Inhalation of Hydrogen-rich Gas before Acute Exercise Alleviates Exercise Fatigue: A Randomized Crossover Study

Abstract

Hydrogen, as an antioxidant, may have the potential to mitigate fatigue and improve selected oxidative stress markers induced by strenuous exercise. This study focused on a previously unexplored approach involving pre-exercise inhalation of hydrogen-rich gas (HRG). Twenty-four healthy adult men first completed pre-laboratories to determine maximum cycling power (W_max) and maximum cycling time (T_max). Then they were subjected to ride T_max at 80% W_max and 60–70 rpm on cycle ergometers after inhaled HRG or placebo gas (air) for 60-minute in a double-blind, counterbalanced, randomized, and crossover design. The cycling frequency in the fatigue modeling process and the rating of perceived exertion (RPE) at the beginning and end of the ride were recorded. Before gas inhalation and after fatigue modeling, visual analog scale (VAS) for fatigue and counter-movement jump (CMJ) were tested, and blood samples were obtained. The results showed that compared to a placebo, HRG inhalation induced significant improvement in VAS, RPE, the cycling frequency during the last 30 seconds in the fatigue modeling process, the ability to inhibit hydroxyl radicals, and serum lactate after exercise (p<0.028), but not in CMJ height and glutathione peroxidase activity. The cycling frequency during the last 30 seconds of all other segments in the fatigue modeling process was within the range of 60–70 rpm. In conclusion, HRG inhalation prior to acute exercise can alleviate exercise-induced fatigue, maintain functional performance, and improve hydroxyl radical and lactate levels.

Keywords

oxidative stress - anti-fatigue - hydroxyl radicals - lactate - recovery

Introduction

Athletic competition, high-intensity training, non-habitual exercise, and other high-load activities frequently lead to exercise-induced fatigue. This type of fatigue not only results in perturbations to bodily tissues through metabolic and mechanical stresses [1], but also diminishes athletic performance[2] and increases incidence of sports injuries [3] [4]. The massive increase in reactive oxygen species (ROS) caused by rigorous and/or prolonged exercise is thought to be one of the contributors to the development of exercise fatigue [5]. When the amount of ROS exceeds the scavenging ability of endogenous antioxidant substances, the oxidation-antioxidative balance is disrupted, generating elevated oxidative stress [6] [7], capable of inhibiting one or more proteins involved in excitation-contraction coupling, leading to a reduction in the production of muscle force (i. e. fatigue) [5]. Therefore, strategies to reduce exercise-induced oxidative stress by increasing exogenous antioxidants hold great promise for alleviating the degree of exercise fatigue and maintaining the performance.

The hydrogen molecule is receiving increasing interest for its selective antioxidant capabilities [8], anti-inflammatory properties, and its role in regulating acid-base homeostasis [9] [10]. Studies have shown that hydrogen molecules administered through the inhalation of gas or oral intake of water (i. e. hydrogen-rich gas (HRG) and hydrogen-rich water (HRW)) can penetrate cell membranes and diffuse rapidly into organelles, thereby selectively reducing OH and ONOO⁻ [11] [12]. It was observed that taking HRW and HRG can attenuate strenuous exercise-induced oxidative damages in human, accelerate heart rate recovery, improve muscular function, reduce exercise-induced fatigue perceptual and alleviate the lactate (LA) response after exercise [13] [14] [15] [16] [17]. However, administration of HRG through inhalation enables the rapid delivery of higher doses of hydrogen to organisms compared to other delivery methods, including drinking HRW [18]. Drinking HRW results in hydrogen reaching peak levels within 10–20 minutes and remaining above baseline for 30–40 minutes [19], whereas the reaction time of hydrogen in the body through inhalation may be up to 3 hours [20]. The longer response time of inhaled HRG may potentially allow for pre-exercise ingestion to influence subsequent exercise. However, it remains to be determined whether inhalation of HRG prior to exercise will have specific effects on subsequent exercise-induced fatigue and exercise performance.

