Abstract PR-04: Blood lipid and lipoprotein levels and the risk of colorectal cancer in the European Prospective Investigation into Cancer and Nutrition
Bibliographic record
Abstract
Abstract Background: Blood lipids and lipoproteins may affect colorectal cancer (CRC) risk, but results are inconsistent. We examined the relation between serum levels of total cholesterol, high density lipoprotein (HDL), low density lipoprotein, triglycerides, apo lipoprotein A–I (apoA), apo lipoprotein B and the incidence of CRC. Methods: A nested case-control study was conducted within the European Prospective Investigation into Cancer and Nutrition (EPIC). A total of 1238 first primary incident colorectal cancer cases were matched to 1238 controls by age, gender, center, time of blood collection and fasting status. Serum levels were quantitatively determined by a colorimetric method. Conditional logistic regression models were used to estimate relative risks (RRs) and 95% confidence intervals (95% CI). Results: After adjustments, levels of HDL and apoA were inversely associated with colon cancer risk (RR for one standard deviation increase of 0.43 mmol/L in HDL and 0.32 g/L in apoA (95% CI) = 0.78 (0.68–0.89); p<0.01 and 0.82 (0.72–0.94); p<0.01, respectively) and with distal cancer risk (0.79 (0.65–0.96); p=0.02 and 0.83 (0.69–1.01); p=0.07, respectively). Although only slightly weaker, associations with proximal colon cancer risk were not statistically significant (0.82 (0.65–1.03); p=0.09 and 0.86 (0.68–1.07); p=0.18, respectively). Inclusion of other biomarkers or exclusion of the first 2 years of follow-up did not influence the association between HDL and colon cancer risk. No association was observed for rectal cancer risk. Conclusion: These findings suggest that high HDL levels may decrease colon cancer risk. The mechanism behind this association should be further investigated. Citation Information: Cancer Prev Res 2010;3(1 Suppl):PR-04.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".