The Effects of Antidepressants and Quetiapine on Heart Rate Variability

• Abstract
• Introduction
• Materials and Methods
• Participants
• Data on medications
• Psychometric measurements
• Heart rate variability
• Statistical analysis
• Results
• Discussion
• References

Abstract

Introduction: The autonomic effects of antidepressants and quetiapine on heart rate variability (HRV) are inconsistent based on past studies. The aim of this study was to explore their influence on the HRV of psychiatric patients without psychotic symptoms.

Methods: A total of 94 patients with depression, anxiety, or somatic symptoms, were recruited into this study. Based on their medication, 4 groups were identified: the no antidepressant group (n=19), the SSRI group (using sertraline or escitalopram, n=53), the other antidepressants group (using venlafaxine or mirtazapine, n=9), and the augmentation group (AG, using an antidepressant+quetiapine, n=13). The HRV of the 4 groups were compared. The correlations between HRV and the medication(s) used were clarified.

Results: Among the 4 groups, the AG had the lowest HRV with its total power (TP), very low frequency power (VLF) and low frequency power (LF) of HRV being significantly different from those of the other groups. Age and using quetiapine were found to be negatively correlated with TP, VLF and LF. With this study group, the autonomic effects of antidepressants were found not to be significant.

Discussion: Among psychiatric patients without psychotic symptoms, quetiapine causes an overt decrease in HRV.

Introduction

Heart rate variability (HRV), a technique used to measure autonomic activity, has been widely applied in psychiatric research [1]. Many types of emotion and psychopathology have been found to be associated with HRV [2] [3]. However, short-term HRV is a sensitive tool, which means that it can be affected by the presence of physical disease, environmental factors, circadian rhythm and the medications being used by an individual [4] [5] [6]. In clinical practice, pharmacological treatment is a major part of the management of psychiatric disorders. Therefore, if we want to understand the association between HRV and psychological factors, the question of whether a given psychiatric medication affects HRV needs to be answered.

The effects of psychiatric medications on HRV have been explored in a number of studies. Among antidepressants, the tricyclic antidepressants (TCAs, such as imipramine and amitriptyline) have been widely found to cause an increase in heart rate and a decrease in HRV [7] [8] [9] [10] [11] [12] [13] [14] [15] [16]. In addition, several studies have pointed out that the effects of TCAs are consistent with parasympathetic inhibition and sympathetic activation, which may be explained by the anticholinergic mechanism of this type of drug [17] [18] [19]. Selective serotonin reuptake inhibitors (SSRIs, such as sertraline, fluoxetine, escitalopram and fluvoxamine) have been found not to affect HRV in an obvious manner in most studies, although some contradictory findings have been published (especially in studies involved paroxetine, which has more anticholinergic property than other SSRIs) [7] [8] [9] [12] [13] [15] [20] [21] [22] [23] [24] [25] [26]. In this context, some researchers have noticed HRV elevation after the administration of an SSRI, but this may simply be associated with an improvement in the patient’s depressive symptoms [27] [28] [29]. The autonomic effects of new generation antidepressants (such as venlafaxine and mirtazapine) have only been explored in a small number of articles. Some studies have found that venlafaxine/mirtazapine individually are able to affect autonomic activity, but again contradictory findings have also been published [9] [19] [23] [30] [31] [32]. In summary, the findings on the “SSRIs, venlafaxine and mirtazapine” are not consistent and, furthermore, these topics have yet been explored to any great degree in a Chinese population. Therefore exploring the effects of the above antidepressants on the autonomic systems of a Han Chinese population became one of our study targets.

The autonomic influence of antipsychotics is another interesting topic. Clozapine and olanzapine have been found to reduce HRV [33] [34] [35] and this has been found to occur in a dose-dependent manner [36]. In contrast, risperidone, amisulpride and haloperidol have been found to have only a limited influence on HRV [37] [38] [39]. Huang et al. [6] in 2013 indicated that antipsychotics with high anticholinergic properties (such as clozapine, olanzapine and chlorpromazine) are associated with a reduction in HRV and they seem to affect both sympathetic and vagal modulation. The study subjects used in the above investigation were mainly schizophrenia patients. However, some antipsychotics (such as olanzapine, quetiapine and aripiprazole) have recently been used for augmentation in patients with major depressive disorder. Whether the findings for patients with schizophrenia can be replicated among patients without psychotic symptoms is another issue worthy of investigation.

The presented study is designed to target some of the above unsolved questions. There are 2 major aims of this study. Firstly, we want to understand the relationship between antidepressants and HRV. This is based on the hypothesis that SSRIs do not significantly affect HRV. Secondly, we wish to explore the influence of quetiapine on HRV, our hypothesis being that quetiapine causes an obvious decrease in HRV among psychiatric patients without psychotic symptoms.

Materials and Methods

Participants

The presented study was performed at National Taiwan University Hospital and National Taiwan University Hospital, Yun-Lin Branch, during 2014. The Institutional Review Board of National Taiwan University Hospital approved this study. All subjects were recruited from psychiatric outpatient clinics. The study population consisted of individuals with depressive, anxiety or somatoform disorders defined by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM-IV-TR). The study subjects needed to be taking one or more of the medications for at least 3 months and there should have been no overt adjustment of the medication recently. Individuals with one of following conditions were excluded: (1) younger than 15 years or older than 70 years; (2) psychotic symptoms, substance/alcohol abuse, or a reality disturbance; (3) cognitive impairment or difficulty reading the questionnaires; (4) physical illness that may have an effect on HRV (such as coronary artery disease, congestive heart failure, arrhythmia and diabetes mellitus); (5) use of non-psychiatric medication that may have an effect on HRV; (6) use of psychiatric medication not listed in this study (detailed descriptions of the medications are presented in “data on medication”). The research is cross-sectional and all information related to a single subject was gathered on a single day. After completing an informed consent form, a diagnostic interview was carried out by a board-certified psychiatrist. The diagnosis of each individual was based on DSM-IV-TR and all diagnoses of each patient were recorded. After these activities had been completed, the researchers asked each patient to complete 4 self-administered questionnaires. Finally, the subject’s HRV data, body mass index (BMI), systolic and diastolic blood pressure, demographic data and data on prescribed medications were collected by the researchers.

