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Schizophrenia Bulletin vol. 39 no. 5 pp. 1139–1149, 2013 doi:10.1093/schbul/sbs085Advance Access publication September 10, 2012

© The Author 2012. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com

Exercise Reveals the Interrelation of Physical Fitness, Inflammatory Response, Psychopathology, and Autonomic Function in Patients With Schizophrenia

Stefanie Ostermann1, Marco Herbsleb2, Steffen Schulz3, Lars Donath2, Sandy Berger1, Daniela Eisenträger1, Tobias Siebert4, Hans-Josef Müller2, Christian Puta2, Andreas Voss3, Holger W. Gabriel2, Kathrin Koch5, and Karl-Jürgen Bär*,1

1Pain and Autonomic Integrative Research (PAIR), Department of Psychiatry and Psychotherapy, University Hospital, Jena, Germany; 2Department of Sports Medicine and Health Promotion, Friedrich-Schiller-University, Jena, Germany; 3Department of Medical Engineering and Biotechnology, University of Applied Sciences, Jena, Germany; 4Department of Motion Science, Friedrich-Schiller-University, Jena, Germany; 5Department of Psychiatry and Psychotherapy, University Hospital, Jena, Germany

*To whom correspondence should be addressed; tel: +49-3641-9390451, fax: +49-3641-9390452, e-mail: Karl-Juergen.Baer@ med.uni-jena.de

Maintaining and improving fitness are associated with a lower risk of premature death from cardiovascular disease. Patients with schizophrenia are known to exercise less and have poorer health behaviors than average. Physical fitness and physi-ological regulation during exercise tasks have not been investi-gated to date among patients with schizophrenia. We studied autonomic modulation in a stepwise exhaustion protocol in 23 patients with schizophrenia and in matched controls, using spirometry and lactate diagnostics. Parameters of physical capacity were determined at the aerobic, anaerobic, and vagal thresholds (VT), as well as for peak output. VT was correlated with psychopathology, as assessed by the Positive and Negative Syndrome Scale, with the inflammatory markers IL-1β, IL-6, and TNF-α and with peak output. The MANOVA for heart and breathing rates, as well as for vagal modulation and com-plexity behavior of heart rate, indicated a profound lack of vagal modulation at all intensity levels, even after the covari-ate carbon monoxide concentration was introduced as a mea-sure of smoking behavior. Significantly decreased physical capacity was demonstrated at the aerobic, anaerobic, and VT in patients. After the exercise task, reduced vagal modulation in patients correlated negatively with positive symptoms and with levels of IL-6 and TNF-α. This study shows decreased physical capacity in patients with schizophrenia. Upcoming intervention studies need to take into account the autonomic imbalance, which might predispose patients to arrhythmias during exercise. Results of inflammatory parameters are sug-gestive of a reduced activity of the anti-inflammatory cholin-ergic pathway in patients, leading to a pro-inflammatory state.

Key words: heart rate/physical exercise/respiration/schizophrenia/vagal threshold/cardiac death/inflammation/physical fitness

Introduction

Regular physical exercise is known to induce profound effects on physiological and psychological processes in humans. Remarkable results of physical exercise were demonstrated for depressive mood, for anxiety, and for cognitive performance in patients at risk for dementia.1 It was even suggested that exercise interventions should be explored as a potential strategy for delaying disease onset of dementia. In addition, cardiorespiratory fitness is a strong and independent mortality predictor for humans.2

Both one-time aerobic exercise tasks and regular exercise training induce a wide range of biochemical and physiological changes. Apart from the beneficial influence on cardiovascular function and obesity, the influence of exercise on mood, on neurotrophic factor levels, and on stress reactivity is well known.1,3 In addition, various interventional studies have shown the beneficial effects of regular training as an add-on treatment for patients with major depression and anxiety disorders.4

People living with schizophrenia are known to exer-cise less and have poorer health behaviors compared with the general population. In addition, these patients report high incidence rates of metabolic syndrome and coro-nary heart disease.5 Thus, regular physical exercise might be an ideal supplementary treatment provided patients could overcome negative symptoms and a lack of motiva-tion. Recent reviews described 3 randomized control tri-als in these patients and concluded that regular exercise programs are possible and that they can have beneficial effects both on physical and mental health.6,7 However, the authors concluded that larger randomized studies were needed before any final conclusion could be drawn.

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We have previously described profound changes of physiological regulation in patients with schizophrenia. A substantial body of evidence shows reduced heart rate variability (HRV) in patients with schizophrenia.8,9 This was described in the acute10,11 and chronic states.12 While a high degree of HRV indicates healthy cardiac regula-tion, decreased HRV is associated with a high incidence of cardiac morbidity and mortality and may predispose patients to cardiac events such as ventricular fibrillation. In contrast to autonomic dysfunction in primary medi-cal conditions such as diabetes or heart failure, decreased HRV in patients with schizophrenia is most likely caused by reduced interaction between prefrontal brain areas and subcortical structures, leading to arousal and an autonomic imbalance. This is characterized by vagal withdrawal and sympathetic predominance.13 In addition, increased breathing rates at rest associated with positive symptoms and reduced cardiorespiratory coupling were shown in patients with schizophrenia.14,15

