Abstract
Decathlon is a multimodality sport that requires the combination of endurance, strength, speed, and agility. A polymorphism present in the gene encoding for alpha-actinin-3 (ACTN3) potentially influences sports performance, since this protein is a structural component of skeletal muscle contributing to muscle contraction effectiveness. Aim: To investigate whether the presence of theACTN3 R577Xpolymorphism is associated with decathlon athletes’ performance in the different modalities of decathlon. Methods: Thirty-one male athletes from the Brazilian national federation of decathlon aged between 18 and 50 years were
alpha-actinin-3 (ACTN3) potentially influences sports performance, since this protein is a structural component of skeletal muscle contributing to muscle contraction effectiveness. Aim: To investigate whether the presence of theACTN3 R577Xpolymorphism is associated with decathlon athletes’ performance in the different modalities of decathlon. Methods: Thirty-one male athletes from the Brazilian national federation of decathlon aged between 18 and 50 years were genotyped for theACTN3 R577Xpolymorphism using real-time polymerase chain reaction (RT-PCR). The athletes’ latest decathlon performances were recorded over ten competitions. The Hardy–Weinberg equilibrium was verified. Pearson’s correlation coefficient was utilized to assess the relationship between the obtained sports performance (score) by event and sets of events (speed events, jumps, and throws) with sig- nificance considered atp< 0.05. Results: Strong and significant correlations were identified between the speed events, the jumping, and the launching performances. Among the ath- letes, the distribution ofACTN3genotypes was as follows:R577R—51.6%,R577X—48.4% , andX577X—0%, indicating a complete absence of homozygosity for the non-functional Xallele in this cohort. No significant differences in sports performance (score) could be observed based on the genotype. Conclusions: Our results may support the importance of theACTN3genotype, specifically, the presence of the577Rallele, as one of the contributive factors for athletes’ performance in modalities that involve muscle strength, power, and speed. However, given the small sample size and the retrospective nature of this study, further research is warranted. Keywords:ACTN3 polymorphism; decathlon; alpha-actinin; sports performance; muscle strength Cells2025,14, 782 https://doi.org/10.3390/cells14110782
Cells2025,14, 782 2 of 12 1. Introduction Alpha-actinin-3 is an actin-binding protein located in the Z-discs of skeletal muscles, where it forms a lattice-like structure and stabilizes the actin filaments within sarcomeres. It is expressed only in fast-twitch skeletal muscle fibers (type II), more specifically in 100% of type IIb and about 50% of type IIa muscle fibers [1]; however, the absence of alpha-actinin-3 does not confer a phenotypic change in the muscle tissue due to a compensatory role of alpha-actinin-2 [2,3]. A common nonsense polymorphism in the gene encoding this protein (ACTN3) is found in 18 to 25% of the general population. This polymorphism involves a cytosine to a thymine (C→T) nucleotide substitution, resulting in the replacement of an arginine (R) with a stop codon (X) and the subsequent synthesis of a truncated, non-functional form of alpha-actinin-3 [4,5]. This ACTN3 R577Xpolymorphism has been reported as influencing athletes’ performance in high-intensity and short-duration sports [6]. In addition, a high frequency of theRXandXXgenotypes is present in endurance sports athletes [7]. It has been shown that theACNT3 577Rgenotype is associated with better performance outcomes in speed, strength, and hypertrophic response adaptations to training, while theACNT3 577Xgenotype is related to better performance in endurance sports [6,8,9]. Decathlon consists of ten athletics events held over two competition days. It requires a mix of different athletic competencies and skills such as speed, agility, and endurance. The decathlon modalities can be categorized into speed events (100 m dash, 110 m hur- dles, and 400 m dash), jumping events (long jump, high jump, and pole vault), throw- ing events (shot put, discus throw, and javelin throw), and endurance running events (middle-distance—1500 m run). As previously mentioned, theACTN3 R577Xpolymorphism has been extensively studied in the context of its influence on physical performance, with evidence suggesting its role in power, speed, and endurance activities. However, despite the physiological demands of decathlon—a sport requiring a unique combination of strength, speed, en- durance, and agility—the relationship between this genetic variant and performance across its ten distinct events has not been previously explored. This study is particularly novel because it examines genotypic
