
==== Front
Heliyon
Heliyon
Heliyon
2405-8440
Elsevier

S2405-8440(24)12156-1
10.1016/j.heliyon.2024.e36125
e36125
Research Article
Causal relationship between serum metabolites and idiopathic pulmonary fibrosis: Insights from a two-sample Mendelian randomization study
Zou Qiong-Chao acd1
Hu Jun-Pei bc1
Cao Yan ce
She Chang ac
Liang Li-Hui 416284649@qq.com
bcd⁎⁎
Liu Zheng-Yu liuzhengyu@hunnu.edu.cn
acd⁎
a Cardiology Department, Hunan Provincial People's Hospital, Changsha, 410000, Hunan Province, China
b Geriatrics Department, Hunan Provincial People's Hospital, Changsha, 410005, Hunan Province, China
c The First Affiliated Hospital of Hunan Normal University, Hunan Provincial People's Hospital, Changsha, 410000, Hunan Province, China
d Research Center for Cardiovascular Epidemiology in Hunan Province, Changsha, 410000, Hunan Province, China
e Department of Emergency, Hunan Provincial People's Hospital, Changsha, 410000, Hunan Province, China
⁎ Corresponding author. Cardiology Department, Hunan Provincial People's Hospital, Changsha, 410000, Hunan Province, China. liuzhengyu@hunnu.edu.cn
⁎⁎ Corresponding author. Geriatrics Department, Hunan Provincial People's Hospital, Changsha, 410005, Hunan Province, China. 416284649@qq.com
1 Contributed equally.

10 8 2024
30 8 2024
10 8 2024
10 16 e3612510 12 2023
5 8 2024
9 8 2024
© 2024 The Authors. Published by Elsevier Ltd.
2024

https://creativecommons.org/licenses/by-nc-nd/4.0/ This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Background

Idiopathic pulmonary fibrosis (IPF) is an irreversible lung disease with unclear pathological mechanisms. In this study, we utilized bidirectional Mendelian randomization (MR) to analyze the relationship between serum metabolites and IPF, and conducted metabolic pathway analysis.

Aim

To determine the causal relationship between serum metabolites and IPF using MR analysis.

Methods

A two-sample MR analysis was conducted to evaluate the causal relationship between 824 serum metabolites and IPF. The inverse variance weighted (IVW) method was used to estimate the causal relationship between exposure and results. Sensitivity analysis was conducted using MR Egger, weighted median, and maximum likelihood to eliminate pleiotropy. Additionally, metabolic pathway analysis was conducted to identify potential metabolic pathways.

Results

We identified 12 serum metabolites (6 risks and 6 protective) associated with IPF from 824 metabolites. Among them, 11 were known and 1 was unknown. 1-Eicosatrienoylglycophorophospholine and 1-myristoylglycophorophospholine were bidirectional MR positive factors, with 1-myristoylglycophorophospholine being a risk factor (1.0013, 1.0097) and 1-eicosatrienoylglycophorine being a protective factor (0.9914, 0.9990). The four lipids (1-linoleoylglycerophoethanolamine*, total cholesterol in large high-density lipoprotein [HDL], cholesterol esters in very large HDL, and phospholipids in very large HDL) and one NA metabolite (degree of unsaturation) were included in the known hazardous metabolites. The known protective metabolites included three types of lipids (carnitine, 1-linoleoylglycerophoethanolamine*, and 1-eicosatrienoylglycerophophophorine), one amino acid (hypoxanthine), and two unknown metabolites (the ratio of omega-6 fatty acids to omega-3 fatty acids, and the ratio of photoshopids to total lipids ratio in chylomicrons and extremely large very low-density lipoprotein [VLDL]). Moreover, sn-Glycerol 3-phosphate and 1-Acyl-sn-glycero-3-phosphocline were found to be involved in the pathogenesis of IPF through metabolic pathways such as Glycerolide metabolism and Glycerophospholipid metabolism.

Conclusion

Our study identified 6 causal risks and 6 protective serum metabolites associated with IPF. Additionally, 2 metabolites were found to be involved in the pathogenesis of IPF through metabolic pathways, providing a new perspective for further understanding the metabolic pathway and the pathogenesis of IPF.

