Sequences in the interferon sensitivity-determining region and core region of hepatitis C virus impact pretreatment prediction of response to PEG-interferon plus ribavirin: data mining analysis

Patterns of Caregiver Factors Predicting Participation in Trauma-Focused Cognitive Behavioral Therapy

This study examined patterns of caregiver factors associated with Trauma- Focused Cognitive Behavioral Therapy (TF-CBT) utilization among trauma-exposed youth. This study included 41 caregivers (caregiver age M = 36.1, SD = 9.88; 93% African American) of youth referred for TF-CBT, following a substantiated forensic assessment of youth trauma exposure. Prior to enrolling in TF-CBT, caregivers reported on measures for parenting stress, attitudes towards treatment, functional impairment, caregiver mental health diagnosis, and caregiver trauma experiences. Classification and regression tree methodology were used to address study aims. Predictors of enrollment and completion included: attitudes towards treatment, caregiver trauma experiences, and parenting stress. Several caregiver factors predicting youth service utilization were identified. Findings suggest screening for caregivers' attitudes towards therapy, parenting stress, and trauma history is warranted to guide providers in offering caregiver-youth dyads appropriate resources at intake that can lead to increased engagement in treatment services.

Factors improving the utility of antiviral therapy for chronic hepatitis B: A nationwide multicenter study in Japan

AIM

The consideration of patients' needs in terms of research outcomes is emphasized in research promotion to eradicate hepatitis B virus according to the Basic Act on Hepatitis Measures in Japan. This study analyzed patients' attitudes toward experienced antiviral therapies for chronic hepatitis B and their need for future therapies.

METHODS

A self-administered questionnaire comprising 124 questions was completed among patients with chronic hepatitis B from 61 core-center hospitals designated to implement and research policies on hepatitis in 47 prefectures from August 2013 to January 2014 (n = 3021, response rate = 51%).

RESULTS

In decision-tree models with 333 variables generated from the questionnaire data, patients' satisfaction with therapy and reduction in anxiety about therapy were dependent on favorable therapeutic effects, sufficient information provided by the physician, and fewer lifestyle disturbances. Medical expenses were not selected at a superior branch because subsidy for antiviral therapy started in 2010. In correspondence analysis of free text answers, patients' need for therapy and support mechanisms differed among their attributes, including a great need for novel therapy in older men, hope for avoidance of lifestyle disturbance in younger men, and alleviation of painful experience with the disease in women.

CONCLUSIONS

Continual provision of sufficient information is necessary to improve the utility of antiviral therapy for chronic hepatitis B as well as for favorable therapeutic effects. The patients believed that novel drugs and support would reduce the diverse burden of the disease on their lives.

Viral and host factors associated with outcomes of hepatitis C virus infection (Review)

Hepatitis C virus (HCV) infection is a major health issue globally. Owing to the progress made in host genetics and HCV molecular virology, emerging data have suggested that the natural course and treatment response in patients with HCV infection are largely determined by complex host‑viral interactions. HCV genotype is the most important viral factor predicting the response to pegylated interferon‑α plus ribavirin therapy. The subtype of HCV genotype 1 is the key viral factor that predicts the efficacy of direct‑acting antiviral therapy. HCV genome heterogeneity and baseline viral load are additionally associated with the treatment response. Multiple host genetic variants localized in genes associated with the immune response have been identified as predictors of spontaneous disease course and therapy outcome in chronic HCV. However, most findings from candidate gene association studies have not been proven universal for all investigated populations and independent studies. Previous findings in independent large genome wide association studies confirmed that interferon‑λ3 gene polymorphisms are associated with spontaneous clearance and treatment responsiveness. A polymorphism of the inosine triphosphatase gene has been identified as a protective factor against ribavirin‑induced anemia and dose reductions. Another genetic variant in the patatin‑like phospholipase domain containing 3 genes is associated with hepatic steatosis and fibrosis in patients with HCV. The present review focused on the identified viral and host factors associated with outcomes of patients with HCV, and assessed the involvement of viral and host genetics in the natural history and treatment outcomes of HCV infection. This will provide novel ideas concerning personalized prevention and individualized clinical management.

