Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2014 Jul 1.
Published in final edited form as: Nurs Outlook. 2013 Jul-Aug;61(4):242–251.e2. doi: 10.1016/j.outlook.2013.05.003

Exploration of a Symptoms Experience in People with Obesity-Related NAFLD

Lori Houghton-Rahrig 1,*, Debra Schutte 2, Alexander von Eye 3, Jenifer Fenton 4, Barbara Given 5, Norm Hord 6
PMCID: PMC3799949  NIHMSID: NIHMS483627  PMID: 23849554

Abstract

Background

Nonalcoholic fatty liver disease (NAFLD) is a highly prevalent condition strongly associated with obesity that can result in premature death. Little is known about a symptoms experience in this progressive disease, preventing health care providers from intervening in the early stages.

Purpose

This study explicated symptoms in persons with NAFLD at higher risk of disease progression defined as the presence of one or two copies of the PNPLA3 gene, (rs738409)-G allele.

Method

Guided by the Symptoms Experience Model, 42 persons >21 years of age with diagnosed NAFLD were recruited from Western Michigan specialty offices in this cross-sectional descriptive study design. The Memorial Symptom Assessment Scale was used to measure the symptoms experience.

Discussion

Participants (97%) experienced 1 or more symptoms (average number of symptoms 12.02, SD 8.817). There was no statistically significant relationship between symptoms and the PNPLA3 (rs738409) variants. Significant predictors of mean frequency, severity and distress of symptoms [TMSAS] (F=2.609; df1=15, df2=25; p=.016) were identified.

Conclusion

Persons with NAFLD experience an average of 12 symptoms.

Keywords: PNPLA3, NAFLD, Symptoms Experience

Obesity-related nonalcoholic fatty liver disease (NAFLD) is a rapidly growing worldwide health concern (Houghton-Rahrig et al., 2011). In the United States alone, 30 to 70 million persons have NAFLD (Adams, Lymp, et al., 2005; American Liver Foundation, 2007; Angulo, 2007; Chan et al., 2007; Choudhury & Sanyal, 2004). Experts estimate that by the year 2030, the prevalence of obesity-related NAFLD will reach epidemic proportions in the U.S. (Younossi et al., 2011). NAFLD, defined as fatty deposits comprising 5% or more of the total liver weight, may progress from simple fatty liver disease to end stage liver disease resulting in premature death (Angulo, 2007; Kistler et al, 2010). In most cases, NAFLD is not discovered until later stages of the disease (Adams, Lymp, et al., 2005). However, if NAFLD is found early, progression can be halted or even reversed through implementation of weight loss strategies (Dixon, Bhathal, Hughes, & O'Brien, 2004; Mummadi, Kasturi, Chennareddygari, & Sood, 2008; Promrat et al., 2010).

Background and Significance

Nonalcoholic fatty liver disease (NAFLD)

NAFLD is strongly associated with obesity, metabolic syndrome and insulin resistance and its incidence is thought to parallel the obesity epidemic (Adler & Schaffner, 1979; Vernon, Baranova, & Younossi, 2011; Younossi et al., 2011). NAFLD can progress from simple fatty liver disease to an inflammatory stage called Nonalcoholic Steatohepatitis (NASH), with or without liver fibrosis to liver cirrhosis followed by liver failure or liver cancer/liver failure resulting in premature death (Adams, Angulo, & Lindor, 2005; Angulo, 2007).

NAFLD is a worldwide health concern. In the U.S., NAFLD is most common in Hispanics, followed by whites and African Americans (Browning et al., 2004; Victor et al., 2004). Further study is needed to determine the incidence of NAFLD in the American Indian and Alaskan Native populations (Bialek et al., 2008; Fischer, Bialek, Homan, Livingston, & McMahon, 2009; Houghton-Rahrig et al., 2011). In the U.S., 1 in every 3 adults and 1 in 10 children have NAFLD (Angulo, 2007a). There is an equal prevalence in male and female adults, but a slightly higher prevalence in young males and white adult males (American Liver Foundation, 2007; Chang et al., 2008; Houghton-Rahrig et al., 2011). Sixty-seven to 78% of persons with a BMI of 30 or greater have NAFLD (Williams et al., 2011).

Severity of disease marker - PNPLA3 gene

While liver biopsy is the gold standard for confirmation, grading, and staging of NAFLD (Yan, Durazo, Tong, & Hong, 2007), only a small percentage of persons have liver biopsies due to the risk of bleeding (Caldwell, Argo, & Al-Osaimi, 2012). Liver function tests, such as ALT and AST, may be elevated in persons with NAFLD triggering an evaluation for NAFLD. Many times, patients may have NAFLD as noted by imaging, but may not have elevated enzymes. For example, in a study of persons with diabetes in Italy, 70% had NAFLD, but did not have elevated liver enzymes (Targher et al., 2007). In addition, imaging modalities can identify persons with NAFLD, but are not sensitive enough to stage and grade disease (Mehta, Thomas, Bell, Johnston, & Taylor-Robinson, 2008). Taken together, no known biomarkers were available at the initiation of this study for grading and staging of disease.

In light of the lack of clinical biomarkers, research to examine potential DNA biomarkers of disease risk and progression are ongoing. For example, a DNA polymorphism (rs738409) in the PNPLA3 (patatin-like phospholipase domain containing 3) gene was found to be strongly associated with NAFLD (Romeo et al., 2010; Sookoian et al., 2009). PNPLA3 encodes a protein called adiponutrin, a triacylglycerol lipase that mediates triacylglycerol hydrolysis in adipocytes. This particular PNPLA3 gene variant (rs738409) is a nonsynonymous polymorphism; that is, it is a substitution of the normally occurring cytosine to guanine that results in an amino acid change from isoleucine to methionine (NCBI Entrez Gene, 2010). Subsequently, the amino acid change is thought to inhibit the normal breakdown of triglycerides resulting in an increased storage of triglyceride in the hepatocytes (He et al., 2010).

Recent studies such as the Dallas Heart Study suggest that the PNPLA3 gene, (rs738409)-G allele, is strongly associated with NAFLD (p=5.9 × 10−10), and this association is more significant after adjusting for BMI, diabetes, alcohol use and ancestry, (p=7.0 × 10−14). Further, the rs738409-G allele is associated with disease progression and severity of disease (Kotronen et al., 2009; Romeo et al., 2008a; Romeo et al., 2010; Sookoian et al., 2009; Tian, Stokowski, Kershenobich, Ballinger, & Hinds, 2010a). In fact, there is a dose effect in that those persons with two copies of the rs738409-G allele were more likely to have advanced disease than those persons with one copy or no copies of the G allele (Valenti et al., 2010). In addition, the PNPLA3 gene polymorphism was also found to be associated with elevated alanine aminotransferase serum levels, a liver function test associated with liver injury in Hispanics (Tian et al., 2010). Interestingly, Hispanics have a higher prevalence of NAFLD (Browning et al., 2004).

