The interactions between monocarboxylate transporter genes MCT1, MCT2, and MCT4 and the kinetics of blood lactate production and removal after high-intensity efforts in elite males: a cross-sectional study

Abstract

Background

This cross-sectional study investigated the relationship between genetic variations in monocarboxylate transporter genes and blood lactate production and removal after high-intensity efforts in humans. The study was conducted to explore how genetic variations in the MCT1, MCT2, and MCT4 genes influenced lactate dynamics and to advance the field of sports genetics by pinpointing critical genetic markers that can enhance athletic performance and recovery.

Methods

337 male athletes from Poland and the Czech Republic underwent two intermittent all-out Wingate tests. Before the tests, DNA samples were taken from each participant, and SNP (single nucleotide polymorphism) analysis was carried out. Two intermittent all-out tests were implemented, and lactate concentrations were assessed before and after these tests.

Results

Sprinters more frequently exhibited the haplotype TAC in the MCT2 gene, which was associated with an increase in the difference between maximum lactate and final lactate concentration. Additionally, this haplotype was linked to higher maximum lactate concentration and was more frequently observed in sprinters. The genotypic interactions AG/T- and GGxT- (MCT1 rs3789592 x MCT4 rs11323780), TTxTT (MCT1 rs12028967 x MCT2 rs3763979), and MCT1 rs7556664 x MCT4 rs11323780 were all associated with an increase in the difference between maximum lactate concentration and final lactate concentration. Conversely, the AGxGG (MCT1 rs3789592 x MCT2 rs995343) interaction was linked to a decrease in this difference. The relationship between maximum lactate concentration and genotypic interactions can be observed as follows: when ATxTT (MCT2 rs3763980 x MCT4 rs11323780) or CTxCT (MCT1 rs10857983 x MCT2 rs3763979) genotypic combinations are present, it leads to a decrease in maximum lactate concentration. Similarly, the combination of CTxCT (MCT1 rs4301628 x MCT2 rs3763979), CT x TT (MCT1 rs4301628 x MCT4 rs11323780), and CTxTT (MCT1 rs4301628 x MCT2 rs3763979) results in decreased maximum lactate concentration.

Conclusions

The TAC haplotype (rs3763980, rs995343, rs3763979) in the MCT2 gene is associated with altered lactate clearance in sprinters, potentially affecting performance and recovery by elevating post-exercise lactate concentrations. While MCT4 rs11323780 is also identified as a significant variant in lactate metabolism, suggesting its role as a biomarker for sprinting performance, further investigation is necessary to clarify underlying mechanisms and consider additional factors. Based on elite male athletes from Poland and the Czech Republic, the study may not generalize to all sprinters or diverse athletic populations. Although genetic variants show promise as biomarkers for sprinting success, athletic performance is influenced by a complex interplay of genetics, environment, and training extending beyond MCT genes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-025-11307-4.

Background

The complex interplay of genetic determinants affecting physiological responses to vigorous physical activity has captivated scholarly interest in elucidating the intricacies of human performance. Among these factors, monocarboxylate transporter (MCT) genes, particularly MCT1, MCT2, MCT3, and MCT4, have emerged as key players in modulating the kinetics of blood lactate (LA) production and removal [1]. LA metabolism is central in high-intensity exercise, particularly in sprinting, where anaerobic conditions lead to significant LA production. MCT1, MCT2, MCT3, and MCT4 are the true LA transporters [2]. MCT1 is ubiquitously expressed except in β cells of the endocrine pancreas [3], MCT2 in skeletal muscle and heart [3, 4], and MCT3 is unique compared to other MCT isoforms as it is mainly found in retinal pigment epithelial cells and the epithelial tissue of the choroid plexus [1]. Finally, MCT4 is abundantly expressed in tissues characterized by vigorous glycolytic and anaerobic metabolism [5]. MCTs have been demonstrated to promote the one-way transport of monocarboxylates across the plasma membrane linked to protons. They can facilitate either the inward or outward movement depending on the existing gradients of the substrate and pH, with the overall rate of monocarboxylate transport being influenced by the difference between the influx and efflux rates. At thermodynamic equilibrium, the ratio of monocarboxylate concentration inside the cell to that outside is equal to the ratio of external to internal hydrogen ion concentrations. Moreover, MCTs can exchange one type of monocarboxylate for another without resulting in a net proton transfer [3, 6, 7]. A diagram illustrating the mechanisms by which MCTs influence the production and removal kinetics of blood LA is shown in Fig. 1.