Predictors of Virological Response in 3,235 Chronic HCV Egyptian Patients Treated with Peginterferon Alpha-2a Compared with Peginterferon Alpha-2b Using Statistical Methods and Data Mining Techniques

Despite the appearance of new oral antiviral drugs, pegylated interferon (PEG-IFN)/RBV may remain the standard of care therapy for some time, and several viral and host factors are reported to be correlated with therapeutic effects. This study aimed to reveal the independent variables associated with failure of sustained virological response (SVR) to PEG-IFN alpha-2a versus PEG-IFN alpha-2b in treatment of naive chronic hepatitis C virus (HCV) Egyptian patients using both statistical methods and data mining techniques. This retrospective cohort study included 3,235 chronic hepatitis C patients enrolled in a large Egyptian medical center: 1,728 patients had been treated with PEG-IFN alpha-2a plus ribavirin (RBV) and 1,507 patients with PEG-IFN alpha-2b plus RBV between 2007 and 2011. Both multivariate analysis and Reduced Error Pruning Tree (REPTree)-based model were used to reveal the independent variables associated with treatment response. In both treatment types, alpha-fetoprotein (AFP) >10 ng/mL and HCV viremia >600 × 10(3) IU/mL were the independent baseline variables associated with failure of SVR, while male gender, decreased hemoglobin, and thyroid-stimulating hormone were the independent variables associated with good response (P < 0.05). Using REPTree-based model showed that low AFP was the factor of initial split (best predictor) of response for either PEG-IFN alpha-2a or PEG-IFN alpha-2b (cutoff value 8.53, 4.89 ng/mL, AUROC = 0.68 and 0.61, P = 0.05). Serum AFP >10 ng/mL and viral load >600 × 10(3) IU/mL are variables associated with failure of response in both treatment types. REPTree-based model could be used to assess predictors of response.

Identifying candidates with favorable prognosis following liver transplantation for hepatocellular carcinoma: Data mining analysis

BACKGROUND AND OBJECTIVES

The optimal cutoff of each value in configuring selection criteria for pre-transplant assessment of hepatocellular carcinoma (HCC) remains uncertain.

METHODS

To build a predictive model for recurrent HCC, we performed data mining analysis on patients who underwent LT for HCC at University Health Network (n = 246). The model was externally validated using a cohort from the Scientific Registry of Transplant Recipients (SRTR) database (n = 9,769).

RESULTS

Among 246 patients, 14.6% (n = 36) experienced recurrent HCC within 2.5 years post-LT. The risk prediction model for recurrent HCC identified two subgroups with low-risk (total tumor diameter [TTD] <4 cm and serum alpha-fetoprotein [AFP] <73 ng/ml, n = 135) and with high-risk (TTD >4 cm and/or AFP >73 ng/ml, n = 111). The reproducibility of the model was validated through the SRTR database; overall patient survival rate was significantly better in low-risk group than high-risk group (P < 0.0001). Using Cox regression model, this yardstick, not Milan criteria, was revealed to efficiently predict post-transplant survival independent of underlying characteristics (P < 0.0001).

CONCLUSIONS

Grouping LT candidates with pre-LT HCC by the cutoffs of TTD 4 cm and AFP 73 ng/ml which were unearthed by data mining analysis efficiently classify patients according by the post-transplant prognosis.

Advanced molecular surveillance of hepatitis C virus

Hepatitis C virus (HCV) infection is an important public health problem worldwide. HCV exploits complex molecular mechanisms, which result in a high degree of intrahost genetic heterogeneity. This high degree of variability represents a challenge for the accurate establishment of genetic relatedness between cases and complicates the identification of sources of infection. Tracking HCV infections is crucial for the elucidation of routes of transmission in a variety of settings. Therefore, implementation of HCV advanced molecular surveillance (AMS) is essential for disease control. Accounting for virulence is also important for HCV AMS and both viral and host factors contribute to the disease outcome. Therefore, HCV AMS requires the incorporation of host factors as an integral component of the algorithms used to monitor disease occurrence. Importantly, implementation of comprehensive global databases and data mining are also needed for the proper study of the mechanisms responsible for HCV transmission. Here, we review molecular aspects associated with HCV transmission, as well as the most recent technological advances used for virus and host characterization. Additionally, the cornerstone discoveries that have defined the pathway for viral characterization are presented and the importance of implementing advanced HCV molecular surveillance is highlighted.

