Introduction
Abdominal obesity and type 2 diabetes (T2D) are among the most prevalent public health challenges today, serving as significant metabolic contributors and central components of metabolic syndrome.1 Over the last four decades, the global health landscape has transitioned from having twice as many underweight individuals as those classified as obese to a scenario where the number of obese individuals now exceeds the underweight population. This shift has positioned obesity as a critical global health issue associated with high prevalence and increased mortality rates.2 Recent estimates indicate that approximately 2 billion people worldwide are overweight or obese, accounting for 30% of the global population.3 Projections suggest that by 2030, nearly half of the adult population in the United States will be obese, with no state reporting an obesity rate below 35%, and some states nearing 60%.4
T2D is one of the most prevalent and severe chronic metabolic diseases worldwide. Shifts in living environments, lifestyles, and dietary habits have contributed to the rise in overweight and obesity, fueling the global T2D epidemic.5 As of 2021, more than 500 million individuals worldwide were living with diabetes, with a prevalence rate exceeding 10%, and diabetes-related healthcare costs estimated at $966 billion. By 2045, these figures are expected to increase to 783 million individuals with diabetes and expenditures surpassing $1.054 trillion, creating a substantial burden on both society and families.6
Fasting insulin (FI) serves as a key intermediary between obesity and T2D. Increased body fat content has long been recognized as a major pathogenic factor in insulin resistance. In the presence of obesity, insulin resistance and fasting hyperinsulinemia often coexist, even in the absence of hyperglycemia.7
Orthopedic diseases, such as cervical disc disorders, osteoporosis (OP), and rheumatoid arthritis (RA), are leading causes of disability globally, placing a significant economic burden on society. Cervical disc disorders, which are prevalent degenerative conditions, are primarily responsible for neck pain and nerve root-related discomfort.8 OP is a common metabolic disorder characterized by decreased bone mass and altered bone microarchitecture.9 RA is an autoimmune disease characterized by synovitis, cartilage damage, and bone erosion in peripheral joints.10 Identifying potential risk and protective factors for orthopedic diseases is crucial for their prevention, particularly those that can be controlled through interventions.
Population-based cross-sectional and cohort studies have demonstrated that abdominal obesity and T2D increase the risk of RA and cervical disc disorders, while T2D may enhance bone mineral density (BMD), acting as a protective factor against OP.11–13 However, discrepancies in the data persist.14 Clarifying the causal relationships between metabolic factors and orthopedic diseases is essential for providing valuable insights into clinical treatment. Previous evidence primarily stems from observational studies, and the complex pathogenic factors associated with abdominal obesity and T2D make these studies susceptible to confounding factors, leading to uncertain causal links between metabolic factors and orthopedic diseases. Unlike traditional observational studies, Mendelian randomization (MR) employs genetic variants as instrumental variables (IVs) to assess the association between exposures and outcomes. MR minimizes confounding factors by utilizing single nucleotide polymorphisms (SNPs) which are randomly assigned at conception and are not influenced by environmental or behavioral factors. This approach can effectively eliminate confounding factors and reverse causality, making MR a valuable tool for exploring the associations between complex diseases and their risk factors.15
Given the uncertainty regarding the causal impact of metabolic factors on orthopedic diseases, an MR design was applied to evaluate the potential causal relationships. This MR analysis explores the possible causal links between metabolic factors (abdominal obesity, FI, and T2D) and orthopedic conditions including cervical disc disorders, OP, and RA.
Materials and Methods
Study Design
This study utilized genome-wide association studies (GWAS) summary data from public databases for MR analysis to explore the causal relationships between metabolic factors (waist-hip ratio (WHR), FI, and T2D) and orthopedic diseases (OP, cervical disc disorders, and RA). Initially, a two-sample MR approach was utilized, incorporating various GWAS summary datasets to investigate the causal relationships between metabolic factors and the three orthopedic diseases in European populations. Subsequently, body mass index (BMI), as a measure of general obesity, was included as a potential confounder in multivariate Mendelian randomization (MVMR) to account for horizontal pleiotropy. To ensure the validity of the MR analysis, three key principles must be satisfied: (1) a strong correlation between the instrumental variable (IV) and the exposure factor; (2) the IV must be independent of any confounding factors; and (3) the IV should influence the specific disease solely through the exposure factor. This study adhered to the STROBE-MR guidelines to ensure that every step of the Mendelian randomization analysis was conducted appropriately. The specific study design process is illustrated in Figure 1.
