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Original Article| Volume 52, ISSUE 5, P695-708.e4, November 2016

Polymorphisms in Cytokine Genes Are Associated With Higher Levels of Fatigue and Lower Levels of Energy in Women After Breast Cancer Surgery

Open AccessPublished:September 21, 2016DOI:https://doi.org/10.1016/j.jpainsymman.2016.04.014

      Abstract

      Context

      Little is known about the phenotypic and molecular characteristics associated with changes over time in fatigue and lack of energy in patients with breast cancer.

      Objectives

      The aim of this study was to identify subgroups (i.e., latent classes) of women with distinct fatigue and energy trajectories; evaluate for differences in phenotypic characteristics between the latent classes for fatigue and energy; and evaluate for associations between polymorphisms in genes for pro- and anti-inflammatory cytokines, their receptors, and their transcriptional regulators and latent class membership.

      Methods

      Patients were enrolled before and followed for six months after breast cancer surgery. Latent class analyses were done to identify subgroups of patients with distinct fatigue and energy trajectories. Candidate gene analyses were done to identify cytokine genes associated with these two symptoms.

      Results

      For both fatigue and lack of energy, two distinct latent classes were identified. Phenotypic characteristics associated with the higher fatigue class were younger age, higher education, lower Karnofsky Performance Status score, higher comorbidity, higher number of lymph nodes removed, and receipt of chemotherapy (CTX). Polymorphisms in interleukin (IL) 1β and IL10 were associated with membership in the higher fatigue class. Phenotypic characteristics associated with the lower energy class included: a lower Karnofsky Performance Status score and a higher comorbidity score. A polymorphism in IL1R1 was associated with membership in the lower energy class.

      Conclusion

      Within each latent class, the severity of fatigue and decrements in energy were relatively stable over the first six months after breast cancer surgery. Distinct phenotypic characteristics and genetic polymorphisms were associated with membership in the higher fatigue and lower energy classes.

      Key Words

      Introduction

      Fatigue is the most common symptom reported by patients who undergo treatment for breast cancer.
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      • Ganz P.A.
      Symptoms: fatigue and cognitive dysfunction.
      Although more than 90% of patients diagnosed with breast cancer will undergo surgery, only a limited number of longitudinal studies have evaluated for preoperative levels of fatigue and changes in fatigue after surgery.
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      Fatigue trajectories during the first 8 months after breast cancer diagnosis.
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      Presurgery psychological factors predict pain, nausea, and fatigue one week after breast cancer surgery.
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      Across these studies, a variety of demographic (e.g., younger age
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      • Diefenbach M.A.
      • Bovbjerg D.H.
      Presurgery psychological factors predict pain, nausea, and fatigue one week after breast cancer surgery.
      ) and clinical (e.g., partial mastectomy,
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      • Liang J.
      • Miaskowski C.
      Changes in and predictors of severity of fatigue in women with breast cancer: a longitudinal study.
      poorer functional status
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      • Guillemin F.
      • Bonnetain F.
      • Velten M.
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      Factors associated with fatigue after surgery in women with early-stage invasive breast cancer.
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      • Liang J.
      • Miaskowski C.
      Changes in and predictors of severity of fatigue in women with breast cancer: a longitudinal study.
      ) characteristics, and psychological factors (e.g., pre-surgical expectancies for fatigue,
      • Montgomery G.H.
      • Schnur J.B.
      • Erblich J.
      • Diefenbach M.A.
      • Bovbjerg D.H.
      Presurgery psychological factors predict pain, nausea, and fatigue one week after breast cancer surgery.
      introversion
      • De Vries J.
      • Van der Steeg A.F.
      • Roukema J.A.
      Determinants of fatigue 6 and 12 months after surgery in women with early-stage breast cancer: a comparison with women with benign breast problems.
      ) were associated with higher levels of fatigue after surgery. In these studies, the length of post-surgical follow-up ranged from one week
      • Montgomery G.H.
      • Schnur J.B.
      • Erblich J.
      • Diefenbach M.A.
      • Bovbjerg D.H.
      Presurgery psychological factors predict pain, nausea, and fatigue one week after breast cancer surgery.
      to one year.
      • Huang H.P.
      • Chen M.L.
      • Liang J.
      • Miaskowski C.
      Changes in and predictors of severity of fatigue in women with breast cancer: a longitudinal study.
      Only one study was identified that used a type of growth mixture modeling (GMM) to identify subgroups of breast cancer patients (n = 290) with distinct fatigue trajectories.
      • Bodtcher H.
      • Bidstrup P.E.
      • Andersen I.
      • et al.
      Fatigue trajectories during the first 8 months after breast cancer diagnosis.
      Using fatigue assessments done before and at four and five months after surgery, groups of patients with persistently high (21%) and persistently low (79%) levels of fatigue were identified. Patients in the high group were more likely to report lower levels of physical activity and higher levels of anxiety. No demographic or clinical characteristics were associated with membership in the higher fatigue group. Although this study provides important information on persistent fatigue, only three assessments were done (i.e., four weeks before surgery and four and eight months after surgery) and the confidence intervals (CIs) were wide, which suggests that a larger sample could result in more reliable estimates.
      In oncology, fatigue is defined as a distressing, persistent sense of physical, emotional, and/or cognitive tiredness for exhaustion related to cancer or cancer treatment that is not proportional to recent activity and interferes with usual functioning.
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      Cancer-related fatigue, version 2.2015.
      In contrast, energy can be defined as an individual's potential to perform physical and mental activity.
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      A theoretical extension of the concept of energy through an empirical study.
      As noted in our previous publication,
      • Aouizerat B.E.
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      Phenotypic and molecular evidence suggests that decrements in morning and evening energy are distinct but related symptoms.
      an increasing body of evidence suggests that fatigue and energy are distinct but related constructs.
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      A theoretical extension of the concept of energy through an empirical study.
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      A concept analysis of energy. Its meaning in the lives of three individuals with chronic illness.
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      Mental energy: assessing the mood dimension.
      For example, instruments like the Profile of Mood States (POMS)
      • McNair D.M.
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      EDITS manual for the profile of mood states.
      have separate scales 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). 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 to 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.
      • Lerdal A.
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      • Lee K.A.
      Lee Fatigue and Energy Scales: exploring aspects of validity in a sample of women with HIV using an application of a Rasch model.
      Only one study was identified that evaluated decrements in energy in patients before breast cancer surgery.
      • Van Onselen C.
      • Aouizerat B.E.
      • Dunn L.B.
      • et al.
      Differences in sleep disturbance, fatigue and energy levels between women with and without breast pain prior to breast cancer surgery.
      In this study, although 32% of the women reported clinically meaningful levels of fatigue, nearly 50% of the patients reported clinically meaningful decrements in energy levels before surgery. No studies were identified that evaluated for subgroups of patients with distinct energy trajectories from before to six months after breast cancer surgery.
      Although some progress has been made in evaluating the role of inflammatory mediators in the development and maintenance of fatigue, recent reviews of fatigue in patients with cancer recommend that additional research be done to establish the underlying mechanisms for fatigue.
      • Bower J.E.
      • Ganz P.A.
      Symptoms: fatigue and cognitive dysfunction.
      • Barsevick A.M.
      • Irwin M.R.
      • Hinds P.
      • et al.
      Recommendations for high-priority research on cancer-related fatigue in children and adults.
      • Saligan L.N.
      • Olson K.
      • Filler K.
      • et al.
      The biology of cancer-related fatigue: a review of the literature.
      In all these reviews, emphasis is placed on understanding the molecular mechanisms that underlie the development of persistent fatigue in oncology patients.
      Cytokines, their receptors, and transcriptional regulators are one class of polypeptides that mediate inflammatory processes (for reviews, see Fagundes et al.,
      • Fagundes C.
      • LeRoy A.
      • Karuga M.
      Behavioral symptoms after breast cancer treatment: a biobehavioral approach.
      Morris et al.,
      • Morris G.
      • Berk M.
      • Walder K.
      • Maes M.
      Central pathways causing fatigue in neuro-inflammatory and autoimmune illnesses.
      Poon et al.
      • Poon D.C.
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      Sickness: from the focus on cytokines, prostaglandins, and complement factors to the perspectives of neurons.
      ). In several studies,
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      • Pace T.W.
      • et al.
      Epigenetic changes associated with inflammation in breast cancer patients treated with chemotherapy.
      • Bower J.E.
      • Ganz P.A.
      • Irwin M.R.
      • et al.
      Cytokine genetic variations and fatigue among patients with breast cancer.
      • 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.
      • Aouizerat B.E.
      • Dodd M.
      • Lee K.
      • et al.
      Preliminary evidence of a genetic association between tumor necrosis factor alpha and the severity of sleep disturbance and morning fatigue.
      genetic and epigenetic mechanisms involved in inflammation were associated with fatigue in oncology patients. Only one study was found that reported on associations between polymorphisms in cytokine genes and decrements in energy in oncology patients undergoing radiation therapy and their family caregivers.
      • Aouizerat B.E.
      • Dhruva A.
      • Paul S.M.
      • et al.
      Phenotypic and molecular evidence suggests that decrements in morning and evening energy are distinct but related symptoms.
      This preliminary evidence provides support for the role of molecular mechanisms involved in inflammatory processes, particularly cytokines, in the etiology of fatigue and decrements in energy in oncology patients.
      Given the paucity of longitudinal studies that aimed to determine distinct phenotypes for fatigue and energy after breast cancer surgery and the need for more molecular-based studies, the purposes of this study, in a sample of women (n = 398) who were assessed before and monthly for six months after surgery were to: identify subgroups (i.e., latent classes) of women with distinct fatigue and energy trajectories, evaluate for differences in phenotypic characteristics between the latent classes for fatigue and energy, and evaluate for associations between polymorphisms in genes for pro- and anti-inflammatory cytokines, their receptors, and their transcriptional regulators and latent class membership. We hypothesized that at least two latent classes would be identified for each symptom using GMM and distinct phenotypic characteristics and genetic polymorphisms would be associated with higher fatigue and lower energy latent class membership.

