Genetic Variants and Hamstring Injury in Soccer

Larruskain, Jon; Celorrio, David; Barrio, Irantzu; Odriozola, Adrián; Gil, Susana María; Fernández-López, Juan Ramón; Nozal, Raúl; Ortuzar, Isusko; Lekue, José António; Aznar, José María

doi:10.1249/mss.0000000000001434

Cited by 38 publications

(103 citation statements)

References 39 publications

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“…Several of the genes associated with Achilles Tendon injuries are also associated with hamstring injuries. Individuals carrying the TNC risk allele T (A allele reported in the paper) at rs2104772 are associated with reduced risk of Hamstring injury (Larruskain et al, 2018). Marathoners show no significantly greater risk of hamstring injuries.…”

Section: Resultsmentioning

confidence: 81%

How do your genetics compare to the world’s elite marathoners?

Sims

Zeng

Falk

et al. 2018

Preprint

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Pathway OME (Formerly Pathway Genomics, http://www.pathway.com) has identified a unique set of genetic traits associated with endurance, anxiety and likelihood of hamstring/Achilles injuries in an elite set of marathon runners. To date, this is the largest database of elite marathon athletes to ever have their exomes sequenced. Proprietary SportIQ Artificial Intelligence (AI) algorithms were developed and used to compare genetic traits in these 1,119 elite marathon runners to a normal population enabling us to develop a 'Marathon Runner' profile to assist athletes in training to compete and achieve their athletic goals. Athlete's whose genetic profiles differ significantly from the "Marathon Runner" profile will gain additional insights on the specific training necessary to prepare their muscles for the demanding conditions of long distance running.

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Section: Resultsmentioning

confidence: 81%

How do your genetics compare to the world’s elite marathoners?

Sims

Zeng

Falk

et al. 2018

Preprint

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show abstract

“…With an abundance of information available to the coach, recent research has focused on being able to better utilise this information to underpin decision making via prediction, either in terms of injury risk [193,194], postinjury recovery times [195], physiology (such as muscle fibre type [196]), training loads and fatigue [197,198], talent identification [199] and training plans [200,201]. These approaches utilise a variety of different statistical modelling techniques, including simple data analysis with a hold-out set for validation (e.g.…”

Section: Prediction Data Mining and Machine Learningmentioning

confidence: 99%

“…These approaches utilise a variety of different statistical modelling techniques, including simple data analysis with a hold-out set for validation (e.g. [194]), more complex data mining techniques (e.g. [202]) and machine learning tools (e.g.…”

Section: Prediction Data Mining and Machine Learningmentioning

confidence: 99%

“…Such a finding could be very useful within elite sport because muscle biopsy testing is highly invasive, limiting its use, while genetic testing is non-invasive. Similarly, Larruskain et al [194] collected hamstring injury data over five seasons in an elite soccer team, along with genetic information. They then created a model of five SNPs, which demonstrated acceptable discriminatory ability to explain previous hamstring injury within that cohort.…”

Section: Prediction Data Mining and Machine Learningmentioning

confidence: 99%

“…As a result of the Larruskain and colleagues [194] study, it is clear that the use of individual pieces of data is likely insufficient in the prediction of complex phenotypes and outcomes, such as injury, while it perhaps is sufficient for less complex phenotypes, as demonstrated by Borizov et al [196], who used genetic information to predict muscle fibre type with success. Data mining refers to the conversion of raw data-such as that collected by the various technological and testing practices utilised in elite sport-to information which can then be analysed [202].…”

Section: Prediction Data Mining and Machine Learningmentioning

confidence: 99%

See 2 more Smart Citations

The Development of a Personalised Training Framework: Implementation of Emerging Technologies for Performance

Pickering

Kiely

2019

JFMK

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Over the last decade, there has been considerable interest in the individualisation of athlete training, including the use of genetic information, alongside more advanced data capture and analysis techniques. Here, we explore the evidence for, and practical use of, a number of these emerging technologies, including the measurement and quantification of epigenetic changes, microbiome analysis and the use of cell-free DNA, along with data mining and machine learning. In doing so, we develop a theoretical model for the use of these technologies in an elite sport setting, allowing the coach to better answer six key questions: (1) To what training will my athlete best respond? (2) How well is my athlete adapting to training? (3) When should I change the training stimulus (i.e., has the athlete reached their adaptive ceiling for this training modality)? (4) How long will it take for a certain adaptation to occur? (5) How well is my athlete tolerating the current training load? (6) What load can my athlete handle today? Special consideration is given to whether such an individualised training framework will outperform current methods as well as the challenges in implementing this approach.

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ACTN3 single nucleotide polymorphism is associated with non-contact musculoskeletal soft-tissue injury incidence in elite professional football players

Clos¹,

Pruna

Lundblad

et al. 2019

Knee Surg Sports Traumatol Arthrosc

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Purpose Muscle injuries are common in professional football, even though prevention protocols are being implemented. Genetics constitutes a novel ield for studying intrinsic injury risks and performance. Since previous studies involving single nucleotide polymorphisms (SNPs) have shown that SNPs inluence muscle injury rate, injury severity and recovery time, the aim was to study the association the SNP of ACTN3 has with those parameters in professional football players. Methods The medical staf team recorded non-contact musculoskeletal soft-tissue injuries in 43 professional football players in 7 diferent seasons (2007-2012 and 2015-2016). Injury rate, injury severity and injury recovery times were established. Players were genotyped by extracting DNA from a blood sample and using a polymerase chain reaction. Results Injury rate was associated with the SNP of ACTN3 (p = 0.003). The 577R allele was more frequent in subjects than in a normal population by showing presence in 93% of the subjects and suggesting that it could inluence football performance. No statistically signiicant diferences in injury severity and recovery time were associated with the SNP of ACTN3. Conclusions Genetics is gaining in importance when assessing injury risk and performance in professional football. ACTN3 can be regarded as a biomarker of injury susceptibility in this discipline. Identifying those players with the highest injury susceptibility through genetics could lead football teams to individualise workloads and prevention protocols. Level of evidence III. Keywords Single nucleotide polymorphism • Football • Muscle injuries • GeneticsEnric Clos and Ricard Pruna contributed equally to the study.

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Genetic Variants and Hamstring Injury in Soccer

Abstract: Genetic variants appear to be involved in the etiology of hamstring injuries but were not found to have predictive value by themselves. Further research, increasing the number of genetic variants and including environmental factors in complex multifactorial risk models, is necessary.

Cited by 38 publications

References 39 publications

How do your genetics compare to the world’s elite marathoners?

How do your genetics compare to the world’s elite marathoners?

The Development of a Personalised Training Framework: Implementation of Emerging Technologies for Performance

ACTN3 single nucleotide polymorphism is associated with non-contact musculoskeletal soft-tissue injury incidence in elite professional football players

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