Data on medications

3 types of psychiatric medications were allowed to be used by the subjects in this study; these were antidepressants, benzodiazepines and quetiapine. The antidepressants consisted of sertraline, escitalopram, venlafaxine, mirtazapine and trazodone. The benzodiazepines consisted of lorazepram, oxazolam, bromazepam, alprazolam, diazepam, clonazepam, estazolam, brotizolam and flunitrazepam. Zolpidem and zopiclone are also included in this type of medicine. Quetiapine is the only antipsychotic that underwent analysis during this study and it is used in Taiwan for augmentation when patients have major depressive disorder. The subjects were separated into 4 groups according to the type of antidepressants/antipsychotic used by the subject. These groups consisted of the “not using antidepressant group” (NG), the “using SSRI group” (SG), the “using other antidepressants group” (OG, namely using venlafaxine or mirtazapine) and the “augmentation group” (AG, using quetiapine and a antidepressant). Trazodone was frequently prescribed at a low dose as an adjunct to act as a sleep enhancer and therefore its use should not affect the groupings. The daily doses of all medications were recorded. Each benzodiazepine dose was converted into an equivalent dose of diazepam for this analysis.

Psychometric measurements

4 self-administered questionnaires were adopted in this study and were used to measure the severity of somatic distress, the amount of hypochondriacal ideation, the severity of depression and the severity of anxiety of each subject.

The Patient Health Questionnaire-15 (PHQ-15) was developed by Kurt Kroenke, Robert L Spitzer et al. in 2002 to measure somatic symptoms [40]. Subjects are asked to rate themselves based on their situation during the month before assessment. The PHQ-15 has 15 items and each has a score from 0–2. A total score of 5–9 is considered to indicate mild somatic distress. A total score of 10–14 is considered to indicate a moderate level of somatic distress. Finally, a score of 15–30 is considered to indicate severe somatic distress [40]. The internal reliability of the PHQ-15 is 0.80. The PHQ-15 is known to have a high coverage based on other questionnaires measuring somatic symptoms (such as the World Health Organization Schedule for Somatoform Disorders Screener and the Symptom Checklist-12). The PHQ-15 is considered to be a useful tool when screening for somatic symptoms and related disorders from the Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM-5) [41].

Michael P. Lucock and Stephen Morley developed the Health Anxiety Questionnaire (HAQ) in 1996 [42]. It is designed to measure anxiety and worry that are related to health topics, including hypochondriacal ideation. HAQ has 21 questions that are assessed using a 4-point Likert scale (0-3). The time that the assessment covers is one week before rating. There are 4 major dimensions within the HAQ based on factor analysis and these are worry and health preoccupation, fear of illness and death, reassurance-seeking behavior and the extent to which symptoms interfere with a person’s life [42]. These concepts include both cognitive and behavioral aspects. The questionnaire’s internal consistency is 0.92 and its test-retest reliability is 0.87. The correlation between HAQ and State-Trait Anxiety Inventory (STAI)-trait is 0.38-0.64, while that between HAQ and the Beck Depression Inventory (BDI) is 0.42 [42].

The Beck Depression Inventory-II (BDI-II) was developed by Aaron T. Beck in 1996 [43]. BDI-II is a self-rated questionnaire that is composed of 21 questions that use a 4-point Likert scale (0–3 points). It is designed for individuals older than 13 years of age. Affective/cognitive and physical/vegetative are the 2 major domains of the depressive symptoms measured by the BDI-II. Subjects are asked to assess their depressive symptoms over the 2 weeks before rating. The test-retest reliability and internal consistency are 0.93 and 0.91, respectively [43]. The BDI-II has been found to be a useful tool when estimating the degree of depression. A score of 14–19 points is usually considered to indicate mild depression; while 20–28 is considered to indicate moderate depression and 29–63 is considered to indicate severe depression.

The Beck Anxiety Inventory (BAI) was also constructed by Aaron T. Beck et al. [44]. The BAI is self-administered and consists of 21 4-point Likert scale questions (0–3 points). The BAI can be used to assess individuals aged 17–80 years old. The somatic aspect of anxiety (panic-like) is especially emphasized in the BAI. Subjects are asked to complete the questionnaire according to their current feelings. The questionnaire’s test-retest reliability is 0.75, while its internal consistency is 0.85–0.93. The correlation between BAI and BDI-II, STAI-state and STAI-trait is 0.66, 0.47 and 0.58, respectively [43] [45]. The publicly accepted cutoffs of the BAI are 8, 16, and 26 points; with 8–15 points indicating mild anxiety; 16–25 points indicating a moderate level of anxiety and 26–63 points indicating a severe level of anxiety.

Heart rate variability

We used a standard method of 5-min HRV measurement and analysis that has been used previously and is described in the literature [4]. For the duration of the measurements, the subjects were asked to sit calmly, breathing with a normal rhythm. They should not fall asleep, fidget, or make any effort to notice any external stimuli. A HRV analyzer (SS1C, Enjoy Research Inc., Taiwan) was adopted for the acquisition, storage, and processing of the electrocardiographic signals. The signals were recorded using an 8-bit analog-to-digital converter using a sampling rate of 512 Hz. The procedure provided a real-time analysis of the digitized electrocardiographic signals. The dataset was simultaneously stored for confirmation. The computer algorithm examined all QRS complexes and rejected unusual signals (such as ventricular premature complex, noise) according to the likelihood of fit to a standard QRS template. Stationary R-R interval values were resampled and interpolated at a rate of 7.11 Hz in order to give continuity in the time domain. The interpolation generated 2 048 data points over 288 s. Subsequently, Fourier transformation was automatically performed and finally the power spectrum was quantified into the various frequency domain measurements that have been described previously [4]. These are total power (TP), very low frequency power (VLF, 0.003–0.04 Hz), low frequency power (LF, 0.04–0.15 Hz), high frequency power (HF, 0.15–0.40 Hz), and ratio of low frequency power to high frequency power (LF/HF) of the HRV; these were calculated in order to provide the frequency domain indices used in our analysis. The above indices were then logarithmically transformed in order to correct for any possible skew in the distributions [5]. Among these indices, HF is usually considered to be a parasympathetic index, whereas LF/HF is thought to represent sympathetic activity or the sympathovagal balance [4] [5]. In addition, the standard deviation of normal to the normal R wave (SDNN), a time domain index, was also calculated and analyzed.

