Skip to main content
NCBI home page
Wiley Open Access Collection logoLink to Wiley Open Access Collection
. 2025 May 10;62(5):e70068. doi: 10.1111/psyp.70068

Acute Psychological Stress and Pulse Wave Velocity: Meta‐Analysis and Recommendations for Future Research

Gabriel Zieff 1,2,3,4,✉, Noora Sharma 1, Keeron Stone 5,6, Patricia Pagan Lassalle 1,2,7, Aiden J Chauntry 1, Erik D Hanson 1,2, Michelle L Meyer 2,7,8, Claudio Battaglini 1,2, Justin B Moore 9, Craig Paterson 1,10, Lee Stoner 1,2,7,11
PMCID: PMC12065061  PMID: 40346916

ABSTRACT

Repeated exposures to acute psychological stress may be associated with cardiovascular disease (CVD) risk, but the mechanisms underlying this relationship are not fully understood. The objective of this meta‐analysis was to determine the effect of acute psychological stress on central pulse wave velocity (PWV) compared to pre‐stress (baseline) levels in adults free of overt CVD. Electronic databases (PubMed, SPORTDiscus, and Google Scholar) were queried from inception to July 2024. Reference lists of eligible studies and previous relevant reviews were also screened. Studies were included if: (i) a noninvasive measure of PWV was used that included a central (aortic) arterial segment; (ii) participants were adults (≥ 18 years) free of overt CVD; and (iii) the acute stressor was purely psychological in nature. Appraisal and Synthesis Methods: Effect sizes were calculated as standardized mean differences (SMD) and pooled using a random‐effects model. The magnitude of effect was adjudicated as trivial (< 0.2), small (0.2), moderate (0.5), or large (0.8). A total of 11,689 studies were identified, from which 7 studies (11 effects, N = 162 participants) were eligible for inclusion. Moderate Acute psychological stress induced a moderate (SMD: 0.51, p < 0.0001; 95% CI: 0.34, 0.68) increase (detrimental) in central PWV, and there was insubstantial heterogeneity between studies (Cochran's Q (10) = 2.62 (p = 0.99)). The small overall number of studies as well as key differences in study methodologies limit the ability to elucidate the magnitude and consistency of stress‐induced increases in PWV. Nonetheless, the present findings suggest that acute psychological stress induces significant increases in central PWV among adults free of overt CVD. The acute PWV response to psychological stress likely contributes to elevated CVD risk over time.

Impact Statement

Psychological stress increases cardiovascular disease (CVD) risk, but mechanisms are not fully understood. Arterial stiffness is a key biomarker of vascular aging that has been reported to increase with acute stress exposure. However, effect sizes have ranged considerably and the literature hasn’t been reviewed. Meta‐analytic findings demonstrate moderate increases in arterial stiffness. Additional research with comparable methodologies is needed. Nonetheless, findings suggest that moderate increases in arterial stiffness induced by acute stress may contribute to CVD risk over time.

1. Introduction

Psychological stress—the perception that demands to a threat or challenge supersede the ability to cope with those demands (Lazarus 1984)—is an independent and ubiquitous risk factor for cardiovascular disease (CVD) (Brotman et al. 2007; Gill et al. 2023). The physiological reaction to stress is an evolutionary trait that enabled our ancestors—and us—to respond to situations that are perceived as threats to our wellbeing and/or survival (McEwen 2007). Factors that influence the physiological (and psychological) responses to stress are wide ranging and include genetics, developmental, and contextual (e.g., perceived racial discrimination) factors, lifestyle behaviors (e.g., physical activity levels) past and present stress exposures, as well as intrapersonal factors (e.g., emotion regulation). Regardless of whether a stressor is physical (e.g., pain, cold) or psychological (e.g., public speaking) in nature, the same cascade of physiological reactions generally ensues, mediated largely by two distinct, yet coordinated systems: the sympathetic nervous system (SNS) and the hypothalamic–pituitary–adrenal (HPA) axis.

The SNS—often colloquially termed the “fight‐or‐flight” response—stimulates the release of catecholamines into the bloodstream, which sharply increases cardiac output and diverts blood flow, and thus oxygen, to essential tissues necessary for immediate survival (e.g., skeletal, cardiac muscle). Though not as fast‐acting, the HPA axis similarly serves to increase the metabolic capacity of the body by releasing the catabolic steroid hormone cortisol, which mobilizes energy stores and makes glucose more readily available to tissues in need. While the physiological response to stress is adaptive in the short term, repetitive exposure dysregulates SNS and HPA systems and taxes numerous downstream biological processes, ultimately contributing to systemic inflammation and heightened CVD risk. Though the precise mechanisms are not fully understood, a biologically plausible means by which repetitive exposure to psychological stress increases CVD risk is through the acceleration of biological vascular aging (Chida and Steptoe 2010a; Everson‐Rose and Lewis 2005; Steptoe and Kivimäki 2012; Walton et al. 1995).

Biological vascular aging refers to the normal vascular structural and functional (e.g., endothelium‐mediated) changes that occur over the lifespan with chronological aging, and is also moderated by CVD risk factors (Kucharska‐Newton et al. 2019; Stoner et al. 2023). The most accepted approach for measuring biological vascular aging is arterial stiffness. Reflecting both arterial structure and function, arterial stiffening results in faster propagation of the pulse‐pressure wave through one or more segments of the arterial tree (Kucharska‐Newton et al. 2019; London and Pannier 2010; Stoner et al. 2023). The time scale of an acute exposure to a psychological stressor is unlikely to affect arterial structure, but rather more plausibly impacts arterial function. Our group has reported that measures of arterial stiffness are sensitive to acute changes in endothelial function (Stoner et al. 2020), while others have similarly demonstrated measures are dependent on nitric oxide (Kinlay et al. 2001; Wilkinson et al. 2002), the primary regulator of endothelial function (Michel and Vanhoutte 2010). Repetitive acute increases in arterial stiffness can lead to structural remodeling, including decreased elastin and increased collagen, and chronically increase arterial stiffness (Stoner et al. 2023).

The gold standard measure of arterial stiffness is pulse wave velocity (PWV) (Laurent et al. 2006; Van Bortel et al. 2012). PWV is defined as distance (length of arterial segment) divided by pulse‐transit time (PTT), with a higher (faster) value reflecting increased arterial stiffness and CVD risk (London and Pannier 2010). PWV can be measured repeatedly in a relatively confined timeframe with minimal subject burden. This is a major advantage in the context of acute psychological stress studies, where cardiovascular parameters, including heart rate, blood pressure (BP), and PWV, generally peak within a short‐lived window of time (e.g., within ≈1–15 min, with large interindividual variation) before more slowly returning toward resting levels (Gentilin et al. 2023; Lackner et al. 2010; Vlachopoulos et al. 2006). The most established and clinically relevant PWV pathlengths include aortic arterial segments since aortic distensibility directly influences myocardial afterload and transmission of pulse pressure to critical end‐organs including the brain and kidneys (Briet et al. 2012; London and Pannier 2010; Pewowaruk et al. 2023). Measurements of PWV encompassing an aortic segment (e.g., carotid‐femoral PWV [cfPWV], brachial‐ankle [baPWV]) confer superior prognostic capacity than traditional CVD risk factors (Stone et al. 2023; Townsend et al. 2015; Yi et al. 2023). Thus, both aortic and composite (e.g., containing both central and peripheral segments) may be appropriate to investigate in the context of stress exposure and CVD risk.

In this study, we define PWV as a construct that represents a composite measure of arterial structure (i.e., stiffness) and function. A number of studies have reported that acute psychological stressors increase measures of PWV (Dutch and Redman 1983; Gentilin et al. 2023; Jatoi et al. 2014; Kume et al. 2020; Kume et al. 2022a; Logan et al. 2020; Szabo 1993; Vlachopoulos et al. 2006; Vlachopoulos et al. 2009), but the effect magnitude has varied greatly with standardized mean differences (SMD) ranging from 0.01 (trivial) (Szabo 1993) to 0.78 (moderate) (Vlachopoulos et al. 2006). The mixed findings may be attributable, at least in part, to the use of relatively small sample sizes (mean: n = 31.3, median: n = 23). To bring clarity to the literature, a meta‐analytic approach can be used to determine a singular effect size estimate.

1.1. Objective

The objective of this meta‐analysis was to determine the effect of acute psychological laboratory stressors, as compared to baseline (pre‐stress) levels, on central PWV, defined herein as including a portion of the aorta, in disease‐free adults. Secondarily, we aimed to provide recommendations for future research surrounding the effects of acute psychological stress on central PWV.

2. Methods

This meta‐analysis follows PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) reporting guidelines. (Page et al. 2021)

2.1. Data Sources and Searches

Two authors (GZ and NS) independently searched electronic databases (PubMed, SPORTDiscus, Google Scholar) using key search terms: (psychological stress OR mental stress OR acute stress) AND (arterial stiffness OR pulse wave velocity OR aortic stiffness). The reference lists of identified articles and previous relevant reviews or editorials were also screened. The search was limited to English language studies published between inception and July 2024.

