Evaluation of Relationships Among National Colorectal Cancer Mortality Rates, Genetic Lactase Non-Persistence Status, and Per Capita Yearly Milk and Milk Product Consumption
Bibliographic record
Abstract
Colorectal cancer (CRC) is one of the leading causes of mortality in Western countries. Its putative pathogenesis revolves around genetic and environmental factors, particularly diet. One of the most studied dietary factors, dairy product intake, is still debated as a protective agent. The role of lactose as a candidate prebiotic (stimulating lactic acid bacteria) and its relation to genetic lactase non-persistence (LNP) status has not been evaluated. We undertook a review and analysis of national per capita dairy product consumption, national LNP prevalence, and national CRC mortality rates (CRCM) to determine whether relationships existed among these variables. Data on these three items were obtained from the available literature. A negative binomial regression model was used to compare national LNP status with national CRCM rates for three time periods. Pearson correlation was used to compare national per capita dairy food intake with national CRCM rates for the approximate midpoint time period of reviewed articles. We found that there was a significant positive correlation between per capita dairy food intake and CRCM rates. However, there was also a significant negative correlation between national LNP prevalence and CRCM rates. Population-based studies supported the suggestion that in both homogeneous high and homogeneous low prevalence LNP countries characterized by low and high dairy food intake respectively, dairy food consumption exerted a protective effect against CRC and CRCM rate. Because some population studies contradict the hypotheses that dairy food intake promotes CRC or that LNP status protects against CRC, we hypothesize that dairy food consumption may operate by two distinct mechanisms--one that operates at low doses in LNP subjects and another in high doses in non-LNP subjects.
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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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".