Dissecting Causal Relationships Between Inflammation-related Proteins, Plasma Metabolites, and Low Back Pain: A Mendelian Randomization Study

doi:10.21203/rs.3.rs-4439134/v1

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Dissecting Causal Relationships Between Inflammation-related Proteins, Plasma Metabolites, and Low Back Pain: A Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-4439134/v1

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Background

We used Mendelian randomization (MR) to investigate the causal relationship between inflammation-associated proteins, LBP , and potential metabolite mediators.

Methods

Using the aggregated data of genome-wide association studies (GWAS), the causal relationship between inflammatory proteins and LBP was explored by two-way MR Analysis, and potential mediating metabolites were identified by two-step MR. Sensitivity analysis was performed to further verify the robustness of the results

Results

After primary analysis and a series of sensitivity analyses, 3 inflammation-related proteins were identified that have a causal relationship with LBP. IL-17C (OR = 0.924; 95% CI, 0.883 - 0.967; P = 0.001 < 0.05) , IL-1 alpha (OR = 0.917; 95% CI, 0.859 -0.978; P = 0.009 < 0.05), were negatively associated with OP, whereas IL-20RA (OR = 1.066; 95% CI, 1.004 -1.131; P =0.036 < 0.05) was positively associated with LBP. This study showed that IL-20RA reduced the risk associated with LBP.

Conclusion

Our study identified a causal relationship between inflammation-related proteins and LBP, with a proportion of the effect mediated by plasma metabolites.

Low back pain

Inflammation-related proteins

Plasma metabolites

Mendelian randomization

Bidirectional analyses

Low Back Pain (LBP) refers to pain or discomfort located in the lower spine, usually including the lower back, sacrum, and pelvic areas. LBP is one of the most common health problems worldwide and is a leading cause of disability and workforce loss in many countries[1]. Low back pain includes a range of different types of pain (e.g., nociceptive, neuropathic, and nociceptive or non-specific) that often overlap[2]. Therefore, understanding the causal relationship between low back pain and potential risk factors, such as inflammation-related proteins, is critical to developing effective prevention and treatment strategies. Such research could shed light on the mechanisms by which pain occurs and develops, providing a basis for targeted interventions.

Inflammatory proteins are involved in regulating the body's inflammatory response, including cytokines, inflammatory cytokines and chemokines. These proteins play an important regulatory role in the inflammatory process, helping to maintain the stability of the body's internal environment. As a key biomarker of systemic metabolic status, inflammation-related proteins have attracted much attention in the study of low back pain[3]. Metabonomics is "omics" an emerging field in the field of science, it provides a comprehensive and systematic method to study the metabolic characteristics of individual[4]. Plasma metabolites serve as key biomarkers of systemic metabolic status, they represent the end products of metabolic processes and can reflect potential pathological changes in bone metabolism. By analyzing the plasma metabolome, researchers can gain insight into the metabolic alterations associated with the development of LBP, identify potential diagnostic and prognostic biomarkers, and further understand the complex metabolic dysregulation of LBP.

The genome-wide association studies (GWAS) have greatly aided in the identification of single nucleotide polymorphisms (SNPs) linked to common complex diseases[5]. Mendelian randomization (MR) analysis uses genetic variation as an instrumental variable (IV) to investigate the relationship between exposure factors and disease outcomes[6, 7]. MR, compared to traditional observational studies, enables more precise and reliable causal inference[8]. In this study, we utilized MR to examine the causal impact of inflammation-related proteins on LBP and evaluate the bidirectional nature of this association.

Study design

We used bidirectional two-sample MR to evaluate the causal relationship between inflammation-related proteins and LBP with Plasma metabolites as the medium. Figure 1 provides a schematic overview of the study design. Initially, we designated the inflammation-related proteins as the exposure and LBP as the outcomes to ascertain their potential roles in either inhibiting or fostering the onset of LBP. LBP was then used as the exposure and inflammation-related proteins as the outcome to investigated changes in metabolites after the occurrence of disease. Then plasma metabolites as mediators to further evaluate this relationship.

