Abstract

Background. Analysis of hepatitis C virus (HCV) RNA kinetics during antiviral therapy may allow estimation of the probability of response.

Methods. To assess clinical and virological correlates and the predictive value of first-phase HCV RNA kinetics during pegylated interferon and ribavirin treatment, we studied 119 patients with chronic hepatitis C who were treated with pegylated interferon and ribavirin. HCV RNA level was measured 5 min before and 2, 14, and 28 days after the start of treatment. For each patient the D0;t0-t20;log10HCV RNA value was calculated, which indicates the relative reduction in HCV RNA level from before treatment to day 2 after logarithmic transformation.

Results. A D0;t0-t20;log10HCV RNA value ⩽0.8 showed a 95% negative predictive value for virological response, whereas one >2.5 had a 93% positive predictive value for virological response, independent of genotype and histology. The D0;t0-t20;log10HCV RNA value was strictly related to final treatment outcome and could differentiate not only patients with a sustained virological response from nonresponders but also patients who experienced relapse from the former. The D0;t0-t20;log10HCV RNA value was highest among patients infected with genotypes 2 and 3 and was lowest among patients infected with genotype 1. It decreased with increasing grades of fibrosis and steatosis and was also inversely related to γ-glutamyl transpeptidase (GGT) level and HOMA-IR (homeostasis model assessment for insulin resistance) score. In multivariate analysis, D0;t0-t20;log10HCV RNA value was the strongest predictor of sustained virological response and appeared to be independently related to viral genotype and GGT level.

Conclusion. HCV RNA kinetics has strong predictive value. It correlates with virological and clinical parameters that are known predictors of antiviral treatment outcome, including insulin resistance. The measurement of HCV load as early as 2 days after the start of pegylated interferon and ribavirin is a useful tool for the prediction of treatment outcome in individual patients and should be used in clinical practice.

Serial measurements of serum hepatitis C virus (HCV) load during antiviral treatment may provide insights into the intrinsic susceptibility of the viral quasispecies to interferon alfa [1, 2]. Mathematical modeling of these measurements has revealed the existence of a biphasic pattern of viral load decline in patients showing susceptibility to interferon alfa [3]. When it occurs, the first phase of the drop in HCV load encompasses the first 24–48 h of treatment [4]. Subsequently, a second phase ensues. This phase is characterized by a slower decline in HCV load and appears to be strictly dependent on the first-phase kinetic [5].

Over the past few years, a number of studies have evaluated the possible relationships between the first-phase kinetics of HCV during interferon alfa treatment and other virological parameters [5-9]. Moreover, it has been suggested that the first-phase kinetics of HCV could somehow predict final treatment outcome as early as 24–48 h after the first dose of interferon [10].

Pegylated interferon alfa and ribavirin are the current treatment for chronic hepatitis C. Three studies have investigated the clinical impact of the first-phase viral kinetics in patients with chronic hepatitis C who were given this treatment combination [11-13]. These studies confirmed and expanded the previous seminal findings obtained in patients treated with unconjugated interferon alfa and showed that evaluation of the early kinetics of HCV allows one to tailor the duration of treatment and to predict its outcome [11-13]. However, these studies were partially limited by having been performed in relatively small and/or selected subsets of patients with chronic hepatitis C. Moreover, host factors such as steatosis, visceral obesity, and insulin resistance—which are currently regarded as major determinants of antiviral treatment outcome in chronic hepatitis C [14-17]—were not evaluated.

In the present study, we investigated a large, well-characterized cohort of patients with chronic hepatitis C to assess the clinical, virological, and biochemical correlates of first-phase HCV RNA kinetics and analyze its possible prognostic value for the outcome of treatment.

Methods

Study design. This was a prospective study of the determinants of the outcome of combination antiviral treatment for chronic hepatitis C, started in our unit in 2002. The study protocol was approved by the Second University of Naples Ethics Committee, and all procedures were performed in accordance with the 1976 Declaration of Helsinki. The primary study end point was the sustained virological response to antiviral treatment.

Patients included and treatment administered. We enrolled in this study 119 previously untreated patients with chronic hepatitis C, who provided informed consent. Inclusion criteria were age >18 years, repeated evidence of elevated alanine aminotransferase (ALT) levels within 12 months before study entry, presence of a measurable serum HCV RNA load, evidence of chronic active hepatitis on liver biopsy, and willingness to cope (at best) with adverse effects and complete the prescribed treatment course. Excluded were patients who showed evidence of alcohol, illicit drug, or potentially hepatotoxic medication use; of coinfection with hepatitis B virus, hepatitis D virus, or human immunodeficiency virus; or of the presence of other liver disorders. All patients were white and came from our geographic area. During the accrual time, 55 patients did not meet the study criteria.

