Exposure to persistent organic pollutants and sperm <scp>DNA</scp> methylation changes in Arctic and European populations
Bibliographic record
Abstract
Persistent organic pollutants (POPs), such as PCBs (polychlorinated biphenyls) and DDT [1,1,1-trichloro-2,2-bis(4-chlorophenyl)ethane], are environmental contaminants with potential endocrine disrupting activity. DNA methylation levels in peripheral blood lymphocytes have been associated with serum concentrations of POPs in Greenland Inuit and Korean populations. Greenland Inuits are characterized by the highest worldwide POP levels. In this cross-sectional study we evaluated the relationship between serum POP concentrations and DNA methylation levels in sperm of non-occupationally exposed fertile men from Greenland, Warsaw (Poland), and Kharkiv (Ukraine). Serum levels of PCB-153 [1,2,4-trichloro-5-(2,4,5-trichlorophenyl)benzene], as a proxy of the total PCBs body burden, and of p,p'-DDE [1,1-dichloro-2,2-bis(4-chlorophenyl)ethylene], the main metabolite of DDT were measured. Sperm DNA methylation level was assessed globally by flow cytometric (FCM) immunodetection of 5-methyl-cytosines and at specific repetitive DNA sequences (Alu, LINE-1, Satα) by PCR-pyrosequencing after bisulfite conversion. Multivariate linear regression analysis was applied to investigate correlations between serum POP concentrations and DNA methylation. No consistent associations between exposure to POPs and sperm DNA methylation at repetitive DNA sequences were detected. A statistically significant global decrease in methylation was associated with exposure to either POP by FCM analysis. This is the first study to investigate environmental exposure to POPs and DNA methylation levels considering sperm as the target cells. Although POP exposure appears to have a limited negative impact on sperm DNA methylation levels in adult males, the global hypomethylation detected by one of the methods applied suggests that further investigation 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 | 0.000 | 0.000 |
| 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".