A game changer for bipolar disorder diagnosis using RNA editing-based biomarkers

• Main Authors: Abraham, J.-D (Author), Cayzac, C. (Author), Checa-Robles, F.J (Author), Chimienti, F. (Author), Courtet, P. (Author), Dubuc, B. (Author), Dupré, P. (Author), Kupfer, D.J (Author), Lang, J.-P (Author), Méreuze, S. (Author), Patel, V. (Author), Salvetat, N. (Author), Vetter, D. (Author), Weissmann, D. (Author)
• Format: Article
• Language: English
• Published: Springer Nature 2022
• Online Access: View Fulltext in Publisher
• ISBN: 21583188 (ISSN)
• DOI: 10.1038/s41398-022-01938-6

In clinical practice, differentiating Bipolar Disorder (BD) from unipolar depression is a challenge due to the depressive symptoms, which are the core presentations of both disorders. This misdiagnosis during depressive episodes results in a delay in proper treatment and a poor management of their condition. In a first step, using A-to-I RNA editome analysis, we discovered 646 variants (366 genes) differentially edited between depressed patients and healthy volunteers in a discovery cohort of 57 participants. After using stringent criteria and biological pathway analysis, candidate biomarkers from 8 genes were singled out and tested in a validation cohort of 410 participants. Combining the selected biomarkers with a machine learning approach achieved to discriminate depressed patients (n = 267) versus controls (n = 143) with an AUC of 0.930 (CI 95% [0.879–0.982]), a sensitivity of 84.0% and a specificity of 87.1%. In a second step by selecting among the depressed patients those with unipolar depression (n = 160) or BD (n = 95), we identified a combination of 6 biomarkers which allowed a differential diagnosis of bipolar disorder with an AUC of 0.935 and high specificity (Sp = 84.6%) and sensitivity (Se = 90.9%). The association of RNA editing variants modifications with depression subtypes and the use of artificial intelligence allowed developing a new tool to identify, among depressed patients, those suffering from BD. This test will help to reduce the misdiagnosis delay of bipolar patients, leading to an earlier implementation of a proper treatment. © 2022, The Author(s).