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Original Article| Volume 50, ISSUE 5, P599-614.e3, November 2015

Phenotypic and Molecular Evidence Suggests That Decrements in Morning and Evening Energy Are Distinct but Related Symptoms

Open AccessPublished:May 29, 2015DOI:https://doi.org/10.1016/j.jpainsymman.2015.05.008

      Abstract

      Context

      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.

      Key Words

      Introduction

      Energy conservation is one of the earliest interventions that was recommended to reduce fatigue associated with cancer and its treatment.
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      • Whitmer K.
      • Sweeney C.
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      A pilot study examining energy conservation for cancer treatment-related fatigue.
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      • et al.
      A randomized clinical trial of energy conservation for patients with cancer-related fatigue.
      In fact, this strategy is included in the latest Fatigue Guidelines published by the National Comprehensive Cancer Network.
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      • et al.
      Studying cancer-related fatigue: report of the NCCN Scientific Research Committee.
      Energy (also termed perceived energy, vigor, and vitality) and fatigue are often thought to be interchangeable symptoms.
      • Gay C.L.
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      • Lee S.Y.
      Sleep patterns and fatigue in new mothers and fathers.
      • Lee K.A.
      • Gay C.
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      • et al.
      Symptom experience in HIV-infected adults: a function of demographic and clinical characteristics.
      For example, on the Memorial Symptom Assessment Scale,
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      • Thaler H.T.
      • Kornblith A.B.
      • et al.
      The Memorial Symptom Assessment Scale: an instrument for the evaluation of symptom prevalence, characteristics and distress.
      fatigue is assessed using the phrase lack of energy.
      However, increasing evidence suggests that fatigue and energy are distinct but related constructs.
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      A concept analysis of energy. Its meaning in the lives of three individuals with chronic illness.
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      A theoretical extension of the concept of energy through an empirical study.
      • O'Connor P.J.
      Mental energy: assessing the mood dimension.
      For example, instruments like the Profile of Mood States (POMS)
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      EDITS manual for the profile of mood states.
      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.
      • Lee K.A.
      • Hicks G.
      • Nino-Murcia G.
      Validity and reliability of a scale to assess fatigue.
      In addition, a recent Rasch analysis of the LFS found that fatigue and energy represented different symptoms.
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      • Lee K.A.
      Development of a short version of the Lee Visual Analogue Fatigue Scale in a sample of women with HIV/AIDS: a Rasch analysis application.
      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,
      • Dunn L.B.
      • Aouizerat B.E.
      • Langford D.J.
      • et al.
      Cytokine gene variation is associated with depressive symptom trajectories in oncology patients and family caregivers.
      sleep disturbance,
      • Miaskowski C.
      • Cooper B.A.
      • Dhruva A.
      • et al.
      Evidence of associations between cytokine genes and subjective reports of sleep disturbance in oncology patients and their family caregivers.
      fatigue,
      • Dhruva A.
      • Aouizerat B.E.
      • Cooper B.
      • et al.
      Cytokine gene associations with self-report ratings of morning and evening fatigue in oncology patients and their family caregivers.
      and attentional fatigue.
      • Merriman J.D.
      • Aouizerat B.E.
      • Langford D.J.
      • et al.
      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.
      • Girgis A.
      • Lambert S.
      • Johnson C.
      • Waller A.
      • Currow D.
      Physical, psychosocial, relationship, and economic burden of caring for people with cancer: a review.
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      • Breitbart W.
      Care for the cancer caregiver: a systematic review.
      • Martin M.Y.
      • Sanders S.
      • Griffin J.M.
      • et al.
      Racial variation in the cancer caregiving experience: a multisite study of colorectal and lung cancer caregivers.
      • Sherwood P.R.
      • Given B.A.
      • Given C.W.
      • et al.
      The impact of a problem-solving intervention on increasing caregiver assistance and improving caregiver health.
      • Yun Y.H.
      • Rhee Y.S.
      • Kang I.O.
      • et al.
      Economic burdens and quality of life of family caregivers of cancer patients.
      • Adelman R.D.
      • Tmanova L.L.
      • Delgado D.
      • Dion S.
      • Lachs M.S.
      Caregiver burden: a clinical review.
      • Williams A.L.
      Psychosocial burden of family caregivers to adults with cancer.
      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,
      • Dhruva A.
      • Lee K.
      • Paul S.M.
      • et al.
      Sleep-wake circadian activity rhythms and fatigue in family caregivers of oncology patients.
      • Langford D.J.
      • Lee K.
      • Miaskowski C.
      Sleep disturbance interventions in oncology patients and family caregivers: a comprehensive review and meta-analysis.
      which contribute to decreases in both morning and evening energy levels.
      Inflammation may influence energy levels through a variety of mechanisms, including activation of immunomodulators,
      • Kamath J.
      • Yarbrough G.G.
      • Prange Jr., A.J.
      • Winokur A.
      The thyrotropin-releasing hormone (TRH)-immune system homeostatic hypothesis.
      alterations in mitochondrial function,
      • Alexander N.B.
      • Taffet G.E.
      • Horne F.M.
      • et al.
      Bedside-to-Bench conference: research agenda for idiopathic fatigue and aging.
      and/or changes in the activity of the hypothalamic-pituitary-adrenal axis.
      • Straub R.H.
      • Buttgereit F.
      • Cutolo M.
      Alterations of the hypothalamic-pituitary-adrenal axis in systemic immune diseases—a role for misguided energy regulation.
      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.
      • Dunn L.B.
      • Aouizerat B.E.
      • Langford D.J.
      • et al.
      Cytokine gene variation is associated with depressive symptom trajectories in oncology patients and family caregivers.
      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.
      • Lee K.A.
      • Hicks G.
      • Nino-Murcia G.
      Validity and reliability of a scale to assess fatigue.
      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.
      • Fletcher B.S.
      • Paul S.M.
      • Dodd M.J.
      • et al.
      Prevalence, severity, and impact of symptoms on female family caregivers of patients at the initiation of radiation therapy for prostate cancer.
      The LFS was chosen for this study because it is relatively short, easy to administer, and has well-established validity and reliability.
      • Miaskowski C.
      • Lee K.A.
      Pain, fatigue, and sleep disturbances in oncology outpatients receiving radiation therapy for bone metastasis: a pilot study.
      • Miaskowski C.
      • Cooper B.A.
      • Paul S.M.
      • et al.
      Subgroups of patients with cancer with different symptom experiences and quality-of-life outcomes: a cluster analysis.
      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.
      • Spielberger C.G.
      • Gorsuch R.L.
      • Suchene R.
      • Vagg P.R.
      • Jacobs G.A.
      Manual for the State-Anxiety (Form Y): Self evaluation questionnaire.
      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.
      • Radloff L.S.
      The CES-D Scale: a self-report depression scale for research in the general population.
      • Sheehan T.J.
      • Fifield J.
      • Reisine S.
      • Tennen H.
      The measurement structure of the Center for Epidemiologic Studies Depression Scale.
      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.
      • Buysse D.J.
      • Reynolds 3rd, C.F.
      • Monk T.H.
      • Berman S.R.
      • Kupfer D.J.
      The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research.
      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.
      • Fletcher B.S.
      • Paul S.M.
      • Dodd M.J.
      • et al.
      Prevalence, severity, and impact of symptoms on female family caregivers of patients at the initiation of radiation therapy for prostate cancer.
      The GSDS has well-established validity and reliability.
      • Lee K.A.
      Self-reported sleep disturbances in employed women.
      In the present study, the Cronbach's alphas for the GSDS total score were 0.84 for patients and 0.79 for FCs.

