Peng Bo, Xiang Xuyu, Tian Han, Xu Kaiqiang, Zhuang Quan, Li Junhui, Zhang Pengpeng, Zhu Yi, Yang Min, Liu Jia, Zhao Yujun, Cheng Ke, Ming Yingzi
Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, China.
Engineering and Technology Research Center for Transplantation Medicine of National Health Commission, Central South University, Changsha, China.
Ren Fail. 2025 Dec;47(1):2493231. doi: 10.1080/0886022X.2025.2493231. Epub 2025 May 14.
Immune monitoring is essential for maintaining immune homeostasis after renal transplantation (RT). Peripheral blood lymphocyte subpopulations (PBLSs) are widely used biomarkers for immune monitoring, yet there is no established standard reference for PBLSs during immune reconstitution post-RT. PBLS data from stable recipients at various time points post-RT were collected. Binary and multiple linear regressions, along with a mixed-effect linear model, were used to analyze the correlations between PBLSs and clinical parameters. Predictive models for PBLS reference values were developed using Gradient Boosting Regressor, and the models' performance was also evaluated in infected recipients. A total of 1,736 tests from 494 stable recipients and 98 tests from 82 infected recipients were included. Age, transplant time, induction therapy, dialysis duration, serum creatinine, albumin, hemoglobin, and immunosuppressant drug concentration were identified as major factors influencing PBLSs. CD4 and CD8 T cells and NK cells increased rapidly, stabilizing within three months post-RT. In contrast, B cells peaked at around two weeks and gradually plateaued after four months. Both static and dynamic predictive models provided accurate reference values for PBLSs at any time post-RT, with the static model showing superior performance in distinguishing stable, infected and sepsis patients. Key factors influencing PBLS reconstitution after RT were identified. The predictive models accurately reflected PBLS reconstitution patterns and provided practical, personalized reference values for PBLSs, contributing to precision-guided care. The study was registered on Chinese Clinical Trial Registry (ChiCTR2300068666).
免疫监测对于肾移植（RT）后维持免疫稳态至关重要。外周血淋巴细胞亚群（PBLSs）是免疫监测中广泛使用的生物标志物，但在RT后免疫重建期间，PBLSs尚无既定的标准参考值。收集了RT后不同时间点稳定受者的PBLS数据。采用二元和多元线性回归以及混合效应线性模型分析PBLSs与临床参数之间的相关性。使用梯度提升回归器建立PBLS参考值的预测模型，并在感染受者中评估模型的性能。共纳入了494名稳定受者的1736次检测和82名感染受者的98次检测。年龄、移植时间、诱导治疗、透析时间、血清肌酐、白蛋白、血红蛋白和免疫抑制剂药物浓度被确定为影响PBLSs的主要因素。CD4和CD8 T细胞以及NK细胞迅速增加，在RT后三个月内趋于稳定。相比之下，B细胞在大约两周时达到峰值，并在四个月后逐渐趋于平稳。静态和动态预测模型均为RT后任何时间的PBLSs提供了准确的参考值，其中静态模型在区分稳定、感染和脓毒症患者方面表现更优。确定了RT后影响PBLS重建的关键因素。预测模型准确反映了PBLS重建模式，并为PBLSs提供了实用的个性化参考值，有助于精准指导护理。该研究已在中国临床试验注册中心注册（ChiCTR2300068666）。