Keywords

Serum metabolites
Idiopathic pulmonary fibrosis
Mendelian randomization
Single-nucleotide polymorphisms
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pmc1 Introduction

Idiopathic pulmonary fibrosis (IPF) is a genetically heterogeneous disease. It involves complex mechanisms and can result in progressive dyspnea and irreversible loss of lung function [[1], [2], [3]]. Early detection and intervention are crucial for slowing disease progression [4]. However, current methods for early detection of IPF have been limited [5]. Serum metabolites are promising biomarkers for IPF, owing to their robust monitoring capabilities, broad applicability, and accessible analysis [6,7]. Although previous studies have identified serum biomarkers for IPF, such as matrix metalloproteinase (MMP), krebs von den lungen-6 (KL-6), and chitinase-3-like protein 1 (YKL-40) [[8], [9], [10], [11]], these biomarkers lack robust evidence to support their causal relationship with the onset of IPF. Moreover, potential confounding and reverse causality make it challenging to draw causal inferences for IPF solely through traditional observational studies. Therefore, it is essential to identify serum biomarkers that can be used to causally monitor or refine the risk of IPF and to further broaden the research into its mechanisms. Mendelian randomization (MR) is a method used to evaluate the causal relationship between circulating biomarkers and disease occurrence. This method employs germline genetic variations as instrumental variables to assess the role of risk factors in disease susceptibility [12]. The random distribution of genetic variation during conception in MR substantially minimizes associations with most confounding factors, thereby enhancing the research credibility of causal inference between biomarkers and disease occurrence [13]. Furthermore, the distribution of these Mendelian genetic variants typically occurs before the onset of the disease, thereby mitigating concerns related to reverse causality.

This study was aimed to effectively screen serum metabolites through the application of MR design, and identify circulating metabolites influencing the risk of IPF. Our goal was to ensure that candidate metabolites shared common genetic signals for causation with IPF and were not influenced by linkage disequilibrium (LD) in MR results. Firstly, we evaluated the serum metabolites of IPF identified through MR and colocalization analysis. Secondly, we conducted bidirectional MR analysis on the selected significant serum metabolites to differentiate between one-way MR-positive serum metabolites, indicating causal relationships that can serve as predictive markers, and bidirectional MR-positive serum metabolites related to IPF. Finally, metabolic pathway analysis was conducted to identify metabolites involved in the pathogenesis of IPF through metabolic pathways (Fig. 1). The experimental findings might contribute to a better understanding of the pathogenesis of IPF, laying a solid foundation for future exploration of potential mechanisms and therapeutic targets for IPF.Fig. 1 Study design and workflow.

Fig. 1

2 Methods

2.1 GWAS data source

We adopted a two-sample MR design to identify serum metabolites related to IPF using the serum metabolite databases from the extensive study conducted by Shin et al. (Table 1). This database included 486 known and 309 unknown metabolites, mainly from liver metabolism and partly from fat [14]. Among these metabolites, 177 known metabolites were categorized into 309 biological groups, such as amino acids, peptides, lipids, and more. The Genome-wide association study (GWAS) collected serum and data from over 20,000 single nucleotide polymorphisms (SNPs) in 7,824 European adults, which were further compiled for analysis. After rigorous quality control, 486 metabolites were considered suitable for GWAS analysis. Furthermore, another study examined the characteristics of 925 metabolites, including lipids, fatty acids, amino acids, and glycolytic precursors [15]. This investigation into lipoprotein subclass-specific lipids addressed the limitations of the original GWAS data. These serum metabolite data are accessible via the Metabolomics GWAS server (https://metabolomics.helmholtz-muenchen.de/gwas/).Table 1 Characteristics of blood metabolites and idiopathic pulmonary fibrosis in Biobank UK project.

