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Original Research

Insulin-like Growth Factor Axis Genetic Score and Sports Excellence

Ben-Zaken, Sigal1; Meckel, Yoav1; Nemet, Dan2,3; Eliakim, Alon2,3

Author Information
Journal of Strength and Conditioning Research: September 2021 - Volume 35 - Issue 9 - p 2421-2426
doi: 10.1519/JSC.0000000000004102
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Abstract

Introduction

The identification of specific genes that may promote athletic excellence is currently extremely puzzling, mainly because the influence of each possible gene on overall heritability is relatively very small. Therefore, using single-nucleotide polymorphism (SNP) assessment, particularly of hormonal genes, as a potential tool to assist in sports selection and for the prediction of future athletic success, is still speculative.

Most previous reports of hormonal gene polymorphisms and athletic performance among professional athletes from a spectrum of sports types focused on variations in the IGF1 polymorphism. The Insulin-like growth factor (IGF) axis plays an important role in muscle growth, differentiation, and function (10,11,22). Several studies have suggested that IGF1 polymorphisms associated with higher circulating IGF1 levels may be advantageous for short-distance running and related to elite performance (3,18,21). Previous studies have also implicated that other polymorphisms that may affect circulating IGF1 levels, such as IGF2 (5), myostatin (MSTN) (7), and IGF-binding protein-3 (IGFBP3) (6), were more frequent among athletes participating in speed sporting events. Interestingly, and in contrast to runners, IGF polymorphisms were not associated with improved swimming performance (37).

Therefore, the aim of this study was to assess the combined prevalence of 6 previously reported polymorphisms related to the IGF axis (IGF1 rs7136446, IGF1 rs35767, IGF1 rs6220, IGF2 rs680, IGFBP3 rs2854744, and MSTN rs1805086) among elite Israeli runners and swimmers. Based on previous reports, we hypothesized that a higher combined genetic score would be found among short-distance runners compared with long-distance runners and that the genetic score of swimmers would be remarkably lower compared with runners.

Methods

Experimental Approach to the Problem

It is well established that excellency in sports require genetic predisposition (23,24). However, it is also known that sports excellence is a multifactorial phenomenon that results from the dynamic relations and interaction between environmental and genetic factors. Moreover, when trying to identify genetic polymorphisms associated with performance variability, one should also keep in mind that each genetic polymorphism makes only a small contribution to final success. Therefore, genetic score tools based on several genetic polymorphisms were developed to distinguish elite athletes (4,13,30,31,35). Most of these genetic scores are based on genetic polymorphisms in genes related to various physiological systems and were mainly aimed to distinguish between endurance and power athletes. In the current study, we propose a novel specific genetic score based on polymorphisms of 6 genes that are solely related to the IGF1 axis (IGF1 -C1245T rs35767, MSTN Lys153Arg 458A>G rs1805086, IGF1 T/C rs6220, IGF1 A/G rs7136446, IGFBP3 A/G rs2854744, and IGF2 A/G rs680, detailed in Table 2). These polymorphisms affect circulating IGF1 levels and were previously reported to be associated with an advantage for speed running events. Although one may argue that muscle IGF-I is more important for athletic performance than circulating IGF-I, previous studies have shown that circulating IGF-I correlates with fitness as well (14). The studied genetic polymorphisms were associated with circulating IGF1. The IGF genetic score was compared between national-level (winners of local championships) and top-level track and field athletes (winners at international competitions, World and European Championships, and the Olympic Games). Moreover, because in contrast to runners, IGF polymorphisms were not associated with improved swimming performance, we compared the prevalence of high IGF1 genetic score between runners and swimmers.

