Escorihuela et al. J Transl Med (2020) 18:4 https://doi.org/10.1186/s12967-019-02184-z Journal of Translational Medicine RESEARCH Open Access Reduced heart rate variability predicts fatigue severity in individuals with chronic fatigue syndrome/myalgic encephalomyelitis Rosa María Escorihuela1*†, Lluís Capdevila2, Juan Ramos Castro3, María Cleofé Zaragozà4, Sara Maurel5, José Alegre6 and Jesús Castro‑Marrero6*† Abstract Background: Heart rate variability (HRV) is an objective, non-invasive tool to assessing autonomic dysfunction in chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME). People with CFS/ME tend to have lower HRV; how‑ ever, in the literature there are only a few previous studies (most of them inconclusive) on their association with illness-related complaints. To address this issue, we assessed the value of different diurnal HRV parameters as potential biomarker in CFS/ME and also investigated the relationship between these HRV indices and self-reported symptoms in individuals with CFS/ME. Methods: In this case–control study, 45 female patients who met the 1994 CDC/Fukuda definition for CFS/ME and 25 age- and gender-matched healthy controls underwent HRV recording-resting state tests. The intervals between consecutive heartbeats (RR) were continuously recorded over three 5-min periods. Time- and frequency-domain analyses were applied to estimate HRV variables. Demographic and clinical features, and self-reported symptom measures were also recorded. Results: CFS/ME patients showed significantly higher scores in all symptom questionnaires (p < 0.001), decreased RR intervals (p < 0.01), and decreased HRV time- and frequency-domain parameters (p < 0.005), except for the LF/HF ratio than in the healthy controls. Overall, the correlation analysis reached significant associations between the question‑ naires scores and HRV time- and frequency-domain measurements (p < 0.05). Furthermore, separate linear regression analyses showed significant relationships between self-reported fatigue symptoms and mean RR (p = 0.005), RMSSD (p = 0.0268) and HFnu indices (p = 0.0067) in CFS/ME patients, but not in healthy controls. Conclusions: Our findings suggest that ANS dysfunction presenting as increased sympathetic hyperactivity may contribute to fatigue severity in individuals with ME/CFS. Further studies comparing short- and long-term HRV record‑ ing and self-reported outcome measures with previous studies in larger CFS/ME cohorts are urgently warranted. *Correspondence: rosamaria.escorihuela@uab.cat; jesus.castro@vhir.org † Rosa María Escorihuela and Jesús Castro-Marrero contributed equally to this work 1 Departament de Psiquiatria i Medicina Legal, Institut de Neurosciències, Universitat Autònoma de Barcelona, Facultat de Medicina, Avinguda Can Domènech, s/n, 08193 Bellaterra, Barcelona, Spain 6 CFS/ME Unit, Vall d’Hebron University Hospital Research Institute, Universitat Autònoma de Barcelona, Passeig de Vall d’Hebron 119‑129, 08035 Barcelona, Spain Full list of author information is available at the end of the article © The Author(s) 2020. 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The Creative Commons Public Domain Dedication waiver (http://creat​iveco​mmons​.org/publi​cdoma​in/ zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Escorihuela et al. J Transl Med (2020) 18:4 Page 2 of 12 Keywords: Autonomic dysfunction, Chronic fatigue syndrome, Fatigue, Heart rate variability, Myalgic encephalomyelitis Background head-up tilt in CFS/ME patients compared with healthy Chronic fatigue syndrome (CFS), also known as myal- controls, but no differences in the baseline supine posi- gic encephalomyelitis (ME) is a debilitating and com- tion. Yataco et al. [14] did not find differences either in plex multisystem condition of unknown aetiology with the baseline supine state or in response to upright tilt. high incapacitating potential due to severe and unex- De Becker et al. [15] found differences in frequency plained fatigue that cannot be alleviated by rest. Along domain parameters after tilting, but not in the supine with disabling fatigue, CFS/ME presents an array of position; and Frith et al. [16] found changes only in fre- core symptoms including autonomic dysfunction, unre- quency domain variables measured at rest. freshing sleep, cognitive impairment (sometimes called In this study we aimed: (1) to explore different diurnal also “brain fog”), post-exertional malaise, and muscle HRV time and frequency domain parameters as potential pain (or myalgias) [1]. biomarker in CFS/ME, and (2) to assess whether these In medical research, heart rate variability (HRV) HRV measures are associated with early self-reported analysis is an objective and non-invasive tool that may symptoms in individuals with CFS/ME. play an important role in describing autonomic dys- function in CFS/ME research and clinical practice, in Methods tracking the natural fluctuations of autonomic func- Participants’ characteristics tion across various time spans, and in predicting the A prospective, cross-sectional, case–control cohort patients’ prognosis. Assessment of autonomic dysfunc- study of 45 consecutive females who met the 1994 CDC/ tion by HRV analysis is easy to perform and offers the Fukuda definition for CFS/ME [17] were recruited important advantage that it can track dynamic changes from a single outpatient tertiary referral centre (CFS/ of cardiac autonomic function within minutes. Recently ME Unit, Vall d’Hebron University Hospital, Barcelona, developed time- and frequency-domain analyses fur- Spain) from January 2014 through March 2015. Twenty- ther enhance the ability of HRV analysis to track active five matched non-fatigued healthy controls were also changes in cardiovascular autonomic function. These included through word-of-mouth and advertisements analyses are likely to