Low-density lipoprotein cholesterol mediates the causal association between systemic lupus erythematosus and asthma: a mediation mendelian randomization study

Abstract

Background

It is well-documented that systemic lupus erythematosus (SLE) is associated with asthma. However, the causal relationship between SLE and asthma, and the potential mediator need to be explained. This study aims to confirm the cause-and-effect relationship between SLE and asthma, and evaluate the mediation effect of lipid in European ancestry.

Methods

A Two-sample Mendelian randomization (MR) study was applied to analyze the causal relationships between SLE and asthma. A two-step MR design was used to explore whether low-density lipoprotein cholesterol (LDL-C) mediates the causal pathway from SLE to asthma outcome. Cochran’s Q statistic methods and MR-Egger regression were used to assess heterogeneity and pleiotropy. Leave-one-out (LOO) sensitivity test was adopted to estimate the effect of removing one of the selected individual SNPs on the overall results. Funnel and forest plots were also conducted to detect the pleiotropy directly.

Results

SLE was significantly associated with higher asthma risk according to inverse-variance weighted (IVW) method [OR (95%CI): 1.093 (1.024–1.166)] (P = 0.007), MR Egger method [OR (95%CI): 1.192 (1.077–1.319)] (P = 0.028) and Maximum likelihood [OR (95%CI): 1.094 (1.036–1.155)] (P = 0.001), which were robust across adequate sensitivity analysis. On the contrary, asthma has no causal relationship with SLE. In addition, LDL-C may mediate a proportion of 6.15% of the total effect between SLE and asthma.

Conclusion

This study demonstrates that patients with SLE may have a higher risk of developing asthma, which may be mediated by LDL-C. Understanding this relationship provides insight into potential mechanisms underlying asthma development in SLE patients and offers a foundation for developing targeted treatment strategies to manage these risks effectively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41927-025-00539-2.

Introduction

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease characterized by immune dysregulation and multi-organ involvement due to immune complex depositions [1]. According to the reports from past five years, the global incidence rate and prevalence of SLE vary considerably around different regions of the world and even in subgroups of population [2]. SLE often manifests with respiratory complications, including upper airway obstruction, which may exacerbate asthma [3, 4]. Asthma, a chronic inflammatory airway disease, shares immunological underpinnings with SLE, such as T-helper 2 cell activation and immunoglobulin E (IgE) production [5]. Epidemiological studies suggest that asthma prevalence is higher in SLE patients than in the general population [6]. A birth cohort found that if the mother has SLE, the offspring are more likely to develop asthma, indicating that there is a genetic relationship between SLE and asthma [7]. Despite these observations, the causal relationship between SLE and asthma remains unclear, and the potential mediators of this association are yet to be elucidated.

Research has shown that SLE patients have significant changes in the body’s lipid metabolism [8]. For instance, high density lipoprotein (HDL) in SLE patients may convert from anti- inflammatory to pro-inflammatory (“dysfunctional HDL”), which elevate levels of oxidized low- density lipoprotein (ox-LDL) to promote atherosclerosis [9]. Lipids-total cholesterol (TC), triglyceride (TG), and HDL were discovered to be significant related to activity of SLE disease [10]. Interestingly, lipid metabolism is an important factor involving in the pathogenesis of asthma [11]. For example, asthma models show that Scd1, Fasn, and Lpcat1 as the lipid metabolism- related genes, locating downstream of the STAT3-SCD1 axis facilitating lung homeostasis, could inhibit allergic airway inflammation [12]. Prostaglandin-endoperoxide synthase 1 (PTGS1), serving as a lipid metabolism-related enzyme, was found to be associated with the epigenetic mechanism of asthma pathogenesis in the respiratory tract [13]. Thus, it is speculated that SLE may further lead to the occurrence or acute exacerbation of asthma through the changes in lipid metabolism.

Although previous traditional epidemiologic studies have provided evidence of a correlation between SLE and asthma, they fail to completely avoid the influence of reverse causation and confounding factors [14]. Mendelian randomization (MR) is a novel statistical approach to estimate the causal relationship between exposure and outcome using genetic variation in non-experimental data [15, 16]. Since there is a temporal relationship between genetic variants, phenotypes, and diseases and they are not affected by confounding factors, MR has been considered to allow causal inference and to compensate for the shortcomings of epidemiological studies.

