Contents

Introduction

Rupture of the anterior cruciate ligament (ACL) is one of the most severe and common musculoskeletal soft tissue knee injuries (Alsayed et al. 2023; Kiapour and Murray 2014). It is associated with immediate pain, rapid swelling, weakness of the knee, and instability, which significantly impair mobility (Logerstedt et al. 2018). The incidence rate of ACL ruptures is estimated at 75–80 per 100,000 per year, especially in physically active adolescents and athletes (15–40 years) (Magnusson et al. 2020). ACL ruptures occur most frequently in athletes who participate in sporting activities that require sudden changes in direction, jumps, and twists (Joseph et al. 2013). The lifetime risk of developing knee osteoarthritis as a result of an ACL injury is 34%, 10–15 years post-injury and surgical repairs (Davis et al. 2021; Suter et al. 2017). It is particularly concerning that these injuries are increasingly affecting young individuals; consequently, there is a growing number of osteoarthritis cases appearing at a younger age, which raises further concern (Murray et al. 2000; Lohmander et al. 2007).

Ligaments have a low healing capacity, poor blood supply, and low cellular content, all of which has been ascribed to contribute to the ACL’s susceptibility to injury (Kiapour and Murray 2014). As a result, ACL ruptures not only cause significant direct healthcare costs, but also lead to indirect costs, such as social and productivity losses and increased absenteeism, collectively creating a long term economic burden (Griffin et al. 2006). The burden of ACL injuries is well recognized, however, the aetiology of ACL rupture remains unclear (Kobayashi et al. 2010). It is known that ACL injuries are multifactorial, meaning that an interaction between various intrinsic and extrinsic risk factors contribute to the susceptibility to these injuries (Hewett et al. 2016; Bittencourt et al. 2016; Meeuwisse 1994). Although research is increasingly focusing on the contributions of biomechanical (modifiable) risk factors and environmental risk factors to ACL injuries (Murphy et al. 2003; Griffin et al. 2006), there is growing evidence that an individual’s genetic predisposition and anthropometric traits contribute to susceptibility to musculoskeletal soft tissue injuries such as ACL ruptures (Joseph et al. 2013; Magnusson et al. 2020; Rahim et al. 2017; Snaebjörnsson et al. 2019).

More than 80 loci encoding genes related to components of the extracellular matrix (ECM) have been identified as being associated with predisposition to ACL rupture (Ribbans et al. 2022). These genes encode proteins which form structural, non-structural and regulatory components of the ECM (Posthumus et al. 2010; Ribbans et al. 2022; Willard et al. 2018; Feldmann 2022; Feldmann et al. 2022; Dlamini et al. 2023). Recently, Dlamini et al. (2023) highlighted genetic variants within two candidate genes, heparan sulphate proteoglycan-2 (HSPG2) and integrin beta 2 (ITGB2), to be associated with ACL rupture susceptibility. They showed that the G-A-C allele combination between HSPG2 (rs2291826 A>G-rs2291827 G>A) and ITGB2 (rs2230528 C>T) variants was associated with reduced risk of ACL rupture as a proxy for gene–gene interaction between these two genes.

The ITGB2 gene encodes integrin β2 subunit, a heterodimeric transmembrane receptor primarily involved in leukocyte adhesion and immune responses (Bednarczyk et al. 2020; Huang et al. 2019). The potential contribution of this gene to ACL rupture susceptibility may arise through the inflammatory pathways and gene–gene interactions (Dlamini et al. 2023). The HSPG2 gene is a multidomain gene that encodes perlecan, a large basement membrane protein (Gubbiotti et al. 2017). Perlecan belongs to the proteoglycan family, which form a major component of extracellular matrix (ECM) ground substance (Gubbiotti et al. 2017; Arikawa-Hirasawa 2022). Perlecan plays a multifaceted role in maintaining the structural and functional integrity of the ECM (Arikawa-Hirasawa 2022). It interacts with various molecules, including collagens and growth factors such as vascular endothelial growth factor (VEGF) and fibroblast growth factor (FGF) to maintain ECM stability and support cellular process (Farach-Carson and Carson 2007; Arikawa-Hirasawa 2022). The expression of HSPG2 contributes to various developmental and cellular processes including bone and cartilage development, angiogenesis, inflammatory responses and wound healing (Farach-Carson and Carson 2007; Martinez et al. 2018). Given the role of HSPG2 in skeletal and cartilage development, it could also contribute to the regulation of height, a known risk factor for ACL ruptures. While the ITGB2 was identified in previous research, the study focused on the HSPG2 due to its stronger biological relevance to ligament integrity and growth regulation.

