Subpopulations of regulatory T cells are associated with subclinical atherosclerotic plaques, levels of LDL, and cardiorespiratory fitness in the elderly

Highlights

• •About one-third of clinically healthy agers already have a subclinical atherosclerotic plaque.
• •Older age and higher blood pressure, which is not classified as hypertension, are positively associated with subclinical atherosclerotic plaques.
• •mTregs but not the total population of Tregs are linked to a subclinical stage of atherosclerosis and could be considered as a biomarker.
• •The subtypes mTregs and rTregs are furthermore linked to factors such as low-density lipoprotein (LDL) or cardio respiratory fitness and may be additional diagnostic indicators.

Abstract

Background

Atherosclerosis forms the pathological basis for the development of cardiovascular disease. Since pathological processes initially develop without clinically relevant symptoms, the identification of early markers in the subclinical stage plays an important role for initiating early interventions. There is evidence that regulatory T cells (Tregs) are involved in the development of atherosclerosis. Therefore, the present study aimed to identify and investigate associations with Tregs and their subsets in a cohort of healthy elderly individuals with and without subclinical atherosclerotic plaques (SAP). In addition, various lifestyle and risk factors, such as cardiorespiratory fitness, were investigated as associated signatures.

Methods

A cross-sectional study was performed in 79 participants (male: n = 50; age = 63.6 ± 3.7 years; body mass index = 24.9 ± 3.1 kg/m²; mean ± SD) who had no previous diagnosis of chronic disease and were not taking medication. Ultrasound of the carotids to identify SAP, cardiovascular function measurement for vascular assessment and a cardiorespiratory fitness test to determine peak oxygen uptake were performed. Additionally, tests were conducted to assess blood lipids and determine glucose levels. Immunophenotyping of Tregs and their subtypes (resting (rTregs) and effector/memory (mTregs)) was performed by 8-chanel flow cytometry. Participants were categorized according to atherosclerotic plaque status. Linear and logistic regression models were used to analyze associations between parameters.

Results

SAP was detected in a total of 29 participants. The participants with plaque were older (64.8 ± 3.6 years vs. 62.9 ± 3.5 years) and had higher peripheral systolic blood pressure (133.8 ± 14.7 mmHg vs. 125.8 ± 10.9 mmHg). The participants with SAP were characterized by a lower percentage of rTregs (28.8% ± 10.7% vs. 34.6% ± 10.7%) and a higher percentage of mTregs (40.3% ± 14.7% vs. 30.0% ± 11.9%). Multiple logistic regression identified age (odds ratio (OR) = 1.20 (95% confidence interval (95%CI): 1.01–1.42)) and mTregs (OR = 1.05 (95%CI: 1.02–1.10)) as independent risk factors for SAP. Stepwise linear regression could reveal an association of peak oxygen uptake (β = 0.441), low-density lipoprotein (LDL) (β = –0.096), and SAP (β = 6.733) with mTregs and LDL (β = 0.104) with rTregs.

Conclusion

While at an early stage of SAP, the total proportion of Tregs gives no indication of vascular changes, this is indicated by a shift in the Treg subgroups. Factors such as serum LDL or cardiopulmonary fitness may be associated with this shift and may also be additional diagnostic indicators. This could be used to initiate lifestyle-based preventive measures at an early stage, which may have a protective effect against disease progression.

Graphical abstract

1. Introduction

Atherosclerosis is an indolent disorder, where the most severe clinical events occur after the rupture of a plaque and thereby cause sudden thrombotic occlusion of the artery. Today, atherosclerosis is the central cause of cardiovascular disease, which, as a disease, is responsible for the majority of deaths worldwide.¹ There can be clinical symptoms because of flow-limiting stenosis due to the plaques, but the development and progression are often subclinical and can be silent for years.^2,3 This corresponds to some extent to model parts of the salutogenetic model, which classifies health and disease as a continuum, and in the early phase one shifts somewhat towards disease on the continuum. Therefore, the identification of early markers in the subclinical stage plays an important role in the ability to initiate early interventions.

There are several risk factors related to the development and progression of atherosclerosis, including health behaviors such as physical inactivity, obesity, malnutrition, and smoking, as well as previous conditions like dyslipidemia, diabetes mellitus, and hypertension.^4,5 The best evidence for the development of plaque is the pathological storage of cholesterol esters and other fats in the inner wall layer of arterial blood vessels in association with vascular inflammation in response to endothelial dysfunction.⁶

