Heart Rate Variability in Women with Systemic Lupus Erythematosus: Association with Health-Related Parameters and Effects of Aerobic Exercise

International Journal of Environmental Research and Public Health Article Heart Rate Variability in Women with Systemic Lupus Erythematosus: Association with Health-Related Parameters and Effects of Aerobic Exercise Elena Martínez-Rosales 1,2,* , Sergio Sola-Rodríguez 1,2 , JoséAntonio Vargas-Hitos 3, Blanca Gavilán-Carrera 4, Antonio Rosales-Castillo 3, Alba Hernández-Martínez 1,2, Enrique G. Artero 1,2, JoséMario Sabio 3and Alberto Soriano-Maldonado 1,2 1Department of Education, Faculty of Education Sciences, University of Almería, 04120 Almería, Spain; [email protected] (S.S.-R.); [email protected] (A.H.-M.); arter[email protected] (E.G.A.); [email protected] (A.S.-M.) 2 SPORT Research Group (CTS-1024), CERNEP Research Center, University of Almer í a, 04120 Almer í a, Spain 3Systemic Autoimmune Diseases Unit, Department of Internal Medicine, Virgen de las Nieves University Hospital, 18014 Granada, Spain; [email protected] (J.A.V.-H.); [email protected] (A.R.-C.); [email protected] (J.M.S.) 4Physical Activity for Health Promotion Research Group (PAHELP), Sport and Health University Research Institute (iMUDS), Department of Physical Education and Sports Faculty of Sport Sciences, University of Granada, 18071 Granada, Spain; [email protected] *Correspondence: [email protected] Received: 16 November 2020; Accepted: 12 December 2020; Published: 18 December 2020   Abstract: Abnormal heart rate variability (HRV) has been observed in patients with systemic lupus erythematosus (SLE). In a combined cross-sectional and interventional study approach, we investigated the association of HRV with inflammation and oxidative stress markers, patient-reported outcomes, and the effect of 12 weeks of aerobic exercise in HRV. Fifty-five women with SLE (mean age 43.5 ± 14.0 years) were assigned to either aerobic exercise (n=26) or usual care (n=29) in a non-randomized trial. HRV was assessed using a heart rate monitor during 10 min, inflammatory and oxidative stress markers were obtained, psychological stress (Perceived Stress Scale), sleep quality (Pittsburg Sleep Quality Index), fatigue (Multidimensional Fatigue Inventory), depressive symptoms (Beck Depression Inventory), and quality of life (36-item Short-Form Health Survey) were also assessed. Low frequency to high frequency power (LFHF) ratio was associated with physical fatigue (p=0.019). Sample entropy was inversely associated with high-sensitivity C-reactive protein (p=0.014) and myeloperoxidase (p=0.007). There were no significant between-group differences in the changes in HRV derived parameters after the exercise intervention. High-sensitivity C-reactive protein and myeloperoxidase were negatively related to sample entropy and physical fatigue was positively related to LFHF ratio. However, an exercise intervention of 12 weeks of aerobic training did not produce any changes in HRV derived parameters in women with SLE in comparison to a control group. Keywords: autonomic nervous system; exercise; inflammation; fatigue; rheumatic disease 1. Introduction Systemic lupus erythematosus (SLE) is a systemic autoimmune disease with multifactorial etiology that predominantly affects women [ 1 ]. In recent years, the diagnosis and treatment of SLE has significantly improved [ 2 ], and deaths due to lupus manifestation have decreased [ 3 ]. However, cardiovascular disease (CVD) mortality remains one of the leading causes of death in SLE patients [ 4 , 5 ]. Int. J. Environ. Res. Public Health 2020,17, 9501; doi:10.3390/ijerph17249501 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2020,17, 9501 2 of 14 The importance of the autonomic nervous system (ANS) on cardiovascular health and prognosis has already been reported [ 6 , 7 ]. In fact, the ANS plays a key role in regulating immune responses to inflammatory