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Short-Term Exercise Training Alters Leukocyte Chemokine Receptors in Obese Adults


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Medicine & Science in Sports & Exercise: August 2017 - Volume 49 - Issue 8 - p 1631-1640
doi: 10.1249/MSS.0000000000001261


Obesity continues to be a worldwide health concern. In the United States, the prevalence of obesity in adults has reached 36.5% (23). Obesity is a primary risk factor that drives the development of insulin resistance, which contributes to type 2 diabetes (T2D) and cardiovascular disease. Chronic low-grade inflammation plays a key role in the development of insulin resistance and its complications (32). The inflammatory environment in obesity is ultimately driven by activation of immune cells, including monocytes/macrophages, T cells, and neutrophils (19,25). Obesity is associated with higher levels of circulating monocytes (26) and greater infiltration of macrophages and T cells into adipose and other organs/tissues (18,19,34).

Leukocytes are recruited to sites of inflammation by chemotactic cytokines known as chemokines (11). Chemokines implicated in immune cell infiltration into adipose and other tissues in obesity include C-C motif ligand 2 (CCL2; also known as monocyte chemoattractant protein [MCP]-1), CCL3 (also known as macrophage inflammatory protein [MIP]-1alpha), and C-X-C motif ligand 8 (CXCL8; also known as interleukin [IL]-8) (33,34). Specific chemokine receptors, differentially expressed on the surface of leukocytes, facilitate migration of immune cells into tissues along a chemokine gradient (11). The importance of leukocyte chemotaxis in driving obesity and T2D is highlighted by findings showing that various chemokine or chemokine receptor knockout models are protected from high-fat diet-induced obesity and insulin resistance (5,16). CCL2, via interaction with its receptor C-C motif chemokine receptor (CCR2), is believed to be the major chemokine involved in obesity-associated macrophage infiltration into adipose (34), which is supported by studies in humans that show elevated CCL2 and CCR2 in obesity (4,20). Although leukocytes appear to express multiple chemokine receptors to respond to various different chemokines (11), some chemokine–chemokine receptor interactions appear more important for specific leukocyte subsets. For example, higher CCR2 expression has been shown on CD14+/CD16− “classic” monocytes when compared with CD14+/CD16+ monocytes (37), suggesting a greater role for CCL2 in migration of CD14+/CD16− monocytes. Furthermore, even though monocytes express CCR5 and C-X-C chemokine receptor (CXCR2), CCL3–CCR5 interactions may also be influential in mediating chemotaxis of T cells, whereas CXCL8 primarily mediates neutrophil chemotaxis through interaction with CXCR2 (11).

Physical activity is a cornerstone in the management, prevention, and treatment of obesity and its related comorbidities. The anti-inflammatory effects of moderate-intensity continuous training (MICT) are hypothesized to play a major role in the health benefits of exercise, particularly in the context of chronic low-grade inflammation associated with obesity (10). Decreased visceral fat mass likely contributes to some of the anti-inflammatory effects of exercise training due to lower proinflammatory adipokine and chemokine secretion, which may be linked to reduced number of proinflammatory macrophages and other immune cells within adipose (10,35). Animal studies also suggest that regular exercise training can prevent infiltration of monocytes/macrophages into adipose tissue during the development diet-induced obesity (17). However, whether the anti-inflammatory effects are due to a direct effect of exercise impacting monocyte migration or are secondary to weight/fat loss are not clear (10,40).

Recently, high-intensity interval training (HIIT) has gained attention for its ability to elicit cardiometabolic benefits, often in a time-efficient manner (9). HIIT involves brief, repeated bursts of vigorous exercise separated by periods of rest. HIIT represents an alternative approach to traditional MICT. HIIT has been shown to promote adaptations similar, or superior, to MICT in healthy and clinical populations (9). However, the potential for HIIT, as opposed to MICT, to have anti-inflammatory effects are much less studied. In an initial study, Robinson et al. (30) found that both HIIT and MICT led to reductions in monocyte toll-like receptor (TLR)-4 expression but only MICT led to reductions in neutrophil TLR4 in overweight and obese adults, suggesting that HIIT and MICT might have different impacts on receptor expression on immune cells. Other research has argued that HIIT may have proinflammatory effects because an acute session of HIIT led to an increase in proinflammatory cytokines, including the chemokines CCL2 and CXCL8, in young healthy men (41). Therefore, despite potential improvements in cardiometabolic health markers (9), it is unclear whether HIIT has the same anti-inflammatory potential as MICT, particularly in the context of obesity.