Subjects

A total of 161 track and field athletes (123 men and 38 women, age 17–50 years) and 94 swimmers (61 men and 33 women, age 16–49 years) participated in the study. Track and field athletes were assigned to 2 main groups according to their main event specialty during their athletic career as follows: 63 short-distance runners (SDRs, major event: 100–200-m sprints and jumps, age 28.0 ± 9.6, 17–50 years mean ± SD) and 98 long-distance runners (LDRs, major event: 5,000 m and marathon, age 29.7 ± 9.1, 17–50 years mean ± SD). Swimmers were also assigned to 2 groups according to their main swimming event during their swimming career as follows: 44 short-distance swimmers (SDSs, major event: 50–100-m swim, age 23.2 ± 7.4, 16–49 years; mean ± SD) and 50 long-distance swimmers (LDSs, major event: 400–1,500 m, age 23.9 ± 8.7, 16–48 years; mean ± SD). Athletes were recruited through their swimming and track and field clubs and by the Israeli national team coaching staff. All athletes were ranked among the top Israeli results in their event and had competed in national-level and international-level meets on a regular basis. Sixty-eight track and field athletes (39 LDRs and 29 SDRs) and 30 swimmers (20 LDSs and 10 SDSs) were classified as top-level athletes (subjects and winners at international competitions, World and European Championships, and the Olympic Games).

The control group consisted of 78 (51 men and 27 women, age 25.7 ± 3.7, 20–36 years; mean ± SD) nonathletic healthy individuals who were not engaged in competitive sports.

The study was approved by the Institutional Review Board of the Hillel Yaffe Medical Center, Hadera, Israel, according to the Declaration of Helsinki. A signed informed consent document was obtained from all subjects. Written parental consent was obtained from all subjects under the age of 18.

Procedures

Genotyping

Genomic DNA was extracted from peripheral ethylenediaminetetraacetic acid-treated anticoagulated blood using a standard protocol. Genotype analyses were performed, as described below, in the Genetics and Molecular Biology Laboratory of the Academic College of Physical Education and Sports Sciences at the Wingate Institute. To ensure proper internal control for each genotype analysis, we used positive and negative controls from different DNA aliquots that had been previously genotyped by the same method, according to recent recommendations for replicating genotype-phenotype association studies (9).

Genotypes were determined using the TaqMan allelic discrimination assay. The Assay-by-Design service (https://www.thermofisher.com/il/en/home.html) was used to setup a TaqMan allelic discrimination assay for the ACSL (rs6552828 A/G). Primers and probe sequences are provided in Table 1.

Table 1 - Primers and probe sequences for TaqMan allelic discrimination assays.
Primer sequences Probe sequences
Forward Reverse Forward: VIC Reverse: FAM
IGF1 A/G (rs7136446)
 AATTGGTTACCTGCTACATTGA GGAGTTAACGCATCTCCTTACTG CGCGTAGTCGAGCG CGCTCGCTGCCCTAAGTGCT
IGFBP3 A/G (rs2854744)
 GGTTCTTGTAGACGACAAGG GTGCAGCTCGAGACTCGCC TCCTCGTGCGCACG CTCGTGCTCACGCC
IGF2 A/G (rs680)
 TGAGTCCCTGAACCAGCAAAG GACGTGCCCACCTGTGAT AGAAAAGAAGGACCCCAGAA AAAAGAAGGGCCCCAGAA
IGF1 -C1245T (rs35767)
 GGATTTCAAGCAGAACTGTGTTTTCA GGTGGAAATAACCTGGACCTTGAAT TTTTTCCGCATGACTCT TTTTTTTTCCACATGCTCT
MSTN Lys(K)-153Arg(R) (rs1805086)
 GAAAACCCAAATGTTGCTTC TGTCTAGCTTATGAG CTTAGGG ATATCCATAGTTGGCCC TTTACTACTTTATTGTATTGTATTTT
IGF1 T/C (rs6220)
 AACAAAGAGATTTCTACCAGTGAAAGG GCCTAGAAAAGAAGGAATCATTGT AGTAAAACCTTGTTT AATAC AGTAAAACCTCGTTT AATA

The polymerase chain reaction (PCR) mixture included 5 ng of genomic DNA, 0.125 μL of TaqMan assay (40*; Applied Biosystems, Inc., Foster City, CA), 2.5 μL of Master mix (Applied Biosystems, Inc.), and 2.375 μL of water. PCR was performed in 96-well PCR plates in an ABI 7300 PCR system (Applied Biosystems, Inc.) and consisted of initial denaturation for 5 minutes at 95° C and 40 cycles with denaturation of 15 seconds at 95° C, as well as annealing and extension for 60 seconds at 63° C. The results were analyzed by the ABI TaqMan 7900HT using the sequence detection system 2.22 software (Applied Biosystems, Inc.).

