The impact of direct pre-competition preparation on changes in metabolite, vitamin, and hormonal biomarker concentrations in professional female volleyball players

Abstract

Background

The training dynamics of professional female volleyball players involve intense pre-competition periods necessitating adaptations to high training loads. This study focuses on the immediate preparation for competition (IPC) analyzing changes in various metabolite, vitamin, and hormonal biomarkers.

Methods

Twelve female volleyball players underwent blood tests four times during an 8-week IPC period before top league season in Poland. Biomarkers such as creatine kinase (CK), lactate dehydrogenase (LDH), folic acid, vitamin B12, calcidiol, cortisol, and insulin-like growth factor 1 were measured using standard biochemical protocols. The athletes adhered to a training regimen that included technical and strength training, supplemented with vitamins B and D3.

Results

Significant changes were observed in the activities of CK and LDH across the measurements, indicating muscle response to training loads. Variations in the concentrations of folic acid and 25-(OH)D3 across the training period were statistically significant. Cortisol levels also showed fluctuations, suggesting hormonal balance shifts in response to training stress. 

Conclusion

The study confirms that a well-structured training regimen during the IPC allows for slight modifications in biochemical and hormonal markers without disrupting their balance. Proper supplementation and targeted training loads help maintain optimal vitamin levels in the blood, aiding in overall athlete preparedness and performance stability.

Introduction

Constant development of female volleyball training programs involves coaches in cooperation with other specialists contributing to individual or team performance (e.g. athletic trainers, exercise physiologists, sports medicine doctors) [1]. Volleyball players are often exposed to high-intensity training periods due to relatively short pre-competition and long competitive period, which requires fast adaptation to training loads [2].

Before the important competition/game individuals/teams are going through the special training procedure in order to achieve the peak form. In the former communists countries (e.g. Russia, Poland, Romania) the immediate preparation for competition (IPC) is often used, in the western countries (e.g. USA, Great Britain, Canada) the taper procedure is commonly used [3]. Although both IPC and tapering are applied before competitive events, IPC typically involves a planned increase in training load followed by a sharp decrease, whereas tapering usually consists solely of a gradual reduction of training volume to optimize performance. Both strategies are based on physiological mechanisms of supercompensation. The purpose of taper strategy is not reaching higher fitness level (e.g. improving strength level) like in the IPC, but to remove the accumulated fatigue by exponentially/linearly reducing the training load in order to maximize performance [4]. Even though tapering is mostly used in individual sports (e.g. swimming, cycling) it could be also beneficial for the sports team athletes. Proper tapering strategies may enhance performance indicators such as VO₂ peak, maximal power, and agility [5], particularly in individual sports. Using the aforementioned strategies, which involve applying excessive training loads without sufficient rest can lead to overreaching/overtraining and therefore to failure of the whole pre-competition preparation training program [6]. Therefore it is common for sports medicine specialists to supervise the process by constant evaluating the sensitive biochemical parameters, which could detect possible overtraining or infections [7]. Monitoring biomarkers especially biochemical (e.g. creatine kinase – CK, lactate dehydrogenase – LDH), hormonal (cortisol, insulin-like growth factor 1 – IGF-1, sex hormone-binding globulin – SHBG) or inflammatory (C-reactive protein – CRP, pro-inflammatory cytokines) could be a good assistance for the volleyball coaches in the process of controlling the training load, enabling them to not rely only on biomechanical tests (e.g. counter movement jump) or intuition [8]. Other, less commonly used but still important biomarkers, such as folic acid (B9) and cobalamin (B12) or D3 vitamin metabolites levels, can additionally inform athletes and medical staff about the possible need for supplementation in selected training periods particularly before competition [9].

As a result of repeated intensive exercises and intensified activity of metabolic pathways involved into energy productions, there is increase demand on certain minerals and vitamins such as B9 and B12.