The purpose of this study was to determine the impact of HIIT versus MICT on circulating chemokine levels and expression of chemokine receptors on leukocytes. We used short-term training modeled after previous studies (30) in attempts to isolate the direct effects of exercise training in the absence of weight loss or changes in body composition. Based on our recently published work showing evidence of potentially greater anti-inflammatory effect of MICT on leukocytes (30), we hypothesized that HIIT and MICT would differentially modulate levels of chemokine and chemokine receptors.


Experimental Design

This study represents a substudy from a larger randomized controlled trial (RCT) examining cardiometabolic adaptions and adherence to HIIT versus MICT in individuals at elevated risk of developing T2D (NCT02164474). After participants were screened and deemed eligible, they were randomized into two groups: HIIT or MICT. A research assistant accessed a password-protected website to retrieve the randomization, which was based on variable permuted blocks. After randomization, both groups underwent the same experimental protocol, which consisted of: (i) baseline (pre) testing, (ii) a 10-session training intervention over a 2-wk duration, and (iii) posttesting approximately 72 h after the final training session to avoid confounding effects of acute exercise.


Eligibility criteria included: inactive (two or less aerobic bouts of >30 min moderate-to-vigorous physical exercise per week; assessed by a standard 7-d physical activity recall interview), between the ages of 30–65 yr, completion of the Canadian Society for Exercise Physiology Physical Activity Readiness Questionnaire-Plus and, if required, cleared for participation in vigorous exercise by a Canadian Society for Exercise Physiology Certified Exercise Physiologist or their physician (14). Exclusion criteria included history of cardiovascular disease, uncontrolled hypertension, previous myocardial infarction or stroke, diagnosed diabetes, taking glucose-lowering or immunomodulatory medications, or any contraindications to exercise. This study design was approved by the University of British Columbia Clinical Research Ethics Board and all subjects provided written informed consent.

The larger RCT was conducted in waves of 10 to 12 participants each. For the present substudy on the impact of HIIT versus MICT on chemokines and chemokine receptors, sample size was calculated a priori using data from Krinninger et al. (20) to determine the smallest meaningful difference in CCR2 expression on CD14+/CD16+ monocytes. Using data from Krinninger et al., we calculated an effect size of 0.93 as the difference between lean and obese women and defined this as the smallest meaningful difference. Using means and SD for CCR2 expression on CD14+/CD16+ monocytes from an optimized antibody panel from data collected on our flow cytometer (n = 24) a sample size of 17 per group was needed with 80% power and alpha of 0.05, assuming a correlation between repeated measures of r = 0.5 (calculated using G-Power version To account for approximately 20% dropout or missing blood samples we aimed to recruit at least 42 (n = 21 per group) obese participants from the larger RCT.

Participants who were classified as obese (body mass index [BMI] > 30 kg·m−2 and/or a waist circumference > 102 cm [men] or 88 cm [women] (7) and/or a > 25% body fat [men] or >30% [women]) from the first five waves of the RCT were included in this substudy. Recruitment occurred between June 2014 and September 2014 with the five waves starting the program between July 2014 (Wave 1) and February 2015 (Wave 5). A total of 43 participants met the inclusion criteria and were randomized to HIIT (n = 22) or MICT (n = 21). Medication use in the sample was minimal. One participant from the HIIT group was taking an anti-hypertensive medication. In the MICT group, one participant was taking a statin and another was on transdermal hormone replacement therapy.


