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Abstract

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Analytic strategy
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

Summary

Background

The relative impact of non-alcoholic fatty liver disease (NAFLD) on health-related quality of life (HRQL) compared to other chronic liver diseases has not been fully explored.

Aim

To compare the domain scores of the 29-item Chronic Liver Disease Questionnaire (CLDQ) for patients with NAFLD to those with chronic hepatitis B and chronic hepatitis C.

Methods

A HRQL questionnaire, CLDQ, was routinely administered to patients attending a liver clinic. Additional clinical and laboratory data were obtained on patients with NAFLD, chronic hepatitis B, and chronic hepatitis C from our quality of life database. Scores for each of the six CLDQ domains were compared using one-way anova and multiple regression.

Results

Complete data were available for 237 patients. NAFLD patients scored lowest on multiple CLDQ domains. Based on the bivariate data, NAFLD patients have the poorest HRQL, followed by chronic hepatitis C and chronic hepatitis B patients. Multivariate analysis showed that some specific domain score correlations remained significant for NAFLD diagnosis, cirrhosis, gender, and body mass index.

Conclusion

NAFLD patients had significantly lower quality of life scores compared with patients with hepatitis B or hepatitis C on multiple CLDQ domains, suggesting that HRQL was severely impaired in patients with NAFLD.


Introduction

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Analytic strategy
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

Chronic liver disease is an important cause of mortality and morbidity. Patients with chronic liver disease demonstrate a variety of symptoms, several of which interfere with life activities and quality of life. Symptoms may vary with type of chronic liver disease.1–11 Knowledge of the differing symptom profiles for chronic liver diseases may help in the development of more comprehensive treatment plans. Research has found that patients with chronic liver diseases, including viral hepatitis, cholestatic liver disease and chronic hepatocellular disease, have more impaired health-related quality of life (HRQL) than the general U.S. population and healthy controls.12

Non-alcoholic fatty liver disease (NAFLD) is a common cause of chronic liver disease. Estimates from the National Health and Nutrition Examination Surveys data place the prevalence of NAFLD in unselected populations between 3% and 23%.1 Risk factors associated with NAFLD include obesity, hyperlipidemia, diabetes mellitus and other conditions associated with metabolic syndrome.1, 3 In fact, in patients with abnormal liver enzymes in the absence of serologic or biochemical markers of liver disease, the prevalence of NAFLD ranges from 66% to 90%, and the prevalence of the progressive form of NAFLD or non-alcoholic steatohepatitis (NASH) is 34–40%.1, 3, 6, 7 From the spectrum of NAFLD, approximately 15% of patients with biopsy-proven NASH can progress to cirrhosis.1–3 Despite a great deal of recent interest in understanding the epidemiology and pathogenesis of NAFLD, treatment options are limited and unproven.1, 3 Furthermore, the impact of NAFLD on patients’ quality of life, as well as its social and economic impact, has not been investigated. In this study, we examined how HRQL is affected by type of liver disease, demographic factors and comorbid risk factors associated with NAFLD, including diabetes, obesity, hypertension, metabolic syndrome and cirrhosis. The second aim was to compare HRQL of NAFLD patients with patients with other forms of chronic liver diseases, specifically hepatitis B (HBV) and hepatitis C (HCV).4, 8

Methods

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Analytic strategy
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

Patient selection

For the purpose of this study, we identified patients from our Liver Disease Quality of Life Database (LDQLD). The population of this data set accrues from patients referred to the Liver Clinic for evaluation, monitoring and treatment of chronic liver diseases. Patients who agreed to participate in a quality of life research study were included. Each patient had an established diagnosis of chronic liver disease by a hepatologist and had completed a validated HRQL questionnaire (Chronic Liver Disease Questionnaire, CLDQ). Clinical and laboratory data from the time of questionnaire administration were also available.

Factors selected from the database for the analysis include demographic (gender, age and race), anthropometric (body mass index, BMI), and comorbid conditions (history of diabetes, hypertension, metabolic syndrome, cirrhosis and alaninaminotransferase (ALT) levels). Cirrhosis was defined histologically by a liver biopsy or clinically as evidenced by the presence of ascites, gastro-oesophageal varices, hepatic encephalopathy or other evidence of hepatic decompensation. Patients with another chronic active medical (e.g., congestive heart failure, malignancy) or active psychiatric condition were excluded. The research protocol was approved by the Institutional Review Board.

Measures

The CLDQ was utilized to measure HRQL, compiled from six domains, during the 2 weeks prior to questionnaire administration. The CLDQ is a liver disease-specific HRQL instrument consisting of 29 items that reflect six domains:

(i) Fatigue (tiredness or fatigue, sleepiness during the day, decreased strength, decreased level of energy, felt drowsy).