Folic acid occurs in small amounts in nature. It is highly photolabile, additionally it is produced in the large intestine by symbiotic bacteria, but is absorbed in the small intestine, which can generate deficiencies. This compound also known as B9 vitamin is involved in the biosynthesis of purine and pyrimidine bases, which are precursors of DNA and RNA. It also participates in the methylation of DNA strands and histone proteins, and its derivative tetrahydrofolate is a cofactor during the production of erythrocytes [10]. Chronic deficiency of vitamins B9 and B12 results in impaired ability to produce normal erythrocytes, leading to the development of megaloblastic anemia and the formation of large blood cells. Those cells transport oxygen in an inefficient way which can contribute to the deterioration of sports performance [11]. In addition, folates play an important role in the repair of damaged muscle fibers as a result of intense efforts, especially with a predominance of eccentric contractions [12]. Tetrahydrofolate (derivative of vitamin B9) participates also, as a coenzyme, in the metabolism of some amino acids (p.ex. serine, glycine), contributing to the regeneration of damaged tissues [11]. Current observations indicate a decrease in folic acid deficiencies among professional athletes. The reason for this fact is the increase in consumption of food products enriched with this vitamin and the increase in supplementation among female athletes compared to the research conducted in the second half of the twentieth century. The most pronounced deficiencies of this vitamin are observed in groups of athletes with dietary restriction caused by necessity to stay in a given weight class (wrestlers, martial arts, bodybuilding) [12].

Recent years have brought many discoveries related to the role of vitamin D in sports training. The presence of the nuclear vitamin D receptor (VDR) has been found in the intestines, kidneys and organs not directly related to the maintenance of calcium metabolism, such as the prostate, brain or skeletal muscles [9, 13]. Research data show that vitamin D deficiency contributes to disorders of the musculoskeletal system and the weakening of its characteristic indicators such as strength and endurance, and may promote faster damage to myocytes due to exercise [14, 15]. In addition, excessive training loads combined with a 25(OH)D3 deficiency of less than 25 nmol/l can also result in a decrease in muscle strength [16]. Vitamin D status is commonly assessed via serum 25(OH)D3 concentration, which reflects both supplementation and dermal synthesis. According to current guidelines, deficiency is defined as < 20 ng/ml, insufficiency as 20–30 ng/ml, and sufficiency as >30 ng/ml [17].

Cortisol is one of the most studied hormones that affect an athlete’s body metabolism and physical performance [18–20]. Together with testosterone they determine anabolic-catabolic balance [21]. Professional training and continuously breaching of hormonal balance is enough strength stressing factor which could sometimes lead to athlete’s overreaching. Decrease of free testosterone/cortisol ratio and IGF-1 to cortisol ratio seems to be good training loads marker [18]. Moreover a long-term elevated level of cortisol may adversely affect motor skills, as well as contribute to the weakening of immunity and the intensification of catabolic transformations, while increased only immediately before a competition may have a positive effect on motivation, psychological functions and muscle strength [22, 23].

The aim of this study was to investigate the impact of intensive pre-competition preparation on selected metabolic and hormonal biomarkers (CK, LDH, folic acid, vitamin B12, 25-(OH)D3, cortisol and IGF-1) in professional female volleyball players. Through analyzing changes in the concentrations/activities of these biomarkers every two weeks over a period of 8 weeks, the study objective was to understand how training optimization can influence the endurance and recovery abilities of athletes in direct preparation for professional-level competitions. The selected biomarkers reflect key components of physical stress (CK, LDH), hormonal adaptation (cortisol, IGF-1), and micronutrient status (vitamins B12, B9, and D), offering a comprehensive picture of the athlete’s response to IPC.

Materials and methods

Participants

The group of 12 female players (age 24.57 ± 4.43 years; height 180.71 ± 8.75 cm; body mass 72.06 ± 9.02 kg) of elite, top league polish volleyball team Developres Rzeszów took part in the study. All of them have at least 10 years of the training experience. They were training from 2 to 3 h daily, with an advantage of anaerobic efforts.

The study protocol met all the guidelines of the Declaration of Helsinki. Each participant was informed about the purpose of the study and the measurement procedures used. Participants were informed about the study objectives, data confidentiality, and their right to withdraw at any time without consequence. Consent to participate was confirmed by signature. The study procedure was approved by Ethics Committee of the Regional Medical Chamber in Kraków (resolution No. 42/KBL/OIL/2015). Consent was obtained in writing from all subjects studied.