After an overnight fast (≥8 h), manual blood pressure was measured according to the Canadian Hypertension Education Program guidelines (28) and a fasting blood sample, collected into two separate ethylenediaminetetraacetic acid-coated Vacutainer (BD) tubes, was obtained from an antecubital vein. Whole blood from one ethylenediaminetetraacetic acid tube was kept at room temperature and transported to the laboratory for flow cytometry analyses. The remaining blood was kept on ice for 10 to 20 min before being centrifuged at 1550g for 15 min at 4°C to isolate plasma, which was stored at −80°C for further batch analysis. Body mass and height were measured to the nearest 0.1 kg and 0.1 cm, respectively (700 Mechanical Column Scale; Seca, Hamburg, Germany). Body composition was measured by whole-body dual-energy X-ray absorptiometry (DXA; Discovery A, Hologic, Marlborough, MA) and analyzed using Hologic Discovery software Version 13.4.2. Participants were instructed to remove all jewellery and metal before DXA scans, which were all performed by the same technician. Measurements for total body (lean mass, fat mass, and fat percent) and estimated visceral adipose tissue (VAT) mass were recorded. Participants completed a continuous incremental ramp maximal exercise test on an electronically braked cycle ergometer (Lode Excalibur, Groningen, The Netherlands) to determine peak heart rate (HRpeak) to prescribe and monitor training intensities. The maximal test consisted of a 4-min warm-up at 30 W and a ramp increase of 15 W·min−1 until volitional exhaustion or the revolutions per min fell below 50. HRpeak was defined as the highest HR achieved.

Training Intervention

The training intervention involved ten exercise sessions performed Monday to Friday over 2 wk, with Saturday and Sunday set as rest days. Training sessions for HIIT or MICT were designed to be progressive and matched for external work. Exercise intensity was determined using HRpeak measured from the baseline maximal exercise test. HIIT consisted of 1 min eliciting approximately 90% HRpeak interspersed with 1 min of low-intensity recovery periods in between each interval, as we have described previously (15,30). Each HIIT session included a standard 3 min warm-up and 2 min cooldown. Participants progressed from 4 × 1 min intervals (day 1) to 10 × 1 min intervals (day 10) with the total duration of each training session lasting from 12 to 25 min, respectively. MICT consisted of continuous moderate-intensity exercise eliciting approximately 65% HRpeak, progressing from 20 min (day 1) to 50 min (day 10). Participants completed days 1 and 10 on a stationary bike and were given the option of several exercise modalities for the other sessions including treadmill walking, stationary cycling, elliptical training, or walking outdoors with intensity closely monitored by a research assistant using HR monitors (FT7; Polar, Kempele, Finland). Of the 10 sessions, 3 sessions (days 4, 7, and 9) were completed independently by the participant at home, with compliance confirmed using downloadable HR monitors. This training protocol was modeled after our previously published work (15,30).


Participants returned to the laboratory approximately 72 h (range 60–74 h) after their last training session for posttesting, which was identical to pretesting.

Blood Analyses

White blood cell count

Total and differential (lymphocyte, monocyte, and granulocyte) white blood cell counts were measured in fasting blood samples using the Coulter AcT diff 2 Analyzer (Beckman Coulter, Pasadena, CA).

Plasma chemokines and adipokines

Fasting plasma concentrations of chemokines/adipokines were measured using multiplex immunoassay. CXCL8 (IL-8) and CCL3 (MIP-1alpha) were measured using the Human High Sensitivity T Cell Multiplex Kit (Cat HSTCMAG-283K; Millipore, Billerica, MA). Leptin and CCL2 (MCP-1) were measured using the Human Adipokine Multiplex Kit (Cat HADK2MAG-61K-01; Millipore). Briefly, plasma samples were spun at 1000g for 15 min at 4°C to pellet out any debris. The assay was completed according to manufacturer’s protocol and samples measured in duplicate using the MAGPIX Bio-Plex reader (Bio-Rad, Hercules, CA). The intra-assay coefficient of variation (CV) based on duplicates was <4% (for both Multiplex Kits) and the inter-assay CV (based on controls run on each plate) was <2% (CXCL8 and CCL3) and <5% (leptin and CCL2). Plasma concentrations were determined using the Bio-Plex Manager 6.1 software. The minimal detectable concentration for each analyte (in pg/mL), calculated by the software, was 0.25 (CXCL8), 1.28 (CCL3), 37 (leptin), and 2.2 (CCL2).

