Ultra-processed food and risk of type 2 diabetes: a systematic review and meta-analysis of longitudinal studies
Why this work is in the frame
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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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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.015 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| 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 it