For the blinding of the present study, the immunological indicators were analyzed by external collaborators who had no knowledge about the procedures of the present study. The researcher responsible for data collection and the participants had no knowledge of the classification of their fitness level. After the recruitment of the sample, 48-h before the tests, the volunteers were screened through a structured anamnesis. For characterization purposes, the volunteers who met the inclusion criteria completed a body composition analysis using dual-energy X-ray absorptiometry (DXA) and were instructed not to perform strenuous physical exercises in the 24 hours before the VO2max test.

Body weight was checked using a digital scale (Micheletti ®, São Paulo Brazil) with accuracy of 0.01kg. Height was measured using a stadiometer with an accuracy of 0.1cm (Sanny®, São Paulo, Brazil). Afterwards, the participants had their body composition assessed using a DXA (LUNAR®/GE PRODIGY - LNR 41.990, Washington, DC, USA) equipped with enCORE software (GE Healthcare®, version 15.0, Madison, WI, USA). The equipment was properly calibrated before the evaluations and followed the same configuration for all participants (Full Body Evaluation, Voltage (kV): 76.0, Current (mA): 0.150, Radiation dose (µGγ): 0.4 (Very low, no health risk)). Subsequently, the values in Kg of bone, fat, lean and fat-free mass were acquired.

Because it is considered a strenuous physical exercise, the maximum cardiorespiratory capacity test was used in this study. The test was performed on a motorized treadmill (Centuriom 300®, Brasília, Distrito Federal, Brazil) located in an air-conditioned environment (24°C). Before the test a warm-up lasting5-min in duration at 4km/h and 0% inclination took place. During the test, heart rate was measured by short-range telemetry using a Polar® strap (Model H10, Vantage NV, Finland). The speed was increased gradually, according to the estimated capacity for each subject, based on the American College of Sports Medicine (ACSM), trying to reach the maximum oxygen consumption (VO2max) within the period of eight to twelve minutes. Details can be seen in the studies by Guazzi et al. (16), and Thompson (17). For the analysis of respiratory gases, an ergoespirometer Model Metalyzer-3B (Micromed®, São Paulo, Brazil) was used, following the “breath by breath” method. With the aid of Metasoft® software, connected to a Cortex® unit calibrated by the closed-circuit method with gas calibration, we determined the maximum oxygen consumption relative to body mass (ml/kg.min-1).

Was used the subjective rating of perception exertion (RPE) scale proposed by Borg (18) to measure the levels of physical effort perceived during the test, thus, the scale was exposed to the participant every 2 minutes. This scale consisted of numerical values between 6 and 20, where 6 indicates resting and 20 maximum effort. The incremental test lasted until the participant reported maximum effort on the Borg scale. We emphasize that there was previous familiarization with the RPE.

The peripheral blood collection (20mL) was performed by a nursing professional before and immediately after the maximal incremental test through the venipuncture method. Samples were collected using a vacuum vacutainer system (BECTON - DICKINSON - VACUTAINER SST BD), and 5mL were placed in tubes with containingethylenediaminetetraacetic acid or k3-EDTA anticoagulants (BD-Vacutainer, EDTA-K2 5.4 mg Plus Plastic) for blood count analysis (hematology analyzer, Cell Dyn-3.000®, São Paulo, Brazil), in which total leukocyte, lymphocyte and neutrophil counts were determined. For conversion to absolute values, the percentage values were multiplied by the absolute White Blood Cells (WBC) and divided by 100.

The lymphocyte subsets were analyzed using a lyse procedure based on a single-platform technique (19). The following 4-color combinations of MoAb were used to analyze antigen expression: fluorescein isothiocyanate (FITC), phycoerythrin (PE), peridin chlorophyll protein (PerCP) and phycoerythrin-cyanine (PC-5). Anti-coagulates whole blood (100μL each) was aliquoted in four 12 x 75 mm polystyrene tubes (Falcon Plastics, Becton Dickinson’s Biociences) containing 20 μL of each monoclonal antibody (MoAb). Tubes were mixed and incubated in the dark for 30 minutes (min) at room temperature. Furthermore, two mL of FACS Lysing Solution (Becton Dickinson’s Biociences), previously prepared in distilled water (1:10, v:v) was added to lyse the red blood cells. Tubes were agitated and incubates for 10 min, and centrifuged at 600g for 5 min in dark, the supernatant fluid was discarded, and the cell pellet was resuspended in cold phosphate buffered saline with Ph 7.2 (PBS, Signa-Aldrich, German) and centrifuged again. The last step was repeated. Finally, the cell pellet was resuspended in 1mL of 0.5% formaldehyde in PBS and cell suspension was kept in the dark at 4oC until flow cytometry analysis. A total of 20.000 events per tube were acquired with Fluorescence Activated Cell Analyzer (FACScan, San Jose, CA, USA) with Cell Quest software (Cell QuestTM® Software, Becton Dickinson Immunocytometry Systems, San Jose, CA, USA). In this way, identify lymphocyte subsets (CD3+, CD3+CD4+, CD3+CD8+, CD3+CD16+CD56+, CD16-CD56+, CD56+). Examples of immunophenotyping can be seen in Figure 2 .