      Materials and Methods

      Patients and Settings

      This analysis is part of a larger, longitudinal study that evaluated neuropathic pain and lymphedema in women who underwent breast cancer surgery. The study methods are described in detail elsewhere.
      • McCann B.
      • Miaskowski C.
      • Koetters T.
      • et al.
      Associations between pro- and anti-inflammatory cytokine genes and breast pain in women prior to breast cancer surgery.
      • Miaskowski C.
      • Cooper B.
      • Paul S.M.
      • et al.
      Identification of patient subgroups and risk factors for persistent breast pain following breast cancer surgery.
      • Miaskowski C.
      • Dodd M.
      • Paul S.M.
      • et al.
      Lymphatic and angiogenic candidate genes predict the development of secondary lymphedema following breast cancer surgery.
      • 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, patients were recruited from breast care centers located in a Comprehensive Cancer Center, two public hospitals, and four community practices.
      Patients were eligible to participate if they were adult women (≥18 years) who were scheduled to undergo unilateral breast cancer surgery; were able to read, write, and understand English; agreed to participate; and gave written informed consent. Patients were excluded if they were having bilateral breast cancer surgery or had distant metastasis at the time of diagnosis. A total of 516 patients were approached, 410 were enrolled (response rate 79.5%), and 398 completed the enrollment assessment. The most common reasons for refusal were too busy, overwhelmed with the cancer diagnosis, or insufficient time available to do the enrollment assessment before surgery.

      Instruments

      The demographic questionnaire obtained information on age, marital status, education, ethnicity, employment status, and living situation. Patients rated their functional status using the Karnofsky Performance Status (KPS) scale that ranged from 30 (I feel severely disabled and need to be hospitalized) to 100 (I feel normal; I have no complaints or symptoms).
      • Karnofsky D.
      • Abelmann W.H.
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      The use of nitrogen mustards in the palliative treatment of carcinoma.
      The Self-Administered Comorbidity Questionnaire (SCQ) was used to evaluate comorbidity.
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      • Stucki G.
      • Liang M.H.
      • Fossel A.H.
      • Katz J.N.
      The Self-Administered Comorbidity Questionnaire: a new method to assess comorbidity for clinical and health services research.
      Patients were asked to indicate if they had one of 13 common medical conditions; if they received treatment for it (proxy for disease severity); and did it limit their activities (indication of functional limitations). For each condition, a patient can receive a maximum of 3 points. The total SCQ score can range from 0 to 39 points. The SCQ has well-established validity and reliability.
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      • Bachmann L.M.
      • Weber U.
      • et al.
      Complex regional pain syndrome 1—the Swiss cohort study.
      • Cieza A.
      • Geyh S.
      • Chatterji S.
      • et al.
      Identification of candidate categories of the International Classification of Functioning Disability and Health (ICF) for a generic ICF core set based on regression modelling.
      The LFS consists of 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 to 10 NRS. Total fatigue and energy scores were calculated as the mean of the 13 fatigue items and the five energy items, with higher scores indicating greater fatigue severity and higher levels of energy. Patients were asked to rate each item based on how they felt “right now.” The LFS has been used with healthy individuals
      • Lee K.A.
      • Hicks G.
      • Nino-Murcia G.
      Validity and reliability of a scale to assess fatigue.
      • Gay C.L.
      • Lee K.A.
      • Lee S.Y.
      Sleep patterns and fatigue in new mothers and fathers.
      and in patients with cancer and HIV.
      • Lee K.A.
      • Portillo C.J.
      • Miramontes H.
      The fatigue experience for women with human immunodeficiency virus.
      • Miaskowski C.
      • Lee K.A.
      Pain, fatigue, and sleep disturbances in oncology outpatients receiving radiation therapy for bone metastasis: a pilot study.
      • Miaskowski C.
      • Paul S.M.
      • Cooper B.A.
      • et al.
      Trajectories of fatigue in men with prostate cancer before, during, and after radiation therapy.
      • 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.
      Cutoff scores of 4.4 or more and 4.8 or less indicate clinically meaningful levels of fatigue severity and low levels of energy, respectively.
      • Dhruva A.
      • Dodd M.
      • Paul S.M.
      • et al.
      Trajectories of fatigue in patients with breast cancer before, during, and after radiation therapy.
      The LFS has well-established validity and reliability.
      • Lee K.A.
      • Hicks G.
      • Nino-Murcia G.
      Validity and reliability of a scale to assess fatigue.
      • Gay C.L.
      • Lee K.A.
      • Lee S.Y.
      Sleep patterns and fatigue in new mothers and fathers.
      In the present study, Cronbach alphas for the fatigue and energy scales were 0.96 and 0.93, respectively.

      Study Procedures

      The study was approved by the Committee on Human Research at the University of California, San Francisco, and by the institutional review boards at each of the study sites. During the preoperative visit, a clinician explained the study, determined the patient's willingness to participate, and introduced her to the research nurse. The research nurse met with the woman, determined eligibility, and obtained written informed consent before surgery. After obtaining consent, patients completed the enrollment questionnaires an average of four days before surgery. Patients completed the LFS at enrollment and monthly for six months (i.e., seven assessments). Medical records were reviewed for disease and treatment information.

      Genomic Analyses

      Gene Selection

      Cytokines, their receptors, and their transcriptional regulators are classes of polypeptides that mediate pro- and anti-inflammatory processes. Cytokine dysregulation is associated with fatigue (for reviews, see Bower and Ganz,
      • Bower J.E.
      • Ganz P.A.
      Symptoms: fatigue and cognitive dysfunction.
      Saligan et al.,
      • Saligan L.N.
      • Olson K.
      • Filler K.
      • et al.
      The biology of cancer-related fatigue: a review of the literature.
      Barsevick et al.
      • Barsevick A.
      • Frost M.
      • Zwinderman A.
      • et al.
      I'm so tired: biological and genetic mechanisms of cancer-related fatigue.
      ). Pro-inflammatory genes promote systemic inflammation and include chemokine (C-C-C motif) ligand 8 (CXCL8, previous gene symbol interleukin 8 [IL8]), interferon gamma [IFNG], IFNG receptor 1 (IFNGR1), IL1 receptor 1 [IL1R1], IL2, IL17A, and members of the tumor necrosis factor (TNF) family (i.e., lymphotoxin alpha [LTA], TNF). Anti-inflammatory genes suppress the activity of pro-inflammatory cytokines and include IL1R2, IL4, IL10, and IL13. Of note, IFNG1, IL1B, and IL6 possess pro- and anti-inflammatory functions. Nuclear factor kappa beta 1 (NFKB1) and NFKB2 are transcriptional regulators of these cytokine genes.
      • Seruga B.
      • Zhang H.
      • Bernstein L.J.
      • Tannock I.F.
      Cytokines and their relationship to the symptoms and outcome of cancer.
      All genes were identified according to the approved symbol stored in the Human Genome Organization Gene Nomenclature Committee database (http://www.genenames.org).

      Blood Collection and Genotyping

      Of the 398 patients who completed the enrollment assessment, 310 provided a blood sample from which DNA could be isolated from peripheral blood mononuclear cells. Genomic DNA was extracted from peripheral blood mononuclear cells using the PUREGene DNA Isolation System (Invitrogen, Carlsbad, CA). DNA was quantitated with a Nanodrop Spectrophotometer (ND-1000) and normalized to a concentration of 50 ng/L. Genotyping was performed blinded to clinical status, and positive and negative controls were included. Samples were genotyped using a custom array on the Golden Gate genotyping platform (Illumina, San Diego, CA) and processed according to the standard protocol using GenomeStudio (Illumina).

      SNP Selection

      A combination of tagging single-nucleotide polymorphism (SNPs) and literature-driven SNPs were selected for analysis. Tagging SNPs were required to be common (i.e., a minor allele frequency ≥0.05) in public databases. SNPs with call rates of less than 95% or Hardy-Weinberg P-values of <0.001 were excluded. As shown in Supplementary Table 1, 83 SNPs from a total of 104 SNPs among 16 candidate genes passed all the quality control filters and were included in the genetic association analyses. Localization of SNPs on the human genome was performed using the GRCh38 human reference assembly. Regional annotations were identified using the University of California Santa Cruz Human Genome Browser GRCh38/hg38 (http://genome.ucsc.edu/cgi-bin/hgTracks?db=hg38). Potential regulatory involvement of SNPs was investigated using a number of Encode data tracks.
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      Linkage disequilibrium (LD) was calculated with Plink v1.90_b39
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      using 1000 Genomes “phase1_release_v2.201001123” variants called from all populations.
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      A map of human genome variation from population-scale sequencing.

