Oxidative stress and antioxidant enzyme activities in elite female Turkish cross-country skiers

Abstract

Background

Oxidative stress–related processes contribute to exercise physiology through redox-sensitive signaling, yet when oxidant production exceeds antioxidant buffering, oxidative modifications may be reflected in circulating biomarkers. Evidence in elite female endurance athletes remains limited, particularly in cross-country skiers, and interpretation should be restricted to biomarker-level descriptions within the constraints of cross-sectional designs.

Methods

This cross-sectional comparative study included 17 elite female cross-country skiers and 17 age- and BMI-matched sedentary women (15–20 years). Following a 10–12 h overnight fast, participants attended the laboratory between 07:00 and 09:00 a.m.; venous blood was collected under standardized resting conditions. Serum catalase (CAT) activity, total glutathione (GSH), and TBARS-derived malondialdehyde (MDA) were quantified using spectrophotometric methods. Between-group differences were tested using the Mann–Whitney U test, with effect sizes expressed as Cliff’s delta (δ) and 95% confidence intervals. Within-group associations among CAT, GSH, and MDA were examined using Spearman correlations (ρ).

Results

Compared with controls, skiers showed higher CAT activity (p < 0.001; δ = 0.765, large) and higher GSH concentrations (p = 0.006; δ = 0.554, large), while TBARS-derived MDA was lower in skiers (p < 0.001; δ = −0.848, large; indicating higher values in controls). Within the skier group, CAT and GSH were moderately correlated (ρ = 0.57, p = 0.017), whereas no significant CAT–GSH association was observed in controls. No significant correlations were detected between CAT and MDA or between GSH and MDA in either group (all p ≥ 0.05).

Conclusions

Elite female cross-country skiers exhibited robust between-group differences in selected circulating redox-related biomarkers at rest, characterized by higher CAT and GSH and lower TBARS-derived MDA relative to sedentary women. These findings describe biomarker-level group separation and should be interpreted as association-level observations without causal attribution, claims of systemic redox homeostasis, or direct inference of training adaptation, recovery, or performance effects (ClinicalTrials.gov identifier: NCT07181889, Date 2025-09-12).

Introduction

Cross-country skiing is among the most physiologically demanding endurance sports, requiring sustained whole-body work from both the upper and lower extremities, often performed in cold outdoor environments. The high aerobic energy turnover associated with prolonged skiing increases mitochondrial oxygen flux and can elevate reactive oxygen species generation, particularly when training and competition loads are substantial [1, 2]. Reactive oxygen species are not solely detrimental; a controlled increase contributes to redox-sensitive signaling processes that support normal physiological regulation. However, when oxidant production exceeds buffering capacity, oxidative modifications of biomolecules may occur and can be reflected in circulating oxidative stress–related markers [3]. Accordingly, measuring selected redox-related biomarkers can provide descriptive information about oxidative and antioxidant-related status in athlete cohorts, while avoiding inferences that exceed the scope of the biomarker panel and study design.

Antioxidant defense involves enzymatic systems, including catalase, and non-enzymatic thiol-based buffering such as reduced glutathione. These components act in concert to limit oxidant-related damage and maintain redox regulation under metabolic stress. Meta-analytic evidence indicates that structured exercise training can modify antioxidant enzyme activities and oxidative stress–related indices, although the direction and magnitude of change may vary according to exercise type, intensity, duration, and participant training status [4]. Moreover, responses may differ across biomarkers, with some interventions producing clearer changes in lipid peroxidation indices than in specific enzymes such as catalase [4, 5]. Observational evidence in adolescent female athletes has reported higher catalase activity and lower MDA alongside smaller changes in glutathione, underscoring that biomarker responses may not be uniform even within similar training contexts [6]. These findings highlight the need to focus on clearly defined biomarkers and to interpret them as partial indicators rather than comprehensive measures of “systemic redox homeostasis.”

