Ultra-Processed Food Intake Is Associated with Non-Alcoholic Fatty Liver Disease in Adults: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Non-alcoholic fatty liver disease (NAFLD) is associated with overweight/obesity, metabolic syndrome and type 2 diabetes (T2D) due to chronic caloric excess and physical inactivity. Previous meta-analyses have confirmed associations between ultra-processed food (UPF) intake and obesity and T2D. We aim to ascertain the contribution of UPF consumption to the risk of developing NAFLD. We performed a systematic review and meta-analysis (PROSPERO (CRD42022368763)). All records registered on Ovid Medline and Web of Science were searched from inception until December 2022. Studies that assessed UPF consumption in adults, determined according to the NOVA food classification system, and that reported NAFLD determined by surrogate (steatosis) scores, imaging or liver biopsy were included. The association between UPF consumption and NAFLD was assessed using random-effects meta-analysis methods. Study quality was assessed, and evidence credibility evaluated, using the Newcastle Ottawa Scale and NutriGrade systems, respectively. A total of 5454 records were screened, and 112 records underwent full text review. From these, 9 studies (3 cross-sectional, 3 case-control and 3 cohort), analysing 60,961 individuals, were included in the current review. Both moderate (vs. low) (pooled relative risk 1.03 (1.00–1.07) (p = 0.04) (I2 = 0%)) and high (vs. low) (1.42 (1.16–1.75) (<0.01) (I2 = 89%)) intake of UPF significantly increased the risk of NAFLD. Funnel plots demonstrate low risk of publication bias. Consumption of UPF is associated with NAFLD with a dose–response effect. Public health measures to reduce overconsumption of UPF are imperative to reduce the burden of NAFLD, and the related conditions, obesity and T2D.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.010 | 0.002 |
| Bibliometrics | 0.000 | 0.003 |
| 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".