{"paper_id":"2e711275-e662-4e12-b11b-9409a5a2a58d","body_text":"Variations in morphological lower inter-limb asymmetry by sex and training level, and its link to performance in recreational runners: A cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Variations in morphological lower inter-limb asymmetry by sex and training level, and its link to performance in recreational runners: A cross-sectional study Joachim D’Hondt, Eva D’Hondt, Dirk Aerenhouts, Kevin Pauw, Peter Clarys, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9008534/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 13 You are reading this latest preprint version Abstract Background Body composition plays a critical role in endurance running. While anthropometric characteristics have been incorporated into performance prediction models, potential inter-limb morphological asymmetries in lower-limb body composition remain largely unexplored. Therefore, this study aimed to ( 1 ) quantify lower-limb morphological asymmetry and examine differences across sex and training level, ( 2 ) compare running performance between low- and high-asymmetry groups using split analyses, and ( 3 ) investigate the link between lean mass (LM), bone mineral content (BMC) and fat mass (FM) asymmetry as well as leg length discrepancy (LLD), and running performance in healthy adult endurance runners with varying training backgrounds. Methods Sixty-eight healthy adult recreational runners (53% male) were stratified by sex and training level (novice, intermediate, trained). Segmental lower-limb body composition (LM, bone mineral density (BMD), BMC, and FM) was assessed using dual-energy X-ray absorptiometry (DXA). LLD was determined anthropometrically as the distance between the anterior superior iliac spine and medial malleolus. Running performance was evaluated using the Cooper 12-minute run test. Low and high asymmetry groups were created by categorizing participants according to the sample median asymmetry values for DXA-derived metrics and using a 2 cm threshold to classify LLD. Group differences were assessed using t-tests and ANOVAs, and multiple linear regression examined predictors of running performance. Results Mean asymmetry magnitudes ranged from 1.91% to 4.04% across metrics. LLD ranged from 0.5 to 0.9 cm. Female runners demonstrated greater LLD than males (p = 0.004), and trained runners showed greater BMD asymmetry than novices (p = 0.006). LM, BMC and FM asymmetry as well as LLD were no significant predictors of Cooper test performance (p = 0.316 to 0.686). Furthermore, no significant differences were found between the low and high asymmetry groups (p = 0.581 to 0.999). Conclusion DXA-derived inter-limb asymmetries at lower limb level and LLD in recreational endurance runners are small and largely independent of sex and training level. Importantly, these asymmetries are not linked to running performance, suggesting limited practical relevance for performance optimization. Trial registration: ClinicalTrials.gov (NCT06808399). Registered on 02 April 2025. Lower Extremity Athletic Performance Running: Dual-energy X-ray absorptiometry Body composition Physical Fitness Introduction Several studies have developed predictive models of endurance running performance, consistently demonstrating that, beyond key individual determinants such as sex, age, VO₂max, and running economy, anthropometric characteristics meaningfully contribute to race outcome ( 1 – 3 ). Most models incorporate at least one anthropometric or body composition variable alongside physiological and training-related parameters ( 1 – 3 ). Although the specific predictors may differ between men and women, higher body mass index (BMI), greater body fat percentage, larger calf circumference, and increased lower-leg volume have repeatedly been identified as negative predictors of endurance running performance ( 2 , 4 , 5 ). These findings indicate that both total body mass and its regional distribution influence the energetic cost of locomotion and, consequently, race performance ( 6 ). However, previous research has largely focused on whole-body measures, with limited consideration of potential inter-limb morphological asymmetries in endurance runners. Over the past decade, inter-limb asymmetry has received growing attention in various athletic populations because of its potential implications for performance and injury risk ( 7 – 9 ). In endurance runners, meaningful asymmetries have been reported across sex and training level ( 10 , 11 ), and some functional, kinetic, and kinematic asymmetries have been suggested to negatively influence determinants of running performance ( 12 ). Nevertheless, inter-limb asymmetry is highly task- and metric-specific ( 9 , 13 ), limiting the generalizability of findings across different assessments. Consequently, the use of arbitrary thresholds (e.g., 10% or 15%) to identify performance impairments is questionable ( 14 ). Moreover, findings derived from functional assessments cannot be directly extrapolated to morphological asymmetry. Indeed, previous work showed that asymmetry in body composition is not necessarily associated with asymmetry in lower-limb functional performance ( 15 , 16 ). Aerobic energy expenditure during running increases by approximately 1% for every additional kilogram added to the trunk, whereas the same mass added distally, such as at the feet, raises metabolic cost by nearly 10% per kilogram ( 17 , 18 ). Given that leg swing accounts for roughly 20% of the net energy cost of running ( 18 ), excess or unevenly distributed mass in the lower limbs may disproportionately elevate energy demand. Although research on this topic remains scarce, it can be hypothesized that asymmetrical lower-limb body composition may further impair running performance. One previous study found no significant association between anatomical asymmetry and metabolic cost in runners of different training levels ( 19 ). However, this investigation was judged to be at high risk of bias according to the Downs and Black assessment tool, due to an unclear study aim, insufficient reporting of primary outcomes, and the absence of actual probability values ( 12 ). Moreover, asymmetry was expressed in absolute rather than relative terms, potentially limiting comparability and interpretability. In the context of performance and health, leg length discrepancies (LLDs) are also often examined. Approximately 90% of individuals present some degree of bone length variation, and around 20% exhibit a LLD greater than 0.9 cm ( 20 ). While often clinically unnoticed, larger discrepancies have been associated with musculoskeletal disorders and lower-limb injuries, including osteoarthritis, stress fractures, and plantar fasciitis ( 21 – 23 ). Greater LLDs have also been linked to altered spatiotemporal gait parameters and ground reaction forces ( 24 ). Consequently, discrepancies exceeding 2 cm have been associated with increased oxygen consumption and energy expenditure during walking ( 25 , 26 ). In contrast, differences smaller than 1 cm do not appear to substantially affect running economy ( 25 , 27 ). Despite these associations with determinants of running performance, the direct relationship between morphological asymmetry and endurance running performance remains unclear. Given the limited evidence regarding (lower-limb) morphological asymmetry in endurance runners and its potential link with performance as well as the absence of established population- and task-specific thresholds, it is uncertain whether correcting body imbalances should be a priority for practitioners aiming to optimize running outcomes. Therefore, the aims of this study were to: ( 1 ) quantify the magnitude of lower inter-limb asymmetry and examine differences in asymmetry across sex and training level, ( 2 ) compare running performance between low- and high-asymmetry groups using split analyses, and ( 3 ) investigate the link between lean mass (LM), bone mineral content (BMC) and fat mass (FM) asymmetry as well as LLD and running performance in healthy adult endurance runners with varying training backgrounds. Materials and methods Participants To ensure a heterogeneous sample, participants were stratified according to sex and running experience (novice, intermediate, trained). Although anecdotally, novice runners were defined as individuals who had not participated in structured or unstructured running training for at least five years and reported a weekly running volume of no more than 10 km during the three months preceding the study. Intermediate runners were those who consistently accumulated between 40 and 150 km of running per month over the previous six months, whereas trained runners reported monthly running volumes exceeding 150 km over the same timeframe. Eligibility criteria required participants to be free from any musculoskeletal injury, surgical interventions, or medical condition affecting the lower limbs, hips, trunk, or spine within the six months preceding study inclusion. Individuals were also excluded if they reported any medical condition or medication use that could influence performance during the Cooper test, or if they regularly engaged (> 2 hours per week) in sports characterized by pronounced unilateral loading, such as tennis or soccer. Procedures This cross-sectional study was conducted over two separate testing sessions, scheduled two weeks apart. The first session involved the collection of anthropometric and DXA data. All participants were instructed to refrain from eating and drinking (i.e., 2 hours prior to the start of the test occasion), from consuming any alcohol (i.e., 12 hours prior to the start of the test occasion), caffeine (i.e., on the day of the test occasion), and (anti)diuretics (i.e., 7 days prior to the test occasion). Prior to testing, participants were instructed to empty their bladder and remove all metal objects (e.g., jewellery). The second session was devoted to the Cooper test, during which participants were allowed to eat and drink as usual but were required to refrain from caffeine and alcohol consumption, consistent with the requirements of the first session. For both test sessions, all participants were instructed to avoid vigorous physical activity during the 24 hours preceding their individually planned test occasion. Anthropometry All anthropometric measurements were performed according to the International Society for the Advancement of Kinanthropometry guidelines ( 28 ). Body height was determined up to 0.1 cm using the SECA 213 stadiometer (SECA, Germany), whilst participants’ body mass (to the nearest 0.002 kg) was measured in minimal clothing using the Radwag WLT60/10/X/3 device (Radwag, Poland). Leg length (i.e., distance between the anterior superior iliac spine and medial malleolus), was assessed using an anthropometric tape (Rosscraft Innovations Inc, Vancouver, Canada) at both sides of the body with an accuracy of 0.1 cm. Dual-energy X-ray absorptiometry Segmental body composition of the lower limbs was assessed by a certified investigator (J.D.) using dual-energy X-ray absorptiometry (DXA; Lunar iDXA, GE Healthcare, Madison, WI, USA). Prior to each measurement session, the DXA scanner was calibrated and subjected to quality assurance procedures in accordance with the manufacturer’s recommendations to ensure measurement reliability. Scans were performed with participants positioned supine on the scanning table and their arms placed alongside the body with thumbs oriented upward. Whole-body scans yielded segment-specific outcomes for the upper and lower extremities, including LM, bone mineral density (BMD), BMC, and FM. Inter-limb asymmetry Inter-limb asymmetry was quantified using the percentage difference method: ((highest value – lowest value) / highest value) × 100 ( 29 ). Running performance assessment Running performance was evaluated using the Cooper 12-minute run test. Prior to testing, participants completed a standardized warm-up consisting of light jogging, knee raises exercises and dynamic stretching exercises. Participants were instructed to cover the maximum possible distance within a 12-minute time frame on a standard 400 m outdoor track (i.c. mean temperature: 9.8 ± 4.2°C; mean relative humidity: 70.0 ± 12.2%). Throughout the test, investigators provided standardized verbal encouragement and