Assessment of causal associations among gut microbes,plasma metabolites and idiopathic pulmonary fibrosis: a bidirectional Mendelian randomization study

doi:10.21203/rs.3.rs-3598979/v1

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Assessment of causal associations among gut microbes,plasma metabolites and idiopathic pulmonary fibrosis: a bidirectional Mendelian randomization study

https://doi.org/10.21203/rs.3.rs-3598979/v1

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BACKGROUND: A collection of interstitial lung disorders, particularly in elderly individuals, with an uncertain cause is known as idiopathic pulmonary fibrosis. A growing body of research indicates that the causes of idiopathic pulmonary fibrosis may include the multifaceted interaction of gut microbes and plasma metabolites. However,uncertainty exists regarding the causative link between idiopathic pulmonary fibrosis, plasma metabolites, and gut microbes.

METHODS:Based on GWAS database,two-sample MR analysis was used to identify gut microbes and plasma metabolites that influence the risk of developing idiopathic pulmonary fibrosis. Sensitivity analysis was used in order to assess the robustness of the results. At last, reverse MR was employed to explore reverse causation.

RESULTS:The development of idiopathic pulmonary fibrosis may be influenced genetically by nine different gut microbes (family.Bacteroidaceae, genus.Bacteroides, genus.Senegalimassilia, etc.) and thirteen different plasma metabolites (glucose, 2-aminobutyrat, aspartylphenylalanine, etc.). Additionally, changes in 10 plasma metabolites and 9 gut microbes may result from idiopathic pulmonary fibrosis.

CONCLUSION:We have established a causal association between gut microbes, plasma metabolites, and idiopathic pulmonary fibrosis for the first time with this research. Our research contributes to advancing both clinical therapies and public health strategies that will effectively reduce the incidence and social burden of IPF.

idiopathic pulmonary fibrosis

gut microbe

plasma metabolite

Mendelian randomization analysis

An uncommon class of chronic interstitial pulmonary fibrosis disorders with an unclear etiology, a very bad prognosis, difficulty curing, and a high mortality rate is known as idiopathic pulmonary fibrosis (IPF)[1].A systematic epidemiology pointed out that the global incidence of IPF is increasing year by year, with a global incidence of about 0.09–1.30/10,000 and a prevalence of about 0.30–4.51/10,000 by 2020 [2].Currently, the mainstays of treatment for IPF are lung transplantation and antifibrotic drugs; however, the high age of patients makes lung transplantation impractical[3].The recommended medications for treating anti-IPF include nidanib and pirfenidone, which can, up to a point, slow down the rate at which the disease progresses but do not considerably enhance the quality of life for patients following the condition or alleviate dyspnea symptoms[4].Studies have shown that the course of IPF is unpredictable and that early diagnosis and timely treatment can effectively improve the prognosis of patients[4, 5].Nevertheless, there is still a need for improved early diagnosis of IPF, which delays the initiation of treatment. Furthermore, many patients receive the incorrect medication for other conditions (such as congestive heart failure or chronic obstructive pulmonary disease) or because they take proven harmful medications (such as corticosteroids and immunosuppressants) [6].Thus, the search for biomarkers related to early IPF diagnosis is urgently needed. These biomarkers will not only enable early disease diagnosis, disease prevention, and improved treatment and prognosis, but they will also offer new targets for medication development[5, 7, 8].

The human gut microbiota is the largest ecosystem in humans and maintains the body's physiological homeostasis[9].Experimental and epidemiological evidence has highlighted the importance of the "gut-lung axis," or the interaction between the lungs and the gut microbiota, in recent years. This suggests that the respiratory and gastrointestinal tracts may interact in both health and disease, though the possible mechanisms are unknown[10].It has been demonstrated in the past that a higher early-life abundance of Bacteroides fragilis and total anaerobes is linked to an increased risk of asthma[11];the gut microbes provides broad protection against respiratory infections, and probiotics based on Lactobacillus and Bifidobacterium species may reduce the frequency of respiratory tract infections in people[12–14].Consequently, it makes sense to suspect a connection between IPF and gut flora. Proteomics and metabolomics in humans have greater potential as biomarkers than genomics[15].By evaluating hundreds of circulating small molecules in an untargeted manner, the human plasma metabolome offers a thorough understanding of human physiology[16],additionally, earlier research has demonstrated that lung disease can be identified by changes in plasma metabolites[17–19].Furthermore, many researchers have discovered that the combination of gut microbes and metabolites can be multidimensional in explaining disease pathogenesis, which facilitates disease early warning and noninvasive diagnosis [20, 21].There have been no studies on the combination of gut microbes and plasma metabolites in IPF, and confirming the causal relationship between the two and IPF correlations, as well as which microbiota taxa or metabolites are most relevant for clinical practice in IPF treatment, is critical for clinical practice in IPF treatment.

