Patients with schizophrenia and bipolar disorder are characterized by different blood RNA editing signatures
Bibliographic record
Abstract
Introduction Mental disorders, such as Bipolar Disorder (BD), Schizophrenia (SZ), and Schizoaffective Disorder (SA), are prevalent and often debilitating conditions that significantly impact individuals’ lives (Scangos et al. Nat Med 2023; 29(2): 317-33). Recent findings have identified blood RNA editing gene modifications that may aid in distinguishing between healthy controls, depressed patients, and those with BD and unipolar depression, improving diagnostic accuracy and treatment strategies (Salvetat et al. Transl Psychiatry 2022; 12(1):182). Objectives This study demonstrates that RNA editing biomarkers can accurately differentiate individuals with SZ, SA, BD, and healthy controls, highlighting the potential of artificial intelligence (AI)-based predictions for diagnosis. Methods A comparative analysis was performed with 85 healthy controls subjects, 39 BD, 31 SZ, and 14 SA patients. Patient samples were collected from two cohorts. Diagnostic assessments were conducted using SCID-1, HDRS, YMRS, and M.I.N.I., while healthy controls had no history of mental disorders or psychotropic medication use. Results Significant biomarkers were combined using a multiclass Random Forest algorithm. The algorithm was trained on 70% of the population. Then, the test was performed on the 30% of the population who never saw the algorithm. The analysis shows clear differentiation between the control group and individuals with BD, SZ, and SA with high sensitivities and specificities for ROC area under the curve (AUC). Conclusions This proof-of-concept analysis provides strong evidence for using RNA editing signature in diagnosis, and potentially in prognosis and treatment prediction. Further validation will be performed using a larger cohort. Disclosure of Interest None Declared
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".