PMCCPMCCPMCC

Search tips
Search criteria 

Advanced

 
Logo of nihpaAbout Author manuscriptsSubmit a manuscriptNIH Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
 
J Pain Symptom Manage. Author manuscript; available in PMC Oct 1, 2011.
Published in final edited form as:
PMCID: PMC2952712
NIHMSID: NIHMS215350
Preliminary Evidence of an Association Between a Functional IL6 Polymorphism and Fatigue and Sleep Disturbance in Oncology Patients and Their Family Caregivers
Christine Miaskowski, RN, PhD, FAAN, Marylin Dodd, RN, PhD, FAAN, Kathryn Lee, RN, PhD, Claudia West, RN, MS, Steven M. Paul, PhD, Bruce A. Cooper, PhD, William Wara, MD, Patrick S. Swift, MD, Laura B. Dunn, MD, and Bradley E. Aouizerat, PhD
School of Nursing (C.M., M.D., K.L., C.W., S.M.P., B.A.C., B.E.A.), School of Medicine (W.W., L.B.D.), and Institute for Human Genetics (B.A.E.), University of California, San Francisco; and Alta Bates Comprehensive Cancer Center (P.S.S.), Berkeley, California, USA
Address correspondence to: Christine Miaskowski, RN, PhD, FAAN, Department of Physiological Nursing, University of California, 2 Koret Way – Box 0610, San Francisco, CA 94143-0610, USA, chris.miaskowski/at/nursing.ucsf.edu
Context
Fatigue and sleep disturbance are common problems in oncology patients and their family caregivers (FCs). However, little is known about factors that contribute to inter-individual variability in these symptoms or to their underlying biologic mechanisms.
Objectives
An evaluation was done on whether genetic variation in a prominent pro-inflammatory cytokine, interleukin 6 (IL6 c.-6101A>T (rs4719714)) was associated with mean ratings of evening fatigue, morning fatigue, and sleep disturbance, as well as with the trajectories of these symptoms.
Methods
Over six months, participants completed standardized measures of fatigue and sleep disturbance. Linear regression was used to assess the effect of the IL6 genotype and other covariates on mean fatigue and sleep disturbance scores. Hierarchical linear modeling was used to determine the effect of IL6 genotype on symptom trajectories. Common allele homozygotes reported higher levels of evening fatigue (P=0.003), morning fatigue (P=0.09), and sleep disturbance (P=0.003) than minor allele carriers. Predictors of baseline level and trajectories of evening fatigue included age, gender, and genotype (intercepts) and baseline level of evening fatigue (slope), respectively. Predictors of baseline level and trajectories of morning fatigue included age and genotype (intercept) and age and baseline level of morning fatigue (slope). Predictors of baseline level and trajectories of sleep disturbance included age and genotype (intercept) and baseline level of sleep disturbance (slope).
Conclusions
Findings provide preliminary evidence of a genetic association between a functional promoter polymorphism in the IL6 gene and severity of evening fatigue, morning fatigue, and sleep disturbance in oncology patients and their FCs.
Keywords: Fatigue, sleep disturbance, interleukin 6, genetics, family caregiver, cancer, cytokines, sickness behavior, radiation therapy
Fatigue is the most common symptom reported by oncology patients undergoing radiation therapy (RT) (13), with prevalence rates of between 25% to 99% (47). In addition, while often not evaluated in oncology patients, prevalence rates for sleep disturbance range from 24% to 95% (810). Both of these symptoms have negative effects on patients’ mood and quality of life (1113). However, the mechanisms that underlie the development of these symptoms remain to be elucidated.
Equally important, recent evidence suggests that fatigue and sleep disturbance occur with similar prevalence rates in family caregivers (FCs) of oncology patients (14, 15). While high levels of these two symptoms may be related to the physical and psychological stressors associated with caring for a patient with cancer (1618), the molecular mechanisms for these symptoms require investigation.
Most studies of fatigue and sleep disturbance in oncology evaluated patients and FCs separately. However, recent evidence suggests that patients and FCs experience similar levels of these two symptoms (19, 20). While the factors that contribute to the development of fatigue and sleep disturbance may be different in patients and FCs, it is plausible to hypothesize that these factors produce similar physiologic processes that are mediated through similar molecular mechanisms.
Several lines of experimental and clinical evidence suggest that pro-inflammatory cytokines may mediate the effects of a variety of physical and psychological stressors (2123). In fact, in a recent study of breast cancer survivors (24), higher levels of ex vivo monocyte production of interleukin-6 (IL-6) and tumor necrosis factor alpha (TNF-α) were found in survivors classified with persistent fatigue compared to nonfatigued individuals. In a follow-up candidate gene study with the same sample of breast cancer survivors (25), the presence of at least one cytosine at IL1-β c.-511 C>T (rs16944) and homozygosity for either variant of the IL6 c.-174 G>C (rs1800795) genotype (i.e., GG or CC) was associated with being in the fatigued group. While the major limitation of these two studies is the small sample size (n=50), they provide preliminary evidence that pro-inflammatory cytokines are associated with increased levels of fatigue in breast cancer survivors.
