Arsenic Speciation and Metallomics Profiling of Human Toenails as a Biomarker to Assess Prostate Cancer Cases: Atlantic PATH Cohort Study
Bibliographic record
Abstract
Chronic exposure to inorganic arsenic and trace metals has been linked to prostate cancer, and altered arsenic methylation capacity may have an important role in arsenic carcinogenesis. Biomarkers may be able to elucidate this role. Our objectives were to characterize profiles of arsenic species and metallome in toenails and urine samples, compare profiles between prostate cancer cases and controls, and determine the discriminant ability of toenail and urine biomarkers. Toenail samples ( n = 576), urine samples ( n = 152), and questionnaire data were sourced from the Atlantic Partnership for Tomorrow's Health (PATH) cohort study. Healthy controls were matched to prostate cancer cases (3:1 ratio) on sex, age, smoking status, and the province of residence. Metallome profiles and proportions of arsenic species were measured in toenail and urine samples. Analysis of covariance (ANCOVA) was used to compare the mean percent monomethylarsonic acid (%MMA), dimethylarsonic acid (%DMA), inorganic arsenic (%iAs), primary methylation index (PMI, MMA/iAs), and secondary methylation index (SMI, DMA/MMA). Multivariate analysis of covariance (MANCOVA) was used to compare selected metal concentrations. Mean %MMA was significantly lower and SMI was significantly higher in toenails from prostate cancer cases compared to controls in unadjusted and adjusted models. Proportions of arsenic species were correlated with total arsenic in toenails. Arsenic speciation in urine was not different between cases and controls, nor were metallome profiles in toenails and urine. Our results indicate that toenails are a viable biomarker for altered arsenic speciation in prostate cancer cases and may have greater utility than urine in this context.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".