Chemokine receptors

Chemokine receptor (CCR2, CCR5, and CXCR2) levels were measured on different leukocyte subsets in whole blood by flow cytometry. Blood was transported to the laboratory and within 10 min of sample collection 10 μL of FcR Blocking Reagent (130-059-901; Miltenyi Biotech, Bergisch Gladbach, Germany) was added to 90 μL of whole blood and incubated in the dark for 10 min at 4°C. After which, 2 μL of CD195 (CCR5) BV421 (562576; BD Horizon, Franklin Lakes, NJ), 5 μL CD182 (CXCR2) FITC (551126; BD Pharmingen, Franklin Lakes, NJ), 2 μL CD192 (CCR2) PE (130-103-829; Miltenyi Biotech), 2 μL CD14 PE-Cy7 (561385; BD Pharmingen), 2 μL CD16 APC (130-091-246; Miltenyi), and 2 μL CD8 APC-H7 (561423; BD Pharmingen) were added and incubated in the dark for 10 min at 4°C. After incubation, 1 mL of red blood cell lysis buffer (120-001-339; Miltenyi Biotech) was added and incubated in the dark for 15 min at room temperature. To exclude dead cells from the analysis, 2 μL of Propidium Iodide (130-093-233; Miltenyi Biotech) was immediately added before sample analysis on a MACSQuant Analyzer flow cytometer (Miltenyi Biotech). The leukocyte populations had established gates based on the characteristic forward and side scatter, as well as staining for common leukocyte markers to establish CD14+/CD16− monocytes and CD14+/CD16+ monocytes, CD16+ granulocytes (neutrophils) and CD8+ lymphocytes (T cells). Fluorescence minus one controls were used to establish positive staining of CCR2, CCR5, and CXCR2. A total of 10,000 CD14+ events were collected for analysis in each subject. Gating strategy is shown in Figure 1. Antibody panel optimization was performed before the study. Compensation was performed before analyses to control for fluorochrome spillover and bank instrument settings were applied to standardize any drift in laser strength over time. CCR2, CCR5, and CXCR2 expression on the leukocyte populations were quantified as median fluorescence intensity (MFI), count per microliter, and percent positive cells using MACSQuant software (Miltenyi Biotech).

Live cells (negative for propidium iodide [PI] staining) were first categorized by characteristic scatter (A) as either granulocytes (P1), monocytes (P2), or lymphocytes (P3). Each cell population was then confirmed by positive expression of cell surface markers within their respective populations: (B) monocytes were confirmed by positive expression of CD14 (UL4) or CD14 and CD16 (UR4), granulocytes were confirmed by positive expression of CD16 (LR4), and (C) lymphocytes were confirmed by positive expression of CD8 (P5). Cell surface expression of CCR2 (D), CCR5 (E), and CXCR2 (F) were then measured on the cell types relative to FMO controls for each chemokine receptor (indicated on each graph). FMO, fluorescence minus one.

Statistical Analysis

Statistical analyses were performed using IBM SPSS Statistics 22. Statistical outliers were determined using an interquartile range with a multiplier of 2.2 based off the method by Hoaglin and Iglewicz (12). Briefly, the 25th and 75th percentile weighted average values were determined using SPSS and then used to calculate the “lower” and “upper” values with a multiplier of 2.2 interquartile range. Based off of these limits, values that fell outside were deemed to be outliers and removed from the analyses. Normality was assessed using Q-Q plots and by Shapiro–Wilks test. Data were log-transformed or square root-transformed if a normal distribution did not exist. All descriptive statistics are reported as mean (SD). Two-factor (group–time) mixed ANOVA with repeated measures on time was used to examine changes in anthropometric measures, blood cell counts, chemokine, and chemokine receptor expression. Significant interactions were followed up with post hoc tests comparing preintervention to postintervention within group. Preplanned contrasts were also conducted to compare preintervention versus postintervention effects within HIIT and MICT separately. Level of significance was set at P ≤ 0.05. Effect sizes were calculated using Cohen d.

Six participants (three HIIT and three MICT) did not complete the intervention or posttesting, five of which dropped out immediately after pretesting due to lack of time and one of which who completed part of the intervention but could not complete the training or posttesting due to illness. Results were analyzed per protocol so these six participants were not included in the analysis, leaving 19 participants in the HIIT group and 18 in the MICT group.


Baseline characteristics of the 37 participants who completed the intervention are shown in Table 1.

Baseline characteristics of participants before the training intervention.


