A comprehensive review of patient-reported outcomes in metabolic dysfunction-associated steatotic liver disease
0 
0
Abstract
The global prevalence of obesity and type 2 diabetes has increased, contributing to an increased worldwide prevalence of metabolic dysfunction-associated steatotic liver disease (MASLD). Currently, one in three adults is affected by MASLD and/or its progressive form, metabolic dysfunction-associated steatohepatitis (MASH), making this liver disease a significant public health challenge. Along with MASH-related cirrhosis, these conditions are poised to become the leading causes of chronic liver disease and liver transplants in the near future. Given the growing burden of MASLD and MASH, it is crucial to understand their impact from the patients’ perspective. One way to do this is by assessing patient-reported outcomes (PROs), including health-related quality of life (HRQL). HRQL can be assessed using generic instruments like the short form 36 version (SF-36) and the European quality of life-5 dimensions questionnaire (EQ-5D), or disease-specific tools such as the chronic liver disease questionnaire for nonalcoholic steatohepatitis (CLDQ-NASH). Given the limitations of each instrument, the best approach generally involves using both generic and disease-specific instruments. Evidence indicates that HRQL scores are significantly lower in individuals with MASLD, especially in areas assessing physical activity and the ability to perform daily living tasks. Fatigue and impaired work productivity are also important PROs for those with MASLD/MASH. These decrements in PROs worsen with disease progression but appear to improve with disease regression, including improvements linked to treatment. In this context, measuring PROs enhances the assessment of other patient-centric outcomes and provides insights for the healthcare community to develop interventions that could improve both clinical and humanistic outcomes for individuals living with MASLD/MASH.
Keywords
INTRODUCTION
Metabolic dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), is a global health problem affecting up to 38% of adults[1]. As curative treatment for the hepatitis C virus and prevention strategies - such as vaccination and effective viral suppression therapies for hepatitis B - expand globally, MASLD may soon become a leading cause of chronic liver disease[2]. Since MASLD is tightly related to the metabolic conditions of obesity, insulin resistance, and type 2 diabetes (T2D), its prevalence rises as these conditions become more widespread globally[2,3].
The more advanced disease state, metabolic dysfunction-associated steatohepatitis (MASH), along with MASH-related cirrhosis, is also increasing, with the most current prevalence rate for MASH noted at 5%, but could be as high as 16% in the general population. Meanwhile, the prevalence of MASH-related cirrhosis among those with MASH has been reported to range between 2% to 7%[1]. However, these rates may be biased by the recruitment criteria of many studies. Nonetheless, they help to demonstrate the potential extent of MASLD and MASH in society[1].
MASLD is related to a greater risk for negative outcomes, including an increased risk for all-cause and cause-specific mortality, a greater risk for cardiovascular diseases, and an elevated risk for chronic kidney disease[4-8]. Among those with MASLD, cardiovascular disease is the leading cause of mortality, followed by extrahepatic cancers (uterine, stomach, pancreatic, esophageal, and colon cancers), and then liver disease. Although these causes of mortality are true for all patients with MASLD, liver disease is the predominant cause of death for those with advanced fibrosis[9-14]. Among those with hepatocellular carcinoma (HCC) who are listed for liver transplantation, MASH-related HCC is now the number one indication for liver transplantation (LT). However, among all transplant recipients, MASH is the second reason for liver transplant[14,15,16].
MASLD and MASH are also associated with a decline in patient-reported outcomes (PROs), a term that encompasses health-related quality of life (HRQL) and productivity at work and in their daily activities[17,18]. In addition, MASLD and MASH have a large fiscal burden resulting from increased use of healthcare resources and lack of productivity at work[18,19]. Therefore, it is imperative to gain knowledge of the comprehensive burden of MASLD/MASH from the perspectives of individuals, providers, and society. This information can be used to guide clinical practice and policy decisions, potentially improving the comprehensive long-term outcomes for these individuals.
Therefore, in this comprehensive review of PROs in MASLD, using PubMed as our main search engine and the key words PROs, HRQL, and NAFLD, we provide an overview of what PROs are, how they are measured, and describe the impact of MASLD and MASH on PROs.
PATIENT-REPORTED OUTCOMES
The concept of PROs was proposed as a measure to help determine the effectiveness of medical treatment. Since then, the inclusion of PROs has become a mandatory endpoint for clinical trials[20]. PROs capture what the patient feels about their health without any assistance from anyone, including clinicians or family members[20,21]. PROs are multidimensional constructs that cover areas such as: symptoms, pain, fatigue, anxiety, physical functioning, intellectual functioning, and HRQL (comprised of physical, mental, social and bodily functioning as well as overall health domains). Health utilities, defined as a single summary HRQL score, are also important to capture when investigating PROs. Health utilities are used to estimate an individual’s health state or their preference for being in a particular health state and are necessary when conducting cost-utility or cost-effectiveness analyses[22,23].