Statistical analysis

The subjects’ demographic factors, data on the medications used by the subjects, the subjects’ psychopathologies and the HRV indices of the subjects were compared across the 4 groups using the Kruskal-Wallis test and the Chi-square test. If there was a significant inter-group difference, post-hoc analysis was performed. In addition, Pearson’s correlation analysis between the HRV indices and the demographic factors, the data on medications used and the psychopathologies present for all subjects was carried out. Categorical items (such as gender and using a medication or not) were changed to dummy variables when performing the correlation analysis. Multiple linear regression analysis was then used to clarify the predictive factors in relation to the HRV results. Finally, the association between quetiapine dosage and several HRV indices was explored using a simple linear regression model.

Results

A total of 94 subjects were recruited into this study. The commonest psychiatric diagnoses among these individuals were somatoform disorders (n=56), major depressive disorder (n=43), panic disorder (n=22) and generalized anxiety disorder (n=18). Subjects with other diagnoses (such as dysthymic disorder, social phobia, obsessive-compulsive disorder and mixed anxiety-depressive disorder) were all less than 5. Based on their psychiatric medications, 4 groups could be created. These consisted of 19 subjects who formed the no-antidepressant group (NG), 53 subjects who formed the SSRI group (SG), 9 subjects who formed the other-antidepressant group (OG) and 13 subjects who formed the augmentation group (AG). The demographic data of the subjects is presented in [Table 1]. Overall, there are no significant inter-group differences in their gender, age, educational level, marital status, living location, course of illness, and BMI.

We also compared the medications, psychopathologies and HRV indices of the 4 groups. The proportion of individuals using trazodone did not show an overt inter-group difference. However, the equivalent dose of benzodiazepine used by the members of the AG is significantly higher than that of the NG. In terms of psychopathologies, the PHQ-15, HAQ, BDI-II, and BAI scores were not found to show inter-group differences. All HRV values were found to be lowest for the AG and among them, the difference in VLF was significant.

In order to gain a further understanding of the relationship between HRV and demographic factors, medication use and psychopathologies, we combined the information on all subjects and performed a correlation analysis. The results are shown in [Table 2]. Age was found to be inversely associated with all HRV indices and it is also worth noting that there also seems to be a relationship between the location where the subject lives and their HRV indices. A significant correlation between HRV and the duration of illness was also found. When the medications used by the subjects were explored, it was noticeable that only “using quetiapine or not” can be seen to be significantly correlated with TP, VLF and LF. Finally, the relationships between psychopathologies and HRV were not found to be significant.

We next performed multiple linear regression analysis on the items that had shown positive findings in the correlation analysis. The results are presented in [Table 3]. The most important predictive factors for TP, VLF and LF were found to be age and using quetiapine. Individuals of a higher age or who are using quetiapine have lower values for these 3 HRV indices. Location and time course of disease did not enter the final models.

We also explored the relationship between the dose of quetiapine and changes in TP, VLF and LF. It was found that the dose of quetiapine showed an inverse correlation with the 3 HRV indexes (significant for TP and VLF; marginally significant for LF); the Spearman’s |r| values are all higher than 0.5. This implies that as the dose of quetiapine increases, the greater the decreases in these HRV indices. These effects can even be observed at a relatively low dosage (25–250 mg/day) of quetiapine ([Fig. 1]).

Discussion

There are 3 major findings in the presented study. First, the use of quetiapine is significantly associated with reduced values for TP, VLF and LF. These effects cannot be explained by the psychopathologies present in the subjects and was found to be dose-dependent. Second, all of the antidepressants investigated in our study, including SSRIs, venlafaxine, mirtazapine and trazodone, do not have an overt influence on HRV indices. Third, any effect of benzodiazepines on HRV is also not obvious.

Quetiapine is the medication most correlated with HRV reduction in this study. Whether an individual is using quetiapine or not and the daily dosage of quetiapine that is used by the individual are both significantly associated with autonomic activity based on the statistical analysis. This is compatible with our hypothesis. Previous studies that have focused on the autonomic effects of quetiapine are limited. The research of Huang et al. [6] separated antipsychotics into those with low muscarinic affinity and high muscarinic affinity and found that the LF, HF and TP of the high muscarinic affinity group are lower than the same indices for low muscarinic group. Quetiapine, olanzapine, clozapine and chlorpromazine are defined as antipsychotics with a high muscarinic activity in that study [6]. LF is usually considered to be an index that combines both sympathetic and parasympathetic activities. The low LF in individuals who are using the above agents may be the result of the drugs’ anticholinergic and anti-adrenergic properties [6]. Olanzapine and clozapine, which are similar to quetiapine, are multi-acting receptor-targeted antipsychotics and there have been a number of reports indicating that they cause an HRV reduction [33] [34] [35] [36] [38]. Our results are compatible with these previous findings. We also noticed that, in addition to TP and LF, VLF is also affected by quetiapine use. Some researchers have considered VLF to represent slow-acting mediating mechanisms such as hormones or temperature rather than the autonomic system [46]. The relationship of VLF and psychiatric medication has been reported previously, but its biological meaning still awaits further investigation [47]. HF, the index of parasympathetic activity, was not found to be associated with quetiapine use in this research. This may be explained by the high proportion of female subjects included in the AG. Females are usually vagal-predominant and under such circumstances any decline in HF that is caused by quetiapine might be less obvious. In this study, all patients received quetiapine for augmentation (combined with at least one antidepressant). Therefore, the interaction between quetiapine and antidepressants also needs to be considered and it is possible that quetiapine only affects HRV under augmentation.

In this study, if we consider TP, VLF, and LF and separate subjects receiving quetiapine from subjects not receiving quetiapine, the Cohen’s d are: TP: 0.787, VLF: 1.079, LF: 0.801. In other studies that have focused on tricyclic antidepressants, the Cohen’s d are 0.54–6.43 (adopting frequency domain indices) [12] [48]. It seems that our findings regarding quetiapine are comparable to those of other studies targeting tricyclic antidepressants, although the effect sizes of the tricyclic antidepressants in some studies are higher.