Mendelian randomization (MR) is a class of methods for investigating causality in epidemiological studies based on whole genome sequencing data that mimic randomized controlled trials in the clinic, with genetic variation serving as an instrumental variable (IV)[22].One study used a Mendelian randomization study to investigate the effect of gut flora and metabolites on IgA nephropathy and discovered a potential causal relationship between actinomycete abundance and a poorer prognosis in patients with IgA nephropathy[23].It has also been discovered that gut microbes and metabolites can act as biomarkers for the idiopathic pulmonary fibrosis process[24].The exact causative link between gut microbes, plasma metabolites, and idiopathic pulmonary fibrosis is uncertain, though.

In order to analyze the causal relationship between gut microbes, plasma metabolites, and idiopathic pulmonary fibrosis, as well as to identify potential biomarkers for gut microbes and plasma metabolites linked to the disease, this paper, which is based on a bidirectional Mendelian randomization study, genotyped 196 intestinal taxon abundances from 18,340 participants recruited from the GWAS dataset, blood metabolite data from 7,824 European populations, and 163,80413 snps from 1028 IPF patients and 196,986 controls.

2.1 Description of the study design

On the basis of combined genome-wide association (GWAS) data, we conducted a bidirectional MR study. For the forward MR analysis, idiopathic pulmonary fibrosis was the outcome and gut microbes and plasma metabolites were the exposures; for the reverse MR analysis, idiopathic pulmonary fibrosis was the exposure and gut microbes and plasma metabolites were the result.Three main MR design assumptions should be met (Fig. 1): It has been observed that: (i) genetic variation as an instrumental variable in bidirectional MR analyses is not associated with the study outcome; (ii) genetic variance as an instrumental variable in bidirectional MR analyses is only strongly associated with exposure; and (iii) genetic variation as an instrumental variable is not associated with confounders when performing a two-way MR analysis.

2.2 Source of the GWAS dataset

The gut microbiome's instrumental variables were taken from the source MiBioGen (https://mibiogen.gcc.rug.nl), a GWAS dataset that gathered 211 gut taxon abundances from 18,340 participants and recruited a total of 24 cohorts from Germany, the Netherlands, the United States, Belgium, the United Kingdom, and Sweden[25].In this study, we eliminated 15 microbes belonging to unidentified taxa, leaving 196 gut taxon abundances—32 families, 119 genera, 20 orders, 16 classes, and 9 phyla—for additional examination.The Human Metabolome GWAS Abstracts database (https://metabolomics.helmholtz-muenchen.de/gwas/) provided the instrumental variables for plasma metabolites. This dataset contains plasma metabolite data from 7824 European populations, including 6056 adults from the UK and 1768 adults from Germany[26].GWAS data for idiopathic pulmonary fibrosis were obtained from the FinnGen database, a dataset derived from a European population and genotyped 16380413 snps from 1028 IPF patients and 196986 controls. The dataset is available at https://gwas.mrcieu.ac.uk/ (GWAS ID:finn-b-IPF).

2.3 Selection of instrumental variables

(i) We set the p-value of all instrumental variables to p < 1×10^− 5 and excluded chain imbalances in all instrumental variables (gut microbial LD r² < 0.001,distance = 10000kb, blood metabolite LD r² < 0.1,distance = 500kb).(ii) We assessed the correlation between instrumental variables and exposure using the F-value, which is calculated as F = (R2/1-R2) × (N-k-1/k) [27], where N denotes the number of sample cases in the GWAS database, k denotes the number of instrumental variables, and R2 denotes the degree to which the instrumental variables explain exposure. (iii) We removed SNPs with allele frequencies ≤ 0.01, palindromic SNPs, and SNPs with F-values < 10.(iv) We selected SNPs that were not associated with confounders.