Today there is evidence that atherosclerosis is a chronic inflammatory disease with signs of an autoimmune component.⁷ The development and progression of plaque is directly linked to circulating immune cells.⁸ Accordingly, inflammation may not only be an epiphenomenon, but may even be a contributor to the development of endothelial dysfunction or arteriosclerosis. First, there is an innate immune response by the activation of macrophages, which leads to the induction of the adaptive immune response.⁹ Around 20% of the cells in atherosclerotic lesions are T cells, and the activation of T cells is known to play a major role in the progression and destabilization of atherosclerotic plaques.¹⁰ T cells contain many pro-inflammatory CD4⁺ T helper cells type 1 (Th₁); as such, atherosclerosis is a known Th₁-related disease. While the role of Th₂ and Th₁₇ cells remains controversial, regulatory T cells (Tregs) are also part of the adaptive immune response and are currently thought to have an impact on atherosclerosis.⁶

Tregs can be generated in the periphery but are mostly produced in the thymus. They play a key role in the immune response and maintain homeostasis in both diseased and normal states. Disruption or lack of Tregs affects autoimmune disease, inflammation, infections, and cancer development^11,12 because they have a suppressive function on various immune cells in the adaptive and innate immune system.¹³ To date there are 3 different ways Tregs suppression is thought to work, although none are completely understood. The most well-known way is by the secretion of anti-inflammatory interleukin-10 and transforming growth factor β.⁹ If Tregs and their immunosuppressive functions are experimentally reduced, the immune balance is disturbed towards a stronger pro-inflammatory reaction, which in turn leads to inflammatory secondary diseases and also favors the development of hypertension and atherosclerosis.¹⁴ Evidence is growing that Tregs may be involved in the development of atherosclerotic plaques. A study by Ait-Oufella et al.¹⁵ demonstrated that a reduction of Tregs affected the number of T cells and macrophages in plaques, reduced inflammation, and increased the healing process in atherosclerosis. Similarly, Mallat et al.¹⁶ found an inverse correlation between the number and function of Tregs and the development of atherosclerosis. However, much less is known about Tregs at the early stage of atherosclerosis. Research has indicated that among patients with subclinical atherosclerosis, there exists a positive correlation between the quantity of Tregs and plasma low-density lipoprotein (LDL) levels, as shown in a previous study.¹⁷ This suggests that Tregs are potentially activated even during a pre-clinical phase. Factors influencing the development of atherosclerosis also have an association with circulating Tregs. Higher levels of cardiorespiratory fitness and a non-obese state are shown to be associated with increased peripheral Tregs.¹⁸ However, additional research is needed here, as several Tregs subsets have been distinguished, each of which have different influences on the immune balance. Resting Tregs (rTregs) exhibit only limited immunosuppressive capacity and are found in the blood as well as in secondary lymphoid tissue. In contrast, memory Tregs (mTregs) are formed to regulate memory effector responses after re-antigen contact. These cells are important for preventing tissue damage after a strong immune response without resulting in excessive immune suppression.^19,20 Currently, there are only limited data available on the relationships between subclinical atherosclerosis, Tregs, and lifestyle factors such as physical activity.

The present exploratory study aimed to identify associations of subpopulations of Tregs with subclinical atherosclerosis in a cohort of apparently healthy elderly patients. In addition, various lifestyle and risk factors, such as cardiorespiratory fitness, were investigated as further associative factors between subclinical atherosclerosis and Tregs and their subsets.

2. Methods

2.1. Study design and participants

The present case control study was designed in a cross-sectional form to evaluate the association of Tregs, as well as different subsets and classical lifestyle risk factors, with the prevalence of atherosclerotic plaques at a subclinical stage. Therefore, we use the term subclinical atherosclerotic plaque (SAP) throughout. As part of the exploratory design, in a second step, all measures of lifestyle and risk factors were associated with Tregs and their subsets to identify possible correlations with Tregs signatures. In total, 79 participants (male: n = 50; age = 63.6 ± 3.7 years; body mass index = 24.9 ± 3.1 kg/m²; mean ± SD) were assigned to the study (Table 1). All participants were healthy and free of any acute illness, including infection or injury. Participants for this study were recruited between August 2020 and December 2021 as part of the Giessen Immunaging study. Inclusion criteria were age >55 years, and postmenopausal in women. Exclusion criteria included excessive alcohol consumption (>2 drinks per day), smoking, body mass index of <18.5 kg/m², myocardial infarction or other cardiac disease, apoplexy, central or peripheral nerve disease, obstructive lung disease, metabolic diseases (diabetes type 1 or 2), relevant systemic diseases (cancer, arthritis, hepatitis, human immunodeficiency virus, autoimmune diseases), taking any medication affecting the immune system in the last 12 weeks or for the treatment of a defined disease.

Table 1.

Characteristics of participants categorized according to atherosclerotic plaque status.

