The relation of autonomic function to physical fitness in patients suffering from alcohol dependence

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The relation of autonomic function to physical fitness in patients suffering from alcohol dependence Marco Herbsleb a,b , Steffen Schulz c , Stephanie Ostermann a , Lars Donath b , Daniela Eisenträger a , Christian Puta b , Andreas Voss c , Holger W. Gabriel b , Karl-Jürgen Bär a,∗ a

Pain & Autonomic Integrative Research (PAIR), Department of Psychiatry and Psychotherapy, University Hospital, Jena, Germany Department of Sports Medicine and Health Promotion, Friedrich-Schiller-University, Jena, Germany c Department of Medical Engineering and Biotechnology, University of Applied Sciences, Jena, Germany b

a r t i c l e

i n f o

Article history: Received 11 August 2012 Received in revised form 9 March 2013 Accepted 26 March 2013 Available online xxx Keywords: Physical exercise Heart rate variability Alcohol dependence Cardiac death Cardio-vascular disease

a b s t r a c t Background: Reduced cardio-vascular health has been found in patients suffering from alcohol dependence. Low cardio-respiratory fitness is an independent predictor of cardio-vascular disease. Methods: We investigated physical fitness in 22 alcohol-dependent patients 10 days after acute alcohol withdrawal and compared results with matched controls. The standardized 6-min walk test (6 MWT) was used to analyze the relationship of autonomic dysfunction and physical fitness. Ventilatory indices and gas exchanges were assessed using a portable spiroergometric system while heart rate recordings were obtained separately. We calculated walking distance, indices of heart rate variability and efficiency parameters of heart rate and breathing. In addition, levels of exhaled carbon monoxide were measured in all participants to account for differences in smoking behaviour. Multivariate analyses of variance (MANOVA) were performed to investigate differences between patients and controls with regard to autonomic and efficiency parameters. Results: Patients walked a significantly shorter distance in comparison to healthy subjects during the 6 MWT. Significantly decreased heart rate variability was observed before and after the test in patients when compared to controls, while no such difference was observed during exercise. The efficiency parameters indicated significantly reduced efficiency in physiological regulation when the obtained parameters were normalized to the distance. Discussion: The 6 MWT is an easily applied instrument to measure physical fitness in alcohol dependent patients. It can also be used during exercise interventions. Reduced physical fitness, as observed in our study, might partly be caused by autonomic dysfunction, leading to less efficient regulation of physiological processes during exercise. © 2013 Elsevier Ireland Ltd. All rights reserved.

1. Introduction Alcohol-use disorders, including specific alcohol related diseases such as alcohol dependence or conditions for which alcohol consumption is a component-cause, such as cardio-vascular disease, contribute significantly to the burden of disease in Europe (Wittchen et al., 2011). Interestingly, the latter group adds more to the global burden of disease than specific alcohol related diseases (Rehm and Scafato, 2011). Thus, in general, alcohol use is associated with tremendous costs for society and there is a need to develop effective treatment and preventive strategies. While many

∗ Corresponding author at: Department of Psychiatry and Psychotherapy, University Hospital Jena, Philosophenweg 3, 07743 Jena, Germany. Tel.: +49 3641 9390451; fax: +49 3641 9390452. E-mail address: [email protected] (K.-J. Bär).

prevention strategies exists for specific alcohol use disorders, it is rather difficult to develop prevention programs for dependent patients with component conditions such as cardio-vascular disease. The assessment of physical fitness is one proven strategy for studying cardio-vascular health in patients suffering from various disorders (Blair and Morris, 2009; Donath et al., 2010a,b; Nocon et al., 2008; Vanhecke et al., 2008). Low cardio-respiratory fitness is a strong and independent predictor of cardio-vascular disease and all-cause mortality (Wei et al., 1999). However, specific fitness levels have rarely been investigated in patients with alcohol related diseases, despite the fact that increased cardio-vascular morbidity and mortality are known to play an important role. Most studies have focused on the therapeutic effect of exercise on the ability to abstain from the substance. Collingwood et al. (1991) reported that physical training might be useful as a supplementary intervention for adolescent substance abusers. Results of this study indicate that

