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Age-dependent immune profile in healthy individuals: an original study, systematic review and meta-analysis

Immunity & Ageing volume 21, Article number: 75 (2024) Cite this article

Abstract

Background

The circulatory peripheral immune system is the most convenient approach for determining an individual’s immune status. Due to various reasons, while previous studies have addressed the critical impact of age, most individual studies did not analyze immunosenescence in a systemic manner, which complicates the possibility of building a reference range for age-dependent immune profiles for effective immune monitoring. To address this gap, this study analyzed a group of healthy individuals to establish age-specific reference ranges of the healthy circulatory immune profile, and a systematic review and meta-analysis were conducted to validate the findings and create generalizable immune cell reference ranges.

Results

Our study recruited a total of 363 healthy Taiwanese adults (median age 42 years [IQR 30, 62], age range 21 to 87 years, 43.3% male), including 158 under 40 years old, 127 between 40–64 years old, and 78 over 64 years old. Significant age-related alterations were observed in both adaptive and innate immune cell subsets. CD8 + T cells decreased and CD4/CD8 ratio increased, with notable increases in NK cells. CD4 + T cells were less impacted by aging, while CD8 + T cells significantly lost CD28 and increased CD31 expression with age. A clear reverse trend in naïve and memory subsets of CD4 + and CD8 + T cells was observed. Detailed reference ranges for immune cell subsets in healthy Taiwanese adults were established. A systematic review included 7,425 adults and a meta-analysis of 12 eligible studies confirmed our findings in Taiwan, enhancing generalizability.

Conclusions

Combined with previous studies and original data through a systematic review and meta-analysis, we highlighted and quantified significant immune profile differences between older and younger individuals. The sex and age-specific reference ranges for peripheral immune cell subsets can serve as a basis for effective immune monitoring of various aging-related illnesses.

Background

The human immune system, crucial for defending against infectious pathogens throughout life, continually adapts to the ever-changing environment by immune cell activation, proliferation, and differentiation. Over time, age-related changes in the immune system have been collectively defined as “immunosenescence” [1, 2]. While the term intuitively implies the progressive deterioration of the immune system with aging, it is being realized that the aging process has diverse effects on the immune system, and these changes are not always detrimental and degenerative [3]. For example, the decrease in naïve T cells, a well-characterized aging-related change in the immune system [4], is associated with decreased vaccine response [5] and increased COVID-19-associated mortality [6]. On the other hand, the emergence of TEMRA cells (effector memory T cells with CD45RA re-expression) during aging could be viewed as a curtailed control of virus-specific T cell expansion to create resources for remaining immune cells [7].

Many previous reports from the literature have investigated the phenotypic characterization of circulatory immune cells in healthy humans to define the “healthy” immune system [8]. While some reports have established the reference values for peripheral blood lymphocytes [9,10,11], these reports failed to consider the potential effects of aging on the immune system. On the other hand, among studies that did consider age's effects on the immune system, they were frequently based on a smaller number of individuals and/or narrow age ranges, and some were based on frozen samples, potentially biased by the viability of immune cells [12, 13]. Furthermore, some studies focus on only specific cell types or fail to account for gender differences [14, 15], thereby overlooking immunosenescence beyond the main lymphocyte subsets [16]. These limitations hinder a comprehensive understanding of age-related changes in the circulatory human immune profile, complicating the definition of immune health across different ages.

In this study, we analyzed the peripheral circulatory T, B, NK cells, and monocyte differentiation subsets in healthy Taiwanese to define the reference range of immune cell populations across different age groups. Furthermore, we conducted a systematic literature review and meta-analysis, integrating our findings with existing research. The meta-analysis aims to corroborate and extend several key findings of age effects on immune cell composition from the literature.

Methods

Enrollment of participants and eligibility criteria

A total of 363 healthy adults, aged between 21 and 87 years old, were recruited from the Health management center of Far Eastern Memorial Hospital between January 2021 and December 2022.