In this study, we aimed to investigate the effect of SLE on asthma and explore the mediation effect of lipid with summary-level data from the largest GWAS project in a two-step MR design.

Materials and methods

Figure 1 shows an overview of the study design, respectively. Two-step MR study design based on public summary-level data derived from genome-wide association studies (GWASs) was adopted to evaluate the possible causal relationship between genetic susceptibility to SLE and asthma risk, as well as the potential inter-medium metabolites.

Fig. 1.

Study design flowchart of the two-step mendelian randomization study

Data sources

In this research, several publicly available summary results of previously completed GWAS were used. First, the summary-level statistical data for SLE (OMIM: 152700; GWAS ID: ebi-a-GCST003156) (including 5,201 cases and 9,066 controls) in a comprehensive and large GWAS were derived from IEU OpenGWAS project (https://gwas.mrcieu.ac.uk/ebi-a-GCST003156) [17, 18]. For the outcome dataset, summary-level GWAS data for asthma from GABRIEL (including 10,365 cases and 16,110 controls recruited from 23 studies; asthma was considered to be present if it had been diagnosed by a physician; childhood-onset asthma was defined as the presence of the disease in a person younger than 16 years of age, and later-onset asthma was defined as disease that developed at 16 years of age or older; some surveys contributed samples to both age-at-onset groups; other subgroups consisted of subjects with asthma that developed at an unknown age, subjects with occupational asthma, and subjects with severe asthma) [19], asthma (ICD10: J45|J46) from UK biobank (including 1,693 cases and 359,501 controls) (http://www.nealelab.is/uk-biobank), and asthma (ICD10: J45|J46) from FinnGen (including 1,778 cases and 131,051 controls) (https://r9.risteys.finngen.fi/) were also extracted from four large GWAS cohorts. For the mediator summary-level GWAS data for 637 metabolites were obtained from Neale lab. More details about genotyping, imputation, quality control and genetic effects calculation were described in the original studies.

Selection of instrumental variable (IVs)

The process of identifying IVs strictly based on the following three assumptions of classical MR analysis: (1) Selected IVs were directly affected exposure; (2) Selected IVs were independent of any confounders; (3) Selected IVs didn’t directly affect the outcome, except through exposure. Genetic variants which were robustly associated with SLE (P < 5 × 10^− 8) and independent (r² < 0.001, 10,000 kb) were considered as IVs. The SNP correlated with outcome and other phenotypes was excluded with PhenoScanner [20] (http://www.phenoscanner.medschl.cam.ac.uk). For these candidate SNPs, F-statistics and variance (r²) were calculated and used to evaluate the strength of each genetic tool. If the F-statistic greater than 10, the instrumental variable has strong potential to predict the outcome and can avoid weak-tool bias.

Statistical analyses

Primary analysis

The MR method was used to explore the causal association of genetically predicted difference of the per logarithm of the odds ratio (log OR) increase in SLE with the risk of asthma by reporting ORs. The exposure for SNP-SLE and outcome for SNP-asthma were harmonized to ensure that the alleles of each SNP were consistent. Three methods including Inverse variance weighted (IVW), Maximum likelihood and MR Egger were used to comprehensively estimate the risk of SLE for asthma, of which the IVW method was considered to be the main method for causal inference. In order to further clarify the causal relationship between SLE and asthma, we also conducted a reverse mendelian randomization analysis.

Mediation analysis

We further performed a mediation analysis using a two-step MR design to explore whether LDL-C mediates the causal pathway from SLE to asthma outcome (Fig. 1). The overall effect can be decomposed into an indirect effect (through mediators) and a direct effect (without mediators) effect [21]. The total effect of SLE on asthma was decomposed into (1) direct effects of SLE on asthma and (2) indirect effects mediated by the mediator. We calculated the percentage mediated by the mediating effect by dividing the indirect effect by the total effect. Meanwhile, 95% confidence intervals were calculated with the delta method [21, 22].

