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Beat-to-beat heart rate and QT interval variability in first episode neuroleptic-naive psychosis
Schizophrenia Research 113 (2009) 176–180
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Schizophrenia Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s c h r e s
Beat-to-beat heart rate and QT interval variability in first episode neuroleptic-naive psychosis Ripu D. Jindal a,b,⁎, Matcheri S. Keshavan a,c, Kevin Eklund a, Angela Stevens b, Debra M. Montrose a, Vikram K. Yeragani d,e a b c d e
University of Pittsburgh School of Medicine, Pittsburgh PA USA University of Ottawa School of Medicine, Ottawa, Canada Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA Wayne State University School of Medicine, Detroit, MI, USA University of Alberta, Edmonton, Alberta, Canada
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Article history: Received 22 February 2009 Received in revised form 2 June 2009 Accepted 3 June 2009 Available online 1 July 2009 Keywords: Psychosis Schizophrenia Sudden death Autonomic dysfunction QT variability RR variability
a b s t r a c t Introduction: Though increased risk of sudden death in patients with schizophrenia is welldocumented, the mechanisms remain unclear. Recent studies report two known risk factors for sudden cardiac death and other arrhythmias in schizophrenia, i.e., decreased RR interval variability (RRV) and increased QT interval variability (QTV). However, these studies did not control for the effects of medication. Herein, we report the results of our study comparing RRV and QTV in first episode neuroleptic-naive psychosis patients with healthy matched controls. Methods: 24 patients with first episode neuroleptic naïve psychosis were matched with 26 healthy controls on age and gender. After an overnight fast, all participants underwent an electrocardiogram recording in the morning. Results: In comparison with matched controls, patients with first episode neuroleptic-naïve psychosis had significantly increased QTV corrected for RRV, and decreased RRV. Conclusions: The observed alterations in RRV and QTV may reflect impaired cardiac autonomic function that could underlie risk for abnormal ventricular repolarization and thereby increase the risk of sudden death and other arrhythmias. Our data suggest that RRV and QTV alterations may be independent of medication effects in first episode psychosis patients. © 2009 Elsevier B.V. All rights reserved.
1. Introduction Sudden cardiac death, as defined by the Task Force on Sudden Cardiac Death of the European Society of Cardiology, is a natural death due to cardiac causes, heralded by abrupt loss of consciousness within an hour of the onset of acute symptoms, or an unwitnessed, unexpected death of someone seen in a stable medical condition less than 24 h previously with no evidence of a noncardiac cause (Priori et al., 2001). Patients with schizophrenia are three times as likely (relative risk 2.9) to ⁎ Corresponding author. 2616 Bear Run Drive, Pittsburgh, Pennsylvania, 15237, USA. Tel.: +1 412 512 9516; fax: +1 412 383 3177. E-mail address: [email protected] (R.D. Jindal). 0920-9964/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.schres.2009.06.003
have sudden unexpected death than the general population (Ruschena et al., 1998), and have greater rates of all cause mortality (Brown et al., 2000; Mortensen and Juel, 1990). Though the exact reasons for the increased mortality in patients with schizophrenia are not clear, factors related to the disease pathology, antipsychotic medications, and lifestyle (e.g. smoking, general neglect of health, decreased access to healthcare services, etc.) may have a role. Among these, there is considerable evidence supporting a link between use of antipsychotic agents and increased rates of sudden death. For example, in a population-based case-control study, current use of antipsychotic agents was associated with a 3-fold increase in risk of sudden cardiac death (Straus et al., 2004). Similarly, in a retrospective cohort study of Tennessee Medicaid enrollees,
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individuals receiving antipsychotic medication were more likely to die unexpectedly than non-users (Ray et al., 2009). The risk was even greater in those being treated with higher doses (Ray et al., 2009). The role of autonomic nervous system in sudden death has received much attention in the recent years. Among various measures of the cardiac autonomic function, decreased RR interval variability (RRV) has been shown to predict potentially fatal ventricular tachycardias in several disease conditions (Bikkina et al., 1998; Dekker et al., 1997; Huikuri et al., 1998; Kleiger et al., 1987; Odemuyiwa et al., 1991) as well as in apparently healthy subjects (Molgaard et al., 1991). Studies of the autonomic system in schizophrenia have detected a number of abnormalities. For example, decreased RRV, a marker of cardiac parasympathetic activity (1996) has been demonstrated in patients with psychosis (Agelink et al., 2001; Bar et al., 2008a; Mujica-Parodi et al., 2005), and may underlie the increased rates of sudden death in patients with schizophrenia. In addition to cardiac autonomic dysregulation, other branches of the autonomic system also seem to be compromised in schizophrenia. For instance, patients