Underlying mechanisms of oxygen uptake kinetics in chronic post-stroke individuals: A correlational, cross-sectional pilot study.

Post-stroke individuals presented deleterious changes in skeletal muscle and in the cardiovascular system, which are related to reduced oxygen uptake ([Formula: see text]) and take longer to produce energy from oxygen-dependent sources at the onset of exercise (mean response time, MTRON) and during post-exercise recovery (MRTOFF). However, to the best of our knowledge, no previous study has investigated the potential mechanisms related to [Formula: see text] kinetics response (MRTON and MRTOFF) in post-stroke populations. The main objective of this study was to determine whether the MTRON and MRTOFF are related to: 1) body composition; 2) arterial compliance; 3) endothelial function; and 4) hematological and inflammatory profiles in chronic post-stroke individuals. Data on oxygen uptake ([Formula: see text]) were collected using a portable metabolic system (Oxycon Mobile®) during the six-minute walk test (6MWT). The time to achieve 63% of [Formula: see text] during a steady state (MTRON) and recovery (MRTOFF) were analyzed by the monoexponential model and corrected by a work rate (wMRTON and wMRTOFF) during 6MWT. Correlation analyses were made using Spearman's rank correlation coefficient (rs) and the bias-corrected and accelerated bootstrap method was used to estimate the 95% confidence intervals. Twenty-four post-stroke participants who were physically inactive took part in the study. The wMRTOFF was correlated with the following: skeletal muscle mass (rs = -0.46), skeletal muscle mass index (rs = -0.45), augmentation index (rs = 0.44), augmentation index normalized to a heart rate of 75 bpm (rs = 0.64), reflection magnitude (rs = 0.43), erythrocyte (rs = -0.61), hemoglobin (rs = -0.54), hematocrit (rs = -0.52) and high-sensitivity C-reactive protein (rs = 0.58), all p < 0.05. A greater amount of oxygen uptake during post-walking recovery is partially related to lower skeletal muscle mass, greater arterial stiffness, reduced number of erythrocytes and higher systemic inflammation in post-stroke individuals.

Introduction

Standing up and walking to the workplace requires proper oxygen uptake (), which is regulated over time by well-controlled mechanisms. Thus, kinetics reflects the efficiency of pulmonary, cardiovascular and skeletal muscle systems’ interaction during physical activity [1]. Sustained submaximal physical activities, such as walking, require a steady-state (). The time interval between oxygen at rest () and the is usually expressed by the mean response time (MRTON) that represents the body’s ability to uptake the oxygen quickly enough in order to produce energy for movement [1]. On the other hand, whether the ending of the activity is considered, the time interval between and during post-activity recovery () is expressed by MRTOFF that represents the amount of needed to restore the body to its resting level of metabolic function (see Fig 1) [1-3].

Fig 1

Oxygen uptake response to the 6-minute walk test.

Oxygen uptake raw data measured breath-by-breath from a sixty-two-year-old woman with stroke and severe motor function impairment. The vertical three dashed lines indicate the sit-to-stand, stand-to-test, and test-to-sit phases, in sequence. Each data point indicates breath-by-breath values averaged every 3 seconds. 6MWT, six-minute walk test; mL/kg/min, milliliter per kilogram per minute; MRTOFF, mean response time of oxygen uptake off-kinetics; MRTON, mean response time of oxygen uptake on-kinetics; s, second; , oxygen uptake; , oxygen uptake during the recovery period; , oxygen uptake at rest; , oxygen uptake during effort at steady-state level.

A decrease in MRTON is related to the early use of oxygen-dependent energy sources and is therefore much more energy efficient than oxygen-independent energy sources [1, 4]. Likewise, MRTOFF is an outcome of kinetics to understand the recovery phase when the is used to produce energy related to thermal, hormonal, and metabolic processes, as well as to resynthesize stored creatine phosphate in the muscle and refill oxygen stores in blood and muscle, used during walking [1-3]. Slower MRTON and MRTOFF are involved with a marked exercise intolerance [1-4]. Thus, understanding the mechanisms limiting is essential for improving bioenergetics kinetics (i.e. on- and off-kinetics), and therefore aerobic endurance [4].

Previous studies have shown that, in post-stroke individuals, both MRTON and MRTOFF are slower than their healthy matched peers [5, 6], which implies an inefficiency energy production at the onset of exercise and during recovery. In addition, bioenergetic kinetics has been described as a limiting factor in the ability of post-stroke individuals to walk in a real-world environment. Previous studies [5-7] suggest that after chronic stroke, individuals have a sluggish capacity to transport, extract and/or consume oxygen, at the onset of exercise (slow on-kinetics) or during the recovery phase (slow off-kinetics), and this is associated with fewer steps/day and the inability to sustain longer periods of activities in the real world [6, 7].

After a stroke, these individuals have deleterious stroke-related skeletal muscle changes, such as a shift from type I to type II fibers, muscle atrophy, intramuscular fat, and muscle fibrosis [8-11]. In addition, they have stroke-related cardiovascular changes, such as endothelial dysfunction, impaired arterial compliance, and increased proinflammatory markers, which reduce the vasodilators (e.g. nitric oxide) and decrease vessel diameter, consequently, affecting blood flow [9]. In particular, the C-reactive protein level, a marker of systemic inflammation, remains elevated during the chronic phase of stroke [12] and is related to anemia in individuals with chronic inflammatory [13, 14]. These alterations are related to a reduction in [9, 15–17] and are potential targets to understand why bioenergetics kinetics is altered in post-stroke individuals. However, to the best of our knowledge, no study investigated which mechanisms are related to kinetics response in chronic post-stroke population. Thus, the main objective of this study was to determine whether the MRT (on and off) is correlated with: 1) body composition; 2) arterial compliance; 3) endothelial function; and 4) hematological and inflammatory profiles in post-stroke individuals. We hypothesize that the underlying mechanisms mentioned above might be involved with kinetics.

Methods

Study design and ethical aspects

This is a correlational, cross-sectional pilot study with a convenience sample (there was no random selection). We followed the STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) guidelines to report our study methods and results. The study protocol was approved by the Ethics and Research Committee at the Federal University of São Carlos, Brazil [number: 62417216.9.0000.5504]. The funders played no role in the design, conduct, or reporting of this study. All participants gave written informed consent before participating in the study.

Setting and participants

The participants were recruited between January 2017 and July 2019 from the local community and nearby cities in the state of São Paulo, Brazil. Individuals included were: 1) 40–80 years of age; 2) stroke diagnosis (ischemic or hemorrhagic) confirmed by computer tomography or magnetic resonance imaging; 3) chronic stroke (time since stroke ≥ 6 months); 4) able to walk independently, including those with a need for aids or orthoses (Functional Ambulation Classification ≥ 3) [18]; 5) physically inactive or insufficiently active [International Physical Activity Questionnaire (IPAQ); < 150 min of moderate-to-vigorous-intensity physical activity per week or < 75 minutes of vigorous-intensity physical activity per week or an equivalent combination of moderate- and vigorous-intensity activity] [19, 20]; and 6) absence of cognitive impairment [Mini-Mental State Examination (MMSE); illiterate (≥ 13 points), elementary and middle (≥ 18 points), and high (≥ 26 points) level literacy] [21]. Individuals excluded were: 1) cardiac surgery and or myocardial infarction; 2) uncontrolled chronic disease; and 3) active/passive smoker and/or regular consumer of alcoholic beverages.

All procedures were carried out over three non-consecutive days with a minimum of 72h interval. On the first day of the assessment, participants were interviewed in order to obtain data on personal characteristics, then height and motor function were measured, and the six-minute walk test was performed between 2 and 6 pm at the Department of Physical Therapy at the Federal University of São Carlos, Brazil. A wearable activity monitor was then placed on the participants’ nonparetic ankle. On the second day of the assessment, participants returned to the department, the activity monitor was removed, and the body composition, arterial compliance and endothelial function were assessed between 8 and 10 am. On the third day of the assessment, a blood sample was collected from participants between 8 and 10 am at the Clinical Analysis Laboratory at UNIMED (a cooperative medical system) in São Carlos, Brazil.

