Genes and personalized training

Original Paper Genes and personalized training DOI: 10.5604/20831862.1198210 Biol. Sport 2016;33:117-126 A genetic-based algorithm for personalized resistance training AUTHORS: Jones N1, Kiely J2, Suraci B3, Collins DJ2, de Lorenzo D4,5, Pickering C6, Grimaldi KA6 1 DNA Sports Performance Ltd, Manchester, UK Institute of Coaching and Performance, University of Central Lancashire, Preston, UK 3 Suraci Consultancy, Portsmouth, UK 4 Departament de Ciències Experimentals i de la Salut, Universitat Pompeu Fabra, CEXS-UPF-PRBB, Barcelona, Catalonia, Spain 5 Centro de Estudios en Genómica y Nutrición-CESGEN, Parc Cientíic i Tecnològic Agroalimentari de LleidaPCiTAL, Lleida, Catalonia, Spain 6 Exercise and Nutritional Genomics Research Centre, DNAFit Ltd, London, UK 2 ABSTRACT: Association studies have identiied dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilizing the idea of personalised training based on athlete’s genetic proile have been conducted. Here we propose an algorithm that allows achieving greater results in response to high- or low-intensity resistance training programs by predicting athlete’s potential for the development of power and endurance qualities with the panel of 15 performance-associated gene polymorphisms. To develop and validate such an algorithm we performed two studies in independent cohorts of male athletes (study 1: athletes from different sports (n=28); study 2: soccer players (n=39)). In both studies athletes completed an eight-week high- or low-intensity resistance training program, which either matched or mismatched their individual genotype. Two variables of explosive power and aerobic itness, as measured by the countermovement jump (CMJ) and aerobic 3-min cycle test (Aero3) were assessed pre and post 8 weeks of resistance training. In study 1, the athletes from the matched groups (i.e. high-intensity trained with power genotype or low-intensity trained with endurance genotype) signiicantly increased results in CMJ (P=0.0005) and Aero3 (P=0.0004). Whereas, athletes from the mismatched group (i.e. high-intensity trained with endurance genotype or lowintensity trained with power genotype) demonstrated non-signiicant improvements in CMJ (P=0.175) and less prominent results in Aero3 (P=0.0134). In study 2, soccer players from the matched group also demonstrated signiicantly greater (P<0.0001) performance changes in both tests compared to the mismatched group. Among non- or low responders of both studies, 82% of athletes (both for CMJ and Aero3) were from the mismatched group (P<0.0001). Our results indicate that matching the individual’s genotype with the appropriate training modality leads to more effective resistance training. The developed algorithm may be used to guide individualised resistance-training interventions. CITATION: Jones N, Kiely J, Suraci B et al. A genetic-based algorithm for personalized resistance training. Biol Sport. 2016;33(2):117–126. Received: 2016-02-29; Reviewed: 2016-03-06; Re-submitted: 2016-03-07; Accepted: 2016-03-08; Published: 2016-04-01. Corresponding author: Nicholas Jones DNA Sports Performance Ltd, Manchester, UK E-mail: nicholasjones@ dna-sports-performance.com Key words: DNA Polymorphism Genotype Personalized training Power Endurance INTRODUCTION mum (1 RM)), volume (total number of sets and repetitions), training frequency, muscle action (concentric vs. eccentric), rest intervals cluding power, strength and endurance events [1, 2]. When prop- between sets, repetition velocity and others [3, 4]. Furthermore, erly performed and combined with adequate nutrition, resistance resistance training can be categorized into two common types: low- training leads to increases in strength, power, speed, muscle size, intensity (~30% of 1 RM and high repetitions) and high-intensity local muscular endurance, coordination, and lexibility and reductions (~70% of 1 RM and low repetitions) resistance training. Low-intensity resistance training is effective for increasing absolute local in body fat and blood pressure [3]. Effective resistance exercise prescription involves manipulation muscular endurance [5], explosive power [6, 7] and preferential of several variables speciic to the targeted goals, such as intensity hypertrophy of slow-twitch muscle ibres [8, 9], while high-intensi- or load per repetition (i.e. percentage of one repetition maxi- ty training (also known as classic strength training) leads to in- - - and athletic potential/capacity across many sporting disciplines in- - - - Resistance exercise training is widely used to enhance general itness Biology of Sport, Vol. 33 No2, 2016 117 Jones N et al. creases in absolute strength [3] and the hypertrophy of all types of each individual will be dependent on the interaction between spe- muscle ibres [10, 11]. ciic training performed and genotype. Indeed, there is a general There is a large variability in both muscle size and strength gains consensus that resistance training programs should be individualized, in response to resistance training between individuals [4]. In a large but little information exists to accurately discern how best to person- study of 585 subjects, Hubal et al. [12] have shown that men and alize training program design to maximize outcomes [3, 4, 12, 13]. women exhibited wide ranges of strength gain (1 RM: 0 to +250%) Muscle iber composition is a heritable (~45%) trait [14], with and skeletal muscle hypertrophy (cross-sectional area: -2 to +59%) large variability between individuals. For example, slow-twitch (Type I) in response to 12 weeks of resistance training, indicating individual content of vastus lateralis ranges from 5-90%. This variability, in training responses may vary widely dependent on factors such as turn, may determine individual’s potential to perform different types genetic heritage. Accordingly, the level of adaptation experienced by of resistance training. Accordingly, data show that Type I muscle TABLE 1. List of genetic variants analysed by DNAFit Peak Performance Algorithm™ Gene Full name Functions and associated phenotypes Polymorphism Endurance or power related allele References ACE Angiotensin I converting enzyme Regulates circulatory homeostasis through the synthesis of vasoconstrictor angiotensin II and the degradation of vasodilator kinins. Alu I/D (rs4646994) Endurance: I Power: D [20, 21] ACTN3 α-actinin-3 Stabilizes the muscle contractile apparatus in fast-twitch muscle ibres. Arg577Ter (rs1815739 C/T) Endurance: 577Ter (T) Power: Arg577 (C) [20, 22] ADRB2 β-2 adrenoreceptor Plays a pivotal role in the regulation of the cardiac, pulmonary, vascular, endocrine and central nervous system. Gly16Arg (rs1042713 G/A) Endurance: 16Arg (A) [23, 24] Gln27Glu (rs1042714 C/G) Endurance: Gln27 (C) [25] Angiotensinogen Angiotensinogen is an essential component of the renin-angiotensin system that regulates vascular resistance and sodium homeostasis, and thus determining blood pressure. Met235Thr (rs699 T/C) Power: 235Thr (C) [26, 27] BDKRB2 Bradykinin receptor B2 Involved in the endothelium-dependent vasodilation. rs1799722 C/T Endurance: T [24] COL5A1 Collagen, type V, α1 Encodes the pro-α1 chain of type V collagen, the rate-limiting component of the of type V collagen trimer assembly. rs12722 C/T (BstUI) Endurance: T [28, 29] CRP C-reactive protein, pentraxin-related Involved in several host defense related functions based on its ability to recognize damaged cells and to initiate their elimination in the blood. rs1205 A/G Endurance: A [30, 31] GABPB1 (NRF2) GA binding protein transcription factor, β subunit 1 (nuclear respiratory factor 2) Encodes a transcriptional regulator of genes involved in activation of cytochrome oxidase expression and nuclear control of mitochondrial function. rs7181866 A/G Endurance: G [32, 33] IL6 Interleukin-6 IL-6 is a pleiotropic cytokine expressed in immune and muscle cells. Involved in a wide variety of biological functions, including regulation of differentiation, proliferation and survival of target cells. -174 C/G (rs1800795) Power: G [34, 35] PPARA Peroxisome proliferator-activated receptor α Regulates liver, heart and skeletal muscle lipid metabolism, glucose homeostasis, mitochondrial biogenesis, cardiac hypertrophy. rs4253778 G/C Endurance: G Power: C [36, 37] PPARGC1A Peroxisome proliferatoractivated receptor γ coactivator 1 α Regulates fatty acid oxidation, glucose utilization, mitochondrial biogenesis, thermogenesis, angiogenesis, formation of muscle ibers. Gly482Ser (rs8192678 G/A) Endurance: Gly482 (G) [38, 39] TRHR Thyrotropinreleasing hormone receptor Stimulates the release of thyroxine, which is important in developing skeletal muscle. rs16892496 A/C Power (muscle mass): C [40] VDR Vitamin D receptor Involved in sustaining normocalcemia by inhibiting the production of parathyroid hormone and has effects on bone and skeletal muscle biology. BsmI A/G (rs1544410) Power: A [41, 42] VEGFA Vascular endothelial growth factor A Growth factor active in angiogenesis, vasculogenesis and endothelial cell growth. rs2010963 G/C Endurance: C [43, 44] - - - - - AGT 118 Genes and personalized training ibres have high resistance to fatigue and are thus suited for low- Ethical approval intensity resistance or aerobic (endurance) training, IIA ibres are The two-stage study was approved by the University of Central Lan- better suited for medium-term anaerobic exercise, and type IIX cashire Ethics Committee according to the Declaration of Helsinki. ibres are adapted for high-intensity (power and strength) exer- Each participant gave written informed consent after procedures were cise [8, 13, 15]. It should be noted that although muscle ibre fully explained. Each participant was free to withdraw from the stud- composition is an informative biomarker, muscle biopsies are ies at anytime. highly invasive. Subsequently, the potential value of non-invasive exercise prescription tools, such as genetic proiling, seems worthy Study design of investigation. Study design utilised a time series trial as explained by Batterham Association studies have linked dozens of genetic variants to train- and Hopkins [45]. Participants of both studies were randomly allo- ing responses and sport-related traits, such as strength, skeletal cated to an eight-week high- or low-intensity resistance-training muscle mass, recovery ability and muscle ibre composition [16-19]. program, after undergoing performance tests for both explosive However, no intervention studies prescribing training on the basis of power and endurance. Participants transitioned from their normal a genetic proile of athletes have been carried out. Here we evaluate training plan to the designed 8-week intervention followed by an an algorithm that facilitates training prescription by using a panel of eight-week wash-out period. The study was double blinded, in that 15 gene polymorphisms associated with physical performance and all were unaware of their ‘genetic potential status’, as determined by muscle-speciic traits to predict an athlete’s potential for development the DNAFit Peak Performance Algorithm™. This also included the of power and/or endurance qualities (Table 1). These polymorphisms lead investigator who coached the participants during the 8 weeks are located within the genes involved in the regulation of muscle of resistance training. ibre type composition and muscle size, cytoskeletal function, mus- Prior to involvement in the study, all participants had undertaken cle damage protection, metabolism, circulatory homeostasis, mito- weekly strength and conditioning programs, supervised by an ac- chondrial biogenesis, thermogenesis and angiogenesis. credited strength and conditioning coach, for a minimum of six months The aim of the present work therefore was to test, in two inde- and maximum of two and half years. These sessions took place in a pendent studies, the hypothesis that genetically matched athletes free weights facility where technique and adherence was closely (i.e. high-intensity trained with power genotype or low-intensity monitored at all times. Participants engaged in a minimum of one, trained with endurance genotype) show greater improvements in and maximum of two (preferentially), sessions per week. No other explosive power (countermovement jump) and aerobic itness (aero- form of resistance training was undertaken during this time, and bic 3-min cycle test) in response to high- or low-intensity resistance participants were actively partaking in other sport-speciic training training compared to mismatched athletes (i.e. high-intensity trained sessions and competitive games in parallel to the intervention. The with endurance genotype or low-intensity trained with power