Conceptual Framework

An adapted Symptoms Experience Model (Armstrong, 2003) guided this study (Adapted with permission from “Symptoms Experience: A Concept Analysis” by T.S. Armstrong, 2003, Oncology Nursing Forum, 28(7), p. 603. Copyright 2003 by ONS). Armstrong defines symptoms experience as the “perception of the frequency, intensity and distress of symptoms as they are produced and expressed” (Armstrong, 2003) [p.603]. An important aspect of the Symptoms Experience Model for the purpose of this study is the proposed interaction of antecedents, such as demographic, disease and individual characteristics, to influence the symptom experience. In this study, genetic influences such as the PNPLA3 (rs738409) polymorphism are examined as an antecedent factor that may influence disease progression. Symptom presence, frequency, intensity and distress are perceived by the individual. Symptoms may occur in clusters and have a catalytic, multiplicative effect upon other symptoms (Armstrong, 2003; Dodd, Miaskowski, & Paul, 2001; Lenz, Pugh, Milligan, Gift, & Suppe, 1997). The Memorial Symptom Assessment Scale (Portenoy et al., 1994), provides a multi-symptom measure of the symptom experience in this study, recognizing the likely complex nature of symptoms in persons with NAFLD. The outcome of the dissertation study from which these two out of four aims for this paper was derived, was health-related quality of life. However, the symptoms experience was the outcome for the two aims used for this analysis. The adapted version of Armstrong's Symptoms Experience Model used in this study did not include the situational and existential meaning of symptoms.

Scientific Gap

Currently, little is known about the symptoms experience throughout the disease trajectory in persons with obesity-related NAFLD (Houghton-Rahrig et al., 2011; Salt, 2004). A lack of knowledge about a potential symptoms experience in this population prevents health care providers from intervening to prevent, halt or reverse the disease, both in the early stages of NAFLD and throughout the disease course (Houghton-Rahrig et al., 2011). NAFLD is more apt to improve or resolve when treated in the early stages of the disease (Mummadi et al., 2008).

Purpose

The purpose of this study was to identify and describe symptoms in persons with NAFLD (Houghton-Rahrig et al., 2011). The first aim of this study was to identify the presence of symptoms in persons with NAFLD, comparing those at higher risk of disease progression based on the presence of one or two copies of the PNPLA3 (rs738409)-G allele variant compared to those at lower risk of disease progression [no copies of the PNPLA3 rs738409-G allele] (Houghton-Rahrig et al., 2011). Once symptoms were determined, our second aim was to compare the extent to which the frequency, intensity, and distress of symptoms in persons with NAFLD differed between those at higher risk of disease progression based on the presence of one or two copies of the PNPLA3 (rs738409)-G variant compared to those at lower risk of disease progression [no copies of the PNPLA3 rs738409-G allele] (Houghton-Rahrig et al., 2011).

Methods

Study Design and Sample

Forty-two participants with NAFLD, diagnosed by imaging (such as ultrasound, MRI or CT) as recorded in their medical record, were enrolled in this correlational, cross-sectional study. NAFLD staging by liver biopsy was only available for 33% of participants (n= 14). Of these 14 participants, 4 had NASH without fibrosis, 5 had NASH with stage 0-1 fibrosis and 5 had NASH with stage 3-4 fibrosis. That is, 9 were in the early stages of disease, and 5 were in the later stage of NAFLD. Participants were recruited from two offices; a gastroenterology office, and a surgical office where gastric banding and general surgery are performed. One participant who was not a patient at either of these offices contacted the Principal Investigator (PI) asking to be included in this study.

Procedures

Participants with diagnosed NAFLD were recruited during office appointments. The medical assistant provided a flyer about the study to eligible patients and asked if they would be interested in participating. If interested, the patients signed a Patient Authorization for Disclosure of Health Information for Research form to allow the PI to talk with the patient. Once signed, the PI met with the potential participants to answer any questions and obtain consent to participate in the study. The PI provided a questionnaire, helped the participant provide a saliva sample, and obtained waist/hip circumference measurements. A medical record audit was also conducted to obtain medical/surgical history, medications, laboratory values, and NAFLD staging and grading results from liver biopsies, if available. Medications were those prescribed as noted in the chart. Serum AST and ALT results were obtained from the chart audit if the patient had elevated liver enzymes during recruitment or in the past as these levels may fluctuate as participants lose and gain weight (Burza et al., 2013).

Instruments

The Charlson Comorbidity Index was used to “count” and control for comorbid conditions in the multiple linear modeling analyses (Charlson, Pompei, Alex, & MacKenzie, 1987). The Memorial Symptom Assessment Scale (MSAS) measured the presence, frequency, intensity and distress of 32 symptoms allowing participants to write in symptoms as well (Portenoy et al., 1994). Four subscales of the MSAS were used to measure symptom attributes:

The Total Memorial Symptom Scale (TMAS) measured the sum of frequency (if applicable), severity (intensity), and distress, averaging the sum of each of the 32 symptoms; averaged using the total number of symptoms. The Memorial Symptom Assessment Scale – Global Distress Index (MSAS-GDI) measured overall distress from the average frequency of 4 psychological symptoms (feeling sad, worrying, feeling irritable and feeling nervous,) and distress of seven physical symptoms [lack of appetite, lack of energy, pain, feeling drowsy, constipation and dry mouth]. The Memorial Symptoms Assessment Physical Scale (MSAS-PHYS) is the average of the frequency, severity (intensity) and distress of 12 physical symptoms [lack of appetite, lack of energy, pain, feeling drowsy, constipation, dry mouth, nausea, vomiting, change in taste, weight loss, feeling bloated and dizziness]. Finally, the MSAS-PSYCH is the average of the frequency, severity (intensity) and distress of six psychological symptoms [feeling sad, worrying, feeling irritable, feeling nervous, difficulty sleeping and difficulty concentrating] (Portenoy et al., 1994) [p.1336].

Oragene® DNA Self-Collection Kits were used to obtain saliva samples for DNA extraction. Genotyping of rs738409 was completed using the Taqman® PCR (Applied Biosystems, Carlsbad, CA). Validity and reliability of instruments are reported in Table 1.

Table 1.

Validity and Reliability of Instruments.

Instruments Reliability Validity
Charlson Comorbidity Index1 High test-retest reliability – controlling for comorbid conditions2,3,4,5 High validity- controlling for comorbid conditions and predicting mortality4,5 x2 = 165, p<.0001
Memorial Symptom Assessment Scale High internal consistency in psychological subscale and most frequently occurring physical symptoms (Cronbach α= 0.83-0.88).6
Moderate internal consistency with less frequently occurring physical symptoms (Cronbach α= 0.58).		 High criterion with Functional Living Index (-.078)6
Rand physical distress (.48)6
Rand psychological distress (.80)6
Open in a new tab

Data Analysis

Data were analyzed using PASW 17 software (SPSS, Inc., Chicago, IL). Participants' data were stratified into two groups, 1) persons at higher risk of disease progression as defined by the presence of one or two copies of the PNPLA3 (rs738409)-G allele and 2) persons at lower risk of disease progression defined as no copies of the PNPLA3 (rs738409)-G allele (Houghton-Rahrig et al., 2011). Individual symptoms (the dependent variables) were grouped into dichotomous variables (present or absent). Likewise, the PNPLA3 gene, (rs738409) variant was grouped into dichotomous variables [presence or absence of G-allele] (Houghton-Rahrig et al., 2011). To determine the likelihood of the presence of one or two copies PNPLA3 (rs738409)-G allele with the presence of each symptoms, Odds ratios and Fisher's exact tests were used (Houghton-Rahrig et al., 2011). Multiple linear regression was used to identify predictors of symptom frequency, intensity and distress.