Thus, in this study we aimed to prepare the basis for upcoming exercise intervention studies by identifying suitable parameters for aerobic exercise intensity training for patients with schizophrenia. In addition, we intended to describe physiological changes in patients with schizo-phrenia during a physical task. We hypothesized that we would find reduced HRV and increased breathing rates for all intensity levels, reflecting altered physiological regulation in patients. We also controlled for smoking severity by means of carbon monoxide measurements.16 In addition, we hypothesized a reduction in physical fit-ness in relation to various physiological respiratory and metabolic thresholds, such as the aerobic threshold, the individual anaerobic threshold, and the vagal threshold (VT) indicating the point at which a disappearance in vagal activity occurs.17 During this exercise task, empha-sis was primarily placed on VT. We hypothesized VT to be reduced in patients, correlating with psychopathol-ogy of the disease. In addition, based on studies of the cholinergic anti-inflammatory pathway showing that the cholinergic system suppresses inflammation, we hypoth-esized that reduced VTs would be associated with an augmented inflammatory response as reflected in levels of interleukin-1β (IL-1β), interleukin-6 (IL-6), or tumor necrosis factor-alpha (TNF-α) after a one-time exercise task.18

Methods

Subject and Exercise Protocol

Twenty-three patients suffering from paranoid schizo-phrenia and 23 healthy controls matched with respect to age, sex, weight, smoking habits, and education were included in the study (see table  1). Inpatients receiving a fixed dose of antipsychotic treatment were studied. Serum drug levels were controlled for legal drugs (eg,

antidepressants and benzodiazepines) and illegal sub-stances (eg, cannabis). In accordance with our inclusion criteria, only subjects with negative results were included in the study. A clinical electrocardiography was recorded prior to the investigation and evaluated by a cardiologist. Similarly, patients were clinically assessed for the pres-ence of any other somatic diseases (eg, heart diseases). Diagnosis of paranoid schizophrenia was established when patients fulfilled DSM-IV criteria (Diagnostic and Statistical Manual of Mental Disorders, 4th ed.). The semi-structured clinical interview SCID-1 was used to approve the clinical diagnosis. Psychotic symptoms were quantified using the Positive and Negative Syndrome Scale (PANSS)19. Control subjects were recruited from hospital staff (n = 8), medical students (n = 3), and the local community (n = 12). Similarly, an interview (SCID-1) and a clinical investigation were performed on controls to exclude any potential psychiatric or other diseases, as well as any interfering medications. Control subjects were matched for total physical activity per week using the short version of the international physical activity ques-tionnaire (IPAQ-total) in metabolic equivalent (MET) minutes per week. The IPAQ-total included intensive, moderate, and walking physical activities. Furthermore, the sitting time per week (IPAQ-sitting) was measured.20

This study complied with the Declaration of Helsinki. All participants gave written informed consent to a protocol approved by the Ethics Committee of the University Hospital, Jena. Patients were informed that refusal to participate in this study would not affect future treatment. Patients were only included after a psychiatrist (S.B.) certified their ability to give full consent to the study protocol. Patients unable to provide informed consent were not included. In accordance with our laboratory procedures, participants were asked to refrain from smoking, exercising, or any eating 2 h prior to the exercise assessment. Carbon monoxide was measured in the exhaled breath of all participants (CO-Check, Neomed GmbH, Korschenbroich, Germany) just before the exercise testing was started.16

Exercise Testing Protocol

The body mass index (BMI) was calculated prior to each test. Participants received instruction regarding the operational aspects of the exercise test and the tests were begun once individuals indicated that they understood the information.

Exercise testing was performed in the upright posi-tion with an electronically braked cycle ergometer (Ergometrics 900, Ergoline, Bitz, Germany) with its seat and handlebar heights correctly set for each subject. After a resting period of 5 min, the incremental bicycle protocol started with the subject pedalling at 15W for 3 min. The power output was then increased by 15W for every 3 min until the subject reached his or her limit of

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tolerance. Exhaustion levels were determined by measur-ing maximum lactate levels (LaPeak) and using the respi-ratory exchange ratio (RER) of carbon dioxide (CO2) output to oxygen (O2) uptake that are known to be the most reliable indicators of maximal effort.

The degree of effort exerted by the participants was further determined using the standardized subjective exhaustion Borg Scale. The attainment of RERmax ≥ 1.05 and a BORG rating of perceived exertion >16 were regarded as indicators of sufficient effort. We encouraged all subjects to aim to maintain a pedalling frequency of 70–80 revolutions per minute throughout the test session. After a patient’s individual limit of tolerance was reached, a cooling down phase ensued, consisting of 3 min pedalling at a slow rate (<40 revolutions/min) at a power output of 15W and the participant remained seated for another 7 min without pedalling.

Physiological Regulation ParametersDuring the resting period and the cycling test, ventilatory indices and gas exchanges were measured continuously on a breath-by-breath basis using an automatic mobile wireless ergospirometer (MetaMax 3B, Cortex, Leipzig, Germany). Before each test, the turbine (flow and volume) was calibrated with a syringe (Hans Rudolph Inc, Kansas City). The gas analyzers were calibrated according to the manufacturer’s guidelines with the same certified calibra-tion gas mixture of 5% CO2 and 15% O2 (Air Liquide Healthcare America Corporation, Plumsteadville, PA). The breath-by-breath gas exchange and ventilatory data were interpolated to give second-by-second values. For data analysis, the second-by-second values were time averaged for 15 s and the average value was aligned with the center of the time interval.

