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Unraveling the genetic association between knee osteoarthritis and hallux deformities

Journal of Orthopaedic Surgery and Research volume 19, Article number: 608 (2024) Cite this article

Abstract

Objective

Knee osteoarthritis (KOA), hallux valgus (HV) and hallux rigidus (HR) are common musculoskeletal problems of the lower extremities. However, their underlying causal relationships are unclear. This study attempts to clarify the cause-and-effect relationship between KOA and the two common hallux deformities (HV and HR).

Design

The summary-level statistics for KOA, HV, and HR were collected from genome-wide association studies (GWAS). The causal analysis of KOA on HV or HR was carried out using two-sample Mendelian randomization (MR). In order to assess the robustness of the MR results, sensitivity analyses were performed. In addition, multivariable MR (MVMR) was implemented to assess the influence of KOA in causation as well as calibrate the effect of anthropometric characteristics. Supplementary backward MR analysis was conducted to determine the causal effect of hallux diseases on KOA.

Results

The univariable analysis indicated that KOA has a causative influence on HR (odds ratio [OR] = 1.29, 95% confidence interval [CI] = 1.18–1.41, P = 2.25E-8) and HV (OR = 1.43, 95% CI = 1.21–1.68, P = 2.76E-5). In the backward MR analyses, hallux deformities did not appear to be the cause of KOA. In the MVMR analysis, after jointly adjusting for the effects of waist-to-hip ratio (WHR), waist circumference (WC), hip circumference (HC) and BMI, the causal impact of KOA on HV and HR remained robust.

Conclusion

In this study, the genetic causality between KOA and increased risk of hallux deformities (HV and HR) is established, which can provide evidence-based recommendations for reducing the incidence of hallux deformities in KOA patients. Further high-level studies are warranted to validate the associations and explore its broader implications.

Introduction

Osteoarthritis, also referred to as degenerative osteoarthropathy, encompasses a collection of persistent degenerative musculoskeletal disorders affecting the cartilage and its adjacent tissues. The principal clinical presentations of this condition include pain, tenderness, stiffness, joint swelling, and restricted movement, which arise from the deterioration and depletion of articular cartilage, reactive hyperplasia of subchondral bone, osteophyte development, ligamentous laxity, and weakening of periarticular muscles [1]. Osteoarthritis can occur in any joint, with knee osteoarthritis (KOA) being the most prevalent form, affecting about 18% of individuals aged 45 and older [2]. As the disease progresses, patients are ultimately treated with total joint arthroplasty to relieve pain and restore joint function. The etiology of KOA is complex, involving traumatic injury [3], genetics [4], obesity [5], and poor joint biomechanics [6] et., among which biomechanical factor has been recognized as a central role in disease progression by analyzing gait.

Throughout the development of KOA, biomechanical alterations occur not limited in the local joint itself, but also in other joints of lower extremity alignment. The foot plays a crucial role in receiving and distributing forces, therefore the potential association between KOA and foot characteristics cannot be ignored. Ideally, feet should display a neutral position in relaxed stance, without excessive supination or pronation. In patients with KOA, a biomechanical link has been established between changed frontal plane knee alignment and altered foot posture [7]. Furthermore, Reilly et al. reported that individuals with osteoarthritis in the middle part of the knee exhibit dorsiflexion and foot posture that fall outside the typical range [8]. In spite of the fact that foot postures are not in pathological condition, they may influence the way people perform everyday tasks in subtle ways. According to recent studies, individuals with medial compartment knee OA tend to exhibit a pronated foot position and show reduced mobile, everted foot kinematics than controls [9,10,11]. The changing foot posture or function can be considered as protective mechanisms of KOA patients. In conservative management, the efficacy of footwear and orthotics in reducing the medial knee loading had been validated, which is based on the principle of laterally shifting the center of pressure by pronating the foot through lateral inclination of the insole [12].

