Responder Interferon λ Genotypes Are Associated With Higher Risk of Liver Fibrosis in HIV–Hepatitis C Virus Coinfection
Bibliographic record
Abstract
BACKGROUND: Liver fibrosis progresses faster in individuals coinfected with human immunodeficiency virus (HIV) and hepatitis C virus (HCV). Interferon λ3 (IFN-λ3) has both antiviral and proinflammatory properties. Genotypes at IFNL single-nucleotide proteins (SNPs; rs12979860CC and rs8099917TT) are linked to higher HCV clearance, potentially via rs8103142. We examined the relationship between IFN-λ genotypes and significant liver fibrosis in HIV-HCV coinfection. METHODS: From the prospective Canadian Co-infection Cohort (n = 1423), HCV RNA-positive participants in whom IFN-λ genotypes were detected and who were free of fibrosis, end-stage liver disease, and chronic hepatitis B at baseline (n = 485) were included. Time to significant fibrosis (defined as an aspartate transaminase level to platelet count ratio index [APRI] of ≥1.5) by IFN-λ genotypes was analyzed using Cox proportional hazards, with adjustment for age, sex, ethnicity, alcohol use, CD4(+) T-cell count, HCV genotype, γ-glutamyl transferase level, and baseline APRI. Haplotype analysis was performed, with adjustment for ethnicity. RESULTS: A total of 125 participants developed fibrosis over 1595 person-years (7.84 cases/100 person-years; 95% confidence interval [CI], 6.58-9.34 cases/100 person-years). Each genotype was associated with an increased fibrosis risk, with adjusted hazard ratios of 1.37 (95% CI, .94-2.02) for rs12979860CC, 1.34 (95% CI, .91-1.97) for rs8103142TT, and 1.79 (95% CI, 1.24-2.57) for rs8099917TT. Haplotype TCT was also linked with a higher risk (hazard ratio, 1.14 [95% CI, .73-1.77]). CONCLUSIONS: IFN-λ SNPs rs12979860, rs8099917, and rs81013142 were individually linked to higher rates of fibrosis in individuals with HIV-HCV coinfection. IFN-λ genotypes may be useful to target HCV treatments to people who are at higher risk of liver disease.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".