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. 2025 Jan 21;8(3):302–310. doi: 10.1016/j.smhs.2025.01.001

Higher BMI might increase risk of ankle–foot sprains: A mendelian randomization study

Xicheng Gu a,1, Xiao'ao Xue a,1, Yi Li a, Ziyi Chen a, He Wang b,⁎⁎,2, Yinghui Hua a,⁎,2
PMCID: PMC13159448  PMID: 42125182

Abstract

Purpose

Although previous research has suggested that a greater body mass index (BMI) may be linked to a higher incidence of ankle–foot sprains, the causal relationship between BMI and these injuries has not been established. This study aims to determine the causal effect of BMI-related features on ankle–foot sprains using a two-sample Mendelian randomization (MR) analysis.

Methods

Exposure single-nucleotide polymorphisms were collected from Genetic Investigation of Anthropometric Traits (GIANT Consortium) for BMI, hip circumference (HIP), hip circumference adjusted for BMI (HIPadjBMI), waist circumference (WC), waist circumference adjusted for BMI (WCadjBMI), waist-to-hip ratio (WHR), and waist-to-hip ratio adjusted for BMI (WHRadjBMI), encompassing a study population of more than 200 ​000 individuals. Furthermore, exposure statistics were gathered from MEC-IEU for body fat percentage (BFP), involving a study population of 454 ​633 individuals. Additionally, outcome statistics for ankle sprains were identified from FinnGen based on hospital discharge records (9 141 cases and 290 ​508 healthy controls). Random-effect, inverse-variance weighted MR was used as the primary method.

Results

BMI (β ​= ​0.173, p ​= ​0.035), BFP (β ​= ​0.341, p ​= ​9.08 ​× ​10−6), hip circumference (β ​= ​0.265, p ​= ​0.001), and WC (β ​= ​0.193, p ​= ​0.045) were found to have positive causal relationships with higher risk of ankle–foot sprains, whereas HIPadjBMI, WCadjBMI, WHR, and WHRadjBMI were found to have no such effect. Additionally, no reverse causal effect was found between ankle–foot sprains and BMI or BFP.

Conclusions

A genetic predisposition to higher BMI-related features can lead to a higher risk of ankle–foot sprains, providing new insight into how to prevent ankle–foot sprains in middle-aged and elderly people.

Keywords: Ankle injuries, Body mass index, Body fat percentage, Mendelian randomization analysis

Abbreviations

BMI

Body Mass Index

BFP

Body Fat Percentage

MR

Mendelian Randomization

GIANT

Genetic Investigation of Anthropometric Traits

HIP

Hip Circumference

HIPadjBMI

Hip Circumference adjusted for BMI

WC

Waist Circumference

WCadjBMI

Waist Circumference adjusted for BMI

WHR

Waist-to-hip Ratio

WHRadjBMI

Waist-to-hip Ratio adjusted for BMI

SNP

Single-Nucleotide Polymorphisms

IV

Instrumental Variable

IVW

Inverse-variance Weighted

GWAS

Genome-Wide Association Studies

1. Introduction

Ankle–foot sprains are one of the most common musculoskeletal injuries, with an incidence of seven events per 1 000 person-years.1,2 For instance, a high incidence of ankle sprain has been reported in various competitive sports, with ankle ligament sprains accounting for approximately 80% of sport-related ankle injuries.3, 4, 5 Besides the high injury risks, about one-third of patients who sustain ankle–foot sprains often develop residual symptoms, such as episodes of giving away, subjective ankle instability, and recurrent sprains, after the initial ankle sprains.6 These individuals often experience a high incidence of recurrent sprains and reduced levels of physical activities, making it difficult for them to regain their preinjury functional.7 Furthermore, there is evidence suggesting that a history of ankle sprains might lead to posttraumatic ankle osteoarthritis or inability to work in the long term.8,9 Consequently, the insurance of proper prevention of ankle–foot sprains becomes imperative.

