Evidence from Mendelian randomization analysis combined with meta-analysis for the causal validation of the relationship between 91 inflammatory factors and lumbar disc herniation

Abstract

Lumbar disc herniation (LDH) is a common spinal disease. In recent years, an increasing number of observational studies have reported the impact of inflammatory factors on LDH. By conducting Mendelian randomization (MR) analysis on 91 inflammatory factors, it is possible to reveal their causal relationship with LDH, providing new insights for prevention and treatment strategies. In this study, a two-sample MR analysis was performed, using 91 inflammatory factors as exposure data, and LDH data from 2 different sources as outcome data. Subsequently, the most significant results from the inverse-variance weighted analysis were subjected to meta-analysis, with multiple corrections applied to the thresholds to ensure result accuracy. Finally, reverse causality MR analysis was conducted to validate the causal relationship between the identified positive inflammatory factors and LDH. Ninety-one cytokines were analyzed in relation to LDH using MR with data from the Finngen and UK Biobank databases. The inverse-variance weighted results from both analyses were then meta-analyzed, and multiple corrections were applied to the significance threshold of the meta-analysis results. Ultimately, only 1 cytokine, tumor necrosis factor-beta levels (genome-wide association study ID: GCST90274840), showed a significant association after the combined MR analysis and multiple corrections, with an odds ratio of 1.073 (95% confidence interval: 1.034–1.113, P = .0154). Furthermore, this positive cytokine did not display any reverse causality with LDH from either data source. Tumor necrosis factor-beta levels are a risk factor for LDH, potentially increasing the risk of developing the condition and exacerbating its symptoms.

1. Introduction

Lumbar disc herniation (LDH) is a common spinal disease primarily caused by degenerative changes or external forces leading to the protrusion of the nucleus pulposus through the annulus fibrosus into the spinal canal, compressing nerve roots or the spinal cord. This results in a range of clinical symptoms, including lower back pain, radiating leg pain, numbness and sensory disturbances, muscle weakness, and bowel or bladder dysfunction.^[1] The global annual incidence of LDH is approximately 5%, meaning that 50 out of every 1000 people will develop this condition each year. The lifetime prevalence of LDH is estimated to be between 2% and 3%, indicating that 2 to 3 out of every 100 people will experience this condition at some point in their lives.^[2] Studies have shown that men are slightly more likely to develop LDH than women, with a male-to-female incidence ratio of about 1.2 to 1.5. Individuals engaged in heavy physical labor, prolonged sitting or standing, and frequent bending and lifting, such as construction workers, drivers, and office workers, are at higher risk of developing LDH.^[3–5]

The pathogenesis of LDH primarily includes several key factors: disc degeneration: with aging, the water content of the nucleus pulposus within the disc decreases, reducing its elasticity and shock-absorbing capacity. Annulus fibrosus degeneration: the annulus fibrosus becomes thinner and more brittle over time, making it prone to rupture, which can lead to nucleus pulposus protrusion.^[6,7] Mechanical stress: prolonged mechanical load and improper posture, such as prolonged sitting, standing, or frequent bending, exert excessive pressure on the lumbar discs, leading to rupture and protrusion. Acute trauma: sudden impacts or twists, such as lifting heavy objects or falling, can cause the annulus fibrosus to tear, allowing the nucleus pulposus to protrude into the spinal canal and compress nerve roots or the spinal cord. Genetic factors: research indicates that LDH has a genetic predisposition, with a higher risk in individuals with a family history of the condition.^[8] Biochemical changes: alterations in the biochemical components of the lumbar discs, such as proteoglycans and collagen fibers, result in changes to the disc’s structure and function. Inflammatory response: the protrusion of the nucleus pulposus can trigger a local inflammatory response, releasing various inflammatory mediators that exacerbate nerve root compression and damage.^[9]

The pathophysiological changes of LDH include: nucleus pulposus protrusion: the nucleus pulposus protrudes through the ruptured annulus fibrosus into the spinal canal, directly compressing the nerve roots or spinal cord, leading to pain and dysfunction.^[10] Nerve root compression: the protruding nucleus pulposus compresses the nerve roots, causing nerve root edema, inflammation, and ischemia, which result in radiating pain, numbness, and muscle weakness.^[11] Changes in spinal stability: degenerative changes and protrusion of the intervertebral disc alter spinal stability, potentially leading to instability or misalignment of the spine, exacerbating symptoms.^[12] Muscle response: pain and dysfunction caused by disc herniation can lead to reflexive spasms of the lower back muscles, further increasing pain, and limiting movement.^[13]