      Statistical Analyses for the Phenotypic Data

      Data were analyzed using SPSS, version 23 (IBM Corp, Armonk, NY),
      SPSS
      IBM SPSS for Windows (version 23).
      and STATA, version 13 (StataCorp LP, College Station, TX).
      StataCorp
      Stata statistical software: Release 14.
      The fatigue and energy data were analyzed separately. Descriptive statistics and frequency distributions were generated for sample characteristics. Independent-sample t-tests, Mann-Whitney U tests, chi-squared analyses, and Fisher exact tests were used to evaluate for differences in demographic and clinical characteristics between the two latent classes. A P-value of <0.05 was considered statistically significant.
      Unconditional GMM with robust maximum likelihood estimation was carried out to identify latent classes with distinct fatigue and energy trajectories using Mplus, version 5.21 (Muthén & Muthén, Los Angeles, CA). These 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 whole sample. Then, the number of latent growth classes that best fit the data was identified using guidelines recommended in the literature.
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      Identifying the correct number of classes in growth mixture models.

      Statistical Analyses for the Genetic Data

      Allele and genotype frequencies were determined by gene counting. Hardy-Weinberg equilibrium was assessed by the chi-squared or Fisher exact tests. Measures of LD (i.e., D′ and R2) were computed from the patients' genotypes with Haploview 4.2 (https://www.broadinstitute.org/haploview/haploview). The LD-based haplotype block definition was based on D′ CI.
      • Gabriel S.B.
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      The structure of haplotype blocks in the human genome.
      For SNPs that were members of the same haploblock, haplotype analyses were conducted to localize the association signal within each gene and to determine if haplotypes improved the strength of the association with the phenotype. Haplotypes were constructed using the program PHASE, version 2.1.
      • Stephens M.
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      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 that were inferred with probability estimates of 0.85 or more, across the five iterations, were retained for downstream analyses. Only inferred haplotypes that occurred with a frequency estimate of ≥15% were included in the association analyses, assuming a dosage model (i.e., analogous to the additive model).
      Ancestry informative markers were used to minimize confounding because of population stratification.
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      • Hoggart C.J.
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      Control of confounding of genetic associations in stratified populations.
      • Tian C.
      • Gregersen P.K.
      • Seldin M.F.
      Accounting for ancestry: population substructure and genome-wide association studies.
      Homogeneity in ancestry among patients was verified by principal component (PC) analysis,
      • Price A.L.
      • Patterson N.J.
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      • et al.
      Principal components analysis corrects for stratification in genome-wide association studies.
      using HelixTree (GoldenHelix, Bozeman, MT). Briefly, the number of PCs was sought that distinguished the major racial/ethnic groups in the sample by visual inspection of scatter plots of orthogonal PCs (i.e., PC1 vs. PC2, PC2 vs. PC3). This procedure was repeated until no discernable clustering of patients by their self-reported race/ethnicity was possible (data not shown). The first three PCs were selected to adjust for potential confounding because of population substructure (i.e., race/ethnicity) by including them in all the logistic regression models. One hundred six ancestry informative markers 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 fit the data, by maximizing the significance of the P-value, was selected for each SNP. Logistic regression analysis, that controlled for significant covariates, and genomic estimates of and self-reported race/ethnicity, was used to evaluate the associations between genotype and fatigue and energy class membership. Only those genetic associations identified as significant from the bivariate analyses were evaluated in the multivariate analyses. A backward stepwise approach was used to create a parsimonious model. Except for genomic estimates of and self-reported race/ethnicity, 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 were estimated using STATA, version 13.
      StataCorp
      Stata statistical software: Release 14.
      As was done in our previous studies,
      • Aouizerat B.E.
      • Dhruva A.
      • Paul S.M.
      • et al.
      Phenotypic and molecular evidence suggests that decrements in morning and evening energy are distinct but related symptoms.
      • 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.
      • McCann B.
      • Miaskowski C.
      • Koetters T.
      • et al.
      Associations between pro- and anti-inflammatory cytokine genes and breast pain in women prior to breast cancer surgery.
      • Miaskowski C.
      • Dodd M.
      • Paul S.M.
      • et al.
      Lymphatic and angiogenic candidate genes predict the development of secondary lymphedema following breast cancer surgery.
      • 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.
      • Miaskowski C.
      • Cataldo J.K.
      • Baggott C.R.
      • et al.
      Cytokine gene variations associated with trait and state anxiety in oncology patients and their family caregivers.
      • Doong S.H.
      • Dhruva A.
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      Associations between cytokine genes and a symptom cluster of pain, fatigue, sleep disturbance, and depression in patients prior to breast cancer surgery.
      • Langford D.J.
      • Schmidt B.
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      Preoperative breast pain predicts persistent breast pain and disability after breast cancer surgery.
      • Alfaro E.
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      Associations between cytokine gene variations and self-reported sleep disturbance in women following breast cancer surgery.
      • Stephens K.
      • Cooper B.A.
      • West C.
      • et al.
      Associations between cytokine gene variations and severe persistent breast pain in women following breast cancer surgery.
      • Merriman J.D.
      • Aouizerat B.E.
      • Cataldo J.K.
      • et al.
      Association between an interleukin 1 receptor, type I promoter polymorphism and self-reported attentional function in women with breast cancer.
      • 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.
      • Saad S.
      • Dunn L.B.
      • Koetters T.
      • et al.
      Cytokine gene variations associated with subsyndromal depressive symptoms in patients with breast cancer.
      • Langford D.J.
      • West C.
      • Elboim C.
      • et al.
      Variations in potassium channel genes are associated with breast pain in women prior to breast cancer surgery.
      • 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.
      based on the recommendations in the literature,
      • Rothman K.J.
      No adjustments are needed for multiple comparisons.
      • Bakan D.
      The test of significance in psychological research.
      and the implementation of rigorous quality controls for genomic data, the nonindependence of SNPs/haplotypes in LD, and the exploratory nature of the analyses, adjustments were not made for multiple testing. In addition, significant SNPs identified in the bivariate analyses were evaluated further using logistic regression analyses that controlled for differences in phenotypic characteristics, potential confounding because of population stratification, and variations 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 due solely to chance. Unadjusted (bivariate) associations are reported for all the SNPs that passed quality control criteria in Supplementary Table 1, to allow for subsequent comparisons and meta-analyses.

      Results

      GMM Analysis for Fatigue

      Two distinct latent classes of fatigue trajectories were identified using GMM (Fig. 1a). A two-class model was selected because its Bayesian Information Criterion was smaller than the one-class and three-class models (Table 1). As listed in Table 2, the majority of the patients were classified into the Higher Fatigue class (n = 244, 61.5%). These patients had estimated fatigue scores that were high before surgery (3.90) and remained high over the six months of the study. Patients in the Lower Fatigue class (n = 153, 38.5%) had estimated fatigue scores that were lower at enrollment (1.60) and that gradually decreased over time.
      Fig. 1
      Fig. 1Observed and estimated a) fatigue and b) energy trajectories for patients in each of the latent classes and the mean fatigue and energy scores for the total sample.
      Table 1Fit Indices for the Lee Fatigue Scale and Lee Energy Scale GMM Class Solutions
      GMMLLAICBICEntropyBLRTVLMR
      Fatigue
       One class
      Latent growth curve with linear and quadratic components; χ2 = 34.60, 19 df, P < 0.02, CFI = 0.99, RMSEA = 0.045.
      −5068.4510,168.9010,232.65n/an/an/a
       Two class
      Two-class model was selected. The BIC was smaller than for the one-class and three-class models, and the BLRT indicated that the two-class solution fit the data better than the one-class solution.
      −5035.4610,106.9310,178.640.6168.73
      P < 0.05.
      68.73
      P < 0.01.
       Three class−5026.2210,096.4410,184.090.7118.48ns18.48ns
      Energy
       One class
      Latent growth curve with linear and quadratic components; χ2 = 50.99, 24 df, P = 0.001, CFI = 0.964, RMSEA = 0.053.
      −5454.4310,930.8710,974.69n/an/an/a
       Two class
      Two-class model was selected. The BIC was smaller than for the one-class and three-class models, and the BLRT indicated that the two-class solution fit the data better than the one-class solution.
      −5430.7810,893.5610,957.300.5347.31
      P < 0.05.
      47.31ns
       Three class−5422.4910,886.9810,970.640.5316.58ns16.58
      P < 0.05.
      AIC = Akaike information criterion; BIC = Bayesian information criterion; BLRT = parametric bootstrapped likelihood ratio test for K-1 (H0) vs. K classes; CFI = comparative fit index; GMM = growth mixture model; LL = log-likelihood; n/a = not applicable; ns = not significant; RMSEA = root mean squared error of approximation; VLMR = Vuong-Lo-Mendell-Rubin likelihood ratio test for K-1 (H0) vs. K classes.
      a Latent growth curve with linear and quadratic components; χ2 = 34.60, 19 df, P < 0.02, CFI = 0.99, RMSEA = 0.045.
      b Two-class model was selected. The BIC was smaller than for the one-class and three-class models, and the BLRT indicated that the two-class solution fit the data better than the one-class solution.
      c P < 0.05.
      d P < 0.01.
      e Latent growth curve with linear and quadratic components; χ2 = 50.99, 24 df, P = 0.001, CFI = 0.964, RMSEA = 0.053.
      Table 2Parameter Estimates for the Lee Fatigue Scale and Lee Energy Scale GMM Latent Classes
      FatigueLower Fatigue Class (n
      Predicted class sizes based on their most likely class membership.
       = 153)
      Higher Fatigue Class (n
      Predicted class sizes based on their most likely class membership.
       = 244)
      Parameter EstimatesMeans (SE)
      Intercept1.60
      P < 0.001.
      (0.36)
      3.90
      P < 0.001.
      (0.22)
      Linear slope−0.09 (0.12)0.13 (0.141
      Quadratic slope0.00 (0.02)−0.02 (0.02)
      Variances
      Intercept0.26 (0.20)2.53
      P < 0.001.
      (0.36)
      Linear slope0
      Random intercepts model only. Random slopes were fixed at zero to assist in estimation.
      0.09
      P < 0.001.
      (0.02)
      Quadratic slope0
      Random intercepts model only. Random slopes were fixed at zero to assist in estimation.
      0
      Random intercepts model only. Random slopes were fixed at zero to assist in estimation.
      EnergyHigher Energy Class (n
      Predicted class sizes based on their most likely class membership.
       = 127)
      Lower Energy Class (n
      Predicted class sizes based on their most likely class membership.
       = 270)
      Parameter EstimatesMeans (SE)
      Intercept5.82
      P < 0.001.
      (0.76)
      4.35
      P < 0.001.
      (0.16)
      Linear slope0.10 (0.37)−0.11 (0.14)
      Quadratic slope0.03 (0.06)0.01 (0.04)
      Variances
      Intercept1.72 (1.60)1.07
      P < 0.001.
      (0.21)
      Linear slope0
      Random intercepts model only. Random slopes were fixed at zero to assist in estimation.
      0
      Random intercepts model only. Random slopes were fixed at zero to assist in estimation.
      Quadratic slope0
      Random intercepts model only. Random slopes were fixed at zero to assist in estimation.
      0
      Random intercepts model only. Random slopes were fixed at zero to assist in estimation.
      GMM = growth mixture model; SE = standard error.
      a Predicted class sizes based on their most likely class membership.
      b P < 0.001.
      c Random intercepts model only. Random slopes were fixed at zero to assist in estimation.