During the entire research period, the athletes took daily dietary supplements: vitamin B complex (Gold-Vit B Forte, Olimp Laboratories, Dębica, Poland) and vitamin D3 (cholecalciferol) at a dose of 2000 IU (Vigantoletten Max, Procter and Gamble, Warsaw, Poland). The vitamin D dose (2000 IU daily/14,000 IU per week) is consistent with general recommendations for maintaining sufficient status in physically active adults during autumn months. Supplementation was introduced systematically for all athletes to ensure adequate micronutrient status during high training loads. No formal deficiency screening was performed prior to the intervention, but the use of supplementation in this group reflects common practice in elite team sports.

Time course of research

All study participants fasted and had their blood drawn in the morning before training four times (T1: 07.08, T2: 21.08, T3: 04.09, T4: 19.09.2023) during different periods of immediate preparation for competition (IPC) (Fig. 1). The last blood collection took place about 10 days before the start of the Top League in Poland. The first blood sample (T1) was collected at the onset of the 8-week IPC period, after a short off-season period of approximately 2 weeks, and serves as a baseline reference. Blood samples were taken from the ulnar vein, in a total amount of approximately 15 mL, by a qualified nurse at the ALAB regional collection point in Rzeszow, where the biochemical analyses were performed (ALAB Laboratory, Poland). Blood samples were analyzed on the day of collection without freezing or long-term storage. Serum concentration of IGF-1 was determined by electrochemiluminescence immunoassay (ECLIA) using the Cobas e801 analyzer (Roche, Switzerland). Enzyme activity (CK, LDH) was determined spectrophotometrically using the Alinity C analyzer (Abbott, USA). The concentration of vitamins was tested by immunochemiluminescence (CMIA) method using the Alinity I analyzer (Abbott, USA).

Fig. 1.

Study design

Training procedure

To the final week of the preparation to the competition team had been training 2-3 h daily 6 days a week, with the advantage of the anaerobic efforts, 5 units of technical training and 3 units of gym training in a week were executed.

The last week of training was conducted according to the protocol described in Table 1.

Table 1.

Training procedure in the final week before match

Days of final week prior to main event	Training content	Training load/intensity/rest intervals
1	Strength training: Warm up: 10 min of general development cardio exercise Gym: Squat, deadlift, bench press, row.	Number of sets: 3–4 Number of repetitions: 8–12 Training intensity: average to high Rest between sets: 60–90 s
2	Technical training Warm up: 10 min of individual technical exercise, technical elements: set, serve, receive Exercises in pairs: player + setter Exercises in groups: tactics, positioning, block.	Training intensity: average Rest between exercises: short, 30–45 s
3	Strength and speed training Warm-up: 10 min of running in place, dynamic stretching Gym: plyometric jumps, spring press, bar squats.	Number of sets: 3–4 Number of repetitions: 6–10 Training intensity: high Rest between sets: 60–90 s
4	Technical training Warm-up: 10 min of individual technical exercise, technical elements: set, serve, receive Exercises in groups: match simulations, tactics, positioning.	Training intensity: average Rest between exercises: short, 30–45 s
5	Recovery training Static stretching 45 min Massage 30 min Sauna 2 sessions (8–10 min each) Swimming (30 min ~ 1000 m)	Training intensity: low
6	Strength and speed training Warm-up: 10 min of running in place, dynamic stretching Gym: plyometric jumps, spring press, bar squats.	Number of sets: 3–4 Number of repetitions: 6–10 Training intensity: high Rest between sets: 60–90 s
7	Day off	N/A

Statistical analysis

The statistical analysis of the collected data was conducted using PQ Stat software (PQStat Software, Poland). Basic descriptive statistics were calculated: arithmetic mean, standard deviation, minimum, and maximum values. A one-way analysis of variance (ANOVA) was used for comparisons across four measurements of the dependent variables. The significance of differences between the individual measurements was calculated using Fisher’s post hoc test. Before conducting the calculations, the distribution of data was verified using the Shapiro-Wilk test, and homogeneity of variances was assessed using Levene’s test. The effect size for the four measurements was reported as ETA2 value (0.01 - weak effect, 0.06 - moderate effect, 0.14 - strong effect), while the effect size between individual measurements was calculated using Cohen’s d (0.2 - weak effect, 0.5 - moderate effect, 0.8 - strong effect). Differences were considered statistically significant at p < 0.05.

