Psychobiological Reactivity to Acute Psychological Stress as a Predictor of Cardiovascular Disease and Mortality: The Multi-Ethnic Study of Atherosclerosis

Aiden J CHAUNTRY; Justin B MOORE; Teresa SEEMAN; Steven J SHEA; Richard P SLOAN; Anna C WHITTAKER; Eli PUTERMAN; Anne I TURNER; William P TYNE; Mark J HUTSON; Mark P FUNNELL; Gabriel ZIEFF; Brook M GELETA; Keeron STONE; Erik D HANSON; Lee STONER; Michael P BANCKS

doi:10.1016/j.psyneuen.2025.107657

. Author manuscript; available in PMC: 2026 Oct 17.

Published in final edited form as: Psychoneuroendocrinology. 2025 Oct 17;183:107657. doi: 10.1016/j.psyneuen.2025.107657

Psychobiological Reactivity to Acute Psychological Stress as a Predictor of Cardiovascular Disease and Mortality: The Multi-Ethnic Study of Atherosclerosis

Aiden J CHAUNTRY ^1,^*, Justin B MOORE ^2,³, Teresa SEEMAN ⁴, Steven J SHEA ^5,⁶, Richard P SLOAN ⁷, Anna C WHITTAKER ⁸, Eli PUTERMAN ⁹, Anne I TURNER ¹⁰, William P TYNE ¹¹, Mark J HUTSON ¹², Mark P FUNNELL ¹³, Gabriel ZIEFF ^9,¹⁴, Brook M GELETA ³, Keeron STONE ^15,¹⁶, Erik D HANSON ¹, Lee STONER ¹⁷, Michael P BANCKS ³

¹Department of Exercise and Sport Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

²Department of Implementation Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA

³Department of Epidemiology and Prevention, Wake Forest University School of Medicine, Winston-Salem, NC, USA

⁴Division of Geriatrics, Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA

⁵Department of Medicine, Columbia University Vagelos College of Physicians and Surgeons, New York, USA

⁶Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, USA

⁷Division of Behavioral Medicine, Department of Psychiatry, Columbia University Irving Medical Center, New York, NY, USA

⁸Faculty of Health Sciences and Sport, University of Stirling, Stirling, Scotland, UK

⁹School of Kinesiology, University of British Columbia, Vancouver, BC, Canada

¹⁰Institute for Physical Activity and Nutrition (IPAN), School of Exercise & Nutrition Sciences, Deakin University, Geelong, Australia

¹¹Faculty of Medicine and Health Sciences, School of Life Sciences, University of Nottingham, Nottingham, UK

¹²School of Sport and Exercise Science, Faculty of Life and Health Sciences, Ulster University, Coleraine, UK

¹³Diabetes Research Centre, University of Leicester, Leicester General Hospital, Leicester, UK

¹⁴Department of Psychology, University of British Columbia, Vancouver, BC, Canada

¹⁵Centre for Cardiovascular Research, Innovation and Development, Cardiff School of Sport and Health Sciences, Cardiff Metropolitan University, Wales, United Kingdom

¹⁶National Cardiovascular Research Network, Wales, United Kingdom

¹⁷Scienceye, NC, USA

AUTHOR CONTRIBUTIONS and CRediT roles

Dr. Chauntry is the guarantor of this work. As such, he takes responsibility for the integrity of the data and the accuracy of the data analysis and affirms that the manuscript is an honest, accurate, and transparent account of the study being reported. All authors meet ICMJE criteria and made substantial intellectual contributions participating in creating and designing the study, analysing and interpreting the data, and reviewing this manuscript. All authors have read and approved the final report for publication.

Chauntry: Conceptualization, Methodology, Formal analysis, Data curation, Writing – original draft, visualization, project administration. Bancks and Moore: Conceptualization, Methodology, Formal analysis, Data curation, Writing – review and editing, visualization, supervision, project administration. Remaining authors: Conceptualization, Methodology, Writing – review and editing, visualization, supervision

Corresponding Author: aidenjc@unc.edu; aidenjc@hotmail.co.uk

PMCID: PMC12685367 NIHMSID: NIHMS2119329 PMID: 41135261

Abstract

BACKGROUND

Dysregulated psychobiological reactivity to acute psychological stress is associated with subclinical cardiovascular disease (CVD) risk, but its association with clinical CVD and mortality remains unclear—especially when considering both exaggerated and blunted responses, non-cardiovascular biomarkers, and potential racial/ethnic differences. This study aimed to test (1) relationships between multi-system stress reactivity and CVD/mortality, and (2) effect modification by race/ethnicity.

METHODS

Participants were CVD-free adults (N=957, age=69±9 years, 56% female, 27% non-Hispanic White, 32% non-Hispanic Black, 41% Hispanic) enrolled in the Multi-Ethnic Study of Atherosclerosis. The following responses to a standardized psychological stress protocol were recorded: blood pressure (BP), heart rate, heart rate variability (HRV), salivary alpha-amylase (sAA), and cortisol. Participants were followed for a median of 8 years. Covariate-adjusted Cox proportional hazard models investigated associations of stress reactivity (baseline-to-stress changes: low/blunted: ≤25^th percentile; intermediate/moderate [reference]: 26–74^th; high/exaggerated: ≥75^th) with incident CVD (N=111) and all-cause mortality (N=114). Race/ethnicity was tested as an effect modifier.

RESULTS

Stress reactivity was not linked with CVD incidence. Blunted diastolic BP reactivity was associated with premature all-cause mortality (HR=1.92, 95% CI: 1.03—3.56). Exaggerated (HR=0.58, 95% CI: 0.35—0.98) and blunted (HR=0.52, 95% CI: 0.30—0.89) sAA reactivity were associated with reduced mortality risk. Race/ethnicity was not an effect modifier (all p for interaction > 0.05).

CONCLUSIONS

Blunted DBP reactivity may serve as an early marker of increased mortality risk; randomized trials should test whether interventions that normalize DBP reactivity improve long-term survival. Further research should explore why dysregulated sAA reactivity was associated with lower mortality risk.