There are 2 SSRIs being investigated in the present study, namely escitalopram and sertraline; these were found not to have any influence on HRV. This is consistent with the findings from other research studies [32]. Among all SSRIs, escitalopram and sertraline are known to have a relatively low affinity for adrenergic and cholinergic receptors. If the autonomic effects of psychiatric medications originate from these 2 pathways, it is quite reasonable that these 2 medications do not have any obvious effect on HRV. However, paroxetine, an SSRI with relatively high anticholinergic properties, has been found to have a limited effect on autonomic activity in some studies [25] [26]. These findings would seem to indicate that affinity to cholinergic receptors may not be the only mechanism that affects HRV.

Our study did not find a significant effect from venlafaxine and mirtazapine on the autonomic system. However, some studies have pointed out an association between venlafaxine, mirtazapine and HRV [31]. Mirtazapine has been suggested to cause a decline of parasympathetic modulation, which may result from its anticholinergic properties [19]. Furthermore, other studies have found decreased vagal activity and increased sympathetic activity to be associated with venlafaxine and with desvenlafaxine [30] [32]. In this context, the cholinergic affinity of venlafaxine is quite low and thus the effect perhaps needs to be understood in terms of mechanisms related to norepinephrine. It is possible that the negative findings for venlafaxine and mirtazapine in our study are related to the relatively small sample size of fewer than 20 subjects that were using these 2 medications. Therefore, a larger sample size is warranted in any future investigation. Our study also found benzodiazepines to have only a limited effect on HRV. Discussions on the relationship between benzodizapine and autonomic functioning remain limited. In this context, decreased sympathetic and parasympathetic modulations that are associated with benzodiazepine use have both been reported [49] [50]. A previous study focused on patients with schizophrenia in Taiwan and this found that benzodiazepine use was not significantly correlated with autonomic activity [6]. This is similar to the present findings. However, an investigation targeting the effects of benzodiazepines on HRV was not a major aim of these studies and therefore a comprehensive specifically designed study is needed to confirm the influence of these drugs on HRV.

Several limitations of this study need to be discussed. Firstly, the sample size of our study is relatively small. There are only 94 subjects in total and the AG and the OG are both fewer than 20 cases. This is likely to result in limitations to the analysis and on the inferences that may be drawn from the findings. Nevertheless, the dose-dependent phenomenon between quetiapine and HRV for the AG well supports the association between this drug and the various indices. Secondly, although we chose patients who were taking regular medications, the time points between the consumption of medications and the measuring HRV may be different for different subjects. When a subject takes a medicine within several hours of measurement, the HRV may reflect the short-term effects of the medications rather than the long-term effects. Thirdly, the psychiatric diagnoses are a little heterogeneous in our study sample. However, in clinical practice, individuals with comorbid depression, anxiety, and somatic complaints are quite common. We measured the degree of depression, the severity of anxiety, the amount of somatic distress and the level of hypochondriacal ideation in all subjects; therefore, the analysis obtained via these questionnaires should have helped to minimize any influence of the diagnostic heterogeneity. In other studies, the influence of depressive, anxiety, and somatoform symptoms on HRV have been reported. Nonetheless in our samples the scores for the above psychopathologies are comparable across different groups. Their influences on autonomic system are also lower than the effects of medicines in the correlation analysis. In addition, it is possible that objective measurements (such as The Hamilton Rating Scale for Depression/Anxiety) may provide more accurate evaluation of the subjects’ psychopathologies, but they were not adopted in this study. Fourthly, our study is cross-sectional. A longitudinal design should be more helpful in terms of clarifying and solving the inconsistencies in the previous literature. Fifthly, only static HRV was measured. A combination of HRV measurement with the tilt-table test or deep breathing might provide more information about the subjects’ autonomic modulation, but these approaches could not be performed because some of our subjects had relevant somatic complaints (such as subjective dizziness or shortness of breath).

In summary, our study reproduces the findings for antipsychotics in a non-psychotic population, namely that quetiapine, a multi-acting receptor-targeted antipsychotic, causes a reduction in HRV. In addition, we also found that escitalopram, sertraline, mirtazapine and venlafaxine do not affect autonomic modulation significantly in Han Chinese. If researchers hope to view HRV as a biomarker for mental status or as an index of treatment response, this information is quite important. In studies of schizophrenia, quetiapine is associated with cardiovascular risk, which may also be understood via its influence on autonomic modulation. For patients using quetiapine, HRV may represent the effect of the medication better than an assessment of emotion; furthermore, when patients are taking SSRIs, this effect can be ignored. We expect our findings to lead to studies with a larger sample size and to future studies on the clinical applications of HRV and drugs.

Conflicts of Interest and Source of Funding

The authors declare that they have no conflict of interest. This work was supported by a grant (104AC-B3) from the Ministry of Education, Aim for the Top University Plan, a grant (NSC-100-2627-B-010-003) from the National Science Council (Taiwan) and a grant (NTUHYL103.N001) from National Taiwan University Hospital, Yun-Lin Branch. We thank Ms. Chieh-Wen Chen for her manuscript production support.

