ORIGINAL RESEARCH article
Front. Physiol.
Sec. Exercise Physiology
Effects of Swinging Exercise on Immune Biomarkers: A Systematic Review and Meta-Analysis with Machine Learning-Based Identification of Responder Profiles
Guodong Zhang 1
siang Wei 2
yanli xie 2
1. Shanxi Agricultural University - Taigu Campus, Jinzhong, China
2. Shanxi Agricultural University College of Animal Science, Jinzhong, China
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Abstract
Objective: This study integrated meta-analysis and machine learning to elucidate the effects of swinging exercise on key immune biomarkers and identify distinct responder profiles. Methods: Following PRISMA guidelines, we systematically searched PubMed, Web of Science, Cochrane Library, Google Scholar, and CNKI databases until February 2025. Results: Fourteen studies involving 440 participants were included for meta-analysis, examining T-cell subsets (CD3+, CD4+, CD8+, CD4+/CD8+ ratio), B-cell immunoglobulins (IgA, IgG, IgM), inflammatory markers (TNF-α, IL-6, IFN-γ), and cardiorenal indices (CK, LDH, BUN). Random-effects models revealed a significant decrease in T-cell markers (SMD = -1.24, 95% CI: -1.58 to -0.90) but a concurrent increase in B-cell markers (SMD = 0.86, 95% CI: 0.42 to 1.30) and cardiorenal markers (SMD = 0.94, 95% CI: 0.55 to 1.33). The effect of swinging exercise on inflammatory markers is not significantly different (P >0.05). Meta-regression showed no significant moderating effects of age, exercise intensity, or duration (all P > 0.05), Machine learning analysis (Random Forest, K-means clustering, PCA) of individual participant data (211 exercisers) identified the CD4+/CD8+ ratio (feature importance = 0.24), IgA (0.19), and IgG (0.18) as the top discriminators between responders and non-responders. Responders exhibited a balanced immune profile characterized by higher CD4+/CD8+ ratios and elevated immunoglobulin levels. Conclusion: Swinging exercise induces a dual immune response: transient T-cell suppression coupled with enhanced humoral immunity. The inter-individual variability highlights the need for personalized training regimens based on immune monitoring. We recommend integrating immune profiling into athletic programming to optimize health and performance outcomes. The observed increase in markers of muscle damage and metabolic stress (CK, LDH, BUN) confirms the substantial physiological stimulus provided by these sports.
Summary
Keywords
swinging sports, Immune function, Meta-analysis, T cells, Immunoglobulins, Machinelearning, Responder profiles, Personalized training
Received
28 August 2025
Accepted
23 December 2025
Copyright
© 2025 Zhang, Wei and xie. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Guodong Zhang
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