stimuli [ 8 ]. Heart rate variability (HRV) is a noninvasive and sensitive measure of ANS function [ 9 ] and is defined as the physiological variation in the duration of intervals between sinus beats [ 10 ]. Autonomic dysfunction is common in autoimmune rheumatic diseases [ 11 ], and specifically, increased sympathetic and decreased parasympathetic activity as reported by several studies in patients with SLE [ 12 – 14 ]. In this sense, patients with SLE have shown abnormal HRV, a surrogate marker of cardiac ANS dysfunction [ 15 ], which may predispose to the onset of fatal arrhythmias in these patients [ 16 ]. Considering that HRV is inversely associated with inflammatory markers in healthy individuals and in patients with CVD [ 17 ], it is of clinical interest to: (i) understand the extent to which HRV might be associated to inflammatory markers and patient-reported outcomes (PROs) and (ii) whether HRV can be enhanced through interventions in women with SLE. Exercise is a potential intervention that significantly increases cardiorespiratory fitness [ 18 , 19 ], improves cardiovascular function and PROs (i.e., fatigue, depression, etc.) [ 20 ] in patients with SLE. Although exercise has shown to decrease cardiovascular morbidity and mortality in the general population [ 21 , 22 ], its benefits in SLE population are understudied to the extent that exercise hardly appear in the EULAR guidelines for the management of this chronic disease [ 23 ]. Benatti and Pedersen [ 24 ] suggested that one of the mechanisms by which exercise might benefit the cardiovascular system in patients with rheumatic diseases is through direct or indirect anti-inflammatory effects. Based on the effects of exercise in the general population [ 25 ] and other chronic conditions [ 26 , 27 ], it might be hypothesized that exercise (and particularly aerobic exercise) could also increase HRV and thus regulate the ANS in women with SLE. Although there have been some studies evaluating HRV after an exercise stress test in this population [ 28 , 29 ], to the best of our knowledge, no prior research has evaluated the effects of an aerobic exercise program on HRV in women with SLE. Therefore, the aims of this study are (1) to cross-sectionally explore the associations of HRV with inflammatory markers and PROs; and (2) to analyze the effect of a 12-week aerobic program in women with SLE on HRV derived parameters. 2. Materials and Methods 2.1. Study Design and Participants This study included data of 58 women with SLE from a non-randomized controlled trial investigating the effects of a 12-week aerobic exercise program on arterial stiffness, inflammation, and cardiorespiratory fitness [ 19 ]. Participants were recruited from the Systemic Autoimmune Diseases Unit of the “Virgen de las Nieves” and “San Cecilio” University Hospitals (Granada, Spain). A comprehensive description of the inclusion and exclusion criteria can be found elsewhere [ 19 ]. The study was approved by the Research Ethics Committee of Granada (ref. No.: 10/2016) and registered at clinicaltrials.gov [NCT03107442] with HRV among the pre-established secondary outcomes. All participants signed written informed consent. The baseline data were used for the cross-sectional analyses of the present study. 2.2. Intervention 2.2.1. Exercise Group The exercise program has been comprehensively described elsewhere [ 19 ] following the Consensus on Exercise Reporting Template (CERT) [ 30 ]. Participants assigned to the exercise group performed two 75-min sessions per week of moderate to vigorous intensity aerobic exercise on a treadmill (BH, Serie i.RC12 Dual, Vitoria-Gasteiz, Spain) for 12 weeks. All sessions began with a warm-up on the treadmill at about 35–40% of the heart rate reserve (HRR) plus 3–4 min of active stretching, while ending with a cool down of static stretching and relaxation. Exercise was prescribed with training Int. J. Environ. Res. Public Health 2020,17, 