Additional evidence for the role of pro-inflammatory cytokines in the development of fatigue and sleep disturbance in both oncology patients and their FCs comes from work by our research team that evaluated whether a functional polymorphism in TNF-α (c.-308 G>A (rs1800629) promoter polymorphism) was associated with overall ratings of fatigue and sleep disturbance as well as with the trajectories of these symptoms (26). No differences were found in patients’ (n=168) and FCs’ (n=85) self-reported levels of fatigue or sleep disturbance. However, in this sample, common allele homozygotes reported significantly higher levels of morning fatigue and sleep disturbance compared to minor allele carriers. Multivariate analyses demonstrated that age and genotype were predictors of both mean symptoms scores, as well as the trajectories of these symptoms.
Based on these initial studies in oncology patients and their FCs (2426), additional candidate gene studies of pro-inflammatory cytokines are warranted to evaluate the role of genetic polymorphisms in the development of fatigue and sleep disturbance. IL6 is a pleiotrophic cytokine, produced by both lymphoid and nonlymphoid cells, that plays a key role in inflammation (27). Within-population differences in IL6 concentrations are known to be due to both genetic and environmental influences (28). This genetic difference is exaggerated during inflammatory events, when IL6 concentration increases in response to diverse stimuli (29). Two single nucleotide polymorphisms (SNP) of IL6 have been studied in detail (i.e., the common -174 G>C variant (30) and the less frequent c.-572 G>C allele (rs1800796) (31)). The -174C allele was associated with higher IL-6 serum concentrations in several studies (3234), with no effect in one study (35), and with lower concentrations in another study (36). With the -572 G>C SNP, carriers of the rare C allele had higher IL-6 concentrations (37). Recent work suggests that genetic variation in the distal IL6 promoter region (i.e., IL6 c.-6331 T>C (rs10499563)) plays an important role in IL6 gene regulation and is associated with increases in IL6 concentration in common allele homozygotes (27).
Taken together, these converging lines of evidence suggest that a genetic variation in the IL6 gene might contribute to differences in the severity of fatigue and sleep disturbance experienced by oncology patients and their FCs. In this study, we sought to extend our previous work on the genetic determinants of symptoms (26) and determine, in the same sample of participants, if a functional polymorphism in IL6 was associated with overall ratings of evening fatigue, morning fatigue, and sleep disturbance as well as with the trajectories of these symptoms. We hypothesized that differences in the severity of evening fatigue, morning fatigue, and sleep disturbance would be associated with genetic variation in IL6, as measured by the c.–6101A>T (rs4719714) functional promoter polymorphism. This polymorphism is in complete linkage disequilibrium (LD) with the IL6 -6331 T>C polymorphism, which is associated with higher serum concentrations of IL6 in common allele homozygotes (27). Therefore, we further hypothesized that common allele homozygotes for the IL6 -6101 A>T polymorphism would report higher levels of these three symptoms.
Participants and Settings
This study is part of a large, longitudinal study of multiple symptoms (15, 19, 20, 26) that recruited 288 participants (i.e., 185 oncology outpatients with breast, prostate, lung, or brain cancer and 103 of their FCs). All patients met the following inclusion criteria: >18 years of age; able to read, write, and understand English; Karnofsky Performance Status (KPS) score of ≥ 60; and scheduled to receive primary or adjuvant RT. Patients were excluded if they had metastatic disease; had more than one cancer diagnosis; or had a diagnosed sleep disorder.
Following their recruitment, patients were asked to identify the person most involved in their care (i.e., their FC). FCs were eligible to participate if they: were >18 years of age; were able to read, write, and understand English; had a KPS score of > 60; were living with the patient; and did not have a diagnosed sleep disorder. Participants were recruited from RT departments located in a Comprehensive Cancer Center and a community-based oncology program. This study was approved by the Institutional Review Boards at the University of California, San Francisco and the community site.
Of the 472 patients approached, 185 consented to participate. The major reasons for refusal were being too overwhelmed with their cancer experience or too busy. In addition, 103 FCs consented to participate. Of the 288 participants, DNA could be recovered from the archived buffy coats of 253 (i.e., 168 patients and 85 FCs). No differences were found in any demographic and clinical characteristics between patients who did and did not choose to participate in the study or in those participants for whom DNA could and could not be recovered from archived specimens.
Instruments
A demographic questionnaire provided information on age, gender, marital status, education, ethnicity, employment status, and 26 comorbidities. In addition, participants completed the KPS scale (38).
The 21-item General Sleep Disturbance Scale (GSDS) was used to evaluate various aspects of sleep disturbance (i.e., quality and quantity of sleep, sleep onset latency, number of awakenings, excessive daytime sleepiness, medication use). Each item is rated on a 0 (never) to 7 (every day) scale. Subscale scores are calculated as well as a total score that is the sum of the 21 items that can range from 0 (no disturbance) to 147 (extreme sleep disturbance). The GSDS has well-established validity and reliability in shift workers, pregnant women, patients with cancer and HIV, and FCs of oncology patients (19, 20, 3941). In this sample, the GSDS Cronbach’s alpha was 0.83.