recorded performance outcomes. Total running distance was calculated as the sum of the number of complete laps completed and the additional distance covered during the final partial lap, which was measured in 10 m increments. Statistical Analyses All analyses were performed using the SPSS software (version 31, SPSS Inc., Chicago, IL, USA). The Shapiro-Wilk test, along with visual inspection of histograms and Q-Q plots, indicated that Cooper test data were normally distributed, whereas raw DXA data and asymmetry magnitudes were non-normally distributed. Within-participant differences for all measures between the dominant (highest value) and non-dominant (lowest value) limb were analysed using Paired samples t tests. Participants were categorized into low and high LM, BMC and FM asymmetry groups using a median split. Additionally, a threshold of 2 cm was applied to distinguish between low and high LLD. A two-way ANOVA (sex × training level) was initially considered; however, this approach was not retained because the assumption of homogeneity of variances was violated, as indicated by a significant Levene’s test. Therefore, between-group comparisons were performed using Independent samples t-tests and One-way ANOVAs to investigate differences in asymmetry magnitude according to sex and training level, respectively. Furthermore, independent samples t-tests were used to compare running performance between low- and high-asymmetry groups. Cohen’s d effect sizes (ES) were reported for the Independent samples t-tests and η² for One-way ANOVAs. Multiple linear regression was performed to examine the relationship between running performance and LM, BMC and FM asymmetry as well as LLD, with sex and training level (dummy coded) included as covariates. Assumptions of linearity, homoscedasticity, and normality of residuals were checked, and multicollinearity was assessed via tolerance and variance inflation factor (VIF), with tolerance < 0.10 or VIF > 10 indicating multicollinearity. Statistical significance was set at p < 0.05. Results A total of 68 recreational runners (53% males) were included to participate in this study (Table 1 ). Table 1 Descriptive characteristics (mean ± standard deviation) of distance runners by sex and training level. Training level Males Females Novice Intermediate Trained Novice Intermediate Trained N 12 11 13 12 10 10 Age (y) 35.1 ± 11.0 34.5 ± 8.6 37.7 ± 9.8 30.5 ± 9.5 36.1 ± 9.6 35.0 ± 7.3 Body weight (kg) 79.7 ± 15.6 73.3 ± 9.0 76.4 ± 9.2 65.7 ± 11.8 60.5 ± 4.9 58.8 ± 4.9 Body height (cm) 183.1 ± 7.6 180.7 ± 6.2 181.6 ± 4.7 167.4 ± 9.6 168.1 ± 6.1 166.3 ± 5.7 BMI (kg/m 2 ) 23.8 ± 5.0 22.5 ± 2.4 23.2 ± 2.9 23.4 ± 3.3 21.5 ± 1.8 21.3 ± 2.1 BMI = body mass index. Significant differences between the dominant and non-dominant limb were observed across all measures (t = 2.964 to 6.943, p < 0.001 to 0.013, ES = 0.796 to 2.093; Table 2 ). Mean asymmetry magnitudes across DXA-derived metrics ranged from 1.91% to 4.04%, irrespective of sex and training level. Mean LLD varied between 0.5 and 0.9 cm across groups. No significant differences in asymmetry magnitude were detected, with the exception of LLD between sexes and BMD across training levels (Table 3 ). Specifically, the female runners displayed greater LLD compared to the male runners (p = 0.004). In addition, trained runners exhibited significantly greater BMD asymmetry compared with novice runners (p = 0.006), whereas no significant differences were observed regarding the remaining group comparisons (p = 0.135 to 0.478). Table 2 Dominant vs. non-dominant limb scores (mean ± standard deviation) by training level and sex. Novice runners Intermediate runners Trained runners Dominant Non-dominant Dominant Non-dominant Dominant Non-dominant Males (n) 12 10 13 LM (g) 3733.0 ± 763.6 3576.8 ± 750.6 3495.6 ± 448.8 3345.9 ± 394.6 3552.1 ± 547.9 3373.2 ± 551.1 BMD (g/cm 2 ) 1.064 ± 0.107 1.029 ± 0.106 1.029 ± 0.125 0.993 ± 0.117 1.031 ± 0.114 0.999 ± 0.114 BMC (g) 240.6 ± 43.6 232.7 ± 42.3 234.3 ± 32.1 224.8 ± 28.9 233.3 ± 30.1 221.9 ± 28.7 FM (g) 983.5 ± 486.0 931.3 ± 460.3 866.7 ± 243.1 793.9 ± 231.4 814.1 ± 295.9 758.0 ± 255.4 Leg length (cm) 95.8 ± 4.7 95.3 ± 4.8 93.3 ± 5.3 92.9 ± 5.3 96.7 ± 4.4 96.2 ± 4.4 Females (n) 12 11 10 LM (g) 2082.6 ± 454.3 1943.1 ± 441.7 2258.2 ± 531.3 2130.9 ± 512.7 2066.8 ± 308.4 1981.7 ± 305.2 BMD (g/cm 2 ) 0.894 ± 0.095 0.868 ± 0.968 0.884 ± 0.104 0.855 ± 0.102 0.870 ± 0.063 0.835 ± 0.047 BMC (g) 156.3 ± 29.0 148.3 ± 29.8 162.9 ± 25.5 154.9 ± 22.4 152.3 ± 14.1 145.6 ± 13.9 FM (g) 1233.1 ± 391.9 1167.3 ± 358.9 814.1 ± 295.9 758.0 ± 255.2 930.2 ± 206.9 879.8 ± 199.4 Leg length (cm) 87.1 ± 6.0 86.4 ± 6.1 83.8 ± 9.9 82.8 ± 9.6 87.2 ± 4.6 86.4 ± 4.7 LM = lean mass, BMD = bone mineral density, BMC = bone mineral content, FM = fat mass, Note: significantly higher (p < 0.05) dominant versus non-dominant limb values are bolded. Table 3 Morphological inter-limb asymmetry (mean ± standard deviation) in recreational runners across sex and training level. LM (%) Male (n = 36) Female (N = 32) t-value p-value ES Novice runners (n = 24) Intermediate runners (n = 21) Trained runners (n = 23) F-value p-value ES 3.36 ± 2.45 3.07 ± 2.64 0.467 0.642 0.113 3.70 ± 2.76 2.34 ± 1.56 3.54 ± 2.85 1.950 0.150 0.057 BMD (%) 1.61 ± 1.40 2.01 ± 1.36 -1.202 0.234 - 0.292 1.25 ± 1.28 1.70 ± 1.05 2.47 ± 1.53 5.217 0.008 0.138 BMC (%) 1.91 ± 1.42 2.21 ± 1.63 -0.824 0.413 -0.200 1.71 ± 1.28 2.20 ± 1.62 2.26 ± 1.66 0.919 0.404 0.027 FM (%) 3.28 ± 3.25 3.42 ± 2.93 -0.189 0.850 -0.046 3.33 ± 2.97 4.04 ± 3.68 2.73 ± 2.55 0.980 0.381 0.029 LLD (cm) 0.5 ± 0.4 0.9 ± 0.6 -3.024 0.004 -0.735 0.6 ± 0.5 0.7 ± 0.7 0.6 ± 0.4 0.183 0.833 0.006 ES = effect size, LM = lean mass, BMD = bone mineral density, BMC = bone mineral content, FM = fat mass, LLD = leg length discrepancy, Note: significant differences (p < 0.05) in the magnitude of inter-limb asymmetry across sexes or training levels are bolded. Independent-samples t-tests, including the predefined split analyses, revealed no significant differences in running performance between participants classified as having low versus high LM asymmetry (t = -0.842, p = 0.404, ES = -0.219), BMC asymmetry (t = -0.556, p = 0.581, ES = -0.15), FM asymmetry (t = -0.001, p = 0.999, ES = 0.00) or between those with low versus high LLD (t = 0.296, p = 0.768, ES = 0.768). In line with these findings, inter-limb LM, BMC and FM asymmetry and LLD magnitude were no significant predictors of running performance (Table 4 ). Table 4 Multiple linear regression of Cooper test performance and lower inter-limb morphological asymmetries ( n = 68). Multiple linear regression Adjusted R² (F-value) B₀ / B β p-value Running performance 0.756 (23.473) 3454.494 < 0.001 Lean mass (LM) asymmetry 5.496 0.030 0.686 Bone mineral content (BMC) asymmetry 14.839 0.049 0.476 Fat mass (FM) asymmetry -7.216 -0.046 0.512 Leg length discrepancy (LLD) 44.143 0.078 0.316 Age -15.630 -0.301 < 0.001 Sex -493.163 -0.523 < 0.001 Training level intermediate runners 559.831 0.547 < 0.001 Training level trained runners 744.339 0.738 < 0.001 B₀ = intercept, B = unstandardized coefficient, β = standardized coefficient. Note: significant predictors (p < 0.05) are bolded. Discussion The main findings of the present study in recreational endurance runners were threefold: ( 1 ) significant (mean) inter-limb asymmetry ranged from 1.91% to 4.04% across DXA-derived metrics, while LLD ranged between 0.5 and 0.9 cm; ( 2 ) apart from greater LLD in female compared with male runners, and higher BMD values in trained runners compared with novices, no significant differences were observed across sex or training level; and ( 3 ) neither inter-limb asymmetries in LM, BMC and FM nor LLD significantly predicted running performance, and no differences in performance were found between athletes with low versus high side-to-side differences. Previous research indicates that magnitudes of morphological asymmetry are generally smaller than functional asymmetry. For example, judokas show lower-limb morphological asymmetries up to 6.4% ( 7 ), compared with isokinetic strength asymmetries up to 11.5% ( 30 ). Similarly, in tennis players, lower-limb morphological asymmetry reaches 2.7%, whereas functional asymmetries during performance tasks are as high as 10.2% ( 16 , 31 , 32 ). Although significant differences were observed between limbs, our study also observed relatively low morphological asymmetry values (≤ 4.0%), while prior studies performed on the same sample have reported mean strength and jump asymmetries up to 19.1% and 13.9%, respectively ( 10 , 33 ). Notably, the largest DXA-derived asymmetries in athletic populations are typically observed in FM ( 34 ), likely because its low absolute values magnify percentage differences. However, because FM distribution cannot be modified through training ( 35 ), the practical relevance of this metric is limited. Sex differences play an important role in endurance running performance ( 36 ). Female runners typically demonstrate shorter contact times, longer flight times, shorter stride lengths, and higher stride frequencies compared to males ( 37 ), which may partly reflect differences in anthropometric and body composition characteristics ( 38 ). In the present study, however, no sex differences were observed in morphological inter-limb asymmetry, except for greater LLD in females. Similarly, while previous research has reported significant differences in morphological asymmetry across training levels at the upper limb ( 16 , 32 ), such differences appear less evident in the lower limbs. In the current study, only BMD asymmetry differed between training levels, with trained runners exhibiting greater asymmetry than novices. Although BMD is influenced by diet, hormones, and genetics ( 39 ), these factors are unlikely to produce inter-limb differences. Instead, the observed asymmetry likely reflects uneven mechanical loading accumulated during endurance running, consistent with Wolf’s law, which states that bone adapts to the forces applied to it ( 40 ). Although the absolute asymmetry values were small, the greater training volume in trained runners suggests that repetitive, asymmetrical loading patterns may contribute to the development of inter-limb BMD differences. Increased distal body mass has been shown to raise the energy cost of running ( 17 , 18 ), while LLDs exceeding 2 cm have been associated with a roughly 5% increase in oxygen consumption during walking ( 25 , 26 ). Therefore, greater inter-limb asymmetry could theoretically impair running performance by increasing metabolic demand. However, our findings indicate that neither LM, BMC and FM asymmetry nor LLD significantly predicts running performance, and no differences were observed between low and high asymmetry groups. These results may be explained by several factors. First, inter-limb differences in our sample were small, with limited variability between participants, and were therefore likely insufficient to meaningfully affect running performance. Second, asymmetry was assessed at segmental rather than at muscle level, potentially masking substantial but opposing muscle-specific asymmetries. Put simply, large asymmetries in different high-volume muscles on opposite limbs could offset each other, resulting in a low overall limb asymmetry score. Finally, given that the combination of LM, BMC and FM contribute substantially to total body mass, and that increased distal mass may impair running economy, limb-level measures may lack the sensitivity to detect performance-relevant asymmetries, particularly in distal segments. This study is not without limitations. First, although DXA is a well-established method for assessing body composition, it is restricted to whole-body or segmental measurements and does not allow for muscle-specific analyses. Future research should therefore consider using magnetic resonance imaging (MRI) or ultrasound measures to quantify muscle-specific inter-limb asymmetries more precisely. Second, running performance was assessed on an outdoor track, where environmental conditions such as temperature and humidity may have influenced performance outcomes ( 41 ). Third, the inability to use a two-way ANOVA due to violation of the homogeneity of variance assumption (significant Levene’s test) limited our ability to examine potential interactions between