The risk factors associated with ankle sprains can be divided into two groups: intrinsic patient-related (e.g., female gender, limited dorsiflexion range of motion, and reduced proprioception) and extrinsic environment-related factors (e.g., type of sports being played and level of participation).10,11 Previous studies have reported body mass index (BMI) as an intrinsic risk factor linked to ankle sprain, with a significant correlation existing between them.10 A.W Watson reported in 1999 that lower BMI was identified in football players who sustain ankle sprains.12 B.R Waterman et al., according to their study at the United States Military Academy, found that increased BMI was a risk factor for ankle sprains in men.13 Hartley et al. reported in 2018 that male collegiate athletes with greater BMI were at a greater risk of sustaining an ankle sprain injury.14 In 2024, Sun et al. found that BMI may be a risk factor for musculoskeletal injury including ankle and foot injury in contemporary and modern dancers.15 Other research also suggested that a higher BMI may be regarded a risk factor for sustaining ankle sprains.4,16 Meanwhile, one meta-analysis showed a greater risk of ankle sprains in patients with a lower BMI.10 Obviously, the previous findings are contradictory and do not provide a conclusion on whether a history of ankle sprains are a result of the alternations of BMI. Consequently, exploring the exact underlying directional causal relationship between BMI and ankle–foot sprains is essential for enhancing the effectiveness of preventive measures against ankle sprains.

Mendelian randomization (MR) is a method of exploring causal links while minimizing the influence of reverse causality and confounding on causal estimates derived from observational data. MR uses single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs). In our case, we use SNPs proven to be associated with BMI-related features as our IVs. Those genetic loci were identified by Locke et al. in a genome-wide association study and Metabochip meta-analysis of BMI in up to 339 ​224 individuals.17 Their work identified 97 BMI-associated loci which account for ∼2.7% of BMI variation, and genome-wide estimates suggest that common variation accounts for > 20% of BMI variation. Since genotypes are randomly assigned at conception, genetic alleles associated with BMI can be used to assign individuals in a randomized manner according to the higher or lower mean levels of these exposures. This process can minimize confounding factors and reverse causality effects. Therefore, MR studies can establish causal links with a degree of certainty comparable to those of randomized clinical trials. To ensure that the causal estimate is valid, three IV assumption principles must be followed. The first is the relevance assumption, wherein the genetic variants must be associated with our exposure (BMI-related features). The second is the independence assumption, wherein the genetic variants must be associated with any potential confounders of the BMI–ankle sprain relationship. The third is the exclusion restriction assumption, wherein genetic variants solely exert influence through BMI-related traits [Fig. 1]. Based on these assumptions, if individuals with a genetic predisposition for higher BMI-related features have a higher risk of ankle–foot sprains, we can attribute the elevated risk of ankle–foot sprains to the higher BMI-related features.

Fig. 1.

Fig. 1

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Core IV assumptions of MR analysis

BMI: Body Mass Index; BFP: Body Fat Percentage; MR: Mendelian Randomization.

In this study, we attempted to use the MR method to investigate the causal relationship between BMI-related features and ankle–foot sprains in order to provide new evidence for preventing ankle–foot sprains. According to our previous research, individuals with higher BMI-related features are at greater risk of sustaining ankle–foot sprains.

2. Methods

2.1. Study design

This study employed a two-sample MR approach to evaluate the directional causal effect of BMI-related features on ankle–foot sprains. The analyses conducted in this study relied on summary-level genome-wide association studies (GWAS) datasets for European ancestry, which are publicly accessible. The chronobiological factor is respected in this study.

2.2. Ethical approval

These datasets used in our study have already obtained ethical approval from their respective institutional review boards and have obtained informed consent from the participants. The report of this study followed the guidelines of Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization.18

2.3. Data sources

This study utilized publicly available GWAS summary statistics, encompassing data from various sources. The statistics include BMI in 339 ​224 individuals from the Genetic Investigation of Anthropometric Traits (GIANT Consortium)17; body fat percentage (BFP) in 454 ​633 individuals from MRC-IEU19; hip circumference (HIP) in 213 ​038 individuals, hip circumference adjusted for BMI (HIPadjBMI) in 211 ​117 individuals, waist circumference (WC) in 232 ​101 individuals, waist circumference adjusted for BMI (WCadjBMI) in 231 ​355 individuals, waist-to-hip ratio (WHR) in 212 ​248 individuals, and waist-to-hip ratio adjusted for BMI (WHRadjBMI) in 210 ​086 individuals from the GIANT Consortium.20