No differences were observed for body composition measures including BMI, estimated VAT mass, percent body fat, total fat mass, and total lean mass after HIIT or MICT (Table 2).

Body composition measures before and after 2 wk of HIIT and MICT.

Blood Cell Count

No significant effects of training were seen on white blood cell, lymphocyte, monocyte or granulocyte counts (see Table, Supplemental Digital Content 1, Hematology counts before and after HIIT and MICT,

Chemokine and Chemokine Receptors

No significant changes were observed in plasma chemokine or leptin concentrations (Table 3).

Circulating chemokine and adipokine levels before and after 2 wk of HIIT and MICT.


There were no statistically significant differences found in surface expression of CCR2 (Figs. 2A–D). There was a group–time interaction (P = 0.019) for CD14+/CD16+ monocytes positive for CCR2 (Table 4) with post hoc tests indicating a reduction (∼3.5%) after MICT only (P = 0.027, Cohen d = 0.6), with no change after HIIT (P = 0.368, Cohen d = 0.2). No differences were observed for percent CCR2 positive cells in any of the other leukocyte subsets (see Table, Supplementary Digital Content 2, Chemokine receptor percent positive and cell counts before and after HIIT and MICT, There was a group–time interaction (P = 0.048) for the neutrophil cell count per μL blood (Table 4); however, post hoc tests indicated no statistically significant differences within groups over time: MICT (P = 0.127, Cohen d = 0.4) and HIIT (P = 0.195, Cohen d = 0.3). No changes in CCR2-positive cell counts per μL blood for the other leukocyte subsets were observed (see Table, Supplementary Digital Content 2, Chemokine receptor percent positive and cell counts before and after HIIT and MICT,

Short-term HIIT and MICT result in different responses on leukocyte chemokine receptor surface expression. Surface expression of CCR2 (A–D), CCR5 (E–H) and CXCR2 (I–L) was measured on CD14+/CD16− monocytes, CD14+/CD16+ monocytes, neutrophils, and T cells by flow cytometry before and after training. *P < 0.05 pre versus post within-group, #P < 0.05 main effect of time.
Chemokine receptor percent positive cells before and after two weeks of HIIT and MICT.


There was a main effect of time (P = 0.012) on the surface expression of CCR5 on CD14+/CD16− monocytes (P = 0.012), CD14+/CD16+ monocytes (P = 0.033) and neutrophils (P = 0.002). Significant increases occurred after HIIT but not MICT for all of these cell types (CD14+/CD16+ monocytes: HIIT: P = 0.025, Cohen d = 0.6; MICT: P = 0.178, Cohen d = 0.3; CD14+/CD16+ monocytes: HIIT: P = 0.024, Cohen d = 0.6; MICT: P = 0.545, Cohen d = 0.1; and, and neutrophils: HIIT: P = 0.006, Cohen d = 0.8; MICT: P = 0.094, Cohen d = 0.4) (Figs. 2E–G). Surface expression of CCR5 was increased after HIIT on these cell subsets by approximately 12%, 18%, and 12%, respectively. The surface expression of CCR5 on T cells was not affected (Fig. 2H), but there was a main effect of time (P = 0.033) on T cells positive for CCR5 (Table 4). Preintervention versus postintervention contrasts revealed an approximately 3% increase after HIIT (P = 0.019, Cohen d = 0.7) but not MICT (P = 0.214, Cohen d = 0.3). A main effect of time (P = 0.033) on neutrophils positive for CCR5 (Table 4) was also seen, with preplanned contrasts indicating increases in both HIIT (∼8%, P = 0.024, Cohen d = 0.6) and MICT (∼7%, P = 0.049, Cohen d = 0.4). No differences in CCR5 positive cell counts per microliter of blood were observed for the leukocyte subsets (see Table, Supplemental Digital Content 2, Chemokine receptor percent positive and cell counts before and after HIIT and MICT,