Day 1 assessment

Clinical assessment

The anthropometric data of height was measured with a standard stadiometer (Welmy R-110, Santa Barbara do Oeste, SP, Brazil). The motor impairment characteristics were measured by the Fugl-Meyer Assessment of Motor Recovery after Stroke (FMA). The motor function domains of the FMA score range from 0 to a maximum of 100 points, and according to their points, participants’ motor function was classified as severe (<50), marked (50–84), moderate (85–94) or slight (95–99) [22]. A single physiotherapist with a background in FMA conducted all the clinical assessments.

The Six-Minute Walk Test (6MWT)

The 6MWT was performed according to the American Thoracic Society standards [23], except that individuals were instructed to “walk as fast as possible”, which is a better predictor of peak metabolic capacity [24]. The protocol of the 6MWT consisted of 2 minutes of sitting rest, 2 minutes of standing rest, 6 minutes of walking, and 6 minutes of sitting rest in recovery, totaling 16 minutes (see Fig 1). A single physiotherapist with a background in functional tests conducted all the 6MWT.

Oxygen uptake on- and off-kinetics

Breath-by-breath ventilatory and metabolic variables [e.g. absolute (mL/min), relative (mL/kg/min), and respiratory exchange ratio (RER)] were measured through Oxycon Mobile® (Mijnhardt/Jäger, Würzburg, Germany), a valid and reliable portable metabolic analyzer [25] during the protocol of the 6MWT. The 6MWT was chosen instead of treadmill or cycle ergometer tests since this functional test accurately reflects real-world walking performance in post-stroke individuals [26] and also the metabolic response of walking on the treadmill is significantly higher both with and without support than that of walking the ground in post-stroke individuals, even at matched speeds [27, 28]. Before each test, the device was calibrated according to the manufacturer’s specifications. Participants were instructed to: 1) not drink alcohol and caffeinated beverages from 24 hours prior to the test; 2) not perform any kind of physical exercise from 72 hours prior to the test; and 3) not consume a large meal from 2 hours prior to the test. Before starting the protocol of the 6MWT, participants rested sitting for 10 minutes in order to stabilize ventilatory and metabolic values.

The steady-state conditions were calculated by the standard deviation of relative over the last one minute in sitting and standing positions, and over the last three minutes of the 6MWT and recovery phase (see Fig 1). The steady-state condition was defined as the standard deviation of relative ≤ 2.0 mL/kg/min and RER values < 1.1 [29]. Participants who did not reach the steady-state condition according to this definition were excluded from the analysis. The relative raw data were pre-processed by removing each 8-point window value above 3 standard deviations of the local mean (removing the outliers) and averaging the breath-by-breath measurements over consecutive periods of 8 breaths (moving average filter), in this order (see Fig 2) [30].

Fig 2

Oxygen uptake on- (A and C) and off-kinetics (B and D) response to the 6-minute walk test. Oxygen uptake raw data (A and B) measured breath-by-breath from a sixty-two-year-old woman with stroke and severe motor function impairment. The outliers (values above 3 standard deviations) were removed and a moving average filter was used by averaging the values over consecutive periods of 8 breaths (C and D). The vertical two dashed lines in each panel indicate the beginning and the end of the posture transition, in sequence. mL/kg/min, milliliter per kilogram per minute; s, second; , oxygen uptake; , oxygen uptake at rest; , oxygen uptake during the recovery period; , oxygen uptake during effort at steady-state level; , oxygen uptake on-kinetics magnitude of response ( - ); , oxygen uptake off-kinetics magnitude of response ( - ).

Afterwards, the on- (60 seconds of rest condition + 360 seconds of 6MWT) and off-kinetics (60 seconds of 6MWT + 360 seconds of recovery) of were analyzed by the monoexponential model following the previous literature [31]. The equations are described below. Please also see Fig 2 for details.

Where represents the at any time (t); is the resting value of in the standing position; is the recovery value of in the sitting position; is the magnitude of response at the onset of walking ( - ); is the magnitude of response during post-walking recovery ( - ); TD is the time delay; and τ is the time constant of the exponential response of interest. For the on-kinetics analysis, we removed the data relative to the first 25-35s after onset (i.e. the cardiodynamic phase) [32]. Each individual curve was assessed visually by two evaluators, and therefore the time between 25 to 35 seconds with less residue was deleted. The mean response time (MRT = TD + τ), i.e. the time required for to achieve 63% of the or , was corrected by the work rate (wMRTON and wMRTOFF, respectively) during the 6MWT in order to take into account the participants’ individual effort and used for analysis [33]. The equations are described below:

Physical activity level

The physical activity level was measured by the StepWatch® Activity Monitor (SAM, Modus Health, Washington, D.C., USA), a wearable activity monitor [34, 35]. The SAM was calibrated and attached to the participants’ nonparetic ankle. The participants were instructed to wear the SAM for 9 days, removing it for sleeping, swimming, and showering. The first and last days of measurements were excluded from the analyses because the device was placed and removed on these days. Participants were given an instruction sheet with detailed information about the care and use of the SAM. The mean steps/day was used to characterize the sample as a sedentary lifestyle (< 5000 steps/day), low active lifestyle (5000–7499 steps/day) and physically active lifestyle (≥ 7500 steps/day) [36].

Day 2 assessment

Body composition, arterial compliance and endothelial function were measured in sequence in the morning at visit 2, in a quiet, dimly lit and humidity and temperature-controlled room (50–60% and 22–24°C, respectively). Participants were instructed to fast overnight (≥ 8h), refrain from caffeinated products (≥ 12h), from vitamin supplements (≥ 72h) and from moderate and vigorous physical activity (≥ 48h) prior to the assessments [37-39]. All female participants were in the menopause period without hormone replacement therapy. The same physiotherapist who was experienced in day 2 assessments carried out all the exams.

Body composition

Weight, skeletal muscle mass (SMM) and body fat mass (BFM) were measured by a bioelectrical impedance analyzer (InBody® 720, InBody Co., Ltd., Seoul, Korea). Body mass index (BMI, kg/m2) was calculated using the following formula: BMI = [(weight in kg)/(height in m)2]. According to BMI, participants were classified as underweight (15.0–19.9 kg/m2), normal weight (20.0–24.9 kg/m2), overweight (25.0–29.9 kg/m2), class I obesity (30.0–34.9 kg/m2), class II obesity (35.0–39.9 kg/m2) and class III obesity (≥ 40 kg/m2) [40]. Skeletal muscle mass index (SMMI, kg/m2) was calculated using the following formula: SMMI = [(skeletal muscle mass in kg)/(height in m)2]. Low SMMI was defined as < 8.87 kg/m2 for men and < 6.42 kg/m2 for women, which are used for the diagnosis of sarcopenia [41]. Body fat mass index (BFMI, kg/m2) was calculated using the following formula: BFMI = [(body fat mass in kg)/(height in m)2] [42].

Arterial compliance

SphygmoCor® XCEL (AtCor Medical Pty. Ltd., Sydney, Australia) was used to calculate carotid-femoral pulse wave velocity (cfPWV), augmentation index (AIx), augmentation index normalized to a heart rate of 75 bpm (AIx75) and reflection magnitude (RM), measures of arterial compliance. For the measurement of cfPWV, a cuff was placed on the participant’s nonparetic upper thigh. The distances from the anterior superior iliac spine to the top of the cuff, from the sternal notch to top of the cuff, and from the sternal notch to the carotid were measured and the values were entered into the SphygmoCor software database. For the measurements of AIx, AIx75 and RM, a cuff was placed on the participant’s nonparetic arm. Five successive sequences of each measurement were performed by the same evaluator in each individual in the supine position after at least 10 minutes of rest. The mean of three similar measurements with a standard deviation of less than 10% was used for analysis [37, 38]. Arterial stiffness was defined as cfPWV ≥ 10 m/s [37].

