Association Between Systemic Immunity-Inflammation Index (SII) and Fatigue, Cancer, and Cancer-Related Fatigue: Insights From NHANES (2005–2018)

IF 2.9 2区 医学 Q2 ONCOLOGY
Cancer Medicine Pub Date : 2025-03-17 DOI:10.1002/cam4.70777
Li Sun, Yanling Wu, Lydia Idowu Akinyemi, Zhiqiu Cao, Zhanhong Fan, Huahua Liu, Ziyi Yang, Leilei Zhang, Feng Zhang
{"title":"Association Between Systemic Immunity-Inflammation Index (SII) and Fatigue, Cancer, and Cancer-Related Fatigue: Insights From NHANES (2005–2018)","authors":"Li Sun,&nbsp;Yanling Wu,&nbsp;Lydia Idowu Akinyemi,&nbsp;Zhiqiu Cao,&nbsp;Zhanhong Fan,&nbsp;Huahua Liu,&nbsp;Ziyi Yang,&nbsp;Leilei Zhang,&nbsp;Feng Zhang","doi":"10.1002/cam4.70777","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objective</h3>\n \n <p>To investigate the association between the systemic immunity-inflammation index (SII) and fatigue, cancer, and cancer-related fatigue (CRF) populations.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>The National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 provided data for this retrospective cross-sectional study. By dividing the platelet count by the neutrophil count and the lymphocyte count, SII was calculated. Participants were categorized into four groups: normal, fatigue, cancer, and cancer-related fatigue (CRF), with the normal group serving as the reference. Binary logistic regression was applied to assess the correlations. The dose–response relationship between SII and outcomes in the four groups was evaluated using restricted cubic splines. Use threshold effect analysis to determine the optimal SII value for each of the three groups. Stratified and subgroup analyses were performed based on sociodemographic factors and confounders, with specific attention to fatigue severity levels (mild, moderate, severe) in the fatigue and CRF groups.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Data analysis included a total of 32,491 participants, including 14,846 in the normal group, 14,581 in the fatigue group, 1520 in the cancer group, and 1544 in the CRF group. The results of binary logistic regression showed that SII was positively correlated with the fatigue group (1.43[1.33, 1.55]), cancer group (1.67 [1.43, 1.95]) and CRF group (1.93 [1.66, 2.25]). Restricted cubic spline analysis revealed a linear relationship between SII and outcomes. The threshold values (k) for each of these groups were identified as 464.78 × 10<sup>3</sup> cells/μL, 448.97 × 10<sup>3</sup> cells/μL, and 454.65 × 10<sup>3</sup> cells/μL, respectively. Stratified analysis indicates that most groups exhibit significant differences. The subgroup analysis indicated that fatigue severity increased with higher SII levels, with the CRF group exhibiting the highest rate of severe fatigue (171% increase).</p>\n </section>\n \n <section>\n \n <h3> Conclusion</h3>\n \n <p>SII is positively correlated with fatigue, cancer, and CRF in a linear way. Higher SII values are associated with greater fatigue, particularly in the CRF population.</p>\n </section>\n </div>","PeriodicalId":139,"journal":{"name":"Cancer Medicine","volume":"14 6","pages":""},"PeriodicalIF":2.9000,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cam4.70777","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Cancer Medicine","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cam4.70777","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ONCOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

Objective

To investigate the association between the systemic immunity-inflammation index (SII) and fatigue, cancer, and cancer-related fatigue (CRF) populations.

Methods

The National Health and Nutrition Examination Survey (NHANES) from 2005 to 2018 provided data for this retrospective cross-sectional study. By dividing the platelet count by the neutrophil count and the lymphocyte count, SII was calculated. Participants were categorized into four groups: normal, fatigue, cancer, and cancer-related fatigue (CRF), with the normal group serving as the reference. Binary logistic regression was applied to assess the correlations. The dose–response relationship between SII and outcomes in the four groups was evaluated using restricted cubic splines. Use threshold effect analysis to determine the optimal SII value for each of the three groups. Stratified and subgroup analyses were performed based on sociodemographic factors and confounders, with specific attention to fatigue severity levels (mild, moderate, severe) in the fatigue and CRF groups.

Results

Data analysis included a total of 32,491 participants, including 14,846 in the normal group, 14,581 in the fatigue group, 1520 in the cancer group, and 1544 in the CRF group. The results of binary logistic regression showed that SII was positively correlated with the fatigue group (1.43[1.33, 1.55]), cancer group (1.67 [1.43, 1.95]) and CRF group (1.93 [1.66, 2.25]). Restricted cubic spline analysis revealed a linear relationship between SII and outcomes. The threshold values (k) for each of these groups were identified as 464.78 × 103 cells/μL, 448.97 × 103 cells/μL, and 454.65 × 103 cells/μL, respectively. Stratified analysis indicates that most groups exhibit significant differences. The subgroup analysis indicated that fatigue severity increased with higher SII levels, with the CRF group exhibiting the highest rate of severe fatigue (171% increase).

Conclusion

SII is positively correlated with fatigue, cancer, and CRF in a linear way. Higher SII values are associated with greater fatigue, particularly in the CRF population.

Abstract Image

求助全文
约1分钟内获得全文 求助全文
来源期刊
Cancer Medicine
Cancer Medicine ONCOLOGY-
CiteScore
5.50
自引率
2.50%
发文量
907
审稿时长
19 weeks
期刊介绍: Cancer Medicine is a peer-reviewed, open access, interdisciplinary journal providing rapid publication of research from global biomedical researchers across the cancer sciences. The journal will consider submissions from all oncologic specialties, including, but not limited to, the following areas: Clinical Cancer Research Translational research ∙ clinical trials ∙ chemotherapy ∙ radiation therapy ∙ surgical therapy ∙ clinical observations ∙ clinical guidelines ∙ genetic consultation ∙ ethical considerations Cancer Biology: Molecular biology ∙ cellular biology ∙ molecular genetics ∙ genomics ∙ immunology ∙ epigenetics ∙ metabolic studies ∙ proteomics ∙ cytopathology ∙ carcinogenesis ∙ drug discovery and delivery. Cancer Prevention: Behavioral science ∙ psychosocial studies ∙ screening ∙ nutrition ∙ epidemiology and prevention ∙ community outreach. Bioinformatics: Gene expressions profiles ∙ gene regulation networks ∙ genome bioinformatics ∙ pathwayanalysis ∙ prognostic biomarkers. Cancer Medicine publishes original research articles, systematic reviews, meta-analyses, and research methods papers, along with invited editorials and commentaries. Original research papers must report well-conducted research with conclusions supported by the data presented in the paper.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信