sex and training level on asymmetry outcomes. Finally, training level was determined using subjective criteria given the absence standardized thresholds, which may have affected the training level specific analyses. Conclusion In conclusion, the present study demonstrates that the significant magnitudes of morphological inter-limb asymmetry in runners are small, with DXA-derived differences generally below 4% and LLD well under clinically relevant thresholds. Apart from greater BMD asymmetry in trained runners and slightly larger LLD in females, asymmetry measures were largely comparable across sex and training level. Importantly, neither morphological inter-limb asymmetries nor LLD were associated with Cooper test performance, and no performance differences were observed between athletes with low and high side-to-side differences. Collectively, these findings indicate that small morphological asymmetries at the lower limb level are unlikely to have a meaningful impact on endurance running performance. From a practical perspective, correcting minor morphological asymmetries solely to enhance performance does not appear justified in healthy adult recreational runners. Nevertheless, future research should investigate whether such asymmetries may be relevant with respect to injury risk. Abbreviations DXA Dual-energy X-ray Absorptiometry FM fat mass BMD bone mineral density BMC bone mineral content LM lean mass BMI body mass index LLD leg length discrepancy Declarations Data availability The datasets generated and/or analysed during the current study are available in the Vrije Universiteit Brussel repository, with persistent identifiers VUB/MOVE/1/000010 VUB/MOVE/1/000013, and VUB/MOVE/1/000015. Acknowledgments The authors would like to thank the participants for participating in this study. Funding The authors declare that no funding was received to support the conduct of this study. Ethics declarations Ethics approval and consent to participate Prior to participation, all individuals received comprehensive verbal and written information regarding the study procedures and provided written informed consent. The study was approved by the Medical Ethics Committee of the Vrije Universiteit Brussel and University Hospital Brussel (B.U.N. 1432023000042), prospectively registered at ClinicalTrials.gov (NCT06808399), and conducted in accordance with the principles of the Declaration of Helsinki. Authors' contributions JD, ED, DA, KDP, PC and LC contributed to the conceptualization of the study. JD was responsible for data collection. JD prepared the original manuscript draft, while ED, DA, KDP, PC and LC provided critical review and revisions. All authors read and approved the final version of the manuscript. Consent for publication Not applicable Competing interests The authors declare no competing interests. References Alvero-Cruz JR, Mathias VP, Romero JG, de Albornoz-Gil MC, Benítez-Porres J, Ordoñez FJ et al. Prediction of Performance in a Short Trail Running Race: The Role of Body Composition. Front Physiol. 2019;10. 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Ground Contact Time Imbalances Strongly Related to Impaired Running Economy. Int J Exerc Sci. 2020;13(4):427–37. Marfell-Jones MJSAD, JH dR. International standards for anthropometric assessment. International Society for the Advancement of Kinanthropometry; 2012. Bishop C, Read P, Chavda S, Turner A. Asymmetries of the Lower Limb: The Calculation Conundrum in Strength Training and Conditioning. Strength Cond J. 2016;38(6):27–32. D'Hondt J, Chapelle L, Pletinckx B, Sadouni M, Bosman L, Lindekens T et al. Intra- and Inter-Limb Isokinetic Strength Asymmetry in Competitive Judokas: Differences in Magnitude Across Competition Levels and Sex. Journal of Human Kinetics. In press. Chapelle L, Bishop C, D'Hondt J, Rommers N, D'Hondt E, Clarys P. Development of upper and lower extremity functional asymmetries in male and female elite youth tennis players: a longitudinal study. J Sport Med Phys Fit. 2023;63(12):1269–84. D'Hondt J, Chapelle L, Van Droogenbroeck L, Aerenhouts D, Clarys P, D'Hondt E. Bioelectrical impedance analysis as a means of quantifying upper and lower limb asymmetry in youth elite tennis players: An explorative study. Eur J Sport Sci. 2022;22(9):1343–54. D'Hondt J, Bishop C, D'Hondt E, Chapelle L, Aerenhouts D, Clarys P et al. Inter-limb range of motion and jumping asymmetry linked to running performance in healthy adult recreational endurance runners. Submitted. D'Hondt J, Chapelle L, Lindekens T, Clarys P. Morphological inter-limb asymmetry in youth judokas is independent of competitive level and sex. Bmc Sports Sci Med R. 2025;17(1). Ramirez-Campillo R, Andrade D, Clemente F, Afonso J, Pérez-Castilla A, Gentil P. A proposed model to test the hypothesis of exercise-induced localized fat reduction (spot reduction), including a systematic review with meta-analysis. Hum Mov. 2021;23(3):1–14. Besson T, Macchi R, Rossi J, Morio CYM, Kunimasa Y, Nicol C, et al. Sex Differences in Endurance Running. Sports Med. 2022;52(6):1235–57. Nelson RC, Brooks CM, Pike NL. Biomechanical comparison of male and female distance runners. Ann N Y Acad Sci. 1977;301:793–807. Roche-Seruendo LE, Latorre-Román PÁ, Soto-Hermoso VM, García-Pinillos F. Do sex and body structure influence spatiotemporal step characteristics in endurance runners? Sci Sport. 2019;34(6):412–e1. Pluijm SMF, Visser M, Smit JH, Popp-Snijders C, Roos JC, Lips P. Determinants of bone mineral density in older men and women: Body composition as mediator. J Bone Min Res. 2001;16(11):2142–51. Andreoli A, Monteleone M, Van Loan M, Promenzio L, Tarantino U, De Lorenzo A. Effects of different sports on bone density and muscle mass in highly trained athletes. Med Sci Sports Exerc. 2001;33(4):507–11. Knechtle B, Di Gangi S, Rüst CA, Villiger E, Rosemann T, Nikolaidis PT. The role of weather conditions on running performance in the Boston Marathon from 1972 to 2018. PLoS ONE. 2019;14(3). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 14 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviews received at journal 08 Apr, 2026 Reviews received at journal 07 Apr, 2026 Reviewers agreed at journal 01 Apr, 2026 Reviewers agreed at journal 31 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers agreed at journal 30 Mar, 2026 Reviewers invited by journal 26 Mar, 2026 Editor invited by journal 17 Mar, 2026 Editor assigned by journal 04 Mar, 2026 Submission checks completed at journal 04 Mar, 2026 First submitted to journal 02 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-9008534\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":false,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":600597527,\"identity\":\"723f3ffa-21d6-4571-b6fb-30db5c610262\",\"order_by\":0,\"name\":\"Joachim D’Hondt\",\"email\":\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA20lEQVRIiWNgGAWjYBAC9gYGBmYGAyDrBhDzMNgwMEgwsOHVwoiqJSGNWC0McC2HidDS3vzwc0HBNga+283HHrz9cT5xw+0Gtgcf8GnpOWYsPcPgNoPknWPphnMSbiduuHOA3XAGPi0zEsyYeYBaDG7kmEnzJNw2NriRwCbNg1dL+jdkLecgWv7g0SI4IwfFlgNyYC34vC/Nc6ZYGqiFR/JGWprknLRkOck7B9sNe/Bo4WNv3/iZ589tOb4bycck3tjY8YCD7gc+a6AA2b+guBoFo2AUjIJRQBEAAB5jTM/EjSi4AAAAAElFTkSuQmCC\",\"orcid\":\"\",\"institution\":\"Vrije Universiteit Brussel\",\"correspondingAuthor\":true,\"prefix\":\"\",\"firstName\":\"Joachim\",\"middleName\":\"\",\"lastName\":\"D’Hondt\",\"suffix\":\"\"},{\"id\":600597528,\"identity\":\"133f48d3-1aa2-45b8-890a-865a8580ec65\",\"order_by\":1,\"name\":\"Eva D’Hondt\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Vrije Universiteit Brussel\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Eva\",\"middleName\":\"\",\"lastName\":\"D’Hondt\",\"suffix\":\"\"},{\"id\":600597529,\"identity\":\"5ec11e14-9698-47a5-a66c-84b90320a9bb\",\"order_by\":2,\"name\":\"Dirk Aerenhouts\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Vrije Universiteit Brussel\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Dirk\",\"middleName\":\"\",\"lastName\":\"Aerenhouts\",\"suffix\":\"\"},{\"id\":600597530,\"identity\":\"773ca4fc-3592-459a-82fd-de5c30a17385\",\"order_by\":3,\"name\":\"Kevin Pauw\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Vrije Universiteit Brussel\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Kevin\",\"middleName\":\"\",\"lastName\":\"Pauw\",\"suffix\":\"\"},{\"id\":600597531,\"identity\":\"f035837a-30a6-4902-9874-2a89b5181ccc\",\"order_by\":4,\"name\":\"Peter Clarys\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Vrije Universiteit Brussel\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Peter\",\"middleName\":\"\",\"lastName\":\"Clarys\",\"suffix\":\"\"},{\"id\":600597534,\"identity\":\"17ff10d6-2b4f-4229-ad02-e0f0cd4807be\",\"order_by\":5,\"name\":\"Laurent Chapelle\",\"email\":\"\",\"orcid\":\"\",\"institution\":\"Vrije Universiteit Brussel\",\"correspondingAuthor\":false,\"prefix\":\"\",\"firstName\":\"Laurent\",\"middleName\":\"\",\"lastName\":\"Chapelle\",\"suffix\":\"\"}],\"badges\":[],\"createdAt\":\"2026-03-02 09:53:28\",\"currentVersionCode\":1,\"declarations\":\"\",\"doi\":\"10.21203/rs.3.rs-9008534/v1\",\"doiUrl\":\"https://doi.org/10.21203/rs.3.rs-9008534/v1\",\"draftVersion\":[],\"editorialEvents\":[],\"editorialNote\":\"\",\"failedWorkflow\":false,\"files\":[{\"id\":104405699,\"identity\":\"8411ed7d-bd09-4dce-84c8-322c8e31cb85\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:23:37\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1028037,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-9008534/v1/718f0ba9-b1f7-46cc-874e-2746d27151e5.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Variations in morphological lower inter-limb asymmetry by sex and training level, and its link to performance in recreational runners: A cross-sectional study\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eSeveral studies have developed predictive models of endurance running performance, consistently demonstrating that, beyond key individual determinants such as sex, age, VO₂max, and running economy, anthropometric characteristics meaningfully contribute to race outcome (\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). Most models incorporate at least one anthropometric or body composition variable alongside physiological and training-related parameters (\\u003cspan additionalcitationids=\\\"CR2\\\" citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e). Although the specific predictors may differ between men and women, higher body mass index (BMI), greater body fat percentage, larger calf circumference, and increased lower-leg volume have repeatedly been identified as negative predictors of endurance running performance (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e4\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e5\\u003c/span\\u003e). These findings indicate that both total body mass and its regional distribution influence the energetic cost of locomotion and, consequently, race performance (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e6\\u003c/span\\u003e). However, previous research has largely focused on whole-body measures, with limited consideration of potential inter-limb morphological asymmetries in endurance runners.