The inclusion criteria were as follows: (1) age twenty years and older, (2) females weighing more than 45 kg, and males weighing more than 50 kg. To determine whether an individual is healthy, all participants were also examined to exclude the following conditions: (1) women who were pregnant or within six months postpartum (including after a miscarriage), (2) individuals who have had major surgery or received a blood transfusion within the last year, (3) individuals who have been vaccinated against measles, German measles, mumps, or polio (oral) with live attenuated vaccines within the last four weeks, (4) individuals currently diagnosed with or undergoing treatment for: syphilis, acquired immunodeficiency syndrome (AIDS), active tuberculosis, diabetes, cardiovascular diseases (arrhythmia, myocardial infarction, and those who have undergone cardiovascular surgery), gastrointestinal ulcer bleeding, kidney-related diseases (chronic renal failure), acute infections, contagious diseases, liver-related diseases (chronic hepatitis, cirrhosis), various cancers, autoimmune diseases (systemic lupus erythematosus, multiple sclerosis, rheumatoid arthritis), (5) participants with coagulopathy, known anemia with hemoglobin levels below 10 mg/dl, or recent trauma. Because the recruitment was performed along at the same time with physical checkups, individuals with incidental findings of illnesses were also excluded. All participants provided the informed consent, and this study was approved by the Institutional Review Board of the National Yang Ming Chiao Tung University (YM109137E) and the Far Eastern Memorial Hospital Research Ethics Committee (108,058-E).

Blood collection and immunophenotyping of peripheral immune cell subsets using flow cytometry

Peripheral blood samples (20 ml each) were collected for all participants before meals at Far Eastern Memorial Hospital. Each sample was processed using a C00162 Duraclone IM Count Tube containing an antibody mixture (Table S1) for staining, followed by adding A09777 VersaLyse buffer to lyze red blood cells. All samples were analyzed using a multicolor flow cytometer Beckman Coulter CYTOFLEX, and after optical scanning by the flow cytometer, data were analyzed using FlowJo software.

Three panels (Figure S1) were designed to identify key immune cell subsets and monocytes, including but not limited to: CD3 + T cells (CD3 +), CD4 + T cells (CD3 + CD4 +), CD8 + T cells (CD3 + CD8 +), B cells, and NK cells [17]. In the T-cell panel, subsets of CD4 + and CD8 + were determined using CD45RA and CD197 (CCR7) to identify naïve (CD197 + CD45RA +), effector memory RA + (CD197-CD45RA + , TEMRA), central memory (CD197 + CD45RA-, TCM), and effector memory (CD197-CD45RA-, TEM) T cells. CD28 is an important costimulatory molecule during T-cell activation and proliferation [18]. CD57 is a surface glycoprotein and CD57 + T cells exhibit impairment in replication capacity [19]. Past research has found a high correlation between the increase in CD57 and the decrease in CD28 with age [20]. CD31, also known as platelet endothelial cell adhesion molecule-1 (PECAM-1), is upregulated in naïve cells, and progressively downregulated during T cell differentiation [21]. Thus, we also evaluated the surface expression of CD28, CD57, and CD31 in CD4 + and CD8 + T cell subsets.

In the B-cell panel, B cells were identified as CD3-CD19 + lymphocytes. Different subsets of B cell maturation were examined, including naïve (CD27-IgD +), memory (CD27 + IgD- and CD27-IgD-), and transitional B cells (CD38highCD24 +). In the NK cell, dendritic cell, and monocyte panel, three groups of NK cells were identified based on their expression of CD3- and different combinations of CD16 and CD56: total NK (CD16 + or CD56 +), CD56high NK (CD16-CD56 +), and CD56low NK (CD16 + CD56-). Additionally, three populations of myeloid cells were identified: CD14 + CD16 + monocytes, CD14 + CD16- (conventional/M1) monocytes, and CD14-CD16 + (non-conventional/M2). Dendritic cells (DC) were characterized as Lineage (CD3-CD19-CD20-CD16-CD56- HLA-DR +) PBMCs.

Statistical analysis of the Taiwanese original data

After removing outliers that deviated more than three standard deviations from the raw data, we conducted a normality test using Q-Q plots. To explore age-related changes in healthy Taiwanese adults, we categorized 363 participants into three age groups: individuals under 40 years, those between 40–64 years, and those over 64 years. One-way Analysis of Variance (ANOVA) was used to compare the differences in absolute numbers among three age groups, with Tukey's Honestly Significant Difference (HSD) post-hoc test for further comparison. Differences between genders were analyzed by independent-sample t-test. Spearman correlation analysis was used to investigate the relationships between immune cell subsets and age. A spearman correlation matrix visualization was generated using the R package “corrplot”. Variables are considered correlated only if the correlation coefficient (r) is greater than 0.2 or less than -0.2, and the p-value is less than 0.05. Finally, we conducted a non-parametric bootstrap to calculate reference range (percentile 2.5–97.5). All data synthesis and analysis in this study were conducted using STATA version 18 and R version 4.3.2. All statistical tests were two-tailed with a significance level of 5%.