Given the wide range of concepts and constructs contained within the term PROs, there are different PRO measures (PROMs) or tools used to obtain the necessary data that inform practitioners about the effects of a disease or intervention from the patient’s perspective. For HRQL and other subjective measures, such as fatigue, there are self-reported survey measurement tools that are considered to be either generic or disease-specific[24]. Generic tools are used across diseases and treatments, which enable comparison of scores between different patient groups[24]. On the other hand, these tools may be less precise when measuring the impact of one particular disease, whereas a disease-specific tool, which is developed with input from people living with the specific disease, may be more responsive to changes over time, whether due to treatment and/or disease progression[24]. However, disease-specific tools cannot be compared among groups of patients with different diseases. As such, many studies will use both a general (generic) and a specific tool for the disease of interest to overcome the limitations of both types of surveys. Although there are objective measures of physical function, such as hand grip strength, treadmill testing, or a six-minute walk test[25], this review focuses solely on self-reported survey tools.
In this context, one very important concept when measuring PROs over time is understanding what changes in PRO scores are considered clinically significant, potentially consequential, and meaningful for patients[26,27]. The minimally clinically important difference (MCID) is the terminology used to denote this change[27]. Therefore, knowing the MCID for each tool is imperative, especially for clinical trials, so that the true effect of an intervention from the patient or individual’s perspective can be appreciated[28]. In general, MCIDs are determined for each PRO using a structured approach. The Food and Drug Administration (FDA) suggests that the MCID can be calculated by subtracting the mean change in scores of patients who did not improve from the mean change in scores of patients who did improve, along with the 95% confidence interval. This method is sometimes referred to as the anchor-based within-patient change[28]. Alternatively, others estimate the MCID based on a score change ranging from ±4% to ±5%[29]. This approach is less robust and may only give a rough assessment of the clinically meaningful change in the scores.
When selecting a measurement tool, one needs to consider several factors, including the tool’s appropriateness for the study, its reliability, validity, and responsiveness to change, its MCID, its feasibility in practice, and the ease of score interpretation[17]. In the following sections, we present some of the more commonly used HRQL tools, fatigue assessment tools, and work/productivity assessment tools for individuals with MASLD.
MEASUREMENT TOOLS
HRQL measurement
Short form 36 version 2
Worldwide, the Short form 36 version 2 (SF-36v2) is the most commonly used generic HRQL instrument[30]. It consists of 36 questions that assess eight areas: physical functioning, social functioning, role-physical, role-emotional, mental health, vitality, bodily pain, and general health perceptions. The
European quality of life-5 dimensions questionnaire
The European quality of life-5 dimensions questionnaire (EQ-5D) is also a commonly used generic HRQL instrument[31-33]. The EQ-5D has five areas: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. Each of these areas is scored using a Likert scale ranging from 1-3, where 1 indicates no problems, 2 indicates some problems, and 3 indicates extreme problems. Lower scores indicate better HRQL and less impairment[31-33]. The EQ-5D is also often used to create health utility scores. These generate quality-adjusted life years (QALYs), which are used in cost-utility or cost-effectiveness models[33].
Chronic liver disease questionnaire-NASH
The chronic liver disease questionnaire (CLDQ) tool was developed in 1999 to measure HRQL among those with chronic liver disease, making it a liver disease-specific tool. The CLDQ has 29 questions that encompass six areas (abdominal symptoms, activity/energy, emotional health, fatigue, systemic symptoms, and worry). Each area is scored on a scale from 0-7. Higher scores indicate better health and fewer symptoms[34]. Over the past two decades, the original CLDQ tool has been refined to specifically measure HRQL related to different liver diseases, such as NAFLD, NASH, HBV, HCV, primary sclerosing cholangitis (PSC), and primary biliary cholangitis (PBC)[35-40]. Refinement of the CLDQ was completed by obtaining additional input from patients with the liver disease of interest through focus group and individual interviews, followed by item reduction, factor analysis, and psychometric assessments to evaluate validity, responsiveness, and reliability[41].
For the purposes of this review, we provide an in-depth analysis of the CLDQ-NASH. The CLDQ-NASH consists of 36 items, retaining 25 of the original 29 questions and the six domains described above. The additional questions better capture the effects of obesity and depression conditions that are more prevalent in those with MASLD and MASH. CLDQ-NASH is now a fully validated instrument, widely used in a large number of clinical trials, as well as in national and global registries[37,40].
Liver disease quality of life questionnaire and the short form
The liver disease quality of life questionnaire (LDQOL) and its short version (SF-LDQOL). The LDQOL was developed in 2000 using the SF-36v2 as its foundation. An additional 75 disease-specific items were added and then grouped into 12 areas (liver disease-related symptoms, liver disease-related effects on activities of daily living, concentration, memory, sexual functioning, sexual problems, sleep, loneliness, hopelessness, quality of social interaction, health distress, and self-perceived stigma of liver disease)[42]. However, given the time required to complete this survey, a shorter version of the LDQOL (the SF-LDQOL) was developed in 2008 to reduce the time of survey completion[43]. The SF-LDQOL retains the SF-36v2 questions, but now includes only 36 disease-specific items and nine areas covering symptoms of liver disease, effects of liver disease, memory and concentration, sleep, hopelessness, distress, loneliness, liver disease stigma, and sexual functioning problems. Investigators determined that the SF-LDQOL took a mean of 18 (±9) min to complete, compared to 38 (±20) min for the LDQOL 1.0[42,43]. [Table 1].