The treatment regimen consisted of either pegylated interferon alfa-2a (180 µg subcutaneously once a week) or pegylated interferon alfa-2b (1.5 µg per kilogram of body weight subcutaneously once a week), as chosen by the physician in charge. All subjects received combination treatment with ribavirin, at 800 mg/day (400 mg twice a day) for those infected with HCV genotype 2 or 3 and at 1000 or 1200 mg/day for subjects infected with genotype 1 weighing <75 or ⩾75 kg, respectively. The predetermined length of treatment was 48 weeks for patients infected with genotype 1 and 24 weeks for patients infected with genotypes other than 1, with a final efficacy evaluation at week 24 of follow-up. Treatment was discontinued at week 12 in patients showing a ⩽2 log10decline in HCV RNA level from baseline. The first 5 doses of pegylated interferon were administered personally in the outpatient clinic office by one of us.

Treatment outcome was assessed as follows: rapid virological response (RVR) and early virological response (EVR) were defined as undetectable HCV RNA at treatment week 4 or 12, respectively; sustained virological response (SVR) was defined as undetectable HCV RNA 24 weeks after treatment discontinuation; relapse was defined as HCV RNA clearance during treatment and reappearance during follow-up; and nonresponse was defined as a failure to clear HCV RNA at any time during treatment [18]. Medication doses were reduced in accordance with current practice recommendations [19]. For the purpose of the evaluation of clinical outcome, an intent-to-treat (ITT) analysis was used (ie, patients discontinuing treatment for adverse events or dropping out before completing at least 12 weeks of treatment were considered to be nonresponsive irrespective of the extent of viral load decline experienced).

Clinical and virological evaluation. The following parameters were obtained and recorded during patient screening: age, sex, height, weight, body mass index, waist circumference, viral genotype (INNO-LiPA; Innogenetics), and serum HCV RNA level (Amplicor HCV Monitor 2.0; Roche). Overweight, obesity, visceral obesity, and insulin resistance were defined according to the current standard criteria [20]. All patients underwent percutaneous liver biopsy before the start of treatment. Liver histology was evaluated by standard techniques, as described elsewhere [21, 22].

Among the laboratory parameters measured at baseline, serum levels of glucose, aminotransferases (ALT and aspartate aminotransferase), γ-glutamyl transpeptidase (GGT), insulin, and ferritin were recorded and included in the analysis. The degree of insulin resistance was measured according to the homeostasis model assessment for insulin resistance: HOMA-IR=FPGL×FIL/0;22.5, where FPGL is the fasting plasma glucose level measured in micromoles per liter and FIL is the fasting insulin level measured in microunits per milliliter. Assays were performed by our hospital laboratory on serum samples obtained from subjects after overnight fasting. Insulin levels were measured by radioimmunoassay (Diagnostic Systems Laboratories).

Blood samples for the kinetics study were obtained after overnight fasting from all patients 5 min before and 2, 14, and 28 days after the administration of the first dose of pegylated interferon and the first ribavirin tablets. Additional samples were obtained on days 90 and 180 of treatment, at the end of treatment, and 6 months later (end of follow-up). Samples were centrifuged at room temperature 30 min after being obtained, and serum samples were cryopreserved at −80°C until further use. For these specimens, HCV RNA levels were measured by branched DNA assay (Versant HCV RNA 3.0; Bayer Diagnostics). The assay has a lower limit of detection of 615 IU/mL. Day 2 samples that tested negative for HCV RNA by the branched DNA assay were reassayed by qualitative polymerase chain reaction (PCR) (Amplicor HCV Monitor 2.0; Roche); this assay has a sensitivity of 50 IU/mL. End-of-treatment and end-of-follow-up samples were assayed by the same qualitative PCR.

To assess first-phase HCV RNA kinetics, we evaluated the relative reduction in HCV RNA level from baseline after logarithmic transformation, calculating for each patient the D0;t0-t20;log10HCV RNA value, where Δ is the difference between 2 HCV RNA determinations, t0is the baseline (day 0) determination, and t2is the determination made on day 2 after the start of treatment.

Statistical evaluation. Analyses were done using SPSS software, version 11.5 (SPSS). The significance level was set at 5%, and all tests were 2-tailed.

The distribution of numerical variables was evaluated by skewness and was found to be mostly nonnormal; thus, nonparametric tests were used. When a posteriori between-group studies were needed, analysis of variance with the Bonferroni post-hoc test was performed.

Differences in numerical variables between groups were assessed by the Mann-Whitney U or the Kruskal-Wallis test for 2 or multiple independent samples, respectively. The possible relationship between 2 numerical variables was assessed by Spearman simple rank correlation.