The ankle sprain-related GWAS summary-level data was extracted from the GWAS data of FinnGen, which is a public–private partnership aiming to collect and analyze genome and clinical endpoints obtained from Finnish nationwide health registries.21 Ankle sprain exposure was defined as the binary variable collected from hospital discharge records, identified by an International Classification of Diseases, 10th Revision diagnostic code: participants with S93 (dislocation, sprain, and strain of joints and ligaments at ankle and foot level) were defined as the cases (n ​= ​9 141), and participants without S90–S99 (injuries to the ankle and foot) were defined as the controls (n ​= ​290 ​508).

In the case of sample overlap, we investigated the original sources of our GWAS data. The outcome data for ankle–foot sprains was from FinnGen, which is a public–private partnership combining genotype data from Finnish biobanks and digital health record data from Finnish health registries.22 The BFP exposure data from MRC-IEU was a population-based cohort from the UK, which had minimal overlap with FinnGen.19 GWAS data obtained from the GIANT Consortium consisted of a number of different cohorts based primarily on European populations. Although several Finnish-based studies were included, the total sample size of the Finnish population was no more than 5 000, accounting for less than 5% of the total sample size.17,20 Therefore, the degree of sample overlaps in this MR study is unlikely to introduce significant relative bias.23

2.4. Selection of genetic instruments

A series of quality controls were performed to filter eligible genetic instruments. First, only SNPs that were strongly associated with exposure factors (BMI, BFP, hip circumference, waist circumference, waist to hip ratio, etc.) with p ​< ​1 ​× ​10−8 were included.24 Second, the linkage disequilibrium clumping was performed using the European reference panel (r2 ​< ​0.001, physical distance > 10 ​000 ​kb) to obtain independent SNPs.25 Third, the F-statistics were calculated using formula F = R2(N−k−1)/k(1−R2) to assess the strength of each SNP and to minimize weak instrumental bias, where R2, N, and k represent the genetic variants, sample size, number of instruments, respectively,26 and a value of above 10 was deemed sufficient.27 Fourth, the ankle sprain-related SNPs with a threshold of 1 ​× ​10−5 were removed, and the MR pleiotropy residual sum and outlier (MR-PRESSO) tests (NbDistribution ​= ​10 ​000) were applied to identify the underlying horizontal pleiotropic outliers.28 Finally, the exposure and outcome SNPs were harmonized to the same effect alleles with the palindromic SNPs removed.

2.5. Main analyses

All statistical analyses were performed using TwoSampleMR version 0.5.7 and MR-PRESSO version 1.0.0 packages in R Version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria). The random-effects inverse-variance weighted (IVW) method was set as the primary approach for estimating causal links (i.e., exposure-on-outcome effects). This method assumes that the aforementioned core IV assumptions were not violated and that all of the IVs were valid.24 Despite prior testing, the estimates may still be subject to bias if residual horizontal pleiotropy exists. Therefore, the MR-Egger29 and weighted median30 methods were employed for sensitivity analysis. The MR-Egger method assumes that all IVs are invalid, while the weighted median method assumes that at least half of the IVs are valid. Other MR methods available in the TwoSampleMR package were also utilized for reference purposes. The MR-Egger intercept test was employed to assess horizontal pleiotropy. The Cochran Q test was used to evaluate heterogeneous effects within the SNP, with a significance level of p ​< ​0.05, indicating statistically significant horizontal pleiotropy and heterogeneity.31 A leave-one-out analysis was conducted to assess the influence of individual SNPs on the stability of the pooled results. The estimates would be considered unstable if a significant estimate became insignificant, which suggests that these results should be interpreted with caution.

2.6. Sensitivity analyses

Given our hypothesis that higher BMI-related features may lead to sprains in the lower limb joints (represented by ankle–foot sprains), rather than injuries to joints across the entire body, we included the corresponding upper limb joints in our analysis as a sensitivity assessment. We obtained the GWAS data on wrist–hand sprain from the GWAS data of FinnGen, with a sample size of 6 697 cases and 279 ​063 controls.22 To determine whether there was a reverse causal relationship between ankle–foot sprains and BMI-related traits, we used the GWAS data on ankle–foot sprain as exposure to study its effect on BMI-related traits. The process of selecting IVs was the same as described above, with the only difference being that SNPs associated with ankle–foot sprains with p ​< ​1 ​× ​10−5 were included to ensure an adequate number of sufficient SNPs for the MR analysis.

