A genetic-based algorithm for personalized resistance-training Nicholas Jones, John Kiely , Bruce Suraci , Dave Collins , David de Lorenzo , Craig Pickering , Keith Grimaldi Biol Sport 2016; 33(2):117-126 ICID: 1198210
Article type: Original article
IC™ Value: 10.00
Abstract provided by Publisher
Association studies have identified dozens of genetic variants linked to training responses and sport-related traits. However, no intervention studies utilising the idea of personalised training based on athlete's genetic profile 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 fitness, 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) significantly 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 low-intensity trained with power genotype) demonstrated only non-significant CMJ and Aero3 (P=0.0134 improvements (P=0.175) ). In study 2, soccer players from the matched group also demonstrated significantly 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, while high responders were predominantly from the cohort whose training was matched according to genotype (83% and 86% for CMJ and Aero3, respectively; 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.