A fatigue severity score was calculated as the mean of the 13 items on the Lee Fatigue Scale (LFS) and could range from 0 to 10, with higher scores indicating higher levels of fatigue. In order to assess diurnal variations in fatigue, participants completed the LFS within 30 minutes of awakening (i.e., morning fatigue) and prior to going to sleep (i.e., evening fatigue). The LFS has well-established validity and reliability in healthy individuals, patients with cancer, and FCs of oncology patients (15, 20, 42). In this sample, the Cronbach’s alphas were 0.96 for morning and 0.94 for evening LFS ratings.
Study Procedures
At the time of the simulation visit (i.e., approximately one week prior to the start of RT), patients were approached by a research nurse to participate in the study. If the FC was present, the research nurse explained the study protocol to both the patient and FC, determined eligibility, and obtained written informed consent. FCs who were not present were contacted by phone to determine their interest in study participation. The research nurse visited these FCs at home to complete the study procedures.
At the time of the simulation visit, participants completed the demographic questionnaire and the GSDS, and had their blood drawn. In addition, they were taught to complete the LFS within 30 minutes of awakening and before going to bed each evening for two consecutive days. The LFS was completed 16 times (i.e., at the time of the simulation visit (i.e., baseline), weekly during the course of RT, every two weeks for two months, and once a month for two months following the completion of RT. The GSDS was completed seven times (i.e., at baseline, at the middle of RT, at the end of RT, and once a month for four months after the completion of RT). Patients and FCs returned the completed questionnaires to the research nurse either in the RT department or by mail.
Genetic Analysis
DNA was amplified by whole genome amplification (GenomiPhi DNA Amplification Kit, GE Healthcare, Piscataway, NJ) from archived buffy coat specimens. The IL6 c.-6101 A>T promoter polymorphism (rs4719714) was screened by TaqMan allelic discrimination assay (Assay ID: C_27953170_10; Applied Biosystems, Foster City, CA).
Statistical Analyses
Data were analyzed using SPSS Version 15.0, and Hierarchical Linear Modeling (HLM) software (43, 44). Descriptive statistics and frequency distributions were generated on the sample characteristics and symptom severity scores. Independent sample t-tests and Chi-square analyses were performed to evaluate for differences in demographic characteristics and mean symptom severity scores between patients and FCs. In addition, to account for the non-independence between some patients and FCs, a multilevel model analysis was performed to confirm the independent sample results.
The gene-counting method was used to determine allele and genotype frequencies. A test for deviation from Hardy-Weinberg expectation, a common indicator of genotype assay error, was evaluated by the goodness-of-fit χ2 test (45).
In order to estimate the association between IL6 gene variation and the average level of each symptom over the six months of the study, mean scores for evening and morning fatigue (i.e., LFS scores) and sleep disturbance (i.e., GSDS scores) were calculated for 16 and seven assessments, respectively. Independent sample t-tests were used to evaluate for differences in mean GSDS scores and morning and evening LFS scores between genotype groups. For these analyses, patient and FC data were combined into a single sample because tests of the potential interaction between IL6 genotype groups in symptom scores were not dependent on patient versus FC status.
Multiple linear regression was used to assess the effect of IL6 genotype and other covariates on mean evening and morning LFS scores and GSDS scores. Predictor variables included: IL6 genotype, age, gender, and ethnicity. Each predictor retained in the final model was required to have a P-value of ≤ 0.10. Although the criterion of P ≤ 0.05 is used as the formal threshold for significance for the majority of the analyses, for both the univariate and multivariate genetic analyses, a P-value of ≤ 0.10 was adopted in order not to miss a suggestive association between genotype and symptom severity score (46).
HLM, based on full maximum likelihood estimation, was used to evaluate the effects of IL6 genotype on the trajectories of fatigue and sleep disturbance (43, 44). First, intra-individual variability in the symptom over time was examined. In this study, time in weeks, refers to the length of time from the simulation visit to four months after the completion of RT (i.e., six months with a total of 16 or seven assessments). Three Level 1 models, which represented that the participants’ symptom level (a) did not change over time (i.e., no time effect), (b) changed at a constant rate (i.e., linear time effect), and (c) changed at a rate that accelerates or decelerates over time (i.e., quadratic effect) were compared. For the Level 1 modeling, the Level 2 model was constrained to be unconditional (i.e., no predictors) and likelihood ratio tests were used to determine the best model. These analyses identified the change parameters that best described individual changes in fatigue and sleep disturbance over time.
Then, inter-individual differences in the trajectories of fatigue and sleep disturbance were examined by modelling the individual change parameters (i.e., intercept, linear and quadratic slopes) as a function of proposed predictors. The predictors that were evaluated in the HLM analyses were those that were identified as significant in the regression analyses. Only predictors that maintained a significant contribution in conjunction with other variables were retained in the final model. Because these analyses were exploratory, a P-value of ≤0.10 was use to indicate statistical significance.