Hallux valgus (HV) and hallux rigidus (HR) are the two most common types of Hallux deformities that affect the joint between the first metatarsal and the proximal phalanx. Although these conditions involve the same joint, they exhibit distinct pathological characteristics. Hallux valgus entails an abnormality in the first metatarsophalangeal joint, resulting in the big toe drifting towards the outside of the foot along with partial dislocation of the joint. Differently, hallux rigidus is attributed to restricted flexion and extension movements (sagittal plane), resulting in joint degeneration. Prior studies have demonstrated the relationship between both hallux deformities and KOA [13]. Within the population of individuals with KOA, there is a notable escalation in the occurrence of HV (OR: 1.71, P < 0.05) or HR (OR: 1.73, P < 0.05), and a significant association has been established between the seriousness of hallux valgus and the existence of KOA [14]. In theory, the relationship between KOA and hallux deformities could be a reciprocal causation. Due to the rotational connection between the heel and tibia, genu varum walking pattern causes tibial inversion leading to subtalar pronation, which ultimately raising the likelihood of developing hallux valgus and hallux rigidus [15]. Conversely, ankle anterior rotation induced by the hallux deformities pain in turn results in elevated dynamic knee loading on inner part of of the knee, ultimately leading to a higher risk of OA [16]. However, there are no randomized clinical trials or prospective studies to date that confirm the causal association between KOA and hallux deformities. Thus, we cannot definitively conclude whether the causal relationship is KOA to hallux deformities or hallux deformities to knee degeneration.

The Mendelian randomization method (MR), akin to a prospective randomized controlled trial, has predominantly been employed in epidemiological investigations concerning etiology. Within MR investigations, exposure is considered an intermediate trait, determined by genotype, and genetic variations (usually single-nucleotide polymorphisms) acts as instrumental variables (IV) to explore connections between genotype and disease, thereby simulating the relationship between exposure and disease [17]. In this particular model, the occurrence of single nucleotide polymorphisms (SNPs) during gamete development is subject to random variation and remains unaffected by confounding factors subsequent to gametogenesis. Given the absence of influence on SNPs by the outcome, the potential for reverse causality effects can be eliminated [18]. Consequently, the utilization of genetic variants strongly linked to exposure enables the investigation of causality with respect to the outcome while accounting for confounding factors and reverse causality [19]. In this study, we introduced bidirectional two-sample MR to explore the potential cause-and-effect relationship between HV or HR and KOA. A multivariable MR (MVMR) model was also used to correct the influence from anthropometric parameters.

Methods

Overall study design

The main objective in conducting two-sample Mendelian randomization analysis was to explore the potential bidirectional causal connection between knee osteoarthritis (KOA) and hallux valgus (HV) or hallux rigidus (HR). In order to establish credible causal inference, the genetic instruments chosen needed to meet the three crucial assumptions of instrumental variable analysis: (i) the SNPs chosen as tools were highly linked to the exposure (relevance assumption); (ii) these instruments were not influenced by confounders of the exposure-outcome relationship (independence assumption); (iii) the SNPs used as tools impacted the outcome only through the exposure and not through other routes (exclusion restriction assumption) [20,21,22]. In the forward MR analysis, the two-sample MR analysis was performed to investigate the causal association between knee osteoarthritis (KOA) and different foot conditions. Subsequently, a multi-variable MR (MVMR) was performed, incorporating anthropometric factors, to ascertain the direct impact of KOA on each foot disorder. In the backward MR analysis, the causal relationship of each foot disorder on KOA was examined using a similar approach. It is important to note that all data utilized in the MR analysis were sourced from publicly available data, eliminating the requirement for further ethical approval.

Data source for KOA

Genetic tools for KOA were derived from a comprehensive GWAS meta-analysis incorporating raw information from the UK Biobank (UKB) [23], Arthritis Research UK OA Genetics (arcOGEN) [24], and the United Kingdom Household Longitudinal Study (UKHLS) [25]. The UKB dataset included both self-reported and hospital-diagnosed cases of OA, while the arcOGEN study identified OA cases based on clinical evidence necessitating total joint arthroplasty or radiographic evidence (Kellgren-Lawrence grade ≥ 2). The UK Household Longitudinal Study (UKHLS) was utilized as a representative control group for the arcOGEN cases. Subsequently, a meta-analysis approach under a fixed-effect model was employed to combine the effect sizes from the UKB and arcOGEN datasets. In total, the GWAS meta-analysis (GWAS ID: ebi-a-GCST007090) included 24,955 patients with KOA and 378,169 healthy controls.