Peak oxygen consumption (VO2peak), peak minute ven-tilation (VEpeak), and peak respiratory exchange ratio

Table 1. Epidemiological Data of Participants

ControlsMean ± SD

PatientsMean ± SD

P value

Epidemiological DataGender (female/male) 6/17 7/16 n.s.Age (years) 28.2 ± 4.1 28.4 ± 5.3 n.s.BMI (kg/m2) 23.6 ± 2.7 24 ± 3.7 n.s.Education8 years at school 0 2 —10 years at school 3 5 —12 years at school (A-Level) 20 16 —SportNo sport 11 9 —<2 h/week 5 6 —2–5 h/week 5 6 —>5 h /week 2 2 —Smoking BehaviorSmokers 19 20 n.s.<5 Cigarettes/day 5 5 —5–10 Cigarettes/day 9 6 —>10 Cigarettes/day 5 9 —Fagerström test for nicotine dependence 0.61 ± 1.1 3.9 ± 2.9 P <.001International Physical Activity Questionnaire (IPAQ)IPAQ-total (MET minutes/week) 2640 ± 2597 1838 ± 2102 n.s.Vigorous physical activity (h/week) 1.41 ± 1.9 1.1 ± 1.4 n.s.Moderate physical activity (h/week) 1.9 ± 2.0 1.5 ± 2.2 n.s.Time spent sitting (min/day) 353 ± 212 316 ± 312 n.s.Walking distance (METs min/week) 1053 ± 999 1143 ± 1983 n.s.Disease-related DataDisease duration (years) — 4.1 ± 3Age of onset — 24.1 ± 6.2PANNS — 74 ± 29MedicationNeuroleptics 19/19Quetiapine (300–500 mg) — 7Olanzapine (5–10 mg) — 6Amisulpride (100–200 mg) — 6

Note: SD, standard deviation; BMI, body mass index; IPAQ, International Physical Activity Questionnaire; PANSS, Positive and Negative Syndrome Scale.P values indicate results of t tests.

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(RERpeak) were defined as the mean of the two highest consecutive values of 15-s averages occurring during exercise (see table 2). The peak power output (Ppeak) was defined as the highest work rate that was sustained for 3 min during the test. When participants were not able to cycle to the end of the last 3-min interval (in two cases), Ppeak was linearly interpolated based on the proportion of the time completed during the terminal stage.

Capillary blood samples of 20 µl were obtained from the ear lobe when the patient was at rest, and again after the end of each exercise level. The lactate concentration in millimoles per liter were measured by the EBIO basic system analyzer, using an enzymatic-amperometric mea-suring system (Eppendorf, Hamburg, Germany).

Physical Fitness Parameters: Aerobic and Anaerobic Thresholds

Maximal parameters (ie, maximal oxygen uptake and maximal power output) attained during exercise test-ing are the most frequently applied indicators of endur-ance capacity and physical fitness. The determination of these maximal performance parameters requires adequate motivation and maximal effort of participants. Therefore, in addition to the measurement of VO2peak and Ppeak we assessed the aerobic threshold (AerT) and the anaerobic threshold (AnT) to determine aerobic fitness. Both thresholds are common in clinical exercise testing and are known to be reproducible submaximal indicators

Table 2. Explanation of Applied Parameters

Parameter Abbreviation

Parameter

Background of Calculation

Significance

Heart rate variability (HRV)

HR Heart beats per minuteTime

domainRMSSDHRV Root mean square of

differences of successive heart beats

Standard deviation Vagal modulation

RSA Respiratory sinus arrhythmia

Variation of heart rate that occurs during a breathing cycle

Vagal modulation

Poincaré plot analyses

SD1 Standard deviation of the instantaneous RR-interval variability

Standard deviation Vagal modulation

Poincaré plot analyses

SD2 Standard deviation of long-term RR-interval variability

Standard deviation

Complexity measure

HCHRV Compression entropy Nonlinear data compression

Vagal modulation

Parameters of physical fitness

AerT Aerobic threshold Power output during initial increase in blood lactate concentrations above resting values

Reflects physical fitness

AnT Anaerobic threshold Power output at a lactate concentration of 1.0 mmol/l above lactate concentration at AerT

Reflects physical fitness

Ppeak Peak power output Maximal power output of participants

Reflects physical fitness

VT Vagal threshold Power output at which no subsequent decline in heart rate variability occurs (measure: SD1)

Vagal modulation

VO2peak Peak oxygen consumption

Maximum capacity of an individuals body to transport and use oxygen during incremental exercise

Reflects physical fitness

Parameters of spirometry

VE Minute ventilation or tidal volume

Total volume of gas entering the lungs per minute

Key indicator for lung function

RER Respiratory exchange ratio

Ratio between the amount of CO2 exhaled and O2 inhaled in one breath

Index of energy consumption and effort

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of endurance and physical fitness. In contrast to the case of peak parameters, the determination of these thresh-olds offers the great advantage that maximal effort and motivation in subjects are not mandatory.

AerT represents the first increase in blood lactate concentrations above resting values during incremental exercise and demarcates the upper limit of a range of exercise intensities (moderate exercise domain) that can be accomplished almost entirely aerobically (table 2). In contrast, AnT describes the maximal lactate steady state and represents the exercise intensity above which a con-tinuous increase in blood lactate is unavoidable during incremental exercise. The AnT indicates the transition between heavy and very heavy exercise domains.

