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Letter to the editor: A genetic-based algorithm for personalized resistance training.

ABSTRACT: In a recent paper entitled "A genetic-based algorithm for personalized resistance training", Jones et al. [1] presented an algorithm of 15 performance-associated gene polymorphisms that they propose can determine an athlete's training response by predicting power and endurance potential. However, from the design of their studies and the data provided, there is no evidence to support these authors' assertions. Progress towards such a significant development in the field of sport and exercise genomics will require a paradigm shift in line with recent recommendations for international collaborations such as the Athlome Project (see www.athlomeconsortium.org). Large-scale initiatives, involving numerous multi-centre and well-phenotyped exercise training and elite performance cohorts, will be necessary before attempting to derive and replicate training and/or performance algorithms.

SUBMITTER: Karanikolou A

PROVIDER: S-EPMC5377557 | biostudies-literature | 2017 Mar

REPOSITORIES: biostudies-literature

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Letter to the editor: A genetic-based algorithm for personalized resistance training.

Karanikolou A A Wang G G Pitsiladis Y Y

Biology of sport 20161111 1

In a recent paper entitled "A genetic-based algorithm for personalized resistance training", Jones et al. [1] presented an algorithm of 15 performance-associated gene polymorphisms that they propose can determine an athlete's training response by predicting power and endurance potential. However, from the design of their studies and the data provided, there is no evidence to support these authors' assertions. Progress towards such a significant development in the field of sport and exercise geno ...[more]

PMID: 28416894

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Project description:Association studies have identified 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 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 non-significant 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 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 (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.

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Letter to the editor: A genetic-based algorithm for personalized resistance training.

Publications

Letter to the editor: A genetic-based algorithm for personalized resistance training.

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