Ultra processed food exposure and cognitive outcomes: A systematic review of observational studies.
Bibliographic record
Abstract
Background: Ultra processed food (UPF) intake has been associated with multiple negative health outcomes. Research investigating UPF intake and cognitive health outcomes has begun. The aim of this review is to summarise the existing evidence of associations between exposure to UPFs, as defined by the NOVA food classification system, and cognitive health outcomes. Methods: We conducted a systematic search across MEDLINE, PsycINFO, Embase, and APA Psych Articles through OVID, and PubMed for relevant studies up-until October 2024. The Newcastle-Ottawa Scale was used to assess the quality of included studies. A narrative approach was used to summarise and integrate results across studies. Results: Three hundred and eighty-three articles were screened and five met the inclusion criteria. All studies were published between 2022 and 2024. The association between UPF intake and four different cognitive outcomes (dementia risk, cognitive impairment risk, cognitive performance and cognitive change trajectories) were explored across the studies. Three out of the five included studies found a significant negative main effect of consuming UPF on the cognitive outcome of interest. All studies identified adverse consequences of consumption in either a sub-group of the population or a sub-group of UPF type. Conclusions: Deleterious effects of UPF consumption on multiple cognitive health outcomes were identified across all studies. However, the results suggest the relationship may be specific to sub-groups of the population or sub-groups of UPF type. Conclusions should be drawn with caution due to the limited number of studies available examining UPF intake according to NOVA and its association with cognitive outcomes, as well as the variability in cognitive measures assessed and other methodological differences across studies.
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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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.008 |
| Bibliometrics | 0.009 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".