Results

Statistically significant changes in creatine kinase activity across four measurements were demonstrated (F = 47.63, p < 0.001), with a strong effect. The post hoc test revealed significant differences between measurement T1 vs. T2, T1 vs. T3, and T1 vs. T4. Table 2 summarizes the changes in creatine kinase activity observed over the 8-week IPC period.

Table 2.

Statistical analysis of creatine kinase (CK) activity at four time points

CK (U/L)						Post Hoc (p-value)/(d-Cohen)
Time Point	x̄	SD		Min	Max	T1	T2	T3	T4
T1	157.91		77.01	50	250	-	< 0.001 d = 0.49	< 0.001 d = 0.47	< 0.001 d = 0.93
T2	229.16		211.50	82	2432	< 0.001 d = 0.49	-	0.96 d = 0.26	0.98 d = 0.45
T3	125.16		62.48	62	233	< 0.001 d = 0.47	0.961 d = 0.50	-	0.95 d = 0.40
T4	105.25		36.72	57	173	< 0.001 d = 0.93	0.98 d = 0.63	0.95 d = 0.40	-
ANOVA	p = < 0.001 F = 47.63 ES = 0.81

Statistically significant changes in lactate dehydrogenase activity were demonstrated across four measurements (F = 17.84, p < 0.001) with a strong effect. The post hoc test revealed significant differences between measurement T1 vs. T2, T1 vs. T3, and T1 vs. T4. Table 3 summarizes the changes in LDH activity observed during the 8-week IPC period.

Table 3.

Statistical analysis of lactate dehydrogenase (LDH) activity at four time points

LDH (U/L)					Post Hoc (p-value)/(d-Cohen)
Time Point	X	SD	Min	Max	T1	T2	T3	T4
T1	160	12.89	137	180	-	< 0.001 d = 2.50	0.010 d = 0.95	< 0.001 d = 1.23
T2	213	29.10	165	252	< 0.001 d = 2.50	-	< 0.001 d = 1.21	< 0.001 d = 1.77
T3	178.83	26.70	152	222	0.010 d = 0.95	< 0.001 d = 1.21	-	0.95 d = 0.26
T4	177.33	15.23	154	199	0.012 d = 1.23	< 0.001 d = 1.77	0.95 D = 0.26	-
ANOVA	p< 0.001 F = 17.84 ES = 0.61

Statistically significant changes in folic acid concentration were shown across four measurements (F = 5.14, p < 0.005) with a strong effect. The post hoc test revealed significant differences between measurement T1 vs. T4; T2 vs. T4, and T3 vs. T4. Table 4 presents changes in vitamin B12 and folate concentrations before and after the supplementation phase.

Table 4.

Statistical analysis of folic acid concentration at four time points

Folic acid (ng/ml)					Post hoc (p-value)/(d-Cohen)
Time Point	X	SD	Min	Max	T1	T2	T3	T4
T1	7.51	1.77	3.8	10.5	-	0.23 d = 0.32	0.40 d = 0.13	< 0.001 d = 1.07
T2	6.82	2.53	3.8	11.1	0.23 d = 0.32	-	0.70 d = 0.13	0.017 d = 0.62
T3	7.15	2.65	3.7	11.2	0.40 d = 0.13	0.70 d = 0.13	-	0.006 d = 0.74
T4	5.31	2.34	3.6	10.2	< 0.001 d = 1.07	0.017 d = 0.62	0.006 d = 0.74	-
ANOVA	p = 0.005 F = 5.14 ES = 0.31

X arithmetic mean, SD standard deviation, ES effect size

No statistically significant differences were found in vitamin B12 concentrations in study participants – results are shown in Table 5.

