Genomic Prediction of Tendinopathy Risk in Elite Team Sports

Predictive utility of the machine learning algorithms in predicting tendinopathy: a meta-analysis of diagnostic test studies

BACKGROUND

Tendinopathy, a degenerative condition of tendon collagen protein, is a common sports injury among elite athletes. Despite its prevalence, the manifestation and progression of tendinopathy remain unclear, and the efficiency of diagnosis and treatment modalities is uncertain. Artificial intelligence and machine learning (ML) have shown positive results in disease diagnosis and treatment evaluation. This systematic review examined many ML methods and their diagnostic yield in predicting tendinopathy.

METHODS

A comprehensive search of electronic databases, including Ovid Medline, EMBASE, PubMed and the Web of Science, was conducted. The quality of the studies was assessed using the Newcastle-Ottawa scale. The statistical analysis was performed using mada package on R software.

RESULTS

Four studies were considered eligible for this meta-analysis, constituting outcomes from 12,611 patients. The ML methods used in the selected studies included random forest, convolutional neural networks and linear support vector machines. The results showed that all selected studies demonstrated the relevance of ML in moderately (Reviewer 2, Comment 2) predicting tendinopathy. The pooled diagnostic yield of the ML algorithms estimated an overall sensitivity of 0.74 (95% CI: 0.64 to 0.82, p =  < 0.001) and an overall specificity of 0.69 (95% CI: 0.49 to 0.85, p = 0.06). The diagnostic odds ratio was 6.01 (95% CI: 1.8 to 20.13), with substantial heterogeneity (I2 = 97.6%).

CONCLUSION

ML methods can predict tendinopathy accurately in elite and non-elite athletes. However, further research is needed to establish the specific clinical features associated with tendinopathy prevalence.

Single Nucleotide Polymorphisms and Tendon/Ligament Injuries in Athletes: A Systematic Review and Meta-analysis

This systematic review and meta-analysis aimed to identify the association between genetic polymorphisms and tendon and ligament injuries in adolescent and adult athletes of multiple competition sports. The PubMed, Web of Science, EBSCO, Cochrane Library, and MEDLINE databases were searched until July 7, 2023. Eligible articles included genetic studies on tendon and ligament injuries and comparisons between injured and non-injured athletes. This review included 31 articles, comprising 1,687 injury cases and 2,227 controls, from a meta-analysis of 12 articles. We identified 144 candidate gene polymorphisms (only single nucleotide polymorphisms were identified). The meta-analyses included vascular endothelial growth factor A (VEGFA) rs699947, collagen type I alpha 1 rs1800012, collagen type V alpha 1 rs12722, and matrix metalloproteinase 3 rs679620. The VEGFA rs699947 polymorphism showed a lower risk of injuries in athletes with the C allele ([C vs. A]: OR=0.80, 95% CI: 0.65-0.98, I 2 =3.82%, p=0.03). The risk of these injuries were not affected by other polymorphisms. In conclusion, the VEGFA rs699947 polymorphism is associated with the risk of tendon and ligament injuries in athletes. This study provides insights into genetic variations that contribute to our understanding of the risk factors for such injuries in athletes.

Predicting Injuries in Elite Female Football Players With Global-Positioning-System and Multiomics Data

PURPOSE

Injury prevention is a crucial aspect of sports, particularly in high-performance settings such as elite female football. This study aimed to develop an injury prediction model that incorporates clinical, Global-Positioning-System (GPS), and multiomics (genomics and metabolomics) data to better understand the factors associated with injury in elite female football players.

METHODS

We designed a prospective cohort study over 2 seasons (2019-20 and 2021-22) of noncontact injuries in 24 elite female players in the Spanish Premiership competition. We used GPS data to determine external workload, genomic data to capture genetic susceptibility, and metabolomic data to measure internal workload.

RESULTS

Forty noncontact injuries were recorded, the most frequent of which were muscle (63%) and ligament (20%) injuries. The baseline risk model included fat mass and the random effect of the player. Six genetic polymorphisms located at the DCN, ADAMTS5, ESRRB, VEGFA, and MMP1 genes were associated with injuries after adjusting for player load (P < .05). The genetic score created with these 6 variants determined groups of players with different profile risks (P = 3.1 × 10-4). Three metabolites (alanine, serotonin, and 5-hydroxy-tryptophan) correlated with injuries. The model comprising baseline variables, genetic score, and player load showed the best prediction capacity (C-index: .74).