with schizophrenia show increased resting pupillary diameter (Bar et al., 2008a). Furthermore, there is evidence that baseline pupillary size correlates with symptom severity, independent of medication (Morris et al., 1997). A recent study by Bar et al. (2008a) demonstrated autonomic dysregulation both at the pupil and heart in patients with schizophrenia. Notably, the two dysregulations were interrelated. Several studies that examine skin conductance suggest that autonomic dysregulation is closely linked with prognosis in schizophrenia. For example, there is evidence that electrodermal activity is a prodromal sign in schizophrenia (Hazlett et al., 1997) and predicts functional outcomes and negative symptoms in psychosis (Schell et al., 2005), as well as rates of relapse on follow-up (Hultman et al., 1996). Electrodermal activity has also been shown to predict performance on tests of neuro-cognition, such as Wisconsin Card sorting task (Schiffer et al., 1996) and Stroop Color Word test (LopesMachado et al., 2002). Similarly, skin conductance hyporesponsivity was associated with scores on the negative symptom scale and perserverative errors (Perry et al., 1998). Most of the patients who participated in the aforementioned studies, were either being treated with antipsychotic medication, or received these medications in the past. There is evidence that some of the commonly used antipsychotic medications influence the autonomic system. For example, among the four second generation antipsychotic medications examined in the study by Agelink et al. (2001), clozapine was associated with a decrease in RRV; RRV correlated inversely with the serum levels of clozapine. Another study has noted an inverse correlation between RRV and blood levels of both clozapine and olanzapine, with clozapine showing a stronger correlation with RRV than olanzapine (Eschweiler et al., 2002). Studies of previously untreated patients with first episode psychoses are likely to help tease apart the effects of disease and medications. There is also evidence that autonomic dysfunction in schizophrenia is a “disease effect”. For example, in one study, tonic electrodermal activity was abnormally elevated only during acute psychotic states, whereas phasic hyporespon-
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siveness relative to level of general activation persisted into remission, suggesting that autonomic deficits can be indicators of both state and trait in schizophrenia (Dawson et al., 1994). In two other studies, psychotic states were associated with decreased RRV (Okada et al., 2003; Toichi et al., 1999). Recently, a Finnish study demonstrated decreased RRV in first episode neuroleptic-naïve patients with diagnosis of schizophrenia or depression with psychotic features (ValkonenKorhonen et al., 2003). Apart from the RRV, beat-to-beat QT interval variability (QTV) has also been shown to predict sudden death and other arrhythmic events (Atiga et al., 1998; Berger et al., 1997; Bonnemeier et al., 2001; Maison-Blanche and Coumel 1997; Vrtovec et al., 2000). Since the QT interval is widely held as the measure of ventricular repolarization time, QTV reflects beatto-beat fluctuations in the myocardial refractory or recovery time. Wider fluctuations in this refractory period may predispose patients to have “reentrant” arrhythmias. Increase in QTV with orthostatic challenge as well as isoproterenol infusions (Yeragani et al., 2000a) suggests that QT interval is partly under sympathetic control. Moreover, Pemoline, a sympathetic stimulant has been shown to increase QTV (Pohl et al., 2003). More recently, Bar et al. (2007) have demonstrated increased QTV in patients with schizophrenia. However, most of the patients in their study had been treated with antipsychotic medications in the past. To our knowledge, no previous study has examined QTV in first episode neuroleptic-naïve patients with psychosis. In this study, we sought to test the hypothesis that first episode patients with psychosis who have not received antipsychotic agents will have significantly decreased RRV and increased QTV compared to their age and gender-matched healthy counterparts. 2. Methods 2.1. Subjects The study subjects were recruited from Western Psychiatric Institute and Clinic, Pittsburgh, Pennsylvania. Twenty four patients with first episode psychosis were enrolled. Among these, three later met diagnostic criteria for schizoaffective disorder, one for schizophreniform disorder, six for paranoid schizophrenia, one for depression with psychotic features, two for undifferentiated schizophrenia and one for schizophrenia disorganized. The remaining patients had a diagnosis of Psychosis Not Otherwise Specified. None of the participants had received any antipsychotic medication prior to their participation in the study. In order to generate a relatively homogenous sample, and to minimize potential confounding effects of age, enrollment was limited to those between the ages of 14 and 30. Twenty six age and gender-matched healthy control subjects were recruited from the same neighbourhoods as the patients. Patients were enrolled only if they met the Diagnostic and Statistical Manual, edition -IV (DSM-IV) (1994) criteria for a Psychotic disorder. Diagnosis was confirmed with the use of Structured Clinical Interview for DSM-IV axis I Disorders–Patient Edition (First, 1995) and consensus discussions using all clinical data. Any current substance use disorder or substance dependence (except for tobacco smoking) within the past 6 months resulted in exclusion from the study. The matched healthy control subjects were only recruited if they did
