Osteoarthritis is a prevalent, chronic degenerative disease that afflicts joint tissues, including articular cartilage, subchondral bone, and synovium. This condition leads to pain and reduced functionality in the affected joint [43, 44]. Knee osteoarthritis is a progressive and multifactorial form of arthritis, accounting for approximately half of all osteoarthritis cases. Its prevalence increases with age and is closely linked to obesity. Among males over 60, the incidence of KOA ranges from 5–15%, while in females aged 60 and older, it's reported to be between 10% and 25%. KOA can cause joint discomfort, muscle weakness, physical limitations, and a decreased quality of life [45]. Risk factors for the development of knee pain and osteoarthritis include being overweight, obese, female gender, and a history of previous knee injuries [46]. Classical epidemiological studies face challenges in establishing a causal relationship between exposure factors and disease outcomes due to the influence of confounding variables. Despite these challenges, the causal connection between obesity and osteoarthritis remains uncertain. This study aims to investigate the association between BMI and the risk of KOA using a two-sample Mendelian randomization approach based on GWA. We utilized four different estimation techniques, including MR-Egger regression, the weighted median method, inverse-variance weighted method, and the weighted median method in our MR analyses. Ultimately, we identified 106 SNPs with significant associations with BMI, serving as instrumental variables. Using these methods and data from the KOA GWAS study, we established a causal link between increased BMI and a higher risk of KOA. The key takeaway from our findings is that they support the idea of weight management as an intervention strategy for both preventing and managing KOA. This offers a promising avenue for further research and intervention in the management of this debilitating condition.
Classical epidemiological studies often face significant challenges in establishing the causal link between exposure factors and disease outcomes due to the influence of confounding variables. The causal connection between obesity and osteoarthritis remains uncertain. This study aims to investigate the relationship between BMI and the risk of knee osteoarthritis through a two-sample Mendelian randomized study utilizing data from GWAS. We utilized four distinct estimation techniques, including MR-Egger regression, the Weighted Median method, the Inverse-Variance Weighted method, and the Weighted Median method, in our Mendelian randomization analyses. Ultimately, our analysis revealed 106 Single Nucleotide Polymorphisms (SNPs) with significant associations as instrumental variables for BMI. By applying these methods to data sourced from the KOA GWAS study, we established a causal relationship between increased BMI and a higher risk of KOA. Our primary conclusion from these findings is that it supports the implementation of weight management as an effective intervention strategy for both the prevention and management of KOA. Therefore, this provides a promising avenue for the management of osteoarthritis, given that obesity may indeed causally elevate the likelihood of developing or worsening this condition. Importantly, weight is a modifiable factor that is more readily controllable compared to other predisposing factors for KOA. The link between obesity and KOA has been well-documented in various studies, and our results align with previous observations. Moreover, we found that the severity of obesity is directly correlated with the clinical impact of KOA, showing a dose-response relationship as BMI increases. Consequently, treatment strategies for KOA should be personalized to address the degree of obesity [47]. BMI has a substantial impact on the likelihood of developing knee osteoarthritis in weight-bearing joints, while this influence is not observed in the case of hand osteoarthritis [48]. Furthermore, when obesity is assessed using BMI, it is associated with a decline in physical function, reduced levels of physical activity, and an increased level of disability over a span of six years. It's worth noting that obesity serves as a strong predictor of prolonged disability in individuals suffering from KOA [49]. Excessive body weight not only places additional stress on weight-bearing joints but also triggers misalignment and unfavorable joint mechanics, particularly in the knees. Consequently, this heightened mechanical stress and the subsequent degradation of cartilage play a pivotal role in the development of osteoarthritis [50–52]. Prior research has provided robust evidence to suggest that BMI might be causally linked to the risk of developing osteoarthritis, including insights from 79 Single Nucleotide Polymorphisms (SNPs) obtained from a Genome-Wide Association Study (GWAS) [53]. Our study's findings are in line with these aforementioned studies, underscoring the significance of the relationship between BMI and osteoarthritis risk. Importantly, our research stands out due to its use of updated data and a more extensive sample size, further strengthening the credibility of our results.