      Attentional Function Index

      The Attentional Function Index consists of 16 items designed to measure attentional function.
      • Cimprich B.
      • Visovatti M.
      • Ronis D.L.
      The Attentional Function Index—a self-report cognitive measure.
      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).
      • Cimprich B.
      • Visovatti M.
      • Ronis D.L.
      The Attentional Function Index—a self-report cognitive measure.
      The Attentional Function Index has well-established reliability and validity. In this study, Cronbach's alpha was 0.95 for both patients and FCs.

      Brief Pain Inventory

      Occurrence of pain was evaluated using the Brief Pain Inventory.
      • Daut R.L.
      • Cleeland C.S.
      • Flanery R.C.
      Development of the Wisconsin Brief Pain Questionnaire to assess pain in cancer and other diseases.
      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).
      • Berger A.M.
      • Wielgus K.K.
      • Young-McCaughan S.
      • et al.
      Methodological challenges when using actigraphy in research.
      • Ancoli-Israel S.
      • Cole R.
      • Alessi C.
      • et al.
      The role of actigraphy in the study of sleep and circadian rhythms.
      Seven sleep/wake and one activity/rest parameters were selected that were identified by a National Cancer Institute-sponsored conference,
      • Berger A.M.
      • Parker K.P.
      • Young-McCaughan S.
      • et al.
      Sleep wake disturbances in people with cancer and their caregivers: state of the science.
      an expert panel that recommended a standard set or research assessments in insomnia,
      • Buysse D.J.
      • Ancoli-Israel S.
      • Edinger J.D.
      • Lichstein K.L.
      • Morin C.M.
      Recommendations for a standard research assessment of insomnia.
      and published studies.
      • Berger A.M.
      • Wielgus K.
      • Hertzog M.
      • Fischer P.
      • Farr L.
      Patterns of circadian activity rhythms and their relationships with fatigue and anxiety/depression in women treated with breast cancer adjuvant chemotherapy.
      • Berger A.M.
      • Farr L.A.
      • Kuhn B.R.
      • Fischer P.
      • Agrawal S.
      Values of sleep/wake, activity/rest, circadian rhythms, and fatigue prior to adjuvant breast cancer chemotherapy.

      Study Procedures

      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.
      • Muthen B.O.
      Latent variable analysis: growth mixture modeling and related techniques for longitudinal data.
      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.
      • Dunn L.B.
      • Cooper B.A.
      • Neuhaus J.
      • et al.
      Identification of distinct depressive symptom trajectories in women following surgery for breast cancer.
      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.
      • Jung T.
      • Wickrama K.A.S.
      An introduction to latent class growth analysis and growth mixture modeling.
      • Nylund K.L.
      • Asparouhov T.
      • Muthen B.O.
      Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study.
      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).
      • Schafer J.L.
      • Graham J.W.
      Missing data: our view of the state of the art.
      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.
      • Seruga B.
      • Zhang H.
      • Bernstein L.J.
      • Tannock I.F.
      Cytokines and their relationship to the symptoms and outcome of cancer.

