5.1 Introduction and aims
The primary aim of this study was to determine whether lipid profiles that are
influenced by chronic hepatitis C (CHC) infection are important determinants of anti- viral treatment outcome with pegylated interferon-α and ribavirin. This was
investigated by a retrospective analysis of lipid profiles in CHC patients, comparing pre treatment lipid profiles in those patients that had achieved a sustained virological response (SVR) (i.e. HCV RNA subsequently not detected > 6 months post treatment) to non-responders.
The second aim was to investigate host genetic polymorphisms associated with anti- viral treatment outcomes. Previous work from the Newcastle HCV research group had suggested carriers of the apoE2 allele were more likey to have spontaneous resolution of acute HCV infection than those with apoE3/E3 who were more likely to become chronic (Price, Bassendine et al. 2006). However this hypothesis driven, candidate gene approach to genetic determinants of treatment outcomes in small clinical cohorts has resulted in largely non-reproducable results. More recently more powerful non- hypothesis driven genome wide association study (GWAS) technology has become available. The GWAS technique is a powerful method of identifying susceptibility single nucleotide polymorphisms (SNP‟s) associated with disease or clinical outcomes. In order for a successful GWA study of HCV treatment outcomes, large case control cohorts are required to compare SVRs and non-responders, with DNA and matched clinical data. Effective research collaborations are therefore essential. The GWAS method utilises chip arrays with hundreds of thousands of SNP‟s
distributed across the whole genome. Statistical analysis can determine whether there are significant differences in alleles, genes or haploytpes between cases and controls. The GWAS approach has the advantage of identifying many previously un-thought of and novel susceptibility genes. Another advantage of GWAS approach is
identification of genes in a complex disease that have only modest effects on risk. GWA studies require large collaborative cohorts with DNA and well characterised clinical phenotypes. Detailed characterisation of HCV treatment outcomes,
demographics and clinical phenotypes with the establishment of a local DNA database enabled effective collaboration in a genome wide association study with the
University of Sydney to investigate genetic determinants of anti-viral treatment outcome in patients with HCV G1 infection. The collaboration was a significant contribution to a landmark genetics study in HCV.
The third section of this chapter stems from the results of the lipid profiles and treatment outcome study and the GWAS to investigate the relationship between the innate interferon response and lipid profiles. Prospective analysis of interferon gamma inducible protein 10 (IP10), a serum marker of hepatic interferon stimulated gene expression in the prospective fasting HCV group was correlated against alterations in fasting lipid profiles. A mechanistic link between host genetics, interferon
susceptibility and lipid profiles in the context of anti-viral treatment outcomes is proposed in the discussion.
5.2 Retrospective lipid profiles and treatment outcomes study 5.2.1 Demographics
129 HCV patients in the retrospective cohort had undergone anti-viral treatment with standard or pegylated interferon-α 2A or 2B and ribavirin and had documented outcomes more than 6 months after completion of therapy. Those that were HCV RNA PCR not detected > 6 months post treatment were considered sustained virological responders (SVR‟s). Those that were still HCV RNA detected were considered non-responders (NR). Relapsers, i.e. those that were HCV RNA not detected during or at the end of treatment but became HCV RNA positive again were included in the non-responders group for this binary analysis. In total there were 72 SVR‟s and 57 non responders. The summary demographics is shown in Table 5-1.
Table 5-1 Retrospective HCV cohort. Treatment outcomes and demographics
Parameter SVR Non Responders
N 72 57 Sex Male Female 63% 37% 65% 35% Age years Male
± SD Female 42.05 ± 9.5 44.0 ± 11.7 48.7 ± 10.2 52.3 ± 11.9 HCV Genotype 1 N (%) 29 (47%) 33 (53%) HCV Genotype 3 N (%) 32 (64%) 18 (36%)
Other HCV genotypes (2, 4, 5, 6 & unknown) 11 (65%) 6 (35%) APOE*E3/E3 62.5% 64.9% APOE*E3/E4 23.6% 29.8% APOE*E2/E3 9.7% 3.5% APOE*E2/E4 2.8% 0 APOE*E4/E4 1.4% 1.8% APOE*E2/E2 0 0
5.2.2 Statistical analysis
The distribution of continuous data was assessed by normality tests. Age, total cholesterol and non-HDL cholesterol conformed to a normal distribution.
Triglycerides and HDL cholesterol levels were positively skewed and therefore log10
transformed to normal distributions before parametric tests were applied. The F test was applied to test the assumption of equal variances and then paired t-tests were used to compare paired total cholesterol, log10 triglycerides, log10 HDL and non-HDL
cholesterol levels pre and post treatment. A two-sample t-test was used to compare the same lipid parameters between SVR‟s and non responders.
Factors associated with achieving a SVR were assessed by a binary logistic regression model. The response was treatment outcome (SVR=1, NR =0). Continuous predictor factors in the model were total cholesterol, non HDL-cholesterol, log10 triglyceride,
log10 HDL cholesterol and age. Categorical factors in the model were sex, HCV
genotype and apoE genotype. All statistical analysis was performed in Minitab Version 15. Statistical significance was defined at the 5% level based on two-tailed test of the null hypothesis.
5.2.3 Results of logistic regression analysis
The primary aim was to examine whether pre-treatment non-fasting lipid levels were associated with treatment outcome. However, HCV genotype, age and sex are known to influence treatment outcome. HCV genotype and host apoE genotype are also known to influence lipid levels as indicated from results chapter 1. Therefore to control for these interactions and confounders, a binary logistic regression analysis in 88 patients was performed in whom complete data including apoE genotype was available. The results of this analysis are shown in table 5-2. The binary logistic regression analysis confirms the negative association of male sex (odds ratio 0.09, 95% CI 0.02-0.37, p=0.001) and increasing age (odds ratio 0.93, 95% CI 0.87-0.99, p= 0.021) with SVR. The important finding was an independent association between higher apoB associated cholesterol (i.e. non HDL cholesterol) and increased odds of achieving SVR (OR 2.14, 95% CI 1.19-3.83, p=0.011) and a negative association of TG/HDL ratio and likelihood of SVR (OR 0.56, 95% CI 0.32 - 0.95, p=0.033). There was no significant association of total cholesterol with SVR (OR 1.2, 95% CI 0.74- 1.97, p= 0.459). Overall apoE genotype was not significantly associated with SVR. However patients with apoE2/E3 had an increased odds ratio of 4.93 of achieving SVR, but this was not statistically significant (95% CI 0.66-36.6, p=0.119) owing to the low frequency of this apoE genotype. Overall SVR rate in HCV genotype 1 was 45% and in genotype 3 was 63%. For those with wild type ApoE3/E3 the SVR rate was 44% for HCV genotype 1 and 68% for HCV genotype 3. Five patients with HCV genotype 3 were apoE2/E3 and all 100% (5/5) achieved an SVR compared to only 38% (5/13) in those with apoE3/E4 (Fishers Exact Test p = 0.0359).
Table 5-2 Results table binary logistic regression analysis
Variable Odds Ratio
(OR)