Assessing Body Mass Index as a Predictor of Running-Related Injuries: A Systematic Review and Meta- analysis

Assessing Body Mass Index as a Predictor of Running-Related Injuries: A Systematic Review and Meta- analysis | 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 Assessing Body Mass Index as a Predictor of Running-Related Injuries: A Systematic Review and Meta- analysis Aynollah Naderi, Farhad Gholami, Hans Degens This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6311307/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Understanding the causes of running-related injuries (RRIs) is essential for identifying high-risk runners and implementing preventive measures to reduce injury risk. This study aims to determine how body mass index (BMI) affects the occurrence of RRIs among runners, crucial for identifying high-risk individuals and implementing preventive measures. Methods We conducted a systematic review and meta-analysis following the 'PECOS' framework for observational studies and PRISMA protocols. We searched Scopus, Web of Science, PubMed, Science Direct, and SPORTDiscus databases until October 2023 for prospective studies on RRIs. Two independent reviewers assessed the methodological risk of bias in the included studies using the ROBINS-E tool. The extracted data included study details, sample characteristics, injury type, number of injuries, and follow-up period. The outcome of interest was RRIs sustained during the study, both overall and specific, and the mean ± standard deviation (SD) of BMI for runners who experienced RRIs and those who did not. Pooled odds ratios [95% confidence interval (CI)] were calculated using a random-effects model. Results In our analysis of 35 studies involving 14,025 runners (median 238; range 21 − 2,207; 57.4% women), we found that BMI significantly predicts RRIs (OR = 1.05, CI:1.02–1.09; P = 0.001), with individuals experiencing such injuries showing higher baseline BMIs (MD = 0.113kg/m², CI:0.031–0.194; P = 0.007). For specific injuries, no significant baseline BMI differences were found for runners with patellofemoral pain syndrome (PFPS) or Achilles tendinopathy (AT) compared to those without (MD = 0.14kg/m²,CI:-0.04-0.33; P = 0.13, and MD = 0.03kg/m²,CI:-0.19-0.25; P = 0.82, respectively). However, individuals with medial tibial stress syndrome (MTSS) had higher BMI (MD = 0.43kg/m²,CI:0.18–0.68;P = 0.001), and those with lower extremity stress fractures had lower BMI (MD=-0.28kg/m²,CI:-0.53-0.03;P = 0.03) compared to their counterparts. Conclusion Runners with RRIs generally have a higher baseline BMI, especially those with MTSS, while those with lower extremity stress fractures have a lower BMI, and BMI does not differentiate those with PFPS or AT. Medial tibial stress syndrome patellofemoral pain syndrome Achilles tendinopathy stress fracture plantar fasciitis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Highlights Runners with RRIs have a higher baseline BMI compared to injury-free counterparts, with each unit increase in BMI corresponding to a 2 to 9 percent higher risk of RRIs. Runners experiencing MTSS have a notably higher baseline BMI, whereas those experiencing lower extremity stress fractures have a significantly lower baseline BMI compared to counterparts who did not. BMI did not emerge as a differentiating factor between runners who experienced PFPS or AT and those who did not. Introduction Running, a widely embraced exercise for its health benefits [ 1 ], confronts participants with the challenge of sports injuries, as indicated by a recent meta-analysis reporting average incidence and prevalence of running-related injuries (RRIs) at 40.2% ± 18.8% and 44.6% ± 18.4%, respectively [ 2 ]. Overuse injuries to the knee (e.g., patellofemoral pain syndrome (PFPS) and iliotibial band syndrome), shin (e.g., medial tibial stress syndrome and tibial stress fracture), calf (e.g., Achilles tendinopathy) and foot (e.g., plantar fasciitis and metatarsal stress fracture) appear to be the most common RRIs [ 2 – 5 ], typically resulting from cumulative loads that exceed the structural capacity of various tissues [ 6 – 9 ]. RRIs lead to disengagement from physical activity, posing various health-related risks and financial burdens to runners [ 10 ]. Such concerns may deter exercise participation, emphasizing the pivotal need to address and overcome barriers like RRIs to sustain an active lifestyle. A comprehensive understanding of the involved etiological factors is crucial to identifying runners prone to injuries and developing preventive measures for reducing the risk of RRIs. A variety of risk factors including shoe characteristics[ 11 ], exercise-related parameters[ 6 ], biomechanical [ 12 ], and demographic variables [ 13 ] have been reported for RRIs. However, for injury prevention strategies to be effective, it is essential that the identified risk factors can be modified and have a biologically plausible mechanism [ 14 ]. Among the demographic risk factors, BMI appears to play a significant role in specific injury patterns and overall injury risk [ 15 , 16 ]. Nonetheless, it is important to acknowledge that many recommendations regarding BMI as a risk factor for RRIs are derived from studies conducted with military personnel rather than recreational and competitive runners[ 17 – 19 ]. Moreover, many preventive recommendations rely on cross-sectional studies, theories, and opinions rather than robust evidence. Recognizing that not all statistically significant factors are causally associated with RRIs is essential [ 20 ]. Consequently, the limited and contradictory available information makes it challenging to draw reliable conclusions regarding the relationship between BMI and RRIs [ 13 ]. Obesity is becoming a global pandemic, and running is increasingly recognized as a method for weight loss and overall health improvement. In addition, the demographic profile of runners has changed as more and more runners have a higher fat percentage than it used to be, particularly those involved in amateur-level events [ 21 ]. Therefore, this systematic review and meta-analysis aims to synthesize previous studies' findings to ascertain the impact of BMI on the occurrence of RRIs among runners. Materials and Methods Design The protocol for this systematic review and meta-analysis was prospectively registered in the PROSPERO database ( Blinded ) and the meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [22]. Search Strategy Two independent investigators (AN and FG) conducted a systematic literature search on Scopus, Web of Science, PubMed, Science Direct, and SPORTDiscus database from inception to October 2023 using specific keywords (running AND injury AND BMI AND Cohort). The search strategy, including the terminology used, is presented in Supplementary Table 1. The search strategy was customized for each database using the keywords and appropriate combination of Boolean operators. Additionally, a manual search of article reference lists and Google Scholar were performed to identify any further relevant articles. The search strategy complied with the Peer Review of Electronic Search Strategies (PRESS) 2015 Checklist to ensure its comprehensiveness [23]. Eligibility Criteria The inclusion criteria, guided by the 'PECOS' framework for observational studies (Population, Exposition, Comparison, Outcome, and Study Design) [24], comprised of the following: (1) Population: healthy runners of any age, sex, and activity level (classified by the authors as novice, recreational, competitive, or mixed cohorts); (2) Exposition: runners BMI at baseline; (3) Comparison: not applicable in this review. Exposure was determined as a baseline measurement of body mass index (BMI); (4) Outcomes: Running-related injuries, whether overall or specific, sustained during running participation. This encompassed various injury definitions (time loss, non-time loss, contact, and non-contact) and injury reporting methods (by medical staff or self-report); (5) Study Design: A prospective cohort design, as it is the preferred approach for acquiring direct and precise estimates of incidence and risk [25]. The study excluded articles that (1) were non-original, including systematic reviews, conference abstracts, book chapters, and gray literature articles; (2) employed intervention, retrospective, cross-sectional, or case-control designs; (3) lacked a risk factor analysis or had insufficient data; (4) focused on risk factors for injured runners or did not specify if all runners were injury-free at baseline; (5) aimed to determine risk factors or injuries for other sports involving running, such as triathlons; (6) were conducted in military settings, aiming to differentiate between recreational and occupational injuries; or (7) were written in languages other than English. The study did not impose limitations on a specific injury definition, encompassing all definitions of RRIs, such as time loss, non-time loss, contact, and non-contact, as well as various injury reporting methods, including medical staff or self-report. Additionally, the study did not impose restrictions based on age, sex, or the method of recruitment. Article Screening The articles obtained from each database were imported into EndNote X7 software (Thomson Reuters, New York, NY, USA) for eligibility screening and duplicate removal. Following duplicate removal, the titles and abstracts of the remaining articles were screened for eligibility based on the a priori inclusion/exclusion criteria to identify eligible articles for full-text review. Two independent reviewers (AN and FG) conducted the article screening, with any discrepancies resolved through discussion or by the third author (HD). Quality assessment Two independent reviewers (AN and FG) assessed the methodological risk of bias in included studies using the Risk of Bias in Non-randomized Studies of Exposures (ROBINS-E tool) (Supplementary Table 2). The ROBINS-E tool is a methodological instrument designed for evaluating the risk of bias in non-randomized studies, such as cohort and case-control studies. It encompasses seven domains of bias, including bias due to confounding, bias in participant selection, bias in exposure classification, bias due to departures from intended exposures, bias due to missing data, bias in outcome measurement, and bias in the selection of reported results. Each domain was categorized as having low, moderate, serious, or critical risk of bias (Supplementary Table 2). In cases of disagreement between assessors, the decision was made through discussion. Data Extraction Two authors (AN and FG) independently extracted data into a spreadsheet database (Microsoft Excel; Washington, US). The extracted data were compared for consistency and any discrepancies were addressed through discussion. The extracted information included study details (e.g., author, year, and design), sample characteristics (e.g., sample size, age, sex, and dropout rate), injury type, number of observed injuries, and length of injury follow-up period. To analyze differences between groups (e.g., injured vs. non-injured), the mean ± standard deviation (SD) of BMI was extracted for runners who experienced RRIs and those who did not. Data synthesis involved the amalgamation of information from various studies, using group classification based on injury definition by the included studies. These groups primarily focused on overall injuries and specific injuries such as medial tibial stress syndrome, Achilles tendinopathy, PFPS, and lower extremity stress fracture. When applicable, data from a single study were included in more than one group. Each of these groups could potentially include a combination of acute, overuse, time loss, or non-time loss injuries, depending on the injury defined in the study (Table 1). (Table 1) Data analysis The meta-analysis was conducted using Comprehensive Meta-Analysis software (version 3.0, Biostat Inc., Englewood, New Jersey, USA). Included studies provided either the mean and standard deviation (SD) of BMI