Keywords: Acute stress, physiological responses, chronic disease prevention, stress reactivity, reactivity hypothesis

1. INTRODUCTION

Cardiovascular disease (CVD) and premature mortality remain critical public health concerns (Vos et al., 2020) and acute psychological stress is an important risk factor for both (Kivimäki and Steptoe, 2018; Steptoe and Kivimäki, 2012). Central to understanding the link between stress and CVD/mortality is “stress reactivity”, the cascade of multi-system biological responses (e.g., blood pressure, cortisol) that evolved to enhance survival/coping when facing acute psychological stressors (Kivimäki and Steptoe, 2018; Steptoe and Kivimäki, 2012). Although stress reactivity is relatively stable within individuals over many years (Hassellund et al., 2010), substantial between-individual variability exists (Kiecolt-Glaser et al., 2020; Whittaker et al., 2021). When frequently mounted over time, dysregulated reactivity—whether exaggerated (e.g., by contributing to biological wear and tear) or blunted (e.g., via chronotropic incompetence and links to CVD risk factors reflecting motivational and behavioral dysregulation, such as depression)—is associated with subclinical CVD risk (Chida and Steptoe, 2010; O’ Riordan et al., 2023; Turner et al., 2020). For example, exaggerated systolic blood pressure (SBP), diastolic blood pressure (DBP), and cortisol reactivity are prospectively associated with increased risk of elevated blood pressure (Chida and Steptoe, 2010; Turner et al., 2020), whereas blunted heart rate reactivity predicts greater carotid intima–media thickness (Turner et al., 2020). Relatively little research has investigated whether heart rate variability (HRV) or salivary alpha-amylase (sAA) reactivity relates to future CVD outcomes, which represents a key gap in the literature. Both HRV and sAA are important markers of autonomic nervous system activity, and although evidence on reactivity is limited, dysregulated resting HRV and sAA levels have been linked to adverse CVD outcomes (Cozma et al., 2017; Thayer et al., 2010). Despite extensive research linking stress reactivity to subclinical CVD, the relationship between dysregulated stress reactivity and clinical CVD risk and mortality remains poorly understood, particularly when considering both exaggerated and blunted responses, as well as non-cardiovascular biomarkers. It also remains unclear whether the strength of associations between stress reactivity and CVD/mortality differs across groups that are disproportionately exposed to acute psychological stress. Addressing this knowledge gap is essential for identifying high-risk populations and developing targeted interventions to reduce health disparities. Notably, racial and ethnic disparities in CVD incidence and premature all-cause mortality have widened across recent decades, with Black and other minoritized populations facing disproportionately greater increases in risk (Tsao et al., 2022). Findings from the Multi-Ethnic Study of Atherosclerosis (MESA) indicate that Black individuals have a 121% higher risk of hypertension and a 72% increased risk of CVD mortality, compared to White individuals (Post et al., 2022). Compared to the racial/ethnic majority, racial and ethnic minorities also experience higher exposure to acute stressors (Phelan and Link, 2015) and have been shown to have dysregulated psychobiological stress reactivity (Anderson, 1989; Knox et al., 2002; Lepore et al., 2006; Majeno et al., 2021; Reimann et al., 2012). Thus, and aligned with the Weathering Hypothesis (Geronimus, 1992; Geronimus et al., 2006), repeated dysregulated psychobiological reactivity to acute psychological stress represents a plausible, yet understudied, psychobiological mechanism that might drive racial/ethnic inequalities in CVD incidence and premature mortality.

The first aim of this study was to test associations between cardiovascular, cortisol, and salivary alpha amylase reactivity to acute psychological stress and 8-year CVD incidence and all-cause mortality risk, in adults free of overt CVD at the time of reactivity testing. We hypothesized that exaggerated and blunted stress reactivity, compared to intermediate reactivity, would be associated with an increased risk of CVD and premature all-cause mortality. The second aim was to investigate whether these associations showed effect modification by race/ethnicity, focusing on non-Hispanic White, non-Hispanic Black, and Hispanic individuals. We hypothesized that harmful associations between dysregulated stress reactivity and CVD/mortality would be stronger and more consistent among Black and Hispanic populations.

2. MATERIAL AND METHODS

This manuscript followed STROBE reporting guidelines. For full details of the methods, refer to Supplementary Material 1 and the MESA methods paper (Bild et al., 2002).

2.1. Study population

The MESA (Bild et al., 2000) is an ongoing cohort study. Between July 2000 and August 2002, 6,814 men and women aged 45–84 years were recruited from six US urban and suburban communities (Baltimore, MD; Chicago, IL; Forsyth County, NC; Los Angeles, CA; Northern Manhattan and the Bronx, NY; and St. Paul, MN). The diverse cohort (38% White, 28% Black, 22% Hispanic, 12% Chinese) has completed six follow-up visits and annual interviews. Institutional review boards approved the study at each site, and all participants provided written informed consent.

This analysis involved 1,040 participants (non-Hispanic white, non-Hispanic black, and Hispanic, aged 54–93 years) who completed the MESA stress II ancillary component (stress reactivity testing), which occurred between 2010–2012 (exam 5) at the MD, CA, and NY field centers. Chinese participants were not recruited at these field centers and were therefore not included in this ancillary study sample. After excluding those with CVD prior to stress reactivity assessment, the final sample was N=957.

2.2. Stress task protocol

Participants abstained from exercise, food, caffeine, and tobacco for 1 hour, with stress reactivity testing occurring between 11:00–18:00. The stress protocol, detailed previously (Castro-Diehl et al., 2016; Lê-Scherban et al., 2018), included an 11-minute resting baseline, two 6-minute psychological stress tasks (Morgan and Turner Hewitt mental arithmetic task and Stroop color word task), each followed by a 6-minute recovery period. Task order was randomized, and participants entered responses via a computer keypad. To induce social evaluation, participants were informed that their performance would be evaluated. The difficulty of both tasks was continuously adjusted based on performance. Participants rated psychological stress (0–7) before and after each task.

2.3. Cardiovascular measures

Continuous cardiovascular data were analyzed in 300-second epochs. Beat-to-beat blood pressure (BP) was recorded using a Nexfin monitor and finger cuff, with a brachial cuff for calibration. Custom software was used for analysis with manual correction via interpolation.

Electrocardiogram recordings assessed heart rate and heart rate variability (HRV) via 3-lead electrodes placed on the shoulders and torso. R-wave errors were manually corrected. HRV was quantified using time-domain (RMSSD) and frequency-domain (LF-HRV [0.04–0.15 Hz], HF-HRV [0.15–0.50 Hz]) measures, analyzed via Fourier transform. The HRV variables were natural log transformed due to severely skewed distributions. In accordance with current recommendations, HRV was not adjusted for respiration as this can reduce variability associated with neural control of the heart (Laborde et al., 2017; Thayer et al., 2011).

2.4. Neuroendocrine measures

Saliva samples were collected pre-stress and 38 min post-stress using Salivette tubes. Samples were stored at −20°C, thawed, and centrifuged before analysis. Salivary alpha-amylase (sAA) was analyzed using an enzyme kinetic method (intra-assay CV <8%; inter-assay CV <12%). Cortisol was measured via a chemiluminescence assay (intra-/inter-assay CV <8%).

