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. 2026 Jul 7;17(1):2686575. doi: 10.1080/20008066.2026.2686575

Cumulative trauma exposure and cardiac autonomic stress reactivity in healthy adults

Exposición acumulativa a traumas y reactividad cardiovascular autonómica al estrés en adultos sanos

Martha Schneider 1,CONTACT, Veronika Eder 1, Christian Rominger 1, Andreas R Schwerdtfeger 1
PMCID: PMC13347849  PMID: 42411660

ABSTRACT

Background: Exposure to traumatic events has been linked to alterations in autonomic stress regulation. While research has primarily focused on clinical populations, less is known about the physiological correlates of cumulative trauma exposure in non-clinical samples.

Objective: The present study investigated whether cumulative lifetime exposure to potentially traumatic events (PTEs) is associated with altered cardiovascular stress reactivity in healthy individuals.

Methods: Sixty-one participants without indications of PTSD completed a standardised speech task, while heart rate variability (HRV), blood pressure and additional hemodynamic indices were continuously recorded.

Results: Higher cumulative trauma exposure was associated with a divergent autonomic response pattern during stress anticipation. Blunted cardiac vagal reactivity (RMSSD) was accompanied by enhanced reactivity in overall autonomic variability (SDNN) and LF-HRV. No associations were found for blood pressure or any other hemodynamic outcome.

Conclusion: Findings suggest that cumulative trauma exposure may be associated with altered cardiac autonomic responding during stress anticipation even in the absence of psychopathology. These results highlight the potential utility of subclinical physiological assessment for detecting stress-system alterations in trauma-exposed individuals.

KEYWORDS: Potentially traumatic events, heart rate variability, stress reactivity

HIGHLIGHTS

  • Cumulative exposure to potentially traumatic events (PTEs) was associated with a divergent cardiac autonomic response pattern during stress anticipation in healthy adults.

  • The divergent pattern was characterised by blunted cardiac vagal reactivity (RMSSD) alongside increases in broader autonomic indices (SDNN, LF-HRV), suggesting that different autonomic regulation metrics respond differently to cumulative trauma exposure.

  • Trauma-related autonomic alterations were specific to the cardiac autonomic domain, with no significant associations observed for blood pressure or any other hemodynamic outcome.

1. Introduction

Stressors, both acute and chronic, can profoundly affect an individual’s health through their impact on physiological stress regulatory systems (Alotiby, 2024). Dysregulated stress responses have been linked to adverse health outcomes, including impaired cardiovascular functioning (O’Riordan et al., 2022). Among these stressors, potentially traumatic events (PTEs) such as serious accidents, physical assault, or witnessing death are particularly potent, as they can trigger the onset of posttraumatic stress disorder (PTSD; American Psychiatric Association, 2013). Exposure to PTEs is quite common across the lifespan, with epidemiological research indicating that approximately 70% of adults worldwide report at least one such event (Benjet et al., 2016). Importantly, not all trauma exposed individuals develop PTSD (Rodrigues et al., 2021), suggesting that individual differences in stress-system functioning may play a role in shaping divergent outcomes following trauma exposure. Physiological stress regulation has been identified as one such key factor (Lee et al., 2018).

Repeated exposure to severe stressors can alter physiological stress reactivity (e.g. Schreier et al., 2020; McLoughlin et al., 2022). The concept of allostatic load (McEwen & Stellar, 1993) proposes that chronic or repeated stress exposure leads to cumulative ‘wear and tear’ across multiple regulatory systems, ultimately resulting in dysregulation of stress-responsive systems. This dysregulation does not follow a uniform pattern and can manifest as prolonged stress responses due to delayed shutdown of physiological reactivity, inadequate blunted responses, or heightened physiological responses (Steptoe et al., 2014; Fava et al., 2022). Complementarily, the stress attenuation hypothesis suggests that repeated stress exposure may specifically lead to compensatory downregulation of stress-sensitive systems and blunted reactivity over time (Trickett et al., 2010; McLoughlin et al., 2022). Both frameworks suggest that cumulative trauma burden may progressively alter physiological responding to subsequent stressors, though the direction of such alterations may vary across individuals and exposure histories.