2.2. Article Selection

Two researchers (GZ and NS) independently completed the screening process. Conflicts between reviewers were resolved by discussion, or when agreement (consensus) was not reached, by the senior author (LS). The term “article” is used synonymously with study (u), and “trial” (k) is the unit included in the meta‐analysis. A given article may have resulted in more than one eligible k if more than one experimental condition (e.g., > 1 type of stressor or > 1 arterial segment was reported for a given (u). In such cases, data for each estimate were extracted separately. Duplicate studies and repeated publications within the same study (i.e., repeated use of the same dataset) were excluded. The full texts of potentially eligible articles were screened, and a study was included in the meta‐analysis if the following criteria were met: (i) a noninvasive measure of PWV was used that included a central (aortic) arterial segment; (ii) participants were adults (mean sample age ≥ 18 years) free of overt CVD and not currently taking vascular‐acting medications; and (iii) the stressor was purely psychological—rather than physical or a combination of psychological and physical (e.g., cold pressor task, socially evaluated cold pressor test) (Schwabe and Schachinger 2018).

2.3. Risk of Bias and Study Quality Assessments

The risk of bias and study quality within studies was independently assessed by two individuals (NS) using the Cochrane Risk‐of‐Bias 2 (RoB 2 Tools, Beta Version 7) tool (https://sites.google.com/site/riskofbiastool/welcome, 2023) and an adapted version (see Supplement) of the National Heart, Lung, and Blood Institute (NHLBI) Study Quality Assessment Tool for Before–After (Pre–Post) Studies With No Control Group (NHLBI, NIH, n.d.). In the event of scoring discrepancies, the lead author (GZ) served as the tiebreaker.

2.4. Data Extraction and Synthesis

Data extracted for each eligible trial included bibliographic information (author, publication year), participant sociodemographic characteristics, details of the stress tasks, and magnitude of effect (mean and SD) in terms of change in PWV from pre (baseline) to post‐stress. When mean differences and associated standard deviations were not published, they were requested from the corresponding study author. We were unable to reach the author of one article (Vlachopoulos et al. 2009). Therefore, image analysis software (ImageJ, Version 1.53) was used to extract data from an included figure (Vlachopoulos et al. 2009). The post‐stress measurement data point used for analysis was the measurement taken immediately following cessation of the stressor. Due to the fast‐acting nature of local (endothelial) and autonomic processes most likely driving PWV responses—and in our attempt to increase the probability of capturing peak responses—when a measure of PWV was not taken immediately following the stressor, the final measurement during the stressor was used as the “post” timepoint (Gentilin et al. 2023; Jatoi et al. 2014). When there was no measure taken immediately after OR during the stress exposure, the first post‐stress measure was used as the “post” timepoint (5 min post‐stress for all) (Kume et al. 2020; Kume et al. 2022b; Logan et al. 2020).

2.5. Data Analysis

Data were analyzed using the “metafor” (Metafor version 4.4–0) (Viechtbauer 2010) package (R, version 4.2.0). In terms of our comparator, scant experimental studies (e.g., with a true non‐stress control condition/group) have assessed acute psychological stress effects on central PWV. To meet what we deemed a minimum article threshold for performing a meta‐analysis, we opted to include quasi‐experimental studies and as such, baseline levels of PWV were considered the comparator in the current meta‐analysis. Outcome measures were expressed as SMD (post‐stress PWV minus baseline PWV) rather than weighted mean difference to accommodate structural and functional differences associated with different arterial segments. SMD values < 0.2, 0.2–0.49, 0.5–0.8 and > 0.8 reflected trivial, small, moderate and large effects, respectively (Cohen 1988). A random‐effects model was used and a corresponding forest plot was created. Heterogeneity (τ 2), was estimated with the restricted maximum likelihood method. The Q‐test for heterogeneity (Cochran 1954) and the I 2 statistic (Higgins and Thompson 2002) are also reported. Studentized residuals and Cook's distances were used to examine potential outliers and/or influential trials (Viechtbauer and Cheung 2010), using previously published thresholds (Viechtbauer and Cheung 2010). Two authors (GZ, NS) conducted the data extraction and analysis. While the data for this meta‐analysis represent a nested structure (i.e., several studies (Kume et al. 2020; Kume et al. 2022a) reported multiple post‐stress PWV measurements), alternative multilevel models did not improve model fit or significantly alter the precision of the meta‐analysis summary effect. Consequently, we presented results from a more readily interpretable standard (single level) meta‐analytic approach.

3. Results

3.1. Literature Search and Trial Selection

A total of 11,689 potentially eligible articles were identified. Of these, 35 articles were identified for full‐text screening, of which 28 were excluded, leaving 11 trials from 7 studies (Gentilin et al. 2023; Jatoi et al. 2014; Kume et al. 2020; Kume et al. 2022a; Logan et al. 2020; Vlachopoulos et al. 2006; Vlachopoulos et al. 2009) for inclusion (Figure 1).

FIGURE 1.

FIGURE 1

Open in a new tab

Preferred reporting items for systematic reviews and meta‐analyses (PRISMA) flowchart of study selection. Flowchart of study exclusions and selections. *This study (Ellins et al. 2008) was a longitudinal study in which acute stress and PWV were administered and measured 3 years apart, respectively. Abbreviations: n, number of articles; PWV, pulse wave velocity.

3.2. Description of the Included Trials

3.2.1. Trial Setting and Participants

Included study characteristics are summarized in Table 1. The studies were carried out in the United States (n = 1) (Logan et al. 2020), Greece (n = 2) (Vlachopoulos et al. 2006; Vlachopoulos et al. 2009), Japan (n = 2) (Kume et al. 2020; Kume et al. 2022a), Italy (n = 1) (Gentilin et al. 2023), and Ireland (n = 1) (Jatoi et al. 2014). The number of participants in each study ranged from N = 15 to N = 85 (mean: N = 29 [SD of mean sample sizes: N = 25.0]; median: N = 19). One study (Logan et al. 2020) included only female participants and two studies included only male participants (Kume et al. 2020; Kume et al. 2022a). The mean age of the participants ranged from 20.1 to 28.8 years (Gentilin et al. 2023; Jatoi et al. 2014; Kume et al. 2020; Kume et al. 2022a; Logan et al. 2020; Vlachopoulos et al. 2006; Vlachopoulos et al. 2009). Across all of the included studies combined, there were a total of N = 203 participants (Gentilin et al. 2023; Jatoi et al. 2014; Kume et al. 2020; Kume et al. 2022a; Logan et al. 2020; Vlachopoulos et al. 2006; Vlachopoulos et al. 2009). However, the meta‐analysis only included N = 162 participants (mean age: 24.3 years [SD of mean ages: 3.1 years], 50% female) because 41 participants from Logan et al. (2020) were assigned to a control group that was not exposed to an acute psychological stressor.

TABLE 1.

Characteristics of included studies.

Reference N Biological sex Mean Age (years) [SD] BMI (kg/m2) Covariates Psychological stressor Duration of stressor (min) “Post‐stress” measurement timing Arterial segment (Device) Nonstress control group or condition? (Y/N) ROB Quality
Vlachopoulos et al. (2006)) 19

F: 10

M: 9

 28.5 [0.6] 27.0 Perceived stress, smoking, caffeine Mental arithmetic 3 Immediately post cfPWV (Complior) Yes Low Fair
Vlachopoulos et al. (2009)) 18

F: 10

M: 8

 26.9 [2.6] NR Peripheral and central SBP and DBP, peripheral and central AP, AIx, HR, smoking Distressing Film 30 Immediately post cfPWV (Complior) Yes Low Good
Jatoi et al. (2014)) 23

F: 14

M: 9

 23 [3.0] 23.3 SBP, DBP, HR Mental arithmetic 3 “During” stressor (precise timing un‐specified)

cfPWV

(Arteriograph)

 No SC Fair
Logan et al. (2020)) 85* F: 85 28.78 [9.84] 23.48 ± 4.10 SBP, Age, BMI TSST (speech task + mental arithmetic) 10 5 min post

cfPWV

(Sphygmocor)

 Yes Low Good
Kume et al. (2020)) 17 M: 17 20.1 [0.7] 22.7 None Mental Arithmetic 5 5 min post

baPWV

haPWV

hbPWV

(Vasera VS‐1500AN)

 Yes Low Good
Kume et al. (2022a)) 15 M: 15 21.7 [0.3] 21.2 ± 0.5 None Mental arithmetic 5 5 min post

baPWV

haPWV

hbPWV

(Vasera VS‐1500AN)

 No Low Good
Gentilin et al. (2023)) 26

F: 13

M: 13

 24.25 [2.85]

F: 21.5

M: 23.7

 Sex Mental arithmetic 10 10th (last) min of stressor

cfPWV

(ECG‐gated Doppler ultrasound [6.6 MHz linear array])

 No Low Good
Open in a new tab

Note: Most BMI standard deviations were not reported. *While Logan et al. (2020) included n = 85 total participants, only the experimental group (n = 44) were included in the current analysis as the control group did not undergo an acute laboratory stressor.