3. Results

Eight sets of BMI-related GWAS data were used as exposure to study their causal association with ankle–foot sprain. Fig. 2 displays the total number of SNPs extracted following a series of steps in which IVs were chosen in accordance with the fundamental IV assumptions. The average age at the first ankle sprain event from the GWAS data of FinnGen was 39.71 years.

Fig. 2.

Fig. 2

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MR estimates between BMI-related features and ankle-foot sprainsMR: Mendelian Randomization; HIPadjBMI: Hip Circumference adjusted for BMI; WCadjBMI: Waist Circumference adjusted for BMI; WHRadjBMI: Waist-to-Hip Ratio adjusted for BMI.

The MR analyses provided suggestive evidence that the traits that may lead to more ankle–foot sprains are BMI (IVW OR ​= ​1.188; 95% CI, 1.012–1.395; p ​= ​0.035), BFP (IVW OR ​= ​1.407; 95% CI, 1.210–1.635; p ​= ​9.08 ​× ​10−6), HIP (IVW OR ​= ​1.303; 95% CI, 1.108–1.533; p ​= ​0.001), and WC (IVW OR ​= ​1.213; 95% CI, 1.005–1.464; p ​= ​0.045). No significant causal effects of HIPadjBMI (IVW OR ​= ​1.142; 95% CI, 0.986–1.322; p ​= ​0.076), WCadjBMI (IVW OR ​= ​1.164; 95% CI, 0.945–1.435; p ​= ​0.154), WHR (IVW OR ​= ​0.960; 95% CI, 0.739–1.246; p ​= ​0.757), and WHRadjBMI (IVW OR ​= ​0.854; 95% CI, 0.680–1.073; p ​= ​1.176) were found for ankle–foot sprains (Fig. 2; see, Supplementary Figs. 1–8, forest plot of MR results). Fig. 3 displays scatter plots of these MR results. Heterogeneity was observed in the analyses pertaining to BFP (Cochran's Q ​= ​382.469, p ​= ​0.020) and WCadjBMI (Cochran's Q ​= ​93.140, p ​= ​0.003) concerning their association with ankle–foot sprains (Table 1). After applying the outlier test using MR-PRESSO, there is still a causal effect of BFP on the outcome (outlier-corrected p ​= ​3.16 ​× ​10−5). In the case of WCadjBMI, no significant outlier was found in the outlier test, indicating a potential bias in the result of the causal effect of WCadjBMI on ankle–foot sprains.

Fig. 3.

Fig. 3

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Scatter plot of SNP effects on BMI-related features and ankle-foot sprains.

Table 1.

Primary MR results of the causal effect of BMI-related traits on ankle–foot sprains.