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besides the improvement of aerobic fitness, patients benefit from a change of abuse risk factors and use patterns as well as the act of participation itself. In most previous reports, VO2max has been calculated from submaximal tests only (Donaghy and Ussher, 2005). Frankel and Murphy (1974) used a step test to assess heart rate decline after a standard task and the results suggested low physical fitness in alcohol-dependent patients. Sinyor et al. (1982) reported qualitative changes of fitness among individuals in an individually tailored fitness program for alcohol-dependent patients that were similar to those found in healthy subjects. In addition, the authors assumed that mere abstinence from alcohol for several weeks would not influence physiological data and fitness. It was suggested that participating in a fitness program increases the likelihood of abstinence in these patients. In another case, Mamen and Martinsen (2009) studied VO2max and lactate levels directly during a maximal test of patients with substance abuse or dependence, indicating that mean values were just below those found in a healthy population. Several reasons might account for these results. Patients with alcohol dependency are known to have a more unhealthy and sedentary lifestyle (Martinsen, 2000) as well as a higher prevalence of cigarette smoking than the general population. In addition, alterations in the autonomic nervous system (ANS) might interfere with the physical fitness of patients. Interestingly, autonomic dysregulation has been shown in various investigations of patients with alcohol dependence where autonomic tests such as the assessment of heart rate or blood pressure variability have been applied (Agelink et al., 1998; Bär et al., 2006, 2008; Jochum et al., 2010, 2011; Kahkonen, 2004). Overall, autonomic balance shows a shift towards a sympathetically dominated state, thus increasing the risk of fatal arrhythmias (Billman et al., 1997). This shift in autonomic balance is more pronounced in acute AWS, which results in tachycardia, increased sweat rate and hypertension (Kahkonen, 2004). This pattern might influence physical capacity during acute alcohol withdrawal as well as in chronic states. In addition, we have previously shown that cardio-respiratory coupling is impaired during alcohol withdrawal (Bär et al., 2008). This might be caused by vagal withdrawal and sympathetic predominance as well as by alcohol toxic neuropathy. This study was performed to lay the ground for later exercise intervention studies. We aimed to investigate the autonomic nervous system during exercise to better understand the relation between autonomic dysfunction and physical fitness in these patients since physical exercise is challenging for the cardiovascular system and side effects need to be taken into account. To analyze the relationship between autonomic dysfunction and physical fitness in patients with alcohol dependence after acute withdrawal from alcohol, we used the standardized 6-min walk test (6 MWT). The 6 MWT was developed to evaluate an individual’s response to exercise in everyday life without sophisticated equipment (Morales-Blanhir et al., 2011). The participant is expected to walk as fast as possible for 6 min. The main advantages of the 6 MWT are its simplicity and the fact that various physiological parameters can easily be determined during the test (American Thoracic Society, 2002). Furthermore, it is inexpensive and has broad applications, given that walking is an everyday activity that almost every person is capable of. However, walking distance and velocity are influenced by numerous factors including age, height, body mass, gender, mood and motivation (Adsett et al., 2011). We hypothesized that autonomic dysfunction would influence individual responses to aerobic exercise in our patients (Hautala et al., 2009). Therefore, we assessed autonomic function using HRV analysis at rest, during the 6-min walk test and thereafter in alcohol dependent patients after they had withdrawn from alcohol for 10 days. In addition, we studied the heart rate, breathing, and ventilatory equivalent efficiency (Marek et al., 2011, 2008) to calculate parameters known to be independent from motivation

Table 1 Epidemiological data of participants. Mean ± SD

Epidemiological data Gender (female/male) Age (years) BMI (kg m−2 ) Munich Alcoholism Test CAGE questionnaire Alcohol consumption No alcohol/week 1–2/week 3–4/week 5–6/week Each day Smokers <5 cigarettes/day 5–10 cigarettes/day >10 cigarettes/day >20 cigarettes/day Fagerström Test for Nicotine dependence IPAQ-total (MET minutes/week) Medication SSRI (S-citalopram) Antipsychotics (Risperidon 1 mg/day)

p value

Controls

Patients

2/20 31 ± 7 24.8 ± 3 – –

3/19 34 ± 8 25.8 ± 5 25.1 ± 7.5 2.8 ± 0.9

n.s. n.s. n.s. n.d. n.d.

