Establishment of the reference intervals of lymphocyte subsets for healthy Chinese Han adults and its influencing factors

Introduction

The maintenance of the normal immune function in the body depends not only on humoral immunity but also on cell-mediated immune responses. Various immune cells are involved in elaborate immune regulation, especially lymphocyte subsets, to produce a reasonable immune reaction. The number of lymphocytes and their subset ratios may change when an immune malfunction occurs in the states of allergic diseases, autoimmune diseases, immunodeficiency diseases, viral infections, and cancer treatments (1-5). Unfortunately, when physicians assess the results of various patients’ examinations, they gravitate towards classical indicators, those from clinical chemistry or humoral immunity, rather than from cellular immunity. One of the most common detections in cellular immunity is lymphocyte subsets, including T lymphocytes (CD3⁺), B lymphocytes (CD3⁻CD19⁺), helper/inducible T lymphocytes (CD3⁺CD4⁺), inhibitory/cytotoxic T lymphocytes (CD3⁺CD8⁺), and natural killer (NK) cells (CD3⁻CD16⁺ or CD56⁺).

One reason cellular immune detections have not been given enough attention is their direct connection to the disease, and another reason could be the reference interval. For the former, fortunately, increasing research is being conducted. Once the peripheral blood white blood cell count and its classification are considered as traditional biomarkers in infectious diseases, in addition to C reactive protein (CRP), procalcitonin (PCT), and IL-6 (6). And the lymphocyte subsets detection has become one of the essential immune monitoring methods for patients with COVID-19 during this pandemic. The lymphocyte subsets provide clinical opportunities to understand the body’s cellular immunity condition and evaluate its immune function or immunoreactivity, which plays a vital role in disease evaluation, prognosis evaluation, and efficacy assessment (7-12). Although most of the early studies are from China, the reference intervals of lymphocyte subsets are not homogenous (7,11). Because the reference intervals of lymphocyte subsets used in laboratories are mainly derived from reagent instructions, textbooks, and literature reports, they may be different. Only a few laboratories have established single-center reference intervals, which are not universally representative due to their potential differences in recruitment criteria and detection systems, including using different instruments and detecting methods. It is challenging to establish reference intervals of peripheral blood lymphocyte subsets due to many factors that affect the results, such as recruitment criteria, the patients’ age and gender, region, and detection systems (13-23). However, it is still necessary to establish appropriate reference intervals for as vast applications as possible.

To establish reference intervals of lymphocyte subsets widely suitable for healthy Chinese Han adults, healthy participants through three centers distributed in different regions (Beijing from northern China, Shanghai from eastern China, Guangzhou from southern China) were recruited in this study. The percentage and absolute count of lymphocyte subsets in peripheral blood samples were simultaneously detected in each center’s three flow cytometers via the single-platform detection method. Multi-center, age-specific, and gender-specific reference intervals of lymphocyte subsets were established, considering the influence of instruments on lymphocyte subset results. We present the following article in accordance with the MDAR reporting checklist (available at https://dx.doi.org/10.21037/atm-21-4031).

Methods

Study design and participants

The present study was conducted at three centers in mainland China: Zhongshan Hospital Affiliated to Fudan University (eastern China), Peking University First Hospital (northern China), and The Second Affiliated Hospital of Guangzhou University of Chinese Medicine (southern China). A total of 1,112 Chinese Han adults aged 18–59 from March 2019 to November 2019 were screened, and 496 were enrolled for analysis. The ethics committees of each center approved this study [ethics approval number: Zhongshan Hospital Affiliated to Fudan University, B2018-115; Peking University First Hospital, (2018) Research No. (145); Guangdong Provincial Hospital of Traditional Chinese Medicine, BF2018-087-01], and all participants provided written informed consent. All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013).

Inclusion criteria

The inclusion criteria were as follows: (I) participation in physical examination; (II) completion of questionnaire survey prompts health assessment.