Data Sources and Study Samples

In this study, we utilized publicly available GWAS summary data for inflammation-related proteins and LBP-related traits. Data on LBP were drawn from the GWAS summary data sources on the FennGenn consortium, which is available at https://www.finngen.fi/en (LBP including 32845 cases and 294770 controls). GWAS data for 91 inflammation-associated proteins and 1400 plasma metabolites were obtained from published studies[9, 10]The summary-level data utilized in this study can be downloaded and has been obtained with ethical approval from the respective institutions overseeing each GWAS involved. The source of GWAS summary data can be found in Supplementary Table 1.

Instrumental Variables Selection

IVs were selected based on the following criteria: (1) strong genome-wide association with the exposure metabolites, (2) independent of potential confounders, and (3) not associated with other intermediate variables in the causal pathway. We selected IVs using a P value threshold of 5×10^− 8.

Primary analysis

The primary strategy utilized in this study to evaluate the initial connection between exposure and outcome was the inverse variance weighted (IVW) method, which is the best unbiased estimate in the absence of pleiotropy[11]. IVW is a method widely used in MR Research that estimates causal effects by weighting the Wald ratios of each SNP[12]. The fixed-effects IVW approach as the main approach, the random-effects IVW approach was utilized if there was substantial heterogeneity. We conducted a bidirectional MR analysis involving two independent samples to assess the reciprocal causal relationship between Inflammation-related proteins and LBP.

Sensitivity analysis

The heterogeneity between SNPs was evaluated by Cochran’s Q test. Horizontal pleiotropy was assessed through the application of the MR-Egger intercept method and the MR-PRESSO method. In cases where outliers were identified, they were excluded from the analysis, followed by a re-evaluation of the MR causal estimates. To further validate the impact of each SNP on the overall causal estimates, a leave-one-out analysis was conducted.

Statistical analysis

Analyses were executed using R statistical software (version 4.2.3, http://www.r-project.org). MR analyses were conducted with the R-based tools “TwoSampleMR” and “MR-PRESSO”[13, 14].

Metabolic pathway analysis

The selected metabolite metabolic pathways were explored utilizing the online tool Metaconflict 6.0(https://www.metaboanalyst.ca/). In this investigation, only metabolites surpassing the recommended threshold were examined for their respective metabolic pathways.

The causal effect of inflammation-related proteins on LBP

As shown in Table 1 and Fig. 2, according to IVW results, three inflammation-related proteins were found to have a causal relationship with LBP. According to the research results of Yu-Hsiang Hsu,Cheng-Nan Chen et al., cell differentiation from intervertebral disc (IVD) and ligamentum flavum (LF) can be affected by inflammatory cytokines. Regulating IL-20 expression provides a potential pathway for controlling stem cell differentiation[15]. So we screened IL-20RA as the main study protein.

IL-17C (OR = 0.924; 95% CI, 0.883–0.967; P = 0.001 < 0.05), IL-1 alpha (OR = 0.917; 95% CI, 0.859–0.978; P = 0.009 < 0.05), were negatively associated with LBP, whereas IL-20RA (OR = 1.066; 95% CI, 1.004–1.131; P = 0.036 < 0.05) was positively associated with LBP. This study showed that IL-20RA reduced the risk associated with LBP.

Sensitivity analyses of the causal effect of inflammation-related proteins on LBP

IL-1 alpha has heterogeneity and asymmetry based on the Cochran’s Q-test and funnel plot (Table 2 and Fig. 3). So Random-effects IVW was used. The MR-Egger intercept method and the MR PRESSO results indicated there were horizontal pleiotropy across SNPs in the causal estimates between IL-1 alpha and LBP (Table 3). However, the outlier-corrected P value was not statistically significant, and no outlier SNP was found.

The causal effect of LBP on inflammation-related proteins

As shown in Table 4, the IVW results showed LBP did not causal effect on the IL-20RA.

The two-step MR analyses of the causal effects between inflammation-related proteins, plasma metabolites, and LBP

As shown in Table 5, IL-20RA has a mediating effect on LBP through 3-methoxytyramine sulfate levels.