geno- investigator selected the same exercises for both groups: deadlift, type). pulldowns, front squat to 90 degrees, dumbbell lat press, step ups to medium high box and vertical jump single effort. Study participants. In Study 1, 55 Caucasian male University ath- monitored for progressive increases in perceived exertion, using a letes, all aged 18-20 years, volunteered for the study, and 28 of them modiied Borg scale, and loads were recorded to ensure progression. (height 180.7 ± 1.5 cm, weight 77.0 ± 2.1 kg) successfully com- The only differences between the training programs were volume pleted it (27 athletes had not completed all aspects of the study due modiications. The high-intensity resistance training program con- to either injury or illness). Each participant was a member of irst or sisted of ten sets of two reps over the eight-week study. This gave a second team, actively competing in British Universities and Colleges total volume of one hundred and twenty reps per session. The low- Sports (BUCS) leagues. The athletes competed in squash (n = 1), intensity resistance training program consisted of three sets of ten swimming (n = 7), running (n = 1), ski/snowboard (n = 4), soccer reps for irst two weeks, three sets of ifteens reps for the next three (n = 1), lacrosse (n = 2), badminton (n = 1), motorsport (n = 1), weeks and three sets of twenty for the last three weeks. This gave a cycling (n = 4), cricket (n = 2), volleyball (n = 1), fencing (n = 1) total volume of one hundred and eighty reps in the irst two weeks, - Each group self-selected training loads for each session, were and rugby union (n = 2). two hundred and seventy in the next three weeks and three hundred - MATERIALS AND METHODS teered to participate in the study, and 39 of them (height 176.1 ± and sixty reps in the last three weeks. 1.0 cm, weight 68.9 ± 1.5 kg) successfully completed it (29 par- Physiological measurements ticipants were withdrawn from the study due to non-adherence of All participants undertook a pre- and post-test measure of explosive set training volumes over the 8 weeks, or injury). Each subject was power and aerobic itness (endurance performance); namely, a coun- a member of college soccer academy who actively competed in BUCS termovement jump (CMJ) and Aerobic 3-min Cycle test (Aero3), us- leagues. ing a Optojump (Microgate, Italia) and Wattbike Pro (Wattbike, Not- - - - In study 2, 68 male soccer players, all aged 16-19 years, volun- Biology of Sport, Vol. 33 No2, 2016 119 Jones N et al. tingham, UK), respectively. Participants performed a standardized performed by multiplying the P value with the number of tests where warm up before every testing session with the CMJ preceding the appropriate. All data are presented as mean (standard deviation; SD). Aero3. Subjects were requested to arrive for testing in a rested and Statistical signiicance was set at a P value < 0.05. hydrated state and to refrain from caffeine intake for at least 12 hours before testing. Testing took place on the same time and weekday on RESULTS each occasion, to ensure a consistent placement within the subject’s Eiciency of diferent training modalities. All performance param- usual schedule. eters increased signiicantly (<0.001) in response to low- and highintensity resistance training when the results of two studies were Genotyping combined. No signiicant differences in explosive power (CMJ: 5.4 Upon enrollment into study each participant volunteered a saliva (5.0) vs. 4.6 (6.1)%, P = 0.547) and aerobic itness (Aero3: 4.3 sample, which was collected through sterile and self-administered (3.8) vs. 4.3 (3.7)%, P = 0.711) gains were observed between buccal swabs. Samples were sent to IDna Genetics laboratory (Nor- low- and high-intensity resistance training groups, indicating that i) wich, UK) within thirty-six hours, where analysis of the genes detailed both training modalities can be used to improve these performance in Table 1 was undertaken. DNA was extracted and puriied using parameters and ii) results of responses to both training types can be the Isohelix Buccalyse DNA extraction kit BEK-50 (Kent, UK). DNA combined for the analysis where appropriate. samples were ampliied by real-time PCR on an ABI7900 real-time thermocycler (Applied Biosystem, Waltham, USA). Association analysis between genotypes and phenotypes With some exceptions for the GABPB1 and VDR gene polymorphisms Calculation of power/endurance ratio in Study 2 (due to the low sample sizes in terms of population genet- Following the analysis, the DNAFit Peak Performance Algorithm™ ics), genotype distributions of 15 gene polymorphisms amongst all was used to determine percentage power/endurance score (P/E) ra- athletes of both studies were in Hardy-Weinberg equilibrium (Table 2). tio, similar to the research conducted by Egorova et al. [46]. Ini- To assess the association between each polymorphism and per- tially, each allele was given a point (0, 1, 2, 3 or 4) depending on formance parameters we used the combined data of two studies. the effect of the polymorphism on performance (power/muscle hy- After Bonferroni’s correction for multiple testing the results were pertrophy or endurance with respect to response to training). The considered signiicant with P < 0.0033 (i.e. 0.05/15). In accordance strength of the rating was based on the evidence from cumulative with the literature data (Table 1), we found that athletes with the literature results averaged over time. The total points for the P/E were ACE DD (P > 0.1 for CMJ, P > 0.1 for Aero3), ACTN3 Arg/Arg (P expressed as a percentage of P/E and then combined to give the = 0.065 for CMJ, P = 0.0038 for Aero3), CRP rs1205 GG (P > balance percentage. A percentage-ranking list was then complied 0.1 for CMJ, P = 0.0833 for Aero3), PPARGC1A Ser/Ser (P = using this score. Every other participant on the list then undertook 0.065 for CMJ, P = 0.0499 for Aero3) and VDR AA (P > 0.1 for high- or low-intensity resistance training. To clarify, someone who is CMJ, P > 0.1 for Aero3) genotypes demonstrated a tendency to 75% power but does low-intensity resistance training would be doing have greater gains in one or two performance tests compared with mismatched genotype training, while a participant rated as 75% the opposite genotype carriers after high-intensity resistance training, endurance that completed