become part of future diagnostic from the local community. All participants were of Cau- criteria for CFS/ME and may serve as a surrogate end- casian descent, from the same geographical area, and point marker in clinical trials [2, 3]. had a sedentary life style at the time of study. Exclusion Previous studies have provided strong evidence for criteria for the study were previous or current diagnosis autonomic nervous system (ANS) involvement in of autoimmune illnesses, multiple sclerosis, psychosis, individuals with CFS/ME, including perturbations in major depression, cardiovascular disorders, haematologi- the structure and function of brain (reflected by fMRI cal disorders, infectious diseases, sleep apnea or thyroid- testing) [4–11]. In brief, cerebral blood flow volume related illnesses, pregnancy or breast-feeding, smoking, changes and reduced HRV may lead to greater exacer- hormone-related drugs, or any symptoms that might be bation of ANS symptoms in individuals with CFS/ME. confused with those of CFS/ME. In the current study, we performed both time and fre- quency domain HRV analyses to examine closely the Setting and data collection relationship between the physiological parameters of All eligible participants attended a first face-to-face inter- HRV and patient-reported symptom measurements. view in which demographic and clinical characteristics Time-domain variables are statistical operations on were collected along with patient-reported outcome R–R intervals that measure the dispersion of the indi- measures. They were asked about their fatigue sever- vidual cardiac cycle length around their mean. Fre- ity using the Fatigue Impact Scale (FIS-40), their auto- quency-domain parameters provide information on nomic dysfunction with both the abbreviated Composite the different constituents (frequencies) of the heart Autonomic Symptom Score (COMPASS-31) and Neu- rate signal and their relative intensity (power) [12]. rovegetative Complaints Questionnaire (NCQ), their There are some discrepancies in the literature about sleep problems using the Pittsburgh Sleep Quality Index the behaviour of the HRV variables in CFS/ME indi- (PSQI), and anxiety and depression through the Hospi- viduals: Yamamoto et al. [13], for example, reported tal Anxiety and Depression Scale (HADS). After the first lower time- and frequency-domain parameters during interview, participants were set dates for the following Escorihuela et al. J Transl Med (2020) 18:4 Page 3 of 12 additional HRV recording sessions. All records were Pittsburgh Sleep Quality Index done at the same location during working days. The Pittsburgh Sleep Quality Index (PSQI) is a 19-item self-administrated questionnaire commonly used to Measures assess sleep disturbances over a 1-month interval. Scores Participants were asked to fill out validated self-reported are acquired on each of seven domains of sleep quality: outcome measures as symptom assessment tools. The subjective sleep quality, sleep latency, sleep duration, following measures were used to evaluate all participants habitual sleep efficiency, sleep disturbances, use of sleep- under the supervision of two trained investigators (JC-M ing medication, and daytime dysfunction. Each domain is and JA) who oversaw participant compliance. scored from 0 to 3 (0 = no problems and 3 = severe prob- lems). The overall PSQI score ranges from 0 to 21 points, Neurovegetative Complaints Questionnaire with scores of ≥ 5 indicating poorer sleep quality [21]. The Neurovegetative Complaints Questionnaire (NCQ) was originally developed to measure neurovegetative, somatic and emotional complaints in post-concussive Hospital Anxiety and Depression Scale patients. The original questionnaire consisted of 28 items To assess anxiety and depression symptoms the Hospital concerning headaches, problems with falling asleep, rest- Anxiety and Depression Scale (HADS) was used; a vali- lessness, chest pain, indigestion, slowness of working, dated 14 item self-reported measure (seven items associ- sensitivity to light, effort, flushing, concentration, dysp- ated with anxiety symptoms and seven with depression) noea, preference for being left alone, tiredness, fainting, over the last week. Each item is scored on a 4-point Lik- heart palpitations, noise, difficulty with doing two tasks ert scale (e.g., 0 = as much as I always do; 1 = not quite simultaneously, preference for working at one’s own pace, so much; 2 = definitely not so much; and 3 = not at all) dizziness, depression, wet hands, crying spells, altered giving maximum subscale scores of 21 for depression libido, irritability, lack of initiative, awakening at night, and anxiety, respectively. Scores of 0–7 are interpreted defeatism, and not being appreciated by others. Par- as normal; scores of 8–10 reflect mild symptoms, 11–14 ticipants had to indicate the frequency of occurrence of moderate, and 15–21 severe for either anxiety or depres- these symptoms on a 4-point Likert scale (1 = no, never; sion. The global HADS score ranges from 0 (no anxiety/ 2 = yes, sometimes; 3 = yes, regularly, and 4 = yes, often). depression) to 42 (severe anxiety/depression symptoms) Higher scores indicated more autonomic complaints [18]. [22]. Fatigue Index Scale The Fatigue Impact Scale (FIS-40) is a 40-item question- Heart rate variability analysis naire designed to assess fatigue symptoms as part of an Heart rate variability was assessed between 3:00 pm underlying chronic condition. It includes three domains and 6:00 pm in a semi-dark room maintained at a tem- reflecting the perceived feeling of fatigue: physical (10 perature between 20 and 24 °C during weekdays. At items), cognitive (10 items) and psychosocial functions the start of each test session, all subjects were asked (20 items). Each item is scored from 0 (no fatigue) to 4 to abstain from alcoholic