2.5. Cardiovascular disease incidence and all-cause mortality

The follow-up for clinical events is ongoing and methods have been previously described (Bild et al., 2002). For this analysis, event ascertainment occurred through December 31, 2019. Only the first recorded event was included in analyses. The primary outcome was incident CVD (myocardial infarction [MI], resuscitated cardiac arrest, definite or probable angina, stroke, stroke death, definite or probable heart failure, coronary heart disease death, other atherosclerotic death, or other CVD-related death. For all-cause mortality (secondary outcome), deaths were classified by the underlying cause on death certificates, with data obtained from state vital statistics offices and the National Death Index. The large number of “pending adjudication” reasons for mortality precluded CVD mortality analyses.

2.6. Covariates

Standard questionnaires and methods (Bild et al., 2002) were used to collect age (continuous; years), biological sex (male/female), race/ethnicity (self-reported: non-Hispanic White; non-Hispanic Black; Hispanic), bioelectrical impedance-measured total body fat (continuous; kg), low density lipoprotein cholesterol (LDL-C; continuous; mg/dL), moderate-to-vigorous physical activity (MVPA) time (Typical Week Physical Activity Survey; continuous; met-min/week), MESA site for reactivity testing (New York, California, or Maryland), annual income (<$24,999; $25,000-$49,999; $50,000-$99,999; >$100,000), highest education achieved (less than high school; completed high school; some college; college or higher education), cigarette smoking status (never; former; current), current use of alcohol (no/yes), diabetes status (2003 American Diabetes Association fasting criteria; normal; impaired fasting glucose; untreated diabetes; treated diabetes), beta blocker use (no/yes), anti-hypertensive medication use (no/yes), lipid-lowering medication use (no/yes), time of baseline saliva collection (continuous), mean self-reported perceived stress levels across the two stress tasks (scale of 1–10, higher scores indicating higher stress; continuous), and the appropriate resting baseline reference value used to calculate each respective stress reactivity variable (continuous). These covariates were collected at exam 5 unless noted and were selected a priori based on established links with stress reactivity and/or CVD/mortality (Carroll et al., 2012; Chauntry et al., 2019; O’ Riordan et al., 2023; Post et al., 2022; Turner et al., 2020; Whittaker et al., 2021).

2.7. Statistical analysis

Data are reported as mean (standard deviation [SD]), n (%), or hazard ratio (HR) with 95% confidence intervals (CIs). Multiple imputation (10 datasets, Rubin’s Rules) addressed missing exposure, covariate and outcome data, and missing data assumptions were checked and verified. Cardiovascular exposure data were averaged across both stress tasks. Separate repeated measures ANOVAs (two time points; baseline, stress) confirmed the stress task perturbed psychobiological measures. Stress reactivity was calculated as average stress minus average baseline (the sample collected 38 min post stress minus baseline sample for cortisol and sAA). Reactivity outliers were winsorized (1^st/99^th percentile). In the absence of standard clinical cutoffs, we followed prior studies (Kupper et al., 2015), and categorized reactivity variables into high (≥75th percentile), intermediate (26^th–74^th percentile, reference), and low (≤25^th percentile) groups (raw cutoff thresholds are shown for each reactivity variable in Supplementary Material 3). The 25/75^th percentile approach was selected over the 33% tertile method to align with similar prior research (Kupper et al., 2015), more meaningfully distinguish extreme reactivity, and better capture potential non-linear associations. As HRV measures usually decrease rather than increase in response to stress, we reversed the classification of HRV reactivity groups.

Separate Cox proportional hazard ratio models assessed each stress reactivity variable in relation to CVD (primary outcome), all-cause mortality (secondary outcome), and cerebrovascular (CVA) and coronary heart (CHD) disease (tertiary outcomes). Model assumptions, including proportional hazards, were assessed and verified. Each reactivity and outcome variable were first examined in an unadjusted model, and then a multivariable-adjusted model with all a priori-selected covariates entered simultaneously (time of saliva collection was only included in cortisol/sAA models). Data in text reflects multivariable-adjusted models. Kaplan-Meier models visualized reactivity associations with CVD and mortality. To test multiplicative effect modification (multiplicative moderation) by race/ethnicity, an interaction term (race/ethnicity*reactivity) was included in each Cox model.

Finally, we conducted sensitivity analyses comparing our findings to those derived from (1) the 33% reactivity tertile approach and (2) the non-imputed dataset. We also reweighted our analytic sample according to the MESA baseline sample using inverse probability of selection weights (IPSW), for our primary (SBP reactivity and CVD incidence) Cox model. Data were analyzed in SAS (version 9.4) with alpha set to p<0.05.

3. RESULTS

3.1. Participant characteristics

This analysis included 957 CVD-free adults. The distribution of demographic and clinical characteristics varied across stress reactivity (shown for SBP reactivity [primary exposure variable] in Table 1). The number of missing data for each variable are reported in Supplementary Material 2.

Table 1.

Participant characteristics according to systolic blood pressure reactivity group (primary exposure variable).