• References

• 1 Servant D, Logier R, Mouster Y et al. Heart rate variability. Applications in psychiatry. L’Encephale 2009; 35: 423-428
  CrossrefPubMedSearch in Google Scholar
• 2 Lo Turco G, Grimaldi Di Terresena L. Spectral analysis of Heart Rate Variability in psychiatric patients: autonomic nervous system evaluation in psychotic, anxiety and depressive disorders. Rivista di psichiatria 2012; 47: 139-148
  PubMedSearch in Google Scholar
• 3 Yeragani VK. Heart rate and blood pressure variability: implications for psychiatric research. Neuropsychobiology 1995; 32: 182-191
  CrossrefPubMedSearch in Google Scholar
• 4 [Anonymous]. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 1996; 93: 1043-1065
  CrossrefPubMedSearch in Google Scholar
• 5 Kuo TB, Lin T, Yang CC et al. Effect of aging on gender differences in neural control of heart rate. Am J Physiol 1999; 277: H2233-H2239
  PubMedSearch in Google Scholar
• 6 Huang WL, Chang LR, Kuo TB et al. Impact of antipsychotics and anticholinergics on autonomic modulation in patients with schizophrenia. J Clin Psychopharmacol 2013; 33: 170-177
  CrossrefPubMedSearch in Google Scholar
• 7 Udupa K, Thirthalli J, Sathyaprabha TN et al. Differential actions of antidepressant treatments on cardiac autonomic alterations in depression: A prospective comparison. Asian J Psychiatr 2011; 4: 100-106
  CrossrefPubMedSearch in Google Scholar
• 8 Licht CM, de Geus EJ, van Dyck R et al. Longitudinal evidence for unfavorable effects of antidepressants on heart rate variability. Biol Psychiatry 2010; 68: 861-868
  CrossrefPubMedSearch in Google Scholar
• 9 Kemp AH, Quintana DS, Gray MA et al. Impact of depression and antidepressant treatment on heart rate variability: a review and meta-analysis. Biol Psychiatry 2010; 67: 1067-1074
  CrossrefPubMedSearch in Google Scholar
• 10 Zimmermann-Viehoff F, Kuehl LK, Danker-Hopfe H et al. Antidepressants, autonomic function and mortality in patients with coronary heart disease: data from the Heart and Soul Study. Psychol Med 2014; 44: 2975-2984
  CrossrefPubMedSearch in Google Scholar
• 11 Dinleyici EC, Kilic Z, Sahin S et al. Heart rate variability in children with tricyclic antidepressant intoxication. Cardiol Res Pract 2013; 2013: 196506
  PubMedSearch in Google Scholar
• 12 van Zyl LT, Hasegawa T, Nagata K. Effects of antidepressant treatment on heart rate variability in major depression: a quantitative review. Biopsychosoc Med 2008; 2: 12
  CrossrefPubMedSearch in Google Scholar
• 13 Volkers AC, Tulen JH, van den Broek WW et al. Effects of imipramine, fluvoxamine and depressive mood on autonomic cardiac functioning in major depressive disorder. Pharmacopsychiatry 2004; 37: 18-25
  Thieme ConnectPubMedSearch in Google Scholar
• 14 Siepmann M, Krause S, Joraschky P et al. The effects of St John’s wort extract on heart rate variability, cognitive function and quantitative EEG: a comparison with amitriptyline and placebo in healthy men. Br J Clin Pharmacol 2002; 54: 277-282
  CrossrefPubMedSearch in Google Scholar
• 15 Evans DL, Staab J, Ward H et al. Depression in the medically ill: management considerations. Depress Anxiety 1996; 4: 199-208
  CrossrefPubMedSearch in Google Scholar
• 16 Rechlin T, Claus D, Weis M et al. Decreased heart rate variability parameters in amitriptyline treated depressed patients: biological and clinical significance. Eur Psychiatry 1995; 10: 189-194
  CrossrefPubMedSearch in Google Scholar
• 17 Srinivasan K, Ashok MV, Vaz M et al. Effect of imipramine on linear and nonlinear measures of heart rate variability in children. Pediatr Cardiol 2004; 25: 20-25
  CrossrefPubMedSearch in Google Scholar
• 18 Yeragani VK, Rao KA, Pohl R et al. Heart rate and QT variability in children with anxiety disorders: a preliminary report. Depress Anxiety 2001; 13: 72-77
  CrossrefPubMedSearch in Google Scholar
• 19 Tulen JH, Bruijn JA, de Man KJ et al. Cardiovascular variability in major depressive disorder and effects of imipramine or mirtazapine (Org 3770). J Clin Psychopharmacol 1996; 16: 135-145
  CrossrefPubMedSearch in Google Scholar
• 20 O’Regan C, Kenny RA, Cronin H et al. Antidepressants strongly influence the relationship between depression and heart rate variability: findings from The Irish Longitudinal Study on Ageing (TILDA). Psychol Med 2014; 1-14
  PubMedSearch in Google Scholar
• 21 Licht CM, de Geus EJ, van Dyck R et al. Association between anxiety disorders and heart rate variability in The Netherlands Study of Depress Anxiety (NESDA). Psychosom Med 2009; 71: 508-518
  CrossrefPubMedSearch in Google Scholar
• 22 Licht CM, de Geus EJ, Seldenrijk A et al. Depression is associated with decreased blood pressure, but antidepressant use increases the risk for hypertension. Hypertension 2009; 53: 631-638
  CrossrefPubMedSearch in Google Scholar
• 23 Koschke M, Boettger MK, Schulz S et al. Autonomy of autonomic dysfunction in major depression. Psychosom Med 2009; 71: 852-860
  CrossrefPubMedSearch in Google Scholar
• 24 Bar KJ, Greiner W, Jochum T et al. The influence of major depression and its treatment on heart rate variability and pupillary light reflex parameters. J Affect Disord 2004; 82: 245-252
  CrossrefPubMedSearch in Google Scholar
• 25 Roose SP, Laghrissi-Thode F, Kennedy JS et al. Comparison of paroxetine and nortriptyline in depressed patients with ischemic heart disease. Jama 1998; 279: 287-291
  CrossrefPubMedSearch in Google Scholar
• 26 Rechlin T, Weis M, Claus D. Heart rate variability in depressed patients and differential effects of paroxetine and amitriptyline on cardiovascular autonomic functions. Pharmacopsychiatry 1994; 27: 124-128