The exact genetic variants and their interaction with extrinsic or intrinsic factors have not been interrogated and require exploration. Some of the intrinsic risk factors include the anthropometric traits such as mass, height and body mass index (BMI), which have been identified as potential factors contributing to susceptibility to musculoskeletal soft tissue injuries such as ACL rupture (Snaebjörnsson et al. 2019; Alsayed et al. 2023). A study by Alsayed et al. (2023) reported that individuals with a BMI greater than 25 kg/m² were at a greater risk of sustaining a sport-related ACL injury compared to individuals with a lower BMI. A high BMI may result in increased mechanical stress on the knee, resulting in knee injury (Alsayed et al. 2023; Snaebjörnsson et al. 2019). It has been noted that females have about 9 times greater risk of ACL injury than men, potentially due to biomechanical, anatomical and hormonal differences (Seneviratne et al. 2004; Bruder et al. 2023). The variability in these factors also has a genetic component, making it important to identify whether these intrinsic risk factors are also confounders when trying to characterise the genetic risk susceptibility for a complex phenotype such as ACL ruptures.

The aim of this study was therefore to investigate the relationship between genetic variants of HSPG2 and anthropometric factors. It was hypothesised that genetic variability at the HSPG2 loci, together with variability in height, may collectively explain the contribution to susceptibility to ACL rupture. In attempting to characterise the risk factors for ACL rupture susceptibility, it is important to identify the confounding factors and determine which factors are associated with each other.

Sample and Methods

Samples

Participant recruitment

A case-control genetic association study was conducted comprising two previously recruited cohorts from Poland and Sweden, respectively, as well as a combined cohort of members from both populations. Each participant was of self-reported European ancestry and provided written informed consent to participate in accordance with principles in the Declaration of Helsinki. All participants in each cohort completed detailed questionnaires covering demographics, lifestyle habits, occupation details, and sporting background including type of sporting activity, duration of participation, and frequency. The questionaries requested medical details related to the ACL injuries, such as the mechanism of injury, and history of other ligament or tendon injuries. Ethical approval for this study was obtained from the Human Research Ethics Committee of the Faculty of Health Science, University of Cape Town (HREC: 026/2023). Approval was also obtained from each of the respective local ethics committees for the individual cohorts recruited from the Regional Ethical Review Board in Umeå, Sweden (dnr. 2011‐200‐31 M), Bioethics Committee for Clinical Research, Regional Medical Chamber, Gdansk, Poland (KB‐8/16).

The ACL rupture group included individuals older than 18 years at the time of recruitment. All the participants had a clinical diagnosis of ACL rupture by physical examination confirmed by magnetic resonance imaging (MRI) or arthroscopy. This group of participants included injuries sustained through contact and non-contact mechanisms. Non-contact ACL rupture was analysed as a subgroup of the cases (NON). The control (CON) group included individuals with no history of an ACL injury or other ligament or tendon injuries. They regularly participated in similar sports activities as the affected individuals and were in the same age group. All participants engaged in regular physical activity, primarily on a recreational level.

Individuals taking chronic medications or diagnosed with a connective tissue disease or other systemic disease known to affect connective tissues were excluded. In addition, individuals with current or previous use of fluoroquinolone antibiotics (within the last 12 months from the time of recruitment) or previous use of local corticosteroids injections into the ACL or surrounding areas were excluded, as these factors can have a negative effect on collagen synthesis and result in matrix degradation thus increasing susceptibility to ACL injuries.

Participant characteristics

The combined cohort, including participants from the Polish and Swedish cohorts, consisted of 502 participants. The cohort consisted of 265 asymptomatic control participants (COMB-CON), 237 participants with ACL rupture (COMB-ACLR), and 135 participants (COMB-NON) who reported sustaining an ACL rupture by a non-contact mechanism (Table 1). The Polish cohort consisted of 291 physically active, unrelated participants recruited between 2008 and 2018 from the Galen Orthopaedics Clinic in Poland. This included 150 asymptomatic control participants (POL-CON), 141 ACL participants (POL-ACLR), and 54 participants who reported a non-contact mechanism (POL-NON). The male controls (n=112) and ACL rupture cases (n=102) were recruited from the Polish soccer league. The female cases (n=39) were recruited from soccer teams and skiing sports. The female controls (n=37) were recruited from sports clubs and wellness centres (Cięszczyk et al. 2017; Feldmann et al. 2022; Lulińska-Kuklik et al. 2019).