and caffeinated beverages, (severe fatigue). The overall score is calculated by adding cigarette smoking, cardioactive medications and heavy together the responses to the 40 questions (ranging from exercise (physical/mental activity) for at least 48 h 0 to 160). Higher scores indicate more functional limita- prior to testing, as their actions might alter their auto- tions due to fatigue [19]. nomic function. After 5 min of rest lying down, par- ticipants were asked to remain in supine position and Composite Autonomic Symptom Score still without speaking or making any movements, and For measuring autonomic dysfunction, all participants HRV data were recorded continuously for 5 min of were screened the Composite Autonomic Symptom natural breathing. Participant’s session ratings of con- were collected and analysed with the ­FitLab® system Score (COMPASS-31), a 31-item questionnaire designed tinuous heart rate (RR or beat-to-beat cardiac intervals) to evaluate the frequency and severity of autonomic function symptoms, grouped in six domains: orthostatic (HealthSportLab.com, Barcelona, Spain); an application intolerance (4 items), vasomotor (3 items), secretomotor that runs on a mobile device (iOS, Apple) connected via (4 items), gastrointestinal (12 items), bladder (3 items) Bluetooth (BTv4) with a cardiac chest band (Polar Elec- and pupillomotor systems (5 items). Added together, the tro Oy, Kempele, Finland) and connected via wireless six domain scores provide a total COMPASS-31 score to a remote server. The system allows performance of ranging from 0 to 100. Higher scores indicate more individual HRV recordings in each session and checks severe autonomic complaints [20]. data quality in real time. All participants performed the Escorihuela et al. J Transl Med (2020) 18:4 Page 4 of 12 three 5-min HRV tests at rest in the supine position. Results The intervals between consecutive heartbeats (R–R) Demographic and clinical characteristics of participants were continuously monitored over three 5-min periods Table 1 presents the demographic, clinical character- on different days. istics and baseline self-reported measures among the ­ itLab® software, Heart rate variability analysis was performed for RR participants. There were no significant differences in intervals in 5 min periods using the F age, BMI or SBP between ill and healthy controls. Heart following the recommendations of the Task Force of the rate and diastolic blood pressure were higher among European Society of Cardiology and the North American ill than healthy controls (p = 0.002 and p = 0.022, Society of Pacing and Electrophysiology [23]. respectively). For the time domain analysis, the mean of RR inter- vals (mean RR), the standard deviation of all RR intervals (SDRR), the root mean square of differences (RMSSD) of successive RR intervals and the mean number of times Table 1 Demographic and clinical characteristics an hour in which the change in successive normal sinus of participants at time of recruitment (RR) intervals exceeds 50 mseg (pNN50) were calcu- Variable Healthy CFS/ME (n = 45) p-valuea lated. For frequency domain analysis, all RR series were controls resampled at 3 Hz using a cubic spline prior to the HRV (n = 25) analysis. Power–frequency analysis of the 5-min record- Age (years) 44.96 ± 1.30 46.41 ± 0.84 N.S. ings was performed sequentially with a fast Fourier trans- BMI (kg/m2) 23.77 ± 0.61 24.59 ± 0.69 N.S. form based on a non-parametric algorithm with a Welsh SBP (mmHg) 115.2 ± 2.15 121.2 ± 1.99 0.057 window after the ectopic-free data were detrended and DBP (mmHg) 74.45 ± 1.56 79.56 ± 1.38 0.022 resampled with a cubic spline interpolation [24]. The HR (bpm) 67.71 ± 1.93 74.72 ± 1.21 0.002 power in the very low frequency (VLF) band (0.0033– 0.04 Hz), the low frequency (LF) band (0.04–0.15 Hz) NCQ 0.40 ± 0.15 10.11 ± 0.28 < 0.001 and the high frequency (HF) band (0.15–0.40 Hz) were FIS-40 calculated from each 5-min spectrum by integrating the Global score (0–160) 17.12 ± 3.25 140.9 ± 1.79 < 0.001 spectral power density in the respective frequency bands.   Physical 4.60 ± 0.94 36.95 ± 0.39 < 0.001 Additional calculations included the LF/HF ratio, as well   Cognitive 4.48 ± 1.03 35.73 ± 0.66 < 0.001 as the normalized LF and HF values (LFnu and HFnu,   Psychosocial 8.04 ± 1.46 68.27 ± 1.04 < 0.001 respectively). For the final analysis of each variable, the COMPASS-31 values obtained in the three independent recordings were Global score (0–100) 27.31 ± 2.42 80.10 ± 2.91 < 0.001 averaged.   Orthostatic intolerance 2.52 ± 0.25 7.45 ± 0.31 < 0.001   Vasomotor 0.48 ± 0.21 1.93 ± 0.24 < 0.001   Secretomotor 0.76 ± 0.18 4.73 ± 0.18 < 0.001 Statistical analysis   Gastrointestinal 5.60 ± 0.74 13.45 ± 0.68 < 0.001 Data were analysed using the IBM SPSS Statistics Pack-   Bladder 0.32 ± 0.11 3.48 ± 0.32 < 0.001 age for Mac OS (version 23.0, SPSS Inc., Chicago, IL).   Pupillomotor 2.96 ± 0.46 10.32 ± 0.55 < 0.001 Descriptive statistics and outcome measures for each PSQI item were shown as mean ± standard error of the mean. Global score (0–21) 4.52 ± 0.63 15.05 ± 0.57 < 0.001   Subjective sleep quality 0.56 ± 0.12 2.23 ± 0.14 < 0.001 ­FitLab® software (HealthSportLab.com, Barcelona, Calculation of HRV parameters was carried out with   Sleep latency 0.72 ± 0.17 1.89 ± 0.16 < 0.001 Spain) and MATLAB environment. Differences between   Sleep duration 0.92 ± 0.17 2.05 ± 0.13 < 0.001 unrelated groups were assessed with the Student’s t-test   Habitual sleep efficiency 0.56 ± 0.22 1.95 ± 0.17 < 0.001 for independent samples, and non-parametric Mann–   Sleep disturbances 1.04 ± 0.07 2.27 ± 0.13 < 0.001 Whitney U test when appropriate. Pearson correlation   Sleeping medication 0.28 ± 0.15 2.11 ± 0.19 < 0.001 analyses were used to test bivariate associations between   Daytime dysfunction 0.44 ± 0.12 2.55 ± 0.11 < 