  Thieme ConnectPubMedSearch in Google Scholar
• 27 Blumenthal JA, Sherwood A, Babyak MA et al. Exercise and pharmacological treatment of depressive symptoms in patients with coronary heart disease: results from the UPBEAT (Understanding the Prognostic Benefits of Exercise and Antidepressant Therapy) study. J Am Coll Cardiol 2012; 60: 1053-1063
  CrossrefPubMedSearch in Google Scholar
• 28 Shea AK, Kamath MV, Fleming A et al. The effect of depression on heart rate variability during pregnancy. A naturalistic study. Clin Auton Res 2008; 18: 203-212
  CrossrefPubMedSearch in Google Scholar
• 29 Glassman AH, Bigger JT, Gaffney M et al. Heart rate variability in acute coronary syndrome patients with major depression: influence of sertraline and mood improvement. Arch Gen Psychiatry 2007; 64: 1025-1031
  CrossrefPubMedSearch in Google Scholar
• 30 Dai F, Lei Y, Chen JD. Inhibitory effects of desvenlafaxine on gastric slow waves, antral contractions, and gastric accommodation mediated via the sympathetic mechanism in dogs. Am J Physiol Gastrointest Liver Physiol 2011; 301: G707-G712
  CrossrefPubMedSearch in Google Scholar
• 31 Terhardt J, Lederbogen F, Feuerhack A et al. Heart rate variability during antidepressant treatment with venlafaxine and mirtazapine. Clin Neuropharmacol 2013; 36: 198-202
  CrossrefPubMedSearch in Google Scholar
• 32 Chang JS, Ha K, Yoon IY et al. Patterns of cardiorespiratory coordination in young women with recurrent major depressive disorder treated with escitalopram or venlafaxine. Prog Neuropsychopharmacol Biol Psychiatry 2012; 39: 136-142
  CrossrefPubMedSearch in Google Scholar
• 33 Agelink MW, Majewski T, Wurthmann C et al. Effects of newer atypical antipsychotics on autonomic neurocardiac function: a comparison between amisulpride, olanzapine, sertindole, and clozapine. J Clin Psychopharmacol 2001; 21: 8-13
  CrossrefPubMedSearch in Google Scholar
• 34 Kim JH, Yi SH, Yoo CS et al. Heart rate dynamics and their relationship to psychotic symptom severity in clozapine-treated schizophrenic subjects. Prog Neuropsychopharmacol Biol Psychiatry 2004; 28: 371-378
  CrossrefPubMedSearch in Google Scholar
• 35 Mueck-Weymann M, Rechlin T, Ehrengut F et al. Effects of olanzapine and clozapine upon pulse rate variability. Depress Anxiety 2002; 16: 93-99
  CrossrefPubMedSearch in Google Scholar
• 36 Eschweiler GW, Bartels M, Langle G et al. Heart-rate variability (HRV) in the ECG trace of routine EEGs: fast monitoring for the anticholinergic effects of clozapine and olanzapine?. Pharmacopsychiatry 2002; 35: 96-100
  Thieme ConnectPubMedSearch in Google Scholar
• 37 Malaspina D, Dalack G, Leitman D et al. Low heart rate variability is not caused by typical neuroleptics in schizophrenia patients. CNS Spectr 2002; 7: 53-57
  CrossrefPubMedSearch in Google Scholar
• 38 Hempel RJ, Tulen JH, van Beveren NJ et al. Cardiovascular variability during treatment with haloperidol, olanzapine or risperidone in recent-onset schizophrenia. J Psychopharmacol 2009; 23: 697-707
  CrossrefPubMedSearch in Google Scholar
• 39 Wang YC, Yang CC, Bai YM et al. Heart rate variability in schizophrenic patients switched from typical antipsychotic agents to amisulpride and olanzapine. 3-month follow-up. Neuropsychobiology 2008; 57: 200-205
  CrossrefPubMedSearch in Google Scholar
• 40 Kroenke K, Spitzer RL, Williams JB. The PHQ-15: validity of a new measure for evaluating the severity of somatic symptoms. Psychosom Med 2002; 64: 258-266
  CrossrefPubMedSearch in Google Scholar
• 41 Lee S, Creed FH, Ma YL et al. Somatic symptom burden and health anxiety in the population and their correlates. J Psychosom Res 2015; 78: 71-76
  CrossrefPubMedSearch in Google Scholar
• 42 Lucock MP, Morley S. The health anxiety questionnaire. Brit J Health Psych 1996; 1: 137-150
  CrossrefPubMedSearch in Google Scholar
• 43 Beck AT, Steer RA, Ball R et al. Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess 1996; 67: 588-597
  CrossrefPubMedSearch in Google Scholar
• 44 Steer RA, Rissmiller DJ, Ranieri WF et al. Structure of the computer-assisted Beck Anxiety Inventory with psychiatric inpatients. J Pers Assess 1993; 60: 532-542
  CrossrefPubMedSearch in Google Scholar
• 45 Julian LJ. Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Arthritis Care Res (Hoboken) 2011; 63 (Suppl. 11) S467-S472
  CrossrefPubMedSearch in Google Scholar
• 46 Taylor JA, Carr DL, Myers CW et al. Mechanisms underlying very-low-frequency RR-interval oscillations in humans. Circulation 1998; 98: 547-555
  CrossrefPubMedSearch in Google Scholar
• 47 Jain FA, Cook IA, Leuchter AF et al. Heart rate variability and treatment outcome in major depression: a pilot study. Int J Psychophysiol 2014; 93: 204-210
  CrossrefPubMedSearch in Google Scholar
• 48 Kemp AH, Brunoni AR, Santos IS et al. Effects of depression, anxiety, comorbidity, and antidepressants on resting-state heart rate and its variability: an ELSA-Brasil cohort baseline study. Am J Psychiatry 2014; 171: 1328-1334
  CrossrefPubMedSearch in Google Scholar
• 49 Agelink MW, Majewski TB, Andrich J et al. Short-term effects of intravenous benzodiazepines on autonomic neurocardiac regulation in humans: a comparison between midazolam, diazepam, and lorazepam. Crit Care Med 2002; 30: 997-1006
  CrossrefPubMedSearch in Google Scholar
• 50 Unoki T, Grap MJ, Sessler CN et al. Autonomic nervous system function and depth of sedation in adults receiving mechanical ventilation. American journal of critical care: an official publication. Am J Crit Care 2009; 18: 42-50 quiz 51
  CrossrefPubMedSearch in Google Scholar

