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Heart rate variability predicts the magnitude of heart rate decrease after fingolimod initiation

Multiple Sclerosis and Related Disorders, Volume 10, November 2016, Pages 86-89



Fingolimod is an immunomodulator with a disease modifying effect on relapsing-remitting multiple sclerosis (RRMS). A heart rate (HR) decrease shortly after fingolimod initiation, however, requires a clinical vigilance. The aim of this study was to prospectively investigate whether cardiac autonomic regulation can predict the magnitude of HR decrease after fingolimod initiation.


Twenty-five patients with RRMS underwent ambulatory 24-h electrocardiogram recording to assess HR variability 20±16 days before fingolimod initiation (baseline) and repeated at the day of fingolimod initiation to assess the magnitude of HR decrease. The percentage of normal RR-intervals with duration more than 50 ms different from the previous normal RR-interval (pNN50) was calculated (among the other HR variability parameters) to assess cardiac autonomic regulation. The maximal HR decrease (ΔHR) after the first dose of fingolimod was assessed in absolute units (beats/min) and in percentage (%).


The maximal ΔHR was −20±11 beats/min (−23±12%) on the average. pNN50 calculated at baseline correlated with ΔHR% (r=−0.657, p<0.001). A HR decrease ≥20% was found in 10/14 patients with pNN50≥10%. The positive and negative predictive values of pNN50≥10% to predict ΔHR≥20% were 83% and 69%, respectively leading to accuracy of 76%.


Cardiac autonomic regulation (pNN50>10%) at baseline can be used to predict the magnitude of HR decrease after the first dose of fingolimod.

Trial registration (NCT01704183).


  • Fingolimod has disease modifying effect on relapsing-remitting multiple sclerosis.
  • Heart rate decrease after fingolimod initiation requires a clinical vigilance.
  • Predefined status of autonomic regulation predicts magnitude of heart rate decrease.
  • Patients at risk for greater heart rate decrease can be identified.

Keywords: Fingolimod, Heart rate, Heart rate variability, Multiple sclerosis.

1. Introduction

Fingolimod is a disease modifying therapy for relapsing-remitting multiple sclerosis (RRMS) (Cohen et al, 2010, Kappos et al, 2006, Kappos et al, 2010, and Pelletier and Hafler, 2012). The therapeutic effects of fingolimod on RRMS are mediated via modulation of sphingosine-1-phosphate (S1P) receptors (Matloubian et al, 2004 and Mehling et al, 2011). The S1P-receptors are found in lymphocytes and neural cells, as well as in cardiovascular system (Brinkmann, 2007;Chun and Hartung, 2010; Mandala et al., 2002).

The initial cardiac effects of fingolimod mimic that of parasympathetic activation (Brinkmann, 2007 and Brinkmann et al, 2010). The first dose of fingolimod results in transient reduction in heart rate (HR) (Cohen et al, 2010, Kappos et al, 2006, Kappos et al, 2010, Rossi et al, 2015, and Simula et al, 2015). However, the magnitude of the initial HR decrease varies between patients. The abrupt decrease in HR can lead to unintended cardiovascular events in susceptible patients. Thus, identification of patients at risk for greater HR decrease is needed to improve drug safety.

HR variability is a marker of cardiac autonomic regulation and can be assessed noninvasively from the ambulatory 24-h electrocardiogram (ECG) recording (Task Force, 1996). HR variability consists of different components mirroring the parasympathetic and sympathetic components of cardiac autonomic regulation.

We hypothesize that measurement of cardiac autonomic regulation can be used as a screening test to predict the magnitude of HR decrease after fingolimod initiation in RRMS patients. In addition, prognostic information of HR variability regarding to the risk for marked HR decrease after the first dose of fingolimod was evaluated.

2. Methods

The patients underwent 24-h ambulatory ECG recording 20±16 days before the initiation of fingolimod (baseline) from which heart rate variability in time domain and in frequency domain were computed. In addition, ambulatory ECG was undertaken at the day of fingolimod initiation to assess average HR on hourly basis. Expanded disability status scale (EDSS) was performed to evaluate neurological disability related to RRMS.

The ethics committee of Kuopio University Hospital approved the study protocol. Prior to participation, written informed consent was obtained from the patients after explanation of the aim and risk of all procedures used. The study was registered at (NCT01704183).

2.1. Study population

Initially, the study consisted of 27 RRMS patients (Simula et al., 2015). Fingolimod was prescribed on clinical basis, according to the accepted drug label and without randomization. The first dose of fingolimod was given at hospital before 10:00 a.m. Patients were followed before discharge at least six hours or until HR started to recover. None of the patients needed overnight observation. Two patients had insufficient ECG-signal for calculation of HR at the last preceding hour before fingolimod initiation and were excluded from the study. Thus, the final population includes 25 patients, 14 (56%) men and 11 (44%) women.

