Circulating Biomarkers of Iron Storage and Clearance of Incident Human Papillomavirus Infection
Bibliographic record
Abstract
BACKGROUND: Iron is an essential mineral for both cellular and pathogen survival and is essential for viral replication. In turn, iron metabolism has been shown to be altered by several viral infections. However, little is known about the association between iron status and human papillomavirus (HPV) natural history. We hypothesize iron to be an HPV cofactor that is associated with longer duration of infection. METHODS: Ferritin and soluble transferrin receptor (sTfR) were measured in baseline serum samples from 327 women enrolled in the Ludwig-McGill cohort. Incident HPV clearance rates (any-type, oncogenic HPV, nononcogenic HPV, and HPV-16) over a 3 year time period were estimated from Cox proportional hazard models accounting for correlations between multiple infections. RESULTS: Women with ferritin levels above the median were less likely to clear incident oncogenic HPV [adjusted hazard ratio (AHR), 0.73; 95% confidence interval (CI), 0.55-0.96] and HPV-16 infections (AHR, 0.29; 95% CI, 0.11-0.73). Using physiologic cutoff points, women with enriched iron stores (>120 μg/L) were less likely to clear incident oncogenic HPV infections than those with low levels of iron (<20 μg/L; AHR, 0.34; 95% CI, 0.15-0.81). CONCLUSION: This study observed that women with the highest ferritin levels were less likely to clear incident oncogenic and HPV-16 infections than women with low ferritin. Rising iron stores may decrease probability of clearing new HPV infection, possibly by promoting viral activity and contributing to oxidative DNA damage. IMPACT: This novel study suggests that elevated iron stores may put women at risk for persistent HPV infection, an early event in cervical carcinogenesis. Further examination of the association between iron status and HPV natural history is warranted.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".