The Association Between Ultra-Processed Food Consumption and Chronic Insomnia in the NutriNet-Santé Study
Bibliographic record
Abstract
BACKGROUND: The consumption of ultra-processed foods (UPF) is on the rise worldwide, and it has been linked to numerous health conditions, such as diabetes, obesity, and cancer. Few studies have focused on the effect of UPF consumption on sleep health and even fewer on chronic insomnia. OBJECTIVE: This study investigated the association between UPF intake and chronic insomnia in a large population-based sample. DESIGN: This was a cross-sectional analysis using the NutriNet-Santé study data, an ongoing Web cohort in France. PARTICIPANTS/SETTING: Thirty-eight thousand five hundred seventy adult males and females who had completed a sleep questionnaire (2014) and at least two 24-hour dietary records were included in the analysis. MAIN OUTCOMES MEASURES: Chronic insomnia was defined according to established criteria. Categorization of food and beverages as UPF was based on the NOVA-Group 4 classification. STATISTICAL ANALYSES PERFORMED: The cross-sectional association between UPF intake and chronic insomnia was assessed using multivariable logistic regression. RESULTS: Among the 38,570 participants (mean age, 50.0 ±14.8 years, 77.0% female) included in the analysis, 19.4% had symptoms of chronic insomnia. On average, UPF represented 16% of the total amount (g/day) of the overall dietary intake. In the fully adjusted model, UPF consumption was associated with higher odds of chronic insomnia (odds ratio [OR] for an absolute 10% greater UPF intake in the diet = 1.06; 95% confidence interval [CI]: 1.02-1.09). Sex-specific OR for chronic insomnia for an absolute 10% greater UPF intake in the diet were 1.09 (1.01-1.18) among males and 1.05 (1.01-1.09) among females. CONCLUSIONS: This large epidemiological study revealed a statistically significant association between UPF intake and chronic insomnia, independent of sociodemographic, lifestyle, diet quality, and mental health status covariates. The findings provide insights for future longitudinal research as well as nutrition- and sleep-focused intervention and prevention programs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".