Fig. 1.

Kinetics of the MCTs in LA production and clearance. Adapted from Halestrap (2012) [7]. Permission obtained from the author [7]

While previous studies have explored individual polymorphisms in MCT genes, their collective role in regulating blood LA kinetics during high-intensity exercise remains underexplored. For instance, MCT1 rs1049434 polymorphism does not significantly affect LA kinetics in anaerobic athletes [8]. However, the AA genotype of MCT1 rs1049434 in power-oriented performance is overrepresented, and these wrestlers have lower blood LA concentrations during two anaerobic performance tests [9]. Nevertheless, our recent analysis reveals, for the first time, a significant impact of genotype on maximum LA levels in sprinters for MCT1 variants rs3789592, rs7556664, rs7169, and rs1049434 (under peer review).We observed a significant impact of genotype on the maximum LA value for the MCT1 rs3789592, rs7556664, rs7169, and rs1049434 in sprinters (unpublished work). The objective of this type of research is to identify genetic variations that are correlated with specific characteristics or traits, such as superior athletic performance (e.g., at a championship level), factors affecting physical fitness (e.g., speed, strength, endurance), or response to training (e.g., physiological indicators of fatigue). This represents a classic model assessing the association between genotype and phenotype in the era of molecular research in sports. However, existing literature has primarily focused on isolated gene effects without addressing the potential synergistic interactions between different MCT genes and their cumulative impact on LA dynamics. Despite the recognized significance of MCT genes in LA metabolism, a critical gap exists in understanding how genetic variations across multiple MCT genes affect the LA’s production and removal during high-intensity efforts in a diverse athletic population. Previous investigations often fail to consider the complex relationships between different MCT gene polymorphisms. This lack of comprehensive analysis hinders the identification of specific genotypic profiles that may predispose individuals to superior performance in sprinting and other anaerobic activities. This study aims to bridge this gap by investigating the individual genetic variations in MCT1, MCT2, and MCT4 and the gene-gene interactions that influence LA kinetics. Through haplotype analysis and genotypic interactions, a nuanced understanding of the genetic determinants contributing to LA maintenance in sprinters aspires to be provided. Expanding the association study of selected genes to include haplotype analysis and intergenic interaction provides a comprehensive view of the impact of individual polymorphisms. A haplotype is defined as a set of DNA variants on a single chromosome often inherited together. Within a population, while a single nucleotide polymorphism (SNP) typically possesses a maximum of two alleles, a haplotype block may encompass more than two haplotypes [10]. A haplotype block comprises multiple polymorphic loci (such as SNPs) that are situated near each other and are highly likely to be inherited together. In contrast to the term allele, which refers to a single alternative DNA sequence at a polymorphic locus, the term haplotype refers to the combination of alleles of polymorphic loci in a haplotype block on a single chromosome [11]. Haplotype analysis and analysis of gene-gene interactions between candidate genes can provide more insights than methods based on individual SNPs. They may be crucial for understanding complex interactions among different gene variants [12]. This study extended the traditional approach to include haplotype analyses within several genes. Such an approach holds the potential to offer an extensive array of information crucial for comprehending the intricate interconnections among gene variants and the pinnacle of sprint performance prowess. This study’s use of such analyses is significant, as previous research in this area has been limited. Using SNP genotyping, we investigated the genetic predisposition for efficient LA utilization in elite sprinters compared to untrained individuals. We purposely selected the sprint discipline because LA generation occurs when the muscles are stressed beyond their aerobic threshold [13]. This study’s focus on elite athletes is crucial for understanding LA kinetics and genetic factors in high-level performance, where physiological demands peak. This approach supports existing research highlighting the importance of elite athletes in uncovering performance-related genetic markers [14]. This article sought to identify and clarify the influence of genetic variations and interactions between the MCT1, MCT2, and MCT4 genes on performance-related LA metrics in athletes. Our study endeavored to enhance the existing knowledge in sports genetics while offering practical implications for training considering genetic predispositions. Ultimately, this work will contribute to ongoing efforts to harness genetic insights for optimizing athletic performance, thereby advancing the field of sports science.