Role of viral and host factors in interferon based therapy of hepatitis C virus infection

The current standard of care (SOC) for hepatitis C virus (HCV) infection is the combination of pegylated interferon (PEG-IFN), Ribavirin and protease inhibitor for HCV genotype 1. Nevertheless, this treatment is successful only in 70-80% of the patients. In addition, the treatment is not economical and is of immense physical burden for the subject. It has been established now, that virus-host interactions play a significant role in determining treatment outcomes. Therefore identifying biological markers that may predict the treatment response and hence treatment outcome would be useful. Both IFN and Ribavirin mainly act by modulating the immune system of the patient. Therefore, the treatment response is influenced by genetic variations of the human as well as the HCV genome. The goal of this review article is to summarize the impact of recent scientific advances in this area regarding the understanding of human and HCV genetic variations and their effect on treatment outcomes. Google scholar and PubMed have been used for literature research. Among the host factors, the most prominent associations are polymorphisms within the region of the interleukin 28B (IL28B) gene, but variations in other cytokine genes have also been linked with the treatment outcome. Among the viral factors, HCV genotypes are noteworthy. Moreover, for sustained virological responses (SVR), variations in core, p7, non-structural 2 (NS2), NS3 and NS5A genes are also important. However, all considered single nucleotide polymorphisms (SNPs) of IL28B and viral genotypes are the most important predictors for interferon based therapy of HCV infection.

Relevance of baseline viral genetic heterogeneity and host factors for treatment outcome prediction in hepatitis C virus 1b-infected patients

BACKGROUND

Only about 50% of patients chronically infected with HCV genotype 1 (HCV-1) respond to treatment with pegylated interferon-alfa and ribavirin (dual therapy), and protease inhibitors have to be administered together with these drugs increasing costs and side-effects. We aimed to develop a predictive model of treatment response based on a combination of baseline clinical and viral parameters.

METHODOLOGY

Seventy-four patients chronically infected with HCV-1b and treated with dual therapy were studied (53 retrospectively -training group-, and 21 prospectively -validation group-). Host and viral-related factors (viral load, and genetic variability in the E1-E2, core and Interferon Sensitivity Determining Region) were assessed. Multivariate discriminant analysis and decision tree analysis were used to develop predictive models on the training group, which were then validated in the validation group.

PRINCIPAL FINDINGS

A multivariate discriminant predictive model was generated including the following variables in decreasing order of significance: the number of viral variants in the E1-E2 region, an amino acid substitution pattern in the viral core region, the IL28B polymorphism, serum GGT and ALT levels, and viral load. Using this model treatment outcome was accurately predicted in the training group (AUROC = 0.9444; 96.3% specificity, 94.7% PPV, 75% sensitivity, 81% NPV), and the accuracy remained high in the validation group (AUROC = 0.8148, 88.9% specificity, 90.0% PPV, 75.0% sensitivity, 72.7% NPV). A second model was obtained by a decision tree analysis and showed a similarly high accuracy in the training group but a worse reproducibility in the validation group (AUROC = 0.9072 vs. 0.7361, respectively).

CONCLUSIONS AND SIGNIFICANCE

The baseline predictive models obtained including both host and viral variables had a high positive predictive value in our population of Spanish HCV-1b treatment naïve patients. Accurately identifying those patients that would respond to the dual therapy could help reducing implementation costs and additional side effects of new treatment regimens.

The impact of interleukin 28b gene polymorphism on the virological response to combined pegylated interferon and ribavirin therapy in chronic HCV genotype 4 infected egyptian patients using data mining analysis

BACKGROUND

Chronic HCV represents one of the common causes of chronic liver disease worldwide with Egypt having the highest prevalence, namely genotype 4. Interleukin IL-28B gene polymorphism has been shown to relate to HCV treatment response, mainly in genotype1.

OBJECTIVES

We aim to evaluate the predictive power of the rs12979860 IL28B SNP and its protein for treatment response in genotype 4 Egyptian patients by regression analysis and decision tree analysis.

PATIENTS AND METHODS

The study included 263 chronic HCV Egyptian patients receiving peg-interferon and ribavirin therapy. Patients were classified into 3 groups; non responders (83patients), relapsers (76patients) and sustained virological responders (104 patients). Serum IL 28 B was performed, DNA was extracted and analyzed by direct sequencing of the SNP rs 12979860 of IL28B gene.

RESULTS

CT, CC and TT represented 56 %, 25 % and 19% of the patients, respectively. Absence of C allele (TT genotype) was significantly correlated with the early failure of response while CC was associated with sustained virological response. The decision tree showed that baseline alpha fetoprotein (AFP ≤ 2.68 ng/ml) was the variable of initial split (the strongest predictor of response) confirmed by regression analysis. Patients with TT genotype had the highest probability of failure of response.

