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School of Nursing, University of California, San Francisco, California, USAInstitute for Human Genetics, University of California, San Francisco, California, USA
Address correspondence to: Christine Miaskowski, RN, PhD, Department of Physiological Nursing, University of California, 2 Koret Way – N631Y, San Francisco, CA 94143-0610, USA.
Little is known about energy levels in oncology patients and their family caregivers.
Objectives
This study sought to identify latent classes of participants, based on self-reported energy levels and evaluate for differences in phenotypic and genotypic characteristics between these classes.
Methods
Energy subscale scores from the Lee Fatigue Scale were used to determine latent class membership. Morning and evening energy scores were obtained just before, during, and for four months after the completion of radiation therapy. Genetic associations were evaluated for 15 proinflammatory and anti-inflammatory cytokine genes.
Results
Two latent classes with distinct morning energy trajectories were identified. Participants who were younger, female, not married/partnered, black, and had more comorbidities, and a lower functional status were more likely to be in the low morning energy class. Two polymorphisms (IL2 rs1479923 and NFKB1 rs4648110) were associated with morning energy latent class membership. Two latent classes with distinct evening energy trajectories were identified. Participants who were younger and male and who had more comorbidities, decreased body weight, and a lower functional status were more likely to be in the moderate evening energy class. Five different polymorphisms (IL1R2 rs4141134, IL6 rs4719714, IL17A rs8193036, NFKB2 rs1056890, and TNFA rs1800683) were associated with evening energy latent class membership.
Conclusion
This study provides preliminary evidence that decrements in morning and evening energy are associated with different phenotypic risk factors and cytokine gene variations.
have separate subscales for fatigue-inertia and energy-vigor. The energy subscale of the POMS evaluates the intensity of energy using a variety of descriptors (e.g., energetic, full of pep, vigorous, active, lively). Like the POMS, the Lee Fatigue Scale (LFS) has two subscales (i.e., a fatigue subscale with 13 items and an energy subscale with five items). The LFS asks participants to rate their level of energy using a 0–10 Numeric Rating Scale (NRS) on five descriptors (i.e., energetic, active, vigorous, efficient, lively). The original psychometric evaluation of the LFS identified these two distinct subscales.
Given these findings, additional research is warranted that provides a more detailed characterization (e.g., diurnal variations, changes in severity) of the symptom of energy.
Our research team has used growth mixture modeling (GMM) to identify subgroups (i.e., latent classes) of oncology patients and their family caregivers (FCs) who differed in their experiences with depression,
Preliminary evidence of an association between an interleukin 6 promoter polymorphism and self-reported attentional function in oncology patients and their family caregivers.
In all these GMM analyses, the phenotypic and molecular data from patients and their FCs were combined for a number of reasons. First, both patients and their FCs experience the stress associated with a cancer diagnosis. For the FC, numerous physical, psychological, social, and economic stressors impact their mental and physical health.
In addition, both groups of individuals have other chronic medical conditions and demands on their time that could result in decreased energy. Finally, both patients and FCs experience significant and comparable levels of sleep disturbance,
Inflammation is mediated in part by changes in proinflammatory and anti-inflammatory proteins, their receptors, and a number of transcriptional regulators that affect both the peripheral and the central nervous systems. Therefore, it is reasonable to hypothesize that variations in cytokine genes may contribute to interindividual variability in morning and evening energy levels.
Given the paucity of research on variations in energy levels in oncology patients and their FCs, the purposes of this study were to identify subgroups of individuals (i.e., latent classes derived using GMM) based on their subjective reports of morning and evening energy levels from before the initiation to four months after the completion of the patients' radiation therapy (RT) and to evaluate for differences in demographic, clinical, and symptom characteristics between these latent classes. In addition, based on the results of the GMM analyses for morning and evening energy, variations in a number of genes that encode for cytokines, their receptors, and related transcription factors were evaluated between the latent classes. Separate analyses were done for morning and evening energy.
Methods
Participants and Settings
This descriptive study is part of a larger longitudinal study that evaluated multiple symptoms in both patients who underwent primary or adjuvant RT and their FCs. The methods for this study are described in detail elsewhere.
In brief, patients and their FCs were recruited from two RT departments located in a Comprehensive Cancer Center and a community-based oncology program at the time of the patient's simulation visit.
Patients were eligible to participate if they were 18 years or older; were scheduled to receive primary or adjuvant RT for breast, prostate, lung, or brain cancer; were able to read, write, and understand English; gave written informed consent; and had a Karnofsky Performance Status (KPS) score of ≥60. Patients were excluded if they had metastatic disease, more than one cancer diagnosis, or a diagnosed sleep disorder. FCs were eligible to participate if they were 18 years or older; were able to read, write, and understand English; gave written informed consent; had a KPS score of ≥60; were living with the patient; and did not have a diagnosed sleep disorder.
Instruments
The demographic questionnaire obtained information on age, gender, marital status, education, ethnicity, employment status, and the presence of a number of comorbid conditions.
Lee Fatigue Scale
The LFS comprises 18 items designed to assess physical fatigue and energy.
Each item was rated on a 0–10 NRS. The energy subscale score was calculated as the mean of the five energy items, with higher scores indicating higher levels of energy. Participants were asked to rate each item based on how they felt right now, within 30 minutes of awakening (i.e., morning energy), and before going to bed (i.e., evening energy). Cutoff scores of ≤6.0 and ≤3.5 indicate low levels of morning and evening energy, respectively.
In this study, Cronbach's alphas for evening and morning energy were 0.95 and 0.96 for patients and 0.95 and 0.96 for FCs, respectively.
Spielberger State-Trait Anxiety Inventories
These inventories consist of 20 items each that are rated from one to four. The scores for each scale are summed and can range from 20 to 80. Cutoff scores of ≥31.8 and ≥32.2 indicate high levels of trait and state anxiety, respectively. The Spielberger State-Trait Anxiety Inventory-State subscale and Spielberger State-Trait Anxiety Inventory-Trait subscale inventories have well-established validity and reliability.
In the present study, the Cronbach's alphas for the Spielberger State-Trait Anxiety Inventory-Trait subscale and Spielberger State-Trait Anxiety Inventory-State subscale were 0.92 and 0.95 for patients and 0.89 and 0.93 for FCs, respectively.
Center for Epidemiological Studies-Depression Scale
The Center for Epidemiological Studies-Depression Scale (CES-D) comprises 20 items selected to represent the major symptoms in the clinical syndrome of depression. Scores can range from 0 to 60, with scores of ≥16 indicating the need for individuals to seek a clinical evaluation for major depression. The CES-D has well-established validity and reliability.
In the present study, the Cronbach's alpha for the CES-D was 0.88 for patients and 0.84 for FCs.
Pittsburgh Sleep Quality Index
The Pittsburgh Sleep Quality Index (PSQI) consists of 19 items designed to assess the quality of sleep in the past month. The global PSQI score is the sum of the seven component scores. Each component score ranges from 0 to 3, and the global PSQI score ranges from 0 to 21. Higher global and component scores indicate a higher level of sleep disturbance. A global PSQI score of >5 indicates a significant level of sleep disturbance. The PSQI has well-established validity and reliability.
In this study, the Cronbach's alphas for the global PSQI score were 0.72 for patients and 0.68 for FCs.
General Sleep Disturbance Scale
The General Sleep Disturbance Scale (GSDS) consists of 21 items designed to assess the quality of sleep in the past week. Each item is rated on a 0 (never) to 7 (every day) NRS. The GSDS total score is the sum of the seven subscale scores, which can range from 0 (no disturbance) to 147 (extreme sleep disturbance). Each mean subscale score can range from 0 to 7. Higher total and subscale scores indicate higher levels of sleep disturbance. Subscale scores of ≥3 and a GSDS total score of ≥43 indicate a significant level of sleep disturbance.
Higher mean scores on a 0–10 NRS indicate greater capacity to direct attention. Scores are grouped into categories of attentional function (i.e., <5.0 low function, 5.0–7.5 moderate function, >7.5 high function).