(ii) Abdominal pain (abdominal bloating, abdominal pain, abdominal discomfort).

(iii) Emotional function (anxious, unhappy, irritable, difficulty sleeping, mood swings, ability to fall asleep at night, felt depressed, problems concentrating).

(iv) Systemic symptoms (bodily pain, shortness of breath, muscle cramps, dry mouth, itching).

(v) Activity (unable to eat as much as preferred, trouble lifting or carrying heavy objects, limitation of diet).

(vi) Worry (concern about liver disease impact on family, worried symptoms will develop to major problems, worried condition will get worse, worried never going to feel better, concerned about transplant availability).

The response options for each question range from ‘all of the time’1 to ‘none of the time’.7 Scores on the six subscales range from 1 to 7. An overall CLDQ score is also calculated (range = 1–7). Higher scores on each CLDQ scale reflect better HRQL. This instrument has been found to be valid and has good test-retest reliability among patients with different chronic liver diseases.9–17

Analytic strategy

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Analytic strategy
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

Health-related quality of life scores, demographic characteristics and comorbid conditions of the three patient subgroups – NAFLD, HBV and HCV – were compared using one-way anova and cross-tabulations with chi-squared tests. Additionally, post hoc Bonferroni tests were examined to identify which patient groups, if any, had statistically significantly different scores. A series of order of least squares (OLS) regression analyses were also conducted to ascertain how diagnosis relates to the HRQL domains, controlling for gender, BMI, cirrhosis, diabetes, hypertension and metabolic syndrome.

Results

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Analytic strategy
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

The study population consists of 237 patients with chronic liver disease. Of these, 131 patients had chronic viral HBV [HBV surface antigen (n = 56)] or HCV [anti-HCV antibody positive (n = 75)] with viraemia. None of the viral hepatitis patients was receiving interferon-based regimens. An additional 106 patients had NAFLD (exclusion of other liver disease and NAFLD documented histologically or radiologically). Patients’ characteristics by diagnosis are described in Table 1. Patients’ mean age was similar across diagnosis. Over two-thirds of NAFLD and HCV patients were White compared to one-fifth of HBV patients. Over two-thirds of the NAFLD patients were female, compared to 43% of HCV patients and 27% of HBV patients. Three-fourths of the NAFLD sample was obese (BMI > 30), which is not surprising given that obesity is a risk factor for NAFLD. Only 36% of HCV and 14% of HBV patients were obese. While almost half of the HCV group had cirrhosis, less than one-fifth of HBV and NAFLD patients had cirrhosis. As expected, patients with NAFLD were much more likely to have diabetes (27%), metabolic syndrome (26%) and hypertension (42%) compared to HCV and HBV patients. HCV patients (84%) were much more likely than HBV (46%) and NAFLD patients (54%) to have elevated ALT levels ≥40.

Table 1.   Descriptive statistics for the overall sample and patient subgroups
  Cases w/ valid data Total sample (n = 237) NAFLD** (n = 106) Hepatitis B (n = 56) Hepatitis C (n = 75)χ2 test or F value –P value
  1. NAFLD, non-alcoholic fatty liver disease.

  2. * A BMI score >30 is considered obese; ** Percentages are based on cases with valid data.

Mean age (s.d.)23746.4 (10.7)46.4 (11.5)45.4 (12.6)47.0 (7.7)F = .3 P > .05
% White (n)22756.4 (128)70.8 (68)19.6 (11)65.3 (49)χ2 = 41.3 P < .001
% Obese* (n)22547.1 (106)75.5 (71)14.3 (8)36.0 (27)χ2 = 58.4 P < .001
% Female (n) 23751.1 (121)69.8 (74)26.8 (15)42.7 (32)χ2 = 30.2 P < .001
% Cirrhosis (n)18728.9 (54)14.1 (9)19.2 (10)49.3 (35)χ2 = 23.6 P < .001
% Diabetes (n)22914.8 (34)26.5 (26)3.6 (2)8.0 (6)χ2 = 19.0 P < .001
% Hypertension (n)22427.7 (62)41.9 (39)16.1 (9)18.7 (14)χ2 = 16.3 P < .001
% Metabolic syndrome (n)22313.5 (20)25.8 (24)3.6 (2)5.3 (4)χ2 = 21.0 P < .001
% ALT < 40 (n)22638.1 (86)46.3 (44)53.6 (30)16.0 (12)χ2 = 23.9 P < .001