Autonomic functioning represents a key pathway through which cumulative stress exposure may exert lasting effects on physiological stress regulation. The autonomic nervous system, through its sympathetic and parasympathetic branches, governs rapid cardiovascular adjustments to environmental demands and plays a central role in the modulation of arousal, emotion regulation and stress recovery (Thayer & Lane, 2000). Repeated and cumulative stress exposure may progressively alter the balance between these branches, resulting in chronically modified autonomic tone that persists beyond acute stress episodes. Altered parasympathetic flexibility, defined as the capacity to dynamically adjust vagal control in response to changing demands, has been associated with impaired emotion regulation and prolonged recovery, though this evidence derives predominantly from clinical populations (McLaughlin et al., 2015; Wassing et al., 2016).

While direct evidence from non-clinical trauma-exposed samples is lacking, research in clinical populations provides important insights into the nature of trauma-related autonomic alterations. Research in individuals with PTSD consistently documents alterations in cardiac autonomic regulation, including reduced vagally mediated HRV at rest and heterogeneous patterns of autonomic reactivity during stress (Schneider & Schwerdtfeger, 2020). Whereas some studies report blunted parasympathetic withdrawal in response to stress, others document exaggerated HRV reductions, particularly in individuals with elevated resting blood pressure (Blechert et al., 2007; Fonkoue et al., 2018). Sympathetic and hemodynamic reactivity are similarly inconsistent, with both augmented and attenuated responses documented across different stressor types and PTSD samples (D’Souza et al., 2024; Fonkoue et al., 2018; Lee et al., 2022). These findings collectively suggest that autonomic functioning is sensitive to the physiological consequences of trauma exposure, providing a basis for examining such alterations more broadly across the spectrum of trauma-exposed individuals.

Against this background, the present study examined whether cumulative lifetime trauma exposure, operationalised as the number of different PTEs experienced, is associated with alterations in cardiovascular stress reactivity in a non-clinical sample. Such an investigation would advance knowledge beyond what can be inferred from clinical populations alone, contributing to a more complete understanding of the physiological correlates of cumulative trauma exposure. Given that the theoretical and empirical accounts outlined above predict heterogeneous patterns of physiological change following cumulative stress exposure, encompassing both heightened and blunted responses, no directional hypothesis was specified a priori. Cumulative trauma exposure was hypothesised to be associated with altered cardiovascular stress reactivity, consistent with the premise that physiological stress-system alterations may extend below the threshold of clinical psychopathology.

2. Methods

2.1. Participants

The study was approved by the Ethics Committee of the University of Graz (GZ.39/93/63) and conducted in accordance with the Declaration of Helsinki. Informed consent was obtained from all participants. Eighty-one individuals participated in the study. Participants were recruited through institutional email distribution, flyers, and word of mouth. Prior to participation, all candidates completed a self-report screening to assess eligibility. Inclusion criteria were age between 18 and 45 years, as HRV shows marked age-related changes (Geovanini et al., 2020; Rastović et al., 2019), which could confound comparisons of cardiovascular stress reactivity. Exclusion criteria were cardiovascular diseases, current use of medication affecting cardiovascular activity, pregnancy, diabetes, and any diagnosed mental disorder. All exclusion criteria were assessed via self-report. Participants abstained from alcohol, caffeine, and nicotine for two hours before testing.

Six participants were excluded due to more than 5% ECG artifacts (using Kubios HRV Scientific’s automatic correction algorithm), three because respiration rate was not derived reliably. Four individuals were excluded for Posttraumatic Stress Disorder Checklist for DSM-5 (PCL-5; Weathers, Litz, et al., 2013) scores ≥ 31, indicating a probability of PTSD. Seven participants were excluded because of inconsistent responses on the Life Events Checklist for DSM-5 (LEC-5, Weathers, Blake, et al., 2013) (e.g. indicating both ‘witnessed it’ and ‘does not apply’ for the same event). The final sample consisted of 61 young adults (mean age = 23.52, SD = 4.18; 63.9% women). Participants reported 2.30 PTEs (SD = 1.78), a mean waist-to-hip ratio (WHR) of 0.77 (SD = 0.05), and high rates of non-smoking (88.5%) and regular physical activity (83.6%; assessed via a yes/no item asking about engagement in regular exercise).