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Figure 1 Diagrams illustrating associations examined in this study. Abbreviations: MR, Mendelian randomization; SNP, single nucleotide polymorphism; WHR, waist-hip ratio; FI, fasting insulin; T2D, type 2 diabetes; MVMR, Multivariate Mendelian randomization; BMI, body mass index.
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Data Sources
Data on BMI and WHR were derived from a large GWAS involving individuals of European ancestry.16 The study combined data from the Genetic Investigation of Anthropometric Traits (GIANT) consortium (n = 212,248) and the UK Biobank (UKB) (n = 485,486), performing a meta-analysis of genome-wide studies. In this GWAS, WHR represented abdominal fat content (abdominal obesity), and BMI served as an indicator of general obesity, comprising a total of 697,734 samples. Data on the FI metabolic phenotype were obtained from the Meta-Analyses of Glucose and Insulin-Related Traits Consortium (MAGIC), comprising up to 281,416 non-diabetic individuals of European ancestry, including 151,013 samples.17 T2D data were sourced from a large-scale GWAS meta-analysis, combining three European-ancestry datasets: the DIAbetes Genetics Replication and Meta-analysis (DIAGRAM), Genetic Epidemiology Research on Aging (GERA), and UKB.18 This dataset included 62,892 cases and 596,424 controls.
Outcome data were obtained from the latest whole-genome genetic datasets from the Finnish database R10 (https://r10.finngen.fi/), which integrates imputed genotype data generated from both new and legacy samples collected from Finnish biobanks and national health registries dating back to 1969. These data, derived from Finnish residents of European ancestry, included outcomes for OP (8,017 cases and 391,037 controls), cervical disc disorders (14,670 cases and 294,770 controls), and RA (13,621 cases and 262,844 controls) (Table 1).
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Table 1 Characteristics of the GWAS Summary Data
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Selection of Instrumental Variables
For WHR and FI, the IVs reported in the original literature were directly utilized. This included 316 WHR-related SNPs (P < 5.0 × 10−⁹) and 43 FI-related SNPs (P < 5.0 × 10−⁸). For T2D, the genome-wide statistical significance threshold was set at P < 5.0 × 10−⁸, with parameters set at R² < 0.001 and kb = 10,000 to mitigate the effects of linkage disequilibrium (LD). SNPs with a minor allele frequency (MAF) of less than 0.01 were excluded. To minimize bias from weak IVs, the F-statistic were used to assess the strength of the association between the IVs and metabolic factors, calculated as: F=[R2×(N-1-K)]/[K×(1-R2)], where R2=β2×2×MAF×(1-MAF), β represents the effect size of the genetic variant related to the exposure factor, MAF represents minor allele frequency, R² indicates the proportion of exposure explained by genetic variation, N denotes the sample size, and K is the number of genetic variants. An F-statistic greater than 10 is considered indicative of a strong IV, with a higher F-statistic suggesting a lower likelihood of weak IVs.19,20
MR Study
The study employed a range of MR techniques, to assess causal relationships, including random or fixed-effects inverse-variance weighted (IVW) analysis, MR-Egger regression, and the weighted median (WM) method. IVW was used as the primary analysis approach. IVW does not account for an intercept term and provides a combined estimate of the causal effect of the exposure on the outcome when the genetic variants satisfy the assumptions of valid IVs.21 However, IVW results overlook the potential presence of gene-level pleiotropy. Therefore MR-Egger and WM were applied as supplementary methods to verify the validity and consistency of the findings.22 MR-Egger regression permits pleiotropy for all SNPs, detecting horizontal heterogeneity via intercept testing and providing estimates after adjusting for pleiotropy.23 WM method weights the causal effect value of each SNP according to its cluster size, yielding a temporary estimate weighted by the most significant SNPs.24
A causal estimate is considered significant if the IVW method P < 0.05, and the direction is consistent with the MR-Egger and WM results. In the multivariate MVMR analysis, IVW was the primary method. To mitigate data bias from multiple measurements, the Bonferroni correction was applied to adjust the P-values. A total of 12 corrections were performed, resulting in a Bonferroni-corrected significance threshold of α < P/12 = 0.0042. Therefore, P < 0.0042 is regarded as a significant impact, 0.0042 < P < 0.05 indicates a suggestive association, and P > 0.05 suggests no significant effect.