Results

Descriptive Statistics

The majority of participants were white females, employed with a high school education. Nearly forty-three percent of participants had no copies of the PNPLA3 (rs738409)-G allele leaving 57.2% with one or two copies of the G-allele. Body Mass Index (BMI) was the sole statistically significant difference between the two genotype groups. Medications that participants were prescribed were similar between genotype groups such as vitamins, antidepressants, antihypertensives, and antihyperglycemics. Table 2 provides descriptive statistics of the demographic, disease and individual characteristics that were used in the analysis of this study.

Table 2.

Descriptive Statistics-Demographic, Disease, and Individual Characteristics; and Symptoms Experience.

Demographic Characteristics PNPLA3 (rs738409) Genotype CC (N=18) PNPLA3 (rs738409) Genotype CG or GG (N=24) Total Sample p-value
Sex: .403c
Male, n, (%) 4 (9.5) 8 (19.0) 12 (28.6)
Female, n, (%) 14 (33.3) 16 (38.1) 30 (71.4)
Age, mean (SD) 43.83 (10.79) 47.04 (10.15) 45.67 (10.42) .330b
Race/Ethnicity
 • White, n, (%) 14 (33.3) 22 (52.4) 36 (85.7)
 • Hispanic, n,(%) 0 (0) 2 (4.8) 2 (4.8)
 • Black, n, (%) 1 (2.4) 0 (0) 1 (2.4)
 • Multi-racial, n, (%) 3 (7.1) 0 (0) 3 (7.1)
 • White vs. “nonwhite” .375a
Education .474c
 • High school or less, n (%) 11 (26.2) 12 (28.6) 23 (54.8)
 • College education, n (%) 7 (16.6) 12 (28.6) 19 (45.2)
Employment, .533c
 • Employed n (%) 9 (21.4) 15 (35.7) 24 (57.1)
 • Unemployed, retired or disabled, n(%) 9 (21.4) 9 (21.4) 18 (42.9)
Access to Healthcare, .653c
 • Outlying areas 11 (26.1) 13 (31.0) 24 (57.1)
 • City or suburb with ≥2 hospitals, n(%) 7 (16.7) 11 (26.2) 18 (42.9)
Disease Characteristics PNPLA3 (rs738409) Genotype CC (N=18) PNPLA3 (rs738409) Genotype CG or GG (N=24) Total Sample p-value
PNPLA3 (rs738409) genotype
 • CC, n, (%) 18 (42.8) - 18 (42.8) -
 • CG, n, (%) - 17 (40.5) 17 (40.5) -
 • GG, n, (%) - 7 (16.7) 7 (16.7) -
BMI, mean (SD) 43.57 (13.24) 36.76 (6.56) 39.68 (10.41) .034b
Disease Characteristics PNPLA3 (rs738409) Genotype CC (N=18) PNPLA3 (rs738409) Genotype CG or GG (N=24) Total Sample p-value
Metabolic Syndrome, n (%) 7 (16.6) 7 (16.6) 14 (33.2) .508a
Waist-hip circumference ratio, mean (SD) .919 (.073) .941 (.076) .932 (.075) .337b
Number of comorbid conditions, mean (SD) 2.4 (1.65) 1.75 (1.15) 2.09 (1.41) .138b
AST elevation now/past, n (%) 5 (12.5) 10 (24.4) 15 (36.6) .422c
ALT elevation now/past, n (%) 8 (19.5) 16 (39.0) 24 (58.5) .209c
Number of medications, mean (SD) 7.22 (3.90) 6.17 (3.65) 6.62 (3.75) .374b
Individual Characteristics PNPLA3 (rs738409) Genotype CC (N=18) PNPLA3 (rs738409) Genotype CG or GG (N=24) Total Sample p-value
Prior Health Knowledge of NAFLD, n (%) 11 (26.2) 17 (40.5) 28 (66.7) .508c
Symptoms Experience PNPLA3 (rs738409) Genotype CC (N=18) PNPLA3 (rs738409) Genotype CG or GG (N=24) Total Sample
Symptoms, mean (SD) 11.67 (9.54) 12.29 (8.44) 12.02 (8.81) .823b
Open in a new tab
a

Fisher's Exact;

b

Independent t-test;

c

Pearson Chi-Square;

Symptoms and the PNPLA3 gene, (rs738409) genotype

We hypothesized that persons at higher risk of disease progression as evidenced by the presence of one or two copies of the PNPLA3 gene, (rs738409)-G allele may experience more symptoms than persons with no copies of the G-allele. In this study, persons with NAFLD experienced 12 symptoms on average (S.D.8.81, range 0-34). Ninety-seven percent of persons experienced one or more symptoms. However, there was no significant relationship between the presence of symptoms and the PNPLA3 (rs738409) genotype. Persons with no copies of the PNPLA3 (rs738409)-G allele had a mean of 11.67, (SE=1.72) symptoms compared to persons with one to two copies of the PNPLA3 (rs738409)-G allele (M=12.29, SE = 2.24). In addition, there was no significant difference between BMI and the number of symptoms experience by participants in this study [F = 1.122, df1 = 1, df2 = 40, p =.296] (Houghton-Rahrig et al., 2011). There was also no difference in the mean number of symptoms between those with a BMI less than 30 (M=11.86) compared to persons with a BMI of 30 or greater [M=12.06] (Houghton-Rahrig et al., 2011). See Table 3 for a comparison of symptom frequencies according to PNPLA3 (rs738409)-genotype.

Table 3. Symptom Frequencies vs. PNPLA3 Gene, (rs738409)-G Allele Odds Ratios.

