潜在类模型
萧条(经济学)
医学
乳腺癌
睡眠障碍
内科学
共病
疾病
星团(航天器)
细胞因子
癌症
肿瘤科
精神科
失眠症
经济
程序设计语言
数学
宏观经济学
统计
计算机科学
作者
Sy-Huey Doong,Anand Dhruva,Laura B. Dunn,Claudia West,Steven M. Paul,Bruce A. Cooper,Charles Elboim,Gary Abrams,John D. Merriman,Dale J. Langford,Heather Leutwyler,Christina Baggott,Kord M. Kober,Bradley E. Aouizerat,Christine Miaskowski
标识
DOI:10.1177/1099800414550394
摘要
Pain, fatigue, sleep disturbance, and depression are common and frequently co-occurring symptoms in oncology patients. This symptom cluster is often attributed to the release of proinflammatory cytokines. The purposes of this study were to determine whether distinct latent classes of patients with breast cancer (n = 398) could be identified based on their experience with this symptom cluster, whether patients in these latent classes differed on demographic and clinical characteristics and whether variations in cytokine genes were associated with latent class membership. Three distinct latent classes were identified: "all low" (61.0%), "low pain and high fatigue" (31.6%), "all high" (7.1%). Compared to patients in the all low class, patients in the all high class were significantly younger, had less education, were more likely to be non-White, had a lower annual income, were more likely to live alone, had a lower functional status, had a higher comorbidity score, and had more advanced disease. Significant associations were found between interleukin 6 (IL6) rs2069845, IL13 rs1295686, and tumor necrosis factor alpha rs18800610 and latent class membership. Findings suggest that variations in pro- and anti-inflammatory cytokine genes are associated with this symptom cluster in breast cancer patients.
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