Heart rate variability of chronic posttraumatic stress disorder in the Korean veterans

Author’s Accepted Manuscript Heart rate variability of chronic posttraumatic stress disorder in the Korean veterans Joo Eon Park, Ji Yeon Lee, Suk-Hoon Kang, Jin Hee Choi, Tae Yong Kim, Hyung Seok So, In Young Yoon www.elsevier.com/locate/psychres

PII: DOI: Reference:

S0165-1781(16)30536-4 http://dx.doi.org/10.1016/j.psychres.2017.05.011 PSY10502

To appear in: Psychiatry Research Received date: 30 March 2016 Revised date: 12 April 2017 Accepted date: 7 May 2017 Cite this article as: Joo Eon Park, Ji Yeon Lee, Suk-Hoon Kang, Jin Hee Choi, Tae Yong Kim, Hyung Seok So and In Young Yoon, Heart rate variability of chronic posttraumatic stress disorder in the Korean veterans, Psychiatry Research, http://dx.doi.org/10.1016/j.psychres.2017.05.011 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 galley proof before it is published in its final citable 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.

Heart rate variability of chronic posttraumatic stress disorder in the Korean veterans Joo Eon Park1, Ji Yeon Lee2, Suk-Hoon Kang2,3*, Jin Hee Choi1, Tae Yong Kim1, Hyung Seok So1,4, In Young Yoon5 1

Department of Psychiatry, Keyo Hospital, Uiwang, Korea

2

Department of Psychiatry, Veteran Health Service Medical Center, Seoul, Korea

3

Center for Sleep Medicine, Veteran Health Service Medical Center, Seoul, Korea

4

Posttraumatic Stress Disorder Clinic, Veteran Health Service Medical Center, Seoul,

Korea 5

Department of Neuropsychiatry, Seoul National University Bundang Hospital,

Gyeonggi-do, Korea *Address for correspondence. Suk-Hoon Kang, MD, Department of Psychiatry, Veterans Health Service Medical Center. 61, Jinhwangdoro-gil, Gandong-Gu, Seoul, 134-791,South Korea. Tel : (82-2)2225-1330, Fax : (82-2) 4776190, E-mail :[email protected]

Abstract Patients with post-traumatic stress disorder (PTSD) have lower heart rate variability (HRV) than the general population, but findings in this area have been inconsistent. This study was conducted to investigate the characteristics of HRV in patients with PTSD and to evaluate associations between PTSD symptoms and HRV indices. Sixty-eight patients with PTSD and 73 controls without PTSD were evaluated. HRV was measured in all subjects after they completed self-reported questionnaires. Patients with PTSD had significantly more depressed moods, anxiety, and poorer sleep quality than individuals in the non-PTSD group. Standard deviations of NN intervals (SDNN), the square root of the mean squared differences of successive NN intervals (RMSSD), and log high-frequency (LNHF) were significantly lower in the PTSD group than in the non-PTSD group. Comparisons of HRV indices among four sub-groups according to presence/absence of PTSD and experiences of combat-related or

other trauma indicated that individuals in the PTSD group who had experienced combatrelated trauma had the lowest HRV indices. These indices included SDNN, RMSSD, and LNHF. Further, SDNN, RMSSD, and HF power were significantly associated with symptoms of hyperarousal. HRV measures might be useful physiological parameters in assessing and monitoring sympathovagal function in patients with PTSD.

Keywords: anxiety; depression; autonomic nervous system; physiological parameter; stress

1.