Symptom Frequency n (%) Number of participants with 1 or 2 copies of PNPLA3 (rs738409)-G allele reporting symptom n (%) OR 95% CI Fisher's Exact two-sided
Lack of energy 30 (71.4) 16 (53.3) .571 [.141, 2.313] .506
Pain 27 (64.3) 17 (63.0) 1.943 [.540, 6.990] .347
Fatigue 25 (59.5) 16 (64.0) 2.000 [.570, 7.013] .348
Feeling irritable 24 (57.2) 13 (54.2) .752 [.217, 2.604] .757
Worrying 22 (52.4) 13 (59.1) 1.182 [.347, 4.019] 1.000
Feeling drowsy 21 (50.0) 13 (61.9) 1.477 [.432, 5.046] .756
Diarrhea 21 (50.0) 13 (61.9) 1.477 [.432, 5.046] .756
Difficulty sleeping 20 (47.6) 12 (60.0) 1.250 [.367, 4.262] .764
Feeling bloated 19 (45.2) 11 (57.9) 1.058 [.310, 3.613] 1.000
Numbness/tingling in hands/feet 18 (42.8) 10 (55.6) .893 [.260, 3.067] 1.000
Feeling sad 17 (40.4) 8 (47.1) .500 [.143, 1.753] .348
Feeling nervous 16 (38.1) 9 (56.3) .943 [.268, 3.315] 1.000
Swelling of arms or legs 16 (38.1) 10 (62.5) 1.429 [.400, 5.099] .750
Dry mouth 15 (35.7) 8 (53.3) .786 [.220, 2.804] .754
Shortness of breath 15 (35.7) 10 (66.7) 1.857 [.500, 6.899] .517
Sweats 15 (35.7) 8 (53.3) .786 [.220, 2.804] .754
Ache or discomfort in/below right lower rib 15 (35.7) 7 (46.7) .515 [.143, 1.852] .347
Difficulty concentrating 14 (33.3) 8 (57.1) 1.000 [.274, 3.656] 1.000
Cough 14 (33.3) 8 (57.1) 1.000 [.274, 3.656] 1.000
Constipation 13 (30.9) 8 (61.5) 1.300 [.342, 4.943] .748
Problems w/sexual activity/interest 12 (28.6) 7 (58.3) 1.071 [.276, 4.154] 1.000
Itching 12 (28.6) 8 (66.7) 1.750 [.432, 7.084] .506
Dizziness 12 (28.6) 7 (58.3) 1.071 [.276, 4.154] 1.000
Weight loss 12 (28.5) 8 (61.5) 1.300 [.342, 4.943] .748
Nausea 11 (26.2) 5 (45.5) .526 [.131, 2.112] .483
Problems with urination 10 (23.8) 7 (70.0) 2.059 [.450, 9.416] .473
Change in the way food tastes 10 (23.8) 7 (70.0) 2.059 [.450, 9.416] .473
Changes in skin 10 (23.8) 7 (70.0) 2.059 [.450, 9.416] .473
Lack of appetite 9 (21.4) 3 (33.3) .286 [.060, 1.355] .139
“I don't look like myself” 8 (19.0) 6 (75.0) 2.667 [.470, 15.136] .431
Hair loss 7 (16.6) 3 (42.9) .500 [.097, 2.584] .438
Vomiting 5 (11.9) 2 (40.0) .455 [.068, 3.057] .636
Difficulty swallowing 5 (11.9) 4 (80.0) 3.400 [.346, 33.397] .371
Mouth sores 4 (9.5) 3 (75.0) 2.429 [.231, 25.510] .623
Open in a new tab

Note. OR = odds ratio; Cl = confidence level. From “Symptoms, Genetics, and Health-Related Quality of Life in Persons with Nonalcoholic Fatty Liver Disease” by L. Houghton-Rahrig, D.L. Schutte, J.I. Fenton, B.A. Given, N.G. Hord., A. Von Eye, 2011, (Dissertation). Copyright 2011 by Lori Houghton-Rahrig.

Symptom Frequency, Intensity and Distress

We also hypothesized that persons at higher risk of disease progression, as evidenced by the presence of one or two copies of the PNPLA3 (rs738409)-G allele, would have more symptom intensity, frequency and/or distress than those persons at lower risk of disease progression, i.e. without the PNPLA3 (rs738409)-G allele. Variables used in the following multiple linear regression models included the demographic, disease, and individual characteristics highlighted in Table 2. The enter method of PASW 17 (SPSS Inc., Chicago, IL) was used in all multiple linear regression analyses. The PNPLA3 gene variant was not a significant predictor of the frequency, intensity and distress of symptoms as measured by the TMAS, MSAS-GDI, MSAS-PHYS or MSAS-PSYCH subscales as hypothesized. However, “nonwhite” race was a significant predictor of all symptom distress. Unemployment was a significant predictor of global symptom distress and psychological symptoms, whereas, health knowledge of NAFLD prior to recruitment into the study was a significant predictor of physical symptoms (See Table 4).

Table 4. MSAS Subscales and significant predictors of symptom attribute.

MSAS Subscale Model df1 = 15, df2 = 25 Predictors (coding) Beta p value
TMAS F=2.609, p=.016 White (1) vs. nonwhite (0)a -.812 .003
MSAS-GDI F=3.331, p=.004 White (1) vs. nonwhite (0)a -.954 .002
Unemployed, retired, disabled (1) vs. employed (0) .529 .036
MSAS-PHYS F=2.726, p=.013 White (1) vs. nonwhite (0)a -.983 <.001
Health Knowledge of NAFLD prior to recruitment into study (1 = Yes) .448 .036
MSAS-PSYCH F=2.944, p=.008 White (1) vs. nonwhite (0)a -.983 .003
Unemployed, retired, disabled (1) vs. employed (0) .945 .006
Open in a new tab
Sa

The term white refers to all Caucasian participants. The term nonwhite refers to participants of all other races, including African American, Hispanic, and multiracial participant.

Discussion

Demographics

Race is a predictor of the total of all symptoms, distress of symptoms, physical and psychological symptoms in this study. Participants in this study who identified their race/ethnicity as Hispanic or Multiracial (“nonwhite” group) experienced more symptoms (M=22.83) compared to persons who identified their race/ethnicity as Caucasian (“white” group) with a mean of 10.22 symptoms [p=.001] (Houghton-Rahrig et al., 2011). These findings are congruent, in some respect, to prior observations that Hispanics have a higher incidence of NAFLD and are at higher risk of disease progression based on the presence of the PNPLA3 (rs738409)-G allele (Browning et al., 2004; Sookoian et al., 2009; Tian, Stokowski, Kershenobich, Ballinger, & Hinds, 2010b; Valenti et al., 2010). The mechanism behind these observations is not clear.

Persons who were unemployed, disabled or retired reported higher distress scores from than those who were employed and those with higher psychological subscale scores. The MSAS-GDI measured the frequency of psychological symptoms of feeling sad, worrying, feeling irritable and feeling nervous. The MSAS-GDI also included the distress of the following physical symptoms such as lack of appetite, lack of energy, pain, feeling drowsy, constipation, and dry mouth (Portenoy et al., 1994).

Disease Characteristics

No disease characteristics were predictors of symptoms in this study. While more symptoms were reported in persons with one or two copies of the PNPLA3 (rs738409)-G allele as noted in Table 4, the relationship was not statistically significant. Further, the PNPLA3 (rs738409)-G allele was not a contributor to the symptoms experience as noted in the MSAS subscales listed in Table 4. Interestingly, persons with no copies of the PNPLA3 (rs738409)-G allele had statistically higher BMI values than persons with one or two copies of the PNPLA3 (rs738409)-G allele as noted in Table 1. This may represent a need to increase our sample size as BMI is similar between PNPLA3 (rs738409) genotype groups in other studies (Romeo et al., 2008; Valenti et al., 2010). Additional participants are needed to increase the power in this study for a more accurate evaluation. In future studies, evaluation with a larger sample size may be beneficial.

Individual Characteristics

Prior knowledge of NAFLD prior to enrollment in the study was a predictor of physical symptoms. In this study, persons who had prior knowledge of the presence of NAFLD had more symptoms (M=13.75, SE=1.56) compared to those who learned about their diagnosis of NAFLD on the day that they were enrolled in the study [M=8.57 symptoms, SE=2.45] (Houghton-Rahrig et al., 2011). Hegyvary notes that symptoms are “red flags” of threats to health (Hegyvary, 1993). Perhaps persons aware that they had NAFLD prior to the study were looking for symptoms to be present or were more aware of symptoms. Further study is needed in this area.

Outcome of Symptoms Experience

In contrast to conventional wisdom that NAFLD is largely asymptomatic, findings from this study suggest that symptoms exist and are, in fact, prevalent in persons with NAFLD. Very few studies are available that measure symptoms in persons with NAFLD. Nonetheless, our findings are consistent with a study of children in NAFLD (Kistler et al., 2010), who also identified symptoms such as fatigue, sadness, and trouble sleeping.

