Evaluation of Renal Function Using Cys-C After Training in a Hot Environment in Collegiate Athlete

  • Michihisa Suwa 
  • Takayuki Komatsu 
  • Yuki Shiota 
  • Atsushi Kubota 
  • Masashi Aoyagi 
  • Kenta Kondo 
  • Yuji Takazawa 
  • Toshiaki Iba 


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Submitted Dec 29, 2024
Published Feb 24, 2025
10.24018/ejsport.2025.4.1.215

Abstract viewed = 193 times

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In this prospective, comparative study, conducted in hot (August) and non-hot environment (November), we aimed to elucidate the usefulness using Cys-C for assessing renal function after training, focused on a hot environment. Eighteen male collegiate baseball players aged 19.8 ± 1.0 years were included. All participants completed training sessions in both hot and non-hot environments, which involved regular training for 3 h in August and November. The training consisted of 45-min running, 45 min of 20-m dash repetitions, and continuous fungo for 60 min. The wet-bulb globe temperature (WBGT) was recorded over time. Urine samples were collected before and after training, and blood samples were collected after training. The primary outcome measures included the serum creatinine ([Cr]) levels, cystatin-C levels ([Cys-C]), and the estimated glomerular filtration rate based on Cr (Cr eGFR) and Cys-C (Cys-C eGFR). The Risk/Injury/Failure/Loss/End-stage kidney disease classification was used to evaluate acute kidney injury (AKI) based on Cr eGFR and Cys-C eGFR. The median WBGT was 37.9°C in August and 16.0°C in November. The serum [Cr] and [Cys-C] levels were significantly higher in August than those in November (p = 0.003 and p = 0.0004, respectively). Cr eGFR was lower in August than in November (p = 0.005). Although, there was one case in August that met the criteria for AKI based on Cr eGFR, no player in both groups showed decreased renal function based on Cys-C eGFR. Even in a hot environment, Cys-C eGFR is useful for preventing overdiagnosis of AKI for athletes when the baseline is unknown.

Keywords: Acute kidney injury Cystatin C Glomerular filtration rate Heat stroke

Introduction

Heat stroke is a severe condition in a spectrum of illnesses that progress from heat exhaustion (Epstein & Yanovich, 2019). Clinically, it poses a significant threat due to central nervous system dysfunction, multi-organ failure, and extreme hyperthermia (typically > 40.5 °C) (Epstein & Yanovich, 2019). To mitigate this risk, it is widely recommended worldwide to discontinue outdoor sports activities when the wet-bulb globe temperature (WBGT) exceeds 30–33.4 °C (Binkleyet al., 2002; FIFA, 2015; Robertset al., 2023; World Athletics Health and Science Department, 2020). However, elite athletes are often compelled to continue training at world-class events, such as the Summer Olympic games, in where the incidence of heat stroke among athletes was reported to be 100 cases (0.88%) (Inoueet al., 2023; Yamasaki & Nomura, 2022).

As the kidneys, along with the brain and liver, are susceptible to early damage, mainly due to reduced blood flow during severe dehydration, acute kidney injury (AKI) reportedly occurs in the early stages of heat stroke. Thus, early detection of AKI is important (Binkleyet al., 2002; Doiet al., 2018; FIFA, 2015; Robertset al., 2023; World Athletics Health and Science Department, 2020).

Serum creatinine concentration ([Cr], mg/dL), commonly used to assess renal function, is influenced by factors, such as muscle mass and hemoconcentration, particularly in athletes. Consequently, serum cystatin-C concentration ([Cys-C], mg/L) has proven to be a more useful marker for renal function assessment in athletes (Binkleyet al., 2002; Mingelset al., 2009; Wołyniecet al., 2020). Previous studies have examined the [Cys-C] or Cys-C-based estimated glomerular filtration rate (Cys-C eGFR) after various types of training, such as marathons, ultramarathons, bicycle mountain races, treadmills, and cycle ergometer (Bongerset al., 2018; Colombiniet al., 2012a, 2012b; Hewinget al., 2015; McCulloughet al., 2011; Poortmanset al., 2013; Pousselet al., 2020; Scherret al., 2011). In five of these studies, blood samples were collected 30 min to 1 h after training to evaluate the direct influence of exercise, while the timing of blood sampling was not specified in the others. Notably, these studies were conducted under conditions where the maximum air temperature was approximately 25 °C, and none were performed in a hot environment.