Participant Characteristics
As summarized in Table 1, the majority of participants were female, White, and well educated. No differences were found between patients and FCs in any demographic characteristics except gender and marital status. Compared to the patients, a higher percentage of the FCs was female and married/partnered. None of the FCs were a patient’s blood relative (i.e., 97.6% were spouses/partners, 2.4% were friends).
Table 1
Table 1
Comparison of the Demographic and Clinical Characteristics of Patients and Family Caregivers
Allelic and Genotypic Characteristics of the Sample
The frequency of the rare allele homozygote group in the entire sample was 3.2% (TT) which is consonant with population estimates available in public databases (dbSNP). The genotype distribution met Hardy-Weinberg expectations (P=0.69). No gender differences were found in the distribution of IL6 genotypes. Given the prohibitively low frequency of the rare allele homozygote group (n = 8), for all of the subsequent analyses, the IL6 genotypes were collapsed into 91 (36%) carriers of the rare allele (AT/TT genotypes) and 162 (64%) common allele homozygotes (AA).
Differences in Mean Fatigue and Sleep Disturbance Scores
As shown in Table 1, no differences were found in mean evening LFS, morning LFS, and GSDS scores between patients and FCs. As shown in Figure 1, common allele homozygotes (i.e., AA) reported significantly higher evening and morning LFS scores, as well as higher total GSDS scores than minor allele carriers (i.e., AT/TT).
Figure 1
Figure 1
Differences in mean evening (A) and morning fatigue scores (B) and sleep disturbance scores (C) between common allele homozygotes (AA) compared to minor allele carriers (AT/TT) for the interleukin 6 c.-6101 A>T polymorphism. All values are plotted (more ...)
Regression Analyses of IL6 Genotype and Symptom Severity
For each of the regression analyses, age, gender (male (0), female (1)), ethnicity (White (0), Nonwhite (1)), and genotype (AA (0), AT/TT (1)) were entered in as predictor variables. For mean evening fatigue, 14.4% of the total variance (P<0.0001) was explained by the optimal combination of all of the variables in the model. The unique contribution (R2 change) of the significant variables in the model is as follows: age (6.6%, P<0.0001), gender (1.7%, P=0.03), and genotype (3.8%, P=0.001) such that younger age, female gender, and the AA genotype were associated with higher mean evening fatigue scores.
For mean morning fatigue, 14.4% of the total variance (P<0.0001) was explained by the optimal combination of all of the variables in the model. The unique contribution (R2 change) of the significant variables in the model is as follows: age (9.4%, P<0.0001) and genotype (1.2%, P=0.06) such that younger age and the AA genotype were associated with higher mean morning fatigue scores.
For mean sleep disturbance, 9.1% of the total variance (P<0.0001) was explained by the optimal combination of all of the variables in the model. The unique contribution (R2 change) of the significant variables in the model is as follows: age (4.0%, P<0.0001) and genotype (3.5%, P=0.002) such that younger age and the AA genotype were associated with higher mean sleep disturbance scores.
Trajectories of Evening Fatigue, Morning Fatigue, and Sleep Disturbance
The first stage of the HLM analyses examined how evening fatigue, morning fatigue, and sleep disturbance levels changed from the time of the simulation visit to four months after the completion of RT. The second stage of the HLM analyses tested the hypothesis that the pattern of change over time in evening fatigue, morning fatigue, and sleep disturbance varied based on predictor variables that were identified in the regression analyses.
Evening Fatigue
The estimates of the quadratic change model are presented in Table 2 (unconditional model). Because the model had no covariates (i.e., unconditional), the intercept represents the estimated amount of evening fatigue (i.e., 4.28) at the time of the simulation visit. The estimated linear rate of change in evening fatigue, for each additional week, was 0.075 (P<0.0001) and the estimated quadratic rate of change per week was −0.003 (P<0.0001). It is important to note that it is the weighted combination of the linear and quadratic terms that define each curve.
Table 2
Table 2
Hierarchical Linear Models of Evening Fatigue, Morning Fatigue, and Sleep Disturbance
As shown in the final model in Table 2, the three variables that predicted inter-individual differences in the intercept for evening fatigue were age, gender, and genotype. Baseline evening fatigue was entered in Level 2 as a predictor of the slope parameters to control for intra-individual differences in evening fatigue at baseline. The variable that predicted inter-individual differences in the slope parameters for evening fatigue was baseline level of evening fatigue.
To illustrate the effects of the four different predictors on participants’ trajectories of evening fatigue, Figure 2 displays the adjusted change curves for evening fatigue that were estimated based on differences in age (i.e., younger or older based on one standard deviation (SD) above and below the mean age of the participants), gender, genotype (AA or AT/TT), and baseline level of evening fatigue (i.e., lower fatigue or higher fatigue calculated based on one SD above and below the mean baseline evening LFS score).