Despite the growing interest in exercise-related oxidative stress, evidence specific to female cross-country skiers remains limited. Available work suggests that oxidative stress–related markers may fluctuate across training phases, and variability may be particularly evident among female athletes [7]. Additional sport-science research in elite skiers has emphasized pronounced metabolic demands but has not consistently included classical redox biomarkers such as CAT, GSH, and MDA in athlete monitoring frameworks [7]. Importantly, interpretation in female cohorts requires attention to biological context. Sex-specific hormonal fluctuations have been proposed as a contributor to heterogeneity in oxidative stress–related findings, and cold exposure may further modify physiological stress responses in endurance sports settings [8, 9]. At the same time, existing reviews emphasize that antioxidant-related responses depend strongly on sport-specific load characteristics, reinforcing the need for investigations that are specific to the demands of cross-country skiing [10]. Taken together, the literature supports a focused descriptive comparison of selected oxidative stress and antioxidant biomarkers in elite female skiers, while recognizing that a limited biomarker panel cannot characterize the entire redox system or establish training adaptation.

Therefore, the primary objective of the present study was to compare catalase activity, reduced glutathione concentration, and TBARS-derived malondialdehyde between elite female cross-country skiers and age-matched sedentary women. The study was designed as a cross-sectional group comparison, and all interpretations are framed accordingly. We hypothesized that skiers would exhibit different resting levels of these biomarkers compared with controls, reflecting group-level differences in enzymatic antioxidant activity, non-enzymatic antioxidant-related status, and a lipid peroxidation index. This work aims to add biomarker-level data in a relatively underrepresented athlete cohort and to inform future longitudinal studies that can more directly evaluate training-phase dynamics and mechanistic pathways.

Method

Study design and participants

This cross-sectional comparative study was conducted in accordance with the STROBE guidelines for observational research and adhered to the principles of the Declaration of Helsinki. Given the observational and single time-point nature of the design, the study was intended to describe between-group differences and associations rather than to infer causality. Ethical approval was obtained from the Scientific Research and Publication Ethics Committee of Mus Alparslan University, Türkiye (Approval No: 27754; Date: 27 October 2021). A total of 34 women voluntarily participated in the study, including 17 elite cross-country skiers (members of the Turkish national junior and senior teams, 2021–2025) and 17 age- and BMI-matched sedentary women. The sedentary group was operationally defined as individuals not participating in any structured endurance or resistance training and not involved in competitive sports during the preceding months. Inclusion criteria were: (a) female sex; (b) age 15–20 years; (c) absence of chronic disease or medication use; (d) no antioxidant supplementation in the past 3 months; (e) no smoking or alcohol consumption. Exclusion criteria included acute infection, musculoskeletal injury, or non-compliance with fasting and rest requirements. Because redox biomarkers may be influenced by hormonal maturation across the 15–20-year range, participant screening included a brief health-history check to minimize major developmental/medical confounding; however, pubertal maturation was not formally staged and is acknowledged as a potential source of biological variability.

All participants (or their legal guardians in the case of those under 18 years of age) provided written informed consent after a full verbal and written explanation of the study procedures and potential risks. For athletes younger than 18 years, parental or guardian consent was obtained in addition to the athlete’s assent.

Menstrual cycle control. To reduce hormonal-related variability in this 15–20-year female cohort, menstrual history was systematically queried during screening. The date of the last menstrual period (LMP) was recorded for all participants, and cycle regularity (typical cycle length/regularity) was documented when available. Importantly, blood sampling was not performed during active menstrual bleeding; appointments were rescheduled when necessary to avoid this period. Although sampling was therefore partially standardized by excluding active menses, blood collection was not strictly synchronized to a single predefined menstrual phase (e.g., early follicular) across all participants; residual cycle-related variability was considered in the interpretation of oxidative stress and antioxidant biomarkers.

To minimize the influence of major confounders known to affect redox biomarkers in young female endurance cohorts, the study implemented several a priori control procedures. Dietary influences were addressed by standardizing morning sampling after an overnight fast and by documenting recent intake through a brief dietary questionnaire complemented by a 24-hour dietary recall, which allowed identification of atypical short-term antioxidant-related consumption prior to testing. Acute exercise effects were reduced by instructing participants to avoid strenuous physical activity before the laboratory visit and by obtaining all measurements under strictly resting conditions following a seated acclimation period in a temperature-controlled environment. Training-context information was documented to support interpretation of biomarker values as resting between-group differences rather than direct evidence of chronic adaptation. In addition, iron status was objectively evaluated using established hematological and biochemical indices, and participants with clinically relevant abnormalities, medication use, or acute illness were excluded to reduce bias related to iron deficiency or systemic inflammatory states. Collectively, these procedures strengthened internal validity by reducing predictable hormonal, nutritional, exercise-related, and iron-status confounding, while the cross-sectional design remained limited to association-level inference.