Exposure Outcome No. of SNPs MR Method p β OR or_lci95 or_uci95 Pleiotropy Test
 Heterogeneity Test
p Cochran's Q p
Body Fat Percentage ankle sprain 329 IVW 9.08 × 10−6∗∗ 0.34 1.41 1.21 1.64 0.51 382.47 0.02
MR-Egger 0.047∗ 0.50 1.64 1.01 2.68
Weighted Median 3.07 × 10−3∗∗ 0.35 1.42 1.13 1.79
Body Mass Index ankle sprain 62 IVW 0.03∗ 0.17 1.19 1.01 1.39 0.97 49.61 0.85
MR-Egger 0.50 0.16 1.18 0.73 1.89
Weighted Median 0.10 0.19 1.21 0.96 1.53
Hip Circumference ankle sprain 48 IVW 1.39 × 10−3∗∗ 0.26 1.3 1.11 1.53 0.89 49.29 0.38
MR-Egger 0.35 0.23 1.26 0.78 2.05
Weighted-Median 0.04∗ 0.25 1.29 1.01 1.64
HipadjBMI ankle sprain 68 IVW 0.08 0.13 1.14 0.99 1.32 0.27 82.56 0.10
MR-Egger 0.13 0.43 1.54 0.89 2.66
Weighted-Median 0.13 0.16 1.18 0.95 1.45
Waist Circumference ankle sprain 35 IVW 0.04∗ 0.19 1.21 1.01 1.46 0.07 32.74 0.53
MR-Egger 0.35 −0.24 0.79 0.48 1.29
Weighted-Median 0.58 0.09 1.09 0.80 1.50
WCadjBMI ankle sprain 60 IVW 0.15 0.15 1.16 0.94 1.43 0.18 93.14 3.04 × 10−3
MR-Egger 0.11 0.82 2.26 0.85 5.98
Weighted-Median 0.66 0.05 1.06 0.81 1.38
Waist/Hip Ratio ankle sprain 26 IVW 0.76 −0.04 0.96 0.74 1.25 0.69 25.73 0.42
MR-Egger 0.75 0.22 1.24 0.34 4.56
Weighted-Median 0.82 0.04 1.04 0.72 1.51
WHRadjBMI ankle sprain 36 IVW 0.18 −0.16 0.85 0.68 1.07 0.88 45.32 0.11
MR-Egger 0.91 −0.07 0.94 0.29 3.07
Weighted-Median 0.37 −0.14 0.87 0.64 1.18
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∗Suggestive significant effect with p ​< ​0.05. ∗∗Significant effect with p ​< ​0.012 5. Bold indicate primary analysis approach. BMI: Body Mass Index; MR: Mendelian Randomization; HIPadjBMI: Hip Circumference adjusted for BMI; WCadjBMI: Waist Circumference adjusted for BMI; WHRadjBMI: Waist-to-Hip Ratio adjusted for BMI; IVW: Inverse-variance Weighted.

The MR analyses showed that none of the BMI-related traits have a causal effect on wrist–hand sprains. However, heterogeneity was observed in the analyses pertaining to BFP concerning its association with wrist–hand sprains (Cochran's Q ​= ​380.759, p ​= ​0.024) (Supplementary Table). Using MR-PRESSO as an outlier test, no significant outlier was found, indicating a potential bias in the result of the causal effect of BFP on wrist–hand sprains (see Supplementary Figs. 9–16, leave-one-out plots of MR results).

When the causal effect of BMI-related traits was investigated using the GWAS data on ankle–foot sprain GWAS data as exposure, only the waist circumference was found to be significantly affected by ankle–foot sprains (IVW β ​= ​0.039; SE ​= ​0.016; p ​= ​0.016). Conversely, no causal association was observed between ankle–foot sprains and other BMI-related traits (Table 2).

Table 2.

Primary MR results of the causal effect of ankle–foot sprains on BMI-related traits.

Exposure Outcome No. of SNPs MR Method p β SE Pleiotropy Test
 Heterogeneity Test
p Cochran's Q p
ankle sprain Body Fat Percentage 72 IVW 0.10 0.01 4.54 × 10−3 0.58 261.06 1.57 × 10−23
MR-Egger 0.60 3.97 × 10−3 7.62 × 10−3
Weighted-Median 0.22 4.71 × 10−3 3.86 × 10−3
ankle sprain Body Mass Index 20 IVW 0.38 0.02 0.02 0.80 43.63 1.06 × 10−3
MR-Egger 1.00 −1.35 × 10−4 0.07
Weighted-Median 0.10 0.03 0.02
ankle sprain Hip Circumference 20 IVW 0.06 0.03 0.02 0.36 16.78 0.60
MR-Egger 0.72 −0.02 0.05
Weighted-Median 0.18 0.03 0.02
ankle sprain HipadjBMI 20 IVW 0.16 0.02 0.02 0.38 21.89 0.29
MR-Egger 0.65 −0.03 0.06
Weighted-Median 0.01 0.06 0.02
ankle sprain Waist Circumference 20 IVW 0.02∗ 0.04 0.02 0.54 23.09 0.23
MR-Egger 0.93 0.01 0.06
Weighted-Median 0.34 0.02 0.02
ankle sprain WCadjBMI 20 IVW 0.05∗ 0.03 0.01 0.75 17.27 0.57
MR-Egger 0.79 0.01 0.05
Weighted-Median 0.66 0.01 0.02
ankle sprain Waist/Hip Ratio 20 IVW 0.14 0.02 0.02 0.94 23.43 0.22
MR-Egger 0.73 0.02 0.06
Weighted-Median 0.43 0.02 0.02
ankle sprain WHRadjBMI 20 IVW 0.25 0.02 0.01 0.92 17.71 0.54
MR-Egger 0.82 0.01 0.05
Weighted-Median 0.26 0.02 0.02
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∗Suggestive significant effect with p ​< ​0.05. ∗∗Significant effect with p ​< ​0.012 5. Bold indicate primary analysis approach. BMI: Body Mass Index; MR: Mendelian Randomization; HIPadjBMI: Hip Circumference adjusted for BMI; WCadjBMI: Waist Circumference adjusted for BMI; WHRadjBMI: Waist-to-Hip Ratio adjusted for BMI; IVW: Inverse-variance Weighted.