influence on physical performance, with evidence suggesting its role in power, speed, and endurance activities. However, despite the physiological demands of decathlon—a sport requiring a unique combination of strength, speed, en- durance, and agility—the relationship between this genetic variant and performance across its ten distinct events has not been previously explored. This study is particularly novel because it examines genotypic distributions and their potential impact on diverse athletic modalities within a cohort of national-level decathlon athletes. Understanding these rela- tionships could provide critical insights into how genetic factors contribute to multisport performance, guiding talent identification, training optimization, and athlete development strategies. Additionally, the findings may further elucidate the mechanistic role of the ACTN3polymorphism in determining physical capabilities across different athletic con- texts, contributing to the broader field of sports genomics. This study, therefore, addresses a significant knowledge gap in the existing body of scholarly literature, offering practical implications for sports science and genetic research. Our aim was to investigate the fre- quency distribution of theACTN3polymorphism in a sample of national decathlon athletes, exploring whether the genotypes can influence performance in different modalities. 2. Materials and Methods In this cross-sectional descriptive study, 31 male decathlon athletes aged 18 to 50 years were recruited for genotyping according to the following inclusion criteria: (i) having participated in the Brazilian, South American, Ibero-American, World Championship, or the Olympic games, and (ii) reporting at least 10 years of competing experience. Data from the athlete’s scores from 1996 to 2015 were collected from the Troféu Brasil competition in São Paulo, the Brazilian University’s Games (JUBS) in Aracaju City, and the website of the Brazilian Athletics Confederation (Confederação Brasileira de Atletismo, CBA). It is
Cells2025,14, 782 3 of 12 important to highlight that the decathlete’s scores presented in this study represent 58.06% of the top rankings in the Olympic games. The sample size was determined a priori using the G*Power software, version 3.1. For this purpose, we considered the following inputs: “t-tests family,” point biserial correlation model, with an r 2 of 0.35 (associative explanation of at least 35%) for two-tailed correlations. Accordingly, we adoptedαof 0.05 andβof 0.95. As such, the minimum sample size indicated for the present study was 30 subjects. 2.1. Ethics This study was designed according to the guidelines of the Declaration of Helsinki and approved by the Ethical Committee for research involving human subjects of the Uni- versity Center of João Pessoa (UNIPÊ), following the National Health Council—Resolution 466/2012 of 12 December 2012, CAAE number #45665215.5.0000.5176. All participants were informed about the objectives, procedures, and potential risks involved in this study and provided their consent. The athletes’ performance was measured by the decathlon score assigned to the tasks. This score reference was taken from the CBA (Tables). Table 1.Presents the points per result in day-one decathlon events.100 m Race Long Jump Shot Put High Jump 400 m Race RES-s Points RES-m Points RES-m Points RES-s Points RES-s Points 10.50 975 7.39 908 14.02 730 1.99 794 49.67 830 10.51 973 7.38 905 14.01 729 1.98 785 49.69 829 10.52 970 7.37 903 13.99 728 1.97 776 49.72 828 10.53 968 7.36 900 13.97 727 1.96 767 49.74 827 10.54 966 7.35 898 13.96 726 1.95 758 49.76 826 10.55 963 7.34 896 13.94 725 1.94 749 49.78 825 10.56 961 7.33 893 13.93 724 1.93 740 49.80 824 10.57 959 7.32 891 13.91 723 1.92 731 49.82 823 10.58 956 7.31 888 13.89 722 1.91 723 49.85 822 10.59 954 7.30 886 13.88 721 1.90 714 49.87 821 RES: result; s: seconds; m: meters. Table 2.Presents the points per result in day-two decathlon events. 110 w/Obstacle Disc Launch Pole Jump Javelin Throw 1500 m Race RES-s Points RES-m Points RES-m Points RES-m Points RES-min Points 14.20 949 44.16 750 4.79