The patients were 43±11y of age, the diagnosis of RRMS was set 10±7y before the study and EDSS was 3.3±1.8 on the average. Five patients (20%) had one or more of the following co-morbidities: two patients (8%) had type-1 diabetes mellitus with insulin-treatment, three patients (12%) were adequately treated with hormonal substitution for hypothyreosis, one patient (4%) had asthma and one patient (4%) had optimally treated hypertension combined with Raynaud phenomenon. Initiation of fingolimod was the only change in the medication during the study. All patients had fingolimod as a second-line treatment for RRMS due to side effects or lack of efficacy during a first-line treatment. Preceding immunomodulative treatment for RRMS was discontinued at least a day before fingolimod initiation if changed from interferon-1b or glatirameracetate or at least two months before shifting from natalizumab.

2.2. Analysis of heart rate and heart rate variability

Twenty-four-hour ambulatory ECG recordings were performed with portable Schiller Medilog AR12plus recorders (Schiller Medilog, Schiller AG, Switzerland). Three bipolar ECG leads (modified chest leads V1 and V5 and modified aVF) were used to record signal with a sampling frequency of 250 Hz. Digital recordings were analyzed with Darwin Holter analysis system (Schiller Medilog, Schiller AG, Switzerland) and exported in MIT-format for subsequent HR variability analysis. During the ECG recordings, the patients were allowed to perform their normal daily activities.

The average HR of the last pre-dose hour before fingolimod dosing was calculated from the ambulatory ECG. After the first dose of fingolimod, the average HR for each consecutive post-dose hour was calculated from the ambulatory ECG, and the nadir of hourly average HR was determined. The difference between pre-dose HR and nadir HR (ΔHR) was calculated and expressed in absolute values (beats/min, bpm) as well as in percentage (%).

Percentage decrease in HR may reflect the hemodynamic changes better than absolute decrease or nadir HR. In this setting, percentage decrease in HR by one fifth was chosen to classify patients to have ΔHR <20% or ΔHR≥20%. The values for different HR variability measures were computed using the WinCPRS software (Absolute Aliens Oy, Turku Finland) according to the recommendations (Task Force, 1996). The standard deviation of all the RR-intervals (SDNN), the percentage of normal RR-interval with duration more than 50 ms different from the previous normal RR-interval (pNN50) and the root mean square of successive differences in RR-interval (rMSSD) were calculated as the time domain measures of HR variability. The power spectra were quantified with non-parametric fast Fourier transformation by measuring the area in three frequency bands: 0.005–0.04 Hz (very low frequency, VLF), 0.04–0.15 Hz (low frequency, LF) and 0.15–0.40 Hz (high frequency, HF). The ratio of the LF power band and HF power band (LF/HF-ratio) was also computed.

2.3. Physiological correlates of heart rate variability

High HR variability is considered to reflect enhanced cardiac parasympathetic regulation. In time domain analysis, pNN50 and rMMSD are indicators of cardiac parasympathetic regulation (Task Force, 1996). In frequency domain analysis, the HF band reflects mainly parasympathetic cardiac modulation (Pomeranz et al., 1985), whereas the LF band is thought to reflect both parasympathetic and sympathetic cardiac modulation (Pagani et al, 1997 and Eckberg, 1997). LF/HF ratio reflects sympatho-vagal balance of cardiac autonomic regulation (Task Force, 1996).

2.4. Statistical analyses

Kolmogorov-Smirnov test was applied to verify the normal distribution of variables. Logarithmic (ln) transformation was made to normalize distributions as needed. Results are expressed as mean±standard deviation (SD). To test the significances of differences between the groups, independent samples t-test was used for continuous variables and Chi-square analysis for categorical variables. A least square regression analysis was used to study univariate linear correlations. The predictive values and accuracy were calculated with standard methods (Fletcher and Fletcher, 2005). All analyses were conducted at the two-tailed level and p-value <0.05 was considered statistically significant. Data were analyzed using IBM SPSS statistics (version 22, IBM Corporation and others, Chicago, USA).

3. Results

3.1. Heart rate response

The average HR at baseline was 81±11 bpm (range 69–109 bpm). All the patients demonstrated HR decrease after fingolimod initiation. The lowest HR (61+8.5 bpm) was reached at the 5th post-dose hour, on the average. The average ΔHR was −20±11 bpm (range: −2–48 bpm) and the mean of ΔHR% was −23±12% (range −2–44%). None of the patients developed symptomatic bradycardia.