Figure 2
Figure 2
Influence of age (A), gender (B), and genotype (C) on inter-individual differences in the intercept for evening fatigue and the influence of baseline level of evening fatigue (D) on the slope parameters for evening fatigue.
Morning Fatigue
The estimates of the quadratic change model are presented in Table 2 (unconditional model). Because the model had no covariates (i.e., unconditional), the intercept represents the estimated amount of morning fatigue (i.e., 2.43) at the time of the simulation visit. The estimated linear rate of change in morning fatigue, for each additional week, was 0.035 (P≤0.01) and the estimated quadratic rate of change per week was −0.002 (P<0.0001).
As shown in the final model in Table 2, the two variables that predicted inter-individual differences in the intercept for morning fatigue were age and genotype. Baseline morning fatigue was entered in Level 2 as a predictor of the slope parameters to control for intra-individual differences in morning fatigue at baseline. The two variables that predicted inter-individual differences in the slope parameters for morning fatigue were age and baseline level of morning fatigue.
To illustrate the effects of the three different predictors on participants’ trajectories of morning fatigue, Figure 3 displays the adjusted change curves for morning fatigue that were estimated based on differences in age (i.e., younger or older based on one SD above and below the mean age of the participants), genotype (AA or AT/TT), and baseline level of morning fatigue (i.e., lower fatigue or higher fatigue calculated based on one SD above and below the mean baseline morning LFS score).
Figure 3
Figure 3
Influence of age (A) and genotype (B) on inter-individual differences in the intercept for morning fatigue and the influence of baseline level of morning fatigue (C) on the slope parameters for morning fatigue.
Sleep Disturbance
The estimates of the linear change model are presented in Table 2 (unconditional model). Because the model had no covariates (i.e., unconditional), the intercept represents the estimated amount of sleep disturbance (i.e., 41.05) at the time of the simulation visit. The estimated linear rate of change in sleep disturbance, for each week, was −0.144 (P<0.0001).
As shown in the final model in Table 2, the two variables that predicted inter-individual differences in the intercept for sleep disturbance were age and genotype. Baseline sleep disturbance was entered in Level 2 as a predictor of the slope parameter to control for intra-individual differences in sleep disturbance at baseline. The variable that predicted inter-individual differences in the slope for sleep disturbance was baseline level of sleep disturbance.
To illustrate the effects of the three different predictors on participants’ trajectories of sleep disturbance, Figure 4 displays the adjusted change curves for sleep disturbance that were estimated based on differences in age (i.e., younger or older based on one SD above and below the mean age of the participants), genotype (AA or AT/TT, and baseline level of sleep disturbance (i.e., lower sleep disturbance or higher sleep disturbance calculated based on one SD above and below the mean baseline GSDS score).
Figure 4
Figure 4
Influence of age (A) and genotype (B) on inter-individual differences in the intercept for sleep disturbance and the influence of baseline level of sleep disturbance (C) on the slope parameter for sleep disturbance.
This study is the first to provide preliminary evidence of a genetic association between a functional promoter polymorphism in the IL6 gene and the severity of evening fatigue, morning fatigue, and sleep disturbance in a sample of oncology patients and their FCs. Consistent with our a priori hypothesis, common allele homozygotes for the IL6 -6101 A>T polymorphism reported higher overall levels of evening fatigue, morning fatigue, and sleep disturbance. While serum levels of IL6 were not available for the participants in this study, the higher symptom severity scores in the common allele homozygotes may be partially explained by the fact that the -6101 A>T SNP is in complete LD with the -6331 T>C SNP. Since Smith and colleagues (27) found that common allele homozygotes for the -6331 T>C SNP had 74% higher serum concentrations of IL6 than the CC genotype and previous studies in oncology patients suggest that higher levels of IL6 are associated with higher levels of fatigue (24, 25), data from this study support the hypothesis that -6101 A>T is an appropriate surrogate marker for the functional -6331 T>C SNP that results in higher serum levels of IL6. Additional research is warranted to replicate this finding and to evaluate serum levels of IL-6 associated with this genetic variation. Nevertheless, the findings provide further evidence that pro-inflammatory cytokines may be involved in the development of the symptoms associated with sickness behavior (47, 48).
Several other findings from this study are worth noting. First, the mean levels of evening fatigue, morning fatigue, and sleep disturbance reported by the entire sample are below the cutpoints for clinically significant levels of these three symptoms (i.e., ≥5.6, ≥3.2, and ≥43.0, respectively) (15). However, when the mean symptom severity scores for the common allele homozygotes were compared to the carriers of the rare allele, they approached these cutpoints (i.e., 4.9 versus 4.1 for evening fatigue, 2.6 versus 2.2 for morning fatigue, and 41.7 versus 35.5 for sleep disturbance; see Figure 1). In addition, the differences in mean evening fatigue, morning fatigue, and sleep disturbance scores between the common allele homozygotes (AA) and the IL-6 minor allele carriers represent not only statistically but clinically significant differences in these three symptoms based on calculations of effect sizes (i.e., d = 0.39, 0.22, and 0.38, respectively). This conclusion is based on reports that suggest that minimally important differences in symptom severity scores are in the range of 0.20 to 0.50 standard deviation units (49, 50).