Anthropometric assessments (height, weight, BMI) were performed barefoot and in light clothing using a calibrated stadiometer and digital scale. Descriptive characteristics are summarized in Table 1.

Table 1.

Anthropometric characteristics of cross-country skiers and sedentary controls

Variable	Group	n	Mean ± SD	Min–Max
Age (years)	Cross-country skiers	17	17.29 ± 1.53	15–20
	Controls	17	17.29 ± 1.45	15–20
Height (m)	Cross-country skiers	17	1.61 ± 0.07	1.50–1.75
	Controls	17	1.59 ± 0.04	1.52–1.67
Weight (kg)	Cross-country skiers	17	58.71 ± 3.44	53–64
	Controls	17	59.06 ± 3.86	52–66

Biochemical sample collection and processing

All participants reported to the laboratory between 07:00 and 09:00 a.m. following a 10–12 h overnight fast to minimize circadian and dietary influences on oxidative stress markers [11]. Upon arrival, participants rested in a seated position for at least 30 min in a temperature-controlled room (22–24 °C) to ensure a true resting baseline. Participants were additionally instructed to refrain from caffeine intake on the morning of sampling to minimize short-term autonomic and metabolic effects that could influence redox-related measures. Subsequently, 3 mL of venous blood was drawn from the antecubital vein using standard aseptic technique and collected into vacuum-sealed serum tubes. Samples were allowed to clot at room temperature for a standardized period prior to centrifugation to improve pre-analytical consistency. Samples were allowed to clot at room temperature and were centrifuged at 5000 rpm for 10 min. The resulting serum aliquots were transferred into polypropylene tubes and stored at − 80 °C until analysis to preserve biochemical integrity; previous research has shown that oxidative stress biomarkers such as reactive oxygen metabolites (ROM) and biological antioxidant potential (BAP) remain stable in human serum stored at − 80 °C for up to 60 months [12]. Serum was aliquoted into multiple polypropylene tubes to reduce repeated freeze–thaw cycles, which can adversely affect redox-sensitive biomarkers.

Biochemical analyses

All analyses were performed in the Central Biochemistry Laboratory of Van Yüzüncü Yıl University Faculty of Medicine using a UV/VIS spectrophotometer (T80+, PG Instruments, UK), calibrated daily according to the manufacturer’s guidelines. Each assay was conducted in triplicate, and the mean value was used for subsequent statistical analysis to improve measurement reliability [13]. All assays were performed by the same trained laboratory technician, and where feasible the technician was not informed of group allocation during the analytical process to reduce expectation bias.

Malondialdehyde (MDA)

MDA, a marker of lipid peroxidation, was quantified using the thiobarbituric acid reactive substances (TBARS) method as originally described by Ohkawa et al. (1979) [14]. Briefly, serum samples were mixed with thiobarbituric acid reagent and incubated at 95 °C under acidic conditions (pH 3.4) to form a pink MDA–TBA complex. Absorbance was read at 532 nm, and MDA concentration was calculated using a standard calibration curve. Because TBARS-based approaches may have limited analytical specificity and may detect other aldehydic products in addition to MDA, values are reported as TBARS-derived MDA estimates and interpreted cautiously.

Glutathione (GSH)

Total GSH concentration was determined according to the method of Beutler et al. (1963) using Ellman’s reagent (5,5’-dithiobis-(2-nitrobenzoic acid), DTNB). The reaction between thiol groups and DTNB produces a yellow-colored product, measured spectrophotometrically at 412 nm [15]. Given the compartment-dependent nature of antioxidant systems, serum GSH was used as a pragmatic circulating indicator; however, intracellular/erythrocyte GSH was not assessed and therefore mechanistic interpretation is limited.

