Huayu Li,1 Yuanhao Tong,2 Jing Li,3 Xiaohan Shi,4 Alphonce MK Nyalali,3,5 Feng Li3 

1Department of Social Medicine of School of Public Health and Department of Pharmacy of The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, 310058, People’s Republic of China; 2Department of Orthopedics, National Center for Orthopedics, Shanghai Sixth People’s Hospital Affiliated to Shanghai JiaoTong University School of Medicine, Shanghai, 200030, People’s Republic of China; 3Department of Neurosurgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, 250117, People’s Republic of China; 4School of Nursing and Rehabilitation, Shandong University, Jinan, 250014, People’s Republic of China; 5Department of Orthopedics and Neurosurgery, Mbeya Zonal Referral Hospital and Mbeya College of Health and Allied Sciences, University of Dar Es Salaam, Mbeya, Tanzania

Correspondence: Feng Li, Department of Neurosurgery, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Sciences, Jinan, People’s Republic of China, Email lidoc@163.com

Background: Patients with glioma experience multidimensional symptoms that reduce their functional status, quality of life, and survival, and these symptoms may be associated with inflammation. This study applied network analysis to examine and visualize the relationship between multidimensional symptom experiences and inflammatory biomarkers and assess the symptom networks of multidimensional symptom experiences over time in patients with glioma.
Methods: Participants diagnosed with glioma were recruited and completed the MD Anderson Symptom Inventory-Brain Tumor Module (MDASI-BT) at three different time points: 2 days after admission (T1), 7 days after surgery (T2), and 1 month after surgery (T3). On the same day as the T1 questionnaire collection, plasma levels of interleukin-1β (IL-1β), IL-6, IL-10, tumor necrosis factor-α (TNF-α), and c-reactive protein (CRP) were measured. Network analysis was employed to explore the relationships among multidimensional symptom experiences and inflammatory biomarkers of patients.
Results: Of the total 334 participants (mean age 54.38 ± 13.16 years), 67.1% had high-grade tumors. In the symptom-cytokine network model, there were positive correlations between “sad and IL-6”, “fatigue and IL-10”, and “sleepy and IL-1β”. Within the symptom network models, “difficulty remembering”, “sad”, and “change in bowel pattern” emerged as the most central symptoms across the three assessments, respectively.
Conclusion: Network analysis provides a novel method for investigating the relationships between multidimensional symptom experiences and inflammatory biomarkers. Additionally, it allows for identifying different core symptoms at various stages of treatment. Clinicians should effectively address and manage symptoms by focusing on special core symptoms and their interconnections within the network.

Keywords: glioma, symptom, inflammatory biomarkers, network analysis