Evidence of Associations between Cytokine Genes and Subjective Reports of Sleep Disturbance in Oncology Patients and Their Family Caregivers
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{"title"=>"Evidence of associations between cytokine genes and subjective reports of sleep disturbance in oncology patients and their family caregivers", "type"=>"journal", "authors"=>[{"first_name"=>"Christine", "last_name"=>"Miaskowski", "scopus_author_id"=>"7005529575"}, {"first_name"=>"Bruce A.", "last_name"=>"Cooper", "scopus_author_id"=>"55722708400"}, {"first_name"=>"Anand", "last_name"=>"Dhruva", "scopus_author_id"=>"14061857500"}, {"first_name"=>"Laura B.", "last_name"=>"Dunn", "scopus_author_id"=>"7102274701"}, {"first_name"=>"Dale J.", "last_name"=>"Langford", "scopus_author_id"=>"16068877300"}, {"first_name"=>"Janine K.", "last_name"=>"Cataldo", "scopus_author_id"=>"55540834500"}, {"first_name"=>"Christina R.", "last_name"=>"Baggott", "scopus_author_id"=>"6506488544"}, {"first_name"=>"John D.", "last_name"=>"Merriman", "scopus_author_id"=>"36572369300"}, {"first_name"=>"Marylin", "last_name"=>"Dodd", "scopus_author_id"=>"35510468700"}, {"first_name"=>"Kathryn", "last_name"=>"Lee", "scopus_author_id"=>"8211331800"}, {"first_name"=>"Claudia", "last_name"=>"West", "scopus_author_id"=>"7402188752"}, {"first_name"=>"Steven M.", "last_name"=>"Paul", "scopus_author_id"=>"55741224100"}, {"first_name"=>"Bradley E.", "last_name"=>"Aouizerat", "scopus_author_id"=>"7103176520"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"22844404", "doi"=>"10.1371/journal.pone.0040560", "sgr"=>"84864241045", "isbn"=>"1932-6203", "scopus"=>"2-s2.0-84864241045", "issn"=>"19326203", "pui"=>"365316249"}, "id"=>"1fde8679-0272-3c0c-9812-98b262bdd219", "abstract"=>"The purposes of this study were to identify distinct latent classes of individuals based on subjective reports of sleep disturbance; to examine differences in demographic, clinical, and symptom characteristics between the latent classes; and to evaluate for variations in pro- and anti-inflammatory cytokine genes between the latent classes. Among 167 oncology outpatients with breast, prostate, lung, or brain cancer and 85 of their FCs, growth mixture modeling (GMM) was used to identify latent classes of individuals based on General Sleep Disturbance Scale (GSDS) obtained prior to, during, and for four months following completion of radiation therapy. Single nucleotide polymorphisms (SNPs) and haplotypes in candidate cytokine genes were interrogated for differences between the two latent classes. Multiple logistic regression was used to assess the effect of phenotypic and genotypic characteristics on GSDS group membership. Two latent classes were identified: lower sleep disturbance (88.5%) and higher sleep disturbance (11.5%). Participants who were younger and had a lower Karnofsky Performance status score were more likely to be in the higher sleep disturbance class. Variation in two cytokine genes (i.e., IL6, NFKB) predicted latent class membership. Evidence was found for latent classes with distinct sleep disturbance trajectories. Unique genetic markers in cytokine genes may partially explain the interindividual heterogeneity characterizing these trajectories.", "link"=>"http://www.mendeley.com/research/evidence-associations-between-cytokine-genes-subjective-reports-sleep-disturbance-oncology-patients", "reader_count"=>21, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>3, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Other"=>1, "Student > Master"=>4, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>3, "Researcher"=>3, "Student > Doctoral Student"=>2, "Student > Ph. D. Student"=>4, "Other"=>1, "Student > Master"=>4, "Student > Bachelor"=>2, "Professor"=>1}, "reader_count_by_subject_area"=>{"Unspecified"=>2, "Nursing and Health Professions"=>4, "Materials Science"=>1, "Medicine and Dentistry"=>7, "Agricultural and Biological Sciences"=>1, "Physics and Astronomy"=>1, "Psychology"=>1, "Social Sciences"=>3, "Pharmacology, Toxicology and Pharmaceutical Science"=>1}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>7}, "Social Sciences"=>{"Social Sciences"=>3}, "Physics and Astronomy"=>{"Physics and Astronomy"=>1}, "Psychology"=>{"Psychology"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>1}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>4}, "Unspecified"=>{"Unspecified"=>2}, "Pharmacology, Toxicology and Pharmaceutical Science"=>{"Pharmacology, Toxicology and Pharmaceutical Science"=>1}}, "reader_count_by_country"=>{"United States"=>3, "United Kingdom"=>1}, "group_count"=>1}

