Blood lipid and lipoprotein concentrations and colorectal cancer risk in the European Prospective Investigation into Cancer and Nutrition
Bibliographic record
Abstract
OBJECTIVE: To examine the association between serum concentrations of total cholesterol, high density lipoprotein cholesterol (HDL), low density lipoprotein cholesterol, triglycerides, apolipoprotein A-I (apoA), apolipoprotein B and the incidence of colorectal cancer (CRC). DESIGN: Nested case-control study. SETTING: The study was conducted within the European Prospective Investigation into Cancer and Nutrition (EPIC), a cohort of more than 520,000 participants from 10 western European countries. PARTICIPANTS: 1238 cases of incident CRC, which developed after enrolment into the cohort, were matched with 1238 controls for age, sex, centre, follow-up time, time of blood collection and fasting status. MAIN OUTCOME MEASURES: Serum concentrations were quantitatively determined by colorimetric and turbidimetric methods. Dietary and lifestyle data were obtained from questionnaires. Conditional logistic regression models were used to estimate incidence rate ratios (RRs) and 95% CIs which were adjusted for height, weight, smoking habits, physical activity, education, consumption of fruit, vegetables, meat, fish, alcohol, fibre and energy. RESULTS: After adjustments, the concentrations of HDL and apoA were inversely associated with the risk of colon cancer (RR for 1 SD increase of 16.6 mg/dl in HDL and 32.0 mg/dl in apoA of 0.78 (95% CI 0.68 to 0.89) and 0.82 (95% CI 0.72 to 0.94), respectively). No association was observed with the risk of rectal cancer. Additional adjustment for biomarkers of systemic inflammation, insulin resistance and oxidative stress or exclusion of the first 2 years of follow-up did not influence the association between HDL and risk of colon cancer. CONCLUSIONS: These findings show that high concentrations of serum HDL are associated with a decreased risk of colon cancer. The mechanism behind this association needs further elucidation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".