9501 3 of 14 intensity progressively increasing in a range from 40% to 75% of each individual’s HRR. In all sessions, heart rate was monitored with a Polar V800 (Polar Inc., Kempele, Finland). Only continuous exercise was performed during the first half of the program. Continuous sessions comprised several bouts of exertion at constant intensity, followed by a couple of minutes of recovery. At 8 weeks, continuous and interval sessions were alternated, and at 12 weeks, the patients performed only interval training sessions, with periods of lower and higher intensity efforts followed by some minutes of rest for hydration. The progression in volume and/or intensity was undertaken by increasing the treadmill speed or inclination according to the perceived exertion of each patient. Lastly, the exercise intensity progressions had to be slightly modified since several patients perceived a 5% HRR intensity increase as very heavy and difficult-to-follow. Therefore, exercise intensity increased by 2.5% instead of 5% in some weeks. 2.2.2. Control Group SLE patients assigned to the control (usual care) group received information about a healthy lifestyle, including physical activity guidelines and basic nutritional information. 2.3. Heart Rate Variability Participants were requested not to drink caffeinated or alcoholic drinks, to fast for at least 3 h, and not to participate in physical activity 24 h before the assessment. R-R intervals were recorded with a Polar V800 (Polar Inc., Kempele, Finland), a validated instrument [ 31 ], placed at the sternum level. Participants were place in supine position in a quiet room (temperature 22–24 ◦ C) between 4 p.m. and 7 p.m., and were instructed to breath normally, stay relaxed and not to speak or fidget during the assessment. HRV was recorded for 10 min, after a period of 5 min, at a sampling frequency of 1000 Hz. HRV raw data was analyzed with Kubios (HRV analysis, Finland). After visual inspection for any premature contractions or ectopic beats in the recording, a 5-min period was manually selected by the evaluator. Kubios filters were applied accordingly based on inter-individual variability and if the sample presented more than 5% of interpolated R-R intervals it was discarded as per manufacturer’s recommendation [32]. The following HRV derived parameters were analyzed: the standard deviation of the average normal-to-normal (NN) interval (SDNN), the square root of the mean squared differences of successive NN intervals (RMSSD), and percentage of consecutive R-R intervals that differ by more than 50 ms (pNN50), low frequency power (LF: 0.04–0.15 Hz), high frequency power (HF: 0.15–0.4 Hz) and LF to HF power ratio (LFHF) indices (which were computed using the fast Fourier transform), Poincar é Plot were standard deviation 1 (SD1), represents short-term variability, and standard deviation 2 (SD2), the long-term variability (compared with SD1); and sample entropy (SampEn). 2.4. Patient-Reported Outcomes Health-related quality of life was assessed using the short version of the Spanish version of the 36-item Short-Form Health Survey (SF-36) [ 33 ]. Depression was assessed through the Beck Depression Inventory-second edition (BDI-II) [ 34 ]. Psychological stress was measured with the Perceived Stress Scale (PSS) [35], and fatigue with the Multidimensional Fatigue Inventory (MFI) [36]. 2.5. Inflammatory and Oxidative Stress Markers Fasting blood samples for biochemical and immunological tests were collected and processed. High-sensitivity CRP (hsCRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNFα ) were measured as markers of inflammation, whereas myeloperoxidase (MPO) was determined as a marker of oxidative stress. Int. J. Environ. Res. Public Health 2020,17, 9501 4 of 14 2.6. Other Measurements Height was measured using a height gauge, weight with a bioimpedance device (InBody R20, Korea), and body mass index (BMI) was calculated (kg/m 2 ). Blood pressure was measured with Mobil-O-Graph ® (IEM GmbH, Stolberg, Germany) [ 37 ]. Disease activity was assessed through the Systemic Lupus Erythematosus Disease Activity Index (SELENA-SLEDAI) [ 38 ]. Physical activity was self-reported with the International Physical Activity Questionnaire [ 39 ]. All participants filled out a socio-demographic and clinical data questionnaire. 