Participants who responded yes to the question about having pain were asked to indicate the cause of their pain and to rate its intensity (i.e., now, least, average, and worst) using a 0 (no pain) to 10 (worst pain imaginable) NRS.
Objective Measure of Sleep Disturbance
Objective data on sleep-wake activity rhythms were obtained by continuous noninvasive monitoring of activity over 48 hours using a wrist motion sensor (Mini Motionlogger Actigraph; Ambulatory Monitoring, Inc., Ardsley, NY).
Patterns of circadian activity rhythms and their relationships with fatigue and anxiety/depression in women treated with breast cancer adjuvant chemotherapy.
The study was approved by the Committee on Human Research at the University of California, San Francisco and at the second site. Approximately one week before the start of RT, patients were invited to participate in the study. If the FC was present, a 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 participation. These FCs completed the enrollment procedures at home.
At the time of the simulation visit (i.e., enrollment), participants (patients and FCs) completed the self-report questionnaires. After the initiation of RT, participants completed the symptom questionnaires at four weeks after the initiation of RT, at the end of RT, and at 4, 8, 12, and 16 weeks after the completion of RT (i.e., seven assessments over six months). In addition, patients' medical records were reviewed for disease and treatment information.
At each of the seven assessments, participants completed the LFS before going to bed each night (i.e., evening energy) and on arising each morning (i.e., morning energy) for two consecutive days. Participants wore the wrist actigraph to monitor nocturnal sleep/rest and daytime wake/activity continuously for two consecutive weekdays and completed a two-day diary. Participants were asked to use the event marker on the wrist actigraph to indicate lights out and lights on time. Because the actual time is important in the calculation of the amount of sleep obtained in the amount of time designated for sleep, having an additional source of information about nap times, bed times, and wake times is important. This information was recorded in a two-day diary. On awakening, the participants used the diary to indicate the number of awakenings during the night.
Methods of Analysis for Phenotypic Data
Data were analyzed using SPSS, version 22 (IBM Corp., Armonk, NY) and Mplus, version 7.11 (Muthén & Muthén, Los Angeles, CA). Descriptive statistics and frequency distributions were generated on the sample characteristics and symptom severity scores. Independent sample t-tests and Chi-squared analyses were done to evaluate for differences in demographic, clinical, and symptom characteristics between patients and FCs and between the GMM latent classes.
Actigraphy files in zero-crossing mode, with 30-second intervals, were analyzed using the Cole-Kripke algorithm in the Action 4 software (Ambulatory Monitoring Inc., Ardsley, NY) by two of the researchers. First, the file was scanned for missing data. Time limits were set for the 48-hour period. The file was reviewed, and intervals were individually set for each day and night period using in order of priority as decision guides: the event marker, diary data, channel data, and cascading movement data.
GMM with robust maximum likelihood estimation was used to identify latent classes (i.e., subgroups of participants) with distinct morning and evening energy trajectories over the six months of the study.
Separate GMM analyses were done for morning and evening energy levels. Because 65% of the participants were in patient-caregiver dyads, models were estimated with dyad as a clustering variable to ensure that any dependency between the morning and evening energy scores for patients and FCs in the same dyad were controlled for in the GMM analysis.
The GMM methods are described in detail elsewhere.
In brief, a single growth curve that represented the average change trajectory was estimated for the total sample. Then, the number of latent growth classes that best fit the data was identified using guidelines recommended by a number of experts.
Missing data for the morning or evening energy scores were accommodated in Mplus, version 7.11 through the use of full information maximum likelihood and the use of the expectation-maximization algorithm. This method assumes that any missing data are ignorable (i.e., missing at random).
Adjustments were not made for missing data in comparisons of the classes identified with the GMM. Therefore, the cohort for each analysis was dependent on the largest set of available data across classes. Differences in demographic, clinical, and symptom characteristics between patients and FCs and between the latent classes were considered statistically significant at the P < 0.05 level.
Methods of Analysis for Genomic Data
Gene Selection
Genes that encode for proinflammatory cytokines and their receptors include interleukin 2 (IL2), IL8, IL17A, and tumor necrosis factor alpha (TNFA), as well as interferon gamma receptor 1 (IFNGR1) and IL1 receptor, type 1 (IL1R1). Genes that encode for anti-inflammatory cytokines and their receptors include IL4, IL10, and IL13, as well as IL1R2. Genes that encode for cytokines with both proinflammatory and anti-inflammatory functions include IFNG, IL1 beta (IL1B), and IL6. Genes that encode for transcription factors, which moderate the levels of cytokine production, include nuclear factor kappa beta 1 (NFKB1) and NFKB2.
Genomic DNA was extracted from archived buffy coats using the PUREGene DNA Isolation System (Invitrogen, Carlsbad, CA). Of the 287 participants recruited, DNA was recovered for 253 (i.e., 168 patients and 85 FCs). No differences were found in any demographic and clinical characteristics between participants who did and did not choose to participate in the study or in those participants for whom DNA could not be recovered from archived specimens.
DNA samples were quantitated with a Nanodrop Spectrophotometer (ND-1000) and normalized to a concentration of 50 ng/μL. Genotyping was performed blinded to clinical status. Samples were genotyped using the GoldenGate genotyping platform (Illumina, San Diego, CA) and processed using GenomeStudio (Illumina, San Diego, CA). Signal intensity profiles and resulting genotype calls for each single nucleotide polymorphism (SNP) were visually inspected by two blinded reviewers.
SNP Selection
A combination of tagging SNPs and literature-driven SNPs were selected for analysis. Tagging SNPs were required to be common (defined as having a minor allele frequency ≥0.05) in public databases. To ensure robust genetic association analyses, quality control filtering of SNPs was performed. SNPs with call rates of <95% or Hardy-Weinberg P-values of <0.001 were excluded.
As shown in Supplemental Table 1 (available at jpsmjournal.com), a total of 92 SNPs among the 15 candidate genes passed all quality control filters and were included in the genetic association analyses. Potential functional roles for these SNPs were examined using PupaSuite 2.0.
Allele and genotype frequencies were determined by gene counting. Hardy-Weinberg equilibrium was assessed by the Chi-squared or Fisher exact tests. Measures of linkage disequilibrium (LD) (i.e., D' and r2) were computed from the participants' genotypes with Haploview 4.2. LD-based haplotype block definition was based on D' CI.
To improve the stability of haplotype inference, the haplotype construction procedure was repeated five times using different seed numbers with each cycle. Only haplotypes inferred with a probability of >0.85, across the five runs, were retained for analysis.
Ancestry informative markers (AIMs) were used to minimize confounding because of population stratification.
A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: utility and applications.
using Helix Tree (Golden Helix, Bozeman, MT). One hundred six AIMs were included in the analysis.
For association tests, three genetic models were assessed for each SNP: additive, dominant, and recessive. Barring trivial improvements (i.e., delta <10%), the genetic model that best fits the data, by maximizing the significance of the P-value, was selected for each SNP. The first three principal components were selected to adjust for potential confounding because of population substructure (i.e., race/ethnicity) by including the three covariates in all regression models.
A backward stepwise approach was used to create a parsimonious model. Except for self-reported race/ethnicity and AIMs, only predictors with a P-value of <0.05 were retained in the final model. Genetic model fit and both unadjusted and covariate-adjusted odds ratios (ORs) were estimated using STATA, version 13 (StataCorp LP, College Station, TX).
Preliminary evidence of an association between an interleukin 6 promoter polymorphism and self-reported attentional function in oncology patients and their family caregivers.
the implementation of rigorous quality controls for genomic data, nonindependence of SNPs/haplotypes in LD, and exploratory nature of the analyses, adjustments were not made for multiple testing. In addition, significant SNPs identified in the bivariate analyses were evaluated using regression analyses that controlled for differences in phenotypic characteristics, potential confounding because of population stratification, and variation in other SNPs/haplotypes within the same gene. Only those SNPs that remained significant were included in the final presentation of the results. Therefore, the significant independent associations reported are unlikely to be solely because of chance. Unadjusted associations are reported for all SNPs that passed quality control criteria in Supplemental Table 1 to allow for subsequent comparisons and meta-analyses.