Methods

This cross-sectional control study investigated the relationship between an individual’s genetics and their immediate response to high-intensity glycolytic loading, explicitly focusing on LA production and removal after exertion. Participants in the experimental and control groups were selected explicitly according to health and performance criteria, which respected the essential agility to perform two intermittent anaerobic all-out tests (Fig. 2). DNA sampling and health questionnaires were conducted before body composition measurements and warm-up. This was followed by the primary glycolytic loading protocol, succeeded by two intermittent all-out Wingate tests (Fig. 2). The LA was measured before the warm-up before the loading protocol between two loading tests at 0’, 3’, 6’, 9’, 20’and 30’ minutes (min) after the loading protocol. Loading protocols were completed during the preparation period. They were conducted in the Physiological Laboratory of the Department of Biomedical Sciences at the University of Physical Education in Warsaw and in the Biomedical Laboratory at the Faculty of Physical Education and Sport at Charles University.

Fig. 2.

Flow chart of formal procedures, DNA sampling, anaerobic loading, and timing of blood lactate samples

Participants

The study involved 337 male athletes from Poland and the Czech Republic, aged 16 to 29 years old, of the Caucasian subpopulation. This sample included 145 elite athletes (the experimental group of 54 individuals from Poland and 91 from the Czech Republic) and 192 physically active individuals who did not participate in competitive speed or strength sports (control group). The participants who had engaged in high-intensity sports or workouts within the last 72 h, sustained an injury in the past three months, and did not have clearance from a sports doctor were not eligible for inclusion. In contrast, individuals in the experimental group needed to achieve a running distance of 400 m in less than 50 s during the current season, participate in at least four workouts per week, and attend 80% of club workouts to be included in the study. The elite athlete group (n = 145) had an average age of 19.79 ± 2.80 years, weight of 73.00 ± 9.52 kg, and height of 182.67 ± 5.67 cm. In contrast, the control group (n = 192) had an average age of 20.94 ± 1.90 years, weight of 78.15 ± 10.18 kg, and height of 180.73 ± 6.50 cm. Before commencing the study, all individuals were provided with a document containing information about the study’s specifics, objectives, processes, possible risks, and the advantages of their involvement. They also filled out a survey form.

Glycolytic loading protocol

A standardized warm-up was conducted before each test. Before the Wingate test, the athletes engaged in a 5–6 min warm-up at 1 W/kg and a cadence of 60 rotations per minute (rpms), which included two 3–5 s accelerations. The protocol for eliciting maximal glycolytic response entailed performing two consecutive all-out anaerobic Wingate tests on a bicycle ergometer (Monark 894 E peak bike, Sweden). Resistance for subjects was set at 7.5% of body weight, and participants remained seated throughout the test, starting from a complete standstill position [15]. Subjects were instructed to stay seated for the entire 30-second sprint. Once the load (7.5% body mass) was set, the subjects’ feet were secured to the pedals. They were then given a 5-second countdown and began the test with maximal effort. Power output was monitored as subjects initiated the test. Subjects pedaled at maximal speed for 30 s. Following the test, subjects had a 4-minute passive rest [16] and then performed a second test following the same procedure (Fig. 2). Upon completing the second Wingate test, the subjects rested on a cycling ergometer for 40 min and provided LA samples. After completing the second Wingate test, the participants engaged in an active rest of 40 min on a cycling ergometer, maintaining a pace of approximately 50 revolutions per minute.

Blood lactate measurement

During the study, the lactate (LA) concentration was tested at various time points: before warm-up during the resting phase, one minute after warm-up, after the first and second Wingate tests, and at 3’, 6’, 9’, 20’, and 30’ into the active rest phase. Blood was collected from the fingertip using sodium heparin-treated end-to-end capillaries, and the LA concentration was measured using the Biosen apparatus. This apparatus employs chip sensor technology and can measure LA within 0.5–40 mmol/l (5–360 mg/dL) (Biosen C-Line Lactate analyzer, EKF Diagnostics).