Abbreviations: AP, augmented pressure; AIx, augmentation index; baPWV, brachial–ankle pulse wave velocity; BMI, body mass index; cfPWV, carotid–femoral pulse wave velocity; DBP, diastolic blood pressure; ECG, electrocardiogram; F, females; haPWV, heart–ankle pulse wave velocity; hbPWV, heart–brachial pulse wave velocity; hfPWV, heart–femoral pulse wave velocity; HR, heart rate; kg, kilogram; M, males; m, meters; MHz, megahertz; Min, minutes; NR, not reported; ROB, risk of bias; SBP, systolic blood pressure; SC, some concerns; TSST, Trier Social Stress Test.

3.2.2. Acute Stressors

A brief description of the acute stress protocols is given in Table 1.

3.3. Risk of Bias and Study Quality Assessment

3.3.1. Risk of Bias

Domain S from the ROB 2 tool is specific to crossover trials. Since the studies by Logan et al. (2020) and Gentilin et al. (2023) were not crossover trials, the Domain S criteria were not considered when determining the overall risk of bias. All studies were assessed to have a “low” risk of bias, besides Jatoi et al. (2014) which was rated as having “some concerns” because the stress tasks were not implemented in a randomized order. Risk of bias ratings of included trials are reported in Table 1. Full descriptions of the risk of bias for each study are reported in Table S1.

3.3.2. Study Quality

Overall, ratings of “good” quality were applied to all studies besides Vlachopoulos et al. (2006) and Jatoi et al. (2014) which were rated as being of “fair” quality because neither reported using a power analysis to determine sample size. Study quality ratings of included trials are reported in Table 1. Full descriptions of study quality ratings for each study are reported in Table S2.

3.4. Synthesis of the Results

3.4.1. Measurement and Characterization of the PWV Response

A number of methodological variables likely impacted the PWV responses that were analyzed. In terms of the measurement technology, five of the seven studies reported cfPWV (Gentilin et al. 2023; Jatoi et al. 2014; Logan et al. 2020; Vlachopoulos et al. 2006; Vlachopoulos et al. 2009),with two having used the Complior (Vlachopoulos et al. 2006; Vlachopoulos et al. 2009), one the Sphygmocor (Logan et al. 2020), one the Arteriograph (Jatoi et al. 2014), and one electrocardiogram (ECG)‐gated ultrasound (Gentilin et al. 2023). The studies by Kume et al. (2020; Kume et al. 2022a) each measured baPWV, heart–ankle PWV, and heart–brachial PWV using the Vasera device. In terms of measurement timing, there were two studies in which a PWV measurement was not taken immediately following the stressor, in which case the final measurement during the stressor was used as the “post” timepoint (Gentilin et al. 2023; Jatoi et al. 2014). In one of these instances (Gentilin et al. 2023), this measurement occurred at minute 10 of a 10‐min stressor. The measurement time point was unclear for the other study (Jatoi et al. 2014) (only described as occurring “during” the stressor), and attempts to contact the authors were unsuccessful. However, the stressor duration in this latter study was only 3 min, and thus it is reasonable to assume that the measurement took place approximately 60–120 s into the 3‐min stressor.

3.4.2. Meta‐Analytic Results

A total of 11 trials were included in the analysis. The observed outcomes (SMDs) ranged from small to large (0.28–0.78) (Figure 2). The random‐effects model produced a moderate effect size SMD (0.51 [95% CI: 0.34, 0.68, p < 0.0001]), indicating a substantial and significant increase in PWV. Based on the Q‐test, there was no evidence of significant heterogeneity (Q(10) = 2.62, p = 0.99, τ 2 = 0.00, I 2 = 0.00%). Visual inspection of the funnel plot (Figure 3) indicated no evidence of publication bias, and the sensitivity analysis indicated that none of the trials unduly influenced the outcome. While no trials unduly influenced the outcome, the study by Logan et al. (2020) had a relatively large weight compared to the rest of the studies. Subsequent analysis after removing Logan et al. (2020) decreased the observed effect trivially, but it remained statistically significant (SMD = 0.50 [95% CI: 0.29, 0.71, p < 0.001]).

FIGURE 2.

FIGURE 2

Open in a new tab

Forest plot showing the observed outcomes and the estimate of the random‐effects model. Lower‐case letters following study years indicate different trials within a given study (e.g., for differing arterial pathlengths). The random‐effects model produced a moderate effect size SMD (0.51 [95% CI: 0.34, 0.68, p < 0.0001]), indicating a substantial and significant increase in pulse wave velocity. Based on the Q‐test, there was no evidence of significant heterogeneity: (Q(10) = 2.62, p = 0.99, τ 2 = 0.00, I 2 = 0.00%). Abbreviations: 95% CI, 95% confidence interval; RE, random effects; SMD, standardized mean difference.

FIGURE 3.

FIGURE 3

Open in a new tab

Funnel plot for studies assessing the acute effects of psychological stress on pulse wave velocity. Visual inspection of the funnel plot indicates no evidence of publication bias.

4. Discussion

The objective of this meta‐analysis was to determine the effect of acute psychological laboratory stressors versus pre‐stress levels on central PWV in disease‐free adults. The main findings were that acute exposure to psychological stress resulted in a significant moderate increase in PWV (SMD = 0.51 [95% CI: 0.34, 0.68, p < 0.0001]). Below, we summarize our findings in the context of the existing literature and discuss potential physiological mechanisms. We also interrogate the methodological differences across studies to identify possible sources of variance in our pooled effect size estimate, as well as provide recommendations to guide future research. A summary of the study's key findings, implications, and recommendations is outlined in Table 2 and Figure 4.

TABLE 2.

Key points, implications, and future directions.

What did we know prior to this study?

  • Psychological stress is linked to increased CVD risk, likely via vascular and autonomic mechanisms

What did not we know prior to this study?

  • Evidence regarding the effects of acute psychological stress on PWV—a key construct reflecting arterial structure (i.e., stiffness) and function—had not yet been systematically reviewed.

What does this study add?

  • Across 7 studies, meta‐analytic findings demonstrate stress‐induced moderate increases in central PWV.

How do we use this new information?

  • Findings can inform future studies about the expected effect size for acute psychological stress on central PWV.

  • Findings support mechanistic understanding of the link between stress and CVD risk which includes autonomic (central) and local effects on vascular function.

What needs to happen next to move the field forward?

  • Given the limited number of studies included, more research is needed with adequate sample sizes obtained via power calculations

  • Use of similar and clinically meaningful arterial segments, PWV measurement techniques, standardized laboratory stressors (and associated recovery periods) will enhance comparability of future research.

  • Longitudinal studies are needed to determine whether stress‐induced increases in central PWV predict future CVD.
Open in a new tab

FIGURE 4.

FIGURE 4

Open in a new tab

Infographic summarizing meta‐analysis: (A) question, (B) methods, (C) potential mechanisms, and (D) results. (A) This meta‐analysis investigated the effect of acute psychological stress on central (containing an aortic segment) PWV in disease‐free adults. (B) PWV reflects arterial structure (i.e., stiffness) and function and is an established biomarker for CVD risk. PWV is the speed at which the forward pressure waveform is transmitted between a proximal (e.g., carotid) and distal (e.g., femoral) arterial segment, with faster PWV reflecting increased arterial stiffness. (C) Stress‐induced increases in PWV may manifest through several pathways. (i) The ANS directly increases sympathetic activity to the arterial system, the myocardium, and the adrenal glands. (ii) Heightened sympathetic activity stimulates SMCs, leading to vasoconstriction. (iii) Increased sympathetic activity at the level of the SA node increases cardiac output, which together with increased SMC tone, raises BP. The hashed red line indicates that increases in PWV also result in quickening and amplification of reflected pressure waves, which increases myocardial burden. (iv) The adrenal glands respond to sympathetic signaling by releasing the primary stress hormones (catecholamines, cortisol) and aldosterone into the bloodstream. The catecholamines promote further increase in SMC tone and cardiac output and promote the release of pro‐inflammatory cytokines into the bloodstream. Cortisol has been reported to compromise endothelial function and elevate BP by decreasing baroreflex sensitivity. Aldosterone may further increase BP by increasing sodium and water reabsorption. (D) The literature search identified 7 studies [11 (k) effects], with a total of n = 162 participants (mean age: 24 ± 3 years). Acute psychological stress led to a moderate (SMD: 0.51 [0.34, 0.68]) increase in PWV. Abbreviations: ANS, autonomic nervous system; BP, blood pressure; CI, confidence interval; CVD, cardiovascular disease; D, distance; HPA, hypothalamic–pituitary–adrenal; n, total sample size; PTT, pulse‐transit time; PWV, pulse wave velocity; SA, sinoatrial; SMC, smooth muscle cell; SMD, standardized mean difference.

