Field Synopsis of Environmental and Genetic Risk Factors of Sporadic Early-Onset Colorectal Cancer and Advanced Adenoma
Bibliographic record
Abstract
BACKGROUND: To systematically appraise and synthesize available epidemiologic evidence on the associations of environmental and genetic factors with the risk of sporadic early-onset colorectal cancer (EOCRC) and early-onset advanced colorectal adenoma (EOCRA). METHODS: Multiple databases were comprehensively searched to identify eligible observational studies. Genotype data from UK Biobank were incorporated to examine their associations with EOCRC in a nested case-control design. Meta-analyses of environmental risk factors were performed, and the strength of evidence was graded based on predefined criteria. Meta-analyses of genetic associations were conducted using the allelic, recessive, and dominant models, respectively. RESULTS: A total of 61 studies were included, reporting 120 environmental factors and 62 genetic variants. We found 12 risk factors (current overweight, overweight in adolescence, high waist circumference, smoking, alcohol, sugary beverages intake, sedentary behavior, red meat intake, family history of colorectal cancer, hypertension, hyperlipidemia, and metabolic syndrome) and three protective factors (vitamin D, folate, and calcium intake) for EOCRC or EOCRA. No significant associations between the examined genetic variants and EOCRC risk were observed. CONCLUSIONS: Recent data indicate that the changing patterns of traditional colorectal cancer risk factors may explain the rising incidence of EOCRC. However, research on novel risk factors for EOCRC is limited; therefore, we cannot rule out the possibility of EOCRC having different risk factors than late-onset colorectal cancer (LOCRC). IMPACT: The potential for the identified risk factors to enhance the identification of at-risk groups for personalized EOCRC screening and prevention and for the prediction of EOCRC risk should be comprehensively addressed by future studies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".