Characteristics	No SAP (n = 49)	SAP (n = 29)	p value
Age (year)	62.9 ± 3.5	64.8 ± 3.6	0.039
Male	30 (61.2)	20 (69.0)	0.626
BMI (kg/m²)	25.0 ± 3.0	24.8 ± 3.2	0.747
Body fat (%)	27.9 ± 6.6	26.0 ± 6.3	0.230
VO2peak (mL/kg/min)	29.9 ± 7.3	30.6 ± 7.3	0.690
Laboratory parameters
Leukocytes (thousands/µL)	5.3 ± 1.1	5.2 ± 1.1	0.810
Lymphocytes (%)	32.9 ± 7.5	32.0 ± 6.7	0.649
Monocytes (%)	8.6 ± 1.7	8.9 ± 2.2	0.748
Neutrophiles (%)	54.0 ± 8.2	55.3 ± 7.5	0.480
Cytomegalovirus positive	24 (49.0)	17 (58.6)	0.389
Fasting glucose (mg/dL)	98.7 ± 8.0	99.9 ± 9.4	0.556
Fasting insulin (mU/L)	6.2 ± 2.5	6.0 ± 2.7	0.895
HOMA-IR	1.5 ± 0.7	1.5 ± 0.7	0.853
Total cholesterol (mg/dL)	220.7 ± 34.3	210.9 ± 30.9	0.217
Triglycerides (mg/dL)	113.3 ± 88.0	88.8 ± 36.1	0.264
HDL cholesterol (mg/dL)	61.9 ± 15.5	61.7 ± 13.8	0.960
LDL cholesterol (mg/dL)	148.2 ± 33.7	144.5 ± 35.7	0.666
Cardiovascular assessment
Peripheral systolic BP (mmHg)	125.8 ± 10.9	133.8 ± 14.7	0.011
Peripheral diastolic BP (mmHg)	74.4 ± 10.3	77.0 ± 9.8	0.281

Notes: All results presented as mean ± SD or n (%). Bold highlights the significant results. p value denotes differences in characteristics between participants with and without SAP.

Abbreviations: BMI = body mass index; BP = blood pressure; HDL = high-density lipoprotein; HOMA-IR = Homeostatic Model Assessment for Insulin Resistance; LDL = low-density lipoprotein; SAP = subclinical atherosclerotic plaques; VO_2peak = peak oxygen uptake.

The examinations of anthropometrics, immunological parameters, and cardiorespiratory fitness took place at the department of Exercise Physiology and Sports Therapy of the Justus-Liebig-University of Gießen. The vascular screening to detect blood pressure and atherosclerotic plaque took place at the University Hospital Gießen and Marburg at Gießen. All participants were assigned to the group “SAP” or “no SAP” according to the ultrasound measurement of carotid intima.

The Medical Ethics committee of the Justus-Liebig-University Giessen approved this study (AZ 100/20). All experimental procedures were performed according to the Declaration of Helsinki, and all participants gave written informed consent before enrollment.

2.2. Blood sampling and anthropometrics measurements

Venous fasting blood samples were taken from each participant between the hours of 08:00–10:00 for further analysis to determine fasting glucose and lipid profile, isolation of peripheral blood mononuclear cells (PBMCs), and to analyze cytomegalovirus (CMV) serostatus. Fasting concentrations of glucose and insulin, total cholesterol, LDL, high-density lipoprotein, and triglycerides were determined in the serum by standard clinical laboratory methods by SYNLAB Medical Care Center (Bad Nauheim, Germany). Body fat (%) was analyzed by bioelectric impedance analysis (BIACORPUS RX 4004M and BodyComposition – Professional Version: 9.0.20413, MEDI CAL HealthCare GmbH, Karlsruhe, Germany).

2.3. Carotid sonography

The participant was placed in a supine position and a pillow was placed under the neck. The examination was performed using a Philips cx50 device (Philips, Eindhoven, Netherlands) with linear transducer operating at a frequency of 3–12 megahertz (MHz). The carotid artery was observed on both sides from the exit from the subclavian artery to the bifurcation into the internal and external carotid artery in cross-section in the B-mode to detect and quantify atherosclerotic wall changes in this area. If changes were present, these were classified as mild, moderate, and severe. The carotid artery was then visualized in a longitudinal section in the area immediately in front of the bifurcation and intima-media thickness was determined approximately 1 cm proximal to the bulb. Three measurements of the intima-media were carried out on each side, and the mean value was calculated to ensure reproducible results.

2.4. Non-invasive assessment of peripheral blood pressure

We used the non-invasive® device (isymed GmbH, Butzbach, Germany) to acquire pulse pressure waveforms by means of oscillometry. The device uses a validated model of the arterial tree that consists of 721 electronic circuits representing all central and peripheral arterial sections. By modulating the circuit's capacitance, resistance, inductance, and voltage, the system replicates an individual's acquired pulse pressure waves. The non-invasive vascular evaluation of brachial systolic and diastolic blood pressure was carried out for all participants after a 15-min rest period. Measurements were performed in a supine position using 4 conventional cuffs adapted to the upper arm and forearm circumferences of the participants. Radial and brachial pulse pressure waves were acquired on both arms with step-by-step deflation of the cuffs. The measurements took place in a room with a comfortable and stable temperature of 22°C and a lack of external stress influences. Participants were advised not to move during the acquisition of pulse pressure waves. Two brachial and 3 radial measurements were performed to guarantee stable and valid results, with a break of 30 s between each measurement phase. The total duration of the examination was 15 min.

