Sport and exercise genomics: the FIMS 2019 consensus statement update

The satisfaction of ecological environment in sports public services by artificial intelligence and big data

In order to gain a more accurate understanding and enhance the relationship between the fitness ecological environment and artificial intelligence (AI)-driven sports public services, this study combines a Convolutional Neural Network (CNN) approach based on residual modules and attention mechanisms with the SERVQUAL evaluation model. The method employed involves the analysis of big data collected from questionnaire surveys, literature reviews, and interviews. This study critically examines the impact of advanced AI technologies on residents' satisfaction with the fitness ecological environment in sports public services and conducts theoretical analysis of the obtained data. The results show that the quality of sports public services empowered by AI significantly influences residents' satisfaction with the fitness ecological environment, such as running, swimming, ball games and other sports with high requirements for sports service quality and ecological environment. Only the good public sports service quality matching with them can meet the needs of the ecological environment for fitness, and stimulate the enthusiasm of the people for fitness. The study also shows that swimming, running and all kinds of ball games account for the largest proportion of all sports. To sum up, the satisfaction of residents' fitness ecological environment is greatly affected by the quality of public sports services, which is mainly reflected in the good and perfect sports environment and facilities that can provide residents with a wealth of fitness options, greatly improving the sports ecological environment. This study is helpful to realize the relationship between sports public service and sports ecological environment. It contributes to understanding the role of AI and deep learning in enhancing the correlation between sports public service and the ecological environment of sports.

Personalized Nutrition for the Enhancement of Elite Athletic Performance

Enhancing athletic performance through the manipulation of nutritional intake has ancient roots, with early guidance from "philosophical giants" like Hippocrates, who describes the balance between diet and exercise. Modern sports nutrition emerged in the 20th century, with research identifying carbohydrate (CHO) intake as beneficial for endurance. Studies like Gordon's in the 1920s linked blood glucose levels to marathon performance, while Cade's research in the 1960s on fluid and electrolyte intake led to the founding of Gatorade and the shift toward drinking during exercise to allegedly prevent dehydration and improve sporting performance. Today, sports nutrition is in a "holding pattern" after significant developments in the 1980s, 1990s, and the 2000s. A new era will involve personalized nutrition, but this development will require a game-changing injection of momentum, recognizing that athletes' responses to nutrition interventions vary widely. New technologies will also need to be developed and perfected, including wearables for real-time biometric monitoring (e.g., heart rate variability, glucose, and sweat composition and rate), which offer potential for tailored nutrition (i.e., diet and hydration) strategies. Applications of genetic and multi-omics technologies (like genomics, transcriptomics, metabolomics, proteomics, and epigenomics) are needed to unlock the potential of personalized sports nutrition by analyzing individual responses to factors such as sleep, nutrition, and exercise. The future lies in fast integration of all available data using next-generation bioinformatics and AI to generate personalized recommendations, with an emphasis on empirical evidence rather than solely commercial interests. As technology matures, sports (and exercise) nutrition will continue refining its practices but will need a paradigm shift to deliver precise interventions that may offer athletes the crucial edge needed to maximize performance while promoting short-term and long-term health.

Big data for neuroscience in the context of predictive, preventive, and personalized medicine

Accurate and precise diagnosis made the medicine the hallmark of evidence-based medicine. While attaining absolute patient satisfaction may seem impossible in the aspect of disease recurrent, personalized their mecidal conditions to their responsive treatment approach may save the day. The last generation approaches in medicine require advanced technologies that will lead to evidence-based medicine. One of the trending fields in this is the use of big data in predictive, preventive, and personalized medicine (3PM). This review dwelled through the practical examples in which big data tools harness neuroscience to add more individualized apporahes to the medical conditions in a bid to confer a more personalized treatment strategies. Moreover, the known breakthroughs of big data in 3PM, big data and 3PM in neuroscience, AI and neuroscience, limitations of big data with 3PM in neuroscience, and the challenges are thoroughly discussed. Finally, the prospects of incorporating big data in 3PM are as well discussed. The review could point out that the implications of big data in 3PM are still in their infancy and will require a holistic approach. While there is a need to carefully sensitize the community, convincing them will come under interdisciplinary and, to some extent, inter-professional collaborations, capacity building for professionals, and optimal coordination of the joint systems.

Prediction of success in sports based on assumed individual genetic predisposition: lack of association with the C > T variant in the ACTN3 gene

BACKGROUND

Prediction of sports success (sports talent) based on individual genetic characteristics is the main goal of sports genetics/genomics. Most often, markers of predisposition to speed-strength sports, or endurance, are single-nucleotide variants in various parts of DNA. One of the most studied variants is the C/T variant in the ACTN3 gene. The accumulated data on the association of this variant with success in various sports is sufficient to conduct a meta-analysis. The purpose of the present review is to analyze the prognostic utility of the data presented in the literature on molecular genetic markers of genetic predisposition to achieve outstanding sports results using the example of the C > T variant of ACTN3 (rs1815739).

MAIN BODY

A total of 42 studies were included in the analysis, with a total number of 41,054 individuals (of which 10,442 were in the athlete group and 30,612 in the control group). For each study included in the analysis, the agreement of genotype frequencies with Hardy-Weinberg equilibrium was tested, as well as the presence of an excess or deficit of heterozygotes. Prediction intervals for the overall effect size (OR-odds ratio) was estimated. Both in the subgroups of athletes and controls, a significant difference FIS from zero was found, suggesting inbreeding or outbreeding, as well as a very wide 95% CI for FIS. A meta-analysis was conducted for dominant, codominant, and recessive inheritance models. The obtained ORs and their 95% CIs were in the range of almost negligible values or have very wide CIs. The evaluation for the recessive model showed 95% PI for the OR lies between 0.74 to 1.92. Statistically, it does not differ from zero, which means that in some 95% of studies comparable to those in the analysis, the true effect size will fall in this interval.

CONCLUSION

Despite numerous attempts to identify genetic variants associated with success in elite sports, progress in this direction remains insignificant. Thus, no sports or sports roles were found for which the C > T variant of the ACTN3 gene would be a reliable prognostic marker for assessing an individual predisposition to achieve high sports performance. The results of the present meta-analysis support the conclusion that neutral gene polymorphism-from evolutionary or adaptive point of view-is not a trait that can be selected or used as a predictive tool in sports.