      Differences in Demographic and Clinical Characteristics Between the Fatigue Classes

      As summarized in Table 3, patients in the Higher Fatigue class were significantly younger and had more education, a lower KPS score, a higher SCQ score, and a higher number of lymph nodes removed. In addition, a higher percentage of patients in the Higher Fatigue class had received neoadjuvant chemotherapy (CTX) before surgery and adjuvant CTX during the first six months after breast cancer surgery.
      Table 3Differences in Demographic and Clinical Characteristics between the Lower Fatigue (n = 153) and Higher Fatigue (n = 244) Classes
      CharacteristicLower Fatigue Class, n = 153 (38.4%)

      Mean (SD)
      Higher Fatigue Class, n = 244 (61.3%)

      Mean (SD)
      Statistic and P Value
      Age (years)57.8 (11.9)53.1 (11.0)t = 4.09, P < 0.0001
      Education (years)15.3 (2.5)15.9 (2.8)t = −2.02, P = 0.04
      Karnofsky Performance Status score96.6 (7.0)91.1 (11.4)t = 5.86, P < 0.0001
      Self-administered Comorbidity Questionnaire score3.8 (2.6)4.6 (3.0)t = −2.64, P = 0.009
      Fatigue severity score at enrollment1.6 (1.6)4.1 (2.2)t = −12.55, P < 0.0001
      Number of breast biopsies in past year1.5 (.8)1.5 (.8)U, P = 0.47
      Number of positive lymph nodes0.8 (1.9)1.0 (2.4)t = −0.88, P = 0.38
      Number of lymph nodes removed4.8 (5.1)6.4 (7.5)t = −2.43, P = 0.016
      n (%)n (%)
      Ethnicityχ2 = 2.82, P = 0.42
       White100 (65.8)155 (63.8)
       Black19 (12.5)21 (8.6)
       Asian/Pacific Islander17 (11.2)32 (13.2)
       Hispanic/mixed ethnic background/other16 (10.5)35 (14.4)
      Married/partnered (% yes)64 (42.1)100 (41.5)FE, P = 0.92
      Work for pay (% yes)71 (46.4)118 (49.0)FE, P = 0.68
      Lives alone (% yes)40 (26.5)54 (22.4)FE, P = 0.40
      Gone through menopause (% yes)96 (63.6)151 (64.3)FE, P = 0.91
      Stage of diseaseU, P = 0.13
       029 (19.0)44 (18.0)
       I66 (43.1)85 (34.8)
       IIA and IIB48 (31.4)92 (37.7)
       IIIA, IIIB, IIIC, and IV10 (6.5)23 (9.4)
      Surgical treatmentFE, P = 1.00
       Breast conservation123 (80.4)195 (79.9)
       Mastectomy30 (19.6)49 (20.1)
      Sentinel node biopsy (% yes)130 (85.0)197 (80.7)FE, P = 0.34
      Axillary lymph node dissection (% yes)50 (32.7)98 (40.3)FE, P = 0.14
      Breast reconstruction at the time of surgery (% yes)33 (21.7)53 (21.7)FE, P = 1.00
      Neoadjuvant chemotherapy (% yes)21 (13.7)58 (23.9)FE, P = 0.014
      Radiation therapy during the first 6 months (% yes)87 (56.9)137 (56.1)FE, P = 0.92
      Chemotherapy during the first 6 months (% yes)36 (23.5)97 (39.8)FE, P = 0.001
      FE = Fisher exact test; SD = standard deviation; U = Mann-Whitney U test.

      Candidate Gene Analyses for Fatigue

      As summarized in Supplementary Table 1, no associations with fatigue latent class membership were observed for SNPs in CXCL8, INFGR1, IL1R2, IL2, IL4, IL6, IL13, 1L17 A, NFKB1, NFKB2, or members of the TNF family (i.e., LTA, TNF). However, genotype frequencies were significantly different between the two latent classes for eight SNPs spanning three genes: IFNG rs2069718, IL1B rs1143629, IL1B rs1143627, IL1B rs16944, IL1B rs1143623, IL10 rs3024496, IL10 rs1878672, and IL10 rs3024491.

      Regression Analyses for IFNG, IL1B, and IL10 Genotypes and Lower vs. Higher Fatigue Classes

      To better estimate the magnitude (i.e., odds ratio) and precision (95% CI) of genotype on the odds of belonging to the higher compared with the Lower fatigue class, multivariate logistic regression models were fit. In these regression analyses, that included genomic estimates of and self-reported race/ethnicity, the only phenotypic characteristics that remained significant in the multivariate model were age (in five-year increments), KPS score (in 10 unit increments), SCQ score, and receipt of CTX in the six months after surgery.
      Two SNPs spanning two different genes remained significant in the multivariate logistic regression analyses (Table 4, Figs. 2a and 2b). For IL1B rs16944 and IL10 rs3024496, a recessive model fit the data best (P = 0.002). In the regression analysis for IL1B rs16944, carrying two doses of the rare A allele (i.e., GG + GA vs. AA) was associated with a 2.98-fold higher odds of belonging to the Higher Fatigue class. In the regression analysis for IL10 rs3024496, carrying two doses of the rare C allele (i.e., TT + TC vs. CC) was associated with a 66% decrease in the odds of belonging to the Higher Fatigue class.
      Table 4Multiple Logistic Regression Analyses for Cytokine Genes and Lower Fatigue vs. Higher Fatigue Classes
      PredictorOdds RatioStandard Error95% CIZP-Value
      IL1B rs169442.981.2151.336, 6.6262.670.008
      Age0.960.0120.936, 0.984−3.170.002
      KPS score0.940.0170.911, 0.978−3.210.001
      SCQ score1.100.0620.998, 1.2431.730.083
      Any chemotherapy2.260.6551.284, 3.9922.830.005
      Overall model fit: χ2 = 56.98, P < 0.0001
      IL10 rs30244960.340.1200.172, 0.682−3.050.002
      Age0.950.0130.930, 0.979−3.58<0.001
      KPS score0.950.0170.916, 0.980−3.090.002
      SCQ score1.120.0631.007, 1.2552.080.037
      Any chemotherapy2.320.6751.315, 4.1062.900.004
      Overall model fit: χ2 = 60.96, P < 0.0001
      Any chemotherapy = receipt of chemotherapy within six months after surgery; CI = confidence interval; IL1B = interleukin 1 beta; KPS = Karnofsky Performance Status; SCQ = Self-administered Comorbidity Questionnaire.
      Multiple logistic regression analyses of candidate gene associations with Lower Fatigue vs. Higher Fatigue classes (n = 301). For each model, the first three principal components identified from the analysis of ancestry informative markers, and self-reported race/ethnicity, were retained in all models to adjust for potential confounding because of race/ethnicity (data not shown). For the regression analyses, predictors evaluated in each model included genotype (IL1B rs16944: GG + GA vs. AA; IL10 rs3024496: TT + TC vs. CC, age (five-year increments), functional status (KPS score in 10-unit increments), self-administered comorbidity questionnaire score, and receipt of chemotherapy within six months after surgery).
      Fig. 2
      Fig. 2a) Differences between the fatigue 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 rs16944 in interleukin 1 beta (IL1B). Values are plotted as unadjusted proportions with corresponding P-value. b) Differences between the fatigue latent classes in the percentages of patients who were homozygous or heterozygous for the common allele (TT + TC) or homozygous for the rare allele (CC) for rs3024496 in interleukin 10 (IL10). Values are plotted as unadjusted proportions with corresponding P-value.

      GMM Analysis for Energy

      Two distinct latent classes of energy trajectories were identified using GMM (Fig. 1b). A two-class model was selected because its Bayesian Information Criterion was smaller than the one-class and three-class models (Table 1). As listed in Table 2, the majority of the patients were classified into the Lower Energy class (n = 270, 68.0%). These patients had estimated energy scores that were low before surgery (4.35) and remained low over the six months of the study. Patients in the Higher Energy class (n = 127, 32.0%) had estimated energy scores that were higher at enrollment (5.82) and that gradually increased over time.