Measurement tools for assessing patient-reported outcomes in patients with MASLD/NAFLD
| Name of tool | Health domains measured | Items | Strengths or limitations | Generic or disease-specific |
| SF-36v2[30] | -Measures 8 domains: physical functioning, social functioning, role-physical, role-emotional, mental health, vitality, bodily pain, and general health, and two summary scales of physical composition and mental composite scores | 36 items | -Most widely used tool -Established population norms for comparison -May not be sensitive to disease change | Generic |
| EQ-5D[31-33] | -Measures five domains: mobility, self-care, usual activities, pain/discomfort, and anxiety/depression | Each area with three levels of functioning | -Brevity and easy administration -Disease-specific patient-reported outcome questionnaire is required to better encompass the symptoms of NAFLD/MAFLD | Generic |
| CLDQ[34] | -Measures six domains: (1) fatigue; (2) activity; (3) emotional function; (4) abdominal symptoms; (5) systemic symptoms; and (6) worry | 29 items using a Likert scale of 1-7 | -Widely used and validated tool to measure health-related quality of life -Translated into many languages -Cannot be compared with other chronic diseases | Disease-specific |
| CLDQ-NAFLD, CLDQ-NASH[37, 40] | -Measures six domains: (1) abdominal symptoms; (2) activity/energy; (3) emotional health; (4) fatigue; (5) systemic symptoms; and (6) worry | 36 items: 29 items from CLDQ and 7 new items to better capture the impact of fatigue in the NAFLD population | Strengths: -Excellent internal consistency, face validity, content validity, and test-retest reliability -Further validation is needed in NAFLD/NASH cirrhosis -Cannot be compared with other chronic diseases | Disease-specific |
| LDQOL[42] | SF-36v2 questions + 75 disease-specific items | -Very thorough assessment -Takes a lot of time (approximately 40-60 min) to complete, making it impractical for use in a clinical setting | Specific | |
| SF LDQOL[43] | -Measures nine domains: symptoms of liver disease, effects of liver disease, memory/concentration, sleep, hopelessness, distress, loneliness, stigma of liver disease, and sexual functioning problems | SF-36v2 questions + 36 disease-specific items | -Retained the validity and reliability of the LDQOL but time to complete was reduced by 50% (20-30 min) -Still requires a long time to complete | Specific |
| FACIT-F[44] | -Measures four primary quality of life domains: physical well-being, social/family well-being, emotional well-being, and functional well-being, and the effect of fatigue on these domains | 16 questions scored (0-4 Likert scale) | -Formatted for ease of self-administration for use with special populations such as the elderly and those living in rural areas -Takes 4-6 min to complete -Appropriate for use in patients with a variety of chronic health conditions and in the general population -Interviewer training is needed to minimize bias against patient responses | Disease-specific |
| FSS[45] | Measures the severity of fatigue and its effect on a person’s activities and lifestyle in patients with a variety of disorders | 9 item scale, scored on a 7-point scale | -Item formulated as statements about the fatigue experience itself, what causes fatigue and how fatigue interferes with daily life. | Generic, but can be modified to disease-specific |
| FIS[46] | Evaluates the effect of fatigue on three domains of daily life: cognitive functioning, physical functioning, and psychosocial functioning | Contains 40 questions with a 5-point Likert scale | -Easy to administer -Limitation is the length of the questionnaire | Generic |
| WPAI-SHP[47] | -Work productivity and activity: presenteeism and absenteeism -Instrument- Specific Health Problem | Consists of six questions: five questions for work productivity and one question on normal daily activities | -7-day recall period -Sufficient validity, reliability, responsiveness, and appropriate interpretation standards to assess the impact of disease on WPAI | Disease-specific |
Fatigue
Functional assessment of chronic illness therapy-fatigue
The functional assessment of chronic illness therapy-fatigue (FACIT-F) scale evaluates different aspects of fatigue in individuals with chronic illness[44]. Using 13 questions, the impact of fatigue is measured over four areas: physical well-being, social/family well-being, emotional well-being, and functional well-being. Each question is answered on a 5-point Likert scale that ranges from 0 to 4, where 0 indicates “not at all” (the issue does not bother them) and 4 indicates the issue bothers them “very much”. To achieve the overall score, all the answers are added together with a maximum score of 52. Higher scores represent better functioning and less severe fatigue. A score of less than 40 indicates the presence of clinically significant fatigue that interferes with one’s daily activities. The FACIT- F has been validated in numerous countries and languages. Population norms for different countries are also available for comparison[44].
Fatigue severity score
The fatigue severity score (FSS) was developed to measure fatigue for different conditions that are chronic in nature[45]. It is a 9-item questionnaire that evaluates the impact of fatigue on daily functioning, motivation, physical activity, work, family, and social life. Respondents are asked to score the severity of their fatigue symptoms using a Likert scale ranging from 1 (completely disagree with the statement) to 7 (completely agree with the statement). A higher score indicates more fatigue and a score > 36 indicates a high level of fatigue[45].
