ITPA Polymorphisms Are Associated with Hematological Side Effects during Antiviral Therapy for Chronic HCV Infection
Bibliographic record
Abstract
BACKGROUND/OBJECTIVE: Genetic polymorphisms in the inosine triphosphatase (ITPA) gene have been associated with the protection from early ribavirin(RBV)-induced hemolytic anemia among patients with chronic hepatitis C virus (HCV) infection. The aim of the present study was to investigate the association between the functional ITPA variants and hematological side effects during antiviral therapy with pegylated interferon (PegIFN) and RBV. PATIENTS AND METHODS: This cohort study included all consecutive Caucasian patients treated for chronic HCV infection with PegIFN and RBV between 2000 and 2009 for whom a serum sample was available for genetic testing. The predicted inosine triphosphate pyrophosphatase (ITPase) activity was based on the genotypes of the SNPs rs1127354 and rs7270101. Decline in hemoglobin (Hb) during antiviral therapy, as well as dose reductions, blood transfusions and use of erythropoietin were assessed. RESULTS: In total, 213 patients were included. The predicted ITPase activity was normal among 152 (71%) patients; 61 (29%) patients had ITPase deficiency. By multivariable linear regression, RBV dose in mg per kilogram (Beta 0.09, 95%CI 0.04-0.13, p<0.001) and normal ITPase activity (Beta 0.89, 95%CI 0.64-1.14, p<0.001) were associated with more Hb decline at week 4 of treatment. Patients with normal ITPase activity underwent more dose adjustments of RBV than patients with ITPase deficiency (19(13%) vs 1(2%),p = 0.014) and received erythropoietin more frequently (12 (8%) vs 0 (0%),p = 0.024). CONCLUSION: Genetic variants in the ITPA gene protected against RBV treatment-induced anemia among Caucasian patients with chronic HCV infection. Patients with normal ITPase activity underwent more dose reductions of RBV and received erythropoietin more frequently.
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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 | 0.002 |
| 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".