CONCLUSIONS

Our model could allow efficient, personalized interventions based on an athlete's vulnerability. However, we emphasize the necessity for further research in female athletes with an emphasis on validation studies involving other teams and individuals. By expanding the scope of our research and incorporating diverse populations, we can bolster the generalizability and robustness of our proposed model.

Performance and healthcare analysis in elite sports teams using artificial intelligence: a scoping review

Introduction

In competitive sports, teams are increasingly relying on advanced systems for improved performance and results. This study reviews the literature on the role of artificial intelligence (AI) in managing these complexities and encouraging a system thinking shift. It found various AI applications, including performance enhancement, healthcare, technical and tactical support, talent identification, game prediction, business growth, and AI testing innovations. The main goal of the study was to assess research supporting performance and healthcare.

Methods

Systematic searches were conducted on databases such as Pubmed, Web of Sciences, and Scopus to find articles using AI to understand or improve sports team performance. Thirty-two studies were selected for review.

Results

The analysis shows that, of the thirty-two articles reviewed, fifteen focused on performance and seventeen on healthcare. Football (Soccer) was the most researched sport, making up 67% of studies. The revised studies comprised 2,823 professional athletes, with a gender split of 65.36% male and 34.64% female. Identified AI and non-AI methods mainly included Tree-based techniques (36%), Ada/XGBoost (19%), Neural Networks (9%), K-Nearest Neighbours (9%), Classical Regression Techniques (9%), and Support Vector Machines (6%).

Conclusions

This study highlights the increasing use of AI in managing sports-related healthcare and performance complexities. These findings aim to assist researchers, practitioners, and policymakers in developing practical applications and exploring future complex systems dynamics.

Predicting Injury and Illness with Machine Learning in Elite Youth Soccer: A Comprehensive Monitoring Approach over 3 Months

The search for monitoring tools that provide early indication of injury and illness could contribute to better player protection. The aim of the present study was to i) determine the feasibility of and adherence to our monitoring approach, and ii) identify variables associated with up-coming illness and injury. We incorporated a comprehensive set of monitoring tools consisting of external load and physical fitness data, questionnaires, blood, neuromuscular-, hamstring, hip abductor and hip adductor performance tests performed over a three-month period in elite under-18 academy soccer players. Twenty-five players (age: 16.6 ± 0.9 years, height: 178 ± 7 cm, weight: 74 ± 7 kg, VO2max: 59 ± 4 ml/min/kg) took part in the study. In addition to evaluating adherence to the monitoring approach, data were analyzed using a linear support vector machine (SVM) to predict illness and injuries. The approach was feasible, with no injuries or dropouts due to the monitoring process. Questionnaire adherence was high at the beginning and decreased steadily towards the end of the study. An SVM resulted in the best classification results for three classification tasks, i.e., illness prediction, illness determination and injury prediction. For injury prediction, one of four injuries present in the test data set was detected, with 96.3% of all data points (i.e., injuries and non-injuries) correctly detected. For both illness prediction and determination, there was only one illness in the test data set that was detected by the linear SVM. However, the model showed low precision for injury and illness prediction with a considerable number of false-positives. The results demonstrate the feasibility of a holistic monitoring approach with the possibility of predicting illness and injury. Additional data points are needed to improve the prediction models. In practical application, this may lead to overcautious recommendations on when players should be protected from injury and illness.

An Overview of Machine Learning Applications in Sports Injury Prediction

Use injuries, i.e., injuries caused by repetitive strain on the body, represent a serious problem in athletics that has traditionally relied on historic datasets and human experience for prevention. Existing methodologies have been frustratingly slow at developing higher precision prevention practices. Technological advancements have permitted the emergence of artificial intelligence and machine learning (ML) as promising toolsets to enhance both injury mitigation and rehabilitation protocols. This article provides a comprehensive overview of recent advances in ML techniques as they have been applied to sports injury prediction and prevention. A comprehensive literature review was conducted searching PubMed/Medline, Institute of Electrical and Electronics Engineers (IEEE)/Institute of Engineering and Technology (IET), and ScienceDirect. Ovid Discovery and Google Scholar were used to provide additional aggregate results and a grey literature search. A focus was placed on papers published from 2017 to 2022. Algorithms of interest were limited to K-Nearest Neighbor (KNN), K-means, decision tree, random forest, gradient boosting and AdaBoost, and neural networks. A total of 42 original research papers were included, and their results were summarized. We conclude that given the current lack of open source, uniform data sets, as well as a reliance on dated regression models, no strong conclusions about the real-world efficacy of ML as it applies to sports injury prediction can be made. However, it is suggested that addressing these two issues will allow powerful, novel ML architectures to be deployed, thus rapidly advancing the state of this field, and providing validated clinical tools.