Characteristic	Total sample n=957	High SBP reactivity n=199	Intermediate (ref) SBP reactivity n=390	Low SBP reactivity n=207
Age (years)	69.1 (9.0)	70.0 (8.6)	67.8 (8.6)	66.5 (8.4)
Sex
Male	423 (44.2)	83 (41.7)	178 (45.6)	78 (37.7)
Female	534 (55.8)	116 (58.3)	212 (54.4)	129 (62.3)
Race/Ethnicity
Non-Hispanic White	262 (27.4)	57 (28.6)	110 (28.2)	50 (24.2)
Non-Hispanic Black	301 (31.5)	67 (33.7)	103 (26.4)	71 (34.3)
Hispanic	394 (41.2)	75 (37.7)	177 (45.4)	86 (41.6)
BMI (kg/m²)	29.3 (5.6)	28.5 (5.1)	29.3 (5.5)	30.3 (6.1)
Total body fat (kg)	26.1 (11.0)	25.3 (10.5)	25.9 (10.6)	27.5 (12.0)
LDL-C (mg/dL)	107.7 (32.8)	113.1 (35.6)	108.3 (30.2)	108.2 (34.9)
MVPA (met-min/week)	5337 (6785)	4893 (4851)	5475 (6303)	6363 (10028)
Annual income
<$24,999	300 (32.0)	68 (34.2)	112 (28.7)	63 (30.4)
$25–49,999	275 (29.4)	56 (28.1)	120 (30.8)	57 (27.5)
$50–99,999	322 (34.4)	65 (32.7)	132 (33.9)	76 (36.7)
>$100,000	40 (4.3)	7 (3.5)	19 (4.9)	6 (2.9)
Missing		3 (1.5)	7 (1.8)	5 (2.4)
Education
Less than high school	177 (18.6)	39 (19.6)	70 (18.0)	36 (17.4)
Completed high school	197 (20.7)	36 (18.1)	79 (20.3)	41 (19.8)
Some college	232 (24.4)	50 (25.1)	97 (24.9)	55 (26.6)
College/higher education	346 (36.3)	74 (37.2)	141 (36.2)	73 (35.3)
Missing		0 (0)	3 (0.8)	2 (1.0)
Cigarette smoking status
Never	446 (46.6)	94 (47.2)	190 (48.7)	93 (44.9)
Former	451 (47.1)	94 (47.2)	176 (45.1)	95 (45.9)
Current	60 (6.3)	11 (5.5)	24 (6.2)	19 (9.2)
Alcohol: current use
No	581 (61.0)	114 (57.3)	247 (63.3)	129 (62.3)
Yes	371 (39.0)	85 (42.7)	143 (36.7)	76 (37.7)
Missing		0 (0)	0 (0)	2 (1.0)
Diabetes (2003 ADA fasting criteria)
Normal	571 (60.0)	129 (64.8)	244 (62.6)	111 (53.6)
IFG	187 (19.7)	42 (21.1)	75 (19.2)	32 (15.5)
Untreated diabetes	12 (1.3)	1 (0.5)	7 (1.8)	4 (1.9)
Treated diabetes	181 (19.0)	26 (13.1)	62 (15.9)	58 (28.0)
Missing		1 (0.5)	2 (0.5)	2 (1.0)
Beta-blockers without diuretics
NO	796 (83.2)	166 (83.4)	324 (83.1)	180 (89.0)
YES	161 (16.8)	33 (16.6)	66 (16.9)	27 (13.0)
Any anti-hypertensive medication
NO	410 (42.8)	90 (45.2)	176 (45.1)	90 (43.5)
YES	547 (57.2)	109 (54.8)	214 (54.9)	117 (56.5)
Any lipid-lowering medication
NO	602 (62.9)	125 (62.8)	244 (65.6)	135 (65.2)
YES	355 (37.1)	74 (37.2)	146 (37.4)	72 (34.8)

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Note: Characteristics are mean (standard deviation) or counts (percentage). SBP = systolic blood pressure; BMI = body mass index; MVPA = moderate-to-vigorous physical activity; LDL-C = low-density lipoprotein-cholesterol; ADA= American Diabetes Association; IFG = impaired fasting glucose. Data for the SBP reactivity groups reflects non-imputed data, such that individuals with missing SBP reactivity data were not categorized to an exposure group, which is the reason for differences in group sample size relative to total sample size. High reactivity (≥75^th percentile), intermediate reactivity (26^th–74^th percentile, reference group), and low reactivity (≤25^th percentile).

3.2. Follow-up time and number of clinical events

The median follow-up time was 7.9 (1.1) years for CVD incidence, 8.0 (1.0) years for all-cause mortality, 7.9 (1.0) years for CVA incidence, and 7.9 (1.0) years for CHD incidence. During follow-up, 111 (11.6%) participants experienced CVD, 114 (11.9%) died, 45 (4.7%) had CHD, and 42 (4.4%) developed CVA.

3.3. Psychobiological responses to the stress protocol

All biological parameters showed significant responses to the stress protocol (all p<.001; Table 2). Self-reported stress levels increased (p< .001, η²=0.51) from pre-stress resting baseline (2.5±1.8) to during the stress protocol (4.3±2.0). Correlations among stress reactivity variables and self-reported stress are shown in Supplementary Material 4.

Table 2.

Biological responses to the psychological stress protocol.

Reactivity measure	Total or reactivity group	Baseline	Stress	Stress reactivity (Δ)	Effect size (η²)
SBP	Total n=957	129.7 (19.7)	145.6 (21.8)	16.0 (12.3)	0.63
	Low n=252	133.5 (21.3)	134.7 (20.5)	1.1 (8.9)
	Int n=465	128.0 (19.2)	143.9 (19.5)	15.9 (4.4)
	High n=240	129.4 (19.2)	160.7 (19.1)	31.3 (7.1)
DBP	Total n=957	64.9 (8.4)	71.4 (9.4)	6.4 (4.8)	0.63
	Low n=253	65.9 (8.6)	66.5 (8.8)	0.6 (3.6)
	Int n=461	64.7 (8.1)	71.1 (8.3)	6.5 (1.7)
	High n=243	64.5 (8.5)	76.8 (8.3)	12.3 (2.5)
Heart rate	Total n=957	68.5 (10.6)	71.6 (11.0)	3.1 (3.9)	0.39
	Low n=250	69.8 (12.0)	68.7 (11.9)	−1.0 (2.5)
	Int n=457	68.0 (10.17)	70.8 (10.3)	2.8 (1.1)
	High n=250	67.1 (10.2)	75.1 (10.7)	8.1 (3.2)
RMSSD	Total n=957	3.0 (0.6)	2.9 (0.6)	−0.1 (0.4)	0.07
	High n=256	3.2 (0.6)	2.7 (0.6)	−0.5 (0.3)
	Int n=469	2.9 (0.6)	2.8 (0.6)	−0.10 (0.1)
	Low n=232	2.8 (0.6)	3.1 (0.6)	0.3 (0.2)
LF-HRV	Total n=957	5.1 (1.2)	4.5 (1.1)	−0.6 (0.8)	0.30
	High n=247	5.7 (1.0)	4.1 (1.0)	−1.6 (0.5)
	Int n=469	5.1 (1.0)	4.5 (1.0)	−0.5 (0.3)
	Low n=241	4.4 (1.1)	4.9 (1.0)	0.5 (0.4)
HF-HRV	Total n=957	4.7 (1.3)	4.4 (1.3)	−0.4 (0.8)	0.17
	high n=256	5.2 (1.2)	3.8 (1.2)	−1.4 (0.5)
	Int n=474	4.7 (1.3)	4.4 (1.3)	−0.3 (0.2)
	Low n=227	4.3 (1.3)	4.9 (1.2)	0.6 (0.5)
sAA	Total n=957	131.2 (136.8)	139.4 (130.8)	8.2 (80.2)	0.01
	Low n=246	223.3 (169.9)	149.3 (140.3)	−74.1 (77.2)
	Int n=466	78.9 (89.6)	85.0 (86.9)	6.2 (17.4)
	High n=245	137.1 (119.9)	231.5 (135.0)	94.4 (67.6)
Cortisol	Total n=957	8.7 (5.4)	9.5 (6.4)	0.8 (4.6)	0.03
	Low n=251	12.5 (5.6)	9.2 (5.5)	−3.4 (2.8)
	Int n=468	6.7 (4.3)	7.0 (4.6)	0.3 (1.1)
	High n=238	8.3 (4.7)	14.8 (7.1)	6.5 (4.7)