Results
Overall Sample
Participant Characteristics
Complete phenotypic and genotypic data were available for 252 participants. Most participants were Caucasian, well educated, and married/partnered. Patients made up 66.3% of the total sample. The mean age of the total sample was 61.5 years. The average participant had more than four comorbid conditions and a mean KPS score of 92. Gender was evenly represented within the total sample, with 46.4% male and 53.6% female participants. Most of the FCs (93%) were the patients' spouses. Approximately 33% of the patients had breast cancer, 54% had prostate cancer, 8% had brain cancer, and 6% had lung cancer.
At enrollment, no significant differences were found between patients and FCs in their ratings of morning energy (5.9 ± 1.9 vs. 5.8 ± 2.1), evening energy (4.5 ± 1.8 vs. 4.3 ± 1.9), morning fatigue (2.3 ± 1.9 vs. 2.3 ± 1.9), evening fatigue (4.2 ± 2.0 vs. 4.5 ± 2.0), attentional fatigue (7.2 ± 1.8 vs. 7.3 ± 1.8), trait anxiety (33.8 ± 10.0 vs. 34.7 ± 9.7), state anxiety (31.0 ± 10.9 vs. 31.0 ± 10.7), worst pain (2.0 ± 3.2 vs. 1.5 ± 3.1), sleep disturbance (39.0 ± 19.6 vs. 38.7 ± 16.7), and depressive symptoms (9.2 ± 8.7 vs. 8.3 ± 7.2).
Morning Energy
Results of GMM Analysis for Morning Energy
Two distinct latent classes of morning energy trajectories were identified using GMM analysis (Fig. 1a). The fit indices for the various models are shown in Table 1. A two-class model was selected because its Bayesian information criterion was smaller than the one-class and three-class models. In addition, each class in the two-class model had a reasonable size and interpretability.
Fig. 1Observed and estimated a) morning and b) evening energy trajectories for participants in each of the latent classes as well as the mean energy scores for the total sample.
This number is the χ2 statistic for the VLMR. When significant, the VLMR test provides evidence that the K-class model fits the data better than the K-1 class model.
Random coefficient latent growth curve model with linear and quadratic components; χ2 = 47.070, 26 degrees of freedom, P < 0.01, comparative fit index = 0.979, and root mean square error of approximation = 0.057.
Two-class model was selected, based on its having the smallest BIC and a significant VLMR. Furthermore, the VLMR is not significant for the three-class model.
Random coefficient latent growth curve model with linear and quadratic components; χ2 = 86.548, 26 degrees of freedom, P < 0.00005, comparative fit index = 0.945, and root mean square error of approximation = 0.096.
Two-class model was selected, based on its having the smallest BIC and a significant VLMR. Furthermore, the VLMR is not significant for the three-class model.
GMM = growth mixture modeling; LL = log likelihood; AIC = Akaike information criterion; BIC = Bayesian information criterion; VLMR = Vuong-Lo-Mendell-Rubin likelihood ratio test; NA = not applicable; NS = not significant.
a P < 0.05.
b P < 0.01.
c This number is the χ2 statistic for the VLMR. When significant, the VLMR test provides evidence that the K-class model fits the data better than the K-1 class model.
d Random coefficient latent growth curve model with linear and quadratic components; χ2 = 47.070, 26 degrees of freedom, P < 0.01, comparative fit index = 0.979, and root mean square error of approximation = 0.057.
e Two-class model was selected, based on its having the smallest BIC and a significant VLMR. Furthermore, the VLMR is not significant for the three-class model.
f Random coefficient latent growth curve model with linear and quadratic components; χ2 = 86.548, 26 degrees of freedom, P < 0.00005, comparative fit index = 0.945, and root mean square error of approximation = 0.096.
The parameter estimates for the two latent classes are listed in Table 2. The latent classes were named based on the cutpoints for a clinically meaningful decrement in morning energy (i.e., ≤6.0). The largest percentage of participants was classified into the low morning energy class (50.8%). These participants had a mean morning energy score of 4.7 at enrollment that increased slightly and then leveled off over the course of the study. Participants in the moderate morning energy class (49.2%) had a mean morning energy score of 6.9 that was stable initially and then increased slightly over the course of the study. No differences were found in the percentage of patients and FCs in the low and moderate morning energy classes.
Table 2GMM Parameter Estimates for Morning and Evening Energy Latent Class
Growth mixture model estimates were obtained with robust maximum likelihood, with dyad as a clustering variable to account for dependency between patients and family caregivers within the same dyad. Quadratic slope variances were fixed at zero to improve estimation.
GMM = growth mixture modeling; NS = not significant.
a P < 0.05.
b P < 0.01.
c P < 0.001.
d Trajectory group sizes are for classification of individuals based on their most likely latent class probabilities.
e Growth mixture model estimates were obtained with robust maximum likelihood, with dyad as a clustering variable to account for dependency between patients and family caregivers within the same dyad. Quadratic slope variances were fixed at zero to improve estimation.
Differences in Demographic and Clinical Characteristics Between the Moderate Morning Energy and Low Morning Energy Classes
As summarized in Table 3, no differences were found between the two morning energy classes for most demographic and clinical characteristics. However, participants in the low morning energy class were more likely to be younger (P < 0.001), female (P = 0.002), not married or partnered (P = 0.039), black as compared with white (P = 0.005), have a higher number of comorbid conditions (P = 0.014), and have a lower KPS score (P < 0.001).
Table 3Differences in Demographic and Clinical Characteristics at Enrollment Between the Two Latent Classes for Morning and Evening Energy
Post hoc contrasts revealed that the difference in ethnicity observed in the low morning energy growth mixture modeling (GMM) class as compared with the moderate morning energy GMM class was because of a decreased number of white participants in the low morning energy GMM class as compared with black participants (P = 0.005).
% White
99 (79.8)
88 (69.3)
0.026
38 (73.1)
149 (74.9)
NS
% Asian/Pacific Islander
12 (9.4)
4 (3.2)
2 (3.8)
14 (7.0)
% Black
16 (12.6)
18 (14.5)
10 (19.2)
24 (12.1)
% Hispanic/mixed/other
11 (8.7)
3 (2.4)
2 (3.8)
12 (6.0)
Lives alone (% yes)
22 (28.2)
31 (34.8)
NS
41 (78.8)
133 (67.2)
NS
Married or partnered (% yes)
94 (75.8)
80 (63.5)
0.039
53 (73.6)
121 (68.0)
NS
Children at home (% yes)
16 (15.4)
20 (18.7)
NS
4 (8.9)
32 (19.3)
NS
Older adult at home (% yes)
1 (1.0)
6 (5.5)
NS
1 (2.2)
6 (3.6)
NS
Work for pay (% yes)
50 (41.0)
65 (52.0)
NS
20 (38.5)
95 (48.7)
NS
Patient/FC (% patient)
78 (62.5)
89 (69.5)
NS
35 (67.3)
132 (66.0)
NS
NS = not significant; KPS = Karnofsky Performance Status; FC = family caregiver.
a Post hoc contrasts revealed that the difference in ethnicity observed in the low morning energy growth mixture modeling (GMM) class as compared with the moderate morning energy GMM class was because of a decreased number of white participants in the low morning energy GMM class as compared with black participants (P = 0.005).
Differences in Symptom Characteristics Between the Moderate Morning Energy and Low Morning Energy Classes
As summarized in Table 4, significant differences were found between the two morning energy classes for most symptoms assessed before the initiation of RT. For those symptom scores with significant between-group differences, participants in the low morning energy class reported higher symptom severity scores than participants in the moderate morning energy class.
