PhD, Associate Professor M.O. Aksenov¹
PhD A.B. Ilyin^2
1Buryat State University, Ulan-Ude
²Russian State University of Physical Education, Sport, Youth and Tourism (GTSOLIFK), Moscow

Keywords: genotype, extensity, intensity, training, weightlifting sports, macro-cycle, load, training process efficiency, training effectiveness.

Introduction. Previous studies [1; 4; 5; 7] on the whole demonstrate that people do not choose the most suitable sport for themselves. This is partly due to the fact that each individual starts training sessions having a certain potential. Some body structure features and body functions are beyond human control. In other words, people are limited to their genetic potential. The ratio of types I and II of muscle fibers limits the possibility of hypertrophy and determines the rates of speed and endurance. Sex determines the functioning of the endocrine system imposing an additional framework on the hypertrophy, and therefore, on strength increase. Age limits muscle mass and speed of nervous processes, which generally limits not only the magnitude of the built up efforts, but also the speed of movements. The coach is unable to create a program for an athlete to move beyond genetically predetermined boundaries of his/her capabilities. However, the study of athletes’ genetic capabilities in certain sports can make it possible to consider individual genetic capabilities and significantly improve fitness indices [5, p. 113].

High achievements in professional sport are 80% dependent on coordinated functioning of the neuromotor system and biological energy, that is, to achieve certain results it is necessary to have inborn traits that are the key to success, but they need to be developed via trainings and science-based programs; at the same time it is necessary to be constantly supervised in order not to get injured accidentally and also to keep records of individual progress. Therefore, all coaches of today use technologies backed up by pedagogical, physiological and biological data. 

Objective of the study was to design and substantiate experimentally the theoretical and methodological foundations for creating a macrostructure of the training process of weightlifters in view of their genetically predisposed training potential.

Methods and structure of the study. In total 178 study group athletes and 365 reference group athletes were under study. The study group (mean age 23.0±6.5 years, 81% males, 19% females) was represented by Masters of Sport (over 88%), International Class Athletes and Honoured Masters of Sport (over 7%); as well as Candidate Masters of Sport and athletes with categories (not more than 5%).  

A computer program was designed to analyze the training process in weightlifting sports. Its target was to spare coaches and athletes the routine work associated with the training load calculations while planning and analyzing the training process. The computer program was registered with Rospatent [6].

Genotypes for three gene polymorphisms were determined: ACE (rs4646994), ACTN3 (rs1815739) and PPARGC1A (rs8192678). The same polymorphisms were identified in the reference group.

Results and discussion. We explored associations of ACE, ACTN3 and PPARGC1A genes with the volumes of specific training loads within the training process macrostructure of elite weightlifters. The following results were obtained (Table 1).

Table 1. Association between ACE gene I/D genotypes and training load parameters within the macro-cycle of elite weightlifters

As seen in Table 1, the reference group weightlifters with the ACE II genotype perform on average =51,352±5,135.12 barbell lifts in the macro-cycle. Athletes with the II genotype are characterized by a low level of adaptation to training volumes. Their macro-cycle load volumes are the largest in comparison with the training volumes of athletes with the heterozygous and DD genotypes. ACE gene contribution was 4.08%.

Table 2 presents data on the association of the ACTN3 gene with the training loads parameters within macro-cycles in weightlifting (Table 2).

Table 2. Association between ACTN3 (R/X) genotypes and training loads parameters within elite weightlifters' macro-cycle

The study of the ACTN3 RX genotype revealed that the average extensity index in the macro-cycle was =18,199.8±909.99 barbell lifts on average. This indicator is minimal in the micro-cycle in comparison with the monozygotic genotypes of the gene, thus a conclusion can be made that elite weightlifters with the RX genotype are the best in terms of training from the optimal load point of view. ACTN3 gene contribution was 3.24%.

Table 3 presents data of the study of the PPARGC1A genotypes with the training loads characteristics in the training macro-cycles of athletes engaged in weightlifting sports (Table 3).