Treatment for LDH includes conservative and surgical options. Adopting correct posture, engaging in appropriate exercise, avoiding excessive weight-bearing, and maintaining a healthy weight can effectively prevent LDH.^[14–19]

The role of cytokines in the study of LDH has gained significant attention. After nucleus pulposus herniation, a local inflammatory response occurs, with pro-inflammatory cytokines such as tumor necrosis factor (TNF), interleukin-1 (IL-1), and interleukin-6 (IL-6) being upregulated in the herniated disc, exacerbating inflammation and compressing nerve roots, leading to pain and neurological dysfunction.^[20] Conversely, anti-inflammatory cytokines like interleukin-10 (IL-10) and transforming growth factor-beta (TGF-β) can reduce inflammation, modulate the local environment, and alleviate nerve damage. The discovery of these factors offers potential therapeutic targets for controlling LDH inflammation.^[21] Additionally, matrix metalloproteinases (MMPs) play a crucial role in disc degeneration by degrading the disc matrix, with their expression regulated by cytokines, particularly TNF-α and IL-1. Research is exploring the possibility of alleviating symptoms by balancing cytokine levels, such as using TNF antagonists for pain relief. Future studies are also focusing on promoting disc regeneration and repair through growth factors and investigating the potential of regulating cytokine signaling for developing regenerative therapies.^[22]

For instance, a study on degenerative changes in LDH discussed the interaction between mechanical stress and biological changes, forming a vicious cycle. During degeneration, the nucleus pulposus loses elasticity and hydration, leading to increased mechanical load, annular fissures, and further herniation that compresses nerves. Concurrently, pro-inflammatory cytokines (such as TNF and IL-1β) are upregulated during disc degeneration, triggering chronic inflammation that accelerates matrix degradation and destabilizes the structure. This interplay between mechanical stress and inflammatory response creates an irreversible degenerative cycle, resulting in persistent low back pain and functional loss. The article suggests breaking this vicious cycle by reducing mechanical load and inhibiting inflammatory responses, providing new directions for the treatment of lumbar disc diseases.^[23]

Another study on cytokines and LDH explored the critical role of cytokines in disc degeneration, particularly their impact on pain generation and changes in disc content. Pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-6 are upregulated in degenerated discs, inducing local inflammation, activating MMPs, and accelerating the degradation of disc matrix, leading to structural instability and loss of hydration. These cytokines also enhance the sensitivity of pain conduction pathways, resulting in chronic pain and referred pain, such as sciatica. Additionally, anti-inflammatory factors like IL-10 and TGF-β play a protective role in modulating the inflammatory response and promoting disc repair. The article suggests that future therapeutic directions may include targeting the inhibition of pro-inflammatory factors to alleviate pain and slow down disc degeneration.^[24]

Currently, there is limited direct research on the causal relationship between cytokines and LDH, although existing studies indicate that cytokines play significant roles in disc degeneration and inflammatory responses. The specific roles of different cytokine phenotypes in LDH remain underexplored, with most research focusing on the overall processes and mechanisms of inflammation, lacking in-depth discussions on the phenotypic differences, functional specificities of various cytokines, and their associations with disease severity. This gap leaves unresolved questions about which specific cytokines are crucial in disease progression, the existence of high-risk phenotypes, and the varying effects of different cytokine phenotypes in patients.^[25,26]

In recent years, research into gene polymorphisms related to LDH has advanced, encompassing methods like genome-wide association studies (GWAS) and single-gene studies. These studies reveal that specific gene polymorphisms may increase the risk of LDH by affecting the metabolism of the disc extracellular matrix or other related pathways.^[27] GWAS analyses of large population genotype data provide new insights into genetic variations associated with LDH, enhancing our understanding of the disease’s genetic basis. Furthermore, gene polymorphisms may regulate inflammation-related genes, altering the expression and release of cytokines that play crucial roles in the inflammatory response of disc degeneration.^[28]

This study employs GWAS data and combines Mendelian randomization (MR) analysis with meta-analysis to explore the potential causal relationship between 91 cytokines and LDH. The aim is to reveal the causal associations of specific cytokines with LDH, elucidate their potential pathological mechanisms, and provide new avenues for developing targeted therapeutic strategies focused on specific cytokines.

2. Methods and materials

2.1. Study design

In the research process, the first step involved collecting exposure and outcome data, followed by data preprocessing. Next, the preprocessed exposure data, comprising 91 inflammatory factors, was subjected to MR analysis with LDH data from 2 other databases. Subsequently, the inverse-variance weighted (IVW) results from both MR analyses were meta-analyzed, with multiple corrections applied to ensure data accuracy. The advantage of combining MR with meta-analysis lies in its ability to integrate results from multiple studies, reduce bias, explore heterogeneity, and enhance the generalizability of the findings. By integrating data from different studies, a more comprehensive assessment of the association between exposure and outcome can be achieved, providing more reliable and credible results, and deepening the understanding of the research question. Finally, reverse MR analysis was conducted on the positive inflammatory factors and LDH to more precisely understand their causal relationship.