Exclusion criteria

The exclusion criteria were as follows: (I) patients infected with hepatitis B virus (HBV), hepatitis C virus (HCV), AIDS (HIV), or syphilis; (II) infection with other acute and chronic infectious diseases; (III) autoimmune disorders, allergic diseases, or cancer; (IV) patients had taken drugs that affect the immune system within 3 months [including glucocorticoids, alkylating agents, antimetabolites, nucleotide reductase or tyrosine kinase inhibitors, botanicals, rapamycin target molecule inhibitors, biological drugs, monoclonal antibodies, and so on (17)]; (V) vaccination within 4 weeks; (VI) patients had received a blood transfusion within one year; (VII) patients had received blood donations within 6 months.

Study methods

Blood collection and sample processing

Approximately 2 mL of blood was collected from each fasting participant in ethylene diamine tetraacetic acid (EDTA) tubes (BD, Catalog No. 367856) and was transported at room temperature to the clinical laboratories of the 3 centers. Within 48 h after being collected, the anticoagulated whole blood sample was dyed and tested in the flow cytometer. To ensure that the number of lymphocytes was within a linear range, a white blood cell (WBC) differential count was performed before the sample was stained.

Flow cytometry

Flow cytometry tests were performed in the clinical laboratories of the 3 centers. In this study, 3 flow cytometers and their corresponding software were used, including FACSCalibur^TM and Multiset^TM software (BD), BD FACSCanto^TMII and FACSCanto^TM software (BD), and BD FACSLyric^TM, and FACSuite^TM software (BD). The antibody detection kit, Multitest^TM IMK kit (BD, Catalog No. 340503), contains antibody Panel A [CD3-fluorescein isothiocyanate (FITC)/CD8-phycoerythrin (PE)/CD45-peridinin-chlorophyll protein (PerCP)/CD4-allophycocyanin (APC) antibodies] and antibody Panel B (CD3-FITC/CD16+56-PE/CD45-PerCP/CD19-APC antibodies). Multitest^TM IMK kit and Trucount^TM Absolute Counting Tubes (BD, Catalog No. 340334) were used to identify and quantify the percentage and number of lymphocyte subsets in erythrocyte-lysed whole blood samples.

To obtain the same test quality across the 3 centers, the flow cytometer’s daily setup and automatic quality control were established before testing the selected samples. The same batch of quality control blood (BD Multi-Check Control, Catalog No. 349702; BD Multi-Check CD4 Low Ctrl, Catalog No. 349705) was used to perform quality control on the flow cytometers to meet the quality evaluation standards, including: (I) the results of quality control products were all in the fluctuation range of quality control; and (II) the results among laboratories were comparable. Meanwhile, to compare the flow cytometry results between different centers, the same batch of BD quality control blood was stained and measured 10 times following the laboratory standard operating procedures (SOPs) according to the instrument and reagent instructions. We took the mean (mean) ±2 standard deviation (SD) as the quality control range, the mean I was defined as the mean value of the measured value, SD = mean × line standard coefficient of variation (CV); when the positive percentage of the test index was ≥30%, the CV value was 8%; when the positive rate was less than 30%, the CV value was 15% [according to the “YY/T 0588-2017 Flow Cytometer Industry Standard implemented on 2018-12-1” (24)]. When 80% and above (≥80%) of the actual measured value fell within the quality control range, it was indicated that the quality control had passed.

The samples were detected according to the SOP for flow cytometry. Briefly, 20 μL antibody Panel A and 20 μL antibody Panel B reagents were added to Trucount A and Trucount B respectively; 50 μL anticoagulated whole blood was added to each tube by reverse aspiration method, they were gently mixed and incubated at room temperature (20–25 °C) in the dark for 15 min, then (1×) lysing solution (diluted by BD FACS™ Lysing Solution 10X Concentrate, Catalog No. 349202) 450 μL was added to the 2 tubes respectively, mixed well, incubated at room temperature for 15 min in the dark, and then tested with the flow cytometer. If not examined immediately, the stained sample was stored in the dark at room temperature and tested within 24 h. Flow cytometers were used to collect the cells, and the software was used to determine the percentage and number of each lymphocyte subset.