Systematic review of the literature

The systematic review and meta-analysis of this study followed the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines [22]. We conducted a comprehensive search of medical electronic databases, including PubMed, MEDLINE, Embase, and Web of Science, to identify relevant studies published from inception to November 2023. Keywords were organized using Medical Subject Headings (MeSH) such as "Lymphocytes," "Reference ranges," "Flow Cytometry," and "Healthy adults." (Table S2). Additionally, the reference lists of included studies were searched manually to increase the retrieval of studies. Two reviewers (STC, YFC) independently screened the full text of the identified studies based on the inclusion and exclusion criteria (Table S3). Eligible studies were selected to extract data, including sample size, country, and the age group of participants. Furthermore, specific details were extracted regarding the flow cytometry protocol used for measuring lymphocytes, such as the gating strategy, single or dual platform, and instrument used. Summary statistics, including mean, median, percentiles, and confidence intervals, were also extracted for absolute immune cell numbers and/or the percentage of immune cell subsets relative to the total numbers.

Meta-analysis of included studies and Taiwan’s data

The meta-analysis included studies that provided data on both the dispersion (e.g., standard deviation) and central tendency (e.g., mean) of immune cell subsets estimation. Studies that only reported a wide age range (\(\ge\) 40 years) were not included in the meta-analysis. Details for feasibility of including studies in a meta-analysis are presented in Table S4.

Two approaches of meta-analysis were utilized to assess the mean and normal ranges (2.5th-97.5th percentile) of immune cell numbers from the included studies and our results in Taiwan. First, corresponding to our data analysis, separate meta-analysis was conducted for three distinct age groups: individuals under 40 years old, those between 40–64 years old, and those over 64 years old. This approach allowed comparisons of immune cell numbers within similar age groups. Second, we combined data from all eligible studies and estimated the relationship between immune cell numbers and continuous age from 18 to 95 years old. This approach provided insights into how immune cell numbers change across the entire age spectrum. Both approaches of meta-analysis were conducted using absolute and percentage of immune cell numbers. Detailed information on the meta-analysis methodology was provided in Additional file 1: Supplementary material. All meta-analysis in our study was conducted using R version 4.3.2.

Results

Significant differences in immune cell subsets across different age groups

A total of 363 healthy Taiwanese adults (median age 42 years [IQR 30, 62], age range 21 to 87 years, 43.3% male) were recruited, including 158 individuals under 40 years old, 127 between 40–64 years old, and 78 over 64 years old. The demographics and average distribution of immune cell subsets in each age group are detailed in Table 1.

Table 1 Demographics and immune cell subsets distribution of study population (n = 363) by three age groups
Full size table

Our data revealed a significant age-related increase in NK cells and CD4/CD8 ratio, along with a decrease in CD8 + T cells and CD19 + B cells (Table 1). In detail, naïve CD4 + T cells (Fig. 1a) and naïve CD8 + T cells (Fig. 1b) decreased as individuals aged, while the numbers of memory cell subsets like the CD8 + effector memory RA + (TEMRA) T cells (Fig. 1b), CD4 + central memory (TCM) T cells, CD4 + effector memory (TEM) T cells, and CD4 + effector memory RA + (TEMRA) T cells (Fig. 1a) were significantly increased with age. Although the numbers of CD4 + T cells remained relatively stable, age-related reverse trends of naïve and memory T lymphocyte subsets were clearly demonstrated (Fig. 1e). In the older age group, there was also an increase in absolute numbers of NK cells, primarily attributed to a significant increase in CD56low NK cells (Fig. 1c). Lastly, although total CD19 + B cells decreased with age (Fig. 1d), the decrease was not due to changes in the naive, memory, or transitional B cell subsets, which remained relatively stable.