Discrepancies observed in the Wingate test and measurements of LA concentration can arise from several factors, including procedural errors, calibration issues with the equipment, biological variability, and the specific conditions under which the testing occurs. To reduce these discrepancies, it is essential to implement standardized testing protocols, conduct regular equipment calibrations, ensure proper participant preparation such as adequate warm up and hydration, and maintain strict control over external influences like the testing environment and timing of measurements [17, 18].In our study, we adhered to validated protocols to address these concerns. However, potential sources of error in our study’s Wingate test and LA concentration measurements may stem from individual differences in each participant’s capacity to achieve maximum pedaling speed. Variability in maximum speed can represent differing intensity levels across individuals, affecting the comparability of results. Furthermore, participants who fully engaged in the test and reached their maximum effort may have encountered significant fatigue, which could influence their performance during the active recovery period (e.g., after 40 min) and subsequently affect various test outcomes, including LA measurements following the second Wingate test. This fatigue can further contribute to variability in the body’s LA removal processes, ultimately affecting the accuracy of the measurements obtained.

DNA sampling and isolation

The biological samples were collected by scraping the inner cheek using two Copan FLOQSwab (Interpath, Australia) samples as per the standard procedure recommended by the manufacturer. Genomic DNA was isolated from the epithelial cells using the standard, commercial High Pure PCR kits (Roche Diagnostics, Germany) following the manufacturer’s instructions. The quality and quantity of the DNA samples were validated using a spectroscopic photometer (NanoPhotometer NP80, Implen, Germany) and then stored at -20 °C for further analysis.

Genotyping analyses

All samples were genotyped in two repeats using the Real-Time PCR system QuantStudio1 (Applied Biosystems, USA). TaqManTM probes were employed for genotyping seven SNPs of the MCT1 (SLC16A1) gene: rs1049434, rs4301628, rs12028967, rs10857983, rs3789592, rs7169, rs7556664, three of the MCT2 (SLC16A7): rs3763980, rs995343, rs3763979, and one of the MCT4 (SLC16A4): rs11323780 (Applied Biosystems, USA). Additionally, custom, non-commercial primer and probe set targeting MCT1 (SLC16A1) rs10776763, and rs6537765 were designed with the Custom TaqMan Assay Design Tool (Thermo Fisher Scientific, Waltham, MA). The specific MCT1 SNPs were chosen due to the limited number of polymorphisms in the gene examined to date, which have either reported associations with metabolic pathways relevant to power athletes or suggested functional roles in the regulation of LA transport and exercise performance (rs1049434, rs7556664, rs7169) [19–24]. The SNPs rs3763980, rs995343, and rs3763979 in the MCT2 gene were selected due to the lack of existing literature connecting these variants with blood LA production and clearance after high-intensity exercise. Likewise, the absence of publications linking MCT4 rs11323780 with LA dynamics in these conditions was noted.

The genotyping process was performed according to the manufacturer’s protocol using TaqPathTM ProAmpTM Master Mix (Applied Biosystems, USA). The PCR reaction components (10 µl of the mixture) were prepared by combining 5.0 µl of the master mix, 0.5 µl of the TaqMan SNP Genotyping Assay, and 4.5 µl of DNA. Similarly, the PCR control reaction components (10 µl of the mixture) included 5.0 µl of the master mix, 0.5 µl of the TaqMan SNP Genotyping Assay, and 4.5 µl of nuclease-free water. The analysis was performed under specific reaction conditions, including initial denaturation at 95 °C, cyclical denaturing at 95 °C, and starter hybridization and extension at 60 °C repeated in 40 cycles. The amplified products were visualized and analyzed using QuantStudio 1 Real-Time PCR Instrument’s Design and Analysis Software version 1.5.1. The samples were analyzed and stored in the Genetic Laboratory of the Department of Biomedical Sciences at Józef Piłsudski University of Physical Education in Warsaw.

Statistical analyses

The compatibility with the normal distribution was assessed using the Shapiro-Wilk test. A t-test was used to examine the differences in MAX LA and LA30’ mean values between the sprinter and control groups. Chi-square tests were conducted to compare genotype frequencies between sprint athletes and control participants for each SNP (Supplementary Table). Haplotype-phenotype associations were analyzed using regression of the trait on haplotype effects while considering ambiguous haplotypes. The analysis employed an iterative two-step Expectation-Maximization (EM) algorithm, with regression coefficients updated based on each individual’s posterior probabilities of haplotype pairs. The dependent variables were the maximum value of LA and the decrease between MAX LA and the final value (LA30’). In contrast, the independent variables included the division into sprinters, controls, and haplotypes. Additionally, regression was utilized to examine the impact of gene x gene interactions on the maximum post-exercise concentration (MAX LA) and the decrease between the final value and LA30’, with genotype and sprinter/control division as independent variables, accounting for all possible interactions. Statistical significance was determined at the < 0.05 standard level. The R program haplo.stat, stats, and SNPassoc packages were employed for the analysis.

