Exploring the causal genetic relationship between 41 inflammatory cytokines and osteoporosis: a bidirectional Mendelian randomization analysis

Abstract

Objectives:

Previous studies reported that many inflammatory factors have associations with osteoporosis. This study use Mendelian randomization (MR) analysis to explore the causal genetic relationship between 41 inflammatory factors and osteoporosis.

Methods:

A bidirectional two-sample MR analysis was performed by employing five Mendelian randomization analysis methods including MR Egger regression, weighted median, inverse-variance weighted and weight mode methods. Summary statistics from the genome-wide association study (GWAS) of 41 inflammatory cytokines and osteoporosis were included in this study. This study examined the MR analysis results for heterogeneity and horizontal pleiotropy.

Results:

Using the inverse variance weighted (IVW) method, this analysis indicated that elevated monocyte chemotactic protein-1 (MCP-1) levels were potentially linked to a 22% increased likelihood of osteoporosis (Odds Ratio (OR) = 1.22, 95% CI: 1.04–1.43, p = 0.014). Additionally, through the IVW approach, we observed that higher tumor necrosis factor-related apoptosis inducing ligand (TRAIL) levels were possibly associated with a 15% greater risk of osteoporosis (OR = 1.12, 95% CI: 1.03–1.29, p = 0.012). Other 39 inflammatory cytokines don’t have casual genetic association with osteoporosis. When this study use MR to estimate the influence of osteoporosis on inflammatory factors, none of the p-values with IVW method were lower than 0.05.

Conclusion:

This is the first bidirectional MR analysis to explore the causal genetic relationship between inflammatory cytokines and osteoporosis. This study found that MCP-1 and TRAIL are probably the upstream factors correlated with osteoporosis, and no inflammatory cytokine was involved in osteoporosis development downstream.

Keywords

Inflammatory factor osteoporosis Mendelian randomization

Introduction

Osteoporosis, which is a major public health problem all around the world, is a skeletal disorder characterised by a decrease in total body bone mass. Osteoporosis may lead to increased bone fragility and the risk of fracture.¹ In a study on osteoporosis conducted by Spanish researchers, it was discovered that individuals who experienced a first major osteoporotic fracture had their healthcare costs double in the first year following the fracture, primarily due to expenses related to inpatient treatment. Besides the cost of hospitalization can reach €12,028.² The economic burden of osteoporosis-related fractures is costing approximately $17.9 in the USA and £4 billion in United Kingdom in every year.³

The interactions between osteoporosis and inflammatory factors had been confirmed by a variety of basic experiments, and a lot of research was also conducted that inflammatory factors are the important factors lead to osteoporosis.⁴ Tumour necrosis factor-alpha (TNFα) may increase osteoblast apoptosis and indirectly stimulate osteoclastogenesis via NF-κB ligand (RANKL), and in postmenopausal osteoporosis TNF-α was found to regulate bone mass decline.⁵ Interleukin-17 (IL-17) regulates bone metabolism in two ways. IL-7 may direct mesenchymal stem cell differentiation towards the osteoblast and indirectly stimulate osteoclast differentiation.⁶ B lymphocytes can promote the generation of osteoclasts.⁷ However, most of the studies are basic medical research, and some studies even come to opposite conclusions.

Due to the selection of statistical methods and the limitation of sample size, it is often difficult to avoid bias caused by confounding factors in previous association studies.⁸ Mendelian randomisation (MR) employs single nucleotide polymorphisms (SNPs) as instrumental variables (IVs) to investigate the causal associations between exposure factors and outcomes, which can effectively avoid the bias of experimental results caused by confounding factors.⁹ To the best of our knowledge, MR has not been used to study the correlation between inflammatory factors and osteoporosis. This study uses the bidirectional Mendelian randomisation analysis to explore the causal genetic association between 41 inflammatory factors and osteoporosis.

Method

The GWAS summary statistics included in this study have already been published. So this study was exempted from ethical review by the Ethics Committee of Henan Provincial People’s Hospital. This study involves human participants and was approved by all. Study cohorts used in this work had already obtained relevant ethical approval and written participant consent. Participants gave informed consent to participate in the study before taking part.