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Note: Data are presented as mean (SD). High reactivity (≥75^th percentile), intermediate reactivity (26^th–74^th percentile, reference group), and low reactivity (≤25^th percentile). Int = intermediate; SBP = systolic blood pressure; DBP = diastolic blood pressure; RMSSD = root mean square of successive differences; LF-HRV = low frequency heart rate variability; HF-HRV = high frequency heart rate variability, sAA = salivary alpha amylase. Reactivity refers to change from baseline to during stress (from baseline to 38 minutes post-baseline for SAA and cortisol). All time effects were significant (p < .001). HRV reactivity groups were reversed to align with other physiological measures; a larger HRV decrease in response to stress was classified as ‘high’ reactivity. Effect size represents the magnitude of change from baseline to stress across the whole sample, with η² values interpreted as small (≥0.01), medium (≥0.06), and large (≥0.14) effects.

3.4. Psychobiological stress reactivity and incident cardiovascular disease

3.4.1. Cox proportional hazard ratio models

In adjusted models, the overall main effect for all stress reactivity variables with incident CVD were non-significant (Table 3); individuals with high (p≥.287) or low (p≥.246) stress reactivity did not have a higher risk of incident CVD compared to the respective intermediate group.

Table 3.

Hazard ratios for stress reactivity exposure variables and cardiovascular disease incidence.

Reactivity variable	Reactivity group	Events / censored / mean person-years	Unadjusted HR (95% CI)	Adjusted HR (95% CI)	Wald χ² (p)
SBP	High	32 / 236 / 5.31	1.06 (0.64, 1.75)	1.01 (0.59, 1.71)	0.54 (.787)
	Int	49 / 384 / 8.71	Reference	Reference
	Low	30 / 226 / 4.90	1.09 (0.65, 1.81)	1.06 (0.62, 1.81)
DBP	High	32 / 234 / 5.38	1.20 (0.72, 2.00)	1.17 (0.69, 2.00)	2.21 (.383)
	Int	43 / 389 / 8.64	Reference	Reference
	Low	35 / 223 / 4.90	1.43 (0.89, 2.32)	1.36 (0.81, 2.27)
Heart rate	High	28 / 264 / 5.87	0.83 (0.49, 1.39)	0.86 (0.50, 1.47)	2.83 (.365)
	Int	48 / 370 / 8.33	Reference	Reference
	Low	34 / 213 / 4.72	1.25 (0.76, 2.05)	1.26 (0.75, 2.12)
RMSSD	High	31 / 263 / 5.87	0.90 (0.54, 1.51)	0.85 (0.49, 1.48)	1.24 (.598)
	Int	47 / 358 / 7.98	Reference	Reference
	Low	33 / 225 / 5.08	1.08 (0.64, 1.83)	0.96 (0.55, 1.67)
LF-HRV	High	28 / 247 / 5.46	0.96 (0.55, 1.68)	0.96 (0.55, 1.68)	2.33 (.371)
	Int	46 / 382 / 8.53	Reference	Reference
	Low	37 / 217 / 4.94	1.41 (0.87, 2.28)	1.33 (0.78, 2.28)
HF-HRV	High	35 / 247 / 5.61	1.19 (0.72, 1.95)	1.12 (0.65, 1.94)	1.40 (.547)
	Int	44 / 377 / 8.34	Reference	Reference
	Low	32 / 223 / 4.98	1.23 (0.65, 2.32)	1.13 (0.59, 2.19)
sAA	High	37 / 229 / 5.35	1.36 (0.87, 2.13)	1.09 (0.68, 1.74)	0.73 (.706)
	Int	44 / 394 / 8.60	Reference	Reference
	Low	30 / 223 / 5.00	1.17 (0.72, 1.88)	0.88 (0.51, 1.54)
Cortisol	High	33 / 218 / 4.90	1.16 (0.75, 1.79)	1.27 (0.81, 2.00)	2.22 (.335)
	Int	53 / 407 / 9.21	Reference	Reference
	Low	25 / 221 / 4.82	0.90 (0.56, 1.46)	0.85 (0.52, 1.41)

Open in a new tab

Note: High reactivity (≥75^th percentile), intermediate reactivity (26^th–74^th percentile, reference group), and low reactivity (≤25^th percentile). Int = intermediate; HR = hazard ratio; SBP = systolic blood pressure; DBP = diastolic blood pressure; RMSSD = root mean square of successive differences; LF-HRV = low frequency heart rate variability; HF-HRV = high frequency heart rate variability, sAA = salivary alpha amylase. Reactivity refers to change from baseline to during stress (from baseline to 38 minutes post-baseline for SAA and cortisol). Wald χ² (p) reflects overall model statistics.

3.4.2. Kaplan-Meier models

Figure 1A displays the Kaplan-Meier curve for our primary exposure (SBP reactivity) and outcome (CVD incidence) data; the remaining CVD Kaplan-Meier curves are found in Supplementary Material 6. The log-rank test did not support statistical differences in CVD incidence for SBP or any other reactivity variable (all Wald χ² p-values>0.05).

3.5. Psychobiological stress reactivity and all-cause mortality

3.5.1. Cox proportional hazard ratio models

In adjusted models, DBP (Wald χ²(2)=8.55, p=.045) and sAA (Wald χ²(2)=8.117, p=.020) reactivity were associated with all-cause mortality. Those with low (p=.040), but not high (p=.186) DBP reactivity had a higher risk of all-cause mortality compared to the intermediate DBP reactivity group (Table 4). For sAA, individuals with high (p=.042) and low (p=.017) reactivity had a lower risk of all-cause mortality compared to the intermediate sAA reactivity group. The remaining stress reactivity variables were not associated with all-cause mortality (all Wald χ² p-values > 0.05); individuals with high (p ≥ 0.274) or low (p ≥ 0.312) reactivity did not have a higher risk compared to the respective intermediate category.

Table 4.

Hazard ratios for stress reactivity exposure variables and all-cause mortality.

