Victoria Kulesh1,2,3, Kirill Peskov1,2,3, Gabriel Helmlinger4, Gennady Bocharov3,5,6
1Research Center of Model-Informed Drug Development, I.M. Sechenov First Moscow State Medical University, 2M&S Decisions LLC, 3Marchuk Institute of Numerical Mathematics RAS, 4Quantitative Medicines LLC, 5Institute for Computer Science and Mathematical Modelling, I.M. Sechenov First Moscow State Medical University, 6Moscow Center of Fundamental and Applied Mathematics at INM RAS
Introduction Antigen-specific CD4+ and CD8+ T-lymphocytes constitute an integral part of the human immune system, regulating both the direct elimination of pathogens and the context-dependent assistance to other immune cells. T-lymphocytes development throughout human life is characterized by a coordinated staging of maturation processes and age-related changes in cell counts and functionality. After egress from the thymus, T-lymphocytes experience sequential differentiation from recent thymic emigrants (RTE) and naïve cells into different types of effector- and memory cells. Aging processes, which comprise several mechanisms such as thymic involution and memory cells accumulation, lead to a decrease in the body’s ability to resist infections and tumors [1]. The number of T-lymphocytes is a global marker of immunological health status, primarily used for diagnostic purposes. However, the use of wide age intervals for reference values, as well as the consideration of only total populations of T-lymphocytes, limit both diagnostic precision and the general understanding of healthy aging. Relationships between various phenotypically distinct T-lymphocyte subpopulations, as well as their age-dependent homeostasis, warrant a comprehensive quantitative evaluation for developing new anti-infectious and T-cell based treatments. Objective The key objective of this study was to quantify age-related changes in the homeostasis of T-lymphocytes in healthy individuals by integrating the available quantitative data covering a wide range of specific cell subpopulations, subject ages and organs. Methods A systematic literature search was conducted in PubMed and Google Scholar following PRISMA guidelines to identify all relevant sources with clinical data on T-lymphocyte numbers in various organs [2]. Piece-wise equal-effect meta-analysis methodology was used to evaluate the age-dependent homeostasis of each T-lymphocyte subpopulation with weighted average calculation within each pre-specified age interval. Weighting by subject numbers rather than via the conventional inverse variance weighing method was implemented due to the observed nonproportional decrease in variance with an increase in subject numbers. Handling of individual-level patient data (IPD) in the meta-analysis was conducted via modified two-stage approach, deriving average estimates from IPD within pre-specified age bins, followed by treating the derived estimates as aggregated for weighted average calculation [3]. Data processing, visualization and meta-analyses were performed using the R Statistics (4.2.3). Results The database comprising 124 distinct clinical studies with 11,722 unique observations from healthy subjects on 21 parameters of immune homeostasis (20 T-lymphocyte subpopulations and the CD4+/CD8+ ratio) was systematically compiled for a meta-analysis. Owing to the wide coverage of age groups from 0 to 111 years, the proposed quantitative analysis resulted in obtaining reference values (in absolute terms and percentages) of various T-lymphocyte subpopulations in blood and other organs within narrow age intervals (up to 5 intervals were implemented within the 0-to-5 year age group alone). A substantial decrease in T-lymphocyte counts was observed in the first 10 years of life for total lymphocyte populations as well as RTE, naïve and effector CD4+ and CD8+ T-lymphocytes. Conversely, blood concentration of memory T-lymphocytes (central-memory and effector-memory cells) tended to increase in older age groups, particularly after ~50 years of age. Notably, the extensive dataset compiled enabled to explore the differences in times-to-maximal cell counts among cell subpopulations. The more differentiated T-lymphocytes reached their maximal cell number later (1–5 years of age) than their less differentiated counterparts (0–1 year of age). Conclusion Our analysis shows that the MBMA approach, which was originally used to extract the pooled effect size for specific therapies, can be used for fundamental and applied immunological research by maximizing information extraction from heterogeneous experimental and clinical data. The proposed results represent the basis for further mechanistic study of human immune status in health and disease. Additionally, the database collected throughout this systematic research effort represents a unique source for the development of quantitative systems pharmacology models and generation of clinically-relevant virtual patients cohorts.
1 – Montecino-Rodriguez E, Berent-Maoz B, Dorshkind K. Causes, consequences, and reversal of immune system aging. J Clin Invest. 2013;123(3):958-965. doi:10.1172/JCI64096 2 – Moher D, Liberati A, Tetzlaff J, Altman DG; PRISMA Group. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. PLoS Med. 2009;6(7):e1000097. doi:10.1371/journal.pmed.1000097 3 – Burke DL, Ensor J, Riley RD. Meta-analysis using individual participant data: one-stage and two-stage approaches, and why they may differ. Stat Med. 2017;36(5):855-875. doi:10.1002/sim.7141
Reference: PAGE 33 (2025) Abstr 11517 [www.page-meeting.org/?abstract=11517]
Poster: Drug/Disease Modelling - Other Topics