CONCLUSIONS

Absence of the C allele was significantly associated with failure of response. The presence of C allele was associated with a favorable outcome. AFP is a strong baseline predictor of HCV treatment response. A decision tree model is useful for predicting the probability of response to therapy.

The assessment of data mining for the prediction of therapeutic outcome in 3719 Egyptian patients with chronic hepatitis C

INTRODUCTION

Decision-tree analysis; a core component of data mining analysis can build predictive models for the therapeutic outcome to antiviral therapy in chronic hepatitis C virus (HCV) patients.

AIM

To develop a prediction model for the end virological response (ETR) to pegylated interferon PEG-IFN plus ribavirin (RBV) therapy in chronic HCV patients using routine clinical, laboratory, and histopathological data.

PATIENTS AND METHODS

Retrospective initial data (19 attributes) from 3719 Egyptian patients with chronic HCV presumably genotype-4 was assigned to model building using the J48 decision tree-inducing algorithm (Weka implementation of C4.5). All patients received PEG-IFN plus RBV at Cairo-Fatemia Hospital, Cairo, Egypt in the context of the national treatment program. Factors predictive of ETR were explored and patients were classified into seven subgroups according to the different rates of ETR. The universality of the decision-tree model was subjected to a 10-fold cross-internal validation in addition to external validation using an independent dataset collected of 200 chronic HCV patients.

RESULTS

At week 48, overall ETR was 54% according to intention to treat protocol. The decision-tree model included AFP level (<8.08 ng/ml) which was associated with high probability of ETR (73%) followed by stages of fibrosis and Hb levels according to the patients' gender followed by the age of patients.

CONCLUSION

In a decision-tree model for the prediction for antiviral therapy in chronic HCV patients, AFP level was the initial split variable at a cutoff of 8.08 ng/ml. This model could represent a potential tool to identify patients' likelihood of response among difficult-to-treat presumably genotype-4 chronic HCV patients and could support clinical decisions regarding the proper selection of patients for therapy without imposing any additional costs.

Model incorporating the ITPA genotype identifies patients at high risk of anemia and treatment failure with pegylated-interferon plus ribavirin therapy for chronic hepatitis C

This study aimed to develop a model for predicting anemia using the inosine triphosphatase (ITPA) genotype and to evaluate its relationship with treatment outcome. Patients with genotype 1b chronic hepatitis C (n = 446) treated with peg-interferon alpha and ribavirin (RBV) for 48 weeks were genotyped for the ITPA (rs1127354) and IL28B (rs8099917) genes. Data mining analysis generated a predictive model for anemia (hemoglobin (Hb) concentration <10 g/dl); the CC genotype of ITPA, baseline Hb <14.0 g/dl, and low creatinine clearance (CLcr) were predictors of anemia. The incidence of anemia was highest in patients with Hb <14.0 g/dl and CLcr <90 ml/min (76%), followed by Hb <14.0 g/dl and ITPA CC (57%). Patients with Hb ≥ 14.0 g/dl and ITPA AA/CA had the lowest incidence of anemia (17%). Patients with two predictors (high-risk) had a higher incidence of anemia than the others (64% vs. 28%, P < 0.0001). At baseline, the IL28B genotype was a predictor of a sustained virological response [adjusted odds ratio 9.88 (95% confidence interval 5.01-19.48), P < 0.0001]. In patients who achieved an early virological response, the IL28B genotype was not associated with a sustained virological response, while a high risk of anemia was a significant negative predictor of a sustained virological response [0.47 (0.24-0.91), P = 0.026]. For high-risk patients with an early virological response, giving >80% of the planned RBV dose increased sustained virological responses by 24%. In conclusion, a predictive model incorporating the ITPA genotype could identify patients with a high risk of anemia and reduced probability of sustained virological response.

The role of viral and host genetics in natural history and treatment of chronic HCV infection

Understanding of the natural history and treatment responsiveness of chronic hepatitis C virus (HCV) infection has evolved rapidly in recent years. Advances in HCV molecular virology and host genetics can now better predict spontaneous clearance and treatment outcomes. HCV genotype is the most important viral factor predicting interferon-α treatment responsiveness; HCV-1 subtype is emerging as a key determinant of the efficacy of direct acting antiviral therapy. Genome-wide association studies have recently identified several clinically important host determinants of the outcomes of peginterferon-α and ribavirin treatment outcome: IL28B polymorphism is associated with spontaneous clearance and treatment responsiveness; ITPA polymorphism protects against ribavirin-induced anaemia and dose reductions; genetic determinants of liver fibrosis progression rate have been proposed. In this review, we evaluate the role of viral and host genetics in the natural history and treatment outcomes of chronic HCV infection, and consider how this knowledge might help individualize clinical management in the era of DAA therapy.