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not meet the DSM-IV criteria for any psychiatric disorder. All participants signed an informed consent after full explanation of the study. The study was approved by the University of Pittsburgh Institutional Review Board. 2.2. Clinical assessments The severity of psychiatric symptoms was rated by the Brief Psychiatric Rating Scale (Overall and Gorham, 1962), the Positive and Negative Syndrome Scale (Kay et al., 1987) and Clinical Global Impressions Ratings (Guy, 1976). 2.3. HRV data collection and analyses All electrocardiographic (ECG) data were collected between 8 am and 10 am with the participants in a supine position. ECG was continuously acquired in lead II configuration in a noise-free environment. All subjects were asked to breathe normally. The ECG signal was digitized at 1000 Hz and the data were saved on a PC for later analyses. The subjects lied down quietly for at least 5 min before the supine data were acquired. We used 256 s of artifact-free data for analyses of RRV and QTV. As per the recommendations of the Task Force of the European Society of Cardiology and North American society of Pacing and Electrophysiology (1996), high frequency RRV and root mean squares of successive differences in RR intervals (RRSD) were calculated as indices of cardiac vagal activity. The QT variability algorithm that we used has been described by Berger et al. (1997) in detail and has been used by his and our groups in previous studies (Yeragani et al., 2002, 2000b). The algorithm uses a graphical interface of digitized ECG (sampled at 1000 Hz which gives a precision of 1 ms to measure the RR and QT intervals) where the time of the ‘R’ wave is obtained using a peak detection algorithm. Then the operator provides the program with the beginning and the end of the QT wave template. The algorithm finds the QT interval for each beat using the time-stretch model. If the operator chooses a longer QT template, all of the QT intervals will be biased accordingly. The output of the algorithm contains beat-to-beat RR intervals and QT intervals. The beat-to-beat RR intervals in milliseconds were sampled at 4 Hz using linear interpolation. The QT intervals were similarly constructed at 4 Hz. The reason to sample the RR and the QT intervals at 4 Hz was to ensure that the same length of time was used for the analysis as the instantaneous RR and QT intervals were equidistant sampled at 0.25 s. We used RR time series free of ventricular premature beats and noise. The RR and QT interval data were then detrended by using the best-fit line prior to the computation of the spectral analyses. The mean RR, detrended RR variance, mean QT interval, detrended QT variance were calculated from the instantaneous RR and QT time series of 1024 points (256 s). Mean RR and mean QT intervals are in ms. The powers are corresponding squared values. Normalized QT variability index (QTVi) was calculated as suggested by Berger et al. (1997): QTVi = Log10 [(detrended QT variance / mean QT interval2) / (detrended RR variance / mean RR interval2)].
This index represents the log-ratio between the QT interval and the RR interval variabilities (detrended), each normalized for the corresponding mean. We used 256 s of data collected in supine posture.
2.4. Spectral analyses RR interval time series (256 s at 4 Hz = 1024 points) was subjected to spectral analyses and the power spectrum was computed using a rectangular window. The powers were integrated in the following bands: TP (total power: 0–0.5 Hz), VLF (very low frequency power: 0.0–0.04 Hz), LF (low frequency power: 0.04–0.15 Hz) and HF (high frequency power (0.15–0.5 Hz)).
2.5. Statistical analysis We used two-tailed t tests or χ2 test to compare demographic variables between the two groups. In cases of nonnormal distributions, data were log transformed. We used Analysis of covariance (ANCOVA) with age as a co-variate to compare the two groups on the log-transformed QTV and RRV variables. A probability value of b0.05 was accepted as significant.
3. Results Demographic and the heart rate variability data are displayed in Table 1. The groups were comparable in terms of age, gender and race. However, a greater proportion of patients gave a history of cigarette smoking than the controls (6 versus 1; χ2 = 0.04). Patients had significantly greater QTVi [F (df, 45) = 6.7; p = 0.01] and lesser log RRSD [F (df,45) = 5.29, p £ 0.03], as well as lesser log RRHF [F (df, 45) = 4.51, p b .04] than the controls. As expected, log RRSD and log RRHF declined with age in the control group (r = .51, p b 0.01 and r = − 0.48, p b 0.01) but not in the patient group. QTVi was not associated with age in either group. None of the HRV variables correlated with the severity of the positive or negative symptoms of psychosis. On post-hoc analyses, QTVi, RRSD and log RRHF values were comparable between those with and without history of cigarette smoking. Even if the data from those with a history of smoking is excluded, patients had significantly greater QTVi.