The study involves the analysis of GWAS database data; therefore, an ethics review is not required. However, we will include the ethical review section from the original data in the article’s ethical statement. Additionally, a flowchart was created to clearly illustrate the research process (Fig. 1).

Figure 1.

The process flowchart of the research methodology.

2.2. Source of GWAS data for 91 inflammatory factors

This study utilized data from the GWAS catalog, analyzing samples from 14,824 individuals of European descent. The research aimed to investigate the relationship between genetic variations and individual biomarker levels. The study benefited from the large dataset provided by the UK Biobank, enabling researchers to gain deeper insights into the impact of genetic factors on various biomarker levels. Specifically, the GWAS identifiers used in this study were GCST91274758 to GCST91274848. Based on recent research, the selection criteria for single nucleotide polymorphisms (SNPs) in the data of 91 inflammatory factors were set as follows: P < 1E-5, F > 10, minor allele frequency (MAF) > 0.01, clump_kb = 10,000, and clump_r2 = 0.001. Ultimately, 2905 SNPs met the criteria after screening.

2.3. Sources of GWAS data on LDH

To avoid overlap between inflammatory factor data and LDH data, LDH outcome data were sourced from 2 different databases. The study aimed to explore the relationship between genetic variations and individual biomarker levels. This research benefited from the extensive datasets provided by Finngen R10 and the UK Biobank, allowing researchers to gain deeper insights into the impact of genetic factors on various biomarker levels. The first set of LDH data came from the Finngen R10 database, with a total cohort of 412,181 individuals, including 9607 cases and 402,574 controls, and 21,306,349 SNPs. The second set of LDH data was sourced from the UK Biobank, with a total cohort of 420,473 individuals, including 7004 cases and 341,077 controls, and 13,978,943 SNPs. Both LDH datasets comprised individuals of European descent. The exclusion criteria for data selection were: prioritizing larger sample sizes, ensuring the data source organizations were as generalized as possible, and matching the study population with the exposure data.

2.4. Criteria for selection of instrumental variables

In MR studies, selecting effective instrumental variables (IVs) is crucial. First, this study used a selection threshold of P < 1E-5 to ensure that only SNPs strongly associated with various inflammatory factors were retained. For all inflammatory factors, the number of relevant SNPs exceeded 3, ensuring representativeness and relevance of the data.

Next, to further screen for robust IVs, this study calculated the F-statistic for each SNP using the formula F = (beta/se)² (Table S1, Supplemental Digital Content, http://links.lww.com/MD/N822). Only SNPs with an F value >10 were retained, a step that helps eliminate weak instruments and enhances the reliability of the study results. Additionally, the MAF was calculated using the effect allele frequency (eaf). If the eaf was <0.5, MAF was set to eaf; otherwise, it was set to 1-eaf. Only SNPs with a MAF >0.01 were retained to exclude rare variants that could affect the study results.

Finally, the filtered data were formatted for MR analysis and adjusted for linkage disequilibrium to avoid its impact on result accuracy. The specific criteria for this adjustment were a distance threshold of 10,000 kilobase pairs (kb) and a linkage disequilibrium threshold of 0.001. These steps ensured the independence of IVs and the precision of the results.

2.5. Statistical analysis

2.5.1. Causal validation of inflammatory factors with LDH

All data analyses in this study were conducted using R version 4.2.1 (https://www.r-project.org/). First, we selected SNP data from the LDH outcome dataset that matched the exposure data for inflammatory factors. We then processed palindromic SNPs based on the criterion action = 2, and excluded data with mr_keep = false.

Prior to conducting MR-PRESSO, we performed horizontal pleiotropy tests on the processed data. SNPs with a P-value <.05 were identified as having horizontal pleiotropy and were considered outliers. These outliers were then excluded using the MR-PRESSO method with parameters NbDistribution = 3000 and SignifThreshold = 0.05. SNPs with P-values >.05 were considered to have no outliers.

Following this refinement, we conducted heterogeneity tests before performing MR analysis. Although data heterogeneity has a minimal impact on results, we optimized the process by using the IVW random-effects model for SNPs with significant heterogeneity (Q_pval < 0.05) and the IVW fixed-effects model for SNPs without significant heterogeneity. Additionally, regardless of heterogeneity, we conducted MR-Egger and weighted median analyses, and calculated odds ratios (OR).