Table 5.

Statistical analysis of vitamin B12 concentration at four time points

Vitamin B12 (pg/ml)					Post hoc(p-value)/(d-Cohen)
Time Point	X	SD	Min	Max	T1	T2	T3	T4
T1	311.66	96.91	228	600	-	0.58 d = 0.16	0.45 d = 1.04	0.51 d = 0.35
T2	324	149.74	46	598	0.58 d = 0.16	-	0.83 d = 0.42	0.91 d = 0.25
T3	279.83	58.50	213	394	0.45 d = 1.04	0.83 d = 0.42	-	0.92 d = 0.15
T4	292.33	106.96	170	556	0.51 d = 0.35	0.91 d = 0.25	0.92 d = 0.15	-
ANOVA	p = 0.874 F = 0.23 ES = 0.02

Statistically significant differences were found in the concentrations of 25(OH)D3 across four measurements (F = 2.89, p < 0.05) with a moderate effect. The post hoc analysis showed significant differences between T1 vs. T4 and T3 vs. T4 measurements. Table 6 summarizes changes in 25(OH)D₃ concentrations over the 8-week IPC period.

Table 6.

Analysis of 25(OH)D3 levels at four time points

25(OH)D3 (ng/ml)					Post hoc(p-value)/(d-Cohen)
Time Point	X	SD	Min	Max	T1	T2	T3	T4
T1	36.33	7.24	29	52	-	0.17 d = 0.32	0.72 d = 0.07	0.033 d = 0.63
T2	34	7.26	23	46	0.17 d = 0.32	-	0.09 d = 0.42	0.40 d = 0.27
T3	36.83	6.10	29	47	0.72 d = 0.07	0.09 d = 0.42	-	0.014 d = 0.77
T4	32.25	5.72	27	48	0.03 d = 0.06	0.40 d = 0.27	0.018 d = 0.77	-
ANOVA	p = 0.05 F = 2.89 ES = 0.20

Cortisol concentration measurements did not show statistically significant differences, although the significance was close to the value indicating a statistical trend (p = 0.06), and the effect size was moderate. The post hoc analysis revealed statistically significant changes between T1 vs. T3 and T1 vs. T4. Table 7 presents the cortisol concentration values across four time points, highlighting pairwise differences despite the lack of overall statistical significance.

Table 7.

Analysis of cortisol levels in four time points

Cortisol (µg/dl)					Post hoc(p-value)/(d-Cohen)
Time point	X	SD	Min	Max	T1	T2	T3	T4
T1	15.65	3.45	11.2	23.1	-	0.83 d = 0.04	0.050 d = 0.69	0.039 d = 0.73
T2	15.75	2.24	12.1	19.2	0.83 d = 0.04	-	0.078 d = 0.76	0.06 d = 0.81
T3	19.01	6.32	14.1	32.9	0.050 d = 0.60	0.078 d = 0.76	-	0.90 d = 0.04
T4	19.25	6.41	11.4	32.4	0.039 d = 0.73	0.06 d = 0.81	0.90 d = 0.04	-
ANOVA	p = 0.06 F = 2.63 ES = 0.19

The analysis of concentrations of IGF-1 did not show any statistically significant changes in four measurements (Table 8).

Table 8.

Analysis of insulin-like growth factor-1 (IGF-1) levels in four time points

IGF-1 (ng/ml)					Post hoc(p-value)/(d-Cohen)
Time point	X	SD	Min	Max	T1	T2	T3	T4
T1	260.5	49.99	210	344	-	0.39 d = 0.30	0.69 d = 0.09	0.51 d = 0.24
T2	278.16	67.60	183	368	0.39 d = 0.30	-	0.21 d = 0.37	0.84 d = 0.06
T3	255.66	55.64	197	342	0.69 d = 0.09	0.21 d = 0.37	-	0.30 d = 0.31
T4	274	63.01	198	350	0.51 d = 0.24	0.84 d = 0.06	0.30 d = 0.31	-
ANOVA	p = 0.57 F = 0.67 ES = 0.05

Additionally, the IGF-1 to cortisol ratio was calculated for each time point to provide a more integrated view of anabolic-catabolic balance. The ratio was highest at T2 (17.66), followed by T1 (16.64), and decreased at T3 (13.45) and T4 (14.23), suggesting a relative shift toward a more catabolic profile in the later stages of IPC, despite all values remaining within physiological norms.