0.001 questionnaires scores and HRV indices or between ques- HADS tionnaires scores themselves. Finally, to further evaluate Global score (0–42) 5.15 ± 0.70 26.68 ± 1.41 < 0.001 the relative relationship between the most significant   Anxiety 3.96 ± 0.41 13.73 ± 0.73 < 0.001 HRV parameters and patient-reported fatigue severity,   Depression 1.16 ± 0.29 12.95 ± 0.68 < 0.001 we performed separate regression analyses in CFS/ME Baseline self-reported measures scores (global and subscales) of symptoms, as patients and healthy controls. The significance threshold explained in “Methods” section. Data are shown as mean ± SEM for each item a Data were analyzed using Student’s t test for independent samples, and the was set at p < 0.05. Mann–Whitney U non-parametric test when appropriate Escorihuela et al. J Transl Med (2020) 18:4 Page 5 of 12 Self‑reported measures anxiety and depression on the HADS questionnaire (a The NCQ scores and the physical, cognitive, psychoso- mean of 10–11 points more) than controls (p < 0.001, cial domains and global FIS-40 scores were an average Table 1). of eight times higher than those reported by the healthy controls (p < 0.001; Table 1). The higher scores on these Heart rate variability indices measures were consistent with the CFS/ME diagnosis. Heart rate variability analysis showed significantly Regarding the autonomic symptoms self-reported in decreased values in CFS/ME patients compared with the COMPASS-31 questionnaire, the CFS/ME patients healthy controls for the time domain parameters: mean reported increases in orthostatic intolerance, vasomo- RR (Fig. 1a, p = 0.009), SDNN (Fig. 1b, p < 0.001), RMSSD tor, secretomotor, gastrointestinal, bladder, pupillomo- (Fig. 1c, p < 0.001) and pNN50 (Fig. 1d, p < 0.001). HRV tor dysfunction (all p < 0.001) and overall COMPASS-31 analysis for the frequency domain revealed significantly score (p < 0.001) compared with the control subjects lower values for the parameters (LF, Fig. 2a, p = 0.011; (Table 1). These results are also consistent with an HF, Fig. 2b, p < 0.001; HFnu Fig. 2c, p = 0.001), and higher increased number of autonomic symptoms reported in LF/HF index (Fig. 2d, p = 0.014) in CFS/ME patients than the NCQ (Table 1). in healthy controls. Regarding the patient-reported sleep symptoms in the PSQI questionnaire, the CFS/ME patients had higher Correlation between clinical measures and HRV scores for sleep quality, sleep latency, sleep duration, and parameters sleep efficiency than controls (all p < 0.001; Table 1). CFS/ Table 2 displays the correlation analysis between self- ME patients also reported increased sleep disturbances, reported symptoms and HRV domain indices, showing greater use of sleep medication and increased daytime a negative association between the global FIS-40 score dysfunction (all p < 0.001; Table 1), and their total PSQI and the subscales with all HRV domain indices (p < 0.01), score was three times higher than in controls (p < 0.001, except for the LF/HF ratio with which the association Table 1). CFS/ME patients reported higher scores of was positive (p < 0.05). Regarding COMPASS-31, the Fig. 1 Comparison of the HRV time-domain parameters among the participants. Values are shown as mean ± SEM of a mean of RR intervals (mean RR), b standard deviation of all R–R intervals (SDNN), c root mean square of successive RR intervals differences (RMSSD), and d the mean number of times in an hour in which the change in successive normal sinus R–R intervals exceeds 50 ms (pNN50). Significance level is indicated above the horizontal line in each chart Escorihuela et al. J Transl Med (2020) 18:4 Page 6 of 12 Fig. 2 Comparison of the HRV frequency-domain measures in the study participants. Values are shown as mean ± SEM of a absolute power of the low frequency band (LF), b absolute power of the high frequency band (HF), c normalized HF value (HFnu) and d ratio of LF-to-HF power (LF/HF). Significance level is indicated above the horizontal line in each chart domains for orthostatic intolerance, secretomotor and with mean RR (no significant correlation), and showed total score correlated negatively with all HRV indices a positive correlation with LF/HF index (p < 0.05). Sleep (p < 0.01), except with LF/HF with which the correlation disturbances correlated negatively with all indices was positive (p < 0.05). The gastrointestinal domain cor- (p < 0.05) except for LF (no significant correlation), and related negatively with all HRV indices (p < 0.01), except positively with LF/HF (p < 0.05). Sleep medication corre- for the LF/HF ratio which correlated positively (p < 0.05). lated negatively with all indices (p < 0.05) except for LF/ The pupillomotor domain showed a negative correlation FH and HFnu (no significant correlation). Daytime dys- with all indices except for LF/HF index. The vasomotor function and overall PSQI score correlated negatively domain presented a significant negative correlation with with all HRV parameters (p < 0.01); except for a signifi- mean RR (p < 0.05); and the bladder item correlated nega- cant positive correlation with LF/HF index (p < 0.01). tively with HFnu (p < 0.05). As showed in Table 2, all correlations were negative for The HADS-depression domain showed a negative cor- the outcomes measures scores, except for LF/HF ratio, relation with all HRV indices (p < 0.01), and a positive thus indicating that lower HRV domain values were asso- correlation with LF/HF ratio (p < 0.05). Finally, HADS- ciated with higher self-reported measure scores. How- anxiety domain showed a negative correlation (p < 0.05), ever, a higher LF/HF index was associated with higher but no correlation with LF/HF index. clinical symptom scores. On the PSQI questionnaire, sleep quality showed a Interestingly, a closer look shows that FIS-40, HADS- negative correlation with the four indices