Causal Insights into Plasma Lipidome and Allergic Rhinitis: Implications from a Bidirectional Two-Sample Mendelian Randomization Analysis

doi:10.21203/rs.3.rs-4617768/v1

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Causal Insights into Plasma Lipidome and Allergic Rhinitis: Implications from a Bidirectional Two-Sample Mendelian Randomization Analysis

https://doi.org/10.21203/rs.3.rs-4617768/v1

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Plasma lipidomes regulate inflammatory responses and influence immune cell function, but the causal relationship between lipids and allergic rhinitis (AR) remains unclear. This study aimed to elucidate this relationship using Mendelian randomization (MR). Summary statistics were obtained from genome-wide association studies (GWAS) involving 179 plasma lipid subtypes and AR outcomes. Sensitivity analyses ensured robustness, heterogeneity, and absence of horizontal pleiotropy. Bayesian Weighted Mendelian Randomization (BWMR) was employed to enhance reliability. A meta-analysis further validated the findings. After FDR correction, several lipid phenotypes were identified as significant risk factors for AR, particularly phosphatidylcholine (18:0_20:4) levels, which remained significant using FinnGen data. Consistent results from MR Egger, weighted median, and BWMR methods underscored the robustness of our findings. This study suggests potential causal associations between specific plasma lipids and AR, offering new perspectives for exploring the biological mechanisms underlying AR and guiding the development of early intervention and treatment strategies.

Health sciences/Medical research/Genetics research

Health sciences/Diseases/Immunological disorders/Inflammatory diseases/Allergy

Allergic rhinitis

Plasma lipidome

Causal inference

Mendelian randomization

Genetic analysis

Allergic rhinitis (AR) is a prevalent chronic inflammatory disease of the nasal mucosa characterized by hypersensitivity reactions to specific allergens, resulting in symptoms such as rhinorrhea, nasal congestion, sneezing, and nasal itching. Globally, AR affects approximately 10–20% of the population, with significant variations in prevalence across different regions and populations ¹. AR not only severely impacts the quality of life of affected individuals but also leads to reduced work and study efficiency, resulting in considerable socioeconomic burden. Additionally, AR is closely associated with other allergic diseases such as asthma, increasing the potential risk for more severe respiratory conditions ^1,2.

Currently, it is known that both environmental and genetic factors contribute to the development of AR ^3,4. Mechanisms related to immune response and inflammation play crucial roles in the pathogenesis of AR ^5–7. Recent studies have shown that lipid metabolism abnormalities may act as pro-inflammatory mediators, regulating the secretion of various inflammatory cytokines such as IL-4, IL-5, IL-10, IL-17, and IL-33, and interferons. These lipid substances potentially contribute to the development of inflammatory and allergic diseases, including AR ⁸. Moreover, inflammation can further alter lipid metabolism, causing various changes in plasma lipid and lipoprotein levels. Understanding these interactions is essential for developing new strategies for the prevention, diagnosis, and treatment of AR ^9,10.

Plasma lipids play vital roles in cell structure and energy metabolism and are increasingly recognized for their involvement in immune responses and allergic diseases ^11,12. Commonly measured plasma lipids, such as HDL-C, LDL-C, TG, and TC, have been identified as risk or protective factors for various diseases, including cardiovascular diseases ¹³, diabetes ¹⁴, and metabolic syndrome ¹⁵. Advances in lipidomics have revealed the complexity and diversity of circulating plasma lipids. Beyond traditional lipid markers, more refined lipid species such as cholesterol esters (CE), ceramides (CER), lysophosphatidylcholines (LPC), and phosphatidylcholines (PC) are important in immune regulation and inflammation ^16–18. Allergic rhinitis is associated with complex alterations in high-density lipoprotein composition and function ^19,20.These lipids can modulate immune cell activity, influence cell membrane fluidity and signal transduction, thus playing roles in the pathogenesis of allergic diseases ²¹.

Randomized controlled trials (RCTs) are considered the gold standard for establishing causality between exposure factors and disease outcomes. However, conducting RCTs to study the effects of plasma lipids on AR poses significant design and ethical challenges. For instance, manipulating lipid levels over a long period could pose health risks to participants, and achieving compliance in dietary or pharmacological interventions is difficult. Moreover, RCTs require substantial financial and logistical resources, making them less feasible for large-scale studies on lipidomics and AR. Thus, alternative approaches like Mendelian randomization (MR) offer a valuable solution by leveraging genetic variants as proxies for lipid levels, enabling robust causal inference without the ethical and practical constraints of RCTs ²².