Update on the management and treatment of hepatitis C virus infection: recommendations from the Department of Veterans Affairs Hepatitis C Resource Center Program and the National Hepatitis C Program Office

Chronic hepatitis C virus (HCV) infection affects approximately 1.3 % of the United States population and 4 % of veterans who use Department of Veterans Affairs medical services. Chronic HCV is the primary cause of cirrhosis, hepatocellular carcinoma (HCC), and end-stage liver disease requiring liver transplantation in the United States. Management of chronic HCV is aimed at halting disease progression, preventing cirrhosis decompensation, reducing the risk of HCC, and treating extrahepatic complications of the infection. As part of a comprehensive HCV management strategy, peginterferon alfa and ribavirin, along with the addition of a hepatitis C protease inhibitor therapy for many genotype 1-infected patients, are the current standard of care. Antiviral therapy should be provided to those individuals who are clinically stable, have moderate liver disease or compensated cirrhosis, and are motivated to pursue therapy. Many patients have comorbid medical and psychiatric conditions, which may affect their adherence to antiviral therapy or worsen while on antiviral therapy. To optimally manage hepatitis C and associated comorbidities, patients benefit from multidisciplinary teams that can provide HCV-specific care and treatment. Sustained virologic response is associated with "cure" of chronic HCV, and results in improved liver disease outcomes and prolonged survival.

Data mining model using simple and readily available factors could identify patients at high risk for hepatocellular carcinoma in chronic hepatitis C

BACKGROUND & AIMS

Assessment of the risk of hepatocellular carcinoma (HCC) development is essential for formulating personalized surveillance or antiviral treatment plan for chronic hepatitis C. We aimed to build a simple model for the identification of patients at high risk of developing HCC.

METHODS

Chronic hepatitis C patients followed for at least 5 years (n=1003) were analyzed by data mining to build a predictive model for HCC development. The model was externally validated using a cohort of 1072 patients (472 with sustained virological response (SVR) and 600 with nonSVR to PEG-interferon plus ribavirin therapy).

RESULTS

On the basis of factors such as age, platelet, albumin, and aspartate aminotransferase, the HCC risk prediction model identified subgroups with high-, intermediate-, and low-risk of HCC with a 5-year HCC development rate of 20.9%, 6.3-7.3%, and 0-1.5%, respectively. The reproducibility of the model was confirmed through external validation (r(2)=0.981). The 10-year HCC development rate was also significantly higher in the high-and intermediate-risk group than in the low-risk group (24.5% vs. 4.8%; p<0.0001). In the high-and intermediate-risk group, the incidence of HCC development was significantly reduced in patients with SVR compared to those with nonSVR (5-year rate, 9.5% vs. 4.5%; p=0.040).

CONCLUSIONS

The HCC risk prediction model uses simple and readily available factors and identifies patients at a high risk of HCC development. The model allows physicians to identify patients requiring HCC surveillance and those who benefit from IFN therapy to prevent HCC.

Algorithm to determine the outcome of patients with acute liver failure: a data-mining analysis using decision trees

BACKGROUND

We established algorithms to predict the prognosis of acute liver failure (ALF) patients through a data-mining analysis, in order to improve the indication criteria for liver transplantation.

METHODS

The subjects were 1,022 ALF patients seen between 1998 and 2007 and enrolled in a nationwide survey. Patients older than 65 years, and those who had undergone liver transplantation and received blood products before the onset of hepatic encephalopathy were excluded. Two data sets were used: patients seen between 1998 and 2003 (n=698), whose data were used for the formation of the algorithm, and those seen between 2004 and 2007 (n=324), whose data were used for the validation of the algorithm. Data on a total of 73 items, at the onset of encephalopathy and 5 days later, were collected from 371 of the 698 patients seen between 1998 and 2003, and their outcome was analyzed to establish decision trees. The obtained algorithm was validated using the data of 160 of the 324 patients seen between 2004 and 2007.