One-way anova was utilized to compare HRQL scores of HBV, HCV and NAFLD patients; post hoc Bonferroni tests were examined to identify which patient groups, if any, had statistically significantly different scores (Table 2). Compared with HBV patients, NAFLD patients had poorer HRQL as measured by the overall CLDQ scale and five of the six CLDQ subscales (only Worry domain scores did not differ between the two patient groups). Patients with NAFLD also had more impairment than patients with HCV in their HRQL as measured by Systemic and Emotion domains of CLDQ. Scores between these patient groups did not differ on the overall CLDQ scale and the four other HRQL subscales. Finally, HBV and HCV patients’ HRQL scores were also similar across the majority of domains measured, although HBV patients had less impairment than HCV patients as measured by Abdominal and Activity domain scores of CLDQ.

Table 2.   Baseline mean health-related quality of life scores (s.d.) by health condition†
 NAFLD (n = 106)Hepatitis B (n = 56)Hepatitis C (n = 75)F value –P value(*)Group differences
  1. A–Mean scores of non-alcoholic fatty liver disease (NAFLD) and hepatitis B patients differ at P < .05. B–Mean scores of NAFLD and hepatitis C patients differ at P < .05. C–Mean scores of hepatitis B and hepatitis C patients differ at P < .05.

  2. P < .05; ** P < .01; *** P < .001; † Chronic Liver Disease Questionnaire (CLDQ) scales range from 1–7.

CLDQ-overall5.4 (1.0)6.0 (.8)5.7 (.8)5.8 **A
Abdominal5.7 (1.4)6.3 (.9)5.6 (1.1)5.6 **A, C
Fatigue4.8 (1.4)5.6 (1.0)5.2 (.9)8.7 ***A
Activity5.3 (1.5)6.3 (1.1)5.6 (1.0)9.0 ***A, C
Emotions5.4 (1.3)5.8 (1.0)5.8 (.9)3.6 *A, B
Systemic5.5 (1.1)6.2 (.9)6.0 (.9)8.6 ***A, B
Worry5.7 (1.4)5.9 (1.1)5.8 (1.0)0.5

Order of least squares multiple regression was also used to assess how diagnosis of different types of chronic liver disease relates to the overall CLDQ score and the six CLDQ subscale scores, controlling for gender, BMI and ALT levels as well as having cirrhosis, diabetes, hypertension and metabolic syndrome. The regression results, including both standardized and unstandardized coefficients, are presented in Table 3. Multivariate regression allowed us to assess how the diagnosis of different types of liver disease relates to the domains of HRQL, while controlling for the influences of other comorbidities as well as patients’ personal and health characteristics. As many of these clinical variables are correlated with the type of liver disease, as previously described, it is important to control for their effects in a multivariate model to determine whether or not the relationship between type of liver disease and HRQL persists. Two additional variables, patient age and race, were also included in the regression models. However, age and race had no unique effects on the CLDQ scores controlling for other variables, and their inclusion did not affect how the other variables related to the outcomes, so they were excluded from the final regression models to achieve the most parsimonious models.

Table 3.   Multiple regression results: predictors of Chronic Liver Disease Questionnaire (CLDQ) and CLDQ subscales standardized (unstandardized) coefficients
PredictorsOutcomes
CLDQ – TotalAbdominalFatigueActivityEmotionsSystemicWorry
  1. BMI, body mass index.

  2. P < .05; ** P < .01; *** P < .001; † Non-alcoholic fatty liver disease is the reference category; ‡ Adjusted Multiple R, or the adjusted correlation coefficient.

Cirrhosis−.15 (−.27)−.14 (−.36)−.05 (−.13)−.10 (−.27)−.07 (−.17)−.23 (−.50)**−.21 (−.54)*
Female−.19 (−.33)*−.21 (−.51)*−.13 (−.29) −.09 (−.21)−.13 (−.28) −.15 (−.29)−.12 (−.29)
BMI−.17 (−.02)−.01 (−.00)−.17 (−.02)−.19 (−.03)*−.01 (−.00)−.20 (−.02)*−.12 (−.02)
Hepatitis C†.14 (.24).00 (.00).08 (.19).12 (.29).33 (.71)**.27 (.55)*.08 (.19)
Hepatitis B†.21 (.39).18 (.47).19 (.45).31 (.84)*.28 (.64)*.26 (.55)*−.03 (−.09)
Diabetes.01 (.01).08 (.29)−.03 (−.09).07 (.26).04 (.05).06 (.17)−.11 (−.38)
Hypertension−.05 (−.10).00 (−.00)−.07 (−.20)−.04 (−.11).08 (.11).12 (.28)−.04 (−.12)
Metabolic syn.16 (.44).05 (.20).18 (.67).09 (.38).07 (.24).07 (.22).13 (.50)
ALT level−.01 (−.02)−.04 (−.10).06 (.14).03 (.08).04 (.09).03 (06)−.11 (−.28)
Adjust MR‡.13.09.09.14.07.18.04

