Nonlinear Redox–Immune Coupling Under Low-Dose-Rate Radiation: A Compartment-Specific Framework for Biological Responses—A Narrative Review

Ionizing radiation induces reactive oxygen species (ROS) and inflammatory signaling that contribute to both therapeutic efficacy and normal tissue toxicity. While the effects of high-dose radiation are well characterized, responses to low-dose-rate radiation (LDRR) remain inconsistent and are not adequately explained by conventional linear dose–response models. To address this gap, we conducted a narrative review of recent experimental studies across multiple biological systems, including body fluids, joint microenvironments, and reproductive tissues, focusing on redox and immune-related responses under LDRR conditions (dose rates: 0.39–3.49 mGy/h). Literature was identified through PubMed/MEDLINE, Web of Science, and Google Scholar, with emphasis on studies published between 2015 and 2026. These studies demonstrate that LDRR elicits nonlinear, dose-dependent effects that vary across biological compartments and involve coordinated changes in oxidative stress, immune signaling, and metabolic regulation. Based on this synthesis, we propose a unifying framework of nonlinear redox–immune coupling, in which oxidative stress functions as a threshold-dependent regulator and immune responses follow a biphasic trajectory characterized by activation at lower dose rates and attenuation or adaptation at higher levels. These responses are strongly influenced by the local microenvironment, resulting in compartment-specific variability. This integrated perspective supports a shift from dose-centric to systems-level interpretations of radiation biology and provides a basis for improving biomarker development, risk assessment, and therapeutic strategies in chronic low-dose radiation exposure settings. Future research priorities include time-resolved mechanistic studies to define compartment-specific redox thresholds, validation of candidate biomarkers under identical multi-compartment experimental conditions (e.g., GSH/GSSG ratio, 8-OHdG, circulating cytokine panels including IL-10/TNF-α ratio), and integration of subject-specific biological variables (e.g., age, sex, and baseline redox capacity) into predictive models of LDRR response.