2.5. Isolation of PBMCs

To isolate PBMCs, fresh peripheral blood was 1:1 diluted by phosphate-buffered saline (Gibco, London, UK) and layered onto EasySept using SepMate 50 mL tubes (STEMCELL Technologies, Vancouver, BC, Canada). After 10 min of centrifugation at 1200 × g the upper layer was collected into a clean tube. Cells were washed and centrifuged for 8 min at 300 × g. Isolated PBMCs were frozen down by resuspending cells in freezing medium Bambanker (NIPPON Genetics, Düren, Germany) consisting of 10% dimethyl sulfoxide and the frozen cells were then stored at –80°C for later analysis.

2.6. Cell phenotyping by flow cytometry

To analyze Tregs and their subsets, frozen PBMCs were thawed at 37°C and washed twice in RPMI 1640 (Gibco) with fetal bovine serum 10% (Gibco). Post washing, pelleted cells were resuspended in phosphate-buffered saline (1 × 10⁶ cells/mL) and stained with fluorescence-coupled antibodies (BioLegend, San Diego, CA, USA): CD3 FITC (clone UCHT1), CD4 Alexa Fluor 700 (clone SK3), CD25 APC (clone BC96), CD45RO PerCP/Cyanine 5.5 (clone UCHL1), CD197/CCR7 Brilliant Violet 421 (clone G043H7), and CD127 PE (clone A019D5). Phenotyping included a live/dead-staining using Zombie Aqua. Incubation expired in the dark at room temperature for 20 min with individual determined amount of fluorescence-coupled antibodies.

The gating strategy used to identify Tregs and their subsets was set as follows:²¹ first, lymphocytes were gated on a forward scatter/side scatter dot plot. A minimum of 250.000 events/tube were acquired. Then singlets were determined. Among the singlets, living cells were determined. Among living cells, the frequency of CD3⁺CD4⁺ were evaluated. T effector cells (Teff) were gated as CD4⁺ CD25⁺CD127^high. Tregs were defined and gated as CD4⁺ CD25⁺CD127^low. Subsets of Tregs were gated as rTregs (CD45RO⁻CCR7⁺) and mTregs (CD45RO⁺CCR7⁻). Combinations of surface markers were classified to define the Tregs and subpopulations additionally described in Fig. 1. The Tregs/Teff ratio was calculated by total number of Tregs divided by total number of Teff. For flow cytometry analysis, we used CytoFLEX S and Kaluza analysis software 2.1 (Beckman Coulter, Indianapolis, IN, USA). Spectral overlap when using more than 1 color was corrected via compensation.

Fig. 1.

Frozen PBMCs were thawed and stained with fluorochrome-conjugated antibodies. Gating strategy: lymphocytes were gated in a FSC/SSC dot plot and dead cells excluded using Zombie Aqua. From the living cell population, CD4 T-cells were gated according to CD3⁺/CD4⁺. Teff were gated as CD4⁺CD25⁺CD127^high. Tregs were defined and gated as CD4⁺CD25⁺CD127^low. Subsets of Tregs were gated as rTregs (CD45RO−CCR7⁺) and mTregs (CD45RO⁺CCR7⁻). APC = allophycocyanin; FITC = fluorescein isothiocyanate; FSC = forward scatter; mTregs = effector/memory regulatory T cells; PBMCs = peripheral blood mononuclear cells; PerCP = peridinin-vhlorophyll-protein; rTregs = resting regulatory T cells; SSC = side scatter; Teff = effector T cells; Tregs = regulatory T cells.

2.7. Cardiorespiratory fitness test

The cardiopulmonary exercise testing was performed on an electric bicycle (Excalibur Sport, Lode, Groningen, the Netherlands) using 2 ramp protocols. Briefly, the exercise test started with a 3-min warm-up period without resistance. The exact ramp protocol was selected depending on the training or fitness status of the participant, with the aim of reaching the maximum load after 15 min. Before the test, all participants were asked about their sports history. Participants with no systematic endurance training or competition experience completed a 2-way ramp. After completing 3 min without load, they started at 50 watts and increased by 25 watts every 3 min; from 100 watts, they increased by 25 watts every 2 min. Trained subjects started at 50 watts after the 3-min warm-up period without load and increased by 50 watts every 3 min. The test was performed until complete exhaustion.