Quality Analysis
To ensure the reliability and stability of the study results, several quality control measures were implemented, including sensitivity analyses, heterogeneity tests, and gene-level pleiotropy assessments. Sensitivity analyses included leave-one-out analyses, where each IV was excluded individually, and the results of the remaining IVs were recalculated to evaluate their impact on the outcomes. The Cochran Q-test was used to assess heterogeneity among IVs.25 If the P > 0.05 with no evidence of heterogeneity, a fixed-effects IVW model was employed as the primary analysis method. When the P < 0.05, indicating significant heterogeneity, a random-effects IVW model was used. MR-Egger regression was utilized to assess horizontal pleiotropy, with a significant deviation of the intercept term from zero indicating the presence of pleiotropy. In these cases, MR-pleiotropy residual sum and outlier (MR-PRESSO) was applied to remove outlier SNPs and eliminate horizontal pleiotropy, after which MR analysis was repeated.26
All analyses in this study were conducted using the TwoSampleMR and MVMR packages in R software (version 4.3.1).
Results
Instrumental Variables
Based on the selection criteria for IVs, SNPs for metabolic-related factors were chosen from GWAS data, resulting in 316 SNPs for WHR, 43 for FI, and 139 for T2D. The strength of these IVs was evaluated using the F-statistic. Among the selected SNPs, 13 with an F-statistic < 10, which were significantly associated with FI, were excluded. All remaining SNPs were used for MR analysis. The characteristics of the final selected SNPs are detailed in Supplementary Table S1.
Two-Sample MR Analysis
The MR analysis results suggest a causal relationship between genetically predicted WHR levels and cervical disc disorders, indicating a suggestive association and significant finding. Specifically, each one standard deviation (SD) increase in WHR was associated with a 1.147-fold increased risk of cervical disc disorders (IVW, p = 0.030, OR = 1.147, 95% CI: 1.014–1.297) (Figure 2). Similarly, genetically predicted FI levels show a causal relationship with cervical disc disorders, with each one SD increase in FI increasing the risk by 1.534 (IVW, p = 0.004, OR = 1.534, 95% CI: 1.145–2.055) (Figure 2), indicating a significant result and suggestive association.
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Figure 2 Mendelian randomization association of genetically determined metabolism-related traits with 3 kinds of orthopedic diseases. Abbreviations: SNP, single nucleotide polymorphism; WHR, waist-hip ratio; FI, fasting insulin; T2D, type 2 diabetes; OR, odds ratio; CI, confidence interval.
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Additionally, genetically predicted T2D levels are causally associated with cervical disc disorders, identifying T2D as a risk factor. Each one SD increase in the Genetic Risk Score (GRS) for T2D raised the risk of cervical disc disorders by 1.080 (IVW, p < 0.001, OR = 1.080, 95% CI: 1.037–1.125) (Figure 2). This finding is significant and indicates a suggestive association. Genetically predicted WHR levels also show a causal relationship with RA, with each one SD increase in WHR increasing the risk of RA by 1.260 (IVW, p < 0.001, OR = 1.260, 95% CI: 1.113–1.426) (Figure 2), signifying a significant result and suggestive association.