for both injured and uninjured groups or odds ratios (OR) with 95% confidence intervals (CI) for the association between BMI and the risk of running injuries. Random effects models were chosen to combine the mean (SD) and OR (95% CI) effect sizes, considering potential variations in participant age, demographics, and running level across studies. The Higgins I 2 statistic was used to assess statistical heterogeneity, categorizing it as low (≤ 25%), moderate (26-50%), large (51-75%), or considerable (> 76%). Publication bias was evaluated by visually examining the funnel plot and Egger's test (P-value) [26]. The significance level was set at P<0.05. Results Search Results The initial search yielded 5,338 studies, and an additional 26 studies were identified from other sources. Of the retrieved studies 2,100 duplicates were removed and 2,480 articles were excluded after titles and abstracts screening. Among the remaining 784 articles, 749 were further excluded during full-text screening. Ultimately, five studies reporting duplicate data were excluded from the quantitative analysis [27-31], leaving 35 articles that met the criteria for quantitative analysis [3, 5, 7-9, 27, 28, 32-59]. The selection process is visually depicted in Figure 1. (Figure 1) Characteristics of Included Studies The included studies were published between 2000 and 2023 and involved a total of 14,025 participants (median 238; range 21-2,207; 57.4% female). Three studies included only women [38, 52, 60], while one study included only men [61] and the remaining studies recruited both sexes. Seventeen articles combined all injury types [3, 5, 7-9, 33-36, 40, 42, 44, 46-48, 55, 58] and categorized as overall running injuries, while others provided estimates for specific injuries such as tibial stress syndrome (n = 4) [28, 32, 45, 50], lower extremity stress fracture (n = 4) [27, 28, 38, 52], PFPS (n = 6) [39, 49, 53, 54, 59], and Achilles tendinopathy (n = 5) [41, 43, 51, 56, 57], and plantar fasciitis. [38] Table 1 provides a summary of all the included articles. In the principal analysis, a total of 2,515 participants were in the injured group and 11,608 participants were in the non-injured group. Among all participants, 1,550 individuals sustained injuries, while 5,773 participants were non-injured. When considering specific injury types, MTSS studies included 177 injured participants and 456 non-injured participants. For Achilles tendinopathy, there were 224 injured participants and 2,838 non-injured participants. Studies on PFPS involved 288 injured participants and 1,452 non-injured participants. Lastly, stress fracture studies included 188 injured participants and 3,283 non-injured participants. The follow-up periods in the studies ranged from 9 weeks to 6 cross-country seasons, and the proportion of participants analyzed varied from 67.4% to 100% of the initial sample size. The studies focused on different types of runners, with 21 studies investigating road runners [5, 8, 9, 33-36, 38-40, 44-49, 51, 53, 54, 56, 58], 8 studying cross-country runners [27, 28, 32, 42, 50, 52, 57, 59], 3 examining track runners (middle- and long-distance), and 3 reporting on long-distance runners [37, 43, 55]. Table 1 provides a summary of the main characteristics of the included studies. Study Quality We conducted a risk of bias assessment for each study, revealing a low to moderate risk of bias across all ROBINS-E domains. This finding provides the strongest evidence with the lowest risk of bias for the overall review. Accordingly, none of the studies were considered to have a high risk of bias, while ten studies were rated with a moderate risk of bias [7, 9, 40, 46, 47, 49-51, 55, 58] and twenty-five studies were rated with low-risk bias [3, 5, 8, 27, 28, 32-39, 41-45, 48, 52-54, 56, 57, 59]. In terms of specific domains of bias, studies were deemed to have a moderate risk in the domains as follows: confounding[40, 47, 50, 51, 58], selection of participants [46, 47, 58], classification of exposures [7, 50, 55], missing data [40, 46, 49, 50, 62], or outcome measurement [9, 51, 55]. Regarding the risk of bias in the selection of reported results, as the final domain of ROBINS-E, most of the included studies were assessed as having a low risk of bias (Supplementary Table 2). Meta-analyses Running related injury Fifteen prospective studies were included in the meta-analyses [3, 5, 7-9, 30, 33, 36, 40, 42, 44, 46, 48, 55, 58] of which four studies found that non-injured runners had a lower BMI compared to injured runners [9, 47, 48, 55]. The remaining studies did not indicate a significant difference in the mean BMI between runners who sustained injuries and those who did not. However, upon combining the data from all the studies, it was found that runners who experienced injuries generally had higher BMI than those who did not experience any injury (pooled SMD = 0.113 kg/m 2 (95% CI: 0.031-0.194); Z=2.71; P=0.007; Figure 2) and the heterogeneity of studies was low and insignificant (I 2 = 21.6 and P=0.21). The funnel plot did not reveal any evidence of publication bias (Supplementary Figure 1), and Egger’s test indicated no selective reporting bias (p=0.77). Additionally, a classic fail-safe N test showed that 22 additional studies would need to be included in the meta-analysis to change the results to nonsignificant. (Figure 2) Six studies were conducted to assess the correlation between BMI and RRIs [3, 9, 34, 35, 44, 55] of which four studies found a positive relationship between BMI and risk of RRIs [9, 34, 35, 55]; however, the remaining two studies did not discover any association [3, 44]. The pooled OR of the included studies indicates that BMI is a significant risk factor for RRIs (OR=1.05 95% CI: 1.02, 1.09); Z=3.39, P=0.001; Figure 3). However, the heterogeneity of included studies was high and statistically significant (I 2 =62.3% and P=0.01). Additionally, conducting a classic fail-safe N test indicated that to render the results nonsignificant, 49 more studies would need to be incorporated into the meta-analysis. (Figure 3) Patellofemoral pain syndrome Six prospective studies were included in the meta-analyses [39, 49, 53, 54, 59, 62]. Individually, none of these studies showed a significant difference in BMI between runners who developed PFPS and those who did not. When the data from these studies were combined, no significant difference was observed in the BMI of the two groups (pooled SMD = 0.14 kg/m 2 , 95% CI: -0.04-0.33; Z=1.50; P=0.13; Figure 4) with no heterogeneity (I 2 = 5.1% and p=0.38). The funnel plot did not reveal any evidence of publication bias (Supplementary Figure 2), and Egger’s test indicated no selective reporting bias (p=0.35). (Figure 4) Achilles tendinopathy Five prospective studies were included in the meta-analyses [41, 43, 51, 56, 57]. Only one study showed a significant difference in BMI between runners with AT and those without [41]. The remaining studies did not find a significant difference in BMI between the two groups. When the data from all the studies were quantified no significant difference in BMI between runners with AT and those without was observed (pooled SMD = 0.03 kg/m 2 ; 95% CI: -0.19-0.25; Z=0.23; P=0.82; Figure 5). A moderate statistically not significant heterogeneity was observed across the trials (I 2 =49.5% and P=0.09). The funnel plot did not reveal any evidence of publication bias (Supplementary Figure 3), and Egger’s test indicated no selective reporting bias (p=0.45). (Figure 5) Medial tibial stress syndrome The meta-analysis included four prospective studies [28, 32, 45, 50] of which two studies reported significantly higher BMI values in runners suffering from MTSS compared to runners who did not suffer from MTSS [28, 45]. The remaining studies did not show a significant difference in BMI between these two groups [32, 50]. The combined data revealed a significantly higher BMI in runners with MTSS compared to those without MTSS (pooled SMD=0.43 kg/m 2 , 95% CI: 0.18-0.68; Z=3.67; P=0.001; Figure 6). The heterogeneity of included trials was moderate and insignificant (I 2 =35.9% and P=0.18). The funnel plot did not reveal any evidence of publication bias (Supplementary Figure 4), and Egger’s test indicated no selective reporting bias (p=0.77). A classical fail-safe N test indicated that 19 additional studies would need to be included in the meta-analysis to alter the results and make them nonsignificant. (Figure 6) Lower extremity stress fracture Four prospective studies were included in the meta-analysis [28, 37, 38, 52] of which two studies found that the runners suffering from lower extremity stress fractures had significantly lower BMI than the runners without lower extremity stress fractures [38, 52]. The remaining studies did not indicate a significant difference in BMI between the two groups. The combined data revealed a significantly lower BMI in runners with a lower extremity stress fracture compared to those without (pooled SMD= -0.28 kg/m 2 , 95% CI: -0.53--0.03; Z=2.22; P=0.03; Figure 7). The heterogeneity appeared to be relatively low and not significant (I 2 =16.2% and P=0.3). The funnel plot did not reveal any evidence of publication bias (Supplementary Figure 5), and Egger’s test indicated no selective reporting bias (p=0.10). Furthermore, the classic fail-safe N test indicated that the inclusion of just 2 more studies in the meta-analysis would be required to render the results to insignificant. (Figure 7) Discussion To the best of our knowledge, this is the first systematic review and meta-analysis exploring the association between BMI and overall RRIs as well as specifically PFPS, iliotibial band syndrome, MTSS, lower extremity stress fractures, Achilles tendinopathy, and plantar fasciitis. Our study found that BMI is a significant contributor of RRIs, with higher BMI being associated with a greater likelihood of RRIs. Specifically, runners who experienced MTSS had higher BMI than those without MTSS experience, while those with lower extremity stress fractures had lower BMI. However, BMI did not appear to have a significant contribution to the occurrence of injuries including PFPS and Achilles tendinopathy. No studies were found on the link between BMI and iliotibial band syndrome risk, with only one study addressing the association between BMI and plantar fasciitis in runners. Running-related injury Our study found that a higher BMI is associated with an increased risk of sustaining a running-related injury (RRI), with an odds ratio of 1.05 (95% CI, 1.02-1.09) per unit increase in BMI. This finding is consistent with a systematic review that reported a positive correlation between BMI and risk of injury, particularly ankle injuries[63]. Nielsen, Buist (30) also found that novice runners with a BMI greater than 30 kg/m 2 have a high risk of injury, while those with a BMI lower than 20 kg/m 2 have a low risk of injury. Additionally, van Poppel, De Koning (64) showed that the risk of RRIs in runners with a BMI greater than 26 kg/m 2 is threefold greater than their counterparts with a normal BMI of 20-26 kg/m 2 . In contrast, a systematic review by Van Gent, Siem (65) found that BMI >26 kg/m 2 was protective against injury occurrence among recreational runners. Although wearing absorptive running shoes is suggested to protect against RRIs, there is no conclusive evidence of this claim. The contribution of BMI to running-related risk of injury may be explained by the additional stress imposed on the body through extra weight. Naderi, Baloochi (66) suggested that BMI is the most important predictor of plantar pressure and impulse before, during, and after a 30-minute run. They also suggest that lower leg muscle strength and endurance may 30-58% contribute to this effect, particularly after 30 minutes of running [66]. This highlights the importance of considering BMI, foot muscle strength, and endurance when assessing injury risk in runners. Vincent, Kilgore III (16) suggest that runners with obesity are able to attenuate impact forces and control loading