Keywords: aging, arterial stiffness, epidemiology, risk factors, T-cells
Abstract
Arterial stiffness measured by total pulse wave velocity (T-PWV) is associated with an increased risk of multiple age-related diseases. T-PWV can be described by structural (S-PWV) and load-dependent (LD-PWV) arterial stiffening. T-cells have been implicated in arterial remodeling, arterial stiffness, and hypertension in humans and animals; however, it is unknown whether T-cells are risk factors for T-PWV or its components. Therefore, we evaluated the cross-sectional associations of peripheral T-cell subpopulations with T-PWV, S-PWV, and LD-PWV. Peripheral blood T-cells were characterized using flow cytometry, and carotid artery stiffness was measured using B-mode ultrasound to calculate T-PWV at the baseline examination in a participant subset of the Multi-Ethnic Study of Atherosclerosis (MESA, n = 1,984). A participant-specific exponential model was used to calculate S-PWV and LD-PWV based on elastic modulus and blood pressure gradients. The associations between five primary (P-significance < 0.01) and 25 exploratory (P-significance < 0.05) immune cell subpopulations, per 1-SD increment, and arterial stiffness measures were assessed using adjusted linear regression models. For the primary analysis, higher CD4+CD28–CD57+, but not CD8+CD28–CD57+, T-cells were associated with higher LD-PWV (β = 0.04 m/s, P < 0.01) after adjusting for covariates. None of the remaining T-cell subpopulations in the primary analysis were associated with T-PWV or S-PWV. For the exploratory analysis, several memory and differentiated/senescence-associated CD4+ and CD8+ T-cell subpopulations were associated with greater T-PWV, S-PWV, and LD-PWV after adjusting for covariates. In conclusion, we highlight novel associations in humans between CD4+ and CD8+ memory and differentiated/senescence-associated T-cell subpopulations and measures of arterial stiffness in MESA. These results warrant longitudinal, prospective studies that examine changes in T-cell subpopulations and arterial stiffness in humans.
NEW & NOTEWORTHY We investigated associations between T-cells and novel measures of structural and load-dependent arterial stiffness in a large multiethnic cohort. The primary analysis revealed that pro-inflammatory, senescence-associated CD4+CD28–CD57+ T-cells were associated with higher load-dependent arterial stiffness. An exploratory analysis revealed that multiple pro-inflammatory CD4+ and CD8+ T-cell subpopulations were associated with both higher structural and load-dependent arterial stiffness. These results suggest that pro-inflammatory T-cells may contribute to arterial stiffness through both arterial remodeling and elevated blood pressure.
INTRODUCTION
Stiffening of the large elastic arteries occurs with aging and is a risk factor for age-related cerebrovascular dysfunction and neurodegenerative disorders, chronic kidney disease, diabetes, cardiovascular diseases (CVD), and death (1–4). Arterial stiffness occurs via multiple mechanisms, including increased pulsatile load from elevated blood pressure (BP) (load-dependent), and structural changes to the vasculature, such as elastin degradation, and collagen deposition (structural stiffening) (5, 6). New methods in measuring arterial stiffness allow for differentiation of the load-dependent (LD-PWV) component from the structural (S-PWV) arterial stiffness component (6).
T-cells have been associated with arterial stiffness and its associated components (e.g., arterial remodeling and blood pressure) (7–13). For example, CD4+CD45RA+ T-cells were associated with lower common carotid artery intima-media thickness and higher proportions of CD4+CD45RO+ (memory) T-cells, and pro-inflammatory CD4+IFN-γ+ (Th1) T-cells were associated with greater common carotid artery intima-media thickness in participants of the Multi-Ethnic Study of Atherosclerosis (MESA) (7, 13). In addition, the treatment with an angiotensin II blocker and statin (telmisartan and rosuvastatin) elicited a coupled reduction in common carotid artery intima-media thickness and pro-inflammatory T-cells CD4+IL-17A+ (Th17) and an increase in anti-inflammatory CD4+ regulatory T-cells (8). Pro-inflammatory CD8+CD57+ and CD8+CD28– T-cells, considered differentiated or senescence-associated, have been associated with increased T-PWV (9, 10), and multiple T-cell subpopulations have also been related to elevated blood pressure or blood pressure regulation (14–16). Although results from MESA have shown that load-dependent stiffening is elevated in adults with hypertension (6), it is not clear whether T-cells are involved in this process.