Catalase (CAT)

CAT activity was assessed following the spectrophotometric method of Aebi (1984). The rate of hydrogen peroxide (H₂O₂) decomposition was monitored by the decrease in absorbance at 240 nm over a 2-min period at 25 °C. Enzyme activity was expressed as units per milliliter (U/mL) based on the change in optical density per minute (ΔA/min) [16]. Similarly, CAT activity was determined in serum, and tissue-specific antioxidant activity could not be inferred from this measure alone.

All biochemical assays were conducted by the same trained laboratory technician to minimize inter-operator variability.

Sample size calculation

A priori sample size estimation was conducted using GPower software (version 3.1.9.7; Heinrich Heine University, Düsseldorf, Germany). Given the cross-sectional design and the planned use of non-parametric between-group comparisons (Mann–Whitney U), the sample size calculation was based on detecting a large between-group difference using the standardized mean difference framework (d = 0.80) as an approximation within GPower. This value was selected as a conservative, planning-level assumption informed by prior reports showing large trained-versus-untrained group differences in oxidative stress and antioxidant biomarkers in endurance athlete populations [17]. With a two-tailed α level of 0.05, power of 0.80 (1 − β), and an allocation ratio of 1:1, the analysis indicated that at least 17 participants per group were required. Accordingly, the final sample (17 cross-country skiers and 17 controls) met the a priori requirement for detecting between-group differences under the planned analytical framework.

Statistical analysis

All statistical analyses were conducted using IBM SPSS Statistics (version 22, IBM Corp., Armonk, NY, USA) and verified in R (version 4.3.1, R Foundation for Statistical Computing, Vienna, Austria) for reproducibility. Data are presented as mean ± standard deviation (SD) along with minimum–maximum values when relevant. Prior to analysis, the distribution of all continuous variables was assessed using the Shapiro–Wilk test, which indicated that several parameters deviated from normality. Consequently, between-group differences in oxidative stress (MDA) and antioxidant biomarkers (CAT, GSH) were evaluated using the non-parametric Mann–Whitney U test [18].

Spearman rank-order correlations (ρ) were employed to examine the associations between CAT, GSH, and MDA within each group. Correlation strength was interpreted according to established thresholds: negligible (< 0.20), low (0.20–0.39), moderate (0.40–0.59), high (0.60–0.79), and very high (≥ 0.80) [19]. To visualize the results, correlation matrices were displayed as color-coded heatmaps (green = positive, orange = negative), and significant associations were further illustrated using scatter plots with separate regression lines for cross-country skiers and controls [20]. Statistical significance was set at p < 0.05. This analytical approach follows current recommendations for transparent and reproducible reporting in sports science and exercise physiology research [19, 21]. Effect sizes for between-group comparisons were quantified using Cliff’s delta (δ), a distribution-free, rank-based dominance measure consistent with Mann–Whitney inference [22]. Cliff’s δ reflects the probability that a randomly selected observation from one group exceeds a randomly selected observation from the other group minus the reverse probability, ranging from − 1 to + 1 (0 indicating no difference) [22]. For each comparison, δ was reported together with 95% confidence intervals; magnitude was interpreted using commonly used benchmarks: negligible (|δ| < 0.147), small (0.147–0.33), medium (0.33–0.474), and large (≥ 0.474) [23].

Results

Anthropometric characteristics of the cross-country skiers and sedentary controls are summarized in Table 1. Both groups consisted of 17 participants. The groups were well matched for age and body weight, confirming baseline comparability. Cross-country skiers were slightly taller on average than controls; however, overall anthropometric profiles indicated a high degree of homogeneity between groups. Detailed descriptive statistics, including minimum and maximum values, are provided in Table 1.

Group comparisons of oxidative stress and antioxidant biomarkers are presented in Table 2.

Table 2.

