The Role of Dietary Patterns and Nutritional Supplements in the Management of Mental Disorders in Children and Adolescents: An Umbrella Review of Meta-Analyses: Le rôle des habitudes alimentaires et des suppléments nutritionnels dans la prise en charge des troubles mentaux chez les enfants et les adolescents : une méta-revue de méta-analyses
Bibliographic record
Abstract
OBJECTIVE: In recent years, the relationship between nutrition and mental health has gained considerable interest. We identified, synthesized, and appraised all meta--analyses of randomized controlled trials (RCTs) and observational studies reporting on the efficacy of dietary patterns and nutrient supplements in the prevention and treatment of mental disorders in children and adolescents. METHODS: Systematic research in MEDLINE, PsycINFO, Scopus, and Cochrane Database of Systematic Reviews was completed on 8 January 2024. RESULTS: Our research found 24 meta-analyses: 14 on RCTs, 8 on observational studies, and 2 combining both. Emerging evidence suggests that omega-3, in particular eicosapentaenoic acid, and Vitamin D may have adjunctive benefits in the treatment of attention deficit hyperactivity disorder (ADHD), while no evidence was found for autism spectrum disorder (ASD). Observational data also indicated that prenatal folic acid supplementation (>400 μg daily) was associated with a reduced risk of ASD in offspring. In terms of dietary habits, several meta-analyses of observational data revealed that healthy dietary patterns (rich in fruits, vegetables, and fibre, low in saturated fats) during the prenatal period, childhood, and adolescence were linked to a significantly reduced risk of internalizing disorders and externalizing disorders. Conversely, unhealthy dietary habits (high in sugars, saturated animal fats, and industrial foods, low in fruits, vegetables, and fibre) are associated with an elevated risk of these mental health issues. However, the number of available studies on dietary interventions for the treatment of depression, ASD, and ADHD was limited, and the results obtained were either nonsignificant or contradictory. CONCLUSION: Our findings emphasize the need to establish clear causal relationships between dietary habits and the risk of mental illness in children and adolescents. Moreover, further investigation of the benefits observed with some nutrient supplements (such as omega-3 and vitamin D for ADHD) through larger-scale RCTs is imperative to establish more robust conclusions.
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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 | 0.026 | 0.051 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.042 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| 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".