Results

The mean values of the MAX LA and LA30’ parameters differed significantly between the sprinter and control group, with a p-value < 0.001. The mean MAX LA value in the sprinter group was 19.97, compared to 15.26 in the control group. Similarly, the mean LA30’ value among sprinters was 9.35, while in the control group, it was 7.71 (Fig. 3). Haplotype analysis showed that TAC in the MCT2 gene (rs3763980, rs995343, rs3763979) was associated with an increase in the difference between MAX LA and final LA (LA 30’) and was more prevalent among sprinters (coefficient = 0.12, p-value = 0.04) (Table 1). The TAC haplotype was also associated with increased MAX LA values. In the sprinters’ group with the TAC haplotype, the mean MAX LA value was 17.81, while in the control group, it was 14.94 and was more prevalent among sprinters (coefficient = 0.13, p-value = 0.0043) (Table 2).

Fig. 3.

LA means responses (mmol/l) at rest (LAr), after the first Wingate test (LAw1), between tests (LAb) immediately after the second Wingate test (LAw2) and 3, 6, 9, 20, 30 minutes (LA3’, LA6’, LA9’, LA20’¸ LA30’) after the second Wingate test in sprinters and control group. The data presented with error bars indicating each time point’s standard deviation (SD)

Table 1.

Association of haplotypes within the MCT2 gene and sprinter division, with a decrease from MAX LA to LA30’ as the dependent variable

	MCT2 (rs3763980)	MCT2 (rs995343)	MCT2 (rs3763979)	frequency	coefficient	SE	z	P - value
Haplotype 1	A	A	C	0.22	-0.03	0.05	-0.48	0.63
Haplotype 2	A	A	T	0.12	-0.07	0.07	1.09	0.28
Haplotype 3	T	A	C	0.22	0.12	0.05	2.02	0.04
Haplotype 4	T	G	C	0.02	-0.16	0.18	-0.90	0.37
Rare haplotype				0.01	-0.09	0.23	-0.39	0.70
Base haplotype	A	G	C	0.42

SE– standard error

Table 2.

Association of haplotypes within the MCT2 gene with sprinters division and control, using MAX LA as the dependent variable

	MCT2 (rs3763980)	MCT2 (rs995343)	MCT2 (rs3763979)	frequency	coefficient	SE	z	P - value
Haplotype 1	A	A	C	0.22	0.04	0.05	0.75	0.45
Haplotype 2	A	A	T	0.11	0.02	0.06	0.35	0.73
Haplotype 3	T	A	C	0.22	0.13	0.05	2.87	0.0043
Haplotype 4	T	G	C	0.02	-0.15	0.15	-0.99	0.323
Rare haplotype				< 0.01	-0.21	0.21	-0.99	0.322

SE– standard error

Next, significant genotype interactions associated with increased differences between MAX LA and final LA were noted. Specifically, interaction effects were observed for genotypes AG/T- and GGxT- (MCT1 (rs3789592) x MCT4 (rs11323780) showed an interaction effect, with a coefficient of 2.07 and 2.014 and a p-value of 0.02. Additionally, TTxTT (MCT1 (rs12028967) x MCT2 (rs3763979)) exhibited an interaction effect with a coefficient of 5.41 and a p-value of 0.03. Furthermore, AGxGG (MCT1 (rs 3789592) x MCT2 (rs 995343)) had an interaction effect with a coefficient of -2.49 and a p-value of 0.03. Finally, an interaction effect was observed for TTxTT MCT1 (rs7556664) x MCT4 (rs11323780) with a coefficient of 2.14 and p-value 0.02 (Table 3).

Table 3.