2.7. Classification of Responders, Non-Responders, and Adverse Responders The inter-individual variability of the patients in the response to the intervention was analyzed by categorizing participants from each group as responders, non-responders or adverse responders using the typical error measurement (TE). The TE was calculated using the equation TE =SDdiff/ √ 2, where SDdiffis the standard deviation of the difference scores observed between the 2 repeats of each measurement [ 40 ]. A responder was defined as an individual who demonstrated an increase (in favor of beneficial changes), an adverse responder was defined as an individual who demonstrated a decrease, and a non-responder was defined as an individual who failed to demonstrate an increase or decrease that was >2 times the TE away from 0. A change more than 2 times the TE means that this response is a true physiological adaptation beyond what might be expected to result from technical and/or biological variability [41]. 2.8. Treatment Allocation and Blinding Randomization was not possible as many participants lived far and were not able to attend the exercise sessions in case of being randomized to exercise. Therefore, participants from the city of Granada were included in the exercise group and participants living outside Granada were included in the control group. To minimize potential selection bias, we aimed to match the groups by age ( ± 2 years), BMI (±1 kg/m2), and SLEDAI (±1 unit). The data analyzer was blinded to the patient allocation. 2.9. Statistical Analysis Normality was tested using visual inspection of histograms and Q-Q plots. As HRV-derived parameters were non-normally distributed, their descriptive analysis was presented using median and interquartile range, while non-parametric test was used for the main analysis. Between-group baseline characteristics were compared with the Student t-test (when normally distributed), Kruskal–Wallis test (when non-normally distributed) for continuous variables and the Chi-square test for categorical variables. To explore the associations of HRV with inflammatory and oxidative stress markers (hsCRP, IL-6, TNFα and MPO) and PROs (aim 1), scatter plots and Spearman’s bivariate correlations were used as preliminary analyses to understand raw associations. Subsequently, quantile regression models were built, including each of the above HRV parameters as dependent variables and each inflammatory marker as independent variables in regression models along with age, heart rate, and disease duration as relevant factors that might confound the association of interest. This same procedure was followed with PROs. Other variables included in the regression model were SLEDAI, systemic damage index (SDI), and smoking. However, neither of these variables affected the regression coefficients; therefore, they were not included. Inflammatory markers (hsCRP, IL-6 and TNF–α) and MPO were winsorized to the highest value due to the presence of outliers. To assess the effects of the exercise intervention (aim 2), the between group differences in the change from baseline in HRV-derived parameters were assessed through quantile regression with baseline values, heart rate, and age as covariables. As we aimed at assessing efficacy, the primary analyses were defined as per-protocol, where patients from the exercise group were included if attendance to the exercise sessions was ≥ 75%. We additionally performed sensitivity analyses including (i) participants with attendance Int. J. Environ. Res. Public Health 2020,17, 9501 5 of 14 ≥ 90%; and (ii) baseline observation carried forward (BOCF). All the analyses were conducted with SPSS v.26 (IBM SPSS Statistics, Chicago, IL, USA). Statistical significance was set at p<0.05. 