Furthermore, we employed more rigorous investigative methods, including a low heterogeneity suggesting increased reliability of MR estimates and the funnel plot exhibiting no indication of asymmetry. In contrast to patients with a normal BMI, high BMI patients exhibited a heightened susceptibility to perioperative and postoperative complications, highlighting a distinct contrast in complication rates between normal and high BMI groups undergoing total knee arthroplasty [54]. Obesity exerted an adverse impact on the outcomes of patients undergoing total knee arthroplasty, leading to a higher incidence of short-term complications and diminished long-term results when compared to non-obese individuals [55–57]. Considering both our findings and the previous studies’ results, it indicates that preventing weight gain is crucial for knee osteoarthritis [58]. Osteoarthritis is a complex and multifactorial condition that affects the entire joint. OA), which is characterized as a chronic and progressive condition that is closely linked to the activation of various pro-inflammatory pathways. Mechanical injuries play a pivotal role as the primary risk factor in the development of OA. These injuries can induce the release of growth factors from the joint matrix, triggering repair mechanisms. However, they can also initiate inflammatory signaling pathways, a phenomenon referred to as "mechanoflammation."[59]
The worldwide prevalence of overweight and obesity has been steadily increasing, underscoring a substantial and urgent public health concern that has unfolded over the past two decades [60]. Obesity has been widely acknowledged as a primary and modifiable risk factor for osteoarthritis, exerting a multitude of effects on the initiation, progression, and severity of symptoms associated with this condition. Notably, obesity emerges as the foremost preventable risk factor that significantly contributes to the development of knee osteoarthritis. The impact of obesity on osteoarthritis has garnered significant attention from researchers and medical professionals. Among the various mechanisms at play, joint mechanical load is widely recognized as a critical factor that either initiates or exacerbates the progression of the disease. There is a well-established positive correlation between increased body mass and the greater loading experienced by the joints in the lower extremities [61]. Obesity-induced osteoarthritis represents a recently characterized phenotypic category in which chronic low-grade inflammation plays a central role. In addition to this persistent inflammatory state, abnormal mechanical loading resulting from increased body weight, especially on weight-bearing joints, is responsible for both the onset and progression of obesity-induced osteoarthritis [62]. Although chronic inflammation, adipocytokines, and metabolic factors are acknowledged as significant contributors to obesity-related osteoarthritis, there has been relatively limited research on the biomechanical effects of obesity on the development of the disease.
Obese individuals often experience knee joint malalignment and hyperextension, which further contribute to the development of osteoarthritis in the knees. Obesity stands out as one of the most significant and controllable risk factors, with far-reaching consequences. It not only amplifies the mechanical stress placed on the tibiofemoral cartilage but also increases the likelihood of osteoararthritis occurring in non-weight-bearing areas. Furthermore, a noteworthy connection exists between obesity and inflammation [63]. Overweight, typically defined as having a BMI greater than 25 kg/m², and obesity, characterized by a BMI exceeding 30 kg/m², are widely recognized as prominent risk factors not only for the onset of osteoarthritis (OA) in a general context but also for knee OA in particular. Additionally, individuals with obesity often exhibit increased thigh girth. To compensate for this, they tend to adopt greater hip abduction and varus malalignment of the knee during walking, with the aim of preventing contact between their thighs. However, this altered alignment reduces the contact area through which mechanical stress is distributed, leading to a preferential load and subsequent damage to the medial aspect of the articular cartilage [64]. Furthermore, the coexistence of obesity and sarcopenia, a condition frequently observed in older individuals due to physical dysfunction, constitutes an independent risk factor for the onset and progression of knee osteoarthritis [65]. Individuals and animals with obesity exhibit elevated levels of pro-inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α), transforming growth factor β1 (TGFβ1), interleukin-1 beta (IL-1β), IL-6, and others, in their bloodstream. These cytokines are primarily