AerT and AnT were determined according to the method described by Dickhuth and Roecker.21 A special software (ERGONIZER, Freiburg, Germany) was used for investigator-independent calculation and was based on an equalizing SPLINE interpolation procedure. The AnT was defined as the power output at a lactate concen-tration of 1.0 mmol/l above the lactate concentration at AerT. The power output that corresponds to the occur-rence of the AerT was corrected for the exercise time with the following formula:

P AerTLA = Pcom +(tTh/180 × 15)

where Pcom is the last completed work rate (W) before occurrence of the AerT and tTh is the time of the incom-plete work rate (s) until AerT occurs. We proceeded simi-larly with the power output at the AnT.

Autonomic Parameters of Heart Rate and Respiration

The LifeShirt (Vivometrics, Inc, Ventura, CA), a multi-function ambulatory device consisting of a Lycra garment, a data recorder a computer-based analysis software (VivoLogic) was used.22 Respiratory inductive plethysmography, a core method which has been demon-strated to provide an accurate non-invasive assessment of respiratory patterns, was employed. In order to obtain the ventilated volume for further analysis, the device was calibrated in two different successive steps, as rec-ommended by the manufacturer. Initially, a qualitative diagnostic calibration procedure was performed using a 5-min period of calm breathing to compute a calibration factor. A  second calibration procedure was conducted based on defined breaths of a fixed volume. The breath-ing rate was defined as the number of breaths per minute (Br/min).

Heart rate time series consisting of successive beat-to-beat intervals (RRI) were extracted from the raw data records. Afterwards, these time series were filtered by applying an adaptive variance estimation algorithm to remove and interpolate ventricular premature beats and artifacts (eg, movement, electrode noise, and extraordi-nary peaks).

Parameters of Time and Frequency Domain of Heart Rate

We obtained the basic heart rate (HR) and the RMSSD (root mean of squared successive difference) as a time domain parameter of HRV (table 2).23 The low frequency (LFHRV, 0.04–0.15 Hz) and high frequency parameters (HFHRV, 0.15–0.40 Hz) of the frequency domain were not calculated, because previous reports have shown that these measures are not conclusive during exercise, due to a break-down of the power spectrum.24 In addition, a Poincaré plot analysis (PPA), respiratory sinus arrhythmia (RSA), and compression entropy (HC) were calculated.

Poincaré Plot Analyses of Heart Rate

PPA is based on a technique derived from nonlinear dynamics and represents the nature of heart beat time series fluctuations. The Poincaré plots are 2-dimensional visual representations (scatter plots) of each RR-interval value in the time series, plotted against the subsequent RR-interval value. The shape of the plot is assumed to be influenced by changes in vagal and sympathetic modulation. Typically, PPA shows an elongated cloud of points aligned along the characteristic line. To illustrate this graphically, an ellipse showing the shape of the cloud of points can be drawn into the plot, such that the center of the ellipse is the mean RR. The standard deviation of the instantaneous RR-interval variability (minor axis of the cloud—SD1 in ms) and the standard deviation of long-term RR-interval variability (transverse diameter of the ellipse—SD2 in ms) can be cal-culated (table 2).25

Respiratory Sinus Arrhythmia

The RSA was quantified as a measure of cardiac vagal activity (table  2). This is a cardiorespiratory phenom-enon characterized by HR fluctuations that are in phase with inhalation and exhalation.26 The LifeShirt uses the peak-to-valley method for assessing each breathing cycle and assesses the HR simultaneously.

Compression Entropy of Heart Rate

Besides linear computational algorithms, nonlinear methods were applied. Here, the entropy (complexity) of a given data set is defined as the smallest algorithm that is capable of generating the data. Although it is theoretically impossible to develop such an algorithm, data compressors represent a sufficient approximation. In this study, we applied the LZ77 algorithm for lossless data compression developed by Lempel and Ziv (1977). They introduced a universal algorithm (ZIP) for lossless data compression using string matching on a sliding win-dow. This algorithm is widely used and implemented in many file compressors such as Winzip. With some modi-fications, this algorithm can be applied for the analysis

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of heart beat time series as introduced by Baumert and colleagues.27 The ratio of the compressed to the original time series length represents an index of entropy and is referred to as HC. Therefore, HC indicates to which degree data from HR time series can be compressed using the detection of recurring sequences. The more frequent certain sequences occur—and therefore the more regular these series are—the higher the compression rate. Thus, reduced variability (reduced short-term fluctuations) of HR results in increased compression and smaller HC values. Entropy reduction appears to reflect a change in sympathetic/parasympathetic HR control.27

Determination of Vagal Threshold

The RR intervals from the last 2 min of rest and each stage of exercise were used for VT determination. For calcula-tion of the VT, values of the instantaneous variability in the RR intervals (SD1) from the Poincaré plot (see above) were plotted in relation to power output using Matlab 2009 (The Math Works Inc, Natick). The deflection point (DP) is the point in time at which no subsequent decline in HRV occurs (see figure 3A for illustration), indicating the moment when parasympathetic activity has decreased and sympathetic activation will increase, in such a way that almost no vagal modulation remains. DP was deter-mined when the HRV dropped below a threshold (mean

value of the HRV plus 3 times the standard deviation during the last 30% of the “exercise time”). In this time period (during the last 30% of the test), the decline in HRV was completed and there were almost no variations in HRV for all subjects observed.