low-intensity resistance training would be while the latter (except for the PPARGC1A polymorphism) better doing matched genotype training. A threshold for 50% was used as responded to the low-intensity training (ACE II: P > 0.1 for CMJ, the splitting value in this process. P = 0.0355 for Aero3; ACTN3 Ter/Ter: P > 0.1 for CMJ, P > 0.1 VDR GG (P > 0.1 for CMJ, P = 0.0311 for Aero3). No signiicant Statistical analysis was conducted in SPSS, Version 20 (Chicago, IL). differences in CMJ and Aero3 gains were observed between different The required sample size for this study was validated using the Mann- genotype groups with respect to the other polymorphisms (data not Whitney test. The chi-square test was used to test genotype distribu- shown). However, given that the latter 10 polymorphisms have tions for deviation from Hardy-Weinberg equilibrium. The non-para- recently been reported to be associated with endurance, power and metric 2-sample paired test was performed matching “before” and muscle-speciic traits, and the fact that each contributing gene can “after” measurements from each individual tested. A 2-sided Mann- explain only a small portion of the observed interindividual differ- Whitney test for 2 independent samples was used to compare gains ences in training-induced effects, we felt justiied in retaining all 15 - in CMJ and Aero3 between groups. Differences in phenotypes between genetic markers for further analysis. test. Spearman’s (non-parametric) correlations were used to assess - Statistical analysis - for Aero3; CRP rs1205 AA: P = 0.0224 for CMJ, P > 0.1 for Aero3; the relationships between the genotype score and performance tests. Efect of diferent training modalities and genetic proiles on performance parameters The squared correlation coeficient R2 was used as a measure of Based on power/endurance genotype score (see Methods), in two explained variance. Bonferroni’s correction for multiple testing was studies we identiied 39 athletes (58.2%) with endurance genotype - - different genotype groups were analysed using ANOVA or unpaired t 120 Genes and personalized training TABLE 2. Genotype distributions and minor allele frequencies of candidate genes in athletes of two studies. Gene and variation Study Genotypes AA AB MAF, % S1 DD 10 ID 11 II 7 I 44.6 S2 14 16 9 43.6 ACTN3 rs1815739 C/T S1 CC 8 CT 10 TT 10 T 53.6 S2 12 21 6 42.3 ADRB2 rs1042713 G/A S1 GG 16 GA 10 AA 2 A 25.0 S2 21 13 5 29.5 ADRB2 rs1042714 C/G S1 CC 5 CG 15 GG 8 G 55.4 S2 14 16 9 43.6 AGT rs699 T/C S1 TT 9 TC 15 CC 4 C 41.1 S2 17 17 5 34.6 BDKRB2 rs1799722 C/T S1 CC 9 CT 14 TT 5 T 42.9 S2 15 17 7 39.7 COL5A1 rs12722 C/T S1 TT 8 TC 17 CC 3 C 41.1 S2 13 17 9 44.9 CRP rs1205 A/G S1 GG 12 GA 12 AA 4 A 35.7 S2 21 12 6 30.8 GABPB1 rs7181866 A/G S1 AA 27 AG 1 GG 0 G 1.8 S2 36 2 1 5.1 IL6 rs1800795 C/G S1 GG 10 GC 13 CC 5 C 41.1 S2 17 16 6 35.9 PPARA rs4253778 G/C S1 GG 21 GC 5 CC 2 C 16.1 S2 26 11 2 19.2 PPARGC1A rs8192678 G/A S1 GG 7 GA 18 AA 3 A 42.9 S2 15 17 7 39.7 TRHR rs16892496 A/C S1 AA 14 AC 9 CC 5 C 33.9 S2 15 17 7 39.7 VDR rs1544410 A/G S1 GG 11 GA 16 AA 1 A 32.1 S2 16 11 12 44.9 VEGFA rs2010963 G/C S1 GG 13 GC 11 CC 4 C 33.9 S2 18 18 3 30.8 Note: MAF - minor allele frequency; S1 - Study 1; S2 - Study 2. *PHW < 0.05 - not consistent with Hardy-Weinberg equilibrium. - ACE rs4646994 I/D - PHW BB 0.2776 0.3005 0.1356 0.5199 0.8011 0.2153 0.6572 0.3005 0.5723 0.8171 0.9122 0.5745 0.1784 0.4576 0.7243 0.0828 0.9233 0.0031* 0.8289 0.4977 0.0736 0.5653 0.0982 0.5745 0.1342 0.5745 0.1009 0.0073* 0.5126 0.6028 and 28 athletes (41.8%) with power genotype proiles. Changes in modality, 34 athletes performed matched training (high-intensity CMJ and Aero3 tests of athletes with predominantly endurance or training with power genotype (n=15) or low-intensity training with power genotype proiles from both studies after 8 weeks of low- and endurance genotype (n=19)), while other 33 athletes completed high-resistance training are presented in Tables 3 and 4. In both mismatched training (high-intensity training with endurance genotype studies it was shown that athletes with endurance genotype proile (n=20) or low-intensity training with power genotype (n=13)). In had greater beneits from the low-intensity resistance training, while study 1, the athletes from the matched group have signiicantly in- athletes with power genotype proile better responded to the high- creased their results in CMJ (P=0.0005) and Aero3 (P=0.0004). intensity resistance training. As expected, the outcomes were more On the other hand, athletes from the mismatched group have shown prominent in the Study 2 with homogeneous cohort (i.e. soccer non-signiicant improvements in CMJ (P=0.175) and less prominent players). Furthermore, we found that power genotype score (%) of results in Aero3 (P=0.0134) (Table 5). In study 2, soccer players athletes from both studies was positively correlated with CMJ (r = from the matched group have also demonstrated signiicantly great- 0.56; P = 0.0005) and Aero3 (r = 0.39; P = 0.0199) increases er (P<0.0001) performance changes in both tests compared to (%) in response to high-intensity training, while endurance genotype mismatched group (Table 5). and Aero3 (r = 0.51; P = 0.0032) increases (%) in response to Determinants of variability in response to resistance training low-intensity training, indicating that power genotype score explained With respect to the changes in CMJ gains (%), the athletes from both 14-32% of the variation in physiological parameters of athletes. studies (n = 67) were divided into tertiles: high responders (increase In accordance with power/endurance genotype score and training in CMJ from 7.4 to 19.4%; n = 23), moderate responders (increase - - - score (%) was positively correlated with CMJ (r = 0.37; P = 0.0399) Biology of Sport, Vol. 33 No2, 2016 121 Jones N et al. TABLE 3. Intergroup comparisons of CMJ increases (%) in response to high- or low-intensity training Increase in CMJ, % Group P1 Low-intensity RT P2 (paired test) High-intensity RT P2 (paired test) All athletes (n = 28) 6.4 (5.8) 0.0009* 4.1 (8.1) 0.131 0.369 Athletes with P genotype (n = 11) 3.8 (5.0) 0.156 7.0 (6.7) 0.125 0.429 8.2 (5.9) 0.0078* 2.2 (8.8) 0.813 0.067 Study 1 Athletes with E genotype (n = 17) P3 = 0.353 P3 = 0.272 Study 2 All athletes (n = 39) 4.6 (4.3) 0.0056* 5.0 (4.7) <0.0001* 0.932 Athletes with P