rate more efficiently than non-obese runners by increasing lower body stiffness and constraining vertical displacement which is likely to minimize the risk of injury. Despite these adaptations, runners with higher BMI need greater force exertion to change their momentum at a given speed, potentially increasing the risk of injury, particularly when their dynamic stability is not adequate for joint control under these circumstances [67]. Additionally, neuromuscular and postural stability is diminished in obese individuals [68], further enhancing the risk of injury, and underscoring the importance of injury prevention programs. Thus, alongside weight reduction strategies, neuromuscular control in the lower extremities should be improved [69]. Gradual improvements in physical fitness, achieved through low-impact aerobics activities and antigravity treadmill training, before engaging in weight-bearing and high-impact exercise like running, may be effective strategies for a runner with a high BMI [70]. Additionally, the use of shock-absorbent orthotic inserts and footwear modifications hold the potential for favorable outcomes [71]. Fredericson (4) recommended that athletic footwear should be replaced after covering a distance of 500 to 700 kilometers of running. Accordingly, the effectiveness of these interventions in preventing RRIs in individuals with a high BMI remains to be explored in future studies. MTSS Our study found that runners experiencing MTSS had in average 0.43 kg/m 2 greater BMI compared to those without MTSS. This aligns with the findings of two previous systematic reviews and meta-analyses [17, 72]. Hamstra-Wright, Bliven (72) reported that higher values of BMI (weighted MD=0.79) is a risk factor for MTSS in athletes and military personnel. However, it is important to note that the meta-analysis included only one study on runners [73] with no subgroup analysis. Similarly, in the study conducted by Reinking, Austin (17) BMI value was 0.34 kg/m 2 greater in active individuals experiencing MTSS compared to their peers without MTSS. However, only one prospective study focused on runners [73], while others included military personnel and athletes engaged in various sports. These findings emphasize the need for further research to explore the potential role of BMI as a risk factor for MTSS, specifically among runners. From a pathomechanical perspective, MTSS is attributed to tibial bending [74] and tibial-fascial traction [75]. BMI is a potential risk factor for MTSS as it can increase tibial-fascial traction and tibial bending during running. Accordingly, a higher BMI can increase traction on the fascia along the medial tibial border by enhancing the contraction of both superficial and deep ankle plantar flexors—specifically the soleus, tibialis posterior, flexor hallucis longus (FHL), and flexor digitorum longus (FDL)—during the stance phase of running [45, 76]. Even though recent MRI and histological studies have not confirmed the tibial-fascial tension theory of MTSS [77], growing evidence suggests that it is caused by repeated chronic loads exerting bending forces on the tibia. According to this theory, bone strains that exceed the modeling threshold cause microdamage. Under most physiological circumstances, microdamage stimulates remodeling and strengthens the bone. However, repetitive or large strains may induce microdamage accumulation that increases skeletal fragility and susceptibility to injury, particularly in the absence of a remodeling response [77, 78]. This suggests that the heavier impact loads associated with increased BMI may be a contributing factor in inducing microdamage accumulation and the incidence of MTSS. With increased BMI, the mechanical load on the tibia increases during physical activity. Thus, runners with higher BMI values may need longer durations and a gradual increase in running load to allow for bone adaptation [79]. The use of pneumatic leg braces [80] and arch support foot orthoses [81] may also help prevent and treat MTSS. In addition to BMI, MTSS may also be attributable to fitness level [72]. A common limitation of the included studies was inconsistency in reporting the fitness level of the participants which needs to be addressed in future studies. Lower extremity Stress fracture The study results indicate that runners who experienced lower extremity SF had on average a 0.28 kg/m 2 lower BMI than those without lower extremity SF. Further subgroup analysis showed that this difference seems to be sex-specific with a higher incidence in female than male runners, similar to that reported in a systematic review Wentz, Liu (82) in military personnel and athletes, where women also exhibited a significantly higher incidence of stress fractures than men. Barrack, Gibbs (83) found that athletes with multiple risk factors associated with the female athlete triad—such as low BMI, low bone density, high exercise frequency, menstrual irregularities, and dietary restraint—exhibited a significantly higher incidence of stress fractures compared to those with only one risk factor. They also noted that individuals with stress fractures had lower body fat percentages [83], suggesting that low adiposity contributes to injury risk. The terminology has been updated to "Relative Energy Deficiency in Sport" (RED-S), emphasizing that both female and active male athletes can suffer injuries due to insufficient energy. Consequently, future research should focus more on the relationship between BMI and sports injuries, especially stress fractures, in athletes with RED-S. Previous studies have linked lower body fat percentages and BMI to narrower bones [84] and low bone mineral density [85] specifically in female athletes, indicating that a lower BMI may increase the risk of stress fractures in female runners; however, further research is necessary to validate this hypothesis. When interpreting the impact of BMI on the risk of stress fractures, the role of muscle mass in absorbing loading forces should be taken into account. Lower extremity stress fractures are a common overuse injury in athletes that typically occur when the bone is subjected to repetitive loading and mechanical stressors that exceed its ability to remodel and adapt. Antigravity treadmills (AGT), with their adjustable body weight support, offer a promising approach for managing bone stress injuries, maintaining fitness in a reduced-gravity environment, and supporting post-injury recovery[70]. AGT running minimizes impact forces while preserving natural running mechanics, which can help athletes sustain fitness and reduce the likelihood of re-injury[70]. However, more high-quality research, particularly randomized controlled trials, is necessary to establish AGT’s effectiveness compared to conventional protocols [70]. Additionally, other low-impact aerobic exercises, such as elliptical training, cycling, and pool running, also provide effective ways to maintain physical fitness during recovery. PFPS Our study found that runners who experienced PFPS did not have a higher BMI compared to those who did not experience PFPS. In a systematic review of cross-sectional studies by Hart, Barton (86), an association was found between BMI and both PFPS and patellofemoral osteoarthritis (PFOA), which could be due to reduced activity levels after symptom development [87]. However, their review did not identify higher BMI as a risk factor for future PFPS in adults or adolescents, and a high BMI was not linked to intervention outcomes in participants with PFPS [86]. These findings suggest that elevated BMI may be secondary to PFPS as it has been reported that PFPS limits running participation, reduces physical activity levels, and contributes to psychological impairments. Therefore, these findings raise questions about the biologically plausible suggestion that high BMI contributes to PFPS development. Some review studies have linked high BMI to knee and PFOA [88-90]. As PFPS is considered a precursor to PFOA [90-92], controlling BMI in adults with PFPS may mitigate mechanical and non-mechanical consequences, potentially slowing or delaying the development and progression of PFOA. However, there are no prospective studies examining the impact of BMI on PFPS progression and the development and progression of PFOA. Longitudinal data is needed to determine whether BMI predicts the development and progression of PFPS and PFOA. Interventions, such as combined diet and exercise training, could be a viable option for individuals with PFPS, particularly those classified as obese. It is essential to prioritize research in this area as there have been no randomized controlled trials targeting weight loss in PFPS. Achilles tendinopathy (AT) Previous research has suggested a link between BMI, body weight, or waist circumference with tendon pathology [93], based on increased absolute tendon load and cytokine levels causing low-grade inflammation in obese individuals [94, 95]. However, our systematic review and meta-analysis did not find any association between BMI and AT. It's worth noting that most studies investigating BMI as a risk factor for AT were in adolescent populations. Additionally, there is a hypothesis that being underweight may be associated with AT pathology due to decreased collagen production [95], leading to a U-shaped relationship between BMI and AT pathology [93]. More cohort studies are needed in diverse populations to investigate this further. Plantar fasciitis The available evidence indicates that increased BMI is associated with plantar fasciitis (PF) in non-athletic populations [96] and military personnel [18]. The higher BMI leads to increased mechanical load, which imposes additional stress on the plantar fascia. However, the incidence of foot pain appears to be more strongly linked to fat mass rather than fat-free mass, suggesting that inflammatory mechanisms related to adiposity may contribute to PF rather than fat mass per se [97, 98]. It is worth noting that there is only one study exploring the association between BMI and PF incidence in runners that challenges the notion that a high BMI, regardless of body composition, applies to all individuals with PF, suggesting that athletes may represent a distinct subgroup. Although the study by Di Caprio, Buda (38) did not provide raw BMI data for PF cases, making it difficult to analyze the role of BMI, the relatively low average BMI of less than 20.6 kg/m 2 in 55% of the participants implies that BMI might play a minor role in athletes, or that there is a cut-off beyond which an increasing BMI poses an increased risk for PF. Strengths and Limitations The study includes some strengths and limitations to be acknowledged. This systematic review adhered rigorously to the PRISMA guidelines[22], ensuring a well-structured analysis. Furthermore, the review comprehensively examined 37 cohort studies that investigated BMI as a potential risk factor for various RRIs, providing evidence to be potentially applied in clinical settings. Our study acknowledges several limitations that warrant consideration. Firstly, the systematic search was restricted to English-language studies, which may have excluded valuable non-English research that could offer additional insights into the relationship between Body Mass Index (BMI) and running-related injuries (RRIs). Additionally, the limited number of studies included in each meta-analysis (except for the overall RRI analysis) necessitates caution in interpreting the results, as smaller sample sizes may affect the reliability of findings. Furthermore, variations in BMI measurements between baseline assessments and injury occurrences could lead to misclassifications of BMI exposures, though such misclassifications are unlikely to directly impact injury risk. Another limitation is that over 80% of the studies analyzed did not report odds ratios (ORs) for the identified risk factors, which are essential for standardizing risk factor reporting. Additionally, while our review identified potential risk factors predominantly through