4.1. Limitations

Several limitations should be kept in mind when considering the current findings. First, the sample sizes of included trials were generally small (range: n = 15 to n = 85; median: 19; inter range [IQR]: 9). Trials with greater sample sizes, including those with time‐matched, neutral control conditions (or groups) are required to more clearly delineate the impact of acute psychological stress on PWV. Despite the lack of statistically significant heterogeneity, there were clear differences in methodologies which may have influenced the observed outcomes. These differences included varied PWV assessment technologies and arterial segments being assessed, as well as different measurement timepoints relative to the end of the stressor. In terms of the latter point, studies with a greater duration between the end of the stressor and when PWV was next measured are more likely to underestimate true PWV reactivity. Additionally, studies used non‐uniform stressors and a wide range of stressor durations, which likely led to inconsistent stress appraisals and therefore responses across participants. Additionally, the majority of trials were mixed sex and, besides Gentilin et al. (2023) did not report sex‐stratified results. It remains unclear whether PWV responses to acute psychological stress differ between sexes. Another limitation was that only four of the seven studies compared their results against a neutral or non‐stress control group or condition (Kume et al. 2020; Logan et al. 2020; Vlachopoulos et al. 2006; Vlachopoulos et al. 2009). Finally, acute laboratory stressors may elicit different physiological responses to real‐world, stress‐inducing scenarios (Johnston et al. 1990; Zanstra and Johnston 2011). While the included studies prioritized internal validity using a regimented stress protocol in a laboratory setting, there is equally a need for future studies to examine the acute stress–PWV relationship with a greater emphasis on external validity. For example, wireless photoplethysmography and/or ambulatory ECG could be employed to track PWV during and following acute stress responses in everyday life (e.g., naturalistic stressors; final examinations, traffic) (Zieff et al. 2023).

4.2. Comparison With the Literature

We found that acute exposure to psychological stress resulted in a significant and moderate increase (SMD = 0.51) in central PWV. No prior meta‐analyses or systematic reviews that we are aware of have summarized the PWV response to acute psychological stress. However, a substantial body of literature has demonstrated that cardiovascular stress reactivity is associated with poor cardiovascular outcomes (Chida and Steptoe 2010b; Moseley and Linden 2006; Sheps et al. 2002; Tabara et al. 2008; Turner et al. 2020; Yuenyongchaiwat 2017). For example, HR and BP reactivity and recovery from acute psychological stress have been associated with HR, BP, and hypertension status 3–10 years later (Carroll et al. 2011; Moseley and Linden 2006). Vascular stress reactivity has also been shown to predict clinical outcomes. Among 569 coronary artery disease patients, the presence of transient endothelial dysfunction in response to acute psychological stress was associated with a 78% increased risk of a 3‐year incident major adverse cardiovascular event (Lima et al. 2019). It is also important to acknowledge that not only exaggerated cardiovascular reactivity, but also blunted reactivity, has been implicated in future health outcomes (Turner et al. 2020), and future work should investigate this possibility within the context of PWV responses to stress (Turner et al. 2020). As far as we are aware, no prior studies have assessed how PWV reactivity to psychological stress predicts future CVD. However, a meta‐analysis of 17 longitudinal studies (n = 15,877, mean follow‐up 7.7 years) indicated that for a 1 m/s increase in cfPWV, the risk of future cardiovascular events increased by 14% and the risk of cardiovascular mortality and all‐cause mortality both increased by 15% (Vlachopoulos et al. 2010). In the current study, on average, PWV increased by 0.30 ± 0.25 m/s, and the clinical relevance of findings from acute stress‐PWV studies needs to be determined in future research. However, acute stress does not equate to chronic, lifetime stress or the CVD risk associated with chronic stress. Nonetheless, the acute stress‐induced increases in central PWV observed in this meta‐analysis reflect direct insults to the vasculature, which over time, likely contribute to the progression of CVD risk and may partially explain the well‐established relationship between chronic stress and cardiovascular morbidity and mortality.

4.2.1. Potential Physiological Mechanisms

While the mechanisms underlying the associations between stress reactivity and CVD risk are not fully understood, two potential key players include endothelial and autonomic dysfunction.

Several reviews have investigated the effects of acute stress on endothelial (Xue et al. 2015) and autonomic (Kim et al. 2018) function. This evidence is relevant, given that work from our team has shown that acute laboratory perturbations to vascular endothelial function (Stoner et al. 2020) and autonomic (Zieff et al. 2023) nervous system directly influence PWV. Xue et al. (2015) performed a meta‐analysis (8 studies, total n = 164) testing the effects of acute stress on endothelial function, with findings demonstrating a 2.5% stress‐induced decrease (worsening) in flow‐mediated dilation, the gold standard non‐invasive assessment of endothelial function (Stoner et al. 2013). Kim et al. conducted another review (37 studies, total n = 14,298) investigating the effects of stress on heart rate variability (HRV), a widely used technique to assess autonomic regulation (Kim et al. 2018). The most frequently reported changes in HRV were decreases in high‐frequency and increases in low‐frequency domains, which reflect reduced parasympathetic activity (Kim et al. 2018). Together, these findings suggest that the effects of psychological stress on PWV are likely mediated by both direct local effects (Ghiadoni et al. 2000) on the vasculature as well as central (e.g., autonomic) mechanisms (Logan et al. 2012) controlling global cardiovascular function.

As mentioned earlier, PWV can be considered a composite measure of arterial structure (i.e., stiffness) and function. Repeated exposure to acute stress over many months or years (e.g., chronic stress) may, in theory, lead to structural changes such as lumen narrowing and/or decreased ratio of elastin to collagen. Acute exposure to psychological stress is unlikely to induce observable changes in arterial structure. Rather, the acute increases in PWV we observed are more likely due to transient changes in autonomic and endothelial function. The mechanisms underlying acute declines in vascular function have not been fully elucidated, but several potential pathways are likely involved. For example, acute psychological stress may impair vascular endothelial function by inducing an unfavorable ratio of vasodilatory (e.g., nitric oxide) to vasoconstrictive (e.g., endothelin‐1) agents (Xue et al. 2015). Acute psychological stress also effects vascular function via central mechanisms, including sympatho‐adrenal activation and concomitant vagal withdrawal (Graham 1990; Jayasinghe et al. 2016; Kim et al. 2018; Sloan et al. 1991). Stress‐induced increases in autonomic activity directly increase cardiac output (Ring et al. 2002) and aldosterone release (Gideon et al. 2020), two key regulators of BP, which is important because BP is known to impact PWV (London and Pannier 2010), and underscores the need for future studies to control for BP (methodologically or statistically). Additionally, it is imperative to acknowledge that psychological stress impacts systems physiology, and alterations in the function of other (non‐cardiovascular) systems have secondary effects on the cardiovascular system which likely impact PWV. For example, acute psychological stress augments HPA release of cortisol (Rimmele et al. 2007), which is also implicated in transient endothelial dysfunction and baroreflex desensitization following stress exposure (Broadley et al. 2005). Figure 4 (Panel C) contains a visual depiction of putative, physiological mechanisms underpinning the relationship between acute psychological stress and PWV.

4.2.2. Unexpected Findings

While it was not our a priori intention to include only young adult samples, the mean age of participants among included studies ranged from 20.1 to 28.8 years. This was likely a result of investigators utilizing convenience sampling within university settings. Nonetheless, results are most generalizable to young adults free of CVD. As such, our findings represent acute stress‐induced increases in PWV among a population relatively unencumbered by age‐ and chronic disease‐related pathophysiological changes to the vascular system.

4.3. Methodological Considerations: Sources of Variability

We did not observe statistically significant heterogeneity in the pooled effect. It is possible that this lack of significance is an artifact related to the limited number of included studies and a high degree of variance, as illustrated by the wide confidence intervals among included studies (Figure 2). Nevertheless, to inform and improve upon future research testing the effects of acute stress on PWV, it is still worthwhile to critically examine differences in study design that perhaps impacted the magnitude of observed effects. Table 3 summarizes our recommendations for improving future studies.

TABLE 3.

Recommendations for minimizing sampling and measurement variance.

Recommendation Example
Study Design/Sample Population driven by research question Young disease‐free adults if interested in avoiding confounds of age and disease‐related changes to arterial system
Sample size driven by power calculation Based on moderate effect size determined from this meta‐analysis
Use of a control condition or group Randomized crossover design with a time‐ and posture‐matched neutral control task
Methods Arterial segment contains an aortic component Carotid–femoral, brachial–femoral, heart–femoral

Valid measurement techniques and report lab‐specific reliability (e.g., ICC)

Within Day ICC: ≥ 0.90

Between Day ICC: ≥ 0.75

Well‐trained operator

(ideally single operator to minimize variance)

Within operator ICC: ≥ 0.90

Between operator ICC: ≥ 0.75
Use of an established stressor containing social‐evaluative threat Trier Social Stress Test
Record PWV ≥ 30 min post‐stress. More frequent measurements for first 15 min to capture peak response.