Association among MCT1, MCT2, and MCT4 genes in the sprinter’s division and control group, with the variable depicting a decrease from MAX LA to LA30’ as the dependent variable

SNP	genotype	estimate	SE	t-value	P-value
MCT1 rs3789592 x MCT4 rs11323780	AG/T-xGG/T-	2.07/2.14	0.87/0.88	2.40/2.44	0.02
MCT1 rs12028967 x MCT2 rs3763979	TTxTT	-5.41	2.51	-2.16	0.03
MCT1 rs 3,789,592 x MCT2 rs 995,343	AGxGG	-2.49	1.11	-2.23	0.03
MCT1 rs7556664 x MCT4 rs11323780	TTxTT	-2.14	0.88	-2.44	0.02

SE– standard error

For tests examining the relationship between MAX LA values and genotypes, linear models showed that the interactions between ATxTT (MCT2 (rs3763980) x MCT4 (rs11323780), coefficient = -1.74, P-value = 0.03), CTxCT (MCT1 (rs10857983) x MCT2 (rs3763979), coefficient = -1.62, P-value = 0.04), CTxCT (MCT1 (rs4301628) x MCT2 (rs3763979), coefficient = -4.58, P-value = 0.04), CT x TT (MCT1 (rs 4301628) x MCT4 (rs 11323780), coefficient = -1.62, P-value = 0.04) and CTxTT (MCT1 (rs 4301628) x MCT2 (rs 3763979), coefficient = -4.58, P-value = 0.04) lowered the LA value which increased the LA value (Table 4).

Table 4.

Statistically significant results for genotype association within MCT1, MCT2, MCT4 genes and sprinters division and control with the variable depicting MAX LA as the dependent variable

SNP	genotype	estimate	SE	t-value	P-value
MCT2 (rs3763980) x MCT4 (rs11323780)	ATxTT	-1.74	0.78	-2.24	0.03
MCT1 (rs10857983) x MCT4 (rs11323780)	CTxTT	-1.62	0.80	-2.03	0.04
MCT1 (rs10857983) x MCT2 (rs3763979)	CTxTT	-4.58	2.20	-2.08	0.04
MCT1 (rs4301628) x MCT4 (rs 11323780)	CTxTT	-1.62	0.80	-2.03	0.04
MCT1 (rs 4301628) x MCT2 (rs 3763979)	CTxTT	-4.58	2.20	-2.08	0.04

SE– standard error

Discussion

Only a small number of polymorphisms in the MCT1 gene (rs1049434, rs7556664, rs7169) have been investigated so far [19–24]. Our study expands upon this by incorporating a broader range of previously examined SNPs while introducing new insights regarding MCT1 polymorphisms rs4301628, rs12028967, rs10857983, and rs3789592. Notably, to our knowledge, this is the first study to explore the relationships between MCT2 (rs3763980, rs995343, rs3763979) and MCT4 (rs11323780) polymorphisms and LA production and removal following high-intensity efforts in elite males. This novel approach enhances our understanding of these genetic variants in the context of athletic performance. The originality of our approach lies in the comprehensive exploration of these genetic variants’ roles in athletic performance, extended by haplotype and gene-gene interaction analysis.

This study aimed to determine the relationship between haplotypes and intergenic interactions with LA utilization. We analyzed variants in LA carrier genes MCT1, MCT2, and MCT4 to establish the links. These are the most significant regulators of transmembrane LA exchange [3, 25]. Experimental work on the genetic basis of adaptive responses to training, including haplotypes, is still rare, and our study is among the first in this area. We measured blood LA. However, it is possible to question the validity of this method by suggesting that muscle LA may be a more accurate indicator. The accumulation of LA occurs due to the increased conversion of pyruvate to LA by LA dehydrogenase, primarily caused by alterations in the intramuscular redox state. Furthermore, the oxidation of the surplus LA depends on its redistribution via blood flow to other muscles, the heart, and the liver [26]. Therefore, muscle LA concentrations reflect blood LA concentrations [27]. Moreover, although LA accumulation in muscle or blood indicates increased proton release and potential for decreased pH levels in cells and blood, we also acknowledge that standard medical practice has accepted an elevated blood LA concentration as the herald of oxygen insufficiency [28].

The literature is abundant in examples of gene variants and allelic forms regulating LA utilization. For instance, Kikuchi’s research was the pioneering study to explore the link between the AA genotype MCT1 rs1049434 and the generation and utilization of LA after strenuous anaerobic activity [9]. Sawczuk et al.‘s research demonstrated that the MCT1 T allele is linked to sprint/power performance in a recessive genetic model. The TT genotype was found to be more common in sprint/power athletes compared to both control and endurance athletes. This suggests that the MCT1 rs1049434 polymorphism may play a role in influencing athletic sprint-power performance in the Polish population [29].