2.4 Mendelian randomization and sensitivity analysis

We used five different approaches to perform Mendelian randomization analysis after extracting gut bacteria and plasma metabolites as exposure and idiopathic pulmonary fibrosis (IPF) as instrumental variables for outcome. MR Egger, Simple mode, Weighted mode, Weighted median, and Inverse variance weighted.The other four methods were employed as supplementary techniques to the inverse variance weighted (IVW) method, which served as the primary analytical technique for establishing causality in this work. The q-value approach was used to FDR-correct the P values of each IVW. When P < 0.05, q > 0.1 suggests a suggestive causal association between exposure and outcome; q-value < 0.1 implies a substantial causal relationship between exposure and result[28].

In the sensitivity test, first, we tested whether the SNPs of the instrumental variables were heterogeneous using Cochran's Q test[29];Second, we used MR-Egger regression for intercept assessment to test for possible multicollinearity under the null causality hypothesis[30];again, we used MRPRESSO to identify more horizontal multiplicity and eliminate the effect of horizontal multiplicity by removing outliers; finally, we used leave one out analysis to identify individual SNPs with potential outliers. in addition, we supplemented the sensitivity analysis with funnel plots and scatter plots.

We screened for positive results mainly by (i) Inverse variance weighted method obtained a p-value < 0.05. (ii) The direction of the beta values of the five methods was consistent. (iii) The results of Cochran's Q test P value and MR-Egger Intercept P value for MR are both greater than 0.05.(iv) A single SNP does not seriously affect the results of MR.

2.5 Reverse Mendelian randomization analysis

We performed reverse Mendelian randomization with idiopathic pulmonary fibrosis (IPF) as the exposure and gut microbes and plasma metabolites as the outcome. We set the p-value of the instrumental variables to p < 5×10^− 6, LD r² < 0.01,distance = 10,000kb; the rest of the GWAS datasets were selected and instrumental variables were screened as described. All the above analyses were analyzed using the R4.2.1 package: TwoSampleMR (version 0.5.6), MR-PRESSO (version 1.2.0).

2.6 Metabolic pathway analysis

We used MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/) to undertake online metabolic pathway prediction of blood metabolites with P_IVW < 0.05 in order to study the metabolic pathways by which blood metabolites contribute causally to idiopathic pulmonary fibrosis (IPF).

3.1 Selection of instrumental variables

In our study, instrumental variables were extracted for 196 gut microbes and 486 plasma metabolites (the instrumental variables we screened to were strongly correlated instrumental variables by quality control, and the F-values of the instrumental variables were > 10). Detailed information on the SNPs of the instrumental variables screened by forward Mendelian randomization and reverse Mendelian randomization is provided in (Supplementary Tables S1-S4).

3.2 Causal effects of gut microbes on idiopathic pulmonary fibrosis

We used five Mendelian randomization methods to analyze the causal relationship between gut microbes and idiopathic pulmonary fibrosis (Fig. 2 and Supplementary Table S5). Based on the IVW estimates, nine gut microbes were identified that had suggestive causal associations with idiopathic pulmonary fibrosis (p value < 0.05, FDR q value > 0.1). Of these, six taxa were identified as having a suggestive positive causal effect on IPF to increase the risk of IPF, specifically phylum.Cyanobacteria (OR = 1.63, 95% confidence interval (CI): 1.04–2.57, p = 0.035), order.Bacillales ( OR: 1.32, 95% CI: 1.01–1.72, p = 0.045), order.Gastranaerophilales ( OR: 1.44, 95% CI: 1.02–2.04, p = 0.039), order.Bacteroidaceae (OR = 1.98, 95% CI: 1.04–3.75, p = 0.038), order.genus. 0.038), genus.Bacteroides (OR = 1.98, 95% CI: 1.04–3.75, p = 0.038), and genus.Senegalimassilia (OR = 2.28, 95% CI: 1.17–4.43, p = 0.015). Three additional taxa were determined to have elevated concentrations negatively associated with the risk of IPF, specifically class.Actinobacteria (OR = 0.61, 95%CI:0.41–0.92, p = 0.018), family.FamilyXIII (OR = 0.38, 95%CI: 0.19–0.75, p = 0.0051) and genus.Oscillibacter (OR = 0.57, 95% CI: 0.41–0.81, p = 0.0014). (Fig. 3)

It is of interest to note that since genus.Bacteroides is a sub-taxonomic unit of family.Bacteroidaceae, the SNPs of some bacterial taxa may overlap. Sensitivity analyses of these 11 causal associations were performed immediately after (Supplementary Table S7), and Cochran's Q test (p value > 0.05) showed no evidence of heterogeneity of SNPs for the instrumental variables. According to the results of MR-Egger regression and MRPRESSO (p value > 0.05), there was no evidence of horizontal multiplicity effect in the results of Mendelian randomization (MR). leave one out analysis results showed that there were no potential outliers in the results of Mendelian randomization. The above results proved that the causal association is plausible.