4. Discussion

In this MR study, potential significant causal associations between several types of BMI-related traits and a higher risk of ankle–foot sprains were identified. To the best of our knowledge, this study is the first to conduct MR-based analysis employing available public data to identify the causal relationships between BMI-related features and ankle–foot sprains. Our findings suggest that those who have higher BFP, BMI, HIP, or WC may face a greater risk of ankle–foot sprains. Our results proved robust after a series of sensitivity analyses and pleiotropy assessments.

4.1. BMI–ankle sprains

Previous studies demonstrated that BMI is associated with a history of ankle sprain and chronic ankle instability.10,16 A study conducted during pediatric emergency room visits reported that overweight children were two to three times likelier to have an ankle sprain,32 possibly due to increased mechanical stress on the ankle joint from excess weight. The higher the BMI, the greater the strain on the musculoskeletal structures, including ligaments and tendons, which may compromise their ability to stabilize the joint during dynamic movements, making it more prone to injury. A cohort study spanning 10 months found that professional soccer players with an increased BMI had a significantly higher risk of noncontact ankle sprains,4 which may be related to altered biomechanical patterns, such as reduced proprioception and changes in the distribution of forces during high-impact activities.

Despite these findings, previous research has been unable to establish whether a higher BMI directly increases the risk of incurring ankle injury, or if other factors are at play. Our MR study contribute to this debate by further proving that a higher BMI has a directional, causal effect on ankle–foot sprains, with no meaningful reverse causal effect, suggesting that the increased risk of injury is more likely due to the mechanical and structural effects of excess body weight on joint stability. Moreover, physical activity was found to be significantly decreased after acute lateral ankle sprain.33 A previous MR study suggests a bidirectional, causal relationship between physical inactivity and adiposity,34 where reduced physical activity may lead to an increase in body fat, while higher adiposity can further hinder the recovery process by compromising muscle strength and joint function. As a result, individuals who experience ankle injuries may become less active, leading to an increase in BMI. This cycle could exacerbate the risk of future ankle sprains, as elevated BMI further destabilizes the ankle joint and reduces the body's ability to protect against injury through neuromuscular adaptation and proper joint mechanics, which are critical for injury prevention.

4.2. Other obesity indicators–ankle sprains

BMI has its own limitations as a measure of both obesity and overall health. When calculating BMI, only body mass and height are considered, leaving out other significant factors, such as the proportion of bone, muscle, and fat. From a physiological perspective, muscle mass and bone mass are crucial determinants of overall strength and mobility. The skeletal muscle plays an essential role in joint stability and proprioception, helping to absorb shock and prevent injuries. In individuals with high muscle mass, such as weightlifters, the greater amount of muscle and bone density offers protective benefits, including a lower risk of certain types of injuries. However, these factors are not reflected in BMI, leading to potential misclassification of their health status. For example, although a weightlifter may have a BMI indicating obesity, their body fat percentage could be much lower than that of someone with a normal BMI, since lean mass (muscle and bone) makes up a larger proportion of their total body weight.35 Despite having positive results from BMI, which can better define body composition, we also employ BFP as exposure to study its causative effect on ankle–foot sprain. The finding that a higher BFP leads to an increased risk of ankle–foot sprains complements the result of BMI to a certain extent. Excess fat mass, especially in the lower limbs, increases the mechanical load on the ankle and foot, leading to greater stress on ligaments and tendons. This additional load, combined with a potential decrease in muscle mass, compromises joint stability and proprioception, making individuals more prone to sprains. HIP and WC were found to have a positive causal effect on ankle–foot sprains. However, no significant effect was detected in the cases of HIPadjBMI and WCadjBMI. This finding suggests that the increased risk of ankle–foot sprains is not directly related to individual differences in HIP and WC. HIP and WC work together with BMI contribute as causal factors leading to ankle–foot sprains.36,37 No significant effect was found in the case of WHR or WHRadjBMI, indicating that body shape has little to do with ankle–foot sprains.38 It is the excess body mass, particularly fat, that plays a critical role in injury risk, rather than body shape, which reflects the distribution of fat in specific body areas like the hips or waist.

