Adipose Organochlorine Concentrations and Risk of Non-Hodgkin Lymphoma in Denmark
Bibliographic record
Abstract
PP-30-208 Background/Aims: Exposure to organochlorines has been examined as a potential risk factor for non-Hodgkin lymphoma with mixed results probably relating to differences in study design and geographic locale. Our purpose was to examine associations between organochlorine concentrations in prediagnostic adipose tissue samples and risk of non-Hodgkin lymphoma. Methods: We conducted a case-cohort study of 256 persons diagnosed with non-Hodgkin lymphoma and 256 sub-cohort persons selected from the 57,053 persons enrolled in the Danish Diet, Cancer, and Health cohort between 1993 and 1997. We measured concentrations of 8 pesticides and 10 polychlorinated biphenyl congeners in adipose tissue, collected upon enrollment and estimated risk of non-Hodgkin lymphoma using Cox regression. Results: The results showed that the risk of non-Hodgkin lymphoma increased significantly (P linear-trend: 0.004) with increasing lipid concentrations of dichlorodiphenyltrichloroethane (DDT). For cis-nonachlor, the upper adipose concentration stratum had a hazard ratio of 2.60 (95% CI: 1.08–6.28) when compared to the lowest stratum and for oxychlordane the 2 upper exposure strata were associated with a significant risk of non-Hodgkin lymphoma when compared to the lowest stratum. The associations found for cis-nonachlor and oxychlordane were strongest amongst women. Adjustment for body mass index, education, smoking status and dietary intake of fat and fruit and vegetables only marginally affected the risk associations found for DDT, oxychlordane, and cis-nonachlor. Conclusion: This study shows a higher risk of non-Hodgkin lymphoma in association with higher exposure to DDT, cis-nonachlor, and oxychlordane. This is the first study of organochlorines and non-Hodgkin lymphoma using prediagnostic adipose tissue samples in the exposure assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| 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) | 0.003 | 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 teacher head, 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".