3.3 Causal effects of plasma metabolites on idiopathic pulmonary fibrosis

In Supplementary Table S6, the causal effects of 486 plasma metabolites on IPF were explored. Based on IVW estimates, a total of 13 plasma metabolites had suggestive causal associations with IPF (p value < 0.05, FDR q value > 0.1), of which there was a subgroup of 7 "unknown" metabolites, and we focused on the remaining 6 known metabolites. Higher genetically predicted concentrations of glucose (OR = 7.30, 95% CI: 1.41–46.60, p = 0.036), 2-aminobutyrate (OR = 6.20, 95% CI: 1.33–28.9, p = 0.02) were associated with an increased risk of IPF. aspartylphenylalanine (OR = 0.23, 95%CI:0.06–0.92, p = 0.038), dihomo-linolenate (20:3n3 or n6) (OR = 0.24, 95%CI:0.06–0.98, p = 0.047), octanoylcarnitine (OR = 0.25, 95%CI:0.009–0.69, p = 0.0072), and 1-linoleoylglycerophosphocholine (OR = 0.10, 95%CI:0.01–0.76, p = 0.025) were protective for idiopathic pulmonary fibrosis factor (Fig. 4). Sensitivity analyses (Supplementary Table S8) showed reliable results, and no heterogeneity or horizontal pleiotropy was observed for SNPs of instrumental variables.

3.4 Causal effects of idiopathic pulmonary fibrosis on gut microbes

Our study used inverse MR to determine the effect of IPF as an exposure on gut microbes. Idiopathic pulmonary fibrosis was positively associated with concentrations of seven gut microbes including: genus Defluviitaleaceae UCG011 (standardized regression coefficient Beta ± SE: 0.03 ± 0.01, p = 0.016), genus Anaerostipes (0.02 ± 0.01, p = 0.029), family Defluviitaleaceae (0.03 ± 0.01, p = 0.018), genus Eubacterium xylanophilum group (0.03 ± 0.01, p = 0.006), genus Allisonella (0.084 ± 0.039, p = 0.031), genus Flavonifractor (0.03 ± 0.01, p = 0.049), and genus Holdemanella (0.03 ± 0.01, p = 0.038) (Supplementary Table S9 and Fig. 5). In addition, idiopathic pulmonary fibrosis was negatively correlated with concentrations of genus Olsenella (-0.04 ± 0.02, p = 0.016) and genus Peptococcus (-0.03 ± 0.01, p = 0.046). Sensitivity analysis showed that Cochran's Q test, MR-Egger regression and MRPRESSO showed reliable results (p > 0.05) (Supplementary Table S11).

3.5 Causal effects of idiopathic pulmonary fibrosis on plasma metabolites

As above, we similarly used inverse MR to determine the causal effect of IPF on plasma metabolites such as Fructose (0.02 ± 0.01, p = 0.025), Glycodeoxycholate (0.06 ± 0.03, p = 0.021), Proline (0.008 ± 0.004, p = 0.043), Tyrosine ( 0.006 ± 0.003, p = 0.044), X-11381 (0.01 ± 0.005, p = 0.049), and Taurocholate (0.21 ± 0.19, p = 0.0059) concentrations were positively causally related. In contrast, IPF was associated with 1-docosahexaenoylglycerophosphocholine (-0.02 ± 0.01, p = 0.0036), 1-heptadecanoylglycerophosphocholine(-0.02 ± 0.01,p = 0.03),Eicosapentaenoate (EPA; 20:5n3) (-0.02 ± 0.01, p = 0.0026),2-linoleoylglycerophosphocholine (-0.02 ± 0.01, p = 0.0026) showed negative causal association (Fig. 6). In sensitivity analyses, the results were stable and not affected by heterogeneity or horizontal pleiotropy. (Supplementary Table S12)

3.7 Metabolic pathway analysis

As shown in Supplementary Table S13, the "Glycolysis/Gluconeogenesis" pathway may be involved in the development of idiopathic pulmonary fibrosis. (p < 0.05)

In conclusion, our two-sample MR study successfully identified some gut microbiota and plasma metabolites that may promote or prevent IPF, and we examined the results by MR Egger、Weighted median、Inverse variance weighted、Simple mode、Weighted mode, almost all of which showed consistent causal relationships. This information is based on the GWAS database. In our study, six gut microbes (family.Bacteroidaceae,genus.Bacteroides,genus.Senegalimassilia, order.Gastranaerophilales, phylum, order.Bacillales) and 2 plasma metabolites (glucose, 2-aminobutyrate) may increase the risk of developing idiopathic pulmonary fibrosis.