      Blood Collection and Genotyping

      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.
      • Conde L.
      • Vaquerizas J.M.
      • Dopazo H.
      • et al.
      PupaSuite: finding functional single nucleotide polymorphisms for large-scale genotyping purposes.

      Statistical Analyses

      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.
      • Gabriel S.B.
      • Schaffner S.F.
      • Nguyen H.
      • et al.
      The structure of haplotype blocks in the human genome.
      Haplotypes were constructed using the PHASE, version 2.1, program.
      • Stephens M.
      • Smith N.J.
      • Donnelly P.
      A new statistical method for haplotype reconstruction from population data.
      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.
      • Halder I.
      • Shriver M.
      • Thomas M.
      • Fernandez J.R.
      • Frudakis T.
      A panel of ancestry informative markers for estimating individual biogeographical ancestry and admixture from four continents: utility and applications.
      • Tian C.
      • Gregersen P.K.
      • Seldin M.F.
      Accounting for ancestry: population substructure and genome-wide association studies.
      Homogeneity in ancestry among participants was verified by principal component analysis
      • Price A.L.
      • Patterson N.J.
      • Plenge R.M.
      • et al.
      Principal components analysis corrects for stratification in genome-wide association studies.
      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).
      As was done in our previous studies,
      • Miaskowski C.
      • Cooper B.A.
      • Dhruva A.
      • et al.
      Evidence of associations between cytokine genes and subjective reports of sleep disturbance in oncology patients and their family caregivers.
      • Dhruva A.
      • Aouizerat B.E.
      • Cooper B.
      • et al.
      Cytokine gene associations with self-report ratings of morning and evening fatigue in oncology patients and their family caregivers.
      • Merriman J.D.
      • Aouizerat B.E.
      • Langford D.J.
      • et al.
      Preliminary evidence of an association between an interleukin 6 promoter polymorphism and self-reported attentional function in oncology patients and their family caregivers.
      based on recommendations in the literature,
      • Rothman K.J.
      No adjustments are needed for multiple comparisons.
      • Hochberg Y.
      • Benjamini Y.
      More powerful procedures for multiple significance testing.
      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.
      • Jung T.
      • Wickrama K.A.S.
      An introduction to latent class growth analysis and growth mixture modeling.
      Figure thumbnail gr1
      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.
      Table 1Fit Indices for Morning and Evening Energy GMM Solutions With Seven Assessments, with Dyad as a Clustering Variable
      GMMLLAICBICEntropyVLMR
      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.
      Morning energy
       One-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.
      −2901.4245834.8475891.318NANA
       Two-class model
      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.
      −2872.1805788.3615866.0080.66758.487
      P < 0.01.
       Three-class model−2865.4865784.9735880.2670.72813.388NS
      Evening energy
       One-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.
      −2928.8765889.7525946.223NANA
       Two-class model
      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.
      −2900.5715847.1435928.3200.70856.609
      P < 0.05.
       Three-class model−2897.1955850.3905949.2140.6136.753NS
      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
      Trajectory group sizes are for classification of individuals based on their most likely latent class probabilities.
      Solutions with Seven Assessments, with Dyad as a Clustering Variable
      Parameter Estimates
      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.
      Moderate Morning Energy

      n = 124 (49.2%)
      Low Morning Energy

      n = 124 (49.2%)
      High Evening Energy

      n = 52 (20.6%)
      Moderate Evening Energy

      n = 200 (79.4%)
      MeansMean (SE)
       Intercept6.945
      P < 0.001.
      (0.276)
      4.704
      P < 0.001.
      (0.261)
      5.759
      P < 0.001.
      (0.845)
      3.955
      P < 0.001.
      (0.225)
       Linear slope0.345
      P < 0.001.
      (0.102)
      −0.253
      P < 0.05.
      (0.127)
      0.706
      P < 0.001.
      (0.197)
      −0.333
      P < 0.05.
      (0.147)
       Quadratic slope−0.033
      P < 0.05.
      (0.015)
      0.053
      P < 0.01.
      (0.019)
      −0.093
      P < 0.01.
      (0.030)
      0.044
      P < 0.05.
      (0.020)
      VariancesVariance (SE)
       Intercept0.967
      P < 0.001.
      (0.201)
      0.891
      P < 0.001.
      (0.207)
      2.056NS (1.449)1.239
      P < 0.001.
      (0.290)
       Linear slope0
      Fixed at zero.
      0.033
      P < 0.01.
      (0.011)
      0.001NS (0.014)0.012NS (0.010)
      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.
      f Fixed at zero.