3. Results The flowchart of the study participants throughout the trial is presented in Figure 1. A total of 58 patients completed the baseline assessment and were included in aim 1 analysis (n=55). Int. J. Environ. Res. Public Health 2020, 17, x 5 of 14 3. Results The flowchart of the study participants throughout the trial is presented in Figure 1. A total of 58 patients completed the baseline assessment and were included in aim 1 analysis (n = 55). Figure 1. Flowchart of the study participants throughout the study. For aim 2, participants were assigned to either the exercise group (n = 26) or the control group (n = 32). At baseline (Tables 1 and 2), the control group showed a higher IL-6 levels (median difference 3.10 pg/mL; p = 0.018), lower score in the physical component summary of the SF-36 (mean difference −4.9 units; p = 0.034), and higher punctuation in depressive symptoms (mean difference 9.0 units; p = 0.011) than the exercise group. Figure 1. Flowchart of the study participants throughout the study. For aim 2, participants were assigned to either the exercise group ( n=26 ) or the control group ( n=32) . At baseline (Tables 1and 2), the control group showed a higher IL-6 levels (median difference 3.10 pg/mL; p=0.018), lower score in the physical component summary of the SF-36 (mean difference − 4.9 units; p=0.034), and higher punctuation in depressive symptoms (mean difference 9.0 units; p=0.011) than the exercise group. Int. J. Environ. Res. Public Health 2020,17, 9501 6 of 14 Table 1. Baseline characteristics of the study participants. All (n=55) Exercise (n=26) Control (n=29) p Mean (SD) Mean (SD) Mean (SD) Age, years 43.5 (14.0) 42.9 (15.1) 43.9 (13.3) 0.808 BMI, kg/m225.4 (4.8) 25.9 (3.4) 25.0 (5.8) 0.491 SBP, mm/Hg 117.5 (10.3) 116.8 (9.9) 118.1 (10.6) 0.653 DBP, mm/Hg 75.3 (9.4) 75.5 (8.7) 75.1 (10.01) 0.843 MBP, mm/Hg 94.6 (8.7) 94.5 (8.3) 94.7 (9.2) 0.937 Mean HR, bpm 76.70 (10.71) 79.11 (9.76) 74.54 (11.23) 0.112 hsCRP, mg/L (median, IQR) 1.6 (2.6–6.5) 2.2 (1.9–7.6) 1.2 (1.5–7.1) 0.218 IL-6, pg/mL (median, IQR) 10.5 (9.4–12.3) 8.2 (7.1–11.7) 11.3 (10.3–14.0) 0.018 TNF-α, pg/mL (median, IQR) 15.6 (15.7–19.8) 16.5 (15.4–21.1) 14.8 (14.3–20.4) 0.385 MPO, ng/mL (median, IQR) 69.6 (79.1–119.6) 60.1 (62.4–126.9) 75.7 (76.3–130.9) 0.385 Smoke (%) 23.6 15.4 31.0 0.237 Menopause (%) 38.2 38.5 37.9 0.968 Dyslipidemia (%) 16.4 19.2 13.8 0.586 Statins (%) 16.4 23.1 10.3 0.203 Immunosuppressants (%) 45.5 46.1 44.8 0.921 Current corticosteroid intake (mg/day) 3.86 (5.1) 4.08 (6.1) 3.70 (4.2) 0.789 Disease duration, years 15.1 (10.1) 14.54 (10.4) 15.6 (9.9) 0.704 Total PA, min/week 94.8 (92.6) 97.5 (95.9) 92.4 (91.1) 0.660 SLEDAI 0.16 (0.764) 0.04 (0.196) 0.28 (1.0) 0.254 SDI 0.42 (1.1) 0.19 (0.63) 0.62 (1.3) 0.145 Psychological Stress (PSS; 0–56; median, IQR) 31.0 (28.9–32.1) 30.0 (27.7–31.6) 31.0 (28.7–33.9) 0.303 Depressive symptoms (BDI-II; 0–63) 12.8 (9.2) 8.0 (6.4–12.7) 17.0 (12.2–19.3) 0.011 Fatigue (MFI-S; 0–20) General Fatigue (median, IQR) 15.0 (12.9–15.1) 14.5 (12.1–15.3) 16.0 (12.5–15.9) 0.498 Physical fatigue 12.8 (4.7) 12.4 (4.8) 13.1 (4.7) 0.577 Reduced Activity (median, IQR) 10.0 (8.7–11.5) 8.0 (7.8–11.5) 11.0 (8.4–12.6) 0.741 Reduced Motivation 9.4 (3.7) 8.5 (3.4) 10.1 (3.9) 0.112 Mental Fatigue 12.2 (2.8) 12.04 (3.0) 12.3 (2.6) 0.720 Health-related quality of life (SF-36; 0–00) * Physical Component Summary 43.0 (8.2) 45.5 (8.5) 40.6 (7.8) 0.034 Mental Component Summary 44.9 (11.0) 47.5 (11.7) 40.4 (11.0) 0.106 * For SF-36 domains total sample size was n=45 due to missing data. Values are the mean (standard deviation; SD), unless otherwise indicated. BMI, body mass index; DBP, diastolic blood pressure; HR, heart rate; hsCRP, high sensitivity C-reactive