Abstract 3228: Tobacco smoking and circulating immune-related biomarkers in monozygotic twins
Bibliographic record
Abstract
Abstract Background: Tobacco smoking is a cause of a variety of cancers by various mechanisms. Paradoxically, smoking also increases the risk of atopy and asthma, which are inversely associated with some cancers such as glioma, colorectal cancer and non-Hodgkin lymphoma, but positively associated with others such as lung cancer. Tobacco smoking may affect the immune system, which may explain some of these associations. We assessed the association between smoking and levels of 27 serum immune/inflammatory markers and DNA methylation in healthy monozygotic (MZ) twins. Methods: 67 MZ twin pairs were identified from the Finnish Twin Cohort Study. Cotinine and immune related biomarkers were measured from fasting serum samples using LC-MS/MS and Luminex multiples assays. Current smoking status was defined by cotinine >3.08 ng/mL. Current smokers were further categorized into low vs high smoking level by the median cotinine (78.17 ng/mL) among smokers. Questionnaire reports of current and former smoking included duration, amount (cigarettes per day [CPD]) and years since quitting. Linear mixed models were used to assess the association between smoking variables and each individual biomarker. For each smoking variable P values were adjusted for multiple comparisons using Pact, taking into account correlations among biomarkers. For biomarkers significantly associated with smoking, we assessed whether blood DNA methylation of biomarker-related genes mediated the smoking-biomarker association. Results: The median age of the study population was 24.8 years (range 21.0-68.9 years) and 56.7% were female twins. 32.1% of the twins were current smokers according to cotinine levels. Current smoking, defined by either cotinine or self-reports, was significantly associated with CCL17, B-cell activating factor (BAFF) and haptoglobin (Hp) levels respectively, after adjusting for multiple comparisons. For instance, serum cotinine was associated with increasing CCL17 levels (Pact for trend test = 0.002): the geometric mean CCL17, adjusted for age and sex among current high-level smokers was approximately 8.2% higher than noncurrent smokers. Similar positive dose-response relationships were observed for self-reported smoking variables with the 3 biomarkers. However, we found no associations between former smoking and any of the 27 biomarkers. We also found that smoking-associated DNA methylation alterations in 3 CpG sites of BAFF affected circulating CCL17 (CpG site: cg11726530) and Hp (cg09158314 and cg21784254) levels, respectively. Conclusion: Current but not former smoking may be associated with alterations in circulating levels of CCL17, BAFF and Hp, suggesting that smoking may promote B-cell activation and affect Th2 immune response, which may play a role in carcinogenesis. Further, preliminary mediation analysis suggests that smoking-induced alterations in these biomarkers may partially mediate through DNA methylation. Citation Format: Jun Wang, David Conti, Marta Epeldegui, Miina Ollikainen, Amie E. Hwang, Ann S. Hamilton, Larry Magpantay, Rachel Tyndale, Thomas M. Mack, Otoniel Martinez-Maza, Jaakko Kaprio, Wendy Cozen. Tobacco smoking and circulating immune-related biomarkers in monozygotic twins [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3228.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".