2.2. Stress task and study procedure

An overview of the study procedure and main variables is provided in Figure 1. Participants were informed prior to enrolment that the study involved measurements of cardiac variables but were not informed of the specific nature of the laboratory task. The experiment was conducted individually in a quiet laboratory. After providing informed consent, participants had their waist-to-hip ratio (WHR) measured and completed demographic questionnaires. ECG electrodes were attached, and participants were seated upright. A three-minute baseline was recorded prior to any information about the stress task being provided. During baseline, participants were instructed to sit quietly and remain still while watching neutral nature images on a computer screen. Following the baseline period, stress was induced using a standardised speech task (Kline et al., 2005), in which participants were instructed to imagine being falsely accused of shoplifting and to prepare a defence speech addressed to a store manager.

Figure 1.

A flow diagram with 4 rounded boxes for Baseline, Preparation, Stress task, and Recovery, linked by arrows and annotated with times. The figure shows a horizontal flow diagram of the experimental session. Four rounded rectangles are arranged from left to right and connected by right pointing arrows. The first rectangle is labeled Baseline, the second is labeled Preparation, the third is labeled Stress task, and the fourth is labeled Recovery. Above the rectangles, straight horizontal arrows indicate the duration of each stage. The Baseline stage lasts 3 minutes, the Preparation stage lasts 3 minutes, the Stress task stage lasts 3 minutes, and the Recovery stage lasts 5 minutes. Above each stage, short upward pointing arrows link the rectangles to the labels positive affect and negative affect, abbreviated as P A and N A. Below each rectangle, a downward pointing arrow leads to a stacked list of cardiovascular variables. The same list is repeated under all four stages and includes root mean square of successive differences, standard deviation of normal to normal intervals, high frequency heart rate variability, low frequency heart rate variability, systolic blood pressure, and diastolic blood pressure. No numerical data or scales are shown.

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Overview of the study procedure and main variables. Note. PA = positive affect, NA = negative affect, RMSSD = root mean square of successive differences, SDNN = standard deviation of normal-to-normal intervals, HF-HRV = high-frequency heart rate variability, LF-HRV = low-frequency heart rate variability, SBP = systolic blood pressure, DBP = diastolic blood pressure.

Participants were told ‘Pretend you are shopping in a department store in a mall. As you are leaving, a security guard accuses you of stealing the belt you purchased there last week. Prepare a defence strategy and address your speech to the store manager.’ Participants had three minutes to prepare, with optional note taking and a visible countdown timer, and were told their speech would be video-recorded and later evaluated. Following the preparation period, participants delivered the speech aloud for three minutes while facing a computer screen, with a visible countdown timer displayed throughout. No actual recording was made. The procedure concluded with a five-minute recovery phase, during which participants viewed neutral nature images on a computer screen. Given the known influence of speech and respiration on HRV, cardiac autonomic analyses focused on the preparation and recovery phases, as data from the speech phase are subject to respiration- and speech-related artifacts (Quintana et al., 2016). Participants were fully debriefed regarding the use of deception at the end of the session. The LEC-5 and PCL-5 were administered after completion of the stress task to avoid potential interference of trauma recall with baseline cardiovascular recordings.

2.3. Variables and instruments

Life Events Checklist for DSM-5 (LEC-5, Weathers et al., 2013a). Exposure to PTEs was assessed using the Life Events Checklist for DSM-5 (LEC-5). The checklist comprises 17 event types (e.g. physical assault, natural disaster, serious accident), for which participants reported their level of exposure using six response options (‘happened to me’, ‘witnessed it’, ‘learned about it’, ‘part of my job’, ‘not sure’, or ‘does not apply’). Events endorsed as ‘happened to me’ were summed to index direct exposure, given their stronger association with physiological and psychological outcomes (May & Wisco, 2016).