Data source for HV and HR

Instruments for assessing HV and HR were sourced from the fifth iteration of biobank examination within the FinnGen group. As a result, there was minimal to no overlap between the dataset pertaining to foot disorders and that related to KOA [26]. The dataset for HV consisted of 8,536 patients with HV and 147,221 healthy participants without a history of arthropathy (GWAS ID: finn-b-M13_HALLUXVALGUS). HV was diagnosed according to the criteria specified in the 10th edition of the International Classification of Diseases (ICD-10) with code M20.1, as well as ICD-9 codes 7350/7271 and ICD-8 codes 737/73,099. The dataset for HR comprised a total of 2,453 patients with HR and 147,221 healthy individuals (GWAS ID: finn-b-M13_HALLUXRIGIDUS). The diagnosis of HR was determined based on the ICD-10 code M20.2, ICD-9 code 7352, or ICD-8 code 73,804. Age, sex, genotyping batch, and 10 principal components were accounted for in the GWAS analysis.

Data source for anthropometric factors as potential confounders

Excessive weight-bearing has been posited as an extrinsic factor in the development of HV [27, 28]. As such, a series of anthropometric factors, which may serve as proxies for excessive weight-bearing, were included as confounders in the MVMR analysis. The genetic instruments for BMI were sourced from the studies conducted by Genetic Investigation of Anthropometric Traits (GIANT) consortium [29], which exhibited minimal to no overlap with the UK Biobank (UKB) or FinnGen cohorts. The GIANT consortium studies encompassed a cohort of 322,154 individuals of European descent (GWAS ID: ieu-a-835). Additionally, we incorporated measurements of waist-to-hip ratio (WHR) adjusted for BMI (GWAS ID: ieu-a-79), waist circumference (WC) after adjusting BMI (GWAS ID: ieu-a-67), and hip circumference (HC) adjusted for BMI (GWAS ID: ieu-a-55) in the MVMR analysis.

Instruments selection

SNPs were selected as instrumental variables for each characteristic using a significance threshold of P < 5.0 × 10− 6, and subsequently clustered (r2 < 0.001, clump window of 10,000 kb) to eliminate linkage disequilibrium (LD). LD was determined using the reference panel from the European-based 1,000 Genome Projects. Due to the small number of SNPs (10 for KOA and 8 for HV) found at the genome-wide significance level of P < 5.0 × 10− 8, it was decided to use a more lenient threshold. This approach of relaxing the threshold has been employed in prior MR studies in instances where only a small number of SNPs were identified [30, 31]. The F-statistic was determined for each chosen SNP by utilizing the designated estimation technique F-statistic=(N-2)×R2/(1-R2), and R2 was used to indicate the phenotypic variance associated with each SNP [32]. To reduce weak instrument bias, SNPs with an F-statistic less than 10 were removed [33]. Prior to conducting the MR analysis, potentially suitable instruments were identified through screening with PhenoScanner V2 to eliminate SNPs associated with confounder at a significance level of P < 5.0 × 10− 4 [34, 35]. Excluding palindromic SNPs with moderate allele frequencies, proxies were not used when SNPs were absent in the outcome dataset.

Statistical analysis

We conducted a series of MR analyses using R software (version 4.2.2) and specific packages, including TwoSampleMR (0.5.5) [36], Mendelian Randomization (0.5.0) [37], and MR-PRESSO package [38]. The results are presented as odds ratios (ORs) with 95% confidence intervals (95% CI), indicating the likelihood of developing the foot disorders with each unit increase in exposure.

Based on the principle that genetic variants (SNPs) are randomly assigned during meiosis, similar to a randomized controlled trial, MR uses SNPs associated with exposures as instrumental variables to infer causal relationships between exposures and outcomes. In this study, we conducted a bi-directional MR analysis to explored the potential causal relationships in both directions: from KOA to hallux deformities (HV and HR) as forward MR, and from hallux deformities to KOA as backward MR. This approach allowed us to determine whether there is a potential reciprocal influence between these conditions, providing a more comprehensive understanding of their interrelationship.