ACE I/D Genotype and Risk of Non-Contact Injury in Moroccan Elite Athletes: A Pilot Study

Background and Objectives: The insertion/deletion (I/D) polymorphism in ACE, the gene encoding the angiotensin-converting enzyme (ACE), has been suggested as a genetic variation that can influence exercise performance and risk of injury in elite athletes. The I allele has been associated with enhanced endurance performance and with reduced inflammation, while the D allele has been associated with improved performance in strength and power activities. However, the role of this genetic variant in the incidence of non-contact injury is underexplored. This study investigated the possible association of ACE I/D genotypes with the risk of non-contact injury in elite Moroccan athletes. Materials and Methods: Forty-three elite male athletes (19 cyclists and 24 field hockey players) from the Moroccan national team participated voluntarily. Non-contact injuries were recorded for all athletes and classified according to the IOC consensus statement by the medical staff of the teams. ACE I/D polymorphism genotyping was performed by polymerase chain reaction (PCR) using genomic DNA from blood samples. Results: There were four cyclists (21.05%) and eight field hockey players (33.33%) with a non-contact injury during the season. The distribution of the ACE I/D genotypes was similar in the athletes with vs. without non-contact injury for cyclists (DD/ID/II 25.00/50.00/25.00% vs. 46.67/40.00/13.33% non-injured, respectively; X2 = 0.69, p = 0.70), field hockey players (DD/ID/II 50.00/50.00/0.00% vs. 50.00/43.75/6.25%; X2 = 0.54, p = 0.76) and for the whole group of athletes (DD/ID/II 41.67/50.00/8.33% vs. 48.39/41.94/9.68%; X2 = 0.22, p = 0.89). In the whole group of athletes, neither the dominant (DD + ID vs. II = OR: 1.17, 95% CI: 0.15-16.56, p = 0.89) nor the recessive (DD vs. ID + II = OR: 1.31, 95% CI: 1.31-4.89, p = 0.69) models showed an increased risk of non-contact injury. Conclusions: The distribution of the ACE I/D genotypes was similar in elite cycling and field hockey athletes with or without non-contact injury during the season. These results indicate that there is no significant association between the ACE I/D polymorphism and the susceptibility to non-contact injury in these athletes. Further research is warranted to validate these findings and to investigate their broader implications for advancing knowledge in sports injury prevention and optimizing athlete management strategies.

Genetic Determinants of Endurance: A Narrative Review on Elite Athlete Status and Performance

This narrative review explores the relationship between genetics and elite endurance athletes, summarizes the current literature, highlights some novel findings, and provides a physiological basis for understanding the mechanistic effects of genetics in sport. Key genetic markers include ACTN3 R577X (muscle fiber composition), ACE I/D (cardiovascular efficiency), and polymorphisms in PPARA, VEGFA, and ADRB2, influencing energy metabolism, angiogenesis, and cardiovascular function. This review underscores the benefits of a multi-omics approach to better understand the complex interactions between genetic polymorphisms and physiological traits. It also addresses long-standing issues such as small sample sizes in studies and the heterogeneity in heritability estimates influenced by factors like sex. Understanding the mechanistic relationship between genetics and endurance performance can lead to personalized training strategies, injury prevention, and improved health outcomes. Future studies should focus on standardized classification of sports, replication studies involving diverse populations, and establishing solid physiological associations between polymorphisms and endurance traits to advance the field of sports genetics.

Towards Precision Sports Nutrition for Endurance Athletes: A Scoping Review of Application of Omics and Wearables Technologies

BACKGROUND

Endurance athletes require tailored nutrition strategies to optimize performance, recovery, and training adaptations. While traditional sports nutrition guidelines provide a foundational framework, individual variability in metabolic responses underscores the need for precision nutrition, informed by genetic, biological, and environmental factors. This scoping review evaluates the application of systems biology-driven sports nutrition for endurance athletes, focusing on 'omics' and wearable technologies.

METHODS

A scoping review of the literature was conducted in PubMed, Scopus, and Web of Science in accordance with the PRISMA-ScR checklist. Research questions, search strategies, and eligibility criteria were guided by the Population-Concept-Context framework with the following inclusion criteria: original research in English, involving endurance athletes, systems biology approaches, and nutritional interventions or continuous glucose monitoring (CGM).

RESULTS

Fifty-two studies were included, with distance runners as the most studied cohort. Eleven studies used metagenomics, eleven CGM, ten nutrigenetics, ten metabolomics, seven multi-omics, one proteomics, one epigenomics, and one lipidomics. Over half (n = 31; 60%) were randomized controlled trials (RCTs) with generally high methodological quality.

CONCLUSIONS

Most studies were proof-of-concept investigations aimed at assessing biomarkers; however, the evidence linking these biomarkers to performance, recovery, and long-term health outcomes in endurance athletes remains insufficient. Future research should focus on well-powered replicated crossover RCTs, multivariate N-of-1 clinical trials, 360-degree systems-wide approaches, and the validation of genetic impacts on nutritional interventions to refine dietary guidelines.

Association between Complex ACTN3 and ACE Gene Polymorphisms and Elite Endurance Sports in Koreans: A Case-Control Study

ACTN3 R577X and ACE I/D polymorphisms are associated with endurance exercise ability. This case-control study explored the association of ACTN3 and ACE gene polymorphisms with elite pure endurance in Korean athletes, hypothesizing that individuals with both ACTN3 XX and ACE II genotypes would exhibit superior endurance. We recruited 934 elite athletes (713 males, 221 females) and selected 45 pure endurance athletes (36 males, 9 females) requiring "≥90% aerobic energy metabolism during sports events", in addition to 679 healthy non-athlete Koreans (361 males, 318 females) as controls. Genomic DNA was extracted and genotyped for ACTN3 R577X and ACE I/D polymorphisms. ACE ID (p = 0.090) and ACTN3 RX+XX (p = 0.029) genotype distributions were significantly different between the two groups. Complex ACTN3-ACE genotypes also exhibited significant differences (p = 0.014), with dominant complex genotypes positively affecting endurance (p = 0.039). The presence of RX+II or XX+II was associated with a 1.763-fold higher likelihood of possessing a superior endurance capacity than that seen in healthy controls (90% CI = 1.037-3.089). Our findings propose an association of combined ACTN3 RX+XX and ACE II genotypes with enhanced endurance performance in elite Korean athletes. While causality remains to be confirmed, our study highlights the potential of ACTN3-ACE polymorphisms in predicting elite endurance.

A critical examination of sport discipline typology: identifying inherent limitations and deficiencies in contemporary classification systems

Exercise scientists (especially in the field of biomolecular research) frequently classify athletic cohorts into categories such as endurance, strength, or mixed, and create a practical framework for studying diverse athletic populations between seemingly similar groups. It is crucial to recognize the limitations and complexities of these classifications, as they may oversimplify the multidimensional characteristics of each sport. If so, the validity of studies dealing with such approaches may become compromised and the comparability across different studies challenging or impossible. This perspective critically examines and highlights the issues associated with current sports typologies, critiques existing sports classification systems, and emphasizes the imperative for a universally accepted classification model to enhance the quality of biomolecular research of sports in the future.