cohort studies with a median of 191 cases, it is suggested that future studies should aim for larger sample sizes—preferably over 200 cases—to detect small to moderate associations more effectively[99]. Moreover, the lack of consistent definitions for RRIs in prospective studies may lead to inaccuracies in estimating their true effects and their relationship with BMI. To address this limitation, future research should adopt standardized definitions for running-related injuries (RRIs), such as the Delphi consensus definition, which characterizes RRI as "running-related (training or competition) musculoskeletal pain in the lower limbs that causes a restriction or cessation of running (distance, speed, duration, or training) for at least 7 days or 3 consecutive scheduled training sessions, or necessitates consultation with a physician or other health professional" [100]. Implementing this standardized definition could facilitate meaningful comparisons across studies, enhance the reliability of findings, and ultimately improve our understanding of the relationship between risk factors and RRIs. Furthermore, the duration of follow-up is crucial for accurate RRI identification, yet only 54% of the reviewed studies met the recommended follow-up duration of at least six months. While BMI is widely used to assess injury risk in runners, it fails to provide a comprehensive evaluation due to the influence of critical factors such as energy availability, training volume, and nutrition [101]. Athelets with low BMI who participate in intense training and experience Relative Energy Deficiency in Sport (RED-S) may suffer from decreased bone density and hormonal imbalances, significantly elevating their risk of injury [101, 102]. Therfore, we suggest that future studies prioritize examining the influence of additional factors such as energy availability, training volume, and nutrition, alongside BMI, to enhance our understanding of injury risk in runners. Additionally, BMI does not accurately reflect essential factors like fat mass and lean muscle distribution, especially when considering the significant differences in healthy BMI ranges between sexes [103], which can lead to misleading conclusions about injury risk. To enhance understanding and develop targeted interventions, future studies should conduct separate analyses for each sex, as the current research lacks sufficient focus on this distinction. Prioritizing these analyses is crucial for clarifying the complex relationship between BMI, body composition, and injury risk. Conclusions Limited evidence on risk factors for RRIs underscores the need for additional high-quality studies to draw definitive conclusions about specific risk factors relevant to RRIs. Our study revealed that higher BMI is associated with an increased risk of RRIs, particularly for MTSS. Lower extremity stress fractures were, on the other hand, associated with lower BMI compared to their counterparts. These insights could guide clinicians in assessing injury risk factors and developing targeted interventions for runners with diverse BMI profiles. Additionally, BMI did not show a significant association with the occurrence of injuries, such as PFPS and Achilles tendinopathy. Moreover, there is limited research on the link between BMI and the risk of iliotibial band syndrome and plantar fasciitis in runners, suggesting that there are other factors than BMI that increase the risk of these injuries. However, further studies are required to explore the correlation between BMI and RRIs, particularly in the cases of iliotibial band syndrome and plantar fasciitis as the evidence was weak. Declarations Acknowledgments: Not applicable. Authors’ Contributions: HD. Administrative support: AN and FG. Provision of study materials or patients: All authors. Collection and assembly of data: All authors. Data analysis and interpretation: AN and FG. Manuscript writing: All authors. Final approval of manuscript: All authors. Funding: This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Availability of Data and Materials: Data is provided within the manuscript and supplementary information files. Competing Interests: The authors declare no conflicts of interest regarding the research, authorship, or publication of this article. Ethics Approval and Consent to Participate: Not applicable. Consent for Publication: Not applicable. References Lee D-C, Brellenthin AG, Thompson PD, Sui X, Lee I-M, Lavie CJ. Running as a key lifestyle medicine for longevity. Progress in cardiovascular diseases. 2017;60(1):45-55. Kakouris N, Yener N, Fong DT. A systematic review of running-related musculoskeletal injuries in runners. Journal of sport and health science. 2021;10(5):513-22. Dallinga J, Van Rijn R, Stubbe J, Deutekom M. Injury incidence and risk factors: a cohort study of 706 8-km or 16-km recreational runners. 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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-6311307","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":446395483,"identity":"65f86058-dbff-4eea-9378-326cf4cc54bd","order_by":0,"name":"Aynollah Naderi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBklEQVRIiWNgGAWjYBACNhjJxt7AxsDYAOIlIEthAn64Fp4DcC0QCpcWSbisRAKaFlzA4EYC24MfZTb5fJLPnz34ueOeHD97jvkDhho7Bj7pA7i0sBv2nEuzbJPOMTfsPVNsLNnzxrCB4VgyAxtfAk5bJHjbDhuwSeeAGAmJG27kALWwHQD6DrvD7IFaJP+2/Tdgkzz+DMiAafmHWwvIFmnetgMGbBIMZtJwWxjb8Gg587BNWuZcsgEbT46ZtGxbAtAvzwpnJPYl8+DUcjz5mOSbMjsD+Xagw962JQBDLHnDhw/f7OTke7BrYRBIbMAimsDAgMMOIOA/gFNqFIyCUTAKRgEEAAAT8lZCG/9fwwAAAABJRU5ErkJggg==","orcid":"","institution":"Shahrood University of Technology","correspondingAuthor":true,"prefix":"","firstName":"Aynollah","middleName":"","lastName":"Naderi","suffix":""},{"id":446395484,"identity":"001d1a27-3d2e-44b1-9902-ec96adf299d1","order_by":1,"name":"Farhad Gholami","email":"","orcid":"","institution":"Shahrood University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Farhad","middleName":"","lastName":"Gholami","suffix":""},{"id":446395485,"identity":"9447d170-7122-4237-bbae-8f695ce3519e","order_by":2,"name":"Hans Degens","email":"","orcid":"","institution":"Manchester Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Hans","middleName":"","lastName":"Degens","suffix":""}],"badges":[],"createdAt":"2025-03-26 10:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6311307/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6311307/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":81208075,"identity":"247968be-8233-4ea6-a7a0-cfc4bb2b72ec","added_by":"auto","created_at":"2025-04-23 12:38:59","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":559136,"visible":true,"origin":"","legend":"\u003cp\u003ePRISMA flow diagram of search and the study selection process.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/cb432f10e5ca6c3955440509.jpeg"},{"id":81208076,"identity":"656324f5-e00f-4635-8d56-7efbbd079de5","added_by":"auto","created_at":"2025-04-23 12:38:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":216125,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the results obtained from random-effect model meta-analyses comparing BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) in individuals with RRIs and those without injuries.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/95e4e33f07e4c081465f37f4.png"},{"id":81208078,"identity":"25649a1b-a207-44e1-9d43-936718969d81","added_by":"auto","created_at":"2025-04-23 12:39:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":128037,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the results obtained from random-effects model meta-analyses, which pooled the OR for the association between BMI and the risk of RRIs.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/5c95c851e2491163c2e1cd35.png"},{"id":81208074,"identity":"ed714d02-40da-4659-90b5-75bf57403236","added_by":"auto","created_at":"2025-04-23 12:38:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":91344,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the results obtained from random-effect model meta-analyses that compared BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) in individuals with patellofemoral pain syndrome\u003cstrong\u003e (\u003c/strong\u003ePFPS) and individuals without PFPS.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/32c5bce03d9808347e037041.png"},{"id":81209310,"identity":"08dcb6fe-59dd-47ed-abd6-3be26292d7ac","added_by":"auto","created_at":"2025-04-23 12:55:00","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":18101,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the results obtained from individual random-effect model meta-analyses regarding BMI as a risk factor for Achilles tendinopathy. CI: confidence interval.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/198f24c25cbe1026c9fa222a.png"},{"id":81208082,"identity":"283bb47e-1700-4c84-bda3-b0ebee4026a2","added_by":"auto","created_at":"2025-04-23 12:39:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":83859,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the results obtained from individual random-effect model meta-analyses regarding BMI as a risk factor for medial tibial stress syndrome. CI: confidence interval.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/0b09e66ec9417a0d2ae7da73.png"},{"id":81208094,"identity":"9ee425fb-e49a-4049-b016-5c142e9fc1be","added_by":"auto","created_at":"2025-04-23 12:39:00","extension":"jpeg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":175415,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the results obtained from individual random-effect model meta-analyses regarding BMI as a risk factor for lower extremity stress fracture. CI: confidence interval.\u003c/p\u003e","description":"","filename":"floatimage7.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/3cfeb2359a73c6526a37a763.jpeg"},{"id":81209312,"identity":"c732f3fd-df36-4663-819e-553d2020397a","added_by":"auto","created_at":"2025-04-23 12:55:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1982684,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/d7f257fb-5e64-46b9-bb45-4002f1b11f22.pdf"},{"id":81208080,"identity":"bec5ae37-ff97-4ad6-9342-e189aad09234","added_by":"auto","created_at":"2025-04-23 12:39:00","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":149190,"visible":true,"origin":"","legend":"","description":"","filename":"supplefile.docx","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/6225cf9105d89ad46561f8ea.docx"},{"id":81208077,"identity":"3424c20f-3f63-482f-88ec-296662cb194d","added_by":"auto","created_at":"2025-04-23 12:39:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":54102,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.docx","url":"https://assets-eu.researchsquare.com/files/rs-6311307/v1/419e3db0f5ed6b6451b3a007.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing Body Mass Index as a Predictor of Running-Related Injuries: A Systematic Review and Meta- analysis","fulltext":[{"header":"Highlights","content":"\u003cul\u003e\n \u003cli\u003eRunners with RRIs have a higher baseline BMI compared to injury-free counterparts, with each unit increase in BMI corresponding to a 2 to 9 percent higher risk of RRIs.\u003c/li\u003e\n \u003cli\u003eRunners experiencing MTSS have a notably higher baseline BMI, whereas those experiencing lower extremity stress fractures have a significantly lower baseline BMI compared to counterparts who did not.\u003c/li\u003e\n \u003cli\u003eBMI did not emerge as a differentiating factor between runners who experienced PFPS or AT and those who did not.