The Impact of Cognitive Behavioral Therapy on Peripheral Interleukin-6 Levels in Depression: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
There is interest in the role of peripheral interleukin-6 (IL-6) in depression and the effect of treatment (e. g., pharmacologic, psychosocial, neurostimulation). However, the relationship between cognitive behavioral therapy (CBT), IL-6 and depression has not yet been established. We conducted a meta-analysis to explore the association between CBT and change of peripheral IL-6 levels in depressive symptoms or major depressive disorder (MDD). A systematic search of online databases (i.e., PubMed, Web of Science, Google Scholar, PsycINFO, and Cochrane Library) was completed from inception to May 2021. In total, 10 eligible papers with 940 participants reporting peripheral IL-6 levels before and after CBT were included in the analysis. The main result indicates that peripheral levels of IL-6 were significantly lower after CBT intervention in individuals with depression, with a small effect (SMD = 0.38, 95% CI: 0.07, 0.69, p = 0.02). The results of subgroup analyses demonstrate that (1) there was a significant decrease in IL-6 for studies that were equal to or <8 weeks in duration vs. more than 8 weeks in duration, and (2) IL-6 was significantly reduced in the Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnosis (i.e., DSM-IV, DSM-IV-TR, or DSM-V) of MDD, but not for the subgroup without DSM diagnosis. Publication year was identified as a potential contributor to heterogeneity of the results from our analysis. Taken together, our findings support the notion that CBT influences peripheral IL-6 in individuals with depression and represents a point of commonality with other antidepressant treatment modalities (e.g., antidepressants). Systematic Review Registration https://doi.org/10.17605/osf.io/tr9yh , identifier: 10.17605/osf.io/tr9yh.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.019 | 0.028 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".