The impact of lipidome on Viral hemorrhagic fevers: a Mendelian randomization study

doi:10.21203/rs.3.rs-4700229/v1

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The impact of lipidome on Viral hemorrhagic fevers: a Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-4700229/v1

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Background

Viral hemorrhagic fevers are a group of clinically similar diseases characterized by a spectrum of acute febrile syndromes ranging from mild to severe, accompanied by vascular damage, plasma leakage, and bleeding. The pathogenesis of viral hemorrhagic fevers is very complex and it related to lipidome.

Methods

In this study, we used 179 lipidomes as exposures and viral hemorrhagic fevers as the outcome. We primarily utilized MR-IVW, co-localization analysis and reverse MR analysis methods for analysis.

Results

Our study identified significant causal relationships between lipidomes and viral hemorrhagic fevers. Sterol ester (27:1/20:5) level(OR[95%CI] = 0.777[0.668,0.904],P = 0.042),phosphatidylcholine(16:0_20:4) level(OR[95%CI] = 0.788[0.689,0.902],P = 0.044),and phosphatidylcholine(O-18:0_20:4) level(OR[95%CI] = 0.745[0.625,0.887],P = 0.05) have a protective effect on viral hemorrhagic fevers.

Conclusions

Our MR analysis results indicate a causal relationship between specific lipidomes and viral hemorrhagic fevers.

Mendelian randomization

lipidome

viral hemorrhagic fevers

Viral hemorrhagic fevers (VHFs) are a group of clinically similar diseases characterized by a spectrum of acute febrile syndromes ranging from mild to severe, accompanied by vascular damage, plasma leakage, and bleeding^[1–3].VHFs can be caused by various RNA viruses, including arenaviruses, filoviruses, hantaviruses, and flaviviruses^{[4, 5]}.VHFs can be transmitted from person to person through direct contact with contaminated body fluids or tissues. Most VHFs are essentially zoonotic, and transmission occurs through different mechanisms depending on the virus involved^[6].These diseases exhibit clinical dynamics, rapidly progressing from asymptomatic to severe and fatal within hours or days, developing through several stages. The pathogenesis of hemorrhagic fevers is complex, involving interactions of various genetic and environmental factors, and the etiology and pathogenesis are not yet fully understood. Recent studies have revealed that genetic factors play an indispensable role in the pathogenesis of VHFs^[7].However, the pathogenesis of VHFs has not been fully elucidated. Therefore, further exploration of the exact causes of VHFs is of significant importance.

Lipidome, as an important substance in the human body, are often associated with the occurrence of various diseases when lipidome are abnormal^[8].However, current research mainly focuses on triglycerides, cholesterol, free fatty acids, and other large lipid substances. To gain a deeper understanding of the role of lipidomes in VHFs, it is necessary to study lipidome abnormalities at different molecular levels because different molecular weights lipidome and components may lead to different biological effects^[9–10].Therefore, it is necessary to conduct further research on the correlation between different molecular weights and lipidome components with VHFs.

Mendelian randomization (MR) as a genetic epidemiological method uses genetic variations as instrumental variables (IVs) to determine causal relationships between risk factors and disease outcomes, where genetic variations are randomly allocated at conception^[11–12].This statistical method avoids the confusion and bias associated with reverse causation because genotype precedes the disease process and is typically not influenced by postnatal lifestyle or environmental factors^[13–15].It can help establish causal relationships between exposure and outcomes of interest by using single nucleotide polymorphisms (SNPs) as instrumental variables^[16].Furthermore, two-sample MR analysis using summary data from two distinct populations enhances the statistical power of MR^[17].

This study uses large-scale Genome-Wide Association Study (GWAS) datasets to analyze the causal relationship between lipidomes and VHFs through two-sample MR analysis. This study may help reveal the genetic characteristics and biological mechanisms of VHFs.

Study Design

We conducted bi-directional MR analysis to assess the potential causal relationships between 179 lipidomes and VHFs, limiting the population to Europeans. The bi-directional MR analysis is conducted in two steps:the first step involves using 179 lipidomes as exposures and VHFs as outcomes for forward MR analysis, while the second step involves using hemorrhagic fever phenotype as exposures and positively identified lipidomes as outcomes for reverse MR analysis^[18]. Figure 1 illustrates an overview of the three hypotheses and the study design. The Fig. 1 shows the workflow in this study.