Introduction

Post-traumatic stress disorder (PTSD) develops after traumatic or stressful events. The diverse clinical symptoms of PTSD, which include intrusion, avoidance, degradation of cognition and mood, and hyperarousal, induce severe distress and affect social and occupational function (American Psychiatric Association., 2013). It is known that 40-90% of the general population experiences at least one traumatic event and that at least 10-20% of these individuals develop PTSD (Kessler et al., 1995). In Korea, the lifetime prevalence of partial PTSD was previously reported to be 2.7%, while that of full PTSD has been reported to be 1.7% (Jeon et al., 2007). An experimental survey of inpatients in the Korean Veteran Health Service Medical Center showed the prevalence rate of PTSD in Vietnam War veterans to be 23% (Chung et al., 2002). The estimated lifetime prevalence of PTSD among adult Americans is 6.8%, which represents a small proportion of those who have experienced trauma in their lives (Kessler et al., 2005). The National Vietnam Veterans Readjustment Study (NVVRS) reported that the lifetime prevalence of PTSD among veterans is 30.9% for men and 26.9% for women (Kulka et al., 1990). Cardiac performance is reciprocally handled by sympathetic excitation and simultaneous vagal inhibition, or vice versa, in most physiological and pathological situations.

The concept of sympathovagal balance was developed to reflect the autonomic state resulting from the sympathetic and parasympathetic influences according to cardiac performance. This sympathovagal balance oscillates from states of inactivity, when homeostatic negative feedback reflexes predominate, to states of excitation, when baroreflex mechanisms are strongly attenuated and central excitatory mechanisms, possibly reinforced by peripheral positive feedback reflexes, are instrumental to enhanced cardiovascular performance (Draghici and Taylor, 2016). Heart rate variability (HRV) provides information regarding sympathovagal balance, including modulation or periodic changes of heart rate (HR) by autonomic nervous system (ANS) function (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). HRV changes continuously due to respiration and in response to endogenous and exogenous cues. HRV can be described not only as a function of time (i.e., in the time domain), but also, with power spectral analysis, as the sum of elementary oscillatory components defined by their frequencies and amplitudes (i.e., in the frequency domain). Previous reports from the field of psychiatry indicate that blunted HRV responses to excitatory stimuli may reflect a variety of pathophysiological states, such as panic disorder or PTSD (Chalmers et al., 2014). However, standard normal and abnormal values for HRV may not be available, as the dynamic equilibrium of the sympathovagal balance and its range of excursions is wide. HRV is also affected by various factors, such as age, gender, lifestyle, physical fitness, and several medical conditions (Malliani, 2005). Psychological distress following exposure to stressful events or serious injury is fairly variable, is associated with increased rates of psychopathology around the world, and significantly contributes to the risk for PTSD (Overstreet et al., 2016). PTSD is characterized by persistent re-experiencing of the traumatic event, avoidance of stimuli associated with the event, and increased arousal. It has long been known to be associated with autonomic

dysregulation, such as elevated heart rate and increased blood pressure in response to stressors (Dennis et al., 2014). PTSD is characterized by a variety of physiological responses and mechanisms. It is well known that norepinephrine and the cortisol secretion system are associated with the stress responses associated with anxiety and fear (Morrison and Ressler, 2014). Persistently elevated HR, hyperarousal, and sympathetic hyperactivation have been shown in patients with PTSD, indicating dysfunctional arousal and stress regulation (Benedek, 2011). Dysregulation of the stress response system has been implicated in a wide range of physical comorbidities of PTSD (Morrison and Ressler, 2014). Several investigations of HRV in patients with PTSD have been carried out. However, their findings have been inconsistent due to differences in sample characteristics, small sample sizes, narrow age ranges, lack of female participants, and failure to control for lifestyle factors that influence the ANS (Agorastos et al., 2013; Cohen et al., 2000; Hauschildt et al., 2011). In particular, posttraumatic stress symptoms following exposure to traumatic events in have been variable in these studies. This may influence findings regarding the severity of PTSD and HRV in patients with PTSD. In a meta-analysis, reduced HF power was found to have a small effect size (Chalmers et al., 2014). Here we compared HRV using time-domain and frequency-domain analyses between subjects with and without PTSD (non-PTSD vs. PTSD). In addition, we compared HRV among 4 subgroups after subdividing the subjects in the PTSD and non-PTSD groups based on experiences of combat-related trauma (veterans) or non-combat traumatic events (nonveterans). .