4.3. Potential mechanism of obesity–ankle sprains

Since the causal relationship between BMI-related traits and ankle sprain was demonstrated in this study, its underlying mechanism remains to be discussed. First, individuals with a high BMI or BFP may have relatively low muscle mass, potentially resulting in reduced ankle range of motion. This, in turn, could lead to less control over gait and balance during motions.39 Next, obese people may have plantar sensory impairment. In comparison to nonobese individuals, obese individuals have larger plantar contact areas and experience higher mean pressure during postural control tasks.40 Consequently, these manifestations may diminish the quality and/or quantity of sensory information originating from the plantar mechanoreceptors, resulting poorer postural control, leading to greater risk of ankle sprains. Furthermore, individuals with higher BMI may have less stable dynamic balance. Obese adults have been shown to have greater difficulty managing mediolateral stability while walking.41 Also, obese people have been shown to experience a decrease in degrees of freedom within their postural control system, potentially resulting in the inhibition of smooth joint motion and constraining dynamic performance.42 Previous reports suggest that the observed postural instability is likely associated with the decline of sensory integration mechanisms, leading to a subsequent decrease in proprioceptive capacities.43 We hypothesize that these alterations in gait control, muscle activity, and proprioception may help to partially explain why obese individuals face higher risk of ankle–foot sprains. Our study only focuses on the causal relationship between BMI and ankle–foot sprains; however, future research could investigate the potential mechanisms underlying the link between higher BMI and ankle–foot sprains.

4.4. Clinical implications

Individuals with higher BMI and BFP may benefit from injury prevention programs that focus on improving joint stability, muscle strength, and proprioception, particularly around the ankle and lower limbs. Exercises that enhance muscle strength and flexibility in these areas could help mitigate the increased mechanical load associated with higher fat mass. However, since those groups of individuals are likelier to sustain ankle–foot sprains, proper cautions during physical exercise must be taken to avoid potential injuries. For athletes, fitness professionals, or individuals at high risk of ankle sprains (e.g., those with higher BFP), personalized fitness plans could be designed to reduce excess body fat and enhance lean mass. Reducing excess fat and increasing muscle mass can improve joint stability, thereby lowering the risk of injuries. For sports medicine clinicians, we recommend closely monitoring athletes' BMI and BFP. Physical training focusing on these aspects should be scheduled regularly. Dieticians’ recommendations should also be considered to help athletes maintain better physical conditions and reduce the risk of ankle–foot injuries. Our findings can also inform public health strategies aimed at reducing obesity and promoting physical activity. Encouraging weight management and improved physical fitness may not only reduce the risk of chronic diseases but also prevent acute injuries like ankle sprains.

4.5. Limitations

This study has limitations that must be acknowledged. First, we identified that higher BMI, BFP, HIP, and WC have positive causal effects on ankle–foot sprains; however, the exposure-response relationship remains unclear. Each of these BMI-related features has its own optimal range. As a result, the exposure-response relationship between these BMI-related traits and ankle–foot sprains should not be linear, necessitating more detailed data and observational studies to establish a more accurate effect-response relationship between these BMI-related features and ankle–foot sprains. Second, public GWAS data for BFP adjusted for BMI was not acquired, which would have provided us with more specific information about the effect of BFP on ankle–foot sprains. Third, although we tried our best to remove the biased SNPs and apply statistical analyses to follow the IV assumptions, the included SNPs might still be associated with unidentified confounders of the association between BMI-related features and ankle–foot sprains and induce residual horizontal pleiotropy. As a result, no complete absolution for the violation of the IV assumptions could be given, and additional research is required to validate our findings. Fourth, no sex or age difference was considered in the available public data, resulting in the limited reliability of our findings when applied to different sex and age groups. When GWAS data of a larger sample size and more detailed groups become available, these results should be replicated. Finally, only European participants were included in the study, which might limit the generalizability of our conclusions to non-European populations.