      Differences in Demographic and Clinical Characteristics Between the Energy Classes

      As summarized in Table 5, patients in the Lower Energy class had a lower KPS score and a higher SCQ score. In addition, the percentage of patients based on their stage of disease differed between the two energy classes. However, post hoc contrasts failed to identify the subgroups that differed between the classes.
      Table 5Differences in Demographic and Clinical Characteristics between the Higher Energy (n = 127) and Lower Energy (n = 270) Classes
      CharacteristicsHigher Energy Class, n = 127 (31.9%)

      Mean (SD)
      Lower Energy

      Class, n = 270 (67.8%)

      Mean (SD)
      Statistic and P Value
      Age (years)56.5 (10.8)54.2 (11.8)t = 1.88, P = 0.061
      Education (years)15.7 (2.2)15.7 (2.8)t = 0.01, P = 0.994
      Karnofsky Performance Status score95.4 (9.4)92.2 (10.6)t = 3.06, P = 0.002
      Self-administered Comorbidity Questionnaire score3.6 (2.3)4.6 (3.0)t = −3.47, P = 0.001
      Mean energy score at enrollment6.1 (2.7)4.4 (2.2)t = −6.26, P < 0.0001
      Number of breast biopsies in past year1.5 (.8)1.5 (.8)U, P = 0.604
      Number of positive lymph nodes0.8 (2.0)1.0 (2.3)t = 0.76, P = 0.450
      Number of lymph nodes removed5.0 (6.3)6.1 (6.9)t = −1.51, P = 0.132
      n (%)n (%)
      Ethnicityχ2 = 1.75, P = 0.627
       White86 (68.3)169 (62.8)
       Black10 (7.9)30 (11.2)
       Asian/Pacific Islander16 (12.7)33 (12.3)
       Hispanic/mixed ethnic background/other14 (11.1)37 (13.8)
      Married/partnered (% yes)50 (39.7)114 (42.7)FE, P = 0.586
      Work for pay (% yes)66 (52.4)123 (45.9)FE, P = 0.236
      Lives alone (% yes)29 (23.0)65 (24.4)FE, P = 0.801
      Gone through menopause (% yes)84 (68.3)163 (62.0)FE, P = 0.256
      Stage of diseaseU, P = 0.040
      Post hoc contrasts of the difference in stage of disease between the Higher Energy and Lower Energy classes failed to identify the subgroups who differed between the classes (P < 0.0083).
       029 (22.8)44 (16.3)
       I51 (40.2)100 (37.0)
       IIA and IIB39 (30.7)101 (37.4)
       IIIA, IIIB, IIIC, and IV8 (6.3)25 (9.3)
      Surgical treatmentFE, P = 0.686
       Breast conservation100 (78.7)218 (80.7)
       Mastectomy27 (21.3)52 (19.3)
      Sentinel node biopsy (% yes)103 (81.1)224 (83.0)FE, P = 0.673
      Axillary lymph node dissection (% yes)40 (31.7)108 (40.0)FE, P = 0.120
      Breast reconstruction at the time of surgery (% yes)28 (22.2)58 (21.5)FE, P = 0.896
      Neoadjuvant chemotherapy (% yes)22 (17.5)57 (21.1)FE, P = 0.421
      Radiation therapy during the first 6 months (% yes)75 (59.1)149 (55.2)FE, P = 0.515
      Chemotherapy during the first 6 months (% yes)34 (26.8)99 (36.7)FE, P = 0.054
      FE = Fisher exact test; SD = standard deviation; U = Mann-Whitney U test.
      a Post hoc contrasts of the difference in stage of disease between the Higher Energy and Lower Energy classes failed to identify the subgroups who differed between the classes (P < 0.0083).

      Candidate Gene Analyses for Energy

      As summarized in Supplementary Table 1, no associations with energy class membership were found for SNPs in any gene except IL1R1. The genotype frequency was significantly different between the two latent classes for one SNP: IL1R1 rs2110726.

      Regression Analyses for IL1R1 Genotype and Higher vs. Lower Energy Classes

      To better estimate the magnitude (i.e., odds ratio) and precision (95% CI) of genotype on the odds of belonging to the Higher compared with the Lower Energy class, a multivariate logistic regression model was fit. In this regression analysis that included genomic estimates of and self-reported race/ethnicity, the only phenotypic characteristics that remained significant in the multivariate model were KPS score (in 10-unit increments) and receipt of CTX in the six months after surgery.
      In the multivariate logistic regression analysis, the association between IL1R1 rs2110726 and the energy phenotype remained significant (Table 6, Fig. 3). For this SNP, a dominant model fit the data best (P = 0.021). In the regression analysis, carrying one or two doses of the rare T allele (i.e., CC vs. CT + TT) was associated with a 50% decrease in the odds of belonging to the Lower Energy class.
      Table 6Multiple Logistic Regression Analysis for Cytokine Genes and Higher Energy vs. Lower Energy Classes
      PredictorOdds RatioStandard Error95% CIZP-Value
      IL1R1 rs21107260.500.1370.296, 0.859−2.520.012
      KPS score0.960.0150.931, 0.989−2.700.007
      Any chemotherapy1.830.5101.059, 3.1592.170.030
      Overall model fit: χ2 = 24.11, P = 0.004
      Any chemotherapy = receipt of chemotherapy within six months after surgery; CI = confidence interval; IL1R1 = interleukin 1 receptor 1; KPS = Karnofsky Performance Status; SCQ = Self-administered Comorbidity Questionnaire.
      Multiple logistic regression analyses of candidate gene associations with Higher Energy vs. Lower Energy classes (n = 301). For each model, the first three principal components identified from the analysis of ancestry informative markers, and self-reported race/ethnicity, were retained in all models to adjust for potential confounding because of race/ethnicity (data not shown). For the regression analysis, predictors evaluated in the model included genotype (IL1R1 rs2110726: CC vs. CT + TT, functional status (KPS score in 10-unit increments) and receipt of chemotherapy within six months after surgery.
      Fig. 3
      Fig. 3Differences between the energy latent classes in the percentages of patients who were homozygous for the common allele (CC) or heterozygous or homozygous for the rare allele (CT + TT) for rss110726 in interleukin 1 receptor 1 (IL1R1). Values are plotted as unadjusted proportions with corresponding P-value.

      Overlap Between the Fatigue and Energy Latent Classes

      An analysis of membership in the fatigue and energy classes in this study revealed that of the 397 patients evaluated: 16.6% (n = 66) were in both the Lower Fatigue and Lower Energy classes; 21.9% (n = 87) were in both the Lower Fatigue and Higher Energy classes; 10.1% (n = 40) were in both the Higher Fatigue and Higher Energy classes; and 51.4% (n = 204) were in both the Higher Fatigue and Lower Energy classes.

      Discussion

      This study is the first to use GMM to identify subgroups of breast cancer patients with distinct trajectories of fatigue and energy and evaluate for associations between a number of cytokine genes and these distinct phenotypes. Because less is known about decrements in energy after breast cancer surgery, this section begins with a discussion of the fatigue findings. In the section that discusses the energy findings, similarities and differences between the two symptoms in phenotypic characteristics and molecular markers are described.