2.

Methods

2.1. Subjects All participants were recruited from the Department of Psychiatry at Veteran Health

Service Medical Center in Seoul. Participants with other health problems, including mental disorders due to brain damage and dysfunction, schizophrenia, bipolar disorder, current substance-use disorder, and current major depressive disorder (MDD) were excluded. Subjects taking medications for hypertension and hyperlipidemia (HTN), diabetes mellitus (DM), or cerebrovascular disease (CVD) were classified as medication-positive. Candidates with uncontrolled HTN, DM, or cardiovascular problems, such as ischemic heart disease, arrhythmia, or heart failure were excluded. Of the 211 participants, 141 were enrolled in the study. Seventy subjects were excluded due to medical problems (total: n = 49; uncontrolled HTN: n = 5, uncontrolled DM: n = 12, ischemic heart disease: n = 8, arrhythmia: n = 10, angina: n = 4, myocardial infarction: n = 5, and mild cognitive impairment: n = 3), the use of beta or calcium-channel blockers (n = 5), and because of artifacts of HRV (n = 16). The study protocol was approved by our institutional review board.

2.2. Procedure All subjects underwent a clinical examination, a psychiatric evaluation, and HRV measurements. PTSD was diagnosed using the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) by a psychiatrist (American Psychiatric Association., 2013). The post-traumatic stress disorder checklist-5 (PCL-5) was used to evaluate symptoms of PTSD in Korean, the Korean version of the Beck Depression Inventory (BDI) was used to estimate mood symptoms, and the Korean version of the Beck Anxiety Inventory (BAI) was used to examine anxiety symptoms. The Korean version of the Pittsburgh Sleep Quality Index (PSQI) was used to assess subjective sleep complaints. All assessments were carried out by the first author and a clinical psychologist, who is experienced in diagnostics and has undergone extensive training in conducting the structured clinical interviews for DSM-5. Among the 68 individuals with PTSD recruited from the outpatient clinic, 45 participants had

symptoms of PTSD due to their experiences in the Vietnam War, and 23 subjects were considered to be part of the PTSD group due to experiences of traumatic events other than combat (natural disaster = 4, vehicular accidents = 4, sexual assault = 1, life threatening injury = 8, sudden death of loved one = 4, other very stressful event = 6). The time since the onset of the traumatic event in years was recorded for all subjects with PTSD. The 73 individuals in the non-PTSD group were recruited via advertisements or from established subject pools. Among these subjects, 43 participants who had experienced the Vietnam War did not meet the criteria for PTSD, and 30 subjects who had no experience of combat were deemed to be non-veterans and were placed in the non-PTSD group. In addition to the clinical interview, participants were asked to complete questionnaires in the interview room of the hospital without the assistance of another person. The HRVs of the subjects were measured seven days after the initial visit.

2.3. Measures 2.3.1. Clinical variables Data regarding clinical variables were collected using a standardized protocol. Body mass index (BMI) was defined as the body weight (kg) divided by the square of the body height (m2). HTN was defined as systolic blood pressure ≥140 mmHg, diastolic blood pressure ≥90 mmHg, or current use of antihypertensive medications. Hyperlipidemia was defined as low density lipoprotein (LDL) cholesterol ≥160 mg/dL. DM was defined as a fasting blood glucose level ≥126 mg/dL or current use of anti-diabetic medications. Information regarding CVDs, such as ischemic brain stroke and brain hemorrhage, was also obtained.