Endothelial function

Endothelial function was measured using brachial artery flow-mediated dilation (baFMD) technique, a non-invasive measure based on endothelium-dependent vasodilation [39]. The participants rested supine for 10 minutes prior to the baFMD procedure. For the measurement of baFMD, a cuff was placed on the participants’ nonparetic forearm, and the arm was abducted to 90 degrees and forearm positioned in supine. An ultrasonography of the brachial artery (M-Turbo, Sonosite, Seattle, WA, USA) was used in a longitudinal plane proximal to the antecubital fossa 1–3 cm. The ultrasound probe (11 MHz) was positioned to view the anterior and posterior lumen-intimate interfaces when measuring the diameter or velocity of the central flow (pulsed Doppler). After the initial images are recorded, an anterior pressure cuff on the forearm was inflated at 220 mmHg for 5 min. To evaluate baFMD, 10 images were captured at a rate of 10 images per second for 1 min, 2 and 3 min after cuff release. Resting brachial flow velocity and peak velocity after cuff release were also recorded. The images were digitally recorded using software Brachial Analyzer (Medical Imaging Applications LLC, Coralville, Iowa, USA) and then further analyzed. The baFMD was calculated using the mean brachial artery diameter as the baseline, compared with the highest mean values obtained after forearm occlusion release using the following formula: FMD (%) = [(peak diameter–baseline diameter)/baseline diameter] x 100. Arterial dysfunction was defined as baFMD < 10% [43].

Day 3 assessment

Hematological and inflammatory profile

Red blood cell (also called erythrocyte, RBC) count, and hemoglobin (Hgb) and hematocrit (Hct) concentrations were measured by an automated hematology analyzer (CELL-DYN Ruby, Abbott Laboratories, Chicago, Illinois, USA), and high-sensitivity C-reactive protein (hs-CRP) analysis was performed by using a chemistry analyzer (Abbot Architect CI 8.200, Abbott Laboratories, Chicago, Illinois, USA). A blood sample was collected from the nonparetic forearm vein in the morning after 10-12h of fasting overnight. Participants were instructed not to perform moderate and vigorous physical activity (≥ 48h), not to attend the exam if any inflammatory process was present, and to maintain their usual diet prior to the exam. Individuals were also asked to report any recent symptom or event during the blood sampling week, such as ongoing or recent upper respiratory infection, recent vaccination, musculoskeletal symptoms and significant headache, and, in case of the presence of any of them, the blood collection was rescheduled. Anemia was defined as Hgb concentrations < 130 g/L for men and < 120 g/L for women [44]. The hs-CRP level was also used to characterize the sample as low- (< 1.0 mg/L), medium- (1.0–3.0 mg/L) and high-grade systemic inflammation (> 3.0 mg/L) [45].

Data analysis

Characteristics of the sample were expressed as absolute numbers (percentage, %), means (standard deviation, SD) or medians (interquartile range, IQR). According to the Shapiro-Wilk test, the wMRTON (W[24] = 0.91, p = 0.03) and wMRTOFF (W[24] = 0.90, p = 0.02) data showed no normality, thus nonparametric tests were used for all analyses. The Wilcoxon signed-rank test was used to determine whether there is a significant difference between baseline and recovery values and between MRTON and MRTOFF, and between wMRTON and wMRTOFF [46].

Spearman’s rank correlation coefficient (rs) was used to determine whether there is a significant correlation between the wMRTON and wMRTOFF with the following: 1) body composition (weight, BMI, BFM, BFMI, SMM and SMMI); 2) arterial compliance (cfPWV, AIx, AIx75 and RM); 3) endothelial function (baFMD); and 4) hematological (RBC, Hgb and Hct) and inflammatory (hs-CRP) profiles. The magnitude of the correlation was based on Munro’s classification (low [0.26 to 0.49], moderate [0.50 to 0.69], high [0.70 to 0.89] and very high [0.90 to 1.00]) [47]. We used bias-corrected and accelerated (BCa) bootstrap resampling with 10,000 replications to estimate 95% confidence interval (CI95). CI95 estimates which did not include zero were considered statistically significant at the level of 5% [46].

All analyses were two-tailed and performed with a significance level of 5% using the Statistical Package for the Social Sciences, version 26.0 (SPSS Inc., Chicago, IL, USA). In addition, we used a syntax file (S1 File) to perform a non-parametric partial correlation in SPSS [48] using the variables with a significant correlation coefficient to control by confounding variables in each variables groups: 1) body composition; 2) arterial compliance; and 3) hematological and inflammatory (hs-CRP) profiles.

Results

Four hundred and forty-three individuals were contacted to participate in the study. Out of 443 subjects, 223 were not assessed for eligibility. Thus, 220 participants were assessed for eligibility, but 176 were not included. In total, 44 participants were recruited, however twenty were excluded from the final analysis due to missing data, inability to reach the steady state during the 6MWT, and refusal to participate after initial consent. Hence, the data for 24 of these individuals were ultimately included for analysis (see Fig 3). All participants completed the 6MWT without stopping and reached the steady-state condition (please see S1-S3 Tables in S1 File), which means that they walked at a constant workload29. Furthermore, there were no complications during the test.

Fig 3

Flow chart for selecting the participants for this correlational, cross-sectional pilot study.

6MWT, six-minute walk test; FAC, functional ambulation category; MMSE, Mini-Mental State Examination.

Data from twenty-four participants after chronic stroke were used for analysis. Participants were, on average, elderly (63%; ≥ 60 years of age), sedentary (83%; < 5000 steps/day) and overweight (54%; BMI 25.0–29.9 kg/m2). Most of them had an ischemic stroke (88%) on the left side (75%) with a severe motor impairment (37%). Additionally, most of the participants did not have arterial stiffness (89%; cfPWV < 10 m/s) but had arterial dysfunction (82%; baFMD < 10%) and medium-grade systemic inflammation (59%; hs-CRP 1.0–3.0 mg/L), and none of them had anemia (100%; Hgb level ≥ 130 g/L for men and ≥ 120 g/L for women) or sarcopenia (100%; SMMI > 8.87 kg/m2 for men and > 6.42 kg/m2 for women) (see Table 1).

Table 1

Participant demographic and clinical characteristics (n = 24).

Characteristics		Interval (min–max)
Men (n = 15) age (years), mean (SD)	60 (11)	44–76
Women (n = 9) age (years), mean (SD)	62 (4)	55–68
Stroke Characteristics
Time since stroke (months), median (IQR)	41 (24 to 60)	6–259
Stroke type, n ischemic (%)	21 (88)
Lesion side, n left (%)	18 (75)
Lower Extremity Fugl Meyer Score, median (IQR)	29 (19 to 32)	11–34
Fugl Meyer Score (Motor function), median (IQR)	76 (33 to 97)	11–99
Slight (96–99), n (%)	7 (29)
Moderate (85–95), n (%)	4 (17)
Marked (50–84), n (%)	4 (17)
Severe (< 50), n (%)	9 (37)
6-Minute Walk Test
Distance achieved (meters), mean (SD)	302.63 (129.01)	120–661.65
Speed achieved (m/s), mean (SD)	0.84 (0.36)	0.33–1.84
StepWatchTM Activity Monitor
Number of Steps (steps/day), median (IQR)	3697 (2733 to 4324)	1547–8568
Body Composition
Weight (kg), mean (SD)	75 (13)	56–106
Body Mass Index (kg/m2), mean (SD)	28.5 (4.6)	20.9–41.4
Body fat massa (kg), mean (SD)	27 (8)	14–43
Body fat mass indexa (kg/m2), mean (SD)	10.2 (3.0)	5.2–15.8
Skeletal muscle massa (kg), mean (SD)	26 (4)	17–35
Skeletal muscle mass indexa (kg/m2), mean (SD)	9.7 (1.0)	7.7–11.9
Arterial compliance
cfPWVb (m/s), median (IQR)	7.9 (7.5 to 9.0)	5.4–15.9
AIxa (%), mean (SD)	24 (11)	2–47
AIx75a (%), mean (SD)	19 (11)	-4–41
Reflection magnitudea (%), mean (SD)	62 (10)	43–80
Endothelial function
baFMDc (%), mean (SD)	5.85 (4)	-3.55–13.31
Hematological and inflammatory profilesc
Erythrocyte (million/mm3), mean (SD)	4.95 (0.61)	3.91–6.24
Hemoglobin (g/dL), mean (SD)	14.5 (1.4)	11.6–16.7
Hematocrit (%), mean (SD)	43.1 (4.9)	33.6–52.5
High-sensitivity C-reactive protein (mg/L), mean (SD)	2.24 (1.36)	0.28–5.80

Note: Continuous variables with normal distribution are presented as means [standard deviations (SDs)]; nonnormal variables are reported as medians [interquartile ranges (IQRs)].

