Mycotoxin exposure and human cancer risk: A systematic review of epidemiological studies
Bibliographic record
Abstract
In recent years, there has been an increasing interest in investigating the carcinogenicity of mycotoxins in humans. This systematic review aims to provide an overview of data linking exposure to different mycotoxins with human cancer risk. Publications (2019 and earlier) of case-control or longitudinal cohort studies were identified in PubMed and EMBASE. These articles were then screened by independent reviewers and their quality was assessed according to the Newcastle-Ottawa scale. Animal, cross-sectional, and molecular studies satisfied criteria for exclusion. In total, 14 articles were included: 13 case-control studies and 1 longitudinal cohort study. Included articles focused on associations of mycotoxin exposure with primary liver, breast, and cervical cancer. Overall, a positive association between the consumption of aflatoxin-contaminated foods and primary liver cancer risk was verified. Two case-control studies in Africa investigated the relationship between zearalenone and its metabolites and breast cancer risk, though conflicting results were reported. Two case-control studies investigated the association between hepatocellular carcinoma and fumonisin B1 exposure, but no significant associations were observed. This systematic review incorporates several clear observations of dose-dependent associations between aflatoxins and liver cancer risk, in keeping with IARC Monograph conclusions. Only few human epidemiological studies investigated the associations between mycotoxin exposures and cancer risk. To close this gap, more in-depth research is needed to unravel evidence for other common mycotoxins, such as deoxynivalenol and ochratoxin A. The link between mycotoxin exposures and cancer risk has mainly been established in experimental studies, and needs to be confirmed in human epidemiological studies to support the evidence-based public health strategies.
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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 | 0.008 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".