2.3.2. HRV The HRV examination was conducted by a physician in a quiet and calm room after the procedure had been explained to the subject. We measured the RR interval using three

electrodes (right arm, left arm, and left leg) and monitored the bipolar leads (I, II, and III). To obtain the best results with convenience of measurement in mind, the electrodes were placed on the chest wall equidistant from the heart rather than on specific limbs. After the three electrodes were attached, the participants were provided with at least 10 minutes to adjust to the environment to minimize any effects from short-term activity. After this period, the medical worker checked the limb-lead conduction, and confirmed that the graph was clear and had no interfering wavelengths. HRV was measured in a resting supine position for 5 minutes using an AFT-800 (Medicore; Seoul, Korea). Overall HRV results were assessed based on the widely used time-domain and frequency-domain analyses. The pre-processing value of the RR interval and electrocardiogram (ECG) data were collected. The HRV value was then calculated during data processing and analysis. To remove noise from the ECG data, a fast Fourier transform (FFT) filter was applied. Following the guidelines of the Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, data were collected over a 5-minute period, and statistical analysis of the time domain was performed (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). Artifacts were removed at the third 30-Hz

ECG signal received from the analog-digital (A/D) converter, which

converts the output signal from the above-mentioned signal detection system into a digital signal. Subsequently, the inflection point was distinguished from the differentiation wave. The time interval from the second R point, which is the interval from the time of the second peak to that of the first peak (the RR point to the first R Point), was obtained by deducting the time of the first peak from the time of the second peak. The above-mentioned RR interval, i.e., the interval from the normal R point to the next normal R point, is called the NN interval. The RR interval can be considered as one cycle of ECG. In other words, the RR interval indicates the time required for one heartbeat. Beat rate per minute data were calculated by multiplying

the A/D converter sampling frequency by 60, and heart rate per minute was calculated by dividing this number by the RR interval. We used the mean of the 5-minute standard deviation of NN intervals (SDNN) for time-domain analyses. The square root of the mean squared differences of successive NN intervals (RMSSD) was also estimated for short-term HRV (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). Frequency analysis partly performs an FFT conversion to carry out frequency analysis based on the output signal of the peak detection. Results acquired by this conversion are calculated from the fields of low-frequency bands (LF) and high-frequency bands (HF). The HF component is an index of parasympathetic system function in the range of 0.15 Hz to 0.4 Hz. The LF component is between 0.05 Hz and 0.15 Hz, and is affected by both the sympathetic nervous system (SNS) and the parasympathetic nervous system (PNS), but mainly reflects the activity of the SNS. The very-low-frequency component (0.003-0.05 Hz) was not analyzed due to poor accuracy in its measurement owing to the 5-minute measurement period (Heathers, 2015). The LF/HF ratio was also excluded, since a recent investigation suggested that its value has no mathematical basis, i.e., it is not a proper index of sympathovagal balance (Heathers, 2015). Raw power was log-transformed before analysis to normalize the relevant distributions.

2.3.3. Psychological variables PTSD symptoms were evaluated using PCL-5, which is a self-report rating scale. The PCL-5, which consists of four items (re-experience, avoidance, negative alterations in cognition and mood, and hyperarousal), was translated into Korean in another study. The subjects rated their symptoms during the past month individually using a 5-point Likert scale, from “1 = not at all” to “5 = absolutely yes”. The total scores ranged from 0 to 80, and a cut-off score of 33 was used, as reported previously (Weathers et al., 2013). Depression was measured using the

Korean version of the BDI. This measurement is a self-report questionnaire that evaluates the existence and level of depression, and assesses cognitive, emotional, motivational, and physiological symptoms. Responses on the BDI are provided on a four-point scale from mild (0) to severe (3) according to the level of symptoms reported for each question (Rhee et al., 1995). Anxiety was estimated using the Korean version of the BAI, which describes 21 anxiety symptoms. Respondents were asked to rate how much each of these symptoms had disturbed them in the past week on a scale ranging from 0 (not at all) to 3 (severely, I could barely stand it). The total score has a minimum of 0 and a maximum of 63 (Yook and Kim, 1997). Sleep quality was assessed using the seven components of the PSQI, which assesses the following: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbance, use of sleeping medication, and daytime dysfunction over the preceding month (Sohn et al., 2012). The global PSQI score is between 0 and 21, and a global PSQI score >5 is known to provide a sensitive and specific measure of poor sleep quality when compared to clinical and laboratory measures.