Table 4Differences in Symptom Severity Scores at Enrollment Between the Two Latent Classes for Morning and Evening Energy
Characteristic
Moderate Morning Energy 122 (49.2%)
Low Morning Energy 128 (50.8%)
P-value
High Evening Energy 112 (20.6%)
Moderate Evening Energy 128 (79.4%)
P-value
Mean (SD)
Mean (SD)
Psychological symptoms at enrollment
STAI-T
30.4 (8.3)
37.6 (10.0)
<0.001
27.5 (6.4)
35.8 (9.9)
<0.001
STAI-S
27.4 (8.1)
34.4 (11.9)
<0.001
25.2 (6.0)
32.5 (11.3)
<0.001
CES-D total
5.6 (5.8)
12.0 (8.9)
<0.001
4.0 (4.3)
10.1 (8.5)
<0.001
PSQI scores at enrollment
Subjective sleep quality
0.8 (0.7)
1.1 (0.7)
<0.001
0.6 (0.5)
1.0 (0.7)
<0.001
Sleep latency
0.7 (0.9)
1.2 (0.9)
<0.001
0.5 (0.8)
1.1 (0.9)
<0.001
Sleep duration
0.7 (0.8)
1.2 (1.0)
<0.001
0.7 (0.7)
1.0 (0.9)
0.020
Habitual sleep efficiency
0.5 (0.8)
0.9 (1.0)
0.002
0.3 (0.7)
0.8 (1.0)
0.003
Sleep disturbance
1.3 (0.5)
1.5 (0.6)
0.001
1.2 (0.5)
1.4 (0.6)
0.003
Use of sleeping medication
0.4 (0.9)
0.9 (1.2)
0.002
0.5 (1.0)
0.7 (1.1)
0.304
Daytime dysfunction
0.5 (0.6)
1.0 (0.6)
<0.001
0.4 (0.6)
0.8 (0.6)
<0.001
PSQI global score
4.8 (2.9)
7.6 (3.7)
<0.001
4.1 (2.3)
6.8 (3.7)
<0.001
General sleep disturbance scores at enrollment
Quality
1.9 (1.6)
2.9 (1.9)
<0.001
1.5 (1.5)
2.7 (1.8)
<0.001
Sleep onset latency
1.2 (1.8)
1.9 (2.1)
0.003
0.8 (1.4)
1.7 (2.1)
0.002
Quantity
4.1 (1.1)
4.7 (1.4)
0.002
4.1 (0.9)
4.5 (1.4)
0.031
Sleep medication
0.2 (0.4)
0.4 (0.7)
0.003
0.2 (0.4)
0.3 (0.6)
0.232
Midsleep awakenings
4.2 (2.6)
4.7 (2.5)
0.114
4.1 (2.6)
4.5 (2.5)
0.219
Early awakenings
1.8 (1.9)
2.8 (2.4)
0.001
1.3 (1.7)
2.6 (2.3)
<0.001
Excessive daytime sleepiness
1.4 (1.2)
2.2 (1.3)
<0.001
1.1 (1.0)
2.0 (1.3)
<0.001
Total GSDS score
32.0 (15.3)
45.6 (19.2)
<0.001
27.9 (13.1)
41.7 (18.8)
<0.001
Actigraphy parameters at enrollment
Sleep period time (minutes)
479.5 (72.5)
489.2 (76.9)
0.323
473.2 (76.9)
487.3 (74.2)
0.238
TST (minutes)
391.4 (82.4)
407.7 (84.3)
0.135
378.6 (95.7)
405.2 (79.5)
0.047
Sleep efficiency
81.5 (14.0)
83.4 (11.7)
0.265
79.3 (16.8)
83.3 (11.6)
0.053
Wake after sleep onset (% of TST)
15.2 (12.8)
12.7 (11.2)
0.124
16.6 (15.1)
13.2 (11.1)
0.078
Wake number
17.7 (8.9)
15.8 (8.6)
0.104
18.7 (9.7)
16.2 (8.5)
0.073
Wake duration (minutes)
3.9 (2.8)
4.2 (6.1)
0.587
3.9 (3.1)
4.1 (5.1)
0.842
Sleep onset latency (minutes)
12.7 (11.8)
16.9 (22.9)
0.081
15.3 (16.6)
14.7 (18.8)
0.844
Fatigue and energy scores at enrollment
Evening fatigue
3.7 (2.1)
4.8 (1.9)
<0.001
2.6 (1.9)
4.7 (1.8)
<0.001
Morning fatigue
1.3 (1.4)
3.2 (2.0)
<0.001
1.2 (1.7)
2.6 (1.9)
<0.001
Evening energy
5.0 (2.0)
3.8 (1.5)
<0.001
5.8 (2.0)
4.1 (1.6)
<0.001
Morning energy
7.0 (1.7)
4.7 (1.6)
<0.001
7.0 (2.1)
5.4 (1.9)
<0.001
Attentional fatigue
7.9 (1.5)
6.5 (1.8)
<0.001
8.4 (1.1)
6.9 (1.8)
<0.001
n (%)
n (%)
Pain (% yes)
43 (36.1)
77 (57.9)
.001
29 (40.3)
91 (50.6)
0.163
STAI-T = Spielberger State-Trait Anxiety Inventory-Trait subscale; STAI-S = Spielberger State-Trait Anxiety Inventory-State subscale; CES-D = Center for Epidemiological Studies-Depression scale; PSQI = Pittsburgh Sleep Quality Index; GSDS = General Sleep Disturbance Scale; TST = total sleep time.
Candidate Gene Analyses of the Two Morning Energy GMM Classes
As summarized in Supplemental Table 1, the genotype frequency was significantly different between the two morning energy classes for eight SNPs and one haplotype: IL1B rs1143643, IL1B rs1143633, IL1B HapA4, IL2 rs1479923, IL6 rs4719714, IL6 rs35610689, NFKB1 rs4648110, TNFA rs1800683, and TNFA rs1041981.
Regression Analyses of Candidate Genes and Morning Energy GMM Latent Classes
To better estimate the magnitude (i.e., OR) and precision (95% CI) of genotype on morning energy class membership (i.e., moderate morning energy vs. low morning energy), multivariable logistic regression analyses were performed that included the following variables in the models: genotype, age, number of comorbid conditions, functional status, and self-reported (i.e., white, black, Asian/Pacific Islander, Hispanic/mixed ethnic background/other) and genomic estimates of race/ethnicity.
The only genetic associations that remained significant in the multivariable analyses were for IL2 rs1479923 (Fig. 2a; Table 5) and NFKB1 rs4648110 (Fig. 2b; Table 5). In the regression analysis for IL2 rs1479923, being homozygous for the rare T allele (i.e., CC + CT vs. TT) was associated with a 75% decrease in the odds of belonging to the low morning energy class. In the regression analysis for NFKB1 rs4648110, being heterozygous or homozygous for the rare A allele (i.e., TT vs. TA + AA) was associated with a 42% decrease in the odds of belonging to the low morning energy class.
Fig. 2a) Differences between the morning energy latent classes in the percentages of participants who were homozygous or heterozygous for the common allele (CC + CT) or homozygous for the rare allele (TT) for rs1479923 in interleukin 2 (IL2). Values are plotted as unadjusted proportions with corresponding P-value. b) Differences between the latent classes in the percentages of patients who were homozygous for the common allele (TT) or heterozygous or homozygous for the rare allele (TA + AA) for rs4648110 in nuclear factor kappa beta 1 (NFKB1). Values are plotted as unadjusted proportions with corresponding P-value.