7.31 888 13.89 722 1.91 723 49.85 822 10.59 954 7.30 886 13.88 721 1.90 714 49.87 821 RES: result; s: seconds; m: meters. Table 2.Presents the points per result in day-two decathlon events. 110 w/Obstacle Disc Launch Pole Jump Javelin Throw 1500 m Race RES-s Points RES-m Points RES-m Points RES-m Points RES-min Points 14.20 949 44.16 750 4.79 846 60.12 740 4:39.33 685 14.21 948 44.11 749 4.78 843 60.05 739 4:39.49 684 14.22 946 44.06 748 4.77 840 59.98 738 4:39.65 683 14.23 945 44.02 747 4.76 837 59.92 737 4:39.80 682 14.24 944 43.97 746 4.75 834 59.85 736 4:39.96 681 14.25 942 43.92 745 4.74 831 59.78 735 4:40.12 680 14.26 941 43.87 744 4.73 828 59.72 734 4:40.28 679 14.27 940 43.82 743 4.72 825 59.65 733 4:40.44 678 14.28 939 43.77 742 4.71 822 59.58 732 4:40.60 677 14.29 937 43.72 741 4.70 819 59.52 731 4:40.76 676 RES: result; s: seconds; m: meters; min: minutes. 2.2. Sampling and Genotyping All participants were asked to refrain from eating for two hours before collecting the buccal epithelium. A sterile swab microtube (Netlab, Rio de Janeiro, Brazil)(Eppendorf) was used for this purpose. Immediately before taking the biomaterial, the subjects thor- oughly rinsed their mouths with warm water. Samples were separated by centrifuga-
Cells2025,14, 782 4 of 12 tion. The resulting aliquots were placed in a refrigerator and kept until DNA profil- ing at−18 ◦ C . Genomic DNA was extracted by the Chelex method as described by Walsh et al. [10] . Allelic variants and genotypes of the SNPrs1815739(C577T) of theACTN3 gene on chromosome 11q13.2 were determined in all participants using real-time poly- merase chain reaction (RT-PCR) (Applied Biosystems, Waltham, MA, USA). The allelic discrimination was performed using a TaqMan ® Pre-Designed SNP Genotyping Assay with fluorescent probes delivered by the validated Assay Id C_590093_1_ (Thermo Fisher Scientific, Waltham, MA, USA), according to the manufacturer’s protocol. In addition, 20% of the DNA samples were tested twice with 100% agreement of the results to en- sure the accuracy of PCR diagnostics. Primers were designed according to the Applied Biosystems protocol for the Context Sequence [VIC/FAM]: CAAGGCAACACTGCCCGAG- GCTGAC[T/C]GAGAGCGAGGTGCCATCATGGGCAT. The following designations of the C (R)andT (X)alleles for thers1815739of theACTN3gene were used: homozygous fully functionalCCgenotype (cytosine/cytosine) orRR(arginine/arginine); heterozygous (inter- mediate)CTgenotype (cytosine/thymine) orRX(arginine/stop codon); and homozygous low functionalTTgenotype (thymine/thymine) orXX(stop codon/stop codon). 2.3. Statistics Descriptive statistics were performed, including measures of central tendency (mean, standard deviation, and variance), along with respective percentages. The normality of distributions was assessed using the Kolmogorov–Smirnov test, and the homogeneity of the variance was examined through the Levene test. Pearson’s correlation coefficient was utilized to assess the relationship between the obtained sports performance (score) in each event and sets of events (speed events, jumps, and throws). The Hardy–Weinberg equilibrium was verified for describing and predicting the genotype and allele frequency in a given population. Allele frequencies were estimated by counting, and the allele observance was compared to its expected frequency based on the Chi-square statistical test. Differences in sports performance (in points) between genotypic groups (R577Rvs.R577X) were analyzed using the non-parametric Mann–WhitneyUtest. For all inferential data analyses, a significance level ofp< 0.05 was considered. The data were analyzed using the statistical software SPSS ® for Windows, version 23. 3. Results The data from the best scores of this study’s participants, according to the modality’s classification, are shown in Table. Table 3.The
points) between genotypic groups (R577Rvs.R577X) were analyzed using the non-parametric Mann–WhitneyUtest. For all inferential data analyses, a significance level ofp< 0.05 was considered. The data were analyzed using the statistical software SPSS ® for Windows, version 23. 3. Results The data from the best scores of this study’s participants, according to the modality’s classification, are shown in Table. Table 3.The scores (in points) obtained by the athletes in this study. Set of Events Sum of Points Average Points Fitness Skills Speed 2.418 806 Explosive strength, displacement speed, agility Jumps 2.188 729 Travel speed, explosive force Throwing and Launching 1.932 644 Maximum strength, explosive strength Medium Distance—1500 m -- 633 Aerobic power Table modalities. Performances in the 100 m sprint had shown significant positive correlations with the long jump (r = 0.727,p< 0.01), shot put (r = 0.375,p< 0.05), high jump (r = 0.526, p< 0.01), 400 m sprint (r = 0.759,p< 0.01), 110 m hurdles (r = 0.625,p< 0.01), pole vault (r = 0.428,p< 0.05), and the 1500 m run (r = 0.463,p< 0.01), suggesting a shared reliance on speed and power output across these disciplines. The 400 m sprint also exhibited significant correlations with the long jump (r = 0.651,p< 0.01), shot put (r = 0.581,p< 0.01), high jump (r = 0.583,p< 0.01), 110 m hurdles (r = 0.640,p< 0.01), discus throw (r = 0.489,p< 0.01),