To evaluate the performance of a treatment outcome classification scheme based on the D0;t0-t20;log10HCV RNA parameter, we calculated the area under the receiver operating characteristic curve (AUROC), entering virological response as the state variable.

Variables that were associated with treatment outcome or extent of viral load decline in univariate analysis (P<.1) or that were judged to be clinically relevant were block-entered into multiple logistic and linear regression models, respectively. The dependent variables were treatment outcome (SVR vs relapse or nonresponse) in the logistic regression analysis and D0;t0-t20;log10HCV RNA value (ie, the day 2 decline in HCV RNA level) in the linear regression analysis. Factor analysis was applied whenever feasible, to rule out collinearity among covariates before entering them into the models. The goodness of fit of the logistic regression model was assessed by the Hosmer-Lemeshow test.

Numerical data are presented as means ± standard deviations or as medians and interquartile ranges. Categorical data are presented as numbers and percentages.

Results

Clinical features of patients and treatment outcome. Included in the study were 119 subjects, whose clinical characteristics are presented in table 1. Sixty-seven patients (56.3%) had a SVR, whereas 29 patients (24.4%) experienced nonresponse and 23 (19.3%) experienced relapse. The outcome of therapy was established on an ITT basis. Among the clinical, analytical, and histological parameters studied, RVR (P<.001), non-genotype 1 (P<.001), lower liver necroinflammatory activity (P<.05) and fibrosis (P<.001) scores, lower serum glucose (P<.05) and GGT (P<.001) levels, and HOMA-IR score (P<.02) were significantly associated with SVR in the univariate analysis.

Table 1

Clinical Features of the Examined Subjects at Study Entry

Table 1

Clinical Features of the Examined Subjects at Study Entry

In this study, we first analyzed patient data to evaluate the relationship between early changes in HCV RNA level and final treatment outcome. Subsequently, we assessed the potential of early HCV RNA kinetics in terms of predicting the final treatment outcome. Finally, we evaluated the relationship between the extent of early HCV RNA changes and various clinical and virological parameters that are known predictors of treatment outcome.

First-phase HCV RNA decline and treatment outcome. We examined the extent of the HCV RNA decline in terms of changes in logarithmic viremia levels from baseline to day 2—the D0;t0-t20;log10HCV RNA value—in patients grouped according to treatment outcome. As depicted in figure 1, the first phase of the HCV RNA decline (0–2 days of treatment) clearly differentiated patients who had SVRs from those who did not, independent of all other parameters. Also, it differentiated patients with SVRs from those who experienced relapse but not the latter from patients who experienced nonresponse.

Figure 1

Extent of logarithmic decline in hepatitis C virus (HCV) RNA level within 2 days of the start of treatment according to final treatment outcome. A, Box plots comparing patients who experienced a sustained virological response (SVR) with those who did not (P<.001). B, Box plots comparing the 3 study groups according to treatment outcome (P<.001for the comparison of patients who experienced a SVR vs those who experienced relapse and for the former vs those who experienced nonresponse; P=.397for the comparison of patients who experienced relapse and those who experienced nonresponse). After 2 days of treatment, patients who subsequently developed a SVR had a significantly greater log decline in HCV RNA level than did either those who experienced relapse or those who experienced nonresponse—median D0;t0-t20;log10HCV RNA values were 2.27 for patients with a SVR versus 1.39 and 1.02 for patients who experienced relapse and nonresponse, respectively (P<.001). C, Linear HCV RNA decline from day 0 to 2 in the 3 study groups, further graphically depicting the difference between patients experiencing a SVR, relapse, and nonresponse in terms of initial HCV RNA decline. Each line represents 1 patient. Thick black lines in each graph represent the median slopes, which clearly differentiate the 3 groups with different treatment outcomes. NR, nonresponse; RLPS, relapse.

Figure 1

Extent of logarithmic decline in hepatitis C virus (HCV) RNA level within 2 days of the start of treatment according to final treatment outcome. A, Box plots comparing patients who experienced a sustained virological response (SVR) with those who did not (P<.001). B, Box plots comparing the 3 study groups according to treatment outcome (P<.001for the comparison of patients who experienced a SVR vs those who experienced relapse and for the former vs those who experienced nonresponse; P=.397for the comparison of patients who experienced relapse and those who experienced nonresponse). After 2 days of treatment, patients who subsequently developed a SVR had a significantly greater log decline in HCV RNA level than did either those who experienced relapse or those who experienced nonresponse—median D0;t0-t20;log10HCV RNA values were 2.27 for patients with a SVR versus 1.39 and 1.02 for patients who experienced relapse and nonresponse, respectively (P<.001). C, Linear HCV RNA decline from day 0 to 2 in the 3 study groups, further graphically depicting the difference between patients experiencing a SVR, relapse, and nonresponse in terms of initial HCV RNA decline. Each line represents 1 patient. Thick black lines in each graph represent the median slopes, which clearly differentiate the 3 groups with different treatment outcomes. NR, nonresponse; RLPS, relapse.