      Fatigue Findings

      In the present study, 62% of the patients reported fatigue scores at the clinically meaningful cutoff score that occurred before surgery and persisted for six months after surgery. This finding contrasts with the previous study by Bodtcher et al.
      • Bodtcher H.
      • Bidstrup P.E.
      • Andersen I.
      • et al.
      Fatigue trajectories during the first 8 months after breast cancer diagnosis.
      who reported that only 21% of their sample reported high levels of fatigue before surgery and that their fatigue decreased at three months and then returned to pretreatment levels at eight months after surgery. Although the demographic and clinical characteristics of the two samples were relatively similar, differences in the instruments used to assess fatigue and in the statistical approaches used to create the latent classes may explain the difference in percentages of patients in the higher fatigue class.
      In terms of demographic characteristics, patients who were younger and had more years of education were more likely to be in the higher fatigue class. Although our findings are consistent with those of Bodtcher et al.
      • Bodtcher H.
      • Bidstrup P.E.
      • Andersen I.
      • et al.
      Fatigue trajectories during the first 8 months after breast cancer diagnosis.
      and Montgomery et al.,
      • Montgomery G.H.
      • Schnur J.B.
      • Erblich J.
      • Diefenbach M.A.
      • Bovbjerg D.H.
      Presurgery psychological factors predict pain, nausea, and fatigue one week after breast cancer surgery.
      they contrast with other studies that found no association
      • Rotonda C.
      • Guillemin F.
      • Bonnetain F.
      • Velten M.
      • Conroy T.
      Factors associated with fatigue after surgery in women with early-stage invasive breast cancer.
      • De Vries J.
      • Van der Steeg A.F.
      • Roukema J.A.
      Determinants of fatigue 6 and 12 months after surgery in women with early-stage breast cancer: a comparison with women with benign breast problems.
      • Huang H.P.
      • Chen M.L.
      • Liang J.
      • Miaskowski C.
      Changes in and predictors of severity of fatigue in women with breast cancer: a longitudinal study.
      between age and severity of fatigue in women after breast cancer surgery. In the study by Montgomery et al.,
      • Montgomery G.H.
      • Schnur J.B.
      • Erblich J.
      • Diefenbach M.A.
      • Bovbjerg D.H.
      Presurgery psychological factors predict pain, nausea, and fatigue one week after breast cancer surgery.
      mediational analyses demonstrated that preoperative expectations of postoperative levels of fatigue accounted in part for the effects of age on the severity of postoperative fatigue. The authors commented that their findings could be explained by Social Learning Theory, which suggests that an individual's previous experiences with fatigue might shape one's expectancies for the symptom.
      • Bandura A.
      Self-efficacy: toward a unifying theory of behavioral change.
      • Bandura A.
      • Adams N.E.
      • Beyer J.
      Cognitive processes mediating behavioral change.
      They hypothesized that older patients may have had previous experiences with surgery and/or fatigue that lowered their expectations for fatigue. An equally important consideration in all the studies cited above is that the mean age of the patients ranged from late 40s to late 50s. Therefore, additional studies are needed that evaluate the association between age and changes in fatigue severity after breast cancer surgery in older age groups.
      Although two studies found no association between education and fatigue severity in breast cancer survivors and women who underwent adjuvant treatment,
      • Bower J.E.
      • Ganz P.A.
      • Aziz N.
      • Fahey J.L.
      Fatigue and proinflammatory cytokine activity in breast cancer survivors.
      • Von Ah D.M.
      • Kang D.H.
      • Carpenter J.S.
      Predictors of cancer-related fatigue in women with breast cancer before, during, and after adjuvant therapy.
      our findings are consistent with those of Huang et al.
      • Huang H.P.
      • Chen M.L.
      • Liang J.
      • Miaskowski C.
      Changes in and predictors of severity of fatigue in women with breast cancer: a longitudinal study.
      who evaluated fatigue trajectories in women who were followed for 12 months after breast cancer surgery. Given these inconsistent findings and the relatively high levels of education in the current and previous studies,
      • Bower J.E.
      • Ganz P.A.
      • Aziz N.
      • Fahey J.L.
      Fatigue and proinflammatory cytokine activity in breast cancer survivors.
      • Von Ah D.M.
      • Kang D.H.
      • Carpenter J.S.
      Predictors of cancer-related fatigue in women with breast cancer before, during, and after adjuvant therapy.
      additional research is warranted to determine the specific factors associated with educational attainment (e.g., different levels of social responsibility, different employment opportunities) that may contribute to variations in fatigue severity.
      Across a series of studies, the evidence is clear that a higher number of comorbidities, poorer performance status, and higher fatigue severity before the initiation of cancer treatment are associated with worse fatigue trajectories or membership in the higher fatigue class (for review, see Bower and Ganz
      • Bower J.E.
      • Ganz P.A.
      Symptoms: fatigue and cognitive dysfunction.
      ). Although stage of disease and type of surgery were not associated with membership in the Higher Fatigue class, in our study, patients who had a higher number of lymph nodes removed and those who received neoadjuvant or adjuvant CTX were more likely to be in the Higher Fatigue class. An examination of the clinical characteristics that predicted higher fatigue severity in the four studies that evaluated patients after breast cancer surgery
      • Bodtcher H.
      • Bidstrup P.E.
      • Andersen I.
      • et al.
      Fatigue trajectories during the first 8 months after breast cancer diagnosis.
      • Montgomery G.H.
      • Schnur J.B.
      • Erblich J.
      • Diefenbach M.A.
      • Bovbjerg D.H.
      Presurgery psychological factors predict pain, nausea, and fatigue one week after breast cancer surgery.
      • Rotonda C.
      • Guillemin F.
      • Bonnetain F.
      • Velten M.
      • Conroy T.
      Factors associated with fatigue after surgery in women with early-stage invasive breast cancer.
      • De Vries J.
      • Van der Steeg A.F.
      • Roukema J.A.
      Determinants of fatigue 6 and 12 months after surgery in women with early-stage breast cancer: a comparison with women with benign breast problems.
      reveal a rather disparate list of risk factors. These inconsistent findings can be partially explained by the number and types of demographic, clinical, symptom, and psychological characteristics that were placed in the various types of multivariate analyses. Future meta-analyses and larger studies with a more comprehensive list of potential predictors would provide insights into the characteristics that clinicians need to assess to identify breast cancer patients who are at higher risk for more severe fatigue.
      IL1B is a pro-inflammatory cytokine that is synthesized in a variety of cells including circulating monocytes and tissue macrophages. In our study, patients who were homozygous for the rare A allele in IL1B rs16944 were more likely to be classified in the higher fatigue class. This SNP lies upstream of the IL1B gene. It lies in a region of histone modification that is suggestive of regulatory activity (i.e., H3K27Ac) and to a lesser extent in a region associated with promoters (i.e., H3K4Me1). However, based on available data in ENCODE, because this region does not contain a DNase Hypersensitivity Cluster or evidence of a transcription binding site, the location of any nearby regulatory DNA elements remains inconclusive. This SNP has been evaluated in a number of studies of fatigue in oncology patients. In contrast to our findings, in a study of 33 fatigue and 14 non-fatigued breast cancer survivors,
      • Collado-Hidalgo A.
      • Bower J.E.
      • Ganz P.A.
      • Irwin M.R.
      • Cole S.W.
      Cytokine gene polymorphisms and fatigue in breast cancer survivors: early findings.
      patients who were heterozygous or homozygous for the rare T allele (A allele in our study) were more likely to be in the non-fatigued group that was assessed using the Multidimensional Fatigue Symptom Inventory.
      • Stein K.D.
      • Jacobsen P.B.
      • Blanchard C.M.
      • Thors C.
      Further validation of the multidimensional fatigue symptom inventory-short form.
      • Stein K.D.
      • Martin S.C.
      • Hann D.M.
      • Jacobsen P.B.
      A multidimensional measure of fatigue for use with cancer patients.
      In other studies of newly diagnosed breast cancer patients who recently completed treatment,
      • Bower J.E.
      • Ganz P.A.
      • Irwin M.R.
      • et al.
      Cytokine genetic variations and fatigue among patients with breast cancer.
      breast cancer survivors,
      • Reinertsen K.V.
      • Grenaker Alnaes G.I.
      • Landmark-Hoyvik H.
      • et al.
      Fatigued breast cancer survivors and gene polymorphisms in the inflammatory pathway.
      and men with prostate cancer who underwent RT,
      • Jim H.S.
      • Park J.Y.
      • Permuth-Wey J.
      • et al.
      Genetic predictors of fatigue in prostate cancer patients treated with androgen deprivation therapy: preliminary findings.
      no associations were found with IL1B rs16944 and the fatigue phenotype. These inconsistent findings may be related to the instruments used to assess fatigue, the methods used to create the “fatigue phenotype” (e.g., dichotomization using a cutpoint vs. latent class analysis), and/or the timing of the assessment of fatigue in relationship to the patient's disease trajectory (e.g., active treatment vs. survivorship).
      IL10 is an anti-inflammatory cytokine that regulates the growth and differentiation of B cells, natural killer cells, and cytotoxic, helper, and regulatory T cells.
      • Moore K.W.
      • de Waal Malefyt R.
      • Coffman R.L.
      • O'Garra A.
      Interleukin-10 and the interleukin-10 receptor.
      In our study, patients who were homozygous for the rare C allele in IL10 rs3024496 were less likely to be in the higher fatigue class. This SNP is in a 3′ untranslated region of the fifth exon of IL10. It is upstream from four TargetScan miRNA that are predicted to be putative miRNA regulatory sites and may be involved in miRNA regulatory actions. However, based on ENCODE data, little support exists for transcription factor binding in this region. Consistent with the findings from the present study, this polymorphism was associated with increased production of IL10 in cell culture.
      • Assis S.
      • Marques C.R.
      • Silva T.M.
      • et al.
      IL10 single nucleotide polymorphisms are related to upregulation of constitutive IL-10 production and susceptibility to Helicobacter pylori infection.
      In addition, in a recent study,
      • Kober K.M.
      • Dunn L.
      • Mastick J.
      • et al.
      Gene expression profiling of evening fatigue in women undergoing chemotherapy for breast cancer.
      the IL10 pathway was found to be differentially expressed between patients with breast cancer who reported low compared with high levels of evening fatigue during CTX.

