Electrocardiogram (ECG) is the key variable to understanding cardiac electrophysiology. Physical exercise has beneficial effects but may also be harmful in the context of cardiovascular diseases. This manuscript provides a method of recording real-time ECG during exercise, which can serve to investigate its effects on cardiac electrophysiology in mice.
Regular physical exercise is a major contributor to cardiovascular health, influencing various metabolic as well as electrophysiological processes. However, in certain cardiac diseases such as inherited arrhythmia syndromes, e.g., arrhythmogenic cardiomyopathy (ACM) or myocarditis, physical exercise may have negative effects on the heart leading to a proarrhythmogenic substrate production. Currently, the underlying molecular mechanisms of exercise-related proarrhythmogenic remodeling are largely unknown, thus it remains unclear which frequency, duration, and intensity of exercise can be considered safe in the context of disease(s).
The proposed method allows to study proarrhythmic/antiarrhythmic effects of physical exercise by combining treadmill training with real-time monitoring of the ECG. Implantable telemetry devices are used to continuously record the ECG of freely moving mice over a period of up to 3 months both at rest and during treadmill training. Data acquisition software with its analysis modules is used to analyze basic ECG parameters such as heart rate, P wave duration, PR interval, QRS interval, or QT duration at rest, during and after training. Furthermore, heart rate variability (HRV) parameters and occurrence of arrhythmias are evaluated. In brief, this manuscript describes a step-by-step approach to experimentally explore exercise induced effects on cardiac electrophysiology, including potential proarrhythmogenic remodeling in mouse models.
Regular physical activity is important for a healthy life. Certain cardiovascular conditions, however, lead to situations where this common-sense agreement is at least questionable. In patients with myocarditis, current data even shows adverse effects of exercise and, thus, it is recommended to pause all exercise for a certain period in these patients1,2,3. In other cardiovascular diseases (CVD) such as inherited arrhythmia syndromes comparatively less evidence on the appropriate level of exercise exists4,5,6,7, making clinical counselling in these cases, mostly for young and physically active patients, very challenging.
Adverse remodeling leading to reduced contractility and heart failure and proarrhythmogenic remodeling leading to arrhythmias and sudden cardiac death have been suggested as hallmarks of exercise-associated harmful effects on the heart8. A large number of studies indicate beneficial effects of moderate exercise over a broad spectrum of different diseases9,10. Extensive training, however, may have detrimental effects on the heart leading to arrhythmias especially in otherwise healthy athletes11. Although structural remodeling processes leading to a vulnerable proarrhythmic substrate production may underlie this paradox situation as demonstrated in marathon runners12, the specific mechanisms of exercise-related adverse remodeling both in healthy people and in patients with cardiovascular diseases remain largely unknown.
In animals, especially in mice, several suitable models have been developed to mimic a broad range of cardiovascular diseases13,14. Also, various exercise models and training protocols have been established in mice15,16,17, including motorized treadmill training, voluntary wheel running (VWR), and swimming17,18. Evaluation of cardiac electrophysiology by ECG monitoring classically depends on a direct conducting connection between the animal and some sort of detection device. Thus, either animals need to be anesthetized, e.g., to obtain ECG recordings using sharp electrodes19, or animals need to be immobilized by a restrainer 20, or data quality is reduced due to motion artifacts, e.g., when using paw-electrodes21 or conductive platforms22 allowing only basic analysis. Thus, none of the above-mentioned approaches are compatible with training protocols and consequently prevent studies on exercise-related mechanisms leading to adverse remodeling in mice. Implantable telemetry devices can overcome these hurdles and are nowadays the most powerful tool and gold standard to evaluate murine electrophysiology in vivo in conscious and moving animals23,24. Current telemetry hardware solutions have been developed to monitor mice in their cages25,26, and commonly require a receiver to be placed underneath the cage for data acquisition, thus making real-time monitoring outside these circumstances challenging. Here we provide an approach to investigate the effects of exercise on cardiac electrophysiology and arrhythmogenesis by real-time ECG recording during treadmill training in mice using implanted telemetry devices. All parameters obtained were analyzed as previously described by Tomsits et al.23.
All animal procedures were conducted in accordance with the guidelines of the Animal Care and Ethics committee of the University of Munich and all procedures were approved by the Government of Bavaria, Munich, Germany (ROB-55.2-2532.Vet_02-16-200). Four male wildtype in-house bred C57BL/6N mice were used in this study.
1. Preparation and surgical implantation of the transmitter
NOTE: For a detailed protocol of transmitter preparation and implantation refer to McCauley et al.26.
2. Data acquisition
3. Data analysis
NOTE: Depending on the individual research aims, various parameters can be obtained and analyzed. This protocol focusses on two aspects: analysis of quantitative ECG traits and the occurrence of arrhythmias before, during, and after training using an approach previously described by Tomsits etal.23; and analysis of heart rate variability (HRV)27.
