Introduction

Chronic hepatitis C virus (HCV) infection is currently a major health problem that is estimated to affect 170 million people worldwide [1]. In a significant proportion of patients, infection leads to liver cirrhosis with potential complications, including impaired liver function, portal hypertension, and/or hepatocellular carcinoma. In addition to these serious complications, several studies have indicated that chronic HCV infection might be associated with considerable impairment of health-related quality of life (HRQOL) regardless of the disease stage [2–6]. However, the precise mechanism of decreased HRQOL in chronic hepatitis C (CHC) patients has not been elucidated, although there are some reports indicating an association with the degree of fibrosis, sex or age [7, 8].

Insulin resistance (IR) in CHC patients is often established early in the course of infection, and related to hepatic steatosis and fibrosis [9–11]. Regarding the mechanism of IR associated with HCV infection, Kawaguchi et al. [12] have reported that HCV core region can directly evoke downregulation of hepatic insulin receptor substrate (IRS)-1 and IRS-2. Moreover, Shintani et al. [13] have demonstrated using HCV core gene transgenic mice that tumor necrosis factor (TNF)-α induced by HCV infection can suppress insulin-induced tyrosine phosphorylation of IRS-1. Recent estimates have indicated that 30–70 % of patients with CHC display some evidence of IR [14, 15].

Therefore, the aim of this study was to investigate the relationship between HRQOL in CHC patients and clinical parameters focused on IR using the 36-item Short Form Health Survey (SF-36) [16].

SF-36 is a widely used self-administered questionnaire designed for use in clinical practice and research, health policy evaluations, and general population surveys. It has demonstrated consistently high reliability and validity in a variety of patient populations including CHC [2–4, 6–8, 16–20]. Although there are several questionnaires, such as Chronic Liver Disease Questionnaire (CLDQ) [21], Liver Disease Symptom Index (LDSI) [22], and Liver Disease Quality of Life (LDQOL) [23] for evaluation of QOL in chronic liver disease patients, we used SF-36 as a non-specific questionnaire to compare HRQOL in CHC patients with that in the general population.

Patients and methods

Study population

We included 190 consecutive patients with CHC diagnosed by HCV antibody test and polymerase chain reaction, who attended Saga Medical School Hospital between January 2005 and December 2008. They were not taking, or had not recently (within 6 months) taken, any antiviral medication. Patients were excluded if they fulfilled the following criteria: (1) hepatitis B virus surface antigen positivity; (2) autoimmune liver disease, alcoholic liver disease (20 g/day alcohol), and/or medication-associated liver damage; (3) taking insulin-sensitizing or antidiabetic medication; and (4) extremity disorders, and/or other significant problems including chronic renal and heart failure. These criteria led to exclusion of 15 patients, which left 175 who were eligible for inclusion. Liver biopsy was performed in 162 of the 175 patients (within 12 months of the study). All 175 patients gave informed consent, and the study was approved by the ethics committee of Saga Medical School in accordance with the Declaration of Helsinki (approved ID number: 2004-04-04 and 2007-04-02).

Assessment of HRQOL

HRQOL of CHC patients was assessed using the SF-36, a 36-item self-administered questionnaire encompassing eight physical and mental health domains and two physical and mental summary scales [24]. We used a Japanese-validated version of SF-36, and the data of the present study were compared to the Japanese normative sample score of SF-36 in 2966 individuals [25]. The eight subscales: physical functioning (PF), role-physical (RP), bodily pain (BP), general health (GH), vitality (VT), social functioning (SF), role-emotional (RE) and mental health (MH), were calculated from the questionnaires as described previously [26–28]. Patients completed SF-36, and the resulting scores were transformed into a scale of 0 (worst possible score) to 100 (best possible score), as recommended by the questionnaire’s originators. Physical and mental summary measures were obtained from the sum scores of the corresponding subscales, that is, PF, RP, BP and GH for the physical component summary (PCS) and VT, SF, RE and MH for the mental component summary (MCS). According to the recommendations given in the manual, subscale scores were calculated if at least half of the items on the respective scale were answered. Transformation of the raw scores was performed using Microsoft Excel (Redmond, WA, USA).