\\u003c/p\\u003e \\u003cp\\u003eOver the past decade, inter-limb asymmetry has received growing attention in various athletic populations because of its potential implications for performance and injury risk (\\u003cspan additionalcitationids=\\\"CR8\\\" citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e). In endurance runners, meaningful asymmetries have been reported across sex and training level (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e11\\u003c/span\\u003e), and some functional, kinetic, and kinematic asymmetries have been suggested to negatively influence determinants of running performance (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Nevertheless, inter-limb asymmetry is highly task- and metric-specific (\\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e9\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e13\\u003c/span\\u003e), limiting the generalizability of findings across different assessments. Consequently, the use of arbitrary thresholds (e.g., 10% or 15%) to identify performance impairments is questionable (\\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e14\\u003c/span\\u003e). Moreover, findings derived from functional assessments cannot be directly extrapolated to morphological asymmetry. Indeed, previous work showed that asymmetry in body composition is not necessarily associated with asymmetry in lower-limb functional performance (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e15\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAerobic energy expenditure during running increases by approximately 1% for every additional kilogram added to the trunk, whereas the same mass added distally, such as at the feet, raises metabolic cost by nearly 10% per kilogram (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e). Given that leg swing accounts for roughly 20% of the net energy cost of running (\\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e), excess or unevenly distributed mass in the lower limbs may disproportionately elevate energy demand. Although research on this topic remains scarce, it can be hypothesized that asymmetrical lower-limb body composition may further impair running performance. One previous study found no significant association between anatomical asymmetry and metabolic cost in runners of different training levels (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e19\\u003c/span\\u003e). However, this investigation was judged to be at high risk of bias according to the Downs and Black assessment tool, due to an unclear study aim, insufficient reporting of primary outcomes, and the absence of actual probability values (\\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e12\\u003c/span\\u003e). Moreover, asymmetry was expressed in absolute rather than relative terms, potentially limiting comparability and interpretability.\\u003c/p\\u003e \\u003cp\\u003eIn the context of performance and health, leg length discrepancies (LLDs) are also often examined. Approximately 90% of individuals present some degree of bone length variation, and around 20% exhibit a LLD greater than 0.9 cm (\\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e20\\u003c/span\\u003e). While often clinically unnoticed, larger discrepancies have been associated with musculoskeletal disorders and lower-limb injuries, including osteoarthritis, stress fractures, and plantar fasciitis (\\u003cspan additionalcitationids=\\\"CR22\\\" citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e21\\u003c/span\\u003e\\u0026ndash;\\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e23\\u003c/span\\u003e). Greater LLDs have also been linked to altered spatiotemporal gait parameters and ground reaction forces (\\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e24\\u003c/span\\u003e). Consequently, discrepancies exceeding 2 cm have been associated with increased oxygen consumption and energy expenditure during walking (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). In contrast, differences smaller than 1 cm do not appear to substantially affect running economy (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e27\\u003c/span\\u003e). Despite these associations with determinants of running performance, the direct relationship between morphological asymmetry and endurance running performance remains unclear.\\u003c/p\\u003e \\u003cp\\u003eGiven the limited evidence regarding (lower-limb) morphological asymmetry in endurance runners and its potential link with performance as well as the absence of established population- and task-specific thresholds, it is uncertain whether correcting body imbalances should be a priority for practitioners aiming to optimize running outcomes. Therefore, the aims of this study were to: (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) quantify the magnitude of lower inter-limb asymmetry and examine differences in asymmetry across sex and training level, (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) compare running performance between low- and high-asymmetry groups using split analyses, and (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) investigate the link between lean mass (LM), bone mineral content (BMC) and fat mass (FM) asymmetry as well as LLD and running performance in healthy adult endurance runners with varying training backgrounds.\\u003c/p\\u003e\"},{\"header\":\"Materials and methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eParticipants\\u003c/h2\\u003e \\u003cp\\u003eTo ensure a heterogeneous sample, participants were stratified according to sex and running experience (novice, intermediate, trained). Although anecdotally, novice runners were defined as individuals who had not participated in structured or unstructured running training for at least five years and reported a weekly running volume of no more than 10 km during the three months preceding the study. Intermediate runners were those who consistently accumulated between 40 and 150 km of running per month over the previous six months, whereas trained runners reported monthly running volumes exceeding 150 km over the same timeframe.\\u003c/p\\u003e \\u003cp\\u003e Eligibility criteria required participants to be free from any musculoskeletal injury, surgical interventions, or medical condition affecting the lower limbs, hips, trunk, or spine within the six months preceding study inclusion. Individuals were also excluded if they reported any medical condition or medication use that could influence performance during the Cooper test, or if they regularly engaged (\\u0026gt;\\u0026thinsp;2 hours per week) in sports characterized by pronounced unilateral loading, such as tennis or soccer.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eProcedures\\u003c/h3\\u003e\\n\\u003cp\\u003eThis cross-sectional study was conducted over two separate testing sessions, scheduled two weeks apart. The first session involved the collection of anthropometric and DXA data. All participants were instructed to refrain from eating and drinking (i.e., 2 hours prior to the start of the test occasion), from consuming any alcohol (i.e., 12 hours prior to the start of the test occasion), caffeine (i.e., on the day of the test occasion), and (anti)diuretics (i.e., 7 days prior to the test occasion). Prior to testing, participants were instructed to empty their bladder and remove all metal objects (e.g., jewellery). The second session was devoted to the Cooper test, during which participants were allowed to eat and drink as usual but were required to refrain from caffeine and alcohol consumption, consistent with the requirements of the first session. For both test sessions, all participants were instructed to avoid vigorous physical activity during the 24 hours preceding their individually planned test occasion.\\u003c/p\\u003e\\n\\u003ch3\\u003eAnthropometry\\u003c/h3\\u003e\\n\\u003cp\\u003eAll anthropometric measurements were performed according to the International Society for the Advancement of Kinanthropometry guidelines (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e28\\u003c/span\\u003e). Body height was determined up to 0.1 cm using the SECA 213 stadiometer (SECA, Germany), whilst participants\\u0026rsquo; body mass (to the nearest 0.002 kg) was measured in minimal clothing using the Radwag WLT60/10/X/3 device (Radwag, Poland). Leg length (i.e., distance between the anterior superior iliac spine and medial malleolus), was assessed using an anthropometric tape (Rosscraft Innovations Inc, Vancouver, Canada) at both sides of the body with an accuracy of 0.1 cm.\\u003c/p\\u003e\\n\\u003ch3\\u003eDual-energy X-ray absorptiometry\\u003c/h3\\u003e\\n\\u003cp\\u003eSegmental body composition of the lower limbs was assessed by a certified investigator (J.D.) using dual-energy X-ray absorptiometry (DXA; Lunar iDXA, GE Healthcare, Madison, WI, USA). Prior to each measurement session, the DXA scanner was calibrated and subjected to quality assurance procedures in accordance with the manufacturer\\u0026rsquo;s recommendations to ensure measurement reliability. Scans were performed with participants positioned supine on the scanning table and their arms placed alongside the body with thumbs oriented upward. Whole-body scans yielded segment-specific outcomes for the upper and lower extremities, including LM, bone mineral density (BMD), BMC, and FM.\\u003c/p\\u003e\\n\\u003ch3\\u003eInter-limb asymmetry\\u003c/h3\\u003e\\n\\u003cp\\u003eInter-limb asymmetry was quantified using the percentage difference method: ((highest value \\u0026ndash; lowest value) / highest value) \\u0026times; 100 (\\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e29\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eRunning performance assessment\\u003c/h2\\u003e \\u003cp\\u003eRunning performance was evaluated using the Cooper 12-minute run test. Prior to testing, participants completed a standardized warm-up consisting of light jogging, knee raises exercises and dynamic stretching exercises. Participants were instructed to cover the maximum possible distance within a 12-minute time frame on a standard 400 m outdoor track (i.c. mean temperature: 9.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.2\\u0026deg;C; mean relative humidity: 70.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;12.2%). Throughout the test, investigators provided standardized verbal encouragement and recorded performance outcomes. Total running distance was calculated as the sum of the number of complete laps completed and the additional distance covered during the final partial lap, which was measured in 10 m increments.\\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eStatistical Analyses\\u003c/h3\\u003e\\n\\u003cp\\u003eAll analyses were performed using the SPSS software (version 31, SPSS Inc., Chicago, IL, USA). The Shapiro-Wilk test, along with visual inspection of histograms and Q-Q plots, indicated that Cooper test data were normally distributed, whereas raw DXA data and asymmetry magnitudes were non-normally distributed. Within-participant differences for all measures between the dominant (highest value) and non-dominant (lowest value) limb were analysed using Paired samples t tests. Participants were categorized into low and high LM, BMC and FM asymmetry groups using a median split. Additionally, a threshold of 2 cm was applied to distinguish between low and high LLD. A two-way ANOVA (sex \\u0026times; training level) was initially considered; however, this approach was not retained because the assumption of homogeneity of variances was violated, as indicated by a significant Levene\\u0026rsquo;s test. Therefore, between-group comparisons were performed using Independent samples t-tests and One-way ANOVAs to investigate differences in asymmetry magnitude according to sex and training level, respectively. Furthermore, independent samples t-tests were used to compare running performance between low- and high-asymmetry groups. Cohen\\u0026rsquo;s d effect sizes (ES) were reported for the Independent samples t-tests and η\\u0026sup2; for One-way ANOVAs. Multiple linear regression was performed to examine the relationship between running performance and LM, BMC and FM asymmetry as well as LLD, with sex and training level (dummy coded) included as covariates. Assumptions of linearity, homoscedasticity, and normality of residuals were checked, and multicollinearity was assessed via tolerance and variance inflation factor (VIF), with tolerance\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.10 or VIF\\u0026thinsp;\\u0026gt;\\u0026thinsp;10 indicating multicollinearity. Statistical significance was set at p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05.\\u003c/p\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eA total of 68 recreational runners (53% males) were included to participate in this study (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDescriptive characteristics (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation) of distance runners by sex and training level.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eTraining level\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c4\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eMales\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"3\\\" nameend=\\\"c7\\\" namest=\\\"c5\\\"\\u003e \\u003cp\\u003eFemales\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eNovice\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eIntermediate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eTrained\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNovice\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eIntermediate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eTrained\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge (y)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e34.