Table 1Exposures	Another name	No. cases	No. SNPs	Category	
Carnitine	NA	7,797	2,545,563	Continuous	
1-myristoylglycerophosphocholine	LysoPC(14:0/0:0)	7,812	2,545,688	Continuous	
1-linoleoylglycerophosphoethanolamine*	glycerophosphoric acid	7,817	2,545,675	Continuous	
1-eicosatrienoylglycerophosphocholine	LysoPC(20:2 (11Z,14Z))	7,809	2,545,640	Continuous	
Total cholesterol in large HDL	NA	21,558	11,861,714	Continuous	
Cholesterol esters in very large HDL	NA	19,273	11,806,428	Continuous	
Phospholipids in very large HDL	NA	19,273	11,820,655	Continuous	
Hypoxanthine	NA	7,287	2,545,756	Continuous	
Ratio of omega-6 fatty acids to omega-3 fatty acids	NA	114,999	12,321,875	Continuous	
Degree of unsaturation	NA	114,999	12,321,875	Continuous	
Phospholipids to total lipids ratio in chylomicrons and extremely large VLDL	NA	111,631	12,321,875	Continuous	
X-12749	NA	7,178	2,545,560	Continuous	
Outcome	No. cases (Female, %)	No. controls (Female, %)	No. SNPs	Category	
IPF	1,369 (NA)	435,866 (NA)	16,137,102	Binary	
Met, Metabolites; IPF, Idiopathic pulmonary fibrosis; No., number of; SD, standard deviation; SNPs, single nucleotide polymorphism.

The SNP database for IPF was derived from Anna Duckworth's GWAS study [16], including previous GWAS and case-control studies from research and Europe, with age and sex appropriately adjusted [16]. The aggregated GWAS summary data were derived from two meta-analyses primarily covering European populations. Following quality control, a total of 16,380,413 variants were retained in the final association analysis. The original study was conducted with the informed consent of all participants, and the IPF dataset is accessible at this website: https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30364-7/fulltext.

2.2 Selection of instrumental variables

To identify high-intensity instrumental variables for the analysis of the relationship between serum metabolites and IPF, we first identified serum metabolites associated with IPF based on a p-value of less than 1 × 10−5 [17,18]. We then ranked the p-values and selected the top 5 % as the threshold. Due to the relatively small sample size, we utilized the 1000 Genome Project as a reference panel for analysis, focusing on a physical distance range of 5000 kb and an r-squared value less than 0.01. Additionally, we calculated the F-statistic for each metabolite to assess the strength of the instrumental variables.

2.3 MR analysis

MR relies on three main assumptions [19]. Firstly, genetic variations must be reliably associated with the exposure of interest. With the emergence of modern large-scale GWASs, genetic variations related to the exposure can be identified in large datasets [20]. Secondly, genetic variations cannot be associated with confounding factors in the exposure-outcome relationship. Potential violation of this hypothesis may occur due to confounding by LD and/or population ancestry [21]. Finally, genetic variation should not affect the outcome unless through the exposure of interest (known as a lack of horizontal pleiotropy) [22]. Large-scale genomic association studies on serum metabolites GWASs often find a close correlation between the genetic determinants of serum metabolites and their coding genes. This close-range effect can be reduced using SNPs for MR analysis to reduce uncertainty and improve the effectiveness of the study. SNPs closely related to metabolites may directly affect gene transcription, thereby affecting the level of metabolites.

We used the inverse variance weighted (IVW) method as the primary method for estimating the causal relationship between metabolites and IPF in our MR analysis [23]. When genetic variation conforms to the hypothesis of three instrumental variables and is unaffected by pleiotropy, IVW method would yield consistent estimates of causal effects between exposure and outcomes.

2.4 Sensitivity analysis

In addition to the IVW method, there are other MR analysis methods that can reduce bias and provide sensitivity analysis. MR Egger's analysis evaluates the heterogeneity of individual SNP estimation and detects horizontal pleiotropy effects [24]. The weighted median (WM) method combines multiple data into a single causal estimate, allowing up to half of SNPs to be invalid instruments [25]. MR pleiotropy residual sum and outlier (MR-PRESSO) global and outlier tests can calculate horizontal pleiotropy and remove outlier SNPs [26]. LD score regression (LDSC) was used to estimate the heritability and genetic correlation of GWAS traits (https://github.com/bulik/LDSC) [27] and SUPER GeNetic covariance Analyzer (SUPERGNOVA) was used to calculate the local genetic covariance of LD genome regions (https://github.com/Qlu lab/SUPERGNOVA) [28]. Finally, we conducted exclusion and single SNP analyses to determine whether a single SNP influenced the central causal relationship. Statistical analysis was performed using R-4.2.3 software (https://www.rstudio.com/) for TwoSampleMR and MR-PRESSO.

2.5 Metabolic pathway analysis

The relationship between metabolic pathways and IPF was explored using MetaboAnalyst 5.0 (https://www.metaboanalyst.ca/). Metabolites were categorized into two groups based on their causal relationships with IPF: positively correlated and negatively correlated. We used two databases, the Small Molecular Pathway Database (SMPDB) [29] and the Kyoto Encyclopedia of Genes and Genomes (KEGG) [30], for metabolite analysis.