Depending on individual research objectives, subsequent analysis of obtained telemetry data will differ widely. Here, we demonstrate feasibility of the method by obtaining good quality data recorded during training periods and provide exemplary results of ECGs and heart rate variability analyses before, during, and after training. Data are presented as mean ± standard error of mean (SEM), all statistical analyses were conducted with a suitable statistical software (see Table of Materials). Statistical significance was assessed by the student's t test. The QT interval is corrected as previously discussed by Roussel et al. using the formula QTc = QT / (√(RR / 100))29.
Successful telemetric ECG recording during training
With this protocol, it is possible to obtain ECG data with clear P, Q, R, S, and T waves in animals during training as demonstrated in Figure 3.
All the measurements from one animal were taken from the same day. Baseline measurements were taken at 10 am ± 10 min before training when animals were still in their permanent housing. Measurements during training were taken from the middle of the 60 min training session ± 10 min on day 3 in the third week of training, post training measurements were taken from the 5 min resting period after training and before retransfer to the permanent housing and recovered measurements were taken 1 h after training ± 10 min. Suitable sections of the ECG tracing for analysis were chosen manually from these defined sections with regard to the readout, e.g., 40 consecutive cycles for data presented in Figure 4.
Evaluation of ECG-derived parameters
Data are used to analyze physiologic changes before, during, and after exercise as shown for one example animal in Figure 4. Heart rate (Figure 4A), PR interval (Figure 4B), QRS duration (Figure 4C), and QTc interval (Figure 4D) are evaluated by averaging 40 consecutive ECG cycles. Heart rate increases to around 800 bpm when the animal is exercising and gradually recovers toward baseline after training. PR interval, QRS duration, and QTc intervals shorten under stress and once stress is over, return to baseline. Exemplary data from one animal are shown.
Detection of tachycardia
Search definitions were used as described in step 3.2.4 for detection of tachycardia and bradycardia episodes. Figure 5A shows sinus rhythm at baseline. A representative trace of sinus tachycardia during training is shown in Figure 5B. Exemplary data from one animal is shown here.
Data quality assessment by evaluation of heart rate variability parameters
HRV analysis is done as described in step 3.3. 5 min sections for HRV analysis is presented in Figure 6. Figure 6A shows the heart rate of a single animal over the course of an experiment. Heart rate increases during training and gradually returns to baseline post training, this trend can also be visualized by the median RR interval as shown in Figure 6B. Figure 6C shows comparable standard deviation of RR intervals (SDRR) obtained at baseline and during training by automated RR annotation, demonstrating data quality. Data obtained is from three mice. The SDRR is the standard deviation of all interbeat intervals (IBI) and is calculated automatically by the software as positive square root of the IBI variance around the mean IBI using the formula:
σx =
Figure 1: Schematic illustration of the telemetry transmitter and lead positioning. Mouse is in supine position; the transmitter is placed intraperitoneally, and leads are fixed subcutaneously in a lead II configuration. Created with Biorender. Please click here to view a larger version of this figure.
Figure 2: Experimental setup. (A) Setup for ECG recording using implantable telemetry before and after training with the signal receiver being kept underneath the animal cage. (B) Setup for real-time ECG monitoring during treadmill training. For optimal signal quality, the signal receiver is placed on the transparent box. Created with Biorender. Please click here to view a larger version of this figure.
Figure 3: Representative ECG during training. Normal sinus rhythm, P-wave, QRS, and T-wave are indicated using capital letters, RR-interval is marked with a bar. Please click here to view a larger version of this figure.
Figure 4: Telemetry over time. Trend graphs show representative results for (A) heart rate (BPM). (B) PR interval (ms). (C) QRS duration (ms). (D) QTc Interval (ms) before (baseline), during (training), immediately after training (post training), and after full recovery (recovered). Data is obtained from one animal by averaging 40 consecutive ECG cycles. Please click here to view a larger version of this figure.
Figure 5: Representative ECGs before and during training. (A) Sinus rhythm before training. (B) Sinus tachycardia during training. Data is from one animal. Please click here to view a larger version of this figure.
Figure 6: Data quality assessment by HRV analysis. (A) Representative heart rate trend of a single animal before (baseline), during (training), and after (post training) exercise. (B) Median RR interval before (baseline) and during training (training) and after full recovery (recovered), shown as mean ± SEM, unpaired Student's t-test, ***p < 0.001. (C) SDRR before (baseline) and during training (training) and after full recovery (recovered), n = 3, shown as mean ± SEM. Please click here to view a larger version of this figure.