We contend that inhaling HRG before high-load exercise would alleviate exercise fatigue by eliminating exercise-induced excess ROS. Evaluating the impact of HRG supplementation versus non-supplementation on fatigue after a specific exercise unit of the same duration and intensity enables exercisers to discern HRG's efficacy in diminishing fatigue following strenuous exercise. Lower fatigue means a lower risk of injury and better athletic performance in subsequent exercise. Therefore, in this double-blind, counterbalanced, randomized, and crossover-designed study, we examined the effects of inhalation HRG or placebo prior to a specific high-intensity cycling ergometer exercise unit on fatigue perceptual, functional performance and metrics of blood sample (i. e. oxidative stress-related blood markers and serum LA) in healthy young adults. Specifically, we hypothesized that compared to the placebo, inhaling HRG before high-intensity exercise at the same load will result in lower level of fatigue and better functional performance at the end of exercise; and such effect would be associated with the reduced level of serum LA and oxidative stress-related markers in the blood samples.

Materials and Methods

Participants

Twenty-four healthy, recreationally active adult men (21±3 yr, 177±5 cm, 71±7 kg; [Table 1]) were recruited for this study. The inclusion criteria were: regular exercise habits, involving engagement in physical activity at least twice a week for one hour or more at a moderate to vigorous intensity level; and the ability to complete high-intensity cycling ergometer exercises. The exclusion criteria were: no history of lower extremity injury in the six months prior to the experiment, no cardiovascular, respiratory and endocrine disease. After a detailed instruction of the experimental procedure, each participant signed an informed consent form. The study protocol conformed to the Declaration of Helsinki and was approved by the Ethical Review Board affiliated with one of the authors (approval number: 2021163H).

Table 1 Participant characteristics.

	A group (n=12)	B group (n=12)	P-value
Age (years)	21.2±2.4	21.5±3.0	0.768
Height (cm)	177.8±4.0	176.9±5.1	0.631
Body weight (kg)	70.8±7.7	70.6±7.1	0.935
BMI (kg/m2)	22.4±2.0	22.5±1.9	0.862
Wmax (W)	213.3±35.0	205.0±25.8	0.513
Tmax (min)	19.0±6.9	22.0±9.5	0.394

Note: W_max, the maximum cycling power; T_max, the maximum cycling time.

Protocol

In this double-blind and randomized study with crossover design ([Fig. 1]), participants completed four study visits in the laboratory consisting of these tests: a maximum cycling power (W_max) test (Visit (I)), a maximum cycling time (T_max) test at 80%W_max (Visit (II)), fatigue modeling using a cycle ergometer following a 60-minute inhalation of HRG (HRG group) and fatigue modeling using a cycle ergometer following a 60-minute inhalation of placebo gas (standard air) (placebo group) (Visit (III) and (IV)). All participants were instructed to avoid vigorous exercise, alcohol, coffee, supplements, medicines, and any specific recovery treatments within 48 hours of each trial period. On the morning of each study visit, all participants consumed a standardized meal provided by the researchers, consisting of milk, bread, and ham sausage. Furthermore, they abstained from consuming any additional food or beverages prior to the visit.

Fig. 1 Crossover study design process.

The parameters of the fatigue model for each participant were determined on study visit (I)and (II), with a minimum resting interval of 72 hours between them. Once the parameters were determined, participants completed the fatigue modeling after inhaling HRG or placebo (i. e. study visit (III) and (IV)) in a randomized order. Visit order was randomly allocated according to balanced permutations generated by a web-based computer program (www.randomizer.org). A resting period of at least 7 days were provided between these two visits. On each of these two visits, at the beginning and end of fatigue modeling task, participants responded to Borg's scale. Additionally, the cycling frequency was required to be maintained at 60–70 rpm throughout the fatigue modeling process. This was continuously monitored and recorded to compare the effects of the two interventions on cycling performance. Before each gas inhalation, participants first completed a Visual Analogue Scale (VAS) test and had blood drawn from the antecubital vein. This was followed by a counter-movement jump (CMJ) tests. After completing the fatigue modeling task, participants first underwent the VAS tests, then immediately performed the CMJ tests, and finally a blood sample was taken (at approximately 2–3 minutes after completion of the fatigue modeling). The VAS tests and blood collection were repeated 30 minutes and 60 minutes after fatigue modeling task ([Fig. 2]). The collected antecubital venous blood samples were analyzed for serum LA levels, the ability to inhibit hydroxyl radicals, and glutathione peroxidase (GSH-PX) activity. Considering the potential influence of women's physiological cycles and endocrine hormone levels, we initially chose male individuals as our research participants when investigating the impact of HRG on blood metabolites.