2.6 Statistical analysis

R software (version 4.2.2) was used to perform the bioinformatics analyses. A p < 0.05 was considered statistically significant.

3 Results

3.1 Causal effect of serum metabolites on IPF

A total of 56,204 independent SNP components were obtained for the 824 metabolites (14, 15). Supplementary Table 1 demonstrates that SNPs related to 12 metabolic products have a causal relationship with IPF and serve as genetic tools in our study. The minimum F-value of the calculated instrumental variable was greater than 10, indicating that the instrumental variable among the 824 metabolites was robust in MR analysis.

3.2 Effect of genetically determined metabolites on IPF

As shown in Fig. 2, 12 significative metabolites were detected, and multiple MR models, including IVW, Egger, and WM, indicated a genetic causal relationship between these metabolites and IPF (Table 2). However, our findings failed to prove the role of IPF in causing the changes in serum metabolites, except for 1-eicosatrienoylglycophorophospholine and 1-myristoylglycophorophospholine (Table 3). Interestingly, these two metabolites exhibited bidirectional MR positive effects in IPF. Moreover, in addition to these two metabolites, the other 10 serum metabolites were also significant in predicting the risk of IPF.Fig. 2 Mendelian randomization results of serum metabolites and idiopathic pulmonary fibrosis (IPF) risk based on inverse variance weighted (IVW) method.

Fig. 2

Table 2 MR Analysis between serum metabolites and IPF.

Table 2Metabolites	IVW	MR-Egger	Weighted median	Penalised weighted median	Maximum likelihood	
OR (95 % CI)	p value	OR (95 % CI)	p value	OR (95 % CI)	p value	OR (95 % CI)	p value	OR (95 % CI)	p value	
Lipid	
Carnitine	(0.9921, 0.9984)	0.0032	(0.9862，1.0029)	0.2007	(0.9879，0.9997)	0.0391	(0.9878，0.9994)	0.0310	(0.9920，0.9985)	0.0037	
1-myristoylglycerophosphocholine	(1.0015,1.0076)	0.0442	(1.0000，1.0102)	0.1074	(1.0013，1.0097)	0.0104	(1.0013，1.0097)	0.0105	(1.0014，1.0078)	0.0045	
1-linoleoylglycerophosphoethanolamine*	(0.9931,0.9996)	0.0269	(0.9892，1.0063)	0.6086	(0.9910，1.0001)	0.0528	(0.9912，0.9998)	0.0425	(0.9930，0.9996)	0.0275	
1-eicosatrienoylglycerophosphocholine	(0.9935,0.9988)	0.0166	(0.9865，1.0009)	0.1010	(0.9914，0.9990)	0.0125	(0.9912，0.9991)	0.0156	(0.9933，0.9988)	0.0055	
Total cholesterol in large HDL	(1.0000,1.0008)	0.0063	(0.9996，1.0011)	0.3441	(1.0001，1.0015)	0.0171	(1.0003，1.0016)	0.0055	(1.0000，1.0008)	0.0345	
Cholesterol esters in very large HDL	(1.0001,1.0012)	0.0226	(0.9997，1.0018)	0.1572	(0.9998，1.0012)	0.2009	(0.9997，1.0012)	0.2128	(1.0002，1.0011)	0.0102	
Phospholipids in very large HDL	(1.0002,1.0010)	0.0049	(0.9995，1.0009)	0.5421	(1.0000，1.0013)	0.0526	(1.0001，1.0014)	0.0229	(1.0002，1.0010)	0.0037	
Nucleotide	
Hypoxanthine	(0.9930,0.9997)	0.0226	(0.9848，1.0052)	0.3413	(0.9897，0.9992)	0.0214	(0.9897，0.9991)	0.0186	(0.9929，0.9997)	0.0329	
NA	
Ratio of omega-6 fatty acids to omega-3 fatty acids	(0.9990,1.0000)	0.0035	(0.9986，0.9999)	0.0265	(0.9988，1.0001)	0.1146	(0.9988，1.0001)	0.1167	(0.9991，1.0000)	0.0347	
Degree of unsaturation	(1.0001,1.0011)	0.0211	(0.9999，1.0013)	0.0745	(0.9999，1.0013)	0.1012	(0.9999，1.0013)	0.1107	(1.0001，1.0011)	0.0169	
Phospholipids to total lipids ratio in chylomicrons and extremely large VLDL	(0.9988,0.9999)	0.0166	(0.9989，1.0006)	0.6076	(0.9984，1.0002)	0.1247	(0.9982，1.0000)	0.0568	(0.9988，0.9999)	0.0138	
Unknown	
X-12749	(1.0012,1.0076)	0.0385	(1.0002，1.0125)	0.0493	(0.9998，1.0101)	0.0601	(0.9996，1.0103)	0.0698	(1.0012，1.0077)	0.0066	
IVW: inverse variance weighted; MR-Egger: Mendelian randomization Egger; CI: confidence interval; OR: odds ratio. The significance of bold values is p < 0.05.

