RESUMO
Genetic variation in the IFNL3-IFNL4 (interferon-λ3-interferon-λ4) region is associated with hepatic inflammation and fibrosis. Whether IFN-λ3 or IFN-λ4 protein drives this association is not known. We demonstrate that hepatic inflammation, fibrosis stage, fibrosis progression rate, hepatic infiltration of immune cells, IFN-λ3 expression, and serum sCD163 levels (a marker of activated macrophages) are greater in individuals with the IFNL3-IFNL4 risk haplotype that does not produce IFN-λ4, but produces IFN-λ3. No difference in these features was observed according to genotype at rs117648444, which encodes a substitution at position 70 of the IFN-λ4 protein and reduces IFN-λ4 activity, or between patients encoding functionally defective IFN-λ4 (IFN-λ4-Ser70) and those encoding fully active IFN-λ4-Pro70. The two proposed functional variants (rs368234815 and rs4803217) were not superior to the discovery SNP rs12979860 with respect to liver inflammation or fibrosis phenotype. IFN-λ3 rather than IFN-λ4 likely mediates IFNL3-IFNL4 haplotype-dependent hepatic inflammation and fibrosis.
Assuntos
Haplótipos , Inflamação/genética , Interleucinas/genética , Fígado/metabolismo , Fibrose/genética , Fibrose/metabolismo , Frequência do Gene , Genótipo , Hepacivirus/fisiologia , Hepatite C/genética , Hepatite C/virologia , Humanos , Inflamação/metabolismo , Interferons , Interleucinas/metabolismo , Desequilíbrio de Ligação , Fígado/patologia , Modelos Logísticos , Análise Multivariada , Polimorfismo de Nucleotídeo Único , Reação em Cadeia da Polimerase Via Transcriptase ReversaRESUMO
BACKGROUND & AIMS: The extent of liver fibrosis predicts long-term outcomes, and hence impacts management and therapy. We developed a non-invasive algorithm to stage fibrosis using non-parametric, machine learning methods designed for predictive modeling, and incorporated an invariant genetic marker of liver fibrosis risk. METHODS: Of 4277 patients with chronic liver disease, 1992 with chronic hepatitis C (derivation cohort) were analyzed to develop the model, and subsequently validated in an independent cohort of 1242 patients. The model was assessed in cohorts with chronic hepatitis B (CHB) (n=555) and non-alcoholic fatty liver disease (NAFLD) (n=488). Model performance was compared to FIB-4 and APRI, and also to the NAFLD fibrosis score (NFS) and Forns' index, in those with NAFLD. RESULTS: Significant fibrosis (⩾F2) was similar in the derivation (48.4%) and validation (47.4%) cohorts. The FibroGENE-DT yielded the area under the receiver operating characteristic curve (AUROCs) of 0.87, 0.85 and 0.804 for the prediction of fast fibrosis progression, cirrhosis and significant fibrosis risk, respectively, with comparable results in the validation cohort. The model performed well in NAFLD and CHB with AUROCs of 0.791, and 0.726, respectively. The negative predictive value to exclude cirrhosis was>0.96 in all three liver diseases. The AUROC of the FibroGENE-DT performed better than FIB-4, APRI, and NFS and Forns' index in most comparisons. CONCLUSION: A non-invasive decision tree model can predict liver fibrosis risk and aid decision making.