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Figshare

  • {"files"=>["https://ndownloader.figshare.com/files/604568"], "description"=>"*<p>p<.05, p<sup>**</sup> <.01, <sup>***</sup>p<.001, n.s.  =  p>.05.</p>a<p>Random coefficients latent growth curve model with linear and quadratic components; Chi<sup>2</sup> = 108.81, 26 df, p<0.001, CFI = 0.921, RMSEA = 0.112.</p>b<p>2-class model was selected, based on its having the smallest BIC, the largest entropy, and a significant VLMR. Further, the VLMR is not significant for the 3-class model, and the 3-class model estimated a class with only 4% of the sample – a class size that is unlikely to be reliable.</p>c<p>This value is the Chi<sup>2</sup> 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.</p><p>Abbreviations: GMM  =  Growth mixture model; LL  =  log likelihood; AIC  =  Akaike Information Criteria; BIC  =  Bayesian Information Criterion; VLMR  =  Vuong-Lo-Mendell-Rubin likelihood ratio test; CFI  =  comparative fit index; RMSEA  =  root mean square error of approximation.</p>", "links"=>[], "tags"=>["indices", "disturbance", "gmm", "solutions", "dyad", "clustering"], "article_id"=>275040, "categories"=>["Physiology", "Neuroscience", "Biotechnology", "Immunology"], "users"=>["Christine Miaskowski", "Bruce A. Cooper", "Anand Dhruva", "Laura B. Dunn", "Dale J. Langford", "Janine K. Cataldo", "Christina R. Baggott", "John D. Merriman", "Marylin Dodd", "Kathryn Lee", "Claudia West", "Steven M. Paul", "Bradley E. Aouizerat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040560.t002", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Fit_indices_for_general_sleep_disturbance_scale_gmm_solutions_over_seven_assessments_with_dyad_as_a_clustering_variable_/275040", "title"=>"Fit indices for general sleep disturbance scale gmm solutions over seven assessments, with dyad as a clustering variable.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 02:03:07"}
  • {"files"=>["https://ndownloader.figshare.com/files/604477"], "description"=>"<p>Abbreviations: A  =  Additive model, Chr  =  chromosome, D  =  Dominant model, Hap  =  haplotype, IFNG  =  interferon gamma, IL  =  interleukin, MAF – minor allele frequency, n/a  =  not applicable, NFKB  =  nuclear factor kappa beta, R  =  Recessive model, SNP  =  single nucleotide polymorphism, TNFA  =  tumor necrosis factor alpha.</p>", "links"=>[], "tags"=>["nucleotide", "polymorphisms", "pro-", "anti-inflammatory", "cytokine", "genes", "disturbance"], "article_id"=>274951, "categories"=>["Physiology", "Neuroscience", "Biotechnology", "Immunology"], "users"=>["Christine Miaskowski", "Bruce A. Cooper", "Anand Dhruva", "Laura B. Dunn", "Dale J. Langford", "Janine K. Cataldo", "Christina R. Baggott", "John D. Merriman", "Marylin Dodd", "Kathryn Lee", "Claudia West", "Steven M. Paul", "Bradley E. Aouizerat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040560.t001", "stats"=>{"downloads"=>14, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Summary_of_single_nucleotide_polymorphisms_analyzed_for_pro_and_anti_inflammatory_cytokine_genes_and_the_growth_mixture_model_analysis_for_general_sleep_disturbance_scale_total_score_/274951", "title"=>"Summary of single nucleotide polymorphisms analyzed for pro- and anti-inflammatory cytokine genes and the growth mixture model analysis for general sleep disturbance scale total score.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 02:02:33"}
