A Game Changer for Bipolar Disorder Diagnosis Using RNA Editing-Based Biomarkers
Open accessAbstract
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.
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RNA editing-based biomarkers have been explored to refine diagnoses in psychiatric conditions such as bipolar disorder.
“Additionally, the application of RNA editing-based biomarkers has been explored as a method to refine diagnoses in psychiatric conditions such as bipolar disorder, further illustrating the impact of innovative diagnostic approaches on mental health care [46].”
From Mental Health in Healthcare Workers Post-COVID-19: A Latin American Review and Insights into Personalized Management Strategies (2024) · cites this paper as [46]
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Self-citation
- Authors
- Salvetat, N., Checa-Robles, F. J., Patel, V., Cayzac, C., Dubuc, B., Chimienti, F., Abraham, J.-D., Dupré, P., Vetter, D., Méreuze, S., Lang, J.-P., Kupfer, D. J., Courtet, P., & Weissmann, D.
- Affiliations
- Centre National de la Recherche Scientifique (FR)
- University of Pittsburgh (US)
- Hôpital Lapeyronie (FR)
- Corresponding author
- Weissmann, Dinah
- ORCID
- Venue
- Translational Psychiatry
- Published
- 2022-05-04
- Volume
- 12
- Issue
- 1
- Pages
- 182-182
- ISSN
- 2158-3188 (ISSN-L)
- PMC
- PMC9064541
- OpenAlex
- W4225393382
- Cited by
- 31
- References
- 88
- Citations by year
- 2026: 5 · 2025: 10 · 2024: 10 · 2023: 5 · 2022: 1
- FWCI
- 4.23
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Translational Psychiatry (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- HAL (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Bipolar Disorder and Treatment
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA regulation and disease
- Health Sciences › Medicine › Pharmacology › Treatment of Major Depression
- Keywords
- bipolar disorder, major depressive disorder, A-to-I RNA editing, biomarkers, differential diagnosis, misdiagnosis, depression subtypes, machine learning classification, artificial intelligence
- MeSH terms
- Artificial Intelligence, Bipolar Disorder, Depressive Disorder, Humans, Biomarkers, RNA Editing
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