Fig. 2 Experimental test sequence and procedure. Note: CMJ, counter-movement jump; VAS, visual analogue scale; W_max, the maximum cycling power; T_max, the maximum cycling time at 80%W_max.

Hydrogen-Rich Gas (HRG)

HRG was prepared by a hydrogen gas generator Hydrogen-oxygen Convalescent machine 2.5 (Zhiheng Hydrogen Health Technology Co., Ltd., Fuzhou, China). The generator can generate 1,800 ml/min of hydrogen-oxygen mixed gas (the composition ratio of hydrogen and oxygen is 2:1). HRG was supplied through a nasal cannula attached to a gas generator. Although we could not measure directly the concentrations of hydrogen and oxygen entering the body due to technical limitations, it is mathematically estimated that the average inspiratory flow rate of a healthy young male at rest is about 500 ml/s, which far exceeds the flow rate of the hydrogen gas generator, diluting the concentration of inhaled hydrogen, so that the maximum concentration of hydrogen in the inhaled body is about 4.08%. Similarly, the oxygen concentration is about 21.66% [15]. Compared to the oxygen content of the air, the increased oxygen is extremely small. Placebo gas (ambient air, 0.00005% hydrogen, 20.9% oxygen) was supplied by a nasal cannula that was connected to a hydrogen gas generator that did not initiate the hydrogen production program.

Fatigue model

On study visit (I), the W_max was tested by a preliminary incremental exercise test. The test began with a load of 50 W for 2 minutes and then the load incremented automatically in steps of 20 W every 2 minutes until volitional exhaustion (operationally defined as a pedal frequency of less than 60 round per minute (rpm) for more than 5 seconds despite strong verbal encouragement) on an Ergoselect 100 cycle ergometer (Ergoline GmbH, Bitz, Germany) [21], participants were asked to maintain the cycling frequency at 60–70 rpm throughout the process. The W_max was calculated according to the equation: W_max=W_out+(t/120)*20 (W_out: workload of the last completed stage; t: time (seconds) in the final stage) [22]. Before the incremental exercise test the position on the cycle ergometer was adjusted for each subject, and settings were recorded so that they could be reproduced at each subsequent experimental sessions. On study visit (II), the T_max test consisted of a 3-minute warm-up at 40% W_max followed by cycling at 80% W_max. The test finished when the pedal frequency was less than 60 rpm for more than 5 seconds despite standardized verbal encouragement.

We then implemented a well-established protocol of fatigue model with equal W_max that has been widely used in previous studies [21] [22] [23]. Specifically, according to the definition of exercise-induced fatigue outlined in the Fifth International Biochemistry of Exercise Conference (1982), the inability to sustain the current exercise load was considered as the criterion for fatigue. In order to ensure that each participant reached a level of fatigue that was comparable for them, the fatigue model employed isophysiological loads. Specifically, the fatigue model consisted of cycling at 60–70 rpm and 40% of their W_max for a warm-up period of 3 minutes, then cycling at the same frequency and at 80% of their W_max for their T_max. To control variables and ensure that post-intervention tests can be compared to determine the effects of HRG, participants performed the same duration of exercise (i. e. their own T_max) within both groups. T_max was pre-determined during a preliminary visit prior to the official experiment.

Measurements

The assessment of fatigue

The primary outcome of fatigue perception was the visual analogue scale (VAS), which was used to examine the subjective feelings of fatigue [24]. Participants can specify their psychometric fatigue level by indicating the location of a continuous 10 cm line between the two endpoints (starting point represents no fatigue, end point represents extreme fatigue, and the midpoint of the line segment represents moderate fatigue).

