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肾脏免疫疾病中的免疫雪崩

Immunological Avalanches in Renal Immune Diseases.

作者信息

Viggiano Davide, Iulianiello Pietro, Mancini Antonio, Iacuzzo Candida, Apicella Luca, Di Pietro Renata Angela, Hamzeh Sarah, Cacciola Giovanna, Lippiello Eugenio, Gigliotti Andrea, Secondulfo Carmine, Bilancio Giancarlo, Gigliotti Giuseppe

机构信息

Department Translational Medical Sciences, University of Campania, 80131 Naples, Italy.

Department of Nephrology and Dialysis, Eboli Hospital, 84025 Eboli, Italy.

出版信息

Biomedicines. 2025 Apr 21;13(4):1003. doi: 10.3390/biomedicines13041003.

Abstract

The complex nature of immune system behavior in both autoimmune diseases and transplant rejection can be understood through the lens of avalanche dynamics in critical-point systems. This paper introduces the concept of the "immunological avalanche" as a framework for understanding unpredictable patterns of immune activity in both contexts. Just as avalanches represent sudden releases of accumulated potential energy, immune responses exhibit periods of apparent stability followed by explosive flares triggered by seemingly minor stimuli. The model presented here draws parallels between immune system behavior and other complex systems such as earthquakes, forest fires, and neuronal activity, where localized events can propagate into large-scale disruptions. In autoimmune conditions like systemic lupus erythematosus (SLE), which affects multiple organ systems including the kidneys in approximately 50% of patients, these dynamics manifest as alternating periods of remission and flares. Similarly, in transplant recipients, the immune system exhibits metastable behavior under constant allograft stimulation. This critical-point dynamics framework is characterized by threshold-dependent activation, positive feedback loops, and dynamic non-linearity. In autoimmune diseases, triggers such as UV light exposure, infections, or stress can initiate cascading immune responses. In transplant patients, longitudinal analysis reveals how monitoring oscillatory patterns in blood parameters and biological age markers can predict rejection risk. In a preliminary study on kidney transplant, all measured variables showed temporal instability. Proteinuria exhibited precise log-log linearity in power law analysis, confirming near-critical-point system behavior. Two distinct dynamic patterns emerged: large oscillations in eGFR, proteinuria, or biological age predicted declining function, while small oscillations indicated stability. During avalanche events, biological age increased dramatically, with partial reversal leaving persistent elevation after acute episodes. Understanding these dynamics has important implications for therapeutic approaches in both contexts. Key findings suggest that monitoring parameter oscillations, rather than absolute values, better indicates system instability and potential avalanche events. Additionally, biological age calculations provide valuable prognostic information, while proteinuria measurements offer efficient sampling for system dynamics assessment. This conceptual model provides a unifying framework for understanding the pathogenesis of both autoimmune and transplant-related immune responses, potentially leading to new perspectives in disease management and rejection prediction.

摘要

通过临界点系统中的雪崩动力学视角,可以理解自身免疫性疾病和移植排斥反应中免疫系统行为的复杂本质。本文引入“免疫雪崩”概念,作为理解这两种情况下不可预测免疫活动模式的框架。正如雪崩代表累积势能的突然释放一样,免疫反应表现出明显的稳定期,随后是由看似微小的刺激引发的爆发性活动。此处提出的模型将免疫系统行为与其他复杂系统(如地震、森林火灾和神经元活动)进行了类比,在这些系统中,局部事件可传播为大规模破坏。在系统性红斑狼疮(SLE)等自身免疫性疾病中,约50%的患者会累及包括肾脏在内的多个器官系统,这些动力学表现为缓解期和发作期交替出现。同样,在移植受者中,免疫系统在持续的同种异体移植刺激下表现出亚稳态行为。这种临界点动力学框架的特征是阈值依赖性激活、正反馈回路和动态非线性。在自身免疫性疾病中,紫外线暴露、感染或压力等触发因素可引发级联免疫反应。在移植患者中,纵向分析揭示了监测血液参数和生物学年龄标志物的振荡模式如何预测排斥风险。在一项关于肾移植的初步研究中,所有测量变量均显示出时间上的不稳定性。蛋白尿在幂律分析中表现出精确的对数-对数线性关系,证实了近临界点系统行为。出现了两种不同的动态模式:估算肾小球滤过率(eGFR)、蛋白尿或生物学年龄的大幅振荡预示功能下降,而小幅振荡则表明稳定。在雪崩事件期间,生物学年龄急剧增加,急性发作后部分逆转但仍持续升高。理解这些动力学对这两种情况下的治疗方法具有重要意义。主要发现表明,监测参数振荡而非绝对值,能更好地指示系统不稳定性和潜在的雪崩事件。此外,生物学年龄计算提供了有价值的预后信息,而蛋白尿测量为系统动力学评估提供了有效的采样方法。这个概念模型为理解自身免疫性和移植相关免疫反应的发病机制提供了一个统一框架,可能会为疾病管理和排斥预测带来新的视角。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c91/12024534/d2f9fff913a1/biomedicines-13-01003-g001.jpg

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