Prediction of peripheral blood lymphocyte subpopulations after renal transplantation.

Journal: Renal failure
Published Date:

Abstract

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).

Authors

  • Bo Peng
    Institute for Environmental and Climate Research, Jinan University, Guangzhou, China.
  • Xuyu Xiang
    Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, China.
  • Han Tian
    State Key Laboratory of Tribology, Department of Mechanical Engineering, Tsinghua University, Beijing, 100084, China.
  • Kaiqiang Xu
    School of Mathematical Sciences, Dalian University of Technology, Dalian 116024, China. Electronic address: 459553299@qq.com.
  • Quan Zhuang
    Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, China.
  • Junhui Li
    1 School of Information Science and Engineering, Shandong University, Jinan 250100, P. R. China.
  • Pengpeng Zhang
    Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, New York, USA; Department of Radiation Oncology, Memorial Sloan-Kettering Cancer Center, New York, USA. Electronic address: zhangp@mskcc.org.
  • Yi Zhu
    2State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangzhou, Guangdong China.
  • Min Yang
    College of Food Science and Engineering, Ocean University of China, Qingdao, 266003, Shandong, China.
  • Jia Liu
    Department of Colorectal Oncology, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Tianjin Key Laboratory of Digestive Cancer, Tianjin, China.
  • Yujun Zhao
    Transplantation Center, The Third Xiangya Hospital, Central South University, Changsha, China.
  • Ke Cheng
    School of Computer Science and Engineering, Jiangsu University of Science and Technology, No. 2 Mengxi Road, Zhenjiang 212003, China.
  • Yingzi Ming
    Transplantation Center, The Third Xiangya Hospital, Central South University, No. 138 Tongzipo Road, Changsha, 410013, Hunan, China. 600941@csu.edu.cn.