2.4. Data analyses Kolmogorov-Smirnov tests were performed to assess the normality of the collected data. Intergroup differences among the four groups (45 veterans with PTSD, 23 non-veterans with PTSD, 43 veterans without PTSD, and 30 non-veterans without PTSD) were assessed using chi-square tests and independent samples t-tests. To compare HRV indices between the groups, analyses of covariance were used with gender, CVD, and duration of PTSD as covariates. Additionally, Bonferroni post-hoc analyses were performed to compare HRV indices among the four groups. To determine the relationships between the four symptoms of PTSD and HRV variables, multiple regression analysis was conducted using gender, cerebrovascular disease, and time since PTSD onset as covariates. The statistical significance

criterion was defined as p < 0.05 for each two-tailed test, and the significance level for comparisons of multiple HRV indices was set at <0.01 following Bonferroni correction. IBM SPSS Statistics for Windows, version 21.0 (IBM Corp., Armonk, NY, USA) was used for statistical analysis.

3.

Results

The mean age was 64.8 years (standard deviation [SD] =10.1) in the PTSD group and 62.7 years (SD = 11.0) in the non-PTSD group. There was a lower proportion of female subjects in the PTSD group than in the non-PTSD group (p = 0.038). Over half of the subjects (n = 88) were veterans. There were no significant differences in of the prevalence of diabetes and hypertension, but there were significantly more subjects with cerebrovascular disease in the PTSD group than in the non-PTSD group. Individuals in the PTSD group had significantly more PTSD symptoms (t = -8.598, p < 0.001), depression (t = -6.767, p < 0.001), anxious mood (t = 6.701, p < 0.001), and poor sleep quality (t = -5.870, p < 0.001) than those in the non-PTSD group. Veterans in the PTSD group had a longer duration of PTSD than non-veterans (39.3 ± 2.4 vs. 9.2 ± 2.6 years, t = 47.100, p < 0.001). Table 2 shows the comparisons of the HRV indices between the PTSD group and the non-PTSD group. The PTSD group had a mean HR of 70.5 beats/minute, while the nonPTSD group had a mean HR of 65.4 beats/minute. This difference was statistically significant (F(1,140) = 7.342, p = 0.003). Time domain analysis revealed that individuals in the PTSD group had higher values of SDNN (F(1,140) = 10.286, p = 0.001) and RMSSD (F(1,140) = 11.152, p = 0.001) than those in the non-PTSD group. Frequency domain analysis revealed that individuals in the PTSD group had lower HF power than those in the non-PTSD group (F(1,140) = 9.151, p = 0.003), although LF power was not significantly different between the

two groups (F(1,140) = 0.732, p = 0.264). We carried out comparisons of HRV indices between participants who were veterans of the Vietnam War and non-veterans to evaluate sympathovagal changes due to combat exposure (Table 3). Time domain analysis indicated that veterans with PTSD had lower SDNN values (F(3,140) = 7.453, p < 0.001) and lower RMSSD values (F(3,140) = 4.869, p = 0.003) than non-veterans without PTSD. Frequency domain analysis indicated that veterans with PTSD had lower HF power than non-veterans without PTSD (F(3,140) = 4.599, p = 0.004). However, LF power was not significantly different between the two groups. Lastly, analyses of the relationships between PTSD symptoms and HRV indices were conducted in all participants (Table 4). Hyperarousal was inversely correlated with SDNN (ß = -0.0177, p = 0.029), RMSSD (ß = -0.202, p = 0.017) and HF power (ß = -0.122, p = 0.038). However, other symptoms of PTSD had no relationship with other HRV indices.

4.

Discussion

We evaluated HRV indices in groups of participants with or without PTSD who had been exposed to combat or other traumatic events. The HRV indices are particularly reduced in patients with PTSD with veteran experience (SDNN, RMSSD, and LNHF).