Table 1

MR estimates of assessing the causal effect of inflammation-related proteins on LBP
Outcome	Exposure	Method	Number of SNPs	Beta	SE	P value	OR (95% CI)
LBP	IL-20RA	IVW	20	0.063	0.030	0.036	1.066 (1.004, 1.131)
LBP	IL-17C	IVW	31	-0.079	0.023	0.001	0.924 (0.883, 0.967)
LBP	IL-1 alpha	IVW	33	-0.087	0.033	0.009	0.917 (0.859, 0.978)

Table 2

Heterogeneity analyses of the causal effect of inflammation-related proteins on LBP
Outcome	Exposure	Pleiotropy test
		MR-Egger pleiotropy test			MR-PRESSO distorted outlier test
		Egger intercept	SE	P value	MR-PRESSO global test P value	Outlier-corrected P value	Outliers Indices	Distortion Test P value
IL-20RA	LBP	-0.002	0.008	0.775	0.45	NA	NA	NA
IL-17C	LBP	0.007	0.005	0.192	0.09	NA	NA	NA
IL-1 alpha	LBP	0.016	0.007	0.033	0.00	NA	NA	NA

Table 3

Pleiotropy analyses of the causal effect of inflammation-related proteins on LBP
Outcome	Exposure	Heterogeneity test
		MR Egger		IVW
		Q	P value	Q	P value
LBP	IL-20RA	MR Egger	19.320	18	0.372
LBP	IL-20RA	IVW	19.410	19	0.431
LBP	IL-17C	MR Egger	35.314	29	0.194
LBP	IL-17C	Inverse variance weighted	37.484	30	0.163
LBP	IL-1 alpha	MR Egger	71.465	31	0.000
LBP	IL-1 alpha	Inverse variance weighted	82.955	32	0.000

Table 4

MR estimates of assessing the causal effect of LBP on inflammation-related proteins
Exposure	Outcome	Method	Number of SNPs	Beta	SE	P value	OR (95% CI)
LBP	IL-20RA	IVW	69	-0.047	0.036	0.188	1.027 (0.969, 1.088 )

Table 5

The two-step MR analyses of the causal effects between inflammation-related proteins, plasma metabolites, and LBP
Outcome	Exposure	Mediators	beta all ± se	beta1 ± se	beta2 ± se	beta indirect ± se	proportion mediated(95%CI)
LBP	IL-20RA	3-methoxytyramine sulfate levels	0.063 ± 0.030	-0.164 ± 0.064	-0.050 ± 0.019	0.008 ± 0.010	0.128 (-0.013,0.029 )

Observational studies reported the association between inflammation-related proteins and LBP. In this study, we conducted a two-sample bidirectional MR analysis using publicly available GWAS summary statistics to investigate the causal relationship between 91 human inflammation-related proteins and the LBP. Our research findings revealed intricate and complex interactions between inflammation-related proteins and the LBP. In the present large-scale MR study, 3 inflammation-related proteins were causally associated with LBP. We suggest that IL-17C (OR = 0.924; 95% CI, 0.883–0.967; P = 0.001 < 0.05), IL-1 alpha (OR = 0.917; 95% CI, 0.859–0.978; P = 0.009 < 0.05), were negatively associated with LBP, whereas IL-20RA (OR = 1.066; 95% CI, 1.004–1.131; P = 0.036 < 0.05) was positively associated with LBP. This study shows that IL-20RA through 3-methoxytyramine sulfate levels reduces the risk associated with LBP.Benyi Yang Et al found that IL-20 promotes the progression of bone disease in the early and late stages of RA. It affects osteoclastogenesis and osteoblastogenesis in RA and, together with other IL-10 family members, affects spondylitis[16]. Mohammed Al-Bogami, Jonas Bystrom Et al .found a statistical association between formerly high levels of IL-20 and low BMD. In addition, IL-20 levels decreased in patients who responded to RA after treatment, suggesting that this cytokine may be involved in TNFA-induced bone loss and promote pOCL differentiation[17]. Shenghui Ni, Fengping Shan et al. found that IL-20 can significantly inhibit the differentiation and maturation of chondrocytes, and anti-IL-20 monoclonal antibody 7E can significantly down-regulate the expression of IL-20 and MMP-13 in the synovium of OA mice,alleviating the injury of OA articular cartilage[18]. Yu-Hsiang Hsu, Ruey-Mo Lin et al. found that inflammation, chemotaxis, matrix degradation and angiogenesis after disc herniation were affected by hypoxia conditions and controlled by IL-1, IL-20 and BMP-2[19]. Bowen Meng et al. found that IL-20 may be involved in osteoclast differentiation through synergistic interaction with RANKL[20]. Yuanbo Liu, and others find IL − 20 through RANKL/nf-kappa B/NFATc1 signaling pathways increase osteoclast formation and osteoclast mediated bone erosion. Inhibition of IL-20 can effectively inhibit osteoclast differentiation and reduce bone resorption[21]. Bowen Meng,Dongle Wu et al. found that IL-20 differentially regulates osteoclast generation in bone marrow mesenchymal stem cells by activating the OPG/RANKL/RANK axis and the NF-κB, MAPK, and AKT pathways[22]. Reynolds et al. found that IL-17 has a pathogenic role in cartilage destruc- tion by inhibiting matrix synthesis by chondrocytes and promot- ing MMP production[23].Kampylafka ,van der Heijde et al. Targeting IL-17 with antibodies has been shown to improve activity and reduce bone loss in patients with rheumatoid disease[24, 25]. Mingmei Meng et al. showed that IL-1α promotes osteoclast formation by weakening osteogenic ability[26]. Hsu, YH et al. found that IL-20 promoted osteoclast differentiation through OPG/RANKL/ RANK axis and NF-κB and MAPK signaling pathways[27]. Y.H. Hsu et al. have shown that plasma levels of IL-20 in fracture patients are positively correlated with sclerosin, a negative regulator of bone formation[28]. Kragstrup,Greisen et al. found that the expression of IL-22R1, IL-20 and IL-24 on the surface of osteoclasts also promoted the secretion of MCP-1/CCL2 by osteoclasts[29] .

