Immune-Ageing Evaluation of Peripheral T and NK Lymphocyte Subsets in Chinese Healthy Adults

Zhenghu Jia; Zhiyao Ren; Dongmei Ye; Jiawei Li; Yan Xu; Hui Liu; Ziyu Meng; Chengmao Yang; Xiaqi Chen; Xinru Mao; Xueli Luo; Zhe Yang; Lina Ma; Anyi Deng; Yafang Li; Bingyu Han; Junping Wei; Chongcheng Huang; Zheng Xiang; Guobing Chen; Peiling Li; Juan Ouyang; Peisong Chen; Oscar Junhong Luo; Yifang Gao; Zhinan Yin

doi:10.1007/s43657-023-00106-0

Immune-Ageing Evaluation of Peripheral T and NK Lymphocyte Subsets in Chinese Healthy Adults

Phenomics. 2023 May 23;3(4):360-374. doi: 10.1007/s43657-023-00106-0. eCollection 2023 Aug.

Authors

Zhenghu Jia^{1

2

3}, Zhiyao Ren^{4

5}, Dongmei Ye⁶, Jiawei Li³, Yan Xu^{1

2}, Hui Liu⁷, Ziyu Meng^{8

9}, Chengmao Yang³, Xiaqi Chen¹⁰, Xinru Mao¹¹, Xueli Luo¹¹, Zhe Yang^{1

2}, Lina Ma^{1

2}, Anyi Deng^{1

2}, Yafang Li^{1

2}, Bingyu Han^{1

2}, Junping Wei^{1

2}, Chongcheng Huang^{1

2}, Zheng Xiang^{1

2}, Guobing Chen⁴, Peiling Li⁶, Juan Ouyang¹¹, Peisong Chen¹², Oscar Junhong Luo⁴, Yifang Gao⁶, Zhinan Yin^{1

2}

Affiliations

¹ Guangdong Provincial Key Laboratory of Tumor Interventional Diagnosis and Treatment, Zhuhai Institute of Translational Medicine Zhuhai People's Hospital Affiliated With Jinan University, Jinan University, Zhuhai, 519000 Guangdong China.
² The Biomedical Translational Research Institute, Health Science Center (School of Medicine), Jinan University, Guangzhou, 510632 Guangdong China.
³ Guangzhou Purui Biotechnology Co., Ltd., Guangzhou, 510660 Guangdong China.
⁴ Department of Systems Biomedical Sciences, School of Medicine, Jinan University, Guangzhou, 510632 Guangdong China.
⁵ Guangzhou Geriatric Hospital, Guangzhou, 510550 Guangdong China.
⁶ Organ Transplantation Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, 510080 Guangdong China.
⁷ Emergency Department, The First Affiliated Hospital of Jinan University, Jinan University, Guangzhou, 510632 Guangdong China.
⁸ NHC Key Laboratory of Hormones and Development, Tianjin Key Laboratory of Metabolic Diseases, Chu Hsien-I Memorial Hospital, Tianjin Medical University, Tianjin, 300134 China.
⁹ Tianjin Institute of Endocrinology, Tianjin Medical University, Tianjin, 300134 China.
¹⁰ Zhongke Regenerative Medicine Technology Co., Ltd, Dongguan, 523808 Guangdong China.
¹¹ Wuhan Purui Medical Laboratory Co., Ltd, Wuhan, 430223 Hubei China.
¹² Department of Clinical Laboratory, Department of Laboratory Medicine, The First Affiliated Hospital of Sun Yat-sen University, Guangzhou, 510080 Guangdong China.

Abstract

Ageing is often accompanied with a decline in immune system function, resulting in immune ageing. Numerous studies have focussed on the changes in different lymphocyte subsets in diseases and immunosenescence. The change in immune phenotype is a key indication of the diseased or healthy status. However, the changes in lymphocyte number and phenotype brought about by ageing have not been comprehensively analysed. Here, we analysed T and natural killer (NK) cell subsets, the phenotype and cell differentiation states in 43,096 healthy individuals, aged 20-88 years, without known diseases. Thirty-six immune parameters were analysed and the reference ranges of these subsets were established in different age groups divided into 5-year intervals. The data were subjected to random forest machine learning for immune-ageing modelling and confirmed using the neural network analysis. Our initial analysis and machine modelling prediction showed that naïve T cells decreased with ageing, whereas central memory T cells (Tcm) and effector memory T cells (Tem) increased cluster of differentiation (CD) 28-associated T cells. This is the largest study to investigate the correlation between age and immune cell function in a Chinese population, and provides insightful differences, suggesting that healthy adults might be considerably influenced by age and sex. The age of a person's immune system might be different from their chronological age. Our immune-ageing modelling study is one of the largest studies to provide insights into 'immune-age' rather than 'biological-age'. Through machine learning, we identified immune factors influencing the most through ageing and built a model for immune-ageing prediction. Our research not only reveals the impact of age on immune parameter differences within the Chinese population, but also provides new insights for monitoring and preventing some diseases in clinical practice.

Supplementary information: The online version contains supplementary material available at 10.1007/s43657-023-00106-0.

Keywords: Immune-ageing modelling; Immunosenescence; Natural killer cell subsets; Phenotype; T cell subset.