Eleven patients (44%) were classified to have HR decrease <20% and 14 (56%) patients to have HR decrease ≥20%. Patients with <20% and ≥20% decrease in HR were similar with respect to age, gender, EDSS, RRMS duration and HR at baseline (Table 1).

Table 1

Characteristics of patients with a heart rate decrease (ΔHR) <20% and ≥20%.


ΔHR<20% ΔHR20% p -Value
n 11 (44%) 14 (56%) ns
Clinical characteristics
 Age 44±11y 42±12y ns
 Women 5 (45%) 9 (64%) ns
 EDSS 3.3±2.1 3.3±1.6 ns
 Disease duration 7.9±5.6y 12±7.9y ns
 HR at baseline (bpm) 78±9.9 83±12 ns
Heart rate variability
 SDNN (ms) 128±37 160±44 0.06
 pNN50 (%) 5.5±5.2 14±9.2 <0.01
 rMSSD (ms) 25±12 44±27 <0.05
 TP (ms2) 10,212±7921 15,392±7219 ns
 VLF (ms2) 2499±1755 3813±2031 ns
 LF (ms2) 788±561 1304±753 0.07
 HF (ms2) 214±228 601±657 0.08
 LF/HF-ratio 5.53±3.72 3.45±1.89 0.08

Abbreviations: bpm, beats/min; HF, high frequency; LF, low frequency; pNN50, percentage of normal RR interval whose duration is more than 50 ms different from the previous normal RR interval; rMSSD, the root mean square of successive differences in RR intervals of filtered beats; SDNN, standard deviation of all filtered RR intervals; TP, total power; VLF, very-low frequency. Values are mean±SD.

3.2. Heart rate variability

In the time domain analysis before fingolimod therapy, SDNN was 146±43 ms, pNN50 was 11±8.8% and rMSSD was 35±24 ms, on the average. In the power spectra, the mean powers of TP, VLF, LF and HF spectral components were 13,112±7827 ms2, 3235±1990 ms2, 1077±712 ms2 and 430±542 ms2, respectively and LF/HF-ratio was 4.37±2.97, on the average.

Enhanced parasympathetic cardiac regulation at baseline (pNN50≥10%) was demonstrated in twelve patients (48%). These patients did not differ from the rest of the patients with respect to age (42±9.7y vs 45±13y), gender (female 8/12; 67% vs 6/13; 46%), EDSS (3.0±1.6 vs 3.6±1.9), RMSS duration (9.6±7.5y vs 11±7.0y) or HR at baseline (81±13 bpm vs 82±10 bpm), respectively.

3.3. Heart rate variability as a predictor of heart rate response

In time domain measures of HR variability, pNN50 correlated significantly with ΔHR% (r=−0.657, p<0.001) (Fig. 1). Also SDNN (r=−0.353, p=0.09) and rMSSD (r=−0.383, p=0.06) tended to correlate (although nonsignificantly) with ΔHR%. In power spectra of HR variability, LF/HF-ratio correlated with ΔHR% (r=0.493, p<0.05) (Fig. 1) and there was also a trend towards correlation between HF power and ΔHR% (r=−0.360, p=0.08).

Fig. 1

Fig. 1

The correlation of pNN50 (index of cardiac parasympathetic regulation) and LF/HF-ratio (index of cardiac sympatho-vagal balance) at baseline with maximal decrease of heart rate (ΔHR%) after the first dose of fingolimod. Threshold values for heart rate drop >20% and enhanced cardiac parasympathetic regulation (pNN50 >10%) are shown with dashed lines.


Enhanced parasympathetic cardiac autonomic regulation (pNN50≥10%) was found in 10/14 patients with ΔHR≥20% (sensitivity 71%) whereas this was the case only in 2/11 (18%) of patients with ΔHR <20% (p<0.05) (specificity 82%). Correspondingly, the positive predictive value of pNN50≥10% to predict HR decrease ≥20% was 83% (10/12 patients) and the negative predictive value was 69% (9/13 patients) thereby leading to 76% (19/25 patients) accuracy.

4. Discussion

The main finding of our study was that assessment of cardiac autonomic regulation could be used to predict the magnitude of HR decrease after fingolimod initiation. Namely, an enhanced parasympathetic cardiac regulation (pNN50≥10%) assessed in advance identified patients who did and did not develop HR decrease ≥20% shortly after the first dose of fingolimod.