In implementation of MR analysis, we firstly conducted Univariable Mendelian Randomization (UVMR) which was the standard MR approach to examines the causal relationship between a single exposure and an outcome. In UVMR analysis, the inverse variance-weighted (IVW) approach was our primary approach, as it aggregated the causal estimates from individual genetic variants (SNPs) by Wald ratio approach to provide a more precise overall estimate [20, 21]. Heterogeneity and pleiotropy may affect the validity of IVW approach. To assess validity and robustness of MRE-IVW findings, we firstly performed assessment of heterogeneity in eligible SNPs through Cochrane’s Q-statistics, with significance set at P < 0.05 to indicate substantial heterogeneity [39, 40]. To address potential pleiotropy, we performed two additional MR analyses using the weighted median (WM) method and MR-Egger regression with [39]. The WM method estimates the causal effect by taking the weighted median of the individual SNP estimates, assuming that at least half of the SNPs are valid [39]. MR-Egger regression, which includes an intercept, allows us to detect and adjust for directional pleiotropy [32]. A non-zero intercept in MR-Egger suggests the presence of directional pleiotropy, and the unbiased causal effect is indicated by the slope of MR-Egger regression with potential presence of pleiotropy and heterogeneity. Moreover, we performed two sensitivity analyses to test the influence of potential outlier SNPs, namely MR pleiotropy residual sum and outlier (MR-PRESSO) and a leave-one-out analysis. In MR-PRESSO, outlier test was performed for each eligible SNP using a threshold of P value at 0.05, an outlier-correct MR-PRESSO was further conducted if any significant outlier SNP was detected [38]. Additionally, a leave-one-out analysis was performed, where each SNP was removed one at a time to assess its impact on the overall causal estimate.

Given the potential genetic associations between knee osteoarthritis (KOA) and other traits that may influence hallux valgus (HV) and hallux rigidus (HR), we further conducted multivariable Mendelian randomization (MVMR) analysis which was an extension of UVMR that allows for the simultaneous assessment of multiple exposures on an outcome [41]. It’s particularly useful for estimate of direct and independent causal effect when exposures are correlated or when there’s potential for horizontal pleiotropy (when genetic variants affect the outcome through pathways other than the exposure of interest). In the MVMR, we included WHR, WC, HC and BMI as potential confounders. We ensured that the SNPs used for each exposure were independent by clustering them based on linkage disequilibrium (r2 < 0.001, clump window of 10,000 kb).

Results

In the forward MR analysis, a total of 72 SNPs were identified as instrumental variables for KOA (Supplementary Table 1). These SNPs collectively accounted for approximately 7.9% of the phenotypic variance, with an average F-statistic of 445.6, suggesting a minimal risk of bias from weak instruments. The results of UVMR analysis were presented in Table 1. The examination of the connection between KOA and HV indicated a favorable association according to the IVW method (OR: 1.29, 95%CI 1.18, 1.41; P = 2.25E-8), with further support from the WM method (OR: 1.33, 95%CI 1.17, 1.51; P = 1.47E-5). Despite the lack of statistical significance, the MR-Egger method produced a direction of association consistent with the IVW and WM method (OR: 1.18, 95%CI 0.88, 1.57; P = 0.26). No outliers were detected in the MR-PRESSO analysis, and there was no notable variation among the included SNPs when applying either the MR-Egger or IVW method. The Egger-intercept was near zero, indicating the absence of significant directional pleiotropy (intercept = 0.0055, P = 0.53). The scatter plots, depicted in Fig. 1, displayed the slope of each fitted line representing the pooled causal estimate from each MR method.

Table 1 Two-sample univariable MR results of the causal effect between KOA and HV or HR
Full size table
Fig. 1
figure 1

MR plots of cause-and-effect inference for KOA on HV. (A) The scatter plot of SNPs associated with KOA and their risk on HV. Each fitted line’s slope indicates the total B-weights (unstandardized slope estimates) from all MR analysis methods. (B) Forest plot of SNPs associated with KOA and pooled MR B-weights (unstandardized slope estimates) for KOA on HV. The B-weights and 95%CI are represented by each dot and its corresponding line. (C) The leave-one-out plots of KOA on HV. The pooled B-weights, which are unstandardized slope estimates, are represented by each dot and its corresponding line after removing the corresponding SNP

Full size image

In the context of KOA and its impact on HR, the findings indicate a positive causal relationship as determined by both the IVW method (OR: 1.43, 95%CI 1.21, 1.68; P = 2.76E-5) and the WM method (OR: 1.45, 95%CI 1.15, 1.82; P = 0.0017). Analysis using the MR-Egger method revealed a negligible intercept (intercept = 0.016, P = 0.74), suggesting no significant pleiotropy in the study. There was no apparent heterogeneity detected by either the MR-Egger method (P = 0.076) or the IVW method (P = 0.086). Furthermore, no outlier SNPs were detected in the MR-PRESSO analysis (Fig. 2).