The Swedish cohort consisted of 211 physically active and unrelated participants recruited between 2011 and 2013 from the orthopedic clinics of two major hospitals in the city of Umeå (in the Vaesterbotten region) and Luleå (in the Norrbotten region) respectively. This cohort comprised 116 asymptomatic control participants (SWD-CON) with no history of ACL injury, 95 participants (SWD-ACLR) with ACL injury, and 79 participants who reported an ACL rupture by a non-contact mechanism (SWD-NON). Most of the recruited participants had a previously described long-term follow-up of the ACL injury (Suijkerbuijk et al. 2019).

Participant sports data were previously published by Suijkerbuijk et al. (2019) and Cięszczyk et al. (2017).The type of sport participation was categorised into non-contact jumping sports, non-contact non-jumping sports, and skiing sports. The participants in the Polish cohort were matched in terms of sport type, level, and frequency of participation. Details on the years of sports participation in the Polish were not available. Most of the participants in the Swedish cohort reported participating in non-contact jumping sports at a recreational level.

Method

DNA extraction

For the participants in the Polish cohort, genomic DNA was extracted from oral epithelial cells using a GEN Elute Mammalian Genomic DNA Miniprep Kit (Sigma; Darmstadt, Germany) according to the manufacturer’s recommendations. For the Swedish cohort, genomic DNA was extracted from venous blood using rapid non-ethanol precipitation as previously described by Lahiri and Nurnberger (1991), with slight modifications from Mokone et al. (2006).

Candidate gene and genetic variants selection 

The candidate gene HSPG2 was selected because of its multifunctional role in different biological pathways involved in remodelling the extracellular matrix (ECM) components and thereby maintaining ECM homeostasis. Participants were genotyped for the following genetic variants: HSPG2 (rs2291826 A>G, rs2281827 G>A). The genetic variants were selected from a previous Whole Exome Sequencing (WES) study, which showed that the variants had a difference in allele frequency distribution of atleast 30% between 10 tendinopathy cases and 10 controls (Gibbon et al. 2018). These genetic variants were also selected because they reported a minor allele frequency of > 5% in the Caucasian population, as reported in the ALFA Allele Frequency dataset and 1000 Genomes Project hosted by the National Centre for Biotechnology Information (NCBI database (http:​/​/​www.​ncbi.​nlm.​nih.​gov/​). The functional significance of the genetic variants remains unknown.

Genotyping

For the standard genotyping protocols, a predesigned TaqMan polymerase chain reaction (PCR) assay was used to genotype the HSPG2 rs2291826 A>G (Assay ID: C_15966515_10) and HSPG2 rs2291827 G>A (Assay ID: C__15966517_10) (ThermoFisher Scientific; Waltham, Massachusetts, USA). Real-time polymerase chain reactions (PCR) were performed for all genetic variants using the Quant Studio™ 3 Real-Time PCR System (Applied Biosystems; Waltham, Massachusetts, USA). Three negative controls (no DNA) and five replicate samples (known genotypes) were included on each 96-walled PCR plate (FG-TCII reaction plate) as a quality control for reliable genotyping and detection of contamination. Genotypes were confirmed by two independent investigators. Genotypes were called using the Thermofisher Cloud^TM Suite (Thermo Fisher Scientific; Waltham, Massachusetts, USA) with an average call rate of 82.7%. All laboratory work was conducted at the University of Cape Town, at the Health through Physical Activity, Lifestyle and Sport (HPALS) Research Centre, which is located at the Sports Science Institute of South Africa (Newlands).

Statistical analysis

The sample size for the study was calculated using QUANTO version 1.2.4 (http:​/​/​keck.​usc.​edu/​biostatistics/​software/​). For the Polish and Swedish cohorts, a sample size of 118 or more cases was required to detect an OR of 1.8 or higher. For the Swedish cohort, a sample size of 85 cases was required to detect an odds ratio (OR) of 2.0 or higher. All calculations assumed minor allele frequencies between 0.2 and 0.5 with a power of 80% and a significance of 5%. The data were analysed with the R Project for Statistical Computing Version 4.3.1 . The distribution of continuous data was analysed using the Shapiro-Wilk normality test, and data was presented as means±standard deviations (Shapiro and Wilk 1965). Non-parametric data was presented as median and interquartile ranges. Categorical data were compared using the chi-square test or Fisher’s exact test (sample size <10) and presented as percentages. One-way analysis of variance (ANOVA) was used for continuous parametric data, while the Mann-Whitney U or Kruskal-Wallis test was used for non-parametric data.