Combining the results of our study and previous studies, we believe that the inhibition of inflammation-related proteins in LBP is mainly related to osteoclasts, while the promotion of LBP is mainly related to osteoblasts. However, given the complex structural and functional characteristics of these proteins, extracting more insights from them remains quite challenging. Nevertheless, our MR study offers new opportunities to delve deeper into the mysteries of these mysterious molecules.

Our study has several significant advantages, which are critical to providing robust evidence of a causal relationship between exposure and LBP risk, with important clinical implications. These advantages include the use of MR designs, the integration of different data sources, and the use of two-way MR analysis. Taken together, these methodological advantages enable a comprehensive exploration of causality and contribute to a deeper understanding of the complex mechanisms underlying LBP development and progression. However, there are still some limitations in this study. First, our analysis was conducted on European populations, which limits its ability to generalize to other ethnic groups. Second, despite our efforts to identify and rule out any abnormal variants, we must acknowledge that horizontal pleiotropy may have a potential impact on our findings. These considerations highlight the need for further investigation, including targeting diverse populations and using reliable methods to minimize the impact of confounding factors to gain a more complete picture of the observed associations.

This study provides a comprehensive evaluation of the causal association between inflammation-related proteins and LBP. The findings underscore the importance of uncovering the complex mechanisms linking inflammation-related proteins and LBP development. Additionally, these results offer new perspectives for targeted interventions focusing on inflammation-related proteins in managing LBP.

Ethics statement

The real data is publicly available, thus ethics approval was not required.

Consent for publication

Not applicable

Availability of data and materials

All data generated or analysed during this study are included in this published article [and its supplementary information files].

Competing interests

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding

This work was supported by Anhui Provincial Natural Science Foundation (2308085MH294), Natural Science Research Project for Anhui Universities (2022AH050510), the Key Research and Development Projects in Anhui Province (202104j07020010), and the Nature Science Foundation of Anhui University of Chinese Medicine (2020yfyzc27).

Authors’ Contributions

Y. W. and H. L. designed the study, analyzed the data, and wrote the manuscript.., ZM H.,Y Z, and QS. C., summarize and analyze dataanalyzed the data. JX.W. and T.J. revised the manuscript. All authors read and approved the final manuscript.

Acknowledgements

Not applicable

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No competing interests reported.

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Dissecting Causal Relationships Between Inflammation-related Proteins, Plasma Metabolites, and Low Back Pain: A Mendelian Randomization Study

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Version 1

Abstract

Figures

Introduction

Materials and methods

Study design

Data Sources and Study Samples

Instrumental Variables Selection

Primary analysis

Sensitivity analysis

Statistical analysis

Metabolic pathway analysis

Results

The causal effect of inflammation-related proteins on LBP

Sensitivity analyses of the causal effect of inflammation-related proteins on LBP

The causal effect of LBP on inflammation-related proteins

The two-step MR analyses of the causal effects between inflammation-related proteins, plasma metabolites, and LBP

Discussion

Conclusion

Declarations

References

Additional Declarations

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