of the HRV depression, overall PSQI and NCQ questionnaires had time domain analysis (p < 0.05), but only with the HF fre- indices lower than − 0.40 and significances greater than quency domain parameter (p < 0.05). Sleep latency did 0.01. In more in detail, the FIS-40 scores had correla- not correlate with any HRV parameter. Sleep duration tion indices below − 0.423 with all HRV time-domain showed a negative correlation with the HRV time domain parameters (mean RR, SDNN, RMSSD and pNN50), parameters (SDNN, RMSSD and pNN50; all p < 0.05), and with the HF and HFnu frequency-domain param- but only a negative correlation with HF for the frequency eters, thus suggesting a robust and consistent associa- domain (p < 0.01). Habitual sleep efficiency correlated tion between fatigue symptoms and these HRV domain significantly negatively with all indices (p < 0.05), except parameters. HADS-depression had a correlation Escorihuela et al. J Transl Med (2020) 18:4 Page 7 of 12 Table 2 Pearson correlation coefficients (r) between HRV parameters (both time- and frequency-domains) and self- reported measures in the sample Mean RR SDRR RMSSD pNN50 LF HF LF/HF HFnu PSQI Sleep quality − .244* − .302* − .302* − .279* − .226 − .275* .174 − .203 Sleep latency − .047 − .190 − .190 − .161 − .124 − .170 .188 − .152 Sleep duration − .223 − .290* − .295* − .251* − .180 − .320** .213 − .216 Habitual sleep efficiency − .182 − .288* − .306* − .285* − .251* − .308* .284* − .249* Sleep disturbances − .301* − 286* − .323** − .291* − .226 − .275* .303* − .283* Sleeping medication − .261* − .407** − .385** − .380** − .285* − .322** .130 − .190 Daytime dysfunction − .297* − .394** − .394** − .385** − .277* − .332** .367** − .403** Global score − .285* − .405** − .409** − .381** − .295* − .373** .302* − .312** NCQ − .332** − .378** − .409** − .399** − .250* − .358** .341** − .409** FIS-40 Physical − .438** − .442** − .494** − .498** –.326** − .435** .309* − .423** Cognitive − .440** − .464** − .507** − .512** –.322** − .451** .360** − .476** Psychosocial − .439** − .462** − .501** − .502** –.337 − .438** .324** − .431** Global score − .442** − .460** − .504** − .507** –.332 − .443** .332** − .443** HADS Anxiety − .334** − .347** − .379** − .377** − .273* − .339** .137 − .283* Depression − .371** − .407** − .446** − .435** − .249* − .377** .308* − .449** Global score − .367** − .390** − .429** − .422** − .271* − .372** .236 − .384** COMPASS-31 Orthostatic intolerance − .367** − .383** − .390** − .362** − .283* − .323** .276* − .314** Vasomotor − .293* − .155 − .170 − .194 − .095 − .092 .060 − .186 Secretomotor − .330** − .379** − .395** − .396** − .291* − .319** .271* − .333** Gastrointestinal − .315** − .278* − .342** − .360** − .203 − .251* .301* − .356** Bladder − .183 − .181 − .239 − .229 − .133 − .225 .219 − .301* Pupillomotor − .322** − .319** − .347** − .353** − .189 − 294* .192 − .289* Global score − .368** − .359** − .400** − .397** − .261* − .317** .307* − .370** Significance: *p < 0.05; **p < 0.01 index below − 0.407 with SDNN, RMSSD and pNN50 were positive, highly significant and higher than 0.71 time-domain parameters, and with HFnu index of the (p < 0.01). Most correlation coefficients (r-values) were frequency domain. Finally, overall PSQI had a nega- higher than 0.8. The r-value between FIS-40 and NCQ tive correlation with SDNN and RMSSD time domain was 0.942 (Table 3). (r = − 0.407 and r = − 0.409, respectively) but did not correlate with any frequency domain parameters. Discussion Finally, the simple linear regression analyses revealed This study, designed to identify HRV analysis-derived clear differences among the study participants, since putative indices associated with self-reported measures CFS/ME patients showed significant relationship in CFS/ME, provides the first evidence of a significant between fatigue severity as measured by the FIS-40 relationship between HRV and fatigue severity in this and HRV domain parameters such as mean RR (Fig. 3a, condition. The findings of this study showed a high asso- r = − 0.056, p = 0.005), RMSSD (Fig. 3b, r = − 0.055, ciation between the questionnaires scores themselves, p = 0.027) and HFnu (Fig. 3c, r = − 0.365, p = 0.007). indicating a close relationship among all the symptoms. A No such associations were found for each HRV domain second finding was that all HRV indices (from frequency in healthy controls. and temporal domain analyses), except for the LF/HF Table 3 shows Pearson’s correlations between global ratio, were negatively correlated with the self-reported scorings of self-reported questionnaires as PSQI, questionnaire scores in both ill and control groups. This NCQ, COMPASS-31, and HADS. All correlations indicates that, as expected, low HRV is associated with high scores for fatigue, autonomic dysfunction, sleep Escorihuela et al. J Transl Med (2020) 18:4 Page 8 of 12 Fig. 3 Correlation analysis between fatigue severity perception and HRV indices among the participants. Total FIS-40 score significantly correlated with a the mean RR, b the RMSSD, and c the HFnu among CFS/ME patients (black squares, upper regression lines) but not for those healthy controls (white circles, bottom regression lines). Significant correlations were found between HRV indices and presence of perceived fatigue among CFS/ME individuals (p < 0.01 for mean RR and HFnu, and p < 0.05 for RMSSD), but not in healthy controls Table 3 Pearson correlations between measures assessed dysfunction, sleep quality and anxiety/depression symp- in the sample toms. This is an important finding because the two types Measures NCQ FIS-40 HADS COMPASS-31 of variables point in the same direction, thus adding robustness to the results. PSQI .824** .813** .729** .816** This is the first study to show this consistency between NCQ .942** .796** .852** the different HRV indices. To some