PTSD Checklist for DSM-5 (PCL-5, Weathers et al., 2013b). Current posttraumatic stress symptoms were assessed using the PTSD Checklist for DSM-5, a 20-item self-report questionnaire corresponding to DSM-5 diagnostic criteria. In the extended Criterion A format, participants rated how much each symptom had bothered them in the past month (0 = not at all to 4 = extremely) with reference to their ‘worst’ PTE reported in the LEC-5. The worst event was self-identified by participants based on subjective appraisal. Total scores (0-80) indicate symptom severity. A cutoff of 31 was applied to identify probable PTSD.

Positive and Negative Affect schedule (PANAS, Krohne et al., 1996). Positive and negative affect were assessed using the German version of the Positive and Negative Affect Schedule. The 20-item self-report scale consists of 10 items each for positive affect (PA) and negative affect (NA), rated on a 5-point Likert scale (1 = not at all, 5 = extremely). Affect ratings were obtained before and after baseline, after preparation period, and after stress task. Internal consistencies (McDonald’s ω) ranged from .93 to .95 for PA and from .93 to .94 for NA.

Physiological variables. Electrocardiography (ECG) was recorded using a BIOPAC MP150 system (1000 Hz). R–R intervals were extracted and artifacts corrected in Kubios HRV Premium (v3.2; Tarvainen et al., 2014), ensuring ≥95% artifact-free data. HRV was quantified using established time- and frequency-domain parameters, selected to capture complementary aspects of autonomic variability. HRV indices were considered the primary physiological outcomes of interest, given their established sensitivity to autonomic stress regulation. Time-domain indices included the standard deviation of normal-to-normal intervals (SDNN), reflecting overall autonomic variability, and the root mean square of successive differences (RMSSD), indexing vagally sensitive cardiac regulation (Thayer & Lane, 2007; Shaffer & Ginsberg, 2017). Frequency-domain indices were derived via fast Fourier transformation and included low-frequency power (LF-HRV, 0.04–0.15 Hz) and high-frequency power (HF-HRV, 0.15–0.40 Hz). LF-HRV seems to reflect baroreflex-mediated autonomic activity, though its precise sympathovagal interpretation remains debated (Goldstein et al., 2011). HF-HRV indexes vagal activity. Time-domain measures were log-transformed prior to analysis.

Hemodynamic indices were included as complementary measures to provide a broader assessment of cardiovascular stress reactivity. Continuous hemodynamic variables including systolic blood pressure (SBP), diastolic blood pressure (DBP), cardiac output (CO), total peripheral resistance (TPR), and stroke volume (STRVOL) were obtained via Finometer® PRO and processed in Beatscope (Wesseling et al., 1993). Respiratory activity was recorded with a piezoelectric chest belt (BIOPAC TSD201, 1000 Hz). A custom Python script filtered the signal (0.05–0.5 Hz), downsampled it to 50 Hz, and computed respiration rate for each three-minute segment. Time-series were visually inspected for signal quality.

2.4. Statistical analysis

Linear mixed-effects models were used for manipulation checks and main analysis to account for within-subject dependence across four study phases (baseline, preparation, stress, recovery). Manipulation checks tested phase effects on physiological and affective responses. Main analyses examined whether PTE exposure moderated cardiovascular reactivity. Study phase (baseline as reference) was entered as a fixed factor, with random intercepts for participants. Stress reactivity was operationalised as phase-related changes relative to baseline. For HRV indices, analyses were restricted to the anticipation and recovery phases, as data from the speech phase is biased due to respiration- and speech-related artifacts (Quintana et al., 2016). To ensure comparability across phases, all physiological indices during the recovery phase were computed from the first three minutes only, consistent with the duration of the other phases.

Respiration rate, sex, age, WHR, smoking status, and physical activity were included as covariates. Age, WHR, and PTE count were grand-mean centred. PTE exposure was treated as a continuous predictor in all models. Significant interaction effects were examined using simple slope analyses at one standard deviation above and below the mean of the continuous PTE variable. For post hoc simple slope analyses, pairwise contrasts were computed as preparation minus baseline, such that positive values indicate increases and negative values indicate decreases relative to baseline. Analyses were conducted in R (R Core Team, 2024; lme4; Bates et al., 2015) with p-values based on Satterthwaite approximation (Kuznetsova et al., 2017).

