High intake of ultra-processed food is associated with dementia in adults: a systematic review and meta-analysis of observational studies
Bibliographic record
Abstract
Abstract Background and aims Poor cardiometabolic health is associated with dementia. Considering previous meta-analyses have confirmed associations between ultra-processed foods (UPFs) and cardiometabolic disease, we were interested in the contribution of UPF consumption to the risk of developing dementia. Methods We performed a systematic review and meta-analysis of all records registered on Ovid Medline and Web of Science from inception until December 2022 [PROSPERO (CRD42023388363)]. Studies that assessed UPF consumption in adults, determined according to NOVA, and that reported dementia (Alzheimer’s disease, vascular dementia and mild cognitive impairment) determined by clearly stated diagnostic criteria (including formal assessment of dementia or use of diagnostic codes) were included. The association between UPF consumption and dementia was assessed using random-effects meta-analysis, controlling for confounding variables. Study quality was assessed using the Newcastle Ottawa Scale and evidence credibility evaluated using the NutriGrade system. Results Seven thousand ten records were screened, and 122 records underwent full text review. From these, 10 observational (8 longitudinal) studies, analysing 867,316 individuals, were included. Included studies adjusted for age, socioeconomic status and co-morbidity, alongside other confounders. High ( vs. low) intake of UPF was associated with increased risk of dementia (pooled relative risk 1.44 (95% confidence interval 1.09–1.90) (p = 0.02)) (I 2 = 97.0%), although m oderate ( vs. low) intake of UPF was not (1.12 (0.96–1.31) (0.13)) (85.0%). Funnel plots demonstrate low risk of publication bias. Conclusion High UPF consumption is associated with dementia. Public health measures to reduce overconsumption of UPFs are imperative to reduce the burden of dementia. Graphical abstract
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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 | 0.016 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.016 | 0.033 |
| Bibliometrics | 0.006 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".