Multiple logistic regression analysis of moderate vs. low morning energy growth mixture modeling (GMM) classes (n = 234) and of high vs. moderate evening energy classes. For each model, the first three principle components identified from the analysis of ancestry informative markers and self-report race/ethnicity were retained in all models to adjust for potential confounding because of race or ethnicity (data not shown). For morning energy GMM regression analyses, predictors evaluated in each model included genotype (IL2 rs1479923 genotype: CC + CT vs. TT; NFKB1 rs4648110 genotype: TT vs. TA + AA), age (5 years' increments), number of comorbid conditions, and functional status (KPS score in 10 unit increments). For evening energy GMM regression analyses, predictors evaluated in each model included genotype (IL1R2 rs4141134: TT vs. TC + CC; IL6 rs4719714: AA vs. AT + TT; IL17A rs8193036: TT vs. TC + CC; NFKB2 rs1056890: CC + CT vs. TT; TNFA rs1800683: GG + GA vs. AA), age (5 years’ increments), gender (female), ethnicity (black as compared with white), and functional status (KPS score in 10 unit increments).
Two distinct latent classes of evening energy trajectories were identified using GMM (Fig. 1b). The fit indices for the various models are shown in Table 1. A two-class model was selected because its Bayesian information criterion was smaller than the one-class and three-class models. In addition, each class in the two-class model had a reasonable size and interpretability.
The parameter estimates for the two latent classes are listed in Table 2. The latent classes were named based on the cutpoints for a clinically meaningful decrement in evening energy (i.e., ≤3.5). The largest percentage of participants was classified into the moderate evening energy class (79.4%). These participants had a mean evening energy score of 4.0 at enrollment that decreased slightly and then leveled off over the course of the study. Participants in the high evening energy class (20.6%) had a mean evening energy score of 5.8 that increased and then decreased slightly over the course of the study. No differences were found in the percentage of patients and FCs in the high and moderate evening energy classes.
Differences in Demographic and Clinical Characteristics Between the High Evening Energy and Moderate Evening Energy Classes
As summarized in Table 3, no differences were found between the two evening energy latent classes for most demographic and clinical characteristics. However, participants in the moderate evening energy class were more likely to be younger (P < 0.001) and male (P = 0.001); and have a greater number of comorbid conditions (P = 0.025), decreased body weight (P = 0.035), and a lower KPS score (P < 0.001).
Differences in Symptom Characteristics Between the High Evening Energy and Moderate Evening Energy Classes
As summarized in Table 4, significant differences were found between the two evening energy classes for most of the symptoms assessed before the initiation of RT. For those symptom scores with significant between-group differences, participants in the moderate evening energy class reported higher symptom severity scores than participants in the high evening energy class.
Candidate Gene Analyses of the Two Evening Energy GMM Classes
As summarized in Supplemental Table 1, the genotype frequency was significantly different between the two latent classes for seven SNPs: IL1R2 rs4141134, IL6 rs4719714, IL17A rs8193036, NKFB2 rs1056890, TNFA rs1800683, TNFA rs1041981, and TNFA rs1800629.
Regression Analyses of Candidate Genes and Evening Energy GMM Latent Classes
To better estimate the magnitude (i.e., OR) and precision (95% CI) of genotype on evening energy class membership (i.e., high evening energy vs. moderate evening energy), multivariable logistic regression analyses were performed that included the following variables in the models: genotype, age, gender, functional status, and self-reported (i.e., white, black, Asian/Pacific Islander, Hispanic/mixed ethnic background/other) and genomic estimates of race/ethnicity.
The genetic associations that remained significant in the multivariable logistic regression analyses were for ILR2 rs4141134 (Fig. 3a; Table 5), IL6 rs4719714 (Fig. 3b; Table 5), IL17A rs8193036 (Fig. 3c; Table 5), NFKB2 rs1056890 (Fig. 3d; Table 5), and TNFA rs1800683 (Fig. 3e; Table 5). In the regression analysis for ILR2 rs4141134, being heterozygous or homozygous for the rare C allele (i.e., TT vs. TC + CC) was associated with a 64% decrease in the odds of belonging to the moderate evening energy class. In the regression analysis for IL6 rs4719714, being heterozygous or homozygous for the rare T allele (i.e., AA vs. AT + TT) was associated with a 73% decrease in the odds of belonging to the moderate evening energy class. In the regression analysis for IL17A rs8193036, being heterozygous or homozygous for the rare C allele (TT vs. CT + CC) was associated with a 61% decrease in the odds of belonging to the moderate evening energy class. In the regression analysis for NFKB2 rs1056890, being homozygous for the rare T allele (i.e., CC + CT vs. TT) was associated with a 9.7-fold increase in the odds of belonging to the moderate evening energy class. In the regression analysis for TNFA rs1800683, being homozygous for the rare A allele (i.e., GG + GA vs. AA) was associated with a 64% decrease in the odds of belonging to the moderate evening energy class.
Fig. 3a) Differences between the evening energy latent classes in the percentages of patients who were homozygous for the common allele (TT) or heterozygous or homozygous for the rare allele (TC + CC) for rs4141134 in interleukin 1 receptor 2 (IL1R2). Values are plotted as unadjusted proportions with corresponding P-value. b) Differences between the latent classes in the percentages of patients who were homozygous for the common allele (AA) or heterozygous or homozygous for the rare allele (AT + TT) for rs4719714 in IL6. Values are plotted as unadjusted proportions with corresponding P-value. c) Differences between the latent classes in the percentages of patients who were homozygous for the common allele (TT) or heterozygous or homozygous for the rare allele (TC + CC) for rs8193036 in IL17A. Values are plotted as unadjusted proportions with corresponding P-value. d) Differences between the latent classes in the percentages of patients who were homozygous or heterozygous for the common allele (CC + CT) or homozygous for the rare allele (TT) for rs1056890 in NFKB2. Values are plotted as unadjusted proportions with corresponding P-value. e) Differences between the latent classes in the percentages of patients who were homozygous or heterozygous for the common allele (GG + GA) or homozygous for the rare allele (AA) for rs1800683 in tumor necrosis factor alpha (TNFA). Values are plotted as unadjusted proportions with corresponding P-value.
This study is the first to identify subgroups of oncology patients and FCs based on their distinct experiences of morning and evening energy and evaluate for associations between these subgroups and polymorphisms in a number of cytokine genes. Although two distinct latent classes were identified for both morning and evening energy, different demographic and clinical characteristics as well as different cytokine gene variations were associated with latent class membership. These findings support our hypothesis that morning and evening energy are distinct but related symptoms.
In terms of the overall phenotypic findings, more than 50% of the participants reported morning energy levels that were well below the cutpoint for clinically meaningful decrements in energy (i.e., ≤6.0). Of note, these clinically meaningful decrements in morning energy levels persisted for four months after the completion of RT (Fig. 1a). In contrast, decrements in evening energy levels were reported by about 80% of the participants. Although the cutoff score for a clinically meaningful decrement in evening energy is ≤3.5, the patients and FCs in the moderate evening energy class had evening energy scores of approximately 4.0 for the entire six months of the study. Across the morning and evening energy classes, 18.7% (n = 47) of the participants were classified into the moderate morning and high evening energy classes; 1.9% (n = 5) were in the low morning and high evening energy classes; 30.6% (n = 77) were in the moderate morning and moderate evening energy classes; and 48.8% (n = 123) were in the low morning and moderate evening energy classes. These findings suggest that a significant number of patients and their FCs have persistent decrements in both morning and evening energy levels.
In terms of demographic and clinical characteristics, younger age, as well as a higher number of comorbid conditions, and a lower KPS score were associated with membership in both the low morning and moderate evening energy classes. A consistent finding across all our GMM studies of common symptoms in oncology patients and their FCs
is that younger participants are classified in the higher symptom class. As explained previously, these age differences may be associated with physiologic (e.g., changes in stress responses
) adaptations associated with aging. Although the most common comorbid conditions in this sample were back problems (49%), arthritis (44%), allergies (43%), and hypertension (30%), additional research is needed to determine which comorbid conditions are associated with more severe decrements in energy.
Again, consistent with our previous analyses of common symptoms in this same sample,
participants with lower KPS scores were more likely to be classified in both the low morning and moderate evening energy classes. The difference in KPS scores between the two morning and the two evening energy classes represent not only statistically significant but also clinically meaningful decrements in functional status (i.e., Cohen's d = 0.64 and d = 0.55, respectively).