Study design

In this study, SNPs were defined as instrumental variables and SNPs were selected from GWAS summary statistics. The three basic principles of two-sample MR were strictly followed in the process of this study: (1) All selected instrumental variables have a high association with exposure. (2) All selected instrumental variables were independent of confounding factors. (3) All selected instrumental variables affect outcomes directly through exposure and there were no confounding factors involved.

Initially, this study established a genome-wide significance threshold of p <5 × 10-8 to identify SNPs strongly associated with osteoporosis and inflammatory factors. Given the limited number of SNPs identified using this criterion, a less stringent cutoff of p <5 × 10-6 was subsequently applied for SNP selection. Second, in order to reduce the impact of linkage disequilibrium (LD), the LD of SNPs that are closely associated with osteoporosis has to meet the condition of kb = 10,000 and r² = 0.001. Third, the F-statistic was used to avoid weak instrument bias and F-statistic has to meet the condition of over 10.

Genome-wide association study summary data for osteoporosis

GWAS summary data of osteoporosis were obtained from FINNGEN (r8.finngen.fi). The GWAS ID in the FINNGEN database is ‘finn-b-M13_OSTEOPOROSIS’, and the dataset comprised 3203 cases and 209,575 controls of European ancestry. Both male and female participants were included. However, as the data were obtained from a public database, and the inclusion and exclusion criteria as well as the specific diagnostic methods are not known. This dataset contained 16,380,452 SNPs.¹⁰ Cases were identified using the M13 code from the International Classification of Diseases, Tenth Revision (ICD-10).¹¹

Summary data from genome-wide association studies for inflammatory cytokines

The summary date contains information on 41 inflammatory factors for 8293 Finnish participants.¹² The data were sourced from the Young Finns Study (YFS) and FINRISK surveys.¹³ In the YFS study, the average age of participants is 37 years, while in the FINRISK survey, it is 60 years. The YFS is a multicentre follow-up study with randomly chosen subjects from the Finnish cities. The present cross-sectional study includes 2019 unrelated individuals who participated in the 2007 follow-up and who had both cytokine measurements and genotype data available.¹² Additionally, no overlapping was observed in the population selection between the exposure and outcome groups.

Statistical analysis

The R software’s ‘TwoSampleMR’ package (version 4.2.3)was utilised to conduct two-sample MR analyses. For the MR analysis, this study used the random effects inverse variance weighted (IVW) as the main method, and other four analysis methods as supplementary methods. The method of IVW assumes that all SNPs are valid, which results in the most accurate results calculated by this method.¹⁴ We utilised MR-Egger regression to assess the bias resulting from horizontal pleiotropic effects and inefficient IVs. The causal effect estimate of the exposure on the outcome is indicated by the slope of the MR-Egger regression, provided that the intercept term is not statistically significant.¹⁵ If half of the SNPs were valid, we used weighted median and weighted modes to estimate the causal effect.¹⁶ p < 0.05 was considered statistically significant and all the statistical tests were two-tailed.

Heterogeneity and sensitivity test

To find the heterogeneity of the effects, this study employed the Rucker’s Q statistic for MR Egger analyses and the Cochran’s Q statistic for MR-IVW analyses, with p > 0.05 denoting no heterogeneity.¹⁷ The impact of directional pleiotropy on risk estimations was evaluated using the MR Egger method’s intercept tests, where p > 0.05 denoted the absence of horizontal pleiotropy.¹⁸ The MR pleiotropy residual sum and outlier (MR-PRESSO) approach was also utilised to evaluate outlier SNPs and probable horizontal pleiotropy when p < 0.05 with the heterogeneity of the effects of SNPs. Outliers detected in the MR analysis may be found using the MR-PRESSO analysis, and horizontal pleiotropy can be found using the global test included in the MR-PRESSO analysis, with p > 0.05 denoting the absence of horizontal pleiotropy.¹⁹ In order to determine if a single SNP had an impact on the causal link, this study also conducted a ‘leave one out’ study.²⁰ Figure 1 shows the flowchart of Mendelian randomisation.

Figure 1.

Flow chart of this Mendelian randomisation study.