Table 1 Demographic and heart rate variability characteristics.
Age (years) Gender (men/women) Race (white/black) QTVi Log RRHF Log RRSD
Controls (n = 26)
Patients (n = 24)
Group effect (t, χ2 or F)
Mean SD
Mean SD
t, F or χ2
22.27 (5.21) 15/11 18/8 − 2.01 (0.27) 3.01 (0.59) 1.74 (0.218)
21.16 (3.97) 16/8 12/12 − 1.77 (.42) 2.72 (.67) 1.61 (.27)
t = .89 0.39 .44 χ2 = .59 2 .24 χ = .16 F (df 45) = 6.7 .01 F (df 45) = 4.51 b0.04 F (df 45) = 5.29 b .03
p value
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4. Discussion One of our main findings was that patients with first episode treatment naïve psychosis have increased QT variability corrected for RR variability (QTV index) as compared to the matched controls. Our results also replicate the evidence for decreased RRV in earlier studies of psychosis. However, we could not replicate the finding of correlation between the severity of psychotic symptoms with HRV abnormalities (Bar et al., 2008b). This may suggest that altered QTV and RRV are not likely to be related to state related measures of symptomatology. Further studies are needed to examine whether these alterations are stable over time and if those reflect the increased risk of cardiac arrhythmias and/or sudden death in patients with schizophrenia. While our observations suggest that QTV and RRV alterations may be related to the core pathology of psychotic disorders, the pathophysiological mechanisms remain unclear. Neuroimaging and neuropathological studies suggest that frontolimbic structures of the brain such as the dorsolateral prefrontal cortex (PFC), insula, orbitofrontal cortex (OFC), medial PFC, and anterior cingulate, have been implicated in the pathogenesis of schizophrenia (Phillips et al., 2003; Shenton et al., 2001). Evidence from the lesion, stimulation, and neuroanatomical studies suggests that these brain structures regulate autonomic function through their reciprocal circuitry and by their projections to cell groups, such as the amygdala, hypothalamus, and brainstem nuclei (Barbas et al., 2003; Ongur and Price, 2000). The relationship between HRV abnormalities and neurobiological alterations in these disorders merits further investigation. One of the strengths of our study was the use of previously untreated first episode psychosis patients, which helps avoid confounds of medications and illness chronicity. Another strength was the fact that we controlled for the time of the day since patients with schizophrenia have been shown to have altered diurnal variation in their HRV (Boettger et al., 2006). A limitation of the study is the relatively small sample size, resulting from the fact that previously untreated first episode first episode patients are difficult to recruit. A second limitation of the study is the lack of control for quality of sleep on the night before the data collection (Bonnet and Arand, 1998). Finally, our study sample is heterogeneous, and included patients with both schizophrenia and non-schizophrenic psychosis. Since increased QTV has also been detected in the studies of depression and panic disorder (Yeragani et al., 2000c), it is possible that increased QTV cuts across diagnostic boundaries and thereby increases risk for sudden death and other arrhythmias in all major psychiatric disorders. Despite these limitations, the study is an important step in unraveling the phenomenon of sudden death in patients with schizophrenia. Further studies will be needed to document the effect of individual antipsychotic medications on QTV and RRV. Continued work will hopefully lead to the development of effective interventions to lower the risk of arrhythmias in patients with schizophrenia. Future work may also help in the early detection of a subset of patients with schizophrenia at greater risk of sudden death, and development of “tailored” antipsychotic treatment interventions in those at greater risk.
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Role of funding source Funding for this study was provided by NIH Grant NIH/NCRR/GCRC grant #M01 RR00056 and Janssen AAGP SRI Alumni Award (P.I Ripu Jindal); the funding sources had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.
Contributors Author Ripu Jindal designed the study, wrote the protocol and the first draft of the paper. Authors Matcheri Keshavan, and Vikram Yeragani managed the literature searches and analyses. Author Angela Stevens and Matcheri Keshavan undertook the statistical analysis. All authors contributed to and have approved the final manuscript. Conflict of interest All authors declare that they have no conflicts of interest.
Acknowledgements We thank Raymond Cho, MD, Rohan Ganguli MD, Gretchen Haas PhD and the clinical core staff of the Center for the Neuroscience of Mental Disorders (MH45156) for their assistance in the diagnostic and psychopathological assessments.