Sensitivity analysis

Several methods were introduced in this study for sensitivity analysis. First, Cochran’s Q test was used to assess the heterogeneity. If the P-value was greater than 0.05, indicating no heterogeneity, the fixed-effects IVW method was considered as the main method; otherwise, the random-effects model was used for instead. Second, the MR-Egger intercept method to used estimate the horizontal pleiotropy of IVs. If the P-value was greater than 0.05, it means no horizontal pleiotropy was observed and IVW method is appropriate. When horizontal pleiotropy is observed through the MR Egger intercept method, the MR Egger method can serve as the main reference method. Third, we conducted a leave-one-out (LOO) sensitivity test to estimate the effect of removing one of the selected individual SNPs on the overall results. Finally, funnel and forest plots were also performed to detect the pleiotropy directly.

All statistical analyses were conduct using the “TwoSampleMR” packages in R software, Version 4.1.2. Two-sided P-value < 0.05 was significant.

Results

The genetic variants associated with IVs

SNPs without proxy and SNPs with wrong causal directions were identified by removing palindromic and ambiguous SNPs. There were 6 SNPs in SLE, 8 SNPs in asthma and 38 SNPs in LDL-C as instrumental variables (Supplementary Table S1).

Estimates of genetic association between SLE and asthma

The results of MR analysis exploring the causal relationship between SLE and asthma are shown in Fig. 2. The IVW results of summary data from 23 cohorts indicated that SLE was significantly associated with higher asthma risk [OR (95%CI): 1.093 (1.024–1.166)] (P = 0.007), which is consistent with results from the other critical MR methods including MR Egger [OR (95%CI): 1.192 (1.077–1.319)] (P = 0.028) and Maximum likelihood [OR (95%CI): 1.094 (1.036–1.155)] (P = 0.001). The variance explained by the SNPs for asthma was 9.6%, and the F-statistic values were all above 10, indicating that no weak instrument bias exists. Sensitivity analyses showed that no significant heterogeneity was apparent in our IVs for asthma (Q P_{MR Egger}-value = 0.502; Q P_IVW-value = 0.204). The P_{MR Egger}-value of 0.120 indicated an unbalanced horizontal pleiotropy. We also analyzed two validation GWAS datasets from different asthma populations, The results showed that there was a consistent positive causal relationship between SLE and asthma, including asthma from FinnGen [OR_IVW (95%CI): 1.062 (1.09–1.108)] (P = 0.005) and asthma from UK biobank [OR_IVW (95%CI): 1.000 (1.000-1.001)] (P = 0.050). The meta-analysis results of these three cohorts also indicated that SLE has a significant promoting effect on asthma [ORIVW (95%CI): 1.050 (1.001–1.120)] (P = 0.050).

Fig. 2.

Forest plot of meta-analysis for bidirectional Mendelian randomization between systemic lupus erythematosus (SLE) and the asthma risk

To comprehensively explore the causality between SLE and asthma, we also conduct the reverse mendelian randomization. However, we failed to find any evidence that asthma may cause SLE (all P > 0.05), and these results were consistent among the IVW, MR Egger, and Maximum likelihood methods.

Estimates of genetic association between SLE and LDL-C

To explore the mediator between SLE and asthma risk, a two-step MR was performed. Firstly, 41 SNPs robustly and independently associated with SLE were used to estimate the risk for 637 metabolites. The initial screening identified 55 metabolites significantly influenced by SLE, including LDL-C (Table S3). Among all three methods, IVW showed that SLE significantly correlated with an increased risk of LDL-C [OR (95%CI): 1.016 (1.002–1.030)] (P = 0.022) (Table 1).

Table 1.