5-day acclimatization phase | ||
Day | Speed (cm/sec) | Time(min) |
1 | 16.7 | 10 |
2 | 18.3 | 20 |
3 | 20 | 30 |
4 | 21.7 | 40 |
5 | 23.3 | 50 |
Remark: 2 min rest intervals after every 15 min |
Table 1: Training regime during acclimatization phase.
5-day training phase | ||
Day | Speed (cm/sec) | Time(min) |
1 | 25.0 | 60 |
2 | 25.0 | 60 |
3 | 25.0 | 60 |
4 | 25.0 | 60 |
5 | 25.0 | 60 |
Remark: 2 min rest intervals after every 15 min |
Table 2: Training regime during training phase.
Current guidelines recommend regular physical activity as it has been demonstrated to be an important modifier of cardiovascular risk factors30. There is also a growing body of evidence that moderate physical activity may protect against atrial fibrillation (AF) both in primary and secondary prevention31,32,33. On the contrary, endurance athletes such as marathon runners have a higher risk to develop AF indicating that endurance training may also have negative effects34,35. Such a U-shaped relationship between arrhythmia risk and training intensity has been clearly shown for AF in otherwise healthy athletes9,36,37,38 and in patients with underlying heart disease, however, only little is known regarding training intensity and arrhythmogenesis4,5,6,7.
To overcome this limitation and to improve patient care, further research on exercise-related effects on cardiac electrophysiology is warranted. To investigate fundamental mechanisms and molecular/cellular adaptations in response to training different models in a number of animal species have been developed15. Given the immanent benefits but also limitations of every model/species, researchers have to choose the most suitable one for each individual research question; regarding electrophysiology and arrhythmia research mouse13,14,39,40 and pig models are widely used13,14,41,42,43. Although training protocols using a motorized treadmill have been developed in pigs, there are a number of significant challenges including (i) the pigs' sedentary behavior, which requires a time- and labor-intense conditioning prior to the experiment as well as stimuli to keep pigs compliant during experiment and (ii) the body size and weight, which may prevent training in older pigs or training over long periods of time15,44. In mice, several exercise protocols have been developed including motorized treadmill training, VWR, or swimming17,18. Although VWR mimics the natural running pattern in rodents and is less stressful compared to forced exercise methods such as swimming and treadmill training, it also has certain disadvantages45. The spontaneous nature of VWR does not allow controlling intensity, duration, or frequency of exercise thus preventing well-controlled experiments. In swimming models, the duration and intensity of training can be easily regulated, the necessary equipment is simple and available at low costs, and the method can be established in most research labs46. Despite these advantages, studying electrophysiology in a swimming model is difficult as there is currently no option to monitor the ECG during swimming. The approach described in this protocol combines an implantable telemetry system with a treadmill exercise model and thus, overcomes the limitations of other training models in the context of electrophysiology research47,48. Using a treadmill allows to control various exercise conditions such as intensity (slope inclination and running speed) or duration. In addition, different training protocols can be studied including endurance exercise training, interval training, and acute exercises. Following this protocol, it is now also possible to record and monitor the ECG using implantable telemetry transmitters while the mouse is running on the treadmill.
Given that mice usually run willingly for only a few minutes, stimuli such as tapping their back with little sticks, blowing puffs of compressed air or electrical stimuli are necessary. These stimuli, however, can induce psychological stress, which can affect the quality of experimental data significantly. Hence, we tried to minimize these stress factors by letting the mouse adapt to the treadmill during an acclimatization phase, with a steady increment of speed and using minimum-to-zero shock intensity as previously described15,17,45.
In general, when recording ECGs, motion artefacts are a major issue, especially during physical activity. Following our proposed protocol, researchers will be able to acquire ECG signals in good quality allowing to clearly distinguish and annotate P, Q, R, S, T (Figure 3). Thus, various ECG parameters such as heart rate, heart rate variability, PR interval, QRS duration, or QT duration can reliably be assessed before, during, and after the training using automated software algorithms. Also, arrhythmias such as tachyarrhythmia, bradyarrhythmia, or pauses can be detected. As heart rate variability analyses-usually performed to investigate the effects of the autonomic nervous system on the heart27,28-depend on sufficient R-wave annotation, data quality can be verified by similarly low SDRR values obtained at rest and during training by automated annotation as shown in Figure 6.