      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
      CharacteristicModerate Morning Energy

      124 (49.2%)
      Low Morning Energy

      128 (50.8%)
      P-valueHigh Evening Energy

      52 (20.6%)
      Moderate Evening Energy

      200 (79.4%)
      P-value
      Mean (SD)Mean (SD)
      Age (yrs)65.2 (9.0)57.9 (12.1)<0.00166.2 (8.7)60.3 (11.6)<0.001
      Education (yrs)16.2 (3.1)15.7 (3.0)NS15.8 (3.2)16.0 (3.0)NS
      Number of comorbid conditions4.2 (2.6)5.0 (2.8)0.0143.9 (2.8)4.8 (2.7)0.025
      Weight (pounds)178.6 (35.8)172.1 (41.6)NS185.0 (36.4)172.2 (38.8)0.035
      KPS score95.6 (8.3)88.2 (13.0)<0.00196.9 (8.3)90.6 (11.9)0.001
      n (%)n (%)
      Gender (% female)54 (43.5)81 (63.3)0.00237 (71.2)80 (40.0)0.001
      Ethnicity
      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).
       % White99 (79.8)88 (69.3)0.02638 (73.1)149 (74.9)NS
       % Asian/Pacific Islander12 (9.4)4 (3.2)2 (3.8)14 (7.0)
       % Black16 (12.6)18 (14.5)10 (19.2)24 (12.1)
       % Hispanic/mixed/other11 (8.7)3 (2.4)2 (3.8)12 (6.0)
      Lives alone (% yes)22 (28.2)31 (34.8)NS41 (78.8)133 (67.2)NS
      Married or partnered (% yes)94 (75.8)80 (63.5)0.03953 (73.6)121 (68.0)NS
      Children at home (% yes)16 (15.4)20 (18.7)NS4 (8.9)32 (19.3)NS
      Older adult at home (% yes)1 (1.0)6 (5.5)NS1 (2.2)6 (3.6)NS
      Work for pay (% yes)50 (41.0)65 (52.0)NS20 (38.5)95 (48.7)NS
      Patient/FC (% patient)78 (62.5)89 (69.5)NS35 (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
      CharacteristicModerate Morning Energy