5. Perspective

This study represents a step forward in our understanding of the complex interplay between BMI-related traits and the incidence of ankle-foot sprains within the European ancestry population. Employing a robust two-sample MR methodology, we conducted an examination of the causal relationship between these traits and ankle-foot sprains, in contrast to prior observational studies with less conclusive results. Our research has yielded a solid causal link between elevated BMI, BFP, HIP, WC, and the heightened risk of ankle-foot sprains. Using data sourced from publicly available genetic consortia, our analysis not only corroborates these causal associations but also quantifies their significance. It unequivocally demonstrates that individuals with elevated BMI and BFP levels face a considerably greater risk of experiencing ankle-foot sprains, underlining the paramount importance of maintaining these traits within a healthy range for proactive injury prevention strategies. These findings resonate across the spectrum of healthcare, from clinical practice to public health policy. They serve as a clarion call for the development and implementation of targeted interventions, aimed at mitigating the burden of ankle-foot sprains, particularly within populations predisposed to higher BMI-related traits. However, it's crucial to acknowledge the inherent complexity of human physiology, and while our study shines a bright light on this critical association, further exploration through longitudinal studies is essential to corroborate our findings and delve deeper into the intricate mechanisms underpinning this relationship. In conclusion, our research not only validates the existence of a causal link but also emphasizes the pivotal role of maintaining healthy BMI-related traits in reducing the incidence of ankle-foot sprains. These insights are poised to revolutionize injury prevention strategies, fostering healthier outcomes and enhancing the overall well-being of individuals in the broader population.

CRediT authorship contribution statement

Xicheng Gu: Writing – review & editing, Writing – original draft, Software, Project administration, Methodology, Formal analysis, Data curation. Xiao'ao Xue: Supervision, Conceptualization. Yi Li: Software, Resources, Methodology. Ziyi Chen: Investigation, Funding acquisition. He Wang: Supervision. Yinghui Hua: Supervision.

Data statement

All data are publicly available, and the codes are available upon reasonable request corresponding to the senior authors. These datasets used in our study have already obtained ethical approval from their respective institutional review boards and have obtained informed consent from the participants. The report of this study followed the guidelines of Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization.

Ethical approval statement

The datasets used in our study have already obtained ethical approval from their respective institutional review boards and have obtained informed consent from the participants. The report of this study followed the guidelines of Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization.

Declaration of competing interest

The authors declare that they have no competing interests.

Acknowledge statement

The results of the study are presented clearly, honestly, and without fabrication, falsification, or inappropriate data manipulation. Results of the present study do not constitute endorsement by ACSM.

Appreciation and thanks are given to Dr. Zixuan Cheng from Zhongshan Hospital, Fudan University for her advises on the code writing. The authors would also like to express their gratitude to Enago (https://enago.cn/) for the expert linguistic services provided.

This work was funded by the National Natural Science Foundation of China [No. 81871823, 8207090113, 82372492] and the Shanghai Science and Technology Committee (22dz1204702).

Footnotes

Appendix A

Supplementary data to this article can be found online at https://doi.org/10.1016/j.smhs.2025.01.001.

Contributor Information

He Wang, Email: hewang@fudan.edu.cn.

Yinghui Hua, Email: hua_cosm@aliyun.com.

Appendix A. Supplementary data

The following are the Supplementary data to this article:

Multimedia component 1
mmc1.xlsx (13.7KB, xlsx)
Multimedia component 2
mmc2.docx (3.6MB, docx)

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Multimedia component 1
mmc1.xlsx (13.7KB, xlsx)
Multimedia component 2
mmc2.docx (3.6MB, docx)

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