The R packages genetics, version 1.3.8.1.3 (González et al. 2007) and SNPassoc version 2.1.0 (Schaid et al. 2012) were used to determine the differences in genotypes and allele frequencies between the groups and to calculate the probabilities of Hardy-Weinberg equilibrium (HWE) for the variants and linkage disequilibrium between the HSPG2 genetic variants. The HSPG2: rs2291826 A>G, rs2291827G>A variants were in a moderate positive LD (D'=0.703), with a significant non-random association (X2=912.35; p<0.001) of the alleles at the two loci. GraphPad Prism version 10.1.2 (GraphPad Software., Boston, Massachusetts, USA) was used to visualize genotype and allele frequencies between the different populations. The St Nicholas House Analysis (SNHA) was used to visually represent the relationship between different variables such as genetic variables, demographic and anthropometric factors and phenotypic traits (Hermanussen et al. 2021).

Results

Participant characteristics

In the combined cohort, the COMB groups (CON vs. ACLR), were covaried for sex and country of recruitment Table 1. The COMB-ACLR group (33.8 ± 11.7 years, n=235) was significantly older than the COMB-CON group (p= 0.036, 31.3 ± 14.2 years, n = 263). The COMB-ACLR group also had a significantly higher mass (p = 0.016, 75.4 ± 14.2 kg, n = 219) compared to the COMB-CON group (72.4 ± 12.7 kg, n=256); however, after the height variable was covaried for sex, the significance was no longer noted. No significant differences were noted for the COMB-NON subgroup.

For the individual cohorts, Polish cohort had younger participants compared to the Swedish cohort participants. The POL-CON group (21.0 ± 1.8 years, n = 149) was significantly younger than the POL-ACLR group (p < 0.001, 31.6 ± 10.0 years, n = 142) and the POL-NON subgroup (p < 0.001, 30.7 ± 9.9 years, n = 56). However, for the Swedish cohort, the SWD-CON group (44.7 ± 11.9 years, n = 114) was significantly older than the SWD-ACLR group (p < 0.001, 37.1 ± 13.3 years, n = 93) and SWD-NON subgroup (p < 0.001, 36.5 ± 13.7 years, n = 78) (Table 1).

The Polish and Swedish groups were similar in height. However, with regards to mass, the Polish cohort had a significantly higher mass and BMI. The POL-ACLR group had a significantly higher mass (p < 0.001, 79.0 ± 14.8 n = 140) and a higher BMI (p < 0.001, 25.0 ± 4.0 kg.m-2, n = 137) than the POL-CON group. Similarly, the high mass and BMI was noted in the POL-NON subgroup (mass: p < 0.001, 81.4 ± 16.7 kg, n = 56; BMI: p < 0.001, 25.1 ± 5.1 kg.m-2, n =56). After the mass and BMI were covaried for age, the significance remained only for the POL-NON subgroup (Table 1).

No significant genotype effect on any of the descriptive variables was noted for the combined cohort (Table 2). For the individual cohorts, genotype effect for height was only noted for the Swedish cohort for the HSPG2 rs2291826 A>G variant distribution where the A/A and A/G genotype was frequently noted in the distribution (p = 0.009) (Table S4).

Genotype and allele frequency

There were no significant differences in the distribution of genotype and allele frequencies for the HSPG2 rs2291826 A>G variant (COMB-CON vs. COMB-ACLR: p = 0.067, and COMB-CON vs. COMB-NON: p = 0.334) between the groups (Figure 1A). Similar findings were noted for the HSPG2 rs2291827 G/A variant: (COMB-CON vs. COMB-ACLR: p=0.482, and COMB-CON vs. COMB-NON: p=0.272) (Figure 1B). No significant differences in either the genotype or allele frequency distributions for the HSPG2 rs2291826 A>G (Figure 2A) and rs2291827 G>A (Figure 2B) variant were noted in the combined cohort when only males or females were examined.

Figure 1  Genotype and allele frequency distributions of (A) HSPG2 rs2291826 A/G and (B) HSPG2 rs2291827 G/A variant for the combined cohort control group (COMB-CON: white bars), combined anterior cruciate ligament rupture group (COMB-ACLR: light grey bars), and ACL rupture by non-contact mechanism subgroup (COMB-NON: dark grey bars). The differences in genotype and allele distributions within each individual cohort are annotated as follows: Poland (circle) and Sweden (square blocks). Details of the HWE and AIC values can be found in Table S5.