Gating Strategy. Tube A: SSC/CD45 is used to gate the lymphocyte population (SSC^lowCD45^bright), SSC/APC is used to gate the Beads, SSC/CD3 is used to gate the CD3⁺ T lymphocytes in the lymphocyte population, and then T lymphocytes are distinguished by CD4⁺CD8⁻ (helper/inducible T lymphocytes) cell group, CD4⁻CD8⁺ (inhibitory/cytotoxic T lymphocytes) cell group, CD4⁺CD8⁺ cell group, CD4⁻CD8⁻ cell group using CD4/CD8. Tube B: SSC/CD45 is used to gate the lymphocyte population (SSC^lowCD45^bright), SSC/APC is used to gate the Beads, SSC/CD3 is used to gate the CD3⁻ lymphocytes in the lymphocyte population, and then CD19/CD16&56 is used to distinguish CD3⁻ lymphocytes to CD19⁺CD16&56⁻ (B) Lymphocyte population, CD19⁻CD16&56⁺ (NK) lymphocyte population.

Statistical analysis

Statistical analysis was carried out using SAS 9.4 software (SAS Institute Inc., Cary, NC, USA). According to the “WST 402-2012 Reference Intervals for Clinical Laboratory Test Items” implemented on 1 August 2013 (25), the outliers of the 3 flow cytometry test results: CD3⁺, CD3⁺CD4⁺, CD3⁺CD8⁺, CD3⁻CD19⁺, CD3⁻CD16⁺CD56⁺ absolute counts (pcs/µL) and percentage (%) and the measured values of CD4⁺/CD8⁺ ratio were arranged in ascending order to obtain adjacent measurements. The maximum value of the value difference was compared with 1/3 of the total distance (R) of all the indicator’s measured values. When the results were greater than R/3, it was considered an outlier and subsequently eliminated. We compared the test results of the 3 flow cytometers: if the data was normally distributed, the analysis of variance (ANOVA) test with a completely random design was used, and the pairwise comparison between the groups used the least significant difference (LSD) test; if the data was non-normally distributed, the Kruskal-Wallis test was used for comparison between the groups, and Nemenyi test was used for pairwise comparison between groups. We used nested ANOVA to test the influence of the above factors on the value of lymphocyte subsets according to the hierarchical relationship of gender-age-region, and combined the scatter plot and Z test results to comprehensively consider whether to group to establish reference intervals for lymphatic subsets (17-19). We used P2.5–P97.5 to set the reference intervals of each lymphatic subset.

Results

Enrollment

This study screened 1,112 potential research participants, of which 504 were successfully enrolled, and 608 were excluded due to abnormalities in the questionnaire survey, physical examinations, and laboratory tests. Among the participants, 8 cases (1.6%) were excluded due to not using or misusing the test reagents and equipment specified, resulting in a final sample size of 496 cases (98.4%) meeting the protocol for analysis. The screening, enrollment, elimination, and final analysis of participants are displayed in Figure 1, and the demographic characteristics of the 496 participants are summarized in Table 1.

Figure 1 Collection, screening, elimination, and final enrollment of participants.

Quality control and comparison of the results in 3 flow cytometers

The quality control of 3 flow cytometers in each center was completed before starting the experiment, and all instruments passed the quality control (Table S1). The detection results of lymphocyte subsets appeared to have no statistical difference among the 3 devices except for the absolute count of CD3⁻CD16⁺CD56⁺ cells and CD3⁺ cells (%). Further pairwise comparisons of the absolute count of CD3⁻CD16⁺CD56⁺ cells and CD3⁺ cells (%) showed no statistical difference. Therefore, the detection results of the 3 flow cytometers were averaged for subsequent analyses. Also, one data of CD3⁻CD19⁺ cells (%) in FACSCanto^TMII was eliminated as outliers before analysis, but the enrolled number was not changed.