The following criteria were used to verify exhaustion: (a) request of the participant to stop due to extreme tiredness and/or perception of intense dyspnea; (b) reached the maximum heart rate (HR_max) based on age was ≥85%; (c) peak respiratory exchange ratio >1.1; (d) oxygen consumption (VO₂) plateau was reached even with increasing workload. Ventilatory and metabolic parameters were collected by respiration using Metalyzer 3-B (Cortex, Leipzig, Germany) and were analyzed. The average of the last test 30 s was used to determine the peak oxygen uptake (VO_2peak).

2.8. Analysis of CMV serostatus

Serum anti-CMV immunoglobulin G antibodies were detected using a semiquantitative sandwich enzyme-linked immunosorbent assay (ELISA-Viditest anti-CMV immunoglobulin G, VIDIA, Vestec, Czech Republic). The procedure followed the manufacturer's instructions. End-point optical density was measured by ELISA reader SPECTROstar Nano (BMG Labtech, Ortenberg, Germany).

2.9. Statistical analysis

The present work was conducted as an exploratory study, and the statistical analysis was performed by SPSS Version 26 (IBM Corp., Armonk, NY, USA). First, the characteristics of participants as well as the T cells and their subsets were compared in terms of the group classifications “SAP” or “no SAP”. Depending on the distribution, which was tested with the Kolmogorov-Smirnoff test, an independent t test or Mann–Whitney U test was used. Next, binary logistic regression was performed to calculate odds ratios (ORs) to determine independent factors affecting atherosclerosis. The dichotomous variable “SAP” or “no SAP” was set as dependent variable. The typical risk factors age, body fat, VO_2peak, fasting glucose, LDL cholesterol, and systolic as well as diastolic blood pressure were set as independent variables. Additionally, Tregs and their subsets were set to a multivariate model. Due to the high multi-collinearity between T cell subtypes, 5 separate models were calculated, whereby in each model Teff, Tregs, or a Tregs subtype would be inserted. In the second part, in order to determine the effect of lifestyle and risk factors on the proportion of Tregs and their subsets, initial univariate linear regression analysis was performed. Stepwise linear regression for multivariate correction was performed when significant relationships in Tregs and their subsets in univariate models were observed. No modification of the α error for multiple testing was taken into consideration because the research of all outcome characteristics was exploratory in nature. For this reason, no sample size calculation was performed. Results are presented as arithmetic mean ± SD unless otherwise stated. Statistical significance level was defined as p < 0.05.

3. Results

3.1. Characteristics of study participants, laboratory parameters, and cardiovascular assessment

Out of the tested 79 participants, SAP was detected in 29 participants. All atherosclerotic wall changes were classified as mild. Regarding the presence of atherosclerotic plaque in the carotid vessels, there was a significant difference between the group with no SAP and this group in terms of age (62.9 ± 3.5 years vs. 64.8 ± 3.6 years; p = 0.039) and peripheral systolic blood pressure (125.8 ± 10.9 mmHg vs. 133.8 ± 14.7 mmHg; p = 0.011). No differences were observed in laboratory parameters, anthropometrics, VO_2peak, or peripheral diastolic blood pressure (Table 1).

3.2. Tregs assessment in participants with and without SAP

The participants with SAP were characterized by a lower percentage of rTregs (28.8% ± 10.7% vs. 34.6% ± 10.7%; p = 0.023). In contrast, a higher percentage of mTregs (40.3% ± 14.7% vs. 30.0% ± 11.9%; p = 0.004) were observed in the group with SAP. There was no difference in the percentage of total Tregs (5.7% ± 1.6% vs. 5.9% ± 1.5%; p = 0.705), Teff (76.2% ± 6.6% vs. 76.4% ± 6.6%), or Tregs/Teff ratio (0.75 ± 0.20 vs. 0.78 ± 0.20) (Fig. 2).

Fig. 2.

Distribution of Teff, Tregs, and their subsets, and Tregs/Teff ratio in study participants categorized according to atherosclerotic plaque status. All results presented as mean ± SD. * denotes significant difference between “no SAP” and “SAP” at level p < 0.05. ** denotes significant difference between “no SAP” and “SAP” at level p < 0.01. mTregs = effector/memory regulatory T cells; rTregs = resting regulatory T cells; SAP = subclinical atherosclerotic plaque; Teff = effector T cells; Tregs = regulatory T cells.