Fig. 1
figure 1

Detailed comparison of absolute numbers of immune cell subsets in three age groups. a Subsets of CD4 T cells (b) Subsets of CD8 T cells. c Subsets of NK cells. d Subsets of B cells. e Comparison of CD4 + T cell subsets and CD8 + T cell subsets distribution across three age groups (blue: < 40 years old, green: 40–64 years old, red: > 64 years old). Black bars: medians. P-values were determined with the Tukey's Honestly Significant Difference (HSD) post-hoc test. **p\(\le\) 0.001 *p\(\le\) 0.05

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Besides changes in the aforementioned T differentiation status, T cells exhibit many other phenotypic changes during aging. With increasing age, a significant increase in absolute numbers of CD8 + CD28-CD57 + T cells were observed, while CD8 + CD31 + T cells (Fig. 1b) decreased significantly with advancing age. Nevertheless, the numbers of CD4 + CD31 + T cells (p = 0.59), CD4 + CD28 + T cells (p = 0.55), and CD4 + CD28null T cells (p = 0.1) remained relatively stable across three age groups (Table 1).

As expected, the number of total monocytes increased with increasing age. Cell numbers of different monocyte subsets (M1, M2, M3) also showed tendencies to increase with age. Nevertheless, the number of dendritic cells was not affected by age.

Correlations between immune cell subsets and age

Since peripheral blood immune subsets showed significant differences between age groups, a Spearman correlation matrix was used to analyze all possible correlation coefficients between immune cell subsets and age, or between the subsets themselves (Figure S2).

The percentage of CD3 + T cells showed a significant negative correlation with age (percentage: r = -0.34, p < 0.0001), while their absolute numbers also decreased. There was a significant correlation between age and CD8 + T cells (absolute: r = -0.27, p < 0.0001; percentage: r = -0.38, p < 0.0001), as were CD4/CD8 ratio (r = 0.31, p < 0.0001), NK cells (absolute: r = 0.21, p < 0.0001), and CD19 + B cells (percentage: r = -0.23, p < 0.0001). Again, CD4 + T cells did not correlate with age in either absolute or percentage numbers. Finally, there was a strong correlation between age and naïve CD8 + T cells (absolute: r = -0.76, p < 0.0001).

Effects of gender on immunosenescence

In our study of 363 healthy adults, which comprising 157 males and 206 females across various age groups, we discovered significant gender-related differences in immune cell numbers. Notably, CD4/CD8 ratio was significantly higher in females than in males (p = 0.017, Table S5). This distinction was also significant in the age group of 40–64 years (p = 0.017, Table S7) and in those over 64 years (p = 0.035, Table S8). Meanwhile, females showed a higher level of memory B cells (p = 0.005, Table S5) and CD4 + CD31 + T cells (p < 0.001, Table S5), while males demonstrated a higher level of monocytes (p = 0.001, Table S5), CD14 + CD16- (conventional/M1) monocytes (p = 0.004, Table S5) and CD8 + CD28-CD57 + T cells (p = 0.021, Table S5).

Given the notable effect of age and gender on the immune system, we established estimated reference ranges for immune cell subsets in Taiwanese males and females separately across different age groups, detailed in Table S6-S8.

Study selection for systemic review

The selection of studies was guided by the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines, as shown in Figure S3. After reviewing the titles and abstracts of 236 articles found in the electronic database, 50 articles were selected for a thorough full-text review. 29 studies [8,9,10,11,12,13, 15, 16, 23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43] met the eligibility criteria for inclusion and the relevant data was extracted as shown in Table S9.

Due to insufficient data for estimation (e.g. only reported median) and/or wide age range, 18 studies [8, 9, 11, 12, 23, 25,26,27,28,29,30, 32, 33, 35, 37, 38, 41, 43] were excluded from the meta-analysis (Table S4). Data from 11 studies [10, 13, 15, 16, 24, 31, 34, 36, 39, 40, 42] and Taiwan’s data were used: 12 studies for the meta-analysis of immune cell absolute numbers and 11 for the meta-analysis of the percentage numbers of immune cells.