While there has been limited research examining symptoms specific to NAFLD, efforts to examine and describe symptoms in liver disease, more broadly, has been done. The Chronic Liver Disease Questionnaire (CLDQ), which has been used to determine quality of life through the measurement symptoms in persons of all liver diseases, was developed from symptoms identified by patients with liver diseases (Younossi, Guyatt, Kiwi, Boparai, & King, 1999). Table 5 provides a comparison of symptoms reported in the present study, symptoms reported in the Kistler study, and symptoms included in the CLDQ (as an indicator of symptoms commonly found in other liver diseases). For example, the CLDQ has been used to measure health-related quality of life in persons with NAFLD citing poorer HRQOL compared to those with hepatitis B or C (Dan et al., 2007). Kistler and others (2010) found symptoms and poor health-related quality of life in children with NAFLD. Finally, this paper, a subsection of the primary author's dissertation work entitled, Symptoms, Genetics, and HRQOL in persons with nonalcoholic fatty liver disease, measured the presence of symptoms in persons with NAFLD (Houghton-Rahrig et al., 2011).

Table 5. Comparison of Symptoms of Chronic Liver Disease with Symptoms Found in Children and Adults with NAFLD.

Symptoms found in development of Chronic Liver Disease Questionnaire (%) Symptoms found in children with NAFLD (%) Symptom mean= 5 Symptoms, Genetics, and HRQOL in Persons with NAFLD (%) Symptom mean=12
Fatigue domain
 Tiredness or fatigue (80) Fatigue (68) Fatigue (59.5)
 Sleepiness during the day (80) Feeling drowsy (50.0)
 Decreased strength (69)
 Decreased level of energy (81) Lack of energy (71.4)
 Felt Drowsy (63)
Abdominal pain domain
 [Feeling bloated]a (66) Swelling abdomen (20) Feeling bloated (45.2)
 Abdominal bloating (58) Liver pain (41) Ache/discomfort rt lower rib area (35.7)
 Abdominal discomfort (55) Diarrhea (35) Diarrhea (50) Constipation (30.9)
Emotional function
 Anxious (69) Feeling nervous (38.1)
 Unhappy Difficulty sleeping (47.6)
 Irritable (55) Irritability (73) Feeling irritable (57.2)
 Difficulty sleeping (61) Difficulty sleeping (47.6)
 Mood swings (52)
 Inability to fall asleep at night (58)
 Felt depressed (56) Feeling sad (40.4)
 Problems concentrating (not available) Trouble concentrating (55) Difficulty concentrating (33.3)
Systemic symptoms
 Bodily pain Headache (60) Pain (64.3)
 Shortness of breath (53) Shortness of breath (35.7)
 Muscle cramps (56) Muscle aches/cramps (53)
 Dry mouth (61) Dry mouth (35.7)
 Itching (53) Itching (28.6)
Numbness/tingling in hands/feet (42.8)
Swelling ankles (15) Swelling of arms/legs (38.1)
Sweats (35.7)
Cough (33.3)
Dizziness (28.6)
Problems with sexual activity/interest (28.6)
Weight loss (28.5)
Problems with urination (23.8)
Nausea (50) Nausea (26.2)
Vomiting (11.9)
Changes in skin (23.8)
“I don't look like myself” (19.0)
Hair loss (16.6)
Mouth sores (9.5)
Activity
 Unable to eat as much as preferred (56)
 Trouble lifting or carrying heavy
 Objects (53), (55)
 Limitation of diet
Worry Worrying (52.4)
 Concern about liver disease impact on family (64)
 Worried symptoms will develop into major problems (73)
 Worried conditions will get worse (83)
 Worried never going to feel better (52)
 Concerned about transplant availability (55)
Open in a new tab

Note. NAFLD = nonalcoholic, fatty liver disease. Adapted from Younossi, Z. M., Guyatt, G., Kiwi, M., Boparai, N., & King, D. (1999). Development of a disease specific questionnaire to measure health related quality of life in patients with chronic liver disease. Gut, 45(2), 295-300. Adapted from Kistler, K. D., Molleston, J., Unalp, A., Abrams, S. H., Behling, C., & Schwimmer, J. B. (2010). Symptoms and quality of life in obese children and adolescents with non-alcoholic fatty liver disease. Alimentary Pharmacology & Therapeutics, 31(3), 396-406. From “Symptoms, Genetics, and Health-Related Quality of Life in Persons with Nonalcoholic Fatty Liver Disease” by L. Houghton-Rahrig, D.L. Schutte, J.I. Fenton, B.A. Given, N.G. Hord., A. Von Eye, 2011, (Dissertation). Copyright 2011 by Lori Houghton-Rahrig.

a

Symptom combined with others in final Chronic Liver Disease Questionnaire.

There are many similar symptoms across these three studies. For example, physical symptoms of fatigue or lack of energy was found in adults and children in these comparative studies (Houghton-Rahrig et al., 2011; Kistler et al., 2010; Younossi et al., 1999). Abdominal discomfort and liver ache or pain were also noted in both studies and as well as in the CLDQ. In addition, persons identified psychological symptoms, such as irritability, with similar frequencies (Houghton-Rahrig et al., 2011; Kistler et al., 2010; Younossi et al., 1999).

Limitations of study

Several limitations are acknowledged. This cross-sectional study used a small sample of 42 participants inhibiting generalizability to the population. In addition, no control group was used to compare a symptoms experience in persons without NAFLD against those with NAFLD. Further research would benefit from the inclusion of a control group of persons without NAFLD, matched by age, gender, weight, and ethnicity, in order to minimize the effects of biases and confounding factors on understanding these relationships (Polit & Hungler, 1995).

Although all participants had a diagnosis of NAFLD, staging of NAFLD from liver biopsy was only available for a small percentage of participants, and, in some cases, these liver biopsies were conducted months to a few years prior to the present study. The lack of liver biopsy results is not uncommon in this population as liver biopsies are rarely conducted unless severe disease is suspected (Angulo, 2007). However, this limited our ability to fully compare the presence and attributes of symptoms across disease stages. In addition, the lack of liver biopsy results conducted during recruitment of the study prohibited comparison of disease stage with PNPLA3 genotype. This experience also highlights the need for other biomarkers of disease stage. In our case, the PNPLA3 (rs738409)-G variant was used to stratify patients based on risk of progression.

While symptoms were prevalent among our participants, we acknowledge that the reported symptoms are also commonly found in other liver diseases as noted in Table 5 by the comparison of the CLDQ, a tool used to measure quality of life in patients with liver disease (Younossi et al., 1999). While not all symptoms in this study and Kistler's study are congruent with the CLDQ, many are. Further comparative studies are needed to control for other comorbidities that may be contributing to symptoms.

While PNPLA3 gene (rs738409) genotype did not contribute to symptoms in our small sample, it cannot be ruled out as a potential biomarker of value in studying NAFLD symptoms. We examined only one polymorphism within the PNPLA3 gene, (rs738409) based on its prior associations with NAFLD risk; however, other genes and other polymorphisms within the PNPLA3 gene may be associated with symptom production. In addition, a larger sample size may have provided additional power to compare the genotypes of CC, CG and GG beyond a binary comparison. To date, the PNPLA3 gene has not been associated with symptoms, but severity of liver disease (Sookoian et al., 2009a).