3. Results

3.1. Manipulation check

Significant phase effects were found for HR, RMSSD, LF-HRV, SBP, DBP, CO, TPR and STRVOL (all ps ≤ .006), as well as for PA and NA (both ps < .001). HF-HRV showed a marginal trend (p = .054). SDNN showed no significant change across phases (p = .13).

3.2. Cardiac autonomic reactivity

Linear mixed-effect models revealed significant interactions between PTE exposure and cardiac reactivity during stress anticipation (baseline to preparation phase) for RMSSD (β = 0.22, p = .011), SDNN (β = 0.31, p = .002) and LF-HRV (β = 0.34, p = .007). The interaction for HF-HRV showed a marginal trend (β = 0.11, p = .071). No significant interactions were found for SBP, DBP, CO, TPR, and STRVOL. The moderating effect of PTE exposure was evident only during the anticipation phase. No significant interactions were observed for recovery. Respiration rate emerged as a significant covariate in the three models yielding significant interactions. Importantly, all three interactions remained statistically significant after adjustment, indicating that the divergent autonomic response pattern was not accounted for by differences in breathing. Sex, age, WHR, smoking status, and physical activity were not significant predictors in any of these models.

Post hoc simple-slope analyses revealed divergent autonomic response patterns across PTE exposure levels. Individuals low in PTE exposure (–1 SD) showed small decreases in SDNN, RMSSD and LF-HRV from baseline to preparation (all ps ≥ .359). At higher levels of PTE exposure (+ 1SD), a differentiated pattern emerged. RMSSD showed no significant change from baseline to preparation (b = 0.11, p = .251), reflecting blunted cardiac vagal reactivity (see Figure 2). In contrast, SDNN and LF-HRV increased significantly from baseline to anticipation (SDNN: b = 0.21, p = .003; LF-HRV: b = 0.65, p = .002, see Figures 3 and 4), indicating an increase in broader autonomic variability and sympathovagal activity during stress anticipation. Additional analyses indicated no baseline differences in RMSSD (Δ = 0.20, p = .103), a trend toward lower baseline SDNN (Δ = 0.18, p = .052), and significantly lower baseline LF-HRV (Δ = 0.54, p = .013) at higher levels of trauma exposure.

Figure 2.

A line graph comparing low and high PTE exposure across 4 phases, showing RMSSD values with overlapping error bars and modest shifts. The figure shows a line graph of heart rate variability indexed by the natural logarithm of the root mean square of successive differences, labeled as Estimated RMSSDln. The horizontal axis lists 4 phases of the stress task: Baseline, Anticipation, Stress, and Recovery. The vertical axis ranges from 3.2 to 3.8 in increments of 0.2, with no units shown. Two lines represent trauma exposure levels derived from the continuous potentially traumatic events variable: Low PTEs at minus 1 standard deviation and High PTEs at + 1 standard deviation. For Low PTEs, the line starts near 3.6 at Baseline, declines to about 3.4 at Anticipation, stays near 3.38 at Stress, then rises to about 3.55 at Recovery. For High PTEs, the line starts near 3.35 at Baseline, is close to 3.37 at Anticipation, dips to about 3.3 at Stress, then increases to about 3.45 at Recovery. Vertical bars around each point depict 95 percent interval estimates, which overlap across exposure groups and phases. A legend on the right labels the 2 PTE Level lines. All data are approximate.

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Interaction of PTE level and Study Phase on HRV (RMSSD). Note. PTE level refers to model-estimated values at ± 1 SD above and below the mean of the continuous PTE variable, to facilitate interpretation of the interaction effect. Error bars show 95% confidence interval.

Figure 3.