The detailed characteristics of the included studies were described in Table S10. Our research included various geography setting: 9 studies in Asia [9, 13, 14, 16, 27, 29, 34, 38, 39], 5 in Middle East [15, 23,24,25, 40], 4 in Europe [8, 12, 26, 28], 4 in South America [10, 31, 35, 37], 3 in Africa [30, 33, 36], 1 in USA [41], and 1 in Australia [11]. These studies all focused on immune cell numbers in healthy adults using flow cytometry, totaling 17 studies [8, 11, 13, 16, 23, 24, 26, 27, 31, 32, 35, 36, 38,39,40, 42, 43] that employed CD45 versus side scatter and 3 studies [8, 9, 24] that used forward and side scatter as a gating strategy. The gating strategy in the remaining studies was not clearly defined. All studies had reported the type of anticoagulants used: 27 used EDTA, 1 used Acid Citrate Dextrose (ACD), and 3 used heparins.

Overall, a total of 7,245 healthy adults were involved in the systematic review, with 4,116 healthy adults aged between 18 and 95 years included in the meta-analysis.

Meta-analyses of age-dependent immune changes

The meta-analyses based on three age groups (see Table S11) showed a similar trend in both percentage and absolute immune cell numbers. We found that the pooled mean of CD3 + T cell decreased in numbers from 1517 cells/\(\mu\) L (or 71.11%) in age < 40 years old to 1337 cells/\(\mu\) L (or 61.13%) in age \(\ge\) 65 years old; CD8 + T cell numbers decreased from 611 cells/\(\mu\) L (or 27.79%) to 525 cells/\(\mu\) L (or 23.88%); CD4/CD8 ratio increased from 1.54 to 1.9; B cell numbers decreased from 244 cells/\(\mu\) L (or 12.99%) to 217 cells/\(\mu\) L (or 8.83%); NK cell numbers increased from 305 cells/\(\mu\) L (or 13.69%) to 355 cells/\(\mu\) L (or 23.59%). Nevertheless, CD4 + T cells (either absolute numbers or percentages) showed no specific pattern across the three age groups.

The meta-analysis considering continuous age in years, showed that the relationship between immune cell numbers and age mirrors the pattern observed in the aggregated results across three age groups (see Fig. 2 and Table S12).

Fig. 2
figure 2

Meta-analysis of absolute numbers and percentage of immune cell subsets according to continuous age (years), including the pooled mean (red line) and the pooled normal range in 2.5th -97.5.th percentile (skyblue region) with the liner curve (blue line) fitting of Taiwan’s data (black hollow dots). **p\(\le\) 0.001 *p\(\le\) 0.05

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Most numbers of immune cell subsets changed with specific pattern across age, except for CD4 + T cells. From ages 18 to 94, the pooled mean of CD3 + T cell numbers decreased from 1512 cells/\(\mu\) L (74.1%) to 1405 cells/\(\mu\) L (62.7%) with advancing age (Fig. 2a and g); CD8 + T cell numbers decreased from 598 cells/\(\mu\) L (or 28.4%) to 507 cells/\(\mu\) L (or 23.9%) (Fig. 2c and i) corresponding with an increased CD4/CD8 ratio from 1.478 to 1.873 (Fig. 2d); NK cell increased from 279 cells/\(\mu\) L (or 10.2%) to 352 cells/\(\mu\) L (or 23.6%) (Fig. 2e and j); B cells decreased from 276 cells/\(\mu\) L (or 13.9%) to 245 cells/\(\mu\) L (or 9.8%) (Fig. 2f and k). Despite an increase in the absolute count of CD4 + T cells from 843 to 910 cells/μL, their percentage declined from 40.9% to 38.8% (Fig. 2b and h). This discrepancy in trends between absolute numbers and percentages suggests no specific age-related changes in CD4 + T cells. Lastly, the linear regression analysis of the Taiwanese immune cell subsets aligned with the normal range established by our meta-analysis, confirming the consistency of trends observed in Taiwan's data with the aggregated findings (see Fig. 2 and Table 2).