extent, our results FIS-40 .843** .839** corroborate those of previous studies. Yamamoto et al. HADS .796** [13] reported lower mean RR, but not SDNN, in CFS/ Significance: **p < 0.001 ME patients than in matched healthy controls in baseline supine position. In contrast, Yataco et al. [14] had pre- viously reported no differences in LF, HF and LF/HF in quality, anxiety and depression symptoms. Finally, this baseline supine position between CFS/ME patients and study reveals a relationship of two HRV indices (RMSSD healthy controls. and HFnu) with fatigue symptoms; that is, low values of During sleep, Boneva et al. [25] found shorter mean RMSSD (obtained from time domain HRV analysis) and RR and reduced LF coupled with higher nor-epineph- low values of HFnu (obtained from frequency domain rine levels and lower aldosterone levels in plasma. The analysis) are specifically associated with high fatigue authors interpreted this as a state of sympathetic ANS symptoms (as assessed by overall FIS-40 score) in the predominance and neuroendocrine disturbances. Moni- CFS/ME patients, but not in healthy controls. As this toring HR during nocturnal sleep in CFS/ME, Rahman relationship did not appear between other HRV variables et al. [26] found decreased RMSSD, HF and LF/HF ratio and other self-reported measures scores, we believe that in CFS/ME patients compared to those healthy controls. this is the first evidence of an association between HRV This result is in line with Meeus et al. review [27] who changes and outcome measures in CFS/ME. concluded that HRV was only reduced during sleep in This study shows that HRV analysis is a clinically useful ME/CFS. non-invasive tool for predicting fatigue severity in CFS/ Lewis et al. [6] used frequency domain analysis to ME. Both the time- and frequency-domain indices were investigate the differences in autonomic dysfunction closely related to self-reported autonomic dysfunction, between two CFS/ME subgroups, POTS vs. non-POTS. sleep quality, and anxiety and depression in clinical out- Interestingly, they found lower LF, HF and VLF in the patients. CFS/ME patients had lower mean RR, SDNN, POTS cases. The authors did not include HRV time RMSSD and pNN50 than healthy controls, and lower LF, domain parameters, and they proposed these frequency HF and HFnu but higher LF/HF. Even though the first set indices as candidate biomarkers for distinguishing of these HRV parameters were obtained from the time between these two CFS/ME phenotypes. domain analysis and the second set from the frequency A relevant feature of the procedure used in the cur- domain analysis, they all indicate that CFS/ME patients rent study for recording the RR intervals is the use of showed decreased HRV associated with autonomic Escorihuela et al. J Transl Med (2020) 18:4 Page 9 of 12 5-min records obtained on 3 different days (similar imbalance might reflect an alteration of the central con- time schedule, 15–18 h) from each participant. The trol pathomechanisms. three values of each variable were averaged and used Studying parasympathetic activity by using HF power for the final analysis. It is likely that this method con- in the frequency domain method and RMSSD in the ferred robustness on the measure and, in consequence, HRV time domain method, previous studies have shown led to a more reliable HRV value, less contaminated by that the HF component changes after electrical vagal everyday variables such as lifestyle habits, food, activ- stimulation, muscarinic receptor blockade, and vagotomy ity, sleep problem, medication, and so on. [24]. We found decreased mean RR, SDNN, RMSSD and In a recent study from our group exploring abnor- pNN50 in CFS/ME patients compared with healthy con- malities of circadian rhythm and dysautonomia in CFS/ trols in the HRV time domain analysis of RR intervals, ME, we found changes in the chronotype and symp- and the frequency domain analysis revealed decreased tom patterns in these patients compared with healthy LF and HF, and HFnu and increased LF/HF index in CFS/ controls. The findings of that study also showed a dif- ME patients. This concurrence in these HRV indices from ference of almost 10 points in self-reported autonomic different domains had not been previously reported. symptoms [28]. Using the COMPASS, which included The robust association between fatigue symptoms, anx- 73 questions that assess autonomic dysfunction symp- iety-depression and HRV indices also deserves mention. toms in CFS/ME, Newton et al. [29] concluded that an All scores of fatigue symptoms (physical, cognitive, psy- overall COMPASS cut-off score ≥ 32.5 was considered chosocial and overall) correlated significantly and nega- as a useful diagnostic criterion for ANS dysfunction in tively with all HRV indices, except for the LF/HF ratio, individuals with CFS/ME. In the current study, CFS/ which showed a positive correlation. This indicates that ME patients reported increased orthostatic intolerance, increased fatigue coincides clearly with a reduced varia- and higher scores of vasomotor, secretomotor, gastro- tion in the time interval between consecutive heartbeats. intestinal, bladder, pupillomotor symptoms and total Anxiety and depression scores were also negatively and COMPASS scores than healthy controls, in line with robustly associated with time domain HRV indices and previous results reported by our group [28] and others with the HF index of the frequency domain, suggesting [30]. that anxiety and depression symptoms were associated The development of telemetric devices capable of with decreased HRV. All PSQI domains except sleep detecting and capturing R–R interval signals, together latency were negatively and robustly associated with the with the applications that facilitate the analysis and pro- main HRV indices, including SDRR, RMSSD, pNN50 vide the