While overall CLDQ and all CLDQ subscales, except Worry, differed among patient groups at the bivariate level, type of chronic liver disease (NAFLD, HBV and HCV) remained independently associated with three of the CLDQ domains in the multivariate models (see Table 3). In all of these CLDQ domains (Emotions, Systemic and Activity), NAFLD patients had poorer HRQL than patients with HCV or HCB.

Turning to other variables included in the multivariate analysis, gender in particular was associated with two CLDQ domains (Abdominal and Activity domains), with women having poorer HRQL than men. Additionally, higher BMI was independently associated with poorer scores on the Activity and Systemic domains, and having cirrhosis was independently associated with poorer Systemic and Worry scores. On the other hand, after controlling for the effects of the other variables in the multivariate model, diabetes, hypertension, metabolic syndrome and ALT levels were not independently associated with any of the CLDQ domains.

Discussion

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Analytic strategy
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

Non-alcoholic fatty liver disease is one of the most common causes of chronic liver disease worldwide. Given its strong association with metabolic syndrome and obesity, its impact on the individuals’ health and health care resource utilization is significant.1, 3, 18, 19 Despite growing awareness of the importance of NAFLD, its epidemiology and pathogenic mechanisms, the full impact of NAFLD on patients’ HRQL has not been established.

Our study is the first to examine HRQL in patients with NAFLD and compare it with other chronic liver diseases. The data show that NAFLD patients have significant abnormalities of HRQL as measured by the CLDQ. In fact, NAFLD patients had lower quality of life scores compared with patients with HBV and HCV on multiple domains of HRQL. In this study, patients with NAFLD had the most impaired HRQL, while patients with HBV had the least impaired HRQL. Our multivariate analyses were consistent with prior research, which demonstrated that obesity and female gender were shown to be associated with poorer HRQL.20–25 Cirrhosis and obesity were associated with Systemic Symptoms. Nevertheless, after controlling for these variables, HRQL of patients with NAFLD remained significantly impaired, a new finding. Identifying that NAFLD persisted in independently affecting HRQL domains even after controlling for obesity is important as obesity itself impacts significantly on aspects of individuals’ quality of life.

Duration of illness is also a consideration in the study of HRQL. This potentially relevant variable is difficult to study in a chronic liver disease population. As chronic liver disease can be asymptomatic for long periods of time, it is difficult to estimate when most chronic liver diseases manifested prior to diagnosis. 1, 3, 4, 8, 26 Nevertheless, our study shows that the quality of life impairment in NAFLD does not seem to be totally explained by obesity, gender, cirrhosis, diabetes, hypertension, ALT levels and metabolic syndrome.

In summary, this is the first study to demonstrate that patients with NAFLD have more impairment in HRQL compared to patients with HBV and HCV, as measured by the CLDQ. This finding remains after controlling for the presence of cirrhosis, diabetes, hypertension, metabolic syndrome, obesity, ALT levels and gender, suggesting that NAFLD is independently associated with poor HRQL. More research is needed to help understand why NAFLD patients have lower quality of life, particularly in these three domains. Possible explanations for NAFLD patients having lower quality of life include abnormalities of glucose metabolism and energetics. As future treatment strategies for NAFLD are being developed, clinical investigators must assess not only the traditional outcomes (such as aminotransferases and liver biopsy) but also patient-related outcomes (such as HRQL). It will only be with this combined approach that the total impact of NAFLD and its treatment can be fully assessed and managed.5, 27–30

Acknowledgements

  1. Top of page
  2. Abstract
  3. Introduction
  4. Methods
  5. Analytic strategy
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References

Declaration of personal interests: None. Declaration of funding interests: This research was supported by a grant from Pfizer Limited, Sandwich, Kent, England and the Liver Outcomes Research Fund, Inova Health Systems Foundation, Falls Church, Virginia. Dr Younossi has received research grants from Pfizer, Roche, Idenix, Vertex, Axcan, Wyeth, Schering-Plough, Human Genome Sciences, Amgen and Gilead Sciences.

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  2. Abstract
  3. Introduction
  4. Methods
  5. Analytic strategy
  6. Results
  7. Discussion
  8. Acknowledgements
  9. References
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