Data Source

The lipidomes data are extracted from the Genome-Wide Association Study (GWAS) data published by Linda et al. in 2023, which includes 7174 Finnish individuals. Through genome-wide association studies on lipidomes, Linda et al. conducted an in-depth investigation into the causal relationship between lipidome metabolism and diseases. After quality control procedures, we selected 179 lipidomes from 13 lipid categories for further study. Information about the samples and data processing included in this study can be found in previous research^[19].As for the outcome, the data on VHFs come from Finn-b-AB1_VIRAL_HEMOR_FEVER_NOS, including 1159 patients and 217,342 controls (Table 1).All original studies obtained ethical approval and informed consent from participants.

Table 1

GWAS datasets used in this Mendelian randomization (MR) study
Dataset	Phenotype/variable	First author (year)	Sample size (cases/controls)	Population	Sex
Exposure	179 lipidomes	Linda Ottensmann (2023)	7174	Sardinian (Italy)	Mixed
Outcome	Viral haemorrhagic fever	Fin biobank (2021)	1159 (1,159/217342)	European	Mixed

Instrument Variables Selection

To ensure the reliability and accuracy of the causal relationship between lipidomes and viral hemorrhagic fever, the following quality control process was adopted to select the most optimal instrumental variables (IVs) (Fig. 1).First, select SNPs that are significantly associated with lipidomes (p < 5×10^− 8) as IVs. Second, remove SNPs with a Minor Allele Frequency (MAF) less than 0.01.Third, perform clustering processing on the extracted SNPs (R² < 0.001 and clumping distance = 10000) to eliminate linkage disequilibrium (LD) between SNPs, ensuring the independence of SNPs^[20].Fourth, MR analysis requires ensuring that the effect of SNPs on exposure corresponds to the effect on the outcome for the same allele, removing ambiguous SNPs for which the effect allele cannot be determined. In the original dataset, palindromic SNPs were specifically examined to avoid unexpected reverse effects. Fifth, to reduce bias from weak instruments, we uniformly calculated the F-statistic for IVs to assess the strength of the selected IVs, and removed IVs with F-statistic < 10.The formula for calculating the F-statistic is F = R²(n-k-1)/k(1-R²), where R² represents the variance in the exposure explained by instrumental SNPs, n is the sample size, and k is the number of instrumental variables^[21].Finally, we used MR-PRESSO to detect potential pleiotropy in the selected SNPs and removed SNPs with pleiotropy, retaining the remaining SNPs for a reevaluation of causal effect estimates^[22].Furthermore, we also extracted SNPs related to VHFs as instrumental variables (IVs) for further reverse MR analysis. The selection process for IVs is consistent with the forward MR analysis.

Mendelian randomization analysis

We conducted MR analysis to assess the causal relationship between lipidomes and VHFs. For lipidomes with only a single SNP as the IV, we used the Wald ratio test to estimate the causal relationship between the selected IV and VHFs. For lipidomes with multiple SNPs as IVs, we employed four common MR methods for evaluation:Inverse Variance Weighting (IVW) ^[23],Weighted Median^[24], MR-Egger regression^[25],and Weighted Mode^[26].Under certain conditions, the IVW method is considered to be the most accurate method for estimating causal relationships^[27].Therefore, the results of multiple IVs are primarily based on the IVW method, with the other three methods serving as supplements. As multiple comparisons were made for each outcome, False Discovery Rate adjustment was applied (P.adjust < 0.05).

Bayesian co-localization analysis

Bayesian colocalization analysis was used to assess the possibility of two features sharing the same causal variable. Bayesian colocalization provided posterior probabilities for five hypotheses:hypothesis 0(PP.H0),hypothesis 1(PP.H1), hypothesis 2(PP.H2),hypothesis 3(PP.H3) and hypothesis 4(PP.H4)^[28].We consider a posterior probability (the P value of PP.H4) > 70% as strong evidence of colocalization. To address bias arising from uneven power between exposure and outcome features, we considered conditional colocalization probability (the P value of PP.H4 = PP.H4/(PP.H3 + PP.H4),assuming that when a significant MR signal is detected, the genetic signal is always present for viral hemorrhagic fevers-related traits in that region^[29].