Sex Differences in the Association between Risk of Anterior Cruciate Ligament Rupture and COL5A1 Polymorphisms in Elite Footballers

BACKGROUND

Single-nucleotide polymorphisms (SNPs) in collagen genes are predisposing factors for anterior cruciate ligament (ACL) rupture. Although these events are more frequent in females, the sex-specific risk of reported SNPs has not been evaluated.

PURPOSE

We aimed to assess the sex-specific risk of historic non-contact ACL rupture considering candidate SNPs in genes previously associated with muscle, tendon, ligament and ACL injury in elite footballers.

STUDY DESIGN

This was a cohort genetic association study.

METHODS

Forty-six (twenty-four females) footballers playing for the first team of FC Barcelona (Spain) during the 2020-21 season were included in the study. We evaluated the association between a history of non-contact ACL rupture before July 2022 and 108 selected SNPs, stratified by sex. SNPs with nominally significant associations in one sex were then tested for their interactions with sex on ACL.

RESULTS

Seven female (29%) and one male (4%) participants had experienced non-contact ACL rupture during their professional football career before the last date of observation. We found a significant association between the rs13946 C/C genotype and ACL injury in women footballers (p = 0.017). No significant associations were found in male footballers. The interaction between rs13946 and sex was significant (p = 0.027). We found that the C-allele of rs13946 was exclusive to one haplotype of five SNPs spanning COL5A1.

CONCLUSIONS

The present study suggests the role of SNPs in genes encoding for collagens as female risk factors for ACL injury in football players.

CLINICAL RELEVANCE

The genetic profiling of athletes at high risk of ACL rupture can contribute to sex-specific strategies for injury prevention in footballers.

Predictive Modeling of Injury Risk Based on Body Composition and Selected Physical Fitness Tests for Elite Football Players

Injuries are one of the most significant issues for elite football players. Consequently, elite football clubs have been consistently interested in having practical, interpretable, and usable models as decision-making support for technical staff. This study aimed to analyze predictive modeling of injury risk based on body composition variables and selected physical fitness tests for elite football players through a sports season. The sample comprised 36 male elite football players who competed in the First Portuguese Soccer League in the 2020/2021 season. The models were calculated based on 22 independent variables that included players' information, body composition, physical fitness, and one dependent variable, the number of injuries per season. In the net elastic analysis, the variables that best predicted injury risk were sectorial positions (defensive and forward), body height, sit-and-reach performance, 1 min number of push-ups, handgrip strength, and 35 m linear speed. This study considered multiple-input single-output regression-type models. The analysis showed that the most accurate model presented in this work generates an error of RMSE = 0.591. Our approach opens a novel perspective for injury prevention and training monitorization. Nevertheless, more studies are needed to identify risk factors associated with injury prediction in elite soccer players, as this is a rising topic that requires several analyses performed in different contexts.

FAAH rs324420 Polymorphism Is Associated with Performance in Elite Rink-Hockey Players

Genetic factors are among the major contributors to athletic performance. Although more than 150 genetic variants have been correlated with elite athlete status, genetic foundations of competition-facilitating behavior influencing elite performances are still scarce. This is the first study designed to examine the distribution of genetic determinants in the athletic performance of elite rink-hockey players. A total of 116 of the world’s top best rink-hockey players (28.2 ± 8.7 years old; more than 50% are cumulatively from the best four world teams and the best five Portuguese teams), who participated at the elite level in the National Rink-Hockey Championship in Portugal, were evaluated in anthropometric indicators/measurements, training conditions, sport experience and sport injuries history. Seven genetic polymorphisms were analyzed. Polymorphism genotyping was performed using the TaqMan® Allelic Discrimination Methodology. Rink-hockey players demonstrated significantly different characteristics according to sex, namely anthropometrics, training habits, sports injuries and genetic variants, such as Vitamin D Receptor (VDR) rs731236 (p < 0.05). The Fatty Acid Amide Hydrolase (FAAH) rs324420 A allele was significantly associated with improved athletic performance (AA/AC vs. CC, OR = 2.80; 95% Cl, 1.23−6.35; p = 0.014; p = 0.008 after Bootstrap) and confirmed as an independent predictor among elite rink-hockey players (adjusted OR = 2.88; 95% Cl, 1.06−7.80; p = 0.038). Our results open an interesting link from FAAH-related biology to athletic performance.