Table 3 MR analysis between IPF and blood metabolites.

Table 3Metabolites	MR Egger	Inverse variance weighted	Maximum likelihood	Weighted median	Panalised weighted median	
OR (95 % CI)	p value	OR (95 % CI)	p value	OR (95 % CI)	p value	OR (95 % CI)	p value	OR (95 % CI)	p value	
Carnitine	（0.0009，21.4072）	0.4591	（0.4871，9.3182）	0.3151	（0.6503，7.3757）	0.2057	（0.1613，5.0916）	0.9111	（0.1619，4.8453）	0.8886	
1-myristoylglycerophosphocholine	（4.4186E-09，308.5737）	0.3140	（0.0003，0.8699）	0.0426	（0.0003，0.9393）	0.0466	（0.0005，26.8073）	0.4340	（0.0005，26.6312）	0.4334	
1-linoleoylglycerophosphoethanolamine*	（0.0762，1787035022.8185）	0.1552	（0.0062，20.7314）	0.6194	（0.0071，18.0203）	0.6071	（0.0317，2188.2664）	0.4556	（0.0374，1890.8798）	0.4409	
1-eicosatrienoylglycerophosphocholine	（6.07045E-08，913.3138）	0.4315	（0.0002，0.4136）	0.0154	（0.0002，0.4052）	0.0156	（0.0002，6.2407）	0.2030	（0.0002，7.4843）	0.2186	
Total cholesterol in large HDL	（0.0333，770.6663）	0.5300	（0.0686，129.5312）	0.5704	（0.0687，140.3244）	0.5601	（0.0209，1290.1503）	0.5585	（0.0185，1456.4117）	0.5669	
Cholesterol esters in very large HDL	（0.1219，2955.4584）	0.2592	（0.0509，104.1552）	0.6683	（0.0499，114.8076）	0.6586	（0.1102，9280.1681）	0.2310	（0.0744，13755.9774）	0.2627	
Phospholipids in very large HDL	（0.0158，383.7577）	0.7282	（0.0443，90.0988）	0.7218	（0.0436，97.4237）	0.7131	（0.0216，1692.2682）	0.5312	（0.0184，1970.2997）	0.5432	
Hypoxanthine	（1.4904E-07，10690893.06581）	0.9777	（0.0060，87.6030）	0.8946	（0.0316，15.7102）	0.8253	（0.0512，791.0648）	0.4521	（0.1169，1301.0834）	0.2905	
Ratio of omega-6 fatty acids to omega-3 fatty acids	（0.0020，38.9699）	0.6155	（0.0486，9.7496）	0.7826	（0.0983，4.5807）	0.6838	（0.0609，56.4408）	0.7232	（0.0674，52.2538）	0.7108	
Degree of unsaturation	（0.0668，108.7245）	0.6015	（0.2587，13.6665）	0.5326	（0.3150，11.9045）	0.4756	（0.2820，100.9284）	0.2644	（0.2731，100.8459）	0.2717	
Phospholipids to total lipids ratio in chylomicrons and extremely large VLDL	（0.0055，15.5996）	0.5479	（0.0825，5.8923）	0.7405	（0.1000，4.6975）	0.7004	（0.0013，0.4545）	0.0129	（0.0012，0.4095）	0.0106	
X-12757	（0.5348，778297.6290）	0.1042	（0.3154，32.2510）	0.3258	（0.3681，29.9518	0.2849	（0.2974，86.9388）	0.2615	（0.2909，100.3596）	0.2578	
CI: confidence interval; MR-Egger: Mendelian randomization Egger; OR: odds ratio; RSS: residual sum of squares.