Furthermore, genetically predicted T2D levels exhibited a causal relationship with OP, with T2D acting as a protective factor. Each one SD increase in the GRS for T2D reduced the risk of OP by 0.925 (IVW, p = 0.010, OR = 0.925, 95% CI: 0.873–0.981) (Figure 2), indicating a significant and suggestive association. Additionally, the analysis of FI-related SNPs (n = 43) without excluding weak IVs reveals a significant causal relationship between genetically predicted FI levels and cervical disc disorders. Each one SD increase in FI raised the risk of cervical disc disorders by 1.469 (IVW, p = 0.032, OR = 1.469, 95% CI: 1.034–2.087) (Figure 2), providing further evidence of a significant result and suggestive association. Supplementary Table S2 presents the results of four MR analysis methods, all of which showed consistent Beta directions across the analyses.
MVMR Analysis
In the MVMR analysis, BMI was adjusted as a confounder. After considering the influence of BMI, MVMR was applied to observe the independent effects of exposures on outcomes. The results of the MVMR analysis indicate that the causal relationship between WHR levels and cervical disc disorders disappeared after adjusting for BMI (IVW, p = 0.107, OR = 1.118, 95% CI: 0.976–1.281). However, FI levels continued to show a positive causal effect on cervical disc disorders (IVW, p < 0.001, OR = 1.716, 95% CI: 1.271–2.317). Similarly, T2D levels maintained a positive causal effect on cervical disc disorders (IVW, p < 0.001, OR = 1.092, 95% CI: 1.048–1.137) (Figure 2).
WHR levels continued to show a positive causal effect on RA (IVW, p = 0.011, OR = 1.203, 95% CI: 1.044–1.385). T2D levels continued to have a protective effect on OP (IVW, p < 0.001, OR = 0.912, 95% CI: 0.867–0.959). Furthermore, after adjusting for BMI, a causal relationship between T2D levels and RA emerged, indicating that T2D is a risk factor for RA. Each unit increase in T2D increased the risk of RA by 1.062 times (IVW, p = 0.016, OR = 1.062, 95% CI: 1.012–1.116) (Figure 3 and Supplementary Table S3).
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Figure 3 Forest plot for the MVMR adjusted for BMI. Abbreviations: WHR, waist-hip ratio; FI, fasting insulin; T2D, type 2 diabetes; OR, odds ratio; CI, confidence interval.
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Sensitivity and Pleiotropy Analyses
The MR-Egger intercept tests revealed P > 0.05 (Table 2), suggesting no evidence of horizontal pleiotropy between the selected SNPs and outcome factors, confirming the validity of the MR methods for causal inference in this study. Cochran’s Q-test for heterogeneity among SNPs showed no significant heterogeneity p > 0.05 for FI after excluding weak IVs (Table 2). Consequently, a fixed-effects IVW model was applied for analysis. For other tests showing significant heterogeneity (P < 0.05) (Table 2), a random-effects IVW model was employed.
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Table 2 Sensitivity Analyses with Directional Pleiotropy Test and Heterogeneity Test
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Leave-one-out sensitivity analyses yielded results consistent with the analysis of all included SNPs, with no SNPs significantly influencing causal associations (Supplementary Table S4). These findings confirm stable causal relationships between the three metabolic factors (WHR, FI, and T2D) and the three orthopedic diseases (cervical disc disorders, osteoporosis, and rheumatoid arthritis).
Discussion
This study utilized publicly available GWAS data and employed both Univariate MR (UVMR) and MVMR methods to explore the causal relationships between three metabolic factors (WHR, FI, and T2D) and various orthopedic diseases (OP, cervical disc disorders, and RA). The UVMR results indicated that T2D played a protective role against OP, while WHR, FI, and T2D appeared to be risk factors for cervical disc disorders, and WHR was a risk factor for RA. The findings from the MVMR analysis, which adjusted for BMI as a confounder, indicated that T2D remained protective against OP, while FI and T2D remained risk factors for cervical disc disorders. Notably, the causal relationship between WHR and cervical disc disorders disappeared when accounting for BMI. Additionally, WHR remained a risk factor for RA, but the positive causal relationship between T2D and RA was attenuated after adjusting for BMI.