Mendelian randomization (MR) is a statistical method that uses genetic variants as instruments to assess causal relationships between risk factors and diseases. Based on Mendel's laws of inheritance, MR assumes that the association between genetic variants and exposure factors (such as plasma lipid levels) is random and independent of potential confounders. The advantage of MR analysis lies in its ability to simulate the design of randomized controlled trials, thus providing robust evidence for causal relationships. Compared to traditional observational studies, MR can better overcome issues of reverse causation and confounding bias, making it a widely recognized approach in causal inference research ^22,23.

Given the public health significance of AR and the potential role of plasma lipids in immune regulation, this study aims to utilize a bidirectional two-sample Mendelian randomization approach, integrating data from multiple genome-wide association studies (GWAS), to investigate the causal relationship between the plasma lipidome and AR. Our objective is to identify specific lipid species and evaluate their potential roles in AR, thereby providing scientific evidence for future preventive strategies and therapeutic interventions. By doing so, we hope to deepen our understanding of the underlying mechanisms of AR and explore novel lipid-based therapeutic targets.

Study Design

Bidirectional two-sample Mendelian randomization (TSMR) was used to analyze the causal relationship between the plasma lipidome and allergic rhinitis (AR). The overall design of this study is shown in Figure 1. To obtain reliable results, three hypotheses need to be satisfied when performing TSMR analysis: (1) there is a strong correlation between genetic variants and exposure factors; (2) there is no correlation between genetic variants and confounders; and (3) genetic variants can only affect the outcome through exposure factors, but not through other methods, that is, horizontal pleiotropy is not allowed (Figure 1). Genetic variants that satisfy these three hypotheses can be included in TSMR analysis as instrumental variables ²².

Data Sources

Summary statistics of the plasma lipidome were obtained from a recent large-scale GWAS study by Ottensmann L et al. This is the largest GWAS study to date on the genetic influence on the human plasma lipidome ²⁴. This study reported univariate and multivariate GWAS of 179 lipid species from 13 lipid classes in 7,174 Finnish individuals from the GeneRISK cohort. These data can be downloaded from the GWAS Catalog (https://www.ebi.ac.uk/gwas/, GCST90277238-GCST90277416).

Outcome information for allergic rhinitis (AR) was derived from two cohorts. The first cohort, the UK Biobank dataset (ukb-b-7178)²⁵, includes 25,486 cases and 87,097 controls, with a total sample size of 112,583 individuals of European descent. The GWAS identified 9,851,867 SNPs, and the data is based on the HG19/GRCh37 build. The diagnostic criteria for AR were based on doctor-diagnosed hayfever or allergic rhinitis. The second cohort, the FinnGen biobank analysis (round 10) (finnagen_R10_ALLERG_RHINITIS) ²⁶, is the largest dataset for AR currently available. This dataset includes 12,240 cases and 392,069 controls, with a total sample size of 404,309 individuals of European descent. The GWAS identified 19,345,529 SNPs, and the data is based on the HG38/GRCh38 build. AR cases were identified using ICD-10 codes J30.10, J30.19, J30.2, J30.3, and J30.4 from hospital records (Table 1).

Although both the plasma lipidome and FinnGen data involve Finnish individuals, the lipidome data are specifically from the GeneRISK cohort, which satisfies the requirements for two-sample MR analysis. We used the tool available at [https://sb452.shinyapps.io/overlap/] to calculate the sample overlap rate. Given the significant difference in sample sizes between the exposure (lipidome data) and outcome (FinnGen data) datasets, even if fully overlapping, the proportion would only be 1.7%. This level of overlap is insufficient to introduce bias and type 1 errors into the analysis, thus satisfying the requirements for two-sample MR analysis ^27,28.A meta-analysis was conducted using these two cohorts to validate the findings and ensure robustness ²².

Table 1 Exposure and Outcome Data Information

Category	Description	Sample Size	Cases	Controls	SNPs	Source	Download Link
Exposure	Plasma Lipidome	7,174 (GeneRISK cohort)	-	-	-	PMID：37907536	[GWAS Catalog](https://www.ebi.ac.uk/gwas/, GCST90277238-GCST90277416)
Outcome	AR(ukb)	112,583	25,486	87,097	9,851,867	ukb-b-7178	https://gwas.mrcieu.ac.uk/
Outcome	AR(finngen)	404,309	12,240	392,069	19,345,529	FinnGen (R10_ALLERG_RHINITIS)	https://finngen.gitbook.io/documentation/data-download

Selection of Instrumental Variables

To obtain more complete results, we used a locus-wide significance threshold of P < 1 × 10^-5 to screen SNPs related to the exposure factors in the lipidome, ensuring an adequate number of SNPs for analysis. While the conventional genome-wide significance threshold is P < 5 × 10^-8, we selected the more lenient threshold to include more SNPs, which is crucial for maintaining sufficient statistical power in our analysis. Additionally, we performed sensitivity analyses using the traditional P < 5 × 10^-8 threshold to minimize the possibility of false positives and to validate the robustness of our findings.

