Ultra-Processed Food Intake and Risk of Insomnia: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objectives: The objective of this investigation was to compile existing observational research and quantify the potential association between ultra-processed foods (UPFs) and the risk of insomnia using meta-analysis. Setting: We conducted a systematic search of the PubMed/MEDLINE, Scopus, and ISI Web of Science databases with no restrictions until 29 June 2024. Odds ratios (OR) and 95% confidence intervals (CI) were aggregated using a random-effects model, while the Newcastle-Ottawa Scale and Egger’s regression asymmetry test assessed study quality and publication bias, respectively. Results: Analysis of data from seven studies showed a significant positive association between higher intake of UPFs and an increased risk of insomnia (OR = 1.53; 95% CI: 1.20, 1.95; I2 = 62.3%; p = 0.014). Subgroup analysis indicated this positive relationship was particularly strong under the NOVA food classification (OR = 1.57; 95% CI: 1.03, 2.40; I2 = 78.5%; p = 0.009; n = 3) and with snack intake (OR = 1.33; 95% CI: 1.04, 1.71; I2 = 0.0%; p < 0.001; n = 2), compared to adherence to Western dietary patterns. Moreover, subgroup analysis based on age group showed that higher UPF intake was significantly associated with increased risk of insomnia among adolescents (OR = 1.55; 95% CI: 1.21, 1.99; I2 = 57.4%; p < 0.001) but not in adults. Conclusions: Our findings underscore a significant association between higher consumption of UPFs and increased risk of insomnia, particularly among adolescents. Further research is necessary to explore the intricacies of this association and to ensure the generalizability of these results.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".