Discussion

Volleyball, like other team sports, involves frequent match play, and the specific schedule that includes a short preparatory period followed by a long competitive phase. Particularly intense loads occur during the pre-competition period, aimed at preparing and quickly adapting to the effort during actual competition [8]. Too little load may result in an inadequateadaptation and a quicker reduction in fitness during a match, whereas excessive load can lead to negative consequences, including fatigue or overtraining [6]. Fatigue, as a result of frequent stretch-shortening cycles associated with alternating eccentric and concentric muscle work (as in jumping), can cause a decline in neuromuscular performance [24, 25]. Overtraining, on the other hand, is characterized by a decrease in overall performance, an increase in central fatigue, and the occurrence of stress negatively affecting mental function [26]. There are many subjective and objective methods for assessing the psycho-physical state of an athlete at any given moment.

During the preparatory period, an increase in volume and intensity of effort is typically accompanied by changes in cellular energy metabolism. Intense efforts can lead to the enhanced metabolic transformations, increasing the demand for B vitamins such as B1, B2, B6, B9, and B12. The latter three play a role in stimulating hematopoietic processes, thereby reducing the risk of anemia in athletes.

Despite the general belief in the necessity of supplementing certain compounds and nutrients, there are no clear recommendations regarding the specific amounts of vitamins for different sports groups [12, 27]. Maintaining optimal levels of vitamin B9 is particularly important among women who additionally engage in professional sports activities.

A deficiency of this nutrient can result in symptoms such as headaches, shallow breathing, or heart palpitations due to the development of megaloblastic anemia [28]. A decrease in folic acid concentration was observed between the first and last measurement, where the average vitamin concentration in serum was at the lower cut-off level (5.31 ng/ml).

This could be due to a reduced vitamin intake in the diet during this period through, for example, changes in dietary structure and a reduction in the nutritional value of meals. During the pre-competition period, increased stress levels may also increase the rate of metabolism of certain compounds and vitamins or reduce their absorption [29].

An important factor for maintaining high mental and physical performance and proper functioning of the nervous system, such as concentration and focus, is ensuring optimal adequate levels of vitamin B12 in the body. Besides its role in red blood cell production, it regulates energy storage in muscle tissue and is essential for proper nerve conduction and regeneration. No changes in vitamin B12 concentration were observed in any of the measurements.

Analysis of changes in the activity of intramuscular conversion enzymes (CK and LDH) allows for assessing the degree of muscle damage caused by single or repeated physical effort. An increase in CK and LDH activity was observed between the first and second measurements and was followed by a decrease in the subsequent two measurements. This could indicate that sports training was directed towards exercises with a predominance of anaerobic processes, and efforts of an eccentric nature, which might also be reflected in the results of tests specific to the discipline, such as the counter-movement jump (CMJ).

Despite significant differences between repeated measurements, individual results did not indicate local or systemic muscle damage (e.g., rhabdomyolysis) in the women studied at any point during the observation period, and the degree of changes in the activity of the tested enzymes only indicates the proper selection of loads and not exceeding the individual functional capabilities of the athletes.

The observed increase in cortisol concentration between the first and third, as well as the first and fourth measurement demonstrates changes in the body’s hormonal homeostasis. Cortisol, along with testosterone, in response to changing training loads can serve as a sensitive marker of changes in the anabolic-catabolic balance, thus being a useful tool in diagnosing early symptoms of overtraining [30, 31].

Moreover, increased mental stress due to upcoming league matches can be a strong stressor stimulating the hypothalamic-pituitary-adrenal axis to increase the release of cortisol [32]. It has been shown that in high-risk sports, such as parachuting, the anticipatory effect of cortisol secretion is independent of the athletes’ experience and frequency of repeated jumps [33].