In this study, we aimed to elucidate the utility of using Cys-C to assess renal function in athletes post-training, with a specific focus on its applicability in hot environments.

Materials and Methods

Study Population

Eighteen male collegiate baseball players (five pitchers, one catcher, six infielders, and six outfielders), aged 19.8 ± 1.0 years were enrolled in this study. All participants had been engaged in their regular training regimen for 12 weeks prior to the intervention. Athletes with any pre-existing health problem or history of heat stroke during the same season were excluded.

Exercise Protocol

To investigate the impact of a hot environment on renal function, this observational study was conducted during the summer (August) in a hot environment and during the autumn (November) in a non-hot environment as a control. The selection of August was based on data from the Japan Ministry of the Environment, which indicates that the risk of heat stroke begins to rise in late June and peaks in August (Ministry of the Environment of Japan, n.d.).

To reflect typical athlete performance, the training sessions were standardized to 150 min in both seasons. Each session included 45 min of running, 45 min of 20-m dash repetitions, and 60 min of continuous fungo practice, with no additional exercise introduced. Unlike studies, such as those by Pousselet al. (2020) and Poortmanset al. (2013), which implemented scheduled drinking protocols, this study allowed athletes to freely consume a sports drink during training. Athletes were encouraged to drink more than 1000 mL based on their thirst to prevent heat stroke without interfering with regular training routines (Fig. 1).

Fig. 1. Experimental protocol for blood and urine sample collection. Urine samples were collected 30 min prior to exercise and 5 min after exercise, while blood samples were collected 15 min post-training.

Additionally, one physician and two nurses were on standby during all sessions to address any cases of heat stroke or other medical issues.

Evaluation Variables

1) Environmental Factors

Air temperature and WBGT were measured every 30 min, from the beginning of the training to the end in each season. The WBGT measuring instruments were installed at a height of approximately 1.5 m above the ground in a well-ventilated, sunny location without interrupting the activities of the athletes (Ministry of the Environment of Japan, n.d.).

2) Rate of Perceived Exertion

After exercising, athletes used the Borg scale to assess their rate of perceived exertion, rating it on a 15-point scale from rest to maximum effort after exercise (Borg, 1982).

3) Biochemical Factors

i) Urine Samples

Urine samples were collected 30 min before the initiation of training and 5 min after training completion. Urinalysis included measurements for occult blood, urinary protein, urinary sugar, and urine-specific gravity.

ii) Blood Samples

To more directly reflect the physiological effects during practice and minimize potential adverse impacts on performance, blood samples were drawn 15 min after the end of training. The samples were collected by medical professionals from the cubital veins with participants seated.

Serum creatine kinase ([CK]) and myoglobin ([Mb]) concentrations were measured as biomarkers of skeletal muscle damage. Renal function was assessed through the measurement of serum [Cr], [Cys-C], β2-microglobulin, and interleukin-6 levels (Trofet al., 2006). Additionally, eGFR was calculated to evaluate renal function. The eGFR was derived from [Cr] and [Cys-C] levels using the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, enabling direct comparison with findings from prior studies (Anderssonet al., 2022; Inkeret al., 2012, 2021; Leveyet al., 2009; Poortmanset al., 2013).

The equations were as follows:

(1)CreGFR=A×([Cr]B)c×0.993age

According to CKD-EPI, in case of male individuals, the coefficients A, B and C are 141, 0.9, and −0.411 or −1.209 at [Cr] ≤0.9 or >0.9, respectively.