Secondarily, we also measured rating of perceived exertion (RPE). RPE was used to quantitatively assess the degree of consciously perceived exertion during the establishment of the fatigue model using a cycle ergometer. It is an indicator of physical stress and subjective effort [25]. On study visit (I), participants were also given standard instructions for overall RPE using the modified version of the Borg CR10 scale [26]. The scale ranged from 0 to 11 with ratio-level properties, incorporating nonlinear spacing of verbal descriptors of the level of intensity. Specifically, 0 indicates not perceived, 0.5 is extremely weak, 3 moderate, 5 strong, 10 extremely strong, and anything above 10 is denoted as 11, signifying the extreme.

The primary outcome of functional performance was CMJ. The CMJ test, a commonly-used neuromuscular fatigue test [27], is utilized to evaluate functional performance subsequent to the induction of post-exercise fatigue [28]. This test can assess an individual's neuromuscular efficiency. As high-intensity activities often cause fatigue that impairs muscle contractile function and neuromuscular coordination. Therefore, the CMJ test can provide a measure of how these functions are affected by fatigue by measuring jump height. We tested the CMJ using a stationary Kistler three-dimensional force platform (Kistler Instrumente AG, Winterthur, Switzerland; collection frequency: 1,000 Hz) and Kistler BioWare 4.0.0 software. Participants were instructed to practice the specific movements involved in the CMJ prior to the actual test. During the CMJ, participants assumed a starting position on the force platform, either upright or slightly squatting, with hands on hips. They then performed a rapid and forceful squatting motion, flexing their knees to approximately 90°, followed by an explosive extension to achieve maximum height. Throughout the flight stage of the jump, participants were instructed to extend their knees and keep their hands on their hips to avoid any sideways displacements. When contacting the ground, participants were instructed to land with their toes first. It was emphasized that intentionally bending the abdomen and knees to prolong the time in the air during landing was not allowed. Each participant completed three trials of the CMJ. The time in the air, defined as the duration during which the vertical ground reaction force was less than 10 N, was used to calculate jump height [29] [30]. The jump height averaged across the three trials was then used in the subsequent analysis.

Secondarily, the entire fatigue modeling process for each participant was divided equally into 10 segments. The cycling frequency during the last 30 seconds of each segment were recorded, and their average values were taken as representing the cycling frequency performance for each respective phase. Each participant cycled for an equivalent duration under identical load conditions in the different intervention groups, thus the cycling frequency performance responded to the participant's functional performance affected by fatigue.

The assessment of blood sample markers

We conducted an analysis of serum LA, a major metabolite that can be affected by hydrogen after strenuous exercise [14]. Additionally, we examined serum GSH-PX and hydroxyl radicals, which serve as markers of blood oxidative stress and can reflect the enzymatic and non-enzymatic reactions involved in the oxidative-antioxidant balance [31]. Antecubital venous blood samples were collected from participants using procoagulant tubes. Once the blood samples had naturally coagulated, an Allegra X-30R high-speed cryogenic centrifuge (Beckman Coulter, Inc., California, USA) was employed for centrifugation at 4°C, 3,000 r/min for 15 minutes, and the resulting supernatant was collected. The ability to inhibit hydroxyl radicals and GSH-PX activity was measured using a Hydroxyl Free Radical assay kit and Glutathione Peroxidase assay kit (Nanjing Jiancheng Bioengineering Institute Co., Ltd., Nanjing, China) and NANODROP 2000C spectrophotometer (Thermo Fisher Scientific, Waltham, USA). Serum LA levels were measured using a detection kit (Beijing Leadman Biochemical Co., Ltd., Beijing, China) and DxC-800 automatic biochemical analyzer (Beckman Coulter, Inc., California, USA). All measurements were performed following established standardized protocols (refer to Supplementary Documents).