protein; IL-6, interleukin-6; mg, milligrams; MBP, mean blood pressure; MPO, myeloperoxidase; PA, physical activity; SBP, systolic blood pressure; SDI, systemic damage index; SLEDAI, systemic lupus erythematosus disease activity index; TNF-α, tumor necrosis factor alpha. Int. J. Environ. Res. Public Health 2020,17, 9501 7 of 14 Table 2. Baseline heart rate variability (HRV) derived parameters of the study participants. All (n=55) Exercise (n=26) Control (n=29) p Median (IQR) Median (IQR) Median (IQR) SDNN, ms 19.59 (13.30–25.80) 15.87 (11.34–25.24) 21.42 (14.55–26.36) 0.376 RMSSD, ms 16.20 (11.55–25.07) 14.82 (8.86–24.86) 17.33 (13.61–26.75) 0.292 pNN50 (%) 0.57 (0.21–3.17) 0.42 (0.22–2.78) 0.70 (0.22–3.48) 0.715 LF, ms2164.12 (76.51–340.51) 157.23 (76.51–345.26) 198.18 (76.51–345.26) 0.607 HF, ms297.20 (39.31–299.42) 93.65 (29.92–334.81) 100.37 (59.40–216.69) 0.607 LFHF 1.57 (0.93–2.81) 1.31 (0.83–3.29) 1.82 (1.08–2.55) 0.980 SD1, ms 11.48 (8.18–17.75) 10.49 (6.27–17.60) 12.27 (9.64–17.60) 0.292 SD2, ms 25.30 (15.54–30.46) 20.86 (18.28–30.42) 25.80 (18.29–30.42) 0.423 SampEn, au 1.70 (1.55–1.83) 1.70 (1.60–1.82) 1.70 (1.51–1.83) 0.692 Values are the median (IQR, interquartile range). HF, high frequency power in absolute value; LF, low frequency power in absolute value; pNN50, percentage of successive normal sinus RR intervals more than 50 ms; RMSSD, root mean square successive difference; SampEn, sample entropy; ms. milliseconds: SD1, standard deviation—poincar é plot crosswise; SD2, standard deviation—poincaréplot lengthwise; SDNN, standard deviation of NN intervals. 3.1. Associations of HRV with Inflammatory, Oxidative Stress Markers, and PROs (Aim 1) The raw association of the HRV parameters with inflammatory markers and PROs is presented in abbreviated form in Table 3(see Table S1 and Figure S1 for more details). SampEn was inversely correlated with hsCRP and MPO (r = − 0.35, p<0.01 and r = − 0.32, p<0.05, respectively). LFHF ratio was positively correlated with IL-6 (r =0.32, p<0.05). There was no association of any time-domain derived parameter with inflammatory markers. Regarding PROs, LFHF ratio was positively correlated with the Physical Fatigue dimension of the MFI (r =0.30, p<0.05). There were no other significant correlations. Int. J. Environ. Res. Public Health 2020,17, 9501 8 of 14 Table 3. Spearman’s correlations between HRV derived parameters, inflammatory markers, and PROs (n=55). hsCRP IL-6 TNF-αMPO SLEDAI SDI PSS BDI MFI-General Fatigue MFI-Physical Fatigue MFI-Reduce Activity MFI-Reduce Motivation MFI-Mental Fatigue SF-36 Physical Component SF-36 Mental Component SDNN −0.05 −0.11 −0.21 0.04 −0.21 −0.14 0.16 −0.11 0.05 −0.14 −0.10 −0.08 −0.06 −0.03 −0.01 RMSSD −0.09 −0.14 −0.17 −0.01 −0.19 −0.03 0.04 −0.04 0.06 −0.09 0.03 0.03 0.04 0.05 −0.05 pNN50 −0.06 −0.14 −0.14 0.05 −0.09 −0.06 0.16 −0.06 0.10 −0.09 0.05 −0.02 0.04 0.07 −0.04 LF −0.03 −0.08 −0.23 −0.08 −0.16 −0.17 0.17 −0.13 0.10 −0.08 −0.10 −0.13 −0.05 −0.03 0.01 HF −0.07 −0.20 −0.23 −0.08 −0.25 −0.15 0.05 −0.14 −0.05 −0.25 −0.13 −0.03 −0.03 0.03 −0.07 LFHF 0.05 0.32 * 0.17 0.20 0.17 0.03 0.08 0.12 0.14 0.30 * −0.13 −0.05 −0.05 −0.11 0.17 SD1 −0.09 −0.14 −0.17 −0.01 −0.19 −0.03 0.04 −0.04 0.06 −0.09 −0.03 0.04 0.04 0.05 −0.05 SD2 −0.03 −0.09 −0.21 0.09 −0.20 −0.17 0.18 −0.14 0.06 −0.14 0.03 −0.09 −0.09 −0.05 0.00 SampEn −0.35 ** −0.16 −0.16 −0.32 * −0.03 −0.05 −0.19 0.15 0.05 0.04 −0.12 0.23 0.23 0.14 0.14 Notes: * p<0.05; ** p<0.01. BDI, Beck depression inventory; HF, high frequency power; hsCRP, high sensitivity C-reactive protein; IL-6, interleukin-6; LF, low frequency power; MFI, multidimension fatigue inventory; MPO, myeloperoxidase; pNN50, percentage of successive normal sinus RR intervals more than 50 ms; PSS, perceived stress scale; RMSSD, root mean square successive difference; SampEn, sample entropy; ms. milliseconds; SD1, standard deviation—poincar é plot crosswise; SD2, standard deviation—poincar é plot