Abbreviations: %, percentage; AIx, augmentation index; AIx75, augmentation index normalized to a heart rate of 75 bpm; baFMD, brachial artery flow-mediated dilation; cfPWV, carotid-femoral pulse wave velocity; g/dL, grams per deciliter; IQR, interquartile range; kg, kilogram; kg/m2, kilogram per meter2; million/mm3, million per cubic millimeter; SD, standard deviation; steps/min, steps per minute.

an = 23.

bn = 19.

cn = 22.

Metabolic and kinetics response to the six-minute walk test

Most of the participants walked at a light (25%, 30–39% predicted reserve) and a moderate (50%, 40–59% predicted reserve) intensity [49, 50] during the 6MWT. Participants took almost twice as long to recover from the 6MWT (wMRTOFF = 0.16 min2/mL/kg) than to adjust toward a steady state (wMRTON = 0.10 min2/mL/kg) and this difference was significant (T = 292, p < 0.001) (see Table 2).

Table 2

Metabolic and kinetics response to the six-minute walk test (n = 24).

Variables	Sitting	Standing	Test	Recovery
V˙O_2 (mL/kg/min)	3.22 (3.00 to 3.70)	3.52 (2.95 to 4.12)	10.59 (8.62 to 12.15)	3.37 (3.02 to 3.90)
ΔV˙O_2 (mL/kg/min)	7.21 (5.69 to 8.82)	6.84 (5.57 to 8.47)	NA	7.03 (5.51 to 8.47)
Predicted V˙O_2MAX (%)	11 (9 to 15)	12 (9 to 17)	38 (33 to 44)	11 (9 to 16)
Predicted V˙O_2R (%)	NA	14 (10 to 20)	42 (37 to 50)	13 (10 to 18)
V˙CO_2 (mL/min)	208.08 (196.20 to 243.41)	247.16 (212.31 to 291.44)	731.85 (579.18 to 841.58)	244.46 (227.58 to 267.19)
RER	0.90 (0.86 to 0.96)	0.92 (0.85 to 1.00)	0.95 (0.86 to 0.99)	0.99 (0.89 to 1.05)
	V˙O_2 on-kinetics	V˙O_2 off-kinetics		p
MRT (s)	46 (41 to 54)	71 (64 to 78)		0.01*
wMRT (min2/mL/kg)	0.10 (0.09 to 0.15)	0.16 (0.14 to 0.21)		0.01*

Note: Variables are reported as medians (interquartile ranges). We used the Wilcoxon signed-rank test to determine whether there is a significant difference between on- and off-kinetics variables. Predicted maximal oxygen uptake [79.9 –(0.39 x age)–(13.7 x sex [0 = male; 1 = female])–(0.127 x weight [lbs])] [50]. Predicted oxygen uptake reserve [ = − at rest sitting].

Abbreviations: %, percentage; min2/mL/kg, minute square per milliliter per kilogram; mL/kg/min, milliliter per kilogram per minute; mL/min, milliliter per minute; MRT, mean response time; NA, not applicable; RER, respiratory exchange ratio; s, second; , carbon dioxide output; , oxygen uptake; , maximal oxygen uptake; , oxygen uptake reserve; wMRT, mean response time corrected for work rate; , oxygen uptake magnitude of response.

*p ≤ 0.05.

Relationship between the body composition and the kinetics

The relationships between the body composition and the kinetics during 6MWT are presented in Table 3. The wMRTON was not correlated with any body composition variable. The wMRTOFF presented a low negative correlation with SMM and SMMI but did not present a correlation with any other body composition variable (please see S1 and S2 Figs in S1 File).

Table 3

Relationship between the oxygen uptake kinetics and the underlying mechanisms (n = 24).

Variables	wMRTON (min2/mL/kg)		wMRTOFF (min2/mL/kg)
	rs [BCa CI95]	p	rs [BCa CI95]	p
Age (years)	0.12 [-0.31, 0.50]	0.57	0.20 [-0.22, 0.51]	0.36
Body Composition
Weight (kg)	0.05 [-0.34, 0.42]	0.82	-0.23 [-0.53, 0.16]	0.28
Body mass index (kg/m2)	0.24 [-0.12, 0.55]	0.27	-0.01 [-0.36, 0.36]	0.96
Body fat massa (kg)	0.21 [-0.18, 0.55]	0.33	0.00 [-0.37, 0.38]	1.00
Body fat mass indexa (kg/m2)	0.25 [-0.14, 0.58]	0.26	0.12 [-0.26, 0.47]	0.59
Skeletal muscle massa (kg)	-0.31 [-0.59, 0.08]	0.15	-0.46 [-0.70, -0.10]	0.03*
Skeletal muscle mass indexa (kg/m2)	-0.19 [-0.53, 0.20]	0.37	-0.45 [-0.71, -0.08]	0.03*
Arterial compliance
cfPWVb (m/s)	0.21 [-0.26, 0.59]	0.38	0.18 [-0.33, 0.59]	0.47
AIxa (%)	0.18 [-0.23, 0.55]	0.40	0.44 [0.05, 0.74]	0.04*
AIx75a (%)	0.36 [-0.04, 0.69]	0.09	0.64 [0.28, 0.87]	< 0.01*
Reflection magnitudeb (%)	0.18 [-0.27, 0.61]	0.42	0.43 [0.08, 0.69]	0.04*
Endothelial Function
baFMDc (%)	-0.23 [-0.59, 0.19]	0.31	-0.18 [-0.52, 0.22]	0.43
Hematological and inflammatory profilesc
Erythrocyte (million/mm3)	-0.38 [-0.63, 0.00]	0.08	-0.61 [-0.76, -0.36]	< 0.01*
Hemoglobin (g/dL)	-0.36 [-0.65, 0.03]	0.10	-0.54 [-0.70, -0.23]	0.01*
Hematocrit (%)	-0.27 [-0.58, 0.15]	0.23	-0.52 [-0.72, -0.17]	0.01*
High-sensitivity C-reactive protein (mg/L)	0.26 [-0.18, 0.60]	0.24	0.58 [0.14, 0.79]	< 0.01*
Motor impairment (FMA)
Upper and lower extremities	0.04 [-0.38, 0.43]	0.86	-0.29 [-0.65, 0.15]	0.18
Lower extremity	0.04 [-0.39, 0.46]	0.85	-0.30 [-0.70, 0.15]	0.16

Note: 95% bias corrected and accelerated confidence intervals reported in square brackets. Confidence intervals based on 10,000 bootstrap samples.

Abbreviations: %, percentage; AIx, augmentation index; AIx75, augmentation index normalized to a heart rate of 75 bpm; baFMD, brachial artery flow-mediated dilation; BCa, bias corrected accelerated; cfPWV, carotid-femoral pulse wave velocity; CI95, 95% confidence interval; FMA, Fugl-Meyer Assessment of Motor Recovery after Stroke; g/dL, grams per deciliter; kg, kilogram; kg/m2, kilogram per meter2; m/s, meter per second; mg/L, milligram per liter; million/mm3, million per cubic millimeter; min2/mL/kg, minute square per milliliter per kilogram; rs, Spearman’s rank correlation coefficient; wMRTOFF, oxygen uptake off-kinetics mean response time corrected for work rate; wMRTON, oxygen uptake on-kinetics mean response time corrected for work rate.