RESULTS

The outcome of the patients at the onset of encephalopathy was predicted through 5 items, and the patients were classified into 6 categories with mortality rates between 23% and89%. When the prognosis of the patients in the categories with mortality rates greater than 50% was predicted as "death", the accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the algorithm were 79, 78, 81, 83, and 75%, respectively. Similar high values were obtained when the algorithm was employed in the patients for validation. The outcome of the patients 5 days after the onset of encephalopathy was predicted through 7 items, and a similar high accuracy was found for both sets of patients.

CONCLUSIONS

Novel algorithms for predicting the outcome of ALF patients may be useful to determine the indication for liver transplantation.

[Current options for predicting therapeutic response in chronically infected patients with hepatitis C virus genotype 1]

Only about 50% of patients chronically infected with hepatitis C virus genotype 1 achieve a successful response to standard treatment with pegylated interferon-alfa and ribavirin. Moreover, the recently approved protease inhibitors will have to be administered together with these drugs. Consequently, predicting response to standard treatment, ideally before starting it, remains an important challenge. Although several baseline predictors of treatment failure have been described, including clinical and virological factors, none of them is able to provide reliable predictions at the individual level. In addition, the development of multivariate models combining several predictive factors has not yet yielded predictions with the requisite reliability for use in clinical practice. Therefore, further research is needed to improve predictive models and to describe new factors that would enable us to predict treatment outcome with greater reliability and reproducibility. The development of candidate selection algorithms that help clinicians to identify which patients could benefit from the new therapies on the basis of their chances of responding to standard therapy is of major interest for both patient well-being and healthcare expense. This review attempts to provide a view of the current options for predicting the response to pegylated interferon-alfa plus ribavirin therapy in patients chronically infected with hepatitis C virus genotype 1.

Pretreatment prediction of anemia progression by pegylated interferon alpha-2b plus ribavirin combination therapy in chronic hepatitis C infection: decision-tree analysis

BACKGROUND

This study aimed to develop a model to predict the development of severe anemia during pegylated interferon alpha-2b plus ribavirin combination therapy.

METHODS

Data were collected from 1081 genotype 1b chronic hepatitis C patients who were treated at 6 hospitals in Japan. These patients were randomly assigned to a model-building group (n = 691) or an internal validation group (n = 390). Factors predictive of severe anemia (hemoglobin, Hb < 8.5 g/dl) were explored using data-mining analysis.

RESULTS

Hb values at baseline, creatinine clearance (Ccr), and an Hb concentration decline by 2 g/dl at week 2 were used to build a decision-tree model, in which the patients were divided into 5 subgroups based on variable rates of severe anemia ranging from 0.4 to 11.8%. The reproducibility of the model was confirmed by the internal validation group (r² = 0.96). The probability of severe anemia was high in patients whose Hb value was <14 g/dl before treatment (6.5%), especially (a) in those whose Ccr was <80 ml/min (11.8%) and (b) those whose Ccr was ≥ 80 ml/min but whose Hb concentration decline at week 2 was ≥ 2 g/dl (11.5%). The probability of severe anemia was low in the other patients (0.4-2.5%).

CONCLUSIONS

The decision-tree model that included Hb values at baseline, Ccr, and an Hb concentration decline by 2 g/dl at week 2 was useful for predicting the probability of severe anemia, and has the potential to support clinical decisions regarding early dose reduction of ribavirin.

Cholesterol and chronic hepatitis C virus infection

Cholesterol is an essential molecule for the life cycle of the hepatitis C virus (HCV). This review focuses on the roles of cholesterol in HCV infection and introduces HCV events related to cholesterol metabolism and applications for cholesterol metabolism as a therapeutic target. HCV appears to alter host lipid metabolism into its preferable state, which is clinically recognized as steatosis and hypocholesterolemia. While hepatic fatty acid and triglyceride syntheses are upregulated in chronic hepatitis C patients, no direct evidence of increased hepatic de novo cholesterol biosynthesis has been obtained. Impaired VLDL secretion from hepatocytes is suggested to increase intracellular cholesterol concentrations, which may lead to hypocholesterolemia. Clinically, lower serum cholesterol levels are associated with lower rates of sustained virological responses (SVR) to pegylated-interferon plus ribavirin therapy, but the reason remains unclear. Clinical trials targeting HMG-CoA reductase, the rate-limiting enzyme in the cholesterol biosynthetic pathway, are being conducted using statins. Anti-HCV actions by statins appear to be caused by the inhibition of geranylgeranyl pyrophosphate synthesis rather than their cholesterol lowering effects. Other compounds that block various steps of cholesterol metabolic pathways have also been studied to develop new strategies for the complete eradication of this virus.