Comparison of oxidative stress and antioxidant biomarkers between cross-country skiers and control group

Parameter	Group	n	Mean ± SD	Mann–Whitney U	p	Cliff’s δ
CAT (U/mL)	Cross-country skiers	17	0.070 ± 0.015	255.0	< 0.001	0.765
	Control group	17	0.038 ± 0.017
GSH (µmol/L)	Cross-country skiers	17	2.93 ± 0.87	224.5	0.006	0.554
	Control group	17	2.09 ± 0.39
MDA (nmol/mL)	Cross-country skiers	17	1.65 ± 0.33	22.0	< 0.001	−0.848
	Control group	17	2.48 ± 0.35

Values are presented as Mean ± SD. U: Mann–Whitney U test statistic. Cliff’s δ: non-parametric effect size ranging from − 1 to + 1; values > 0 indicate higher values in cross-country skiers, whereas values < 0 indicate higher values in controls. Magnitude was interpreted as negligible (< 0.147), small (0.147–0.33), medium (0.33–0.474), and large (≥ 0.474) based on |δ|

CAT Catalase, GSH Glutathione, MDA Malondialdehyde, SD Standard deviation, U Mann–Whitney U test statistic, δ Cliff’s delta effect size, CI Confidence interval. 

Cross-country skiers demonstrated significantly higher CAT and GSH levels compared with sedentary controls (both p < 0.01), whereas MDA levels were significantly lower in the skier group (p < 0.001). The between-group separation was large for all biomarkers when quantified using Cliff’s delta (δ): CAT (δ = 0.765, large), GSH (δ = 0.554, large), and MDA (δ = −0.848, large; indicating higher values in the control group). Accordingly, the results indicate strong group differences characterized by higher antioxidant biomarker levels (CAT, GSH) and lower TBARS-derived MDA in trained athletes under rank-based comparison (Table 2).

Spearman correlation coefficients (ρ) describing the relationships among CAT, GSH, and MDA within each group are reported in Table 3. In cross-country skiers, a moderate positive correlation was observed between CAT and GSH levels (ρ = 0.57, p < 0.05). In contrast, no significant association between these variables was detected in the sedentary control group. No meaningful correlations were found between CAT and MDA or between GSH and MDA in either group.

Table 3.

Spearman correlation matrix of oxidative stress and antioxidant biomarkers (CAT, GSH, MDA) in cross-country skiers and controls

Variable pair	Skiers (n = 17) ρ	95% CI	p value	Controls (n = 17) ρ	95% CI	p value
CAT – GSH	0.57	0.12 to 0.82	0.017*	0.11	−0.39 to 0.56	0.674
CAT – MDA	−0.07	−0.53 to 0.42	0.789	0.18	−0.33 to 0.61	0.489
GSH – MDA	0.08	−0.42 to 0.54	0.760	0.32	−0.19 to 0.69	0.211

ρ = Spearman’s rank correlation coefficient; CI Confidence interval; p values are from two-tailed Spearman correlation tests

CAT  Catalase activity, GSH Glutathione (total), MDA Malondialdehyde, n sample size per grou

*p < 0.05

Spearman correlation coefficients (ρ) and corresponding p-values for the associations among CAT, GSH, and MDA within each group are reported in Table 3. To facilitate interpretation of the key relationships, scatter plots are presented in Fig. 1, illustrating CAT versus GSH (Panel A) and CAT versus MDA (Panel B). In these plots, each point represents an individual participant, with trend lines displayed separately for cross-country skiers and controls. Spearman’s ρ coefficients and p-values are annotated within each panel.

Fig. 1.

Scatter plots showing the relationships between (A) CAT and GSH and B CAT and MDA

Discussion

This cross-sectional comparative study examined resting circulating redox-related biomarkers in elite female cross-country skiers and age- and BMI-matched sedentary women, focusing on catalase activity, reduced glutathione, and TBARS-derived malondialdehyde. Given the single time-point design, our findings are interpreted as between-group differences and association-level observations rather than evidence of training-induced adaptation or “systemic redox homeostasis.” This framing is important because a limited biomarker panel and an observational design cannot establish causal pathways, quantify whole-system redox regulation, or support functional claims related to recovery or performance.

Cross-country skiers demonstrated higher CAT activity and GSH concentration and lower TBARS-derived MDA than controls, with statistically significant differences and large non-parametric effect sizes derived from rank-based comparisons. Specifically, CAT was higher in skiers with p < 0.001 and a large effect (Cliff’s δ = 0.765), GSH was higher with p = 0.006 and a large effect (δ = 0.554), and MDA was lower with p < 0.001 and a large effect favoring controls (δ = −0.848). Under the dominance interpretation of Cliff’s δ, these values indicate strong group separation in the rank ordering of observations, meaning that the probability of higher CAT and GSH values is substantially greater in skiers than controls, while the probability of higher TBARS-derived MDA values is substantially greater in controls [22, 23]. However, these effect sizes quantify between-group separation rather than the cause of that separation.

