No Association between Circulating Levels and Genetic Variants of IL-6 and TNF-α and Colon Adenoma
Bibliographic record
Abstract
Background: Interleukin 6 (IL-6) and tumor necrosis factor alpha (TNF-alpha), two important inflammatory cytokines, have been inconsistently associated with risk of colon neoplasia in epidemiological studies. However, research to date has not adequately assessed whether race-specific differences may exist in associations between biomarkers and genetic variants of these cytokines and colorectal adenoma - the precursor lesions of colorectal cancer. We sought to determine whether circulating levels of IL-6 and TNF-alpha, or genetic polymorphisms in IL-6and TNF -alpha were associated with colon adenoma and if so, whether that association differed by race. Methods: We analyzed the associations of circulating levels and single nucleotide polymorphisms (SNPs) of IL-6 and TNF-alpha with risk of colon adenomas in a colonoscopy -based case-control study of 401 incident adenoma cases and 1,050 controls. We used multivariate unconditional logistic regression models to estimate the odds ratios (OR) and 95% confidence intervals (95% CI) for levels or genotypes (log additive models) of IL-6 and TNF-alpha. Results: Compared to the bottom tertile of IL-6, the adjusted ORs were 1.06 (0.75 - 1.44) and 1.01 (0.72 - 1.40), respectively for the 2nd and 3rd tertiles (P trend = 0.10); the corresponding ORs for TNF-alpha were: 0.85 (0.63 - 1.15) and 1.01 (0.75 - 1.36), respectively (P trend = 0.39). Race-stratified analyses did not reveal any significant associations. There were also no statistically significant associations between IL-6 and TNF-alpha SNPs and colon adenoma. Conclusions: Our results do not support pre-diagnostic levels of IL-6, TNF-alpha or their genetic variants as significant risk factors for the development of colon adenoma. doi: http://dx.doi.org/10.4021/gr529w
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".