Abstract B95: A dietary pattern that is associated with C-peptide and risk of colorectal cancer in women
Bibliographic record
Abstract
Abstract Higher serum C-peptide concentrations is associated with increased risk of colorectal cancer (CRC). Therefore, we used diet and C-peptide information from a subsample of women from a large cohort and applied stepwise linear regression to identify food groups that correlated with serum levels of C-peptide. These food contributors then formed dietary patterns for predicting the risk of CRC in the main cohort of women. In up to 22 years of follow-up, we ascertained 985 cases of CRC and 758 colon cancer cases. After adjusting for confounders, we observed that the C-peptide pattern, characterized by higher meat, fish, and sweetened beverage intake, but lower coffee, high fat dairy, and whole grains intake, showed direct association with CRC risk (comparing extreme quintiles, RR=1.35, 95% CI=1.07–1.70. p trend=0.009). In stratified analysis, we did not observe an association between the C-peptide pattern and colon cancer among lean and active women (RR comparing extreme quintiles=0.84, 95% CI=0.50–1.41, p trend=0.23). However, among women who were overweight or sedentary, RR for the same comparison was 1.58 (95% CI=1.20–2.07, p trend=0.002) (p for interaction=0.007). In addition, the association between the C peptide pattern and serum C peptide concentrations is also stronger in the sedentary or overweight group (r=0.23, p<0.0001 vs r=0.15, p=0.01 among lean and active women). In conclusion, we derived a dietary pattern that correlated with C peptide concentrations. This pattern was associated with an increase of colon cancer, especially among women who were at risk for insulin resistance. Citation Information: Cancer Prev Res 2011;4(10 Suppl):B95.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".