*p ≤ 0.05.

an = 23.

bn = 19.

cn = 22.

There was a low negative correlation between wMRTOFF and SMM, when controlled by BMI (r[20] = -0.48, p = 0.03), BFM (r[20] = -0.48, p = 0.03) and BMFI (r[20] = -0.47, p = 0.03) and a correlation that approached the significance when controlled by the weight (r[20] = -0.40, p = 0.06). There was a low negative correlation between wMRTOFF and SMMI, when controlled by BFM (r[20] = -0.49, p = 0.02) and a correlation that approached the significance when controlled by the weight (r[20] = -0.38, p = 0.08). Moreover, there was a moderate negative correlation between wMRTOFF and SMMI, when controlled by BMI (r[20] = -0.53, p = 0.01) and BFMI (r[20] = -0.51, p = 0.02).

Relationship between the arterial compliance and the kinetics

The relationships between the arterial compliance variables and the kinetics during 6MWT are presented in Table 3. The wMRTON was not correlated with any of the arterial compliance variables. The wMRTOFF presented the following findings: 1) a low positive correlation with the percentage of AIx and the percentage of RM; and 2) a moderate positive correlation with the percentage of AIx75. There was no correlation between the wMRTOFF and cfPWV (please see S3 Fig in S1 File).

When controlled by cfPWV, the wMRTOFF presented a high positive correlation with the AIx75 (r[16] = 0.76, p < 0.01) and a correlation that approached the significance with AIx (r[16] = 0.47, p = 0.05), but there was no correlation with RM (r[16] = 0.29, p = 0.25).

Relationship between the endothelial function and the kinetics

The relationship between the endothelial function assessed through baFMD and the kinetics during 6MWT is presented in Table 3. Neither wMRTON nor wMRTOFF was correlated with baFMD (please see S4 Fig in S1 File).

Relationship between the hematological and inflammatory profiles and the kinetics

The relationships between the hematological and inflammatory profiles and the kinetics during 6MWT are presented in Table 3. The wMRTON was neither correlated with any of the hematological variables nor with hs-CRP. However, the wMRTOFF presented the following findings: 1) a moderate negative correlation with the number of RBC, the Hgb level, and the percentage of Hct; and 2) a moderate positive correlation with the hs-CRP level (please see S5 Fig in S1 File).

We also found a moderate negative correlation between hs-CRP and: 1) the number of RBC (rs = -0.52, BCa CI95 [-0.82, -0.05], p = 0.01); 2) the Hgb level (rs = -0.54, BCa CI95 [-0.78, -0.17], p = 0.01); and 3) the percentage of Hct (rs = -0.59, BCa CI95 [-0.84, -0.20], p < 0.01) (please see S6 Fig in S1 File). Furthermore, the wMRTOFF showed correlations approaching the significance with hs-CRP when controlled by: 1) the number of RBC (r[19] = 0.39, p = 0.08); 2) the Hgb level (r[19] = 0.41, p = 0.07); and 3) the percentage of Hct (r[19] = 0.40, p = 0.07). However, the wMRTOFF presented a low negative correlation with the number of RBC (r[19] = -0.45, p = 0.04) when controlled by hs-CRP.

Relationship between the kinetics and the underlying mechanisms according to age

Considering a large age range, we divided the sample into two groups (adults [19–59 years] and the elderly [≥ 60 years]) and carried out the aforementioned analyses in each group. Neither wMRTON nor wMRTOFF was correlated with any variable in the adult group (please see S4 Table in S1 File). In the elderly group, the wMRTOFF presented a high negative correlation with SMMI and showed correlations that approached the significance with SMM, AIx75 and hs-CRP (please see S5 Table in S1 File).

Discussion

This study investigated whether the delay in response at the onset of a short bout of walking (wMRTON) and during post-walking recovery (wMRTOFF) correlated with: 1) body composition; 2) arterial compliance; 3) endothelial function; and 4) hematological and inflammatory profiles in post-stroke individuals. This study unprecedently showed that wMRTOFF presented correlation with SMM, SMMI, AIx, AIx75, RM, RBC, Hgb, Hct, and hs-CRP. However, the wMRTON presented no correlation with the evaluated variables.

Among the mechanisms related to the VO2 response kinetics, we assessed some related to the muscular and cardiovascular systems. Regarding the muscular system, our findings suggested that the skeletal muscle mass seems to play a more significant limiting role in the regulation of during the recovery phase than at the onset of walking. The highest quantity of mitochondria in our body is found in the skeletal muscle mass in order to provide substantial amounts of adenosine triphosphate (ATP), our energy currency [51]. After a stroke, the loss of skeletal muscle mass is characterized by a decrease in mitochondria-rich slow-twitch muscle fibers [52]. As the recovery process is also energy-dependent, such as the resynthesis of the intramuscular store of phosphocreatine [53], a lower number of mitochondria available means less ATP production and energy, and therefore slows down recovery.

Considering the cardiovascular system, our findings suggested that most evaluated variables (i.e. compliance and function arterial variable, and hematological and inflammatory variables) seem to play a limiting role in kinetics of poststroke individuals when walking. Nevertheless, greater distensibility of the arterial blood vessels was also related to shorter recovery time. Indeed, according to the Hagen-Poiseuille law, the blood flow rate is directly proportional to the radius to the fourth power of the vessel lumen [54], so any change in blood vessel diameter results in considerable variation in blood flow rate and, consequently, in the amount of oxygen transported. During moderate-intensity, as observed during 6MWT, kinetics may be limited by intramyocyte derangements perturbations in some chronic diseases [55]. However, for some more deconditioned patients, 6MWT could be a high-intensity exercise. In this context, other systemic disturbances, such as the reduced arterial compliance (as observed by AIx and AIx75, both in %), were related to wMRTOFF, which could explain that the sluggish kinetic is associated to arterial stiffness present in these patients.

Furthermore, the levels of hemoglobin, hematocrit, and the erythrocytes seem to play a supporting role in the time it may take for them to recovery after walking, but a contrasting result was found regarding the hs-CRP levels, an inflammatory biomarker. Since almost all oxygen transported from the lungs to body tissues is bound to hemoglobin [54], a higher number of red blood cells may shorten recovery time. It is noteworthy that even in non-anemic individuals, hs-CRP levels had a moderate negative correlation with the erythrocyte count. Previous studies showed that systemic inflammation may directly impair the production of erythropoietin [13, 14], a glycoprotein cytokine that stimulates erythrocyte production in the bone marrow.

In addition, the inflammatory state has been associated with insufficient production of vasodilators [56], such as nitric oxide, which impairs the endothelium-dependent vasodilation and explains the non-correlation between endothelial function (baFMD) and on- and off-kinetics response. In addition, despite the fact that previous studies showed a positive association between baFMD and peak () [15], it seems that once impairment of endothelial function is installed, it does not play a significant response in submaximal physical activities.

It is also worth highlighting that the age and motor function do not seem to be factors limiting the speed of the to achieve a steady state during walking or for the recovery after walking (please see Table 3 and S7 Fig in S1 File). Recently, George et al. [57] observed that aging per se does not determine the response. According to the study, even with an age-related reduction in the , inactive elderly individuals took as long as their much younger and inactive matched counterparts to adjust to exercise. Similar to age, factors that alter the biomechanics of the body inferred from the Fugl-Meyer Assessment scale, such as spasticity and muscle co-contractions, do not relate to response kinetics. On the other hand, Ribeiro et al. [29] and Billinger et al. [58] showed correlations between the Fugl-Meyer Assessment scale and energy cost and , respectively, other measurements of aerobic endurance [4]. These findings together reinforce the importance of a multicomponent rehabilitation program for improving physical activity tolerance in this population.