Notable differences were found between sprinters and the control group, suggesting distinct characteristics associated with athletic performance. The current study identified that the TAC haplotype (rs3763980, rs995343, rs3763979) in the MCT2 gene was associated with an increase in the difference between MAX LA and final LA in sprinters. The frequency of this haplotype in the control population was 0.19 and in sprinters 0.26. The increase in the difference between MAX LA and final LA suggests that individuals possessing the TAC haplotype might have a slower LA clearance rate from their blood after intense exercise. This could imply that the TAC haplotype affects the efficiency of the MCT2 transporter, which plays a role in LA transport. This inefficiency might lead to higher LA concentrations persisting for longer post-exercise. For athletes, particularly sprinters, this genetic trait might influence their performance and recovery times. Higher post-exercise LA concentrations could affect muscle fatigue and recovery, although the exact implications would depend on various factors, including overall training and conditioning. These suggestions could be supported by the fact that the TAC haplotype was associated with an increase in the MAX LA value. These data imply that sprinters with this haplotype produce or accumulate more LA during intense exercise and may clear LA more slowly.

In this study, specific genotype interactions increased or decreased the difference between MAX LA and final LA. Specifically, AG/T-x GGxT- (MCT1, rs3789592 x MCT4, rs11323780), TTxTT (MCT1, rs12028967 x MCT2, rs3763979), TTxTT (MCT1, rs12028967 x MCT2, rs3763979) affected the rise and AGxGG (MCT1, rs3789592 x MCT2, rs995343) the drop between the values. An association with an increase in the difference between MAX LA and final LA suggests a slower LA clearance, whereas the reduction suggests a faster LA utilization. Therefore, the relevant combinations of genetic variants might be beneficial or disadvantageous in LA production and removal.

Another important finding was that there was a relationship between MAX LA values and genotype interactions. The following decreased MAX LA value: ATxTT (MCT2 rs3763980 x MCT4 rs11323780), CTxTT (MCT1 rs10857983 x MCT4 rs11323780), CTxTT (MCT1 rs10857983 x MCT2 rs3763979), CTxTT (MCT1 rs4301628 x MCT4 rs11323780), CTxTT (MCT1 rs4301628 x MCT2 rs3763979). This suggests that individuals with these specific genotype interactions might have more efficient LA production or clearance mechanisms.

According to the SNP National Center for Biotechnology Information (NCBI) database [30], no publications about examined MCT1 SNPs (rs3789592, rs12028967, rs10857983 rs4301628) exist apart from MCT1 rs7556664 and rs1049434. MCT1 rs7556664 A is linked with better survival of patients with multiple myeloma [23], and wrestlers with the AA genotype of MCT1 rs1049434 have lower LA concentrations [9]. In the case of MCT2, there are no publications for MCT2 rs3763979. Rs 995,343 is described in the prognosis of cancer patients [31, 32]. Next, rs3763980 is linked to response to methotrexate [33, 34]. There is no literature data for MCT4 rs11323780, either. As these current experiments are the first of their kind, there was no available data on these SNPs in the existing literature. Consequently, comparisons with other studies were not possible, further highlighting the novelty of this research.

There are a few limitations to this study. Practical constraints precluded the inclusion of insights on Polish and Czech females, highlighting the need for future research in this area. Results might not apply to non-elite or female athletes, limiting the applicability of the findings across the different groups. Further investigations should encompass elderly individuals and those from diverse ethnic backgrounds to determine the potential impact of these variables on the outcomes. Additionally, it is imperative to conduct a replication study with a larger sample size to evaluate the reliability and consistency of the observed effects in our study. The study’s limitations also include the lack of analysis of the interactions between genetic variability and cultural factors, epigenetics, and the environment. Cultural elements, including dietary habits, patterns of physical activity, and training methodologies, can affect the expression and efficiency of genetic variations in MCT transporters. For instance, specific cultural dietary practices may enhance or impede the optimal functioning of LA transporters, which could subsequently affect the accumulation and clearance of LA. Future research should encompass various ethnic groups and genders to assess genetic variability across diverse populations comprehensively. Additionally, the adoption of multi-gene analyses is suggested, as this approach examines the interactions of multiple genetic variants rather than concentrating on a single gene. By integrating data from various genetic markers, a better understanding of the polygenic nature of many traits can be achieved. Finally, cross-sectional design can show associations but cannot establish causality between genetic variations and LA kinetics.