Fatigue impact score
The fatigue impact score (FIS) can also be used to measure fatigue in those with a chronic disease or condition[46]. The FIS contains 40 questions across three domains: cognitive, physical, and psychosocial functioning. Each question is scored on a 5-point Likert scale ranging from 0 (no problem) to 4 (an extreme problem). The maximum score is 160 and higher scores demonstrate greater fatigue[46]. [Table 1].
The work productivity and activity impairment-specific health problem
The work productivity and activity impairment-specific health problem (WPAI-SHP) measures the impact of individuals’ health state on their ability to work productively and participate in activities of their choice[47]. The WPAI produces scores in four areas: absenteeism (work time missed), presenteeism (impairment at work/reduced on-the-job effectiveness), work productivity loss (overall work impairment / absenteeism plus presenteeism) and activity impairment. The WPAI:SHP consists of six questions: five related to work productivity and one related to activity impairment. Higher scores indicate higher levels of impairment[47].
PATIENT-REPORTED OUTCOMES DATA IN MASLD
Studies using the aforementioned tools have reported that those with MASLD experience significant impairment in their HRQL, particularly in areas related to physical activity/functioning. They also report higher levels of fatigue and decreased work productivity in terms of presenteeism, compared to those without MASLD[48-54]. Disease progression, including advanced fibrosis, cirrhosis and hepatocellular cancer, is an independent predictor of decreased HRQL and higher levels of fatigue[49-54]. In fact, a recent study found that reports of fatigue were actually prognostic for adverse outcomes (liver events and mortality) among those with MASLD and MASH[53,55]. Additionally, depression rates are higher in patients with MASLD than in the general population[56].
Currently, the first-line treatment for MASLD remains diet and exercise; however, implementation of an effective intervention can be complicated by the level of fatigue present[57]. Therefore, it is important to have a better understanding of fatigue and the drivers of fatigue among those with MASLD. Fatigue is a multidimensional state that is made up of both a peripheral component (peripheral fatigue) and a central component (central nervous system, CNS)[25,58]. Liver disease-related fatigue is considered a pathological state, as it does not improve with rest after exertion or with treatment of underlying conditions, such as anemia. In fact, in liver-related fatigue, the central fatigue component may be dominant due to the presence of the inflammatory milieu associated with obesity and MASLD. This liver inflammation sets off a cascade of events affecting the neural, humoral, and immune-mediated networks, which change the neurotransmission within the CNS. Central fatigue is typically characterized by a lack of motivation, difficulty sleeping, depression and anxiety, social withdrawal, and cognitive impairment, often described as brain fog. While central fatigue may be more dominant, peripheral fatigue may be present, also manifesting as the decreased ability to be physically active despite having the capacity to do physical activity, particularly evident in those without advanced steatosis or fibrosis[25,53,58-62].
Obesity and metabolic abnormalities may also play a role in driving other fatigue mechanisms such as autonomic dysfunction, mitochondrial abnormalities, and other inflammatory mechanisms. Additionally, studies have shown that being female and older are more common predictors of lower PRO scores[50].
Despite these significant findings obtained via measurement tools, most individuals with MASLD remain unaware that many of their reported symptoms of fatigue, stomach pain, anxiety, depression, poor focus and memory, and impaired sleep are related to their liver disease[50]. This underscores the importance of assessing PROs during a clinical visit to prescribe individualized treatment. Furthermore, having baseline measurements in these areas can also help practitioners evaluate prescribed treatment and intervention outcomes, as studies have shown that improvement in PROs can indicate disease regression, whereas worsening of PROs may signal disease progression[63,64]. This suggestion is especially important now that medication is available for those most at risk for MASLD-related adverse outcomes (MASLD with fibrosis stage 2 and stage 3)[64]. In clinical trials, patients who experienced fibrosis regression without disease progression demonstrated improved HRQL, especially in the domains of physical functioning and worry[63,64].
PATIENT-REPORTED OUTCOMES AND STIGMA
Although stigma has been studied in other liver diseases, there is a paucity of research on stigma and its effects among those with MASLD[65-68]. Two recent studies were undertaken to begin filling this gap of knowledge[69,70]. The first study explored the perception of stigma among providers and patients, where investigators found a discrepancy between the NAFLD-related terms that patients and providers felt were stigmatizing. Specifically, patients reported stigma related to the terms “obese” and “overweight,” but not to NAFLD. In contrast, physicians felt the term “fatty” was stigmatizing, as was “non-alcoholic,” especially in the Middle East and North Africa regions. However, only a few felt that the term “steatotic liver disease” would be stigmatizing[69]. As such, given the name change from NAFLD to MASLD, future research is needed to determine whether the name change will reduce stigma among patients and providers.
The second study explored the relationship between stigma and HRQL among 2,000 patients from around the world with MASLD. Those who reported stigmatization due to NAFLD or obesity had substantially lower HRQL scores. Patients’ self-blame for their liver disease also contributed to their lower HRQL. Research work must focus on helping patients with MASLD overcome self-blame to improve their HRQL and engage in their prescribed treatments[70].