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Schizophrenia Research j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s c h r e s
Beat-to-beat heart rate and QT interval variability in first episode neuroleptic-naive psychosis Ripu D. Jindal a,b,⁎, Matcheri S. Keshavan a,c, Kevin Eklund a, Angela Stevens b, Debra M. Montrose a, Vikram K. Yeragani d,e a b c d e
University of Pittsburgh School of Medicine, Pittsburgh PA USA University of Ottawa School of Medicine, Ottawa, Canada Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA Wayne State University School of Medicine, Detroit, MI, USA University of Alberta, Edmonton, Alberta, Canada
a r t i c l e
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Article history: Received 22 February 2009 Received in revised form 2 June 2009 Accepted 3 June 2009 Available online 1 July 2009 Keywords: Psychosis Schizophrenia Sudden death Autonomic dysfunction QT variability RR variability
a b s t r a c t Introduction: Though increased risk of sudden death in patients with schizophrenia is welldocumented, the mechanisms remain unclear. Recent studies report two known risk factors for sudden cardiac death and other arrhythmias in schizophrenia, i.e., decreased RR interval variability (RRV) and increased QT interval variability (QTV). However, these studies did not control for the effects of medication. Herein, we report the results of our study comparing RRV and QTV in first episode neuroleptic-naive psychosis patients with healthy matched controls. Methods: 24 patients with first episode neuroleptic naïve psychosis were matched with 26 healthy controls on age and gender. After an overnight fast, all participants underwent an electrocardiogram recording in the morning. Results: In comparison with matched controls, patients with first episode neuroleptic-naïve psychosis had significantly increased QTV corrected for RRV, and decreased RRV. Conclusions: The observed alterations in RRV and QTV may reflect impaired cardiac autonomic function that could underlie risk for abnormal ventricular repolarization and thereby increase the risk of sudden death and other arrhythmias. Our data suggest that RRV and QTV alterations may be independent of medication effects in first episode psychosis patients. © 2009 Elsevier B.V. All rights reserved.
1. Introduction Sudden cardiac death, as defined by the Task Force on Sudden Cardiac Death of the European Society of Cardiology, is a natural death due to cardiac causes, heralded by abrupt loss of consciousness within an hour of the onset of acute symptoms, or an unwitnessed, unexpected death of someone seen in a stable medical condition less than 24 h previously with no evidence of a noncardiac cause (Priori et al., 2001). Patients with schizophrenia are three times as likely (relative risk 2.9) to ⁎ Corresponding author. 2616 Bear Run Drive, Pittsburgh, Pennsylvania, 15237, USA. Tel.: +1 412 512 9516; fax: +1 412 383 3177. E-mail address: [email protected] (R.D. Jindal). 0920-9964/$ – see front matter © 2009 Elsevier B.V. All rights reserved. doi:10.1016/j.schres.2009.06.003
have sudden unexpected death than the general population (Ruschena et al., 1998), and have greater rates of all cause mortality (Brown et al., 2000; Mortensen and Juel, 1990). Though the exact reasons for the increased mortality in patients with schizophrenia are not clear, factors related to the disease pathology, antipsychotic medications, and lifestyle (e.g. smoking, general neglect of health, decreased access to healthcare services, etc.) may have a role. Among these, there is considerable evidence supporting a link between use of antipsychotic agents and increased rates of sudden death. For example, in a population-based case-control study, current use of antipsychotic agents was associated with a 3-fold increase in risk of sudden cardiac death (Straus et al., 2004). Similarly, in a retrospective cohort study of Tennessee Medicaid enrollees,
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individuals receiving antipsychotic medication were more likely to die unexpectedly than non-users (Ray et al., 2009). The risk was even greater in those being treated with higher doses (Ray et al., 2009). The role of autonomic nervous system in sudden death has received much attention in the recent years. Among various measures of the cardiac autonomic function, decreased RR interval variability (RRV) has been shown to predict potentially fatal ventricular tachycardias in several disease conditions (Bikkina et al., 1998; Dekker et al., 1997; Huikuri et al., 1998; Kleiger et al., 1987; Odemuyiwa et al., 1991) as well as in apparently healthy subjects (Molgaard et al., 1991). Studies of the autonomic system in schizophrenia have detected a number of abnormalities. For example, decreased RRV, a marker of cardiac parasympathetic activity (1996) has been demonstrated in patients with psychosis (Agelink et al., 2001; Bar et al., 2008a; Mujica-Parodi et al., 2005), and may underlie the increased rates of sudden death in patients with schizophrenia. In addition to cardiac autonomic dysregulation, other branches of the autonomic system also seem to be compromised in schizophrenia. For instance, patients with schizophrenia show increased resting pupillary diameter (Bar et al., 2008a). Furthermore, there