Result

Impact of inflammatory cytokines on osteoporosis

There were 16 SNPs in the MR analysis between monocyte chemotactic protein-1 (MCP-1) and osteoporosis. By using the IVW method, we found that higher MCP-1 levels were suggestively associated with 22% higher odds for osteoporosis (OR = 1.22, 95% CI: 1.04–1.43, p = 0.014). A 22% increased risk of osteoporosis due to MCP-1 may suggest a moderate but meaningful genetic influence. By the Weighted median method, this study found that higher MCP-1 levels were suggestively associated with 24% higher odds for osteoporosis (OR = 1.24, 95% CI: 1.00–1.54, p = 0.049). A 24% increased risk of osteoporosis due to MCP-1 may suggest a moderate but meaningful genetic influence. The MR Egger analysis did not identify a statistically significant association. However, the results demonstrated a comparable changing trend to those of the IVW and weighted median methods (OR = 1.16, 95% CI: 1.07–1.40, p = 0.662).

There were 16 SNPs in the MR analysis between tumour necrosis factor-related apoptosis inducing ligand (TRAIL) and osteoporosis. By the IVW method, this study found that higher TRAIL levels were suggestively associated with 12% higher odds for osteoporosis (OR = 1.12, 95% CI: 1.03–1.29, p = 0.012). A 12% increased risk of osteoporosis due to TRAIL may suggest a moderate but meaningful genetic influence. By the weighted median method, we found that higher TRAIL levels were suggestively associated with 18% higher odds for osteoporosis (OR = 1.18, 95% CI: 1.02–1.37, p = 0.022). A 18% increased risk of osteoporosis due to TRAIL may suggest a moderate but meaningful genetic influence. The MR Egger analysis did not reveal a statistically significant association; however, the findings showed a comparable changing trend to those of the IVW and weighted median methods (OR = 1.18, 95% CI: 1.00–1.38, p = 0.069). The results of the analysis of MCP-1 and TRAIL are shown in Table 1.

Table 1.

The results of the analysis of MCP-1 and TRAIL.

Exposure/Outcome	nSNP	Method	b	se	pavl	Estimate
TRAIL/osteoporosis	16	MR Egger	0.16	0.08	0.069	1.18 (1.00–1.38)
		Weighted median	0.17	0.07	0.022	1.18 (1.02–1.37)
		IVW	0.14	0.06	0.012	1.15 (1.03–1.29)
		Simple mode	0.18	0.14	0.202	1.20 (0.92–1.57)
		Weighted mode	0.19	0.08	0.041	1.21 (1.02–1.43)
MCP-1/osteoporosis	16	MR Egger	0.10	0.22	0.662	1.16 (1.07–1.40)
		Weighted median	0.22	0.11	0.050	1.24 (1.00–1.54)
		IVW	0.20	0.08	0.014	1.22 (1.04–1.43)
		Simple mode	0.22	0.19	0.256	1.25 (0.86–1.82)
		Weighted mode	0.23	0.19	0.252	1.26 (0.86–1.85)

TRAIL: tumour necrosis factor-related apoptosis inducing ligand, IVW: inverse variance weighted, MCP-1: monocyte chemotactic protein-1, SNP: single nucleotide polymorphism, MR: Mendelian randomization.

Other 39 inflammatory cytokines don’t have casual genetic association with osteoporosis, all the p-values with IVW method were above 0.05. The results of the association between all 41 inflammatory factors and osteoporosis are shown in Figure 2. There was no heterogeneity in the association between MCP-1 and osteoporosis with MR Egger (p = 0.761) and the IVW method (p = 0.674). There was no heterogeneity in the association between TRAIL and osteoporosis with MR Egger (p = 0.916) and the IVW method (p = 0.940). The results of the MR Egger test of horizontal pleiotropy showed no horizontal pleiotropy both in MCP-1 (p = 0.174) and TRAIL (p = 0.734). The funnel plots, scatter plots and leave-one out sensitivity analyses of Mendelian randomization analysis are shown in Figures 3 and 4.

Figure 2.

Results of Mendelian randomization analysis of the association between inflammatory factors and osteoporosis.

Figure 3.

Funnel plots (a), scatter plots (b) and leave-one out sensitivity analyses (c) of Mendelian randomisation analysis between MCP-1 and osteoporosis.

Figure 4.

Funnel plots (a), scatter plots (b) and leave-one out sensitivity analyses (c) of Mendelian randomisation analysis between TRAIL and osteoporosis.