The intestines and lungs have the same mucosal origin and the presence of crosstalk between the intestinal and pulmonary compartments, a bi-directional connection defined as the intestinal-pulmonary axis, which involves different signaling pathways for immune cell and epithelial cell responses, host and microbiota in both locations[31].Bacteroides are potential definers in the human colon that can enter the extraintestinal sterile tissue from the intestinal mucosa and cause different disease conditions[32].It has been shown that three pulmonary commensal microorganisms of the genera genus.Bacteroides and Prevotella promote the fibrotic pathogenesis of the lung through IL-17R signaling during bleomycin-induced pulmonary fibrosis in mice[33].Whereas our results indicate that the gut microbes family.Bacteroidaceae and genus.Bacteroides are risk factors for IPF, it is clear at the genetic level that family.Bacteroidaceae and genus.Bacteroides are indeed able to promote the process of pulmonary fibrosis, which is in line with existing studies[33]combined, it is more than possible to clarify that the family Mycobacteriaceae and the genus Mycobacteria are risk factors for IPF and that they are causally related. In addition, our study found for the first time that genus.Senegalimassilia,order.Gastranaerophilales, phylum.Cyanobacteria, and order.Bacillales are risk factors for IPF, which has not been reported in the literature before. On the contrary, genus.Oscillibacter, family.FamilyXIII and class.Actinobacteria were protective factors for IPF. Oscillibacter is a group of low G + C Gram-positive bacteria that are widely found in the digestive tract of humans and animals and are capable of fermenting butyrate-based short-chain fatty acids (SCFA).[34]Butyrate can be transported into the blood or tissues and attenuates bleomycin-induced fibrosis in the skin and lungs by inhibiting the expression of histone deacetylases (HDACs)[35],This may be the mechanism by which this taxon reduces the risk of IPF. There are relevant studies showing increased abundance of class.Actinobacteria in COPD[36],Our study, on the other hand, suggests that the bacterium is a protective factor against IPF, implying the diversity of the bacterium's role in lung disease.

While metabolomics is a promising approach for understanding the underlying mechanisms of illness progression, systematic studies of metabolites in IPF are uncommon[37].This study also determined the causal relationship between plasma metabolites and IPF, and the results showed that 13 serum metabolites were causally associated with IPF and suggestivity, because there were 7 "unknown" subgroups, so we mainly focused on the remaining 6 known metabolites, of which glucose, 2-aminobutyrate were risk factors for IPF.Glucose is made up of 12-carbon atoms, 12-hydrogen atoms and 6-oxygen atoms. It can exist in a hexagonal ring structure or an open chain structure[38].It is the basic substrate that all eukaryotic cells break down to produce energy in the form of ATP in a process called glycolysis[39]It has been shown that the glycolytic pathway is disrupted in patients with IPF, and mass spectrometry of IPF and microarrays of the lungs show reduced levels of the late glycolytic metabolites fructose 1,6-bisphosphate (fructose 1,6-BP) and phosphoenolpyruvate (PEP), which may be attributed to reduced phosphofructokinase (PFK) expression[37],This process may lead to a buildup of glucose, accelerating the progression of IPF.Therefore, there is good reason to believe that increased glucose levels promote the development of IPF. In addition, four metabolites, aspartylphenylalanine, dihomo-linolenate (20:3n3 or n6), octanoylcarnitine, and 1-linoleoylglycerophosphocholine, were found for the first time to be protective against IPF. Their causal role with IPF has not been previously reported, except for a report showing that octanoylcarnitine is a molecular metabolite significantly under-expressed in patients with lung nodules[40].