Race and ethnicity were also self-reported. While self-report is common in research, this study may have benefited from ancestry genotyping testing to determine dominant race/ethnicity, especially in the multi-racial groups. Tian et al. examined the PNPLA3 variant using ancestry genotyping as alcoholic fatty liver disease as is NAFLD, more common in the Hispanic population (Tian et al., 2010).

Medications may be a contributor to the symptoms experienced in this sample. For example, symptoms of dry mouth may be due to the administration of diuretic or allergy medications. Further study is needed to investigate the role of medications with symptoms in this population.

A larger effect size was used in the power calculation for this study. It is possible that the relationship between symptoms and the PNPLA3 gene, (rs738409)-G allele may be weak. A larger sample size may highlight these differences. Despite these limitations, this research provides a critical and comprehensive foundational description of the symptom experience in persons with NAFLD.

A possible alternative explanation for the symptoms reported by our participants is that they are due to the presence of obesity. In our study, there was not a significant relationship between symptoms and BMI. However, the potential role of obesity and production of symptoms remains an important question. Fatty deposits in the liver are hypothesized to set up a systemic inflammatory response that influences symptom production of “mood disorders, sleep disturbances, cognitive impairments, and sickness behaviors such as fatigue, malaise, lethargy, loss of appetite and loss of social interest”(D'Mello & Swain, 2011) [p.G750]. Higher circulating levels of TNF-α have been noted in obese persons with NASH (the inflammatory stage of NAFLD) compared to obese patients serving as controls for the study (Baranova et al., 2007). D'Mello and Swain (2011) purport that TNF-α, IL-1β and IL-6 play a role in a symptoms experience in persons with chronic liver disease and may provide an explanation for the presence of generalized, common symptoms other than liver discomfort reported by participants in this study.

In our study, 33% had liver biopsies determining all had NASH and on average, the total population experienced a mean of 12 symptoms with a higher percentage of persons experiencing some of the symptoms noted in the sickness behavior or signs of inflammation such as fatigue (59.5%) lack of energy (71.4%), difficulty concentrating (33.3%), difficulty sleeping (47.6%), lack of appetite (21.4%) and mood disorders such as feeling sad (40.4), irritable (57.2%) or nervous (38.1%), While preliminary work has been conducted in cancer populations of the role of cytokines and symptom production, (Aouizerat et al., 2009; Illi et al., 2012) further work is needed in this case to provide insight about the mechanisms underlying a symptoms experience in persons with NAFLD.

Implications for Nursing Practice and Research

Hegyvary (1993) defines symptoms as the “red flags to health” (p.146). In this population, symptoms may be triggers for health care providers to evaluate patients for the presence of NAFLD, especially persons at higher risk such as those with obesity, metabolic syndrome or diabetes. Once identified, interventions implemented in the early stages of NAFLD will reverse the disease progression. The identification of symptoms in this study suggests that persons with NAFLD may need assistance with symptom monitoring and management. Nurses are experts in the monitoring and treatment of symptoms as well as in supporting self-management of symptoms, as they promote health, manage chronic disease and treat the human response (American Nurses Association, 2010). Timely access to care in the early identification of NAFLD and treatment of symptoms in persons with chronic disease from NAFLD will promote optimal care for this population.

Further research with a larger sample size and more race/ethnic diversity is needed to determine a symptoms experience in persons with NAFLD. Research should include staging and grading of NAFLD as recommended by the American Association for the Study of Liver Disease (Chalasani et al., 2012). Longitudinal studies are needed to determine the symptoms experience along the trajectory of NAFLD from simple fatty liver disease, to NASH with and without fibrosis, liver cirrhosis, and liver cancer. Lastly, further study is needed to understand the physiological mechanisms of symptoms as charged by the National Institute of Nursing Research initiatives in order to effectively prevent, treat and manage NAFLD in all stages of disease (National Institute of Nursing Research, 2011).

Acknowledgments

Funding was provided by the Michigan Nurse Corps Initiative provided by the Michigan Department of Community Health and Michigan Department of Labor and Economic Growth, 2009-2010; Summer Genetics Institute, National Institute of Nursing Research, 2010; and the National Institute of Health, National Institute of Nursing Research 1F31NR012600-01 NRSA, January – December, 2011.

Footnotes

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Contributor Information

Lori Houghton-Rahrig, Kirkhof College of Nursing, Grand Valley State University, Grand Rapids, Michigan.

Debra Schutte, College of Nursing, Michigan State University, East Lansing, Michigan.

Alexander von Eye, Department of Psychology, Michigan State University, East Lansing, Michigan.

Jenifer Fenton, College of Food Science and Human Nutrition, Department of Agriculture, Michigan State University, East Lansing, Michigan.

Barbara Given, College of Nursing, Michigan State University, East Lansing, Michigan.

Norm Hord, College of Food Science and Human Nutrition, Department of Agriculture, Michigan State University, East Lansing, Michigan.