Baseline: 2–3 measurements

Immediately post: 0, 5, 10, 15 min

Recovery: 30, 45, 60 min
Analysis Adjustments for relevant covariates

Primary: BP

Secondary: e.g., BMI, age, medications
Open in a new tab

Abbreviations: BMI, body mass index; BP, blood pressure; cfPWV, carotid–femoral PWV; ICC, intraclass correlation coefficient; min, minutes; PWV, pulse wave velocity; TSST, Trier Social Stress Test.

4.3.1. Measurement of Pulse Wave Velocity

4.3.1.1. Differential Arterial Segments

One major between‐study difference was the measured arterial segments. These included carotid–femoral, heart–ankle, heart–brachial, and brachial–ankle segments. These segments are correlated (Choo et al. 2014; Lee et al. 2018; Tanaka et al. 2009) but not directly comparable due to differences in structure and function (Fryer et al. 2021; Stone et al. 2021). For example, compared to central arterial segments such as the ascending and descending aorta, the greater concentration of smooth muscle cells in the walls of muscular peripheral arteries makes them more susceptible to changes in tone and diameter via autonomic and vasoactive responses (Leloup et al. 2015). The greater propensity for acute changes in tone and diameter in peripheral arteries may in theory translate to greater stress‐induced increases in PWV in these arteries compared to central arteries. Equally important to acknowledge, chronological and biological aging of the vasculature generally accelerates central arterial stiffening relative to stiffening of peripheral arteries, resulting in amplification and quickening of the arterial wave reflection, thereby increasing cardiac afterload and compromising coronary perfusion (Fortier and Agharazii 2016; London and Pannier 2010). As such, age and progression of arterio‐ and atherosclerotic processes may influence the extent to which central versus peripheral arteries are acutely impacted by psychological stress. Under ideal circumstances with a greater number of included trials, this would normally prompt sensitivity analysis by segment type. Due to the small number of trials (k = 11), subgroup analysis by arterial segment was not performed, but will be of interest as additional investigations into stress effects on PWV emerge.

4.3.1.2. Technique to Determine Pulse Transit Time

The pre‐ejection period (PEP) is the electromechanical delay associated with the conversion of electrical signal into the mechanical action of the heart. Some PWV techniques such as ECG‐gated Doppler ultrasound, which was used by Gentilin et al. (2023) include this PEP‐associated delay, which biases PTT (and subsequently, PWV) estimates. In contrast, Vlachopoulos et al. (2006; Vlachopoulos et al. 2009) used applanation tonometry, which avoids the PEP‐related delay. Differences between studies in their ascertainment of PTT impact studies' PWV estimates. We acknowledge that ECG‐gated ultrasound is more financially feasible for many researchers. Nonetheless, whenever possible, it is recommended for future studies testing effects of acute stress on PWV to use techniques that truly measure PTT (e.g., from foot of proximal to foot of distal pressure waveforms).

4.3.2. Type and Duration of Psychological Stressor

4.3.2.1. Stressor Type

Physiological stress responses can arise from psychological, cognitive, and physical stressors. However, stress responses to these distinct provocations are not necessarily uniform (Finke et al. 2021). For example, cardiovascular effects from the cold‐pressor test have been shown to be primarily driven by alpha‐adrenergic mechanisms (influencing vasoconstriction), whereas effects from mental arithmetic tasks may be driven more by beta‐adrenergic activation (influencing cardiac stimulation and vasodilation) (Montoya et al. 1997). The current meta‐analysis excluded physical stressors to help minimize variability and focus on psychological stress as a CVD risk factor. Even among psychological stressors, whether the task incorporates social‐evaluative threat can impact the magnitude of physiological reactivity (Schwabe et al. 2008). This point is made clear in a seminal meta‐analysis (total n = 6153, 208 studies) by Dickerson and Kemeny (Dickerson and Kemeny 2004), which found that, in addition to uncontrollability, social‐evaluative threat was a “key ingredient” for acute stressors to be able to reliably induce a robust physiological response.

All but one (Vlachopoulos et al. 2009) of the included studies utilized a mental arithmetic component in their stressor, with various levels of social‐evaluative threat incorporated (Gentilin et al. 2023; Jatoi et al. 2014; Kume et al. 2020; Kume et al. 2022a; Logan et al. 2020; Vlachopoulos et al. 2006). Indeed, mental arithmetic is one of the most commonly cited types of stressors utilized in psychophysiological research, and has been reported in the literature for decades including as far back as 1959 by Brod et al. 1959 investigation of hemodynamic effects of stress (Sloan et al. 1991). Cortisol and affective responses to the Trier Social Stress Test (TSST)—which includes a mental arithmetic component—have been shown to significantly and moderately correlate with responses to a naturalistic (oral course exam) stressor on a separate day (Henze et al. 2017). However, parallel research is needed in the context of vascular and autonomic stress biomarkers. Nonetheless, previous findings reported that cardiovascular reactivity to arithmetic tasks predicts future CVD risk (Carroll et al. 2011; Moseley and Linden 2006), and that arithmetic may be a superior stressor in eliciting predictive reactivity responses compared to other types of lab stressors (Yuenyongchaiwat 2017). Future research should also take into account that the most suitable (e.g., robust, ecologically valid) stressors may depend on the population being studied (Allen et al. 2017).

4.3.2.2. Stressor Duration

Another factor that may influence the PWV response to acute psychological stress is the duration of the stressor. Among the articles included in the present meta‐analyses, the stressors ranged in duration from 3 to 30 min (mean: 9.42 min, median: 5 min, IQR: 7 min). Ring et al. (2002) reported that, over the course of a 28‐min mental arithmetic task in 30 healthy young adults, cardiac output rose during the first half of the task and then returned to baseline levels, whereas total peripheral resistance increased to a greater extent in the latter portion of the stressor (Ring et al. 2002). These findings suggest that, at least in prolonged mental arithmetic tasks, cardiovascular reactivity may initially be driven by cardiac mechanisms, which then give way to vascular mechanisms as the stressor progresses. To aid comparability among future research examining acute stress effects on PWV, we recommend that future studies use established protocols with standardized protocols (e.g., TSST uses 10‐min stressor) and recovery durations.

5. Conclusions

Chronic psychological stress is a known contributor to elevated CVD risk (Brotman et al. 2007), and severe levels of psychological stress are commonplace in today's society (http://www.apa.org/news/press/releases/stress//snapshot, 2015). A small overall number of studies have examined stress‐induced PWV responses using clinically relevant central arterial segments. Results from the current meta‐analysis indicate that acute psychological stress causes transient increases in PWV, potentially leading to arterial stiffening and CVD risk over time. However, the magnitude and consistency of stress‐induced increases in PWV are not fully clear—particularly given the heterogeneity in stress protocols and arterial pathlengths. Future studies are warranted to investigate (i) the extent to which PWV responses to laboratory‐based stressors are influenced by lifestyle (e.g., exercise, mindfulness, diet, sleep) and lifestyle‐associated factors (e.g., resilience, cardiorespiratory fitness), and (ii) how PWV reactivity and/or recovery to acute stressors predicts future CVD risk and outcomes.

Author Contributions

Gabriel Zieff: conceptualization, investigation, writing – original draft, methodology, validation, visualization, writing – review and editing, software, formal analysis, project administration, data curation, resources. Noora Sharma: methodology, formal analysis, data curation, validation, visualization, writing – review and editing, investigation. Keeron Stone: conceptualization, investigation, writing – original draft, methodology, validation, visualization, writing – review and editing. Patricia Pagan Lassalle: investigation, methodology, validation, visualization, writing – review and editing, software, formal analysis. Aiden J. Chauntry: writing – review and editing. Erik D. Hanson: conceptualization, investigation, methodology, validation, writing – review and editing, supervision. Michelle L. Meyer: conceptualization, investigation, writing – review and editing, validation. Claudio Battaglini: conceptualization, investigation, validation, writing – review and editing. Justin B. Moore: conceptualization, validation, writing – review and editing. Craig Paterson: conceptualization, investigation, methodology, validation, writing – review and editing, data curation, software, formal analysis, visualization, writing – original draft. Lee Stoner: conceptualization, investigation, writing – original draft, methodology, validation, writing – review and editing, supervision, resources, visualization.

Conflicts of Interest

The authors declare no conflicts of interest.

Supporting information

Data S1.

PSYP-62-e70068-s001.docx (40.1KB, docx)

Acknowledgments

We would like to thank Oliver Bontkes for his assistance with the risk of bias and study quality ratings.

Funding: The authors received no specific funding for this work.