The purpose of this study was to evaluate the cross-sectional relationships between T-cell populations and aspects of arterial stiffness in a large, multiethnic, community-based cohort. We hypothesized that a higher proportion of pro-inflammatory T-cells (Th1, Th17, CD4+CD28–CD57+, and CD8+CD28–CD57+) would be associated with higher total pulse wave velocity (T-PWV), LD-PWV, and S-PWV, and that higher proportions of T-regulatory cells (TREG) would be associated with lower T-PWV, LD-PWV, and S-PWV.
MATERIALS AND METHODS
MESA Description
The MESA is an ongoing, prospective cohort study investigating subclinical CVD that completed recruitment in 2000–2002 (17). MESA participants were free of known CVD at baseline and were recruited from six US communities. The cohort comprised 6,814 men and women ranging from 45 to 84 yr at baseline and was 38% White, 28% African-American, 22% Hispanic, and 12% Chinese. Written informed consent was obtained from all participants for the study protocol. The study protocol was approved by the MESA field center Institutional Review Boards.
The present analyses leveraged existing data from two prior ancillary studies in MESA for a secondary cross-sectional analysis of immune cell associations with carotid stiffness. The current study includes a subset of MESA participants with carotid distensibility measurements (n = 6,359) (6) and immune cell phenotype data from the baseline examination (n = 2,200) (18, 19). Demographic, medical history, and laboratory data for the present study were also obtained at the baseline examination (17).
Carotid Ultrasound Imaging and Blood Pressure Measurements
The carotid ultrasound image analysis has been described (6). The distal right common carotid artery was measured using B-mode ultrasound and a Logiq 700 ultrasound system (General Electric Medical Systems). Participants rested in the supine position for 10 min, and repeated measures of brachial blood pressure were obtained using an automated upper arm sphygmomanometer (DINAMAP; GE Medical Systems, Milwaukee, WI) before carotid ultrasound image acquisition.
Calculating Total, Load-Dependent, and Structural Carotid Stiffness
T-PWV, LD-PWV, and S-PWV were calculated from the carotid ultrasound images using the Bramwell–Hill equation, as previously described (6). PWV was reported as meters per second (m/s). A participant-specific exponential model was used to differentiate the structural and load-dependent components of carotid artery stiffness (20). S-PWV was calculated as carotid PWV (21) at the reference blood pressure of 120/80 mmHg for all participants. LD-PWV was calculated as the difference between carotid T-PWV calculated at the individuals’ measured blood pressure (BP) and the S-PWV.
Immune Cell Phenotyping
The immune cell isolation procedures have been described extensively (18, 22). Peripheral blood mononuclear cells (PBMCs) were isolated using CPT tubes and cryopreserved. PBMCs were thawed and then either labeled with cell surface markers or activated with phorbol myristate acetate and ionomycin in the presence of Brefeldin A. Cells were fixed in paraformaldehyde and analyzed using a MACSQuant 10 flow cytometer (Miltenyi Biotec, Germany). The immune cell subpopulations were reported as percentages. Natural killer, B-cells, CD4, and CD8 were reported as a percent of gated lymphocytes, CD4 subsets as a percent of CD4+ cells, and CD8 subsets as a percent of CD8+ cells. Monocyte subsets were reported as a percent of CD14+ gated monocytes, and B-cell subsets were reported as a percent of CD19+ B-cells (18, 22). The flow cytometry gating strategy is published (18).
Statistical Analysis
Associations of immune cell subpopulations (independent variable), evaluated per 1-SD increment, with arterial stiffness measures (dependent variable) were estimated using weighted linear regression models. Multiple testing was adjusted for the five primary exposures using Bonferroni correction, with a significance threshold of P ≤ 0.01. Analyses of the secondary immune cell populations were exploratory and utilized a significance threshold of P < 0.05. The models were adjusted for age, sex, race/ethnicity, cell phenotyping batch, study site, education, and log-transformed cytomegalovirus (CMV) titers. We also assessed cell-by-age, cell-by-race/ethnicity, and cell-by-sex interaction terms (F test with 3 degrees of freedom), with a P-interaction significance <0.05. To account for the sampling design, analyses were weighted using empirical sampling weights.