Age, gender, and geographic distribution of lymphocyte subsets

Figure S1 shows the scatter plot of the age and gender distribution of each lymphocyte subset. It can be seen from the figure that women had higher CD4⁺ cells (%) than men, while men’s absolute counts of CD3⁻CD16⁺CD56⁺ cells were higher than women’s; the absolute counts of CD3⁻CD19⁺ cells, CD3⁻CD19⁺ cells (%), and CD4⁺/CD8⁺ ratios were age-specific. In detail, the absolute counts and percentages of CD3⁻CD19⁺ cells in men under 30 years old were higher than those in the over 30 years old group, and the CD4⁺/CD8⁺ ratio of people over 40 years old was higher than that of people under 40 years old. The results of the nested ANOVA showed that gender affected the lymphocyte subsets results except for the number of CD3⁺ cells and CD3⁺CD8⁺ cells (%); the absolute count of CD3⁺ cells, CD3⁺CD4⁺ cells (%), the percentage and number of CD3⁺CD8⁺ cells, and CD4⁺/CD8⁺ ratios were statistically different among different age groups; besides, the absolute counts of CD3⁻CD19⁺ cells in 3 regions had statistical differences (Table S2). The Z test results and difference analysis by gender are shown in Table S3: the absolute count of CD3⁻CD16⁺CD56⁺ cells, the ZZ* difference of CD3⁻CD16⁺CD56⁺ cells (%) and CD3⁺CD4⁺ cells (%) are greater than 0, which implies that the difference was statistically significant. In summary, there was a gender difference in the absolute counts of CD3⁻CD16⁺CD56⁺ cells and CD3⁺CD4⁺ cells (%), while the CD4⁺/CD8⁺ ratio had an age difference; the above indicators need to be identified to establish reference intervals.

Reference intervals of lymphocyte subsets

Refer to Table 2 for the reference intervals of lymphocyte subsets for healthy Chinese Han adults.

Discussion

Previous studies have shown that many laboratories have established reference intervals of lymphocyte subsets based on the region or the laboratory. These studies have used different detection platforms, detection methods, and enrollment criteria; moreover, most have been single-center studies, thereby not representative, which results in limitations. This study was the first multi-center study on the reference intervals of lymphocyte subsets for healthy Chinese Han adults in different regions with 3 flow cytometers (FACSCanto^TMII, FACSLyric^TM, FACSCalibur^TM) in each center using a single-platform detection method. The influencing factors of reference intervals, such as detection platform and method, age, and gender of the detection participant, were discussed. We found that there was no significant difference in the results from different instruments when using the standardized methods; the influence of gender on lymphocyte subsets was mainly in the percentage and absolute count of CD3⁻CD16⁺CD56⁺ cells and the percentage of CD4⁺ cells (%), while age mainly affected the ratio of CD4⁺/CD8⁺.

Methods and instruments

Compared with other studies, we found that the detection method’s influence and detection platform on the results cannot be ruled out. The cytometry-based absolute count methods for immune cells mainly include a single-platform method and a dual-platform method. To obtain known volume (such as 50 μL) absolute count of lymphocyte subsets in whole blood, single-platform refers to the use of known concentrations of microspheres to directly obtain the absolute count results of each lymphocyte subset, mainly including the use of BD Trucount tubes, and Beckman-Coulter’s Flow-Count Fluorospheres. In contrast, the dual-platform method first obtains each lymphocyte subset percentage through flow cytometry and then multiplies it by the number of lymphocytes obtained by the hemocytometer (26,27). Compared with dual-platform detection used in previous research, the single-platform method is not affected by the blood cell counter’s detection performance. It has fewer practical steps and more effortless operations. In recent years, the single-platform method has been applied more frequently (18,24). The detection platform mainly refers to the detection instruments used, including the flow cytometer and blood cell counter. In this study, to avoid the excessive influence of detection methods and platforms on the results, three flow cytometers common in clinical were used in the 3 centers. A standardized single-platform method was used. As a result, there was no significant difference in the detection results between the instruments, indicating that the results of lymphocyte subsets are comparable if the quality control and the detection method are consistent. In terms of detection methods, previous studies have shown that laboratories using the dual-platform method may have inter-chamber deviations due to the counting results obtained from different blood counters (27). Still, this difference can be reduced by an optimized dual-platform method to achieve the same precision as the single platform method (28). At present, there is no comparative study on the use of different single-platform detection methods or the duplicated detection method with other instruments. However, compared to using various detection methods, the results from different flow cytometers using the same method may be more confidently interpreted. In addition, the flow cytometric single-platform method could be used to enumerate any cell and its subsets, including monocytes and granulocytes, but these require the use of relative antibody panels.