produced by macrophages originating from adipose tissue. They play a crucial role in regulating various metabolic processes, including the proliferation and apoptosis of adipocytes, facilitating lipolysis, inhibiting lipid synthesis, and reducing blood lipid levels. These effects are mediated through autocrine and paracrine mechanisms [66, 67]. Elevated concentrations of serum leptin, OPN (osteopontin), SOST (sclerostin), and resistin were noted in individuals with knee osteoarthritis, and these biomarkers displayed a positive correlation among themselves. These observations present a promising avenue for uncovering novel targets that could be harnessed in medical interventions aimed at managing KOA [68–71]. Numerous molecules have exhibited associations with inflammation, a process believed to initiate within the joints in the context of osteoarthritis. Furthermore, various research investigations have delved into the genetic predisposition to OA, exploring the role of specific genes in this context. Moreover, Furthermore, it is widely accepted that genetics play a substantial role, contributing to approximately 40% of knee osteoarthritis cases [72]. A comprehensive genome-wide association study (GWAS) was executed to identify susceptibility risk loci for osteoarthritis of both the hip and the knee, utilizing samples from Iceland and the UK Biobank [73]. Numerous single nucleotide polymorphisms (SNPs) within genes, including the T-1486C SNP in TLR9, and TLR7 and TLR8 SNPs, have emerged as potential genetic associations with knee osteoarthritis. Understanding these potential mechanisms is crucial for a more profound comprehension of the involvement of macrophages in obesity-related osteoarthritis. Changes in the phenotype of transgenic mice, engineered to manipulate Toll-like receptor (TLR) pathways, provide supportive evidence for the delicate balance between pro-inflammatory and anti-inflammatory signaling pathways activated by damage-associated molecular patterns (DAMPs) in the context of OA. Clinical findings also underscore the participation of Toll-like receptors (TLRs) in OA pathogenesis, encompassing genetic variations and surrogate markers indicative of disease activity [74, 75]. A heightened prevalence of the S allele and SS genotype of the rs2070600 polymorphism has been observed in patients with knee osteoarthritis, with the SS genotype in individuals with obesity further escalating the risk of developing OA [76]. Furthermore, the combination of rs419598, rs315943, rs1799750, and rs9005 haplotypes has been associated with the radiological progression of knee osteoarthritis [77, 78]. The IL-6 rs1800795, IL-18 rs1946518, and MMP-13 rs2252070 mutations have also been linked to an increased susceptibility to knee osteoarthritis [79, 80]. However, several comprehensive meta-analyses have shown no significant association between the variants rs1143634, rs16944, rs419598, rs1799750, and knee osteoarthritis [81–83]. Establishing a causal relationship between BMI and osteoarthritis presents a challenge in traditional observational studies due to the potential influence of confounding variables and the risk of reverse causality. The Mendelian Randomization approach emerges as a promising tool for addressing these challenges. By employing genetic variations as instrumental variables for exposure factors, MR allows researchers to infer causal relationships between these factors and outcomes, thus minimizing biases inherent in observational studies. Several studies have employed the Mendelian Randomization method to investigate the association between BMI and osteoarthritis. Our research outcomes align with those of previous studies, yet our study distinguishes itself through several noteworthy aspects. Firstly, we utilized the most recent data and a considerably larger sample size for knee osteoarthritis patients who underwent surgery. Moreover, our investigative methods were characterized by a higher level of rigor and increased reliability [84, 48]. However, it's crucial to acknowledge that MR studies are susceptible to bias arising from pleiotropy, a situation in which genetic variants are associated with multiple variables simultaneously [85]. Addressing these complexities is essential to ensure the accuracy and validity of our findings.
To address the potential issue of pleiotropy, we employed a weighted median estimator, a robust method that provides reliable estimates even when up to 50% of the Single Nucleotide Polymorphisms used as instruments may not be entirely valid. Additionally, we implemented MR-Egger regression, which serves a dual purpose by not only assessing the presence of unbalanced pleiotropy but also offering a causal estimate of the effect of exposure on the outcome in cases where such pleiotropy is detected [86–88].