Influence of osteoporosis on inflammatory cytokines

A total of 21 SNPs were extracted as IVs for osteoporosis. A total of 1 SNP was not accessible in the result GWAS for β-NGF, CTACK, GROA, IL-13, IL-16, IL-18, IL-1B, IL-18RA, IL-2, IL-2RA, IL-5, IL-7, IL-8, IL-9, IP-10, M-CSF, MIF, MIG, MIP-1A, RANTES, SCGF-B and TNF-α. Twenty SNPs were included in the MR analysis since there were no proxy SNPs available. A total of 19 SNPs could be included in the MR analysis since 2 SNPs for MCP-3 and TNF-β were not available in the GWAS summary date. The F-statistic values were all above 10 in all the inflammatory cytokines SNPs. However, none of the p-values with IVW method was lower than 0.05. Which means no inflammatory cytokine was involved in osteoporosis development downstream. The results of the correlation between osteoporosis and 41 inflammatory factors are shown in Figure 5.

Figure 5.

Results of Mendelian randomisation analysis of the association between osteoporosis and inflammatory factors.

Discussion

To the best of our knowledge, this is the first study to analyse the causal genetic association between inflammation and osteoporosis using bidirectional Mendelian randomisation. These findings suggest that MCP-1 and TRAIL may have a potential causal role in osteoporosis. Besides no inflammatory cytokine was involved in osteoporosis development downstream. The conclusion is different from previous observational studies. A sizable sample size from the GWAS summary data were used in this study. This study also tested the results for heterogeneity and pleiotropy. Therefore, the present results were reliable and conformed to the three assumptions of Mendelian randomisation.

Some studies have also explored the correlation between inflammatory factors and osteoporosis via Mendelian randomisation and arrived at diverse conclusions. Yi et al.²¹ conducted a significant causal correlation between osteoporosis and MCP-3. The occurrence of osteoporosis may also lead to elevated levels of MCP3 (β = −0.466 (−0.714 to −0.217), p < 0.01]. Xu et al.²² conducted that MIP-1α and IL-12p70 expression associated negatively and causally with osteoporosis (β = −0.017 (MIP-1α), β = −0.011 (IL-12p70)). Conversely, TRAIL was associated with a decreased risk of fractures (OR = 0.980). Liu et al.²³ conducted that MR Results showed that FGF21 overexpression reduced bone mineral density through a two-sample, mediating Mendelian analysis.

TRAIL, the full name of which is tumour necrosis factor-related apoptosis-inducing ligand, also known as APO2L, belongs to the TNF family.²⁴

Many clinical studies have detected the content of TRAIL in osteoporosis patients and its correlation with clinical characteristics. TRAIL levels are closely associated with age, the content of TRAIL in pregnant women is the lowest and the content in postmenopausal women is the highest.²⁵ Many cohort studies have also demonstrated that TRAIL content in osteoporosis patients is higher than that in normal people.^26,27 Serum levels of TRAIL were significantly higher in patients with osteoporosis compared with controls (66.1 ± 45.3 pmol/l versus 50.0 ± 20.1 pmol/l, p < 0.01).²⁸ The level of TRAIL showed a positive and independent correlation with arms fat mass (p < 0.05) and trunk fat mass (p < 0.001).²⁹ This may also contribute to osteoporosis. While OPG serum levels were associated with the erythrocyte sedimentation rate (R = 0.215, p < 0.05), TRAIL serum levels had a correlation with C-reactive protein (CRP) levels (R = 0.201, p < 0.05).³⁰ TRAIL may lead to osteoporosis by affecting the inflammatory environment.

TRAIL is the receptor for OPG, and this mechanism induces osteoclast differentiation leading to osteoporosis.³¹ Studies found that the occurrence of osteoporosis and vascular calcification is highly time overlapping, so the study speculated that TRAIL may not only induce vascular calcification at the same time may cause osteoporosis. Calcification of intraosseous vessels may lead to insufficient blood supply to the bone and then lead to bone loss and osteoporosis.^32
–34

Meanwhile, many studies have shown that TRAIL antagonists can reverse osteoporosis. TRAIL can be used as a target for drug therapy in the treatment of osteoporosis. Tetrandrine could potentially play a role in anti-osteopenia treatment, as it inhibits RANKL-induced osteoclastogenesis by enhancing TRAIL degradation and promoting osteoblast development.³⁵ Wu et al. found that Onc201 inhibited osteoclast-relevant genes expression and promoted TRAIL-mediated proliferation and differentiation of osteoclasts. They found that Onc201 provided protection in the mouse model of bone loss.³⁶

