Interleukin 28B polymorphisms are the only common genetic variants associated with low‐density lipoprotein cholesterol (LDL‐C) in genotype‐1 chronic hepatitis C and determine the association between LDL‐C and treatment response
Bibliographic record
Abstract
Low-density lipoprotein cholesterol (LDL-C) levels and interleukin 28B (IL28B) polymorphism are associated with sustained viral response (SVR) to peginterferon/ribavirin (pegIFN/RBV) for chronic hepatitis C (CHC) infection. IL28B has been linked with LDL-C levels using a candidate gene approach, but it is not known whether other genetic variants are associated with LDL-C, nor how these factors definitively affect SVR. We assessed genetic predictors of serum lipid and triglyceride levels in 1604 patients with genotype 1 (G1) chronic hepatitis C virus (HCV) infection by genome-wide association study and developed multivariable predictive models of SVR. IL28B polymorphisms were the only common genetic variants associated with pretreatment LDL-C level in Caucasians (rs12980275, P = 4.7 × 10(-17), poor response IL28B variants associated with lower LDL-C). The association was dependent on HCV infection, IL28B genotype was no longer associated with LDL-C in SVR patients after treatment, while the association remained significant in non-SVR patients (P < 0.001). LDL-C was significantly associated with SVR for heterozygous IL28B genotype patients (P < 0.001) but not for homozygous genotypes. SVR modelling suggested that IL28B heterozygotes with LDL-C > 130 mg/dL and HCV RNA ≤600 000 IU/mL may anticipate cure rates >80%, while the absence of these two criteria was associated with an SVR rate of <35%. IL28B polymorphisms are the only common genetic variants associated with pretreatment LDL-C in G1-HCV. LDL-C remains significantly associated with SVR for heterozygous IL28B genotype patients, where LDL-C and HCV RNA burden may identify those patients with high or low likelihood of cure with pegIFN/RBV therapy.
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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 | 0.001 |
| 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 | 0.001 |
| 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".