Breast cancer and microbial cancer incidence in female populations around the world: A surprising hyperbolic association
Bibliographic record
Abstract
Current literature on cancer epidemiology typically discusses etiology of cancer by cancer type. Risks of different cancer types are, however, correlated at population level and may provide etiological clues. We showed previously an unexpected very high positive correlation between breast cancer (BC) and young-adult Hodgkin disease incidence rates. In a population-based case-control study of BC, older ages at the first Epstein-Barr virus exposure, indicated by older ages at onset of infectious mononucleosis, were associated with elevated BC risk. Here we examine BC risk in association with microbial cancer (MC) risk in female populations across the world. MC cancers are cervical, liver and stomach cancers with established causal associations with human papillomaviruses, hepatitis viruses, and helicobacter pylori, respectively. We examined age-adjusted BC and MC incidence rates in 74 female populations around the world with cancer registries. Our analysis suggests that BC and MC rates are inversely associated in a special mathematical form such that the product of BC rate and MC rate is approximately constant across world female populations. A differential equation model with solutions consistent to the observed inverse association was derived. BC and MC rates were modeled as functions of an exposure level to unspecified common factors that influence the 2 rates. In conjunction with previously reported evidence, we submit a hypothesis that BC etiology may have an appreciable link with microbial exposures (and/or immunological responses to them), the lack of which, especially in early life, may elevate BC risk.
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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 | 0.008 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".