PATIENT-REPORTED OUTCOMES IN LIVER TRANSPLANT RECIPIENTS
It is important to note that issues related to the post-transplant status of patients with liver disease can be very different from those associated with chronic liver disease. In the United States, the number of patients receiving liver transplants continues to increase each year, with over 10,000 patients receiving a LT in 2023. In addition, the one-year and five-year survival rates are improving dramatically (1-year: 90%, 5-year: 70%). Several recent studies examining survival post-liver transplant for those with MASLD have confirmed that their survival was similar to those transplanted for other liver diseases[71,72]. Nevertheless, post-transplant co-morbid metabolic diseases, such as obesity and T2D, are common in those transplanted for MASH, and these factors can negatively impact patients’ HRQL scores even after transplant[72-74]. Although there is evidence of substantial improvement in HRQL post-LT, PROs specifically for post-transplant patients with MASLD have not been fully investigated[75].
FUTURE RESEARCH
As the understanding of MASLD and its spectrum of disease evolves, investigating PROs along this continuum will be essential to providing more nuanced care. Although data from the first medication-based regimen for MASH suggest not only histologic improvement but also improvement in PROs, other regimens are in clinical trials and may show additional benefits that will be important to study. Although documenting the improvement of PROs in clinical trials is important, understanding the long-term effectiveness of these regimens in real-world practice is critical and represents another important area for further study. Finally, studies investigating PROs before and after LT in patients with MASH/NASH and MASH/NASH-related HCC are also needed.
CONCLUSION
In this review, we defined PROs and summarized the features of some of the more commonly used instruments for assessing PROs in individuals with MASLD. With the use of these tools, MASLD/MASH has been shown to have a significant negative impact on patients’ PROs, especially in the advanced stages of the disease. In fact, study findings indicate that individuals with MASLD/MASH experience worse physical and mental health, increased fatigue, and decreased productivity at work compared to the general population. These findings challenge the perception that MASLD is an asymptomatic disease or one characterized by only vague complaints of fatigue, anxiety and/or abdominal discomfort. Additionally, assessment of fatigue through a validated self-report tool is crucial for patients with MASLD/MASH, as the presence of fatigue has been shown to have prognostic value. Despite this knowledge, areas of investigation remain, especially the long-term PRO scores for individuals receiving treatment with resmetirom and other regimens expected to be approved in the future, as well as for those who have undergone LT.
DECLARATIONS
Authors’ contributions
Concept design, critical review of the manuscript: Sapmaz A
Manuscript writing and editing: Paik A
Study design, manuscript writing, and critical review of the manuscript: Henry L
Critical review of the manuscript: Younossi ZM
Availability of data and materials
Not applicable.
Financial support and sponsorship
None.
Conflicts of interest
Younossi ZM has received research funding and/or served as a consultant to Intercept, Cymabay, Boehringer Ingelheim, BMS, GSK, Novo Nordisk, AstraZeneca, Siemens, Madridgal, Merck, and Abbott. The other authors declared that there are no conflicts of interest.
Ethical approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Copyright
© The Author(s) 2024.
REFERENCES
1. Younossi ZM, Golabi P, Paik JM, Henry A, Van Dongen C, Henry L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology 2023;77:1335-47.
2. Paik JM, Henry L, Younossi Y, Ong J, Alqahtani S, Younossi ZM. The burden of nonalcoholic fatty liver disease (NAFLD) is rapidly growing in every region of the world from 1990 to 2019. Hepatol Commun 2023;7:e0251.
3. Younossi ZM, Stepanova M, Younossi Y, et al. Epidemiology of chronic liver diseases in the USA in the past three decades. Gut 2020;69:564-8.
4. Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M. Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 2016;64:73-84.
5. Sanyal AJ, Van Natta ML, Clark J, et al; NASH Clinical Research Network (CRN). Prospective study of outcomes in adults with nonalcoholic fatty liver disease. N Engl J Med 2021;385:1559-69.
6. Simon TG, Roelstraete B, Khalili H, Hagström H, Ludvigsson JF. Mortality in biopsy-confirmed nonalcoholic fatty liver disease: results from a nationwide cohort. Gut 2021;70:1375-82.
7. Park H, Dawwas GK, Liu X, Nguyen MH. Nonalcoholic fatty liver disease increases risk of incident advanced chronic kidney disease: a propensity-matched cohort study. J Intern Med 2019;286:711-22.
8. Younossi ZM, Paik JM, Stepanova M, Ong J, Alqahtani S, Henry L. Clinical profiles and mortality rates are similar for metabolic dysfunction-associated steatotic liver disease and non-alcoholic fatty liver disease. J Hepatol 2024;80:694-701.
9. Golabi P, Paik JM, Eberly K, de Avila L, Alqahtani SA, Younossi ZM. Causes of death in patients with non-alcoholic fatty liver disease (NAFLD), alcoholic liver disease and chronic viral hepatitis B and C. Ann Hepatol 2022;27:100556.