Table 2 Summary of immune cell subsets change associated with aging
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Discussion

In this study, we observed significant age-related changes in both adaptive and innate immune cell subsets among healthy adults. Key findings included a decrease in CD8 + T cells and an increase in the CD4/CD8 ratio with aging, accompanied by a notable increase in NK cells and a decrease in B cells. Interestingly, the numbers of CD4 + T cells appear less impacted by the aging process compared to CD8 + T cells and other subsets of immune cells. Additionally, our findings from Taiwan revealed a clear reverse trend of naïve and memory functional subsets in both CD4 + and CD8 + T cells. We also established a robust reference range of peripheral blood immune cell subsets in Taiwanese healthy adults. Finally, we complemented the original data with a systemic review and meta-analysis of 12 studies to estimate the normal ranges for major immune cell subsets. Our analyses concluded the key immunosenescence characteristics beyond our original data, significantly enhancing the generalizability of our findings and aligning the results with most studies in the meta-analysis. The results and analyses are of high translational and clinical value and can serve as a basis for implanting immune monitoring in preventive and therapeutic scenarios.

A clear reverse trend of naïve and memory functional subsets in both CD4 + and CD8 + T cells could be explained by a process known as thymic involution. Thymic involution significantly impacts cellular aging by reducing the number of naïve T cells and narrowing the diversity of the T-cell receptor (TCR) repertoire, which contributes to a weakened and exhausted immune system [44]. The age-related decrease in naïve T cells may partially explain the reduced capacity to respond to new antigens and the increased susceptibility to malignancies in older adults [45, 46]. Furthermore, as reviewed by Yu et al., the increase in highly differentiated memory T cell subsets may have pathogenic potential in cardiovascular diseases by releasing pro-inflammatory cytokines and cytotoxic mediators [47]. These findings highlight the critical role of lymphocyte numbers of changes in various age-related diseases and conditions. However, recent studies suggest that many age-related changes in the immune system may be beneficial adaptations to the normal aging process [3]. For example, thymic involution may be essential for minimizing energy expenditure in an organ that, while not crucial for survival, requires significant energy to maintain; similarly, an increase in memory subsets of lymphocytes may enhance the ability to combat pathogens more efficiently. Although immunosenescence may contribute to age-related diseases, its overall impact on aging remains a debated topic.

The finding that CD4 + T cells showed greater resistance to age-related phenotypic changes compared to CD8 + T cells aligned with results from prior studies [48, 49]. While the total number of CD4 + T cells remained relatively stable, we observed a significant change in the composition of memory subsets with aging. That included a decrease in naïve subsets and a corresponding increase in memory subsets in both CD4 + and CD8 + T cells with a more pronounced magnitude observed in CD8 + T cell. This may also explain why the ratio of CD4 + to CD8 + T cells significantly increased with age. However, the exact mechanisms involved remain unclear.

With aging and increased exposure to chronic antigens, previous research has also suggested an expansion of CD28 − CD57 + T cells, predominantly within the CD8 + compartment [20, 50], a trend that was also confirmed in our study. The CD8 + CD28 − CD57 + T cells are characterized as advanced-differentiated cells, with significantly shortened telomeres, reduced proliferation capacity, and a shorter replicative lifespan [50]. These characteristics may contribute to immune incompetence, susceptibility to persistent viral infections, and reduced response to anti-cancer treatment or vaccination in older adults [51]. Along with thymic involution, the age-related increase of these senescent subsets also results in a marked decrease in antigenic diversity in older individuals [52]. This evidence highlights the importance of monitoring CD8 + CD28 − CD57 + T cells in aging, and our data suggest that their decline is a typical feature of immune aging. Of note, many aforementioned aging-associated changes in T cells (especially CD8 +) have been associated with CMV infection [53]. CMV infection drives the expansion of CMV-specific memory T cells with aging, and these cells frequently reside in the late-differentiated CD28-CD57 + compartment. Despite numerous research, CMV's dynamic, bidirectional interaction with immunosenescence remains a poorly understood complex phenomenon [54].

Besides T cells, alterations in the numbers of NK cells are also a significant marker of immune aging. A remodeling of NK cell subsets characterized by a decrease in the number of the more immature CD56high NK cells and an increase of CD56low subsets was clearly shown. Few studies included in our systematic review have evaluated the numbers of CD56high and CD56low NK cells separately. CD56low NK cells are mature subsets mainly involved in cytotoxic activities, while CD56high NK cells are progenitor subsets but primarily responsible for producing cytokines like TNF-α and IFN-γ [55]. During the aging process, CD56low NK cells continue to differentiate and increase the expression of CD57 and CD16 markers, activating receptors such as NKG2D, and experience telomere shortening [56]. Previous research based on single-cell RNA expression analysis also found the context of aging in NK cells, with expansion of low cytotoxic subsets and decreased virus defense responses [57]. In contrast to T cells, although we already know that the levels of CMV antibodies are closely associated with the aging of T cell immunity [48], whether CMV serostatus affects the numbers of NK cells is still controversial [56]. Overall, there is a great number of knowledges regarding the alterations in NK cells numbers and function as individuals age; however, there remains a gap in understanding the underlying factors that contribute to these changes.