Gender

Many previous studies have shown that gender is one of the influencing factors of lymphocyte subsets. In this study, we found that women’s CD4⁺ cells (%) were higher than men’s, while men’s absolute counts of NK cells (CD3⁻CD16⁺CD56⁺) were higher than women’s, which is consistent with the results of some previous studies. Studies in South Korea (19), Qatar (21), Brazil (22), Italy (29), and Israel (30) have also observed gender differences in CD4⁺ cells (%) and the absolute counts of NK cells. However, studies in Qatar (21), Brazil (22), Italy (29), and Israel (30) reported that there are gender differences in the absolute counts of CD4⁺ cells, as well as in Singapore (15), Hong Kong (18), South Korea (19), and Italy (29). The results of gender differences in NK cells (%) reported in the Israeli (31) study were not observed in this study. In addition, this study did not observe the results of gender differences in CD3⁺ cells (%), CD3⁺ cell counts, and CD4⁺/CD8⁺ ratios reported in some studies (16,17,19,20,22,32). The inconsistency of these results may be attributed to differences in the inclusion criteria, sample size, and age distribution of the participants.

Age

The number of lymphocyte subsets presents different trends with age. This study showed that CD3⁺CD4⁺ cells increased slightly with age, while CD3⁺CD8⁺ cells showed a downward trend. The CD4⁺/CD8⁺ ratio of people over 40 was significantly higher than that of people under 40. As far as CD4⁺ cells are concerned, reports in Malawi (5), Spain (33), Israel (31), and Cuba (29) showed the same age change trend as this research, while the study results of Hong Kong (16), on the contrary, demonstrated that CD4⁺ cells decreased with age. Studies in Singapore (13), Germany (14), Israel (31), and Cuba (29) are consistent with the results of this study in terms of CD8⁺ cells, while studies in Spain (33) showed that CD8⁺ cells were positively correlated with age. In terms of the CD4⁺/CD8⁺ ratio, the studies of Germany (14), Spain (33), and Cuba (29) had the same age trends as this study. The possible explanation for the change of lymphocyte subsets with age in this study is that the telomere length of CD3⁺CD4⁺ cells is relatively constant, and less affected by age. Simultaneously, the telomere length of CD3⁺CD8⁺ cells is more susceptible to erosion with immune aging. Its sensitivity to telomerase induction also decreases with age; thereby, its number is reduced with age (30).

Races and regions

People of different races and regions may have differences in the number of lymphocyte subsets due to genetic factors, ecological environment, lifestyle, and nutritional status. In our study, except for the differences in CD19⁺ cells in Guangzhou, the differences of other indicators of lymphocyte subsets in Shanghai, Beijing, and Guangzhou were not found to be statistically significant. Although there was no gender difference in CD19⁺ cells, we still could not rule out that this difference in Guangzhou was caused by gender bias in the enrollment. Horizontal comparison of T lymphocyte subsets of different races in China showed that the absolute counts of CD3⁺, CD4⁺, and CD8⁺ T cells in the Guangxi Han population were slightly higher than those in the Guangxi Zhuang population. Still, the difference is not statistically significant (34). The T lymphocyte subsets tests using the same Trucount tube showed differences in T lymphocyte subsets between the Han and other ethnic groups in China, such as the Yunnan Dai population, the Sichuan Yi population, and the Hunan Miao population, were not statistically significant (34).