The fitness level factor was categorized based on VO2max levels following the recommendations proposed by Cooper (20), considering age and gender. The VO2max levels of the participants were classified as poor (n=3), fair (n=2), good (n=11), excellent (n=5) and superior (n=11). Given these results, we divided the sample into two groups:

For analyses considering the age factor, we used the median split and divided the sample into two groups:

All data processing was performed in a blinded manner by a collaborator external to the present research. Descriptive data analysis was performed based on the recommendations of Mishra (21). To this end, the normality of the data of the independent variables (considering the time factor) were verified by the Shapiro-Wilk, Asymmetry and Kurtosis tests (-1.96 to 1.96). Levene’s test verified the assumption of homogeneity of variances for each independent variable (age & fitness level). In addition, we verified the sphericity of the data using Mauchly’s test. For the comparative analyses considering the factors time (Pre vs. Post), age (Younger vs. Older) and fitness level (Less conditioned vs. More conditioned), we used the Generalized Linear Model (GLM) followed by Bonferroni post-hoc. Thus, the effect size between the differences was checked by eta-squared-partial (η²p) considering the magnitude (22): Small<0.20; Medium>0.20 and <0.50; Large>0.50. For all analyses we considered a significance of p<0.05. SPSS Statistic software (IBM®, version 26.0, USA) was used for all analyses.

Aging is closely linked to reduced efficiency of the immune system (23). This may be related to the thymus gland (i.e., primary lymphoid organ), which begins the involution process after puberty (24, 25). Lawton (26) highlights that thymic involution occurs at a rate of approximately 3% per year. According to Miller (27), the main function of the thymus is to provide the mechanisms for T-cell maturation. In a review study, Yan et al. (28), indicated that advancing thymic involution process reduces the efficiency of the gland with respect to promoting T-cell maturation, which generates decay of the lymphocyte population. According to Wikby et al. (29), a low proliferative response of lymphocytes or the ratio of CD4/CD8 T cells is closely related to aging, pointing lower values in subjects with advanced chronological age. This may justify the findings of the present study regarding the effect of the chronological age factor on CD3+CD4+ and CD3+CD8+ lymphocytes and the CD4/CD8 ratio.

Jacome Burbano, Cherfils-Vicini & Gilson (30) emphasize that among the immune cells that suffer the greatest effect of aging are neutrophils. Such cells are part of the innate immune system and act as the body’s first line of defense against infection (31). According to Petri & Sanz (32), neutrophils migrate to extravascular sites of infection or tissue damage through a process called chemotaxis, and this process is what becomes most defective as the immune system ages. Thus reducing the speed and efficiency of neutrophils, and can cause increased low-level generalized inflammation (33). According to Bartlett et al. (10), one way to restore neutrophils and improve their function is to engage in physical activity on a regular basis. The fact that the sample in the present study was physically active may justify the findings on the small effect of chronological age on neutrophil percentage.

Lawton (26) reports that healthy lifestyle habits (e.g., organic food and exercise) can rejuvenate the immune system and may increase its efficiency. In addition, he points out that in general, the immune system has a biological age rhythm that may differ from chronological age, and this concept is called immune age. Thus, it is possible for younger subjects to have an immune age like that of an older subject, and the reverse is also true. In this sense, because our sample consisted of healthy physically active subjects, it is possible to suggest that the immune age is balanced between the groups (Younger & Older). This would be a possible explanation as to why the present study found no significant effect of chronological age on the other immunological variables analyzed.

The influence of exercise intensity and fitness on immune responses gained prominence with the development of the famous “J-curve” (9). A consensus that currently exists is that regular moderate-intensity physical activity entails greater immunovigilance when compared to sedentary individuals (34). In addition, fitness level, light to moderate physical activity, and even performing exercise before or after an influenza or COVID-19 vaccination appears to increase the antibody response to vaccination (35, 36).

Regarding the comparison of immune responses in physically active individuals after performing a maximal exercise test, there seems to be no difference when compared in groups according to fitness levels. The results of the present study did not show a statistically significant difference between individuals with superior or excellent fitness compared to those with good, fair or poor fitness in any of the immune markers, except for CD3+CD4+ (% and mm3) and CD16-56+ (%) which showed statistically significant differences. To the best of our knowledge, this was the first study to compare immune responses after a maximal exercise session in physically active individuals divided into groups based on current fitness level.

However, according to the study conducted by Dorneles et al. (37), fitness status seems to directly affect T-lymphocyte function, i.e., those individuals with lower fitness have higher circulating T-lymphocytes and a consequent higher pro-inflammatory state. Furthermore, in another study conducted by Dorneles et al. (38), it was identified that after three sessions of high-intensity interval training (HIIT) with 48h of recovery between sessions, low fitness exerted a negative impact on several immune system markers. Thus, although fitness level is shown to play a key role on the immune system, there appear to be no differences in immune responses immediately after a single maximal exercise session, however, it is not known how this immune response behaves in the following hours at different fitness levels.

The main limitations of the present study were: (i) not having a group of sedentary subjects, which could have provided us with more robust answers regarding the effect of fitness level on immunological factors after strenuous exercise and dividing groups by each fitness level, and not only into higher and lower; (ii) The group of less-conditioned subjects was composed mostly of subjects with good fitness levels; (iii) not having a group composed predominantly of pediatric, middle-aged, or elderly subjects, which would allow us more precise findings on the effect of advanced chronological age on the immune system after performing strenuous exercise; (iv) not having performed more extended analyses over time (e. g., after 24 h of the exercise session) and assessments of the risk of upper respiratory tract infection. We suggest that future studies conduct investigations like the present study considering sedentary subjects, fitness levels divided by all categories and different age groups, as well as differences between male and female sexes.

The main finding of the present study was that for physically active adult men, strenuous exercise will generate similar immune responses immediately after, regardless of fitness level. Thus, in physically active men, fitness level is apparently not a protective factor for negative immune system responses immediately following strenuous exercise. Thus, sports professionals and fitness coaches cannot overload subjects with high fitness levels on the assumption that they will be less susceptible to the negative effects of strenuous exercise on immunity. Such care will optimize recovery between strenuous physical training sessions and may prevent possible occurrences of overreaching and/or overtraining.