Interactions between Plasma Levels of 25-Hydroxyvitamin D, Insulin-Like Growth Factor (IGF)-1 and C-Peptide with Risk of Colorectal Cancer
Bibliographic record
Abstract
BACKGROUND: Vitamin D status and levels of insulin-like growth factor (IGF)-1 and C-peptide have been implicated in colorectal carcinogenesis. However, in contrast to vitamin D IGF-1 is not an easily modifiable risk factor. METHODS: Combining data from the Health Professionals Follow up Study (HPFS) and the Nurses' Health Study cohort (NHS) additive and multiplicative interactions were examined between plasma 25-hydroxyvitamin D (25(OH)D) and IGF-1, IGFBP-3 as well as C-peptide levels in 499 cases and 992 matched controls. For the various analytes, being high or low was based on being either above (or equal) or below the medians, respectively. RESULTS: Compared to participants with high 25(OH)D and low IGF-1/IGFBP-3 ratio (reference group), participants with a high IGF-1/IGFBP-3 ratio were at elevated risk of colorectal cancer when 25(OH)D was low (odds ratio (OR): 2.05 (95% CI: 1.43 to 2.92), but not when 25(OH)D was high (OR:1.20 (95% CI: 0.84 to 1.71, p(interaction): additive =0.06, multiplicative =0.25). Similarly, compared to participants with high 25(OH)D and low molar IGF-1/IGFBP-3 ratio and low C-peptide levels (reference group), participants with a combination of either high IGF-1/IGFBP-3 ratio or high C-peptide were at elevated risk for colorectal cancer when 25(OH)D was low (OR =1.90, 95% CI: 1.22 to 2.94) but not when 25(OH)D was high (OR=1.15, 95% CI: 0.74 to 1.77, p(interaction): additive=0.004; multiplicative=0.04). CONCLUSION: The results from this study suggest that improving vitamin D status may help lower risk of colorectal cancer associated with higher IGF-1/IGFBP-3 ratio or C-peptide levels.
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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 | 0.006 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".