(2)Cys−CeGFR=133×([Cr]B0.8)D×0.996age

Similarly, for male individuals, the coefficient D is −0.499 or −1.328 at [Cys-C] ≤0.8 or >0.8, respectively.

4) Definition of AKI

Although [Cys-C] is more appropriate than [Cr] for assessing renal function in athletes, (Binkleyet al., 2002; Mingelset al., 2009; Wołyniecet al., 2020) the Kidney Disease Improving Global Outcomes (KDIGO) classification, the most recent AKI diagnostic criteria in general use, cannot reflect [Cys-C] (Khwaja, 2012; Mehtaet al., 2007). The classification based directly on [Cys-C] has been reported. However, it was deemed inappropriate for this study, as it requires a 48-h monitoring period to diagnose AKI (Zhouet al., 2016). Therefore, this study adopted the Risk/Injury/Failure/Loss/End-stage kidney disease (RIFLE) classification, which is currently the only classification system capable of diagnosing AKI based on changes in the GFR (Bellomoet al., 2004; Kellumet al., 2002).

Given the lack of baseline GFR data, an eGFR of 100 mL/min/1.73 m², reflecting the typical range for healthy adults, was used as a reference point, consistent with previous studies (Pickering & Endre, 2010; Wołyniecet al., 2020). According to the RIFLE classification, the “Risk” category, the mildest form of AKI, was defined as a reduction in eGFR >25% from baseline, corresponding to an eGFR <75 mL/min/1.73 m² (Bellomoet al., 2004; Kellumet al., 2002). After calculating the Cr eGFR and Cys-C eGFR, AKI was diagnosed if the criteria of the RIFLE classification were met based on the Cys-C eGFR, given its applicability and utility for athletic populations.

Statistical Analysis

Before comparing each dependent variable, the Kolmogorov–Smirnov test was used to assess the normality. Continuous variables were presented as means ± standard deviations (SDs) evaluated by the Wilcoxon signed-rank test between August and November. A significance level of p < 0.05 was applied for all comparisons. Data analysis was conducted using the EZR software program, version 1.55 (Saitama Medical Center, Jichi Medical University, Saitama, Japan) (Kanda, 2013).

Ethical Considerations

The study was approved by the Ethics Committee in Juntendo University Graduate School of Health and Sports Science, Chiba, Japan (approval reference number: 2021–27), and conformed to the principles outlined in the Declaration of Helsinki. All participants provided written informed consent before enrollment.

Results

An overview of participant characteristics is presented in Table I. Fig. 2 shows the mean WBGTs as 37.9 °C in August and 16.0 °C in November. The Borg scale scores revealed no difference between both environments (17.1 ± 0.6 vs. 16.7 ± 0.8, p = 0.2). Moreover, none of the athletes reported subjective symptoms of heat stroke during exercise.

Parameter Mean ± SD
Age (years) 19.8 ± 1.0
Height (cm) 172.1 ± 4.4
Weight (kg) 73.2 ± 7.9
BMI (kg/m2) 24.7 ± 2.2
Baseball career length (years) 11.3 ± 2.1
Table I. General Baseline Characteristics of Participants

Fig. 2. Ambient temperature and wet-bulb globe temperature. The average WBGT for August was 37.9 °C, highlighting the intervention’s occurrence in a challenging hot environment.

Height and weight were 172.1 ± 4.4 and 73.2 ± 7.9, respectively, and BMI was 24.7 ± 2.2 kg/m2.