Fig. 2
figure 2

MR plots of cause-and-effect inference for KOA on HR. (A) The scatter plot of SNPs associated with KOA and their risk on HR. Each fitted line’s slope indicates the total B-weights (unstandardized slope estimates) from all MR analysis methods. (B) Forest plot of SNPs associated with KOA and pooled MR B-weights (unstandardized slope estimates) for KOA on HR. The B-weights and 95%CI are represented by each dot and its corresponding line. (C) The leave-one-out plots of KOA on HR. The pooled B-weights, which are unstandardized slope estimates, are represented by each dot and its corresponding line after removing the corresponding SNP

Full size image

Given the established causal relationship of KOA on HV and HR, a MVMR was conducted to assess the direct impact of KOA while accounting for anthropometric variables. After adjusting for BMI, HC, WC, and WHR collectively, KOA was found to have an independent association with HV, exhibiting a stronger causal effect compared to UVMR analysis (OR: 1.70, 95%CI 1.48, 1.95; P = 5.98E-14). None of the included anthropometric factors showed significant associations with HV in the presence of KOA (Table 2). The results of MVMR analysis also demonstrated a significant independent association with HR, with a comparable magnitude of effect to that observed in UVMR analysis (OR: 1.43, 95%CI 1.21, 1.68; P = 2.76E-5).

Table 2 Multivariable MR results of KOA on HV and HR
Full size table

A combined 38 SNPs were discovered in the backward MR assessments for predicting HV, while 20 SNPs were found for predicting HR, all with an F-statistic greater than 10 (Supplementary Tables 2 and 3). The IVW method did not reveal significant associations between HV (odds ratio [OR]: 1.03, 95% confidence interval [CI] 0.99, 1.07; P = 0.11) or HR (OR: 1.00, 95% CI 0.99, 1.02; P = 0.64) and KOA. These non-significant findings were further confirmed through the MR-Egger method and WM method (Supplementary Figs. 1, 2).

Discussion

KOA, HV and HR are common musculoskeletal disorders of the lower extremities. All three disorders are prevalent in older women, but whether there is a causal association between these diseases is not clear. In current study, we delved into the causal associations between osteoarthritis and hallux deformities by using MR approaches, and our data demonstrated that genetically predicted KOA were associated to increased risk of HV and HR. The reverse MR analysis did not show any indication that liability to HV and HR was related to KOA. Furthermore, the direct causal correlation between KOA and HV or HR was proposed when adjusted by BMI, HC, WC, and WHR jointly, suggesting that KOA could be a standalone risk factor for the onset of HV and HR. Our study presents novel evidence that KOA, independent of BMI, HC, WC, and WHR jointly, is the predominant factor correlated with HV and HR. These findings on causal relationship between KOA and HV or HR offer fresh perspectives on KOA and foot musculoskeletal disorders.

As one of the most prevalent degenerative joint disorders, KOA predominantly affects the medial tibiofemoral compartment, which reflects overall biomechanical changing in the lower extremity during locomotor activities. In lower extremity, the foot plays an important role in absorbing and distributing forces while walking, thus observation on the changing foot characteristics and mechanics in KOA is growing. HV and HR are both common forefoot deformities that affect the first metatarsophalangeal joint. Epidemiologic studies have shown that age, gender, and obesity are all factors in the onset of HV and HR, mirroring the epidemiological characteristics of KOA [14, 42, 43]. Speculation has arisen regarding a potential direct causal relationship between KOA and HV or HR due to shared epidemiologic characteristics and the coordinating function of the knee and foot during walking. In the past decades, many scholars have reported the association between KOA and hallux deformities. In 2009, Hayal reported that the coexisting foot deformity (pes planus and HV) exacerbate knee functional impairment among patients with osteoarthritis and a significant correlation existed between hallux valgus angle and WOMAC scores (r = 0.362, P < 0.001) [44]. Soon after, Hylton et al. demonstrated that the prevalence of HV was associated with pain in knee [45]. In 2013, Akinobu and his colleagues investigated the prevalence of HV in a rural Japanese community and found that the total occurrence of confirmed radiographic HV was 22.8% out of 806 cases, and KOA was found to be significantly linked to a higher risk of HV (OR = 1.71, P = 0.028) [14]. In addition, Akinobu et al. has since discovered that grip strength and maximum walking speed were negatively affected by severe HV deformity (HV angle ≥ 30 degrees), and painful HV reduced usual and maximum walking speeds irrespective of the presence or absence of KOA through further study in 2018 [16]. Compared to HV, studies on KOA-HR association are limited. Recently, the study, also from the team of Akinobu, proposed that KOA is an independent risk factor for HR, with KOA having odds ratios of 1.73 for HR (P < 0.05) [14]. Although the above studies revealed an association between KOA and hallux deformities, including HR and HV, they were all based on cross-sectional designs. The inherent issues of reverse causality and residual confounding in such studies made it impossible to establish causality. In addition, the studies were primarily conducted in Asian communities with relatively small sample size, thus the generalizability of their findings was limited. Hence, our study contributed greatly to this topic by showing the causality between KOA and hallux deformities.