is evidence that baseline pupillary size correlates with symptom severity, independent of medication (Morris et al., 1997). A recent study by Bar et al. (2008a) demonstrated autonomic dysregulation both at the pupil and heart in patients with schizophrenia. Notably, the two dysregulations were interrelated. Several studies that examine skin conductance suggest that autonomic dysregulation is closely linked with prognosis in schizophrenia. For example, there is evidence that electrodermal activity is a prodromal sign in schizophrenia (Hazlett et al., 1997) and predicts functional outcomes and negative symptoms in psychosis (Schell et al., 2005), as well as rates of relapse on follow-up (Hultman et al., 1996). Electrodermal activity has also been shown to predict performance on tests of neuro-cognition, such as Wisconsin Card sorting task (Schiffer et al., 1996) and Stroop Color Word test (LopesMachado et al., 2002). Similarly, skin conductance hyporesponsivity was associated with scores on the negative symptom scale and perserverative errors (Perry et al., 1998). Most of the patients who participated in the aforementioned studies, were either being treated with antipsychotic medication, or received these medications in the past. There is evidence that some of the commonly used antipsychotic medications influence the autonomic system. For example, among the four second generation antipsychotic medications examined in the study by Agelink et al. (2001), clozapine was associated with a decrease in RRV; RRV correlated inversely with the serum levels of clozapine. Another study has noted an inverse correlation between RRV and blood levels of both clozapine and olanzapine, with clozapine showing a stronger correlation with RRV than olanzapine (Eschweiler et al., 2002). Studies of previously untreated patients with first episode psychoses are likely to help tease apart the effects of disease and medications. There is also evidence that autonomic dysfunction in schizophrenia is a “disease effect”. For example, in one study, tonic electrodermal activity was abnormally elevated only during acute psychotic states, whereas phasic hyporespon-
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siveness relative to level of general activation persisted into remission, suggesting that autonomic deficits can be indicators of both state and trait in schizophrenia (Dawson et al., 1994). In two other studies, psychotic states were associated with decreased RRV (Okada et al., 2003; Toichi et al., 1999). Recently, a Finnish study demonstrated decreased RRV in first episode neuroleptic-naïve patients with diagnosis of schizophrenia or depression with psychotic features (ValkonenKorhonen et al., 2003). Apart from the RRV, beat-to-beat QT interval variability (QTV) has also been shown to predict sudden death and other arrhythmic events (Atiga et al., 1998; Berger et al., 1997; Bonnemeier et al., 2001; Maison-Blanche and Coumel 1997; Vrtovec et al., 2000). Since the QT interval is widely held as the measure of ventricular repolarization time, QTV reflects beatto-beat fluctuations in the myocardial refractory or recovery time. Wider fluctuations in this refractory period may predispose patients to have “reentrant” arrhythmias. Increase in QTV with orthostatic challenge as well as isoproterenol infusions (Yeragani et al., 2000a) suggests that QT interval is partly under sympathetic control. Moreover, Pemoline, a sympathetic stimulant has been shown to increase QTV (Pohl et al., 2003). More recently, Bar et al. (2007) have demonstrated increased QTV in patients with schizophrenia. However, most of the patients in their study had been treated with antipsychotic medications in the past. To our knowledge, no previous study has examined QTV in first episode neuroleptic-naïve patients with psychosis. In this study, we sought to test the hypothesis that first episode patients with psychosis who have not received antipsychotic agents will have significantly decreased RRV and increased QTV compared to their age and gender-matched healthy counterparts. 2. Methods 2.1. Subjects The study subjects were recruited from Western Psychiatric Institute and Clinic, Pittsburgh, Pennsylvania. Twenty four patients with first episode psychosis were enrolled. Among these, three later met diagnostic criteria for schizoaffective disorder, one for schizophreniform disorder, six for paranoid schizophrenia, one for depression with psychotic features, two for undifferentiated schizophrenia and one for schizophrenia disorganized. The remaining patients had a diagnosis of Psychosis Not Otherwise Specified. None of the participants had received any antipsychotic medication prior to their participation in the study. In order to generate a relatively homogenous sample, and to minimize potential confounding effects of age, enrollment was limited to those between the ages of 14 and 30. Twenty six age and gender-matched healthy control subjects were recruited from the same neighbourhoods as the patients. Patients were enrolled only if they met the Diagnostic and Statistical Manual, edition -IV (DSM-IV) (1994) criteria for a Psychotic disorder. Diagnosis was confirmed with the use of Structured Clinical Interview for DSM-IV axis I Disorders–Patient Edition (First, 1995) and consensus discussions using all clinical data. Any current substance use disorder or substance dependence (except for tobacco smoking) within the past 6 months resulted in exclusion from the study. The matched healthy control subjects were only recruited if they did