Predictive value of the first-phase HCV RNA kinetics. In view of the clear-cut differences observed as early as 2 days after the start of treatment, we attempted to assess the predictive value of the D0;t0-t20;log10HCV RNA parameter for final treatment outcome. To do this, we first calculated the AUROC of the D0;t0-t20;log10HCV RNA testing for SVR, and we found a highly significant result (AUROC, 0.81±0.04; 95% confidence interval, 0.73–0.89; P<.001). We therefore derived 2 cutoff values for the D0;t0-t20;log10HCV RNA parameter (mean ± standard deviation, approximated to the next 0.1). A D0;t0-t20;log10HCV RNA value ⩽0.8 had a negative predictive value of 95% and a positive predictive value of 67%, whereas a D0;t0-t20;log10HCV RNA value >2.5 had a positive predictive value of 88% and a negative predictive value of 54%. Thus, using a rigorous ITT analysis we found that a decline in HCV RNA level ⩽0.8 logs as early as 2 days after the start of treatment predicted an unfavorable outcome (relapse or nonresponse) with 95% accuracy. Conversely, a 2-day drop of >2.5 logs implied an 88% probability of the development of a SVR (table 2). These predictive values were independent of any other parameter (such as viral genotype, liver fibrosis stage, and patient age or sex) and were based on an ITT analysis of the primary study end point. We also performed a per-protocol outcome analysis excluding the 5 patients who withdrew prematurely because of adverse events. In this analysis, the negative predictive value of an HCV RNA decline ⩽0.8 logs was 95%, and the positive predictive value of a decline >2.5 logs was 93%.

Table 2

Prediction of Treatment Outcome on the Basis of Cutoff Values for the Day 2 Decline in Hepatitis C Virus (HCV) RNA Level (Intent-to-Treat Analysis)

Table 2

Prediction of Treatment Outcome on the Basis of Cutoff Values for the Day 2 Decline in Hepatitis C Virus (HCV) RNA Level (Intent-to-Treat Analysis)

Correlates of first-phase HCV RNA kinetics. We subsequently analyzed the correlation between the first-phase HCV kinetics and various clinical and virological parameters. The D0;t0-t20;log10HCV RNA value correlated with RVR (P<.001), whereas it showed no relationship with patient age or sex or with the subtype of pegylated interferon alfa administered (data not shown).

Among histological parameters (figure 2), liver fibrosis stage was significantly related to the first-phase decline. In figure 2A, it can be seen that subjects with mild or moderate fibrosis (F1 or F2) had significantly greater D0;t0-t20;log10HCV RNA values than did patients with advanced fibrosis or cirrhosis (F3 or F4) (D0;t0-t20;log10HCV RNA value, 2.04±1.0and 1.4±0.82respectively; P=.001). Moreover, the extent of the HCV RNA decline was found to progressively decrease with increasing fibrosis stage (figure 2B).

Figure 2

Relationship between D0;t0-t20;log10hepatitis C virus (HCV) RNA value (logarithmic decline in HCV RNA level 2 days after the start of treatment) and liver histological parameters. A, D0;t0-t20;log10HCV RNA values for patients with fibrosis grades F1 or F2 compared with those with fibrosis grades F3 or F4. B, D0;t0-t20;log10HCV RNA values for patients with different degrees of liver fibrosis. C, D0;t0-t20;log10HCV RNA values for patients with different degrees of liver steatosis.

Figure 2

Relationship between D0;t0-t20;log10hepatitis C virus (HCV) RNA value (logarithmic decline in HCV RNA level 2 days after the start of treatment) and liver histological parameters. A, D0;t0-t20;log10HCV RNA values for patients with fibrosis grades F1 or F2 compared with those with fibrosis grades F3 or F4. B, D0;t0-t20;log10HCV RNA values for patients with different degrees of liver fibrosis. C, D0;t0-t20;log10HCV RNA values for patients with different degrees of liver steatosis.

The degree of early HCV RNA decline was also related to the extent of liver steatosis. As shown in figure 2C, an almost linear indirect relationship was observed between steatosis grade and the D0;t0-t20;log10HCV RNA parameter. Interestingly, this relationship did not exist among subjects with the highest degrees of steatosis. This paradoxical finding was due to the prevalence of genotype 3-infected subjects (100%) within the subgroup of 6 patients with grade 3 steatosis (ie, involving >66% of hepatocytes). Coherently, the inverse relationship between steatosis and D0;t0-t20;log10HCV RNA value was strongest when we restricted the analysis to genotype 1-infected patients (data not shown).