In addition,

hyperarousal had significant correlations with symptom clusters of PTSD and SDNN, RMSSD, and LNHF in all study participants. In time domain analyses, SDNN values reflect the overall degree of activity of the ANS (Draghici and Taylor, 2016). Physiologically healthy persons have higher SDNN values, which reflect physiological resilience in the face of stress (Chalmers et al., 2014). In a study of 125 female veterans, 37 subjects with PTSD had reduced SDNN values when compared to non-PTSD subjects, suggesting that the flexibility of the entire ANS is reduced

and that the ability to cope with stress is lowered in individuals with PTSD (Lee and Theus, 2012). However, there is inconsistent evidence regarding the nerve system affected the most by the pathology of PTSD. RMSSD is frequently used to evaluate short-term variations in HR, which reflect the degree of activity of the PNS (Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). The significantly reduced RMSSD in our PTSD group when compared to the non-PTSD group might result from the decline of the function of the PNS due to various traumas. The RMSSD index correlated with the frequency domain measure of HF in HRV (Berntson et al., 2005). The HF component is primarily a marker of parasympathetic activity, as respiratory sinus arrhythmia is mediated exclusively by vagal mechanisms (Draghici and Taylor, 2016; Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology, 1996). Thus, emotional states such as constant stress, anxiety, and fear may decrease HF power (Dishman et al., 2000). Numerous studies have reported on reduced HF power in individuals with PTSD relative to controls in spite of small sample sizes. These studies were analyzed in a metaanalysis of HRV (Chalmers et al., 2014). LF power is known to be associated with mental stress and fatigue, and to result in the loss of body energy (Boneva et al., 2007). However, traditional interpretations of the role of the LF component in PTSD studies have proven controversial. Previous research has shown that LF power is better determined by the sympathetic tone, although some studies report that LF power reflects activity of both the SNS and the PNS (Malliani, 1999; Reyes del Paso et al., 2013). Our findings show that there are non-significant differences in the LF component between groups. This suggests that vagal tone might be more changed than sympathetic tone in PTSD. We also assumed that LF power more closely reflects activation of the SNS, as SDNN, which is associated with overall HRV, had significantly lower values in our study (Hauschildt et al., 2011).

To evaluate the effect of combat exposure on HRV, subjects in both groups (nonPTSD and PTSD) were divided into veterans who either took part in the Vietnam War and non-veterans. Previous studies have reported that different traumatic events might induce different somatic symptoms (Afari et al., 2014). In addition, a study of a female veteran group with combat experience indicated that they had lower SDNN and RMSDD values than non-military subjects with PTSD (Lee and Theus, 2012). Thus, we hypothesize that combat exposure might be a more significant stressor than other traumatic events in patients with PTSD, and that HRV values might decrease more according to the degree of trauma. In our study, veterans with PTSD had the lowest HRV values among the four groups, although post-hoc analyses indicated that there was no difference between nonveterans with PTSD and veterans with PTSD. Our study thus had some limitations, although combat exposure led to larger decreases in HRV. However, combat trauma may not always have a more negative effect on subjects when compared to other traumatic events. In our PTSD groups, veterans had longer durations of PTSD than non-veterans. Duration of PTSD might not be related to HRV measures. In addition, even trauma-related alterations in parasympathetic tone are known to be related to vulnerability to psychopathology in PTSD (Afari et al., 2014; Beauchaine and Thayer, 2015; Lee and Theus, 2012). The pathogenesis of PTSD might thus be a consequence of interactions between social factors, individual genetic factors, individual susceptibility, and environmental factors, as well as psychological traumatic events (Auxemery, 2012). Our findings regarding the relationships between symptoms of PTSD and HRV indices are inconsistent with those of previous studies. In 227 participants including 107 young adults with PTSD, all PTSD symptoms, particularly re-experiencing and avoidance symptoms, were found to be negatively associated with all HRV indices, such as SDNN, RMSSD, HF, and LF (Dennis et al., 2014). Meanwhile, associations between LF power