Correspondence

• References

• 1 Servant D, Logier R, Mouster Y et al. Heart rate variability. Applications in psychiatry. L’Encephale 2009; 35: 423-428
  CrossrefPubMedSearch in Google Scholar
• 2 Lo Turco G, Grimaldi Di Terresena L. Spectral analysis of Heart Rate Variability in psychiatric patients: autonomic nervous system evaluation in psychotic, anxiety and depressive disorders. Rivista di psichiatria 2012; 47: 139-148
  PubMedSearch in Google Scholar
• 3 Yeragani VK. Heart rate and blood pressure variability: implications for psychiatric research. Neuropsychobiology 1995; 32: 182-191
  CrossrefPubMedSearch in Google Scholar
• 4 [Anonymous]. Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. Circulation 1996; 93: 1043-1065
  CrossrefPubMedSearch in Google Scholar
• 5 Kuo TB, Lin T, Yang CC et al. Effect of aging on gender differences in neural control of heart rate. Am J Physiol 1999; 277: H2233-H2239
  PubMedSearch in Google Scholar
• 6 Huang WL, Chang LR, Kuo TB et al. Impact of antipsychotics and anticholinergics on autonomic modulation in patients with schizophrenia. J Clin Psychopharmacol 2013; 33: 170-177
  CrossrefPubMedSearch in Google Scholar
• 7 Udupa K, Thirthalli J, Sathyaprabha TN et al. Differential actions of antidepressant treatments on cardiac autonomic alterations in depression: A prospective comparison. Asian J Psychiatr 2011; 4: 100-106
  CrossrefPubMedSearch in Google Scholar
• 8 Licht CM, de Geus EJ, van Dyck R et al. Longitudinal evidence for unfavorable effects of antidepressants on heart rate variability. Biol Psychiatry 2010; 68: 861-868
  CrossrefPubMedSearch in Google Scholar
• 9 Kemp AH, Quintana DS, Gray MA et al. Impact of depression and antidepressant treatment on heart rate variability: a review and meta-analysis. Biol Psychiatry 2010; 67: 1067-1074
  CrossrefPubMedSearch in Google Scholar
• 10 Zimmermann-Viehoff F, Kuehl LK, Danker-Hopfe H et al. Antidepressants, autonomic function and mortality in patients with coronary heart disease: data from the Heart and Soul Study. Psychol Med 2014; 44: 2975-2984
  CrossrefPubMedSearch in Google Scholar
• 11 Dinleyici EC, Kilic Z, Sahin S et al. Heart rate variability in children with tricyclic antidepressant intoxication. Cardiol Res Pract 2013; 2013: 196506
  PubMedSearch in Google Scholar
• 12 van Zyl LT, Hasegawa T, Nagata K. Effects of antidepressant treatment on heart rate variability in major depression: a quantitative review. Biopsychosoc Med 2008; 2: 12
  CrossrefPubMedSearch in Google Scholar
• 13 Volkers AC, Tulen JH, van den Broek WW et al. Effects of imipramine, fluvoxamine and depressive mood on autonomic cardiac functioning in major depressive disorder. Pharmacopsychiatry 2004; 37: 18-25
  Thieme ConnectPubMedSearch in Google Scholar
• 14 Siepmann M, Krause S, Joraschky P et al. The effects of St John’s wort extract on heart rate variability, cognitive function and quantitative EEG: a comparison with amitriptyline and placebo in healthy men. Br J Clin Pharmacol 2002; 54: 277-282
  CrossrefPubMedSearch in Google Scholar
• 15 Evans DL, Staab J, Ward H et al. Depression in the medically ill: management considerations. Depress Anxiety 1996; 4: 199-208
  CrossrefPubMedSearch in Google Scholar
• 16 Rechlin T, Claus D, Weis M et al. Decreased heart rate variability parameters in amitriptyline treated depressed patients: biological and clinical significance. Eur Psychiatry 1995; 10: 189-194
  CrossrefPubMedSearch in Google Scholar
• 17 Srinivasan K, Ashok MV, Vaz M et al. Effect of imipramine on linear and nonlinear measures of heart rate variability in children. Pediatr Cardiol 2004; 25: 20-25
  CrossrefPubMedSearch in Google Scholar
• 18 Yeragani VK, Rao KA, Pohl R et al. Heart rate and QT variability in children with anxiety disorders: a preliminary report. Depress Anxiety 2001; 13: 72-77
  CrossrefPubMedSearch in Google Scholar
• 19 Tulen JH, Bruijn JA, de Man KJ et al. Cardiovascular variability in major depressive disorder and effects of imipramine or mirtazapine (Org 3770). J Clin Psychopharmacol 1996; 16: 135-145
  CrossrefPubMedSearch in Google Scholar
• 20 O’Regan C, Kenny RA, Cronin H et al. Antidepressants strongly influence the relationship between depression and heart rate variability: findings from The Irish Longitudinal Study on Ageing (TILDA). Psychol Med 2014; 1-14
  PubMedSearch in Google Scholar
• 21 Licht CM, de Geus EJ, van Dyck R et al. Association between anxiety disorders and heart rate variability in The Netherlands Study of Depress Anxiety (NESDA). Psychosom Med 2009; 71: 508-518
  CrossrefPubMedSearch in Google Scholar
• 22 Licht CM, de Geus EJ, Seldenrijk A et al. Depression is associated with decreased blood pressure, but antidepressant use increases the risk for hypertension. Hypertension 2009; 53: 631-638
  CrossrefPubMedSearch in Google Scholar
• 23 Koschke M, Boettger MK, Schulz S et al. Autonomy of autonomic dysfunction in major depression. Psychosom Med 2009; 71: 852-860
  CrossrefPubMedSearch in Google Scholar
• 24 Bar KJ, Greiner W, Jochum T et al. The influence of major depression and its treatment on heart rate variability and pupillary light reflex parameters. J Affect Disord 2004; 82: 245-252
  CrossrefPubMedSearch in Google Scholar
• 25 Roose SP, Laghrissi-Thode F, Kennedy JS et al. Comparison of paroxetine and nortriptyline in depressed patients with ischemic heart disease. Jama 1998; 279: 287-291
  CrossrefPubMedSearch in Google Scholar
• 26 Rechlin T, Weis M, Claus D. Heart rate variability in depressed patients and differential effects of paroxetine and amitriptyline on cardiovascular autonomic functions. Pharmacopsychiatry 1994; 27: 124-128
  Thieme ConnectPubMedSearch in Google Scholar