2 17 3 – – 17 4 10 3 – 0.68 ± 1

– 1 2 3 16 19 0 3 13 3 3.6 ± 2.4

n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. n.d. <.001

3460 ± 3242

2451 ± 2527

n.s.

1 –

2 1

Abbreviations: n.s.: not significant; n.d.: not done; SSRI: selective serotonin reuptake inhibitors.

and exertion. Since heavy alcohol consumption contributes to a pro-inflammatory state by interfering with the body’s natural defense system (Wang et al., 2010), we controlled for altered levels of cortisol, interleukin-1␤ (IL-1␤), interleukin-6 (IL-6), and tumor necrosis factor-alpha (TNF-␣) after exercise (Gabriel and Kindermann, 1997). 2. Material and methods 2.1. Participants Nineteen male and three female patients admitted for alcohol detoxification to the inpatient unit were investigated in our study after the acute phase had elapsed and medication for withdrawal symptoms had been discontinued for 10 days (±1 day, see Table 1). Most patients were known to the staff psychiatrist. All patients had a history of alcohol dependence according to DSM-IV criteria and were treated with clomethiazole (half-life: 4–6 h) for withdrawal symptoms according to in-house guidelines (Banger et al., 1992) up to 5 days after admission. Medication was discontinued after withdrawal symptoms had ceased. All patients completed the CAGE questionnaire (Buchsbaum et al., 1991) and the Munich Alcoholism Test (MALT; Feuerlein et al., 1979) to confirm the diagnosis. Serum drug levels were controlled for legal drugs (e.g., antidepressants, benzodiazepines) and illegal substances (e.g., cannabis). In accordance with our inclusion criteria, only subjects with negative results were included in the study. A clinical ECG was recorded and evaluated by a cardiologist. Similarly, serum electrolytes were obtained prior to the investigation and patients were clinically assessed for the presence of any other somatic diseases (e.g., heart diseases). Healthy control subjects were matched for total physical activity per week in metabolic equivalent (MET) minutes per week using the short version of the International Physical Activity Questionnaire (IPAQ-total). The IPAQ-total included intensive, moderate, and walking physical activities. Furthermore, the sitting time per week (IPAQ-sitting) was measured (Ainsworth et al.,

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2000). All participants were screened by a structured clinical interview for any additional psychiatric diseases. Two patients received 10 mg S-citalopram for a dysphoria-like state during withdrawal without any additional symptoms that would have indicated a depressive disorder. One control subject received S-citalopram for a mild binge eating disorder and was included to balance the potential effect of S-citalopram. All other control subjects were completely healthy as assessed by a careful clinical interview for psychiatric disorders and other diseases, by physical examination and by a routine electrocardiogram to exclude any interfering conditions. Carbon monoxide was measured in the exhaled breath of all participants (CO-Check, Neomed GmbH, Korschenbroich, Germany) before and after the test to account for differences in smoking behaviour (Jarvis et al., 1980). 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 certified their ability to give full consent to the study protocol. 2.2. Exercise testing protocol of the 6-min walk test The body mass index (BMI) was calculated prior to each test and participants received instruction regarding the operational aspects of the exercise test. The 6 MWT was performed on a straight hospital corridor with a hard surface. Participants were instructed to walk the largest possible distance in 6 min at a self-chosen walking speed. A starting line, which marked the beginning and end of each 60-m lap, was marked on the floor using coloured tape. During the test, standardized verbal encouragements from the test leader at 1 min intervals were used to encourage all subjects (American Thoracic Society, 2002). Patients also were asked to rate their perceived level of exhaustion using the Borg scale at 1-min intervals during the test. The total distance was calculated including the final incomplete lap. Heart rate variability, blood lactate and gas exchange parameters were measured for 10 min after the 6 MWT at rest in a chair located near the starting position. 2.3. Spirometry During the 6 MWT, ventilatory indices and gas exchanges were measured continuously on a breath-by-breath basis using a portable spiroergometric system (MetaMax 3B, Cortex, Leipzig, Germany). This was worn by each participating subject around his or her neck, in addition to a chest-belt for telemetric heart rate recordings (Polar Electro, Kempele, Finland). Before each test, the turbine (flow and volume) was calibrated with a 3-l syringe (Hans Rudolph Inc., Kansas City, US). The gas analyzers were calibrated according to the manufacturer’s guidelines with the same certified calibration gas mixture of 5% CO2 and 15% O2 (Air Liquide Healthcare America Corporation, Plumsteadville, PA, US). 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 over the last 5 min (from the second to sixth minute) of the 6 MWT. The first minute of the 6 MWT was excluded from calculations because pulmonary oxygen uptake and ventilatory response generally need a minute to achieve steady-state conditions following the onset of moderate dynamic exercise (Whipp and Ward, 1990). We obtained the following parameters: oxygen uptake (VO2 , l min−1 ), ventilation per minute (VE, l min−1 ); respiratory frequency (f, min−1 ) ventilation equivalent for oxygen uptake (VE/VO2 ) and the respiratory exchange ratio (RER). Capillary blood samples of 20 ␮l were