10. Adams LA, Anstee QM, Tilg H, Targher G. Non-alcoholic fatty liver disease and its relationship with cardiovascular disease and other extrahepatic diseases. Gut 2017;66:1138-53.
11. Ballestri S, Mantovani A, Girolamo MD, Baldelli E, Capitelli M, Lonardo A. Liver fibrosis in nonalcoholic fatty liver disease patients: noninvasive evaluation and correlation with cardiovascular disease and mortality. Metab Target Organ Damage 2023;3:1.
12. Kalligeros M, Henry L, Younossi ZM. Metabolic dysfunction-associated steatotic liver disease and its link to cancer. Metabolism 2024;160:156004.
13. Mantovani A, Petracca G, Beatrice G, et al. Non-alcoholic fatty liver disease and increased risk of incident extrahepatic cancers: a meta-analysis of observational cohort studies. Gut 2022;71:778-88.
14. Ekstedt M, Hagström H, Nasr P, et al. Fibrosis stage is the strongest predictor for disease-specific mortality in NAFLD after up to 33 years of follow-up. Hepatology 2015;61:1547-54.
15. Younossi ZM, Stepanova M, Ong J, et al. Nonalcoholic steatohepatitis is the most rapidly increasing indication for liver transplantation in the United States. Clin Gastroenterol Hepatol 2021;19:580-9.e5.
16. Noureddin M, Vipani A, Bresee C, et al. NASH leading cause of liver transplant in women: updated analysis of indications for liver transplant and ethnic and gender variances. Am J Gastroenterol 2018;113:1649-59.
17. Younossi ZM, Yilmaz Y, Yu ML, et al; Global NASH Council. Clinical and patient-reported outcomes from patients with nonalcoholic fatty liver disease across the world: data from the global non-alcoholic steatohepatitis (NASH)/non-alcoholic fatty liver disease (NAFLD) registry. Clin Gastroenterol Hepatol 2022;20:2296-306.e6.
18. Stepanova M, Henry L, Younossi ZM. Economic burden and patient-reported outcomes of nonalcoholic fatty liver disease. Clin Liver Dis 2023;27:483-513.
19. Younossi ZM, Paik JM, Henry L, et al. The growing economic and clinical burden of nonalcoholic steatohepatitis (NASH) in the United States. J Clin Exp Hepatol 2023;13:454-67.
20. US Food and Drug Administration. Guidance for industry: patient-reported outcome measures: use in medical product development to support labelling claims. Available from: https://www.fda.gov/downloads/Drugs/GuidanceComplianceRegulatoryInformation/Guidances/UCM193282.pdf. [Last accessed on 19 Nov 2024].
21. Mercieca-Bebber R, King MT, Calvert MJ, Stockler MR, Friedlander M. The importance of patient-reported outcomes in clinical trials and strategies for future optimization. Patient Relat Outcome Meas 2018;9:353-67.
23. Horsman J, Furlong W, Feeny D, Torrance G. The Health Utilities Index (HUI): concepts, measurement properties and applications. Health Qual Life Outcomes 2003;1:54.
24. van der Plas SM, Hansen BE, de Boer JB, et al. Generic and disease-specific health related quality of life of liver patients with various aetiologies: a survey. Qual Life Res 2007;16:375-88.
25. Younossi ZM, Kremer AE, Swain MG, et al. Assessment of fatigue and its impact in chronic liver disease. J Hepatol 2024;81:726-42.
26. Guyatt G, Walter S, Norman G. Measuring change over time: assessing the usefulness of evaluative instruments. J Chronic Dis 1987;40:171-8.
27. Cook CE. Clinimetrics corner: the minimal clinically important change score (MCID): a necessary pretense. J Man Manip Ther 2008;16:E82-3.
28. US Food and Drug Administration. Incorporating clinical outcome assessments into endpoints for regulatory decision-making. Available from: https://www.fda.gov/media/132505/download. [Last accessed on 19 Nov 2024].
29. Jaeschke R, Singer J, Guyatt GH. Measurement of health status. Ascertaining the minimal clinically important difference. Control Clin Trials 1989;10:407-15.
30. Ware JE Jr, Sherbourne CD. The MOS 36-item short-form health survey (SF-36). I. Conceptual framework and item selection. Med Care 1992;30:473-83.
31. Devlin NJ, Brooks R. EQ-5D and the EuroQol group: past, present and future. Appl Health Econ Health Policy 2017;15:127-37.
32. Coretti S, Ruggeri M, McNamee P. The minimum clinically important difference for EQ-5D index: a critical review. Expert Rev Pharmacoecon Outcomes Res 2014;14:221-33.
33. EUROQQOL. EQ-5D-5L. Available from: https://euroqol.org/information-and-support/euroqol-instruments/eq-5d-5l/. [Last accessed on 20 Nov 2024].
34. Younossi ZM, Guyatt G, Kiwi M, Boparai N, King D. Development of a disease specific questionnaire to measure health related quality of life in patients with chronic liver disease. Gut 1999;45:295-300.
