Investigating genetic and pro-fibrogenic immune markers of Hepatitis C outcomes in HIV-Hepatitis C (HCV) co-infected individuals
Bibliographic record
Abstract
In HIV-Hepatitis C (HCV) co-infected individuals, impaired HCV-specific immune response and high inflammation leads to adverse outcomes like lower HCV spontaneous clearance and faster liver fibrosis progression. Liver fibrosis can be a precursor to advanced, possibly irreversible liver damage and is therefore an important intervention point, especially in the early stages. While HCV cure is possible, high costs of direct-acting antiviral agents (DAAs), low treatment uptake, HCV re-infection, and other hepatotoxic exposures remain problems in the co-infected population. Characterizing the genetic and immune markers of the underlying immunological mechanisms triggered by HCV persistence in co-infected persons can help in understanding disease etiology and improve treatment decision-making by identifying higher-risk individuals. This is especially important because HCV viral cure is less likely when fibrosis has progressed to advanced cirrhosis. Several host genetic and immune factors have been studied in other populations as markers of HCV pathogenesis. We wanted to examine their roles in the Canadian HIV-HCV co-infected population, which has a unique genetic mix due to an overrepresentation of Aboriginal peoples. OBJECTIVES: 1) Test the association of HCV spontaneous clearance and three single nucleotide polymorphisms (SNPs) near the Interferon Lambda 3 (IFNL3) gene (rs12979860, rs8099917, functional variant rs8103142) and compare the SNP frequencies between Canadian whites and Aboriginal peoples 2) test the association of IFNL SNPs with significant liver fibrosis after HCV clearance fails and 3) assess whether pro-fibrogenic immune and genetic markers improve ability to predict three-year risk of significant liver fibrosis over clinical risk factors alone using a case-cohort design. All study samples were derived from eligible subpopulations of the Canadian Co-infection Cohort (CCC) and were analyzed using Cox proportional hazards. RESULTS: Aim 1: The IFNL genotypes of interest were linked with clearance rates at least three times higher than in those lacking the genotypes, after adjusting for sex and ethnicity. The major "beneficial" alleles, genotypes and haplotypes were all more frequent among Aboriginal peoples than whites, but this only partially explained why Aboriginal individuals had higher clearance rates. Aim 2: Each IFNL genotype, associated with pro-inflammatory responses and higher clearance, was linked with a higher risk of significant liver fibrosis. The relationship with rs8099917 TT was strongest, indicating a 79% increase in fibrosis risk. Haplotype analysis also supported the link with higher risk of liver fibrosis. Aim 3: Specific immune markers were measured from first available plasma or serum in the randomly selected subcohort and fibrosis cases only. Prediction metrics (discrimination, calibration and risk classification) were compared between Model 1 (selected clinical predictors only) and Model 2 (clinical predictors from Model 1 plus selected markers at IFNL rs8099917 and 5 immune markers: IL-8, sICAM-1, RANTES, hsCRP, and sCD14). Both models were well-calibrated. The improvement in discrimination with model 2 was small, but the model with the markers fit and classified risk better.CONCLUSIONS: Specific IFNL genotypes indicated a higher likelihood of spontaneous HCV clearance in co-infected Canadians. They were far more common in Aboriginal peoples, who cleared more often. Other mechanisms likely also contribute as IFNL genotypes did not fully account for their higher clearance rates. Once clearance fails, the same IFNL polymorphisms, reflecting a pro-inflammatory response, also are linked with a higher risk of developing significant liver fibrosis. Other markers of heightened hepatic inflammation can improve ability to predict 3-year risk of significant liver fibrosis, but require further cost-benefit analyses and external validation in other populations.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".