Sensitivity analysis

Heterogeneity and pleiotropy tests were conducted on the 179 lipidomes to ensure the accuracy of IVW results. Cochran’s Q test and associated p-values were used to assess heterogeneity. In the presence of heterogeneity (P < 0.05), the Inverse Variance Weighting (IVW) method adopts a random-effects model. Alternatively, a fixed-effects model is used^[30].To detect pleiotropic effects, we used the MR-Egger intercept test to assess whether horizontal pleiotropy exists:if the intercept of the MR-Egger regression (P > 0.05),it indicates the absence of horizontal pleiotropy^[25].Furthermore, we conducted leave-one-out analysis on the identified significant results to determine whether the causal relationship in MR analysis is driven by individual SNPs^[31].

Reverse Mendelian randomization analysis

In this study, we used MR analysis to conduct reverse MR analysis on VHFs and identified significant lipidomes(VHFs as exposure and lipidomes as outcomes) to examine the presence of reverse causal relationships. The reverse MR analysis method is the same as the forward MR analysis.

Statistical analysis

All statistical analyses were carried out using R software version 4.2.1 (R Foundation for Statistical Computing, Vienna, Austria).MR analyses were conducted using the TwosampleMR (version 0.5.6)and MR-PRESSO (version 1.0) R packages.

Instrumental variables for lipidomes and VHFs

After stringent correlation filtering (p < 5×10^− 8) and clumping (R² = 0.001, window size = 10,000 kb),a total of 4427 instrumental variables were extracted from the GWAS of 179 lipidomes for viral hemorrhagic fever (Supplementary Table 1).

For reverse MR analysis, SNP selection for VHFs was based on p < 5×10^− 8 and clumping (R² = 0.001, window size = 10,000 kb).For VHFs,17 IVs were selected for Sterol ester (27:1/20:5) levels, Phosphatidylcholine (16:0_20:4) levels, and Phosphatidylcholine (O-18:0_20:4) levels (Supplementary Table 2).In our study, the F-statistic for IVs is greater than 10,indicating a low likelihood of bias from weak instrumental variables.

Results of the MR analysis

We used four MR methods to analyze the causal relationship between 179 lipidomes and VHFs. We identified potential causal relationships between 7 lipidomes and VHFs (Fig. 2).With IVW as the primary method and the other three methods as supplements, we cross-validated the potential causal relationships between 7 lipidomes and VHFs, ultimately confirming 3 lipidomes associated with VHFs (Fig. 3).We primarily focus on these 3 relatively robust causal relationships.

We also conducted Leave-one-out analysis on the identified significant lipidomes, further validating the robustness of our results(Fig. 4).In the absence of heterogeneity, horizontal pleiotropy, and outliers, the results of the MR analysis are reliable.

Red Lines Represent Estimations from the IVW Test.A.MR Leave-one-out sensitivity analysis for Sterol ester (27:1/20:5) levels on Viral haemorrhagic fevers,B.MR Leave-one-out sensitivity analysis for Phosphatidylcholine (16:0_20:4) on Viral haemorrhagic fevers,C.MR Leave-one-out sensitivity analysis for Phosphatidylcholine (O-18:0_20:4) levels on Viral haemorrhagic fevers.

The impact of 179 lipidomes on VHFs

Through IVW analysis, our study results (Table 2) significantly revealed a substantial negative association between three lipidomes and the risk of VHFs. These lipidomes include levels of stero lester (27:1/20:5) levels (OR[95%CI] = 0.777[0.668,0.904],P = 0.042),phosphatidylcholine(16:0_20:4) levels (OR[95%CI] = 0.788[0.689,0.902],P = 0.044),and phosphatidylcholine(O-18:0_20:4) levels (OR[95%CI] = 0.745[0.625,0.887],P = 0.05).