No gender differences were observed in response to both social and sports-related competition-related stressors [34, 35]. As mentioned in the introduction, a moderate increase in cortisol concentration can have a positive impact on the perception of the significance of an event, also in relation to a volleyball match [36].

The systematic increase in cortisol concentration among the volleyball players in these studies, based on other indicators, seems to have a more psychological than physiological basis and may serve to focus attention and concentration on performing the task at hand, i.e., defeating the opposing team. In the fourth week of measurement, an increase to 19 ug/dl (still within the norm) may indicate a very favorable direction of change in this hormone’s concentration from a psychological capability perspective. In studies by Roli, it was shown that over the course of a volleyball season, the testosterone to cortisol ratio decreased by 30% suggesting the occurrence of overtraining [7]. In the discussed studies during the short preparatory period for the season, it cannot be fully talked about changes in the anabolic-catabolic status since testosterone concentration was not determined, but the slight change in cortisol as mentioned earlier may have a beneficial psychological effect.

It should also be considered that fluctuations in sex hormones across the menstrual cycle may have influenced some of the biochemical and hormonal markers measured in this study. For example, elevated estradiol levels in the late follicular phase have been reported to exert a protective effect on muscle cell membranes, potentially attenuating exercise-induced increases in CK activity. Conversely, higher progesterone levels during the luteal phase can influence substrate metabolism and may be associated with altered hypothalamic–pituitary–adrenal axis activity, leading to modifications in cortisol secretion patterns. Both estradiol and progesterone have also been implicated in modulating growth hormone secretion and, indirectly, IGF-1 concentrations. Without controlling for menstrual cycle phase at the time of blood sampling, these physiological variations could have introduced additional interindividual variability and partially masked or amplified the training-related changes observed in the present study.

In light of scientific reports, the action of IGF-1 appears to be pleiotropic [37]. It has a particularly significant effect in the context of mediating the action of the growth hormone (GH), and it also displays neuroprotective effects as well as impacts nutrient metabolism [38, 39]. In competitive sports, it is banned as a doping agent [40]. Data regarding changes in resting IGF-1 concentrations as a result of training are not unequivocal. Studies among judokas showed an increase in the factor during the season [41], while others showed a decrease [42] or no changes [43, 44]. No changes were observed in both male and female volleyball players [43, 45]. An increase in IGF-1 concentration was achieved among female volleyball players only when they were subjected to various forms of strength training [46] which is also consistent with earlier results among non-training women subjected to strength training [47, 48]. The results of this study also do not provide evidence of post-training changes in the concentration of insulin-like growth factor-1 among female volleyball players during a several-week training period.

Despite the fact that no clinical deficiencies in folic acid, vitamin B12 or vitamin D were observed in any of the athletes, daily supplementation was introduced from the start of the training period. This reflects a common practice in elite-level team sports, where supplementation is used prophylactically to stabilize micronutrient status during periods of high training load, regardless of measured insufficiencies [27]. Such an approach is particularly relevant in female athletes, who may have increased susceptibility to subclinical deficiencies due to hormonal, nutritional, or lifestyle factors [27].

The observed trends in CK and LDH activities support the hypothesis of muscular adaptation to repeated anaerobic and eccentric training loads. While both enzymes showed initial increases (T1–T2) followed by a gradual normalization (T3–T4), the amplitude of these changes remained within physiological ranges. It’s important to note that interindividual variability in enzyme release is influenced by sex, muscle mass, ethnicity, training status, and genetic predispositions. For example, previous studies have identified “high responders” and “low responders” to exercise-induced CK elevation, independent of perceived muscle soreness or performance markers [26]. The decline in enzyme activity during the taper phase may reflect improved muscle resilience and adaptation to eccentric loading.

Interestingly, cortisol levels were relatively high already at T1, even though the athletes had recently returned from a ~ 2-week off-season break. While still within reference limits, the early elevation might reflect incomplete recovery or anticipatory psychological stress related to the resumption of structured training. Literature indicates that cortisol concentrations can remain elevated after prolonged competitive seasons, particularly in athletes with high psychological commitment or inadequate rest periods [22, 31, 49]. Therefore, preseason endocrine profiling could offer valuable insight into baseline readiness.