As every experimental technique, this method does not come without pitfalls and contains several critical steps. Sterile conditions and a short operation time are requirements for successful transmitter implantation, proper wound healing, and fast animal recovery post-surgery. Sutures must not be too tight, or they will cause skin necrosis. In general, the surgical procedure requires practical experience, and results will improve over time. Lead positioning influences the recorded main vector, best results are obtained with a steep lead two position, as it results in higher P- and R-wave amplitudes, which in turn are critical requirements for later ECG analysis. Training mice can be challenging as not all animals train willingly. A well-designed acclimatization protocol, including introduction to the treadmill environment, slow increments in conveyor belt speed and positive enhancement of good training behavior, e.g., with food pellets, can help to condition the animals to train better and to reduce the need for potentially interfering stimuli during the experiments. It is important to reduce all stimuli to the absolute minimum as they may affect data quality. However, the most critical step is the optimal positioning of the telemetry receiver during the treadmill training as it directly determines the quality of the data obtained. The receiver position must be determined for each pair of animals training at the same time, as it varies depending on the exact position of telemetry device and leads as well as on the individual animals running pattern. The position is found by trial and error, visually judging the signal quality in real time. All ECG traits to be analyzed must be clearly visible before experiments can start. Given the high murine heart rate, many data points accumulate even with short recording periods. This and the overall low signal amplitude, naturally leading to a lower signal-to-noise ratio in rodents than in humans or large animals, make data analysis extremely challenging, as we have previously discussed23. A major limitation of this protocol besides the costly equipment needed to perform telemetry and treadmill training is the high technical demand on the surgical procedure and on data analysis, limiting accessibility to beginners in the field.
In sum, the ECG is a brilliant tool to study cardiac electrophysiology and arrhythmogenesis. In humans, stress tests to record ECGs during exercise are routinely performed and allow to assess training-associated effects on cardiac electrophysiology. Mice are the most commonly used species in research, several exercise protocols have been developed, but monitoring the real-time ECG during training was not possible so far. Our proposed protocol allows to obtain ECG recordings during periods of exercise in mice for the first time. This will enable researchers to study both exercise-related mechanisms leading to beneficial cardiac adaptations and maladaptive, proarrhythmic remodeling and will, thus, eventually result in improved patient care in the future.
The authors have nothing to disclose.
This work was supported by the German Research Foundation (DFG; Clinician Scientist Program in Vascular Medicine (PRIME), MA 2186/14-1 to P. Tomsits), the German Centre for Cardiovascular Research (DZHK; 81X2600255 to S. Clauss), the Corona Foundation (S199/10079/2019 to S. Clauss), and the ERA-NET on Cardiovascular Diseases (ERA-CVD; 01KL1910 to S. Clauss). The funders had no role in manuscript preparation.
14-gauge needle | Sterican | 584125 | |
Any mouse | e.g. Jackson Laboratories | ||
Bepanthen | Bayer | 1578675 | |
Carprofen 0.005 mg/µL | Zoetis | 53716-49-7 | |
Data Exchange Matrix 2.0 (MX2) | Data Science International | Manages communication between PhysioTel and PhysioTel HD telemetry implants and the acquisition computer. | |
Enrofloxacin 25 mg/ml | Baytril | 400614.00.00 | |
Fentanyl 0.5 mg/10 mL | Braun Melsungen | ||
Fine forceps | Fine Science Tools | 11295-51 | |
Five Lane Treadmill for Mouse | Panlab – Harvard Apparatus | 76-0896 | Includes treadmill unit, touchscreen control unit, a sponge , and cables |
Iris scissors | Fine Science Tools | 14084-08 | |
Isoflurane 1 mL/mL | Cp-Pharma | 31303 | |
Isoflurane vaporizer system | Hugo Sachs Elektronik | 34-0458, 34-1030, 73-4911, 34-0415, 73-4910 | Includes an induction chamber, a gas evacuation unit and charcoal filters |
LabChart Pro 8.1.16 | ADInstruments | ||
Magnet | Data Science International | ||
Modified Bain circuit | Hugo Sachs Elektronik | 73-4860 | Includes an anesthesia mask for mice |
Modular connectors | Data Science International | Connecting cables between Reciever, Signal Interface and Matrix 2.0 (MX2) | |
Novafil s 5-0 | Medtrocin/Covidien | 88864555-23 | |
Octal BioAmp | ADInstruments | FE238-0239 | Amplifier for recording Surface ECG |
Octenisept | Schülke | 121418 | |
Oxygen 5 L | Linde | 2020175 | Includes a pressure regulator |
PhysioTel ETA-F10 transmitter | Data Science International | ||
PhysioTel receiver RPC-1 | Data Science International | Signal reciever | |
Ponemah 6.42 | Data Science International | ECG Analysis Software | |
Powerlab | ADInstruments | 3516-1277 | Suface ECG Acquisition hardware device. Includes ECG electrode leads |
Prism 8.0.1 | Graph Pad | ||
Radio Device (Sony AF/AM) | Sony | ||
Signal Interface | Data Science International | Acquires and synchronizes digital signals with telemetry data in Ponemah v6.x. | |
Spring scissors | Fine Science Tools | 91500-09 | |
Surgical platform | Kent Scientific | SURGI-M | |
Tergazyme 1% | Alconox | 13051.0 | Commercial cleaning solution |
Tweezers | Kent Scientific | INS600098-2 |