To enhance the reliability of the results, we performed a meta-analysis of the MR results from the 2 LDH datasets and applied multiple corrections to the significance P-values using the Bonferroni method to reduce the likelihood of Type I errors. Ultimately, only 1 inflammatory factors showed a significant association after MR analysis and multiple corrections.

2.5.2. The causal link between LDH and positive inflammatory factors

In this process, we treated the data for inflammatory factors as outcome data and LDH as exposure data, using the same instrumental variable selection and data analysis methods as in the forward analysis. The primary purpose of this step was to verify the directionality of the causal relationship between the 2. Therefore, we applied the same thresholds and criteria to select effective IVs and conducted similar data processing and analysis to determine the directionality between them.

2.6. Sensitivity analysis

Horizontal pleiotropy means that different treatments or interventions may have varying effects on different individuals or situations, which could be mistakenly attributed to differences between the experimental and control groups rather than the actual treatment effect. To minimize the impact of horizontal pleiotropy on the experimental results, we conducted horizontal pleiotropy tests on the GWAS data. SNPs showing horizontal pleiotropy (P-value < .05) were processed using MR-PRESSO to exclude outliers, with specific exclusion criteria set at NbDistribution = 3000 and SignifThreshold = 0.05 (Table S2, Supplemental Digital Content, http://links.lww.com/MD/N822).

Heterogeneity refers to the presence of diversity or variation in the study subjects, observations, or experimental conditions. In statistics and research methodology, heterogeneity typically signifies differences among samples or individuals, which may stem from individual characteristics, environmental conditions, or other factors. Heterogeneity is common in research and can manifest in various forms, including physiological and psychological differences among individuals, socioeconomic disparities, and environmental influences. While this diversity enhances the generalizability and representativeness of research findings, it also increases the complexity and difficulty of interpreting results. During analysis, we conducted heterogeneity tests on the data. For SNPs with significant heterogeneity (Q_pval < 0.05), we used the IVW random-effects model in MR analysis. For those without significant heterogeneity, we used the fixed-effects model to ensure the accuracy and reliability of the results (Table S3, Supplemental Digital Content, http://links.lww.com/MD/N822).

2.7. Ethical statement

Participants in FinnGen provided informed consent for biobank research on basis of the Finnish Biobank Act. Alternatively, separate research cohorts, collected before the Finnish Biobank Act came into effect (in September 2013) and the start of FinnGen (August 2017) were collected on the basis of study-specific consent and later transferred to the Finnish biobanks after approval by Fimea, the National Supervisory Authority for Welfare and Health. Recruitment protocols followed the biobank protocols approved by Fimea. The Coordinating Ethics Committee of the Hospital District of Helsinki and Uusimaa (HUS) approved the FinnGen study protocol (number HUS/990/2017).

The FinnGen study is approved by the THL (approval number THL/2031/6.02.00/2017, amendments THL/1101/5.05.00/2017, THL/341/6.02.00/2018, THL/2222/6.02.00/2018, THL/283/6.02.00/2019, and THL/1721/5.05.00/2019), the Digital and Population Data Service Agency (VRK43431/2017-3, VRK/6909/2018-3 and VRK/4415/2019-3), the Social Insurance Institution (KELA) (KELA 58/522/2017, KELA 131/522/2018, KELA 70/522/2019 and KELA 98/522/2019) and Statistics Finland (TK-53-1041-17).

The Biobank Access Decisions for FinnGen samples and data utilized in FinnGen Data Freeze 5 include the following datasets: THL Biobank BB2017_55, BB2017_111, BB2018_19, BB_2018_34, BB_2018_67, BB2018_71, BB2019_7, BB2019_8 and BB2019_26; Finnish Red Cross Blood Service Biobank 7.12.2017; Helsinki Biobank HUS/359/2017; Auria Biobank AB17-5154; Biobank Borealis of Northern Finland_2017_1013; Biobank of Eastern Finland 1186/2018; Finnish Clinical Biobank Tampere MH0004; Central Finland Biobank 1-2017; and Terveystalo Biobank STB 2018001.