genotype (n = 17) 1.0 (4.6) 0.578 7.1 (5.9) 0.0059* 0.0046* Athletes with E genotype (n = 22) 7.1 (1.0) 0.002* 3.2 (2.5) 0.0005* 0.0008* 0.547 P3 = 0.0002* P3 = 0.0056* Studies 1 and 2 <0.0001* 4.6 (6.1) 0.0002* 2.3 (4.8) 0.1465 7.1 (5.9) 0.0006* 0.0052* 7.6 (4.0) <0.0001* 2.8 (5.7) 0.051 0.0012* All athletes (n = 67) 5.4 (5.0) Athletes with P genotype (n = 28) Athletes with E genotype (n = 39) P3 = 0.0022* P3 = 0.0098* Note: *P < 0.05 - statistically different values between groups; P - power; E - endurance, RT - resistance training. P1 - comparison between athletes with different training types (i.e. low-intensity vs. high-intensity); P2 - signiicant increases in CMJ (paired test); P3 - comparison between athletes with different genotype proiles (i.e. power genotype vs. endurance genotype) of the same training modality TABLE 4. Intergroup comparisons of Aero3 increases (%) in response to high- or low-intensity training Increase in Aero3, % Group P1 Low-intensity RT P2 (paired test) High-intensity RT P2 (paired test) Study 1 All athletes (n = 28) 2.6 (3.1) 0.0103* 4.4 (4.4) 0.0017* 0.618 Athletes with P genotype (n = 11) 2.0 (4.3) 0.3125 6.0 (3.9) 0.0625 0.178 Athletes with E genotype (n = 17) 3.0 (2.2) 0.0078* 3.4 (4.6) 0.0391* 0.541 P3 = 0.776 P3 = 0.284 Study 2 All athletes (n = 39) 5.8 (3.7) <0.0001* 4.2 (3.3) <0.0001* 0.218 Athletes with P genotype (n = 17) 1.7 (0.5) 0.0156* 6.8 (2.5) 0.002* 0.002* 8.7 (1.6) 0.002* 2.1 (2.3) 0.0161* <0.0001* Athletes with E genotype (n = 22) P3 = 0.0001* P3 = 0.002* Studies 1 and 2 All athletes (n = 67) 4.3 (3.8) <0.0001* 4.3 (3.7) <0.0001* 0.711 Athletes with P genotype (n = 28) Athletes with E genotype (n = 39) 1.8 (2.8) 0.0171* 6.5 (2.9) <0.0001* 0.0004* 6.0 (3.5) <0.0001* 2.6 (3.3) 0.0004* 0.0013* P3 = 0.0004* P3 = 0.0026* in CMJ from 2.7 to 7.2%; n = 22) and non- or low responders linear trend for the proportion of matched-trained athletes among (increase in CMJ from -8.4 to 2.5%; n=22). There was a signiicant the high (increase in Aero3 from 6.0 to 13.2%; n = 22) responders linear trend for the proportion of matched-trained athletes among (86.4%), moderate (increase in Aero3 from 2.0 to 5.9%; n = 23) the high responders (82.6%), moderate responders (50.0%) and responders (47.8%) and non- or low (increase in Aero3 from -6.1 non- or low responders (18.2%) (χ2=18.7, P < 0.0001). Similarly, to 1.9%; n = 22) responders (18.2%) (χ2=20.5, P < 0.0001). In when considering increases of Aero3 (%), we found a signiicant other words, among non- or low responders to any type of resistance - - - - - Note: *P < 0.05 - statistically different values between groups; P - power; E - endurance, RT - resistance training. P1 - comparison between athletes with different training types (i.e. low-intensity vs. high-intensity); P2 - signiicant increases in Aero3 (paired test); P3 - comparison between athletes with different genotype proiles (i.e. power genotype vs. endurance genotype) of the same training modality 122 Genes and personalized training TABLE 5. Comparisons of CMJ and Aero3 increases (%) in response to resistance training between matched and mismatched groups. Study Group Matched athletes Study 1 n =14 P3 Mismatched athletes P1 (paired test) n = 14 P2 (paired test) Change in CMJ, % 7.8 (5.9) 0.0005* 2.9 (7.2) 0.175 0.0596 Change in Aero3, % 4.0 (3.1) 0.0004* 2.8 (4.3) 0.0134* 0.2456 Study 2 n =20 n = 19 Change in CMJ, % 7.1 (4.1) <0.0001* 2.4 (3.5) 0.0053* <0.0001* Change in Aero3, % 7.7 (2.2) <0.0001* 1.9 (1.8) 0.0004* <0.0001* Studies 1 and 2 n =34 n =33 Change in CMJ, % 7.4 (4.9) <0.0001* 2.6 (5.3) 0.0152* <0.0001* Change in Aero3, % 6.2 (3.2) <0.0001* 2.3 (3.1) <0.0001* <0.0001* Note: *P1 and P2 < 0.05 - signiicant increases in CMJ and Aero3 (paired test); *P3 < 0.05 - signiicant difference between matched and mismatched groups. Matched athletes - high-intensity trained with endurance genotype or low-intensity trained with power genotype; mismatched athletes - highintensity trained with power genotype or low-intensity trained with endurance genotype. training, 82% of athletes (both for CMJ and Aero3) were from the aerobic itness responses may be partly explained by genetic factors mismatched group, while high responders were predominantly and selected training modalities. Another important inding was that matched athletes (83% and 86% for CMJ and Aero3, respectively; among non- or low responders to resistance training, most athletes P < 0.0001 for the comparison between non- or low responders and were from the mismatched group, while high responders were pre- high responders). Accordingly, after 8 weeks of resistance training dominantly matched athletes. These results suggest personalized the odds of achieving more favorable outcomes in CMJ and Aero3 training prescription based on genetic proiling may help some indi- were 21 and 28.5 times, respectively, greater (P < 0.0001) for viduals overcome unresponsiveness to resistance training. matched than mismatched genotype training (when irst and third tertiles were compared). Exercise training response is inluenced by a multitude of determinants including genetics, environmental factors, measurement power are under moderate to high genetic control with heritabilities To the best of our knowledge, this is the irst study to examine the ranging between 30 and 84% [17, 47]. Numerous studies reported eficacy of using genetic proiling methods to target training of both the association between individual differences in strength/anaerobic power and endurance qualities of athletes. The results of our study power phenotypes in response to resistance/anaerobic power training demonstrated that all performance parameters increased signii- and gene variations [16, 17]. Accordingly, several gene polymor- cantly in response to 8-weeks of either low- or high-intensity resistance phisms in our study were found to be individually linked with training training without differences between the two training modalities, responses. For instance, the II genotype of the ACE and XX (Ter/Ter) however, the magnitude of training effects was strongly related to the genotype of the ACTN3 genes (known as endurance markers) were association between genetic proile and training modality. Our main associated (or tended to correlate) with increases in aerobic itness inding is that matching individual genotype with the appropriate in