3

taken from the ear lobe when the patient was at rest before, and after the end of the 6 MWT. Lactate concentrations (in mmol l−1 ) were quantified using the EBIO basic system analyzer, employing an enzymatic–amperometric measuring system (Eppendorf, Hamburg, Germany). 2.4. Efficiency for heart rate, breathing and ventilatory equivalent The primary measurement in the 6 MWT is the total walking distance (WD). As described above, different conditions such as familiarization, motivation and cooperation can, however, significantly influence the WD obtained and might lead to considerable differences in physiological responses (Morales et al., 1999). To overcome these problems, we calculated physiological parameters adjusted to the individual WD since they are independent of patient cooperation (Marek et al., 2011). The walking distance per minute was divided by mean heart rate per minute giving the walking distance per heart beat. This calculation provides a quantification of the heart rate efficiency as originally described by Marek and his colleagues (Marek et al., 2008). Similar calculations were performed for breathing rate (walking distance per breath) and ventilation equivalent for oxygen uptake (walking distance per litre of oxygen uptake per minute of ventilation). 2.5. Data analysis of respiratory and heart rate signals The LifeShirt (Vivometrics, Inc., Ventura, CA, U.S.A.), a multifunction ambulatory device and consisting of a Lycra garment, a data recorder and a computer-based analysis software (VivoLogic) was used to obtain data (Grossman, 2004; Rachow et al., 2011). We employed respiratory inductive plethysmography, a core method which has demonstrated accurate, non-invasive assessment of respiratory patterns. In order to obtain the ventilated volume for further analysis, the device was calibrated in two different successive steps, as recommended 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 breathing 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 (e.g., movement, electrode noise and extraordinary peaks). 2.5.1. Parameters of time domain. We obtained the basic heart rate (HR) and the RMSSD (root mean of squared successive difference) as a time domain parameter of heart rate variability (Task-Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology Task-Force, 1996). 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, since previous reports have shown that these measures are not conclusive during exercise, due to a break down of the power spectrum (Boettger et al., 2010). In addition, compression entropy (Hc) was calculated. 2.5.2. Compression entropy (Hc). Besides linear computational algorithms, nonlinear methods were also applied. Lempel and Ziv (1977) introduced a universal algorithm (ZIP) for lossless data compression using string-matching on a sliding window. With some modifications, this algorithm can be applied to the analysis of heart beat time series. Here, Hc (calculated as the ratio of the length of the compressed data series to the length of the original data series)