A main effect of time (P = 0.043) was observed for the surface expression of CXCR2 on CD14+/CD16− monocytes. Preplanned contrasts within groups did not reveal statistically significant changes in HIIT (P = 0.281, Cohen d = 0.3) or MICT (P = 0.080, Cohen d = 0.5). On CD14+/CD16+ monocytes, there was a main effect of time (P = 0.003) on the surface expression of CXCR2, with an approximately 13% reduction after MICT (P = 0.032, Cohen d = 0.8) but no difference after HIIT (P = 0.388, Cohen d = 0.4). There were no changes after training in surface expression of CXCR2 on T cells or neutrophils (Figs. 2K, L) or any changes on percent positive cells for CXCR2 (see Table, Supplemental Digital Content 2, Chemokine receptor percent positive and cell counts before and after HIIT and MICT, for any of the leukocyte subsets measured. No changes in CXCR2 positive cell counts per μL blood for the leukocyte subsets were seen (see Table, Supplemental Digital Content 2, Chemokine receptor percent positive and cell counts before and after HIIT and MICT,


The results of this study demonstrate that, in obese adults, short-term exercise training in the absence of weight loss has direct effects on leukocyte chemokine receptors. HIIT and MICT appear to differentially modulate chemokine receptors on specific immune cells, indicating that the intensity and/or pattern of exercise impact adaptive responses across immune cell subsets. Overall, 2 wk of MICT led to a reduction in CCR2 and CXCR2 on CD14+/CD16+ monocytes. In contrast, HIIT resulted in an overall increase of CCR5 on CD14+/CD16− monocytes, CD14+/CD16+ monocytes, neutrophils and T cells. These changes occurred in the absence of weight loss or alterations in circulating chemokines, suggesting that exercise training has a direct influence on chemokine receptor expression in circulating leukocytes.

In a previous study by Krinninger et al. (20), obese women demonstrated higher surface expression of CCR2 and CCR5 on both CD14+/CD16− and CD14+/CD16+ monocytes compared with lean women. These monocytes also had greater chemotactic activity, which was suggested to be due to the increased CCR2 expression. Although studies indicate a greater expression of these chemokine receptors during obesity (2,20), studies on the effects of exercise training on chemokine receptors are lacking. Given the importance of chemokine receptors in mediating chronic low-grade inflammation in obesity (5,16) such information may improve our understanding of the anti-inflammatory effects of exercise training in obesity.

Impact of MICT on Chemokine Receptors

Changes in CCR2 expression

Our study shows that MICT, but not HIIT, decreased the percent of CD14+/CD16+ monocytes that were positive for CCR2. We are only aware of previous studies that have examined CCR2 after acute exercise, which have shown mixed results. CCR2 expression on CD14+/CD16− and CD14+/CD16+ monocytes was unchanged in the hours after moderate-intensity leg or arm cycling exercise in healthy young men (22). Okutsu et al. (24) also reported no change in CCR2 expression on CD14+ monocytes in young men after an acute bout of aerobic exercise, although, when peripheral blood mononuclear cells (PBMCs) were incubated with postexercise serum there was an increase in CD14+ monocyte CCR2 expression. However, Wells et al. (38) reported decreased CCR2 expression on CD14+ monocytes after an acute bout of resistance training in resistance-trained young men. These mixed results may be due to the difference in exercise modality (aerobic vs resistance) and/or study sample (e.g., healthy vs resistance trained). Our study is the first, to our knowledge, in obese adults and we are the first to report that aerobic training (2 wk of MICT) can lead to reductions in CD14+/CD16+ monocyte CCR2.

Although CD14+/CD16+ monocytes only constitute ∼10% of total circulating monocytes, they show a greater inflammatory potential (10) and are involved in inflammatory-related pathologies such as obesity and cardiovascular disease (26). The main function of CCR2 and its ligand CCL2 is monocyte trafficking to sites of inflammation (11) and during obesity, there is a greater degree of accumulation of these proinflammatory monocytes into tissues such as adipose (34). Although CCR2 is more highly expressed on CD14+/CD16− compared with CD14+/CD16+ monocytes (37), surface expression and percent positive CCR2 CD14+/CD16+ monocytes have been reported to be higher in obese compared with lean women (20). A decrease in the percent of CD14+/CD16+ monocytes that are positive for CCR2 after MICT may therefore be considered more substantial due to the higher inflammatory potential of this monocyte subset. A reduction of percent CD14+/CD16+ monocytes that are positive for CCR2 as seen in our study may indicate that MICT is better able to reduce potential for proinflammatory monocyte migration, which would potentially decrease the accumulation of proinflammatory monocytes into tissues such as adipose.

