<p>IL-6 −572C>G and CARD8 304T>A Genetic Polymorphisms are Associated with the Absolute Neutrophil Count in Patients with Hematological Malignancies Under Chemotherapy: An Application of Multilevel Models to a Preliminary Pharmacogenetic Study</p>
Bibliographic record
Abstract
Purpose: Neutropenia is a common event in patients undergoing cytotoxic chemotherapy for the treatment of a hematological malignancy. Some polymorphisms, as IL-6 − 572C>G (rs1800796), IL-1β − 31 G>A (rs1143627), and CARD8 304T>A (rs2043211), in genes related to the inflammatory process, could affect the level of absolute neutrophil count (ANC) after chemotherapy. Since an efficient inflammatory process enhances neutrophil survival, we hypothesize that these polymorphisms are associated with ANC. Patients and Methods: We carried out a prospective cohort study in two hospitals in Santiago, Chile. The patients included were adults diagnosed with acute myeloblastic leukemia, acute lymphoblastic leukemia, or non-Hodgkin’s lymphoma, undergoing cytotoxic chemotherapy. We use a multilevel linear regression model to test our hypothesis. The best model was selected using the Akaike’s information criterion (AIC). Results: We analyzed 1726 hemograms and ANCs from 172 hospitalizations from 32 patients. The results show that CC and CG genotypes of IL-6 − 572 C>G polymorphism are associated with higher ANCs compared with the GG genotype (Ln (ANC) ∼ 0.81 IC95% 0.02– 1.55). Similarly, TT and AT genotypes of CARD8 304T>A polymorphism were related to higher ANCs compared with AA (Ln (ANC) ∼ 0.95 IC95% 0.02– 1.82). IL-1β genetic polymorphism had no statistically significant association with ANC. Conclusion: IL-6 rs1800796 − 572C>G and CARD8 rs2043211 304T>A polymorphisms are associated with the absolute neutrophil count in patients undergoing cytotoxic chemotherapy for treatment of hematological malignancies. Our findings might be useful to improve the safety of chemotherapy through predictive ANC models. Keywords: pharmacogenetics, neutropenia, hematological neoplasms, leukemia, lymphoma, interleukin-6, CARD8 protein
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".