3. Results

3.1. Causal validation of the inflammatory factors with LDH

In this study, MR analyses were conducted using 91 cytokines with LDH data from both the Finngen R10 database and the UK Biobank (UKB). The IVW results from both MR analyses were then meta-analyzed, followed by multiple corrections for the significance thresholds. Ultimately, a significant association was found for 1 group of cytokines, specifically Tumor necrosis factor-beta (TNF-beta) levels (GWAS ID: GCST90274840), with a strong correlation identified with 43 SNPs, including rs10282173, rs10740467, rs112317147, rs113244922, rs113664581, rs113800750, rs118033381, rs12743015, rs12883833, rs1382846, rs138471526, rs139511528, rs147423408, rs16852556, rs192303297, rs2074475, rs2364485, rs247096, rs2523882, rs2904602, rs55914821, rs58634322, rs7210455, rs72854560, rs76481949, rs76913603, rs77897196, rs78051721, and rs9267798, and others.

The analysis results indicated that the MR analysis of TNF-beta levels with LDH in the Finngen R10 database yielded an OR of 1.056 (95% confidence interval [CI]: 1.009–1.106, P = 1.106), with β values of 0.055 for the IVW method, 0.072 for the MR-Egger method, and 0.047 for the weighted median method (Table S4, Supplemental Digital Content, http://links.lww.com/MD/N822). The β values from the 3 primary methods were consistent, suggesting that TNF-beta levels are a risk factor for LDH. An MR scatter plot was also created for this analysis (Fig. 2).

Figure 2.

Combined MR plots of TNF-beta on Finngen R10 outcomes. MR = Mendelian randomization, TNF-beta = tumor necrosis factor-beta.

For the MR analysis of TNF-beta levels with LDH in the UKB database, the IVW results showed an OR of 1.103 (95% CI: 1.033–1.178, P = .0078), with β values of 0.0036 for the IVW method, 0.060 for the MR-Egger method, and 0.107 for the weighted median method, all indicating that TNF-beta levels are a risk factor for LDH (Table S4, Supplemental Digital Content, http://links.lww.com/MD/N822). Scatter plots were also generated for this analysis (Fig. 3).

Figure 3.

Combined MR plots of TNF-beta on UKB outcomes. MR = Mendelian randomization, TNF-beta = tumor necrosis factor-beta, UKB = UK Biobank.

Regarding the MR analysis results of TNF-beta levels with LDH in both the Finngen R10 and UKB databases, the main IVW results were further meta-analyzed using the “meta” package in R (Table S5, Supplemental Digital Content, http://links.lww.com/MD/N822), with multiple corrections applied using the Bonferroni method (Table S6, Supplemental Digital Content, http://links.lww.com/MD/N822). The post-meta-analysis and multiple correction results indicated an OR of 1.073 (95% CI: 1.034–1.113, P = .0002). And the results of meta-analysis were used to draw a forest plot (Fig. 4). This evidence shows a significant association between TNF-beta levels and LDH after combined MR analysis, meta-analysis, and multiple corrections.

Figure 4.

Forest plot of positive results after meta-analysis.

3.2. The causal link between LDH and positive inflammatory factors

In the reverse MR validation of this study, the significant cytokine phenotype TNF-beta levels was used as the outcome data, while the 2 sets of LDH data served as exposure data. The results of the reverse MR analysis did not reveal a significant association between TNF-beta levels and LDH.

Specifically, when LDH data from the Finngen R10 database was used as the exposure factor, the IVW results of the MR analysis showed an OR of 0.991 (95% CI: 0.977–1.003, P = 1.131). When using LDH data from the UKB as the exposure factor, the IVW results indicated an OR of 0.995 (95% CI: 0.980–1.011, P = .565). These findings suggest that LDH from both databases did not show a significant association with TNF-beta levels (P > .05) (Table S7, Supplemental Digital Content, http://links.lww.com/MD/N822). Therefore, there was no evidence of a reverse causal relationship between TNF-beta levels and LDH from the 2 different data sources.

4. Discussion

This study utilized GWAS data to explore the causal relationship between 91 cytokines and LDH through bidirectional MR analysis and meta-analysis. To enhance result accuracy, multiple corrections were applied to the thresholds after the MR analysis and subsequent meta-analysis. Ultimately, a significant causal association was found only for TNF-beta levels with LDH, with no evidence of reverse causality between the 2.