Monitoring Variables Influence on Random Forest Models to Forecast Injuries in Short-Track Speed Skating

Injuries limit the athletes' ability to participate fully in their training and competitive process. They are detrimental to performance, affecting the athletes psychologically while limiting physiological adaptations and long-term development. This study aims to present a framework for developing random forest classifier models, forecasting injuries in the upcoming 1 to 7 days, to assist the performance support staff in reducing injuries and maximizing performance within the Canadian National Female Short-Track Speed Skating Program. Forty different variables monitored daily over two seasons (2018-2019 and 2019-2020) were used to develop two sets of forecasting models. One includes only training load variables (TL), and a second (ALL) combines a wide array of monitored variables (neuromuscular function, heart rate variability, training load, psychological wellbeing, past injury type, and location). The sensitivity (ALL: 0.35 ± 0.19, TL: 0.23 ± 0.03), specificity (ALL: 0.81 ± 0.05, TL: 0.74 ± 0.03) and Matthews Correlation Coefficients (MCC) (ALL: 0.13 ± 0.05, TL: -0.02 ± 0.02) were computed. Paired T-test on the MCC revealed statistically significant (p < 0.01) and large positive effects (Cohen d > 1) for the ALL forecasting models' MCC over every forecasting window (1 to 7 days). These models were highly determined by the athletes' training completion, lower limb and trunk/lumbar injury history, as well as sFatigue, a training load marker. The TL forecasting models' MCC suggests they do not bring any added value to forecast injuries. Combining a wide array of monitored variables and quantifying the injury etiology conceptual components significantly improve the injury forecasting performance of random forest models. The ALL forecasting models' performances are promising, especially on one time windows of one or two days, with sensitivities and specificities being respectively above 0.5 and 0.7. They could add value to the decision-making process for the support staff in order to assist the Canadian National Female Team Short-Track Speed Skating program in reducing the number of incomplete training days, which could potentially increase performance. On longer forecasting time windows, ALL forecasting models' sensitivity and MCC decrease gradually. Further work is needed to determine if such models could be useful for forecasting injuries over three days or longer.

Impact of Gender and Feature Set on Machine-Learning-Based Prediction of Lower-Limb Overuse Injuries Using a Single Trunk-Mounted Accelerometer

Even though practicing sports has great health benefits, it also entails a risk of developing overuse injuries, which can elicit a negative impact on physical, mental, and financial health. Being able to predict the risk of an overuse injury arising is of widespread interest because this may play a vital role in preventing its occurrence. In this paper, we present a machine learning model trained to predict the occurrence of a lower-limb overuse injury (LLOI). This model was trained and evaluated using data from a three-dimensional accelerometer on the lower back, collected during a Cooper test performed by 161 first-year undergraduate students of a movement science program. In this study, gender-specific models performed better than mixed-gender models. The estimated area under the receiving operating characteristic curve of the best-performing male- and female-specific models, trained according to the presented approach, was, respectively, 0.615 and 0.645. In addition, the best-performing models were achieved by combining statistical and sports-specific features. Overall, the results demonstrated that a machine learning injury prediction model is a promising, yet challenging approach.

CLINICAL APPLICATION OF GENE MEDICINE IN PREVENTING SPORTS INJURIES

ABSTRACT Introduction: Using gene therapy to transfer specific genes to implant therapeutic proteins into damaged tissues is a more promising way to treat sports injuries. The combination of tissue engineering and gene therapy will potentially promote the regeneration and repair of various damaged tissues. Objective: This article explores the adaptive relationship between gene selection therapy and athletes in sports. Methods: We selected students of related majors in sports schools to conduct specific genetic testing and measure the muscle area, fatigue level, muscle damage, and other related indicators before and after exercise. Results: After a series of physical fitness assessments, an increase in the gene sequence, as well as changes in the biochemical indices, were confirmed Conclusions: The muscle gain of the test subject during training is better than other genotypes. Level of evidence II; Therapeutic studies - investigation of treatment results.