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quantifies the extent to which data from heart beat time series can be compressed, i.e., how often repetitive sequences occur. Reduced short-term fluctuations (reduced variability) of HR thus result in increased compression and thus smaller Hc values. Entropy reduction appears to reflect a change in sympathetic/parasympathetic HR control (Baumert et al., 2004). 2.5.3. Blood counts and tests. Blood samples were collected from an antecubital vein at rest, both before, as well as 10 min after the end of the exercise test. After centrifugation, aliquots of plasma were snap-frozen and stored at −80 ◦ C until cytokine levels could be determined. Enzymelinked immunosorbent assays (ELISA’s) for the cytokines IL-1␤ (R&D Systems, Minneapolis, MN, USA), IL-6 (Beckman-Coulter, Krefeld, Deutschland) and TNF␣ (R&D Systems, Minneapolis, MN, USA) were used. All samples were measured with the same assay for each cytokine. ELISAs for IL-1␤ and TNF␣ 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. 2.6. Statistical analyses For statistical analysis, SPSS for Windows (version 17.0) was used. All parameters were tested for normal distribution with

the Kolmogorov–Smirnov test. A multivariate analysis of variance (MANOVA) was performed applying the factor GROUP to investigate differences between patients and controls at baseline for the following parameters: heart rate, breathing rate, RMSSD and compression entropy Hc. ANOVAs were then calculated for single parameters. In the next step, a repeated measures MANOVA was performed for normalized parameters to the walking distance (HRtest , RMSSDtest , breathing ratetest , Hctest ) to analyse differences during the walking test and both recovery phases using the factors TIME, GROUP and TIME × GROUP interaction. For descriptive analysis, values of patients and controls were compared by means of a t-test at baseline and during recovery phases 1 and 2 (Fig. 1). The source data of normalized parameters are displayed in Table 2. Both MANOVAs described above were repeated while including the carbon monoxide concentrations of exhaled air as a covariate to account for differences in smoking habits. Similarly, efficiency parameters of heart rate, breathing rate and the ventilatory equivalent were compared using a MANOVA, applying the factor GROUP. A t-test was applied for single parameters (Fig. 2). Additionally, a MANCOVA was used to compare efficiency parameters. This also took into account carbon monoxide concentrations as a covariate to account for smoking habits. Lactate concentrations, inflammatory parameters (including carbon monoxide concentrations), the subjective level of exhaustion and assessed basic autonomic and spirometric data were compared using t-tests for descriptive analysis (Table 2).

Fig. 1. Indices of autonomic function of controls (white) and patients (blue) are displayed. Heart rate at rest (A) and heart rate normalized to individual distance walked during the test and thereafter are shown. Similarly, breathing rate (B), parasympathetic modulation as indicated by RMSSD values (C), and complexity of heart rate regulation (D) are also shown. Boxes indicate data between the 25th and 75th percentile with the horizontal bar reflecting the median ( = mean; – = 1st and 99th percentile). Significant differences of Bonferroni corrected pair-wise comparisons are indicated: *p < .05; **p < .01; ***p < .001. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)

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Table 2 Physiological parameters obtained in the study. Mean ± SD Controls Parameters Distance (m) Borg scale Carbon monoxidebaseline (parts/million) Carbon monoxiderecovery (parts/million) Lactatebaseline (mmol l−1 ) Lactaterecovery (mmol l−1 ) Hematocritbaseline Hematocritrecovery Inflammatory and stress parameters Cortisolbaseline (␮g dl−1 ) Cortisolbaseline (␮g dl−1 ) IL-1␤baseline (pg ml−1 ) IL-1␤recovery (pg ml−1 ) IL-6baseline (pg ml−1 ) IL-6recovery (pg ml−1 ) TNF␣baseline (pg ml−1 ) TNF␣recovery (pg ml−1 ) Autonomic parameters Heart ratetest (beats/min) Heart raterecovery 1 (beats/min) Heart raterecovery 2 (beats/min) Breathing ratetest (breaths/min) Breathing raterecovery 1 (breaths/min) Breathing raterecovery 2 (breaths/min) RMSSDtest (ms) RMSSDrecovery 1 (ms) RMSSDrecovery 2 (ms) Compression entropytest Compression entropyrecovery 1 Compression entropyrecovery 2 Spirometry parameters VO2test (l min−1 ) VEtest (l min−1 ) RERtest VE/VO2test

p value Patients

700 13 9.7 9.9 1.02 3.0

± ± ± ± ± ±

70 2 11.3 10 0.9 1.9

640 15 19.9 18.9 1.31 3.55

± ± ± ± ± ±

80 3 12.2 10.6 0.8 1.7

<.05 n.s. <.01 <.05 n.s. n.s.