The significance of bold values is p < 0.05.

Out of the twelve metabolites identified, eleven were known serum metabolites, with 5 associated with an increased risk of IPF and six associated with protection against IPF. Additionally, an unknown serum metabolite was found to be associated with potential risk for IPF. Carnitine, a protective lipid serum metabolite, showed the most significant causal relationship with IPF (P-weighted median = 0.031, two-tailed). Among the known hazardous metabolites, there were four types of lipids (1-myristoylglycophorophosphocline, total cholesterol in large high-density lipoprotein (HDL), cholesterol esters in very large HDL, and phospholipids in very large HDL), and one unknown metabolite (degree of unsaturation). The known protective metabolites included three types of lipids (Carnitine, 1-linoleoylglycerophoethanolamine*, 1-eicosatrienoylglycerophophophorine), one amino acid (Hypoxanthine), and two NA metabolites (the ratio of omega-6 fatty acids to omega-3 fatty acids and the ratio of phospholipids to total lipids ratio in chylomicrons and extremely large very low-density lipoprotein [VLDL]). These findings were significant for a deeper understanding of the pathogenesis of IPF and might provide insights into potential treatment strategies (Supplementary Figs. 1–2).

3.3 Sensitivity analysis

In the Two-Sample Mendelian Randomization (TSMR) analysis, no evidence of heterogeneity or horizontal pleiotropy was found between 12 Metabolites and IPF, as indicated by the heterogeneity test and MR-Egger intercept test (Supplementary Table 2).

3.4 Genetic correlation

Both LDSC and SUPERGNOVA were used to estimate the genetic correlation between metabolites and IPF. LDSC calculated the global correlation, whereas SUPERGNOVA estimated local genetic correlation. Our study did not identify a significant causal correlation between metabolites and IPF using LDSC. Furthermore, we did not observe significant local genetic covariance between the 12 metabolites identified by the IVW method. These results suggest that our findings were not influenced by genetic confounding or causal SNPs.

3.5 Metabolic pathway analysis

As shown in Table 4, the two metabolic pathways binding to metabolites exhibited a positive causal relationship with IPF. The most significant metabolic pathways associated with IPF was Glycerophorid metabolism and Glycerophorid metabolism. The metabolites involved in the occurrence of IPF, named 1-Acyl-sn-glyco-3-phosphocline, sn-Glycerol 3-phosphate, and sn-Glycerol 3-phosphate, were part of the two pathways (Glycerophospholipid metabolism and Glyceroolipid metabolism).Table 4 Important metabolic pathways in the pathogenesis of IPF.

Table 4Metabolic pathway	Metabolites involved	p value	Database	
Glycerophospholipid metabolism	1-Acyl-sn-glycero-3-phosphocholine，sn-Glycerol 3-phosphate	0.0031	KEGG	
Glycerolipid metabolism	sn-Glycerol 3-phosphate	0.0407	KEGG SMP	
KEGG: Kyoto Encyclopedia of Genes and Genomes; SMPDB: Small Molecule Pathway Database.

4 Discussion

To the best of our knowledge, this is the first study to utilize a MR approach to establish the genetic causality between 12 metabolites and IPF. Our study identified components of the sn-Glycerol 3-phosphate and 1-Acyl-sn-glycero-3-phosphate metabolic pathways to be involved in the occurrence and development of IPF. The GWAS data and a two-sample MR method were used to explore the causal association between IPF and metabolites. Rigorous sensitivity analyses were conducted to eliminate confounding factors, thereby elevating the robustness and credibility of our findings.

In our bidirectional MR study, we found that Carnitine, a well-established metabolite, had remarkable protective efficacy against IPF. Carnitine is a vitamin-like biological factor that plays a crucial role in regulating fatty acid metabolism and energy utilization. Previous investigations conducted by Sonia Zambrano and her research team have shown that carnitine can upregulate PPAR-γ, thereby effectively mitigating the progression of renal fibrosis and myocardial fibrosis in rat models [31,32]. Multiple studies further substantiated Carnitine's intervention in fibrosis by ameliorating inflammation, modulating oxidative stress, and impeding the production and release of fibrosis-inducing factors [31,[33], [34], [35], [36], [37]]. Results of our MR research corroborated Carnitine's protective role in the onset and development of IPF, highlighting its potential as a predictive biomarker for assessing the occurrence and impact of IPF. This finding is of great significance in clinical practice, offering novel avenues for early prevention and therapeutic strategies in IPF and deepening our understanding of Carnitine's biological effects.