A line graph showing estimated SDNN in natural log across 4 study phases for low and high PTE levels, with vertical error bars. The figure shows a line graph of estimated standard deviation of normal to normal intervals in natural log across study phases for 2 trauma exposure levels. The horizontal axis lists 4 phases: Baseline, Anticipation, Stress, and Recovery. The vertical axis is labeled Estimated SDNN in natural log, with a numeric scale from 3.6 to 4.1 in steps of 0.1. One line represents low potentially traumatic event exposure at minus 1 standard deviation, and a second line represents high potentially traumatic event exposure at + 1 standard deviation. Each phase has a point estimate with a vertical bar showing a 95 percent confidence interval. At Baseline, the low exposure line starts near 3.85 while the high exposure line starts near 3.63. At Anticipation, both lines meet around 3.73. At Stress, both lines rise, with low exposure near 3.80 and high exposure near 3.76. At Recovery, the low exposure line is slightly higher than at Stress, while the high exposure line slopes downward. All data are approximate.

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Interaction of PTE level and Study Phase on HRV (SDNN). Note. PTE level refers to model-estimated values at ± 1 SD above and below the mean of the continuous PTE variable, to facilitate interpretation of the interaction effect. Error bars show 95% confidence interval.

Figure 4.

A line graph comparing estimated low frequency heart rate variability for low and high PTEs across baseline, anticipation, stress, recovery. The figure shows a line graph with error bars illustrating estimated low frequency heart rate variability values for 2 trauma exposure groups over 4 study phases. The horizontal axis is labeled Baseline, Anticipation, Stress, and Recovery. The vertical axis is labeled Estimated low frequency heart rate variability and runs from 6.0 to 7.5 in increments of 0.5. One line represents Low potentially traumatic events at minus 1 standard deviation, and the other represents High potentially traumatic events at plus 1 standard deviation. At Baseline, the low exposure group is higher, around 7.2, while the high exposure group is lower, around 6.5. From Baseline to Anticipation, the low exposure line slopes downward to about 6.9, whereas the high exposure line slopes upward to about 6.9, so the 2 lines meet. From Anticipation to Stress, both lines rise, peaking near 7.3 for low exposure and 7.25 for high exposure. From Stress to Recovery, both lines drop, with low exposure ending around 7.0 and high exposure around 6.8. Error bars extend above and below each mean. All data are approximate.

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Interaction of PTE level and Study Phase on HRV (LF-HRV). Note. PTE level refers to model-estimated values at ± 1 SD above and below the mean of the continuous PTE variable, to facilitate interpretation of the interaction effect. Error bars show 95% confidence interval.

4. Discussion

The present study examined whether cumulative lifetime exposure, operationalised as the number of different PTEs reported, is associated with alterations in cardiovascular stress reactivity in a non-clinical sample. Higher levels of trauma exposure were associated with a divergent autonomic response pattern during stress anticipation. Specifically, individuals at higher levels of PTE exposure showed no significant change in RMSSD from baseline to preparation, reflecting blunted cardiac vagal reactivity. The observed increases in SDNN suggest heightened overall autonomic variability during stress anticipation, potentially reflecting a global mobilisation of autonomic resources. The concurrent increases in LF-HRV may indicate enhanced baroreflex-mediated autonomic activity, though the precise sympathovagal interpretation of this index remains contested (Goldstein et al., 2011). Although respiration rate was a significant covariate, all three interactions remained statistically significant after adjustment, indicating that breathing differences did not account for the observed effects. The findings indicate that cumulative trauma exposure may be associated with altered cardiac autonomic responding during stress anticipation. No effects were found for hemodynamic reactivity, suggesting that alterations were confined to cardiac autonomic rather than vascular responses.

Importantly, individuals at higher levels of trauma exposure started from a significantly lower baseline in LF-HRV, with a similar trend observed for SDNN. This pattern may reflect chronic downregulation of resting autonomic activity associated with cumulative trauma burden. During stress anticipation, this chronically suppressed system may respond with disproportionate autonomic mobilisation upon encountering stress cues, a pattern consistent with theoretical accounts of stress sensitisation, in which repeated trauma exposure progressively lowers the threshold for physiological reactivity to subsequent stressors (Minassian et al., 2015). It should be noted, however, that the observed increases in SDNN and LF-HRV may partly reflect regression to the mean, given the significantly lower baseline values observed at higher levels of trauma exposure. This constitutes a methodological consideration that warrants caution in interpreting these specific effects.