The association between SLE, LDL-C and asthma from two-step MR analysis

Effects	Exposure	Outcome	SNPs	Beta (95% CI)	OR (95% CI)	P-value
aTotal effect	SLE	Asthma	6
	IVW			0.089 (0.024, 0.153)	1.093 (1.024, 1.166)	0.007
	Maximum Likelihood			0.090 (0.035, 0.144)	1.094 (1.036, 1.155)	0.001
	MR Egger			0.175 (0.074, 0.277)	1.192 (1.077, 1.319)	0.028
bDirect effect	SLE	LDL-C	41
	IVW			0.016 (0.002, 0.029)	1.016 (1.002, 1.030)	0.022
	Maximum Likelihood			0.016 (0.004, 0.028)	1.016 (1.004, 1.028)	0.010
	MR Egger			0.025 (0.005, 0.045)	1.025 (1.005, 1.046)	0.015
cDirect effect	LDL-C	Asthma	38
	IVW			0.342 (0.055, 0.628)	1.407 (1.057, 1.874)	0.019
	Maximum Likelihood			0.344 (0.057, 0.632)	1.411 (1.058, 1.881)	0.019
	MR Egger			0.512 (0.005, 1.019)	1.668 (1.005, 2.770)	0.048

SLE, systemic lupus erythematosus; LDL-C, low-density lipoprotein cholesterol; CI, confidence interval

^aThe causal effect of SLE on asthma in TS-MR analysis

^bThe causal effect of SLE on LDL-C in TS-MR analysis

^cThe causal effect of LDL-C on asthma in TS-MR analysis

Estimates of genetic association between LDL-C and asthma

Then, IVs were extracted with the genome-wide significance threshold of 5 × 10^− 8 and applied to estimate the casual relationship between 637 metabolites and asthma. The subsequent analysis revealed 31 significant metabolite-asthma associations, again including LDL-C (Table S4). Interestingly, one-unit increase in LDL-C increased the risk of asthma with IVW methods [OR (95% CI): 1.407 (1.057–1.874)] (P = 0.019). In addition, another two methods also showed significant association between SLE to LDL-C risk or LDL-C to asthma risk (all P < 0.05) (Table 1).

Proportion of the association between SLE and asthma mediated by LDL-C

The Delta method was utilized to estimate the mediating effect of LDL-C on the relationship between SLE and asthma by calculating the mediating proportion. The effect coefficient was used with β being the total effect of SLE on asthma, β1 being the direct effect of SLE on LDL-C, and β2 being the direct effect of LDL-C on asthma. All β values obtained from IVW methods were used. Finally, the mediating effect was calculated to be 0.0615, accounting for 6.15% of the overall effect (Table 2; Fig. 3).

Table 2.

Mediating effect of LDL-C in the association between SLE and asthma

Exposure	Mediator	Outcome	Total effecta	Mediated effectb	Mediated proportionc
SLE	LDL-C	Asthma	0.089 (0.024, 0.153)	0.005 (0.001, 0.014)	6.15% (1.69%, 12.93%)

SLE, systemic lupus erythematosus; LDL-C, low-density lipoprotein cholesterol

^aThe causal effect of SLE on asthma in TS-MR analysis

^bThe causal effect of SLE on LDL-C in TS-MR analysis

^cThe effect of SLE on asthma mediated through LDL-C

Fig. 3.

Schematic diagram of mediating effect of two-step mendelian randomization

Discussion

The causal relationships between SLE and asthma, as well as potential mediator, were explored in this two-step MR analysis. According to our findings, patients with SLE may suffer from asthma, and lipid-lowering especially LDL-C may be one of the effective ways to prevent this process.

In this study, we found that SLE patients have an increased risk of asthma. In fact, this is not the first time that this phenomenon has been reported. As early as 2009, Hersh et al. found that SLE patients have common lung involvement through a cohort study [23]. It’s not unique that a retrospective study from Taiwan found that the risk of asthma in SLE patients was significantly higher than that in healthy people, especially in the elderly over 60 years old [24]. In addition, two other studies found that mothers with SLE also increase the risk of childhood asthma, and maternal antibodies and inflammatory factors may mediate this process [7, 25]. In this present study, we further confirm this phenomenon from the genetic susceptibility with MR, providing an important theoretical basis for SLE patients to prevent asthma.