Our analysis found that 1-eicosatrienoylglycophorophospholine and 1-myristoylglycophorophospholine exhibited bidirectional MR positive effects in IPF, suggesting that these two metabolites may have potential value as biomarkers and also potentially becoming novel therapeutic targets for IPF. 1-Myristoylglycerophosphate is a lysophospholipid (LyP) and serves as a precursor to lysophosphatidic acid (LPA), a bioactive glycerophospholipid with various forms characterized by differing chain lengths and degrees of fatty acid saturation [38]. LPA is implicated in many physiological processes, including fibrosis, systemic sclerosis, cancer, inflammation, atherosclerosis, obesity, asthma, and multiple sclerosis [[39], [40], [41], [42], [43], [44]]. Besides, LPA can induce fibrotic conditions in the lung, kidney, and liver, mediated through mechanisms involving epithelial cell apoptosis, vascular permeability, and fibroblast migration and proliferation [45,46]. Our MR study underscored the clinical significance of 1-myristoylglycerophosphate by elucidating its positive association with the elevated risk of IPF.

Hypoxanthine, which belongs to the nucleotide class, is a purine metabolite that has been identified to have a causal relationship with fibrosis. Purines are pivotal in modulating the immune response during wound healing and fibrosis, serving as critical signaling hubs in scar tissue formation within fibrotic regions [39]. Our MR study reinforced the protective role of hypoxanthine in the initiation and progression of IPF, contributing to the ongoing exploration of potential drug targets for this condition. In contrast, inosine, another purine metabolite and precursor to adenosine, remains controversial regarding its involvement in the onset and development of fibrosis. Metabolic analysis using gas chromatography with mass spectrometry of lung tissue from IPF patients has shown an increase in inosine's molecular weight compared to normal lung tissue [47]. Moreover, adenosine levels have been found to be associated with the progression of IPF [48]. An alternative study suggests that inosine, acting as a potential receptor agonist in certain adenosine A3 formulations, may inhibit fibrosis by suppressing the proliferation of hepatic stellate cell-derived sodium channel regulatory factor cells [49]. Interestingly, our research did not find a direct correlation between inosine and IPF, highlighting the multifactorial nature of fibrosis development. This finding suggests new avenues for future exploration, such as investigating other metabolites and cytokines, to gain deeper insights. Despite the absence of a confirmed link between inosine and IPF as a significant metabolic product, its biological effects and functions warrant continued attention and investigation, potentially offering applications in other domains.

1-Eicosatrienoylglycerophosphocline belongs to the serum metabolite of lipids. Both eicosatrienoylglycerophosphocline (ETEPC) and epoxyicosatrienoic acid (EETs) contain eicosatrienoic acid. The biological activity of EET is closely related to the synthesis of ETEPC and its lipid metabolism pathway. ETEPC serves as a precursor for EET synthesis, which can be produced through the metabolism of ETEPC by lipoxygenase; EET, in turn, can regulate the metabolism and levels of ETEPC through different pathways. Current research has indicated that EETs have inhibitory effects on pulmonary fibrosis (50). EETs can reduce the occurrence and progression of fibrosis by inhibiting molecular signaling pathways and inflammatory responses related to IPF, reducing oxidative stress and cell apoptosis, and inhibiting the proliferation and migration of reticulocytes and pulmonary artery endothelial cells [50,51]. Our MR studies also demonstrated the preventive and inhibitory effects of 1-eicosatrienoylglycophorine on fibrosis.