Viral genotype was in fact confirmed to be a major virological determinant of the extent of the HCV RNA decline over the first 2 days of treatment. Figure 3shows the relationship between viral factors and the first-phase HCV RNA kinetics. Patients infected with HCV genotype 1 had significantly lower D0;t0-t20;log10HCV RNA values than did those infected with genotype 2 or 3. In contrast, no statistically significant difference was observed between genotype 2-infected and genotype 3-infected patients (figure 3A). We also found a major differ ence in D0;t0-t20;log10HCV RNA values among genotype 1-infected patients with or without a SVR (1.89±0.76vs 0.93±0.68; P<.001). The same difference was observed for genotype 3-infected, but not genotype 2-infected, patients (figure 3B).

Figure 3

Relationship between D0;t0-t20;log10hepatitis C virus (HCV) RNA value (logarithmic decline in HCV RNA level 2 days after the start of treatment) and viral genotype. A, D0;t0-t20;log10HCV RNA values for patients infected with different HCV genotypes. B, D0;t0-t20;log10HCV RNA values for patients infected with different HCV genotypes according to final treatment outcome (sustained virological response [SVR] vs relapse [RLPS] or nonresponse [NR]).

Figure 3

Relationship between D0;t0-t20;log10hepatitis C virus (HCV) RNA value (logarithmic decline in HCV RNA level 2 days after the start of treatment) and viral genotype. A, D0;t0-t20;log10HCV RNA values for patients infected with different HCV genotypes. B, D0;t0-t20;log10HCV RNA values for patients infected with different HCV genotypes according to final treatment outcome (sustained virological response [SVR] vs relapse [RLPS] or nonresponse [NR]).

To complete the univariate analysis of factors associated with early HCV RNA decline, we looked at its possible correlation with the remaining clinical and analytical parameters recorded. Interestingly, we found that both serum ALT and GGT levels were inversely related to the extent of the early HCV RNA decline (for GGT, r=-0.475and P<.001; for ALT, r=-0.312 and P=.001) (figure 4Aand 4B). In particular, a strict indirect relationship was observed between serum GGT levels and D0;t0-t20;log10HCV RNA value. Because GGT levels correlated with liver fibrosis or steatosis and with insulin resistance (data not shown), we looked at the possible effect of body composition and metabolic parameters on the first-phase HCV kinetics. No significant correlation between D0;t0-t20;log10HCV RNA value and body mass index or waist circumference was found. In contrast, serum insulin levels and HOMA-IR scores were inversely correlated with D0;t0-t20;log10HCV RNA value (Spearman ρ, −0.282 and −0.292, respectively; P<.005) (figure 4C).

Figure 4

Relationship between D0;t0-t20;log10hepatitis C virus (HCV) RNA value (logarithmic decline in HCV RNA level 2 days after the start of treatment) and biochemical parameters. A, D0;t0-t20;log10HCV RNA values for patients with different levels (by quartile) of γ-glutamyl transpeptidase (GGT). B, Correlation between D0;t0-t20;log10HCV RNA value and baseline alanine aminotransferase (ALT) level. ULN, upper limit of normal. C, Correlation between D0;t0-t20;log10HCV RNA value and baseline serum insulin level.

Figure 4

Relationship between D0;t0-t20;log10hepatitis C virus (HCV) RNA value (logarithmic decline in HCV RNA level 2 days after the start of treatment) and biochemical parameters. A, D0;t0-t20;log10HCV RNA values for patients with different levels (by quartile) of γ-glutamyl transpeptidase (GGT). B, Correlation between D0;t0-t20;log10HCV RNA value and baseline alanine aminotransferase (ALT) level. ULN, upper limit of normal. C, Correlation between D0;t0-t20;log10HCV RNA value and baseline serum insulin level.

Finally, we performed a multivariate analysis of factors independently associated with treatment outcome (SVR vs non-SVR) or D0;t0-t20;log10HCV RNA value. We built a logistic regression model that included HCV genotype, necroinflammatory activity, fibrosis stage, GGT level, glycemia, HOMA-IR score, and D0;t0-t20;log10HCV RNA value. In this model, the D0;t0-t20;log10HCV RNA value was independently associated with treatment outcome. Together with the fibrosis stage, the D0;t0-t20;log10HCV RNA value was the strongest predictor of the final treatment outcome (table 3).