Figure 2  Genotype and allele frequency distributions of (A) HSPG2 rs2291826 A/G and (B) HSPG2 rs2291827 G/A variant for the combined cohort control group (COMB-CON: white bars), combined anterior cruciate ligament rupture group (COMB-ACLR: light grey bars), and ACL rupture by non-contact mechanism subgroup (COMB-NON: dark grey bars). Details of the HWE and AIC values can be found in Table S4 (males) and Table S5 (females).

Relationship between Genetic variants and Anthropometric Factors

A positive correlation in mass and BMI was found in all individual cohorts; COMB (r = 0.78), (POL: r = 0.81), and SWD: r = 0.78) respectively Figure 3. BMI was consistently influenced by mass in all the cohorts and height was similarly distributed between cases and controls between the cohorts. There appears to be a consistent correlation between sex with mass and height in all cohorts, with a negative correlation in the Swedish cohort (r = -0.43) (Figure 3C).

The combined (r = 0.73) and Polish cohorts (r = 0.68) had a strong positive correlation between sex and height cohort while a strong negative correlation was noted in the Swedish cohort (r = -0.75), which may be ascribed to the sex-specific differences in height noted between the cohorts. A positive correlation was found between COR and height (r = 0.25) in the COMB cohort, which could possibly be influenced by the height of the Polish participants. No positive correlation between the HSPG2: rs2291826 A>G and rs2291827 G>A was noted with the combined and Polish cohorts. However, a negative correlation of the HSPG2 rs2261826 A/G with height (r = -0.19) was found in the Swedish cohort. A positive correlation was found between the two HSPG2 rs2291826 A>G and rs2291827 G>A (r = 0.66) variants, which is expected, considering that the genetic variants are in a moderate positive linkage disequilibrium (D' = 0.703).

Figure 3  St Nicholas House analysis (SNHA) plot for the combined cohort and individual populations. The grey lines indicate a positive correlation and the red line a negative correlation (COR: country of recruitment, BMI: body mass index).

Discussion

The study aimed to investigate the relationship between genetic variants of HSPG2: rs2291826 A>G and rs2291827 G>A and anthropometric traits with the risk of ACL rupture in a combined group of participants recruited from Poland and Sweden. It was hypothesised that genetic variability at the HSPG2 loci together with variability in height may collectively explain the contribution to susceptibility to ACL rupture. There was no significant association between anthropometric traits and the risk of ACL rupture. A genotype effect for height was found in the Swedish cohort for the HSPG2 rs2291826 A>G variant, where genotypes A/A and A/G were frequently noted in the distribution. The SNHA plot also showed a negative correlation between genetic variant HSPG2 rs2291826 A>G and height in the Swedish cohort, suggesting that individuals with genotypes A/A and A/G tend to be shorter.

In the combined cohort, significant associations in age and mass were found between the ACL rupture group and the control groups. The COMB-ACLR group was significantly older (p=0.036) and had a higher mass (p = 0.016) compared to the CON groups. However, when mass was adjusted for sex, significance was removed, suggesting that sex and mass may be confounding factors. The results are consistent with previous studies that emphasize the influence of age and mass on the risk of ACL rupture (Hurd et al. 2008; Adouni et al. 2024). Excessive mass can increase mechanical stress on the knee joint and change the kinematics of the joint, which can lead to knee injuries such as ACL ruptures (Adouni et al. 2024). In the individual cohorts, the Polish cohort was significantly younger compared to the Swedish cohort, with the POL-ACLR group having a higher mass and BMI compared to the CON group (p<0.001) (including non-contact ACL rupture). After adjusting for age, the significance of higher BMI was only maintained for the POL-NON subgroup. This suggests that age may play a role in the mass and BMI differences observed in the Polish cohort. Similar trends have been observed in previous studies where younger people tended to participate in high risk sporting activities, possibly predisposing them to injury (Motififard et al. 2024; Snaebjörnsson et al. 2019).

