Activation of the interleukin-23/Th17 axis in major depression: a systematic review and meta-analysis
Bibliographic record
Abstract
The interleukin-23/Th17 axis is a promising modifiable target for depression. However, its association with depression has not been systematically evaluated. We systematically searched four databases (EMBASE, Web of Science, Pubmed and PsycINFO) for studies comparing patients with major depression and healthy controls for plasma/serum levels of Th17 cells and their canonical cytokines (interleukin-17A [IL-17A], IL-22, granulocyte macrophage colony stimulating factor [GM-CSF]). We also compared counts of Th1, Th2 and Th9 cells between depressed/non-depressed patients and their respective canonical cytokines. We performed random-effects meta-analysis of the standardised mean difference (SMD) in immune measures between groups. Risk of bias was assessed using the Newcastle-Ottawa scale. Of 3154 studies screened, 36 studies were included in meta-analysis. Patients with depression had elevated IL-17A compared to controls (SMD = 0.80 [95% CI 0.03 to 1.58], p = 0.042), an association moderated by antidepressant use (Z = 2.12, p = 0.034). Patients with depression had elevated GM-CSF (SMD = 0.54 [95% CI 0.16 to 0.91], p = 0.0047), and a trend towards higher Th17 counts (SMD = 0.44 [- 0.01 to 0.88], p = 0.052). Whilst the Th2-associated cytokine IL-5 was elevated in depression (SMD = 0.36 [95% CI 0.05 to 0.66], p = 0.02), Th2 cell counts (p = 0.97), Th1 cell counts (p = 0.17) and interferon-γ (p = 0.22) were not. Data for Th9 cells, IL-9 and IL-22 were insufficient for meta-analysis. Respectively, 22, 25 and 5 studies were good, fair and poor in quality. Patients with major depression show peripheral over-activation of the IL-23/Th17 axis. Future interventional studies should test whether this is a modifiable target for depression.
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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 | 0.009 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.031 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".