35. Younossi ZM, Stepanova M, Henry L. Performance and validation of chronic liver disease questionnaire-hepatitis C version (CLDQ-HCV) in clinical trials of patients with chronic hepatitis C. Value Health 2016;19:544-51.
36. Younossi ZM, Stepanova M, Younossi I, Racila A. Development and validation of a hepatitis B-specific health-related quality-of-life instrument: CLDQ-HBV. J Viral Hepat 2021;28:484-92.
37. Younossi ZM, Stepanova M, Henry L, et al. A disease-specific quality of life instrument for non-alcoholic fatty liver disease and non-alcoholic steatohepatitis: CLDQ-NAFLD. Liver Int 2017;37:1209-18.
38. Younossi ZM, Stepanova M, Younossi I, Racila A. Development and validation of a primary sclerosing cholangitis-specific health-related quality of life instrument: CLDQ-PSC. Hepatol Commun 2023;7:e0049.
39. Younossi ZM, Stepanova M, Younossi I, Racila A. Validation of a primary biliary cholangitis-specific version of chronic liver disease questionnaire: CLDQ-PBC. Clin Transl Gastroenterol 2024;15:e1.
40. Younossi ZM, Stepanova M, Younossi I, Racila A. Validation of chronic liver disease questionnaire for nonalcoholic steatohepatitis in patients with biopsy-proven nonalcoholic steatohepatitis. Clin Gastroenterol Hepatol 2019;17:2093-100.e3.
41. Schulz KH, Kroencke S, Ewers H, Schulz H, Younossi ZM. The factorial structure of the chronic liver disease questionnaire (CLDQ). Qual Life Res 2008;17:575-84.
42. Gralnek IM, Hays RD, Kilbourne A, et al. Development and evaluation of the liver disease quality of life instrument in persons with advanced, chronic liver disease-the LDQOL 1.0. Am J Gastroenterol 2000;95:3552-65.
43. Kanwal F, Spiegel BM, Hays RD, et al. Prospective validation of the short form liver disease quality of life instrument. Aliment Pharmacol Ther 2008;28:1088-101.
44. FACIT.org. Functional assessment of chronic illness therapy - fatigue scale (FACIT-fatigue). Available from: https://www.facit.org/measures/facit-fatigue. [Last accessed on 19 Nov 2024].
45. Kleinman L, Zodet MW, Hakim Z, et al. Psychometric evaluation of the fatigue severity scale for use in chronic hepatitis C. Qual Life Res 2000;9:499-508.
46. Fisk JD, Ritvo PG, Ross L, Haase DA, Marrie TJ, Schlech WF. Measuring the functional impact of fatigue: initial validation of the fatigue impact scale. Clin Infect Dis 1994;18 Suppl 1:S79-83.
47. Reilly MC, Zbrozek AS, Dukes EM. The validity and reproducibility of a work productivity and activity impairment instrument. Pharmaco economics 1993;4:353-65.
48. Younossi ZM, Golabi P, Henry L. A comprehensive review of patient-reported outcomes in patients with chronic liver diseases. J Clin Gastroenterol 2019;53:331-41.
49. Verma M, Paik JM, Younossi I, Tan D, Abdelaal H, Younossi ZM. The impact of hepatocellular carcinoma diagnosis on patients’ health-related quality of life. Cancer Med 2021;10:6273-81.
50. Younossi Z, Aggarwal P, Shrestha I, et al. The burden of non-alcoholic steatohepatitis: a systematic review of health-related quality of life and patient-reported outcomes. JHEP Rep 2022;4:100525.
51. Yamamura S, Nakano D, Hashida R, et al. Patient-reported outcomes in patients with non-alcoholic fatty liver disease: a narrative review of chronic liver disease questionnaire-non-alcoholic fatty liver disease/non-alcoholic steatohepatitis. J Gastroenterol Hepatol 2021;36:629-36.
52. Barberá A, White TM, Arora AK, Henry L, Lazarus JV, Younossi ZM. Patient-reported outcomes in metabolic dysfunction-associated steatotic liver disease. Semin Liver Dis 2024;Online ahead of print.
53. Younossi ZM, Stepanova M, Myers RP, Younossi I, Henry L. The potential role of fatigue in identifying patients with NASH and advanced fibrosis who experience disease progression. Clin Gastroenterol Hepatol 2023;21:970-7.e1.
54. McSweeney L, Breckons M, Fattakhova G, et al. Health-related quality of life and patient-reported outcome measures in NASH-related cirrhosis. JHEP Rep 2020;2:100099.
55. Younossi ZM, Paik JM, Golabi P, Younossi Y, Henry L, Nader F. The impact of fatigue on mortality of patients with non-alcoholic fatty liver disease: data from National health and nutrition examination survey 2005-2010 and 2017-2018. Liver Int 2022;42:2646-61.
56. Weinstein AA, De Avila L, Kannan S, et al. Interrelationship between physical activity and depression in nonalcoholic fatty liver disease. World J Hepatol 2022;14:612-22.