Reactivity variable	Reactivity group	Events / censored / Mean Person-years	Unadjusted HR (95% CI)	Adjusted HR (95% CI)	Wald χ² (p)
SBP	High	37 / 231 / 5.64	1.39 (0.83, 2.34)	1.13 (0.67, 1.91)	2.12 (.462)
	Int	44 / 389 / 9.30	Reference	Reference
	Low	33 / 223 / 5.27	1.33 (0.77, 2.29)	1.35 (0.75, 2.46)
DBP	High	36 / 230 / 5.69	1.56 (0.93, 2.62)	1.40 (0.85, 2.35)	8.55 (.045)
	Int	38 / 395 / 9.22	Reference	Reference
	Low	40 / 219 / 5.30	1.85 (1.06, 3.20)	1.92 (1.03, 3.56)
Heart rate	High	39 / 253 / 6.20	1.32 (0.80, 2.17)	1.33 (0.80, 2.20)	2.33 (.368)
	Int	43 / 376 / 8.94	Reference	Reference
	Low	32 / 215 / 5.07	1.33 (0.82, 2.18)	1.28 (0.77, 2.13)
RMSSD	High	43 / 252 / 6.22	1.56 (0.96, 2.56)	1.31 (0.77, 2.26)	2.23 (.406)
	Int	38 / 367 / 8.59	Reference	Reference
	Low	34 / 224 / 5.40	1.42 (0.77, 2.62)	1.20 (0.63, 2.30)
LF-HRV	High	38 / 238 / 5.75	1.22 (0.74, 1.20)	1.37 (0.73, 2.54)	4.53 (.710)
	Int	49 / 378 / 9.09	Reference	Reference
	Low	27 / 227 / 5.36	0.92 (0.54, 1.57)	0.79 (0.46, 1.35)
HF-HRV	High	38 / 244 / 5.97	1.28 (0.80, 2.07)	1.22 (0.66, 1.88)	1.14 (.650)
	Int	44 / 376 / 8.89	Reference	Reference
	Low	32 / 223/ 5.34	1.20 (0.67, 2.17)	0.97 (0.53, 1.81)
sAA	High	26 / 240 / 5.72	0.70 (0.43, 1.13)	0.58 (0.35, 0.98)	8.12 (.020)
	Int	59 / 379 / 9.14	Reference	Reference
	Low	29 / 224 / 5.34	0.82 (0.52, 1.31)	0.52 (0.30, 0.89)
Cortisol	High	29 / 222 / 5.24	1.08 (0.68, 1.72)	1.06 (0.65, 1.74)	0.32 (.856)
	Int	50 / 410 / 9.78	Reference	Reference
	Low	35 / 211 / 5.18	1.34 (0.86, 2.08)	1.12 (0.71, 1.77)

Open in a new tab

Note: High reactivity (≥75th percentile), intermediate reactivity (26th–74th percentile, reference group), and low reactivity (≤25th percentile). Int = intermediate; SBP = systolic blood pressure; DBP = diastolic blood pressure; RMSSD = root mean square of successive differences; LF-HRV = low frequency heart rate variability; HF-HRV = high frequency heart rate variability, sAA = salivary alpha amylase. Reactivity refers to change from baseline to during stress (from baseline to 38 minutes post-baseline for SAA and cortisol). Wald χ² (p) reflects overall model statistics, bold text indicates significant (p < 0.05) overall model effect.

3.5.2. Kaplan-Meier models

As shown in Figure 1B, all-cause mortality rates differed by DBP reactivity group (Wald χ²(2)=6.04, p=.048). Mortality risk was higher in adults with low versus intermediate DBP reactivity (p=.018), but no significant difference was found between the high and intermediate groups (p=.055). There were no differences in all-cause mortality risk across the remaining stress reactivity exposure variables (all Wald χ²(2) p-values >0.05); the associated Kaplan-Meier curves are displayed in Supplementary Material 7.

3.6. Effect modification by race/ethnicity for CVD incidence and all-cause mortality

We observed no effect modification by race/ethnicity for the associations between stress reactivity measures and CVD incidence or all-cause mortality (all p-for-interaction >0.05). Stratified findings are shown in Supplementary Table 5.

3.7. Supplementary analyses

No stress reactivity variable was associated with risk of CHD or CVA in either unadjusted or adjusted models (all adjusted Wald χ² p-values > 0.23 for CHD; > 0.16 for CVA). Race/ethnicity did not modify any associations (all interaction p-values > 0.16). Full results for these tertiary outcomes are available in the Supplementary Material.

3.8. Sensitivity analyses

The mean IPSW was 1.0026 (SD=.0522). In the covariate-adjusted model without IPSW, the HR was 1.010 (95% CI: 0.593–1.713, p=.976) for high versus intermediate SBP reactivity and 1.060 (95% CI: 0.621–1.813, p=.828) for low versus intermediate SBP reactivity. With IPSW adjustment, these HRs became 1.014 (95% CI: 0.598–1.721, p=.956) and 1.068 (95% CI: 0.628–1.818, p=.805), respectively. Our findings were largely consistent when derived from the 33% reactivity tertile approach and the non-imputed dataset.

4. DISCUSSION

This study examined whether exaggerated or blunted psychobiological reactivity to acute psychological stress predicted 8-year CVD incidence and all-cause mortality in initially CVD-free adults. No significant associations were found for CVD risk. Blunted DBP reactivity was linked to increased mortality risk, whereas both exaggerated and blunted sAA reactivity were associated with reduced mortality risk. No statistical effect modification by race/ethnicity was observed.

4.1. Acute psychobiological stress reactivity and cardiovascular disease incidence

Our findings contrast with prior research linking exaggerated and blunted SBP, DBP, heart rate, and cortisol stress reactivity with subclinical and clinical CVD risk (O’ Riordan et al., 2023; Treiber et al., 2003; Turner et al., 2020). Possible explanations include the modest follow-up duration (~8 years) and that our study population was initially free from overt CVD, whereas previous research has found stronger associations in individuals with pre-existing disease or higher CVD risk profiles (Treiber et al., 2003). Supporting this, our results align with another study that found no relationship between stress reactivity and risk of (10-year) clinical CVD events in initially CVD-free individuals (Shaffer et al., 2012). It is possible that robust regulatory mechanisms—such as autonomic flexibility and vascular resilience—may help buffer against the adverse effects of dysregulated stress reactivity in healthy populations (Kivimäki and Steptoe, 2018; Shaffer et al., 2012; Steptoe and Kivimäki, 2012; Treiber et al., 2003). Overall, our findings, based on a relatively large sample (N=957), question whether stress reactivity is associated with CVD incidence in disease-free populations (Shaffer et al., 2012; Treiber et al., 2003), and we extend the literature by demonstrating this null association with additional stress reactivity measures, including HRV and sAA.