Cervical disc disorders are common orthopedic diseases caused by degenerative changes in the cervical intervertebral discs, resulting in neck pain and nerve root-related discomfort.8 This disorder is characterized by a detrimental cycle involving altered cell metabolism, matrix, and biomechanics, ultimately leading to mechanical instability and loss of the shock-absorbing function of the intervertebral discs.27 Over the past 30 years, the prevalence and disability rates of this disease have significantly increased. By 2015, more than one-third of the global population had experienced neck pain lasting longer than three months.28
Most existing literature investigates the relationship between obesity and lumbar diseases, with fewer studies focusing on obesity and cervical diseases, typically using BMI as a measure of obesity levels. However, actual fat distribution varies significantly among individuals, leading to the growing recognition that WHR better reflects fat distribution and central obesity. This makes WHR a more accurate indicator of metabolic status than BMI.29
The UVMR results indicate that WHR is a risk factor for cervical disc disorders, while MVMR analysis suggests that this relationship may be attributed to BMI. Sun et al also found that BMI negatively affects the risk of cervical spondylosis in MR studies, demonstrating that general obesity (as indicated by BMI) is a risk factor for cervical spondylosis (IVW, OR = 1.166, 95% CI: 1.052–1.292, P = 0.003).30 However, observational studies regarding whether obesity causes cervical spondylosis have been inconsistent.
Teraguchi et al used MRI to examine the prevalence and distribution of intervertebral disc degeneration across the entire spine in a small population (n = 975) and identified a significant correlation between obesity (assessed by BMI) and intervertebral disc degeneration in all regions (cervical, thoracic, and lumbar).11 Sheng et al, however, found no significant association between overweight and obesity (assessed by BMI) and cervical diseases in a cross-sectional sample from the 2014 Medical Expenditure Panel Study (MEPS) (n = 23,048), which is considered representative of the US population.14 Their study further confirmed the absence of a significant association between obesity and cervical diseases, aligning with the prevailing opinion that no significant relationship exists between obesity and cervical spondylosis. Interestingly, studies on orthopedic diseases in children have shown a high correlation between obesity and neck pain.31,32 Dianat et al found that children with a BMI below 17.33 were less likely to report neck pain (OR = 0.63, 95% CI: 0.42–0.95), suggesting that lower weight may reduce the risk of neck pain.33 Azabagic et al found that the most common chronic pain site associated with overweight and obesity (assessed by BMI) was the neck (OR = 1.212), and Krul et al reported a higher frequency of chronic neck pain in overweight or obese children (assessed by BMI) (OR = 2.60; 95% CI: 1.30–5.19).34,35
The MVMR results show that after excluding the effect of BMI, BMI-adjusted WHR (WHRadjBMI), which directly measures abdominal fat content and is independent of obesity level, is not associated with cervical disc disorders, consistent with previous observational studies.14,36 This may be because mechanical factors are the primary pathogenic contributors to disc diseases, and the cervical spine bears less body weight than the lumbar spine, reducing the impact of abdominal obesity on the cervical spine.37 In children, head weight (indicated by head circumference) is positively correlated with newborn BMI, with the head accounting for 20 to 25% of total body weight at birth, which decreases to 4 to 6% by adulthood.38 Consequently, children’s cervical spines bear greater head weight than those of adults. To more accurately assess local obesity levels in the head and neck region, some scholars have proposed the concept of subcutaneous fat tissue thickness (SFTT) at the cervical level. A retrospective study based on this concept found a close correlation between SFTT at the C3 level and intervertebral disc degeneration, with females having an SFTT > 9.64mm being more likely to develop spinal degeneration, and males showing a similar trend with SFTT > 8.21mm.39 Unfortunately, GWAS data related to neck circumference or neck fat distribution could not be obtained.