      Energy Findings

      In the present study, 68.0% of the patients reported energy scores that were below the cutoff score for clinically meaningful decrements in energy levels (i.e., ≤4.8). Of note, these decrements in energy levels persisted in the Lower Energy class over the six months of the study. Comparisons of these findings with our previous study of patients and family caregivers
      • Aouizerat B.E.
      • Dhruva A.
      • Paul S.M.
      • et al.
      Phenotypic and molecular evidence suggests that decrements in morning and evening energy are distinct but related symptoms.
      are difficult because diurnal variations in energy levels were not evaluated in the present study.
      Similar to the fatigue findings, in the bivariate analyses, a lower KPS score, a higher SCQ score, and receipt of CTX during the six months after breast cancer surgery were associated with membership in the Lower Energy class. In addition, although stage of disease was associated with membership in the Lower Energy class (but not fatigue class membership) because of the relatively small sample sizes for the highest stage of disease, post hoc contrasts failed to identify subgroup differences. Again, although direct comparisons with our previous study are not possible,
      • Aouizerat B.E.
      • Dhruva A.
      • Paul S.M.
      • et al.
      Phenotypic and molecular evidence suggests that decrements in morning and evening energy are distinct but related symptoms.
      a lower KPS score was associated with membership in the Low Morning and Moderate Evening energy classes. In both studies, KPS scores were approximately 90, which suggest that even with high functional status scores, some patients can experience significant decrements in energy levels.
      Two additional characteristics warrant consideration. First, for both fatigue and energy, the receipt of adjuvant CTX was associated with membership in the class with the poorer outcomes. Although higher levels of fatigue are associated with the receipt of CTX,
      • Berger A.M.
      Patterns of fatigue and activity and rest during adjuvant breast cancer chemotherapy.
      • Wright F.
      • D'Eramo Melkus G.
      • Hammer M.
      • et al.
      Trajectories of evening fatigue in oncology outpatients receiving chemotherapy.
      • Wright F.
      • D'Eramo Melkus G.
      • Hammer M.
      • et al.
      Predictors and trajectories of morning fatigue are distinct from evening fatigue.
      this study is the first to document an association between receipt of adjuvant CTX and decrements in energy levels. Second, for both symptoms, clinically meaningful levels of fatigue and decrements in energy levels, before surgery, were associated with membership in the Higher Fatigue and Lower Energy classes, respectively. Taken together, these findings suggest that clinicians need to assess for both fatigue severity and decrements in energy levels before and after breast cancer surgery.
      Only one SNP in the IL1R1 gene was associated with membership in the Lower Energy class. In this study, patients who were heterozygous or homozygous for the rare T allele in rs2110726 were less likely to be in the Lower Energy class. This SNP is located in the 3′ untranslated of the IL1R1 gene. It is 35 bases upstream from a polymorphic CpG methylation site (i.e., rs200426703). Although population data are not available for rs200426703 directly, rs2110726 is in high LD (R2 = 0.0232341, D′ = 1.0) with an SNP (i.e., rs3732134) that is 30 bases downstream from the methylated site (i.e., 65 bases downstream from rs2110726 and flanking rs200426703; see Supplementary Fig. 1). Methylated sites may be involved in gene regulation
      • Illingworth R.S.
      • Bird A.P.
      CpG islands—'a rough guide'.
      and methylation may be allelic specific.
      • Shoemaker R.
      • Deng J.
      • Wang W.
      • Zhang K.
      Allele-specific methylation is prevalent and is contributed by CpG-SNPs in the human genome.
      The strong LD observed between the SNPs flanking this region including the methylated polymorphic site suggests that rs2110726 may be a proxy for the genotype at the methylated site (i.e., rs200426703). Therefore, polymorphisms in rs2110726 may be related to any gene regulation activity that may occur at the polymorphic methylated site. In a previous study with the same sample,
      • McCann B.
      • Miaskowski C.
      • Koetters T.
      • et al.
      Associations between pro- and anti-inflammatory cytokine genes and breast pain in women prior to breast cancer surgery.
      patients who were heterozygous or homozygous for the rare T allele had a lower odds of reporting preoperative breast pain. Taken together, these findings are consistent with studies of IL1 function in mice, in which removal of IL1R function or blockade of IL1A led to a decrease in inflammation and pain behaviors.
      • Torres R.
      • Macdonald L.
      • Croll S.D.
      • et al.
      Hyperalgesia, synovitis and multiple biomarkers of inflammation are suppressed by interleukin 1 inhibition in a novel animal model of gouty arthritis.
      Additional functional studies are needed to determine if the minor allele of rs2110726 is associated with a decrease in IL1R1 function and, therefore, a decrease in the pro-inflammatory effects of IL1A.

      Limitations

      Several study limitations need to be acknowledged. Although a growing body of evidence suggests that diurnal variations in both fatigue and energy warrant consideration in future studies,
      • Aouizerat B.E.
      • Dhruva A.
      • Paul S.M.
      • et al.
      Phenotypic and molecular evidence suggests that decrements in morning and evening energy are distinct but related symptoms.
      • Miaskowski C.
      • Paul S.M.
      • Cooper B.A.
      • et al.
      Trajectories of fatigue in men with prostate cancer before, during, and after radiation therapy.
      • Dhruva A.
      • Dodd M.
      • Paul S.M.
      • et al.
      Trajectories of fatigue in patients with breast cancer before, during, and after radiation therapy.
      • Wright F.
      • D'Eramo Melkus G.
      • Hammer M.
      • et al.
      Trajectories of evening fatigue in oncology outpatients receiving chemotherapy.
      • Wright F.
      • D'Eramo Melkus G.
      • Hammer M.
      • et al.
      Predictors and trajectories of morning fatigue are distinct from evening fatigue.
      • Dimsdale J.E.
      • Ancoli-Israel S.
      • Ayalon L.
      • Elsmore T.F.
      • Gruen W.
      Taking fatigue seriously, II: variability in fatigue levels in cancer patients.
      • Jim H.S.
      • Small B.
      • Faul L.A.
      • et al.
      Fatigue, depression, sleep, and activity during chemotherapy: daily and intraday variation and relationships among symptom changes.
      in the present study, patients were asked to evaluate their levels of fatigue and energy at the time they completed the questionnaire. In addition, because most patients had early-stage breast cancer, differences in fatigue and energy class memberships associated with stage of disease could not be evaluated. Third, no direct measurements of systemic levels of inflammatory markers were obtained to provide additional information on the underlying mechanisms for fatigue severity and decrements in energy. Finally, the genetic associations identified in this study warrant confirmation in future studies, and functional studies are needed to confirm the impact of these polymorphisms on inflammatory mediators.

      Conclusions

      Findings from this study suggest that within each latent class, the severity of fatigue and decrements in energy were relatively stable from before to six months after breast cancer surgery. In addition, distinct phenotypic characteristics and genetic polymorphisms were associated with membership in the higher fatigue and lower energy classes. Although these findings warrant confirmation in future studies, the phenotypic and genetic findings from this study support the growing body of literature that suggests that fatigue and energy are distinct but related symptoms.
      • Lerdal A.
      A theoretical extension of the concept of energy through an empirical study.
      • Aouizerat B.E.
      • Dhruva A.
      • Paul S.M.
      • et al.
      Phenotypic and molecular evidence suggests that decrements in morning and evening energy are distinct but related symptoms.
      • Lerdal A.
      A concept analysis of energy. Its meaning in the lives of three individuals with chronic illness.
      • O'Connor P.J.
      Mental energy: assessing the mood dimension.
      Additional research is warranted to evaluate for differences in the underlying mechanisms for both symptoms. Future studies can focus on an evaluation of additional immune pathways and molecular markers of metabolic and neuroendocrine function.
      • Saligan L.N.
      • Olson K.
      • Filler K.
      • et al.
      The biology of cancer-related fatigue: a review of the literature.
      A better understanding of the molecular mechanisms that underlie these two symptoms could lead to the earlier identification of high-risk patients and the development and testing of novel mechanistically based interventions.

      Disclosures and Acknowledgments

      This study was funded by grants from the National Cancer Institute (NCI, CA107091 and CA118658) and the Oncology Nursing Foundation. Dr. Christine Miaskowski is an American Cancer Society Clinical Research Professor and is supported by a K05 award from the NCI (CA168960). This project is supported by NIH/NCRR UCSF-CTSI (grant number UL1 RR024131). Its contents are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. The authors declare no conflicts of interest.