Genetic Score

Genetic scores have been previously used for genetic differentiation between elite power and endurance athletes (4,30,31,35). We computed the combined influence of 6 IGF axis polymorphisms (IGF-GS) according to a model we had previously used (4). First, we scored each genotype within each polymorphism. We assumed an additive model (equaling 0, 1, or 2), based on the number of alleles associated with sports excellence that was carried by each subject for each polymorphism. Thus, we assigned a genotype score (GS) of 2, 1, or 0 to each individual genotype, theoretically associated with the high, moderate, or low potential for sports excellence, respectively. All studied polymorphisms are listed in Table 2. All genetic polymorphisms did not differ by age or sex. A univariate analysis of variance (ANOVA) did not reveal a significant age difference among tested genetic polymorphisms (F(2,334) = 1.657, p = 0.192 for IGF2 rs680; F(2,334) = 1.241, p = 0.290 for IGFBP3 rs2854744; F(2,334) = 1.385, p = 0.252 for IGF1 rs7136446; F(2,334) = 0.399, p = 0.971 for IGF1 rs6220; F(2,334) = 0.369, p = 0.692 for MSTN rs1805086; F(2,334) = 0.669, p = 0.513 for IGF1 rs35767).

Table 2 - Genetic scoring of IGF1 axis genetic polymorphisms.*
Symbol Polymorphism MAF (%) Genetic score: “for the various genotypes”
IGF1 A/G (rs7136446) 28 AA = 0, AG = 1, GG = 2
-C1245T (rs35767) 30 CC = 0, CT = 1, TT = 2
T/C (rs6220) 36 TT = 0, TC = 1, CC = 2
IGF2 A/G (rs680) 30 AA = 0, AG = 1, GG = 2
IGFBP3 A/G (rs2854744) 47 AA = 0, AG = 1, GG = 2
MSTN Lys(K)-153Arg(R) (rs1805086) 6 AA = 0, AG = 1, GG = 2
*MAF = Minor Allele Frequency.

Chi-square analysis did not reveal significant genotype frequency differences by sex for the tested polymorphisms (χ2(2) = 1.676, p = 0.433 for IGF2 rs680; χ2(2) = 0.320, p = 0.852 for IGFBP3 rs2854744; χ2(2) = 4.075, p = 0.130 for IGF1 rs7136446; χ2(2) = 1.285, p = 0.526 for IGF1 rs6220; χ2(2) = 0.312, p = 0.856 for MSTN rs1805086; χ2(2) = 2.510, p = 0.285 for IGF1 rs35767).

Insulin-like growth factor-GS was calculated by computing the combined influence of 6 IGF axis polymorphisms (IGF1 -C1245T rs35767, MSTN Lys153Arg 458A>G rs1805086, IGF1 T/C rs6220, IGF1 A/G rs7136446, IGFBP3 A/G rs2854744, and IGF2 A/G rs680 detailed in Table 2). Insulin-like growth factor-GS is the Euclidean distance from the perfect genetic score for these polymorphisms. Thus, IGF-GS = SQRT ([2-GS-IGF1 -C1245T]2 + [2-GS- MSTN Lys153Arg]2 + [2-GS- IGF1 T/C]2 + [2-GS- IGF1 A/G]2 + [2-GS- IGFBP3 A/G]2 + [2-GS- IGF2 A/G]2). The IGF-GS was transformed into a 0–100 scale for easier interpretation as follows: IGF-GS = 100 − (100/24) × {SQRT ([2-GS_IGF1 -C1245T]2 + [2-GS- MSTN Lys153Arg]2 + [2-GS- IGF1 T/C]2 + [2-GS- IGF1 A/G]2 + [2-GS- IGFBP3 A/G]2 + [2-GS-IGF2 A/G]2)}, where 24 is the result of multiplying 6 (the number of studied polymorphisms) by 4, which is the score given to the “worst” genotype. An IGF-GS of 100 represents an “optimal” genetic profile, that is, all GSs are 2. By contrast, an IGF-GS of 0 represents the “worst” possible profile for sports excellence, that is, all GSs are 0.