The results of urinalysis and blood biochemical tests are listed in Table II. There was no presence of urine occult blood, proteinuria, or urinary sugar levels before or after training in either group. While there was no significant difference in urine-specific gravity before training, it significantly increased after training in August compared with that in November (1.031 ± 0.005 vs. 1.026 ± 0.008, p = 0.001). Additionally, both [Cr] and [Cys-C] were higher in August than in November (1.20 ± 0.130 vs. 1.10 ± 0.119 mg/dL, p = 0.003; 0.93 ± 0.101 vs. 0.79 ± 0.072 mg/L, p = 0.0004, respectively). Cr eGFR and Cys-C eGFR were lower in August compared to those in November (90.8 ± 13.2 vs. 99.9 ± 12.5 mL/min/1.73 m2, p = 0.005; 102.4 ± 14.0 vs. 122.1 ± 9.1 mL/min/1.73 m2, p = 0.0006, respectively). Although [CK], which exceeded the upper limit of normal in both groups (515 ± 327 vs. 445 ± 221 U/L, p = 0.58), was not significantly different between the groups, [Mb] was elevated only in November (98.6 ± 47.4 vs. 180.4 ± 79.2 ng/mL, p = 0.0003). Although, one case was classified as AKI according to Cr eGFR in August, when referencing Cys-C eGFR, no players in both groups had AKI.

Parameter, Normal range August mean ± SD November mean ± SD p-value
Cr (mg/dL), 0.61–1.04 1.20 ± 0.130 1.10 ± 0.119 0.0031
Cys-C (mg/L), 0.61–1.00 0.93 ± 0.101 0.79 ± 0.072 0.0004
CK (U/L), 60–270 515 ± 327 445 ± 221 0.5798
Mb (ng/mL), ≤154.9 98.6 ± 47.4 180.4 ± 79.2 0.0003
β2-MG (mg/L), 0.9–1.9 1.8 ± 0.2 1.4 ± 0.1 0.0007
IL-6 (pg/mL), ≤7.0 3.6 ± 1.7 3.1 ± 0.9 0.4078
Urine specific gravity, 1.006–1.030
Pre-training 1.025 ± 0.008 1.025 ± 0.008 0.636
Post-training 1.031 ± 0.005 1.026 ± 0.008 0.0011
Cr eGFR (mL/min/1.73 m2) 90.8 ± 13.2 99.9 ± 12.5 0.0049
Cys-C eGFR (mL/min/1.73 m2) 102.4 ± 14.0 122.1 ± 9.1 0.0006
Table II. Urine and Blood Parameters

[Cr] and [Cys-C] were higher in August; Cr eGFR and Cys-C eGFR were lower in August; urine specific gravity was higher and more concentrated after exercise in August.

Discussion

Although the elevation in urine-specific gravity and [Cr] following training in a hot environment with an average WBGT of 37.9 °C indicated intravascular dehydration, no athlete exhibited renal dysfunction when assessed using Cys-C eGFR rather than Cr eGFR.

Previous studies have suggested that the elevation of [Cys-C] immediately after marathon running, even in non-hot environments, is influenced primarily by cytokine release associated with high-intensity exercise, though dehydration may also play a role (Hewinget al., 2015; McCulloughet al., 2011). For instance, Pousselet al. (2020, p. 71) reported that ultramarathon runners in non-hot environments who maintained adequate fluid intake did not develop AKI. In contrast, our study revealed that even in a hot environment leading to intravascular dehydration, no athletes developed renal dysfunction during moderate-intensity exercise, unlike the high intensity of marathon running.

This finding could be attributed to the athletes’ heat acclimatization from approximately 12 weeks of prior training in hot conditions. Heat acclimatization reduces the likelihood of a substantial increase in core body temperature (Ministry of the Environment of Japan, n.d.). Notably, short-term heat acclimatization enhances the reabsorption of sodium ions in sweat glands, resulting in decreased sodium ion concentration in sweat. Consequently, while water loss exceeds sodium loss, core body temperature remains regulated through efficient heat dissipation. However, this process also induces a shift of intracellular water into the extracellular space, contributing to intravascular dehydration (Boulteret al., 2011).

