Calcium Intake and Risk of Colorectal Cancer According to Tumor-infiltrating T Cells
Bibliographic record
Abstract
Abstract Calcium intake has been associated with a lower risk of colorectal cancer. Calcium signaling may enhance T-cell proliferation and differentiation, and contribute to T-cell–mediated antitumor immunity. In this prospective cohort study, we investigated the association between calcium intake and colorectal cancer risk according to tumor immunity status to provide additional insights into the role of calcium in colorectal carcinogenesis. The densities of tumor-infiltrating T-cell subsets [CD3+, CD8+, CD45RO (PTPRC)+, or FOXP3+ cell] were assessed using IHC and computer-assisted image analysis in 736 cancer cases that developed among 136,249 individuals in two cohorts. HRs and 95% confidence intervals (CI) were calculated using Cox proportional hazards regression. Total calcium intake was associated with a multivariable HR of 0.55 (comparing ≥1,200 vs. <600 mg/day; 95% CI, 0.36–0.84; Ptrend = 0.002) for CD8+ T-cell–low but not for CD8+ T-cell–high tumors (HR = 1.02; 95% CI, 0.67–1.55; Ptrend = 0.47). Similarly, the corresponding HRs (95% CIs) for calcium for low versus high T-cell–infiltrated tumors were 0.63 (0.42–0.94; Ptrend = 0.01) and 0.89 (0.58–1.35; Ptrend = 0.20) for CD3+; 0.58 (0.39–0.87; Ptrend = 0.006) and 1.04 (0.69–1.58; Ptrend = 0.54) for CD45RO+; and 0.56 (0.36–0.85; Ptrend = 0.006) and 1.10 (0.72–1.67; Ptrend = 0.47) for FOXP3+, although the differences by subtypes defined by T-cell density were not statistically significant. These potential differential associations generally appeared consistent regardless of sex, source of calcium intake, tumor location, and tumor microsatellite instability status. Our findings suggest a possible role of calcium in cancer immunoprevention via modulation of T-cell function.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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 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".