Of note, age and KPS scores were retained in the final phenotypic regression models for both morning and evening energy (Table 5). Taken together, these consistent findings across multiple symptoms suggest that clinicians need to consider an individual's age and KPS score as part of their evaluation of symptom burden.
In this study, the findings regarding ethnic differences in energy levels are inconsistent. In the bivariate, but not in the multivariate analyses, whites compared with blacks were less likely to be classified in the low morning energy class. In contrast, in the multivariate analysis for evening energy, which controlled for self-report and genomic estimates of race/ethnicity, being black as compared with white was associated with a 95% reduction in the odds of belonging to the moderate evening energy class. These inconsistent findings may be related to the relatively small number of ethnic minorities in this sample and warrant evaluation in future studies.
As shown in Table 2, only two demographic and one clinical characteristic distinguished between the morning and evening energy latent classes. First, women were more likely to be classified into the lower morning energy class and men were more likely to be classified into the moderate evening energy class. However, gender remained significant in the final regression model only for evening energy. Although no studies were found on gender differences in energy levels, findings on gender differences in the occurrence and severity of other symptoms are inconsistent.
Second, participants who were married or partnered were less likely to be classified in the low morning energy class. This finding may be attributed to increased levels of social support.
Symptoms of hormonal therapy and social support: is there a connection? Comparison of symptom severity, symptom interference and social support among breast cancer patients receiving and not receiving adjuvant hormonal treatment.
Finally, although an explanation is not readily apparent and warrants investigation in future studies, participants in the moderate evening energy class had a lower body weight than participants in the high evening energy class.
It should be noted that no differences were found in the distribution of patients and FCs in either the morning or the evening energy latent classes. This finding suggests that the mechanisms that contribute to lower levels of morning and evening energy are not solely dependent on the characteristics of the cancer or its treatment.
This study is the first to report on differences in a number of symptom severity scores between the morning and the evening energy latent classes (Table 4). In terms of state and trait anxiety, both the low morning and moderate evening energy classes had anxiety scores at enrollment that were above the clinically meaningful cutoff scores. Previous research has documented that 10%–20% of patients experience clinically significant levels of anxiety at the initiation of RT.
It is reasonable to hypothesize that higher levels of anxiety could contribute to sleep disturbance and decrements in energy.
For both of the sleep disturbance measures, participants in the low morning and moderate evening energy latent classes reported total GSDS and PSQI global scores that were above the clinically meaningful cutoff scores. The use of sleep medications was the only subscale score on both of the sleep disturbance measures that differentiated between the morning and evening energy classes. Although detailed information on the specific sleep medications and their duration of use are not available, the chronic use of sleep medications is associated with disrupted sleep patterns and drowsiness on awakening.
In terms of the objective sleep parameters, except for total sleep time, no differences were found in any of the actigraphy parameters between either the morning or evening energy latent classes. However, patients in the moderate evening energy class reported a significantly longer sleep time (i.e., 26.6 minutes) than the high evening energy class. Additional research is warranted to confirm these findings.
This study is the first to identify genotypic differences in morning and evening energy. Variations in two genes (IL2 and NFKB1) were associated with morning energy. The SNP in IL2 (rs1479923) is located immediately downstream of the IL2 gene and has no known function. Presumably, it is in LD with an unmeasured causal polymorphism(s). In the present study, being homozygous for the rare T allele was associated with a 64% decrease in the odds of being in the low morning energy class. Although IL2 plays a role in immune activation and homeostasis,
ongoing characterization of this cytokine continues to reveal novel functions. For example, the demonstration of altered IL2 gene expression in peripheral leukocytes in response to psychological stress
suggests that changes in energy levels can mediate and be mediated by immune activation.
For NFKB1, the SNP rs4648110 is located in the intronic region of the gene and has no known function. However, NFKB is an important nuclear transcription factor that regulates a large number of cytokines and is critical for the regulation of inflammation. Increased transcription of NFKB can increase inflammation and angiogenesis as well as cell survival and growth.
rs4648110 was associated with a decreased risk of colon cancer. In the present study, individuals who were heterozygous or homozygous for the rare A allele had a 42% decrease in the odds of being in the low morning energy class. Consistent with findings from our study, these authors suggested that this SNP might be in LD with a functional SNP that decreases the transcription of NFKB1, which results in decreases in inflammatory responses and the associated occurrence of colon cancer.
One could hypothesize that a decrease in inflammatory responses would result in higher energy levels.
Variations in five different genes (IL1R2, IL6, IL17A, NFKB2, and TNFA) were associated with evening energy. IL1R2 rs4141134 is located in the immediate promoter of the gene in a glucocorticoid receptor binding site. Carrying one or two doses of the rare C allele was associated with a 64% decrease in the odds of being in the moderate evening energy class. In a previous analysis with the same sample (13), this SNP was part of an IL1R2 haplotype that was associated with an increased odds of belonging to a group of participants with subsyndromal levels of depressive symptoms. It is unknown if being homozygous for the common T allele results in alterations in glucocorticoid receptor binding to the IL1R2 promoter, which results in altered expression of the IL-1-RII protein. Changes in glucocorticoid signaling under situations of perceived stress results in redistribution of energy levels so that an individual has the capacity to respond to the potential threat. Persistent stress (e.g., because of cancer or the need to care for a family member with cancer), coupled with suboptimal cytokine function because of gene variations, may result in dysregulation of glucocorticoid function and subsequent energy impairments.
Preliminary evidence of an association between a functional interleukin-6 polymorphism and fatigue and sleep disturbance in oncology patients and their family caregivers.
where we observed an association between carrying one or two doses of the rare T allele in IL6 rs4719714 with overall lower levels of morning and evening fatigue and sleep disturbance, a similar association was observed with evening energy. Being heterozygous or homozygous for the rare T allele was associated with a 73% decrease in the odds of being in the moderate evening energy class. Although it does not appear to be functional, rs4719714 is in near perfect LD with rs10499563.
The rare allele at rs10499563 is associated with decreased production of IL6. Decreased production of this proinflammatory cytokine could result in decreased fatigue and/or increased energy.
IL-17A, a proinflammatory cytokine that regulates localized inflammatory responses within tissues,
was associated with evening but not morning energy. Being heterozygous or homozygous for the rare C allele in rs8193036 was associated with a 61% decrease in the odds of being in the moderate evening energy class. Although this SNP, which is located in the promoter region of IL17A, has no known function, it could influence the regulation of IL17A gene expression by altering transcription factor binding in this region.
NFKB2 and NFKB1 each encode for one-half of the heterodimeric protein NFKB, which is a central transcriptional modulator of inflammation.
Being homozygous for the rare T allele in NFKB2 rs1056890 was associated with a 9.7-fold increase in the odds of being in the moderate evening energy class. This SNP is located immediately downstream of the gene. Previously, we identified an association between sleep disturbance and variations in NFKB2 in both the current (rs7897947)
In both of these studies, being homozygous or heterozygous for the rare allele was associated with less sleep disturbance. These inconsistent findings warrant confirmation in future studies.
In terms of TNFA, being homozygous for the rare A allele in rs1800683 was associated with a 64% decrease in the odds of being in the moderate evening energy class. TNFA rs1800683 is located in the intron region of the gene. Although this SNP is associated with altered TNFA gene expression, the direction of the relationship differs among studies reported in the literature.
larger samples may identify additional latent classes as well as different phenotypic and molecular characteristics associated with latent class membership. Although vigorous quality control analyses and adjustment for potential confounding because of population substructure were performed, some of the relationships identified may be because of Type 1 error. The common and unique predictors associated with latent class membership for both morning and evening energy must be interpreted with caution until they are replicated in future studies. The genetic associations observed in the present study require validation in an independent cohort. Ideally, future studies need to evaluate for changes in serum cytokines and gene expression associated with these polymorphisms.