Blood Counts and Tests

Blood samples were collected from an antecubital vein at rest and 10 min after the end of the exercise test. For evaluat-ing the cell counts of erythrocytes, platelets and leucocytes, a commercial blood cell counter (Act-Diff; Beckman Coulter GmbH, Krefeld, Germany) was used. The hematocrit was determined before and after exercise to evaluate changes in plasma volume and was used for correcting confounds caused by fluid loss after exercise. After centrifugation, ali-quots of plasma were snap-frozen and stored at −80°C until cytokine levels could be determined. Enzymelinked immu-nosorbent assays (ELISAs) for the cytokines IL-1β (R&D Systems, Minneapolis, MN, USA), IL-6 (Beckman-Coulter, Krefeld, Deutschland), and TNF-α (R&D Systems, Minneapolis, MN) were used. All samples were measured with the same assay for each cytokine. ELISAs for IL-1β and TNF-α were quantified using a Dynatech MR 4000 (Dynex, Denkendorf, Germany). IL-6 was assessed using the Access Immunoassay System (Beckman Coulter, Krefeld, Germany). The intra- and interassay variation coefficients for the ELISA kits were below 5% and 10%, respectively.

Statistical Analyses

For statistical analysis, SPSS for Windows (ver-sion 17.0) was used. All parameters were tested for

Fig. 1. Changes of heart rate (A), breathing rate (B), respiratory sinus arrhythmia (C), and the complexity parameter compression entropy (D) are shown for controls (white boxes) and patients (blue boxes) during an incremental exercise task. At each workload-step, an independent two-tailed t test was calculated to compare patients and controls for descriptive analysis. While differences between heart rate were only shown at rest and during low intensity levels (A), reduced variability (C) and complexity (D) were evident at all intensity levels. Similarly, breathing rates were increased in patients (B) at all levels. Boxes indicate data between the 25th and 75th percentile with the horizontal bar reflecting the median (▯ = mean; – = 1st and 99th percentile; x = minimum and maximum of data). Significant differences of Bonferroni corrected pair-wise comparisons are indicated: *P < .05; **P < .01; ***P < .001. (For color, please see figure online.)

Fig. 2. Significantly reduced physical fitness is shown at the aerobic and anaerobic thresholds for patients compared with controls. Similarly, patients show reduced peak physical capacity and decreased workloads at the vagal threshold. Boxes indicate data between the 25th and 75th percentile with the horizontal bar reflecting the median (▯ = mean; – = 1st and 99th percentile; x = minimum and maximum of data). Significant differences of Bonferroni corrected pair-wise comparisons are indicated: *P < .05; **P < .01; ***P < .001. (For color, please see figure online.)

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normal distribution with the Kolmogorov-Smirnov test. Collected data were investigated as follows: first, we per-formed a multivariate analysis of variance (MANOVA) for repeated measures, including obtained physiological parameters. Second, we performed a MANOVA focused on different physiological thresholds with respect to power output of patients and controls. In a last analy-sis, the relation of fitness parameters to psychopathology and inflammatory values was investigated.

The repeated measures MANOVA was performed to investigate an overall effect for the factors GROUP (patients and controls), POWER OUTPUT (baseline, 15W, 30W, 45W, 60W, 75W, and 90W) and GROUP × POWER OUTPUT interaction for the physiological parameters (HR, breathing rate, RMSSD, SD1, SD2, RSA, and HC). Repeated measures ANOVAs were cal-culated to analyze the effect of POWER OUTPUT, GROUP, and GROUP × POWER OUTPUT interaction for single parameters. At each workload-step, a planned post hoc t test was calculated to compare patients and controls for descriptive analysis. The post-test resting condition (recovery) was not included in any analysis, because peak power output was significantly different between patients and control subjects. The MANOVA was repeated in terms of a MANCOVA using the carbon monoxide concentration of exhaled air as a covariate to remove the influence of smoking from the results.

A second MANOVA was performed applying the factor GROUP (patients and controls) and including parameters of physical capacity (normalized to weight) at the AerT, AnT, and VTs, as well as for peak output. ANOVAs were calculated for each parameter. The second MANOVA was repeated in terms of a MANCOVA using the carbon mon-oxide concentration of exhaled air as a covariate to ana-lyze the influence of smoking on the physical capacity of patients and control subjects. In addition, we performed a comparison of fitness parameters (VT, AerT, AnT) with respect to medication by means of a simple t test for descriptive analysis. Blood counts and interleukins were compared by means of independent t tests for descriptive analysis (table 3). Spearman’s rank correlation coefficient was calculated to investigate a potential correlation of VT of patients with PANNS scores, interleukins, and peak-power output. Similarly, we correlated the duration of the disease with parameters of physical fitness.

Results

All parameters were log-transformed before being included in analysis to achieve normal distribution after the Kolmogorov-Smirnov test had indicated non-normal distribution of some parameters.

The repeated measures MANOVA for physiological parameters (HR, breathing rate, RMSSD, SD1, SD2, RSA, and HC) indicated an effect for the factor GROUP [F(38,7) = 2.5, P < .03], a significant overall effect for the