Cells2025,14, 782 5 of 12 pole vault (r = 0.612,p< 0.01), and javelin throw (r = 0.500,p< 0.01), emphasizing the centrality of anaerobic capacity and total-body coordination in these events. Long jump performance had been significantly related to nearly all other events, including the shot put (r = 0.558,p< 0.01), high jump (r = 0.789,p< 0.01), 110 m hurdles (r = 0.829,p< 0.01), discus throw (r = 0.580,p< 0.01), pole vault (r = 0.623,p< 0.01), and javelin throw (r = 0.385, p< 0.05), with a near-significant trend for the 1500 m run (r = 0.328,p= 0.071), reflecting the importance of lower-limb power and agility. Shot put scores had shown highly significant correlations with the high jump (r = 0.583,p< 0.01), 110 m hurdles (r = 0.651,p< 0.01), discus throw (r = 0.736,p< 0.01), pole vault (r = 0.699,p< 0.01), and javelin throw (r = 0.640, p< 0.01), indicating that strength-oriented events shared overlapping skill sets. Similarly, high jump scores were strongly associated with the 110 m hurdles (r = 0.745,p< 0.01), discus throw (r = 0.712,p< 0.01), pole vault (r = 0.790,p< 0.01), and javelin throw (r = 0.397, p< 0.05), suggesting a common neuromuscular profile that favors both vertical power and coordination. The 110 m hurdles stood out as a multidimensional event, significantly correlating with the long jump (r = 0.829,p< 0.01), shot put (r = 0.651,p< 0.01), high jump (r = 0.745,p< 0.01), 400 m sprint (r = 0.640,p< 0.01), discus throw (r = 0.675,p< 0.01), pole vault (r = 0.607,p< 0.01), and javelin throw (r = 0.433,p< 0.05), evidencing its reliance on speed, technique, and strength. Discus throw performance correlated significantly with the shot put (r = 0.736,p< 0.01), high jump (r = 0.712,p< 0.01), 110 m hurdles (r = 0.675,p< 0.01), pole vault (r = 0.805,p< 0.01), and javelin throw (r = 0.608,p< 0.01), reinforcing the technical strength nexus across field events. Pole vault scores demonstrated strong and significant associations with the high jump (r = 0.790,p< 0.01), discus throw (r = 0.805,p< 0.01), javelin throw (r = 0.546,p< 0.01), and shot put (r = 0.699,p<
hurdles (r = 0.675,p< 0.01), pole vault (r = 0.805,p< 0.01), and javelin throw (r = 0.608,p< 0.01), reinforcing the technical strength nexus across field events. Pole vault scores demonstrated strong and significant associations with the high jump (r = 0.790,p< 0.01), discus throw (r = 0.805,p< 0.01), javelin throw (r = 0.546,p< 0.01), and shot put (r = 0.699,p< 0.01), further highlighting this interconnected cluster. The javelin throw significantly correlated with the shot put (r = 0.640,p< 0.01), high jump (r = 0.397,p< 0.05), 110 m hurdles (r = 0.433,p< 0.05), discus throw (r = 0.608,p< 0.01), and pole vault (r = 0.546,p< 0.01), but showed no significant relationships with the sprints or the 1500 m run. Finally, the 1500 m run had limited associations, reaching statistical significance only with the 100 m sprint (r = 0.463,p< 0.01), whereas its correlations with other events remained weak or nonsignificant, pointing to the relative independence of aerobic endurance performance within the decathlon structure. Furthermore, the Pearson correlation coefficient between speed and jumps was notably high (r = 0.786,p< 0.01), suggesting that athletes with superior sprinting performance tended to excel in jumping events as well. Similarly, the correlation between jumps and launching/throwing was also substantial (r = 0.747,p< 0.01), reflecting a shared perfor- mance foundation rooted in lower-body power and explosive strength. The association between speed and launching/throwing, while slightly lower (r = 0.634,p< 0.01), remained moderately strong, underscoring the interdependence of neuromuscular and biomechanical attributes across these event clusters (Table). In Table, the genotypes and allelic frequencies for the entire sample are presented. The results show an equitable distribution of theRR(n = 16, 51.6%) andRX(n = 15, 48.4%) genotypes, with the absence of theXXgenotype (0%) and a consequent high allelic frequency for theRallele amongst the athletes (0.76). This study’s population adhered to the Hardy–Weinberg equilibrium (p= 0.758; q = 0.242;χ 2 = 3.119,p> 0.05).