and intrusions or avoidance symptoms were reported in 52 trauma-exposed individuals (Hauschildt et al., 2011). Another study indicated that only the re-experience symptom was associated with HRV in 19 patients with PTSD (Norte et al., 2013). These discordant findings might be due to different sample characteristics. Previous studies included no data regarding the time since onset of PTSD or the duration of PTSD. The average ages of the participants in these studies were 30-40 years, while the participants with chronic PTSD in our study were in their sixties. A recent study reported that hyperarousal symptoms arise when numbing leads to re-experiencing, which then leads to later long-term hyperarousal (Doron-LaMarca et al., 2015). Another report indicates that smoking, alcohol dependence, and sleep disturbance might lead to a reduced association between PTSD symptoms and HRV measures (Dennis et al., 2014). Our participants might include samples with cigarette consumption because of out of patients and volunteers. Previous studies have suggested that hyperarousal symptoms seem to be due to high sympathetic activity coupled with low parasympathetic cardiac tone (Blechert et al., 2007). However, there is no clear evidence regarding the nerve system most affected by the pathology of PTSD. It is interesting to note that dysfunction in parasympathetic activity after traumatic events might be a significant predictor of the development of PTSD (Berntson et al., 2005). Although the SDNN index could

not be used to distinguish between changes in HRV due to increased sympathetic

tone and those due to withdrawal of vagal tone (Draghici and Taylor, 2016), the relative consistency of our findings across the HRV measures were reduced, with the exception of low-frequency power, which was not different among the groups. Lower HRV measures have previously been studied in association with altered connectivity between the anterior cingulate cortex and the insula, which is connected to the amygdala and medial prefrontal cortex. This altered connectivity has been shown to trigger fear and threat responses in individuals with PTSD (Etkin and Wager, 2007).

This study has several limitations. The present sample consisted of elderly subjects who were predominantly men. This limits the generalizability of our findings. It was also difficult to control factors such as smoking, caffeine intake, and alcohol consumption completely, as all of the participants were outpatients. In addition, symptoms of PTSD were investigated using the PCL-5 tool, which is a self-report scale. To obtain more objective results, clinician assessment tools are required in addition to subjective report scales. Nevertheless, this study evaluated HRV indices based on whether participants in the PTSD and non-PTSD groups had been exposed to combat exposure. The experience of combat-related trauma may precipitate greater changes in parasympathetic tone when compared to other traumatic events. In addition, hyperarousal symptoms were associated with lower HRVs. Our findings suggest the possibility that the HRV indices of SDNN, RMSSD, and HF may be used as potential markers for the evaluation of PTSD symptoms.

Conflicts of Interest The authors have no conflicts of interest.

Acknowledgment This study was supported by a grant from the Korean Mental Health Technology R&D Project, Ministry of Health & Welfare, Republic of Korea (HM15C1058).

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Table 1. Demographic and Clinical Characteristics Non-PTSD

PTSD

(N=73)

(N=68)

62.7 (11.0)

Female (N, %)

t/χ2

p

64.8 (10.1)

-1.189

0.236

9 (12.3)

2 (2.9)

4.313

0.038*

Veterans (N, %)

43 (58.9)

45 (66.2)

0.794

0.373

BMI (kg/m2)

25.1 (3.6)

24.1 (3.2)

1.727

0.086

Smoking (N, %)

35 (47.9)

24 (35.3)

2.315

0.172

Alcohol (N, %)

36 (49.3)

24 (35.3)

2.831

0.125

Coffee (cup/day)

2.1 (1.3)

1.9 (1.4)

1.457

0.452

Education (years)

12.3 (3.6)

11.9 (3.5)

0.679

0.498

PCL-5

21.3 (13.9)

44.1 (17.3)

-8.598

<0.001*

BDI

12.4 (10.1)

26.0 (13.5)

-6.767

<0.001*

BAI

12.0 (9.5)

26.0 (13.5)

