Investigating genetic and pro-fibrogenic immune markers of Hepatitis C outcomes in HIV-Hepatitis C (HCV) co-infected individuals
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,001 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».