Table 3

Multiple Logistic Regression Analysis of Predictors of Treatment Outcome and Multiple Linear Regression Analysis of Predictors of First-Phase Hepatitis C Virus (HCV) RNA Kinetics

Table 3

Multiple Logistic Regression Analysis of Predictors of Treatment Outcome and Multiple Linear Regression Analysis of Predictors of First-Phase Hepatitis C Virus (HCV) RNA Kinetics

By means of a multivariate linear regression model that included fibrosis, steatosis, HCV genotype, ALT and GGT levels, and insulinemia, we found that the independent predictors of the first-phase viral response to treatment were HCV genotype and GGT level (table 3).

Discussion

The currently available treatment for chronic hepatitis C has an overall suboptimal efficacy in the face of significant toxicity [19]. During-treatment predictors of outcome have succeeded in avoiding the useless financial and human burdens of ineffective treatments in refractory subjects [23, 24]. It is presently recommended that medications be withdrawn whenever a RVR or an EVR is not achieved [19]. The results of the present study show that it is possible to predict treatment outcome with reasonable confidence in a substantial number of cases as early as 2 days after the start of treatment. Indeed, our clinical experience suggests that evaluation of first-phase HCV RNA kinetics may in many cases function as an in vivo antiviral susceptibility test.

Three major findings arise from our study. First, evaluation of the HCV RNA decline after the first 2 days of combination antiviral treatment clearly discriminates among patient groups with different outcomes. Second, the application of 2 different cutoff values for day 2 HCV RNA decline allows, early during the course of treatment and with good accuracy, prediction of the final treatment outcome in almost half of patients with chronic hepatitis C in clinical practice. This finding, however, awaits independent external validation. Third, early virological events after the start of treatment are strictly related to a number of factors relevant to both the host and the virus. Interestingly, all of these factors have long been applied to the a priori prediction of treatment outcome [23].

To account for the pretreatment HCV RNA level, we evaluated the relative reduction in HCV RNA level from baseline, calculating for each patient a D0;t0-t20;log10HCV RNA value. This procedure allows the comparison of patients, although the results are less reliable in cases of low baseline HCV RNA level because of the relatively low sensitivity of the assay used (615 IU/mL). Nonetheless, through this approach we were able to verify that patient groups with different final treatment outcomes may be differentiated as early as 2 days after the start of treatment.

We obtained clinically valuable data by measuring HCV RNA levels 2 days after the start of treatment. At this early stage, the extent of the viral load decline has positive and negative predictive values comparable to those of the RVR or EVR evaluations performed much later [30-33]. To account for real-world clinical experience, in addition to an ITT analysis we also ran a per-protocol prediction analysis that excluded patients who showed excellent virological responses but discontinued treatment prematurely. This allowed us to improve the positive predictive value of the day 2 HCV RNA decline, which reached a 93% probability of a SVR for a decrease >2.5 logs.

Accumulating evidence suggests that insulin resistance is a key pathophysiologic feature of chronic hepatitis C, one that correlates with steatosis and fibrosis progression [25, 26]. Recent data suggest that the greater the pretreatment insulin resistance, the smaller the chance of a SVR [16, 17], and that insulin resistance tends to wane after treatment-induced viral clearance [17, 27]. Our findings corroborate these data, showing for the first time that the deleterious effect of insulin resistance is manifest very early during the course of treatment. It appears that insulin resistance affects the direct antiviral effect of interferon, as emerging data from in vitro and in vivo studies are showing [28, 29].

Our study has some limitations. It is a single-center analysis, performed on genetically homogeneous patients. Moreover, only HCV genotypes 1, 2, and 3 were represented. Finally, the quantitative assay used has a relatively low sensitivity (615 IU/mL) compared with those of the latest real-time PCR-based tests, which were not available at the time we initiated the study.

In conclusion, the study of first-phase HCV RNA kinetics appears to provide important information on the intrinsic susceptibility of HCV to interferon. We believe that evaluation of the early HCV RNA kinetics during combination antiviral treatment should enter into clinical practice. Furthermore, our prediction model based on 2 cutoff values could have 2-fold relevance: it might allow immediate discontinuation of treatment when it appears to be ineffective, and it could boost patient adherence when a favorable outcome may be foreseen despite significant toxicity. Early withdrawal of ineffective medications could also dramatically reduce health and financial costs. We found for the first time that insulin resistance affects treatment response at a very early stage. Unraveling the molecular bases of this influence will likely provide novel tools for the management of chronic hepatitis C.

Acknowledgments

We thank Drs Marie-Françoise Tripodi, Aldo Marrone, Vittorio Attanasio, and Ludovico Tallarico for their support in the recruitment of patients and Luca Vallefuoco, Loredana Costigliola, Geltrude Fiorillo, and Francesco Crispi for their valuable technical collaboration.

Financial support. Italian Ministero della Salute and Regione Campania.