Aside from NK cells, other subsets of innate immune cells showed significant age-related changes. There was an increase in the absolute number of monocytes, while dendritic cells remained unchanged. This aligned with the general expectation that peripheral cells of innate immunity may be better preserved with age compared to the adaptive immune system [3, 41], while their changes contribute to an age-dependent increase in chronic inflammation, or “inflammaging” [58].

Compared to other immune cells, conflicting results have been shown regarding peripheral B cells. B cells are antigen-presenting cells (APCs) essential for humoral immunity by secreting antibodies and modulating T cell activation. Our original data showed an age-dependent decrease in total B cell number, but subset homeostasis was unchanged. One study included in our review has found that B cell subsets are increased in the peripheral blood of healthy elderly adults [24], while others have not [12, 16]. However, functional changes in B lymphocytes associated with aging have been reported. Frasca et al. [59] observed that although the frequencies of influenza vaccine-specific memory B cells and plasmablasts were similar in young and elderly adults, older individuals exhibited a diminished fold-increase in serum titers following vaccination. This suggests a compromised ability in older adults to differentiate from memory B cells to plasma cells. Consequently, older individuals may show a reduced capacity to generate antibodies even after immunization, underlining a critical aspect of immunosenescence. Longitudinal studies involving standardized methods for B cell count and functional analysis will be critical to uncovering additional effects of aging on B cell immunity.

Our study also demonstrated distinct age-dependent changes in innate and adaptive immunity between males and females. The significant increase in the CD4/CD8 ratio, memory B cells, and CD4 T cell subsets in females and the increase in monocyte numbers in males suggests hormonal influences. Previous reports [60] indicate that estrogen enhances both cell-mediated and humoral immune responses, and genes associated with adaptive immunity are elevated in post-pubertal females [61]. Recently, a study investigating the epigenome signature of aging revealed that 15 times more monocyte epigenetic loci were activated in men than women. In contrast, B cell-specific loci were significantly inactivated in men [62]. Additionally, our results revealed an increased CD8 + CD28-CD57 + T cells in males compared to females. As a double-edged sword, the faster aging of the T cell compartment in males may benefit autoimmune conditions but also contribute to increased immunosenescence and risk of malignancy [61].

Our original study includes a detailed analysis of immune cell subsets and employs a robust systematic review and meta-analysis to integrate existing related research, thereby increasing the overall confidence and validation of our results. Evidently, age and gender differences should be considered when interpreting immune profiles in clinical practice when immune monitoring is necessary. Therefore, establishing a reference range is crucial for accurately assessing diverse patients in clinical settings, particularly for managing age-related immune changes in geriatrics and immunocompromised hosts. For example, the number of circulatory naïve T cells could predict the efficacy of influenza vaccination [63] and severity of COVID-19 infection [6]. Measuring immunosenescence provided a better prediction of mortality [64] than using well-established cardiovascular disease risk factors.

Nevertheless, several limitations should be noted. First, we only examined immune cell subsets in peripheral blood and did not analyze tissues like the spleen, lymph nodes, or interstitial mucosa, where most immune cells are located. Additionally, we did not investigate genetic factors or CMV serology status, which could affect immune responses based on prior studies. Nevertheless, given the high prevalence rate of CMV infection in the overall population, a large comprehensive study investigating a large population of only CMV-uninfected individuals will be difficult to execute. Lastly, estimations of immune cell numbers can vary depending on the gating strategy used and can also differ between laboratories, potentially introducing heterogeneity into meta-analyses.