We also found an interaction for the percent neutrophils that were positive for CCR2. The mean differences and effect sizes indicated that MICT tended to be lower, whereas HIIT was higher, after training but specific post hoc tests did not indicate statistical significance on the pairwise comparisons. This trend and significant interaction effect for granulocytes is in line with the potential for MICT to reduce CCR2 on monocytes.

Changes in CXCR2 expression

Two weeks of MICT also led to a decrease in CXCR2 MFI expression on CD14+/CD16+ monocytes. An overall decrease in CXCR2 MFI expression on CD14+/CD16− monocytes after exercise training was also observed. CXCR2 expression has been shown to increase after an acute bout of exercise in vascular endothelial cells of skeletal muscle in healthy young men, with this increase suggested to be beneficial in the stimulation of angiogenesis (8). Although this increase in CXCR2 expression was described as beneficial, the study was focused on young healthy men who most likely do not have an accumulation of monocytes and other leukocytes into their tissues as seen during obesity. The overall decrease in CXCR2 expression on both monocyte subsets observed in our study may be seen as potentially anti-inflammatory based on reduced potential for monocyte trafficking. Although CXCR2 is expressed on monocytes and T cells, its expression is highest on neutrophils and it is believed to play a predominant role in neutrophil chemotaxis (11). We did not see any impact of exercise training on CXCR2 on neutrophils or T cells, indicating that the effects were specific to monocytes. These findings of different effects on immune cell subsets highlight the potential importance and added insight of studying chemokine receptors on different leukocytes.

Impact of HIIT on Chemokine Receptors

Changes in CCR5

Two weeks of HIIT showed an overall increase of CCR5 on all leukocyte subsets studied. There was an increase of CCR5 surface expression on CD14+/CD16− monocytes, CD14+/CD16+ monocytes and neutrophils. Further analysis demonstrated an increase in the percent of CD14+/CD16+ monocytes and T cells that were positive for CCR5. Although these consistent increases in CCR5 in all leukocytes were seen after HIIT, MICT did also lead to an increase in the percent CCR5 positive CD14+/CD16+ monocytes.

There are very few studies exploring the effects of exercise on CCR5. In obese adults who showed elevated CCR5 in adipose tissue, three months of exercise reduced adipose tissue expression of CCR5 alongside a decrease in percent body fat (2). Dorneles et al. (5) also reported decreased expression of whole blood CCR5 24 h after an acute session of strength training in young men (6). On the other hand, CCR5 on neutrophils was increased after an acute bout of high intensity aerobic exercise in young men (29). These conflicting results may be due to the lack of leukocyte distinction in the study by Dorneles et al. (5) compared with the specificity of neutrophil CCR5 expression in the latter study (29). Our results appear to indicate that an increase in CCR5 after short-term HIIT is a consistent response across multiple immune cells including monocytes, neutrophils and T cells.

CCR5 is recognized for mediating T-cell trafficking and type 1 adaptive immunity but is also involved in monocyte/macrophage migration and CCR5 is expressed on neutrophils (11). Therefore, an increase in CCR5 expression, such as we observed on all leukocyte subsets after HIIT, could be indicative of increased potential for T cell, monocyte, and/or neutrophil migration to tissues. However, the increase in CCR5 expression after HIIT may also be indicative of an alternative immunomodulatory function of exercise. Along with its function in T-cell trafficking, CCR5 has also been implicated in muscular endurance. Lei et al. (21) have shown that CCR5 activation by orosomucoid (ORM)-1 increases muscle glycogen storage and enhances muscle endurance, an effect that appears related to CCR5-mediated activation of skeletal muscle 5′ AMP-activated protein kinase (27). These mechanistic studies have been completed in mice but studies in humans have shown upregulated levels of ORM-1 in PBMCs after an acute bout of resistance exercise (3), providing some preliminary evidence that ORM-1 and CCR5 interactions may be influenced by exercise.