Research on single-gene variants of cytokines has made significant strides in revealing their key roles in inflammatory responses and disease susceptibility. Variants of cytokines such as IL-1, TNF-α, IL-6, TGF-β, and IL-10 have been linked to various chronic inflammatory diseases, autoimmune disorders, and cancers. For example, IL-1 gene polymorphisms have been associated with a higher risk of inflammatory diseases like rheumatoid arthritis, osteoarthritis, and Crohn disease, leading to excessive inflammatory responses and increased tissue damage.^[29] TNF-α gene variants not only relate to chronic obstructive pulmonary disease and systemic lupus erythematosus but may also worsen disease progression by promoting inflammation. IL-6 polymorphisms are linked to cardiovascular diseases, diabetes, and chronic inflammation, with gene variations potentially causing its overexpression in chronic inflammatory contexts, exacerbating metabolic dysregulation and vascular damage. Additionally, TGF-β plays a crucial role in regulating immunity and cell proliferation, with mutations associated with cancer and fibrotic diseases, potentially promoting fibrosis or tumor growth through abnormal signaling pathways. IL-10, as an anti-inflammatory cytokine, has gene polymorphisms closely related to inflammatory bowel diseases like ulcerative colitis, where mutations may weaken anti-inflammatory responses, intensifying the inflammatory processes of the disease. Overall, single-gene studies of cytokines provide new insights into the mechanisms of inflammation and their complex associations with diseases.^[30]

A genetic study on LDH delved into the genetic basis of disc degeneration, focusing particularly on gene polymorphisms associated with degenerative diseases and their manifestations in different populations. The study found that gene variations in pro-inflammatory cytokines such as IL-1 and IL-6 are closely linked to the progression of intervertebral disc degeneration. These genes promote the degradation and degeneration of the disc matrix by upregulating MMPs like MMP-3 and MMP-9. Notably, polymorphisms in the IL-6 gene showed a higher incidence in females, indicating that sex differences play a crucial role in the disease’s pathogenesis. Additionally, SNPs in the CILP gene were significantly associated with the risk of disc degeneration, with notable differences observed among various ethnic groups. The TGF-β signaling pathway was also a key focus of the research; while TGF-β typically has a protective effect on the discs, its excessive activation can lead to fibrosis, further exacerbating degenerative changes. These polymorphisms not only provide new insights into the genetic mechanisms underlying disc degeneration but also offer potential targets and evidence for developing personalized treatment strategies in the future.^[31]

Another study on TGF-β analyzed the association between the TNF-β gene polymorphism (rs909253) and the inflammatory and metabolic responses in patients with acute ischemic stroke. The research indicated that individuals carrying the rs909253 polymorphism exhibited significantly elevated levels of TNF-β, which were correlated with inflammatory markers such as C-reactive protein and white blood cell counts. This suggests that the polymorphism may exacerbate inflammatory responses, potentially increasing the severity of stroke. Moreover, this polymorphism was also associated with metabolic indicators, such as blood glucose and lipid levels, indicating its possible impact on metabolic function, further aggravating the pathological processes of stroke. The study highlights the important role of the rs909253 polymorphism in inflammatory diseases and metabolic disorders, potentially influencing TNF-β expression to promote inflammation and metabolic imbalance, thus elevating stroke risk in patients. This finding offers potential directions for personalized treatment, especially in stroke and other inflammation-related diseases, where early interventions based on genetic polymorphisms may improve patient outcomes.^[32]

However, this study’s advantages over single-gene research lie in its comprehensiveness and depth. By utilizing GWAS data and bidirectional MR analysis, the research encompassed 91 cytokines, offering a broader perspective and revealing causal relationships between LDH and cytokines rather than merely simple correlations. Additionally, the use of meta-analysis and multiple corrections enhanced the accuracy and robustness of the results, a level of statistical rigor that is often less common in single-gene studies. Through integrative analysis across multiple genes, this research provided deeper insights into gene interactions while excluding reverse causality through bidirectional analysis, clarifying the unidirectional impact of TNF-beta levels on LDH. This clarity offers more defined directions for future treatment strategies.

TNF-beta, also known as Lymphotoxin-alpha (LT-alpha), is a member of the TNF family. Despite its similar name to TNF-alpha, TNF-beta has distinct biological characteristics and functions. It is a cytokine produced by lymphocytes, primarily secreted by activated T cells and B cells.^[33] The TNF-beta gene is located in the major histocompatibility complex region on chromosome 6, adjacent to the TNF-alpha gene.^[34] TNF-beta is a trimeric protein that shares a similar three-dimensional structure and receptor-binding characteristics with TNF-alpha.^[35] TNF-beta can bind to the same receptors as TNF-alpha, including TNF Receptor 1 (TNFR1) and TNF Receptor 2 (TNFR2).