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Introduction","content":"\u003cp\u003eRunning, a widely embraced exercise for its health benefits [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], confronts participants with the challenge of sports injuries, as indicated by a recent meta-analysis reporting average incidence and prevalence of running-related injuries (RRIs) at 40.2% \u0026plusmn; 18.8% and 44.6% \u0026plusmn; 18.4%, respectively [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Overuse injuries to the knee (e.g., patellofemoral pain syndrome (PFPS) and iliotibial band syndrome), shin (e.g., medial tibial stress syndrome and tibial stress fracture), calf (e.g., Achilles tendinopathy) and foot (e.g., plantar fasciitis and metatarsal stress fracture) appear to be the most common RRIs [\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], typically resulting from cumulative loads that exceed the structural capacity of various tissues [\u003cspan additionalcitationids=\"CR7 CR8\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. RRIs lead to disengagement from physical activity, posing various health-related risks and financial burdens to runners [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Such concerns may deter exercise participation, emphasizing the pivotal need to address and overcome barriers like RRIs to sustain an active lifestyle.\u003c/p\u003e \u003cp\u003eA comprehensive understanding of the involved etiological factors is crucial to identifying runners prone to injuries and developing preventive measures for reducing the risk of RRIs. A variety of risk factors including shoe characteristics[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], exercise-related parameters[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], biomechanical [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and demographic variables [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] have been reported for RRIs. However, for injury prevention strategies to be effective, it is essential that the identified risk factors can be modified and have a biologically plausible mechanism [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the demographic risk factors, BMI appears to play a significant role in specific injury patterns and overall injury risk [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Nonetheless, it is important to acknowledge that many recommendations regarding BMI as a risk factor for RRIs are derived from studies conducted with military personnel rather than recreational and competitive runners[\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Moreover, many preventive recommendations rely on cross-sectional studies, theories, and opinions rather than robust evidence. Recognizing that not all statistically significant factors are causally associated with RRIs is essential [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Consequently, the limited and contradictory available information makes it challenging to draw reliable conclusions regarding the relationship between BMI and RRIs [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eObesity is becoming a global pandemic, and running is increasingly recognized as a method for weight loss and overall health improvement. In addition, the demographic profile of runners has changed as more and more runners have a higher fat percentage than it used to be, particularly those involved in amateur-level events [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Therefore, this systematic review and meta-analysis aims to synthesize previous studies' findings to ascertain the impact of BMI on the occurrence of RRIs among runners.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eDesign\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe protocol for this systematic review and meta-analysis was prospectively registered in the PROSPERO database (\u003cstrong\u003eBlinded\u003c/strong\u003e) and the meta-analysis was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSearch Strategy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo independent investigators (AN and FG) conducted a systematic literature search on Scopus, Web of Science, PubMed, Science Direct, and SPORTDiscus database from inception to October 2023 using specific keywords (running AND injury AND BMI AND Cohort). The search strategy, including the terminology used, is presented in Supplementary Table 1. The search strategy was customized for each database using the keywords and appropriate combination of Boolean operators. Additionally, a manual search of article reference lists and Google Scholar were performed to identify any further relevant articles. The search strategy complied with the Peer Review of Electronic Search Strategies (PRESS) 2015 Checklist to ensure its comprehensiveness [23].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEligibility Criteria\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe inclusion criteria, guided by the \u0026apos;PECOS\u0026apos; framework for observational studies (Population, Exposition, Comparison, Outcome, and Study Design) [24], comprised of the following: (1) Population: healthy runners of any age, sex, and activity level (classified by the authors as novice, recreational, competitive, or mixed cohorts); (2) Exposition: runners BMI at baseline; (3) Comparison: not applicable in this review. Exposure was determined as a baseline measurement of body mass index (BMI); (4) Outcomes: Running-related injuries, whether overall or specific, sustained during running participation. This encompassed various injury definitions (time loss, non-time loss, contact, and non-contact) and injury reporting methods (by medical staff or self-report); (5) Study Design: A prospective cohort design, as it is the preferred approach for acquiring direct and precise estimates of incidence and risk [25].\u003c/p\u003e\n\u003cp\u003eThe study excluded articles that (1) were non-original, including systematic reviews, conference abstracts, book chapters, and gray literature articles; (2) employed intervention, retrospective, cross-sectional, or case-control designs; (3) lacked a risk factor analysis or had insufficient data; (4) focused on risk factors for injured runners or did not specify if all runners were injury-free at baseline; (5) aimed to determine risk factors or injuries for other sports involving running, such as triathlons; (6) were conducted in military settings, aiming to differentiate between recreational and occupational injuries; or (7) were written in languages other than English.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe study did not impose limitations on a specific injury definition, encompassing all definitions of RRIs, such as time loss, non-time loss, contact, and non-contact, as well as various injury reporting methods, including medical staff or self-report. Additionally, the study did not impose restrictions based on age, sex, or the method of recruitment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eArticle Screening\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe articles obtained from each database were imported into EndNote X7 software (Thomson Reuters, New York, NY, USA) for eligibility screening and duplicate removal. Following duplicate removal, the titles and abstracts of the remaining articles were screened for eligibility based on the a priori inclusion/exclusion criteria to identify eligible articles for full-text review. Two independent reviewers (AN and FG) conducted the article screening, with any discrepancies resolved through discussion or by the third author (HD).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo independent reviewers (AN and FG) assessed the methodological risk of bias in included studies using the Risk of Bias in Non-randomized Studies of Exposures (ROBINS-E tool) (Supplementary Table 2). The ROBINS-E tool is a methodological instrument designed for evaluating the risk of bias in non-randomized studies, such as cohort and case-control studies. It encompasses seven domains of bias, including bias due to confounding, bias in participant selection, bias in exposure classification, bias due to departures from intended exposures, bias due to missing data, bias in outcome measurement, and bias in the selection of reported results. Each domain was categorized as having low, moderate, serious, or critical risk of bias (Supplementary Table 2). In cases of disagreement between assessors, the decision was made through discussion.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Extraction\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTwo authors (AN and FG) independently extracted data into a spreadsheet database (Microsoft Excel; Washington, US). The extracted data were compared for consistency and any discrepancies were addressed through discussion. The extracted information included study details (e.g., author, year, and design), sample characteristics (e.g., sample size, age, sex, and dropout rate), injury type, number of observed injuries, and length of injury follow-up period. To analyze differences between groups (e.g., injured \u003cem\u003evs.\u003c/em\u003e non-injured), the mean \u0026plusmn; standard deviation (SD) of BMI was extracted for runners who experienced RRIs and those who did not. Data synthesis involved the amalgamation of information from various studies, using group classification based on injury definition by the included studies. These groups primarily focused on overall injuries and specific injuries such as medial tibial stress syndrome, Achilles tendinopathy, PFPS, and lower extremity stress fracture. When applicable, data from a single study were included in more than one group. Each of these groups could potentially include a combination of acute, overuse, time loss, or non-time loss injuries, depending on the injury defined in the study (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Table 1)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe meta-analysis was conducted using Comprehensive Meta-Analysis software (version 3.0, Biostat Inc., Englewood, New Jersey, USA). Included studies provided either the mean and standard deviation (SD) of BMI for both injured and uninjured groups or odds ratios (OR) with 95% confidence intervals (CI) for the association between BMI and the risk of running injuries. Random effects models were chosen to combine the mean (SD) and OR (95% CI) effect sizes, considering potential variations in participant age, demographics, and running level across studies. The Higgins I\u003csup\u003e2\u003c/sup\u003e statistic was used to assess statistical heterogeneity, categorizing it as low (\u0026le; 25%), moderate (26-50%), large (51-75%), or considerable (\u0026gt; 76%). Publication bias was evaluated by visually examining the funnel plot and Egger\u0026apos;s test (P-value) [26]. The significance level was set at P\u0026lt;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSearch Results\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe initial search yielded 5,338 studies, and an additional 26 studies were identified from other sources. Of the retrieved studies 2,100 duplicates were removed and 2,480 articles were excluded after titles and abstracts screening. Among the remaining 784 articles, 749 were further excluded during full-text screening. Ultimately, five studies reporting duplicate data were excluded from the quantitative analysis [27-31], leaving 35 articles that met the criteria for quantitative analysis [3, 5, 7-9, 27, 28, 32-59]. The selection process is visually depicted in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Figure 1)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCharacteristics of Included Studies\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe included studies were published between 2000 and 2023 and involved a total of 14,025 participants (median 238; range 21-2,207; 57.4% female). Three studies included only women [38, 52, 60], while one study included only men [61] and the remaining studies recruited both sexes. \u0026nbsp;Seventeen articles combined all injury types [3, 5, 7-9, 33-36, 40, 42, 44, 46-48, 55, 58] and categorized as overall running injuries, while others provided estimates for specific injuries such as tibial stress syndrome (n = 4) [28, 32, 45, 50], lower extremity stress fracture (n = 4) [27, 28, 38, 52], PFPS (n = 6) [39, 49, 53, 54, 59], and Achilles tendinopathy (n = 5) [41, 43, 51, 56, 57], and plantar fasciitis. [38] \u0026nbsp;Table 1 provides a summary of all the included articles.