In addition to mechanical factors, significant fat infiltration around the cervical spine creates an inflammatory environment within disc tissues and induces abnormal adipokine production. On one hand, the excessive production of inflammatory factors such as IL-1β, TNF-α, and IL-6 accelerates disc matrix degradation, cell senescence, and cell mortality.40 On the other hand, adipokines like leptin (primarily produced by white adipose tissue) and resistin (linked to insulin resistance) activate immune regulation and inflammatory signaling pathways within disc tissues, further enhancing the expression of inflammatory factors.41
Most existing studies examine the relationship between T2D and lumbar diseases, commonly suggesting that obesity induced by T2D is the primary pathogenic factor.42 The current study, however, indicates that both T2D and FI are risk factors for cervical disc disorders. The high-glucose environment in patients with T2D accelerates disc degeneration, with advanced glycation end-products (AGEs) associated with diabetes accumulating in the annulus fibrosus, nucleus pulposus, and cartilage endplate, ultimately contributing to disc degeneration independent of obesity.43 In vitro experiments reveal that elevated blood glucose concentrations inhibit nucleus pulposus cell proliferation and disrupt disc cell homeostasis.44 Furthermore, since insulin shares structural similarities with Insulin-Like Growth Factor-1 (IGF-1) and IGF-2, hyperinsulinemia may interfere with IGF signaling pathways, accelerating disc degeneration.45,46
RA is a chronic inflammatory disease characterized by symmetric synovitis, cartilage damage, and bone erosion in peripheral joints. Its global prevalence ranges from approximately 0.25% to 1.0%, affecting 1 in every 200 individuals. The incidence is 2 to 3 times higher in women than in men, with peak onset occurring between ages 50 and 59.10,47
Previous MR studies have confirmed that WHR is a risk factor for RA (OR = 1.41; 95% CI, 1.22–1.62).48 Similarly, observational studies have shown that abdominal obesity increases the risk of RA compared to general obesity (HR = 1.22, 95% CI: 1.06–1.41), aligning with the UVMR results.49 When individuals first exhibit symptoms of RA, their immune system may have already been in a state of chronic inflammation for years. Obesity itself is a chronic inflammatory state, with adipose tissue, particularly visceral fat, promoting the production of various inflammatory factors and adipokines such as IL-1, IL-6, IL-17, TNF-α, interferon-γ, and leptin. This induces systemic inflammation, increases peripheral tissue damage, and triggers autoimmune responses, which may lead to and exacerbate systemic inflammatory conditions, including inflammatory arthritis.40,41,48 Martin et al proposed new expressions for different body fat distributions: favorable adiposity (FA), which has higher subcutaneous fat but lower visceral fat, and unfavorable adiposity (UFA), characterized by higher subcutaneous and visceral fat.50 They used MR studies to confirm that both FA and UFA are risk factors for RA, consistent with the UVMR and MVMR results.
T2D and RA share common inflammatory pathological mechanisms. Studies have shown that abnormal glucose metabolism, insulin resistance, and T2D are frequently observed in patients with RA.51 Zhang et al confirmed through MR studies that RA is a risk factor for T2D (OR = 1.04; 95% CI, 1.02–1.05).52 However, limited evidence suggests that the risk of RA increases after a T2D diagnosis. Lu et al found that female patients with T2D, especially younger women, had a significantly increased risk of RA (OR = 1.46, 95% CI: 1.24–1.72), while no such association was observed in men.12 Lahiri et al used data from the European Prospective Investigation of Cancer-Norfolk and the Norfolk Arthritis Register Study (EPIC-2-NOAR Study) and found that T2D (HR = 2.54, 95% CI: 1.26–5.09) and obesity (HR = 2.75, 95% CI: 1.39–5.46) were both associated with an increased risk of inflammatory polyarthritis.53 This finding provides new insights into the MR research, suggesting that T2D and RA may have a mutual causal relationship.
A high-glucose environment enhances the interaction between thioredoxin-interacting protein (TXNIP) and the NOD-like receptor protein 3 (NLRP3) inflammasome, activating caspase 1 and promoting IL-1β release. Additionally, hyperglycemia directly induces β-cell apoptosis through Fas receptors, further upregulating IL-1β. Synovial fluid of patients with RA contains high levels of IL-1β, which regulates leukocyte recruitment, induces matrix metalloproteinases (MMPs), promotes cartilage degradation, inhibits new matrix synthesis, causes joint damage, and induces bone erosion by stimulating osteoclast differentiation and activation.51,54 Studies have shown that IL-1 blockers (eg, Anakinra and Canakinumab) can simultaneously improve the progression of both diseases.55
OP is a prevalent metabolic disorder characterized by reduced bone mass, altered bone microarchitecture, increased bone fragility, and an elevated risk of fractures. Due to its significant public health impact, it is often referred to as the “silent epidemic of the 21st century”.9 The gold standard for diagnosing OP remains dual-energy X-ray absorptiometry for measuring BMD.
