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

Front Psychiatry. 2022 Jul 11:13:843400. doi: 10.3389/fpsyt.2022.843400. eCollection 2022.

Authors

Zhi Xu¹, Lei Chen¹, Yunyun Hu², Tian Shen¹, Zimu Chen¹, Tingting Tan¹, Chenjie Gao¹, Suzhen Chen¹, Wenji Chen³, Bingwei Chen⁴, Yonggui Yuan^{1

2}, Zhijun Zhang⁵

Affiliations

¹ Department of Psychosomatics and Psychiatry, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
² Key Laboratory of Developmental Genes and Human Disease, Ministry of Education, Institute of Life Sciences, Southeast University, Nanjing, China.
³ Department of General Practice, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.
⁴ Department of Epidemiology and Biostatistics, School of Public Health, Southeast University, Nanjing, China.
⁵ Department of Neurology, School of Medicine, Zhongda Hospital, Southeast University, Nanjing, China.

Abstract

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.

Keywords: bipolar disorder; circadian rhythm; conversion; machine learning; predictive model.