Statistical analysis

Statistical analyses were performed using SPSS 25.0 (IBM, Chicago, IL, USA). A value of p<0.05 was considered statistically significant. Descriptive statistics (i. e. mean, standard deviation (SD)) were used to summarize the demographic characteristics of the participants. Shapiro-Wilk tests were used to examine whether the data were distributed normally. Data distributed normally were described using “mean±SD”, and those not distributed normally were described using “median (interquartile range)” (M (P25, P75)). Since cycling frequencies between 60–70 rpm meet the requirements, the existence of the statistically significant differences in this range is not meaningful. Whereas cycling frequency below 60 rpm indicates an inability to maintain cycling requirements and can be used to distinguish differences in functional performance. Paired t-test models were thus utilized to examine the difference in the segments with the last 30 seconds cycling frequency of less than 60 rpm during fatigue modeling process between the HRG group and placebo group. Two-way (group×time) repeated measures ANOVAs were utilized to examine the outcomes that were distributed normally; and generalized estimating equations (GEE) were utilized to examine the outcomes that were not distributed normally. The model factors for the two-way ANOVAs and GEE analyses included group, time, and their interaction. Fisher's least significant difference (LSD) post-hoc analyses were performed when a significant interaction was observed. Linear regression analysis was used to explore the association between the percentage changes in the outcomes of fatigue and functional performance, and the outcomes of blood sample that were significantly affected by HRG within the HRG group.

Results

All 24 participants completed all study tests, and their data were included in the analysis. Their anthropometric characteristics and W_max, T_max results were presented in [Table 1]. Before the intervention, there were no significant differences in all test results between the HRG group and placebo group (p>0.150, [Table 2]). Participants reported no discernible discrepancy in their subjective feelings between the two inhalations, suggesting a successful blinding. The CMJ, the cycling frequency during the last 30 seconds of the last segment in the fatigue modeling process, and blood sample markers were distributed normally, and thus ANOVA models and paired t-test models were used for the comparison in them. The VAS and RPE were not distributed normally, therefore the GEE models were used.

Table 2 The results of the two trials before and after the crossover of the main test indicators.

		The first phase experiment		The second phase experiment		Overall after combination
		A group (n=12) (HRG intervention)	B group (n=12) (placebo intervention)	A group (n=12) (placebo intervention)	B group (n=12) (HRG intervention)	HRG group (n=24)	placebo group (n=24)
RPE	Pre	1.3 (0.6, 2.4)	1.8 (0.63, 2.4)	2.0 (1.6, 2.0)	1.3 (1.0, 2.8)	2.0 (1.5, 2.0)	2.0 (1.6, 3.0)
	0 min post	9.5 (8.3,10.8)	11.0 (10.0, 11.0)*	10.0 (9.3, 11.0)	9.0 (8.3, 10.0)*	9.0 (9.0, 10.0)	10.5 (10.0, 11.0)*
CMJ (cm)	Pre	33.26±7.18	34.12±3.93	32.16±9.05	34.85±4.74	34.06±6.01	33.14±6.90
	0 min post	34.96±6.88	33.33±4.28	31.72±7.50	35.07±6.33	35.01±5.74	32.53±6.03*
VAS (mm)	Pre	12.5 (9.1, 18.5)	11.8 (4.0, 15.0)	10.5 (6.8, 30.0)	14.3 (7.9, 20.8)	12.5 (9.0, 19.3)	15.5 (11.0, 23.8)
	0 min post	70.5 (53.2, 78.5)	75.8 (64.0, 88.3)	78.8 (71.1, 91.6)	73.5 (58.3, 80.9)	72.5 (55.0, 80.1)	76.8 (68.0, 89.0)*
	30 min post	31.0 (17.6, 51.0)	27.5 (14.8, 50.9)	39.0 (17.3, 52.5)	20.0 (10.3, 35.5)	22.5 (15.8, 36.8)	31.3 (17.0, 50.9)
	60 min post	17.3 (6.1, 25.0)	11.5 (7.1, 30.0)	21.3 (8.3, 34.8)	12.0 (6.5, 21.3)	15.3 (6.5, 24.6)	17.3 (8.3, 31.8)
LA (mmol/L)	Pre	2.52±0.69	2.36±0.55	2.40±0.63	2.85±0.76	2.68±0.73	2.38±0.58
	0 min post	8.85±3.28	9.83±3.16	10.08±2.67	8.14±3.57	8.49±3.37	9.96±2.86
	30 min post	2.47±1.05	3.24±0.79	3.67±1.20	2.72±1.14	2.60±1.08	3.46±1.02*
	60 min post	1.77±0.63	2.20±0.79	2.77±0.84	2.14±0.89	1.95±0.75	2.49±0.85*
OH·- (U/ml)	Pre	474.29±247.35	401.86±157.94	499.26±194.38	542.57±236.36	501.38±242.43	445.21±188.26
	0 min post	633.87±277.01	382.74±233.21*	494.79±152.59	647.14±150.38*	637.06±222.43	434.69±201.67*
	30 min post	536.67±220.02	456.73±103.03	569.15±140.61	479.35±255.36	495.77±233.58	515.90±137.91
	60 min post	512.30±256.27	457.77±280.95	505.48±203.00	155.58±176.77	485.96±221.96	493.63±243.92
GSH-PX (μmol/L)	Pre	222.67±67.33	218.59±76.15	202.24±64.43	213.29±61.85	219.17±64.58	210.41±70.31
	0 min post	188.61±89.57	171.08±46.23	188.79±61.94	201.26±81.67	197.28±85.23	178.69±54.06 #
	30 min post	206.36±70.60	191.88±54.75	199.63±48.46	205.95±83.74	209.24±76.02	195.78±51.91
	60 min post	259.58±138.78	194.73±74.92	202.80±58.52	188.21±124.52	229.64±134.24	197.06±66.06