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;8.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e37.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e30.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e36.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e35.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBody weight (kg)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e79.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;15.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e73.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e76.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e65.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;11.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e60.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e58.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBody height (cm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e183.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;7.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e180.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e181.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e167.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e168.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e166.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMI (kg/m\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e23.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.0\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e22.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e23.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e23.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e21.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e21.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003eBMI\\u0026thinsp;=\\u0026thinsp;body mass index.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSignificant differences between the dominant and non-dominant limb were observed across all measures (t\\u0026thinsp;=\\u0026thinsp;2.964 to 6.943, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001 to 0.013, ES\\u0026thinsp;=\\u0026thinsp;0.796 to 2.093; Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Mean asymmetry magnitudes across DXA-derived metrics ranged from 1.91% to 4.04%, irrespective of sex and training level. Mean LLD varied between 0.5 and 0.9 cm across groups. No significant differences in asymmetry magnitude were detected, with the exception of LLD between sexes and BMD across training levels (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Specifically, the female runners displayed greater LLD compared to the male runners (p\\u0026thinsp;=\\u0026thinsp;0.004). In addition, trained runners exhibited significantly greater BMD asymmetry compared with novice runners (p\\u0026thinsp;=\\u0026thinsp;0.006), whereas no significant differences were observed regarding the remaining group comparisons (p\\u0026thinsp;=\\u0026thinsp;0.135 to 0.478).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eDominant vs. non-dominant limb scores (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation) by training level and sex.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"7\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eNovice runners\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003eIntermediate runners\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003eTrained runners\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eDominant\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eNon-dominant\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eDominant\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eNon-dominant\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eDominant\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNon-dominant\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eMales (n)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e13\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLM (g)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e3733.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;763.6\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3576.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;750.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e3495.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;448.8\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e3345.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;394.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e3552.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;547.9\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3373.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;551.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMD (g/cm\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.064\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.107\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1.029\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.106\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.029\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.125\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.993\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.117\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1.031\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.114\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.999\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.114\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMC (g)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e240.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;43.6\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e232.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;42.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e234.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;32.1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e224.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;28.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e233.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;30.1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e221.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;28.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFM (g)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e983.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;486.0\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e931.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;460.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e866.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;243.1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e793.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;231.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e814.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;295.9\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e758.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;255.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLeg length (cm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e95.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e95.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e93.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e92.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;5.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e96.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e96.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eFemales (n)\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c3\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003e12\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c5\\\" namest=\\\"c4\\\"\\u003e \\u003cp\\u003e11\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"2\\\" nameend=\\\"c7\\\" namest=\\\"c6\\\"\\u003e \\u003cp\\u003e10\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLM (g)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2082.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;454.3\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1943.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;441.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2258.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;531.3\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e2130.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;512.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e2066.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;308.4\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1981.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;305.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMD (g/cm\\u003csup\\u003e2\\u003c/sup\\u003e)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.894\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.095\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.868\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.968\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.884\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.104\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.855\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.102\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.870\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.063\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.835\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.047\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMC (g)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e156.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;29.0\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e148.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;29.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e162.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;25.5\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e154.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;22.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e152.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;14.1\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e145.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;13.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFM (g)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e1233.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;391.9\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e1167.3\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;358.9\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e814.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;295.9\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e758.0\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;255.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e930.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;206.9\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e879.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;199.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLeg length (cm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e87.1\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.0\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e86.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;6.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e83.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.9\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e82.8\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;9.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e87.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.6\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e86.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;4.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"7\\\"\\u003eLM\\u0026thinsp;=\\u0026thinsp;lean mass, BMD\\u0026thinsp;=\\u0026thinsp;bone mineral density, BMC\\u0026thinsp;=\\u0026thinsp;bone mineral content, FM\\u0026thinsp;=\\u0026thinsp;fat mass, Note: significantly higher (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) dominant versus non-dominant limb values are bolded.