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not meet the DSM-IV criteria for any psychiatric disorder. All participants signed an informed consent after full explanation of the study. The study was approved by the University of Pittsburgh Institutional Review Board. 2.2. Clinical assessments The severity of psychiatric symptoms was rated by the Brief Psychiatric Rating Scale (Overall and Gorham, 1962), the Positive and Negative Syndrome Scale (Kay et al., 1987) and Clinical Global Impressions Ratings (Guy, 1976). 2.3. HRV data collection and analyses All electrocardiographic (ECG) data were collected between 8 am and 10 am with the participants in a supine position. ECG was continuously acquired in lead II configuration in a noise-free environment. All subjects were asked to breathe normally. The ECG signal was digitized at 1000 Hz and the data were saved on a PC for later analyses. The subjects lied down quietly for at least 5 min before the supine data were acquired. We used 256 s of artifact-free data for analyses of RRV and QTV. As per the recommendations of the Task Force of the European Society of Cardiology and North American society of Pacing and Electrophysiology (1996), high frequency RRV and root mean squares of successive differences in RR intervals (RRSD) were calculated as indices of cardiac vagal activity. The QT variability algorithm that we used has been described by Berger et al. (1997) in detail and has been used by his and our groups in previous studies (Yeragani et al., 2002, 2000b). The algorithm uses a graphical interface of digitized ECG (sampled at 1000 Hz which gives a precision of 1 ms to measure the RR and QT intervals) where the time of the ‘R’ wave is obtained using a peak detection algorithm. Then the operator provides the program with the beginning and the end of the QT wave template. The algorithm finds the QT interval for each beat using the time-stretch model. If the operator chooses a longer QT template, all of the QT intervals will be biased accordingly. The output of the algorithm contains beat-to-beat RR intervals and QT intervals. The beat-to-beat RR intervals in milliseconds were sampled at 4 Hz using linear interpolation. The QT intervals were similarly constructed at 4 Hz. The reason to sample the RR and the QT intervals at 4 Hz was to ensure that the same length of time was used for the analysis as the instantaneous RR and QT intervals were equidistant sampled at 0.25 s. We used RR time series free of ventricular premature beats and noise. The RR and QT interval data were then detrended by using the best-fit line prior to the computation of the spectral analyses. The mean RR, detrended RR variance, mean QT interval, detrended QT variance were calculated from the instantaneous RR and QT time series of 1024 points (256 s). Mean RR and mean QT intervals are in ms. The powers are corresponding squared values. Normalized QT variability index (QTVi) was calculated as suggested by Berger et al. (1997): QTVi = Log10 [(detrended QT variance / mean QT interval2) / (detrended RR variance / mean RR interval2)].
This index represents the log-ratio between the QT interval and the RR interval variabilities (detrended), each normalized for the corresponding mean. We used 256 s of data collected in supine posture.
2.4. Spectral analyses RR interval time series (256 s at 4 Hz = 1024 points) was subjected to spectral analyses and the power spectrum was computed using a rectangular window. The powers were integrated in the following bands: TP (total power: 0–0.5 Hz), VLF (very low frequency power: 0.0–0.04 Hz), LF (low frequency power: 0.04–0.15 Hz) and HF (high frequency power (0.15–0.5 Hz)).
2.5. Statistical analysis We used two-tailed t tests or χ2 test to compare demographic variables between the two groups. In cases of nonnormal distributions, data were log transformed. We used Analysis of covariance (ANCOVA) with age as a co-variate to compare the two groups on the log-transformed QTV and RRV variables. A probability value of b0.05 was accepted as significant.
3. Results Demographic and the heart rate variability data are displayed in Table 1. The groups were comparable in terms of age, gender and race. However, a greater proportion of patients gave a history of cigarette smoking than the controls (6 versus 1; χ2 = 0.04). Patients had significantly greater QTVi [F (df, 45) = 6.7; p = 0.01] and lesser log RRSD [F (df,45) = 5.29, p £ 0.03], as well as lesser log RRHF [F (df, 45) = 4.51, p b .04] than the controls. As expected, log RRSD and log RRHF declined with age in the control group (r = .51, p b 0.01 and r = − 0.48, p b 0.01) but not in the patient group. QTVi was not associated with age in either group. None of the HRV variables correlated with the severity of the positive or negative symptoms of psychosis. On post-hoc analyses, QTVi, RRSD and log RRHF values were comparable between those with and without history of cigarette smoking. Even if the data from those with a history of smoking is excluded, patients had significantly greater QTVi.