calculation of HRV indices, may facilitate the and HF. This result is consistent with those of a previous use of these signals as biomarker in research and clini- study reporting lower nocturnal RMSSD and HF in CFS/ cal practice. This study breaks new ground in the use of ME patients than in healthy controls [26]. In the current mHealth technology for the real-time analysis of cardiac study, sleep efficiency, disturbances, sleeping medication variability in CFS/ME in a controlled situation. For exam- and total scores were also associated with HFnu. Inter- ple, mHealth has been defined as the use of mobile com- estingly, the correlations of HF and HFnu with the ques- puting and communication technologies in healthcare tionnaires scores were stronger than those of the LF and and public health [31, 32]. The improvement in the speed LF/HF indices. Overall, the significance of the HF and of the processors, the smaller and longer-lasting batter- HFnu when present was greater than that of LF or LF/HF ies, the greater memory capacity and very precise built- in all the correlations analysed, suggesting that HF and in sensors enables more accurate monitoring of health or HFnu may be more specific correlates of fatigue and parameters in real time and in natural situations [33]. comorbid health conditions than LF or LF/HF. Heart rate variability is considered an index of car- Regarding the COMPASS-31 results, four of the six diac autonomic modulation. In the frequency domain, domains (orthostatic intolerance, secretomotor, gastroin- vagal (parasympathetic) activity is the major contribu- testinal, and pupillomotor) as well as the total score were tor to HF variability, whereas both vagal and sympathetic associated with decreased HRV time domain and HF and activity contributes to LF variability. The LF/HF ratio is HFnu parameters, which again corroborates the specific- considered an index of sympathovagal balance. For time ity of those measures as biomarker that correlate with domain indices, vagal (parasympathetic) activity is the fatigue and comorbid conditions. We stress that orthos- main contributor to pNN50 and RMSSD, whereas SDNN tatic intolerance, secretomotor and total scores were also is a measure of total variability, analogous to the total associated with LF and the LF/HF ratio, but with a lower power index in the frequency domain [24]. Autonomic degree of significance than the other parameters men- function in CFS/ME shows sympathetic hyperactivity tioned above. Vasomotor and bladder domains were not and parasympathetic hypoactivity and this autonomic associated with HRV, thus indicating certain specificity Escorihuela et al. J Transl Med (2020) 18:4 Page 10 of 12 in the association between the autonomic dysfunction people with CFS/ME and also assess the natural course of scored by COMPASS-31 and HRV domain parameters. cardiac autonomic dysfunction, taking advantage of the With the exception of the LF/HF ratio, a significant nega- capabilities offered by mHealth technology; (2) we did 24-h Holter ECG recorders); however, the ­FitLab® system tive association was found between all the HRV param- not use a ‘gold standard’ for the HRV measurements (i.e. eters and symptoms of sleep disturbances, anxiety/ depression, autonomic dysfunction and, most signifi- using the new mobile telemetric devices has proven to be cantly, the fatigue scores. Thus, low values for the HRV an accurate and valid method for HRV measurements; indices were associated with high scores of the clinical (3) we did not include men with CFS/ME; this is an symptoms. area we are currently exploring; (4) Short-term (5-min) Finally, the results showed a robust relationship HRV analysis was performed only for time- and spectral between the self-reported measure score of fatigue domains; perhaps other complex approaches for analys- assessed by FIS-40 scale and mean RR, RMSSD and ing long-term HRV over the entire 24 h (e.g., approxi- HFnu HRV indices in CFS/ME patients, but not in mate and sample entropy, detrended fluctuation analysis, healthy controls. Interestingly, HF domain together with long-term exposure, non-linear vs. linear dynamic meth- mood status (assessed by the Profile of Mood Status) and ods, and so on) may provide valuable additional infor- blood biomarkers (such as serum dehydroepiandroster- mation in relation to the prediction of fatigue status in one sulfate levels, cortisol and TNF-α), HF improved in individuals with CFS/ME. CFS/ME patients after a session of isometric yoga [34]. These changes may be related to a short-term fatigue- relieving effect of sitting isometric yoga and the ensuing Conclusions increase in vagal nerve functioning observed due to the This is the first study to investigate the relationship reduction of the heart rate and the increase in high fre- between HRV domain indices and patient-reported quency power. These results indicate the importance of measures in individuals with CFS/ME. Our findings show the physiological parameters involved in the R–R vari- that HRV analysis using mHealth technology is an objec- ability, and of the assessment of fatigue severity status in tive, non-invasive tool that can be useful for clinical use individuals with CFS/ME. to predict fatigue severity, which may therefore be part of the pathogenesis in CFS/ME, since both the time- and Strengths and limitations frequency-domain indices were closely associated with We believe that this study is unique for several reasons: ANS dysfunction in this condition. This study provides a (1) the sample size is adequate in a study involving CFS/ rationale for future longitudinal studies in larger CFS/ME ME patients; (2) the reproducibility of HRV recordings cohorts, including men, would be of great help to assess and their technical quality were satisfactory: the use of the impact of the HRV measurement