Changes in chemokines and adipokines

There were no changes in plasma chemokines or the adipokine, leptin (Table 4). The short-term nature of our exercise intervention and the lack of weight loss or visceral adipose tissue reduction likely explain why no changes in chemokines or leptin were seen. The lack of change in circulating inflammatory mediators is in line with a previous investigation that found no change in plasma IL-6 and tumor necrosis factor-alpha after 6 wk of HIIT or MICT in overweight young men (1). As levels of chemokines are known to impact receptor expression (24), our results indicate that the changes seen in CCR2 and CXCR2 on monocytes after MICT and CCR5 on all leukocytes after HIIT do not appear related to altered circulating levels of their respective ligands.


We purposefully chose to study short-term exercise in this initial study of leukocyte chemokine receptors to avoid the possible confounding effects of weight loss or body composition changes on these inflammatory parameters (40). Thus, the effects seen on leukocyte chemokine receptors may only reflect aspects of the initial adaptive response to exercise in previously inactive obese adults. Longer-term studies are warranted to determine the impacts of prolonged exercise training.

A previous study has shown that acute infusion of lipids and/or glucose may impact levels of circulating CCL2 (13) and Wells et al. (39) have shown that protein ingestion after resistance exercise impacts monocyte CCR2 expression. It is therefore possible that nutrition may influence the impact of exercise on immune cell chemokine receptor expression. In our study, participants were instructed not to change their dietary habits over the course of the 2-wk intervention but we did not specifically control diet throughout. The objective measures of body mass and body composition by DXA provide evidence that energy balance was maintained but it is possible that subtle changes in diet, in addition to the HIIT or MICT exercise interventions, influenced the results.

Of our 32 female participants, 15 (40.5%) were premenopausal, three (8.1%) perimenopausal and 14 (37.8%) were postmenopausal. It was not possible to standardize the menstrual phase of each female participant for pretesting and posttesting as the study protocol dictated that measures were taken 23 d apart (pretesting always occurred on a Friday 1 wk before beginning the training intervention and posttesting was always completed on the Monday after completion of training). We are not aware of any data indicating that chemokines or their receptors are altered with menstruation, and it seems unlikely that systematic differences in the timing of menstrual cycle phase for pretesting and posttesting would arise within a randomized study, but it should be noted that that our study sample and design were not able to control for menopause or menstrual cycle.

We were not able to determine the impact of chemokine receptor changes on infiltration of different leukocytes into tissues. Because visceral adipose and the arterial wall are considered most important inflammatory sites for metabolic and cardiovascular health (31,36) such studies are not feasible in humans with existing methods. It would be interesting to determine whether the changes in chemokine receptors translate into altered tissue immune cell infiltration to impact inflammation in future work. Complementing circulating leukocyte measures with subcutaneous adipose tissue or skeletal muscle biopsies would be one potential approach. Similarly, the chemotactic potential or migratory capability of each leukocyte subset was not measured in the present study. Based on our findings, it would be of interest in future studies to measure chemotactic potential of different cell types (i.e. monocytes, neutrophils and T cells) after exercise training to determine if altered chemokine receptors translate into increased or decreased ex vivo migration.


Short-term exercise training resulted in changes in chemokine receptor expression in the absence of weight or visceral fat loss and without changes in circulating chemokines. Specifically, MICT led to decreased CCR2 and CXCR2 on monocytes and HIIT resulted in increased CCR5 on monocytes, neutrophils and T cells in obese adults. These findings indicate that exercise has direct effects on immune cells in adults with obesity, in the absence of changes in systemic chemokines or fat loss, with the type of exercise (HIIT or MICT) resulting in different responses. The implication of reduced CCR2 and CXCR2 after MICT and of increased CCR5 after HIIT will require further research, but overall, these findings indicate, based on leukocyte chemokine receptor expression, that MICT may reduce monocyte infiltration, whereas HIIT may increase monocyte, T cell, and neutrophil infiltration. These findings indicate distinct effects of exercise on chemokine receptors, and future studies are required to explore the effect of different exercise intensities and modalities on leukocyte trafficking during obesity.

This study was part of a larger study that was supported by a Foundation Grant from the Canadian Institutes of Health Research (CIHR) awarded to M. E. J. (333266). M. E. J. is supported by a Michael Smith Foundation for Health Research Scholar Award (Award 5917) and a J. P. L. is supported by a CIHR New Investigator Award (MSH-141980). The authors declare no conflicts of interest. The results of the present study do not constitute endorsement by ACSM.


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