Machine learning methods in sport injury prediction and prevention: a systematic review

PURPOSE

Injuries are common in sports and can have significant physical, psychological and financial consequences. Machine learning (ML) methods could be used to improve injury prediction and allow proper approaches to injury prevention. The aim of our study was therefore to perform a systematic review of ML methods in sport injury prediction and prevention.

METHODS

A search of the PubMed database was performed on March 24th 2020. Eligible articles included original studies investigating the role of ML for sport injury prediction and prevention. Two independent reviewers screened articles, assessed eligibility, risk of bias and extracted data. Methodological quality and risk of bias were determined by the Newcastle-Ottawa Scale. Study quality was evaluated using the GRADE working group methodology.

RESULTS

Eleven out of 249 studies met inclusion/exclusion criteria. Different ML methods were used (tree-based ensemble methods (n = 9), Support Vector Machines (n = 4), Artificial Neural Networks (n = 2)). The classification methods were facilitated by preprocessing steps (n = 5) and optimized using over- and undersampling methods (n = 6), hyperparameter tuning (n = 4), feature selection (n = 3) and dimensionality reduction (n = 1). Injury predictive performance ranged from poor (Accuracy = 52%, AUC = 0.52) to strong (AUC = 0.87, f1-score = 85%).

CONCLUSIONS

Current ML methods can be used to identify athletes at high injury risk and be helpful to detect the most important injury risk factors. Methodological quality of the analyses was sufficient in general, but could be further improved. More effort should be put in the interpretation of the ML models.

Functional COL1A1 variants are associated with the risk of acute musculoskeletal soft tissue injuries

Studies have reported the association of the COL1A1 Sp1 binding site variant (rs1800012) with the risk of acute musculoskeletal soft tissue injuries. Interaction with the COL1A1 promoter variant (rs1107946) has also been proposed to modulate acute injury risk. Conversely, neither of these loci have been associated with chronic musculoskeletal soft tissue phenotypes. Therefore, the primary aim of this study involved characterizing these variants in a cohort of participants with chronic Achilles tendinopathy. Second, this study aimed to support the contribution of the rs1107946 and rs1800012 variants to the profile predisposing for acute musculoskeletal soft tissue injuries including Achilles tendon and anterior cruciate ligament (ACL) ruptures. A hypothesis-driven association study was conducted. In total, 295 control participants, 210 participants with clinically diagnosed Achilles tendinopathy, and 72 participants with Achilles tendon ruptures recruited independently from South Africa and the United Kingdom were genotyped for the prioritized variants. In addition, a cohort including 232 control participants and 234 participants with surgically diagnosed ACL ruptures was also analyzed. Although no associations were observed in the recruited cohorts, the rare rs1800012 TT genotype was associated with decreased ACL injury risk when the results from the current study were combined with that from previously published studies (P = .040, OR: 2.8, 95% CI: 1.0-11.0). In addition, the G-T (rs1107946-rs1800012) inferred haplotype was associated with decreased risk for Achilles tendon ruptures. These results support previous observations and reiterate the heterogeneity of musculoskeletal phenlotypes whereby certain markers may be common to the predisposing profiles while others may be unique.

Can Genetic Testing Identify Talent for Sport?

Elite athlete status is a partially heritable trait, as are many of the underpinning physiological, anthropometrical, and psychological traits that contribute to elite performance. In recent years, our understanding of the specific genetic variants that contribute to these traits has grown, such that there is considerable interest in attempting to utilise genetic information as a tool to predict future elite athlete status. In this review, we explore the extent of the genetic influence on the making of a sporting champion and we describe issues which, at present, hamper the utility of genetic testing in identifying future elite performers. We build on this by exploring what further knowledge is required to enhance this process, including a reflection on the potential learnings from the use of genetics as a disease prediction tool. Finally, we discuss ways in which genetic information may hold utility within elite sport in the future, including guiding nutritional and training recommendations, and assisting in the prevention of injury. Whilst genetic testing has the potential to assist in the identification of future talented performers, genetic tests should be combined with other tools to obtain an accurate identification of those athletes predisposed to succeed in sport. The use of total genotype scores, composed of a high number of performance-enhancing polymorphisms, will likely be one of the best strategies in the utilisation of genetic information to identify talent in sport.