Statistical Analyses

The SPSS statistical package, version 25.0, was used to perform all the statistical procedures (SPSS, Chicago, IL). Levene's tests of homogeneity of variance were conducted on the IGF-GS to test variance equality among groups (control, LDR, SDR, LDS, and SDS). To test the differences in IGF-GS between sports types and controls, 1-way ANOVA was performed, with the sports type (control, LDR, SDR, LDS, and SDS) considered as independent variable and IGF-GS as dependent variable. To test the differences in IGF-GS between sports types and competition level, 2-way ANOVA was performed with sports type (LDR, SDR, LDS, and SDS) and competition level (top and national) as independent fixed factors and IGF-GS as dependent variable. To further explore the differences in IGF-GS between subgroups, we used an unpaired Student's t-test to compare IGF-GS between top-level and national-level athletes in each group (SDR, LDR, SDS, and LDS) and between top-level athletes from different groups. Data are shown as mean ± SD. Statistical significance was set at p value <0.05. We also evaluated the ability of IGF-GS to distinguish top-level athletes from national-level athletes by receiver operating characteristic (ROC) curves (38). We calculated the area under the ROC curve (AUC) and 95% confidence intervals (CIs). We used binary logistic regression to assess the odds ratio (OR) of athletic status (Figure 1).

Figure 1.
Figure 1.:
IGF-GS among runners (A) and swimmers (B). Black circles represent short-distance events and gray squares represent long-distance events. A) Top-level SDRs exhibit high IGF-GS compared with national-level SDRs, whereas no difference in IGF-GS between top-level and national-level LDRs. B) Top-level LDSs exhibit high IGF-GS compared with national-level LDSs (nonsignificant), whereas no difference in IGF-GS between top-level and national-level SDSs. Top-level SDRs exhibit high IGF-GS compared with top-level SDSs. No significant difference in IGF-GS was found between top-level LDRs and LDSs. IGF-GS = IGF genetic score; SDR = short-distance runner; LDR = long-distance runner; LDS = long-distance swimmer; and SDS = short-distance swimmer.

Results

The subjects' mean IGF-GS is presented in Table 3. Levene's tests of homogeneity of variance conducted on IGF-GS revealed nonsignificant (p > 0.05) variance differences among groups. A follow-up 1-way ANOVA revealed no significant differences in IGF-GS among sports type and controls (F(4,328) = 0.877, p = 0.478). Significant difference in IGF-GS was found among competition levels (F(2,330) = 4.044, p = 0.018). Top-level athletes exhibited high IGF-GS (26.6 + 12.0) compared with national-level athletes (22.6 + 11.8, p = 0.008) and controls (22.5 + 11.9, p = 0.024).

Table 3 - IGF-GS among runners and swimmers (mean ± SD).*
Top National Total p
Score n Score n Score n
Controls 22.5 ± 11.9 78
Runners
 LDR 24.8 ± 10.5 39 23.0 ± 12.1 59 23.7 ± 11.4 98
 SDR 30.8 ± 11.9 29 20.5 ± 11.5 34 25.2 ± 12.7 63 §
Swimmers
 LDS 27.5 ± 14.6 20 23.9 ± 11.9 30 25.3 ± 13.1 50
 SDS 19.9 ± 8.5 10 22.7 ± 11.6 34 22.1 ± 10.9 44
*IGF-GS = IGF genetic score; SDR = short-distance runner; LDR = long-distance runner; LDS = long-distance swimmer; SDS = short-distance swimmer.
p = 0.003, top-level SDRs vs. controls.
p = 0.001, top-level vs. national-level SDRs.
§p = 0.022 top-level SDRs vs. top-level SDSs.

Two-way ANOVA for IGF-GS by sports type and competition level reveals significant main effect for competition level, Wilks' λ = 0.989, F(1,331) = 3.71, p = 0.05, ηp2 = 0.011 and significant interactional effect of sports by competition level, Wilks' λ = 0.976, F(3,329) = 2.617, p = 0.05, ηp2 = 0.024. The significant interactional effect suggests a different trend for the IGF-GS differences between top-level and national-level athletes and swimmers. While top-level SDRs exhibit high IGF-GS compared with national-level SDRs (30.8 + 11.9 vs. 20.5 + 11.5 for top-level and national-level SDRs, respectively), top-level LDRs exhibit similar IGF-GS compared with national-level LDRs (24.8 + 10.5 vs. 23.0 + 12.0 for top-level and national-level LDRs, respectively). On the other hand, while top-level SDSs exhibit similar IGF-GS compared with national-level SDSs (19.9 ± 8.5 vs. 22.7 ± 11.6 for top-level and national-level SDRs, respectively), top-level LDSs exhibit high IGF-GS compared with national-level LDSs (27.5 ± 14.6 vs. 23.9 + 11.9 for top-level and national-level LDSs, respectively).