Clinical implications of this study

Although this study has a potential mechanistic nature, our results may point to strategies that aim to accelerate these responses of kinetics, and thus reduce the deleterious effects on the bioenergetic machinery of the muscles and on the cardiovascular function of these patients. Endurance exercise training seems to be the most effective therapeutic modality for the speeding up of the kinetics response [1]. Both young people and the elderly showed a faster response to VO2 kinetics after brief sessions (≤ 3 sessions) of aerobic training protocols [59, 60], which earlier could decrease effort and increase tolerance during the performance of activities of daily living among the stroke individuals who meet the exercise recommendations for stroke survivors [61]. However, there is little evidence of improvement in VO2 kinetics related to any type of exercise in post-stroke individuals. We found only one study [62] that observed improvement in kinetics following a low-intensity endurance training protocol. These individuals have stroke-related cardiovascular and skeletal muscle change (e.g. skeletal muscle fiber shift, and smaller peripheral artery blood flow and diameter in the stroke-affected side) [9, 15, 58] determining the kinetics response [1, 2], therefore the underlying mechanisms bearing on response kinetics during physical activity and exercise might differ from other populations.

Study limitations

Our results must be interpreted with caution because of some limiting factors: (1) participants were chosen from a convenience sample (non-probability sampling), and therefore there is a possibility of sample selection bias; (2) correlational study; (3) small sample size; (4) the lack of cardiac and pulmonary function assessments; and (5) the on-kinetics was measured in the standing position, and off-kinetics in the sitting position in order to ensure participant´s safety. However, this is a first exploratory study on the limiting mechanisms in bioenergetics kinetics response to walking, and we believe future research with larger and more heterogeneous samples (e.g. levels of physical activity and sedentary behavior, and types and chronicity of strokes) with different measurements [heart function (e.g. cardiac output, ejection fraction and diastolic function) and lung function (e.g. airway resistance and functional residual capacity)] is required to better understand how to improve bioenergetic kinetics response to activities of daily living. Furthermore, taking into account that the is related to gait patterns in post-stroke individuals [27, 28], it is reasonable to assess whether the gait pattern during overground walking using three-dimensional kinematics or inertial sensors is related to kinetics. It is also reasonable to consider sophisticated analyses, such as multiple regression and covariance analysis, and variables that have a direct bearing on the kinetics, such as [52].

Conclusion

In conclusion, a slower off-kinetics response to walking is partially related to body composition, arterial compliance, and hematological and inflammatory profiles. Lower skeletal muscle mass, greater arterial stiffness, a reduced number of erythrocytes and higher systemic inflammation have been related to a greater amount of oxygen uptake during the recovery phase in post-stroke individuals.

(SAV)

Click here for additional data file.

(XLSX)

Click here for additional data file.

(DOCX)

Click here for additional data file.

27 Aug 2020

PONE-D-20-21333

Underlying mechanisms of oxygen uptake kinetics in chronic post-stroke individuals: a correlational, cross-sectional pilot study

PLOS ONE

Dear Dr. Ribeiro,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Oct 11 2020 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: http://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols

We look forward to receiving your revised manuscript.

Kind regards,

Yuji Ogura, Ph.D.

Academic Editor

PLOS ONE

Journal Requirements:

When submitting your revision, we need you to address these additional requirements.

1. Please ensure that your manuscript meets PLOS ONE's style requirements, including those for file naming. The PLOS ONE style templates can be found at

https://journals.plos.org/plosone/s/file?id=wjVg/PLOSOne_formatting_sample_main_body.pdf and

https://journals.plos.org/plosone/s/file?id=ba62/PLOSOne_formatting_sample_title_authors_affiliations.pdf

2. Please include captions for your Supporting Information files at the end of your manuscript, and update any in-text citations to match accordingly. Please see our Supporting Information guidelines for more information: http://journals.plos.org/plosone/s/supporting-information.

Additional Editor Comments (if provided):

Both experts raised critical concerns especially regarding the weakness of study design and data analysis. They thought that the conclusion of this manuscript is not supported by the results at the present form, whereas they found that this manuscripts has some merit. Please address all the concerns of the two reviewers and submit a revised version of the manuscript.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Partly

Reviewer #2: Partly

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: N/A

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: No

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The paper is focused on the interesting research issue where the oxygen consumption for walking in post-stroke patients. The paper is generally well written, however, there are several concerns with the current manuscript. In particular, the research design may include potential limitation.

Major comments

1. Author should clearly document the validity of the study design. As numerous papers demonstrate that the energy cost of "6-min walk test" was depending on the gait pattern such as walking speed, cadence, stride length, and were depended on the physical characters including disease condition. Thus, the authors should document the reason for using the "6-min walk test" rather than the controlled exercise (such as treadmill walking, or cycle ergometer)

2. As mentioned above, the VO2 for "6-min walk test" was associated with health status. Therefore, the authors should firstly demonstrate the gat pattern of the "6-min walk test" was independent from the obtained variables (SMI, PWV, inflammatory, etc..)

3. The conclusion of the present investigation was not supported by the finding. The relationship of variables with physical activity levels was not demonstrated.

Minor

1. The validity/accuracy of the gas analyzer should be documented.

2. The speed and gait pattern for "6-min walk test" should be shown.

3. The correlations should be demonstrated by illustrations NOT only P value.

4. Authors should be use the partial correlation, as the variables are not independent.

5. As the present study is cross-sectional analysis, the conclusion should be limited to "relationship" or "correlation" NOT "explain".

Reviewer #2: PLOS One review

The authors present the results of a correlative pilot study on onset and offset kinetics in individuals who survived a stroke event.

Comments:

1. Introduction needs a description of a mechanisms on how the stroke event actually causes these changes that affect the O2 kinetics. Is the kinetics worse because of poor health that contributed to stroke and age or did the stroke event induce these deleterious changes that were not present before the event? For examples how does a stroke event contribute to muscle fiber shift, muscle atrophy endothelial dysfunction etc.?

2. Methods

- “This is a correlational, cross-sectional pilot study with a convenience sample.” This sentence is unclear.

- Considering a large age range, could the analyses be done separately for the older (>65) and younger individuals (<45) in order to check the age effect? If the differences are insignificant, the results can be placed in the supplementary material.

3. Limitations

- Correlative study is also a limitation.

4. Conclusions

- Correlations cannot be used to explain the biological reactions. The manuscript needs the wording adjustment to state these conclusions more cautiously.

5. Figures

- The consort diagram should be presented first as it was described first too.

**********

6. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

[NOTE: If reviewer comments were submitted as an attachment file, they will be attached to this email and accessible via the submission site. Please log into your account, locate the manuscript record, and check for the action link "View Attachments". If this link does not appear, there are no attachment files.]

While revising your submission, please upload your figure files to the Preflight Analysis and Conversion Engine (PACE) digital diagnostic tool, https://pacev2.apexcovantage.com/. PACE helps ensure that figures meet PLOS requirements. To use PACE, you must first register as a user. Registration is free. Then, login and navigate to the UPLOAD tab, where you will find detailed instructions on how to use the tool. If you encounter any issues or have any questions when using PACE, please email PLOS at figures@plos.org. Please note that Supporting Information files do not need this step.

28 Sep 2020

Reviewers’ Comments to the Authors:

Reviewer #1: The paper is focused on the interesting research issue where the oxygen consumption for walking in post-stroke patients. The paper is generally well written, however, there are several concerns with the current manuscript. In particular, the research design may include potential limitation.

Major comments

1. Author should clearly document the validity of the study design. As numerous papers demonstrate that the energy cost of "6-min walk test" was depending on the gait pattern such as walking speed, cadence, stride length, and were depended on the physical characters including disease condition. Thus, the authors should document the reason for using the "6-min walk test" rather than the controlled exercise (such as treadmill walking, or cycle ergometer).