References

  1. Adams LA, Angulo P, Lindor KD. Nonalcoholic fatty liver disease. Canadian Medical Association Journal. 2005;172(7):899–905. doi: 10.1503/cmaj.045232. 172/7/899 [pii]10.1503/cmaj.045232. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Adams LA, Lymp JF, St Sauver J, Sanderson SO, Lindor KD, Feldstein A, Angulo P. The natural history of nonalcoholic fatty liver disease: a population-based cohort study. Gastroenterology. 2005;129(1):113–121. doi: 10.1053/j.gastro.2005.04.014. [DOI] [PubMed] [Google Scholar]
  3. Adler M, Schaffner F. Fatty liver hepatitis and cirrhosis in obese patients. American Journal of Medicine. 1979;67(5):811–816. doi: 10.1016/0002-9343(79)90740-x. [DOI] [PubMed] [Google Scholar]
  4. American Liver Foundation. Fatty Liver. 2007 Retrieved October 29 2008. [Google Scholar]
  5. American Nurses Association. Nursing's social policy statement: The essence of the profession. 3rd. Silver Spring: Nursesbooks.org; 2010. [Google Scholar]
  6. Angulo P. Obesity and nonalcoholic fatty liver disease. Nutrition Reviews. 2007;65(6):S57–S63. doi: 10.1111/j.1753-4887.2007.tb00329.x. [DOI] [PubMed] [Google Scholar]
  7. Aouizerat BE, Dodd M, Lee K, West C, Paul SM, Cooper BA, Miaskowski C. Preliminary evidence of a genetic association between tumor necrosis factor alpha and the severity of sleep disturbance and morning fatigue. Biol Res Nurs. 2009;11(1):27–41. doi: 10.1177/1099800409333871. [DOI] [PubMed] [Google Scholar]
  8. Armstrong TS. Symptoms experience: A concept analysis. Oncology Nursing Forum. 2003;30(4):601–606. doi: 10.1188/03.ONF.601-606. [DOI] [PubMed] [Google Scholar]
  9. Baranova A, Schlauch K, Elariny H, Jarrar M, Bennett C, Nugent C, Younossi ZM. Gene expression patterns in hepatic tissue and visceral adipose tissue of patients with non-alcoholic fatty liver disease. Obes Surg. 2007;17(8):1111–1118. doi: 10.1007/s11695-007-9187-y. [DOI] [PubMed] [Google Scholar]
  10. Bialek SR, Redd JT, Lynch A, Vogt T, Lewis S, Wilson C, Bell BP. Chronic liver disease among two American Indian patient populations in the southwestern United States, 2000-2003. J Clin Gastroenterol. 2008;42(7):949–954. doi: 10.1097/mcg.0b013e318054492a. [DOI] [PubMed] [Google Scholar]
  11. Birim O, Maat AP, Kappetein AP, van Meerbeeck JP, Damhuis RA, Bogers AJ. Validation of the Charlson comorbidity index in patients with operated primary non-small cell lung cancer. Eur J Cardiothorac Surg. 2003;23(1):30–34. doi: 10.1016/s1010-7940(02)00721-2. [DOI] [PubMed] [Google Scholar]
  12. Browning JD, Szczepaniak LS, Dobbins R, Nuremberg P, Horton JD, Cohen JC, Hobbs HH. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity. Hepatology. 2004;40(6):1387–1395. doi: 10.1002/hep.20466. [DOI] [PubMed] [Google Scholar]
  13. Burza MA, Romeo S, Kotronen A, Svensson PA, Sjoholm K, Torgerson JS, Peltonen M. Long-Term Effect of Bariatric Surgery on Liver Enzymes in the Swedish Obese Subjects (SOS) Study. PLoS One. 2013;8(3):e60495. doi: 10.1371/journal.pone.0060495. [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Caldwell SH, Argo CK, Al-Osaimi AMS. Nonalcoholic fatty liver disease. In: Schiff ER, Maddrey WC, Sorrell MF, editors. Schiff's diseases of the liver. 11th. Hoboken, N.J.: Wiley-Blackwell; 2012. pp. 868–910. [Google Scholar]
  15. Chalasani N, Younossi Z, Lavine JE, Diehl AM, Brunt EM, Cusi K, Sanyal AJ. The diagnosis and management of non-alcoholic fatty liver disease: Practice guidelines by the American Association for the Study of Liver Disease, American College of Gastroenterology, and the American Gastroenterological Association. Hepatology. 2012;55(6):2005–2023. doi: 10.1002/hep.25762. [DOI] [PubMed] [Google Scholar]
  16. Chan H, Chan HL, de Silva HJ, Leung NW, Lim SG, Farrell GC, Asia-Pacific Working Party on, Nafld How should we manage patients with non-alcoholic fatty liver disease in 2007? J Gastroenterol Hepatol. 2007;22(6):801–808. doi: 10.1111/j.1440-1746.2007.04977.x. [DOI] [PubMed] [Google Scholar]
  17. Chang Y, Ryu S, Sung E, Woo HY, Oh E, Cha K, Kim WS. Nonalcoholic fatty liver disease predicts chronic kidney disease in nonhypertensive and nondiabetic Korean men. Metabolism. 2008;57(4):569–576. doi: 10.1016/j.metabol.2007.11.022. [DOI] [PubMed] [Google Scholar]
  18. Charlson ME, Pompei P, Alex KL, MacKenzie CR. A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation. Journal of Chronic Disease. 1987;40(5):373–383. doi: 10.1016/0021-9681(87)90171-8. [DOI] [PubMed] [Google Scholar]
  19. Charlson ME, Szatrowski TP, Peterson J, Gold J. Validation of a combined comorbidity index. J Clin Epidemiol. 1994;47(11):1245–1251. doi: 10.1016/0895-4356(94)90129-5. [DOI] [PubMed] [Google Scholar]
  20. Choudhury J, Sanyal AJ. Clinical aspects of fatty liver disease. Semin Liver Dis. 2004;24(4):349–362. doi: 10.1055/s-2004-860864. [DOI] [PubMed] [Google Scholar]
  21. D'Mello Charlotte, Swain Mark G. Liver-Brain Inflammatory Axis. American Journal of Physiology - Gastrointestinal and Liver Physiology. 2011 doi: 10.1152/ajpgi.00184.2011. [DOI] [PubMed] [Google Scholar]
  22. Dan AA, Kallman JB, Wheeler A, Younoszai Z, Collantes R, Bondini S, Younossi Z. Health-related quality of life in patients with non-alcoholic fatty liver disease. Alimentary Pharmacology & Therapeutics. 2007;26(6):815–820. doi: 10.1111/j.1365-2036.2007.03426.x. [DOI] [PubMed] [Google Scholar]
  23. de Groot V, Beckerman H, Lankhorst GJ, Bouter LM. How to measure comorbidity. A critical review of available methods. J Clin Epidemiol. 2003;56(3):221–229. doi: 10.1016/s0895-4356(02)00585-1. [DOI] [PubMed] [Google Scholar]
  24. Dixon JB, Bhathal PS, Hughes NR, O'Brien PE. Nonalcoholic fatty liver disease: Improvement in liver histological analysis with weight loss. Hepatology. 2004;39(6):1647–1654. doi: 10.1002/hep.20251. [DOI] [PubMed] [Google Scholar]
  25. Dodd MJ, Miaskowski C, Paul SM. Symptom clusters and their effect on the functional status of patients with cancer. Oncol Nurs Forum. 2001;28(3):465–470. [PubMed] [Google Scholar]
  26. Fischer GE, Bialek SP, Homan CE, Livingston SE, McMahon BJ. Chronic liver disease among Alaska-Native people, 2003-2004. Am J Gastroenterol. 2009;104(2):363–370. doi: 10.1038/ajg.2008.57. ajg200857 [pii]10.1038/ajg.2008.57. [DOI] [PubMed] [Google Scholar]
  27. He S, McPhaul C, Li JZ, Garuti R, Kinch L, Grishin NV, Hobbs HH. A sequence variation (I148M) in PNPLA3 associated with nonalcoholic fatty liver disease disrupts triglyceride hydrolysis. J Biol Chem. 2010;285(9):6706–6715. doi: 10.1074/jbc.M109.064501. M109.064501 [pii]10.1074/jbc.M109.064501. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Hegyvary ST. Patient care outcomes related to managment of symptoms. Annual Review of Nursing Research. 1993;11:145–168. [PubMed] [Google Scholar]
  29. Houghton-Rahrig L, Schutte DL, Fenton JI, Given BA, Hord NG, Von Eye A. Symptoms, genetics, and health-related quality of life in persons with nonalcoholic fatty liver disease(Doctor of Philosophy Dissertation) Michigan State University, East Lansing, MI: UMI Proquest database; 2011. [Google Scholar]