      122 (49.2%)
      Low Morning Energy

      128 (50.8%)
      P-valueHigh Evening Energy

      112 (20.6%)
      Moderate Evening Energy

      128 (79.4%)
      P-value
      Mean (SD)Mean (SD)
      Psychological symptoms at enrollment
       STAI-T30.4 (8.3)37.6 (10.0)<0.00127.5 (6.4)35.8 (9.9)<0.001
       STAI-S27.4 (8.1)34.4 (11.9)<0.00125.2 (6.0)32.5 (11.3)<0.001
       CES-D total5.6 (5.8)12.0 (8.9)<0.0014.0 (4.3)10.1 (8.5)<0.001
      PSQI scores at enrollment
       Subjective sleep quality0.8 (0.7)1.1 (0.7)<0.0010.6 (0.5)1.0 (0.7)<0.001
       Sleep latency0.7 (0.9)1.2 (0.9)<0.0010.5 (0.8)1.1 (0.9)<0.001
       Sleep duration0.7 (0.8)1.2 (1.0)<0.0010.7 (0.7)1.0 (0.9)0.020
       Habitual sleep efficiency0.5 (0.8)0.9 (1.0)0.0020.3 (0.7)0.8 (1.0)0.003
       Sleep disturbance1.3 (0.5)1.5 (0.6)0.0011.2 (0.5)1.4 (0.6)0.003
       Use of sleeping medication0.4 (0.9)0.9 (1.2)0.0020.5 (1.0)0.7 (1.1)0.304
       Daytime dysfunction0.5 (0.6)1.0 (0.6)<0.0010.4 (0.6)0.8 (0.6)<0.001
       PSQI global score4.8 (2.9)7.6 (3.7)<0.0014.1 (2.3)6.8 (3.7)<0.001
      General sleep disturbance scores at enrollment
       Quality1.9 (1.6)2.9 (1.9)<0.0011.5 (1.5)2.7 (1.8)<0.001
       Sleep onset latency1.2 (1.8)1.9 (2.1)0.0030.8 (1.4)1.7 (2.1)0.002
       Quantity4.1 (1.1)4.7 (1.4)0.0024.1 (0.9)4.5 (1.4)0.031
       Sleep medication0.2 (0.4)0.4 (0.7)0.0030.2 (0.4)0.3 (0.6)0.232
       Midsleep awakenings4.2 (2.6)4.7 (2.5)0.1144.1 (2.6)4.5 (2.5)0.219
       Early awakenings1.8 (1.9)2.8 (2.4)0.0011.3 (1.7)2.6 (2.3)<0.001
       Excessive daytime sleepiness1.4 (1.2)2.2 (1.3)<0.0011.1 (1.0)2.0 (1.3)<0.001
       Total GSDS score32.0 (15.3)45.6 (19.2)<0.00127.9 (13.1)41.7 (18.8)<0.001
      Actigraphy parameters at enrollment
       Sleep period time (minutes)479.5 (72.5)489.2 (76.9)0.323473.2 (76.9)487.3 (74.2)0.238
       TST (minutes)391.4 (82.4)407.7 (84.3)0.135378.6 (95.7)405.2 (79.5)0.047
       Sleep efficiency81.5 (14.0)83.4 (11.7)0.26579.3 (16.8)83.3 (11.6)0.053
       Wake after sleep onset (% of TST)15.2 (12.8)12.7 (11.2)0.12416.6 (15.1)13.2 (11.1)0.078
       Wake number17.7 (8.9)15.8 (8.6)0.10418.7 (9.7)16.2 (8.5)0.073
       Wake duration (minutes)3.9 (2.8)4.2 (6.1)0.5873.9 (3.1)4.1 (5.1)0.842
       Sleep onset latency (minutes)12.7 (11.8)16.9 (22.9)0.08115.3 (16.6)14.7 (18.8)0.844
      Fatigue and energy scores at enrollment
       Evening fatigue3.7 (2.1)4.8 (1.9)<0.0012.6 (1.9)4.7 (1.8)<0.001
       Morning fatigue1.3 (1.4)3.2 (2.0)<0.0011.2 (1.7)2.6 (1.9)<0.001
       Evening energy5.0 (2.0)3.8 (1.5)<0.0015.8 (2.0)4.1 (1.6)<0.001
       Morning energy7.0 (1.7)4.7 (1.6)<0.0017.0 (2.1)5.4 (1.9)<0.001
       Attentional fatigue7.9 (1.5)6.5 (1.8)<0.0018.4 (1.1)6.9 (1.8)<0.001
      n (%)n (%)
       Pain (% yes)43 (36.1)77 (57.9).00129 (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.
      Figure thumbnail gr2
      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.
      Table 5Multiple Logistic Regression Analyses for Morning Energy and Evening Energy
      PredictorOdds RatioSE95% CIZP-value
      Morning energy
       IL2 rs14799230.250.1510.077, 0.816−2.300.022
       Age0.700.0580.599, 0.826−4.29<0.001
       Number of comorbid conditions1.150.0701.020, 1.2942.280.023
       KPS score0.570.0910.415, 0.776−3.55<0.001
      Overall model fit: χ2 = 64.5, P < 0.0001, R2 = 0.1990
       NFKB1 rs46481100.580.1590.337, 0.990−2.000.046
       Age0.700.0570.600, 0.827−4.29<0.001
       Number of comorbid conditions1.150.0701.020, 1.2962.290.022
       KPS score0.570.0900.414, 0.772−3.58<0.001
      Overall model fit: χ2 = 62.7, P < 0.0001, R2 = 0.1934
      Evening energy
       IL1R2 rs41411340.360.1570.153, 0.845−2.350.019
       Age0.780.0790.641, 0.951−2.460.014
       Gender0.400.1520.189, 0.842−2.410.016
       Ethnicity0.030.0420.001, 0.655−2.220.027
       KPS score0.500.1210.310, 0.801−2.880.004
      Overall model fit: χ2 = 43.97, P < 0.0001, R2 = 0.1811
       IL6 rs47197140.270.1020.126, 0.563−3.460.001
       Age0.770.0780.631, 0.938−2.590.010
       Gender0.380.1480.180, 0.817−2.480.013
       Ethnicity0.020.0370.001, 0.736−2.130.033
       KPS score0.480.1190.293, 0.780−2.960.003
      Overall model fit: χ2 = 50.42, P < 0.0001, R2 = 0.2077
       IL17A rs81930360.390.1450.192, 0.811−2.530.011
       Age0.780.0780.643, 0.951−2.470.014
       Gender0.380.1470.181, 0.811−2.510.012
       KPS score0.500.1230.312, 0.811−2.820.005
      Overall model fit: χ2 = 43.97, P < 0.0001, R2 = 0.1815
       NFKB2 rs10568909.7010.2671.218, 77.2252.150.032
       Age0.760.0770.628, 0.932−2.660.008
       Gender0.410.1550.196, 0.862−2.350.019
       KPS score0.520.1260.327, 0.839−2.690.007
      Overall model fit: χ2 = 46.23, P < 0.0001, R2 = 0.1904
       TNFA rs18006830.360.1610.148, 0.863−2.290.022
       Age0.770.0770.631, 0.934−2.650.008
       Gender0.390.1480.186, 0.821−2.480.013
       Ethnicity0.030.0480.001, 0.826−2.070.038
       KPS score0.510.1230.322, 0.822−2.780.005
      Overall model fit: χ2 = 43.00, P < 0.0001, R2 = 0.1771
      KPS = Karnofsky Performance Status; NFKB1 = nuclear factor kappa beta 1; IL1R2 = interleukin 1 receptor 2; IL6 = interleukin 6; IL17A = interleukin 17A; NFKB2 = nuclear factor kappa beta 2; TNFA = tumor necrosis factor alpha.
      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).

      Evening Energy

      Results of GMM Analysis for Evening Energy

      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.
      • Jung T.
      • Wickrama K.A.S.
      An introduction to latent class growth analysis and growth mixture modeling.
      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.
      Figure thumbnail gr3
      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.