The markedly higher CAT activity in skiers is consistent with literature showing that endurance-oriented cohorts often present higher resting antioxidant enzyme activity, although responses differ across protocols, populations, and training contexts [9, 24–27]. In the present dataset, CAT showed both statistical separation and a large dominance effect size, indicating that elevated CAT is a robust distinguishing feature between the groups at rest. At the same time, the cross-sectional design does not allow attribution of CAT differences to endurance training exposure itself, nor can it distinguish training effects from other correlated characteristics of elite athletes, such as long-term lifestyle, nutrition, or selection factors. Accordingly, the findings are best reported as a strong between-group difference in an enzymatic antioxidant marker, compatible with prior endurance exercise literature, without inferring “adaptive upregulation” as a demonstrated process [9, 24–27].

Although hormesis is frequently discussed as a conceptual framework in exercise redox literature, it is not used here as an explanatory conclusion because mechanistic confirmation would require longitudinal exposure tracking and molecular readouts beyond the current design [1, 28–32].

Skiers also showed higher GSH, again with a statistically significant difference and a large effect size. Prior evidence indicates that exercise exposure can modulate glutathione availability and recycling and that dietary substrate availability and recovery context influence this system [6, 25]. Here, the magnitude of δ suggests that GSH is not merely different on average but shows meaningful rank separation between groups, supporting its role as a discriminating circulating marker in this comparison. Nevertheless, interpretation must account for the biological compartment dependence of glutathione. Serum GSH does not directly represent intracellular glutathione pools or tissue-specific redox buffering. Therefore, the observed difference is framed as a circulating non-enzymatic antioxidant-related marker difference rather than evidence of enhanced whole-body glutathione homeostasis or cellular antioxidant capacity. This distinction addresses the concern that limited biomarker panels cannot justify system-level claims.

MDA values were lower in skiers, with a large negative δ indicating substantially higher values in controls. Studies have reported associations between sustained training exposure and lower resting lipid peroxidation indices, including MDA, particularly under standardized sampling conditions [4, 33–36]. In the present data, MDA is best described as a TBARS-derived index of lipid peroxidation rather than a definitive measure of MDA concentration. TBARS can capture multiple aldehydic products and may overestimate “true” MDA, which reduces specificity [37]. Accordingly, we interpret the observed difference as lower TBARS-reactive products in skiers at rest, while avoiding the stronger claim that lipid peroxidation is unequivocally reduced. This phrasing aligns with methodological guidance advocating cautious terminology when oxidative damage markers are measured with assays that have known specificity constraints [38].

In cross-country skiers, CAT and GSH were moderately correlated (ρ = 0.57, p < 0.05), whereas this association was not evident in controls. Given n = 17 per group and multiple correlation tests, these relationships should be considered exploratory and interpreted conservatively. The correlation result indicates that, within the skier group, higher CAT values tended to occur alongside higher GSH values, but it does not establish coordinated regulation, shared mechanisms, or pathway activation. While mechanistic literature describes redox-sensitive signaling pathways that can influence antioxidant systems [39], the present study did not measure molecular mediators, and therefore no claims regarding pathway activation, including Nrf2 activation, are supported by these data. In addition, the modest sample size limits precision, and the possibility of type I error cannot be excluded without multiplicity control; thus, the correlation findings are presented as descriptive rather than confirmatory.

Menstrual status, diet, training period and recent training load, and iron status are recognized confounders for circulating redox biomarkers in young female endurance cohorts. In the present protocol, menstrual history was systematically assessed by recording the date of the last menstrual period for all participants, and blood sampling was rescheduled when necessary to avoid collection during active menstrual bleeding, which may help reduce acute cycle-related variability in biomarker values [40]. Dietary influences were addressed through standardized morning sampling following an overnight fast, complemented by documentation of recent intake using a brief dietary questionnaire and a 24-hour dietary recall. Acute exercise effects were minimized by instructing participants to avoid strenuous exercise prior to testing and by obtaining samples after a seated rest period under controlled ambient conditions. Training-context information was documented to support cautious interpretation of resting biomarker values. Iron status, which can influence oxidative stress biology in endurance athletes, was objectively evaluated using standard hematological and biochemical indices, and individuals with clinically relevant abnormalities, medication use, or acute illness were excluded. Nevertheless, residual confounding cannot be fully excluded, and the cross-sectional design limits inference to association-level interpretations. Therefore, these measures strengthen internal consistency and reduce predictable sources of bias, but they cannot fully eliminate unmeasured or residual confounding inherent to observational comparisons of elite athletes and sedentary controls.