Post hoc t-test analysis revealed that top-level SDR's mean IGF-GS (30.8 ± 11.9) was significantly higher compared with controls (22.5 ± 11.9), national-level SDRs (20.5 ± 11.5), and top-level SDSs (19.9 ± 8.5) (p = 0.003, 0.001, and 0.006, respectively).

Receiver operating characteristic analysis showed a significant discriminating accuracy of IGF-GS (AUC = 0.275; 95% CI: 0.154–0.397; p < 0.005; sensitivity = 0.324, 1-specificity = 0.733) in identifying top-level SDRs compared with national-level SDRs at the values of 25. Subjects with IGF-GS above 25 had an increased OR of being top-level SDRs (OR: 4.2; 95% CI: 0.68–26.09; p < 0.001). No significant discriminating accuracy of IGF-GS between top-level and national-level athletes was found for LDRs and swimmers.

Discussion

This study assessed the combined prevalence of 6 polymorphisms related to the IGF axis among elite Israeli runners and swimmers. Consistent with our hypothesis, top-level short-distance runners had a significantly higher IGF-GS compared with national-level short-distance runners and short-distance swimmers as presented in Figure 1.

Our IGF axis genetic panel included 6 polymorphisms that were found to affect circulating levels of IGF1 and were previously related to sports performance and excellence. For example, the C allele of IGF1 T/C (rs6220) polymorphism is associated with increased levels of circulating IGF1 (34). The IGF1 C1245T polymorphism (rs35767), which is also related to increased levels of circulating IGF1 (8,17,26,28,29,33), was found to be associated with sports excellence (3). A greater frequency of the TT genotype was demonstrated among Israeli athletes (4.8%) compared with controls (3). Interestingly, however, although TT polymorphism carriers were both endurance and power athletes, the endurance athletes were of national level and the power athletes were international and Olympic top-level athletes. These results suggest that the IGF1 T/T polymorphism is of greater benefit to power sports performance and excellence.

In addition, in relation to the G allele of another IGF1 gene polymorphism (rs7136446), which is associated with increased circulating IGF1 levels (34), stronger muscular performance (19), and speed sports performance (1), it was shown that carrying the GG genotype was significantly greater among sprinters compared with weightlifters (2). This supports the perception that among typical power sports, the IGF1 polymorphism is more important for speed sports events rather than strength events.

IGF2 (rs680) GG genotype frequency was significantly greater among sprinters compared with weightlifters (5), suggesting as well that carrying this IGF2 polymorphism may be beneficial, mainly for speed but not for strength sports. Interestingly, previous studies demonstrated that circulating IGF2 concentrations were lower among individuals homozygous for the G allele (27) and that higher levels of plasma IGF1 levels were found in individuals carrying the GG rs680 genotype (16). This may indicate that the possible beneficial influence of IGF2 rs680 polymorphism on speed performance is not necessarily mediated through its effect on circulating IGF2 but rather through its effect on IGF1 levels.

The MSTN gene is expressed almost exclusively in skeletal muscle cells and functions as a negative regulator of muscle growth, at least partially through its inhibitory effect on circulating IGF1 levels. The MSTN Lys(K)-153Arg(R) polymorphism (rs1805086) affects skeletal muscle phenotype (32) with mutant R allele frequency (associated with greater IGF function) of 3–4% and mutant homozygocity (RR) below 1% among Caucasians (12,15,20). Carrying the MSTN rare R allele was associated with greater muscle mass and strength (20) and with elite compared with national-level long-distance and short-distance runners, suggesting the possible importance of this polymorphism for excellence in both sprints and long-distance running (7).

The IGFBP3 promoter region A-202C polymorphism (rs1805086) can influence circulating IGFBP3 levels with the C allele expressing significantly lower activity compared with the A allele, resulting in lower circulating IGFBP3 levels (29,36). IGFBP3 levels are regulated by GH and serve as a reservoir of IGF1, and therefore, IGFBP3 low levels may reflect lower IGF1 activity. On the other hand, lower circulating IGFBP3 levels alter the stability of the IGF1-IGFBP3-ALS complex and, therefore, may increase free IGF1 levels—the actual bioactive growth factor. Thus, the genetic effect on the IGF axis does not necessarily operate through a direct effect on IGF1 but instead indirectly by modifying its binding proteins. Previous studies have demonstrated higher maximal oxygen consumption among CC genotype carrier athletes (18).