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Allen, A. P. , Kennedy P. J., Dockray S., Cryan J. F., Dinan T. G., and Clarke G.. 2017. “The Trier Social Stress Test: Principles and Practice.” Neurobiology of Stress 6: 113–126. 10.1016/j.ynstr.2016.11.001. [DOI] [PMC free article] [PubMed] [Google Scholar]
  2. Briet, M. , Boutouyrie P., Laurent S., and London G. M.. 2012. “Arterial Stiffness and Pulse Pressure in CKD and ESRD.” Kidney International 82, no. 4: 388–400. 10.1038/KI.2012.131. [DOI] [PubMed] [Google Scholar]
  3. Broadley, A. J. M. , Korszun A., Abdelaal E., et al. 2005. “Inhibition of Cortisol Production With Metyrapone Prevents Mental Stress‐Induced Endothelial Dysfunction and Baroreflex Impairment.” Journal of the American College of Cardiology 46, no. 2: 344–350. 10.1016/j.jacc.2005.03.068. [DOI] [PubMed] [Google Scholar]
  4. Brod, J. , Fencl V., Hejl Z., and Jirka J.. 1959. “Circulatory Changes Underlying Blood Pressure Elevation During Acute Emotional Stress (Mental Arithmetic) in Normotensive and Hypertensive Subjects.” Clinical Science 18: 269–279. https://pubmed.ncbi.nlm.nih.gov/13804603/. [PubMed] [Google Scholar]
  5. Brotman, D. J. , Golden S. H., and Wittstein I. S.. 2007. “The Cardiovascular Toll of Stress.” Lancet 370, no. 9592: 1089–1100. 10.1016/S0140-6736(07)61305-1. [DOI] [PubMed] [Google Scholar]
  6. Carroll, D. , Phillips A. C., Der G., Hunt K., and Benzeval M.. 2011. “Blood Pressure Reactions to Acute Mental Stress and Future Blood Pressure Status: Data From the 12‐Year Follow‐Up of the West of Scotland Study.” Psychosomatic Medicine 73, no. 9: 737–742. 10.1097/PSY.0B013E3182359808. [DOI] [PubMed] [Google Scholar]
  7. Chida, Y. , and Steptoe A.. 2010a. “Response to Mental Stress, Arterial Stiffness, Central Pressures, and Cardiovascular Risk.” Hypertension 56, no. 3: e29. 10.1161/HYPERTENSIONAHA.110.156679. [DOI] [PubMed] [Google Scholar]
  8. Chida, Y. , and Steptoe A.. 2010b. “Greater Cardiovascular Responses to Laboratory Mental Stress Are Associated With Poor Subsequent Cardiovascular Risk Status: A Meta‐Analysis of Prospective Evidence.” Hypertension 55, no. 4: 1026–1032. 10.1161/hypertensionaha.109.146621. [DOI] [PubMed] [Google Scholar]
  9. Choo, J. , Shin C., Barinas‐Mitchell E., et al. 2014. “Regional Pulse Wave Velocities and Their Cardiovascular Risk Factors Among Healthy Middle‐Aged Men: A Cross‐Sectional Population‐Based Study.” BMC Cardiovascular Disorders 14, no. 1: 1–8. 10.1186/1471-2261-14-5/tables/4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Cochran, W. G. 1954. “The Combination of Estimates From Different Experiments.” Biometrics 10, no. 1: 101. 10.2307/3001666. [DOI] [Google Scholar]
  11. Cohen, J. 1988. Statistical Power Analysis for the Behavioral Sciences, 567. Lawrence Erlbaum Associates, Publishers. 10.1234/12345678. [DOI] [Google Scholar]
  12. Dickerson, S. S. , and Kemeny M. E.. 2004. “Acute Stressors and Cortisol Responses: A Theoretical Integration and Synthesis of Laboratory Research.” Psychological Bulletin 130, no. 3: 355–391. 10.1037/0033-2909.130.3.355. [DOI] [PubMed] [Google Scholar]
  13. Dutch, J. , and Redman S.. 1983. “Psychological Stress and Arterial Pulse Transit Time.” New Zealand Medical Journal 96, no. 737: 607–609. https://pubmed.ncbi.nlm.nih.gov/6575313/. [PubMed] [Google Scholar]
  14. Ellins, E. , Halcox J., Donald A., et al. 2008. “Arterial Stiffness and Inflammatory Response to Psychophysiological Stress.” Brain, Behavior, and Immunity 22, no. 6: 941–948. 10.1016/J.BBI.2008.01.009. [DOI] [PubMed] [Google Scholar]
  15. Everson‐Rose, S. A. , and Lewis T. T.. 2005. “Psychosocial Factors and Cardiovascular Diseases.” Annual Review of Public Health 26: 469–500. 10.1146/annurev.publhealth.26.021304.144542. [DOI] [PubMed] [Google Scholar]
  16. Finke, J. B. , Zhang X., Plein D., Schilling T. M., Schächinger H., and Larra M. F.. 2021. “Combining Mental and Physical Stress: Synergy or Interference?” Physiology & Behavior 233: 113365. 10.1016/J.PHYSBEH.2021.113365. [DOI] [PubMed] [Google Scholar]
  17. Fortier, C. , and Agharazii M.. 2016. “Arterial Stiffness Gradient.” Pulse 3, no. 3–4: 159–166. 10.1159/000438852. [DOI] [PMC free article] [PubMed] [Google Scholar]
  18. Fryer, S. , Stone K., Paterson C., et al. 2021. “Central and Peripheral Arterial Stiffness Responses to Uninterrupted Prolonged Sitting Combined With a High‐Fat Meal: A Randomized Controlled Crossover Trial.” Hypertension Research 44, no. 10: 1332–1340. 10.1038/S41440-021-00708-Z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Gentilin, A. , Cevese A., Schena F., and Tarperi C.. 2023. “Mental Stress Augments Central Artery Stiffness in Young Individuals of Both Sexes.” Biological Psychology 178: 108513. 10.1016/j.biopsycho.2023.108513. [DOI] [PubMed] [Google Scholar]
  20. Ghiadoni, L. , Donald A. E., Cropley M., et al. 2000. “Mental Stress Induces Transient Endothelial Dysfunction in Humans.” Circulation 102: 2473–2478. 10.1161/01.cir.102.20.2473. [DOI] [PubMed] [Google Scholar]
  21. Gideon, A. , Sauter C., Fieres J., Berger T., Renner B., and Wirtz P. H.. 2020. “Kinetics and Interrelations of the Renin Aldosterone Response to Acute Psychosocial Stress: A Neglected Stress System.” Journal of Clinical Endocrinology and Metabolism 105, no. 3: e762–e773. 10.1210/CLINEM/DGZ190. [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Gill, A. , Aldosoky W., Ong M. B. H., Mir T., Dar T., and Abohashem S.. 2023. “A Narrative Review on Mental Stress and Cardiovascular Disease: Evidence, Mechanisms, and Potential Interventions.” Hear Mind 7, no. 2: 62–69. 10.4103/HM.HM-D-22-00002. [DOI] [Google Scholar]
  23. Graham, R. M. 1990. “Adrenergic Receptors: Structure and Function.” Cleveland Clinic Journal of Medicine 57, no. 5: 481–492. 10.3949/CCJM.57.5.481. [DOI] [PubMed] [Google Scholar]
  24. Henze, G. I. , Zänkert S., Urschler D. F., et al. 2017. “Testing the Ecological Validity of the Trier Social Stress Test: Association With Real‐Life Exam Stress.” Psychoneuroendocrinology 75: 52–55. 10.1016/j.psyneuen.2016.10.002. [DOI] [PubMed] [Google Scholar]
  25. Higgins, J. P. T. , and Thompson S. G.. 2002. “Quantifying Heterogeneity in a Meta‐Analysis.” Statistics in Medicine 21, no. 11: 1539–1558. 10.1002/SIM.1186. [DOI] [PubMed] [Google Scholar]
  26. 2015. “Stress in America Snapshot.” http://www.apa.org/news/press/releases/stress//snapshot.aspx.
  27. 2023. “Risk of Bias Tools.” https://sites.google.com/site/riskofbiastool/welcome.
  28. Jatoi, N. A. , Kyvelou S. M., and Feely J.. 2014. “The Acute Effects of Mental Arithmetic, Cold Pressor and Maximal Voluntary Contraction on Arterial Stiffness in Young Healthy Subjects.” Artery Research 8, no. 2: 44–50. 10.1016/j.artres.2014.02.002. [DOI] [Google Scholar]
  29. Jayasinghe, S. U. , Lambert G. W., Torres S. J., Fraser S. F., Eikelis N., and Turner A. I.. 2016. “Hypothalamo‐Pituitary Adrenal Axis and Sympatho‐Adrenal Medullary System Responses to Psychological Stress Were Not Attenuated in Women With Elevated Physical Fitness Levels.” Endocrine 51: 369–379. 10.1007/s12020-015-0687-6. [DOI] [PubMed] [Google Scholar]