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the principal analysis, a total of 2,515 participants were in the injured group and 11,608 participants were in the non-injured group. Among all participants, 1,550 individuals sustained injuries, while 5,773 participants were non-injured. When considering specific injury types, MTSS studies included 177 injured participants and 456 non-injured participants. For Achilles tendinopathy, there were 224 injured participants and 2,838 non-injured participants. Studies on PFPS involved 288 injured participants and 1,452 non-injured participants. Lastly, stress fracture studies included 188 injured participants and 3,283 non-injured participants.\u003c/p\u003e\n\u003cp\u003eThe follow-up periods in the studies ranged from 9 weeks to 6 cross-country seasons, and the proportion of participants analyzed varied from 67.4% to 100% of the initial sample size. The studies focused on different types of runners, with 21 studies investigating road runners [5, 8, 9, 33-36, 38-40, 44-49, 51, 53, 54, 56, 58], 8 studying cross-country runners [27, 28, 32, 42, 50, 52, 57, 59], 3 examining track runners (middle- and long-distance), and 3 reporting on long-distance runners [37, 43, 55]. Table 1 provides a summary of the main characteristics of the included studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy Quality\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe conducted a risk of bias assessment for each study, revealing a low to moderate risk of bias across all ROBINS-E domains. This finding provides the strongest evidence with the lowest risk of bias for the overall review. Accordingly, none of the studies were considered to have a high risk of bias, while ten studies were rated with a moderate risk of bias [7, 9, 40, 46, 47, 49-51, 55, 58] and twenty-five studies were rated with low-risk bias [3, 5, 8, 27, 28, 32-39, 41-45, 48, 52-54, 56, 57, 59]. In terms of specific domains of bias, studies were deemed to have a moderate risk in the domains as follows: \u0026nbsp;confounding[40, 47, 50, 51, 58], selection of participants [46, 47, 58], classification of exposures [7, 50, 55], missing data [40, 46, 49, 50, 62], or outcome measurement [9, 51, 55]. Regarding the risk of bias in the selection of reported results, as the final domain of ROBINS-E, most of the included studies were assessed as having a low risk of bias (Supplementary Table 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeta-analyses\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRunning related injury\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFifteen prospective studies were included in the meta-analyses [3, 5, 7-9, 30, 33, 36, 40, 42, 44, 46, 48, 55, 58] of which four studies found that non-injured runners had a lower BMI compared to injured runners [9, 47, 48, 55]. The remaining studies did not indicate a significant difference in the mean BMI between runners who sustained injuries and those who did not. However, upon combining the data from all the studies, it was found that runners who experienced injuries generally had higher BMI than those who did not experience any injury (pooled SMD = 0.113 kg/m\u003csup\u003e2\u003c/sup\u003e (95% CI: 0.031-0.194); Z=2.71; P=0.007; Figure 2) and the heterogeneity of studies was low and insignificant (I\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 21.6 and P=0.21). The funnel plot did not reveal any evidence of publication bias (Supplementary Figure 1), and Egger\u0026rsquo;s test indicated no selective reporting bias (p=0.77). Additionally, a classic fail-safe N test showed that 22 additional studies would need to be included in the meta-analysis to change the results to nonsignificant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Figure 2)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix studies were conducted to assess the correlation between BMI and RRIs [3, 9, 34, 35, 44, 55] \u0026nbsp;of which four studies found a positive relationship between BMI and risk of RRIs [9, 34, 35, 55]; however, the remaining two studies did not discover any association [3, 44]. The pooled OR of the included studies indicates that BMI is a significant risk factor for RRIs (OR=1.05 95% CI: 1.02, 1.09); Z=3.39, P=0.001; Figure 3). However, the heterogeneity of included studies was high and statistically significant (I\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e=62.3% and P=0.01). Additionally, conducting a classic fail-safe N test indicated that to render the results nonsignificant, 49 more studies would need to be incorporated into the meta-analysis. \u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Figure 3)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatellofemoral pain syndrome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSix prospective studies were included in the meta-analyses [39, 49, 53, 54, 59, 62]. Individually, none of these studies showed a significant difference in BMI between runners who developed PFPS and those who did not. When the data from these studies were combined, no significant difference was observed in the BMI of the two groups (pooled SMD = 0.14 kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: -0.04-0.33; Z=1.50; P=0.13; Figure 4) with no heterogeneity (I\u003csup\u003e2\u0026nbsp;\u003c/sup\u003e= 5.1% and p=0.38). The funnel plot did not reveal any evidence of publication bias (Supplementary Figure 2), and Egger\u0026rsquo;s test indicated no selective reporting bias (p=0.35).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Figure 4)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAchilles tendinopathy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFive prospective studies were included in the meta-analyses [41, 43, 51, 56, 57]. Only one study showed a significant difference in BMI between runners with AT and those without [41]. The remaining studies did not find a significant difference in BMI between the two groups. When the data from all the studies were quantified no significant difference in BMI between runners with AT and those without was observed (pooled SMD = 0.03 kg/m\u003csup\u003e2\u003c/sup\u003e; 95% CI: -0.19-0.25; Z=0.23; P=0.82; Figure 5). A\u0026nbsp;moderate statistically not significant heterogeneity was observed across the trials (I\u003csup\u003e2\u003c/sup\u003e=49.5% and P=0.09). The funnel plot did not reveal any evidence of publication bias (Supplementary\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFigure 3), and Egger\u0026rsquo;s test indicated no selective reporting bias (p=0.45).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Figure 5)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMedial tibial stress syndrome\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe meta-analysis included four prospective studies [28, 32, 45, 50] of which two studies reported significantly higher BMI values in runners suffering from MTSS compared to runners who did not suffer from MTSS [28, 45]. The remaining studies did not show a significant difference in BMI between these two groups [32, 50]. The combined data revealed a significantly higher BMI in runners with MTSS compared to those without MTSS (pooled SMD=0.43 kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: 0.18-0.68; Z=3.67; P=0.001; Figure 6).\u0026nbsp;The heterogeneity of included trials was moderate and insignificant (I\u003csup\u003e2\u003c/sup\u003e=35.9% and P=0.18). The funnel plot did not reveal any evidence of publication bias (Supplementary\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFigure 4), and Egger\u0026rsquo;s test indicated no selective reporting bias (p=0.77). A classical fail-safe N test indicated that 19 additional studies would need to be included in the meta-analysis to alter the results and make them nonsignificant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Figure 6)\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLower extremity stress fracture\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour prospective studies were included in the meta-analysis [28, 37, 38, 52] of which two studies found that the runners suffering from lower extremity stress fractures had significantly lower BMI than the runners without lower extremity stress fractures [38, 52]. The remaining studies did not indicate a significant difference in BMI between the two groups. The combined data revealed a significantly lower BMI in runners with a lower extremity stress fracture compared to those without (pooled SMD= -0.28 kg/m\u003csup\u003e2\u003c/sup\u003e, 95% CI: -0.53--0.03; Z=2.22; P=0.03; Figure 7).\u0026nbsp;The heterogeneity appeared to be relatively low and not significant (I\u003csup\u003e2\u003c/sup\u003e=16.2% and P=0.3). The funnel plot did not reveal any evidence of publication bias (Supplementary\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eFigure 5), and Egger\u0026rsquo;s test indicated no selective reporting bias (p=0.10). \u0026nbsp;Furthermore, the classic fail-safe N test indicated that the inclusion of just 2 more studies in the meta-analysis would be required to render the results to insignificant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(Figure 7)\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTo the best of our knowledge, this is the first systematic review and meta-analysis exploring the association between BMI and overall RRIs as well as specifically PFPS, iliotibial band syndrome, MTSS, lower extremity stress fractures, Achilles tendinopathy, and plantar fasciitis. \u0026nbsp;Our study found that BMI is a significant contributor of RRIs, with higher BMI being associated with a greater likelihood of RRIs. Specifically, runners who experienced MTSS had higher BMI than those without MTSS experience, while those with lower extremity stress fractures had lower BMI. However, BMI did not appear to have a significant contribution to the occurrence of injuries including PFPS and Achilles tendinopathy. No studies were found on the link between BMI and iliotibial band syndrome risk, with only one study addressing the association between BMI and plantar fasciitis in runners.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRunning-related injury\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study found that a higher BMI is associated with an increased risk of sustaining a running-related injury (RRI), with an odds ratio of 1.05 (95% CI, 1.02-1.09) per unit increase in BMI. This finding is consistent with a systematic review that reported a positive correlation between BMI and risk of injury, particularly ankle injuries[63]. Nielsen, Buist (30) also found that novice runners with a BMI greater than 30 kg/m\u003csup\u003e2\u003c/sup\u003e have a high risk of injury, while those with a BMI lower than 20 kg/m\u003csup\u003e2\u003c/sup\u003e have a low risk of injury. Additionally, van Poppel, De Koning (64) showed that the risk of RRIs in runners with a BMI greater than 26 kg/m\u003csup\u003e2\u003c/sup\u003e is threefold greater than their counterparts with a normal BMI of 20-26 kg/m\u003csup\u003e2\u003c/sup\u003e. In contrast, a systematic review by Van Gent, Siem (65) found that BMI \u0026gt;26 kg/m\u003csup\u003e2\u003c/sup\u003e was protective against injury occurrence among recreational runners. Although wearing absorptive running shoes is suggested to protect against RRIs, there is no conclusive evidence of this claim.