Conclusion
In summary, both the phenotypic and molecular findings suggest that morning and evening energy are distinct but related symptoms. Additional research is warranted to identify specific demographic and clinical characteristics that contribute to more severe decrements in morning and evening energy. In addition, future molecular analyses will assist with the identification of common and distinct mechanisms for morning and evening energy.
Disclosures and Acknowledgments
This research was supported by a grant from the National Institute of Nursing Research (NR04835) and partially supported by a UCSF Academic Senate grant to Drs. Dunn and Aouizerat. Dr. Miaskowski is funded by the American Cancer Society as a Clinical Research Professor and the National Cancer Institute (K05 CA16890). Dr. Dhruva is funded through a Mentored Patient-Oriented Research Career Development Award from the National Institutes of Health (K23 AT005340). The authors declare no conflicts of interest.
Appendix
Supplemental Table 1Single Nucleotide Polymorphisms in Cytokine Genes Analyzed for Morning and Evening Energy
Gene
SNP
Position
Chr
MAF
Alleles
Morning Energy
Evening Energy
Chi Square
P-value
Model
Chi Square
P-value
Model
IFNG1
rs2069728
66834051
12
0.079
G>A
0.67
0.717
A
0.73
0.694
A
IFNG1
rs2069727
66834490
12
0.411
A>G
0.14
0.931
A
3.82
0.148
A
IFNG1
rs2069718
66836429
12
0.442
C>T
1.32
0.518
A
1.88
0.391
A
IFNG1
rs1861493
66837463
12
0.264
A>G
1.02
0.601
A
4.26
0.120
A
IFNG1
rs1861494
66837676
12
0.279
T>C
0.12
0.944
A
2.35
0.309
A
IFNG1
rs2069709
66839970
12
0.008
G>T
NA
NA
NA
NA
NA
NA
IFNG1
HapA3
12
1.02
0.601
4.25
0.120
A
IFNG1
HapA5
12
0.14
0.931
3.82
0.148
A
IFNGR1
rs9376268
137574444
6
0.246
G>A
1.02
0.600
A
0.34
0.843
A
IL1B
rs1071676
106042060
2
0.198
G>C
0.99
0.610
A
1.60
0.450
A
IL1B
rs1143643
106042929
2
0.331
G>A
8.46
0.015
A
2.69
0.261
A
IL1B
rs1143642
106043180
2
0.095
C>T
1.08
0.582
A
0.59
0.744
A
IL1B
rs1143634
106045017
2
0.196
C>T
1.25
0.534
A
1.79
0.408
A
IL1B
rs1143633
106045094
2
0.345
G>A
7.87
0.020
A
2.70
0.259
A
IL1B
rs1143630
106046282
2
0.103
C>A
2.69
0.261
A
1.45
0.484
A
IL1B
rs3917356
106046990
2
0.432
G>A
0.90
0.637
A
1.47
0.479
A
IL1B
rs1143629
106048145
2
0.353
T>C
2.23
0.328
A
1.10
0.577
A
IL1B
rs1143627
106049014
2
0.390
T>C
0.69
0.708
A
2.29
0.318
A
IL1B
rs16944
106049494
2
0.380
G>A
0.49
0.795
A
1.03
0.597
A
IL1B
rs1143623
106050452
2
0.248
G>C
1.36
0.508
A
1.49
0.475
A
IL1B
rs13032029
106055022
2
0.428
C>T
0.91
0.633
A
1.33
0.513
A
IL1B
HapA1
1.87
0.393
3.51
0.173
IL1B
HapA3
FE
1.000
FE
0.552
IL1B
HapA4
9.27
0.010
2.75
0.253
IL1B
HapA5
1.26
0.533
1.66
0.436
IL1B
HapB1
1.99
0.370
0.98
0.611
IL1B
HapB7
0.77
0.681
1.00
0.608
IL1B
HapB9
1.06
0.588
1.34
0.512
IL1B
HapB11
2.33
0.312
1.00
0.608
IL1R1
rs949963
96533648
2
0.213
G>A
1.72
0.423
A
0.47
0.792
A
IL1R1
rs2228139
96545511
2
0.066
C>G
1.73
0.421
A
1.10
0.576
A
IL1R1
rs3917320
96556738
2
0.068
A>C
FE
0.711
A
FE
0.644
A
IL1R1
rs2110726
96558145
2
0.333
C>T
0.19
0.909
A
1.39
0.500
A
IL1R1
rs3917332
96560387
2
0.124
A>T
1.61
0.447
A
1.86
0.395
A
IL1R2
rs4141134
96370336
2
0.401
T>C
1.37
0.505
A
FE
0.033
D
IL1R2
rs11674595
96374804
2
0.233
T>C
1.14
0.564
A
1.15
0.563
A
IL1R2
rs7570441
96380807
2
0.393
G>A
0.79
0.674
A
3.83
0.147
A
IL1R2
HapA1
2.49
0.289
0.83
0.660
IL1R2
HapA2
0.19
0.910
1.38
0.502
IL1R2
HapA4
1.22
0.545
5.17
0.076
IL2
rs1479923
119096993
4
0.302
C>T
FE
0.025
R
0.52
0.770
A
IL2
rs2069776
119098582
4
0.244
T>C
0.32
0.851
A
1.10
0.578
A
IL2
rs2069772
119099739
4
0.238
A>G
1.86
0.395
A
2.73
0.255
A
IL2
rs2069777
119103043
4
0.054
C>T
FE
0.214
A
FE
0.613
A
IL2
rs2069763
119104088
4
0.287
T>G
3.01
0.222
A
1.29
0.525
A
IL2
HapA1
0.27
0.872
0.66
0.968
IL2
HapA2
2.08
0.354
2.55
0.280
IL2
HapA3
0.26
0.879
0.97
0.617
IL2
HapA5
5.64
0.060
0.52
0.770
IL4
rs2243248
127200946
5
0.101
T>G
0.49
0.785
A
0.52
0.771
A
IL4
rs2243250
127201455
5
0.260
C>T
NA
NA
NA
NA
NA
NA
IL4
rs2070874
127202011
5
0.219
C>T
3.45
0.179
A
0.12
0.942
A
IL4
rs2227284
127205027
5
0.399
C>A
2.55
0.279
A
3.56
0.168
A
IL4
rs2227282
127205481
5
0.401
C>G
2.03
0.362
A
2.88
0.237
A
IL4
rs2243263
127205601
5
0.124
C>G
FE
1.000
A
FE
0.584
A
IL4
rs2243266
127206091
5
0.203
G>A
2.25
0.325
A
0.85
0.653
A
IL4
rs2243267
127206188
5
0.205
G>C
2.33
0.312
A
0.76
0.682
A
IL4
rs2243274
127207134
5
0.262
G>A
2.61
0.272
A
2.95
0.229
A
IL4
HapA1
1.96
0.376
2.63
0.268
IL4
HapA10
3.02
0.220
0.69
0.708
IL6
rs4719714
22643793
7
0.196
A>T
7.19
0.027
A
FE
0.005
D
IL6
rs2069827
22648536
7
0.071
G>T
4.88
0.087
A
1.43
0.490
A
IL6
rs1800796
22649326
7
0.095
C>G
NA
NA
NA
NA
NA
NA
IL6
rs1800795
22649725
7
0.355
C>G
1.34
0.512
A
1.80
0.406
A
IL6
rs2069835
22650951
7
0.066
T>C
FE
1.000
A
FE
0.157
A
IL6
rs2066992
22651329
7
0.091
G>T
NA
NA
NA
NA
NA
NA
IL6
rs2069840
22651652
7
0.308
C>G
3.38
0.185
A
3.75
0.153
A
IL6
rs1554606
22651787
7
0.405
G>T
1.28
0.528
A
1.13
0.567
A
IL6
rs2069845
22653229
7
0.405
A>G
1.28
0.528
A
1.13
0.567
A
IL6
rs2069849
22654236
7
0.039
C>T
NA
NA
NA
NA
NA
NA
IL6
rs2069861
22654734
7
0.083
C>T
FE