4.2. Acute psychobiological stress reactivity and all-cause mortality

Blunted DBP reactivity was associated with a 92% increased risk of all-cause mortality, independent of resting DBP and covariates such as antihypertensive and beta-blocker use. This suggests that DBP reactivity has unique prognostic value beyond resting DBP, potentially due to different genetic underpinnings (De Geus et al., 2007). Possible mechanisms linking blunted DBP reactivity to mortality need to be directly tested, but may involve links with CVD risk factors, such as depression and adiposity (O’ Riordan et al., 2023; Turner et al., 2020), and motivational dysregulation, including reduced engagement in health-promoting behaviors such as physical activity and medical adherence (Chauntry et al., 2019; O’ Riordan et al., 2023; Whittaker et al., 2021; Whittaker and Chauntry, 2021). Our findings contribute to the growing body of evidence indicating that blunted DBP reactivity may be an important risk factor for all-cause mortality. We build on prior research in clinical populations, such that in a cohort of systolic heart failure patients (N = 100; median follow-up = 4 years), those in the lowest DBP (but not SBP) reactivity category (mean Δ = −2.4 mmHg) in response to a public-speaking stressor had an approximately twofold higher risk of all-cause mortality, compared with those in the intermediate (mean Δ = 7.3 mmHg) DBP response category (HR = 2.04, 95% CI = 1.15–3.60) (Kupper et al., 2015). A larger heart failure cohort (N = 199; median follow-up = 5 years) showed similar results: patients in the lowest DBP (but not SBP) reactivity tertile (mean Δ = 5 mmHg) during a public-speaking task had a 49% higher risk of mortality or cardiovascular hospitalization, compared with those in the intermediate tertile (mean Δ = 14 mmHg; HR = 1.49, 95% CI = 1.03–2.16). Higher SBP reactivity was associated with lower mortality/hospitalization risk compared with the intermediate group (Sherwood et al., 2017). Finally, in post–coronary artery bypass graft patients (N = 521; median follow-up = 3 years), reactivity to mirror-tracing and public-speaking tasks were assessed six months post-surgery. Across follow up, participants in the lowest (Δ ≤ 9 mmHg) DBP reactivity quartile experienced a 20% incidence of mortality and other adverse clinical events, which was double the event rate observed in the highest (Δ ≥ 19 mmHg) quartile (10% events) (Herd et al., 2003).

Collectively, prior evidence and our findings provide compelling support that blunted DBP reactivity might be a predictor of future mortality risk. Our study extends this literature by demonstrating this association in response to new stress paradigms (mental arithmetic and Stroop tasks), in a larger and more diverse sample, over a longer follow-up period, and with more comprehensive adjustment for potential confounders. To our knowledge, we also provide the first evidence that these risk patterns are observed in a disease-free, community-based population. Further research is needed to elucidate the possible underlying mechanisms and clarify why similar associations with mortality are not observed for other cardiovascular reactivity measures, including SBP. One possible explanation relates to differences in vascular and cardiac regulatory pathways. DBP responses primarily index peripheral vascular resistance and small-artery compliance, with additional influence from heart rate. SBP responses are more strongly determined by cardiac output and stroke volume, but are also shaped by large-artery stiffness and wave reflections (Everson et al., 2001; Wentzel et al., 2025). Consequently, dysregulated vascular pathways may help explain why blunted DBP (but not SBP) responses are associated with premature mortality, and these patterns can reflect autonomic dysfunction, impaired sympathetic vasoconstriction, and reduced vascular compliance or reserve. However, further research incorporating additional cardiac and vascular measures is needed to confirm this speculative hypothesis.

Unexpectedly, both high and low sAA responses to stress were associated with lower risk of all-cause mortality. The link between sAA reactivity and future subclinical or clinical disease has been largely unexplored (Turner et al., 2020), which is a novel aspect of this present study. Our findings challenge the widely accepted assumption that moderate psychobiological responses to stress are always the most desirable (Turner et al., 2020; Whittaker et al., 2021). One speculative explanation is that a moderate sAA response reflects ineffective stress and autonomic regulation, where the body neither fully mobilizes resources to cope nor conserves energy for recovery. However, it is not clear why this effect would only be apparent for sAA reactivity rather than across all biological stress responses, especially measures that also index autonomic nervous system activity, such as HRV (Laborde et al., 2017). One possibility is that sAA predominantly indexes sympathetic-adrenal-medullary activation, and captures adrenergic responses that may be more proximally tied to stress-related disease mechanisms than HRV and other cardiovascular measures (Nater and Rohleder, 2009). In contrast, although interpretations for LF-HRV (and LF/HF) are contested, other HRV indices, especially RMSSD and HF-HRV, are often taken to reflect parasympathetic withdrawal and may be less sensitive to subtle autonomic dysregulation in disease-free samples, where signal-to-noise is lower (Laborde et al., 2017). Further research directly comparing sAA, HRV, and other autonomic responses within the same cohort is needed to determine whether sAA captures unique aspects of autonomic dysfunction not reflected in traditional HRV-based markers.

4.3. Effect modification by race/ethnicity

Our analyses suggest that relationships between stress reactivity and CVD/mortality are generally consistent across racial/ethnic groups. This was tested because various psychosocial, structural, and environmental factors (e.g., discrimination, acculturation stress, healthcare access, disparities in coping and health behaviors) aligned the Weathering Hypothesis (Geronimus, 1992), may drive racial/ethnic disparities in CVD and mortality by exacerbating the effects of acute stress and reactivity dysregulation (Birditt et al., 2023; Geronimus et al., 2006; Williams et al., 2019). Future studies with confirmed statistical power must verify that race/ethnicity does not modify the reactivity-CVD/mortality link across disease-free and clinical populations.

4.4. Methodological considerations

This study has several limitations. The laboratory stress protocol does not reflect real-world stress exposures, limiting ecological validity. The stress tasks likely induced only mild social-evaluative threat, and it is plausible that factors such as past psychological trauma and childhood adversity contributed to large cortisol decreases in response to stress for some participants (Whittaker et al., 2021), potentially explaining the small (albeit statistically significant) average stress-induced cortisol increase (Dickerson and Kemeny, 2004). The analyzed sample represents a subset of MESA and despite efforts to ensure representativeness, selection bias cannot be ruled out. As an observational study, causality cannot be inferred, and residual confounding may remain despite extensive covariate adjustment. Additionally, some important covariates may have been overlooked. Generalizability was limited by the fact that Chinese American MESA participants were not enrolled at sites offering the stress reactivity protocol. Analyses were conducted using reactivity categories to enhance interpretability and facilitate comparability with prior studies (Kupper et al., 2015; Sherwood et al., 2017). However, categorizing continuous variables can reduce statistical power, introduce cut-point dependence, and limit the ability to detect subtle associations. Future studies could consider continuous and non-linear modelling approaches to assess whether alternative analytic strategies yield additional insight. Our study-specific reactivity cutoffs also limit external validity. Finally, we did not apply multiple testing corrections, so caution is warranted regarding the potential for Type I error. This decision was made because our analyses were hypothesis-driven and preplanned, and standard correction methods—assuming independent tests—might have been overly conservative given the physiological interdependence of stress reactivity measures, thereby increasing the risk of Type II error.