Similarly, Brazil’s analysis of lymphocyte subsets of people in different regions of the country showed that except for CD3⁺ and CD4⁺ cells with statistical differences in particular areas, the differences in lymphocyte subsets between most places were not statistically significant (35). A horizontal comparison of the results of this study with similar single-platform studies found that the absolute counts of lymphatic subpopulations studied in Qatar (19), especially T lymphocyte subsets, were higher than the results of this study. However, the results of Singapore (13) showed that the absolute counts and percentages of the lymphocyte subgroups, especially the T cell subgroups, and the CD4⁺/CD8⁺ ratio were similar to this study. In contrast, the research results on T cell subgroups in India (36) and Brazil (35) showed that the absolute counts of CD4⁺T cells were higher than in this study. The differences between the results of these studies may be related to differences in ethnic composition. For example, nearly 80% of the participants in the Singapore study (13) were of Chinese descent, which was similar to the ethnic composition of this study, while the ethnic composition of participants in other studies was entirely different from this study, the study participants in Qatar (19) were all Arabs, and the studies in Brazil (35) and India (36) were all multi-ethnic populations. These differences may also be due to the significant differences in the age and gender distribution of the participants in these studies. In particular, 90% of the participants in Qatar (19) study were under 45 years old, and 64% were male; more than 85% of the participants in the Indian (36) study were under 40 years old, and more than 60% of the participants in the Brazil (35) study were men. These confounding factors make it difficult for us to simply attribute the differences between lymphocyte subgroups studied worldwide to differences in race. The differences in lymphocyte subsets between different races need to be confirmed through an international multi-center study that strictly controls various confounding factors.

Limitations of our study include strict enrollment criteria, restriction of age, and rough lymphocytic grouping. The strict selection criteria lead to the high elimination rate at the screening stage, and the sample size analyzed was relatively small. In this study, children, juveniles, and people over 60 years old were not included, and we only distinguished Th/Tc cells, B cells, and NK cells. Moreover, despite having little effect on the reference intervals, gender bias may have existed in Guangzhou.

Conclusions

Despite the limitations, the present study established reference intervals of healthy Chinese Han adults’ lymphocyte subsets, considering detection methods and instruments, gender, and age. This study was the first multi-center study, simultaneously using 3 flow cytometers in each center with a single-platform method to establish reference intervals of lymphocyte subsets. Gender and age were the main influencing factors. Although the ethnicity and region of the participants may also have an impact, more research is needed to clarify the extent of such influence. Therefore, it is recommended that laboratories and clinics in various regions cooperate to establish reference intervals of lymphocyte subsets used for cellular immune monitoring with a similar and standardized method to facilitate more accurate and reasonable clinical decision-making. Future research will expand the age range and establish reference intervals for more defined immune cell subsets than T cells, B cells, and NK cells.

Acknowledgments

We gratefully acknowledge all the individuals for their participation in this study.

Funding: Financial support for this work was provided by the Shanghai Municipal Health Commission, Constructing Project of Clinical Key Disciplines in Shanghai (shslczdzk03302), and Beckton Dickinson Medical Devices (Shanghai) Co., Ltd.

Footnote

Reporting Checklist: The authors have completed the MDAR reporting checklist. Available at https://dx.doi.org/10.21037/atm-21-4031

Data Sharing Statement: Available at https://dx.doi.org/10.21037/atm-21-4031

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/atm-21-4031). The authors report that the financial support for this work was provided by the Shanghai Municipal Health Commission, the Constructing Project of Clinical Key Disciplines in Shanghai (shslczdzk03302), and Beckton Dickinson Medical Devices (Shanghai) Co., Ltd (payments were made to BD FACSLyric^TM instruments); the receipt of equipment is from Beckton Dickinson Medical Devices (Shanghai) Co., Ltd (BD FACSLyric^TM instruments). The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The ethics committees of each center approved this study [ethics approval number: Zhongshan Hospital Affiliated to Fudan University, B2018-115; Peking University First Hospital, (2018) Research No. (145); Guangdong Provincial Hospital of Traditional Chinese Medicine, BF2018-087-01], and all participants provided written informed consent. All procedures performed in this study involving human participants were in accordance with the Declaration of Helsinki (as revised in 2013).

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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(English Language Editor: J. Jones)