response to low-intensity resistance training, while the ACE DD mode of training led to more substantial resistance training beneits, and ACTN3 RR (Arg/Arg) genotypes (known as power/strength mark- for both power and endurance matched participants. More specii- ers) carriers demonstrated greater improvement of performance pa- cally, in the irst athletes from the matched group demonstrated rameters in response to high-intensity resistance training, which is signiicantly enhanced results in explosive power and aerobic itness, consistent with previous indings [48-51]. while the gains realized by the mismatched athletes were of lesser The likely mechanism through which the polygenic proile (i.e. magnitude. Importantly, these results were replicated in the second proile composed of 15 polymorphisms) of athletes was associated study, using a homogenous cohort of athletes. with training responses could be the link between genetic variations There was also a positive correlation between power genotype and skeletal muscle characteristics, such as muscle ibre composition. score of athletes and performance changes in response to high-in- Of note, 5 of 15 gene polymorphisms (ACE I/D, ACTN3 rs1815739 tensity training, as well as a positive correlation between endurance C/T, PPARA rs4253778 G/C, PPARGC1A rs8192678 G/A and VEG- genotype score and increases in performance tests in response to FA rs2010963 G/C) included in our panel, have recently been re- low-intensity training: indings suggesting that the commonly observed ported to be associated with muscle ibre type [18]. It is well known heterogeneity in resistance training-induced explosive power and that slow-twitch muscle ibres better respond to low-intensity resis- - - - - - errors and others. Studies suggest that muscle strength and explosive DISCUSSION Biology of Sport, Vol. 33 No2, 2016 123 Jones N et al. tance or aerobic (endurance) training, while fast-twitch muscle ibres effective during resistance training program, one might speculate that are better suited for high-intensity (power and strength) training [8, even in this case the manipulation of training variables is necessary 13, 15]. Consequently, elite endurance athletes have a remarkably for long-term resistance training progression. Fourth, the results of high proportion of slow-twitch muscle ibres, whereas muscles of top our study may be applicable only for speciic training goals, such as sprinters and weightlifters predominantly consist of fast-twitch mus- improvement of explosive power and aerobic performance with one cle ibres [15]. Interestingly, Sukhova et al. [52] have shown that of two different modalities. Although loads of < 45% of 1 RM (i.e., speed skaters whose muscle ibre composition did not correspond to performed with very high repetitions) may increase strength in un- their distance specialty (i.e. speed skaters with increased proportion trained individuals [54], whereas trained weightlifters appear respon- of slow-twitch muscle ibres who performed sprint training and speed sive only to heavier loading [55]. Further research analyzing genetic skaters with predominantly fast-twitch muscle ibres who performed determinants of improvement of absolute strength and skeletal mus- endurance training) had destructive alterations of their muscles (with cle hypertrophy is needed. Finally, in our study we have used a possible negative effect on physical performance), indicating that validated panel of a limited number (n=15) of gene polymorphisms individuals should train and select sports in accordance with their associated with power/strength, endurance and other muscle-specif- genetic potential. One might speculate that non- or low-responders ic traits, which could explain only 14-32% of the variation in phys- to different training modalities in our study genetically were not iological parameters of athletes in our study. Undoubtedly there are suited for selected resistance training types. On the other hand, there likely to be many more genetic variants associated with responses are many more factors at the molecular, cellular, tissue and organ to different modalities of resistance training that remain to be identi- system levels that may determine individual responses to resistance ied. Therefore, it is logical to conclude that the picture we see in the training. For instance, Petrella et al. [53] have demonstrated that future may become clearer as more genetic markers are included in extreme responders (in terms of hypertrophy of muscle ibres) to a the panel. 16-week resistance training program showed a markedly higher activation of their satellite cells and greater myonuclei addition compared CONCLUSIONS In conclusion, our results suggest that using genetic proiling to bet- with moderate responders and non-responders. Our study has some limitations, which have to be pointed out. ter match individual genotype with appropriate training modality may Firstly, this was a relatively small study: only 28 athletes from Study be a powerful tool to aid more personalized, and precise, resistance 1 and 39 athletes from Study 2 completed the resistance training training prescription in the future. programs. However, the power calculation suggested that the sample size was suficient to adequately fulill the study’s main objective. Secondly, the sample was taken from a wide range of sporting dis- Acknowledgements ciplines, all of which were commonly exposed to different forms and The authors would like to acknowledge the University of Manchester’s levels of training and competition stresses: a factor which could Sport Department and Athletic Union as well as the Portsmouth conceivably inluence training responses. Furthermore, the low num- College for the allowing their students/athletes the chance to volun- ber of weekly training sessions, which were by necessity completed teer as participants in the study. Also thanks must go to all the in tandem with sport-speciic training, may well have confounded coaches of DNA Sports Performance Ltd and Suraci Consultancy who the experimental manipulation. However, athletes from Study 2 were took part in data collection and training for the participants. DNAFit™ all soccer players and thus represented the homogeneous group with supported this original research by providing all genetic testing. Fi- more signiicant results. Further studies