Clinical and laboratory assessments

All venous blood samples were taken after a 12-h overnight fast. For the oral glucose tolerance test (OGTT), patients ingested a solution containing 75 g glucose, and venous blood samples were collected at 0, 30, 60, 90 and 120 min for the measurement of plasma glucose and serum insulin concentrations. Glucose was determined by a glucokinase method and insulin was measured using a chemiluminescent enzyme immunoassay kit (Abbott Japan, Tokyo, Japan). Glucose tolerance was categorized into 3 groups according to the criteria of the World Health Organization [29] as follows: (1) normal glucose tolerance (NGT; fasting plasma glucose level <110 and 2-h plasma glucose level <140 mg/dL); (2) impaired glucose tolerance (IGT; fasting glucose level ≤126 mg/dL and 2-h glucose level ≥140 and ≤200 mg/dL); and (3) diabetes mellitus (DM; fasting glucose level ≥126 mg/dL or 2-h glucose level ≥200 mg/dL).

IR was evaluated by the homeostasis model assessment of insulin resistance (HOMA-IR) method, which was calculated as follows: HOMA-IR = fasting plasma glucose × fasting serum insulin/405. Body mass index (BMI) was calculated as kg/m2. Serum HCV-RNA levels were identified by 2 different quantitative polymerase chain reaction (PCR) assays: one was Amplicore HCV Monitor version 2.0 and the other was COBAS Taq-Man HCV Monitor Test (both Roche Diagnostics, Tokyo, Japan). High viral load was defined as ≥100 kIU/mL by Amplicore method and ≥5.0 logIU/mL by Taq-Man method. The HCV genotype was determined on the basis of the sequence of the core region [30].

Liver histology

Percutaneous liver biopsy was performed under ultrasound imaging within 12 months before SF-36. Histological hepatic fibrosis and inflammation were scored using the METAVIR scoring system [31]. Grade of fibrosis was classified from F0 to F4, with varying degrees of fibrosis as follows: F0, no fibrosis; F1, portal fibrosis without septa; F2, portal fibrosis with rare septa; F3, numerous septa without cirrhosis; and F4, cirrhosis. Based on the degree of lymphocyte infiltration and hepatocyte necrosis, activity was classified from A0 to A3, with higher scores indicating more severe inflammation.

Statistical analysis

Comparisons between groups were made using the Mann–Whitney U test for continuous variables and the χ 2 or Fisher’s exact probability test for categorical data. Multivariate analysis was performed using logistic regression analysis. Relationships between continuous variables were analyzed by Pearson’s correlation coefficient test. Data are expressed as mean ± SD. P < 0.05 was considered statistically significant. All statistical analyses were performed using SAS software (Cary, NC, USA).

Results

Patient characteristics

Clinical, biochemical, virological and histological characteristics of all 175 CHC patients are summarized in Table 1. Forty-four (25 %) patients were considered obese, with BMI >25 kg/m2, and 64 (37 %) patients were evaluated for IR, with HOMA-IR >2. About one-third of the patients showed abnormal glucose tolerance; with respect to degrees of glucose tolerance, 67, 21 and 12 % of patients showed NGT, IGT and DM, respectively. A large majority of HCV was genotype 1b (76 %). Liver biopsy samples were obtained from 162 patients, and 152 (94:4 %) indicated minimal to moderate necroinflammatory activity, although 10 patients had severe activity. Regarding fibrosis in the biopsy samples, F1, F2, F3 and cirrhosis (F4) were seen in 83 (51 %), 51 (31 %), 22 (14 %) and 6 (4 %) patients, respectively. Liver biopsy was not performed in the remaining 13 of the 175 patients because of refusal.