Table 3. Association of PPARGC1A G/S genotypes with training load characteristics in macro-cycles of elite weightlifters

As seen from Table 3 the training load volumes are related to the PPARGC1A SS genotype; in particular, athletes engaged in weightlifting spots with the SS genotype were proved to maintain a high level of sports achievements with small amounts of training in the meso- and macrostructure of the training process. The PPARGC1A gene contribution was 7.34%.

Conclusion. The continuous, over 20 years long, experimental study of representative samples of over 1,000 athletes and experimental analysis make it possible to assert the existence of two types of athletes in weightlifting sports – fast trained and slowly trained. Based on that we developed an educational model of the weightlifting training process design customized to genetic predispositions to training. The genotypes to be considered the genetic markers for fast training capability in weightlifting sports based on the study results are as follows: ID ACE, RX ACTN3 and SS PPARGC1A.

Practical recommendations. The experiments showed that high training loads should not be planned in the training macro-cycles for weightlifters with ID ACE, RX ACTN3 and SS PPARGC1A genotypes. Their annual training volumes should not exceed 19,000 barbell (weights) lifts. 

References

1. Aksenov M.O. Assotsiatsiya gena ACTN3 s pokazatelyami bioimpedansnogo analiza vysokokvalifitsirovannykh sportsmenov tyazheloatleticheskikh vidov sporta [ACTN3 gene association with indicators of bioelectrical impedance analysis of elite athletes in weightlifting sports]. Teoriya i praktika fiz. kultury, 2016, no. 2, p. 80.
2. Aksenov M.O., Aksenova A.V. Postroenie trenirovochnogo protsessa sportsmenov tyazheloatleticheskikh vidov sporta na osnove dannykh bioimpedansnogo analiza [Weight lifter training process organization based on bioimpedance analysis data]. Teoriya i praktika fiz. kultury, 2015, no. 12, pp. 74–76.
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6. Patent RF # 2016610865, 21.01.2016. Aksenov M.O. Kompyuternaya programma «Sport 3.0». Rospatent # 2016610865, 2016 [Patent of the RF No. 2016610865, January 21, 2016. Aksenov M.O. Sport 3.0 software. Rospatent No. 2016610865, 2016].
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10. Gineviciene V., Jakaitiene A., Aksenov M.O., Aksenova A.V., Druzhevskaya A.M., Astratenkova I.V., Egorova E.S., Gabdrakhmanova L.J., Tubelis L., Kucinskas V., Utkus A. Association analysis of ACE, ACTN3 and PPARGC1А gene polymorphisms in two cohorts of European strength and power athletes. Biology of Sport, vol. 33, no. 3, pp. 199–206.
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The work was supported by a grant from Buryat State University in 2017.

Corresponding author: 6730@mail.ru

Abstract

Objective of the study was to provide theoretical grounds for the training and competitive loads to be optimized within the frame of the long-term training process design in weightlifting sports in view of the athletes' individual genetic predispositions. We explored associations of the ACE, ACTN3 and PPARGC1A gene polymorphisms with the quantitative and qualitative parameters of the training process in weightlifting sports. Our analysis of the allele frequency for ACE, ACTN3 and PPARGC1A genes made it possible to expand our knowledge of the types of bodily adaptation to training loads. We found the genetic effects being significant for the sport mastery progress rates. We assumed the genetic factors being of effect on the individual competitive success forecasts within the frame of the long-term training process in weightlifting sports. We developed and implemented new methods designed to analyze and plan training loads versus relevant qualitative and quantitative progress parameters. New training and competitive load rating algorithms were offered for the weightlifting sports. We offered theoretical grounds for the training process design to facilitate the efforts to develop and implement new training methods to attain goals of the training and competitive process as required by the physical fitness and sport mastery improvement mission. We used the experimental data to develop the training and competitive load integrated rating method. The proposed training and competitive load rating algorithm makes it possible to clearly and simply rate the training process efficiency with the training loads being harmonized with the actual athletes’ training capacity levels in every physical training domain. The study found the ACE, ACTN3 and PPARGC1A genotypes in Masters of Sport and World Class Athletes in weightlifting, and the finding makes it possible to customize athletes' training loads by variations of the qualitative and quantitative parameters of the training loads. The study findings were proved beneficial for the training process design in the weightlifting sports.