Logistic ridge regression to predict bipolar disorder using mRNA expression levels in the N-methyl-D-aspartate receptor genes.

Abstract Background It is hypothesized that demographic variables and mRNA expression levels in the N-methyl-D-aspartate receptor (NMDAR) genes can be employed as potential biomarkers to predict bipolar disorder using artificial intelligence and machine learning approaches. Methods To determine bipolar status, we established a logistic ridge regression model resulting from the analysis of age, gender, and mRNA expression levels in 7 NMDAR genes in the blood of 51 bipolar patients and 139 unrelated healthy individuals in the Taiwanese population. The NMDAR genes encompasses COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR. We also compared our approach with various state-of-the-art algorithms such as support vector machine and C4.5 decision tree. Results The analysis revealed that the mRNA expression levels of COMT, GCAT, NRG1, PSAT1, SHMT2, SLC1A4, and SRR were associated with bipolar disorder. Moreover, the logistic ridge regression model (area under the receiver operating characteristic curve = 0.922) performed maximally among predictive models to infer the complicated relationship between bipolar disorder and biomarkers. Additionally, the results for the age- and gender-matched cohort were similar to those of the unmatched cohort. Limitations The cross-sectional study design limited the predictive value. Conclusion This is the first study demonstrating that the mRNA expression levels in the NMDAR genes may be altered in patients with bipolar disorder, thereby supporting the NMDAR hypothesis of bipolar disorder. The study also indicates that the mRNA expression levels in the NMDAR genes could serve as potential biomarkers to distinguish bipolar patients from healthy controls using artificial intelligence and machine learning approaches.