TNF-beta has three main biological functions: immune regulation, inflammatory response, and antitumor activity.^[25] It plays a crucial regulatory role in the immune system by promoting the activation and proliferation of T cells, B cells, and natural killer cells.^[36] By binding to TNFR1 and TNFR2, TNF-beta can induce apoptosis, inflammation, and cell growth.^[24,37] It is involved in various inflammatory responses, promoting leukocyte infiltration and the release of inflammatory mediators.^[38] Elevated levels of TNF-beta are often observed in chronic inflammatory diseases such as rheumatoid arthritis and inflammatory bowel disease, where it contributes to the pathological process. Additionally, TNF-beta has direct antitumor activity, capable of inducing tumor cell apoptosis and inhibiting tumor angiogenesis, and its mechanism of action is used in some cancer therapies to enhance antitumor immune responses.^[26,39]

Studies have found that TNF-beta regulates the activation, differentiation, and apoptosis of immune cells through signal pathways mediated by TNFR1 and TNFR2.^[40] TNF-beta also plays an important role in immune checkpoint regulation, affecting the immune surveillance function of T cells. In animal models, the absence or blockade of TNF-beta significantly reduces inflammatory responses and tissue damage, suggesting its potential application in the treatment of inflammatory diseases.^[41] The expression of TNF-beta at inflammation sites is positively correlated with the severity of various inflammatory diseases, further supporting its potential as a therapeutic target.^[42] Using genetic engineering techniques, researchers have developed monoclonal antibodies and fusion proteins specifically targeting TNF-beta to enhance antitumor immune responses.^[43] TNF-beta, when used in combination with other antitumor drugs, has shown synergistic effects, potentially improving therapeutic outcomes and reducing side effects.^[44,45]

Existing research indicates that TNF-beta plays a significant role in LDH. Elevated levels of TNF-beta trigger chronic inflammatory responses in disc tissues, enhancing the local pro-inflammatory environment and leading to the apoptosis of nucleus pulposus cells and degradation of the extracellular matrix. Studies have shown that excessive TNF-beta expression not only directly promotes the apoptosis of nucleus pulposus cells but also accelerates degenerative changes and structural damage of the disc by upregulating MMPs such as MMP-3 and MMP-9. Compared to TNF-alpha, TNF-beta may have a unique role in regulating the cytokine network associated with LDH.^[46] Due to its critical influence on cytokine expression levels, TNF-beta is increasingly recognized as a potential therapeutic target. Controlling TNF-beta expression may become an important strategy for slowing disc degeneration and inflammation.^[47]

A study on TNF-β in inflammatory environments explored its pro-inflammatory effects in chondrocytes and its impact on joint degeneration. The research found that TNF-β exacerbates the inflammatory response in chondrocytes by activating key pro-inflammatory factors such as NF-κB and Cox-2. Additionally, TNF-β upregulates MMP-9 and MMP-13, enzymes responsible for degrading collagen and proteoglycans in cartilage, leading to tissue degeneration. TNF-β not only accelerates cartilage degradation through these enzymes but also aggravates tissue damage and degenerative changes by influencing apoptotic pathways. These mechanisms underscore the critical role of TNF-β in the pathogenesis of inflammatory diseases such as arthritis and highlight its potential as a target for future therapeutic interventions. Moreover, the inflammatory response induced by TNF-β continues to activate matrix-degrading enzymes, causing chondrocytes to gradually lose their structural function. The overexpression of MMPs not only exacerbates cartilage degeneration but can also induce inflammatory responses in neighboring tissues, leading to broader tissue damage. Additionally, NF-κB, as a major regulator of apoptosis and inflammation, further promotes chondrocyte death when overactivated. These findings provide new directions for targeting TNF-β in treatment, particularly in reducing inflammation and tissue damage associated with arthritis. Overall, these results establish a solid foundation for exploring the feasibility of TNF-β as a therapeutic target, especially in managing degenerative joint diseases.^[48]

Another study on the inflammatory response in LDH delved into the significance of inflammation in the spontaneous resorption of herniated discs. The research demonstrated that when herniated disc tissue is exposed to the epidural space, pro-inflammatory cytokines such as TNF-alpha, TNF-beta, and IL-1β are activated, leading to the recruitment and activation of immune cells like macrophages. These immune cells release matrix-degrading enzymes, such as MMP-3 and MMP-9, which break down the components of the nucleus pulposus and annulus fibrosus, facilitating the spontaneous resorption process. Although the inflammatory response aids in the degradation and absorption of herniated tissue, the accompanying local inflammation may exacerbate pain and symptoms of nerve root compression, such as sciatica. Furthermore, the study indicated that the composition of the herniated material significantly impacts the effectiveness of absorption. Herniated discs rich in nucleus pulposus, due to their high water and proteoglycan content, are more readily broken down by the immune system, while those with a higher cartilage content are less likely to undergo spontaneous resorption. Thus, the spontaneous absorption process is dependent not only on the intensity of the inflammatory response but also on the tissue composition of the herniation.^[49]

