Prospective Association of Energy Balance Scores Based on Metabolic Biomarkers with Colorectal Cancer Risk
Bibliographic record
Abstract
Abstract Background: Energy balance–related factors, such as body mass index (BMI), diet, and physical activity, may influence colorectal cancer etiology through interconnected metabolic pathways, but their combined influence is less clear. Methods: We used reduced rank regression to derive three energy balance scores that associate lifestyle factors with combinations of prediagnostic, circulating levels of high-sensitivity C-reactive protein (hsCRP), C-peptide, and hemoglobin A1c (HbA1c) among 2,498 participants in the Cancer Prevention Study-II Nutrition Cohort. Among 114,989 participants, we verified 2,228 colorectal cancer cases. We assessed associations of each score with colorectal cancer incidence and by tumor molecular phenotypes using Cox proportional hazards regression. Results: The derived scores comprised BMI, physical activity, screen time, and 14 food groups, and explained 5.1% to 10.5% of the variation in biomarkers. The HR and 95% confidence interval (CI) for quartile 4 versus 1 of the HbA1c+C peptide–based score and colorectal cancer was 1.30 (1.15–1.47), the hsCRP-based score was 1.35 (1.19–1.53), and the hsCRP, C-peptide, and HbA1c-based score was 1.35 (1.19–1.52). The latter score was associated with non-CIMP tumors (HRQ4vsQ1: 1.59; 95% CI: 1.17–2.16), but not CIMP-positive tumors (Pheterogeneity = 0.04). Conclusions: These results further support hypotheses that systemic biomarkers of metabolic health—inflammation and abnormal glucose homeostasis—mediate part of the relationship between several energy balance–related modifiable factors and colorectal cancer risk. Impact: Results support cancer prevention guidelines for maintaining a healthful body weight, consuming a healthful diet, and being physically active. More research is needed on these clusters of exposures with molecular phenotypes of tumors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 | 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".