Depression and inflammation among children and adolescents: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Increasing evidence suggests that youth with Major Depressive Disorder (MDD) exhibit early indicators of cardiovascular disease. A leading hypothesized mechanism of this association is via inflammatory pathways, however, results examining this direct association are mixed. Our objective was to synthesize and quantify observational studies examining the association of depression and inflammation among children and adolescents. METHODS: Electronic searches were conducted in MEDLINE, Embase, PsycINFO, and Scopus, yielding 2,757 non-duplicate records from 1946 to 2019. The included studies measured depression or depressive symptoms and examined its association with inflammation in participants younger than 18 years. All relevant articles were reviewed and data extracted by two independent coders. Estimates were examined by using random-effects meta-analysis. RESULTS: Twenty-two studies (20,791 participants) were included. Significant associations were observed between concurrent depression and CRP (n = 7; r = 0.12; 95% confidence interval [CI] = 0.04 to 0.19), and IL-6 (n = 7; r = 0.17; 95% CI= 0.10 to 0.24). Longitudinal analyses revealed that depression is a significant predictor of IL-6 (n = 3; r = 0.29; 95% CI= 0.04 to 0.50) and conversely, that inflammation (measured by CRP or IL-6) predicts future depression (n = 4; r = 0.04; 95% CI= 0.00 to 0.08). LIMITATIONS: Results are limited by the small number of studies preventing examination of some moderator variables. Findings are correlational, not causal. CONCLUSION: Depression is positively associated with concurrent and future inflammation among children and adolescents. Results suggest that bidirectional associations may exist between depression and a pro-inflammatory state.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.048 |
| Bibliometrics | 0.005 | 0.006 |
| 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".