Cells2025,14, 782 6 of 12 Table 4.Pearson correlation coefficient among scores in the decathlon events for the entire sample. Tests R. 100 m L.J S. Put H.J C. 400 m R.110 m w/H T. of Disc Pole Jump T. of the dart R. 1500 m 100 m race r 1 0.727 ** 0.375 * 0.526 ** 0.759 ** 0.625 ** 0.336 0.428 * 0.292 0.463 ** p 0 0.038 0.002 0 0 0.064 0.016 0.111 0.009 400 m race r 0.759 ** 0.651 ** 0.581 ** 0.583 ** 1 0.640 ** 0.489 ** 0.612 ** 0.500 ** 0.285 p 0 0 0.001 0.001 0 0.005 0 0.004 0.12 Long jump r 0.727 ** 1 0.558 ** 0.789 ** 0.651 ** 0.829 ** 0.580 ** 0.623 ** 0.385 * 0.328 p 0 0.001 0 0 0 0.001 0 0.033 0.071 Shot put r 0.375 * 0.558 ** 1 0.583 ** 0.581 ** 0.651 ** 0.736 ** 0.699 ** 0.640 ** 0.035 p 0.038 0.001 0.001 0.001 0 0 0 0 0.853 High jump r 0.526 ** 0.789 ** 0.583 ** 1 0.583 ** 0.745 ** 0.712 ** 0.790 ** 0.397 * 0.04 p 0.002 0 0.001 0.001 0 0 0 0.027 0.832 110 m race with ob. r 0.625 ** 0.829 ** 0.651 ** 0.745 ** 0.640 ** 1 0.675 ** 0.607 ** 0.433 * 0.309 p 0 0 0 0 0 0 0 0.015 0.09 Disc launch r 0.336 0.580 ** 0.736 ** 0.712 ** 0.489 ** 0.675 ** 1 0.805 ** 0.608 ** −0.155 p 0.064 0.001 0 0 0.005 0 0 0 0.406 Pole jump r 0.428 * 0.623 ** 0.699 ** 0.790 ** 0.612 ** 0.607 ** 0.805 ** 1 0.546 ** 0.037 p 0.016 0 0 0 0 0 0 0.001 0.842 Javelin throw r 0.292 0.385 * 0.640 ** 0.397 * 0.500 ** 0.433 * 0.608 ** 0.546 ** 1 −0.129 p 0.111 0.033 0 0.027 0.004 0.015 0 0.001 0.49 1500 m race r 0.463 ** 0.328 0.035 0.04 0.285 0.309 −0.155 0.037 −0.129 1 p 0.009 0.071 0.853 0.832 0.12 0.09 0.406 0.842
0 0 0 0 0.001 0.842 Javelin throw r 0.292 0.385 * 0.640 ** 0.397 * 0.500 ** 0.433 * 0.608 ** 0.546 ** 1 −0.129 p 0.111 0.033 0 0.027 0.004 0.015 0 0.001 0.49 1500 m race r 0.463 ** 0.328 0.035 0.04 0.285 0.309 −0.155 0.037 −0.129 1 p 0.009 0.071 0.853 0.832 0.12 0.09 0.406 0.842 0.49 r: correlation coefficient; R = race; L.J. = long jump; S. put = shot put; H.J. = high jump; w/H = with hurdle; T = throw. ** The correlation is significant at the 0.01 level (two-sided). * Correlation is significant at the 0.05 level (two-sided).
Description
The study explores genetic influences on decathlon performance.