involving untrained (unit) nally the authors would also like to acknowledge the hard work and subjects and strength athletes with more carefully controlled total effort of the participants in this study, who without their hours and training loads are warranted. Third, the subjects of our studies per- hours of testing and training these results would have remained hid- formed a short-term, nonperiodized resistance training. It has been den from the world. shown that systematically varying volume and intensity (i.e. periodized programs with the stable training variables [3]. Therefore, although Conlict of interests: the authors declared no conlict of interests we have shown that genetically matched nonperiodized training was regarding the publication of this manuscript. REFERENCES 1. American College of Sports Medicine. American College of Sports Medicine position stand. Progression models in resistance training for healthy adults. Med Sci Sports Exerc. 2009;41(3):687-708. 2. Vikmoen O, Ellefsen S, Trøen Ø, Hollan I, - - - - - training) is most effective for long-term progression compared with 124 Hanestadhaugen M, Raastad T, Rønnestad BR. Strength training improves cycling performance, fractional utilization of VO2 max and cycling economy in female cyclists. Scand J Med Sci Sports. 2015 Apr 18. doi: 10.1111/sms.12468. 3. Kraemer WJ, Ratamess NA. Fundamentals of resistance training: progression and exercise prescription. Med Sci Sports Exerc. 2004;36(4):674-688. 4. McGlory C, Phillips SM. Exercise and the Regulation of Skeletal Muscle Hypertrophy. Prog Mol Biol Transl Sci. 2015;135:153-173. 5. Campos GE, Luecke TJ, Wendeln HK, Toma K, Hagerman FC, Murray TF, Ragg KE, Ratamess NA, Kraemer WJ, Staron RS. Muscular adaptations in response to three different resistancetraining regimens: speciicity of repetition maximum training zones. Eur J Appl Physiol. 2002;88(1-2):50-60. 6. Wilson GJ, Newton RU, Murphy AJ, Humphries BJ. The optimal training load for the development of dynamic athletic performance. Med Sci Sports Exerc. 1993;25(11):1279-1286. 7. McBride JM, Triplett-McBride T, Davie A, Newton RU. The effect of heavy- vs. light-load jump squats on the development of strength, power, and speed. J Strength Cond Res. 2002;16(1):75-82. 8. Netreba AI, Popov DV, Liubaeva EV, Bravyĭ IaR, Prostova AB, Lemesheva IuS, Vinogradova OL. Physiological effects of using the low intensity strength training without relaxation in single-joint and multi-joint movements. Ross Fiziol Zh Im I M Sechenova. 2007;93(1):27-38. 9. Mitchell CJ, Churchward-Venne TA, West DW, Burd NA, Breen L, Baker SK, Phillips SM. Resistance exercise load does not determine training-mediated hypertrophic gains in young men. J Appl Physiol (1985). 2012;113(1):71-77. 10. Fry AC. The role of resistance exercise intensity on muscle ibre adaptations. Sports Med. 2004;34(10):663-679. 11. Kosek DJ, Kim JS, Petrella JK, Cross JM, Bamman MM. Eficacy of 3 days/wk resistance training on myoiber hypertrophy and myogenic mechanisms in young vs. older adults. J Appl Physiol (1985). 2006;101(2):531-544. 12. Hubal MJ, Gordish-Dressman H, Thompson PD, Price TB, Hoffman EP, Angelopoulos TJ, Gordon PM, Moyna NM, Pescatello LS, Visich PS, Zoeller RF, Seip RL, Clarkson PM. Variability in muscle size and strength gain after unilateral resistance training. Med Sci Sports Exerc. 2005;37(6):964-972. 13. Pipes TV. Strength training and iber types. Scholastic Coach. 1994;63:6770. 14. Simoneau J-A, Bouchard C. Genetic determinism of iber type proportion in human skeletal muscle. FASEB J. 1995;9:1091-1095. 15. Andersen JL, Schjerling P, Saltin B. Muscle, genes, and athletic performance. Sci Am. 2000;283(3):48-55. 16. Bray MS, Hagberg JM, Pérusse L, Rankinen T, Roth SM, Wolfarth B, Bouchard C. The human gene map for performance and health-related itness phenotypes: the 2006-2007 update. Med Sci Sports Exerc. 2009;41(1):3573. 17. Hughes DC, Day SH, Ahmetov II, Williams AG. Genetics of muscle strength and power: polygenic proile similarity limits skeletal muscle performance. J Sports Sci. 2011;29(13):1425-34. 18. Ahmetov II, Vinogradova OL, Williams AG. Gene polymorphisms and iber-type composition of human skeletal muscle. Int J Sport Nutr Exerc Metab. 2012;22(4):292-303. 19. Ahmetov II, Fedotovskaya ON. Current Progress in Sports Genomics. Adv Clin Chem. 2015;70:247-314. 20. Ma F, Yang Y, Li X, Zhou F, Gao C, Li M, Gao L. The association of sport performance with ACE and ACTN3 genetic polymorphisms: a systematic review and meta-analysis. PLoS One. 2013;8(1):e54685. 21. Wang G, Mikami E, Chiu LL, DE Perini A, Deason M, Fuku N, Miyachi M, Kaneoka K, Murakami H, Tanaka M, Hsieh LL, Hsieh SS, Caporossi D, Pigozzi F, Hilley A, Lee R, Galloway SD, Gulbin J, Rogozkin VA, Ahmetov II, Yang N, North KN, Ploutarhos S, Montgomery HE, Bailey ME, Pitsiladis YP. Association analysis of ACE and ACTN3 in elite Caucasian and East Asian swimmers. Med Sci Sports Exerc. 2013;45(5):892-900. 22. Yang N, Arthur DG, Gulbin JP, Hahn AG, Beggs AH, Easteal S, North K. ACTN3 genotype is associated with human elite athletic performance. Am J Hum Genet. 2003;73(3):627-631. 23. Wolfarth B, Rankinen T, Mühlbauer S, Scherr J, Boulay MR, Pérusse L, Rauramaa R, Bouchard C. Association between a beta2-adrenergic receptor polymorphism and elite endurance performance. Metabolism. 2007;56(12):1649-1651. 24. Tsianos GI, Evangelou E, Boot A, Zillikens MC, van Meurs JB, Uitterlinden AG, Ioannidis JP. Associations of polymorphisms of eight muscle- or metabolism-related genes with performance in Mount Olympus marathon runners. J Appl Physiol (1985). 2010;108(3):567-574. 25. McCole SD, Shuldiner AR, Brown MD, Moore GE, Ferrell RE, Wilund KR, Huberty A, Douglass LW, Hagberg JM. Beta2- and beta3-adrenergic receptor polymorphisms and exercise hemodynamics in postmenopausal women. J Appl Physiol (1985). 2004;96(2):526-530. 26. Gomez-Gallego F, Santiago C, GonzálezFreire M, Yvert T, Muniesa CA, Serratosa L, Altmäe S, Ruiz JR, Lucia A. The C allele of the AGT Met235Thr polymorphism is associated with power sports performance. Appl Physiol Nutr Metab. 2009;34(6):1108-1111. 27. Zarębska A, Sawczyn S, Kaczmarczyk M, Ficek K, Maciejewska-Karłowska A, Sawczuk M, Leońska-Duniec A, Eider J, Grenda A, Cięszczyk P. Association of rs699 (M235T) polymorphism in the AGT gene with power but not endurance athlete status. J Strength Cond Res. 2013;27(10):2898-2903. 