The analysis of aerobics intelligent fitness system for neurorobotics based on big data and machine learning

In modern society, people's pace of life is fast, and the pressure is enormous, leading to increasingly prominent issues such as obesity and sub-health. Traditional fitness methods cannot meet people's needs to a certain extent. Therefore, this work aims to use technology to change people's lifestyles and compensate for traditional fitness methods' shortcomings. Firstly, this work overviews neurorobotics, providing neural perception and control functions for aerobics intelligent fitness system. Secondly, the connection between big data and machine learning (ML), big data technology products, and the ML process are discussed. The Spark big data platform builds node data for calculation, and the decision tree algorithm is used for data preprocessing. These are important for future intelligent fitness analysis. This work proposes an aerobics intelligent fitness system based on neurorobotics technology and big data analysis and develops a recommendation system for the best fitness exercise. This system utilizes neural perception and control functions, combined with big data and ML technology, to solve the obesity and sub-health problems faced by people in fast-paced and high-pressure lifestyles. By harnessing the computational capabilities of the Spark big data platform and applying the decision tree algorithm for data preprocessing, the system can furnish users with personalized fitness plans and optimization recommendations. This work conducts a model performance study on 35 % aerobic fitness data on intelligent fitness Android v1.0.8 to evaluate the system's data processing ability and training effectiveness. Moreover, the aerobics intelligent fitness system models based on neurorobotics, big data, and ML are evaluated. The results indicate that normalizing the data using the Min-Max method leads to a decrease in the F1 value and a reduction in data set errors. Consequently, the dataset studied by the system model is beneficial to improving the work efficiency of the aerobics intelligent fitness system. After the comprehensive human quality of the system model is evaluated, the actual average score of the comprehensive human quality of the 13 users tested before the aerobics intelligent fitness system test is 91.44, and the average prediction score is 90.88. The results of the two tests are similar. Thus, using the intelligent fitness system can enable the user to obtain system feedback according to the actual training effect, thereby playing a guiding role in the intelligent fitness of aerobics for the user. This work designs and implements the aerobics intelligent fitness system close to the human body's training effect, further enhancing the specialization and individualization of sports and fitness.

A Systematic Review and Meta-analysis of the Association Between ACTN3 R577X Genotypes and Performance in Endurance Versus Power Athletes and Non-athletes

BACKGROUND

Previous studies reported differences in genotype frequency of the ACTN3 R577X polymorphisms (rs1815739; RR, RX and XX) in athletes and non-athletic populations. This systematic review with meta-analysis assessed ACTN3 R577X genotype frequencies in power versus endurance athletes and non-athletes.

METHODS

Five electronic databases (PubMed, Web of Science, Scopus, Science Direct, SPORTDiscus) were searched for research articles published until December 31st, 2022. Studies were included if they reported the frequency of the ACTN3 R577X genotypes in power athletes (e.g., weightlifters) and if they included a comparison with endurance athletes (e.g., long-distance runners) or non-athletic controls. A meta-analysis was then performed using either fixed or random-effects models. Pooled odds ratios (OR) were determined. Heterogeneity was detected using I2 and Cochran's Q tests. Publication bias and sensitivity analysis tests were computed.

RESULTS

After screening 476 initial registrations, 25 studies were included in the final analysis (13 different countries; 14,541 participants). In power athletes, the RX genotype was predominant over the two other genotypes: RR versus RX (OR 0.70; 95% CI 0.57-0.85, p = 0.0005), RR versus XX (OR 4.26; 95% CI 3.19-5.69, p < 0.00001), RX versus XX (OR 6.58; 95% CI 5.66-7.67, p < 0.00001). The R allele was higher than the X allele (OR 2.87; 95% CI 2.35-3.50, p < 0.00001) in power athletes. Additionally, the frequency of the RR genotype was higher in power athletes than in non-athletes (OR 1.48; 95% CI 1.25-1.75, p < 0.00001). The RX genotype was similar in both groups (OR 0.84; 95% CI 0.71-1.00, p = 0.06). The XX genotype was lower in power athletes than in controls (OR 0.73; 95% CI 0.64-0.84, p < 0.00001). Furthermore, the R allele frequency was higher in power athletes than in controls (OR 1.28; 95% CI 1.19-1.38, p < 0.00001). Conversely, a higher frequency of X allele was observed in the control group compared to power athletes (OR 0.78; 95% CI 0.73-0.84, p < 0.00001). On the other hand, the frequency of the RR genotype was higher in power athletes than in endurance athletes (OR 1.27; 95% CI 1.09-1.49, p = 0.003). The frequency of the RX genotype was similar in both groups (OR 1.07; 95% CI 0.93-1.24, p = 0.36). In contrast, the frequency of the XX genotype was lower in power athletes than in endurance athletes (OR 0.63; 95% CI 0.52-0.76, p < 0.00001). In addition, the R allele was higher in power athletes than in endurance athletes (OR 1.32; 95% CI 1.11-1.57, p = 0.002). However, the X allele was higher in endurance athletes compared to power athletes (OR 0.76; 95% CI 0.64-0.90, p = 0.002). Finally, the genotypic and allelic frequency of ACTN3 genes were similar in male and female power athletes.

CONCLUSIONS

The pattern of the frequencies of the ACTN3 R577X genotypes in power athletes was RX > RR > XX. However, the RR genotype and R allele were overrepresented in power athletes compared to non-athletes and endurance athletes. These data suggest that the RR genotype and R allele, which is associated with a normal expression of α-actinin-3 in fast-twitch muscle fibers, may offer some benefit in improving performance development in muscle strength and power.

Genotypic and Allelic Distribution of the CD36 rs1761667 Polymorphism in High-Level Moroccan Athletes: A Pilot Study

Previous studies have shown that variations in the CD36 gene may affect phenotypes associated with fat metabolism as the CD36 protein facilitates the transport of fatty acids to the mitochondria for oxidation. However, no previous study has tested whether variations in the CD36 gene are associated with sports performance. We investigated the genotypic and allelic distribution of the single-nucleotide polymorphism (SNP) rs1761667 in the CD36 gene in elite Moroccan athletes (cyclists and hockey players) in comparison with healthy non-athletes of the same ethnic origin. Forty-three Moroccan elite male athletes (nineteen cyclists and twenty-four field hockey players) belonging to the national teams of their respective sports (athlete group) were compared to twenty-eight healthy, active, male university students (control group). Genotyping of the CD36 rs1761667 (G>A) SNP was performed via polymerase chain reaction (PCR) and Sanger sequencing. A chi-square (χ2) test was used to assess the Hardy-Weinberg equilibrium (HWE) and to compare allele and genotype frequencies in the "athlete" and "control" groups. The genotypic distribution of the CD36 rs1761667 polymorphism was similar in elite athletes (AA: 23.81, AG: 59.52, and GG: 16.67%) and controls (AA: 19.23, AG: 69.23, and GG: 11.54%; χ2 = 0.67, p = 0.71). However, the genotypic distribution of the CD36 rs1761667 polymorphism was different between cyclists (AA: 0.00, AG: 72.22, and GG: 27.78%) and hockey players (AA: 41.67, AG: 50.00, and GG: 8.33%; χ2 = 10.69, p = 0.004). Specifically, the frequency of the AA genotype was significantly lower in cyclists than in hockey players (p = 0.02). In terms of allele frequency, a significant difference was found between cyclists versus field hockey players (χ2 = 7.72, p = 0.005). Additionally, there was a predominance of the recessive model in cyclists over field hockey players (OR: 0.00, 95% CI: 0.00-0.35, p = 0.002). Our study shows a significant difference between cyclists and field hockey players in terms of the genotypic and allelic frequency of the SNP rs1761667 of the CD36 gene. This divergence suggests a probable association between genetic variations in the CD36 gene and the type of sport in elite Moroccan athletes.