Sensitivity analyses were conducted for levels of stero lester (27:1/20:5), phosphatidylcholine(16:0_20:4), and phosphatidylcholine(O-18:0_20:4). The MR-Egger intercept test found no evidence of pleiotropy (P > 0.05) (Fig. 3).The test results showed no significant heterogeneity among the selected IVs(P > 0.05)(Fig. 3).MR-PRESSO did not detect horizontal pleiotropy (Fig. 3).Through heterogeneity and sensitivity analyses, we was found that the estimated values of sterol ester (27:1/20:5) levels, phosphatidylcholine(16:0_20:4) levels, and phosphatidylcholine (O-18:0_20:4) levels remained consistent across the four methods (Fig. 5).

Table 2

MR results of causal effects between 3 lipidomes and VHFs
exposure	outcome	method	nsnp	beta	se	p-value	or	or_lci95	or_uci95
Sterol ester (27:1/20:5) levels	Viral haemorrhagic fevers	IVW	25	-0.251993243	0.077013981	0.041641248	0.77724999	0.668351767	0.903891598
Phosphatidylcholine (16:0_20:4) levels	Viral haemorrhagic fevers	IVW	25	-0.237846298	0.068965819	0.043927638	0.788323849	0.68865194	0.902421753
Phosphatidylcholine(O-18:1_20:4) levels	Viral haemorrhagic fevers	IVW	21	-0.294355404	0.089240208	0.0505524	0.745011664	0.625461056	0.887413172

nsnp: number of single nucleotide polymorphism, WM: weighted median estimator, IVW: inverse-variance weighted method, OR: odd ratio, CI: confidence interval.

A.Sterol ester (27:1/20:5) levels, B. Phosphatidylcholine (16:0_20:4),C. Phosphatidylcholine (O-18:0_20:4).

Reverse MR analysis

Through reverse MR analysis, we did not find a reverse causal relationship between the three lipidomes and VHFs(Table 3).This result suggests that VHFs may not affect the corresponding lipidomes.

Table 3

MR results of causal effects between VHFs and lipidomes
exposure	outcome	method	nsnp	beta	se	p-value	or	or_lci95	or_uci95
Viral haemorrhagic fevers	Sterol ester (27:1/20:5) levels	IVW	17	0.011429995	0.018798063	0.543160199	1.011495567	0.974906015	1.049458375
Viral haemorrhagic fevers	Phosphatidylcholine (16:0_20:4) levels	IVW	17	-0.002428381	0.027777157	0.930334741	0.997574565	0.944715355	1.053391381
Viral haemorrhagic fevers	Phosphatidylcholine(O-18:1_20:4) levels	IVW	17	0.00158936	0.019103939	0.933696176	1.001590623	0.964780794	1.039804879

nsnp :number of single nucleotide polymorphism, WM: weighted median estimator, IVW: inverse-variance weighted method, OR: odd ratio, CI: confidence interval.

Co-localization results of Sterol ester(27:1/20:5) and VHFs

Co-localization analysis showed a significant correlation between Sterol ester(27:1/20:5) and VHFs(Fig. 6),that the P value of PP.H4(PP.H4/PP.H3 + H4)is 0.839.The results of co-localization analysis between Phosphatidylcholine(16:0_20:4) and VHFs were negative(Figure.6),that the P value of PP.H4(PP.H4/PP.H3 + H4) is 0.480.The results of co localization analysis between Phosphatidylcholine(O-18:0_20:4) and VHFs were negative(Figure.6),that the P value of PP.H4(PP.H4/PP.H3 + H4) is 0.422.

Using GWAS summary data, we identified three lipidomes significantly associated with VHFs:sterol ester (27:1/20:5) levels, phosphatidylcholine (16:0_20:4) levels and phosphatidylcholine (O-18:0_20:4) levels. Therefore, our discussion primarily focuses on sterol ester and phosphatidylcholine.

Our study shows that sterol ester (27:1/20:5) levels are protective factors for VHFs. Lipidomess have been proven to be closely associated with disease development. Sterol esters are a type of lipidome formed by the esterification of sterols and fatty acids. They play a role in various biological processes in the human body, including the structure and function of cell membranes, cell signaling and hormone synthesis. Sterol esters are precursors to hormone synthesis, including sex hormones and glucocorticoids, and can regulate the metabolism of VHFs through hormone levels, thereby slowing down the onset and development of VHFs. Different molecular species and proportions of sterol esters may impact these processes, leading to variations in the association between sterol esters and VHFs. One of the mechanisms of VHFs is a massive outbreak of inflammation ^[32].Sterol esters may have anti-inflammatory effects, for instance, current sterol ester drugs can inhibit the release of inflammatory mediators, migration, and activation of inflammatory cells ^[33],thereby slowing down the release of inflammatory factors during VHFs.