The study presents several notable advantages over research focusing solely on cytokine expression levels. First, by employing GWAS data and bidirectional MR analysis, the study can more accurately identify causal relationships between cytokines (like TNF-beta) and LDH, rather than mere correlations. This causal inference method effectively mitigates the influence of confounding factors, making the results more reliable. Second, the study implemented multiple corrections for the thresholds in the meta-analysis following MR analysis, enhancing the rigor of the statistical analysis and the credibility of the results. This approach ensures that the identified causal relationships possess higher statistical significance. Finally, by concentrating on the significant causal association between TNF-beta levels and LDH, the research not only offers new insights into the role of cytokines in disease mechanisms but also lays the groundwork for subsequent targeted therapeutic strategies. Thus, this study provides deeper insights and more practical conclusions regarding the mechanisms of action of cytokines and their biomarkers.

The findings of this study have significant medical implications. Although there are few direct studies on the causal relationship between TNF-beta and LDH, existing evidence suggests that TNF-beta plays an important role in disc degeneration and inflammatory responses.^[31] TNF-beta may exacerbate LDH symptoms by promoting the release of inflammatory mediators and nerve activation.^[50] Further research is expected to reveal the specific mechanisms of TNF-beta in LDH and provide new approaches for targeting TNF-beta in treatment.^[51,52]

In summary, studying the relationship between inflammatory factors and LDH not only aids in formulating effective public health policies but also promotes the development of medical interventions and prevention strategies. Regulating TNF-beta levels could potentially reduce the incidence of LDH and improve public health.^[53]

The study opens several avenues for further research on the causal relationship between TNF-beta and LDH. Firstly, the significant association with TNF-beta could serve as a new target for early disease screening and personalized treatment. Mechanistic studies will reveal its role in cell signaling and inflammatory responses, potentially uncovering new biomarkers. Additionally, exploring other cytokines could help establish a comprehensive causal network, enhancing our understanding of cytokines’ roles in disease. Integrating genomic data could provide a foundation for precision medicine, while long-term follow-up studies will assess the consistency and clinical relevance of these relationships across different populations. Delving into these areas holds promise for significant advancements in disease prevention and treatment.

Based on numerous observational studies, this research genetically validated the causal association between 91 inflammatory factors and LDH, achieving a completely randomized controlled trial at the genetic level. This method avoids the confounding factors present in observational studies and provides a more precise understanding of the relationship between the 2. The combination of MR analysis and meta-analysis makes the findings more reliable and credible than single analyses.

However, the study also has limitations. Due to the data source constraints, the sample population in the study is primarily of European descent, so the results may not fully represent the global population. Future research should expand to other races and regions to further validate and extend these findings. Nonetheless, this study provides a scientific basis for further research and clinical application of TNF-beta’s role in LDH risk. Scientifically regulating TNF-beta levels could significantly improve public health and reduce the incidence of LDH.

5. Conclusions

The study found a significant association between TNF-beta levels and LDH. Specifically, elevated TNF-beta levels were identified as a risk factor for LDH, increasing the risk and exacerbating disease progression. This suggests that interventions targeting TNF-beta levels could potentially treat LDH or reduce its incidence.

Acknowledgments

Firstly, we express our profound thanks to all individuals and researchers who participated in the GWAS data for this research. Additionally, we extend our sincere gratitude and respect to the personnel involved with the associated public databases. Lastly, our heartfelt appreciation goes out to every author who played a role in contributing to this study.

Author contributions

Conceptualization: Jingze Yang, Wanxian Xu, Daolei Chen, Yichen Liu, Xingbo Hu.

Data curation: Jingze Yang, Daolei Chen, Xingbo Hu.

Formal analysis: Jingze Yang, Wanxian Xu, Daolei Chen, Yichen Liu, Xingbo Hu.

Funding acquisition: Xingbo Hu.

Investigation: Jingze Yang, Wanxian Xu, Daolei Chen, Yichen Liu.

Methodology: Jingze Yang, Wanxian Xu, Daolei Chen, Yichen Liu, Xingbo Hu.

Project administration: Jingze Yang, Wanxian Xu, Xingbo Hu.

Resources: Jingze Yang, Xingbo Hu.

Software: Jingze Yang, Wanxian Xu.

Supervision: Daolei Chen, Xingbo Hu.

Validation: Jingze Yang, Daolei Chen, Yichen Liu, Xingbo Hu.

Visualization: Jingze Yang, Wanxian Xu, Daolei Chen, Yichen Liu, Xingbo Hu.

Writing – original draft: Jingze Yang, Wanxian Xu, Daolei Chen, Yichen Liu.

Writing – review & editing: Xingbo Hu.