A strength of this study is the focus on elite female cross-country skiers, together with standardized sampling, triplicate assays, and rank-consistent effect size reporting. Importantly, the interpretive scope is intentionally limited to the measured biomarkers. Key limitations include the restricted biomarker panel, the specificity constraints of TBARS-derived MDA, and the compartment dependence of serum antioxidant markers, which collectively limit generalization to “systemic redox homeostasis.” In addition, the cross-sectional design precludes causal inference, and functional outcomes such as performance or recovery were not measured; therefore, practical recommendations were removed or reframed as hypothesis-generating. Broader panels and more specific assays, together with longitudinal sampling across training phases and objective training load quantification, would strengthen causal and mechanistic interpretation. Consensus guidance also emphasizes careful terminology and methodological rigor when assessing oxidative reactions and damage in vivo, supporting our conservative interpretive approach [38]. In line with this guidance, conclusions were restricted to biomarker-level differences at rest and do not extend to system-level regulation or applied performance contexts.

Conclusion

Elite female cross-country skiers exhibited higher CAT activity and GSH concentration and lower TBARS-derived MDA than sedentary controls, with statistically significant differences and large non-parametric effect sizes. These findings describe robust between-group separation in selected circulating redox-related biomarkers at rest and should be interpreted at the association level, without causal attribution or claims regarding systemic redox homeostasis, training adaptation, recovery, or performance outcomes. The present results provide a focused description of biomarker differences under standardized sampling conditions and support future longitudinal research with expanded redox panels and more specific lipid peroxidation assays to clarify temporal dynamics and determinants of these between-group differences.

Abbreviations

ROS

Reactive Oxygen Species

GSH

Reduced Glutathione

CAT

Catalase

MDA

Malondialdehyde

ROM

Reactive Oxygen Metabolites

TBARS

Thiobarbituric Acid Reactive Substances

Authors’ contributions

SS: Writing – original draft, Writing – review and editing, Investigation, Methodology. MFB: Formal Analysis, Investigation, Writing – original draft, Data curation, Methodology, Supervision, Visualization. HD: Writing – original draft, Writing – review and editing, Investigation, Methodology, Supervision, Writing – original draft. OMF: Formal Analysis, Investigation, Writing – original draft, Data curation. MZK: Visualization, Methodology, Writing – original draft, Writing – review and editing. MFS: Investigation, Methodology, Supervision, Writing – original draft. SA: Formal Analysis, Investigation, Writing – original draft, Data curation. CS: Visualization, Methodology, Writing – original draft, Writing – review and editing. GA: Visualization, Methodology, Writing – original draft, Writing – review and editing. SK: Visualization, Methodology, Writing – original draft.

Funding

The author(s) declare that they have received no financial support for the research and/or publication of this article.

Data availability

The datasets generated and/or analyzed during the current study are not publicly available due to ethical restrictions related to participant confidentiality and the inclusion of sensitive clinical data. However, the data are available from the corresponding authors upon reasonable request, subject to approval by the Ethics Committee of Muş Alparslan University.

Declarations

Ethics approval and consent to participate

Ethical approval for this study was obtained from the Scientific Research and Publication Ethics Committee of Muş Alparslan University, Türkiye, affiliated with Muş Alparslan University (Approval No: 27754; Date: 27 October 2021). The study was conducted in accordance with the principles of the Declaration of Helsinki.

Prior to participation, all participants were informed about the purpose, procedures, potential risks, and benefits of the study. Written informed consent was obtained from all participants. For participants under the age of 18 years, written informed consent was additionally obtained from their parents or legal guardians. Participation was voluntary, and participants were free to withdraw from the study at any time without any consequences.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.