-6.701

<0.001*

PSQI

7.2 (4.0)

11.2 (4.1)

-5.870

<0.001*

DM med (N, %)

13 (17.8)

19 (27.9)

2.060

0.151

HD med (N, %)

32 (43.8)

32 (47.1)

1.148

0.701

3 (4.1)

11 (16.2)

5.732

0.017*

Age (years)

CVD med (N, %)

Data are presented as mean (standard deviation) for continuous variables and number (%) for categorical variables. * p<0.05, independent t-test or chi square test Abbreviations: PTSD, post-traumatic stress disorder; BMI, body mass index; PCL, post-traumatic stress disorder checklist; BDI, Beck Depression Inventory; BAI, Beck Anxiety Inventory; PSQI, Pittsburgh Sleep Quality Index; DM, diabetes mellitus; med, taking medications; HD, heart disease; CVD, cerebrovascular disease; med, take a medication

Table 2. Comparison of HRV indices between PTSD and non-PTSD groups Non-PTSD

PTSD

(N=73)

(N=68)

HR

65.4 (1.2)

SDNN

F

p

70.5 (1.2)

7.342

0.003*

28.6 (1.3)

21.9 (1.4)

10.286

0.001*

RMSSD

23.1 (1.5)

15.6 (1.6)

11.152

0.001*

LNLF

4.3 (0.1)

4.1 (0.2)

0.732

0.264

LNHF

4.3 (0.1)

3.7 (0.1)

9.151

0.003*

Data are presented as mean (standard error). Analysis of covariance was carried out using gender, cerebrovascular disease and time since PTSD onset as covariates. * Significant p-value is <0.01 following Bonferroni correction. Abbreviations: HRV, heart rate variability; PTSD, post-traumatic stress disorder; HR, heart rate; SDNN, standard deviation of the NN interval; RMSSD, the square root of the mean squared differences of successive NN intervals; LNLF, log low frequency; LNHF, log high frequency

Table 3. Comparison of HRV indices in veterans and non-veterans with or without PTSD Non-PTSD

PTSD

Nonveteransa (N=30)

Veteransb (N=43)

Nonveteransc (N=23)

Veteransd (N=45)

F

p

Post-hoc

HR

65.0 (1.6)

65.8 (1.4)

73.2 (2.1)

68.9 (1.7)

3.741

0.013

-

SDNN

32.6 (2.3)

26.5 (1.9)

24.0 (2.2)

20.3 (1.4)

7.453

<0.001*

a > c, d

RMSSD

24.8 (2.4)

22.3 (2.6)

16.9 (1.7)

14.5 (1.3)

4.869

0.003*

a, b > d

LNLF

4.7 (0.2)

4.1 (0.1)

4.4 (0.3)

4.0 (0.2)

2.384

0.072

LNHF

4.5 (0.2)

4.2 (0.2)

3.8 (0.3)

3.5 (0.2)

4.599

0.004*

a>d

Data are presented as mean (standard error). Analysis of covariance was carried out using gender, cerebrovascular disease and time since PTSD onset as covariates. * Significant p-value is <0.01 following Bonferroni correction. Abbreviations: HRV, heart rate variability; PTSD, post-traumatic stress disorder; HR, heart rate; SDNN, standard deviation of the NN interval; RMSSD, the square root of the mean squared differences of successive NN intervals; LNLF, log low frequency; LNHF, log high frequency

Table 4. Relationship between PTSD symptoms and HRV indices

Hyper-arousal

ß standardized coefficient

p

0.105

-0.177

0.029*

RMSSD

0.104

-0.202

0.017*

LNHF

0.101

-0.122

0.038*

Dependent variables

Adjusted R

SDNN

2

Multiple linear regression analysis performed adjusting for age, gender, and CVD as covariates. *p<0.05 Abbreviations: SDNN, standard deviation of the NN interval; RMSSD, the square root of the mean squared differences of successive NN intervals; LNHF, log high frequency