\u003c/p\u003e\n\u003cp\u003eThe contribution of BMI to running-related risk of injury may be explained by the additional stress imposed on the body through extra weight. Naderi, Baloochi (66) suggested that BMI is the most important predictor of plantar pressure and impulse before, during, and after a 30-minute run. They also suggest that lower leg muscle strength and endurance may 30-58% contribute to this effect, particularly after 30 minutes of running [66]. This highlights the importance of considering BMI, foot muscle strength, and endurance when assessing injury risk in runners. Vincent, Kilgore III (16) suggest that runners with obesity are able to attenuate impact forces and control loading rate more efficiently than non-obese runners by increasing lower body stiffness and constraining vertical displacement which is likely to minimize the risk of injury. Despite these adaptations, runners with higher BMI need greater force exertion to change their momentum at a given speed, potentially increasing the risk of injury, particularly when their dynamic stability is not adequate for joint control under these circumstances [67]. Additionally, neuromuscular and postural stability is diminished in obese individuals [68], further enhancing the risk of injury, and underscoring the importance of injury prevention programs. Thus, alongside weight reduction strategies, neuromuscular control in the lower extremities should be improved [69]. Gradual improvements in physical fitness, achieved through low-impact aerobics activities and antigravity treadmill training, before engaging in weight-bearing and high-impact exercise like running, may be effective strategies for a runner with a high BMI [70]. Additionally, the use of shock-absorbent orthotic inserts and footwear modifications hold the potential for favorable outcomes [71]. Fredericson (4) recommended that athletic footwear should be replaced after covering a distance of 500 to 700 kilometers of running. Accordingly, the effectiveness of these interventions in preventing RRIs in individuals with a high BMI remains to be explored in future studies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMTSS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study found that runners experiencing MTSS had in average 0.43 kg/m\u003csup\u003e2\u003c/sup\u003e greater BMI compared to those without MTSS. This aligns with the findings of two previous systematic reviews and meta-analyses [17, 72]. Hamstra-Wright, Bliven (72)\u0026nbsp;reported that higher values of BMI (weighted MD=0.79) is a risk factor for MTSS in athletes and military personnel. However, it is important to note that the meta-analysis included only one study on runners [73] with no subgroup analysis. Similarly, in the study conducted by Reinking, Austin (17) BMI value was 0.34 kg/m\u003csup\u003e2\u003c/sup\u003e greater in active individuals experiencing MTSS compared to their peers without MTSS. However, only one prospective study focused on runners [73], while others included military personnel and athletes engaged in various sports. These findings emphasize the need for further research to explore the potential role of BMI as a risk factor for MTSS, specifically among runners.\u003c/p\u003e\n\u003cp\u003eFrom a pathomechanical perspective, MTSS is attributed to tibial bending [74] and tibial-fascial traction [75]. BMI is a potential risk factor for MTSS as it can increase tibial-fascial traction and tibial bending during running. Accordingly, a higher BMI can increase traction on the fascia along the medial tibial border by enhancing the contraction of both superficial and deep ankle plantar flexors\u0026mdash;specifically the soleus, tibialis posterior, flexor hallucis longus (FHL), and flexor digitorum longus (FDL)\u0026mdash;during the stance phase of running [45, 76]. Even though\u0026nbsp;recent MRI and histological studies have not confirmed the tibial-fascial tension theory of MTSS\u0026nbsp;[77], growing evidence suggests that it is caused by repeated chronic loads exerting bending forces on the tibia. According to this theory, bone strains that exceed the modeling threshold cause microdamage. Under most physiological circumstances, microdamage stimulates remodeling and strengthens the bone. However, repetitive or large strains may induce microdamage accumulation that increases skeletal fragility and susceptibility to injury, particularly in the absence of a remodeling response\u0026nbsp;[77, 78]. This suggests that the heavier impact loads associated with increased BMI may be a contributing factor in inducing microdamage accumulation and the incidence of MTSS. With increased BMI, the mechanical load on the tibia increases during physical activity. Thus, runners with higher BMI values may need longer durations and a gradual increase in running load to allow for bone adaptation\u0026nbsp;[79]. The use of pneumatic leg braces [80]\u0026nbsp;and arch support foot orthoses\u0026nbsp;[81]\u0026nbsp;may also help prevent and treat MTSS. In addition to BMI, MTSS may also be attributable to fitness level\u0026nbsp;[72]. A common limitation of the included studies was inconsistency in reporting the fitness level of the participants which needs to be addressed in future studies.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLower extremity Stress fracture\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study results indicate that runners who experienced lower extremity SF had on average a 0.28 kg/m\u003csup\u003e2\u003c/sup\u003e lower BMI than those without lower extremity SF. Further subgroup analysis showed that this difference seems to be sex-specific with a higher incidence in female than male runners, similar to that reported in a systematic review Wentz, Liu (82) in military personnel and athletes, where women also exhibited a significantly higher incidence of stress fractures than men. Barrack, Gibbs (83) found that athletes with multiple risk factors associated with the female athlete triad\u0026mdash;such as low BMI, low bone density, high exercise frequency, menstrual irregularities, and dietary restraint\u0026mdash;exhibited a significantly higher incidence of stress fractures compared to those with only one risk factor. They also noted that individuals with stress fractures had lower body fat percentages [83], suggesting that low adiposity contributes to injury risk. The terminology has been updated to \u0026quot;Relative Energy Deficiency in Sport\u0026quot; (RED-S), emphasizing that both female and active male athletes can suffer injuries due to insufficient energy. Consequently, future research should focus more on the relationship between BMI and sports injuries, especially stress fractures, in athletes with RED-S. Previous studies have linked lower body fat percentages and BMI to narrower bones [84] and low bone mineral density [85] specifically in female athletes, indicating that a lower BMI may increase the risk of stress fractures in female runners; however, further research is necessary to validate this hypothesis.\u003c/p\u003e\n\u003cp\u003eWhen interpreting the impact of BMI on the risk of stress fractures, the role of muscle mass in absorbing loading forces should be taken into account. Lower extremity stress fractures are a common overuse injury in athletes that typically occur when the bone is subjected to repetitive loading and mechanical stressors that exceed its ability to remodel and adapt.\u003cspan dir=\"RTL\"\u003e\u0026nbsp;\u003c/span\u003eAntigravity treadmills (AGT), with their adjustable body weight support, offer a promising approach for managing bone stress injuries, maintaining fitness in a reduced-gravity environment, and supporting post-injury recovery[70]. AGT running minimizes impact forces while preserving natural running mechanics, which can help athletes sustain fitness and reduce the likelihood of re-injury[70]. However, more high-quality research, particularly randomized controlled trials, is necessary to establish AGT\u0026rsquo;s effectiveness compared to conventional protocols [70]. Additionally, other low-impact aerobic exercises, such as elliptical training, cycling, and pool running, also provide effective ways to maintain physical fitness during recovery.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePFPS\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study found that runners who experienced PFPS did not have a higher BMI compared to those who did not experience PFPS. In a systematic review of cross-sectional studies by Hart, Barton (86), an association was found between BMI and both PFPS and patellofemoral osteoarthritis (PFOA), which could be due to reduced activity levels after symptom development [87]. However, their review did not identify higher BMI as a risk factor for future PFPS in adults or adolescents, and a high BMI was not linked to intervention outcomes in participants with PFPS [86]. These findings suggest that elevated BMI may be secondary to PFPS as it has been reported that PFPS limits running participation, reduces physical activity levels, and contributes to psychological impairments. Therefore, these findings raise questions about the biologically plausible suggestion that high BMI contributes to PFPS development.\u003c/p\u003e\n\u003cp\u003eSome review studies have linked high BMI to knee and PFOA [88-90]. \u0026nbsp;As PFPS is considered a precursor to PFOA [90-92], controlling BMI in adults with PFPS may mitigate mechanical and non-mechanical consequences, potentially slowing or delaying the development and progression of PFOA. However, there are no prospective studies examining the impact of BMI on PFPS progression and the development and progression of PFOA. Longitudinal data is needed to determine whether BMI predicts the development and progression of PFPS and PFOA. Interventions, such as combined diet and exercise training, could be a viable option for individuals with PFPS, particularly those classified as obese. It is essential to prioritize research in this area as there have been no randomized controlled trials targeting weight loss in PFPS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAchilles tendinopathy (AT)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrevious research has suggested a link between BMI, body weight, or waist circumference with tendon pathology [93], based on increased absolute tendon load and cytokine levels causing low-grade inflammation in obese individuals [94, 95]. However, our systematic review and meta-analysis did not find any association between BMI and AT. It\u0026apos;s worth noting that most studies investigating BMI as a risk factor for AT were in adolescent populations. Additionally, there is a hypothesis that being underweight may be associated with AT pathology due to decreased collagen production [95], leading to a U-shaped relationship between BMI and AT pathology [93]. More cohort studies are needed in diverse populations to investigate this further.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePlantar fasciitis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe available evidence indicates that increased BMI is associated with plantar fasciitis (PF) in non-athletic populations [96] and military personnel [18]. The higher BMI leads to increased mechanical load, which imposes additional stress on the plantar fascia. However, the incidence of foot pain appears to be more strongly linked to fat mass rather than fat-free mass, suggesting that inflammatory mechanisms related to adiposity may contribute to PF rather than fat mass \u003cem\u003eper se\u003c/em\u003e [97, 98]. It is worth noting that there is only one study exploring the association between BMI and PF incidence in runners that challenges the notion that a high BMI, regardless of body composition, applies to all individuals with PF, suggesting that athletes may represent a distinct subgroup. Although the study by Di Caprio, Buda (38) did not provide raw BMI data for PF cases, making it difficult to analyze the role of BMI, the relatively low average BMI of less than 20.6 kg/m\u003csup\u003e2\u003c/sup\u003e in 55% of the participants implies that BMI might play a minor role in athletes, or that there is a cut-off beyond which an increasing BMI poses an increased risk for PF.\u003cstrong\u003e\u003cspan dir=\"RTL\"\u003e \u003c/span\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eLimitations\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study includes some strengths and limitations to be acknowledged. This systematic review adhered rigorously to the PRISMA guidelines[22], ensuring a well-structured analysis. Furthermore, the review comprehensively examined 37 cohort studies that investigated BMI as a potential risk factor for various RRIs, providing evidence to be potentially applied in clinical settings.