      Discussion

      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
      • Dunn L.B.
      • Aouizerat B.E.
      • Langford D.J.
      • et al.
      Cytokine gene variation is associated with depressive symptom trajectories in oncology patients and family caregivers.
      • Miaskowski C.
      • Cooper B.A.
      • Dhruva A.
      • et al.
      Evidence of associations between cytokine genes and subjective reports of sleep disturbance in oncology patients and their family caregivers.
      • Dhruva A.
      • Aouizerat B.E.
      • Cooper B.
      • et al.
      Cytokine gene associations with self-report ratings of morning and evening fatigue in oncology patients and their family caregivers.
      • Illi J.
      • Miaskowski C.
      • Cooper B.
      • et al.
      Association between pro- and anti-inflammatory cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression.
      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
      • Hasan K.M.
      • Rahman M.S.
      • Arif K.M.
      • Sobhani M.E.
      Psychological stress and aging: role of glucocorticoids (GCs).
      • Garrido P.
      Aging and stress: past hypotheses, present approaches and perspectives.
      ) and/or psychological (e.g., response shift
      • Ahmed S.
      • Schwartz C.
      • Ring L.
      • Sprangers M.A.
      Applications of health-related quality of life for guiding health care: advances in response shift research.
      • Sprangers M.A.
      • Schwartz C.E.
      The challenge of response shift for quality-of-life-based clinical oncology research.
      ) 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,
      • Dunn L.B.
      • Aouizerat B.E.
      • Langford D.J.
      • et al.
      Cytokine gene variation is associated with depressive symptom trajectories in oncology patients and family caregivers.
      • Miaskowski C.
      • Cooper B.A.
      • Dhruva A.
      • et al.
      Evidence of associations between cytokine genes and subjective reports of sleep disturbance in oncology patients and their family caregivers.
      • Dhruva A.
      • Aouizerat B.E.
      • Cooper B.
      • et al.
      Cytokine gene associations with self-report ratings of morning and evening fatigue in oncology patients and their family caregivers.
      • Dhruva A.
      • Aouizerat B.E.
      • Cooper B.
      • et al.
      Differences in morning and evening fatigue in oncology patients and their family caregivers.
      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).
      • Osoba D.
      Interpreting the meaningfulness of changes in health-related quality of life scores: lessons from studies in adults.
      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.
      • Miaskowski C.
      Gender differences in pain, fatigue, and depression in patients with cancer.
      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.
      • Ochayon L.
      • Tunin R.
      • Yoselis A.
      • Kadmon I.
      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.
      • Stiegelis H.E.
      • Ranchor A.V.
      • Sanderman R.
      Psychological functioning in cancer patients treated with radiotherapy.
      In addition, equally high numbers of FCs experience psychological distress associated with providing care to cancer patients.
      • Couper J.
      • Bloch S.
      • Love A.
      • et al.
      Psychosocial adjustment of female partners of men with prostate cancer: a review of the literature.
      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.
      • Buysse D.J.
      Insomnia.
      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,
      • Gaffen S.L.
      • Liu K.D.
      Overview of interleukin-2 function, production and clinical applications.
      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
      • Glaser R.
      • Kennedy S.
      • Lafuse W.P.
      • et al.
      Psychological stress-induced modulation of interleukin 2 receptor gene expression and interleukin 2 production in peripheral blood leukocytes.
      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.
      • Kandel E.S.
      NFkappaB inhibition and more: a side-by-side comparison of the inhibitors of IKK and proteasome.
      In two studies,
      • Seufert B.L.
      • Poole E.M.
      • Whitton J.
      • et al.
      IkappaBKbeta and NFkappaB1, NSAID use and risk of colorectal cancer in the Colon Cancer Family Registry.
      • Curtin K.
      • Wolff R.K.
      • Herrick J.S.
      • Abo R.
      • Slattery M.L.
      Exploring multilocus associations of inflammation genes and colorectal cancer risk using hapConstructor.
      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.
      • Seufert B.L.
      • Poole E.M.
      • Whitton J.
      • et al.
      IkappaBKbeta and NFkappaB1, NSAID use and risk of colorectal cancer in the Colon Cancer Family Registry.
      • Curtin K.
      • Wolff R.K.
      • Herrick J.S.
      • Abo R.
      • Slattery M.L.
      Exploring multilocus associations of inflammation genes and colorectal cancer risk using hapConstructor.
      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.
      • Garrido P.
      Aging and stress: past hypotheses, present approaches and perspectives.
      Consistent with findings from our previous study in this same sample,
      • Miaskowski C.
      • Dodd M.
      • Lee K.
      • et al.
      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.
      • Smith A.J.
      • D'Aiuto F.
      • Palmen J.
      • et al.
      Association of serum interleukin-6 concentration with a functional IL6 -6331T>C polymorphism.
      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,
      • Ivanov S.
      • Linden A.
      Interleukin-17 as a drug target in human disease.
      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.
      • Oeckinghaus A.
      • Hayden M.S.
      • Ghosh S.
      Crosstalk in NF-kappaB signaling pathways.
      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)
      • Miaskowski C.
      • Cooper B.A.
      • Dhruva A.
      • et al.
      Evidence of associations between cytokine genes and subjective reports of sleep disturbance in oncology patients and their family caregivers.
      and an independent sample (rs1056890).
      • Alfaro E.
      • Dhruva A.
      • Langford D.J.
      • et al.
      Associations between cytokine gene variations and self-reported sleep disturbance in women following breast cancer surgery.
      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.
      • Kroeger K.M.
      • Carville K.S.
      • Abraham L.J.
      The -308 tumor necrosis factor-alpha promoter polymorphism effects transcription.
      • Kroeger K.M.
      • Steer J.H.
      • Joyce D.A.
      • Abraham L.J.
      Effects of stimulus and cell type on the expression of the -308 tumour necrosis factor promoter polymorphism.