factor POWER OUTPUT [F(3,42) = 146.5, P < .001] and a significant effect for the POWER OUTPUT × GROUP interaction [F(3,42)  =  31.2, P < .008], which is sugges-tive of differential changes of physiological parameters during the exercise task in both groups. ANOVAs for specific parameters revealed a significant effect for the factor POWER OUTPUT for HR (F = 52.2, P < .001), breathing rate (F  =  4.3, P < .04), RMSSD (F  =  123.5, P < .001), SD1 (F = 126.1, P < .001), SD2 (F = 101.9, P < .001), RSA (F = 101.7, P < .001), and for HC (F = 206.6, P < .001). In addition, we observed significant differ-ences for the factor GROUP for HR (F = 5.6, P < .02), RMSSD (F = 11.0, P < .002), SD1 (F = 11.6, P < .001); SD2 (F = 5.9, P < .02), RSA (F = 14.2, P < .001), and HC (F = 17.2, P < .001). Significant POWER OUTPUT × GROUP interactions were observed for RMSSD (F = 6.2, P < .001), SD1 (F = 4.9, P < .001), SD2 (F = 8.5, P < .001), and RSA (F = 2.5, P < .02). Descriptive analyses for patients and controls by means of planned post hoc t tests are indicated in figure 1 and see online supplementary figure 1. Because the carbon monoxide concentration of exhaled air was different in patients and controls (being a measure of smoking behaviour [table 3]), we included this parameter as a covariate in the above described repeated measures MANOVA and calculated a repeated measures MANCOVA. Results showed significant differences for the factor POWER OUTPUT [F(2,42) = 44.8, P < .022] and POWER OUTPUT × GROUP interaction [F(2,42) = 50.8, P < .019], reducing the likelihood that results were pro-foundly influenced by smoking behavior. The ANCOVAs for specific parameters revealed a significant effect for the factor POWER OUTPUT for HR (F = 17.8, P < .001), RMSSD (F = 39.0, P < .001), SD1 (F = 38.5, P < .001), SD2 (F = 20.1, P < .001), RSA (F = 30.8, P < .001), and HC (F = 74.4, P < .001). No significant effect was found for the breathing rate (P = .8) in this ANCOVA. In addition, we observed significant differences for the factor GROUP for HR (F = 5.5, P < .02), RMSSD (F = 5.9, P < .02), SD1 (F = 6.3, P < .016), RSA (F = 7.9, P < .007), and HC (F = 11.2, P < .002). No significant effect was observed for the breathing rate (P = .6) and SD2 (P = .07). Significant POWER OUTPUT × GROUP interactions were observed for HR (F = 2.1, P < .05), RMSSD (F = 2.2, P < .04), SD2 (F = 5.9,P < .001), and RSA (F = 2.5, P < .02), while no differences were observed for breathing rate (P = .5), SD1 (P = .1), and HC (P = .6).

The MANOVA for parameters of physical capacity (AerT, AnT, peak workload, and VT) revealed a significant effect for the factor GROUP [F(4,36) = 3.0, P < .029]. ANOVAs showed significant differences between patients and controls for AerT (F = 5.4, P < .026; figure 2), AnT (F = 6.8, P < .012; figure 2), peak output (F = 9.1, P < .004; figure 2), and VT (F = 6.2, P < .001; figure 2). Even after including the carbon monoxide concentration of the exhaled air as a covariate in the MANCOVA, we still observed significant differences for the factor GROUP [F(4,34) = 4.5, P < .005]

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as well as for single parameters (AerT [P < .011], AnT [P < .004], peak output [P < .001], and VT (P < .001]).

In addition, we compared VT, AerT, and AnT with respect to prescribed medication (olanzapine, quetiapine, and amisulpride) by means of a t test. The only observed difference was found for the VT of patients taking olan-zapine (n = 6, 68.5 ± 19) when compared with the threshold of patients taking quetiapine (n = 7, 51.5 ± 9.3; P < .01).

Correlation Analysis

To analyze an assumed association between symptoms and VT, we calculated the Spearman’s rank correlation coefficient. An association was observed for PANSS total (r = −.452, P < .03), for PANSS positive (r = −.443, P < .034; figure 3B), and for the general psychopathology scale of PANSS (r = −.439, P < .036).

VT data of patients were correlated with concentra-tions of the inflammatory markers IL-1β, IL-6, and TNF-α after the exercise test. We observed a negative correlation of VT with IL-6 (r = −.453, P < .03) and with TNF-α (r = −.48, P < .02; figure 3C). No such correlation was observed in healthy subjects.

Peak power output (in W/kg) correlated with VT of patients (r  =  .403, P < .05), indicating a relationship between physical fitness and vagal modulation in patients. A trend was observed for the correlation of the duration of the disease with peak power output (r = −.398, P < .06) indicating that a longer disease duration might be associated with reduced physical fitness.

Discussion

The one-time physiological exercise task performed by patients with schizophrenia revealed three major findings. First, it supports findings on altered HRV and respiration previously described at rest in these patients in compari-son to controls. Second, it demonstrates reduced physi-cal fitness of patients in relation to various physiological thresholds. Third, it underlines the importance of vagal function in relation to clinical state, physical fitness, and inflammatory response.

Previous studies have described reduced HRV and increased breathing rates in patients at rest.8,15,28 Although a similar pattern of HRV alterations was described in rela-tives as well, some criticism was raised with respect either to smoking, agitation caused by the laboratory environ-ment, or the influence of medication. While many authors have described altered HRV patterns in drug-naive or unmedicated patients,28,29 the influence of smoking and investigation-related distress have not been addressed in detail. Here, we have shown that reduced vagal modulation dominates not only the pattern of cardiac regulation at rest but at all intensity levels up to 90W (figure 1C, see online supplementary figure 1A–C). Similarly, the complexity of cardiac regulation is not only decreased at rest, as previously shown,30 but during physical exercise as well. As previous

Fig. 3. The calculation of the vagal threshold is depicted in A, using data of one patient and one control subject for illustration. The deflection point (DP) is the point in time at which no subsequent decline in heart rate variability occurs. DP was determined when the heart rate variability dropped below a threshold (mean value of the heart rate variability plus 3 times the standard deviation during the last 30% of the “exercise time”). In this time period (the last 30% of the test), the decline in heart rate variability was completed and there were almost no variations in heart rate variability among all subjects observed. The negative correlation between positive symptoms and vagal threshold is shown in (B), indicating that a reduced vagal threshold is associated with an increased amount of positive symptoms. (C) Shows the association of a reduced VT with increased TNF-α values after the exercise task.