ACTN3 XX Genotype Negatively Affects Running Performance and Increases Muscle Injury Incidence in LaLiga Football Players

The aim of this study was to investigate the association of the ACTN3 rs1815739 polymorphism with match running performance and injury incidence in top-level professional football players. A total of 315 top-level professional football players from the first division of Spanish football (i.e., LaLiga) participated in this prospective and descriptive study. The ACTN3 rs1815739 genotype was identified for each player using genomic DNA samples. During LaLiga 2021-2022, players' performance was obtained through a validated camera system in all official matches. Additionally, the incidence of non-contact injuries was obtained by each team's medical staff according to the International Olympic Committee (IOC) statement. From the study sample, 116 (36.8%) players had the RR genotype, 156 (49.5%) had the RX genotype, and 43 (13.7%) had the XX genotype. The anthropometric characteristics of the players were similar across genotypes. However, the total running distance (p = 0.046), the distance at 21.0-23.9 km/h (p = 0.042), and the number of sprints (p = 0.042) were associated with the ACTN3 genotype. In all these variables, XX players had lower match performance values than RR players. Additionally, total and match injury incidences were higher in XX players than in RR players (p = 0.026 and 0.009, respectively). The rate of muscle injuries was also higher in XX players (p = 0.016). LaLiga football players with the ACTN3 XX genotype had lower match running performance and a higher incidence of non-contact injuries over the season.

Human exons and introns classification using pre-trained Resnet-50 and GoogleNet models and 13-layers CNN model

BACKGROUND

Examining functions and characteristics of DNA sequences is a highly challenging task. When it comes to the human genome, which is made up of exons and introns, this task is more challenging. Human exons and introns contain millions to billions of nucleotides, which contributes to the complexity observed in this sequences. Considering how complicated the subject of genomics is, it is obvious that using signal processing techniques and deep learning tools to build a strong predictive model can be very helpful for the development of the research of the human genome.

RESULTS

After representing human exons and introns with color images using Frequency Chaos Game Representation, two pre-trained convolutional neural network models (Resnet-50 and GoogleNet) and a proposed CNN model having 13 hidden layers were used to classify our obtained images. We have reached a value of 92% for the accuracy rate for Resnet-50 model in about 7 h for the execution time, a value of 91.5% for the accuracy rate for the GoogleNet model in 2 h and a half for the execution time. For our proposed CNN model, we have reached 91.6% for the accuracy rate in 2 h and 37 min.

CONCLUSIONS

Our proposed CNN model is faster than the Resnet-50 model in terms of execution time. It was able to slightly exceed the GoogleNet model for the accuracy rate value.

Advancing athletic assessment by integrating conventional methods with cutting-edge biomedical technologies for comprehensive performance, wellness, and longevity insights

In modern athlete assessment, the integration of conventional biochemical and ergophysiologic monitoring with innovative methods like telomere analysis, genotyping/phenotypic profiling, and metabolomics has the potential to offer a comprehensive understanding of athletes' performance and potential longevity. Telomeres provide insights into cellular functioning, aging, and adaptation and elucidate the effects of training on cellular health. Genotype/phenotype analysis explores genetic variations associated with athletic performance, injury predisposition, and recovery needs, enabling personalization of training plans and interventions. Metabolomics especially focusing on low-molecular weight metabolites, reveal metabolic pathways and responses to exercise. Biochemical tests assess key biomarkers related to energy metabolism, inflammation, and recovery. Essential elements depict the micronutrient status of the individual, which is critical for optimal performance. Echocardiography provides detailed monitoring of cardiac structure and function, while burnout testing evaluates psychological stress, fatigue, and readiness for optimal performance. By integrating this scientific testing battery, a multidimensional understanding of athlete health status can be achieved, leading to personalized interventions in training, nutrition, supplementation, injury prevention, and mental wellness support. This scientifically rigorous approach hereby presented holds significant potential for improving athletic performance and longevity through evidence-based, individualized interventions, contributing to advances in the field of sports performance optimization.

From Microcosm to Macrocosm: The -Omics, Multiomics, and Sportomics Approaches in Exercise and Sports

This article explores the progressive integration of -omics methods, including genomics, metabolomics, and proteomics, into sports research, highlighting the development of the concept of "sportomics." We discuss how sportomics can be used to comprehend the multilevel metabolism during exercise in real-life conditions faced by athletes, enabling potential personalized interventions to improve performance and recovery and reduce injuries, all with a minimally invasive approach and reduced time. Sportomics may also support highly personalized investigations, including the implementation of n-of-1 clinical trials and the curation of extensive datasets through long-term follow-up of athletes, enabling tailored interventions for athletes based on their unique physiological responses to different conditions. Beyond its immediate sport-related applications, we delve into the potential of utilizing the sportomics approach to translate Big Data regarding top-level athletes into studying different human diseases, especially with nontargeted analysis. Furthermore, we present how the amalgamation of bioinformatics, artificial intelligence, and integrative computational analysis aids in investigating biochemical pathways, and facilitates the search for various biomarkers. We also highlight how sportomics can offer relevant information about doping control analysis. Overall, sportomics offers a comprehensive approach providing novel insights into human metabolism during metabolic stress, leveraging cutting-edge systems science techniques and technologies.

Genetics and athletic performance: a systematic SWOT analysis of non-systematic reviews

Exercise genetics/genomics is a growing research discipline comprising several Strengths and Opportunities but also deals with Weaknesses and Threats. This "systematic SWOT overview of non-systematic reviews" (sSWOT) aimed to identify the Strengths, Weaknesses, Opportunities, and Threats linked to exercise genetics/genomics. A systematic search was conducted in the Medline and Embase databases for non-systematic reviews to provide a comprehensive overview of the current literature/research area. The extracted data was thematically analyzed, coded, and categorized into SWOT clusters. In the 45 included reviews five Strengths, nine Weaknesses, six Opportunities, and three Threats were identified. The cluster of Strengths included "advances in technology", "empirical evidence", "growing research discipline", the "establishment of consortia", and the "acceptance/accessibility of genetic testing". The Weaknesses were linked to a "low research quality", the "complexity of exercise-related traits", "low generalizability", "high costs", "genotype scores", "reporting bias", "invasive methods", "research progress", and "causality". The Opportunities comprised of "precision exercise", "omics", "multicenter studies", as well as "genetic testing" as "commercial"-, "screening"-, and "anti-doping" detection tool. The Threats were related to "ethical issues", "direct-to-consumer genetic testing companies", and "gene doping". This overview of the present state of the art research in sport genetics/genomics indicates a field with great potential, while also drawing attention to the necessity for additional advancement in methodological and ethical guidance to mitigate the recognized Weaknesses and Threats. The recognized Strengths and Opportunities substantiate the capability of genetics/genomics to make significant contributions to the performance and wellbeing of athletes.