MCP-1, the full name of which is monocyte chemoattractant protein-1, also known as chemokine ligand 2 (CCL2), is from the family of CC chemokines.³⁷ Recent studies found that MCP-1 is a key mediator of bone metabolism, involving the production of osteoclasts, the recruitment of monocytes and macrophages and bone resorption.³⁸

Relevant clinical studies have confirmed that the content of MCP-1 in osteoporosis patients is abnormal. Wang et al. use ELISA to measure the MCP-1 content in osteoporosis patients, and they found that MCP‑1 was finally demonstrated to be increased in the serum of postmenopausal osteoporosis patients, and the content of MCP-1 was correlated with the severity of bone loss.³⁹ Yang et al.⁴⁰ found that MCP-1 levels were increased in postmenopausal women and elevated MCP-1 levels were linked with decreased bone mineral density.

MCP-1 can be used as a target for drug therapy in the treatment of osteoporosis.^36
–38 Frazier et al. found that the content of MCP-1 in the osteoporosis model mice was higher, and MCP-1 level was closely related to estrogen level. MCP-1 mRNA was inhibited to a maximum of 300 pg/ml estrogen, with an inhibition range of 50%–90%. The authors suggested that estrogen regulates osteoporosis through MCP-1.⁴¹ Siddiqui et al. found that intermittent PTH regulates the secretion of MCP-1 by osteoblasts, which lead to increased TGF-β signal path; this regulatory mechanism will directly affect the bone mass.⁴²

In this study, only two inflammatory factors had casual genetic association with osteoporosis, and none of the inflammatory factors was associated with osteoporosis when osteoporosis was included as the exposure variable. which may be the most different with the previous studies. The reasons for this difference were analysed. First, the concept of osteoporosis in this study is osteoporosis in a broad sense, primary osteoporosis and secondary osteoporosis are not classified. The confounding factors may cause a certain deviation of the experimental results. Second, most of the studies on the correlation between inflammatory factors and osteoporosis are basic research, while this study is a real-world cohort study, which is the reason for the inconsistent results. Lastly, osteoporosis may be caused by a variety of factors, and Mendelian randomisation considers inflammatory factors to be only confounding factors. Meanwhile, the reverse Mendelian randomisation study did not find that osteoporosis would have an impact on inflammatory factors, which is inconsistent with the conclusion of the relevant observational studies. Osteoporosis maybe not a major driver of inflammation and the limitations of the GWAS database may affect the test power.

This study also has limitations. First, the date of this MR analysis was derived from the GWAS dataset. Conclusions may be updated as more research results are obtained. Further bidirectional MR analyses with larger datasets may be needed to confirm the findings. Second, the concept of osteoporosis included in this study is rather general and does not distinguish between primary, secondary and idiopathic osteoporosis. Undoubtedly, these are the limitations of this research. In the future, we will further explore the correlation between different types of osteoporosis and inflammatory factors. Third, all the enrolled patients were European, and the results may not be generalisable to Asians due to racial differences. Furthermore, the research group will conduct functional studies to explore the MCP-1/TRAIL mechanisms in bone cell models in the future.

Conclusion

This is the first bidirectional Mendelian randomisation analysis to explore the causal genetic relationship between inflammatory cytokines and osteoporosis. The present findings suggest that MCP-1 and TRAIL may have a potential causal role in osteoporosis. Besides no inflammatory cytokine was involved in osteoporosis development downstream.

Footnotes

ORCID iD

Weiran Hu

Author contributions

Kai Zhang and Hongqiang Wang, analysis of data, drafting and revising manuscript and final approval. Xinge Shi, design, acquisition, analysis of data, drafting and revising manuscript and final approval. Chuang Wang and Runze Liu, data collection and final approval. Weiran Hu, conception and design of work, drafting and revising manuscript and final approval.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship and/or publication of this article: This work was supported by the Science and Technology Research Program of Henan Province (242102310107) and the Medical Science and Technology Research Program of Henan Province (LHGJ20240006).

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Data availability statement

All of the data are described in the manuscript. Data available on request from the authors. The datasets used and/or analysed in the present study are available from the corresponding author upon reasonable request.

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