28. Posthumus M, Schwellnus MP, Collins M. The COL5A1 gene: a novel marker of endurance running performance. Med Sci Sports Exerc. 2011;43(4):584-589. 29. Brown JC, Miller CJ, Posthumus M, Schwellnus MP, Collins M. The COL5A1 gene, ultra-marathon running performance, and range of motion. Int J Sports Physiol Perform. 2011;6(4):485496. 30. Obisesan TO, Leeuwenburgh C, Phillips T, Ferrell RE, Phares DA, Prior SJ, Hagberg JM. C-reactive protein genotypes affect baseline, but not exercise training-induced changes, in C-reactive protein levels. Arterioscler Thromb Vasc Biol. 2004;24(10):1874-1879. 31. Kuo HK, Yen CJ, Chen JH, Yu YH, Bean JF. Association of cardiorespiratory itness and levels of C-reactive protein: data from the National Health and Nutrition Examination Survey 19992002. Int J Cardiol. 2007;114(1):2833. 32. He Z, Hu Y, Feng L, Lu Y, Liu G, Xi Y, Wen L, McNaughton LR. NRF2 genotype improves endurance capacity in response to training. Int J Sports Med. 2007;28(9):717-721. 33. Eynon N, Sagiv M, Meckel Y, Duarte JA, Alves AJ, Yamin C, Sagiv M, Goldhammer E, Oliveira J. NRF2 intron 3 A/G polymorphism is associated with endurance athletes’ status. J Appl Physiol (1985). 2009;107(1):76-79. 34. Ruiz JR, Buxens A, Artieda M, Arteta D, Santiago C, Rodríguez-Romo G, Lao JI, Gómez-Gallego F, Lucia A. The -174 G/C polymorphism of the IL6 gene is associated with elite power performance. J Sci Med Sport. 2010;13(5):549-553. 35. Eider J, Cieszczyk P, Leońska-Duniec A, Maciejewska A, Sawczuk M, Ficek K, Kotarska K. Association of the 174 G/C polymorphism of the IL6 gene in Polish power-orientated athletes. J Sports Med Phys Fitness. 2013;53(1):88-92. 36. Ahmetov II, Gavrilov DN, Astratenkova IV, Druzhevskaya AM, Malinin AV, Romanova EE, Rogozkin VA. The association of ACE, ACTN3 and PPARA gene variants with strength phenotypes in middle school-age children. J Physiol Sci. 2013;63(1):79-85. 37. Lopez-Leon S, Tuvblad C, Forero DA. Sports genetics: the PPARA gene and athletes’ high ability in endurance sports. A systematic review and meta-analysis. Biol Sport. 2016;33:3-6. 38. Lucia A, Gómez-Gallego F, Barroso I, Rabadán M, Bandrés F, San Juan AF, Chicharro JL, Ekelund U, Brage S, Earnest CP, Wareham NJ, Franks PW. PPARGC1A genotype (Gly482Ser) predicts exceptional endurance capacity - - - - - Genes and personalized training Biology of Sport, Vol. 33 No2, 2016 125 Jones N et al. - - - - - in European men. J Appl Physiol (1985). 2005;99(1):344-348. 39. Maciejewska A, Sawczuk M, Cieszczyk P, Mozhayskaya IA, Ahmetov II. The PPARGC1A gene Gly482Ser in Polish and Russian athletes. J Sports Sci. 2012;30(1):101-113. 40. Liu XG, Tan LJ, Lei SF, Liu YJ, Shen H, Wang L, Yan H, Guo YF, Xiong DH, Chen XD, Pan F, Yang TL, Zhang YP, Guo Y, Tang NL, Zhu XZ, Deng HY, Levy S, Recker RR,Papasian CJ, Deng HW. Genome-wide association and replication studies identiied TRHR as an important gene for lean body mass. Am J Hum Genet. 2009;84(3):418-423. 41. Wang P, Ma LH, Wang HY, Zhang W, Tian Q, Cao DN, Zheng GX, Sun YL. Association between polymorphisms of vitamin D receptor gene ApaI, BsmI and TaqI and muscular strength in young Chinese women. Int J Sports Med. 2006;27(3):182-186. 42. Windelinckx A, De Mars G, Beunen G, Aerssens J, Delecluse C, Lefevre J, Thomis MA. Polymorphisms in the vitamin D receptor gene are associated with muscle strength in men and women. Osteoporos Int. 2007;18(9):1235-1242. 43. Prior SJ, Hagberg JM, Paton CM, Douglass LW, Brown MD, McLenithan JC, Roth SM. DNA sequence variation in the promoter region of the VEGF gene impacts VEGF gene expression and maximal oxygen consumption. Am J Physiol Heart Circ Physiol. 2006;290(5):1848-1855. 126 44. Ahmetov II, Khakimullina AM, Popov DV, Missina SS, Vinogradova OL, Rogozkin VA. Polymorphism of the vascular endothelial growth factor gene (VEGF) and aerobic performance in athletes. Hum Physiol. 2008;34:477-481. 45. Batterham AM, Hopkins WG. A decision tree for controlled trails. Sportsci. 2005;9:33-39. 46. Egorova ES, Borisova AV, Mustaina LJ, Arkhipova AA, Gabbasov RT, Druzhevskaya AM, Astratenkova IV, Ahmetov II. The polygenic proile of Russian football players. J Sports Sci. 2014;32(13):1286-93. 47. Calvo M, Rodas G, Vallejo M, Estruch A, Arcas A, Javierre C, Viscor G, Ventura JL. Heritability of explosive power and anaerobic capacity in humans. Eur J Appl Physiol. 2002;86(3):218-225. 48. Montgomery HE, Marshall R, Hemingway H, Myerson S, Clarkson P, Dollery C, Hayward M, Holliman DE, Jubb M, World M, Thomas EL, Brynes AE, Saeed N, Barnard M, Bell JD, Prasad K, Rayson M, Talmud PJ, Humphries SE. Human gene for physical performance. Nature. 1998;393(6682):221-222. 49. Folland J, Leach B, Little T, Hawker K, Myerson S, Montgomery H, Jones D. Angiotensin-converting enzyme genotype affects the response of human skeletal muscle to functional overload. Exp Physiol. 2000;85:575-579. 50. Pescatello LS, Kostek MA, GordishDressman H, Thompson PD, Seip RL, Price TB, Angelopoulos TJ, Clarkson PM, Gordon PM, Moyna NM, Visich PS, Zoeller RF, Devaney JM, Hoffman EP. ACE ID genotype and the muscle strength and size response to unilateral resistance training. Med Sci Sports Exerc. 2006;38(6):1074-1081. 51. Pereira A, Costa AM, Izquierdo M, Silva AJ, Bastos E, Marques MC. ACE I/D and ACTN3 R/X polymorphisms as potential factors in modulating exercise-related phenotypes in older women in response to a muscle power training stimuli. Age (Dordr). 2013;35(5):1949-1959. 52. Sukhova ZI, Ivanitskaia VV, Makarova LF, Poluéktova BP, Iazvikov VV. Features of the ultrastructural organization of the muscles of skaters in relation to their sport specialization and muscle iber composition. Arkh Anat Gistol Embriol. 1985;89(12):87-90. 53. Petrella JK, Kim JS, Mayhew DL, Cross JM, Bamman MM. Potent myoiber hypertrophy during resistance training in humans is associated with satellite cell-mediated myonuclear addition: a cluster analysis. J Appl Physiol (1985). 2008;104(6):1736-42. 54. Stone WJ, Coulter SP. Strength/endurance effects from three resistance training protocols with women. J Strength Cond Res. 1994;8:231-234. 55. Häkkinen K, Komi PV, Alén M, Kauhanen H. EMG, muscle ibre and force production characteristics during a 1 year training period in elite weightlifters. Eur J Appl Physiol Occup Physiol. 1987;56(4):419-27.