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studies have shown, increased breathing rates were also asso-ciated with shallow breathing in patients with schizophre-nia.14,15 Here, we present evidence that this prevailing pattern remains during physical exercise as well. Because autonomic dysregulation due to anxiety or investigation-induced dis-tress normally settles during physical exercise, we suggest, in accordance with our hypothesis, that the autonomic imbal-ance described in patients with schizophrenia is a core fea-ture of the disease that does not normalize in the context of a one-time physical exercise. Whether regular exercise has the potential to modify this autonomic imbalance in patients with schizophrenia remains to be investigated. The autonomic imbalance in patients during exercise is moreover

reflected in the finding that higher intensity levels were asso-ciated with relatively increased breathing rates in patients compared with healthy controls, whereas HRs were not significantly increased during higher workloads (ie, >15W, figure 1A). This finding matches with the significant inter-action effect for RSA that likewise indicates an abnormality in vagal modulation and cardiorespiratory coupling across the different exercise intensities in patients. These results are relevant for all exercise-related activities of patients, because they argue for decreased vagal modulation in favor of aug-mented sympathetic modulation. Sympathetic modulation is associated with an increased risk of arrhythmias due to complex effects on cardiac electrophysiological properties.

Table 3. Physiological Parameters Obtained in the Study

ParametersControlsMean ± SD

PatientsMean ± SD P value

Borg90W 14 ± 2 15 ± 2 n.s.Carbon monoxidepre (parts/million) 9.3 ± 9.9 26.0 ± 25.0 P < .005Carbon monoxidepost (parts/million) 7.0 ± 5.2 19.0 ± 15.5 P < .001Lactatebaseline (mmol/) 1.02 ± 0.61 0.98 ± 0.56 n.s.Lactate90W (mmol/l) 2.5 ± 1.78 2.42 ± 1.42 n.s.pHbaseline 7.45 ± 0.02 7.45 ± 0.02 n.s.pH90W 7.42 ± 0.02 7.43 ± 0.03 n.s.Blood GasespCO2/baseline (mmHg) 38.5 ± 3.4 38.6 ± 3.2 n.s.pCO2/90W (mmHg) 36.9 ± 4.5 37.6 ± 4.2 n.s.pO2/baseline (mmHg) 78.5 ± 15.9 75.2 ± 14.0 n.s.pO2/90W (mmHg) 81.3 ± 6.1 78.2 ± 6.6 n.s.HCO3

− (mmol/l) 27.6 ± 2.4 27.5 ± 2.1 n.s.HCO3

– (mmol/l) 24.9 ± 3.1 25.9 ± 2.9 n.s.Blood CountHemoglobinbaseline (mmol/l) 9.3 ± 0.7 9.2 ± 1.3 n.s.Hemoglobinrecovery (mmol/l) 9.8 ± 0.9 9.5 ± 1.5 n.s.Hematocritbaseline (%) 44 ± 3 43 ± 5 n.s.Hematocritrecovery (%) 46 ± 4 45 ± 6 n.s.White blood cellsbaseline (×109/l) 6.6 ± 1.3 8.1 ± 1.6 n.s.White blood cellsrecovery (×109/l) 9.6 ± 2.5 9.6 ± 2.1 n.s.Red blood cellsbaseline (×106/ml) 4.7 ± 0.3 4.7 ± 0.5 n.s.Red blood cellsrecovery (×106/ml) 5.0 ± 0.4 4.9 ± 0.6 n.s.Lymphocytesbaseline 30.7 ± 6.0 26.0 ± 7.0 n.s.Lymphocytesrecovery 31.3 ± 5.7 28.0 ± 7.7 n.s.Plateletsbaseline (109/l) 250.4 ± 60.7 262.7 ± 48.3 n.s.Plateletsrecovery (109/l) 290.2 ± 81.7 292.4 ± 58.2 n.s.Inflammatory ParametersIL-1βbaseline (pg/ml) 0.08 ± 0.05 0.12 ± 0.08 n.s.IL-1βrecovery (pg/ml) 0.13 ± 0.09 0.15 ± 0.08 n.s.IL-6baseline (pg/ml) 1.41 ± 1.44 1.89 ± 2.77 n.s.IL-6recovery (pg/ml) 2.69 ± 3.74 4.85 ± 11.71 n.s.TNFαbaseline (pg/ml) 0.69 ± 0.36 0.63 ± 0.28 n.s.TNFαrecovery (pg/ml) 0.95 ± 0.41 1.0 ± 0.58 n.s.Parameters Measured in SpirometryVO2baseline (l/min) 0.45 ± 0.16 0.45 ± 0.15 n.s.VO2peak (l/min) 2.64 ± 0.61 2.02 ± 0.53 P < .001VO2rel peak (ml/min/kg) 36.1 ± 8.6 27.1 ± 6.2 P < .001VEbaseline (l/min) 11.8 ± 4.3 11.9 ± 3.7 n.s.VEpeak (l/min) 84.6 ± 22.8 59.4 ± 14.8 P < .001RERbaseline 0.81 ± 0.06 0.91 ± 0.07 P < .001RERpeak 1.06 ± 0.07 1.09 ± 0.06 n.s.