Yusof S. The Predictive Ability of Total Genotype Score and Serum Metabolite Markers in Power-Based Sports Performance Following Different Strength Training Intensities — A Pilot Study

Muscular power is one of the factors that contribute to an athlete’s performance. This study aimed to explore the predictive ability of total genotype score (TGS) and serum metabolite markers in power-based sports performance following different strength training (ST) intensities. We recruited 15 novice male field hockey players (age = 16.27 ± .12 years old, body mass index = 22.57 ± 2.21 kg/m2) and allocated them to; high-intensity strength training (HIST, n=5), moderate intensity strength (MIST, n=5), and control group (C, n=5). Both training groups completed an eight-week ST intervention. Pre- and post-training muscular power (vertical jump) was measured. The participants were genotyped for; ACE (rs1799752), ACTN3 (rs1815739), ADRB3 (rs4994), AGT (rs699), BDKRB2 (rs1799722), PPARA (rs4253778), PPARGC1A (rs8192678), TRHR (rs7832552), and VEGF (rs1870377). TGS was calculated to annotate for strength-power (STP) and endurance (END) qualities. Subsequently, serum metabolomics analysis was conducted using Liquid chromatography-mass spectrometry Quadrupole-Time-of-Flight (LC-MS QTOF) to profile differentially expressed metabolite changes induced by training. Multiple regression analysis was conducted to explore the ability of TGS and differentially expressed metabolite markers to predict muscular power changes following the intervention. Multiple Regression revealed that only TGS STP might be a significant predictor of muscular power changes following MIST (adjusted R2=.906, p<.05). Additionally, ST also resulted in significant muscular power improvement (p<.05) and perturbation of the sphingolipid metabolism pathway (p<.05). Therefore, selected gene variants may influence muscular power. Therefore, STP TGS might be able to predict muscular power changes following MIST.

Factors associated with high-level endurance performance: An expert consensus derived via the Delphi technique

There is little agreement on the factors influencing endurance performance. Endurance performance often is described by surrogate variables such as maximum oxygen consumption, lactate threshold, and running economy. However, other factors also determine success and progression of high-level endurance athletes. Therefore, the aim was to identify the relevant factors for endurance performance assessed by international experts by adhering to a structured communication method (i.e., Delphi technique). Three anonymous evaluation rounds were conducted initiated by a list of candidate factors (n = 120) serving as baseline input variables. The items that achieved ≥70% of agreement in round 1 were re-evaluated in a second round. Items with a level of agreement of ≥70% in round 2 reached consensus and items with a level of agreement of 40-69% in round 2 were re-rated in a third round followed by a consensus meeting. Round 1 comprised of 27 panellists (n = 24 male) and in round 2 and 3 18 (n = 15 male) of the 27 panellists remained. Thus, the final endurance expert panel comprised of 18 international experts (n = 15 male) with 20 years of experience on average. The consensus report identified the following 26 factors: endurance capacity, running economy, maximal oxygen consumption, recovery speed, carbohydrate metabolism, glycolysis capacity, lactate threshold, fat metabolism, number of erythrocytes, iron deficiency, muscle fibre type, mitochondrial biogenesis, hydrogen ion buffering, testosterone, erythropoietin, cortisol, hydration status, vitamin D deficiency, risk of non-functional overreaching and stress fracture, healing function of skeletal tissue, motivation, stress resistance, confidence, sleep quality, and fatigue. This study provides an expert-derived summary including 26 key factors for endurance performance, the "FENDLE" factors (FENDLE = Factors for ENDurance Level). This consensus report may assist to optimize sophisticated diagnostics, personalized training strategies and technology.

Genetic profiles to identify talents in elite endurance athletes and professional football players

The genetic profile that is needed to identify talents has been studied extensively in recent years. The main objective of this investigation was to approach, for the first time, the study of genetic variants in several polygenic profiles and their role in elite endurance and professional football performance by comparing the allelic and genotypic frequencies to the non-athlete population. In this study, genotypic and allelic frequencies were determined in 452 subjects: 292 professional athletes (160 elite endurance athletes and 132 professional football players) and 160 non-athlete subjects. Genotyping of polymorphisms in liver metabolisers (CYP2D6, GSTM1, GSTP and GSTT), iron metabolism and energy efficiency (HFE, AMPD1 and PGC1a), cardiorespiratory fitness (ACE, NOS3, ADRA2A, ADRB2 and BDKRB2) and muscle injuries (ACE, ACTN3, AMPD1, CKM and MLCK) was performed by Polymerase Chain Reaction-Single Nucleotide Primer Extension (PCR-SNPE). The combination of the polymorphisms for the “optimal” polygenic profile was quantified using the genotype score (GS) and total genotype score (TGS). Statistical differences were found in the genetic distributions between professional athletes and the non-athlete population in liver metabolism, iron metabolism and energy efficiency, and muscle injuries (p<0.001). The binary logistic regression model showed a favourable OR (odds ratio) of being a professional athlete against a non-athlete in liver metabolism (OR: 1.96; 95% CI: 1.28–3.01; p = 0.002), iron metabolism and energy efficiency (OR: 2.21; 95% CI: 1.42–3.43; p < 0.001), and muscle injuries (OR: 2.70; 95% CI: 1.75–4.16; p < 0.001) in the polymorphisms studied. Genetic distribution in professional athletes as regards endurance (professional cyclists and elite runners) and professional football players shows genetic selection in these sports disciplines.

Psychosocial aspects of sports medicine in pediatric athletes: Current concepts in the 21st century

Behavioral aspects of organized sports activity for pediatric athletes are considered in a world consumed with winning at all costs. In the first part of this treatise, we deal with a number of themes faced by our children in their sports play. These concepts include the lure of sports, sports attrition, the mental health of pediatric athletes (i.e., effects of stress, anxiety, depression, suicide in athletes, ADHD and stimulants, coping with injuries, drug use, and eating disorders), violence in sports (i.e., concepts of the abused athlete including sexual abuse), dealing with supervisors (i.e., coaches, parents), peers, the talented athlete, early sports specialization and sports clubs. In the second part of this discussion, we cover ergolytic agents consumed by young athletes in attempts to win at all costs. Sports doping agents covered include anabolic steroids (anabolic-androgenic steroids or AAS), androstenedione, dehydroepiandrostenedione (DHEA), human growth hormone (hGH; also its human recombinant homologue: rhGH), clenbuterol, creatine, gamma hydroxybutyrate (GHB), amphetamines, caffeine and ephedrine. Also considered are blood doping that includes erythropoietin (EPO) and concepts of gene doping. In the last section of this discussion, we look at disabled pediatric athletes that include such concepts as athletes with spinal cord injuries (SCIs), myelomeningocele, cerebral palsy, wheelchair athletes, and amputee athletes; also covered are pediatric athletes with visual impairment, deafness, and those with intellectual disability including Down syndrome. In addition, concepts of autonomic dysreflexia, boosting and atlantoaxial instability are emphasized. We conclude that clinicians and society should protect our precious pediatric athletes who face many challenges in their involvement with organized sports in a world obsessed with winning. There is much we can do to help our young athletes find benefit from sports play while avoiding or blunting negative consequences of organized sport activities.

TTN Variants Are Associated with Physical Performance and Provide Potential Markers for Sport-Related Phenotypes

TTN encodes the third myofilament, titin, which plays structural, mechanical, regulatory, and developmental roles in sarcomeres. The aim of this research was to determine the interaction between novel and previously described TTN variants and athletic performance, as well as competition level, in Caucasians. Firstly, 100 athletes and 47 controls were recruited, and whole-genome sequencing was performed. Secondly, 348 athletes (108 endurance, 100 sprint/power, 140 mixed-sport athletes) and 403 volunteers were included, and real-time PCR was performed. We found a significant overrepresentation of the rs10497520 CT and TT genotypes in the sprint/power athlete group (95% CI, 1.41-3.66, p = 0.0013). The rs10497520 T carriers were 2.17 times more likely to become sprint/power athletes (95% CI 1.35-3.49, p = 0.0021). We also found that the likelihood of having the TT genotype was higher for the highly elite and sub-elite sprint/power athletes. Possessing at least one TAA (rs10497520, rs55837610, rs72648256) haplotype resulted in an increase in the log-odds ratio by 0.80 (p = 0.0015), 1.42 (p = 0.003), and 0.77 (p = 0.044) for all, highly elite, and sub-elite sprint/power athletes, respectively. We demonstrated that harbouring the rs10497520 T allele, individually and in a haplotype combination, increased the chance of being an elite sprint/power athlete, indicating that this allele may be favourable for sprint/power performance.