  30. Johnston, D. W. , Anastasiades P., and Wood C.. 1990. “The Relationship Between Cardiovascular Responses in the Laboratory and in the Field.” Psychophysiology 27, no. 1: 34–44. 10.1111/j.1469-8986.1990.tb02175.x. [DOI] [PubMed] [Google Scholar]
  31. Kim, H. G. , Cheon E. J., Bai D. S., Lee Y. H., and Koo B. H.. 2018. “Stress and Heart Rate Variability: A Meta‐Analysis and Review of the Literature.” Psychiatry Investigation 15, no. 3: 235–245. 10.30773/PI.2017.08.17. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Kinlay, S. , Creager M. A., Fukumoto M., et al. 2001. “Endothelium‐Derived Nitric Oxide Regulates Arterial Elasticity in Human Arteries In Vivo.” Hypertens 38, no. 5: 1049–1053. 10.1161/HY1101.095329. [DOI] [PubMed] [Google Scholar]
  33. Kucharska‐Newton, A. M. , Stoner L., and Meyer M. L.. 2019. “Determinants of Vascular Age: An Epidemiological Perspective.” Clinical Chemistry 65, no. 1: 108–118. 10.1373/clinchem.2018.287623. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Kume, D. , Nishiwaki M., Hotta N., and Endoh H.. 2020. “Impact of Acute Mental Stress on Segmental Arterial Stiffness.” European Journal of Applied Physiology 120, no. 10: 2247–2257. 10.1007/S00421-020-04448-9. [DOI] [PubMed] [Google Scholar]
  35. Kume, D. , Nishiwaki M., Takahara R., and Hotta N.. 2022a. “Three‐Minute Bench Step Exercise as a Countermeasure for Acute Mental Stress‐Induced Arterial Stiffening Daisuke.” PLoS One 17, no. 12: e0279761. 10.1371/journal.pone.0279761. [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Kume, D. , Nishiwaki M., Takahara R., and Hotta N.. 2022b. “The Effectiveness of Bench Step Exercise for Ameliorating Acute Mental Stress‐Induced Arterial Stiffening.” European Journal of Applied Physiology 122, no. 8: 1875–1884. 10.1007/S00421-022-04962-Y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Lackner, H. K. , Goswami N., Papousek I., et al. 2010. “Time Course of Cardiovascular Responses Induced by Mental and Orthostatic Challenges.” International Journal of Psychophysiology 75, no. 1: 48–53. 10.1016/j.ijpsycho.2009.11.003. [DOI] [PubMed] [Google Scholar]
  38. Laurent, S. , Cockcroft J., Van Bortel L., et al. 2006. “Expert Consensus Document on Arterial Stiffness: Methodological Issues and Clinical Applications.” European Heart Journal 27, no. 21: 2588–2605. 10.1093/eurheartj/ehl254. [DOI] [PubMed] [Google Scholar]
  39. Lazarus, R. 1984. Stress, Appraisal, and Coping. Springer. https://books.google.com/books?hl=en&lr=&id=i‐ySQQuUpr8C&oi=fnd&pg.=PR5&ots=DhFMlwkfNh&sig=Us4wVH‐‐akaibYnR‐p0SMzSLk. [Google Scholar]
  40. Lee, S. W. , Han S. H., Yoo T. H., et al. 2018. “Relationship Between Brachial‐Ankle and Heart‐Femoral Pulse Wave Velocities and the Rapid Decline of Kidney Function.” Science Reports 8, no. 1: 1–9. 10.1038/s41598-018-19,334-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Leloup, A. J. A. , Van Hove C. E., Heykers A., Schrijvers D. M., De Meyer G. R. Y., and Fransen P.. 2015. “Elastic and Muscular Arteries Differ in Structure, Basal NO Production and Voltage‐Gated Ca2+ ‐Channels.” Frontiers in Physiology 6: 375. 10.3389/FPHYS.2015.00375. [DOI] [PMC free article] [PubMed] [Google Scholar]
  42. Lima, B. B. , Hammadah M., Kim J. H., et al. 2019. “Association of Transient Endothelial Dysfunction Induced by Mental Stress With Major Adverse Cardiovascular Events in Men and Women With Coronary Artery Disease.” JAMA Cardiology 4, no. 10: 988–996. 10.1001/JAMACARDIO.2019.3252. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Logan, J. G. , Barksdale D. J., Carlson J., Carlson B. W., and Rowsey P. J.. 2012. “Psychological Stress and Arterial Stiffness in Korean Americans.” Journal of Psychosomatic Research 73, no. 1: 53–58. 10.1016/j.jpsychores.2012.04.008. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Logan, J. G. , Teachman B. A., Liu X., Farber C. R., Liu Z., and Annex B. H.. 2020. “Acute Psychological Stress, Autonomic Function, and Arterial Stiffness Among Women.” International Journal of Psychophysiology 155: 219–226. 10.1016/j.ijpsycho.2020.06.015. [DOI] [PMC free article] [PubMed] [Google Scholar]
  45. London, G. M. , and Pannier B.. 2010. “Arterial Functions: How to Interpret the Complex Physiology.” Nephrology, Dialysis, Transplantation 25: 3815–3823. 10.1093/ndt/gfq614. [DOI] [PubMed] [Google Scholar]
  46. McEwen, B. S. 2007. “Physiology and Neurobiology of Stress and Adaptation: Central Role of the Brain.” Physiological Reviews 87, no. 3: 873–904. 10.1152/PHYSREV.00041.2006. [DOI] [PubMed] [Google Scholar]
  47. Michel, T. , and Vanhoutte P. M.. 2010. “Cellular Signaling and NO Production.” Pflügers Archiv 459, no. 6: 807–816. 10.1007/S00424-009-0765-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  48. Montoya, P. , Brody S., Beck K., Veit R., and Rau H.. 1997. “Differential ?‐ and ?‐Adrenergic Activation During Psychological Stress.” European Journal of Applied Physiology 75, no. 3: 256–262. 10.1007/s004210050157. [DOI] [PubMed] [Google Scholar]
  49. Moseley, J. V. , and Linden W.. 2006. “Predicting Blood Pressure and Heart Rate Change With Cardiovascular Reactivity and Recovery: Results From 3‐Year and 10‐Year Follow Up.” Psychosomatic Medicine 68, no. 6: 833–843. 10.1097/01.PSY.0000238453.11324.D5. [DOI] [PubMed] [Google Scholar]
  50. NHLBI, NIH . “Study Quality Assessment Tools.” https://www.nhlbi.nih.gov/health‐topics/study‐quality‐assessment‐tools.
  51. Page, M. J. , McKenzie J. E., Bossuyt P. M., et al. 2021. “The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews.” BMJ 372: n71. 10.1136/BMJ.N71. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Pewowaruk, R. , Korcarz C., De B. I., et al. 2023. “Carotid Artery Stiffness Mechanisms Are Associated With End Organ Damage and all‐Cause Mortality: MESA (Multi‐Ethnic Study of Atherosclerosis).” Journal of the American Heart Association 12, no. 7: 27517. 10.1161/jaha.122.027517/suppl_file/jah38343-sup-0001-supinfo.pdf. [DOI] [PMC free article] [PubMed] [Google Scholar]
  53. Rimmele, U. , Zellweger B. C., Marti B., et al. 2007. “Trained Men Show Lower Cortisol, Heart Rate and Psychological Responses to Psychosocial Stress Compared With Untrained Men.” Psychoneuroendocrinology 32, no. 6: 627–635. 10.1016/j.psyneuen.2007.04.005. [DOI] [PubMed] [Google Scholar]
  54. Ring, C. , Burns V. E., and Carroll D.. 2002. “Shifting Hemodynamics of Blood Pressure Control During Prolonged Mental Stress.” Psychophysiology 39, no. 5: 585–590. 10.1017/S0048577202011320. [DOI] [PubMed] [Google Scholar]
  55. Schwabe, L. , Haddad L., and Schachinger H.. 2008. “HPA Axis Activation by a Socially Evaluated Cold‐Pressor Test.” Psychoneuroendocrinology 33, no. 6: 890–895. 10.1016/j.psyneuen.2008.03.001. [DOI] [PubMed] [Google Scholar]
  56. Schwabe, L. , and Schachinger H.. 2018. “Ten Years of Research With the Socially Evaluated Cold Pressor Test: Data From the Past and Guidelines for the Future.” Psychoneuroendocrinology 92: 155–161. 10.1016/j.psyneuen.2018.03.010. [DOI] [PubMed] [Google Scholar]
  57. Sheps, D. S. , McMahon R. P., Becker L., et al. 2002. “Mental Stress‐Induced Ischemia and All‐Cause Mortality in Patients With Coronary Artery Disease: Results From the Psychophysiological Investigations of Myocardial Ischemia Study.” Circulation 105, no. 15: 1780–1784. 10.1161/01.CIR.0000014491.90666.06. [DOI] [PubMed] [Google Scholar]
