The association of ultra-processed food intake with neurodegenerative disorders: a systematic review and dose-response meta-analysis of large-scale cohorts
Bibliographic record
Abstract
Objectives Our systematic review and meta-analysis aimed to uncover the relationship between UPFs intake and neurodegenerative disorders, including multiple sclerosis (MS), Parkinson’s disease (PD), Alzheimer’s disease (AD), cognitive impairment, and dementia.Setting A systematic search was conducted using the Scopus, PubMed/MEDLINE, and ISI Web of Science databases without any limitation until June 24, 2023. Relative risk (RR) and 95% confidence interval (CI) were pooled by using a random-effects model, while validated methods examined quality and publication bias via Newcastle-Ottawa Scale, Egger’s regression asymmetry, and Begg’s rank correlation tests, respectively.Results Analysis from 28 studies indicated that a higher UPFs intake was significantly related to an enhanced risk of MS (RR = 1.15; 95% CI: 1.00, 1.33; I2 = 37.5%; p = 0.050; n = 14), PD (RR = 1.56; 95% CI: 1.21, 2.02; I2 = 64.1%; p = 0.001; n = 15), and cognitive impairment (RR = 1.17; 95% CI: 1.06, 1.30; I2 = 74.1%; p = 0.003; n = 17), although not AD or dementia. We observed that a 25 g increment in UPFs intake was related to a 4% higher risk of MS (RR = 1.04; 95% CI: 1.01, 1.06; I2 = 0.0%; p = 0.013; n = 7), but not PD. The non-linear dose–response relationship indicated a positive non-linear association between UPF intake and the risk of MS (Pnonlinearity = 0.031, Pdose-response = 0.002). This association was not observed for the risk of PD (Pnonlinearity = 0.431, Pdose-response = 0.231).Conclusion These findings indicate that persistent overconsumption of UPFs may have an adverse impact on neurodegenerative conditions, potentially leading to a decline in quality of life and reduced independence as individuals age.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.020 | 0.043 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| 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".