Not all elderly individuals experience age-related diseases, as evidenced by the healthy older adults in our study and, remarkably, by centenarians [65]. Research has shown that centenarians exhibit well-balanced levels of inflammatory and anti-inflammatory responses and infections do not necessarily shorten their lifespan. Future studies should try to monitor an individual’s immune system over its lifespan, focusing on the association between the immune system and ideal aging, which involves maintaining a dynamic and resilient balance in response to various events over time. In contrast, an inability to adapt could lead to maladaptation, manifesting as increased severity of age-related diseases and increased mortality in both acute and chronic healthcare settings.

Conclusions

Since healthy aging involves maintaining a dynamic equilibrium of the immune system with the environment, it becomes essential to identify phenotypic changes in circulating immune cells across age in healthy adults. Examining a total of 363 healthy Taiwanese adults, this study thus established detailed reference ranges of peripheral immune subsets for different age groups to provide values for clinical decision-making and contribute to the effective management of various age-related diseases. Besides novel observations, results were also validated through a rigorous systematic review and meta-analysis.

Data availability

The authors will provide the raw data supporting the conclusions of this article upon request without restriction.

Abbreviations

CMV:

Cytomegalovirus

EDTA:

Ethylenediaminetetraacetic Acid

DC:

Dendritic Cells

HLA-DR:

Human Leukocyte Antigen—DR isotype

IFN-γ:

Interferon-gamma

TNF-α:

Tumor Necrosis Factor-alpha

NK:

Natural Killer) cells

PBMCs:

Peripheral Blood Mononuclear Cells

PECAM-1:

Platelet Endothelial Cell Adhesion Molecule-1

Q-Q plots:

Quantile-Quantile plots

RA:

Retinoic Acid

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Acknowledgements

The authors express their gratitude to Prof. Chyong-Mei Chen for her valuable expertise and support in conducting the meta-analysis. The authors also thank Far-Eastern Memorial Biobank for providing samples for technical controls and as a storage facility for excess study samples.

Funding

This study was supported by Taiwan Ministry of Science and Technology grant 106–2628-B-010 and Far Eastern Memorial Hospital-National Yang Ming University collaboration grant 107DN11, 108DN18, and 110DN18.

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Author notes

  1. Syuan-Ting Chang and Yi-Fang Chuang contributed equally to this work.

Authors and Affiliations

  1. School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Syuan-Ting Chang

  2. Institute of Public Health, National Yang Ming Chiao Tung University, Taipei, Taiwan

    Yi-Fang Chuang

  3. Department of Psychiatry, Far Eastern Memorial Hospital, New Taipei, Taiwan

    Yi-Fang Chuang

  4. Graduate Program in Biomedical Informatics and Graduate Institute of Medicine, Yuan Ze University, Taoyuan, Taiwan

    Ai-Hsien Li, Yang-Teng Fan, Man-Ru Liao & Yen-Ling Chiu

  5. Department of Medical Research, Far Eastern Memorial Hospital, New Taipei, Taiwan

    I-Yu Chen, Ruo-Wei Hung & Yen-Ling Chiu

  6. Science Officer, Cancer Epidemiology Unit, Nuffield, Department of Population Health , University of Oxford, Richard Doll Building, Old Road Campus, Headington, Oxford, OX3 7LF, UK

    Tienyu Owen Yang

  7. Health Management Center, Far Eastern Memorial Hospital, New Taipei, Taiwan

    Ai-Hsien Li & Man-Ru Liao

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Contributions

YLC and YFC designed and initiated the study. YLC, IYC, and RWH recruited study populations. IYC and RWH carried out experiments. STC analyzed the experiments. MRL assisted in the analysis of experiments. AHL, YTF, and TOY helped with the execution and analysis of the study. STC, YFC, and YLC interpreted the data and wrote the manuscript. All authors contributed to reading and completing the manuscript and have approved the final version.

Corresponding author

Correspondence to Yen-Ling Chiu.

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Ethics approval and consent to participate

All participants provided informed consent, and the study received approval from the Institutional Review Board of the National Yang Ming Chiao Tung University (YM109137E) and the Far Eastern Memorial Hospital Research Ethics Committee (108058-E).

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The authors declare no competing interests.

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Chang, ST., Chuang, YF., Li, AH. et al. Age-dependent immune profile in healthy individuals: an original study, systematic review and meta-analysis. Immun Ageing 21, 75 (2024). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12979-024-00480-x

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Keywords

Immunity & Ageing

ISSN: 1742-4933

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