Table 1 Patient characteristics (n = 175)
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One woman had a low serum albumin level of 2.7 g/dL, but her other hepatic function tests were well preserved, such as 80 % of prothrombin activity and 1.5 mg/dL of total bilirubin level. Therefore, we included her in this study as compensated liver cirrhosis. One woman showed a high serum level of total bilirubin of 2.7 mg/dL. However, her liver histology showed a METAVIR fibrosis stage of F2. Her indirect bilirubin level was 2.2 mg/dL; therefore, we assumed that her bilirubinemia was constitutional jaundice, such as Gilbert disease. Therefore, we included her in the F2 group.

Assessment of HRQOL

We compared the HRQOL scores in CHC patients in our study with those in the Japanese normative sample, and there was no difference except for evaluation of GH (Fig. 1).

Fig. 1
figure 1

SF-36 mean subscale scores in CHC patients. Dots are Japanese normative sample mean scores. In physical and mental component summary (PCS and MCS), mean score was 50, calculated from Japanese normative sample scores. BP bodily pain, GH general health, MH mental health, PF physical functioning, RE role-emotional, RP role-physical, SF social functioning, VT vitality

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We evaluated factors associated with HRQOL scores in CHC patients. Dividing PCS and MCS score into 2 groups by each median score (PCS 57, MCS 46), univariate and multivariate analysis were performed. Univariate analysis showed that older age, female sex, low hemoglobin, high fasting insulin level, high HOMA-IR value, and impaired glucose tolerance were significant factors associated with lower PCS. In contrast, there was no significant factor associated with MCS (Table 2). Multivariate analysis after adding fibrosis factor to the significant factors by univariate analysis indicated that high HOMA-IR value was the only significant factor associated with lower PCS (OR 2.92, p < 0.01, 95 % CI 1.37–6.18) (Table 3).

Table 2 Univariate analysis of factors associated with PCS and MCS
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Table 3 Multivariate analysis of factors associated with decline in physical component summary in CHC patients
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Correlations between SF-36 subscale, PCS and MCS scores and HOMA-IR

The correlation coefficients between eight subscales of SF-36 and HOMA-IR are shown in Table 4. RP and GH scores associated with PCS were strongly and negatively correlated with HOMA-IR. Relationships between PCS, MCS and HOMA-IR are shown by scatter plots (Fig. 2). PCS had a negative correlation with HOMA-IR value (r = −0.234, p = 0.018) (Fig. 2a); meanwhile, MCS was not associated with HOMA-IR (r = 0.09, p = 0.48) (Fig. 2b). Similar results were obtained for analysis of patients (n = 152) excluding F4 stage fibrosis or DM pattern in 75 g OGTT, which might have influenced their HRQOL or insulin resistance (Fig. 2c for PCS: r = −0.224, p = 0.02 and Fig. 2d for MCS: r = 0.084, p = 0.58).

Table 4 Correlation coefficient between eight subscale scores of SF-36 (PF, RP, GH, VT, SF, RE, MH) and HOMA-IR
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Fig. 2
figure 2

Correlations between physical component summary (PCS) score and homeostasis model assessment of insulin resistance (HOMA-IR) (a, c). Correlations between mental component summary (MCS) score and HOMA-IR (b, d). PCS was associated with HOMA-IR in all patients (a r = −0.234, p = 0.018 by Pearson’s correlation coefficient test) and in those excluding F4 stage fibrosis or diabetes mellitus (DM) pattern in 75 g OGTT (c, r = −0.224, p = 0.02). MCS was not associated with HOMA-IR in all patients (b r = 0.09, p = 0.48) and in those excluding F4 stage fibrosis or DM pattern in 75 g OGTT (d r = 0.084, p = 0.58)

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Discussion

This cross-sectional study regarding HRQOL of Japanese CHC patients indicated that the impairment of the physical aspect of HRQOL was significantly related to IR evaluated by HOMA-IR, but was not associated with the demographic factors, inflammatory activity, fibrosis stage or viral factors.