3.3. Analysis of traditional and immunological risk factors for SAP

In the next step, we set the presence of SAP as the dependent variable and include traditional risk factors of atherosclerosis and the different subsets of Tregs as covariates in separate multivariate logistic regression models to determine independent risk factors. Due to high collinearity between the Tregs subsets, we calculated 5 different models. Of the 5 models, 2 revealed significant results, as presented in Table 2. The multivariate logistic regression model including rTregs (χ² = 17.487; p = 0.025) revealed a higher systolic blood pressure (OR = 1.07; 95% confidence interval (95%CI): 1.01–1.14; p = 0.038) as an independent risk factor for SAP. In the second model (χ² = 19.262; p = 0.014), higher age (OR = 1.20; 95%CI: 1.01–1.42; p = 0.038) and a higher proportion of mTregs (OR = 1.05, 95%CI: 1.02–1.10; p = 0.040) are independent risk factors for SAP. The multivariate logistic regression model including Teff (χ² = 14.810; p = 0.063), Tregs (χ² = 19.262; p = 0.064), and Tregs/Teff ratio (χ² = 14.584; p = 0.068) revealed no significant associations (data not shown).

Table 2.

Multivariate logistic regression to identify independent risk factors of SAP.

Model	Model rTregs χ² = 17.487 p = 0.025	Model mTregs χ² = 19.262 p = 0.014
	OR (95%CI) p value	OR (95%CI) p value
Age (year)	1.16 (0.99–1.37) 0.073	1.20 (1.01–1.42) 0.038
Body fat (%)	0.95 (0.83–1.08) 0.395	0.98 (0.86–1.11) 0.722
VO2peak (mL/kg/min)	0.97 (0.87–1.08) 0.573	0.98 (0.88–1.09) 0.667
Fasting glucose (mg/dL)	0.96 (0.88–1.03) 0.249	0.96 (0.89–1.13) 0.332
LDL cholesterol (mg/dL)	1.00 (0.99–1.02) 0.702	1.01 (0.99–1.02) 0.765
Peripheral systolic BP (mmHg)	1.07 (1.01–1.14) 0.038	1.06 (0.99–1.12) 0.094
Peripheral diastolic BP (mmHg)	1.00 (0.93–1.07) 0.902	1.01 (0.94–1.08) 0.847
rTregs	0.95 (0.90–1.01) 0.100	–
mTregs	–	1.05 (1.02–1.10) 0.040

Note: Bold highlights the significant results.

Abbreviations: 95%CI = 95% confidence interval; BP = blood pressure; LDL = low-density lipoprotein; mTregs = effector/memory regulatory T cells; OR = odds ratio; rTregs = resting regulatory T cells; SAP = subclinical atherosclerotic plaques; VO_2peak = peak oxygen uptake.

3.4. Relationships between Tregs and overall characteristics, laboratory parameters, and cardiovascular assessment

To detect further factors that influence the proportion of Tregs as well as their subsets, we performed a simple linear regression analysis (Table 3). In the linear regression, we observed significant correlations between Tregs subsets, overall characteristics, laboratory and cardiovascular parameters. A correlation was found between rTregs and VO_2peak (β = –0.349; p = 0.039), total cholesterol (β = 0.086; p = 0.026), LDL cholesterol (β = 0.107; p = 0.004), and the presence of SAP (β = –5.841; p = 0.020), respectively. A correlation was also found between mTregs and percentage of body fat (β = −0.607; p = 0.011), VO_2peak (β = 0.560; p = 0.008), total cholesterol (β = –0.095; p = 0.044), LDL cholesterol (β = –0.120; p = 0.009), and the presence of SAP (β = 9.759; p = 0.002). There was no association with total Tregs or Tregs/Teff ratio.

Table 3.

Results of univariate linear regression analysis of participant characteristics on Treg, Treg/Teff ratio, rTregs, and mTregs.

	Tregs β (p)	Tregs/Teff ratio β (p)	rTregs β (p)	mTregs β (p)
Age (year)	0.031 (0.476)	0.001 (0.760)	–0.152 (0.656)	–0.256 (0.553)
Sex (male)	–0.211 (0.554)	0.001 (0.765)	0.220 (0.932)	–4.108 (0.204)
Body fat (%)	–0.014 (0.589)	0.001 (0.912)	0.251 (0.189)	–0.607 (0.011)
VO2peak (mL/kg/min)	0.009 (0.697)	0.001 (0.957)	–0.349 (0.039)	0.560 (0.008)
CMV (positive)	–0.258 (0.453)	0.001 (0.877)	–2.113 (0.373)	–0.296 (0.925)
Fasting glucose (mg/dL)	0.023 (0.259)	0.001 (0.342)	0.011 (0.941)	0.002 (0.992)
Fasting insulin (mU/L)	0.105 (0.135)	0.002 (0.006)	0.076 (0.880)	0.387 (0.544)
Total cholesterol (mg/dL)	–0.004 (0.486)	0.001 (0.859)	0.086 (0.026)	–0.095 (0.044)
Triglycerides (mg/dL)	–0.001 (0.761)	0.001 (0.487)	0.020 (0.258)	–0.022 (0.305)
HDL cholesterol (mg/dL)	–0.013 (0.296)	0.001 (0.301)	–0.114 (0.190)	0.133 (0.214)
LDL cholesterol (mg/dL)	–0.002 (0.763)	0.001 (0.956)	0.107 (0.004)	–0.120 (0.009)
SAP (yes)	–0.174 (0.626)	–0.002 (0.745)	–5.841 (0.020)	9.759 (0.002)
Peripheral systolic BP (mmHg)	–0.004 (0.743)	0.001 (0.976)	–0.070 (0.467)	0.150 (0.213)
Peripheral diastolic BP (mmHg)	0.012 (0.464)	0.001 (0.486)	0.052 (0.669)	–0.045 (0.769)