Our findings have important implications. The selected genes and their polymorphisms, haplotypes, and intergenic could potentially function as genetic markers in the future, enabling the prediction of success in sprinting. In a broader term, association studies of selected genes expanded to include haplotype analysis, and intergenic interaction analyses emerge as a tool for a comprehensive understanding of how individual polymorphisms affect athletic success.

The current work is valuable for both the scientific and athletic sectors, as it deepens the understanding of how specific genetic markers in the MCT1, MCT2, and MCT4 genes affect LA dynamics during high-intensity exercise. This advancement contributes to the field of exercise physiology and muscle performance. For athletes and coaches, the outcomes offer a foundation for creating personalized training programs tailored to individual genetic characteristics, enhancing performance and recovery. Future research possibilities include broadening genetic analysis to examine additional variants and their long-term implications on training adaptations, studying the interaction between environmental factors and genetics to inform training approaches under different conditions, and incorporating diverse populations to achieve more generalized findings. Practical applications could lead to customized sports training, targeted recovery strategies in sports medicine, and improved athlete selection based on genetic predispositions toward specific sports.

Conclusions

In conclusion, this study uniquely identifies the specific interactions among monocarboxylate transporter genes MCT1, MCT2, and MCT4 and their significant impact on LA kinetics following high-intensity exercise in elite male athletes. Unlike previous research, haplotype analysis and gene-gene interactions were employed in this study. The TAC haplotype in MCT2 was associated with an increase in the difference between MAX LA and final LA in sprinters. Additionally, distinct genotypic interactions, such as AG/T- and GGxT- (MCT1 x MCT4) and TTxTT (MCT1 x MCT2), were shown to correspond to more significant differences between peak and final LA levels, offering new insights into the influence of genetic combinations on athletic performance.

Furthermore, our findings suggest that MCT4 rs11323780 shows a notable association with power athlete status as it interacts with other genetic variants of MCT1 and MCT2. MCT4 has been associated with preferential expression in type II (fast-twitch) muscle fibers [35]. Therefore, MCT4 rs11323780 may have the potential as a biomarker for sprinting performance. These contributions enhance our understanding of the genetic basis for LA dynamics, providing a foundation for individualized strategies in optimizing athletic performance based on specific genetic profiles. The findings are based on a sample of elite male athletes from Poland and the Czech Republic, limiting their generalizability to all sprinters or athletes of different demographics. The study identifies genetic variants as potential biomarkers for sprinting success, but claiming these markers can universally predict performance may be excessive. Multiple factors, including genetics, environment, and training, are influenced by athletic performance, extending beyond MCT genes.

Electronic supplementary material

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Abbreviations

BW

Body weight

EM

Expectation-Maximization

LA

Blood lactate

MCT

Monocarboxylate transporter

MAX LA

Maximum lactate concentration

LA30’

Final lactate concentration, i.e., measured at 30 min post-workout

Author contributions

E.M. conceptualized the study and developed the methodology. E.M., P.S., and A.G. conducted the validation and formal analysis, while E.M. led the investigation and collected data together with A. Mróz, P.V., D.K., and M.P. E.M. and A.B. prepared the original draft and were responsible for the review and editing. E.M. handled the visualization and was in charge of project administration and supervision. E.M. and A. Mastalerz funded the project.

Funding

This research was funded by the National Science Centre MINIATURA 6 entitled “Polygenetic determinants of lactic acid utilization in elite athletes” in 2022/2023 and the Ministry of Education and Science in 2023/2024 as part of the University Research Project of the University of Physical Education in Warsaw– UPB No. 1.

Data availability

The data for this study is available in the European Variation Archive (EVA) at EMBL-EBI, under accession number PRJEB85239 (https://www.ebi.ac.uk/eva/?eva-study=PRJEB85239).

Declarations

Ethics approval and consent to participate

The Bioethics Commission of the District Medical Chamber in Gdansk authorized the research and informed consent (resolution KB-2/21, dated February 3, 2021). All methods utilized in the study adhered to the Helsinki Declaration of the World Medical Association (2013). All participants provided informed consent to participate in the study.

Consent for publication

All authors have read the manuscript and agreed to its content.

Competing interests

The authors declare no competing interests.