A Systematic Review and Meta-Analysis of Immune Signatures Involved in the Prevalence of Exhaustion in Humans, Mice, and Bovine Species

Introduction: Immune exhaustion is known to occur in chronic infections as well as cancer, and is characterized by constant antigenic stimulation and gradual loss of T cell function and upregulation of immune exhaustion markers. This systematic review aims to explore conserved and divergent exhaustion signatures across humans, mice, and bovines to advance our understanding for better translational outcomes. Methods: Following PRISMA 2020 guidelines, studies from 2011–2023 were analyzed using a random-effects model (metaprop, logit transformation). Subgroup analyses (species, disease type), sensitivity and heterogeneity (I²) analyses were performed. Publication bias was performed to evaluate biomarker variability. Results: Meta-analysis identified an almost consistent role of IL-6 (48%), IL-2 (51%), TNF-α (46%), IFN-γ (41%), and IL-10 (48%) in immune exhaustion throughout various chronic disease conditions. Co-inhibitory receptors such as PD-1 (51%), TIM-3 (54%), LAG-3 (59%), and CTLA-4 (64%) were highly upregulated. Extreme heterogeneity (I² >95%) pointed towards considerable variation mainly due to species differences, disease category, and methodological factors, with fewer murine and bovine studies compared to human literature. Discussion: The confluence of pro-inflammatory cytokines with elevated checkpoint receptor expression highlights the fact that immune exhaustion is conserved across mammals. However, the limitation in extrapolation attributed to high heterogeneity and species imbalance exists in the study. Furthermore, the variations in disease models and technical differences in detection methods point towards a need for standardization and integrated comparative analyses to boost biomarker interpretation. Conclusion: Co-inhibitory receptors and various cytokines appear to be the conserved contributors to immune exhaustion across species. Further, enriching cross-species data sets and accounting for methodological variability will improve the translational importance and support in developing more precise therapeutic interventions.