RESULTS
Participant Descriptives
Of the 6,814 MESA participants, 1,984 had immune cell, covariate, and arterial stiffness data. Participant characteristics and descriptive statistics are presented in Supplemental Table S1.
Primary Analysis
After adjusting for multiple hypothesis testing, a 1-SD higher proportion of CD4+CD28–CD57+ T-cells was associated with higher LD-PWV, but not T-PWV or S-PWV (Table 1). There were no associations between the other T-cell subsets specified in our a priori hypotheses, including CD4+IFN-γ+, CD4+IL-17A+, CD4+CD25+CD127–, or CD8+CD28–CD57+ T-cells with T-PWV, S-PWV, or LD-PWV (Table 1).
Table 1.
Linear regression estimates for T-cells included in primary hypotheses with total, structural, and load-dependent carotid artery stiffness, weighted
| Markers | Phenotype | β | SE | 95% CI | P Value |
|---|---|---|---|---|---|
| T-PWV | |||||
| CD4+IFN-γ+ | Th1 CD4+ T-cells | 0.07 | 0.04 | (–0.00, 0.15) | 0.06 |
| CD4+IL-17A+ | Th17 CD4+ T-cells | –0.01 | 0.04 | (–0.08, 0.07) | 0.87 |
| CD4+CD25+CD127– | Regulatory CD4+ T-cells | –0.01 | 0.04 | (–0.08, 0.07) | 0.84 |
| CD4+CD28–CD57+ | Differentiated/immunosenescent CD4+ T-cells | 0.06 | 0.04 | (–0.01, 0.14) | 0.09 |
| CD8+CD28–CD57+ | Differentiated/immunosenescent CD8+ T-cells | 0.05 | 0.04 | (–0.02, 0.12) | 0.13 |
| S-PWV | |||||
| CD4+IFN-γ+ | Th1 CD4+ T-cells | 0.04 | 0.03 | (–0.03, 0.11) | 0.23 |
| CD4+IL17A+ | Th17 CD4+ T-cells | –0.004 | 0.03 | (–0.07, 0.06) | 0.90 |
| CD4+CD25+CD127– | Regulatory CD4+ T-cells | 0.01 | 0.03 | (–0.06, 0.07) | 0.82 |
| CD4+CD28–CD57+ | Differentiated/immunosenescent CD4+ T-cells | 0.02 | 0.03 | (–0.05, 0.08) | 0.55 |
| CD8+CD28–CD57+ | Differentiated/immunosenescent CD8+ T-cells | 0.03 | 0.03 | (–0.03, 0.09) | 0.34 |
| LD-PWV | |||||
| CD4+IFN-γ+ | Th1 CD4+ T-cells | 0.03 | 0.02 | (0.00, 0.06) | 0.03 |
| CD4+IL17A+ | Th17 CD4+ T-cells | –0.002 | 0.01 | (–0.03, 0.03) | 0.91 |
| CD4+CD25+CD127– | Regulatory CD4+ T-cells | –0.02 | 0.01 | (–0.04, 0.01) | 0.30 |
| CD4+CD28–CD57+ | Differentiated/immunosenescent CD4+ T-cells | 0.04 | 0.01 | (0.01, 0.07) | 0.003* |
| CD8+CD28–CD57+ | Differentiated/immunosenescent CD8+ T-cells | 0.02 | 0.01 | (–0.00, 0.05) | 0.09 |
The data are weighted to account for the sampling design. Models are adjusted for age, analytical batch, sex, race/ethnicity, MESA study site, education, and log-transformed cytomegalovirus titers. CI, confidence interval; LD-PWV, load-dependent pulse wave velocity; MESA, Multi-Ethnic Study of Atherosclerosis; S-PWV, structural pulse wave velocity; T-PWV, total pulse wave velocity. *Significance for primary exposures P < 0.01.