Note: IL-1β, Interleukin-1β; IL-6, Interleukin-6, TNF-α, tumor necrosis factor-alpha; VO2, oxygen consumption; VE, ventilation; RER, respiratory exchange ratio.

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Apart from direct effects on various repolarizing channels, indirect effects on receptor profile and cell metabolism have been described as pro-arrhythmogenic mechanisms of the sympathetic branch.

Most studies in patients with schizophrenia run the risk of being confounded by the influence of smoking severity. Although we tried to control for this confounding factor, it is rather difficult to find healthy young subjects who smoke the same amount of cigarettes per day as our patients do. We therefore measured the concentration of carbon monoxide (table 3) in the exhaled air of all subjects studied, as it can be used to estimate smoking habits.16 As in the Fagerström test for nicotine dependence (table 1), the measured amount of carbon monoxide indicates a significantly stronger nico-tine use of patients. We have therefore included the carbon monoxide concentration as a covariate in the analysis of our data. Because overall differences remained between patients and controls, we concluded that results were not caused by differences in smoking habits. However, covarying carbon monoxide in the analysis of physical fitness parameters seemed to improve statistical results between patients and controls. Future studies need to pay close attention to this result to elucidate the underlying cause.

The assessment of physical fitness was the second aim of our study. In accordance with our hypothesis, we found reduced physical fitness in patients compared with healthy controls, which was indicated by reduced workloads at the AerT, the individual AnT, and the VT. The AerT represents the workload associated with the first increase in blood lac-tate concentrations above resting values. Thus, in contrast to peak workloads, it is less affected by motivation. We conclude, therefore, that decreased workloads of patients at both the AerT and AnT argue for reduced physical fit-ness of patients. An interesting aspect of this study was that we applied a relatively new measure: the VT. This is defined as the point in time during exercise at which no subsequent decline in HRV occurs (figure  3A) and almost no vagal modulation remains. We found that this point is reached in patients at significantly lower workloads. This illustrates, on the one hand, the previously described reduced activity of the efferent vagal system in these patients, and, on the other, the close relation between the vagal system and physical fit-ness.31 Moreover, this breaking point during physical activity was associated with general psychopathology corroborating previous results. Assuming that VT constitutes an indicator of vagal modulation in relation to physical fitness this find-ing supports previous evidence showing a close relationship between physical fitness and psychopathology in patients with schizophrenia. In addition, we observed a correlation between the inflammatory response after this exercise task with low vagal modulation, because VT correlated nega-tively with TNF-α and IL-6 in patients. There is increas-ing evidence for a correlation between physical fitness and the degree of exercise-associated inflammatory response in healthy people.32 Our results suggest similar mechanisms in patients, potentially in connection with a reduced activity of

the anti-inflammatory cholinergic pathway18,33 in patients as inflammatory parameters (apart from TNF-α) were increased in patients, albeit not significantly but on a descrip-tive basis (table 3). Longitudinal studies should investigate whether regular physical exercise in patients with schizo-phrenia influences these alterations in vagal modulation and, maybe as a direct consequence, the association between VT and inflammatory parameters. Future studies need to carefully describe disease-related data such as duration and previous types of medication, age of onset, or the number of episodes because a correlative trend was observed between duration of the disease and peak power output that might be indicative of such an association. Comparable findings were described in patients with major depression.34

Some limitations need to be addressed. The main limitation is that antipsychotic treatment of patients has influenced our results. However, many studies that have investigated drug-naive patients have shown similar HRV and respiratory findings.8,9,28,29,35 In addition, we have mainly abstained from comparing peak parameters of power output or oxygen consumption with physiological parameters. We aimed to exclude any motivational bias from being associated with peak parameters. Although patients smoked more cigarettes per day than controls, the inclusion of carbon monoxide as a measure of smok-ing behavior in our calculation reduced the potential influence of this confounder. Furthermore, differences of the VTs between patients taking olanzapine and quetiap-ine need to be taken with caution because this analysis included a very small number of patients. Similarly, we tried to match the amount of physical activity between patients and controls by means of the IPAQ. We can-not, however, exclude the possibility that patients over-estimated their physical activity and shortcomings due to the assessment interval measuring the last 7 days only. In addition, the methods applied in this study were unable to disentangle the close relationship of decreased physi-cal capacity and autonomic alterations in more detail.

Maintaining or improving fitness is associated with a lower risk of premature deaths from all-causes and cardio-vascular disease in men.3 Preventing fitness loss with increas-ing age, regardless of whether BMI changes, is important for mortality risk reduction. Our study shows reduced physical capacity, even after adjusting for weight, and decreased vagal modulation in patients with schizophrenia to be associated with psychopathology and a pro-inflammatory response after exercise. This underlines the need for larger random-ized, maybe even longitudinal, studies in patient with schizo-phrenia investigating the effects of repeated, regular physical exercise on physical fitness, psychopathological symptoms as well as autonomic and immunological alterations.

Supplementary Material

Supplementary figure 1 is available at http://schizophreni abulletin.oxfordjournals.org.

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Acknowledgments

The authors thank Birgit Dorschner for technical sup-port during ergospirometry. The Authors have declared that there are no conflicts of interest in relation to the subject of this study.

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