For all three symptoms, younger age was associated with higher symptom severity scores at the initiation of RT. However, age was only associated with the trajectory of morning fatigue. The finding of increased symptom severity scores in younger oncology patients is consistent with previous reports (51, 52), as well as with our findings with TNF-α (26). Future studies with larger samples need to evaluate the interaction between age and IL6 genotype because a study of the general population demonstrated that plasma levels of IL6 increased with age in both men and women (r=0.28 and r=0.22, P<0.001, respectively) (28).
The finding that female participants reported higher levels of evening fatigue at the initiation of RT warrants additional investigation. Studies of gender differences in fatigue in oncology patients and their FCs have produced inconsistent results (53) with some studies reporting no differences (3, 54) and others reporting higher severity scores in females (5557). One potential reason for the gender differences in evening fatigue found in this study is that female patients and FCs may have additional responsibilities (e.g., child care) that would result in higher levels of self-reported fatigue, particularly in the evening.
The fact that even after controlling for baseline levels of the three symptoms, the severity of evening fatigue, morning fatigue, and sleep disturbance at the initiation of RT predicted the trajectories of these three symptoms has important implications for clinicians. While clinicians may assess fatigue severity at the initiation of cancer treatment and prescribe interventions for patients with high levels of fatigue, data from this study suggest that individuals with lower levels of morning fatigue and sleep disturbance may be at greater risk for worse symptom trajectories over the course of the patient’s treatment.
Another interesting finding is that no differences in mean fatigue and sleep disturbance scores were found between patients and FCs. In both groups of participants, fatigue and sleep disturbance scores were in the moderate range. This finding adds support to the idea that both patients and FCs are affected by the cancer experience and require support with symptom management (15, 17, 19)
Several limitations of this study need to be acknowledged. Because of the relatively small sample size, additional research is warranted to validate this candidate gene association in a large, independent sample and to test for gene × environment interactions, as well as for gene × gene interactions (i.e., epistasis). Future studies need to evaluate additional SNPs for IL-6. In addition, the measurement of plasma IL6 levels may allow for more definitive conclusions to be drawn about the association between IL6 genotype and symptom severity. This type of analysis might help to determine the underlying mechanisms for fatigue and sleep disturbance in oncology patients and their FCs.
In conclusion, these findings provide preliminary evidence that pro-inflammatory cytokines may play a role in the development of fatigue and sleep disturbance in oncology patients and their FCs who are experiencing a variety of stressors associated with cancer and its treatment. Additional studies of genetic variation in other pro-inflammatory cytokines are warranted to test this hypothesis.
Acknowledgments
This research was supported by a grant from the National Institute of Nursing Research (NINR, NR04835). Dr. Aouizerat is funded through the National Institutes of Health Roadmap for Medical Research Grant (K12RR023262). Dr. Dunn is supported in part by funds from the Mount Zion Health Fund. Additional support for Dr. Miaskowski’s program of research was provided through unrestricted grants from Endo Pharmaceuticals, PriCara Unit of Ortho-McNeil, Inc., and Purdue Pharma LP.
Footnotes
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.
1. Lawrence DP, Kupelnick B, Miller K, Devine D, Lau J. Evidence report on the occurrence, assessment, and treatment of fatigue in cancer patients. J Natl Cancer Inst Monogr. 2004;32:40–50. [PubMed]
2. Jereczek-Fossa BA, Marsiglia HR, Orecchia R. Radiotherapy-related fatigue. Crit Rev Oncol Hematol. 2002;41:317–325. [PubMed]
3. Hickok JT, Roscoe JA, Morrow GR, Mustian K, Okunieff P, Bole CW. Frequency, severity, clinical course, and correlates of fatigue in 372 patients during 5 weeks of radiotherapy for cancer. Cancer. 2005;104:1772–1778. [PubMed]
4. Bower JE. Cancer-related fatigue: links with inflammation in cancer patients and survivors. Brain Behav Immun. 2007;21:863–871. [PubMed]
5. Hofman M, Ryan JL, Figueroa-Moseley CD, Jean-Pierre P, Morrow GR. Cancer-related fatigue: the scale of the problem. Oncologist. 2007;12 Suppl 1:4–10. [PubMed]
6. Levy M. Cancer fatigue: a review for psychiatrists. Gen Hosp Psychiatry. 2008;30:233–244. [PubMed]
7. Stone PC, Minton O. Cancer-related fatigue. Eur J Cancer. 2008;44:1097–1104. [PubMed]
8. Graci G. Pathogenesis and management of cancer-related insomnia. J Support Oncol. 2005;3:349–359. [PubMed]
9. Lee K, Cho M, Miaskowski C, Dodd M. Impaired sleep and rhythms in persons with cancer. Sleep Med Rev. 2004;8:199–212. [PubMed]
10. Savard J, Morin CM. Insomnia in the context of cancer: a review of a neglected problem. J Clin Oncol. 2001;19:895–908. [PubMed]