Note: Pre, before the intervention; 0 min post, immediately after the intervention; 30 min post, 30 minutes after the intervention; 60 min post, 60 minutes after the intervention; OH·^-, the ability to inhibit hydroxyl radicals; GSH-PX, glutathione peroxidase activity;*indicates a significant difference between the groups at the same time, p<0.05.

The effects of HRG on fatigue perception

The GEE models demonstrated a significant interaction between group and time for the primary outcome of fatigue perception, that is, the VAS (W=9.940, p=0.019). Post-hoc analyses indicated that immediately post-exercise, the VAS of the HRG group were significantly lower than those of the placebo group (p=0.008), but no significant differences were observed between HRG group and placebo group at 30 and 60 minutes post-exercise (p>0.258). Similarly, the GEE models showed a significant interaction between group and time for RPE (W=6.016, p=0.014). Post-hoc analyses revealed that immediately post-exercise, the RPE of the HRG group were significantly lower than those of the placebo group (p<0.001).

The effects of HRG on functional performance

The primary two-way repeated measures ANOVA models showed no significant interaction between group and time for CMJ (F=2.723, p=0.113), although greater performance in CMJ was observed after the intervention in HRG group than in the placebo group (35.01 cm VS 32.53 cm). Secondarily, paired t-test models showed that the cycling frequency during the last 30 seconds of the last segment in the fatigue modeling process was significantly higher in the HRG group compared to the placebo group (t=9.659, p<0.001). The cycling frequency during the last 30 seconds of all other segments in the fatigue modeling process were within the range of 60–70 rpm ([Table 3]).

Table 3 Results for the last 30 seconds of cycling frequency of ten phases throughout the fatigue modelling process.

	Phase 1	Phase 2	Phase 3	Phase 4	Phase 5	Phase 6	Phase 7	Phase 8	Phase 9	Phase 10
HRG group (n=24)	65.01±3.79	65.96±4.34	66.22±3.85	66.41±3.99	66.12±3.75	66.22±3.63	65.90±3.91	65.94±5.14	66.12±3.81	65.38±3.96
placebo group (n=24)	64.14±3.44	64.71±3.77	65.61±4.29	66.44±4.30	67.22±4.81	66.67±4.68	66.66±5.01	65.63±5.59	66.60±7.08	53.45±3.97*

Note: Cycling frequencies between 60–70 rpm meet the requirements, the existence of the statistically significant differences in this range is not meaningful; cycling frequency below 60 rpm indicates an inability to maintain cycling requirements;*indicates a significant difference in cycling frequency between the two groups, p<0.001.