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMorphological inter-limb asymmetry (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;standard deviation) in recreational runners across sex and training level.\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"12\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c12\\\" colnum=\\\"12\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eLM (%)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMale\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;36)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eFemale\\u003c/p\\u003e \\u003cp\\u003e(N\\u0026thinsp;=\\u0026thinsp;32)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003et-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003eES\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003eNovice runners\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;24)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003eIntermediate runners\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;21)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eTrained runners\\u003c/p\\u003e \\u003cp\\u003e(n\\u0026thinsp;=\\u0026thinsp;23)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eF-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003eES\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.36\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.45\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.07\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.64\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.467\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.642\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e0.113\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.76\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.34\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.56\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e3.54\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.85\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e1.950\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.150\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.057\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMD (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.61\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.40\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.01\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.202\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.234\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e-\\u003c/b\\u003e0.292\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.25\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e1.70\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.05\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2.47\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e5.217\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.008\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.138\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBMC (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.91\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.42\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2.21\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.63\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.824\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.413\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.200\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e1.71\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.28\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e2.20\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.62\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2.26\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;1.66\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.919\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.404\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.027\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFM (%)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e3.28\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.25\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3.42\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.93\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.189\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.850\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.046\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e3.33\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.97\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e4.04\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;3.68\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e2.73\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.55\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.980\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.381\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.029\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLLD (cm)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.5\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.9\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.6\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.024\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e0.004\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e \\u003cp\\u003e-0.735\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e0.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c8\\\"\\u003e \\u003cp\\u003e0.7\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\"\\u0026plusmn;\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e0.6\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e0.183\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003e0.833\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c12\\\"\\u003e \\u003cp\\u003e0.006\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003ctfoot\\u003e \\u003ctr\\u003e\\u003ctd colspan=\\\"12\\\"\\u003eES\\u0026thinsp;=\\u0026thinsp;effect size, LM\\u0026thinsp;=\\u0026thinsp;lean mass, BMD\\u0026thinsp;=\\u0026thinsp;bone mineral density, BMC\\u0026thinsp;=\\u0026thinsp;bone mineral content, FM\\u0026thinsp;=\\u0026thinsp;fat mass, LLD\\u0026thinsp;=\\u0026thinsp;leg length discrepancy, Note: significant differences (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) in the magnitude of inter-limb asymmetry across sexes or training levels are bolded.\\u003c/td\\u003e\\u003c/tr\\u003e \\u003c/tfoot\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eIndependent-samples t-tests, including the predefined split analyses, revealed no significant differences in running performance between participants classified as having low versus high LM asymmetry (t = -0.842, p\\u0026thinsp;=\\u0026thinsp;0.404, ES = -0.219), BMC asymmetry (t = -0.556, p\\u0026thinsp;=\\u0026thinsp;0.581, ES = -0.15), FM asymmetry (t = -0.001, p\\u0026thinsp;=\\u0026thinsp;0.999, ES\\u0026thinsp;=\\u0026thinsp;0.00) or between those with low versus high LLD (t\\u0026thinsp;=\\u0026thinsp;0.296, p\\u0026thinsp;=\\u0026thinsp;0.768, ES\\u0026thinsp;=\\u0026thinsp;0.768). In line with these findings, inter-limb LM, BMC and FM asymmetry and LLD magnitude were no significant predictors of running performance (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eMultiple linear regression of Cooper test performance and lower inter-limb morphological asymmetries (\\u003cem\\u003en\\u003c/em\\u003e\\u0026thinsp;=\\u0026thinsp;68).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eMultiple linear regression\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eAdjusted R\\u0026sup2;\\u003c/p\\u003e \\u003cp\\u003e(F-value)\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eB₀ / B\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003eβ\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ep-value\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003eRunning performance\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e0.756 (23.473)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e3454.494\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLean mass (LM) asymmetry\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e5.496\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.030\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.686\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eBone mineral content (BMC) asymmetry\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e14.839\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.049\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.476\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFat mass (FM) asymmetry\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-7.216\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.046\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.512\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLeg length discrepancy (LLD)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e44.143\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.078\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.316\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eAge\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-15.630\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.301\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSex\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-493.163\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.523\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTraining level intermediate runners\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e559.831\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.547\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTraining level trained runners\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e744.339\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.738\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e\\u003cb\\u003e\\u0026lt;\\u0026thinsp;0.001\\u003c/b\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eB₀ = intercept, B\\u0026thinsp;=\\u0026thinsp;unstandardized coefficient, β\\u0026thinsp;=\\u0026thinsp;standardized coefficient. Note: significant predictors (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.05) are bolded.\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eThe main findings of the present study in recreational endurance runners were threefold: (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) significant (mean) inter-limb asymmetry ranged from 1.91% to 4.04% across DXA-derived metrics, while LLD ranged between 0.5 and 0.9 cm; (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) apart from greater LLD in female compared with male runners, and higher BMD values in trained runners compared with novices, no significant differences were observed across sex or training level; and (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) neither inter-limb asymmetries in LM, BMC and FM nor LLD significantly predicted running performance, and no differences in performance were found between athletes with low versus high side-to-side differences.\\u003c/p\\u003e \\u003cp\\u003ePrevious research indicates that magnitudes of morphological asymmetry are generally smaller than functional asymmetry. For example, judokas show lower-limb morphological asymmetries up to 6.4% (\\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e7\\u003c/span\\u003e), compared with isokinetic strength asymmetries up to 11.5% (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e30\\u003c/span\\u003e). Similarly, in tennis players, lower-limb morphological asymmetry reaches 2.7%, whereas functional asymmetries during performance tasks are as high as 10.2% (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e31\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e). Although significant differences were observed between limbs, our study also observed relatively low morphological asymmetry values (\\u0026le;\\u0026thinsp;4.0%), while prior studies performed on the same sample have reported mean strength and jump asymmetries up to 19.1% and 13.9%, respectively (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e10\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e33\\u003c/span\\u003e). Notably, the largest DXA-derived asymmetries in athletic populations are typically observed in FM (\\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e34\\u003c/span\\u003e), likely because its low absolute values magnify percentage differences. However, because FM distribution cannot be modified through training (\\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e35\\u003c/span\\u003e), the practical relevance of this metric is limited.