The SNHA revealed a strong positive correlation between mass and BMI in all cohorts: COMB cohort (r = 0.78), the SWD cohort (r = 0.78), and an even stronger correlation in the Polish cohort (r = 0.81) (Figure 3). This is to be expected as BMI is a mass-related index and the heights between the cases and controls were similar. However, the broader associations involving anthropometric traits such as mass and BMI, suggests these factors may influence ACL injury risk (Kızılgöz et al. 2019; Snaebjörnsson et al. 2019; Alsayed et al. 2023). A higher BMI can lead to excessive stress on the knee joint and can place additional strain on ligaments, cartilage, and tendons as well as decreased neuromuscular control around the knee joints, potentially increasing the risk of ACL ruptures (Widmyer et al. 2013; Snaebjörnsson et al. 2019; Motififard et al. 2024). When exploring the relationship between sex, height and mass, a negative correlation was found in the Swedish cohort, especially between sex and height (r = -0.75) and sex and mass (r = -0.43). However, a strong positive correlation between sex and height was also found in the combined cohort and the Polish cohort. The differences in the patterns could reflect the sex-specific differences in anthropometric traits influenced by the differences in the average height of individuals recruited in the two countries. In particular, males in the Swedish cohort were shorter and weighed less than males in the Polish cohort, which may have influenced the direction and strength of the observed correlation.

Although a strong association was found between anthropometric traits and the risk of ACL rupture, no correlation was found between the genetic variants HSPG2: rs2291826 A>G, rs2291827 G>A and anthropometric traits in the combined as well as in the Polish cohort. However, a moderate negative correlation between genetic variant HSPG2 rs2291826 A/G and height was found in the Swedish cohort (r=-0.019) (Figure 3C). A genotype effect on height related to HSPG2 rs2291826 A>G was also noted in the Swedish cohort. Although the effect size is small, the results suggest that individuals with an A/A and A/G genotype of HSPG2 rs2291826 A>G may be shorter. The findings are important because short individuals who weigh more (or have a higher BMI) may be at increased risk of ACL rupture due to the biomechanical load on the knee joint relative to stature. Although the HSPG2 rs2291826 variant may not directly influence ACL rupture risk, but it could have an indirect modifying effect on height. The HSPG2 gene encodes perlecan, a multifunctional protein found in the basement membrane (Iozzo 2005; Farach-Carson and Carson 2007). Perlecan is mainly expressed in the cartilage matrix and plays an important role in regulating the availability and activities of growth factors such as fibroblast growth factors (FGF) and bone morphogenetic proteins (BMPs) (Iozzo 2005). These growth factors play an important role in the proliferation and differentiation of chondrocytes, an important process for skeletal development and endochondral ossification (Hayes et al. 2022). The exact biological mechanisms are still unclear and should be investigated in a larger cohort.

The study had several limitations, including the sample size of each cohort, in which controls and cases were not matched by sex and mass. In addition, the cohorts were not matched by age. The Polish cohort had younger participants, while the Swedish cohort had older participants. In addition, positive correlations of COR with height and sex could indicate possible recruitment bias or population-specific characteristics in the cohort, such as regional differences in height and sex distribution, which could have an influence on the analysis if not considered. Sport participation data were self-reported, and participants were not matched for participation in contact and non-contact sports.

Conclusion

The combined analysis of participant characteristics and the St. Nicholas house plot highlighted the role of anthropometric traits, particularly mass and BMI. The positive correlations between mass and BMI across all cohorts emphasize the importance of mass as a key factor in the risk of ACL rupture, while the sex-and population-specific differences in the relationships between height and mass suggest that ACL injury patterns are influenced by both genetic and environmental factors. There was no positive association between the genetic variants tested and anthropometric characteristics of participants. For this reason, it is hypothesised that for these two genetic variants tested, the variability in ACL risk is most likely not largely influenced by a genetic component of these measurements as influenced by HSPG2. The variability observed in the risk to ACL rupture can therefore be tested using the two variants HSPG2 rs2291826 A>G and rs2291827 G

Acknowledgements

The statistical analysis and manuscript were the result of participating in the 7^th International Summer School (University of Potsdam) funded by KoUP University of Potsdam, in Gülpe, Brandenburg, Germany from the 3^rd of July to 12^th July 2024. I extend great thanks to PD Dr. Christiane Scheffler, Dr. Detlef Groth, Prof. Michael Hermanussen and Prof. Barry Bogin for accommodation and patience, their expertise and adventures they created to make the summer schools enjoyable. I extend utmost appreciation to my supervisors, Professor Alison September, Professor Malcolm Collins and Dr Mary-Jane Laguette, for the opportunity to participate in the Summer School and for their invaluable support and guidance during my MSc journey.

Funding statement

The National Research Foundation (NRF) supported L.Ramoshaba. The research study was supported by the South African Medical Research Council (SAMRC) through its Division of Research Capacity Development under the Mid-Career Scientist Programme, which is funded by the South African National Department of Health (SANDH).

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