Outcomes of Genetic Testing-Based Cardiac Rehabilitation Program in Patients with Acute Myocardial Infarction after Percutaneous Coronary Intervention

Objective

There can be extreme variability between individual responses to exercise training, and the identification of genetic variants associated with individual variabilities in exercise-related traits could guide individualized exercise programs. We aimed to screen the exercise-related gene sensitivity of patients with acute myocardial infarction after PCI by establishing the gene spectrum of aerobic exercise and cardiopulmonary function sensitivity, test the effect of individualized precision exercise therapy, and provide evidence for the establishment of a precision medicine program for clinical research.

Methods

Aerobic exercise- and cardiopulmonary function-related genes and single-nucleotide polymorphisms (SNPs) were obtained by data mining utilizing a major publicly available biomedical repository, the NCBI PubMed database. Biological samples from all participants underwent DNA testing. We performed SNP detection using Samtools. A total of 122 patients who underwent PCI were enrolled in the study. We screened the first 24 cases with a high mutation frequency for aerobic exercise- and cardiopulmonary function-related genes and the last 24 cases with a low mutation frequency and separated them into two groups for the exercise intervention experiment.

Results

In both the low mutation frequency group and the high mutation frequency group, after 8 weeks of exercise intervention, 6 MWT distance, 6 MWT%, VO₂/kg at peak, and VO₂/kg at AT were significantly improved, and the effect in the high mutation frequency group was significantly higher than that in the low mutation frequency group (6 MWT distance: 468 vs. 439, P=0.003; 6 MWT%: 85 vs. 77, P=0.002, VO₂/kg at peak: 14.7 vs. 13.3, P=0.002; VO₂/kg at AT: 11.9 vs. 13.3, P=0.003).

Conclusions

There is extreme variability between individual responses to exercise training. The identification of genetic variants associated with individual variabilities in exercise-related traits could guide individualized exercise programs. We found that the subjects with a high mutation frequency in aerobic exercise and cardiopulmonary function-related genes achieved more cardiorespiratory fitness benefits in the aerobic exercise rehabilitation program and provided evidence for the establishment of a precision medicine program for clinical research.

Consumer Group Identification Algorithm for Ice and Snow Sports

As an important part of the modern sports industry system, the quality and level of its development are related to whether China's sports industry can successfully become a pillar industry of the national economy. Therefore, the development of the ice and snow sports industry is to promote the expansion of China's sports industry scale high quality development of the national economy and an important way to build sports power. Participative sports consumption is the most important part of sports consumption and the development of the sports industry. The sports industry separated from participative sports consumption is water without source and tree without roots, while participative sports consumption demand is the power source of participative sports consumption. At present, there is no systematic and complete research on participation sports consumption demand. In order to understand the causes and demand state of residents' participation sports consumption demand and provide entry points for enterprises to formulate marketing strategies, this study constructs an organic system with participation sports service products as consumption objects, centering on the demanding state of participation sports consumers. In the system, on the theory of supply and demand, under the guidance of consumption economics theory, adhere to the combination of theoretical research and empirical analysis, the combination of macroplanning and microdesign, the combination of qualitative analysis and quantitative analysis, through the empirical investigation and receipt collection of residents' participation sports consumption demand, the use of systematic analysis, literature method, and survey method, through mathematical analysis, and other research methods, the paper explores the main causes and demand conditions of residents' participation sports consumption demand in different consumption states and excavates the main causes and demand conditions of participating sports consumption demand in different consumption states under different sports levels.

Genetics and sports performance: the present and future in the identification of talent for sports based on DNA testing

The impact of genetics on physiology and sports performance is one of the most debated research aspects in sports sciences. Nearly 200 genetic polymorphisms have been found to influence sports performance traits, and over 20 polymorphisms may condition the status of the elite athlete. However, with the current evidence, it is certainly too early a stage to determine how to use genotyping as a tool for predicting exercise/sports performance or improving current methods of training. Research on this topic presents methodological limitations such as the lack of measurement of valid exercise performance phenotypes that make the study results difficult to interpret. Additionally, many studies present an insufficient cohort of athletes, or their classification as elite is dubious, which may introduce expectancy effects. Finally, the assessment of a progressively higher number of polymorphisms in the studies and the introduction of new analysis tools, such as the total genotype score (TGS) and genome-wide association studies (GWAS), have produced a considerable advance in the power of the analyses and a change from the study of single variants to determine pathways and systems associated with performance. The purpose of the present study was to comprehensively review evidence on the impact of genetics on endurance- and power-based exercise performance to clearly determine the potential utility of genotyping for detecting sports talent, enhancing training, or preventing exercise-related injuries, and to present an overview of recent research that has attempted to correct the methodological issues found in previous investigations.

Advances in sports genomics

Sports genomics is the scientific discipline that focuses on the organization and function of the genome in elite athletes, and aims to develop molecular methods for talent identification, personalized exercise training, nutritional need and prevention of exercise-related diseases. It postulates that both genetic and environmental factors play a key role in athletic performance and related phenotypes. This update on the panel of genetic markers (DNA polymorphisms) associated with athlete status and soft-tissue injuries covers advances in research reported in recent years, including one whole genome sequencing (WGS) and four genome-wide association (GWAS) studies, as well as findings from collaborative projects and meta-analyses. At end of 2020, the total number of DNA polymorphisms associated with athlete status was 220, of which 97 markers have been found significant in at least two studies (35 endurance-related, 24 power-related, and 38 strength-related). Furthermore, 29 genetic markers have been linked to soft-tissue injuries in at least two studies. The most promising genetic markers include HFE rs1799945, MYBPC3 rs1052373, NFIA-AS2 rs1572312, PPARA rs4253778, and PPARGC1A rs8192678 for endurance; ACTN3 rs1815739, AMPD1 rs17602729, CPNE5 rs3213537, CKM rs8111989, and NOS3 rs2070744 for power; LRPPRC rs10186876, MMS22L rs9320823, PHACTR1 rs6905419, and PPARG rs1801282 for strength; and COL1A1 rs1800012, COL5A1 rs12722, COL12A1 rs970547, MMP1 rs1799750, MMP3 rs679620, and TIMP2 rs4789932 for soft-tissue injuries. It should be appreciated, however, that hundreds and even thousands of DNA polymorphisms are needed for the prediction of athletic performance and injury risk.