  30. Illi J, Miaskowski C, Cooper B, Levine JD, Dunn L, West C, Aouizerat BE. Association between pro- and anti-inflammatory cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression. Cytokine. 2012;58(3):437–447. doi: 10.1016/j.cyto.2012.02.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Jepsen P, Vilstrup H, Andersen PK, Lash TL, Sorensen HT. Comorbidity and survival of Danish cirrhosis patients: a nationwide population-based cohort study. Hepatology. 2008;48(1):214–220. doi: 10.1002/hep.22341. [DOI] [PubMed] [Google Scholar]
  32. Kistler KD, Molleston J, Unalp A, Abrams SH, Behling C, Schwimmer JB. Symptoms and quality of life in obese children and adolescents with non-alcoholic fatty liver disease. Alimentary Pharmacology & Therapeutics. 2010;31(3):396–406. doi: 10.1111/j.1365-2036.2009.04181.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Kotronen A, Peltonen M, Hakkarainen A, Sevastianova K, Bergholm R, Johansson LM, Yki-Jarvinen H. Prediction of non-alcoholic fatty liver disease and liver fat using metabolic and genetic factors. Gastroenterology. 2009;137(3):865–872. doi: 10.1053/j.gastro.2009.06.005. S0016-5085(09)00913-5 [pii]10.1053/j.gastro.2009.06.005. [DOI] [PubMed] [Google Scholar]
  34. Lenz ER, Pugh LC, Milligan RA, Gift A, Suppe F. The middle-range theory of unpleasant symptoms: An update. Advances in Nursing Science. 1997;19(3):14–27. doi: 10.1097/00012272-199703000-00003. [DOI] [PubMed] [Google Scholar]
  35. Mehta SR, Thomas EL, Bell JD, Johnston DG, Taylor-Robinson SD. Non-invasive means of measuring hepatic fat content. World J Gastroenterol. 2008;14(22):3476–3483. doi: 10.3748/wjg.14.3476. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Mummadi RR, Kasturi KS, Chennareddygari S, Sood GK. Effect of bariatric surgery on nonalcoholic fatty liver disease: Systematic review and meta-analysis. Clinical Gastroenterology and Hepatology. 2008;6(12):1396–1402. doi: 10.1016/j.cgh.2008.08.012. S1542-3565(08)00831-8 [pii]10.1016/j.cgh.2008.08.012. [DOI] [PubMed] [Google Scholar]
  37. National Institute of Nursing Research. Bringing Science to Life: NINR Strategic Plan. Bethesda, Maryland: National Institutes of Health; 2011. [Google Scholar]
  38. NCBI Entrez Gene. PNPLA3 patatin-like phospholipase domain containing 3 [Homo sapiens] 2010 Retrieved May 19, 2010, from http://www.ncbi.nlm.nih.gov/gene/80339.
  39. Polit DF, Hungler BP. Nursing research: Principles and methods. 5th. Philadelphia: J.B. Lippincott Company; 1995. [Google Scholar]
  40. Portenoy RK, Thaler HT, Kornblith AB, McCarthy Lepore J, Friedlander-Klar H, Kiyasu E, et al. The Memorial Symptom Assessment Scale: An instrument for the evalution of symptom prevalence, charactertistics and distress. European Journal of Cancer. 1994;30A(9):1326–1336. doi: 10.1016/0959-8049(94)90182-1. [DOI] [PubMed] [Google Scholar]
  41. Promrat K, Kleiner DE, Niemeier HM, Jackvony E, Kearns M, Wands JR, Wing RR. Randomized controlled trial testing the effects of weight loss on nonalcoholic steatohepatitis. Hepatology. 2010;51(1):121–129. doi: 10.1002/hep.23276. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Romeo S, Kozlitina J, Xing C, Pertsemlidis A, Cox D, Pennacchio LA, Hobbs HH. Genetic variation in PNPLA3 confers susceptibility to nonalcoholic fatty liver disease. Nature Genetics. 2008;40(12):1461–1465. doi: 10.1038/ng.257. ng.257 [pii]10.1038/ng.257. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Romeo S, Sentinelli F, Dash S, Yeo GS, Savage DB, Leonetti F, Baroni MG. Morbid obesity exposes the association between PNPLA3 I148M (rs738409) and indices of hepatic injury in individuals of European descent. Int J Obes (Lond) 2010;34(1):190–194. doi: 10.1038/ijo.2009.216. ijo2009216 [pii]10.1038/ijo.2009.216. [DOI] [PubMed] [Google Scholar]
  44. Salt WB., 2nd Nonalcoholic fatty liver disease (NAFLD): A comprehensive review. Journal of Insurance Medicine. 2004;36(1):27–41. [PubMed] [Google Scholar]
  45. Sookoian S, Castano GO, Burgueno AL, Fernandez Gianotti T, Rosselli MS, Pirola CJ. A nonsynonymous gene variant in the adiponutrin gene is associated with nonalcoholic fatty liver disease severity. Journal of Lipid Research. 2009;50(10):2111–2116. doi: 10.1194/jlr.P900013-JLR200. P900013-JLR200 [pii]10.1194/jlr.P900013-JLR20. [DOI] [PMC free article] [PubMed] [Google Scholar]
  46. Targher G, Bertolini L, Padovani R, Rodella S, Tessari R, Zenari L, et al. Arcaro G. Prevalence of nonalcoholic fatty liver disease and its association with cardiovascular disease among type 2 diabetic patients. Diabetes Care. 2007;30(5):1212–1218. doi: 10.2337/dc06-2247. [DOI] [PubMed] [Google Scholar]
  47. Tian C, Stokowski RP, Kershenobich D, Ballinger DG, Hinds DA. Variant in PNPLA3 is associated with alcoholic liver disease. Nature Genetics. 2010;42(1):21–23. doi: 10.1038/ng.488. ng.488 [pii]10.1038/ng.488. [DOI] [PubMed] [Google Scholar]
  48. Valenti L, Al-Serri A, Daly AK, Galmozzi E, Rametta R, Dongiovanni P, Day CP. Homozygosity for the patatin-like phospholipase-3/adiponutrin I148M polymorphism influences liver fibrosis in patients with nonalcoholic fatty liver disease. Hepatology. 2010;51(4):1209–1217. doi: 10.1002/hep.23622. [DOI] [PubMed] [Google Scholar]
  49. Vernon G, Baranova A, Younossi ZM. Systematic review: The epidemiology and natural history of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis in adults. Alimentary Pharmacology & Therapeutics. 2011;34(3):274–285. doi: 10.1111/j.1365-2036.2011.04724.x. [DOI] [PubMed] [Google Scholar]
  50. Victor Ronald G, Haley Robert W, Willett DuWayne L, Peshock Ronald M, Vaeth Patrice C, Leonard David, Hobbs Helen H. The Dallas Heart Study: a population-based probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. The American Journal of Cardiology. 2004;93(12):1473–1480. doi: 10.1016/j.amjcard.2004.02.058. [DOI] [PubMed] [Google Scholar]
  51. Williams CD, Stengel J, Asike MI, Torres DM, Shaw J, Contreras M, Harrison SA. Prevalence of nonalcoholic fatty liver disease and nonalcoholic steatohepatitis among a largely middle-aged population utilizing ultrasound and liver biopsy: a prospective study. Gastroenterology. 2011;140(1):124–131. doi: 10.1053/j.gastro.2010.09.038. [DOI] [PubMed] [Google Scholar]
  52. Yan E, Durazo F, Tong M, Hong K. Nonalcoholic fatty liver disease: pathogenesis, identification, progression, and management. Nutr Rev. 2007;65(8 Pt 1):376–384. doi: 10.1301/nr.2007.aug.376-384. [DOI] [PubMed] [Google Scholar]
  53. 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(2):295–300. doi: 10.1136/gut.45.2.295. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Younossi ZM, Stepanova M, Afendy M, Fang Y, Younossi Y, Mir H, Srishord M. Changes in the prevalence of the most common causes of chronic liver diseases in the United States from 1988 to 2008. Clin Gastroenterol Hepatol. 2011;9(6):524–530. doi: 10.1016/j.cgh.2011.03.020. [DOI] [PubMed] [Google Scholar]

RESOURCES