Furthermore, heat acclimatization enhances the expression of intracellular heat shock protein 70 in tubular cells, which protects renal function by preventing the depletion of kidney-resident macrophages (Gotoet al., 2022). This mechanism likely contributed to the absence of AKI, despite the potential risk of renal parenchymal damage caused by dehydration-related reductions in renal blood flow. Our findings suggest that even in environments with high WBGT, renal dysfunction may be prevented through completed heat acclimatization and the implementation of appropriate hydration strategies.

Our study highlighted the utility of the RIFLE classification based on Cys-C eGFR as a definition of AKI in athletes, particularly in hot environments, to minimize the risk of overdiagnosis. While the KDIGO classification can be applied even when baseline [Cr] is unknown, it does not incorporate [Cys-C], a parameter particularly relevant for athletes due to its independence from muscle mass and hemoconcentration (Khwaja, 2012; Mehtaet al., 2007).

Additionally, although there are AKI classifications that directly reflect [Cys-C], they are not well-suited for athletes because they require 48-h monitoring to establish a diagnosis (Zhouet al., 2016). In this context, our findings suggest that, until a diagnostic classification specifically incorporating [Cys-C] in situations where baseline values are unavailable is developed, the RIFLE classification using Cys-C eGFR could serve as a practical and effective tool. Despite being a classical diagnostic criterion, it offers an approach tailored to avoid overdiagnosis of AKI in athletes, particularly in challenging conditions, such as hot environments.

Study Strengths and Limitations

This study is the first to evaluate renal function following training, particularly in a hot environment, which is a significant strength. However, several limitations should be acknowledged.

First, pre-training blood examination data were not available, which makes it challenging to determine precise baseline renal function. Blood sampling was not performed before practice to avoid interference with normal training routines. However, as the study participants were healthy university athletes, and no cases of renal dysfunction were observed in [Cys-C] measurements post-training, it is reasonable to assume that none of the participants had pre-existing renal dysfunction.

Second, core body temperature was not measured, leaving uncertainty regarding the extent of organ damage caused by heat stress. Monitoring core body temperature was impractical during regular practice sessions. Nevertheless, the elevation of [Mb], which reflects cellular damage, was observed only in the non-hot environment, suggesting that heat acclimatization in August had been completed, likely providing protection against core body temperature-related renal dysfunction.

Third, renal function was assessed only immediately after training, limiting the evaluation of potential renal dysfunction in the subsequent days when the effects of rhabdomyolysis might become apparent. Although [CK] levels were elevated post-training with no significant differences between August and November, no athletes exhibited renal dysfunction. This finding indicates that with proper heat acclimatization and adequate hydration, training can be conducted safely in hot environments without compromising renal function, even in the presence of mild rhabdomyolysis.

Fourth, body weight changes before and after training and the precise volume of fluid intake were not measured, so dehydration was assessed indirectly using urine-specific gravity. While participants were instructed to consume at least 1000 mL of sports drinks during training to account for their condition, individual variations in fluid intake could have influenced the results.

Finally, the findings may not directly apply to other endurance sports conducted in hot environments, such as marathons, which have been the focus of previous studies, or to contact and collision sports, which involve a higher incidence of muscle damage (Bongerset al., 2018; Colombiniet al., 2012a, 2012b; Hewinget al., 2015; McCulloughet al., 2011; Poortmanset al., 2013; Pousselet al., 2020; Scherret al., 2011).

Despite these limitations, this study provides important insights into renal function assessment in athletes. It suggests that [Cys-C], which has been validated as a reliable biomarker in previous studies, could also be a valuable tool for evaluating renal function in hot environments. Future studies should address these limitations to further confirm and expand upon these findings.

Conclusions

To avoid the overdiagnosis of AKI, particularly in hot environments, [Cys-C] has demonstrated potential utility for athletes. Additionally, the RIFLE classification appears to be a suitable criterion for diagnosing AKI in athletes when baseline renal function is unknown. This remains the case until a diagnostic classification incorporating [Cys-C] is developed specifically for such scenarios.

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