Our study has many strengths. First, it leverages a large, well-characterized prospective cohort with rigorous protocols for data collection and clinical event adjudication, along with considerable racial/ethnic diversity. Second, the inclusion of multiple biological stress markers provided a comprehensive, multisystem assessment of stress reactivity. Lastly, statistical approaches were employed to account for selective attrition.

4.5. Future directions

Future studies should expand to other underrepresented groups (e.g., Asian and Indigenous populations), and consider additional biomarkers (e.g., inflammatory, vascular, neural), stress tasks (e.g., more ecologically valid tasks, active vs. passive tasks), and response patterns (e.g., recovery, habituation). Later work could use clustering to assess stress reactivity across multiple biological systems (Brindle et al., 2016) or ecological momentary assessment and wearable devices to capture reactivity to real-world, naturalistic stressors.

4.6. Conclusions

Among adults free from CVD at the time of reactivity testing, no significant associations emerged between dysregulated stress reactivity and 8-year CVD incidence. Both exaggerated and blunted sAA reactivity were associated with reduced risk of all-cause mortality, whereas blunted DBP reactivity predicted increased mortality risk. Race/ethnicity was not an effect modifier between stress reactivity and CVD incidence or all-cause mortality. Our DBP findings are consistent with prior research and suggest that blunted DBP reactivity may serve as an early marker of increased all-cause mortality risk. Further investigation is warranted to test whether adding DBP stress reactivity to risk-stratification frameworks improves clinical decision-making and whether interventions that normalize DBP reactivity reduce all-cause mortality.

Supplementary Material

NIHMS2119329-supplement-1.docx^{(1.5MB, docx)}

SUPPLEMENTARY DATA

Supplementary data are available online.

Highlights.

Blunted DBP stress reactivity predicted greater risk of all-cause mortality.
Both blunted and exaggerated sAA reactivity was linked to lower mortality risk.
No association was found between stress reactivity and incident CVD.
Race/ethnicity did not modify stress reactivity and CVD/mortality association.

ACKNOWLEDGEMENTS

Thank you to Jake C. Diana for his support with the initial stages of the SAS coding. The authors thank the other investigators, the staff, and the participants of the MESA study and MESA stress ancillary study for their valuable contributions. A full list of participating MESA investigators and institutions can be found at http://www.mesa-nhlbi.org.This manuscript has been reviewed by MESA for scientific content and consistency of data interpretation with previous MESA publications.

FUNDING

The 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 National Heart, Lung, and Blood Institute, and by grants UL1-TR-000040, UL1-TR-001079, and UL1-TR-001420 from the National Center for Advancing Translational Sciences (NCATS). AJC, JBM, EDH and LS are supported by National Heart, Lung, and Blood Institute Grants R01HL157187 and R01HL162805A. MPF is supported by the NIHR Applied Research Collaboration East Midlands (ARC EM). The funding agencies played no role in the design of the study, data analysis and interpretation, manuscript preparation, or the decision to publish the findings.

Footnotes

COMPETING INTERESTS

The authors have no relevant financial or non-financial interests to disclose. The views expressed in this manuscript are those of the authors and do not necessarily represent the views of the National Heart, Lung, and Blood Institute; the National Eye Institute; the National Institutes of Health; or the U.S. Department of Health and Human Services.

Declaration of interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Declarations of interest:

none

ETHICS APPROVAL

Institutional review boards approved the study at each site, and all participants provided written informed consent. Consent to publish was obtained.

Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

DATA AVAILABILITY

Data from the MESA study are publicly available through request from the Biologic Specimen and Data Repository Information Coordinating Center of the National Institutes of Health: https://biolincc.nhlbi.nih.gov/studies/mesa/. The SAS code used in the manuscript is available upon reasonable request by emailing the corresponding author.

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Data Availability Statement

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PERMALINK

Psychobiological Reactivity to Acute Psychological Stress as a Predictor of Cardiovascular Disease and Mortality: The Multi-Ethnic Study of Atherosclerosis

Aiden J CHAUNTRY

Justin B MOORE

Teresa SEEMAN

Steven J SHEA

Richard P SLOAN

Anna C WHITTAKER

Eli PUTERMAN

Anne I TURNER

William P TYNE

Mark J HUTSON

Mark P FUNNELL

Gabriel ZIEFF

Brook M GELETA

Keeron STONE

Erik D HANSON

Lee STONER

Michael P BANCKS

Abstract

BACKGROUND

METHODS

RESULTS

CONCLUSIONS

1. INTRODUCTION

2. MATERIAL AND METHODS

2.1. Study population

2.2. Stress task protocol

2.3. Cardiovascular measures

2.4. Neuroendocrine measures

2.5. Cardiovascular disease incidence and all-cause mortality

2.6. Covariates

2.7. Statistical analysis

3. RESULTS

3.1. Participant characteristics

Table 1.

3.2. Follow-up time and number of clinical events

3.3. Psychobiological responses to the stress protocol

Table 2.

3.4. Psychobiological stress reactivity and incident cardiovascular disease

3.4.1. Cox proportional hazard ratio models

Table 3.

3.4.2. Kaplan-Meier models

Figure 1.

3.5. Psychobiological stress reactivity and all-cause mortality

3.5.1. Cox proportional hazard ratio models

Table 4.

3.5.2. Kaplan-Meier models

3.6. Effect modification by race/ethnicity for CVD incidence and all-cause mortality

3.7. Supplementary analyses

3.8. Sensitivity analyses

4. DISCUSSION

4.1. Acute psychobiological stress reactivity and cardiovascular disease incidence

4.2. Acute psychobiological stress reactivity and all-cause mortality

4.3. Effect modification by race/ethnicity

4.4. Methodological considerations

4.5. Future directions

4.6. Conclusions

Supplementary Material

Highlights.

ACKNOWLEDGEMENTS

FUNDING

Footnotes

DATA AVAILABILITY

REFERENCES

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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