\\u003c/p\\u003e \\u003cp\\u003eSex differences play an important role in endurance running performance (\\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e36\\u003c/span\\u003e). Female runners typically demonstrate shorter contact times, longer flight times, shorter stride lengths, and higher stride frequencies compared to males (\\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e37\\u003c/span\\u003e), which may partly reflect differences in anthropometric and body composition characteristics (\\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e38\\u003c/span\\u003e). In the present study, however, no sex differences were observed in morphological inter-limb asymmetry, except for greater LLD in females. Similarly, while previous research has reported significant differences in morphological asymmetry across training levels at the upper limb (\\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e16\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e32\\u003c/span\\u003e), such differences appear less evident in the lower limbs. In the current study, only BMD asymmetry differed between training levels, with trained runners exhibiting greater asymmetry than novices. Although BMD is influenced by diet, hormones, and genetics (\\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e39\\u003c/span\\u003e), these factors are unlikely to produce inter-limb differences. Instead, the observed asymmetry likely reflects uneven mechanical loading accumulated during endurance running, consistent with Wolf\\u0026rsquo;s law, which states that bone adapts to the forces applied to it (\\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e40\\u003c/span\\u003e). Although the absolute asymmetry values were small, the greater training volume in trained runners suggests that repetitive, asymmetrical loading patterns may contribute to the development of inter-limb BMD differences.\\u003c/p\\u003e \\u003cp\\u003eIncreased distal body mass has been shown to raise the energy cost of running (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e17\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e18\\u003c/span\\u003e), while LLDs exceeding 2 cm have been associated with a roughly 5% increase in oxygen consumption during walking (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e25\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e26\\u003c/span\\u003e). Therefore, greater inter-limb asymmetry could theoretically impair running performance by increasing metabolic demand. However, our findings indicate that neither LM, BMC and FM asymmetry nor LLD significantly predicts running performance, and no differences were observed between low and high asymmetry groups. These results may be explained by several factors. First, inter-limb differences in our sample were small, with limited variability between participants, and were therefore likely insufficient to meaningfully affect running performance. Second, asymmetry was assessed at segmental rather than at muscle level, potentially masking substantial but opposing muscle-specific asymmetries. Put simply, large asymmetries in different high-volume muscles on opposite limbs could offset each other, resulting in a low overall limb asymmetry score. Finally, given that the combination of LM, BMC and FM contribute substantially to total body mass, and that increased distal mass may impair running economy, limb-level measures may lack the sensitivity to detect performance-relevant asymmetries, particularly in distal segments.\\u003c/p\\u003e \\u003cp\\u003eThis study is not without limitations. First, although DXA is a well-established method for assessing body composition, it is restricted to whole-body or segmental measurements and does not allow for muscle-specific analyses. Future research should therefore consider using magnetic resonance imaging (MRI) or ultrasound measures to quantify muscle-specific inter-limb asymmetries more precisely. Second, running performance was assessed on an outdoor track, where environmental conditions such as temperature and humidity may have influenced performance outcomes (\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e41\\u003c/span\\u003e). Third, the inability to use a two-way ANOVA due to violation of the homogeneity of variance assumption (significant Levene\\u0026rsquo;s test) limited our ability to examine potential interactions between sex and training level on asymmetry outcomes. Finally, training level was determined using subjective criteria given the absence standardized thresholds, which may have affected the training level specific analyses.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eIn conclusion, the present study demonstrates that the significant magnitudes of morphological inter-limb asymmetry in runners are small, with DXA-derived differences generally below 4% and LLD well under clinically relevant thresholds. Apart from greater BMD asymmetry in trained runners and slightly larger LLD in females, asymmetry measures were largely comparable across sex and training level. Importantly, neither morphological inter-limb asymmetries nor LLD were associated with Cooper test performance, and no performance differences were observed between athletes with low and high side-to-side differences. Collectively, these findings indicate that small morphological asymmetries at the lower limb level are unlikely to have a meaningful impact on endurance running performance. From a practical perspective, correcting minor morphological asymmetries solely to enhance performance does not appear justified in healthy adult recreational runners. Nevertheless, future research should investigate whether such asymmetries may be relevant with respect to injury risk.\\u003c/p\\u003e\"},{\"header\":\"Abbreviations\",\"content\":\"\\u003cdiv class=\\\"DefinitionList\\\"\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eDXA\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eDual-energy X-ray Absorptiometry\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eFM\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003efat mass\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eBMD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ebone mineral density\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eBMC\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ebone mineral content\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eLM\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003elean mass\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eBMI\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003ebody mass index\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv class=\\\"DefinitionListEntry\\\"\\u003e \\u003cdiv class=\\\"Term\\\"\\u003eLLD\\u003c/div\\u003e \\u003cdiv class=\\\"Description\\\"\\u003e \\u003cp\\u003eleg length discrepancy\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe datasets generated and/or analysed during the current study are available in the Vrije Universiteit Brussel repository, with persistent identifiers VUB/MOVE/1/000010 VUB/MOVE/1/000013, and VUB/MOVE/1/000015.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgments\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors would like to thank the participants for participating in this study.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that no funding was received to support the conduct of this study.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics declarations\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics approval and consent to participate\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003ePrior to participation, all individuals received comprehensive verbal and written information regarding the study procedures and provided written informed consent. The study was approved by the Medical Ethics Committee of the Vrije Universiteit Brussel and University Hospital Brussel (B.U.N. 1432023000042), prospectively registered at ClinicalTrials.gov (NCT06808399), and conducted in accordance with the principles of the Declaration of Helsinki.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026apos; contributions\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eJD, ED, DA, KDP, PC and LC contributed to the conceptualization of the study. JD was responsible for data collection. JD prepared the original manuscript draft, while ED, DA, KDP, PC and LC provided critical review and revisions. All authors read and approved the final version of the manuscript.\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u003c/p\\u003e\\n\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\u003cli\\u003e\\u003cspan\\u003eAlvero-Cruz JR, Mathias VP, Romero JG, de Albornoz-Gil MC, Ben\\u0026iacute;tez-Porres J, Ordo\\u0026ntilde;ez FJ et al. Prediction of Performance in a Short Trail Running Race: The Role of Body Composition. Front Physiol. 2019;10.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSalinero JJ, Soriano ML, Lara B, Gallo-Salazar C, Areces F, Ruiz-Vicente D, et al. Predicting race time in male amateur marathon runners. J Sport Med Phys Fit. 2017;57(9):1169\\u0026ndash;77.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKnechtle B, Barandun U, Knechtle P, Zingg MA, Rosemann T, R\\u0026uuml;st CA. Prediction of half-marathon race time in recreational female and male runners. Springerplus. 2014;3.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eLucia A, Esteve-Lanao J, Olivan J, Gomez-Gallego F, San Juan AF, Santiago C, et al. Physiological characteristics of the best Eritrean runners-exceptional running economy. Appl Physiol Nutr Metab. 2006;31(5):530\\u0026ndash;40.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHerrmann FR, Graf C, Karsegard VL, Mareschal J, Achamrah N, Delsoglio M, et al. Running performance in a timed city run and body composition: A cross-sectional study in more than 3000 runners. Nutrition. 2019;61:1\\u0026ndash;7.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMyers MJ, Steudel K. Effect of Limb Mass and Its Distribution on the Energetic Cost of Running. J Exp Biol. 1985;116(May):363\\u0026ndash;73.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Chapelle L, Lindekens T, Clarys P. Morphological Inter-Limb Asymmetry in Youth Judokas is Independent of Competitive Level and Sex. Bmc Sports Sci Med R. 2025.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Chapelle L. Change of Direction Asymmetry in Youth Elite Tennis Players: A Longitudinal Study. Int J Sports Med. 2024;45(6):436\\u0026ndash;42.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Vanhoudt M, Maluta D, Chapelle L, Aerenhouts D. Does a muscle fatigue-inducing protocol alters the magnitude of jump inter-limb asymmetry in healthy adolescents? J Hum Kinetics. 2025.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Chapelle L, Bishop C, Aerenhouts D, De Pauw K, Clarys P et al. Test-retest and inter-rater reliability of a field-based test battery to assess lower limb isometric strength and inter-limb asymmetry in healthy adult distance runners of various training levels. J Strength Cond Res. Unpublished results.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Aerenhouts D, Bishop C, De Pauw K, Clarys P, D'Hondt E et al. The association between the magnitude of isometric strength inter-limb asymmetry and running performance in healthy adult distance runners. Biol Sport. Unpublished results.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Chapelle L, Bishop C, Aerenhouts D, De Pauw K, Clarys P et al. Association Between Inter-Limb Asymmetry and Determinants of Middle- and Long-distance Running Performance in Healthy Populations: A Systematic Review. Sports Med-Open. 2024;10(1).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Bishop C, Chapelle L, De Pauw K, Clarys P, D'Hondt E et al. Consistency Between Lower Limb Preference and Dominance in Healthy Adult Distance Runners: Exploring its Variability Across Tasks and Time. J Strength Cond Res. Unpublished results.