Note: Bold highlights the significant results.

Abbreviations: BP = blood pressure; CMV = cytomegalovirus; HDL = high-density lipoprotein; LDL = low-density lipoprotein; mTregs = effector/memory regulatory T cells; rTregs = resting regulatory T cells; SAP = subclinical atherosclerotic plaques; Teff = effector T cells; Tregs = regulatory T cells; VO_2peak = peak oxygen uptake.

3.5. Multivariate analysis of Tregs subsets

In the next step, stepwise linear regression analysis was performed to detect association when adjusted for cofactors (Table 4). Therefore, only the significant Tregs subsets from simple regression analysis were considered. The analysis demonstrated that in a ranked regression, the rTregs are positively affected by LDL cholesterol (β = 0.104; p = 0.006). It showed that the presence of atherosclerosis (β = 6.733; p = 0.034) is associated with an increased number of mTregs. A higher VO_2peak (β = 0.441; p = 0.048) was associated with a higher frequency of mTregs, whereas the amount of LDL cholesterol (β = –0.096; p = 0.047) was negatively associated with the mTregs subset. There was no significant correlation with other variables.

Table 4.

Results of stepwise multiple regression analysis for LDL cholesterol, VO_2peak, and SAP on subsets of Tregs after adjusting for other factors.

Model	Adjusted R²	β	Standard error	Standard β	p
rTregs	0.094				0.006
LDL cholesterol (mg/dL)		0.104	0.036	0.327	0.006
mTregs	0.171				0.002
VO2peak (mL/kg/min)		0.441	0.219	0.232	0.048
LDL cholesterol (mg/dL)		–0.096	0.048	–0.234	0.047
SAP (yes)		6.733	3.109	0.238	0.034

Abbreviations: LDL = low-density lipoprotein; mTregs = effector/memory regulatory T cells; rTregs = resting regulatory T cells; SAP = subclinical atherosclerotic plaques; Tregs = regulatory T cells; VO_2peak = peak oxygen uptake.

4. Discussion

The present study describes a cohort of clinically healthy elderly individuals, which was defined based on the inclusion criteria as the absence of chronic diseases and intake of medication. However, a focused diagnosis then showed that 37% of the patients could be classified with SAP based on a very precise screening of the carotid artery. It is important to note that we did not deliberately recruit a sick collective alongside a healthy collective based on inclusion and exclusion criteria, but that we identified a subgroup within a healthy collective that, according to certain indicators in our salutogenetic model, is shifted somewhat further towards disease on the continuum between health and disease. Phenotypically, the condition of SAP was associated with older age and higher peripheral and central systolic blood pressure. However, the participants did not differ in any other typical clinical risk factors. Regarding the immunological data, participants with SAP differed in the peripheral distribution of Tregs subpopulations as indicated by a proportional shift from rTregs to mTregs. The number of mTregs and rTregs were associated with LDL cholesterol, while only mTregs were positively associated with VO_2peak and the condition of SAP after multiple adjustments. Overall, a multifactorial and interdependent network of cardiovascular risk factors and immunologic markers was found, which could be useful for the early detection of arteriosclerotic plaques.

The number of SAPs in the cohort included corresponds with previous studies where the prevalence of subclinical carotid atherosclerosis is about 50%.²² Since age is certainly a significant risk factor for vascular disease, this difference is not surprising. Our findings suggest that systolic blood pressure has a major impact on the occurrence of SAP, as there is a difference between the groups. Interestingly, blood pressure in the SAP group can be classified as high normal and not as hypertensive, according to the classifications suggested by the European Society of Cardiology.

Regarding other typical risk factors, we did not find any significant difference in fasting serum glucose, body mass index, or the different markers of cholesterol.