  • {"files"=>["https://ndownloader.figshare.com/files/604432"], "description"=>"<p>For each model, the first three principle components identified from the analysis of ancestry informative markers as well as self-report race/ethnicity (White, Asian/Pacific Islander, Black, Hispanic/Mixed background/Other) were retained in all models to adjust for potential confounding due to race or ethnicity (data not shown). Predictors evaluated in the model included genotype (IL6 rs35610689: AA versus AG+GG; NFKB2 rs7897947: TT versus TG + GG), age (5 year increments), and functional status (KPS score, 10 point increments). Patient versus family caregiver (FC) status could not be included in the regression analyses because no FCs were included in the higher sleep disturbance class.</p>", "links"=>[], "tags"=>["logistic", "regression", "analyses", "interleukin", "rs35610689", "kappa", "beta", "subunit", "rs7897947", "higher", "disturbance"], "article_id"=>274900, "categories"=>["Physiology", "Neuroscience", "Biotechnology", "Immunology"], "users"=>["Christine Miaskowski", "Bruce A. Cooper", "Anand Dhruva", "Laura B. Dunn", "Dale J. Langford", "Janine K. Cataldo", "Christina R. Baggott", "John D. Merriman", "Marylin Dodd", "Kathryn Lee", "Claudia West", "Steven M. Paul", "Bradley E. Aouizerat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040560.t006", "stats"=>{"downloads"=>3, "page_views"=>11, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Multiple_Logistic_Regression_Analyses_for_Interleukin_6_IL6_rs35610689_and_Nuclear_Factor_Kappa_Beta_2_Subunit_NFKB2_rs7897947_to_Predict_Higher_Sleep_Disturbance_Class_/274900", "title"=>"Multiple Logistic Regression Analyses for Interleukin 6 (IL6) rs35610689 and Nuclear Factor Kappa Beta 2 Subunit (NFKB2) rs7897947 to Predict Higher Sleep Disturbance Class.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 02:02:12"}
  • {"files"=>["https://ndownloader.figshare.com/files/604204"], "description"=>"<p>Panel B illustrates differences between the latent classes in the percentages of participants who were homozygous for the common allele (TT) or heterozygous or homozygous for the minor allele (TG+GG) for rs7897947 in nuclear factor kappa beta 2 (NFKB2).</p>", "links"=>[], "tags"=>["illustrates", "differences", "latent", "classes", "percentages", "homozygous", "allele", "heterozygous", "rs35610689", "interleukin"], "article_id"=>274673, "categories"=>["Physiology", "Neuroscience", "Biotechnology", "Immunology"], "users"=>["Christine Miaskowski", "Bruce A. Cooper", "Anand Dhruva", "Laura B. Dunn", "Dale J. Langford", "Janine K. Cataldo", "Christina R. Baggott", "John D. Merriman", "Marylin Dodd", "Kathryn Lee", "Claudia West", "Steven M. Paul", "Bradley E. Aouizerat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040560.g002", "stats"=>{"downloads"=>1, "page_views"=>7, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Panel_A_illustrates_differences_between_the_latent_classes_in_the_percentages_of_participants_who_were_homozygous_for_the_common_allele_AA_or_heterozygous_or_homozygous_for_the_minor_allele_AG_GG_for_rs35610689_in_interleukin_6_IL6_/274673", "title"=>"Panel A illustrates differences between the latent classes in the percentages of participants who were homozygous for the common allele (AA) or heterozygous or homozygous for the minor allele (AG+GG) for rs35610689 in interleukin 6 (IL6).", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 02:00:49"}
  • {"files"=>["https://ndownloader.figshare.com/files/604043"], "description"=>"<p>Observed and estimated General Sleep Disturbance Scale (GSDS) trajectories for participants in each of the latent classes, as well as the mean GSDS scores for the total sample.</p>", "links"=>[], "tags"=>["disturbance", "trajectories", "latent", "gsds", "scores"], "article_id"=>274512, "categories"=>["Physiology", "Neuroscience", "Biotechnology", "Immunology"], "users"=>["Christine Miaskowski", "Bruce A. Cooper", "Anand Dhruva", "Laura B. Dunn", "Dale J. Langford", "Janine K. Cataldo", "Christina R. Baggott", "John D. Merriman", "Marylin Dodd", "Kathryn Lee", "Claudia West", "Steven M. Paul", "Bradley E. Aouizerat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040560.g001", "stats"=>{"downloads"=>2, "page_views"=>12, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Observed_and_estimated_General_Sleep_Disturbance_Scale_GSDS_trajectories_for_participants_in_each_of_the_latent_classes_as_well_as_the_mean_GSDS_scores_for_the_total_sample_/274512", "title"=>"Observed and estimated General Sleep Disturbance Scale (GSDS) trajectories for participants in each of the latent classes, as well as the mean GSDS scores for the total sample.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 01:59:47"}