In current study, we verified that KOA is a contributing factor for HV and HR via multi-variable MR approach using the most recent genetic information from genome-wide association studies. Several possible reasons exist for the higher risk of KOA linked to HV. The main explanation mentioned is the biomechanical mechanism that is a high knee adduction moment [46]. Previous studies have confirmed that KOA patients exhibited higher external knee adduction moments (KAM) while walking in comparison to matched control subjects [47,48,49]. The increased external KAM resulted in outwardly shifted ground reaction force, which then reduced the ground reaction force lever arm [49]. Patients with KOA respond to this change by adopting a subtalar-pronation gait and it has been shown that a greater degree of foot pronation (internal tibial rotation, rearfoot eversion from the front plane, and forefoot inversion from the front plane) was associated with the reduced medial knee loading [50]. The relationship between foot pronation and HV is well documented in many studies, with excessive pronation identified as a primary factor contributing to HV [51,52,53]. In addition, accumulating evidence shows that TKA may alter foot kinematics and hindfoot alignment following knee realignment [7]. Changes in midfoot and hindfoot characteristics in turn ultimately influence forefoot morphology. However, due to the lack of relevant literature, this explanation still needs to be supported by further study. For HR, we start by confirming that HR and HV have different pathogenic profiles, that is HR occurs primarily as a result of joint degeneration due to restriction of normal flexion and extension movements (sagittal plane), unlike the lateral deviation of the big toe in HV [54]. The extent of varus misalignment can also impact the movement of the foot while walking, potentially resulting in a compensatory reaction to maintain normal foot function during ambulation [10]. Thus, we proposed that the pronation of foot also restricted normal movements of metatarsal. But equally limited by the lack of relevant studies, this conclusion still needs to be justified.

Several strengths are apparent in this study. Most notably, it is the first study to demonstrate a direct causal relationship between KOA and hallux deformities (HR and HV), highlighting the independent risk role of KOA in the development of HV and HR. Furthermore, in order to strengthen the validity of the causal estimates, a thorough and comprehensive analysis using bidirectional and multivariate MR techniques was conducted. Additionally, a range of sensitivity analyses were employed to ensure the stability and coherence of the causal association. Moreover, the utilization of a substantial number of SNPs as instrumental variables (IVs) further enhances the dependability of the conclusions.

Despite its strengths, this study does have some limitations. To begin with, it should be noted that the study participants exclusively consisted of individuals of European descent, potentially restricting the generalizability of the findings to different ethnic groups. In addition, Since HV and HR is predominantly female, there is insufficient data to stratify by sex and the KOA-associated joint deformities (knee valgus or knee varus) were also not reflected in the data. Furthermore, this study did not encompass other characteristics of foot, such as flatfoot and rearfoot eversion. Lastly, the lack of in-depth and exhaustive biological studies makes it difficult to explain our conclusions in more detail. Therefore, further high-level studies are warranted to validate the associations.

In summary, our present study, utilizing genetic epidemiology methods, provides evidence suggesting a causal relationship between KOA, HV, and HR. Individuals with KOA have a higher likelihood of developing HV and HR regardless of their anthropometric characteristics.

Data availability

No datasets were generated or analysed during the current study.

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Acknowledgements

Not applicable.