11. Lee TS, Kilbreath SL, Refshauge KM, et al. Quality of life of women treated with radiotherapy for breast cancer. Support Care Cancer. 2008;16:399–405. [PubMed]
12. Reich M, Lesur A, Perdrizet-Chevallier C. Depression, quality of life and breast cancer: a review of the literature. Breast Cancer Res Treat. 2008;110:9–17. [PubMed]
13. Vistad I, Fossa SD, Kristensen GB, Dahl AA. Chronic fatigue and its correlates in long-term survivors of cervical cancer treated with radiotherapy. BJOG. 2007;114:1150–1158. [PubMed]
14. Fletcher BA, Miaskowski C, Dodd MJ, Schumacher KL. A review of the literature on the symptom experience of family caregivers of patients with cancer. Oncol Nurs Forum. 2008;35:E23–E44. [PubMed]
15. Fletcher BS, Paul SM, Dodd MJ, et al. Prevalence, severity, and impact of symptoms on female family caregivers of patients at the initiation of radiation therapy for prostate cancer. J Clin Oncol. 2008;26:599–605. [PubMed]
16. Jensen S, Given BA. Fatigue affecting family caregivers of cancer patients. Cancer Nurs. 1991;14:181–187. [PubMed]
17. Passik SD, Kirsh KL. A pilot examination of the impact of cancer patients' fatigue on their spousal caregivers. Palliat Support Care. 2005;3:273–279. [PubMed]
18. Carter PA. Caregivers' descriptions of sleep changes and depressive symptoms. Oncol Nurs Forum. 2002;29:1277–1283. [PubMed]
19. Fletcher BA, Schumacher KL, Dodd M, et al. Trajectories of fatigue in family caregivers of patients undergoing radiation therapy for prostate cancer. Res Nurs Health. 2009;32:125–139. [PMC free article] [PubMed]
20. Miaskowski C, Paul SM, Cooper BA, et al. Trajectories of fatigue in men with prostate cancer before, during, and after radiation therapy. J Pain Symptom Manage. 2008;35:632–643. [PMC free article] [PubMed]
21. Calcagni E, Elenkov I. Stress system activity, innate and T helper cytokines, and susceptibility to immune-related diseases. Ann N Y Acad Sci. 2006;1069:62–76. [PubMed]
22. Kiecolt-Glaser JK, Preacher KJ, MacCallum RC, et al. Chronic stress and age-related increases in the proinflammatory cytokine IL-6. Proc Natl Acad Sci U S A. 2003;100:9090–9095. [PubMed]
23. McEwen BS. Central effects of stress hormones in health and disease: understanding the protective and damaging effects of stress and stress mediators. Eur J Pharmacol. 2008;583:174–185. [PMC free article] [PubMed]
24. Collado-Hidalgo A, Bower JE, Ganz PA, Cole SW, Irwin MR. Inflammatory biomarkers for persistent fatigue in breast cancer survivors. Clin Cancer Res. 2006;12:2759–2766. [PubMed]
25. Collado-Hidalgo A, Bower JE, Ganz PA, Irwin MR, Cole SW. Cytokine gene polymorphisms and fatigue in breast cancer survivors: early findings. Brain Behav Immun. 2008;22(8):1197–1200. Epub 2008 Jul 9. [PMC free article] [PubMed]
26. Aouizerat BE, Dodd M, Lee K, et al. Preliminary evidence of a genetic association between tumor necrosis factor alpha and the severity of sleep disturbance and morning fatigue. Biol Res Nurs. 2009;11:27–41. [PubMed]
27. Smith AJ, D'Aiuto F, Palmen J, et al. Association of serum interleukin-6 concentration with a functional IL6 -6331T>C polymorphism. Clin Chem. 2008;54:841–850. [PubMed]
28. Pantsulaia I, Trofimov S, Kobyliansky E, Livshits G. Genetic and environmental influences on IL-6 and TNF-alpha plasma levels in apparently healthy general population. Cytokine. 2002;19:138–146. [PubMed]
29. Song M, Kellum JA. Interleukin-6. Crit Care Med. 2005;33:S463–S465. [PubMed]
30. Olomolaiye O, Wood NA, Bidwell JL. A novel NlaIII polymorphism in the human IL-6 promoter. Eur J Immunogenet. 1998;25:267. [PubMed]
31. Terry CF, Loukaci V, Green FR. Cooperative influence of genetic polymorphisms on interleukin 6 transcriptional regulation. J Biol Chem. 2000;275:18138–18144. [PubMed]
32. Brull DJ, Montgomery HE, Sanders J, et al. Interleukin-6 gene -174g>c and -572g>c promoter polymorphisms are strong predictors of plasma interleukin-6 levels after coronary artery bypass surgery. Arterioscler Thromb Vasc Biol. 2001;21:1458–1463. [PubMed]
33. Boiardi L, Casali B, Farnetti E, et al. Relationship between interleukin 6 promoter polymorphism at position -174, IL-6 serum levels, and the risk of relapse/recurrence in polymyalgia rheumatica. J Rheumatol. 2006;33:703–708. [PubMed]
34. Ravaglia G, Forti P, Maioli F, et al. Associations of the -174 G/C interleukin-6 gene promoter polymorphism with serum interleukin 6 and mortality in the elderly. Biogerontology. 2005;6:415–423. [PubMed]
35. Walston J, Arking DE, Fallin D, et al. IL-6 gene variation is not associated with increased serum levels of IL-6, muscle, weakness, or frailty in older women. Exp Gerontol. 2005;40:344–352. [PubMed]
36. Fishman D, Faulds G, Jeffery R, et al. The effect of novel polymorphisms in the interleukin-6 (IL-6) gene on IL-6 transcription and plasma IL-6 levels, and an association with systemic-onset juvenile chronic arthritis. J Clin Invest. 1998;102:1369–1376. [PMC free article] [PubMed]
37. Malarstig A, Wallentin L, Siegbahn A. Genetic variation in the interleukin-6 gene in relation to risk and outcomes in acute coronary syndrome. Thromb Res. 2007;119:467–473. [PubMed]
38. Karnofsky D, Abelmann WH, Craver LV, Burchenal JH. The use of nitrogen mustards in the palliative treatment of carcinoma. Cancer. 1948;1:634–656.