lengthwise; SDI, systemic damage index; SDNN, standard deviation of NN intervals; SF-36, short form health survey; SLEDAI, systemic lupus erythematosus disease activity index; TNFα , tumor necrosis factor alpha. Int. J. Environ. Res. Public Health 2020,17, 9501 9 of 14 The quantile regression models evaluating the association between HRV parameters, inflammatory markers, and PROs are presented in Table 4adjusted by age, heart rate and disease duration. Only significant correlations were explored. LFHF ratio was associated with the physical fatigue dimension of the MFI (unstandardized coefficient (B) =0.89; 95% confidence interval (CI) 0.15 to 1.62; p=0.019 ) but there was no association with IL-6 (B =0.48; 95% CI − 0.31 to 1.27; p>0.05). SampEn was inversely associated with hsCRP (B = − 4.82; 95% CI − 8.62 to − 1.03; p=0.014) and MPO (B = − 106.51; 95% CI − 182.54 to − 30.50; p=0.007). We did not find associations of HRV derived parameters with SLEDAI or SDI. Table 4. Quantile regression analysis evaluating the association between different components of heart rate variability, inflammatory markers, and PROs in women with systemic lupus erythematosus (n=55). B SE CI 95% p LFHF IL-6 0.48 0.39 −0.31 1.27 0.231 MFI-Physical Fatigue 0.89 0.37 0.15 1.62 0.019 SampEn hsCRP −4.82 1.89 −8.62 −1.03 0.014 MPO − 106.51 37.85 − 182.54 −30.50 0.007 hsCRP, high sensitivity C-reactive protein; IL-6, interleukin-6, LFHF, low frequency to high frequency ratio; MFI, multidimensional fatigue inventory; MPO, myeloperoxidase; SampEn, sample entropy; adjusted by age, heart rate and disease duration. 3.2. Effects of the Exercise Intervention on HRV-Derived Parameters (Aim 2) The HRV signals from 5 participants from the control group were excluded due to excessive interpolated beats (>5%). Full HRV data at baseline and week 12 was obtained from 44 participants (21 exercise and 23 control). The primary analyses revealed no significant between-group differences between changes in HRV derived parameters (Table 5) in all domains, and these results were consistent in sensitivity analyses in which participants from the exercise group were included only when attendance of the exercise sessions was ≥90% (Table S2) and in BOCF analyses (Table S3). Table 5. Per-protocol (primary) analyses assessing the effects of 12-week progressive aerobic exercise on HRV derived parameters in women with systemic lupus erythematosus (participants in the exercise group were included if attendance was ≥75%). Change from Baseline at Week 12 Exercise (n=21) Control (n=23) Median Difference (95% CI) p Median (SE) Median (SE) SDNN 2.70 (2.36) 4.18 (2.91) −1.48 (−12.00 to 6.37) 0.539 RMSSD 2.03 (3.52) 2.75 (4.33) −0.72 (−12.05 to 9.74) 0.831 pNN50 0.21 (1.93) 0.28 (2.96) −0.07 (−5.87 to 6.16) 0.960 LF (ms) 2.50 (81.86) −22.31 (57.00) 24.81 (−142.07 to 169.88) 0.858 HF (ms) 4.76 (98.31) 6.91 (73.40) −2.15 (−140.79 to 129.24) 0.932 LFHF −0.12 (1.30) 0.05 (1.01) −0.17 (−01.45 to 2.30) 0.652 SD1 1.44 (2.49) 1.95 (3.07) −0.51 (−8.53 to 6.90) 0.831 SD2 3.10 (2.51) 5.22 (3.04) −2.45 (−11.91 to 6.33) 0.539 SampEn 0.02 (0.07) 0.01 (0.08) 0.01 (−0.31 to 0.23) 0.741 The analyses were adjusted for baseline values, mean heart rate, and age. Values are the median (standard error). HF, high frequency power in absolute value; LF, low frequency power in absolute value; pNN50, percentage of successive normal sinus RR intervals more than 50 ms; RMSSD, root mean square successive difference; SampEn, sample entropy; ms. milliseconds; SD1, standard deviation—poincar é plot crosswise; SD2, standard deviation—poincaréplot lengthwise; SDNN, standard deviation of NN intervals. Regarding responders, non-responders, and adverse responders, in the control group we observed significant differences in RMSSD between responders against non-responders and adverse responders (p=0.37 and p=0.002, respectively) and between non-responder and adverse responder (p=0.37). In the