In reverse MR, the pathogenesis of idiopathic pulmonary fibrosis may be caused by seven intestinal microorganisms including genus Defluviitaleaceae UCG011, genus Anaerostipes, family Defluviitaleaceae, genus Eubacterium xylanophilum group, genus Allisonella, genus Flavonifractor, genus Holdemanella and six plasma metabolites including Fructose, Glycodeoxycholate, Proline, Tyrosine, X-11381 and Taurocholate, which may be associated with an increase in two gut microorganisms including genus Olsenella and genus Peptococcus and four plasma metabolites including 1-docosahexaenoylglycerophosphocholine, 1- heptadecanoylglycerophosphocholine, Eicosapentaenoate (EPA; 20:5n3) and linoleoylglycerophosphocholine were decreased. Taken together, these gut microbes and plasma metabolites have the potential to be early diagnostic biomarkers for IPF and need to be followed up with further clinical studies.

Our study connects gut microbes and plasma metabolites for the first time, providing potential biomarkers associated with idiopathic pulmonary fibrosis using a multi-omics study and facilitating the study of the mechanisms of idiopathic pulmonary fibrosis. In addition, this study has some limitations. First, after FDR correction, our study only provided evidence of suggestive associations of gut microbes, plasma metabolites, and idiopathic pulmonary fibrosis, and we need more observational studies and experiments to confirm their relationships. Second, we used loose thresholds to assess the results, which may lead to some false positives in the results.

In conclusion, MR is an excellent study design for opportunistic genes to elucidate causal relationships between potential risk factors and associated diseases. Recently, there have been relevant Mendelian randomization studies demonstrating the causal association of smoking, dietary intake, and telomere length with IPF [41–43], and by exploring the factors that regulate IPF, MR studies can help to promote public health policies and clinical interventions to effectively reduce the incidence and social burden of IPF. Meanwhile, we identified possible IPF-induced alterations in intestinal flora and plasma metabolites by inverse MR, which provides new possibilities for early diagnosis of IPF.

Author contributions

WD and XY took part in the research design. WD, XW, JW, and BZ contributed to the paper's authoring. WD, XW, JW, and BZ took part in the research's execution. LC produced the data. Each author approved the submitted version of the article and participated to its writing.

Funding

This work was supported by the Natural Science Foundation of Fujian Province of China(2022J011409). This work was also supported by the Xiamen Medical College Science and Technology Subjects in China ( K2023-07).

Ackowledgments

We are appreciative of the MiBioGen and OpenGWAS databases.

Data Availability Statement

The raw data provided in the study are included in the article/supplementary material and further queries can be directed to the corresponding author.

Ethics statement

The study received approval from the Institute of Institute of Respiratory Research's ethical committee at Xiamen Medical