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur study acknowledges several limitations that warrant consideration. Firstly, the systematic search was restricted to English-language studies, which may have excluded valuable non-English research that could offer additional insights into the relationship between Body Mass Index (BMI) and running-related injuries (RRIs). Additionally, the limited number of studies included in each meta-analysis (except for the overall RRI analysis) necessitates caution in interpreting the results, as smaller sample sizes may affect the reliability of findings. Furthermore, variations in BMI measurements between baseline assessments and injury occurrences could lead to misclassifications of BMI exposures, though such misclassifications are unlikely to directly impact injury risk.\u003c/p\u003e\n\u003cp\u003eAnother limitation is that over 80% of the studies analyzed did not report odds ratios (ORs) for the identified risk factors, which are essential for standardizing risk factor reporting. Additionally, while our review identified potential risk factors predominantly through cohort studies with a median of 191 cases, it is suggested that future studies should aim for larger sample sizes\u0026mdash;preferably over 200 cases\u0026mdash;to detect small to moderate associations more effectively[99].\u003c/p\u003e\n\u003cp\u003eMoreover, the lack of consistent definitions for RRIs in prospective studies may lead to inaccuracies in estimating their true effects and their relationship with BMI. To address this limitation, future research should adopt standardized definitions for running-related injuries (RRIs), such as the Delphi consensus definition, which characterizes RRI as \u0026quot;running-related (training or competition) musculoskeletal pain in the lower limbs that causes a restriction or cessation of running (distance, speed, duration, or training) for at least 7 days or 3 consecutive scheduled training sessions, or necessitates consultation with a physician or other health professional\u0026quot; [100]. Implementing this standardized definition could facilitate meaningful comparisons across studies, enhance the reliability of findings, and ultimately improve our understanding of the relationship between risk factors and RRIs. Furthermore, the duration of follow-up is crucial for accurate RRI identification, yet only 54% of the reviewed studies met the recommended follow-up duration of at least six months.\u003c/p\u003e\n\u003cp\u003eWhile BMI is widely used to assess injury risk in runners, it fails to provide a comprehensive evaluation due to the influence of critical factors such as energy availability, training volume, and nutrition\u0026nbsp;[101]. Athelets with low BMI who participate in intense training and experience Relative Energy Deficiency in Sport (RED-S) may suffer from decreased bone density and hormonal imbalances, significantly elevating their risk of injury [101, 102]. Therfore, we suggest that future studies prioritize examining the influence of additional factors such as energy availability, training volume, and nutrition, alongside BMI, to enhance our understanding of injury risk in runners.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Additionally, BMI does not accurately reflect essential factors like fat mass and lean muscle distribution, especially when considering the significant differences in healthy BMI ranges between sexes [103], \u0026nbsp;which can lead to misleading conclusions about injury risk. To enhance understanding and develop targeted interventions, future studies should conduct separate analyses for each sex, as the current research lacks sufficient focus on this distinction. Prioritizing these analyses is crucial for clarifying the complex relationship between BMI, body composition, and injury risk.\u0026nbsp;\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eLimited evidence on risk factors for RRIs underscores the need for additional high-quality studies to draw definitive conclusions about specific risk factors relevant to RRIs. Our study revealed that higher BMI is associated with an increased risk of RRIs, particularly for MTSS. Lower extremity stress fractures were, on the other hand, associated with lower BMI compared to their counterparts. These insights could guide clinicians in assessing injury risk factors and developing targeted interventions for runners with diverse BMI profiles. Additionally, BMI did not show a significant association with the occurrence of injuries, such as PFPS and Achilles tendinopathy. Moreover, there is limited research on the link between BMI and the risk of iliotibial band syndrome and plantar fasciitis in runners, suggesting that there are other factors than BMI that increase the risk of these injuries. However, further studies are required to explore the correlation between BMI and RRIs, particularly in the cases of iliotibial band syndrome and plantar fasciitis as the evidence was weak.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHD. Administrative support: AN and FG. Provision of study materials or patients: All authors. Collection and assembly of data: All authors. Data analysis and interpretation: AN and FG. Manuscript writing: All authors. Final approval of manuscript: All authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData is provided within the manuscript and supplementary information files.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest regarding the research, authorship, or publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLee D-C, Brellenthin AG, Thompson PD, Sui X, Lee I-M, Lavie CJ. 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The association between body mass index and musculoskeletal foot disorders: a systematic review. Obesity reviews. 2012;13(7):630-42.\u003c/li\u003e\n\u003cli\u003eTanamas SK, Wluka AE, Berry P, Menz HB, Strauss BJ, Davies‐Tuck M, et al. Relationship between obesity and foot pain and its association with fat mass, fat distribution, and muscle mass. Arthritis care \u0026amp; research. 2012;64(2):262-8.\u003c/li\u003e\n\u003cli\u003eBahr R, Holme I. Risk factors for sports injuries\u0026mdash;a methodological approach. British journal of sports medicine. 2003;37(5):384-92.\u003c/li\u003e\n\u003cli\u003eYamato TP, Saragiotto BT, Lopes AD. A consensus definition of running-related injury in recreational runners: a modified Delphi approach. journal of orthopaedic \u0026amp; sports physical therapy. 2015;45(5):375-80.\u003c/li\u003e\n\u003cli\u003eThuany M, Hill L, Alvero-Cruz JR, Knechtle B, Gomes TN. The relationship between training volume and bmi in the expression of running performance in runners: a mediation model. Journal of Science in Sport and Exercise. 2023;5(2):142-8.\u003c/li\u003e\n\u003cli\u003eGallant TL, Ong LF, Wong L, Sparks M, Wilson E, Puglisi JL, et al. Low Energy Availability and Relative Energy Deficiency in Sport: A Systematic Review and Meta-analysis. Sports Medicine. 2024:1-15.\u003c/li\u003e\n\u003cli\u003eVan der Worp MP, Ten Haaf DS, van Cingel R, de Wijer A, Nijhuis-van der Sanden MW, Staal JB. Injuries in runners; a systematic review on risk factors and sex differences. PloS one. 2015;10(2):e0114937.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"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":"[email protected]","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":"Medial tibial stress syndrome, patellofemoral pain syndrome, Achilles tendinopathy, stress fracture, plantar fasciitis","lastPublishedDoi":"10.21203/rs.3.rs-6311307/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6311307/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eUnderstanding the causes of running-related injuries (RRIs) is essential for identifying high-risk runners and implementing preventive measures to reduce injury risk. This study aims to determine how body mass index (BMI) affects the occurrence of RRIs among runners, crucial for identifying high-risk individuals and implementing preventive measures.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e We conducted a systematic review and meta-analysis following the 'PECOS' framework for observational studies and PRISMA protocols. We searched Scopus, Web of Science, PubMed, Science Direct, and SPORTDiscus databases until October 2023 for prospective studies on RRIs. Two independent reviewers assessed the methodological risk of bias in the included studies using the ROBINS-E tool. The extracted data included study details, sample characteristics, injury type, number of injuries, and follow-up period. The outcome of interest was RRIs sustained during the study, both overall and specific, and the mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) of BMI for runners who experienced RRIs and those who did not. Pooled odds ratios [95% confidence interval (CI)] were calculated using a random-effects model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn our analysis of 35 studies involving 14,025 runners (median 238; range 21\u0026thinsp;\u0026minus;\u0026thinsp;2,207; 57.4% women), we found that BMI significantly predicts RRIs (OR\u0026thinsp;=\u0026thinsp;1.05, CI:1.02\u0026ndash;1.09; P\u0026thinsp;=\u0026thinsp;0.001), with individuals experiencing such injuries showing higher baseline BMIs (MD\u0026thinsp;=\u0026thinsp;0.113kg/m\u0026sup2;, CI:0.031\u0026ndash;0.194; P\u0026thinsp;=\u0026thinsp;0.007). For specific injuries, no significant baseline BMI differences were found for runners with patellofemoral pain syndrome (PFPS) or Achilles tendinopathy (AT) compared to those without (MD\u0026thinsp;=\u0026thinsp;0.14kg/m\u0026sup2;,CI:-0.04-0.33; P\u0026thinsp;=\u0026thinsp;0.13, and MD\u0026thinsp;=\u0026thinsp;0.03kg/m\u0026sup2;,CI:-0.19-0.25; P\u0026thinsp;=\u0026thinsp;0.82, respectively). However, individuals with medial tibial stress syndrome (MTSS) had higher BMI (MD\u0026thinsp;=\u0026thinsp;0.43kg/m\u0026sup2;,CI:0.18\u0026ndash;0.68;P\u0026thinsp;=\u0026thinsp;0.001), and those with lower extremity stress fractures had lower BMI (MD=-0.28kg/m\u0026sup2;,CI:-0.53-0.03;P\u0026thinsp;=\u0026thinsp;0.03) compared to their counterparts.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eRunners with RRIs generally have a higher baseline BMI, especially those with MTSS, while those with lower extremity stress fractures have a lower BMI, and BMI does not differentiate those with PFPS or AT.\u003c/p\u003e","manuscriptTitle":"Assessing Body Mass Index as a Predictor of Running-Related Injuries: A Systematic Review and Meta- analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-23 12:38:52","doi":"10.21203/rs.3.rs-6311307/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2025-04-26T12:51:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-23T15:19:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24225805084045492598517066357653083110","date":"2025-04-15T12:15:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"267647124404435764046514314141996129397","date":"2025-04-15T08:21:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-02T13:27:34+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-04-02T13:20:05+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-03-31T07:04:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-31T07:02:51+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Sports Science, Medicine and Rehabilitation","date":"2025-03-26T10:11:55+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"a2165001-ab4e-436f-869e-3c700f82e2af","owner":[],"postedDate":"April 23rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-04-23T12:38:52+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-23 12:38:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6311307","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6311307","identity":"rs-6311307","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}