57. Younossi ZM, Zelber-Sagi S, Henry L, Gerber LH. Lifestyle interventions in nonalcoholic fatty liver disease. Nat Rev Gastroenterol Hepatol 2023;20:708-22.
58. Gerber LH, Weinstein AA, Mehta R, Younossi ZM. Importance of fatigue and its measurement in chronic liver disease. World J Gastroenterol 2019;25:3669-83.
59. Austin P, Gerber L, Paik JM, Price JK, Escheik C, Younossi ZM. Aerobic capacity and exercise performance in nonalcoholic fatty liver disease. J Sports Med Phys Fitness 2019;59:1376-88.
60. Canada JM, Abbate A, Collen R, et al. Relation of hepatic fibrosis in nonalcoholic fatty liver disease to left ventricular diastolic function and exercise tolerance. Am J Cardiol 2019;123:466-73.
61. Mascaró CM, Bouzas C, Montemayor S, et al. Association between stages of hepatic steatosis and physical activity performance in adults with metabolic syndrome: a cross-sectional analysis in FLIPAN study. Nutrients 2022;14:1790.
62. Weinstein AA, Escheik C, Oe B, Price JK, Gerber LH, Younossi ZM. Perception of effort during activity in patients with chronic hepatitis C and nonalcoholic fatty liver disease. PM R 2016;8:28-34.
63. Younossi ZM, Stepanova M, Noureddin M, et al. Improvements of fibrosis and disease activity are associated with improvement of patient-reported outcomes in patients with advanced fibrosis due to nonalcoholic steatohepatitis. Hepatol Commun 2021;5:1201-11.
64. Younossi ZM, Stepanova M, Taub RA, Barbone JM, Harrison SA. Hepatic fat reduction due to resmetirom in patients with nonalcoholic steatohepatitis is associated with improvement of quality of life. Clin Gastroenterol Hepatol 2022;20:1354-61.e7.
65. Schomerus G, Leonhard A, Manthey J, et al. The stigma of alcohol-related liver disease and its impact on healthcare. J Hepatol 2022;77:516-24.
66. Wahlin S, Andersson J. Liver health literacy and social stigma of liver disease: a general population e-survey. Clin Res Hepatol Gastroenterol 2021;45:101750.
67. Batchelder AW, Heo M, Foley JD, et al; HERO Study Group. Shame and stigma in association with the HCV cascade to cure among people who inject drugs. Drug Alcohol Depend 2023;253:111013.
68. Smith-Palmer J, Cerri K, Sbarigia U, et al. Impact of stigma on people living with chronic hepatitis B. Patient Relat Outcome Meas 2020;11:95-107.
69. Younossi ZM, Alqahtani SA, Alswat K, et al; Global NASH Council. Global survey of stigma among physicians and patients with nonalcoholic fatty liver disease. J Hepatol 2024;80:419-30.
70. Younossi ZM, AlQahtani SA, Funuyet-Salas J, et al; Global NASH Council. The impact of stigma on quality of life and liver disease burden among patients with nonalcoholic fatty liver disease. JHEP Rep 2024;6:101066.
71. UNOS. Data and trends. Available from: https://unos.org/data/. [Last accessed on 19 Nov 2024].
72. Serrano MT, Sabroso S, Esteban LM, et al. Mortality and causes of death after liver transplantation: analysis of sex differences in a large nationwide cohort. Transpl Int 2022;35:10263.
73. Vitale A, Trapani S, Russo FP, et al; Associazione Italiana per lo Studio del Fegato (AISF).; Società Italiana Trapianti d’Organo (SITO).; Centro Nazionale Trapianti (CNT). Waiting list mortality and 5-year transplant survival benefit of patients with MASLD: an Italian liver transplant registry study. JHEP Rep 2024;6:101147.
74. Stepanova M, Henry L, Garg R, Kalwaney S, Saab S, Younossi Z. Risk of de novo post-transplant type 2 diabetes in patients undergoing liver transplant for non-alcoholic steatohepatitis. BMC Gastroenterol 2015;15:175.
Cite This Article
Open Access
How to Cite
Sapmaz, A.; Paik, A.; Henry, L.; Younossi, Z. M. A comprehensive review of patient-reported outcomes in metabolic dysfunction-associated steatotic liver disease. Metab. Target. Organ. Damage. 2024, 4, 42. http://dx.doi.org/10.20517/mtod.2024.70
Download Citation
Export Citation File:
Type of Import
Tips on Downloading Citation
Citation Manager File Format
Type of Import
Direct Import: When the Direct Import option is selected (the default state), a dialogue box will give you the option to Save or Open the downloaded citation data. Choosing Open will either launch your citation manager or give you a choice of applications with which to use the metadata. The Save option saves the file locally for later use.
Indirect Import: When the Indirect Import option is selected, the metadata is displayed and may be copied and pasted as needed.
About This Article
Special Issue
Copyright
Comments
Comments must be written in English. Spam, offensive content, impersonation, and private information will not be permitted. If any comment is reported and identified as inappropriate content by OAE staff, the comment will be removed without notice. If you have any queries or need any help, please contact us at support@oaepublish.com.