Wang J, Hu K, Cai X, Yang B, He Q, Wang J, Weng Q. Targeting PI3K/AKT signaling for treatment of idiopathic pulmonary fibrosis. Acta Pharm Sinica B. 2022;12(1):18–32.
Luppi F, Kalluri M, Faverio P, Kreuter M, Ferrara G. Idiopathic pulmonary fibrosis beyond the lung: understanding disease mechanisms to improve diagnosis and management. Respir Res. 2021;22(1):109.
Spagnolo P, Kropski JA, Jones MG, Lee JS, Rossi G, Karampitsakos T, Maher TM, Tzouvelekis A, Ryerson CJ. Idiopathic pulmonary fibrosis: Disease mechanisms and drug development. Pharmacol Ther. 2021;222:107798.
Maher TM, Strek ME. Antifibrotic therapy for idiopathic pulmonary fibrosis: time to treat. Respir Res. 2019;20(1):205.
Hamai K, Iwamoto H, Ishikawa N, Horimasu Y, Masuda T, Miyamoto S, Nakashima T, Ohshimo S, Fujitaka K, Hamada H et al. Comparative Study of Circulating MMP-7, CCL18, KL-6, SP-A, and SP-D as Disease Markers of Idiopathic Pulmonary Fibrosis. Disease markers 2016, 2016:4759040.
Amaral AF, Colares PFB, Kairalla RA. Idiopathic pulmonary fibrosis: current diagnosis and treatment. Jornal brasileiro de pneumologia: publicacao oficial da Sociedade Brasileira de Pneumologia e Tisilogia. 2023;49(4):e20230085.
Prasad JD. Biomarkers in idiopathic pulmonary fibrosis: Are we there yet? Respirol (Carlton Vic). 2020;25(7):674–5.
Drakopanagiotakis F, Wujak L, Wygrecka M, Markart P. Biomarkers in idiopathic pulmonary fibrosis. Matrix biology: journal of the International Society for Matrix Biology. 2018;68–69:404–21.
Chen Y, Zhou J, Wang L. Role and Mechanism of Gut Microbiota in Human Disease. Front Cell Infect Microbiol. 2021;11:625913.
Dang AT, Marsland BJ. Microbes, metabolites, and the gut-lung axis. Mucosal Immunol. 2019;12(4):843–50.
Vael C, Nelen V, Verhulst SL, Goossens H, Desager KN. Early intestinal Bacteroides fragilis colonisation and development of asthma. BMC Pulm Med. 2008;8:19.
Schuijt TJ, Lankelma JM, Scicluna BP, de Sousa e Melo F, Roelofs JJ, de Boer JD, Hoogendijk AJ, de Beer R, de Vos A, Belzer C, et al. The gut microbiota plays a protective role in the host defence against pneumococcal pneumonia. Gut. 2016;65(4):575–83.
Luoto R, Ruuskanen O, Waris M, Kalliomäki M, Salminen S, Isolauri E. Prebiotic and probiotic supplementation prevents rhinovirus infections in preterm infants: a randomized, placebo-controlled trial. J Allergy Clin Immunol. 2014;133(2):405–13.
Jespersen L, Tarnow I, Eskesen D, Morberg CM, Michelsen B, Bügel S, Dragsted LO, Rijkers GT, Calder PC. Effect of Lactobacillus paracasei subsp. paracasei, L. casei 431 on immune response to influenza vaccination and upper respiratory tract infections in healthy adult volunteers: a randomized, double-blind, placebo-controlled, parallel-group study. Am J Clin Nutr. 2015;101(6):1188–96.
Ramautar R, Berger R, van der Greef J, Hankemeier T. Human metabolomics: strategies to understand biology. Curr Opin Chem Biol. 2013;17(5):841–6.
Pietzner M, Stewart ID, Raffler J, Khaw KT, Michelotti GA, Kastenmüller G, Wareham NJ, Langenberg C. Plasma metabolites to profile pathways in noncommunicable disease multimorbidity. Nat Med. 2021;27(3):471–9.
Mao Y, Ma Z, Xu C, Lv Z, Dong W, Liu X. Pathogenesis of ventilator-induced lung injury: metabolomics analysis of the lung and plasma. Metabolomics: Official journal of the Metabolomic Society. 2022;18(8):66.
Lokhov PG, Trifonova OP, Maslov DL, Archakov AI. Blood plasma metabolites and the risk of developing lung cancer in Russia. Eur J cancer prevention: official J Eur Cancer Prev Organisation (ECP). 2013;22(4):335–41.
Wendt CH, Samorodnitsky S, Lock EF, Kruk M, Morris A, Leung JM, Kunisaki KM, Griffin TJ. Lung and Plasma Metabolome in HIV-Associated Obstructive Lung Disease. J Acquir Immune Defic Syndr. 2022;91(3):312–8.
Dong C, Yang Y, Wang Y, Hu X, Wang Q, Gao F, Sun S, Liu Q, Li L, Liu J, et al. Gut microbiota combined with metabolites reveals unique features of acute myocardial infarction patients different from stable coronary artery disease. J Adv Res. 2023;46:101–12.
Coker OO, Liu C, Wu WKK, Wong SH, Jia W, Sung JJY, Yu J. Altered gut metabolites and microbiota interactions are implicated in colorectal carcinogenesis and can be non-invasive diagnostic biomarkers. Microbiome. 2022;10(1):35.
Bowden J, Holmes MV. Meta-analysis and Mendelian randomization: A review. Res synthesis methods. 2019;10(4):486–96.
Wang F, Li N, Ni S, Min Y, Wei K, Sun H, Fu Y, Liu Y, Lv D. The Effects of Specific Gut Microbiota and Metabolites on IgA Nephropathy-Based on Mendelian Randomization and Clinical Validation. Nutrients 2023, 15(10).