  • {"files"=>["https://ndownloader.figshare.com/files/604329"], "description"=>"<p>Abbreviations: STAI-S  =  Spielberger State-Trait Anxiety Inventory – State subscale; STAI-T  =  Spielberger State-Trait Anxiety Inventory – Trait subscale; CES-D  =  Center for Epidemiological Studies – Depression scale; GSDS  =  General Sleep Disturbance Scale; TST  =  Total sleep time.</p>", "links"=>[], "tags"=>["baseline", "severity", "scores", "latent", "classes", "disturbance"], "article_id"=>274809, "categories"=>["Physiology", "Neuroscience", "Biotechnology", "Immunology"], "users"=>["Christine Miaskowski", "Bruce A. Cooper", "Anand Dhruva", "Laura B. Dunn", "Dale J. Langford", "Janine K. Cataldo", "Christina R. Baggott", "John D. Merriman", "Marylin Dodd", "Kathryn Lee", "Claudia West", "Steven M. Paul", "Bradley E. Aouizerat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040560.t005", "stats"=>{"downloads"=>3, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Differences_in_Baseline_Symptom_Severity_Scores_Between_the_Two_Latent_Classes_for_General_Sleep_Disturbance_Scale_Total_Score_/274809", "title"=>"Differences in Baseline Symptom Severity Scores Between the Two Latent Classes for General Sleep Disturbance Scale Total Score.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 02:01:39"}
  • {"files"=>["https://ndownloader.figshare.com/files/604373"], "description"=>"<p>Differences in Baseline Demographic and Clinical Characteristics Between the Two Latent Classes for General Sleep Disturbance Scale Total Score.</p>", "links"=>[], "tags"=>["baseline", "characteristics", "latent", "classes", "disturbance"], "article_id"=>274852, "categories"=>["Physiology", "Neuroscience", "Biotechnology", "Immunology"], "users"=>["Christine Miaskowski", "Bruce A. Cooper", "Anand Dhruva", "Laura B. Dunn", "Dale J. Langford", "Janine K. Cataldo", "Christina R. Baggott", "John D. Merriman", "Marylin Dodd", "Kathryn Lee", "Claudia West", "Steven M. Paul", "Bradley E. Aouizerat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040560.t004", "stats"=>{"downloads"=>0, "page_views"=>3, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Differences_in_Baseline_Demographic_and_Clinical_Characteristics_Between_the_Two_Latent_Classes_for_General_Sleep_Disturbance_Scale_Total_Score_/274852", "title"=>"Differences in Baseline Demographic and Clinical Characteristics Between the Two Latent Classes for General Sleep Disturbance Scale Total Score.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 02:01:55"}
  • {"files"=>["https://ndownloader.figshare.com/files/604523"], "description"=>"*<p>p<.05, **p<.01, <sup>***</sup>p<.001.</p>a<p>Trajectory group sizes are for classification of individuals based on their most likely latent class probabilities.</p>b<p>Growth mixture model estimates were obtained with robust maximum likelihood, with dyad as a clustering variable to account for dependency between patients and caregivers within the same dyad. Quadratic slope variances were fixed at zero to improve estimation.</p><p>Abbreviations: GMM  =  Growth mixture model; S.E.  =  standard error.</p>", "links"=>[], "tags"=>["parameter", "estimates", "disturbance", "latent", "dyad", "clustering"], "article_id"=>275001, "categories"=>["Physiology", "Neuroscience", "Biotechnology", "Immunology"], "users"=>["Christine Miaskowski", "Bruce A. Cooper", "Anand Dhruva", "Laura B. Dunn", "Dale J. Langford", "Janine K. Cataldo", "Christina R. Baggott", "John D. Merriman", "Marylin Dodd", "Kathryn Lee", "Claudia West", "Steven M. Paul", "Bradley E. Aouizerat"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0040560.t003", "stats"=>{"downloads"=>1, "page_views"=>6, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_GMM_parameter_estimates_for_general_sleep_disturbance_scale_latent_class_a_solution_with_7_assessments_with_dyad_as_a_clustering_variable_/275001", "title"=>"GMM parameter estimates for general sleep disturbance scale latent class<sup>a</sup> solution with 7 assessments, with dyad as a clustering variable.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2013-02-20 02:02:51"}

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Relative Metric

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