Microplastics and mental health: The role of ultra-processed foods
Bibliographic record
Abstract
Ultra-processed foods now dominate the food supplies of high-income countries, with over 50% of energy intake coming from ultra-processed foods in the United States. Observational data has revealed that greater ultra-processed food consumption is associated with adverse mental health outcomes, while data from randomized controlled trials has demonstrated improvements to mental health following reduction in ultra-processed food intake. Ultra-processed foods are known to contain high concentrations of microplastics, largely due to both the processing and packing procedures. In light of recent findings which demonstrated alarming microplastic concentrations in the human brain, we propose that microplastics may partially mediate the adverse mental health effects of increasing ultra-processed food intake. In this viewpoint, we discuss the overlapping mechanisms for adverse mental health, paucity of research in the area, and propose a Dietary Microplastic Index (DMI) to study this potential relationship.
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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 | 0.000 | 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 | 0.000 |
| 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".