0.864
A
FE
0.398
A
IL6
rs35610689
22656903
7
0.242
A>G
7.09
0.029
A
1.54
0.463
A
IL6
HapA4
3.17
0.210
4.58
0.101
IL6
HapA6
1.40
0.497
2.58
0.275
IL8
rs4073
70417508
4
0.498
T>A
0.19
0.911
A
1.61
0.446
A
IL8
rs2227306
70418539
4
0.366
C>T
0.85
0.655
A
2.79
0.247
A
IL8
rs2227543
70419394
4
0.374
C>T
1.09
0.580
A
2.83
0.243
A
IL8
HapA1
0.11
0.949
0.11
0.945
IL8
HapA3
0.85
0.655
2.79
0.247
IL8
HapA4
0.19
0.911
1.61
0.446
IL10
rs3024505
177638230
1
0.138
C>T
0.03
0.987
A
0.70
0.705
A
IL10
rs3024498
177639855
1
0.236
A>G
1.48
0.478
A
1.29
0.526
A
IL10
rs3024496
177640190
1
0.459
T>C
1.29
0.524
A
0.07
0.967
A
IL10
rs1878672
177642039
1
0.452
G>C
0.67
0.714
A
0.06
0.971
A
IL10
rs3024492
177642438
1
0.207
T>A
0.43
0.809
A
1.00
0.608
A
IL10
rs1518111
177642971
1
0.267
G>A
4.42
0.110
A
0.89
0.641
A
IL10
rs1518110
177643187
1
0.267
G>T
4.42
0.110
A
0.89
0.641
A
IL10
rs3024491
177643372
1
0.448
G>T
0.98
0.613
A
0.01
0.995
A
IL10
HapA5
0.60
0.742
1.72
0.424
A
IL10
HapA6
4.17
0.124
0.84
0.658
A
IL10
HapA8
0.48
0.789
0.99
0.608
A
IL10
HapA9
0.02
0.991
0.67
0.715
A
IL13
rs1881457
127184713
5
0.192
A>C
0.71
0.702
A
3.47
0.177
A
IL13
rs1800925
127185113
5
0.227
C>T
0.07
0.966
A
0.71
0.700
A
IL13
rs2069743
127185579
5
0.021
A>G
NA
NA
NA
NA
NA
NA
IL13
rs1295686
127188147
5
0.252
G>A
1.11
0.575
A
0.74
0.692
A
IL13
rs20541
127188268
5
0.174
C>T
0.25
0.883
A
1.03
0.598
A
IL13
HapA1
0.64
0.727
0.34
0.846
IL13
HapA4
0.46
0.797
2.47
.291
IL17A
rs4711998
51881422
6
0.293
G>A
2.87
0.239
A
0.38
0.828
A
IL17A
rs8193036
51881562
6
0.255
T>C
2.83
0.243
A
FE
0.017
D
IL17A
rs3819024
51881855
6
0.374
A>G
0.03
0.986
A
2.17
0.338
A
IL17A
rs2275913
51882102
6
0.345
G>A
1.20
0.548
A
0.53
0.767
A
IL17A
rs3804513
51884266
6
0.027
A>T
NA
NA
NA
NA
NA
NA
IL17A
rs7747909
51885318
6
0.225
G>A
2.40
0.301
A
0.70
0.703
A
NFKB1
rs3774933
103645369
4
0.444
T>C
0.83
0.660
A
0.15
0.928
A
NFKB1
rs170731
103667933
4
0.397
A>T
0.52
.773
A
0.29
0.863
A
NFKB1
rs17032779
103685279
4
0.023
T>C
NA
NA
NA
NA
NA
NA
NFKB1
rs230510
103695201
4
0.366
T>A
1.07
0.586
A
0.29
0.875
A
NFKB1
rs230494
103706005
4
0.477
A>G
3.89
0.143
A
0.81
0.668
A
NFKB1
rs4648016
103708706
4
0.017
C>T
NA
NA
NA
NA
NA
NA
NFKB1
rs4648018
103709236
4
0.025
G>C
NA
NA
NA
NA
NA
NA
NFKB1
rs3774956
103727564
4
0.479
C>T
4.47
0.107
A
0.91
0.635
A
NFKB1
rs10489114
103730426
4
0.025
A>G
NA
NA
NA
NA
NA
NA
NFKB1
rs4648068
103737343
4
0.366
A>G
3.72
0.156
A
0.38
0.826
A
NFKB1
rs4648095
103746914
4
0.052
T>C
FE
0.091
A
FE
0.794
A
NFKB1
rs4648110
103752867
4
0.205
T>A
FE
0.043
A
1.10
0.577
A
NFKB1
rs4648135
103755716
4
0.060
A>G
FE
0.238
A
FE
0.808
A
NFKB1
rs4648141
103755947
4
0.188
G>A
4.76
0.093
A
3.34
0.188
A
NFKB1
rs1609798
103756488
4
0.337
C>T
0.99
0.608
A
2.15
0.342
A
NFKB1
HapA1
0.92
0.633
0.20
0.903
NFKB1
HapA9
0.79
0.675
A
0.08
0.961
NFKB2
rs12772374
104146901
10
0.229
A>G
0.92
0.633
A
0.76
0.683
A
NFKB2
rs7897947
104147701
10
0.085
T>G
1.43
0.490
A
1.59
0.452
A
NFKB2
rs11574849
104149686
10
0.317
G>A
1.04
0.595
A
1.76
0.414
A
NFKB2
rs1056890
104152760
10
0.317
C>T
0.45
0.798
A
FE
0.013
R
TNFA
rs2857602
31533378
6
0.360
T>C
0.29
0.864
A
0.24
0.885
A
TNFA
rs1800683
31540071
6
0.388
G>A
FE
0.048
R
FE
0.027
R
TNFA
rs2239704
31540141
6
0.370
G>T
0.44
0.801
A
0.25
0.882
A
TNFA
rs2229094
31540556
6
0.256
T>C
1.74
0.419
A
3.97
0.138
A
TNFA
rs1041981
31540784
6
0.388
C>A
FE
0.048
R
FE
0.027
A
TNFA
rs1799964
31542308
6
0.202
T>C
0.10
0.950
A
2.23
0.328
A
TNFA
rs1800750
31542963
6
0.019
G>A
NA
NA
NA
NA
NA
NA
TNFA
rs1800629
31543031
6
0.157
G>A
1.49
0.475
A
9.59
0.008
A
TNFA
rs1800610
31543827
6
0.105
C>T
2.16
0.340
A
3.32
0.190
A
TNFA
rs3093662
31544189
6
0.072
A>G
3.90
0.142
A
1.64
0.439
A
TNFA
HapA1
5.81
0.055
5.03
0.081
TNFA
HapA5
0.07
0.968
2.07
0.355
TNFA
HapA8
0.25
0.883
0.10
0.952
SNP = single nucleotide polymorphism; Chr = chromosome; MAF = minor allele frequency; IFNG = interferon gamma; A = additive model; NA = not assayed because SNP violated Hardy-Weinberg expectations (P < 0.001) or because MAF was <0.05; FE = Fisher exact test; IL = interleukin; Hap = haplotype; D = dominant model; NFKB = nuclear factor kappa beta; R = recessive model; TNFA = tumor necrosis factor alpha.
Preliminary evidence of an association between an interleukin 6 promoter polymorphism and self-reported attentional function in oncology patients and their family caregivers.
Patterns of circadian activity rhythms and their relationships with fatigue and anxiety/depression in women treated with breast cancer adjuvant chemotherapy.
A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: utility and applications.
Symptoms of hormonal therapy and social support: is there a connection? Comparison of symptom severity, symptom interference and social support among breast cancer patients receiving and not receiving adjuvant hormonal treatment.
Preliminary evidence of an association between a functional interleukin-6 polymorphism and fatigue and sleep disturbance in oncology patients and their family caregivers.
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