Gong GC, Song SR, Su J. Pulmonary fibrosis alters gut microbiota and associated metabolites in mice: An integrated 16S and metabolomics analysis. Life Sci. 2021;264:118616.
Kurilshikov A, Medina-Gomez C, Bacigalupe R, Radjabzadeh D, Wang J, Demirkan A, Le Roy CI, Raygoza Garay JA, Finnicum CT, Liu X, et al. Large-scale association analyses identify host factors influencing human gut microbiome composition. Nat Genet. 2021;53(2):156–65.
Shin SY, Fauman EB, Petersen AK, Krumsiek J, Santos R, Huang J, Arnold M, Erte I, Forgetta V, Yang TP, et al. An atlas of genetic influences on human blood metabolites. Nat Genet. 2014;46(6):543–50.
Milligan BG. Maximum-likelihood estimation of relatedness. Genetics. 2003;163(3):1153–67.
Storey JD, Tibshirani R. Statistical significance for genomewide studies. Proc Natl Acad Sci U S A. 2003;100(16):9440–5.
Hemani G, Bowden J, Davey Smith G. Evaluating the potential role of pleiotropy in Mendelian randomization studies. Hum Mol Genet. 2018;27(R2):R195–r208.
Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25.
Corrêa RO, Castro PR, Moser R, Ferreira CM, Quesniaux VFJ, Vinolo MAR, Ryffel B. Butyrate: Connecting the gut-lung axis to the management of pulmonary disorders. Front Nutr. 2022;9:1011732.
Zafar H, Saier MH Jr.. Gut Bacteroides species in health and disease. Gut Microbes. 2021;13(1):1–20.
Yang D, Chen X, Wang J, Lou Q, Lou Y, Li L, Wang H, Chen J, Wu M, Song X, et al. Dysregulated Lung Commensal Bacteria Drive Interleukin-17B Production to Promote Pulmonary Fibrosis through Their Outer Membrane Vesicles. Immunity. 2019;50(3):692–706e697.
Yang J, Li Y, Wen Z, Liu W, Meng L, Huang H. Oscillospira - a candidate for the next-generation probiotics. Gut Microbes. 2021;13(1):1987783.
Park HJ, Jeong OY, Chun SH, Cheon YH, Kim M, Kim S, Lee SI. Butyrate Improves Skin/Lung Fibrosis and Intestinal Dysbiosis in Bleomycin-Induced Mouse Models. Int J Mol Sci 2021, 22(5).
Mendez R, Banerjee S, Bhattacharya SK, Banerjee S. Lung inflammation and disease: A perspective on microbial homeostasis and metabolism. IUBMB Life. 2019;71(2):152–65.
Zhao YD, Yin L, Archer S, Lu C, Zhao G, Yao Y, Wu L, Hsin M, Waddell TK, Keshavjee S, et al. Metabolic heterogeneity of idiopathic pulmonary fibrosis: a metabolomic study. BMJ open respiratory research. 2017;4(1):e000183.
Roque W, Romero F. Cellular metabolomics of pulmonary fibrosis, from amino acids to lipids. Am J Physiol Cell Physiol. 2021;320(5):C689–c695.
Cummings JH, Stephen AM. Carbohydrate terminology and classification. Eur J Clin Nutr. 2007;61(Suppl 1):5–18.
Liu Z, Wang L, Du M, Liang Y, Liang M, Li Z, Gao Y. Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma. J Oncol. 2022;2022:9460019.
Zhu J, Zhou D, Yu M, Li Y. Appraising the causal role of smoking in idiopathic pulmonary fibrosis: a Mendelian randomization study. Thorax 2023.
Zhang Y, Gan Y, Zhang H. Dietary intake and incidence risk of idiopathic pulmonary fibrosis: a Mendelian randomization study. BMC Pulm Med. 2023;23(1):376.
Duckworth A, Gibbons MA, Allen RJ, Almond H, Beaumont RN, Wood AR, Lunnon K, Lindsay MA, Wain LV, Tyrrell J, et al. Telomere length and risk of idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease: a mendelian randomisation study. The Lancet Respiratory medicine. 2021;9(3):285–94.

No competing interests reported.

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Assessment of causal associations among gut microbes,plasma metabolites and idiopathic pulmonary fibrosis: a bidirectional Mendelian randomization study

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Abstract

Figures

1. Introduction

2. Materials and Methods

2.1 Description of the study design

2.2 Source of the GWAS dataset

2.3 Selection of instrumental variables

2.4 Mendelian randomization and sensitivity analysis

2.5 Reverse Mendelian randomization analysis

2.6 Metabolic pathway analysis

3. Results

3.1 Selection of instrumental variables

3.2 Causal effects of gut microbes on idiopathic pulmonary fibrosis

3.3 Causal effects of plasma metabolites on idiopathic pulmonary fibrosis

3.4 Causal effects of idiopathic pulmonary fibrosis on gut microbes

3.5 Causal effects of idiopathic pulmonary fibrosis on plasma metabolites

3.7 Metabolic pathway analysis

4. DISCUSSION

5. Conclusion

Declarations

References

Additional Declarations

Supplementary Files

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