To avoid weak instrument bias, we calculated the F statistic for each IV (F = β²_exposure / SE²_exposure)²⁹ and excluded IVs with F < 10. Linkage disequilibrium analysis was performed with r² < 0.001 and clumping window > 10000 kb. Palindromic SNPs with intermediate allele frequencies were excluded.In the reverse MR analysis, a genome-wide significance threshold of P < 5 × 10^–8 was used to screen SNPs related to allergic rhinitis as the exposure. The same linkage disequilibrium criteria were applied, and palindromic and incompatible SNPs were excluded (Figure 1). SNPs that could not be matched in the GWAS outcome statistics were also excluded.We utilized the PhenoScanner website (http://www.phenoscanner.medschl.cam.ac.uk/) to exclude SNPs of exposure directly associated with the outcomes.

Statistical Analysis

We utilized R version 4.3.1 (http://www.Rproject.org) for all analyses. To assess the causal relationship between the plasma lipidome and allergic rhinitis (AR), we conducted inverse variance weighted (IVW)³⁰, MR Egger, weighted median ³¹, and Bayesian weighted Mendelian randomization (BWMR) analyses using TwoSampleMR version 0.5.7. The IVW method was our primary analytical approach as it combines multiple random variables to reduce overall variance by weighting each variable based on divergence, providing the most accurate estimates when all genetic variants are valid instruments ³². MR Egger adjusts for potential pleiotropy by allowing an intercept term, weighted median offers consistent estimates even with up to 50% invalid instruments, and BWMR reduces potential biases ³³, particularly for high-throughput and highly correlated data. P values from the IVW method were adjusted using the False Discovery Rate (FDR) ³⁴ to account for multiple testing and enhance reliability (Figure 1) ³⁵.

Sensitivity Analysis

Sensitivity analysis was conducted to ensure the robustness of our findings. First, the intercept term of the MR-Egger ^36,37 regression model was examined to assess potential pleiotropic effects. If the P-value of the intercept term exceeded 0.05, it suggested minimal influence of genetic pleiotropy, indicating that the IVs solely impacted the risk of AR through lipidomes. Cochran's Q test ³⁸ was employed to investigate the heterogeneity of the IVs and its potential impact on the causal estimate. A leave-one-out analysis ³⁹ was performed to assess the sensitivity of the results by conducting MR analysis repeatedly, gradually eliminating one SNP at a time. Funnel plots and scatter plots were used to visually assess the data, where scatter plots demonstrated that outliers had minimal effect on the data, and funnel plots showed a high degree of association and a lack of heterogeneity. Additionally, the MR-PRESSO test³⁵ was used to evaluate any disparities between the outcomes of MR analysis before and after correction, identifying and correcting for outliers contributing to bias ⁴⁰.

Ethical Approval

The GWAS data used in this study were public de-identified data. The ethics committee approved these data; therefore, there was no need for additional ethical approval ^24–26.

IVs were screened according to the conditions described above. The details of the SNPs that were eventually included in the TSMR analysis of the plasma lipidome and allergic rhinitis (AR) are presented in Supplementary Table 1. After harmonization, the number of SNPs involved in each pair of lipid species and allergic rhinitis ranged from 10 to 34. The F-statistics of all SNPs ranged from 18.79 to 1969.17, indicating that there are no weak IVs. In the reverse Mendelian randomization analysis, SNPs related to allergic rhinitis as the exposure were also screened using a genome-wide significance threshold of P < 5 × 10^-8. The number of SNPs after clumping ranged from 23 to 31. To identify suitable IVs for this study, we utilized the PhenoScanner website. Any SNPs of exposure found to be directly associated with the outcomes were excluded ahead of the MR analysis. Furthermore, we comprehensively assessed the selected SNPs to evaluate their potential pleiotropic effects, utilizing the MR Pleiotropy RESidual Sum and Outlier (MR-PRESSO) test. Fortunately, none of the SNPs exhibited any indications for removal after undergoing this evaluation.