      Limitations

      Some study limitations need to be acknowledged. Although the sample sizes for the GMM analyses were adequate,
      • Kroeger K.M.
      • Carville K.S.
      • Abraham L.J.
      The -308 tumor necrosis factor-alpha promoter polymorphism effects transcription.
      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
      GeneSNPPositionChrMAFAllelesMorning EnergyEvening Energy
      Chi SquareP-valueModelChi SquareP-valueModel
      IFNG1rs206972866834051120.079G>A0.670.717A0.730.694A
      IFNG1rs206972766834490120.411A>G0.140.931A3.820.148A
      IFNG1rs206971866836429120.442C>T1.320.518A1.880.391A
      IFNG1rs186149366837463120.264A>G1.020.601A4.260.120A
      IFNG1rs186149466837676120.279T>C0.120.944A2.350.309A
      IFNG1rs206970966839970120.008G>TNANANANANANA
      IFNG1HapA3121.020.6014.250.120A
      IFNG1HapA5120.140.9313.820.148A
      IFNGR1rs937626813757444460.246G>A1.020.600A0.340.843A
      IL1Brs107167610604206020.198G>C0.990.610A1.600.450A
      IL1Brs114364310604292920.331G>A8.460.015A2.690.261A
      IL1Brs114364210604318020.095C>T1.080.582A0.590.744A
      IL1Brs114363410604501720.196C>T1.250.534A1.790.408A
      IL1Brs114363310604509420.345G>A7.870.020A2.700.259A
      IL1Brs114363010604628220.103C>A2.690.261A1.450.484A
      IL1Brs391735610604699020.432G>A0.900.637A1.470.479A
      IL1Brs114362910604814520.353T>C2.230.328A1.100.577A
      IL1Brs114362710604901420.390T>C0.690.708A2.290.318A
      IL1Brs1694410604949420.380G>A0.490.795A1.030.597A
      IL1Brs114362310605045220.248G>C1.360.508A1.490.475A
      IL1Brs1303202910605502220.428C>T0.910.633A1.330.513A
      IL1BHapA11.870.3933.510.173
      IL1BHapA3FE1.000FE0.552
      IL1BHapA49.270.0102.750.253
      IL1BHapA51.260.5331.660.436
      IL1BHapB11.990.3700.980.611
      IL1BHapB70.770.6811.000.608
      IL1BHapB91.060.5881.340.512
      IL1BHapB112.330.3121.000.608
      IL1R1rs9499639653364820.213G>A1.720.423A0.470.792A
      IL1R1rs22281399654551120.066C>G1.730.421A1.100.576A
      IL1R1rs39173209655673820.068A>CFE0.711AFE0.644A
      IL1R1rs21107269655814520.333C>T0.190.909A1.390.500A
      IL1R1rs39173329656038720.124A>T1.610.447A1.860.395A
      IL1R2rs41411349637033620.401T>C1.370.505AFE0.033D
      IL1R2rs116745959637480420.233T>C1.140.564A1.150.563A
      IL1R2rs75704419638080720.393G>A0.790.674A3.830.147A
      IL1R2HapA12.490.2890.830.660
      IL1R2HapA20.190.9101.380.502
      IL1R2HapA41.220.5455.170.076
      IL2rs147992311909699340.302C>TFE0.025R0.520.770A
      IL2rs206977611909858240.244T>C0.320.851A1.100.578A
      IL2rs206977211909973940.238A>G1.860.395A2.730.255A
      IL2rs206977711910304340.054C>TFE0.214AFE0.613A
      IL2rs206976311910408840.287T>G3.010.222A1.290.525A
      IL2HapA10.270.8720.660.968
      IL2HapA22.080.3542.550.280
      IL2HapA30.260.8790.970.617
      IL2HapA55.640.0600.520.770
      IL4rs224324812720094650.101T>G0.490.785A0.520.771A
      IL4rs224325012720145550.260C>TNANANANANANA
      IL4rs207087412720201150.219C>T3.450.179A0.120.942A
      IL4rs222728412720502750.399C>A2.550.279A3.560.168A
      IL4rs222728212720548150.401C>G2.030.362A2.880.237A
      IL4rs224326312720560150.124C>GFE1.000AFE0.584A
      IL4rs224326612720609150.203G>A2.250.325A0.850.653A
      IL4rs224326712720618850.205G>C2.330.312A0.760.682A
      IL4rs224327412720713450.262G>A2.610.272A2.950.229A
      IL4HapA11.960.3762.630.268
      IL4HapA103.020.2200.690.708
      IL6rs47197142264379370.196A>T7.190.027AFE0.005D
      IL6rs20698272264853670.071G>T4.880.087A1.430.490A
      IL6rs18007962264932670.095C>GNANANANANANA
      IL6rs18007952264972570.355C>G1.340.512A1.800.406A
      IL6rs20698352265095170.066T>CFE1.000AFE0.157A
      IL6rs20669922265132970.091G>TNANANANANANA
      IL6rs20698402265165270.308C>G3.380.185A3.750.153A