Funding

This work was supported by research grants from the National Natural Science Foundation of China (82202717, 82300994), Medical discipline Construction Project of Pudong Health Committee of Shanghai (PWYts2021-08), Science, Technology and Innovation Action Project of Shanghai Science and Technology Commission (23J11900400), Peak Disciplines (Type IV) of Institutions of Higher Learning in Shanghai, the Natural Science Foundation of Jiangxi Province (20232BAB216034), the China Postdoctoral Science Foundation (2023M741520), and the Young Research and Cultivation Fund of the First Affiliated Hospital of Nanchang University (YFYPY202141).

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  1. Zhengtao Lv and Mingchao Lin contributed equally to this work.

Authors and Affiliations

  1. Department of Orthopedics, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, People’s Republic of China

    Zhengtao Lv & Yaping Ye

  2. The Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People’s Republic of China

    Mingchao Lin, KuoYang Sun, Zunwen Lin & Junming Huang

  3. Department of Sports Medicine, Orthopedic Hospital, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People’s Republic of China

    Mingchao Lin, KuoYang Sun, Zunwen Lin & Junming Huang

  4. Clinical innovation & Research Center (CIRC), Shenzhen Hospital, Southern Medical University, Shenzhen, 518100, People’s Republic of China

    Jiaming Zhang

  5. Jiangxi Provincial Key Laboratory of Spine and Spinal Cord Diseases, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People’s Republic of China

    Zunwen Lin & Junming Huang

  6. Postdoctoral Innovation Practice Base, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, 330006, People’s Republic of China

    Zunwen Lin & Junming Huang

  7. Department of Joint Surgery, Shanghai East Hospital, School of Medicine, Tongji University, Shanghai, 200092, People’s Republic of China

    Feng Yin

  8. Shanghai Institute of Stem Cell Research and Clinical Translation, Shanghai, 200120, People’s Republic of China

    Feng Yin

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  1. Zhengtao Lv
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Contributions

Zhengtao Lv and Yaping Ye conceived and revised the study. Junming Huang and Jiaming Zhang performed the experiments. Zhengtao Lv, KuoYang Sun, Mingchao Lin, Feng Yin, and Zunwen Lin analyzed the data. Jun-ming Huang and Zhengtao Lv wrote the paper. Zhengtao Lv, Mingchao Lin and Yaping Ye revised the manuscript. Every author has participated in the completion of the ultimate draft and given their approval for the release of the final document.

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Correspondence to Junming Huang or Yaping Ye.

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13018_2024_5107_MOESM1_ESM.jpg

Supplementary Material 1: Supplementary Fig. 1. MR plots of cause-and-effect inference for HV on KOA. (A) The scatter plot of SNPs associated with HV and their risk on KOA. Each fitted line’s slope indicates the total B-weights (unstandardized slope estimates) from all MR analysis methods. (B) Forest plot of SNPs associated with HV and pooled MR B-weights (unstandardized slope estimates) for HV on KOA. The B-weights and 95%CI are represented by each dot and its corresponding line. (C) The leave-one-out plots of HV on KOA. The pooled B-weights, which are unstandardized slope estimates, are represented by each dot and its corresponding line after removing the corresponding SNP

13018_2024_5107_MOESM2_ESM.jpg

Supplementary Material 2: Supplementary Fig. 2. MR plots of cause-and-effect inference for HR on KOA. (A) The scatter plot of SNPs associated with HR and their risk on KOA. Each fitted line’s slope indicates the total B-weights (unstandardized slope estimates) from all MR analysis methods. (B) Forest plot of SNPs associated with HR and pooled MR B-weights (unstandardized slope estimates) for HR on KOA. The B-weights and 95%CI are represented by each dot and its corresponding line. (C) The leave-one-out plots of HR on KOA. The pooled B-weights, which are unstandardized slope estimates, are represented by each dot and its corresponding line after removing the corresponding SNP

Supplementary Material 3: Supplementary Table 1. Summary statistics for SNPs to instrument knee osteoarthritis

Supplementary Material 4: Supplementary Table 2. Summary statistics for SNPs to instrument hallux valgus

Supplementary Material 5: Supplementary Table 3. Summary statistics for SNPs to instrument hallux rigidus

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Lv, Z., Lin, M., Zhang, J. et al. Unraveling the genetic association between knee osteoarthritis and hallux deformities. J Orthop Surg Res 19, 608 (2024). https://doi.org/10.1186/s13018-024-05107-x

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Keywords

Journal of Orthopaedic Surgery and Research

ISSN: 1749-799X