Since subclinical atherosclerosis is now understood to be an inflammatory process, we also found an immunological correlate of the early stage of vascular disease. It is well known that T cells play a key role in the development of arteriosclerosis. Thus, Tregs are present in every state of atherosclerotic plaque.¹⁰ While previous data reported either an inverse correlation between the presence of Tregs and atherosclerosis development¹⁶ or no effect of Tregs on atherosclerosis,²³ the distinction made here between the subpopulations can resolve this supposed contradiction. We show for the first time that despite a comparable total number of Tregs, higher proportions of mTregs and lower proportions of rTregs can be detected in the group with SAP. Physiologically, rTregs decrease with age whereas mTregs typically increase.²⁴ Our multivariate regression model revealed that an increase of mTregs is independent of age and blood pressure. Accordingly, such a difference in Tregs subsets could be diagnostically indicative of early atherosclerosis, possibly making complex vascular diagnostics unnecessary. This seems plausible when looking at the characteristics of the mTregs, a cell type that is highly proliferative and active but vulnerable to apoptosis.²⁵ With their increased effector function, they appear to mitigate tissue damage during enhanced responses of pro-inflammatory effector memory cells.²⁰ In general, the accumulation of Teff is observed during the progression of atherosclerosis.^26,27 A key inductor of differentiation of T_H appears to be oxidized LDL (oxLDL). Antigen-presenting cells take up oxLDL and process it so that antigens from apolipoprotein B can be presented. After T_H cells make contact with the antigen of apolipoprotein B, they develop the full phenotype of Teff or Tregs.²⁶ Thus, our results suggest that even the early stage of atherosclerosis could be a driver of regulatory memory recognition. A later decline in the total number of Tregs as atherosclerosis progresses, which has been shown in other studies,28, 29, 30 could also be due to increased oxLDL levels since this condition is associated with apoptosis of Tregs.^31,32 Therefore, identifying and characterizing subsets of Tregs even before the total number of Tregs is affected seems to be of specific importance for detecting the development of atherosclerosis.

Whereas rTregs are positively associated with serum LDL, mTregs show a negative association. It is speculated that elevated LDL stimulates the release of rTregs from secondary lymphoid organs, as a signal of early immunological activation. This assumption is supported by the work of Guasti et al.,¹⁷ who found an association between elevated LDL levels and Tregs in a preclinical phase of atherosclerosis. However, the association they found was specifically related to the total amount of Tregs. The difference between their results and ours could be due to LDL levels. Guasti et al.¹⁷ detected highly elevated levels of LDL, while only slightly increased LDL levels were measured in our group. As a consequence, an initial shift in subpopulations could be responsible for changes in an otherwise constant proportion of Tregs. We speculate that only in the further progression of atherogenesis, rTregs express a phenotype with effector memory recognition, as suggested by their independent association with the presence of SAP. However, further studies are needed to confirm these suggestions.

Our results also show an independent association between VO_2peak and mTregs. Most previous studies have only investigated associations between VO_2peak and the total population of Tregs, without differentiating the subpopulations. Here, it was found that increased cardiorespiratory fitness is positively associated with the total number of circulating Tregs.^18,33,34 Only Dorneles et al.³⁴ previously showed that Tregs expressing CD39⁺, which have been characterized as mTregs, are associated with moderate to high levels of VO_2peak. We are left to speculate about any physiological connection between cardiopulmonary fitness and altered Tregs ratios. It is known that more intensive sporting activities have pro-inflammatory components related to the stress-induced immune response or muscle damage. In these cases, Tregs also become active and develop their effector functions. Without sufficient Tregs, regeneration processes function only to a limited extent, so regular exercise can promote differentiation into a memory subtype here.

Nevertheless, the present study has some limitations. Although it involves a highly standardized study population, the sample size remains relatively small. Furthermore, the study design itself, characterized by its exploratory nature, should be regarded as a limitation. Future larger trials should encompass other potential risk factors that might impact the immune system and SAP. In the present study, the presence of atherosclerotic plaque was measured with ultrasonography of the carotid vessel. This method is easy to access, non-invasive, and cost-effective. Therefore it is widely used to predict cardiovascular risk, despite the fact that it is not possible to distinguish between vulnerable and non-vulnerable plaques.^35,36 As such, we were not able to define which plaques were at high risk and which might be silent for years. Moreover, the comparison with previous results is complicated as there are different methods and no clear gold standard for measuring the subsets of Tregs. They are tested heterogeneously, and the phenotyping of Tregs and their subsets differs between studies. Nevertheless, the present method of assessment represents a widely used and accepted one.^23,35, 36, 37, 38

5. Conclusion

Overall, the present study shows that about one-third of all older people initially classified as healthy already have SAP. At this early stage, the total proportion of Tregs does not indicate vascular change, which is instead reflected by a shift in Tregs subsets. Factors such as serum LDL or cardiopulmonary fitness may be associated with this shift and may also be additional diagnostic indicators. This information could be used to initiate lifestyle-based preventive measures at an early stage, which may have a protective effect against disease progression.

Peer review under responsibility of Shanghai University of Sport.