RNA Sequencing of Whole Blood Defines the Signature of High Intensity Exercise at Altitude in Elite Speed Skaters

Although high altitude training has been increasingly popular among endurance athletes, the molecular and cellular bases of this adaptation remain poorly understood. We aimed to define the underlying physiological changes and screen for potential biomarkers of adaptation using transcriptional profiling of whole blood. Seven elite female speed skaters were profiled on the 18th day of high-altitude adaptation. Whole blood RNA-seq before and after an intense 1 h skating bout was used to measure gene expression changes associated with exercise. In order to identify the genes specifically regulated at high altitudes, we have leveraged the data from eight previously published microarray datasets studying blood expression changes after exercise at sea level. Using cell type-specific signatures, we were able to deconvolute changes of cell type abundance from individual gene expression changes. Among these were PHOSPHO1, with a known role in erythropoiesis, and MARC1 with a role in endogenic NO metabolism. We find that platelet and erythrocyte counts uniquely respond to altitude exercise, while changes in neutrophils represent a more generic marker of intense exercise. Publicly available data from both single cell atlases and exercise-related blood profiling dramatically increases the value of whole blood RNA-seq for the dynamic evaluation of physiological changes in an athlete’s body.

EFFECTS OF GENETIC VARIATIONS OF MLCK2, AMPD1, AND COL5A1 ON MUSCLE ENDURANCE

ABSTRACT Introduction: Although potential relationships with genetic variants of MLCK2, AMPD1 and COL5A1 have been detected in molecular studies evaluating sports performance from the genetic perspective, there are limited data in terms of muscle endurance and physical fitness. Materials and Methods: This study aimed to evaluate these variants in terms of lower limb muscle endurance and physical fitness in thirty-three soccer players. Genotypes were determined by High Resolution Melting (HRM) analysis in qPCR after genomic DNA was isolated from buccal swab samples from the participants. Measurements of lower limb muscle endurance, the dynamic leap and balance test (DLBT), and the standing broad jump test (SBJ) were taken for all the participants. Results: Greater height (p = 0.006), higher DLBT (p = 0.016) and SBJ (p = 0.033) scores, as well as greater left hip adduction (p <0.001), were detected in those with the CT genotype for AMPD1 as compared to those with CC. For MLCK rs28497577, it was found that the players carrying the AA genotype were taller (p = 0.046), heavier (p = 0.049), and had greater left knee extension (p=0.014) and left foot plantar flexion (p =0.040) than those carrying the C allele. Those with the CT genotype for COL5A1 rs12722 had greater right hip extension (p = 0.040) and right knee extension (p = 0.048) than those with the CC genotype. Conclusions: Our results showed that MLCK2 and COL5A1 gene variants are associated with body composition and lower limb muscle endurance, and the presence of the AMPD1 CT genotype may contribute positively to balance, correct positioning, controlled strength, and hip mobility. Evidence level II; Comparative prospective study .

New Opportunities to Advance the Field of Sports Nutrition

Sports nutrition is a relatively new discipline; with ~100 published papers/year in the 1990s to ~3,500+ papers/year today. Historically, sports nutrition research was primarily initiated by university-based exercise physiologists who developed new methodologies that could be impacted by nutrition interventions (e.g., carbohydrate/fat oxidation by whole body calorimetry and muscle glycogen by muscle biopsies). Application of these methods in seminal studies helped develop current sports nutrition guidelines as compiled in several expert consensus statements. Despite this wealth of knowledge, a limitation of the current evidence is the lack of appropriate intervention studies (e.g., randomized controlled clinical trials) in elite athlete populations that are ecologically valid (e.g., in real-life training and competition settings). Over the last decade, there has been an explosion of sports science technologies, methodologies, and innovations. Some of these recent advances are field-based, thus, providing the opportunity to accelerate the application of ecologically valid personalized sports nutrition interventions. Conversely, the acceleration of novel technologies and commercial solutions, especially in the field of biotechnology and software/app development, has far outstripped the scientific communities' ability to validate the effectiveness and utility of the vast majority of these new commercial technologies. This mini-review will highlight historical and present innovations with particular focus on technological innovations in sports nutrition that are expected to advance the field into the future. Indeed, the development and sharing of more “big data,” integrating field-based measurements, resulting in more ecologically valid evidence for efficacy and personalized prescriptions, are all future key opportunities to further advance the field of sports nutrition.

Molecular Big Data in Sports Sciences: State-of-Art and Future Prospects of OMICS-Based Sports Sciences

Together with environment and experience (that is to say, diet and training), the biological and genetic make-up of an athlete plays a major role in exercise physiology. Sports genomics has shown, indeed, that some DNA single nucleotide polymorphisms (SNPs) can be associated with athlete performance and level (such as elite/world-class athletic status), having an impact on physical activity behavior, endurance, strength, power, speed, flexibility, energetic expenditure, neuromuscular coordination, metabolic and cardio-respiratory fitness, among others, as well as with psychological traits. Athletic phenotype is complex and depends on the combination of different traits and characteristics: as such, it requires a "complex science," like that of metadata and multi-OMICS profiles. Several projects and trials (like ELITE, GAMES, Gene SMART, GENESIS, and POWERGENE) are aimed at discovering genomics-based biomarkers with an adequate predictive power. Sports genomics could enable to optimize and maximize physical performance, as well as it could predict the risk of sports-related injuries. Exercise has a profound impact on proteome too. Proteomics can assess both from a qualitative and quantitative point of view the modifications induced by training. Recently, scholars have assessed the epigenetics changes in athletes. Summarizing, the different omics specialties seem to converge in a unique approach, termed sportomics or athlomics and defined as a "holistic and top-down," "non-hypothesis-driven research on an individual's metabolite changes during sports and exercise" (the Athlome Project Consortium and the Santorini Declaration) Not only sportomics includes metabonomics/metabolomics, but relying on the athlete's biological passport or profile, it would enable the systematic study of sports-induced changes and effects at any level (genome, transcriptome, proteome, etc.). However, the wealth of data is so huge and massive and heterogenous that new computational algorithms and protocols are needed, more computational power is required as well as new strategies for properly and effectively combining and integrating data.

Using Genomic Techniques in Sports and Exercise Science: Current Status and Future Opportunities

ABSTRACT

The past two decades have built on the successes of the Human Genome Project identifying the impact of genetics and genomics on human traits. Given the importance of exercise in the physical and psychological health of individuals across the lifespan, using genomics to understand the impact of genes in the sports medicine field is an emerging field. Given the complexity of the systems involved, high-throughput genomics is required to understand genetic variants, their functions, and ultimately their effect on the body. Consequently, genomic studies have been performed across several domains of sports medicine with varying degrees of success. While the breadth of these is great, they focus largely on the following three areas: 1) performance; 2) injury susceptibility; and 3) sports associated chronic conditions, such as osteoarthritis. Herein, we review literature on genetics and genomics in sports medicine, offer suggestions to bolster existing studies, and suggest ways to ideally impact clinical care.

A meta-analysis on the association of ACE and PPARA gene variants and endurance athletic status

BACKGROUND

Genetics has an important role in determining the athletic ability and endurance performance potential. This study aimed to investigate the variable results obtained from endurance athletes and control participants in terms of angiotensin-converting enzyme (ACE) and peroxisome proliferator-activated receptor alpha (PPARA) polymorphism distributions.