      IL6rs15546062265178770.405G>T1.280.528A1.130.567A
      IL6rs20698452265322970.405A>G1.280.528A1.130.567A
      IL6rs20698492265423670.039C>TNANANANANANA
      IL6rs20698612265473470.083C>TFE0.864AFE0.398A
      IL6rs356106892265690370.242A>G7.090.029A1.540.463A
      IL6HapA43.170.2104.580.101
      IL6HapA61.400.4972.580.275
      IL8rs40737041750840.498T>A0.190.911A1.610.446A
      IL8rs22273067041853940.366C>T0.850.655A2.790.247A
      IL8rs22275437041939440.374C>T1.090.580A2.830.243A
      IL8HapA10.110.9490.110.945
      IL8HapA30.850.6552.790.247
      IL8HapA40.190.9111.610.446
      IL10rs302450517763823010.138C>T0.030.987A0.700.705A
      IL10rs302449817763985510.236A>G1.480.478A1.290.526A
      IL10rs302449617764019010.459T>C1.290.524A0.070.967A
      IL10rs187867217764203910.452G>C0.670.714A0.060.971A
      IL10rs302449217764243810.207T>A0.430.809A1.000.608A
      IL10rs151811117764297110.267G>A4.420.110A0.890.641A
      IL10rs151811017764318710.267G>T4.420.110A0.890.641A
      IL10rs302449117764337210.448G>T0.980.613A0.010.995A
      IL10HapA50.600.7421.720.424A
      IL10HapA64.170.1240.840.658A
      IL10HapA80.480.7890.990.608A
      IL10HapA90.020.9910.670.715A
      IL13rs188145712718471350.192A>C0.710.702A3.470.177A
      IL13rs180092512718511350.227C>T0.070.966A0.710.700A
      IL13rs206974312718557950.021A>GNANANANANANA
      IL13rs129568612718814750.252G>A1.110.575A0.740.692A
      IL13rs2054112718826850.174C>T0.250.883A1.030.598A
      IL13HapA10.640.7270.340.846
      IL13HapA40.460.7972.47.291
      IL17Ars47119985188142260.293G>A2.870.239A0.380.828A
      IL17Ars81930365188156260.255T>C2.830.243AFE0.017D
      IL17Ars38190245188185560.374A>G0.030.986A2.170.338A
      IL17Ars22759135188210260.345G>A1.200.548A0.530.767A
      IL17Ars38045135188426660.027A>TNANANANANANA
      IL17Ars77479095188531860.225G>A2.400.301A0.700.703A
      NFKB1rs377493310364536940.444T>C0.830.660A0.150.928A
      NFKB1rs17073110366793340.397A>T0.52.773A0.290.863A
      NFKB1rs1703277910368527940.023T>CNANANANANANA
      NFKB1rs23051010369520140.366T>A1.070.586A0.290.875A
      NFKB1rs23049410370600540.477A>G3.890.143A0.810.668A
      NFKB1rs464801610370870640.017C>TNANANANANANA
      NFKB1rs464801810370923640.025G>CNANANANANANA
      NFKB1rs377495610372756440.479C>T4.470.107A0.910.635A
      NFKB1rs1048911410373042640.025A>GNANANANANANA
      NFKB1rs464806810373734340.366A>G3.720.156A0.380.826A
      NFKB1rs464809510374691440.052T>CFE0.091AFE0.794A
      NFKB1rs464811010375286740.205T>AFE0.043A1.100.577A
      NFKB1rs464813510375571640.060A>GFE0.238AFE0.808A
      NFKB1rs464814110375594740.188G>A4.760.093A3.340.188A
      NFKB1rs160979810375648840.337C>T0.990.608A2.150.342A
      NFKB1HapA10.920.6330.200.903
      NFKB1HapA90.790.675A0.080.961
      NFKB2rs12772374104146901100.229A>G0.920.633A0.760.683A
      NFKB2rs7897947104147701100.085T>G1.430.490A1.590.452A
      NFKB2rs11574849104149686100.317G>A1.040.595A1.760.414A
      NFKB2rs1056890104152760100.317C>T0.450.798AFE0.013R
      TNFArs28576023153337860.360T>C0.290.864A0.240.885A
      TNFArs18006833154007160.388G>AFE0.048RFE0.027R
      TNFArs22397043154014160.370G>T0.440.801A0.250.882A
      TNFArs22290943154055660.256T>C1.740.419A3.970.138A
      TNFArs10419813154078460.388C>AFE0.048RFE0.027A
      TNFArs17999643154230860.202T>C0.100.950A2.230.328A
      TNFArs18007503154296360.019G>ANANANANANANA
      TNFArs18006293154303160.157G>A1.490.475A9.590.008A
      TNFArs18006103154382760.105C>T2.160.340A3.320.190A
      TNFArs30936623154418960.072A>G3.900.142A1.640.439A
      TNFAHapA15.810.0555.030.081
      TNFAHapA50.070.9682.070.355
      TNFAHapA80.250.8830.100.952
      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.

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