Obesity and risk of depressive disorder in children and adolescents: A meta‐analysis of observational studies
Bibliographic record
Abstract
Abstract Purpose This meta‐analysis evaluated the relationship between overweight/obesity and depressive disorders in children and adolescents. Methods We examined the databases of PubMed, Embase and Web of Science for pertinent observational studies released up until 20 February 2022. The pooled relative risks (RRs) and 95% confidence intervals (CIs) of obesity and overweight with depressive disorder were calculated by means of random‐effects models. The Newcastle‐Ottawa Quality Assessment Scale and Agency for Healthcare Research and Quality scale were adopted to evaluate the study quality. Results Finally, for this meta‐analysis, we evaluated 22 observational publications covering 175 135 participants (5 cohort study articles, 1 case–control study article and 16 cross‐sectional study articles). A significant positive association was found between obesity and the risk of depression (RR 1.32, 95% CI 1.09–1.60, I2 = 79.90%, Pheterogeneity < 0.001) and in the association between obesity and depressive symptoms (RR 1.16, 95% CI: 1.00–1.35, I2 = 25.0%, Pheterogeneity = 0.247). On sensitivity analysis, the pooled RRs remained robust. Subgroup analysis indicated that obese children and teenagers in western countries were more prone to depression. Conclusion Evidence from this meta‐analysis, based on observational studies, supported the idea that obese children and adolescents are more likely to experience depression and depressive symptoms.
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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 | 0.017 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.053 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".