AccScience Publishing / EJMO / Online First / DOI: 10.36922/EJMO026020013
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ORIGINAL RESEARCH ARTICLE

An exploratory study on establishing reference intervals for circulating immune cells and an immune age prediction model in healthy young and middle-aged adults

Wei-wu Chen1† ,  Rong-zheng Chen1† ,  Yin-yin Fan2† ,  Shang Cai1 ,  Qian Yin1 ,  Shi-cheng Li1 ,  Yue-hong Kong1 ,  Hong Zhang2* ,  Li-yuan Zhang1*
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1 Center for Cancer Diagnosis and Treatment, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
2 Laboratory Medicine Center, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China
†These authors contributed equally to this work.
EJMO, 026020013 https://doi.org/10.36922/EJMO026020013
Received: 7 January 2026 | Revised: 15 April 2026 | Accepted: 7 May 2026 | Published online: 28 May 2026
© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

Introduction: “Immune age” quantifies the immune system’s aging status, offering a new perspective for predicting disease risk and guiding health management. Although advanced technologies such as genomics have been used to develop immune age models, their high cost and complexity hinder their widespread application in healthy populations.

Objective: To establish reference intervals for circulating immune cells in healthy young and middle-aged adults, and to explore and construct an immune age prediction model using machine learning.

Methods: A study involving 124 healthy individuals measured 36 circulating immune cell parameters to establish population-based reference intervals. Six machine learning regression models were evaluated to identify the optimal model for predicting immune age. An open-source, visually enhanced web-based user interface was subsequently developed to improve model interpretability and provide a user-friendly experience.

Results: This study established preliminary reference intervals for 36 circulating immune cell parameters and identified significant age-related correlations in several of them. With increasing age, both the percentage and the absolute count of naive T cells declined significantly, whereas the percentage and absolute count of terminally differentiated T cells increased significantly. These changes are consistent with the established hallmarks of immunosenescence. Among six prediction models, the gradient boosting regressor demonstrated the best performance, achieving a mean absolute error of 6.295 years and a coefficient of determination of 0.491 on an independent test set. This indicates the model has preliminary predictive potential. Furthermore, this study exploratorily developed a web-based visualized tool for predicting immune age.

Conclusion: This study has preliminarily established reference intervals for circulating immune cells and exploratorily built an immune age prediction model for healthy young and middle-aged adults.

Keywords
Healthy population
Immune age
Circulating immune cells
Machine learning
Reference intervals
Funding
This work was supported by Suzhou Gusu Health Talent Program (GSWS2022028); Open Project of State Key Laboratory of Radiation Medicine and Protection of Soochow University (GZN1202302); New medical technology project of the Second Affiliated Hospital of Soochow University (23zl001); Multi-center Clinical Research Project for Major Diseases in Suzhou (DZXYJ202304); Gusu health talent research Fund (GSWS2022053); The National Natural Science Foundation of China (82102824); and Suzhou Applied Basic Research (Medical and Health) Science and Technology Innovation Project (SYW2024095).
Conflict of interest
The authors declare that there is no conflict of interest.
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Eurasian Journal of Medicine and Oncology, Electronic ISSN: 2587-196X Print ISSN: 2587-2400, Published by AccScience Publishing
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