Identifying Baseline Predictors of Relapse and Stratifying Immune Composition in Major Depressive Disorder
Bibliographic record
Abstract
A major challenge in the treatment of major depressive disorder (MDD) is relapse, which is defined as the return of depressive symptoms during a period of remission. Relapse rates in MDD are high, with approximately 50% of individuals relapsing following treatment of their first depressive episode, therefore early intervention to prevent relapse is crucial. Evidence suggests that immune dysregulation may be linked to longitudinal changes in depressive severity. However, it is currently unknown whether inflammation can predict future relapse in MDD. The objective of this project was to identify potential immune predictors of relapse in participants that responded to a treatment or a combination of treatments for MDD. A secondary objective was to investigate immune composition in efforts to stratify MDD individuals into more homogenous groups and further explore these groups in relation to clinical symptoms. This project is part of the Wellness Monitoring for Major Depressive Disorder longitudinal study (NCT02934334) of responders to antidepressant treatment conducted at 6 clinical sites across Canada. Montgomery Asberg Depression Rating Scale (MADRS) scores were used to assess depression severity and to categorize participants into ultrastable, unstable, and relapse groups. Plasma immune profiles were generated using the LEGENDplex Human Th Cytokine Panel immunoassay. Principal Component Analysis and Kruskal-Wallis tests of individual immune cytokines did not show differences between ultrastable, unstable, or relapse groups. Principal Component Analysis did reveal two cytokine clusters. Hierarchical Clustering analysis identified three distinct immune biotypes characterized by differing levels of Th cytokines and validated the presence of the cytokine clusters. Neither of these outcomes was predictive of relapse in this cohort. Our findings have shown that immune composition may serve as an important factor in parsing heterogeneity that is observed in this disorder through identification of distinct immune biotypes and highly interconnected cytokine subnetworks in major depression. The potential for immune biotypes for optimizing treatment regimens and relapse prevention necessitates further investigation and replication.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".