Authors’ Response: As suggested by the reviewer, the reason for using the 6MWT rather than the controlled exercise is described in the methods section. Although several studies demonstrate that treadmill walking increases biomechanical symmetry in post-stroke individuals, it is accompanied by higher metabolic demands both with and without support, even at matched speeds (Brouwer et al., 2009; IJmker et al., 2013). Due to unease or instability associated with treadmill walking, post-stroke individuals present higher cadence, shorter stance time and shorter strides using it when compared to overground. Moreover, even though handrails are used, there is a greater contribution of the muscle activation pattern associated with the paretic limb during treadmill walking compared to walking overground. All in all, it is reasonable to hypothesize that the six-minute test, a functional test, reflects better the metabolic response during activities of daily living related to walking rather than treadmill walking.

Page 6: “The 6MWT was chosen instead of treadmill or cycle ergometer tests since this functional test accurately reflects real-world walking performance in post-stroke individuals26 and also the metabolic response of walking on the treadmill is significantly higher both with and without support than that of walking the ground in post-stroke individuals, even at matched speeds27,28.”

References

26 Fulk GD, He Y, Boyne P, Dunning K. Predicting home and community walking activity poststroke. Stroke. 2017;48(2):406-411. doi: 10.1161/STROKEAHA.116.015309.

27 Brouwer B, Parvataneni K, Olney SJ. A comparison of gait biomechanics and metabolic requirements of overground and treadmill walking in people with stroke. Clin Biomech. 2009;24(9):729-734. doi: 10.1016/j.clinbiomech.2009.07.004.

28 Ijmker T, Houdijk H, Lamoth CJ, Jarbandhan AV, Rijntjes D, Beek PJ, et al. Effect of balance support on the energy cost of walking after stroke. Arch Phys Med Rehabil. 2013;94(11):2255-2261. doi: 10.1016/j.apmr.2013.04.022.

2. As mentioned above, the VO2 for "6-min walk test" was associated with health status. Therefore, the authors should firstly demonstrate the gait pattern of the "6-min walk test" was independent from the obtained variables (SMI, PWV, inflammatory, etc..).

Authors’ Response: As suggested by the reviewer, we analyzed the correlations between motor function (data about gait patterns were not collected) and the obtained variables (please see Table below). Except for body fat mass and body fat mass index (which did not correlate with VO2 kinetics), the obtained variables have not been correlated with motor function. However, we agree that this aspect is relevant and have included it as a limitation of the study. The analysis of gait patterns using 3D kinematics or inertial sensors during the test could help advance this topic, as it is clinically relevant.

Page 20: “Furthermore, taking into account that the V̇O2 is related to gait patterns in post-stroke individuals27,28, it is reasonable to assess whether the gait pattern during overground walking using three-dimensional kinematics or inertial sensors is related to V̇O2 kinetics.”

3. The conclusion of the present investigation was not supported by the finding. The relationship of variables with physical activity levels was not demonstrated.

Authors’ Response: We agree with the reviewer’s assessment. We removed the statement “which might imply in intolerance to physical activity” from the abstract and conclusion section of the main text.

Minor

1. The validity/accuracy of the gas analyzer should be documented.

Authors’ Response: Thank you for your suggestion. The validity/accuracy of the gas analyzer has now been documented.

Page 6: “Breath-by-breath ventilatory and metabolic variables [e.g. absolute V̇O2 (mL/min), relative V̇O2 (mL/kg/min), and respiratory exchange ratio (RER)] were measured using an Oxycon Mobile® (Mijnhardt/Jäger, Würzburg, Germany), a valid and reliable portable metabolic analyzer25 during the protocol of the 6MWT.”

Reference

25 Rosdahl H, Gullstrand L, Salier-Eriksson J, Johansson P, Schantz P. Evaluation of the oxycon mobile metabolic system against the Douglas bag method. Eur J Appl Physiol. 2010;109(2):159-171. doi: 10.1007/s00421-009-1326-9.

2. The speed and gait pattern for "6-min walk test" should be shown.

Authors’ Response: Thank you for this suggestion. The data about speed during the 6MWT have been added to Table 1. However, data about gait patterns were not collected, instead we collected data about the motor function after stroke by the Fugl-Meyer Assessment of Motor Recovery after Stroke.

3. The correlations should be demonstrated by illustrations NOT only P value.

Authors’ Response: We agree with the suggestion. We have also represented all correlations graphically in the Supplementary Material (Figures S1 to S7).

4. Authors should be use the partial correlation, as the variables are not independent.

Authors’ Response: Thank you for pointing this out. According to the Shapiro-Wilk test, the wMRTON (W[24] = 0.908, p = 0.032) and wMRTOFF (W[24] = 0.896, p = 0.017) data showed no normality, therefore we used a syntax file to perform a non-parametric partial correlation in SPSS (https://www.ibm.com/support/pages/partial-rank-correlations-spss).

Syntax file

NONPAR CORR

/MISSING = LISTWISE

/MATRIX OUT(*).

RECODE rowtype_ ('RHO'='CORR') .

PARTIAL CORR

/significance = twotail

/MISSING = LISTWISE

/MATRIX IN(*).

Page 17: “According to the Shapiro-Wilk test, the wMRTON (W[24] = 0.91, p = 0.03) and wMRTOFF (W[24] = 0.90, p = 0.02) data showed no normality, thus nonparametric tests were used for all analyses”

Page 17: “In addition, we used a syntax file (Supplementary Material) to perform a non-parametric partial correlation in SPSS48 using the variables with a significant correlation coefficient to control by confounding variables in each variable groups: 1) body composition; 2) arterial compliance; and 3) hematological and inflammatory (hs-CRP) profiles.”

Page 14: “There was a low negative correlation between wMRTOFF and SMM, when controlled by BMI (r[20] = -0.48, p = 0.03), BFM (r[20] = -0.48, p = 0.03) and BMFI (r[20] = -0.47, p = 0.03), and a correlation that approached the significance when controlled by the weight (r[20] = -0.40, p = 0.06). There was a low negative correlation between wMRTOFF and SMMI, when controlled by BFM (r[20] = -0.49, p = 0.02) and a correlation that approached the significance when controlled by the weight (r[20] = -0.38, p = 0.08). Moreover, there was a moderate negative correlation between wMRTOFF and SMMI, whilst controlling for BMI (r[20] = -0.53, p = 0.01) and BFMI (r[20] = -0.51, p = 0.02).

Page 15: “When controlled by cfPWV, the wMRTOFF presented a high positive correlation with the AIx75 (r[(16] = 0.76, p < 0.01) and and a correlation that approached the significance AIx (r[16] = 0.47, p = 0.05), but there was no correlation with RM (r[16] = 0.29, p = 0.25).”

Page 15: “Furthermore, the wMRTOFF showed correlations that approached the significance with hs-CRP when controlled by: 1) the number of RBC (r[19] = 0.39, p = 0.08); 2) the Hgb level (r[19] = 0.41, p = 0.07); and 3) the percentage of Hct (r[19] = 0.40, p = 0.07). However, the wMRTOFF presented a low negative correlation with the number of RBC (r[(19] = -0.45, p = 0.04) when controlled by hs-CRP.”

Page 20: “It is also reasonable to consider sophisticated analyses, such as multiple regression and covariance analysis, and variables that have a direct bearing on the V̇O2 kinetics, such as the V̇O2PEAK52.”

Reference

48 International Business Machines Corporation (IBM). Partial rank correlations in SPSS. Available from: https://www.ibm.com/support/pages/partial-rank-correlations-spss. [Accessed Sep 25th, 2020].

5. As the present study is cross-sectional analysis, the conclusion should be limited to "relationship" or "correlation" NOT "explain".

Authors’ Response: To further balance the implications of our results with the potential limitations of a cross-sectional study, we changed “explained” to “related” in the first sentence of the conclusion in the abstract and conclusion section of the main text.

