Sweet Misery: Association of Sugar Consumption With Anxiety and Depression—A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Food choices we make have profound implications on mental well-being, and this is an area that demands detailed investigation. Hence, this systematic review was conducted to synthesize evidence to elucidate if sugar consumption is associated with anxiety and depression. METHODOLOGY: Electronic databases of PubMed, Cochrane, Scopus, Web of Science, Google scholar, and EMBASE were searched for relevant articles. Cross-sectional, cohort, and case-control studies assessing the influence of sugar consumption on anxiety and depression were included. The Newcastle Ottawa Scale was used for assessing the risk of bias. A systematic review was conducted according to PRISMA guidelines. RESULTS: Twenty-six articles were included for systematic review, out of which 14 were cross-sectional, 10 were cohort, and two were case-control studies. Risk of bias was assessed across all included studies. Nineteen studies were rated as high quality, while the remaining seven were of moderate quality. There was a considerable degree of heterogeneity between the studies, with a wide range of age groups and a lack of consistency in tools to measure anxiety and/or depression, and hence a meta-analysis was not conducted. However, an overall positive association was observed between high intake of sugar and increased risk of anxiety and depressive symptoms in different populations across the globe. CONCLUSION: Reducing sugar intake may serve as a modifiable risk factor for mental disorders, underscoring the need for public health interventions. A further understanding of the causal directions, as well as mediation mechanisms underlying the complex relationship between sugar consumption and mental disorders, is essential.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.008 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".