Table 1 Demographic and heart rate variability characteristics.
Age (years) Gender (men/women) Race (white/black) QTVi Log RRHF Log RRSD
Controls (n = 26)
Patients (n = 24)
Group effect (t, χ2 or F)
Mean SD
Mean SD
t, F or χ2
22.27 (5.21) 15/11 18/8 − 2.01 (0.27) 3.01 (0.59) 1.74 (0.218)
21.16 (3.97) 16/8 12/12 − 1.77 (.42) 2.72 (.67) 1.61 (.27)
t = .89 0.39 .44 χ2 = .59 2 .24 χ = .16 F (df 45) = 6.7 .01 F (df 45) = 4.51 b0.04 F (df 45) = 5.29 b .03
p value
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4. Discussion One of our main findings was that patients with first episode treatment naïve psychosis have increased QT variability corrected for RR variability (QTV index) as compared to the matched controls. Our results also replicate the evidence for decreased RRV in earlier studies of psychosis. However, we could not replicate the finding of correlation between the severity of psychotic symptoms with HRV abnormalities (Bar et al., 2008b). This may suggest that altered QTV and RRV are not likely to be related to state related measures of symptomatology. Further studies are needed to examine whether these alterations are stable over time and if those reflect the increased risk of cardiac arrhythmias and/or sudden death in patients with schizophrenia. While our observations suggest that QTV and RRV alterations may be related to the core pathology of psychotic disorders, the pathophysiological mechanisms remain unclear. Neuroimaging and neuropathological studies suggest that frontolimbic structures of the brain such as the dorsolateral prefrontal cortex (PFC), insula, orbitofrontal cortex (OFC), medial PFC, and anterior cingulate, have been implicated in the pathogenesis of schizophrenia (Phillips et al., 2003; Shenton et al., 2001). Evidence from the lesion, stimulation, and neuroanatomical studies suggests that these brain structures regulate autonomic function through their reciprocal circuitry and by their projections to cell groups, such as the amygdala, hypothalamus, and brainstem nuclei (Barbas et al., 2003; Ongur and Price, 2000). The relationship between HRV abnormalities and neurobiological alterations in these disorders merits further investigation. One of the strengths of our study was the use of previously untreated first episode psychosis patients, which helps avoid confounds of medications and illness chronicity. Another strength was the fact that we controlled for the time of the day since patients with schizophrenia have been shown to have altered diurnal variation in their HRV (Boettger et al., 2006). A limitation of the study is the relatively small sample size, resulting from the fact that previously untreated first episode first episode patients are difficult to recruit. A second limitation of the study is the lack of control for quality of sleep on the night before the data collection (Bonnet and Arand, 1998). Finally, our study sample is heterogeneous, and included patients with both schizophrenia and non-schizophrenic psychosis. Since increased QTV has also been detected in the studies of depression and panic disorder (Yeragani et al., 2000c), it is possible that increased QTV cuts across diagnostic boundaries and thereby increases risk for sudden death and other arrhythmias in all major psychiatric disorders. Despite these limitations, the study is an important step in unraveling the phenomenon of sudden death in patients with schizophrenia. Further studies will be needed to document the effect of individual antipsychotic medications on QTV and RRV. Continued work will hopefully lead to the development of effective interventions to lower the risk of arrhythmias in patients with schizophrenia. Future work may also help in the early detection of a subset of patients with schizophrenia at greater risk of sudden death, and development of “tailored” antipsychotic treatment interventions in those at greater risk.
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Role of funding source Funding for this study was provided by NIH Grant NIH/NCRR/GCRC grant #M01 RR00056 and Janssen AAGP SRI Alumni Award (P.I Ripu Jindal); the funding sources had no further role in study design; in the collection, analysis and interpretation of data; in the writing of the report; and in the decision to submit the paper for publication.
Contributors Author Ripu Jindal designed the study, wrote the protocol and the first draft of the paper. Authors Matcheri Keshavan, and Vikram Yeragani managed the literature searches and analyses. Author Angela Stevens and Matcheri Keshavan undertook the statistical analysis. All authors contributed to and have approved the final manuscript. Conflict of interest All authors declare that they have no conflicts of interest.
Acknowledgements We thank Raymond Cho, MD, Rohan Ganguli MD, Gretchen Haas PhD and the clinical core staff of the Center for the Neuroscience of Mental Disorders (MH45156) for their assistance in the diagnostic and psychopathological assessments.
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