The effects of HRG on blood sample markers

Two-way repeated measures ANOVA models showed a significant interaction between group and time for LA and the ability to inhibit hydroxyl radicals (LA: F=8.993, p=0.001; the ability to inhibit hydroxyl radicals: F=4.665, p=0.006), but not for GSH-PX activity (F=0.314, p=0.815). Only main effect of time on GSH-PX activity was observed (F=4.148, p=0.021). Post-hoc analyses revealed that there were no significant differences between the LA of HRG group and placebo group immediately after exercise (F=3.998, p=0.058), but the LA of HRG group was significantly lower than that of placebo group at 30 minutes and 60 minutes after exercise (F>5.500, p<0.028). The LA of HRG group returned to pre-exercise levels at 30 minutes after exercise, while in the placebo group, it returned to pre-exercise levels at 60 minutes after exercise. The ability to inhibit hydroxyl radicals in HRG group was significantly higher than that in placebo group immediately after exercise (F=8.671, p=0.009), but no significant difference was observed at 30 minutes and 60 minutes after exercise (F<0.146, p>0.707).

The association between the fatigue performance and the blood sample markers

Based on the aforementioned results, we conducted a linear regression analysis to examine the relationship between blood sample markers outcomes (i. e. LA, the ability to inhibit hydroxyl radicals) and fatigue perception (i. e. VAS and RPE scores) that were significant influenced by HRG within the HRG group. No significant correlation between the percentage change in fatigue perception and that in blood sample markers was observed (r<0.245, p>0.271).

Discussion

To our knowledge, this study is the first study to explore the effects of pre-exercise HRG inhalation on exercise fatigue, functional performance and blood markers after exercise. It was observed that the pre-exercise inhalation of HRG can help alleviate the fatigue severity and to some extent improve functional performance (i. e. the cycling frequency during the last 30 seconds of the last segment in the fatigue modeling process) under the fatigue condition. Meanwhile, HRG also accelerated the clearance of LA after strenuous exercise and enhanced the ability to inhibit hydroxyl radicals, potentially demonstrating the underlying mechanisms through which HRG can help alleviate the burden of fatigue after exercise.

The primary findings here are that compared to the placebo, inhaling HRG before exercise can significantly alleviate subjective fatigue perception after high-load exercise, as assessed by VAS; and improve the ability to inhibit hydroxyl radicals. This is in consistent with previous studies showing that hydrogen can alleviate the fatigue and improve oxidative stress markers [15] [17] [32]. Additionally, the accelerated recovery of LA by HRG was observed, which may be attributed to its capacity to reduce excessive ROS, mitigate oxidative damage to mitochondria caused by ROS, and enhance aerobic pathway utilization [33]. It has been suggested that HRW has an alkalizing effect that can help neutralize LA [34], but it is still unclear if HRG can have a similar effect, thus neutralizing LA and reducing its concentration.

The development of exercised-related fatigue and decline in functional performance may arise from increased ROS. The high-load physical exertion can disrupt electron flow in mitochondria, trigger auto-oxidation of catecholamines, impede nicotinamide adenine dinucleotide phosphate activity, or result in ischemia-reperfusion, consequently inducing oxidative stress and elevating ROS generation [35]. The impact of ROS on mitochondrial function is recognized as a significant contributor to fatigue [36] [37]. Mitochondria are particularly susceptible to the effects of ROS-induced oxidative damage of lipids, proteins, and DNA, resulting in a decline in electron transfer and ATP synthesis. This leads to a decrease in the efficiency of the aerobic pathway and an increase in the utilization of the anaerobic pathway, which can result in elevated levels of inorganic phosphate and LA [33]. The changes in redox status caused by increased ROS also trigger an increase in intracellular calcium ions and the inactivation of intracellular enzymes, which can alter the action potential of muscle contraction and interfere with the intramuscular potassium ion transport system. Additionally, ROS can modify muscle contractile proteins (muscle contraction) and calcium pumps (muscle control) [38], and damage one or more proteins involved in excitation-contraction coupling, leading to a reduction in the production of muscle force [5]. However, HRG may counteract these issues by eliminating ROS [8] [39] [40] thus lead to the observed improvement of the cycling frequency during the last 30 seconds of the last segment in the fatigue modeling process in this work.