using mHealth tech the same location (medical lab), the same timing in the and fatigue severity (i.e. HR monitoring to prevent post- day and the week, the same device and the recordings of exertional malaise) both at baseline and after an exercise HRV were routinely performed by the same researcher; challenge (consecutive 2-day CPET) in patients with (3) the assessment of the ‘fatigue status’ and other core CFS/ME. Studies of this kind are also urgently needed symptoms was based on patient-reported questionnaires to understand the pathophysiologic mechanisms due to that have been widely validated in the Spanish CFS/ME neurohormonal imbalance, especially related with HPA population and on an a priori threshold independent of axis and ANS dysfunction in the physical functioning and HRV analysis; (4) from a practical point of view, we con- health-related well-being in people with CFS/ME. sider that the current study will help to demonstrate the relevance of HRV time- and frequency-domain analyses Abbreviations for the quantification and monitoring of fatigue severity ANS: autonomic nervous system; BMI: body mass index; CDC: Centers for Disease Control and Prevention; CFS/ME: chronic fatigue syndrome/myalgic and its clinical impacts in CFS/ME. However, this study encephalomyelitis; COMPASS-31: 31-item composite autonomic symptom has several limitations that need to be addressed: (1) the score; DBP: diastolic blood pressure; FIS-40: 40-item fatigue index scale; HPA: study has a cross-sectional design and the sample size hypothalamic–pituitary–adrenal axis; HADS: Hospital Anxiety and Depression Scale; HF: relative power of the high-frequency band (0.15–0.4 Hz); HR: heart is relatively small; however, there is sufficient evidence rate; HRV: heart rate variability; LF: relative power of the low-frequency band to suggest a close relationship between ANS dysfunc- (0.04–0.15 Hz); LF/HF: ratio of LF-to-HF power; MRI: magnetic resonance imag‑ tion (assessed by HRV analysis) and most (if not all) of ing; NCQ: Neurovegetative Complaints Questionnaire; PEM: post-exertional malaise; POTS: postural orthostatic tachycardia syndrome; POMS: Profile of the multisystem symptoms in CFS/ME. The primary Mood Status; PSQI: Pittsburgh Sleep Quality Index; pNN50: percentage of suc‑ cause of sympathetic dysfunction in ME/CFS remains cessive RR intervals that differ by more than 50 ms; RR intervals: time between to be defined. Therefore, longitudinal large-scale stud- two successive heart beats; SBP: systolic blood pressure; SDNN: standard deviation of NN intervals; SDRR: standard deviation of RR intervals; RMSSD: ies are now warranted to elucidate the HRV changes in Escorihuela et al. J Transl Med (2020) 18:4 Page 11 of 12 root mean square of successive RR interval differences; VLF: absolute power of chronic fatigue syndrome: a systematic literature review. Eur J Clin Invest. the very-low-frequency band (0.0033–0.04 Hz). 2014;44:516–26. https​://doi.org/10.1111/eci.12256​. 6. Lewis I, Pairman J, Spickett G, Newton JL. Clinical characteristics of a novel Acknowledgements subgroup of chronic fatigue syndrome patients with postural orthos‑ The authors are grateful to all the participants who took part in the study. tatic tachycardia syndrome. J Intern Med. 2013;273:501–10. https​://doi. We also thank Michael Maudsley and Prof. Raquel Bailón (Aragon Institute of org/10.1111/joim.12022​. Engineering Research (BSICoS), University of Zaragoza, Spain) for reviewing 7. Gotts ZM, Ellis JG, Deary V, Barclay N, Newton JL. The association between the draft and providing critical comments/suggestions, and linguistic advice daytime napping and cognitive functioning in chronic fatigue syndrome. on the final manuscript. PLoS ONE. 2015;10:e0117136. https​://doi.org/10.1371/journ​al.pone.01171​ 36. Authors’ contributions 8. Castro-Marrero J, Zaragoza MC, González-Garcia S, Aliste L, Sáez- Conceptualization/conception of overall study design: RME, JC-M, JRC, LC, Francàs M, Romero O, Ferré A, Fernández de Sevilla T, Alegre J. Poor MCZ, JA. Collection and formal statistical analysis of data: RME, SM, MCZ, JRC, self-reported sleep quality and self-related quality of life in patients LC. Interpretation of the results and drafting of the paper: RME, JC-M, JRC, LC, with chronic fatigue syndrome/myalgic encephalomyelitis. J Sleep Res. SM, MCZ, JA. All authors read and approved the final manuscript. 2018;27:e12703. https​://doi.org/10.1111/jsr.12703​. 9. Nisenbaum R, Jones JF, Unger ER, Reyes M, Reeves WC. A population- Funding based study of the clinical course of chronic fatigue syndrome. Health This work was partially supported by the Spanish Ministry of Economy and Qual Life Outcomes. 2003;1:49. https​://doi.org/10.1186/1477-7525-1-49. Competitiveness (MINECO, Grant no: DEP2015-68538-C2-1-R). 10. Shan ZY, Kwiatek R, Burnet R, Del Fante P, Staines DR, Marshall-Gradisnik SM, Barndena LR. Medial prefrontal cortex deficits correlate with unre‑ Availability of data and materials freshing sleep in patients with chronic fatigue syndrome. NMR Biomed. The dataset supporting the findings of this work are available from the cor‑ 2017;30:e3757. https​://doi.org/10.1002/nbm.3757. responding author upon reasonable request. 11. Reeves WC, Heim C, Maloney EM, Youngblood LS, Unger ER, Decker MJ, Jones JF, Rye DB. Sleep characteristics of persons with chronic fatigue Ethics approval and consent to participate syndrome and non-fatigued controls: results from a population-based The study protocol was approved by the local Institutional Human Research study. BMC Neurol. 2006;6:41. https​://doi.org/10.1186/1471-2377-6-41. Ethics Committee of Vall d’Hebron University Hospital (Ref nº: CEIC/PR 12. Xhyheri B, Manfrini O, Mazzolini M, Pizzi C, Bugiardini R. Heart rate variabil‑ AG-174/2014). 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