A Large Study of Immune Aging in T Cell and Natural Killer Cell Populations
The overall population size of broad immune cell categories, such as T cells, remains remarkably consistent across a lifespan, even given a reduced supply of replacement T cells as the thymus atrophies and hematopoietic stem cell populations become dysfunctional. In an environment of limited supply, numbers are kept up in the face of continued attrition by increased replication, which leads to increased cellular senescence in immune cell populations as ever more cells hit the Hayflick limit. Another aspect of immune aging is a progressively larger shift in the relative count of different types of immune cell, such as diminished numbers of naive T cells capable of responding to novel pathogens.
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 cell and natural killer (NK) cell subsets, the phenotype and cell differentiation states in 43,096 healthy Chinese 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 CD28-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.