Causal effects of lipidomes and AR

Through FDR correction (P_FDR <0.05), we identified several lipid phenotypes as risk factors for allergic rhinitis (AR): Sterol ester (27:1/20:4) levels, Phosphatidylcholine (18:0_20:4) levels, Phosphatidylcholine (18:0_22:5) levels, Sterol ester (27:1/20:5) levels, and Phosphatidylcholine (20:4_0:0) levels. Notably, Phosphatidylcholine (18:0_20:4) levels remained significant after FDR correction when using FinnGen data for AR outcomes as auxiliary validation (P_FDR = 0.017, P = 0.0003). For detailed data, refer to Table S1-S4. For Sterol ester (27:1/20:4) levels in relation to AR (UK Biobank data), the IVW method estimated the risk (OR) as 1.009 (95% CI = 1.004-1.014, P_FDR = 0.027, P = 0.0003). Similarly, for Phosphatidylcholine (18:0_20:4) levels in relation to AR (UK Biobank data), the IVW method estimated the risk (OR) as 1.009 (95% CI = 1.004-1.014, P_FDR = 0.027, P = 0.0003). Consistent results were obtained from the MR Egger, weighted median, and Bayesian Weighted Mendelian Randomization (BWMR) methods, further validating these findings (Figures 2 and 3). For Phosphatidylcholine (18:0_20:4) levels in relation to AR (FinnGen data), the IVW method estimated the risk (OR) as 1.068 (95% CI = 1.03-1.106, P_FDR = 0.017, P = 0.0003). For Phosphatidylcholine (18:0_22:5) levels in relation to AR (FinnGen data), the IVW method estimated the risk (OR) as 1.115 (95% CI = 1.055-1.178, P_FDR = 0.010, P = 0.0001). The consistent results from other methods reinforced these associations. For Sterol ester (27:1/20:5) levels in relation to AR (FinnGen data), the IVW method estimated the risk (OR) as 1.084 (95% CI = 1.035-1.134, P_FDR = 0.025, P = 0.0006). Finally, for Phosphatidylcholine (20:4_0:0) levels in relation to AR (FinnGen data), the IVW method estimated the risk (OR) as 1.081 (95% CI = 1.04-1.123, P_FDR = 0.010, P = 0.0001) (Figure 2).

Sensitivity analysis results

To validate the robustness of our findings, sensitivity analyses were performed using the stricter P < 5 × 10^-8 threshold. Although this stricter threshold resulted in fewer SNPs (ranging from 2 to 8), the positive associations identified with the P < 1 × 10^-5 threshold remained significant after FDR correction, reinforcing the reliability of our causal inferences. Interestingly, using the P < 5 × 10^-8 threshold allowed us to identify more positive results, which was crucial for maintaining sufficient statistical power. However, using fewer SNPs with the stricter threshold increased the risk of results being influenced by individual SNP effects. Therefore, the more inclusive threshold was preferred to ensure a robust Mendelian Randomization (MR) analysis (Table S7-8).

For all associations, the MR-Egger intercept test excluded the notion of horizontal pleiotropy, indicating no pleiotropic bias. The MR-Egger intercept test showed no significant evidence of directional pleiotropy (P values > 0.05), suggesting minimal influence of genetic pleiotropy on the IVs' impact on AR through lipidomes. Cochran's Q test was used to evaluate the heterogeneity of the IVs, with Q_pval values ranging from 0.207 to 0.955, indicating no substantial heterogeneity (Table 2). The leave-one-out analysis confirmed that no single SNP significantly influenced the overall results, reinforcing the stability of our findings (Figures 4 and 5). Additionally, funnel plots and scatter plots demonstrated a high degree of association and a lack of heterogeneity, further validating the robustness of the identified causal relationships.