Exploratory Analysis
Total arterial stiffness.
In exploratory analyses, each 1-SD higher CD4+CD45RA+ and CD4+CD38+ T-cells were associated with lower T-PWV, whereas each 1-SD higher CD4+CD45RO+, CD4+CD28–CD57+CD45RA+, and CD8+CD57+ T-cells were associated with higher T-PWV (Table 2). No associations were observed for other CD4+ T-helper (Th) or CD8+ T-cytotoxic (Tc) subsets; however, each 1-SD higher proportion of innate immune CD3+γδ+ T-cells was associated with higher T-PWV (Table 2). There were no associations between other immune cells evaluated and T-PWV (Supplemental Table S2).
Table 2.
Secondary analysis linear regression estimates for immune cells included in secondary analyses with total carotid artery stiffness, weighted
| Markers | Phenotype | β | SE | 95% CI | P Value |
|---|---|---|---|---|---|
| CD4+ | Pan CD4+ T-cells | –0.01 | 0.04 | (–0.08, 0.06) | 0.70 |
| CD4+CD45RA+ | CD4+ naïve or TEMRA T-cells | –0.09 | 0.04 | (–0.16, –0.02) | 0.01Ⴕ |
| CD4+CD38+ | Activated CD4+ T-cells | –0.14 | 0.04 | (–0.22, –0.06) | <0.001Ⴕ |
| CD4+IL4+ | Th2 CD4+ T-cells | 0.02 | 0.04 | (–0.06, 0.09) | 0.68 |
| CD4+CD25+ | T regulatory/activated T-cells | 0.02 | 0.04 | (–0.06, 0.10) | 0.61 |
| CD4+CD45RO+ | CD4+ memory T-cells | 0.09 | 0.04 | (0.01, 0.16) | 0.02Ⴕ |
| CD4+CD28– | Differentiated/immunosenescent CD4+ T-cells | 0.05 | 0.04 | (–0.03, 0.12) | 0.21 |
| CD4+CD57+ | Differentiated/immunosenescent CD4+ T-cells | 0.03 | 0.04 | (–0.05, 0.11) | 0.44 |
| CD4+CD28–CD57+CD45RA+ | CD4+ TEMRA T-cells | 0.08 | 0.04 | (0.01, 0.15) | 0.03Ⴕ |
| CD8+ | Pan CD8+ T-cells | 0.03 | 0.04 | (–0.04, 0.1) | 0.38 |
| CD8+CD45RA+ | CD8+ naïve or TEMRA T-cells | –0.02 | 0.04 | (–0.09, 0.05) | 0.64 |
| CD8+CD38+ | Activated CD8+ T-cells | –0.05 | 0.04 | (–0.12, 0.03) | 0.23 |
| CD8+IFN-γ+ | Tc1 CD8+ T-cells | 0.01 | 0.04 | (–0.07, 0.09) | 0.73 |
| CD8+IL-4+ | Tc2 CD8+ T-cells | –0.04 | 0.05 | (–0.14, 0.05) | 0.37 |
| CD8+IL-17A+ | Tc17 CD8+ T-cells | –0.01 | 0.04 | (–0.09, 0.08) | 0.87 |
| CD8+CD45RO+ | CD8+ memory T-cells | 0.04 | 0.04 | (–0.03, 0.11) | 0.27 |
| CD8+CD28– | Differentiated/immunosenescent CD8+ T-cells | 0.02 | 0.04 | (–0.05, 0.09) | 0.6 |
| CD8+CD57+ | Differentiated/immunosenescent CD8+ T-cells | 0.10 | 0.04 | (0.03, 0.17) | 0.004Ⴕ |
| CD8+CD28–CD57+CD45RA+ | CD8+ TEMRA T-cells | 0.04 | 0.04 | (–0.03, 0.11) | 0.27 |
| CD3+γδ | γδ T-cells | 0.09 | 0.03 | (0.02, 0.15) | 0.01Ⴕ |
The data are weighted to account for the sampling design. Models are adjusted for age, sex, race/ethnicity, MESA study site, analytical batch, education, and log-transformed cytomegalovirus titers. CI, confidence interval; MESA, Multi-Ethnic Study of Atherosclerosis; TEMRA, effector memory T-cell reexpressing CD45RA. ႵSignificance for secondary exposures P < 0.05.
