The Effect of Lactose Maldigestion on the Relationship Between Dairy Food Intake and Colorectal Cancer: A Systematic Review
Bibliographic record
Abstract
Dairy food consumption has been inconsistently shown to protect against colorectal cancer (CRC) in case-based studies, and no clear benefits against recurrent colonic polyps (CRP) have been reported. Based on population-based studies we have hypothesized that dairy food intake may have anti-CRC effects at both low intake lactase non-persistent (LNP) populations and at high intake lactase persistent (LP) subjects. We separately analyse existing case-based studies and divide origins into high LNP (>or= 80% LNP prevalence), low LNP (prevalence <or= 20%) and mid LNP countries (21-79% prevalence), which coincide with low, high, and mid quantity dairy food intake regions, respectively. Odds ratios and relative risks (RR) of highest versus lowest dairy intake within each group are analyzed together for assessment of protection against CRC and CRP. Eighty studies met stipulated criteria. Thirteen analyzed the effect on recurrent polyps. Forest plots from 2 regions, high LNP (low dairy food intake) RR = 0.84 [95% confidence interval (CI) = 0.73-0.97) and low LNP (high dairy food intake) RR = 0.80 (95% CI = 0.73-0.88) demonstrated significant protection against CRC. In mixed LNP/LP populations (mid dairy food intake) nonsignificant protection was found RR = 0.92 (95% CI = 0.79-1.06). Similar regional analysis for CRP failed to show significant protective effect in any region. This meta-analysis supports that the highest level of dairy food consumption protects subjects in both high and low LNP regions but not in areas with significant mixed LNP/LP populations. In both groups, dairy foods had no effect on polyp formation, suggesting it may only protect against CRC at late stages of promotion. These results raise the possibility that LNP/LP status may be partly responsible for the discrepant results with respect to the relationship between dairy food consumption and CRC.
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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) | 0.001 | 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) | 0.000 | 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".