Can Artificial Sweeteners Increase the Risk of Cancer Incidence and Mortality: Evidence from Prospective Studies
Bibliographic record
Abstract
Cancer has become a major challenge in the global disease burden. Artificial sweeteners are a class of chemical compounds that are used as food and beverage addition agent to replace sugar. However, the health effects of consuming artificial sweeteners are still unclear. This meta-analysis was performed to evaluate the role of artificial sweeteners on cancer. The databases PubMed, Cochrane Library, MEDLINE, Web of Science and EMBASE were searched up until July 2022. A Newcastle−Ottawa scale (NOS) was used to estimate the study quality. A total of 25 observational studies were included with a total of 3,739,775 subjects. The intake of artificial sweeteners had no apparent association with overall cancer incidence and mortality. However, in Europe, artificial sweeteners’ intake could increase the risk of cancer incidence (HR/RR = 1.07, 95% CI = [1.02, 1.12], I2 = 25.8%, P = 0.223), which appears to be related to a shift in nutritional behaviors in the countries. Significant results were also observed in subgroups with aspartame and a mixed intake of artificial sweeteners. Moreover, higher risk was observed for artificial sweeteners intake in all-cause mortality (HR/RR =1.13, 95% CI = [1.03, 1.25], I2 = 79.7%, p < 0.001) and a J-shaped association between them was found. More data from well-conducted studies and clinical trials are required.
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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 | 0.022 | 0.056 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.034 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".