However, we did not observe a significant direct association between the HRG-induced improvements in fatigue and the ability to inhibit hydroxyl radicals, indicating that the observed fatigue alleviation may potentially arise from another potential pathway. The prefrontal cortex (PFC) of the brain is considered a key area for processing fatigue-related information [41]. Higher levels of fatigue are correlated with reduced activation in the PFC [42]. In one of our recent studies, we showed that the HRG helps significantly increase the excitability of the PFC after the high-load exercise, and the changes in PFC excitability are associated with the changes of the subjective perception of fatigue, as assessed by RPE [43]. This may reveal a “central” pathway through which HRG can alleviate exercise-induced fatigue. Taken together, the results here showed that the benefits of HRG for fatigue alleviation may not only arise from alleviating oxidative stress in the periphery, but also from the modulation of the excitability of the cortical regions, including the PFC. It is thus highly demanded to comprehensively characterize the underlying pathways through which intaking hydrogen can alleviate the exercise-related fatigue by measuring both peripheral and central elements pertaining to the development of fatigue.

Several limitations should be acknowledged in our study. First, it is important to note that the hydrogen generator utilized in this study also produces oxygen. Although the concentration of oxygen inhaled into the human body is extremely minute and does not independently impact fatigue performance (the minimum concentration of oxygen required to affect fatigue and oxidative capacity is 70–96% [44] [45]), there remains the possibility of a synergistic effect in combination with hydrogen. Second, currently the appropriate dose and timing of HRG has not been determined. In this study, we designed the HRG protocol based upon the knowledge from previous studies [15], which, however, did not induce expected significant improvement in CMJ height (though higher height of the CMJ was observed after inhaling HRG than placebo). This insignificance may be related to the insufficient statistical power due to relatively small size, suggesting that future work with larger sample size of participant is needed to determine the protocol that can maximize the benefits of HRG. Third, the slightly higher concentrations of serum LA detected in this study may be attributed to the freezing of blood samples at -80°C (the storage conditions for oxidative markers of blood) and are not directly comparable to other studies. But this did not affect the results of this study. Fourth, due to the considerable individual variability and wide range of oxidative stress markers, the oxidative stress biomarkers tested in this study were not comprehensive enough to fully reflect the processes behind oxidative stress. Future study should employ metabolomics and related methodologies to screen for hydrogen's metabolic markers, aiming to elucidate these mechanisms through pathway enrichment analysis. In addition, more research is needed in the future to validate the confidence and validity of assays for their ability to inhibit hydroxyl radicals. Fifth, hydrogen is not suitable for use in all situations. When the intensity of recreation and training is low, the body's own ability to remove ROS is sufficient and inhaling HRG will not produce an effect. Whether HRG can still improve performance in elite athletes or play a decisive role in higher-level competitions remains to be investigated. This also may affect whether hydrogen gas will be considered a banned substance for performance enhancement.

Conclusions

Our study demonstrated that inhalation of HRG prior to high-intensity exercise is a novel strategy to alleviate exercise fatigue and maintain functional performance after exercise. The ability to inhibit hydroxyl radicals, and accelerate LA recovery may be contributing factors to the observed effects of HRG. Inhalation of HRG before strenuous or non-habitual exercise can serve as part of the preparatory activities, extending the duration of recreational and training sessions. This practice helps in mitigating the cellular damage related to fatigue, accelerating the elimination of LA in the body, and may lessen the physical responses induced by LA.

Conflict of Interest

The authors declare that they have no conflict of interest.

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Publication History

Received: 04 March 2024

Accepted: 29 April 2024

Accepted Manuscript online:
02 May 2024

Article published online:
10 September 2024