10.2 11.7 0.16 0.29 1.6 2.1 0.82 0.99

± ± ± ± ± ± ± ±

4.4 5.1 0.33 0.75 2.1 2.6 0.38 0.41

8.1 7.5 0.07 0.13 2.6 5.2 0.96 1.1

± ± ± ± ± ± ± ±

3.9 2.6 0.04 0.06 2.5 10 0.47 0.58

n.s. <.001 n.s. n.s. n.s. n.s. n.s. n.s.

139.9 98.0 83.3 28.9 20.9 17.5 4.7 13.6 16.8 0.31 0.57 0.64

± ± ± ± ± ± ± ± ± ± ± ±

13.4 16.8 13.0 5.4 3.9 2.9 3.2 8.1 7.9 0.05 0.11 0.09

135.7 106.1 95.3 29.6 22.3 19.2 4.1 8.7 8.1 0.31 0.47 0.49

± ± ± ± ± ± ± ± ± ± ± ±

16.2 14.4 14.4 5.4 3.8 3.5 3.8 5.5 6.1 0.07 0.11 0.11

n.s. n.s. <.01 n.s. n.s. n.s. n.s. <.05 <.001 n.s. <.01 <.001

2.25 55.5 0.92 25.18

± ± ± ±

0.53 11.5 0.08 3.9

1.76 54.7 1.0 30.96

± ± ± ±

0.29 11.3 0.07 3.5

<.001 n.s. <.005 <.001

IL: interleukin; TNF: tumor necrosis factor; RMSSD: root mean square of successive differences; VO2 : oxygen uptake; VE: ventilation per minute; VE/VO2 : ventilatory equivalent of oxygen uptake; RER: respiratory exchange ratio.

In addition, we used the IPAQ to perform an exploratory analysis of the influence of daily activity on the autonomic parameters of patients and controls. 3. Results The MANOVA which compared autonomic parameters at baseline (heart rate, breathing rate, RMSSD, Hc) between patients and controls revealed a significant overall difference between groups [F(4, 39) = 5.38; p < .001]. ANOVAS for single parameters revealed significant differences for heart rate (F = 7.0; p < .01), RMMSD (F = 15.6; p < .001), and Hc (F = 17.9; p < .001), while no such difference was observed for breathing rates (p = .68). The repeated measures MANOVA performed for normalized parameters to analyse differences during the 6 MWT and both recovery phases revealed a significant difference for the factor GROUP [F(4, 39) = 4.77; p < .003], TIME [F(8, 35) = 58.5; p < .001] and GROUP × TIME interaction [F(8, 35) = 2.64; p < .022], suggesting that autonomic parameters of both groups are influenced differentially by the exercise task. Follow-up ANOVAs for single parameters showed a significant effect for the factor GROUP for heart ratetest (F = 7.8; p < .008), breathing ratetest (F = 5.9; p < .019) and RMMSDtest (F = 7.1; p < .011), while no differences were observed for Hctest (p < .2) during the test, nor for the two recovery periods. Significant p values below .001 were observed for the factor TIME for all four parameters during the walking period and both recovery

