Cytokine gene variation is associated with depressive symptom trajectories in oncology patients and family caregivers
ABSTRACT PURPOSE: Depressive symptoms are common in cancer patients and their family caregivers (FCs). While these symptoms are characterized by substantial interindividual variability, the factors that predict this variability remain largely unknown. This study sought to confirm latent classes of oncology patients and FCs with distinct depressive symptom trajectories and to examine differences in phenotypic and genotypic characteristics among these classes. METHOD: 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 Center for Epidemiological Studies-Depression (CES-D) scores obtained prior to, during, and for four months following completion of radiation therapy. One hundred four single nucleotide polymorphisms (SNPs) and haplotypes in 15 candidate cytokine genes were interrogated for differences between the two largest latent classes. Multivariate logistic regression analyses assessed effects of phenotypic and genotypic characteristics on class membership. RESULTS: Four latent classes were confirmed: Resilient (56.3%), Subsyndromal (32.5%), Delayed (5.2%), and Peak (6.0%). Participants who were younger, female, non-white, and who reported higher baseline trait and state anxiety were more likely to be in the Subsyndromal, Delayed, or Peak groups. Variation in three cytokine genes (i.e., interleukin 1 receptor 2 [IL1R2], IL10, tumor necrosis factor alpha [TNFA]), age, and performance status predicted membership in the Resilient versus Subsyndromal classes. CONCLUSIONS: Findings confirm the four latent classes of depressive symptom trajectories previously identified in a sample of breast cancer patients. Variations in cytokine genes may influence variability in depressive symptom trajectories.
- SourceAvailable from: Janine K Cataldo
[Show abstract] [Hide abstract]
- "of two SNPs ( i . e . , rs11674595 , rs7570441 ) . Each additional dose of IL1R2 HapA2 was associated with a 2 . 08 increase in the odds of belonging to the high sustained sleep disturbance class . Prior to this study , no as - sociations were found between this haplotype and sleep distur - bance . However in another study from our research team ( Dunn et al . , 2013 ) , a different haplotype in the same region ( i . e . , a 3 - SNP haplotype composed of the rare C allele of rs4141134 , the common T allele of rs11674595 , and the rare A allele of rs7570441 ) was associated with a 2 - fold increase in the odds of belonging to the class with a higher level of depressive symptoms . While the functions "
ABSTRACT: To attempt to replicate the associations found in our previous study of patients and family caregivers between interleukin 6 (IL6) and nuclear factor kappa beta 2 (NFKB2) and sleep disturbance and to identify additional genetic associations in a larger sample of patients with breast cancer. Patients with breast cancer (n = 398) were recruited prior to surgery and followed for six months. Patients completed a self-report measure of sleep disturbance and provided a blood sample for genomic analyses. Growth mixture modeling was used to identify distinct latent classes of patients with higher and lower levels of sleep disturbance. Patients who were younger and who had higher comorbidity and lower functional status were more likely to be in the high sustained sleep disturbance class. Variations in three cytokine genes (i.e., IL1 receptor 2 (IL1R2), IL13, NFKB2) predicted latent class membership. Polymorphisms in cytokine genes may partially explain inter-individual variability in sleep disturbance. Determination of high risk phenotypes and associated molecular markers may allow for earlier identification of patients at higher risk for developing sleep disturbance and lead to the development of more targeted clinical interventions.European journal of oncology nursing: the official journal of European Oncology Nursing Society 09/2013; 18(1). DOI:10.1016/j.ejon.2013.08.004 · 1.79 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Personalized medicine applies knowledge about the patient's individual characteristics in relation to health and intervention outcomes, including treatment response and adverse side-effects, to develop a tailored treatment plan. For women with breast cancer, personalized medicine has substantially improved the rate of survival, however, a high proportion of these women report multiple, co-occurring psychoneurological symptoms over the treatment trajectory that adversely affect their quality of life. In a subset of these women, co-occurring symptoms referred to as symptoms clusters, can persist long after treatment has ended. Over the past decade, research from the field of nursing and other health sciences has specifically examined the potential underlying mechanisms of the psychoneurological symptom cluster in women with breast cancer. Recent findings suggest that epigenetic and genomic factors contribute to inter-individual variability in the experience of psychoneurological symptoms during and after breast cancer treatment. While nursing research has been underrepresented in the field of personalized medicine, these studies represent a shared goal; that is, to improve patient outcomes by considering the individual's risk of short- and long-term adverse symptoms. The aim of this paper is to introduce a conceptual model of the individual variations that influence psychoneurological symptoms in women with breast cancer, including perceived stress, hypothalamic-pituitary adrenocortical axis dysfunction, inflammation, as well as epigenetic and genomic factors. The proposed concepts will help bring nursing research and personalized medicine together, in hopes that this hitherto neglected and understudied area of biomedical research convergence may ultimately lead to the development of more targeted clinical nursing strategies in breast cancer patients with psychoneurological symptoms.Current Pharmacogenomics and Personalized Medicine (Formerly Current Pharmacogenomics) 09/2013; 11(3):224-230. DOI:10.2174/18756921113119990004
- [Show abstract] [Hide abstract]
ABSTRACT: Subgroups of patients with breast cancer may be at greater risk for cytokine-induced changes in cognitive function after diagnosis and during treatment. The purposes of this study were to identify subgroups of patients with distinct trajectories of attentional function and evaluate for phenotypic and genotypic (i.e., cytokine gene polymorphisms) predictors of subgroup membership. Self-reported attentional function was evaluated in 397 patients with breast cancer using the Attentional Function Index before surgery and for six months after surgery (i.e., seven time points). Using growth mixture modeling, three attentional function latent classes were identified: High (41.6%), Moderate (25.4%), and Low-moderate (33.0%). Patients in the Low-moderate class were significantly younger than those in the High class, with more comorbidities and lower functional status than the other two classes. No differences were found among the classes in years of education, race/ethnicity, or other clinical characteristics. DNA was recovered from 302 patients' samples. Eighty-two single nucleotide polymorphisms among 15 candidate genes were included in the genetic association analyses. After controlling for age, comorbidities, functional status, and population stratification due to race/ethnicity, IL1R1 rs949963 remained a significant genotypic predictor of class membership in the multivariable model. Carrying the rare "A" allele (i.e., GA+AA) was associated with a twofold increase in the odds of belonging to a lower attentional function class (OR: 1.98; 95% CI: 1.18, 3.30; p=.009). Findings provide evidence of subgroups of women with breast cancer who report distinct trajectories of attentional function and of a genetic association between subgroup membership and an IL1R1 promoter polymorphism.Cytokine 12/2013; 65(2). DOI:10.1016/j.cyto.2013.11.003 · 2.87 Impact Factor