Plasma and Urinary Alkylresorcinol Metabolites as Potential Biomarkers of Breast Cancer Risk in Finnish Women: A Pilot Study
Bibliographic record
Abstract
Alkylresorcinols (ARs) are shown to be good biomarkers of consumption of rye and whole-grain wheat products in man. The aim of this pilot study was to investigate AR metabolites as potential biomarkers of breast cancer (BC) risk in Finnish women since intake of cereal fiber and its components has been proposed to reduce this risk through an effect on the enterohepatic circulation of estrogens. This was a cross-sectional and observational pilot study. A total of 20 omnivores, 20 vegetarians, and 16 BC women (6-12 mo after operation) were investigated on 2 occasions 6 mo apart. Dietary intake (5-days record), plasma/urinary AR metabolites [3,5-dihydroxybenzoic acid (DHBA) and 3-(3,5-dihydroxyphenyl)-1-propanoic acid (DHPPA)] and plasma/urinary enterolactone were measured. The groups were compared using nonparametric tests. We observed that plasma DHBA (P = 0.007; P = 0.03), plasma DHPPA (P = 0.02; P = 0.01), urinary DHBA (P = 0.001; P = 0.003), urinary DHPPA (P = 0.001; P = 0.001), and cereal fiber intake (P = 0.007; P = 0.003) were significantly lower in the BC group compared to the vegetarian and omnivore groups, respectively. Based on measurements of AR metabolites in urine and in plasma, whole-grain rye and wheat cereal fiber intake is low in BC subjects. Thus, urinary and plasma AR metabolites may be used as potential biomarkers of BC risk in women. This novel approach will likely also facilitate studies of associations between rye and whole-grain wheat cereal fiber intake and other diseases. Our findings should, however, be confirmed with larger subject populations.
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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 | 0.000 |
| 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) | 0.001 | 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".