Structural arterial stiffness.
Each 1-SD higher CD4+CD45RA+ and CD4+CD38+ T-cells were associated with lower S-PWV, whereas each 1-SD higher CD8+CD57+ T-cell subpopulation was associated with higher S-PWV (Table 3). Similar to T-PWV, no associations with CD4+ Th or CD8+ Tc subsets were observed, whereas each 1-SD higher CD3+γδ+ T-cells was associated with higher S-PWV. No other immune cell subset was associated with S-PWV (Supplemental Table S2).
Table 3.
Secondary analysis linear regression estimates for immune cells included in secondary analyses with structural carotid artery stiffness, weighted
| Markers | β | SE | 95% CI | P Value |
|---|---|---|---|---|
| CD4+ | –0.02 | 0.03 | (–0.08, 0.05) | 0.63 |
| CD4+CD45RA+ | –0.07 | 0.03 | (–0.13, –0.01) | 0.03Ⴕ |
| CD4+CD38+ | –0.11 | 0.04 | (–0.18, –0.04) | 0.002Ⴕ |
| CD4+IL4+ | 0.03 | 0.03 | (–0.03, 0.10) | 0.31 |
| CD4+CD25+ | 0.03 | 0.03 | (–0.04, 0.09) | 0.47 |
| CD4+CD45RO+ | 0.06 | 0.03 | (–0.00, 0.13) | 0.06 |
| CD4+CD28– | 0.01 | 0.03 | (–0.06, 0.07) | 0.86 |
| CD4+CD57+ | –0.003 | 0.03 | (–0.07, 0.07) | 0.94 |
| CD4+CD28–CD57+CD45RA+ | 0.05 | 0.03 | (–0.02, 0.11) | 0.17 |
| CD8+ | 0.04 | 0.03 | (–0.02, 0.10) | 0.23 |
| CD8+CD45RA+ | –0.02 | 0.03 | (–0.08, 0.04) | 0.55 |
| CD8+CD38+ | –0.03 | 0.04 | (–0.10, 0.04) | 0.41 |
| CD8+IFN-γ+ | 0.02 | 0.04 | (–0.06, 0.09) | 0.65 |
| CD8+IL4+ | 0.004 | 0.04 | (–0.08, 0.09) | 0.92 |
| CD8+IL17A+ | –0.01 | 0.04 | (–0.09, 0.06) | 0.70 |
| CD8+CD45RO+ | 0.04 | 0.03 | (–0.02, 0.10) | 0.17 |
| CD8+CD28– | 0.004 | 0.03 | (–0.06, 0.07) | 0.91 |
| CD8+CD57+ | 0.07 | 0.03 | (0.01, 0.13) | 0.027Ⴕ |
| CD8+CD28–CD57+CD45RA+ | 0.02 | 0.03 | (–0.04, 0.08) | 0.59 |
| CD3+γδ | 0.09 | 0.03 | (0.03, 0.15) | 0.003Ⴕ |
The data are weighted to account for the sampling design. Models are adjusted for age, sex, race/ethnicity, MESA study site, analytical batch, education, and log-transformed cytomegalovirus titers. CI, confidence interval; MESA, Multi-Ethnic Study of Atherosclerosis. ႵSignificance for secondary exposures P < 0.05.
Load-dependent arterial stiffness.
Each 1-SD higher value of CD4+CD28–, CD4+CD57+, CD4+CD28–CD57+CD45RA+, and CD8+CD57+ T-cells was associated with higher LD-PWV, whereas each 1-SD higher CD8+IL-4+ T-cell population was associated with lower LD-PWV (Table 4). No other T-cell subpopulations were associated with LD-PWV. Finally, each 1-SD higher proportion of CD3–CD16+CD56+ cells was associated with higher LD-PWV (Supplemental Table S2).
Table 4.