phases. A GROUP × TIME interaction was found for the parameter heart ratetest (F = 6.7; p < .008), RMMSDtest (F = 7.8; p < .001), Hctest (F = 10.5; p < .001), while no difference was observed for the breathing ratetest (p < .9). Results of descriptive comparisons of autonomic parameters were shown in Fig. 1 and source data of normalized parameters are displayed in Table 2. To study the influence of smoking on autonomic parameters, we included the concentration of carbon monoxide in the exhaled air as a covariate. Both the MANCOVA at baseline [F(4, 38) = 4.81; p < .003] and the repeated measures MANCOVA for normalized parameters (GROUP: [F(4, 38) = 3.6; p < .015]; GROUP × TIME: [F(4, 34) = 3.5; p < .005]) remained significantly different after including this putative confounding factor. These results suggest that, neither before nor during the 6-min walk test did severity of smoking lead to observed differences in autonomic parameters between groups. In addition, we computed a MANOVA of efficiency parameters for heart rate, breathing rate and the ventilatory equivalent and found a significant overall difference [F(3, 40) = 9.7; p < .001]. This difference remained even after including carbon monoxide concentration as a covariate to control for smoking [F(3, 39) = 6.5; p < .001]. The results of the ANOVAs for single parameters indicated a reduced efficiency for all parameters. These are displayed in Fig. 2. Assessed lactate concentrations, inflammatory parameters, carbon monoxide concentrations, the subjective level of exhaustion and basic autonomic and spirometric data were compared using t-tests for descriptive analysis, as shown in Table 2.

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3.1. Correlation analysis The IPAQ values, indicating the amount of daily activity, showed a negative correlated negatively with breathing frequency during walking (r = −.502; p < .01) in healthy controls, indicating the beneficial effect of daily physical activity. No such correlation was observed in patients.

4. Discussion

Fig. 2. Efficiency parameters are displayed in the figure. The walking distance per minute was divided by mean heart rate per minute giving the walking distance per heart-beat. This was significantly lower in patients (A). Similar calculations were performed for breathing rate (B) (walking distance per breath) and the ventilation equivalent for oxygen uptake (C) (walking distance per 1 l of ventilation in relation to oxygen uptake) indicating significantly less efficient regulation in patients. Boxes indicate data between the 25th and 75th percentile with the horizontal bar reflecting the median ( = mean; – = 1st and 99th percentile). Significant differences of Bonferroni corrected pair-wise comparisons are indicated: *p < .05; ***p < .001. (For interpretation of the references to color in this figure, the reader is referred to the web version of the article.)

Addiction to various substances is a growing global problem among adolescents, as well as adults of all ages. In the context of alcohol and drug rehabilitation, exercise has the potential to encourage a healthy lifestyle which is generally incompatible with substance abuse. Our study investigated autonomic dysfunction during exercise in patients with alcohol dependence and investigated physiological efficiency with respect to autonomic parameters. Overall, we observed reduced fitness and decreased efficiency during exercise in patients with alcohol dependence. These results are of value because they help to establish if and which intensity of training programs are appropriate for patients in interventional studies. In particular, we showed that patients with alcohol dependence walked a significantly shorter distance during the 6-min walk test in comparison to control subjects. When comparing our data with reference values, patients reached about 85–100% of reference values, while control subjects were within the normal range of 91–107% (Chetta et al., 2006; Enright and Sherrill, 1998). This indicates that patients were slightly less fit than control subjects even when compared with a larger reference group. However, our patients performed better in this test than patients suffering from cardiac diseases (Rubim et al., 2006). In addition, the comparison of control subjects’ data with reference values suggests that the performance of our controls was well within the normal range of other studies (Chetta et al., 2006). As shown in Fig. 1, autonomic dysfunction was present in patients before and after the exercise intervention. In particular, heart rate was increased due to reduced vagal modulation. In addition, the compression entropy value showed reduced complexity. This corroborates our previous findings (Bär et al., 2006, 2007), although one important difference needs to be pointed out: patients of this study were investigated 10 days after acute withdrawal in contrast to previous investigations of patients, which took place during acute alcohol withdrawal. Interestingly, during the walking test, no difference in autonomic regulation was observed. During exercise, cardiac regulation of patients and controls was characterized by vagal withdrawal and by sympathetic activation, overshadowing autonomic differences at rest. This underlines previous findings on limitations of HRV assessment during exercise (Boettger et al., 2010). Nevertheless, the difference in autonomic regulation between both groups reappeared after exercise, during the resting period. To investigate the impact of autonomic dysfunction on performance of investigated subjects, we calculated efficiency parameters as previously shown (Marek et al., 2011, 2008). These parameters show clearly that although autonomic parameters such as heart rate or RMSSD did not differ during the exercise test, both groups were not similarly efficiently regulated. Patients were significantly less efficient, as shown by a smaller walking distance per heart beat and breath. Efficiency has been suggested as a meaningful complement to the sole consideration of the distance walked in the 6 MWT assessment of physical fitness since it is independent of patient’s cooperation. Therefore, one might conclude that decreased walking distance is not just caused by reduced motivation but also by altered physiological regulation.