Potential conflicts of interest. All authors: no conflicts.

References

1
Zeuzem
S
Schmidt
JM
Lee
JH
Rüster
B
Roth
WK
Effect of interferon alfa on the dynamics of hepatitis C virus turnover in vivo
Hepatology
 , 
1996
, vol. 
23
 (pg. 
366
-
71
)
2
Ferenci
P
Predicting the therapeutic response in patients with chronic hepatitis C: the role of viral kinetic studies
J Antimicrob Chemother
 , 
2004
, vol. 
53
 (pg. 
15
-
8
)
3
Neumann
AU
Lam
NP
Dahari
H
, et al.  . 
Hepatitis C viral dynamics in vivo and the antiviral efficacy of interferon-alpha therapy
Science
 , 
1998
, vol. 
282
 (pg. 
103
-
7
)
4
Layden-Almer
JE
Cotler
SJ
Layden
TJ
Viral kinetics in the treatment of chronic hepatitis C
J Viral Hepat
 , 
2006
, vol. 
13
 (pg. 
499
-
504
)
5
Layden
JE
Layden
TJ
Reddy
KR
Levy-Drummer
RS
Poulakos
J
Neumann
AU
First phase viral kinetic parameters as predictors of treatment response and their influence on the second phase viral decline
J Viral Hepat
 , 
2002
, vol. 
9
 (pg. 
340
-
5
)
6
Boulestin
A
Kamar
N
Sandres-Sauné
K
, et al.  . 
Twenty-four hour kinetics of hepatitis C virus and antiviral effect of alpha-interferon
J Med Virol
 , 
2006
, vol. 
78
 (pg. 
365
-
71
)
7
Bekkering
FC
Stalgis
C
McHutchison
JG
Brouwer
JT
Perelson
AS
Estimation of early hepatitis C viral clearance in patients receiving daily interferon and ribavirin therapy using a mathematical model
Hepatology
 , 
2001
, vol. 
33
 (pg. 
419
-
23
)
8
Berg
T
Sarrazin
C
Herrmann
E
, et al.  . 
Prediction of treatment outcome in patients with chronic hepatitis C: significance of baseline parameters and viral dynamics during therapy
Hepatology
 , 
2003
, vol. 
37
 (pg. 
600
-
9
)
9
Zeuzem
S
Lee
JH
Franke
A
, et al.  . 
Quantification of the initial decline of serum hepatitis C virus RNA and response to interferon alfa
Hepatology
 , 
1998
, vol. 
27
 (pg. 
1149
-
56
)
10
Jessner
W
Gschwantler
M
Steindl-Munda
P
, et al.  . 
Primary interferon resistance and treatment response in chronic hepatitis C infection: a pilot study
Lancet
 , 
2001
, vol. 
358
 (pg. 
1241
-
2
)
11
Kronenberger
B
Herrmann
E
Micol
F
von Wagner
M
Zeuzem
S
Viral kinetics during antiviral therapy in patients with chronic hepatitis C and persistently normal ALT levels
Hepatology
 , 
2004
, vol. 
40
 (pg. 
1442
-
9
)
12
Carlsson
T
Reichard
O
Norkrans
G
, et al.  . 
Hepatitis C virus RNA kinetics during the initial 12 weeks treatment with pegylated interferon-alpha 2a and ribavirin according to virological response
J ViralHepat
 , 
2005
, vol. 
12
 (pg. 
473
-
80
)
13
Lindh
M
Alestig
E
Arnholm
B
, et al.  . 
Response prediction and treatment tailoring for chronic hepatitis C virus genotype 1 infection
J Clin Microbiol
 , 
2007
, vol. 
45
 (pg. 
2439
-
45
)
14
Poynard
T
Ratziu
V
McHutchison
J
, et al.  . 
Effect of treatment with peginterferon or interferon alfa-2b and ribavirin on steatosis in patients infected with hepatitis C
Hepatology
 , 
2003
, vol. 
38
 (pg. 
75
-
85
)
15
Bressler
BL
Guindi
M
Tomlinson
G
Heathcote
J
High body mass index is an independent risk factor for nonresponse to antiviral treatment in chronic hepatitis C
Hepatology
 , 
2003
, vol. 
38
 (pg. 
639
-
44
)
16
D'Souza
R
Sabin
CA
Foster
GR
Insulin resistance plays a significant role in liver fibrosis in chronic hepatitis C and in the response to antiviral therapy
Am J Gastroenterol
 , 
2005
, vol. 
100
 (pg. 
1509
-
15
)
17
Romero-Gómez
M
Del Mar Viloria
M
Andrade
RJ
, et al.  . 
Insulin resistance impairs sustained response rate to peginterferon plus ribavirin in chronic hepatitis C patients