Secondary analysis linear regression estimates for immune cells included in secondary analyses with load-dependent carotid artery stiffness, weighted
| Markers | β | SE | 95% CI | P Value |
|---|---|---|---|---|
| CD4+ | 0.02 | 0.01 | (–0.03, 0.03) | 0.91 |
| CD4+CD45RA+ | –0.02 | 0.01 | (–0.05, 0.01) | 0.12 |
| CD4+CD38+ | –0.03 | 0.02 | (–0.06, 0.01) | 0.10 |
| CD4+IL4+ | –0.02 | 0.01 | (–0.05, 0.01) | 0.19 |
| CD4+CD25+ | –0.01 | 0.02 | (–0.03, 0.02) | 0.73 |
| CD4+CD45RO+ | 0.03 | 0.01 | (–0.00, 0.05) | 0.09 |
| CD4+CD28– | 0.04 | 0.02 | (0.01, 0.07) | 0.004Ⴕ |
| CD4+CD57+ | 0.03 | 0.02 | (0.00, 0.06) | 0.028Ⴕ |
| CD4+CD28–CD57+CD45RA+ | 0.04 | 0.01 | (0.01, 0.06) | 0.013Ⴕ |
| CD8+ | –0.01 | 0.01 | (–0.03, 0.02) | 0.65 |
| CD8+CD45RA+ | 0.002 | 0.01 | (–0.02, 0.03) | 0.85 |
| CD8+CD38+ | –0.02 | 0.02 | (–0.05, 0.01) | 0.26 |
| CD8+IFN-γ+ | –0.002 | 0.02 | (–0.03, 0.03) | 0.90 |
| CD8+IL4+ | –0.05 | 0.02 | (–0.08, –0.01) | 0.01Ⴕ |
| CD8+IL17A+ | 0.01 | 0.02 | (–0.03, 0.04) | 0.66 |
| CD8+CD45RO+ | –0.004 | 0.01 | (–0.03, 0.02) | 0.75 |
| CD8+CD28– | 0.02 | 0.01 | (–0.01, 0.04) | 0.24 |
| CD8+CD57+ | 0.03 | 0.01 | (0.01, 0.06) | 0.01Ⴕ |
| CD8+CD28–CD57+CD45RA+ | 0.02 | 0.01 | (–0.01, 0.05) | 0.11 |
| CD3+γδ | –0.01 | 0.01 | (–0.03, 0.02) | 0.69 |
The data are weighted to account for the sampling design. Models are adjusted for age, sex, race/ethnicity, MESA study site, analytical batch, education, and log-transformed cytomegalovirus titers. CI, confidence interval; MESA, Multi-Ethnic Study of Atherosclerosis. ႵSignificance for secondary exposures P < 0.05.
Age interactions.
Significant interactions with age were observed for CD4+CD45RA+ (Pinteraction = 0.01), CD4+CD25+CD127– (Pinteraction = 0.008), CD4+IFN-γ+ (Pinteraction = 0.01), CD4+IL-4+ (Pinteraction = 0.03), and CD4+CD45RO+ (Pinteraction = 0.03) with LD-PWV. Age-stratified results (≥65 and <65 yr) are presented in Supplemental Table S3.
Race/ethnicity and sex interactions.
We observed significant interactions with race/ethnicity for CD8+CD38+ (Pinteraction = 0.003), CD3+γδ+ (Pinteraction = 0.03), and CD3–CD16+CD56+ cells (Pinteraction = 0.03) with T-PWV. There was also a CD8+CD38+ T-cell by race/ethnicity interaction for S-PWV (Pinteraction < 0.001) and for CD3+γδ+ T-cells and LD-PWV (Pinteraction = 0.047). The race/ethnicity stratified results are presented in Supplemental Table S4. There were no sex interactions (all Pinteractions > 0.05).