39. Lee KA, DeJoseph JF. Sleep disturbances, vitality, and fatigue among a select group of employed childbearing women. Birth. 1992;19:208–213. [PubMed]
40. Lee KA, Portillo CJ, Miramontes H. The influence of sleep and activity patterns on fatigue in women with HIV/AIDS. J Assoc Nurses AIDS Care. 2001;12 Suppl:19–27. [PubMed]
41. Lee KA. Self-reported sleep disturbances in employed women. Sleep. 1992;15:493–498. [PubMed]
42. Lee KA, Hicks G, Nino-Murcia G. Validity and reliability of a scale to assess fatigue. Psychiatry Res. 1991;36:291–298. [PubMed]
43. Raudenbush S, Bryk A. Hierarchical linear models: Applications and data analysis methods. 2nd ed. Thousand Oaks, CA: Sage Publications; 2002.
44. Raudenbush S, Bryk A, Cheong YF, Congdon R. HLM6: Hierarchical linear and nonlinear modeling. Lincolnwood, IL: Scientific Software International; 2004.
45. Gomes I, Collins A, Lonjou C, et al. Hardy-Weinberg quality control. Ann Hum Genet. 1999;63:535–538. [PubMed]
46. Rothman KJ. No adjustments are needed for multiple comparisons. Epidemiology. 1990;1:43–46. [PubMed]
47. Dantzer R, Capuron L, Irwin MR, et al. Identification and treatment of symptoms associated with inflammation in medically ill patients. Psychoneuroendocrinology. 2008;33:18–29. [PMC free article] [PubMed]
48. Dantzer R, Kelley KW. Twenty years of research on cytokine-induced sickness behavior. Brain Behav Immun. 2007;21:153–160. [PMC free article] [PubMed]
49. Osoba D. Interpreting the meaningfulness of changes in health-related quality of life scores: lessons from studies in adults. Int J Cancer Suppl. 1999;12:132–137. [PubMed]
50. Sloan JA, Dueck A. Issues for statisticians in conducting analyses and translating results for quality of life end points in clinical trials. J Biopharm Stat. 2004;14:73–96. [PubMed]
51. Miaskowski C, Cooper BA, Paul SM, et al. Subgroups of patients with cancer with different symptom experiences and quality-of-life outcomes: a cluster analysis. Oncol Nurs Forum. 2006;33:E79–E89. [PubMed]
52. Pud D, Ben Ami S, Cooper BA, et al. The symptom experience of oncology outpatients has a different impact on quality-of-life outcomes. J Pain Symptom Manage. 2008;35:162–170. [PubMed]
53. Miaskowski C. Gender differences in pain, fatigue, and depression in patients with cancer. J Natl Cancer Inst Monogr. 2004;32:139–143. [PubMed]
54. Servaes P, Verhagen C, Bleijenberg G. Fatigue in cancer patients during and after treatment: prevalence, correlates and interventions. Eur J Cancer. 2002;38:27–43. [PubMed]
55. Gaugler JE, Given WC, Linder J, et al. Work, gender, and stress in family cancer caregiving. Support Care Cancer. 2008;16:347–357. [PubMed]
56. Akechi T, Kugaya A, Okamura H, Yamawaki S, Uchitomi Y. Fatigue and its associated factors in ambulatory cancer patients: a preliminary study. J Pain Symptom Manage. 1999;17:42–48. [PubMed]
57. Husain AF, Stewart K, Arseneault R, et al. Women experience higher levels of fatigue than men at the end of life: a longitudinal home palliative care study. J Pain Symptom Manage. 2007;33:389–397. [PubMed]