Ultra-processed food and risk of type 2 diabetes: a systematic review and meta-analysis of longitudinal studies
Bibliographic record
Abstract
BACKGROUND: The consumption of some food groups is associated with the risk of diabetes. However, there is no evidence from meta-analysis which evaluates the consumption of ultra-processed products in the risk of diabetes. This study aimed to review the literature assessing longitudinally the association between consumption of ultra-processed food and the risk of type 2 diabetes and to quantify this risk through a meta-analysis. METHODS: We conducted a systematic review and meta-analysis with records from PubMed, Latin American and Caribbean Literature in Health Sciences (LILACS), Scielo, Scopus, Embase, and Web of Science. We included longitudinal studies assessing ultra-processed foods and the risk of type 2 diabetes. The review process was conducted independently by two reviewers. The Newcastle Ottawa scale assessed the quality of the studies. A meta-analysis was conducted to assess the effect of moderate and high consumption of ultra-processed food on the risk of diabetes. RESULTS: In total 2272 records were screened, of which 18 studies, including almost 1.1 million individuals, were included in this review and 72% showed a positive association between ultra-processed foods and the risk of diabetes. According to the studies included in the meta-analysis, compared with non-consumption, moderate intake of ultra-processed food increased the risk of diabetes by 12% [relative risk (RR): 1.12; 95% confidence interval (CI): 1.06-1.17, I2 = 24%], whereas high intake increased risk by 31% (RR: 1.31; 95% CI: 1.21-1.42, I2 = 60%). CONCLUSIONS: The consumption of ultra-processed foods increased the risk for type 2 diabetes as dose-response effect, with moderate to high credibility of evidence.
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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 | 0.018 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.036 |
| Bibliometrics | 0.007 | 0.009 |
| 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) | 0.003 | 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".