DISCUSSION
In a multiethnic, community-based cohort of adults, we assessed the cross-sectional associations between circulating immune cell subpopulations and carotid arterial stiffness measures. In accordance with our hypotheses, higher differentiated/senescence-associated CD4+CD28–CD57+ T-cells were associated with higher load-dependent carotid artery stiffness (LD-PWV). This finding suggests that CD4+CD28–CD57+ T-cells may be related to the risk of arterial stiffening caused by elevated blood pressure. The null findings for Th1 (CD4+IFN-γ+), Th17 (CD4+IL-17A+), TREG (CD4+CD25+CD127–), and CD8+CD28–CD57+ T-cells and LD-PWV are consistent with a previous MESA study that similarly reported no associations of these cells with blood pressure (23). Th1, Th17, CD4+TREG, and CD4+ or CD8+ CD28–CD57+ T-cells were also not associated with total (T-PWV) or structural (S-PWV) carotid artery stiffness. In the exploratory analyses, which were not adjusted for multiple hypothesis testing, several memory and differentiated/senescence-associated T-cell subpopulations were associated with T-PWV, S-PWV, and S-PWV.
Results from the exploratory analyses are consistent with prior studies on changes in T-cell populations during aging and their roles in chronic inflammation (24–29). In the present study, lower CD4+CD45RA+ T-cells were associated with higher T-PWV and S-PWV, whereas higher CD4+CD45RO+ (memory) T-cells were associated with higher T-PWV. Associations of these subsets with carotid artery stiffness are generally consistent with a prior study in MESA demonstrating a similar cellular pattern was associated with measures of subclinical atherosclerosis (13). Although we are unable to establish whether CD4+CD45RA+ T-cells are naïve or effector memory T-cells reexpressing CD45RA (TEMRA) in MESA, the direction of the observed associations suggests this subpopulation is representative of naïve T-cells. For instance, we observed higher CD4+CD45RA+ T-cells were associated with lower T-PWV and S-PWV, whereas higher circulating levels of CD4+ TEMRA cells were associated with higher T-PWV and LD-PWV. These data are consistent with naïve T-cells being associated with lower arterial stiffness. In addition to CD4+ TEMRA cells, other terminally differentiated or senescence-associated T-cell subpopulations, such as CD4+CD28– and CD4+CD57+ T-cells (LD-PWV) and CD8+CD57+ T-cells (T-PWV, S-PWV, and LD-PWV), were associated with higher PWV measures. These results suggest that TEMRA, CD28–, or CD57+ T-cell subsets, which reflect different states of terminal differentiation or senescence, may play roles in arterial stiffening caused by structural or functional alterations. The associations between senescence-associated CD8+ T-cells and T-PWV, S-PWV, and LD-PWV extend findings from small clinical studies into a large multiethnic cohort (9, 10, 14) and also provide novel associations between senescence-associated CD4+ T-cells and carotid arterial stiffness.
To further understand the effect of age on the associations between T-cells and carotid artery stiffness, we tested for an interaction effect of age and observed several significant relationships. When stratifying by age, higher Th2 cells (CD4+IL-4+) were associated with lower LD-PWV in adults <65 yr of age. Among adults of age ≥65 yr, there was an association of higher CD4+CD45RA+ T-cells with lower LD-PWV. We also observed higher Th1 and CD4+ memory T-cells were associated with higher LD-PWV in adults older than 65 yr, which are generally consistent with previous findings in mid-life adults with hypertension (15, 16). Collectively, these results might suggest that loss of naïve T-cells, which are generally higher in young adults, and accumulation of memory T-cells, which are generally higher in older adults, are related to the risk of higher carotid artery stiffness. Animal models of hypertension and aging have consistently shown that T-cells induce elevations in blood pressure or arterial stiffness and arterial remodeling (11, 12, 15, 30–32). These effects are likely caused by a shift toward either circulating or tissue-infiltrating T-cell subpopulations that produce inflammatory cytokines or reactive oxygen species. It is possible that these mechanisms are conserved in humans and explain associations in the current study, but this requires validation.
Our study has several limitations. First, because these data are cross-sectional, we are unable to make causal inferences, and indeed, it is possible that measures of vascular stiffness influence T-cell composition. Second, we used cryopreserved cells, which may not fully recapitulate the cells found in circulation or resident in tissues; however, data have shown that most cell types are highly correlated after cryopreservation (33). Third, we lack more comprehensive measures of cellular senescence and other T-cell phenotypes or an evaluation of their molecular mediators (34–36). Fourth, the independent or joint contributions of age, hypertension, and atherosclerosis toward arterial stiffness are complex and difficult to disentangle and may exhibit different immune cell repertoires. We are unable to determine whether the roles of T-cells in age-associated or atherosclerosis-associated inflammation were particularly important in driving the relationships with arterial stiffening. Future longitudinal studies are warranted to help address these questions. Fifth, a majority of the findings were from exploratory analyses, not corrected for multiple hypothesis testing.