Gastroenterology
 , 
2005
, vol. 
128
 (pg. 
636
-
41
)
18
Feld
JJ
Hoofnagle
JH
Mechanism of action of interferon and ribavirin in treatment of hepatitis C
Nature
 , 
2005
, vol. 
436
 (pg. 
967
-
72
)
19
Hoofnagle
JH
Seeff
LB
Peginterferon and ribavirin for chronic hepatitis C
N Engl J Med
 , 
2006
, vol. 
355
 (pg. 
2444
-
51
)
20
Expert Panel on Detection Evaluation and Treatment of High Blood Cholesterol in Adults
Executive summary of the third report of the National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, and Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III)
JAMA
 , 
2001
, vol. 
285
 (pg. 
2486
-
97
)
21
Adinolfi
LE
Gambardella
M
Andreana
A
Tripodi
MF
Utili
R
Ruggiero
G
Steatosis accelerates the progression of liver damage of chronic hepatitis C patients and correlates with specific HCV genotype and visceral obesity
Hepatology
 , 
2001
, vol. 
33
 (pg. 
1358
-
64
)
22
Brunt
EM
Janney
CG
Di Bisceglie
AM
Neuschwander-Tetri
BA
Bacon
BR
Nonalcoholic steatohepatitis: a proposal for grading and staging the histological lesions
Am J Gastroenterol
 , 
1999
, vol. 
94
 (pg. 
2467
-
74
)
23
Mihm
U
Herrmann
E
Sarrazin
C
Zeuzem
S
Review article: predicting response in hepatitis C virus therapy
Aliment Pharmacol Ther
 , 
2006
, vol. 
23
 (pg. 
1043
-
54
)
24
Poordad
F
Reddy
KR
Martin
P
Rapid virologic response: a new mile stone in the management of chronic hepatitis C
Clin Infect Dis
 , 
2008
, vol. 
46
 (pg. 
78
-
84
)
25
Hui
JM
Sud
A
Farrell
GC
, et al.  . 
Insulin resistance is associated with chronic hepatitis C virus infection and fibrosis progression
Gastroenterology
 , 
2003
, vol. 
125
 (pg. 
1695
-
704
)
26
Fartoux
L
Poujol-Robert
A
Guechot
J
Wendum
D
Poupon
R
Serfaty
L
Insulin resistance is a cause of steatosis and fibrosis progression in chronic hepatitis C
Gut
 , 
2005
, vol. 
54
 (pg. 
1003
-
8
)
27
Kawaguchi
T
Ide
T
Taniguchi
E
, et al.  . 
Clearance of HCV improves insulin resistance, beta-cell function, and hepatic expression of insulin receptor substrate 1 and 2
Am J Gastroenterol
 , 
2007
, vol. 
102
 (pg. 
570
-
6
)
28
Kawaguchi
T
Yoshida
T
Harada
M
, et al.  . 
Hepatitis C virus down-regulates insulin receptor substrates 1 and 2 through up-regulation of suppressor of cytokine signaling 3
Am J Pathol
 , 
2004
, vol. 
165
 (pg. 
1499
-
508
)
29
Persico
M
Capasso
M
Persico
E
, et al.  . 
Suppressor of cytokine signaling 3 (SOCS3) expression and hepatitis C virus-related chronic hepatitis: insulin resistance and response to antiviral therapy
Hepatology
 , 
2007
, vol. 
46
 (pg. 
1009
-
15
)
30
Lee
WM
Reddy
KR
Tong
MJ
, et al.  . 
Early hepatitis C virus-RNA responses predict interferon treatment outcomes in chronic hepatitis C. The Consensus Interferon Study Group
Hepatology
 , 
1998
, vol. 
28
 (pg. 
1411
-
5
)
31
Brouwer
JT
Hansen
BE
Niesters
HG
Schalm
SW
Early prediction of response in interferon monotherapy and in interferon-ribavirin combination therapy for chronic hepatitis C: HCV RNA at 4 weeks versus ALT
J Hepatol
 , 
1999
, vol. 
30
 (pg. 
192
-
8
)
32
Castro
FJ
Esteban
JI
Juárez
A
, et al.  . 
Early detection of nonresponse to interferon plus ribavirin combination treatment of chronic hepatitis C
J Viral Hepat
 , 
2002
, vol. 
9
 (pg. 
202
-
7
)
33
Davis
GL
Wong
JB
McHutchison
JG
Manns
MP
Harvey
J
Albrecht
J
Early virologic response to treatment with peginterferon alfa-2b plus ribavirin in patients with chronic hepatitis C
Hepatology
 , 
2003
, vol. 
38
 (pg. 
645
-
52
)

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