Table 2 Assessment of Pleiotropy and Heterogeneity in MR Analysis

Exposure	Outcome	Pleiotropy			Heterogeneity
		MR-Egger intercept		MR-PRESSO	Cochrane’s Q P-value
		egger_intercept	pval	Global Test`$Pvalue	IVW	MR-Egger
Sterol ester (27:1/20:4) levels	AR(ukb)	-0.0008	0.352	0.346	0.211	0.210
Phosphatidylcholine (18:0_20:4) levels	AR(ukb)	-0.0003	0.651	0.364	0.207	0.176
Phosphatidylcholine (18:0_20:4) levels	AR(finngen)	0.0010	0.859	0.304	0.230	0.190
Phosphatidylcholine (18:0_22:5) levels	AR(finngen)	-0.0138	0.070	0.272	0.215	0.331
Sterol ester (27:1/20:5) levels	AR(finngen)	-0.0023	0.705	0.810	0.749	0.706
Phosphatidylcholine (20:4_0:0) levels	AR(finngen)	-0.0002	0.976	0.962	0.955	0.938

MR-Egger intercept and p-values are used to assess pleiotropy, where non-significant p-values indicate no pleiotropic bias. The MR-PRESSO Global Test's p-values further evaluate pleiotropy. Cochrane’s Q p-values assess heterogeneity, with non-significant values indicating no substantial heterogeneity. AR: Allergic Rhinitis.

Additionally, for Phosphatidylcholine (18:0_20:4) levels, which remained significant after FDR correction in both UK Biobank and FinnGen datasets, we conducted a meta-analysis of the outcomes. As shown in the attached figure, the random effects model estimated an overall odds ratio (OR) of 1.0351 (95% CI = 0.9799-1.0933, P = 0.2169), indicating a consistent association across both cohorts despite the observed heterogeneity (I² = 89%, τ² = 0.001, P < 0.01) (Figure 6).

Reverse Mendelian Randomization Analysis

In our exploration of the causal effects of AR on the plasma lipidome, we utilized the IVW method as the principal analysis in a two-sample MR study. Despite conducting multiple test adjustments via the FDR method, we identified two lipid traits in the UK Biobank cohort with suggestive significance: Phosphatidylcholine (18:2_20:3) levels (beta = -0.5165, P = 0.0449, 95% CI = -1.0213 to -0.0117) and Phosphatidylinositol (18:0_20:3) levels (beta = -0.5020, P = 0.0450, 95% CI = -0.9929 to -0.0112). However, these associations did not retain significance after FDR correction (P_FDR = 0.993). For the FinnGen cohort, no lipid traits were identified at a significance level of P < 0.05, even before FDR correction (Table S5,S6). This suggests that AR does not have a robust causal effect on the plasma lipidome in the FinnGen dataset. These results imply that while there may be suggestive associations between AR and specific lipid traits in the UK Biobank data, they do not meet the stringent criteria for statistical significance after correction for multiple testing. The lack of significant findings in the FinnGen cohort further supports the conclusion that the observed effects of AR on the plasma lipidome are not robust.

This study employed Mendelian randomization to explore the causal relationship between the plasma lipidome and allergic rhinitis (AR). Our findings indicate that specific plasma lipids, including certain subtypes of sterol esters and phosphatidylcholine, are significantly positively associated with the incidence of AR. Elevated levels of these lipid molecules are linked to an increased risk of AR. Reverse two-sample MR analysis showed no causal effect of AR on these lipid molecule levels, suggesting that the observed associations are more likely due to lipid levels influencing AR risk rather than the reverse.

Sterol esters and phosphatidylcholine are important plasma lipid components involved in cell membrane structure, lipid metabolism, and immune regulation ^11,41,42. Existing literature suggests these lipids may affect immune cell function and inflammatory mediator release, contributing to allergic diseases ^42,43. Additionally, AR patients have a high level of apolipoprotein in nasal mucus, which may be involved in lipid metabolism and have immunomodulatory properties ⁴⁴. Recent studies have identified an immune response network associated with blood lipid levels, suggesting a complex interplay between lipid metabolism and immune function ⁴⁵.Our findings align with existing studies, highlighting their potential role in AR development. Traditional lipidomics research focuses on standard lipids like HDL-C, LDL-C, TG, and TC. Our study's advantage is the use of 179 plasma lipid subtypes, offering a comprehensive understanding of the plasma lipidome's complexity and diversity, revealing more mechanisms related to AR ²⁴.

Previous observational studies have suggested that obesity and overweight may increase the risk of AR ^46,47. However, a recent Mendelian randomization study concluded that obesity and lipids do not genetically increase the risk of AR ⁴⁸. This discrepancy might be due to the fact that the MR study only analyzed traditional lipids without a detailed examination of plasma lipidome subtypes, potentially leading to bias. Our study addresses this gap by examining refined lipid species, providing a more nuanced understanding of their roles in AR.While our study provides genetic evidence of a causal relationship between specific subtypes of plasma lipids and AR, the biological mechanisms need further elucidation. Sterol esters and phosphatidylcholine may influence AR through various pathways, such as antigen presentation by immune cells, T cell differentiation, and cell membrane fluidity. Future research should explore these mechanisms at the molecular and cellular levels.