To gain a more integrated view of the endocrine balance, we also calculated the IGF-1 to cortisol ratio for each time point. The ratio was highest at T2 (17.66), followed by T1 (16.64), and decreased at T3 (13.45) and T4 (14.23). This trend suggests a moderate shift toward a more catabolic state in the latter part of the IPC phase, even though individual hormone values remained within normal ranges. These findings support the usefulness of ratio-based markers for monitoring anabolic-catabolic dynamics in applied sport settings [26, 50].

Study limitations

This study has several limitations that should be acknowledged:

1. Baseline status: No pre-supplementation screening for vitamin or hormone levels was conducted. This limits the ability to estimate the effect size of supplementation on observed changes.

2. Sample size and generalizability: The study was conducted on a single elite female volleyball team with a relatively small and homogeneous sample (n = 12), which reduces the statistical power and limits the generalizability of the findings to recreational or non-European populations.

3. Menstrual cycle phase: The phase of the menstrual cycle at the time of blood sampling was not controlled for hormonal fluctuations during the cycle may have influenced biochemical and hormonal markers, particularly cortisol and IGF-1 levels.

4. Measurement frequency: Biomarker concentrations were assessed at four fixed time points over the 8-week IPC period. More frequent measurements might have provided a more detailed profile of physiological adaptation to training.

5. Nutritional intake: No dietary control or tracking was conducted during the study. This makes it difficult to isolate the effects of supplementation from natural dietary influences.

6. Psychological and performance variables: Due to logistical constraints, psychological or performance-related variables (e.g., jump height, fatigue scores) were not included. Their inclusion could have provided a more comprehensive view of training effectiveness.

Conclusions

The conducted study confirms that properly selected training and loads applied during the pre-competition period in elite-level athletes contribute only to minor changes in the concentrations of biochemical and hormonal indicators, without causing a disturbance in their balance. Moreover, supplementation of athletes allows for maintaining proper vitamin concentrations in the blood despite variations in training intensity during this period. Furthermore, coaches and sports medicine staff are encouraged to monitor athletes’ biomarkers in conjunction with training loads and to consider individual physiological variables, including the menstrual cycle, when interpreting results and planning individualized interventions.

Practical implications

The findings of this study offer relevant insights for coaches, trainers, and sports medicine professionals working with elite female volleyball players. The observed patterns of biomarker fluctuations suggest that intensive pre-competition training, when supported by appropriate supplementation (e.g., B vitamins, vitamin D3), does not cause hormonal or metabolic disruption. Regular monitoring of selected biochemical indicators such as CK, LDH, and vitamin levels may serve as a practical tool to individualize training loads, assess recovery status, and identify early signs of nutritional deficiency or overreaching. Implementing such biomarker-based monitoring protocols could enhance training effectiveness and reduce the risk of performance decline.

Authors’ contributions

Conceptualization, Ł.R., P.K. and T.P.; methodology, Ł.R., K.S., M.M., M.Majerek.; software, T.A., T.P., A.P., M. Majerek.; validation, A.P., W.B. and W.C.; formal analysis, Ł.R., O.C.L., A.P., K.S., W.P.; investigation, T.A.; resources, Ł.R., P.K.; data curation, A.P., W.B.; writing—original draft preparation, Ł.R., M.M., T.P., K.S., A.Piotrowska.; writing—review and editing, Ł.R., A.Piotrowska., O.C.L., T.P.,W.P.; visualization, A.P., P.K.; supervision, T.A., Ł.R., W.C.; project administration, P.K., Ł.R.; funding acquisition, W.C., P.K., A.P., W.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data availability

All data supporting the findings of this study are available within the paper.

Declarations

Consent to participate

Written informed consent was obtained from all individual participants included in the study. Participation was voluntary, and participants could withdraw at any time without consequences.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Conflict of interest

No potential conflict of interest was reported by the author(s).

Institutional review board statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by Bioethical Commission of the Regional Medical Chamber in Krakow, Poland (opinion No. 42/KBL/OIL/2015).