MA et al performed a meta-analysis on the association between T2D and BMD involving 3437 patients with diabetes and 19139 controls.13 The analysis found significantly higher BMD at the femoral neck, hip, and spine in patients with T2D compared to non-diabetic individuals. However, this study did not include subgroup analyses. Qiu et al conducted a meta-analysis examining the relationship between diabetes and low bone mass, which included 4599 patients with diabetes and 19741 controls.56 They found no association between T2D and low bone mass, but identified a significant link between type 1 diabetes and low bone density. These results remained consistent in subgroup analyses. Zhou et al utilized data from the US National Health and Nutrition Examination Survey (NHANES) for a cross-sectional study and European GWAS summary statistics for a MR study.57 They found a positive correlation between fasting glucose levels and BMD at the hip, femoral neck, and first lumbar vertebra (L1) in patients with T2D. Additionally, FI was positively correlated with hip BMD in patients with T2D, who had higher hip BMD than non-diabetic individuals.
Obesity associated with T2D was previously thought to indirectly increased BMD. However, numerous recent MR studies have confirmed a direct protective role of T2D against OP.58–60 Ma et al demonstrated, through mediated MR analysis, that while BMI increases the risk of OP, this effect is mediated by T2D.61 The MR results further suggest that T2D acts as a direct protective factor against OP, while FI does not show a significant association with OP, possibly due to the inclusion of non-diabetic individuals in the GWAS. Notably, MR findings suggest FI may be a potential protective factor against OP, though the results were not significant [UVMR: OR = 0.648, P = 0.154; MVMR: OR = 0.813, P = 0.272], which is generally consistent with previous studies. High glucose levels in T2D reduce RANKL levels, inhibiting the RANKL/RANK/OPG pathway, which disrupts osteoclast differentiation and function, thus preventing bone matrix degradation and increasing BMD.62 Elevated insulin levels in circulation may also contribute to the higher BMD in patients with T2D, as insulin, structurally similar to insulin-like growth factor 1 (IGF-1), can bind to IGF-1 receptors on osteoblasts, facilitating bone anabolic processes.45
This study has several limitations. First, the results may not be generalizable to non-European populations, as the GWAS dataset were of European ancestry. Differences in genetics, cultural practices, and lifestyles may influence the observed associations. Future studies should incorporate more diverse cohorts to validate the generalizability of these results across various ethnic and demographic groups. Second, due to limitations within the dataset, we were unable to perform subgroup analyses based on age, sex, or disease severity, which may have provided further insight into differential risk profiles and disease mechanisms. Further subgroup analyses of orthopedic disease datasets could be performed. Third, the complex pathogenesis of abdominal obesity and T2D complicates the complete exclusion of confounding factors, which may account for the variability in results observed across different MR studies.63 Additional studies incorporating multi-omics data and pathway-specific analyses may better elucidate these relationships and clarify causal mechanisms.
Conclusions
In conclusion, this study suggests that abdominal obesity and T2D are associated with an increased risk of RA, with T2D exhibiting a causal relationship with a heightened risk of cervical disc disorders and a reduced risk of OP. These findings enhance the understanding of the relationships between metabolic factors and common orthopedic diseases, providing valuable insights for clinical practice and disease management.
Data Sharing Statement
The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article.
Ethics Approval
This study was conducted in accordance with the declaration of Helsinki. This study was conducted with approval from the Ethics Committee of Affiliated Hospital of Shandong Traditional Chinese Medicine University.
Acknowledgments
All data used in this study were obtained from openly available databases and consortiums. We express our sincere appreciation to them.
Funding
This work was supported by the Shandong Provincial Natural Science Foundation (No. ZR20210H229) and the Science and Technology Plan Projects of Shandong Provincial Administration of Traditional Chinese Medicine (No. 2021Q107).
Disclosure
The authors declare no competing interests in this work.
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