The main finding of this study is that a significantly higher IGF-GS was found among top-level short-distance runners compared with national-level short-distance runners and short-distance swimmers. This result strengthens the current notion that IGF-related gene polymorphisms may be important for speed rather than for endurance or even strength sports events. Interestingly and more importantly, we demonstrated that a short-distance runner with IGF-GS greater than 25 had a 4.2 times higher likelihood of being a top-level sprinter and not “only” a national-level sprinter. Whether the IGF axis genetic score may or should be used for the selection of sprinters in early stages of their athletic career needs to be further investigated.

Consistent with previous reports (37), and with our second hypothesis, the IGF-GS was low among short-distance swimmers. This may possibly indicate that the insulin-like growth factor system is less significant for top-level short-distance swimming performance. Interestingly, however, IGF-GS was significantly higher among top-level long-distance swimmers compared with national-level long-distance swimmers. Although the mechanism for the difference between runners and swimmers is yet to be determined, it is possible that excellence in swimming relies mostly on anthropometric features of the swimmer (in particular limb length) and on his/her swimming technical skills (25). This may mask physiologic and metabolic variations and even genetic polymorphism differences related to these capabilities; therefore, enabling tall swimmers with a greater arm span and better technical expertise to excel in most swimming events. The finding of higher IGF-GS among top-level long-distance swimmers may raise the possibility that genetic speed characteristics are required (at least to some extent) to distinguish between top-level to national-level long-distance swimmers.

The study has several limitations. First, although the sample size is relatively small for genetic studies, our study population of elite short-distance and long-distance runners and swimmers is unique and reflect (by definition) only a very small portion of the general population. Second, we included 6 polymorphisms that were previously reported to be related with sports performance. It is possible that other genes may affect sports selection and excellence as well. Third, genetic scores demonstrate the additive effect of the polymorphisms selected and does not indicate the relative importance of each specific gene. Fourth, we were unable to collect anthropometric measures such as muscle and fat mass of the subjects. Fifth, the age range of the athletes was larger than the age range of the controls. However, the association between IGF axis genetic score and the subject's athletic status relates to their performance during their competitive age, which is similar to the age ranges of the control subjects, and not to their current age. Finally, we selected polymorphisms that mediate circulating IGF1 levels. It is believed that many of the IGF-I effects on athletic performance are mediated by local muscular rather than systemic effects. In addition to the direct effect of IGF1 on muscle mass and muscle performance, local IGF plays important role in exercise-associated muscle damage repair and as a result may improve the recovery process and trainability. We are not aware of current studies on the effects of IGF polymorphisms on local muscle IGF isoforms activity.

In summary, the combined assessment of 6 SNPs, all known to modulate circulation IGF1 levels, was associated with a higher genetic score among top-level short-distance runners, emphasizing and strengthening once more the possible importance of the IGF system to land speed sports events and not to swimming speed events. Whether the evaluation of this IGF genetic score can be used for sports selection in young athletes or whether a multipotent athlete who wants to develop a competitive career and carries a high IGF axis genetic score (i.e., >25) should prefer sprint running over swimming or even over other power sports events is currently speculative and needs to be further studied. Moreover, it should be always kept in mind that while a favorable genetic predisposition is essential, environmental and psychological characteristics, including training equipment and facilities, adequate nutrition, familial support, persistence, and adaptive and motivational features, are crucial for elite-level sports success as well.

Practical Applications

Despite seemingly similar physiological and metabolic characteristics, running and swimming may carry different genetic polymorphisms. Specific IGF1 polymorphisms may be advantageous mainly for land (and not water) speed sports events. A specific IGF axis genetic score may distinguish top-level short-distance runners from national-level short-distance runners. While the practical use of genetic testing in sports is very limited, it is possible that at some point in the (near?) future, assessment of genetic polymorphisms and genetic scores would be used as part of the complete toolbox assisting athletes and their coaching stuff in both talent identification and orientation.

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Keywords:

IGF axis; genetic score; sports performance

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