      Appendix

      Supplementary Table 1Summary of Single-Nucleotide Polymorphisms Analyzed for Pro- and Anti-Inflammatory Cytokine Genes and the Growth Mixture Model Analysis for Fatigue and Energy Scores
      GeneSNPPositionChrMAFAllelesFatigueEnergy
      χ2P-ValueModelχ2P-ValueModel
      IFNG1rs206972868154003120.110G > A1.0230.6A2.3370.311A
      IFNG1rs206972768154442120.384A > G0.1380.933A0.0510.975A
      IFNG1rs206971868156381120.494C > TFE0.035R1.0010.606A
      IFNG1rs186149368157415120.266A > G1.5280.466A1.2050.547A
      IFNG1rs186149468157628120.273T > C1.8410.398A1.0730.585A
      IFNG1rs206970968159922120.003G > Tn/an/an/an/an/an/a
      IFNG1HapA31.6180.4451.3740.503
      IFNG1HapA50.1260.9390.0130.994
      IFNGR1rs937626813721161360.254G > A1.0530.591A1.9740.373A
      IL1Brs107167611282985520.189G > C1.4670.48A2.5050.286A
      IL1Brs114364311283072420.383G > A0.4780.787A1.6470.439A
      IL1Brs114364211283097520.082C > T0.0650.968A0.3840.825A
      IL1Brs114363411283281220.187C > T1.220.543A2.1830.336A
      IL1Brs114363311283288920.392G > A0.7450.689A1.7220.423A
      IL1Brs114363011283407720.115C > A2.4660.291A3.4570.178A
      IL1Brs391735611283478520.450G > A1.2930.524A0.1290.938A
      IL1Brs114362911283594020.389T > CFE0.011R2.1110.348A
      IL1Brs114362711283680920.397T > CFE0.005R1.9850.371A
      IL1Brs1694411283728920.386G > AFE0.002R1.8980.387A
      IL1Brs114362311283825120.277G > CFE0.028D1.0450.593A
      IL1Brs1303202911284283720.448C > T0.910.634A0.0420.979A
      IL1BHapA12.0560.3581.2430.537
      IL1BHapA40.3890.8231.4560.483
      IL1BHapA61.260.5332.270.321
      IL1BHapB14.7470.0934.4430.108
      IL1BHapB64.1850.1230.2430.885
      IL1BHapB80.7830.6760.20.905
      IL1R1rs94996310215332520.223G > A2.0840.353A3.9950.136A
      IL1R1rs222813910216518820.053C > G0.7210.697A3.4020.182A
      IL1R1rs391732010217641420.047A > Cn/an/an/an/an/an/a
      IL1R1rs211072610217782120.317C > T1.5810.454AFE0.021D
      IL1R1rs391733210218006320.187A > T0.3220.851A2.0110.366A
      IL1R1HapA11.4870.4755.0740.079
      IL1R1HapA20.2270.8923.9650.138
      IL1R1HapA30.2960.8631.9740.373
      IL1R2rs414113410199006320.362T > C0.5080.776A0.0350.983A
      IL1R2rs1167459510199452920.258T > C0.1110.946A1.1840.553A
      IL1R2rs757044110200053220.408G > A0.2410.886A0.0270.986A
      IL1R2HapA10.3420.8430.0140.993
      IL1R2HapA2FE0.697FE0.076
      IL1R2HapA40.1520.9270.6310.73
      IL2rs147992312244923140.308C > T2.050.359A1.0650.587A
      IL2rs206977612245082040.184T > Cn/an/an/an/an/an/a
      IL2rs206977212245197740.241A > G1.2420.538A1.3770.502A
      IL2rs206977712245528140.047C > Tn/an/an/an/an/an/a
      IL2rs206976312245632640.277T > G0.9210.631A1.0670.587A
      IL2HapA10.7670.6810.0050.997
      IL2HapA20.9980.6071.1420.565
      IL2HapA31.2420.5381.3770.502
      IL4rs224324813267295150.086T > G3.5290.171A3.1420.208A
      IL4rs224325013267346150.269C > Tn/an/an/an/an/an/a
      IL4rs207087413267401750.245C > Tn/an/an/an/an/an/a
      IL4rs222728413267703250.387C > An/an/an/an/an/an/a
      IL4rs222728213267748650.390C > Gn/an/an/an/an/an/a
      IL4rs224326313267760650.124C > G2.2020.333A0.5030.778A
      IL4rs224326613267809650.237G > An/an/an/an/an/an/a
      IL4rs224326713267819350.237G > Cn/an/an/an/an/an/a
      IL4rs224327413267913950.261G > An/an/an/an/an/an/a
      IL4HapA12.8020.2460.0010.999
      IL4HapA34.50.1051.0530.591
      IL4HapX12.6180.271.6350.441
      IL6rs47197142272109370.255A > T1.510.47A0.050.975A
      IL6rs20698272272583670.069G > T1.7680.413A2.1180.347A
      IL6rs18007962272662670.134C > Gn/an/an/an/an/an/a
      IL6rs18007952272702570.285C > G0.9890.61A0.0450.978A
      IL6rs20698352272825170.061T > Cn/an/an/an/an/an/a
      IL6rs20669922272862970.049G > T2.8190.244A2.430.297A
      IL6rs20698402272895270.333C > G3.0810.214A1.7470.418A
      IL6rs15546062272908770.319G > T1.6680.434A0.3060.858A
      IL6rs20698452273052970.319A > G1.6680.434A0.3060.858A
      IL6rs20698492273153670.024C > Tn/an/an/an/an/an/a
      IL6rs20698612273203470.056C > T0.4050.817A0.8130.666A
      IL6rs356106892273420070.259A > G0.9460.623A0.3230.851A
      IL6HapA1010.1180.943
      IL6HapA53.2470.1972.9790.225
      IL6HapA81.2540.5340.4760.788
      CXCL8rs40737374030640.455T > A0.3470.841A0.4190.811A
      CXCL8rs22273067374133740.366C > T0.1460.93A0.4590.795A
      CXCL8rs22275437374219240.368C > T0.380.827A1.0860.581A
      CXCL8HapA10.3470.8410.4190.811
      CXCL8HapA40.1740.9160.7640.682
      IL10rs302450520676655810.129C > T1.1260.57A2.8530.24A
      IL10rs302449820676818310.204A > G3.2330.199A0.8110.667A
      IL10rs302449620676851810.421T > CFE0.007R3.1950.202A
      IL10rs187867220677036710.416G > CFE0.043D1.5930.451A
      IL10rs302449220677076610.190T > An/an/an/an/an/an/a
      IL10rs151811120677129910.303G > A0.4170.812A0.3870.824A
      IL10rs151811020677151510.301G > T0.3340.846A0.4330.806A
      IL10rs302449120677170010.408G > TFE0.043D1.5880.452A
      IL10HapA10.6620.7180.3860.824
      IL10HapA22.7630.2511.5350.464
      IL10HapA82.2760.3210.8960.639
      IL13rs188145713265671650.210A > C1.7850.41A1.5760.455A
      IL13rs180092513265711650.233C > T1.5530.46A0.8460.655A
      IL13rs206974313265758250.019A > Gn/an/an/an/an/an/a
      IL13rs129568613266015050.265G > A0.1920.908A0.3290.848A
      IL13rs2054113266027150.212C > T2.790.248A0.120.942A
      IL13HapA10.220.8960.4340.805
      IL13HapA42.7130.2580.0550.973
      IL17 Ars47119985218555460.346G > A1.4670.48A0.5790.749A
      IL17 Ars81930365218569460.327T > C0.5150.773A4.5040.105A
      IL17 Ars38190245218598760.372A > G1.8070.405A0.5850.746A
      IL17 Ars22759135218623460.361G > A1.410.494A1.4660.48A
      IL17 Ars38045135218839860.023A > Tn/an/an/an/an/an/a
      IL17 Ars77479095218945060.217G > A1.9440.378A1.280.527A
      NFKB1rs377493310250518140.409T > C1.1820.554A0.1820.913A
      NFKB1rs17073110252774540.358A > T0.010.995A1.2610.532A
      NFKB1rs1703277910254509240.011T > Cn/an/an/an/an/an/a
      NFKB1rs23051010255500840.410T > A1.510.47A0.5050.777A
      NFKB1rs23049410256581140.434A > G0.2960.863A1.1450.564A
      NFKB1rs464801610256851240.010C > Tn/an/an/an/an/an/a
      NFKB1rs464801810256904240.018G > Cn/an/an/an/an/an/a
      NFKB1rs377495610258736840.435C > T0.0880.957A0.8890.641A
      NFKB1rs1048911410259023040.018A > Gn/an/an/an/an/an/a
      NFKB1rs464806810259714740.363A > G0.1790.914A1.870.393A
      NFKB1rs464809510260671840.052T > CFE0.569AFE0.845A
      NFKB1rs464811010261266340.170T > A2.3180.314A0.2150.898A
      NFKB1rs464813510261551240.061A > GFE1AFE0.363A
      NFKB1rs464814110261574340.180G > A3.5130.173A0.5950.743A
      NFKB1rs160979810261628440.337C > T0.1810.914A2.130.345A
      NFKB1HapA11.3520.5090.3750.829
      NFKB1HapA90.1420.9311.5690.456A
      NFKB2rs12772374102397153100.168A > G3.6070.165A2.1160.347A
      NFKB2rs7897947102397953100.221T > G0.3470.841A2.070.355A
      NFKB2rs11574849102399938100.070G > A3.9450.139A5.7650.056A
      NFKB2rs1056890102403012100.305C > T0.5760.75A2.2120.331A
      TNF SFrs28576023156560060.341T > C0.8720.647A0.7110.701A
      TNF SFrs18006833157229360.390G > A1.4160.493A2.7620.251A
      TNF SFrs22397043157236360.335G > T0.3270.849A1.3690.504A
      TNF SFrs22290943157277860.278T > C1.0120.603A4.7030.095A
      TNF SFrs10419813157300660.386C > A1.2220.543A2.7960.247A
      TNF SFrs17999643157453060.224T > C1.0330.597A1.340.512A
      TNF SFrs18007503157518560.016G > An/an/an/an/an/an/a
      TNF SFrs18006293157525360.149G > A1.9510.377A4.0370.133A
      TNF SFrs18006103157604960.100C > T1.0880.58A0.1530.926A
      TNF SFrs30936623157641160.074A > G2.2620.323A1.2280.541A
      TNF SFHapA10.280.8692.2590.323
      TNF SFHapA50.1980.9061.1340.567
      TNF SFHapA60.0170.9912.8770.237
      A = additive model; Chr = chromosome (GRCh38 human reference assembly); D = dominant model; Hap = haplotype; IFNG = interferon gamma; IL = interleukin; MAF = minor allele frequency; n/a = not assayed because SNP violated Hardy-Weinberg expectations (P < 0.001) or because MAF was <.05; NFKB = nuclear factor kappa beta; R = recessive model; SNP = single-nucleotide polymorphism; TNF SF = tumor necrosis factor superfamily.
      Supplementary Fig. 1
      Supplementary Fig. 1Screenshot of the UCSC Genome Browser displaying a portion of the 3′ untranslated region of the IL1R1 gene on chromosome 2 of the hg19 (GRCh38) assembly. Assembly tracks show scale, scaffold and positions of the region, and gaps in the assembly. The gene model is provided by the “RefSeq Genes” track. The genomic loci of single-nucleotide polymorphism (SNP) rs200426703 in blue (annotated by dbSNP release 142) is shared with a CpG methylation site in orange (assayed by the Illumina HumanMethyl 450K bead array as part of the ENCODE project). This loci resides in a region flanked by SNP rs2110726 35 base pairs upstream and rs3732134 30 base pairs downstream. SNP loci for which population data is available from the 1000 Genomes project are identified in the “1000 Genomes Phase 1 Integrated Variant Calls” track in black.

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