Phosphatidylcholine is a glycerophospholipid consisting of choline as a head group. And it is a major class of phospholipid compounds that form cell membranes. It plays an important role in energy storage, signal transduction, and glycerophospholipid metabolism ^[34].Phosphatidylcholine also plays a significant role in the kidneys. It is one of the main components of cell membranes, contributing to the maintenance of cell structure and function. In the kidneys, phosphatidylcholine is not only present in cell membranes but also involved in regulating the function of renal tubules and glomeruli.The metabolism and balance of phosphatidylcholine are crucial for kidney health and function. Research indicates that abnormalities in phosphatidylcholine metabolism may be related to the occurrence and development of certain kidney diseases. Therefore, the role of phosphatidylcholine in the kidneys is vital for maintaining normal kidney function and health. Our study shows a significant negative association between phosphatidylcholine (16:0_20:4) levels and phosphatidylcholine (O-18:0_20:4) levels with the risk of VHFs. This result supports the potential protective role of phosphatidylcholine in the onset of VHFs.

This study identified causal relationships between 3 lipidomes and VHFs through MR analysis, but it cannot deny the possibility of associations between other lipidomes and VHFs. While MR analysis uses genotypes as instrumental variables to infer associations between phenotypes and diseases, making it suitable for causal inference, this study still has certain limitations. First,the study population adopted in this study is the European population and the study results are only applicable to the European population. More comprehensive research should be conducted in case-control environments of different racial groups. Secondly, the inclusion of 179 lipidomes may affect the statistical power due to insufficient case numbers and different characteristics of the population. Moreover, the dataset of VHFs in this study is entirely derived from the UK Biobank, and validation should be performed in different datasets to ensure the robustness of the research results. Due to the structural diversity of lipidomes and the complex metabolic pathways, the association mechanisms between different lipidomes and VHFs remain an active research area. This study has added a new dimension to the understanding and cognition of VHFs from a novel perspective. It has also opened up new research directions for the prevention and treatment of VHFs, laying a foundation for further exploration in this field.

In conclusion, there may be a significant causal relationship between lipidomes and VHFs. We found that the levels of sterol ester (27:1/20:5), phosphatidylcholine (16:0_20:4) and phosphatidylcholine (O-18:0_20:4) are protective factors for VHFs, potentially playing a protective role in the onset of VHFs.

Data availability statement

The original contributions presented in the study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Author Disclosure Statement

The authors declare that they have no conflicts of interest to disclose.

Authors’ Contributions

Na-na Li, Jia-xi Xu, Jian-nan Zhang and Kai Kang formulated the study design, conducted data analysis and interpretation, and drafted the manuscript. Na-na Li, Jia-xi Xu, Kuo Wang, Peng fei Chen, Bo-wen Liu and Jia-ning Zhang performed data analysis and interpretation. Ying-fei Zhi and Jia Xu conceived and designed the study and revised the manuscript. All authors reviewed and approved the final manuscript. Na-na Li and Jia-xi Xu contributed equally to this work.

Funding Information

This study was supported by the Heilongjiang Province Key R&D Program (GA23C007)

Conflict of interest

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.

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The impact of lipidome on Viral hemorrhagic fevers: a Mendelian randomization study

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Abstract

Background

Methods

Results

Conclusions

Figures

Background

Methods

Study Design

Data Source

Instrument Variables Selection

Mendelian randomization analysis

Bayesian co-localization analysis

Sensitivity analysis

Reverse Mendelian randomization analysis

Statistical analysis

Results

Instrumental variables for lipidomes and VHFs

Results of the MR analysis

The impact of 179 lipidomes on VHFs

Reverse MR analysis

Co-localization results of Sterol ester(27:1/20:5) and VHFs

Discussion

Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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