METHODS

Multiple electronic databases were investigated independently by two researchers. A meta-analysis was conducted on the association of ACE insertion/deletion (I/D) polymorphism and PPARA G/C polymorphisms with endurance athletes. Odds ratios (OR) and 95% confidence intervals (CI) were estimated. Twenty-six studies were identified for the ACE I/D for 2979 endurance athletes and 10048 control participants while seven studies were identified for PPARA G/C for 901 endurance athletes and 2292 control participants.

RESULTS

There was a significant difference in ACE genotype distribution between endurance athletes and control (II vs. ID+DD: OR=1.48; 95% CI=0.30-2.67; p=0.001). On the other hand, there was no a significant difference in PPARA G/C polymorphism genotype distribution between endurance athletes and control (GC+CC vs. GG: OR=0.93; 95% CI=-0.46-2.32; p=0.192; GC+GG vs CC: OR=0.62; 95% CI=-1.75-2.99; p=0.604).

CONCLUSIONS

The results have shown that ACE I/D polymorphism may be associated with endurance performance in sports and that the predominance of the ACE II genotype in a person may play an advantageous role in being an endurance athlete. However, this effect has not been observed in PPARA G/C polymorphism.

Goals in Nutrition Science 2020-2025

Five years ago, with the editorial board of Frontiers in Nutrition, we took a leap of faith to outline the Goals for Nutrition Science - the way we see it (1). Now, in 2020, we can put ourselves to the test and take a look back. Without a doubt we got it right with several of the key directions. To name a few, Sustainable Development Goals (SDGs) for Food and Nutrition are part of the global public agenda, and the SDGs contribute to the structuring of international science and research. Nutritional Science has become a critical element in strengthening work on the SDGs, and the development of appropriate methodologies is built on the groundwork of acquiring and analyzing big datasets. Investigation of the Human Microbiome is providing novel insight on the interrelationship between nutrition, the immune system and disease. Finally, with an advanced definition of the gut-brain-axis we are getting a glimpse into the potential for Nutrition and Brain Health. Various milestones have been achieved, and any look into the future will have to consider the lessons learned from Covid-19 and the sobering awareness about the frailty of our food systems in ensuring global food security. With a view into the coming 5 years from 2020 to 2025, the editorial board has taken a slightly different approach as compared to the previous Goals article. A mind map has been created to outline the key topics in nutrition science. Not surprisingly, when looking ahead, the majority of scientific investigation required will be in the areas of health and sustainability. Johannes le Coutre, Field Chief Editor, Frontiers in Nutrition.

Effect of ACTN3 R577X Genotype on Injury Epidemiology in Elite Endurance Runners

The p.R577X polymorphism (rs1815739) in the ACTN3 gene causes individuals with the ACTN3 XX genotype to be deficient in functional α-actinin-3. Previous investigations have found that XX athletes are more prone to suffer non-contact muscle injuries. This investigation aimed to determine the influence of the ACTN3 R577X polymorphism in the injury epidemiology of elite endurance athletes. Using a cross-sectional experiment, the epidemiology of running-related injuries was recorded for one season in a group of 89 Spanish elite endurance runners. ACTN3 R577X genotype was obtained for each athlete using genomic DNA samples. From the study sample, 42.7% of athletes had the RR genotype, 39.3% had the RX genotype, and 18.0% had the XX genotype. A total of 96 injuries were recorded in 57 athletes. Injury incidence was higher in RR runners (3.2 injuries/1000 h of running) than in RX (2.0 injuries/1000 h) and XX (2.2 injuries/1000 h; p = 0.030) runners. RR runners had a higher proportion of injuries located in the Achilles tendon, RX runners had a higher proportion of injuries located in the knee, and XX runners had a higher proportion of injuries located in the groin (p = 0.025). The ACTN3 genotype did not affect the mode of onset, the severity, or the type of injury. The ACTN3 genotype slightly affected the injury epidemiology of elite endurance athletes with a higher injury rate in RR athletes and differences in injury location. However, elite ACTN3 XX endurance runners were not more prone to muscle-type injuries.

Whole genome sequencing of elite athletes

Whole genome sequencing (WGS) has great potential to explore all possible DNA variants associated with physical performance, psychological traits and health conditions of athletes. Here we present, for the first time, annotation of genomic variants of elite athletes, based on the WGS of 20 Tatar male wrestlers. The maximum number of high-quality variants per sample was over 3.8 M for single nucleotide polymorphisms (SNPs) and about 0.64 M for indels. The maximum number of nonsense mutations was 148 single nucleotide variants (SNVs) per individual. Athletes' genomes on average contained 18.9 nonsense SNPs in a homozygous state per sample, while non-athletes' exomes (Tatar controls, n = 19) contained 18 nonsense SNPs. Finally, we applied genomic data for the association analysis and used reaction time (RT) as an example. Out of 1884 known genome-wide significant SNPs related to RT, we identified four SNPs (KIF27 rs10125715, APC rs518013, TMEM229A rs7783359, LRRN3 rs80054135) associated with RT in wrestlers. The cumulative number of favourable alleles (KIF27 A, APC A, TMEM229A T, LRRN3 T) was significantly correlated with RT both in wrestlers (P = 0.0003) and an independent cohort (n = 43) of physically active subjects (P = 0.029). Furthermore, we found that the frequencies of the APC A (53.3 vs 44.0%, P = 0.033) and LRRN3 T (7.5 vs 2.8%, P = 0.009) alleles were significantly higher in elite athletes (n = 107) involved in sports with RT as an essential component of performance (combat sports, table tennis and volleyball) compared to less successful (n = 176) athletes. The LRRN3 T allele was also over-represented in elite athletes (7.5%) in comparison with 189 controls (2.9%, P = 0.009). In conclusion, we present the first WGS study of athletes showing that WGS can be applied in sport and exercise science.

Effect of ACTN3 Genotype on Sports Performance, Exercise-Induced Muscle Damage, and Injury Epidemiology

Genetic factors play a significant role in athletic performance and its related phenotypes such as power, strength and aerobic capacity. In this regard, the lack of a muscle protein due to a genetic polymorphism has been found to affect sport performance in a wide variety of ways. α-actinin-3 is a protein located within the skeletal muscle with a key role in the production of sarcomeric force. A common stop-codon polymorphism (rs1815739; R577X) in the gene that codes for α-actinin-3 (ACTN3) produces individuals with the XX genotype that lack expression of a functional α-actinin-3. In contrast, individuals with the R-allele (i.e., RX vs. RR genotypes) in this polymorphism can express α-actinin-3. Interestingly, around ~18% of the world population have the XX genotype and much has been debated about why a polymorphism that produces a lack of a muscle protein has endured natural selection. Several investigations have found that α-actinin-3 deficiency due to XX homozygosity in the ACTN3 R577X polymorphism can negatively affect sports performance through several structural, metabolic, or signaling changes. In addition, new evidence suggests that α-actinin-3 deficiency may also impact sports performance through indirect factors such a higher risk for injury or lower resistance to muscle-damaging exercise. The purpose of this discussion is to provide a clear explanation of the effect of α-actinin-3 deficiency due to the ACTN3 XX genotype on sport. Key focus has been provided about the effect of α-actinin-3 deficiency on morphologic changes in skeletal muscle, on the low frequency of XX athletes in some athletic disciplines, and on injury epidemiology.