However, while our study contributes valuable insights, the precise underlying cause of the causal relationship between BMI and osteoarthritis remains incompletely understood. Previous research has suggested that individuals with higher BMI may experience increased joint loading, potentially expediting the aging process of the articular surface. This phenomenon could, in turn, play a role in the onset and progression of osteoarthritis [89]. Furthermore, obesity has the potential to incite inflammation through the elevation of intermediates generated by lipid metabolism. This inflammatory process may contribute to the emergence of joint symptoms and structural alterations in knee osteoarthritis. Notably, these components exhibit distinct associations with the aforementioned symptoms and changes, underscoring the intricate interplay between inflammation and metabolism in the development and progression of KOA [90, 91]. This complexity warrants further investigation for a comprehensive understanding of the mechanisms at play. Nonetheless, there remain various facets of the adipokine network, especially the intricate interactions among inflammatory pathways, mechanical influences, and metabolic processes within the context of cartilage and bone disorders, which continue to elude comprehensive understanding. Without a doubt, delving deeper into the complex mechanisms governing the actions of peripheral and central adipokines could prove immensely advantageous in the quest to develop future treatments for osteoarthritis. It is crucial to acknowledge certain limitations when interpreting our findings. The validation of genetic polymorphisms can be a challenging endeavor, and despite our utilization of the MR-Egger method, the potential for misclassification in genetic polymorphisms cannot be entirely eradicated, and definitive exclusion of such misclassification remains elusive. Furthermore, the GWAS dataset for BMI employed in this study encompassed a diverse population, whereas the data pertaining to knee osteoarthritis originated from a European population. This divergence in population stratification could introduce bias, and the direct extrapolation of our findings to other populations remains uncertain, underscoring the need for further research in this regard. It is also noteworthy that sex and age are associated with variations in obesity and body composition. Consequently, women often exhibit a higher percentage of body fat compared to men with the same BMI. Over-identification in the two-sample Mendelian randomization (MR) study is conceivable, and it has the potential to magnify the relationship between SNP and exposure [92]. These complexities underscore the need for cautious interpretation and the consideration of potential confounding factors in our analyses.
Nonetheless, this meta-analysis has several notable strengths. One of its key advantages is the utilization of the Mendelian randomization design to examine the relationship between BMI and knee osteoarthritis. This approach effectively mitigates the influence of residual confounding and minimizes the potential for reverse causation bias. Importantly, it provides estimates for both the gene exposure and gene-outcome associations for various genetic variants. Moreover, the study drew upon the most current data obtained from published Genome-Wide Association Studies (GWAS) research and GWAS meta-analyses. This encompassed a substantial sample size and a diverse array of genetic variations, lending robustness to the findings. Given that this study focused on knee osteoarthritis patients undergoing surgery, it becomes imperative to explore whether weight reduction can ameliorate their condition, potentially reducing the necessity for surgical interventions. A meta-analysis indicated that achieving a significant reduction in knee pain, joint stiffness, and improved physical function necessitates a substantial weight loss. Specifically, a 25% reduction from the initial weight is deemed essential to attain a 50% decrease in each subscale of the Western Ontario and McMaster Universities Osteoarthritis (WOMAC) score. Various interventions have demonstrated effectiveness in reducing knee pain, including bariatric surgery, low-calorie diets, and exercise, particularly when integrated into intensive programs that combine weight loss and exercise. Among these methods, bariatric surgery stands out as the most effective, while physical exercise plays a critical role in maintaining lean body mass and preventing sarcopenia [93]. It's important to recognize that osteoarthritis remains an incurable condition, and its underlying causes are not fully elucidated. Consequently, the most effective strategy in addressing knee osteoarthritis is prevention. The primary goal of treatment is to alleviate clinical symptoms, and the reduction of body weight emerges as a significant avenue for KOA management. Furthermore, considering that many KOA patients are not obese, there is room for further investigation into the causality of BMI in relation to KOA.