Please cite this article in press as: Herbsleb, M., et al., The relation of autonomic function to physical fitness in patients suffering from alcohol dependence. Drug Alcohol Depend. (2013), http://dx.doi.org/10.1016/j.drugalcdep.2013.03.016

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In healthy subjects, a shift of immune reactivity can be observed upon exposition to physical exercise, leading to an acute increase in IL-6 concentrations and leucocyte counts (Gabriel and Kindermann, 1997). To date, the immune response to acute exercise has not been investigated in patients suffering from alcohol dependence. Therefore, we controlled for inflammatory markers after the 6 MWT in patients and controls. Although alcohol dependence is associated with an altered inflammatory defence system, we have not observed differences between patients and controls with respect to immune reactivity after exercise. In addition, about 60% of alcohol misusers are thought to suffer from alcohol-related myopathy, which may have contributed to our findings. It has been reported that chronic skeletal myopathy caused by alcohol misuse leads to decreased muscle glycogen content and type II fiber atrophy, which is associated with reduced muscle strength, mass and function (Preedy et al., 2003; Preedy and Peters, 1990a,b; Urbano-Marquez and Fernandez-Sola, 2004). Furthermore, reduced muscle strength persists even after 5 years of alcohol abstinence, indicating the severity of damage to the muscle (Estruch et al., 2004). Some limitations need to be addressed. A relatively small number of patients and controls was included and results need to be replicated in a larger population. Patients smoked more cigarettes per day than controls and carbon monoxide concentrations assessed in the exhaled air were significantly different. However, including carbon monoxide as a measure of smoking behaviour in our calculation reduced the potential influence of this confounder on differences obtained between patients and controls. In addition, two patients received 10 mg S-citalopram for dysphoria-like states during alcohol withdrawal. To control for a putative, however unlikely, effect of this drug on autonomic function (Bär et al., 2010; Koschke et al., 2009) we included one control subject receiving 10 mg S-citalopram for a mild form of binge eating disorder. Similarly, we tried to match the amount of physical activity between patients and controls using the IPAQ. We cannot, however, exclude the possibility that patients overestimated their physical activity and shortcomings due to the fact that the assessment interval measured only the last 7 days. In addition, the methods applied in this study were unable to disentangle in detail the close relationship between decreased physical capacity and autonomic alterations. In conclusion, we show that patients investigated in our study are less physically fit than controls as assessed by the 6 MWT. In addition, autonomic dysfunction in patients was observed before and after the test and might be reflected in reduced efficiency during the test. Prospective studies are needed to examine physical fitness and autonomic function in more detail and to evaluate whether the 6 MWT might be a valuable tool to study changes of physical fitness in interventional studies of these patients. It is especially worth investigating whether the improvement of performance in the 6 MWT correlates with better patient physical health and the duration of abstinence. This would suggest that the 6 MWT is a very good tool to assess physical fitness in this population in a way which is simple, easily assessable and applicable. Role of funding source This work was funded by internal funds of the University Hospital Jena only. Contributors M. Herbsleb contributed in recruitment of patients and controls, writing the manuscript. Steffen Schulz contributed in writing of

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Please cite this article in press as: Herbsleb, M., et al., The relation of autonomic function to physical fitness in patients suffering from alcohol dependence. Drug Alcohol Depend. (2013), http://dx.doi.org/10.1016/j.drugalcdep.2013.03.016