IR or abnormal glucose metabolism are known to be clinical characteristics of HCV-infected patients [9–13]. Previous reports have indicated that eradication of HCV by interferon (IFN) therapy improves IR [32] and HRQOL scores [33, 34]. We have previously shown that sustained viral disappearance induced by IFN treatment improves systemic as well as hepatic insulin sensitivity, and decreases serum levels of soluble TNF receptor [35]. Lecube et al. [36] have demonstrated that serum levels of proinflammatory cytokines in CHC patients are higher than those in patients with chronic liver disorder due to other causes. Moreover, it has also been reported that the levels of circulating inflammatory cytokines such as interleukin (IL)-1, IL-6, IL-8 and TNF-α are related to fatigue in patients with acute myelogenous leukemia and myelodysplastic syndrome [37]. Judging from these reports, we assume that HCV infection increases systemic IR and production of inflammatory cytokines, which lead to impairment of HRQOL.

However, the limitation of this study was that there was no comparison of the relationship of QOL with IR between CHC and other chronic liver disease patients, including hepatitis B or fatty liver disease, and no data regarding inflammatory cytokines in the present study. A previous study has indicated that there was a significant association between declined physical functioning and elevated HOMA-IR in the general elderly population, not related to HCV or liver disease [38]. Therefore, we cannot conclude whether the association of HRQOL and IR is characteristic of HCV-infected patients, and whether it is mediated via cytokines. It should be clarified whether this association is dependent on disease etiology.

Our study indicated that IR was only associated with the physical component of HRQOL and not with the mental component. Meanwhile, Tillman et al. [39] have reported that MCS of SF-36, but not PCS, in CHC patients was significantly lower than that in patients with non-HCV liver disease such as chronic hepatitis B, primary biliary cirrhosis, primary sclerosing cholangitis, and autoimmune hepatitis. Although that study had some differences in age, race and the setting of the control group from our study, these are not enough to explain the discrepancies in the results. Bonkovsky et al. [7] have reported that there is a significant difference in BMI between high and low scores of PCS but not MCS in CHC patients. Moreover, a systematic review has indicated that PCS in CHC patients with sustained virological response by IFN treatment was improved [40]. These reports support our results. At present, however, it is controversial which component, mental and/or physical, in QOL is impaired by HCV infection.

The causal relationship between HRQOL and IR remains largely speculative because our study was cross-sectional. Although it has been reported that glucose intolerance or high plasma glucose level might cause weak muscle strength and impair physical function [41], it is possible that impairment of physical aspects influences IR. Longitudinal studies are needed to verify the detailed mechanism of this relationship.

The present study failed to demonstrate the difference in HRQOL defined by SF-36 scores between healthy individuals and CHC patients, except for general health score. This result was different from previous studies that have indicated that CHC patients have a diminished HRQOL compared with healthy controls across all SF-36 scores [2–6]. The reason for this discrepancy is outlined below. First, almost all the patients were willing to visit our tertiary hospital for IFN treatment in the future, so their QOL might be better than that in the general HCV-infected patients. Second, a previous large cross-sectional survey of unselected HCV-positive patients contained many with low household income, untreated diabetes, or a history of intravenous drug use, which were shown to be independent predictors of reduced HRQOL [42]. Our study did not include such patients.

Previous reports suggest that advanced liver fibrosis, especially cirrhosis, is strongly associated with decline of QOL [7, 8]. In the present study, we could not find a significant difference in HRQOL score between mild (F0–F2) and severe (F3, F4) fibrosis. However, we cannot deny the association between QOL and liver fibrosis, because our result might have been due to the small number of cases of liver cirrhosis (n = 6). Liver fibrosis evokes IR, therefore, further studies are necessary to elucidate the relationship between fibrosis and IR and QOL.

In conclusion, this study shows that diminished HRQOL, especially physical domains, in CHC patients is associated with IR. Improvement in IR due to weight reduction by diet and/or exercise, or using insulin sensitizers, might improve HRQOL in CHC patients, following good adherence to IFN treatment, although the relationship between IR and HRQOL warrants further exploration.