In a population-based cohort free of clinical CVD, memory and terminally differentiated/senescence-associated CD4+ and CD8+ T-cell subpopulations were associated with greater arterial stiffness as estimated by T-PWV, S-PWV, and LD-PWV. Although longitudinal studies are warranted to confirm these associations, our study highlights novel relationships between multiple senescence-associated T-cell subpopulations and carotid artery stiffness and its associated components (S-PWV and LD-PWV) in a large, multiethnic cohort of older adults.
DATA AVAILABILITY
MESA data can be requested from the Collaborative Health Studies Coordinating Center (CHSCC) upon approval of a paper proposal using this data. The instructions are at https://www.mesa-nhlbi.org/. MESA data are also available via BIOLINCC (https://biolincc.nhlbi.nih.gov/home/). The corresponding author can be contacted for the analytic methods. The ultrasound images are held internally at MESA to protect participant privacy. Any questions or interest in the ultrasound images, immune cell data, or other study materials should be directed to MESA via the study authors or the CHSCC.
SUPPLEMENTAL MATERIAL
Supplemental Tables S1–S4: https://figshare.com/s/a424244fdb45f2f1e8f1.
GRANTS
The research reported in this article was supported by R00HL129045, R01HL120854, and R01HL135625 from the National Heart, Lung, and Blood Institute (NHLBI), and the Translational Training in Aging and Alzheimer’s Disease Related Disorders (T32AG033534) from the National Institute on Aging (NIA). MESA was supported by contracts 75N92020D00001, HHSN268201500003I, N01-HC-95159, 75N92020D00005, N01-HC-95160, 75N92020D00002, N01-HC-95161, 75N92020D00003, N01-HC-95162, 75N92020D00006, N01-HC-95163, 75N92020D00004, N01-HC-95164, 75N92020D00007, N01-HC-95165, N01-HC-95166, N01-HC-95167, N01-HC-95168, and N01-HC-95169 from the NHLBI, and by Grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS).
DISCLOSURES
R.P. has patent applications related to arterial stiffness calculation methods and B.M.P. serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. None of the other authors has any conflicts of interest, financial or otherwise, to disclose.
AUTHOR CONTRIBUTIONS
T.M.D., P.B., R.P., J.A.D., B.M.P., R.P.T., M.F.D., T.M.H., A.D.G., J.D., and N.C.O. conceived and designed research; R.P., M.F.D., and A.D.G. performed experiments; P.B., R.P., M.F.D., and A.D.G. analyzed data; T.M.D., J.A.D., B.M.P., R.P.T., T.M.H., J.D., and N.C.O. interpreted results of experiments; T.M.D. prepared figures; T.M.D. drafted manuscript; T.M.D., P.B., R.P., J.A.D., B.M.P., R.P.T., M.F.D., C.M.S., A.L.L., S.A.H., T.M.H., A.G.B., A.D.G., J.D., and N.C.O. edited and revised manuscript; T.M.D., J.D., and N.C.O. approved final version of manuscript.
ACKNOWLEDGMENTS
The authors thank the other investigators, the staff, and the participants of the MESA study for valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org. Graphical abstract was created with a licensed version of BioRender.com.
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Supplementary Materials
Supplemental Tables S1–S4: https://figshare.com/s/a424244fdb45f2f1e8f1.
Data Availability Statement
MESA data can be requested from the Collaborative Health Studies Coordinating Center (CHSCC) upon approval of a paper proposal using this data. The instructions are at https://www.mesa-nhlbi.org/. MESA data are also available via BIOLINCC (https://biolincc.nhlbi.nih.gov/home/). The corresponding author can be contacted for the analytic methods. The ultrasound images are held internally at MESA to protect participant privacy. Any questions or interest in the ultrasound images, immune cell data, or other study materials should be directed to MESA via the study authors or the CHSCC.