In contrast to the increase in SDNN and LF-HRV, the blunted RMSSD response warrants separate consideration. Cardiac vagal withdrawal during stress anticipation is generally regarded as a functional component of the normal stress response, facilitating rapid cardiac adjustments in preparation for active coping (Porges, 2007). Its absence at higher levels of trauma exposure, alongside increases in broader autonomic variability indices, suggest a divergence between parasympathetic and overall autonomic stress responding following cumulative trauma exposure. Whether this divergence reflects maladaptive dysregulation or a form of functional physiological adaptation, however, cannot be determined from the present data. On one hand, reduced parasympathetic modulation, as suggested by the blunted RMSSD pattern, may compromise the capacity to downregulate physiological arousal following stress exposure (McLaughlin et al., 2015; Minassian et al., 2015). This could potentially increase vulnerability to future stress-related outcomes in some individuals. Alternatively, the observed pattern may reflect a form of physiological adaptation that supports stress tolerance in the context of cumulative trauma exposure, consistent with resilience-oriented accounts of physiological adjustment (Bonanno, 2004). Distinguishing between these interpretations requires prospective-longitudinal designs that track autonomic functioning and psychological outcomes over time following trauma exposure. The present findings should therefore be interpreted in light of both the contributions and limitations of the current study design.

Several methodological strengths of the present study merit consideration. Excluding participants with potential PTSD ensured that associations are not merely based on PTSD symptomatology. Respiratory activity was included as a covariate in all models, as breathing patterns vary with emotional and cognitive states and can thereby influence HRV (Ritz, 2024). Furthermore, the focus on cumulative trauma exposure, rather than limiting the assessment to a single traumatic event, reflects a more comprehensive assessment of trauma burden. This broader perspective acknowledges that many individuals experience different types of trauma exposure across the lifespan, thereby providing a more comprehensive understanding of how cumulative trauma exposure can influence physiological stress reactivity.

Despite several strengths, certain limitations should be noted. Lifetime PTSD diagnosis and treatment history were not assessed, although both may have enduring effects on autonomic functioning (Gupta, 2013). Frequency, chronicity, and subjective impact of individual PTEs were not measured. Although participants with mental disorders were excluded, the presence of subclinical depressive symptoms, which have been associated with altered cardiac reactivity (Liu et al., 2022), cannot be ruled out. The cross-sectional design precludes causal inference, and the relatively young and gender-imbalanced sample restricts generalizability, as age and gender influence HRV and stress reactivity (Geovanini et al., 2020). As noted above, regression to the mean cannot be excluded as a contributing factor to the observed increases in SDNN and LF-HRV, given the lower baseline values at higher exposure levels. The absence of significant associations for some autonomic indices and the presence of baseline differences in LF-HRV further underscore the need for replication in larger and more diverse samples. Furthermore, future research should examine whether similar response patterns occur under different stressor conditions.

Overall, the findings of the current study indicate that cumulative trauma exposure could be associated with a divergent cardiac autonomic response pattern during stress anticipation in healthy individuals, even in the absence of PTSD. The specificity of effects to cardiac autonomic indices, with no significant associations observed for hemodynamic responses, highlights the potential value of HRV-based measures as tools for detecting physiological correlates of cumulative trauma exposure in non-clinical samples, though their clinical utility remains to be established. Prospective longitudinal designs are essential to determine whether such autonomic alterations predict subsequent stress-related psychopathology or cardiovascular risk. Future work should further examine their reversibility and potential moderation by protective factors such as social support or emotion regulation capacity.

Acknowledgements

The authors acknowledge the financial support by the University of Graz. The authors thank Josef Tatschl for his valuable assistance in evaluating the breathing signal measurements

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.

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Associated Data

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

Data Availability Statement

The participants of this study did not give written consent for their data to be shared publicly, so due to the sensitive nature of the research supporting data is not available.

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