  58. Sloan, R. P. , Korten J. B., and Myers M. M.. 1991. “Components of Heart Rate Reactivity During Mental Arithmetic With and Without Speaking.” Physiology & Behavior 50, no. 5: 1039–1045. 10.1016/0031-9384(91)90434-P. [DOI] [PubMed] [Google Scholar]
  59. Steptoe, A. , and Kivimäki M.. 2012. “Stress and Cardiovascular Disease.” Nature Reviews. Cardiology 9, no. 6: 360–370. 10.1038/NRCARDIO.2012.45. [DOI] [PubMed] [Google Scholar]
  60. Stone, K. , Fryer S., Meyer M. L., et al. 2021. “The Aortic‐Femoral Arterial Stiffness Gradient: An Atherosclerosis Risk in Communities (ARIC) Study.” Journal of Hypertension 39, no. 7: 1370–1377. 10.1097/HJH.0000000000002808. [DOI] [PMC free article] [PubMed] [Google Scholar]
  61. Stone, K. , Veerasingam D., Meyer M. L., et al. 2023. “Reimagining the Value of Brachial‐Ankle Pulse Wave Velocity as a Biomarker of Cardiovascular Disease Risk‐A Call to Action on Behalf of VascAgeNet.” Hypertension 80, no. 10: 1980–1992. 10.1161/hypertensionaha.123.21314. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Stoner, L. , Higgins S., and Paterson C.. 2023. “The 24‐h Activity Cycle and Cardiovascular Outcomes: Establishing Biological Plausibility Using Arterial Stiffness as an Intermediate Outcome.” American Journal of Physiology. Heart and Circulatory Physiology 325, no. 6: H1243–H1263. 10.1152/AJPHEART.00258.2023. [DOI] [PMC free article] [PubMed] [Google Scholar]
  63. Stoner, L. , Stone K., Zieff G., et al. 2020. “Endothelium Function Dependence of Acute Changes in Pulse Wave Velocity and Flow‐Mediated Slowing.” Vascular Medicine 25, no. 5: 26588. 10.1177/1358863X20926588. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Stoner, L. , Tarrant M. A., Fryer S., and Faulkner J.. 2013. “How Should Flow‐Mediated Dilation Be Normalized to Its Stimulus?” Clinical Physiology and Functional Imaging 33, no. 1: 75–78. 10.1111/J.1475-097X.2012.01154.X. [DOI] [PubMed] [Google Scholar]
  65. Szabo, A. 1993. “The Combined Effects of Orthostatic and Mental Stress on Heart Rate, T‐Wave Amplitude, and Pulse Transit Time.” European Journal of Applied Physiology and Occupational Physiology 67, no. 6: 540–544. 10.1007/BF00241651. [DOI] [PubMed] [Google Scholar]
  66. Tabara, Y. , Kohara K., Nakagawa S., et al. 2008. “Effects of Obesity and Smoking on Mental Stress‐Induced Blood Pressure and Augmentation Index Responses in Normotensive Young Males: The J‐SHIPP Study.” Hypertension Research 31, no. 6: 1219–1224. 10.1291/hypres.31.1219. [DOI] [PubMed] [Google Scholar]
  67. Tanaka, H. , Munakata M., Kawano Y., et al. 2009. “Comparison Between Carotid‐Femoral and Brachial‐Ankle Pulse Wave Velocity as Measures of Arterial Stiffness.” Journal of Hypertension 27, no. 10: 2022–2027. 10.1097/HJH.0B013E32832E94E7. [DOI] [PubMed] [Google Scholar]
  68. Townsend, R. R. , Wilkinson I. B., Schiffrin E. L., et al. 2015. “Recommendations for Improving and Standardizing Vascular Research on Arterial Stiffness: A Scientific Statement From the American Heart Association.” Hypertension 66, no. 3: 698–722. 10.1161/HYP.0000000000000033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Turner, A. I. , Smyth N., Hall S. J., et al. 2020. “Psychological Stress Reactivity and Future Health and Disease Outcomes: A Systematic Review of Prospective Evidence.” Psychoneuroendocrinology 114: 104599. 10.1016/j.psyneuen.2020.104599. [DOI] [PubMed] [Google Scholar]
  70. Van Bortel, L. M. , Laurent S., Boutouyrie P., et al. 2012. “Expert Consensus Document on the Measurement of Aortic Stiffness in Daily Practice Using Carotid‐Femoral Pulse Wave Velocity.” Journal of Hypertension 30, no. 3: 445–448. 10.1097/HJH.0b013e32834fa8b0. [DOI] [PubMed] [Google Scholar]
  71. Viechtbauer, W. 2010. “Conducting Meta‐Analyses in R With the Metafor Package.” Journal of Statistical Software 36, no. 3: 1–48. 10.18637/JSS.V036.I03. [DOI] [Google Scholar]
  72. Viechtbauer, W. , and Cheung M. W.‐L.. 2010. “Outlier and Influence Diagnostics for Meta‐Analysis.” Research Synthesis Methods 1, no. 2: 112–125. 10.1002/JRSM.11. [DOI] [PubMed] [Google Scholar]
  73. Vlachopoulos, C. , Aznaouridis K., and Stefanadis C.. 2010. “Prediction of Cardiovascular Events and All‐Cause Mortality With Arterial Stiffness. A Systematic Review and Meta‐Analysis.” Journal of the American College of Cardiology 55, no. 13: 1318–1327. 10.1016/j.jacc.2009.10.061. [DOI] [PubMed] [Google Scholar]
  74. Vlachopoulos, C. , Kosmopoulou F., Alexopoulos N., Ioakeimidis N., Siasos G., and Stefanadis C.. 2006. “Acute Mental Stress has a Prolonged Unfavorable Effect on Arterial Stiffness and Wave Reflections.” Psychosomatic Medicine 68, no. 2: 231–237. 10.1097/01.psy.0000203171.33348.72. [DOI] [PubMed] [Google Scholar]
  75. Vlachopoulos, C. , Xaplanteris P., Alexopoulos N., et al. 2009. “Divergent Effects of Laughter and Mental Stress on Arterial Stiffness and Central Hemodynamics.” Psychosomatic Medicine 71, no. 4: 446–453. 10.1097/PSY.0b013e318198dcd4. [DOI] [PubMed] [Google Scholar]
  76. Walton, K. G. , Pugh N. D., Gelderloos P., and Macrae P.. 1995. “Stress Reduction and Preventing Hypertension: Preliminary Support for a Psychoneuroendocrine Mechanism.” Journal of Alternative and Complementary Medicine 1, no. 3: 263–283. https://www.liebertpub.com/doi/pdf/10.1089/acm. [DOI] [PubMed] [Google Scholar]
  77. Wilkinson, I. B. , Qasem A., McEniery C. M., Webb D. J., Avolio A. P., and Cockcroft J. R.. 2002. “Nitric Oxide Regulates Local Arterial Distensibility In Vivo.” Circulation 105, no. 2: 213–217. 10.1161/HC0202.101970. [DOI] [PubMed] [Google Scholar]
  78. Xue, Y.‐T. , Tan Q., Li P., et al. 2015. “Investigating the Role of Acute Mental Stress on Endothelial Dysfunction: A Systematic Review and Meta‐Analysis.” Clinical Research in Cardiology 104, no. 4: 310–319. 10.1007/s00392-014-0782-3. [DOI] [PubMed] [Google Scholar]
  79. Yi, T. , Gao L., Fan F., et al. 2023. “Association Between Pulse Wave Velocity and the 10‐Year Risk of Atherosclerotic Cardiovascular Disease in the Chinese Population: A Community‐Based Study.” Journal of Clinical Hypertension (Greenwich, Conn.) 25, no. 3: 278–285. 10.1111/JCH.14642. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Yuenyongchaiwat, K. 2017. “Cardiovascular Response to Mental Stress Tests and the Prediction of Blood Pressure.” Indian Journal of Psychological Medicine 39, no. 4: 413–417. 10.4103/0253-7176.211744. [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Zanstra, Y. J. , and Johnston D. W.. 2011. “Cardiovascular Reactivity in Real Life Settings: Measurement, Mechanisms and Meaning.” Biological Psychology 86, no. 2: 98–105. 10.1016/j.biopsycho.2010.05.002. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Zieff, G. , Stone K., Paterson C., et al. 2023. “Pulse‐Wave Velocity Assessments Derived From a Simple Photoplethysmography Device: Agreement With a Referent Device.” Frontiers in Cardiovascular Medicine 10: 163. 10.3389/FCVM.2023.1108219 /BIBTEX. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Data S1.

PSYP-62-e70068-s001.docx (40.1KB, docx)

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Articles from Psychophysiology are provided here courtesy of Wiley

RESOURCES