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eDos\\u0026rsquo;Santos T, Thomas C, Jones PA. Assessing interlimb asymmetries: Are we heading in the right direction? Strength Conditioning J. 2021;43(3):91\\u0026ndash;100.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eL\\u0026oacute;pez-Sierra P, Calleja-Gonz\\u0026aacute;lez J, Arede J, Ib\\u0026aacute;\\u0026ntilde;ez SJ. Monitoring Morphological and Muscular Asymmetries in Elite Basketball: Field and Lab Measures of Neuromuscular Health. Symmetry-Basel. 2026;18(1):159.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChapelle L, Bishop C, D'Hondt J, D'Hondt E, Clarys P. Morphological and functional asymmetry in elite youth tennis players compared to sex- and age-matched controls. J Sport Sci. 2022;40(14):1618\\u0026ndash;28.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSteudel K, Myers MJ. Effect of Limb Mass and Its Distribution on the Energetic Cost of Running. Am Zool. 1986;26(4):A63\\u0026ndash;A.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eModica JR, Kram R. Metabolic energy and muscular activity required for leg swing in running. J Appl Physiol. 2005;98(6):2126\\u0026ndash;31.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eSeminati E, Nardello F, Zamparo P, Ardig\\u0026ograve; LP, Faccioli N, Minetti AE. Anatomically Asymmetrical Runners Move More Asymmetrically at the Same Metabolic Cost. PLoS ONE. 2013;8(9).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKnutson GA. Anatomic and functional leg-length inequality: a review and recommendation for clinical decision-making. Part I, anatomic leg-length inequality: prevalence, magnitude, effects and clinical significance. Chiropr Osteopat. 2005;13:11.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBrunet ME, Cook SD, Brinker MR, Dickinson JA. A survey of running injuries in 1505 competitive and recreational runners. J Sports Med Phys Fit. 1990;30(3):307\\u0026ndash;15.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eHarvey WF, Yang M, Cooke TDV, Segal NA, Lane N, Lewis CE, et al. Association of Leg-Length Inequality With Knee Osteoarthritis A Cohort Study. Ann Intern Med. 2010;152(5):287\\u0026ndash;W92.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMessier SP, Pittala KA. Etiologic Factors Associated with Selected Running Injuries. 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Ground Contact Time Imbalances Strongly Related to Impaired Running Economy. Int J Exerc Sci. 2020;13(4):427\\u0026ndash;37.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eMarfell-Jones MJSAD, JH dR. International standards for anthropometric assessment. International Society for the Advancement of Kinanthropometry; 2012.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBishop C, Read P, Chavda S, Turner A. Asymmetries of the Lower Limb: The Calculation Conundrum in Strength Training and Conditioning. Strength Cond J. 2016;38(6):27\\u0026ndash;32.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Chapelle L, Pletinckx B, Sadouni M, Bosman L, Lindekens T et al. Intra- and Inter-Limb Isokinetic Strength Asymmetry in Competitive Judokas: Differences in Magnitude Across Competition Levels and Sex. Journal of Human Kinetics. In press.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eChapelle L, Bishop C, D'Hondt J, Rommers N, D'Hondt E, Clarys P. Development of upper and lower extremity functional asymmetries in male and female elite youth tennis players: a longitudinal study. J Sport Med Phys Fit. 2023;63(12):1269\\u0026ndash;84.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Chapelle L, Van Droogenbroeck L, Aerenhouts D, Clarys P, D'Hondt E. Bioelectrical impedance analysis as a means of quantifying upper and lower limb asymmetry in youth elite tennis players: An explorative study. Eur J Sport Sci. 2022;22(9):1343\\u0026ndash;54.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Bishop C, D'Hondt E, Chapelle L, Aerenhouts D, Clarys P et al. Inter-limb range of motion and jumping asymmetry linked to running performance in healthy adult recreational endurance runners. Submitted.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eD'Hondt J, Chapelle L, Lindekens T, Clarys P. Morphological inter-limb asymmetry in youth judokas is independent of competitive level and sex. Bmc Sports Sci Med R. 2025;17(1).\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRamirez-Campillo R, Andrade D, Clemente F, Afonso J, P\\u0026eacute;rez-Castilla A, Gentil P. A proposed model to test the hypothesis of exercise-induced localized fat reduction (spot reduction), including a systematic review with meta-analysis. Hum Mov. 2021;23(3):1\\u0026ndash;14.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eBesson T, Macchi R, Rossi J, Morio CYM, Kunimasa Y, Nicol C, et al. Sex Differences in Endurance Running. Sports Med. 2022;52(6):1235\\u0026ndash;57.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eNelson RC, Brooks CM, Pike NL. Biomechanical comparison of male and female distance runners. Ann N Y Acad Sci. 1977;301:793\\u0026ndash;807.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eRoche-Seruendo LE, Latorre-Rom\\u0026aacute;n P\\u0026Aacute;, Soto-Hermoso VM, Garc\\u0026iacute;a-Pinillos F. Do sex and body structure influence spatiotemporal step characteristics in endurance runners? Sci Sport. 2019;34(6):412\\u0026ndash;e1.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003ePluijm SMF, Visser M, Smit JH, Popp-Snijders C, Roos JC, Lips P. Determinants of bone mineral density in older men and women: Body composition as mediator. J Bone Min Res. 2001;16(11):2142\\u0026ndash;51.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eAndreoli A, Monteleone M, Van Loan M, Promenzio L, Tarantino U, De Lorenzo A. Effects of different sports on bone density and muscle mass in highly trained athletes. Med Sci Sports Exerc. 2001;33(4):507\\u0026ndash;11.\\u003c/span\\u003e\\u003c/li\\u003e \\u003cli\\u003e\\u003cspan\\u003eKnechtle B, Di Gangi S, R\\u0026uuml;st CA, Villiger E, Rosemann T, Nikolaidis PT. The role of weather conditions on running performance in the Boston Marathon from 1972 to 2018. PLoS ONE. 2019;14(3).\\u003c/span\\u003e\\u003c/li\\u003e\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-sports-science-medicine-and-rehabilitation\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ssmr\",\"sideBox\":\"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/ssmr/default.aspx\",\"title\":\"BMC Sports Science, Medicine and Rehabilitation\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Lower Extremity, Athletic Performance, Running: Dual-energy X-ray absorptiometry, Body composition, Physical Fitness\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-9008534/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-9008534/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003ch2\\u003eBackground\\u003c/h2\\u003e \\u003cp\\u003eBody composition plays a critical role in endurance running. While anthropometric characteristics have been incorporated into performance prediction models, potential inter-limb morphological asymmetries in lower-limb body composition remain largely unexplored. Therefore, this study aimed to (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e1\\u003c/span\\u003e) quantify lower-limb morphological asymmetry and examine differences across sex and training level, (\\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2\\u003c/span\\u003e) compare running performance between low- and high-asymmetry groups using split analyses, and (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e3\\u003c/span\\u003e) investigate the link between lean mass (LM), bone mineral content (BMC) and fat mass (FM) asymmetry as well as leg length discrepancy (LLD), and running performance in healthy adult endurance runners with varying training backgrounds.\\u003c/p\\u003e\\u003ch2\\u003eMethods\\u003c/h2\\u003e \\u003cp\\u003eSixty-eight healthy adult recreational runners (53% male) were stratified by sex and training level (novice, intermediate, trained). Segmental lower-limb body composition (LM, bone mineral density (BMD), BMC, and FM) was assessed using dual-energy X-ray absorptiometry (DXA). LLD was determined anthropometrically as the distance between the anterior superior iliac spine and medial malleolus. Running performance was evaluated using the Cooper 12-minute run test. Low and high asymmetry groups were created by categorizing participants according to the sample median asymmetry values for DXA-derived metrics and using a 2 cm threshold to classify LLD. Group differences were assessed using t-tests and ANOVAs, and multiple linear regression examined predictors of running performance.\\u003c/p\\u003e\\u003ch2\\u003eResults\\u003c/h2\\u003e \\u003cp\\u003eMean asymmetry magnitudes ranged from 1.91% to 4.04% across metrics. LLD ranged from 0.5 to 0.9 cm. Female runners demonstrated greater LLD than males (p\\u0026thinsp;=\\u0026thinsp;0.004), and trained runners showed greater BMD asymmetry than novices (p\\u0026thinsp;=\\u0026thinsp;0.006). LM, BMC and FM asymmetry as well as LLD were no significant predictors of Cooper test performance (p\\u0026thinsp;=\\u0026thinsp;0.316 to 0.686). Furthermore, no significant differences were found between the low and high asymmetry groups (p\\u0026thinsp;=\\u0026thinsp;0.581 to 0.999).\\u003c/p\\u003e\\u003ch2\\u003eConclusion\\u003c/h2\\u003e \\u003cp\\u003eDXA-derived inter-limb asymmetries at lower limb level and LLD in recreational endurance runners are small and largely independent of sex and training level. Importantly, these asymmetries are not linked to running performance, suggesting limited practical relevance for performance optimization.\\u003c/p\\u003e\\u003ch2\\u003eTrial registration:\\u003c/h2\\u003e \\u003cp\\u003eClinicalTrials.gov (NCT06808399). Registered on 02 April 2025.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Variations in morphological lower inter-limb asymmetry by sex and training level, and its link to performance in recreational runners: A cross-sectional study\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-03-10 07:00:14\",\"doi\":\"10.21203/rs.3.rs-9008534/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"decision\",\"content\":\"Revision requested\",\"date\":\"2026-04-14T09:15:03+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-10T10:16:01+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-08T23:57:08+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-07T08:48:36+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"81504994982244811465061115038658060142\",\"date\":\"2026-04-01T07:07:44+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"103550295849949500997316901722291526704\",\"date\":\"2026-03-31T12:04:35+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"151919241963131890376438280711591244981\",\"date\":\"2026-03-30T21:33:52+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"233689436481813661217298107031802042264\",\"date\":\"2026-03-30T12:24:12+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-03-26T11:20:51+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorInvited\",\"content\":\"\",\"date\":\"2026-03-17T14:09:44+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-03-04T06:39:38+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-03-04T06:38:33+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"BMC Sports Science, Medicine and Rehabilitation\",\"date\":\"2026-03-02T09:42:35+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"bmc-sports-science-medicine-and-rehabilitation\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"ssmr\",\"sideBox\":\"Learn more about [BMC Sports Science, Medicine and Rehabilitation](http://bmcsportsscimedrehabil.biomedcentral.com/)\",\"snPcode\":\"\",\"submissionUrl\":\"https://www.editorialmanager.com/ssmr/default.aspx\",\"title\":\"BMC Sports Science, Medicine and Rehabilitation\",\"twitterHandle\":\"BMC_series\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"BMC Series\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"58179a96-9d9a-44d2-b998-d557c85d255e\",\"owner\":[],\"postedDate\":\"March 10th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-05-15T13:23:22+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-03-10 07:00:14\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-9008534\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-9008534\",\"identity\":\"rs-9008534\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}