Mendelian randomization provides a robust tool for assessing causal relationships, reducing confounding factors and reverse causation. However, limitations include the genetic variants' inability to capture all genetic factors affecting plasma lipid levels and the need for further validation of MR assumptions. Sample selection and genetic background may also affect generalizability ^22,28.Our findings have potential public health and clinical significance for AR prevention and treatment. Modulating plasma lipid levels through dietary, lifestyle, or pharmacological interventions could reduce AR risk or alleviate symptoms. Future research should validate these findings and explore clinical applications.

In conclusion, our bidirectional Mendelian randomization analysis suggests a potential causal relationship between certain subtypes of plasma lipids, specifically subtypes of sterol esters and phosphatidylcholine, and allergic rhinitis (AR). Our findings indicate that elevated levels of these specific molecular subtypes of lipids may increase the risk of AR. While our study aims to reduce the impact of confounding factors and reverse causality, these results should be interpreted with caution. Further research is needed to explore the underlying biological mechanisms and validate these findings. Our study provides preliminary insights into the role of plasma lipid subtypes in AR, which may inform future preventive and therapeutic strategies.

AR: Allergic Rhinitis

BWMR: Bayesian Weighted Mendelian Randomization

CE: Cholesterol Esters

CI: Confidence Interval

FDR: False Discovery Rate

GWAS: Genome-Wide Association Study

HDL-C: High-Density Lipoprotein Cholesterol

IV: Instrumental Variable

IVW: Inverse Variance Weighted

LDL-C: Low-Density Lipoprotein Cholesterol

LPC: Lysophosphatidylcholines

MR: Mendelian Randomization

OR: Odds Ratio

PC: Phosphatidylcholines

Q: Cochran's Q Test

SNP: Single Nucleotide Polymorphism

TG: Triglycerides

TC: Total Cholesterol

UKB: UK Biobank

Acknowledgments

We thank the teams of the OpenGWAS, FinnGen, and UK Biobank databases for making the summary data publicly available, and we would like to acknowledge the principal investigators of the studies who made their data openly accessible for research.

Authors’ contributions

The study was designed by HY and QL. Statistical analyses were performed by HY. The manuscript was written by HY and YL. All authors contributed to the interpretation of data and commented on the manuscript. All authors contributed tog the article and approved the submitted version.

Funding

This study was supported by funding from Dr. Qiang Liu's Southwest Medical University University-level Research Project 2023 (No. 2023QN112).

Availability of data and materials

All data used in this study are available in the public repository. The code involved in the data analysis process can be obtained by contacting the corresponding author.

Data Availability Statement

All data used in this study are available in the public repository. The code involved in the data analysis process can be obtained by contacting the corresponding author.

Conflicts of Interest

The authors declare no competing interests.

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No competing interests reported.

TableS1.harmonisedataforexposureandukboutcomes.xls
Table S1. harmonise data for exposure and ukb outcomes
TableS2.harmonisedataforexposureandfinngenoutcomes.xls
Table S2. harmonise data for exposure and finngen outcomes
TableS3.MRanalysisresultswithfdrcorrectionukboutcome.xls
Table S3.MR analysis results with fdr correction (ukb as outcome)
TableS4.MRAnalysisresultswithfdrcorrectionfinngenOutcome.xls
Table S4.MR Analysis results with fdr correction (finngen as outcome)
TableS5.ReverseMendelianRandomizationresultsUKBasexposure.xls
Table S5. Reverse Mendelian Randomization results (ukb as exposure)
TableS6.ReverseMendelianRandomizationanalysisresultsfinngenisexposure.xls
Table S6. Reverse Mendelian Randomization results (finngen as exposure)
TableS7.MRanalysisresultswithP5108thresholdukbasoutcome.xls
Table S7. MR analysis results with P 5 × 10^-8 threshold (ukb as outcome）
TableS8.MRanalysisresultswithP5108thresholdfinngenasoutcome.xls
Table S8. MR analysis results with P 5 × 10^-8 threshold (finngen as outcome)
TableS9STROBEMRchecklist.docx
Table S9. STROBE-MR checklist

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Editor invited by journal
29 Jul, 2024
Submission checks completed at journal
24 Jul, 2024
First submitted to journal
21 Jun, 2024

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Causal Insights into Plasma Lipidome and Allergic Rhinitis: Implications from a Bidirectional Two-Sample Mendelian Randomization Analysis

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