• 27 Blumenthal JA, Sherwood A, Babyak MA et al. Exercise and pharmacological treatment of depressive symptoms in patients with coronary heart disease: results from the UPBEAT (Understanding the Prognostic Benefits of Exercise and Antidepressant Therapy) study. J Am Coll Cardiol 2012; 60: 1053-1063
  CrossrefPubMedSearch in Google Scholar
• 28 Shea AK, Kamath MV, Fleming A et al. The effect of depression on heart rate variability during pregnancy. A naturalistic study. Clin Auton Res 2008; 18: 203-212
  CrossrefPubMedSearch in Google Scholar
• 29 Glassman AH, Bigger JT, Gaffney M et al. Heart rate variability in acute coronary syndrome patients with major depression: influence of sertraline and mood improvement. Arch Gen Psychiatry 2007; 64: 1025-1031
  CrossrefPubMedSearch in Google Scholar
• 30 Dai F, Lei Y, Chen JD. Inhibitory effects of desvenlafaxine on gastric slow waves, antral contractions, and gastric accommodation mediated via the sympathetic mechanism in dogs. Am J Physiol Gastrointest Liver Physiol 2011; 301: G707-G712
  CrossrefPubMedSearch in Google Scholar
• 31 Terhardt J, Lederbogen F, Feuerhack A et al. Heart rate variability during antidepressant treatment with venlafaxine and mirtazapine. Clin Neuropharmacol 2013; 36: 198-202
  CrossrefPubMedSearch in Google Scholar
• 32 Chang JS, Ha K, Yoon IY et al. Patterns of cardiorespiratory coordination in young women with recurrent major depressive disorder treated with escitalopram or venlafaxine. Prog Neuropsychopharmacol Biol Psychiatry 2012; 39: 136-142
  CrossrefPubMedSearch in Google Scholar
• 33 Agelink MW, Majewski T, Wurthmann C et al. Effects of newer atypical antipsychotics on autonomic neurocardiac function: a comparison between amisulpride, olanzapine, sertindole, and clozapine. J Clin Psychopharmacol 2001; 21: 8-13
  CrossrefPubMedSearch in Google Scholar
• 34 Kim JH, Yi SH, Yoo CS et al. Heart rate dynamics and their relationship to psychotic symptom severity in clozapine-treated schizophrenic subjects. Prog Neuropsychopharmacol Biol Psychiatry 2004; 28: 371-378
  CrossrefPubMedSearch in Google Scholar
• 35 Mueck-Weymann M, Rechlin T, Ehrengut F et al. Effects of olanzapine and clozapine upon pulse rate variability. Depress Anxiety 2002; 16: 93-99
  CrossrefPubMedSearch in Google Scholar
• 36 Eschweiler GW, Bartels M, Langle G et al. Heart-rate variability (HRV) in the ECG trace of routine EEGs: fast monitoring for the anticholinergic effects of clozapine and olanzapine?. Pharmacopsychiatry 2002; 35: 96-100
  Thieme ConnectPubMedSearch in Google Scholar
• 37 Malaspina D, Dalack G, Leitman D et al. Low heart rate variability is not caused by typical neuroleptics in schizophrenia patients. CNS Spectr 2002; 7: 53-57
  CrossrefPubMedSearch in Google Scholar
• 38 Hempel RJ, Tulen JH, van Beveren NJ et al. Cardiovascular variability during treatment with haloperidol, olanzapine or risperidone in recent-onset schizophrenia. J Psychopharmacol 2009; 23: 697-707
  CrossrefPubMedSearch in Google Scholar
• 39 Wang YC, Yang CC, Bai YM et al. Heart rate variability in schizophrenic patients switched from typical antipsychotic agents to amisulpride and olanzapine. 3-month follow-up. Neuropsychobiology 2008; 57: 200-205
  CrossrefPubMedSearch in Google Scholar
• 40 Kroenke K, Spitzer RL, Williams JB. The PHQ-15: validity of a new measure for evaluating the severity of somatic symptoms. Psychosom Med 2002; 64: 258-266
  CrossrefPubMedSearch in Google Scholar
• 41 Lee S, Creed FH, Ma YL et al. Somatic symptom burden and health anxiety in the population and their correlates. J Psychosom Res 2015; 78: 71-76
  CrossrefPubMedSearch in Google Scholar
• 42 Lucock MP, Morley S. The health anxiety questionnaire. Brit J Health Psych 1996; 1: 137-150
  CrossrefPubMedSearch in Google Scholar
• 43 Beck AT, Steer RA, Ball R et al. Comparison of Beck Depression Inventories -IA and -II in psychiatric outpatients. J Pers Assess 1996; 67: 588-597
  CrossrefPubMedSearch in Google Scholar
• 44 Steer RA, Rissmiller DJ, Ranieri WF et al. Structure of the computer-assisted Beck Anxiety Inventory with psychiatric inpatients. J Pers Assess 1993; 60: 532-542
  CrossrefPubMedSearch in Google Scholar
• 45 Julian LJ. Measures of anxiety: State-Trait Anxiety Inventory (STAI), Beck Anxiety Inventory (BAI), and Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Arthritis Care Res (Hoboken) 2011; 63 (Suppl. 11) S467-S472
  CrossrefPubMedSearch in Google Scholar
• 46 Taylor JA, Carr DL, Myers CW et al. Mechanisms underlying very-low-frequency RR-interval oscillations in humans. Circulation 1998; 98: 547-555
  CrossrefPubMedSearch in Google Scholar
• 47 Jain FA, Cook IA, Leuchter AF et al. Heart rate variability and treatment outcome in major depression: a pilot study. Int J Psychophysiol 2014; 93: 204-210
  CrossrefPubMedSearch in Google Scholar
• 48 Kemp AH, Brunoni AR, Santos IS et al. Effects of depression, anxiety, comorbidity, and antidepressants on resting-state heart rate and its variability: an ELSA-Brasil cohort baseline study. Am J Psychiatry 2014; 171: 1328-1334
  CrossrefPubMedSearch in Google Scholar
• 49 Agelink MW, Majewski TB, Andrich J et al. Short-term effects of intravenous benzodiazepines on autonomic neurocardiac regulation in humans: a comparison between midazolam, diazepam, and lorazepam. Crit Care Med 2002; 30: 997-1006
  CrossrefPubMedSearch in Google Scholar
• 50 Unoki T, Grap MJ, Sessler CN et al. Autonomic nervous system function and depth of sedation in adults receiving mechanical ventilation. American journal of critical care: an official publication. Am J Crit Care 2009; 18: 42-50 quiz 51
  CrossrefPubMedSearch in Google Scholar