Page 1: “A greater amount of oxygen uptake during post-walking recovery is partially related to lower skeletal muscle mass, greater arterial stiffness, reduced number of erythrocytes and higher systemic inflammation in post-stroke individuals.”

Page 20: “In conclusion, the slower V̇O2 off-kinetics response to walking is partially related to body composition, arterial compliance, and hematological and inflammatory profiles.”

Reviewer #2: The authors present the results of a correlative pilot study on onset and offset kinetics in individuals who survived a stroke event.

Comments:

1. Introduction needs a description of a mechanisms on how the stroke event actually causes these changes that affect the O2 kinetics. Is the kinetics worse because of poor health that contributed to stroke and age or did the stroke event induce these deleterious changes that were not present before the event? For examples how does a stroke event contribute to muscle fiber shift, muscle atrophy endothelial dysfunction etc.?

Authors’ Response: The references used in the Introduction section were about stroke-related muscular and cardiovascular changes and not about poor health-related changes following stroke, but indeed it was unclear. Therefore, we have made some changes in order to clarify this.

Page 3: “After a stroke, these individuals have deleterious stroke-related skeletal muscle changes, such as a shift from type I to type II fibers, muscle atrophy, intramuscular fat, and muscle fibrosis.8-11 In addition, they have stroke-related cardiovascular changes, such as endothelial dysfunction, impaired arterial compliance, and increased proinflammatory markers,…”

References

8 Sions JM, Tyrell CM, Knarr BA, Jancosko A, Binder-Macleod SA. Age- and stroke-related skeletal muscle changes a review for the geriatric clinician. J Geriatr Phys Ther. 2012;35(3):155–161. doi: 10.1519/JPT.0b013e318236db92.

9 Billinger SA, Coughenour E, MacKay-Lyons MJ, Ivey FM. Reduced cardiorespiratory fitness after stroke: biological consequences and exercise-induced adaptations. Stroke Res Treat. 2012;2012:959120. doi: 10.1155/2012/959120.

10 Silva-Couto MA, Prado-Medeiros CL, Oliveira AB, Alcântara CC, Guimarães AT, Salvini TF, et al. Muscle atrophy, voluntary activation disturbances, and low serum concentrations of IGF-1 and IGFBP-3 are associated with weakness in people with chronic stroke. Phys Ther. 2014;94(7):957–967. doi: 10.2522/ptj.20130322.

11 Faturi FM, Santos GL, Ocamoto GN, Russo TL. Structural muscular adaptations in upper limb after stroke: a systematic review. Top Stroke Rehabil. 2019;26(1):73–79. doi: 10.1080/10749357.2018.1517511.

12 Eikelboom JW, Hankey GJ, Baker RI, McQuillan A, Thom J, Staton J, et al. C-reactive protein in ischemic stroke and its etiologic subtypes. J Stroke Cerebrovasc Dis. 2003;12(2):74–81. doi: 10.1053/jscd.2003.16.

2. Methods

- “This is a correlational, cross-sectional pilot study with a convenience sample.” This sentence is unclear.

Authors’ Response: We have added the information “there was no random selection” in order to make the sentence clearer.

Page 4: “This is a correlational, cross-sectional pilot study with a convenience sample (there was no random selection).”

- Considering a large age range, could the analyses be done separately for the older (>65) and younger individuals (<45) in order to check the age effect? If the differences are insignificant, the results can be placed in the supplementary material.

Authors’ Response: We appreciate this comment and agree that the analyses could be done separately in order to check the age effect. It was not possible to divide the sample into older (> 65) and younger (< 45) as we had only one individual aged below 45 years old. However, we were able to divide the sample into adults (19-59 years) and the elderly (≥ 60 year). Thus, we performed the analyses in each group (see tables below) and added the following information to the main text.

Page 16: “Considering a large age range, we divided the sample into two groups (adults [19-59 years] and the elderly [≥ 60 years]) and carried out the aforementioned analyses in each group. Neither wMRTON nor wMRTOFF was correlated with any variable in the adult group (please see Supplementary Material, Table S4). In the elderly group, the wMRTOFF presented a high negative correlation with SMMI and showed correlations that approached the significance with SMM, AIx75 and hs-CRP (please see Supplementary Material, Table S5).”

3. Limitations

- Correlative study is also a limitation.

Authors’ Response: We agree that this is a potential limitation of the study. We have added the suggested content to the manuscript on the Study limitations section.

Page 19: “Our results must be interpreted with caution because of some limiting factors: (1) participants were chosen from a convenience sample (non-probability sampling), and therefore there is a possibility of a sample selection bias; (2) correlational study;…”

4. Conclusions

- Correlations cannot be used to explain the biological reactions. The manuscript needs the wording adjustment to state these conclusions more cautiously.

Authors’ Response: Thank you for pointing this out. As suggested by the reviewer, we have made changes throughout the manuscript to make adjustments to state these conclusions more cautiously. The main modifications were the following:

1. We have removed these statements from the Discussion section

“It explained 21% of the variation in the time it may take for them to recovery after 6MWT.”

“About 18-41% of the variation in recovery time after walking was explained by the degree of arterial compliance.”

2. We have replaced the statement “Furthermore, the levels of hemoglobin, hematocrit, and the erythrocytes count explained 27-37% of the variation in the time it may take for them to recovery after walking, and a similar result (34%) was found regarding the hs-CRP levels, an inflammatory biomarker.” by

Page 17: “Furthermore, the levels of hemoglobin, hematocrit, and the erythrocytes seem to play a supporting role in the time it may take for them to recovery after walking, but a contrasting result was found regarding the hs-CRP levels, an inflammatory biomarker”

3. We have replaced the statement “It is noteworthy that even in non-anemic individuals, hs-CRP levels explained 27% of the variation in the erythrocyte count.” by

Page 17: “It is noteworthy that even in non-anemic individuals, hs-CRP levels had a moderate negative correlation with the erythrocyte count.”

5. Figures

- The consort diagram should be presented first as it was described first too.

Authors’ Response: We appreciate the reviewer’s feedback. We have made certain that the Figures are presented in the order they have been described.

Pages 2, 6 and 7: Figure 1 (Oxygen uptake response to the 6-minute test.)

Page 7: Figure 2 [Oxygen uptake on- (A and C) and off-kinetics (B and D) response to the 6-minute walk test.]

Page 13: Figure 3 (Flow chart for selecting the participants for this correlational, cross-sectional pilot study.)

Submitted filename: Response to Reviewers.docx

Click here for additional data file.

22 Oct 2020

Underlying mechanisms of oxygen uptake kinetics in chronic post-stroke individuals: a correlational, cross-sectional pilot study

PONE-D-20-21333R1

Dear Dr. Ribeiro

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

If your institution or institutions have a press office, please notify them about your upcoming paper to help maximize its impact. If they’ll be preparing press materials, please inform our press team as soon as possible -- no later than 48 hours after receiving the formal acceptance. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information, please contact onepress@plos.org.

Kind regards,

Yuji Ogura, Ph.D.

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: (No Response)

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The current manuscript is appropriately improved according to the reviewer comments.

I have no additional comments.

Reviewer #2: (No Response)

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: No

27 Oct 2020

PONE-D-20-21333R1

Underlying mechanisms of oxygen uptake kinetics in chronic post-stroke individuals: a correlational, cross-sectional pilot study

Dear Dr. Ribeiro:

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now with our production department.

If your institution or institutions have a press office, please let them know about your upcoming paper now to help maximize its impact. If they'll be preparing press materials, please inform our press team within the next 48 hours. Your manuscript will remain under strict press embargo until 2 pm Eastern Time on the date of publication. For more information please contact onepress@plos.org.

If we can help with anything else, please email us at plosone@plos.org.

Thank you for submitting your work to PLOS ONE and supporting open access.

Kind regards,

PLOS ONE Editorial Office Staff

on behalf of

Dr. Yuji Ogura

Academic Editor

PLOS ONE