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

IF 3.1 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

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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.

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全身免疫-炎症指数（SII）与疲劳、癌症和癌症相关疲劳之间的关系：来自NHANES的见解（2005-2018）

目的探讨全身免疫炎症指数（SII）与疲劳、癌症及癌症相关疲劳（CRF）人群的关系。方法2005 - 2018年美国国家健康与营养检查调查（NHANES）为本回顾性横断面研究提供数据。通过血小板计数除以中性粒细胞计数和淋巴细胞计数，计算SII。参与者被分为四组：正常组、疲劳组、癌症组和癌症相关疲劳组（CRF），正常组作为参考。采用二元逻辑回归评估相关性。使用受限三次样条评估四组患者SII与预后之间的剂量-反应关系。使用阈值效应分析来确定三组中每组的最佳SII值。根据社会人口学因素和混杂因素进行分层和亚组分析，特别关注疲劳组和CRF组的疲劳严重程度（轻度、中度、重度）。数据分析共纳入32491名参与者，其中正常组14846人，疲劳组14581人，癌症组1520人，CRF组1544人。二元logistic回归结果显示，SII与疲劳组（1.43[1.33,1.55]）、癌症组（1.67[1.43,1.95]）、CRF组（1.93[1.66,2.25]）呈正相关。限制性三次样条分析显示SII与预后呈线性关系。各组的阈值(k)分别为464.78 × 103 cells/μL、448.97 × 103 cells/μL和454.65 × 103 cells/μL。分层分析表明，大多数组表现出显著差异。亚组分析表明，疲劳程度随着SII水平的提高而增加，CRF组表现出最高的严重疲劳率（增加171%）。结论SII与疲劳、肿瘤、CRF呈线性正相关。更高的SII值与更严重的疲劳有关，特别是在CRF人群中。

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来源期刊

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.