A decrease in HR was found in all (100%) our RRMS patients after the first dose of fingolimod. In the previous studies, the incidence of bradycardia after fingolimod initiation has been reported to vary between 0.5% and 29%, depending on the cohort and the predefined threshold level for bradycardia (Cohen et al, 2010, Gold et al, 2014, Kappos et al, 2006, Kappos et al, 2010, and Rossi et al, 2015). In our study, the maximal HR decrease was 23% on the average, but none of the patients developed symptoms of bradycardia or a nadir HR below 40 bpm.

Heart rate decrease after fingolimod initiation was greater in patients with parasympathetic predominance of cardiac autonomic control (high pNN50) at baseline. On the other hand, LF/HF-ratio indicating sympatho-vagal balance, correlated also significantly with ΔHR, but high sympathetic dominance associated with a less pronounced HR decrease. Both these findings suggest that the magnitude of HR decrease after fingolimod initiation is related to status of cardiac autonomic regulation assessed before fingolimod therapy.

In line with us, Rossi et al. (2015) has reported that the lowest HR (but not the magnitude of HR decrease) after fingolimod initiation was associated with high parasympathetic cardiac regulation at baseline. In that study, HR variability was assessed by means of Valsalva maneuver and deep breathing test whereas in our study it was based on 24-h ambulatory ECG recording. HR variability assessed from Valsalva maneuver and deep breathing test are well-established and reliable methods, but require dedicated devices. On the other hand, HR variability analysis based on 24-h ambulatory ECG recording is easy to perform with all the existing ambulatory ECG analysis systems.

Development of HR decrease ≥20% shortly after the first dose of fingolimod was predicted by a measure indicating enhanced parasympathetic cardiac regulation (pNN50≥10%) with high positive predictive value of 83% and high, 76%, accuracy. A transient HR decrease after fingolimod initiation is a well-known phenomenon but practical identification of patients susceptible to a greater HR decrease has been lacking so far. According to our findings, a simple time domain measure of HR variability, pNN50, provides valuable information regarding to the risk of HR decrease after the first dose of fingolimod.

Parasympathetic stimulation of the heart is mediated by acetylcholine-receptors, which activate G-protein coupled inwardly rectifying potassium (GIRK) channels on the cell membrane. On the other hand, fingolimod results in initial agonism of the S1P-receptors leading also to activation of the myocardial GIRK channels (Brinkmann, 2007 and Koyrakh et al, 2005). Thus, parasympathetic cardiac activation via acetylcholine receptors and the effects of fingolimod via S1P-receptor agonism activate eventually the GIRK-channels similarly. During enhanced parasympathetic cardiac regulation, the effect of fingolimod on the GIRK-channels may also become augmented and thus, predispose a patient to a greater HR decrease.

In our study, HR variability was calculated from ambulatory 24-h ECG recordings which was primarily undertaken to screen cardiac rhythm and conduction problems before fingolimod initiation. While interpreting our results, one should bear in the mind that the values of HR variability based on short-term and long-term ECG recordings are not comparable due to methodological concerns (Task Force, 1996).

5. Conclusion

Assessment of cardiac autonomic regulation by HR variability based on an ambulatory ECG recording can be used to predict the magnitude of HR decrease after the first dose of fingolimod. Specifically, an enhanced parasympathetic cardiac regulation predisposes a RRMS patient to a HR decrease shortly after the fingolimod initiation. This finding helps to identify patients at risk for greater HR decrease after the first dose of fingolimod and thereby improves drug safety.

Declaration of funding

This work was funded by the government as a non-commercial TEVO-funding (Grant 12178).

Conflict of interest

SS has been the congress representative of Mikkeli Central Hospital sponsored by industry (BiogenIdec, Boehringer Ingelheim, Genzyme, GlaxoSmithKline, Novartis, OrionPharma, Sanofi, TEVA) and has been a speaker sponsored by industry (Biogen, Merck, Novartis, TEVA).

TPL has received a research grant from the Finnish Foundation for Cardiovascular Research.

TML: None.

PH has been the congress representative of Kuopio University Hospital sponsored by industry (BiogenIdec, Genzyme, TEVA).

JEKH has received research grants from the Finnish Foundation for Cardiovascular Research and the European Union Seventh Framework Programme and has been a speaker sponsored by industry (Cardiome, Biotronic, Ambeg, MSD, AstraZeneca).


This work was funded by the government as a non-commercial TEVO-funding (Grant 12178). The funding source did not contribute to the work in any part.


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a Department of Neurology, Mikkeli Central Hospital, Mikkeli, Finland

b Department of Clinical Physiology and Nuclear Medicine, Kuopio University Hospital and University of Eastern Finland, Kuopio, Finland

c Neuro Center, Kuopio University Hospital and University of Eastern Finland, Kuopio, Finland

d Heart Center Kuopio University Hospital and University of Eastern Finland, Kuopio, Finland

Corresponding author.