Further, we use the two-step MR to confirm the mediating role of 637 metabolites in the causal relationship between SLE and asthma. Among the 637 metabolites, only LDL-C emerged as the sole metabolite exhibiting a significant mediating role in the SLE-asthma causal association. On the one hand, several studies have found that SLE patients have dyslipidemia, such as abnormal LDL-C and HDL-C, which increases the incidence of atherosclerosis, renal dysfunction and other diseases [26–28]. However, another study using mendelian randomization found that SLE had negative causal links for LDL-C [29]. In this case, we use high-quality GWAS data to verify the positive causal relationship between SLE and LDL-C, which is helpful to further study the mechanism of SLE-related lipid metabolism. On the other hand, previous studies on the relationship between blood lipids and asthma have shown ambiguous results. In a prospective cohort study, Rebecca et al. found that high levels of LDL-C were associated with asthma [30]. Coincidentally, Yang et al. discovered that asthma was associated with higher LDL-C levels, which was amplified in overweight subjects [31]. Patrick et al. believe that reducing LDL-C level and limiting lipid consumption can help reduce the risk of asthma and the deterioration of asthma symptoms [32]. Similarly, we also verified the previous research results through MR.

Although the relationship between SLE and LDL-C, LDL-C and asthma has been widely reported, the mediating role of LDL-C in the pathogenesis of asthma caused by SLE in our research was reported for the first time, which may provide a strategy for the prevention and treatment of asthma in SLE patients. A study shows that cytokines such as TNFα may mediate the relationship between SLE and asthma because it was a common genetic risk factor for them [33]. Another study showed that the elevated IgG in the serum of SLE patients may be related to immune diseases such as asthma in children [34]. Thus, immune factors and immune processes play a key role between SLE and asthma. Recent studies have found that lipid metabolism is closely related to the immune function of the body. It is well known that LDL-C is responsible for the transport of cholesterol, indicating that elevated LDL-C may lead to cholesterol accumulation. Interestingly, it has been reported that the accumulation of excessive cholesterol can affect mitochondrial metabolism and release its mtDNA to the cytoplasm, thus activating the AIM2 inflammatory body in macrophages, leading to an inflammatory cascade reaction [35]. In Ldl-r/XBP1 conditional knockout mice, serum IgG and other immunoglobulin levels were significantly reduced, suggesting that LDL-C may directly affect the level of IgG and other immune factors. In a word, LDL-C may affect the production of immune factors and the regulation of immune process, thus mediating the occurrence of asthma in SLE patients. Therefore, we believe that abnormal blood lipid indicators may be the potential links between SLE and asthma. In the future, further longitudinal and mechanistic studies are needed to determine whether there is a true interaction or shared pathophysiological pathway.

However, our study also has several limitations. Firstly, this is a European-based study, and our findings should be extended to other populations with caution. Secondly, we did not conduct subgroup analysis to further explore this casual association in different population, because the non-European datasets, which include SLE, asthma and LDL-C at the same time are not available. This gap will be filled in the future. Thirdly, confounders such as smoking, socioeconomic status, and atopy, along with important findings like demographics, disease severity, medication use (such as corticosteroids), and comorbidities were not analyzed in depth. The reason is that the existing GWAS database does not include the important information, which will be discussed in the future.

Conclusion

Our study provided robust evidence for a potential causal relationship between SLE and an increased risk of asthma mediated by LDL-C, which improving our understanding of the mechanisms SLE and asthma and providing a treatment strategy for SLE patients.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Author contributions

HY: Data curation, Formal analysis, Writing - Original Draft and Writing - Review & Editing. TXW: Writing - Original Draft and Writing - Review & Editing. LL: Administration, Methodology, Software and Writing - Review & Editing. ZH: Conceptualization, Resources, Writing - Review & Editing and Funding acquisition.

Funding

This study was supported by grants from Hunan Provincial Natural Science Foundation of China (No. 2025JJ70211, 2025JJ81117); Shaoyang City science and technology plan guiding project (No. 2023ZD0081, 2023ZD0083, 2024PT6135); Scientific research project of the First Affiliated Hospital of Shaoyang University (No. 24FY1001, 23FY1001).

Data availability

The data presented in this study are openly available in Table S2.

Declarations

Ethics approval and consent to participate

This study utilized publicly available data from participant studies that had already received ethical approval from a committee responsible for human experimentation. No additional ethical approval was necessary for this particular study.

Consent for publication

Not applicable.

Disclosure statement

The authors have nothing to disclose.

Competing interests

The authors declare no competing interests.