The ratio of omega-6 fatty acids to omega-3 fatty acids quantifies the balance between these two types of fatty acids in the human diet. These fatty acids play crucial roles in various physiological functions in the human body, including cell membrane synthesis, cell signaling, inflammatory responses, and immune regulation [52,53]. Current research has underscored the potential of excessive omega-6 fatty acid consumption to promote fibrosis development. This propensity can be attributed to the generation of bioactive compounds like prostaglandin E2 (PGE2) by omega-6 fatty acids, which can trigger inflammation and exacerbate fibrosis progression [54,55]. Moreover, omega-6 fatty acids can influence cell growth and gene expression, potentially accelerating fibrosis progression [56,57]. In contrast, omega-3 fatty acids, also known as polyunsaturated fatty acids, play a protective role in preventing and mitigating the onset of fibrosis by suppressing inflammatory reactions [58] and enhancing antioxidant activity [59]. Our MR study suggests that the ratio of omega-6 fatty acids to omega-3 fatty acids may be a critical determinant in the development of IPF. Specifically, an elevated omega-3 to omega-6 ratio may accelerate the process of IPF, while reducing this ratio may have a preventive effect on IPF, likely due to the physiological attributes of omega-3 fatty acids. This conclusion serves as a crucial reference for developing strategies to prevent and treat IPF. Thus, increasing daily dietary intake of omega-3 fatty acids could be an effective intervention to reduce the omega-3 to omega-6 ratio, thereby preventing and managing IPF.

The degree of unsaturation refers to the number of unsaturated bonds present in blood metabolites. This metric is significant as it reflects metabolic changes resulting from various biochemical reactions, thereby serving as a biomarker for assessing metabolic disorders and susceptibility to diseases [60,61]. Previous studies have supported a connection between the number of unsaturated bonds in lipid components and other blood metabolites with fibrotic diseases [62]. Our MR research findings provided initial evidence of a potential positive correlation between the degree of unsaturation and IPF, which offered new insights into the management and prevention of IPF, and further enhanced our understanding of the regulatory role of lipid metabolism in the pathogenesis of IPF. It may also pave the way for developing novel targets for regulating lipid metabolism, ultimately improving diagnostic accuracy and treatment outcomes for IPF. These discoveries hold promise for exerting a positive and lasting impact on the treatment and prognosis of patients with IPF.

Additionally, 1-linoleoylglycerophoethanolamine* is a phospholipid molecule that plays pivotal roles in various biological processes within the human body, including cell membrane composition and signal transduction [63]. Our MR study represents a pioneering effort in elucidating the connection between 1-linoleoylglycerophoethanolamine* and IPF, and suggest that this metabolite may act as a protective factor in serum, potentially reducing the risk of IPF. However, its precise role within the pathophysiological mechanisms of IPF remains ambiguous, making 1-linoleoylglycerophoethanolamine* a compelling subject for further investigation. A recently study has identified a novel etiological mechanism involving HMGCS2-mediated lipid metabolism in alveolar type II epithelial cells (AECII), which plays a significant role in the onset and progression of pulmonary fibrosis. This finding highlights a potential new target for clinical intervention [64].

Through metabolic pathway analysis, we identified a significant correlation between IPF and the glycerophospholipid and glyceride metabolism pathways, both of which involved the metabolic processing of the glycerol scaffold. Specifically, our findings highlighted a strong association with a phosphatidylglycerol molecule known as sn-Glycerol 3-phosphate, posing an increased risk of IPF. Consequently, sn-Glycerol 3-phosphate emerges as a promising candidate for a potential diagnostic marker and therapeutic target in the context of IPF.

However, our study had several limitations. Firstly, a larger sample size and more comprehensive GWAS data are necessary for a thorough exploration of the roles of metabolites, particularly amino acids, in IPF. Secondly, the GWAS databases used in this research were primarily composed by European populations. Therefore, caution should be exercised when generalizing these results to other racial or ethnic groups, as their applicability to these populations remains uncertain.

5 Conclusion

We identified six primary causal risk factors and six protective serum metabolites closely linked with IPF. Our research highlighted carnitine as the most significant protective element against the development of IPF. Additionally, we identified two metabolites, specifically sn-Glycerol 3-phosphate and 1-Acyl-sn-glycero-3-phosphocholine, playing pivotal roles in the pathogenesis of IPF through metabolic pathways. These metabolites were demonstrated to be crucial for the metabolic processes influencing the progression of IPF.

Ethical recognition

Due to the use of publicly available data, no separate ethical approval is required.

Consent for publication

All the authors have read and approved the final version of manuscript and give consent.

Funding

This work was supported by 10.13039/501100001809 National Natural Science Foundation of China (No. 82070520 ), 10.13039/501100004735 Natural Science Foundation of Hunan Province (No. 2020JJ8015 ), and Scientific research project approved by 10.13039/100017695 Hunan Provincial Health Commission (202103012120 ).

Data availability statement

All data utilized in this study were sourced from the publicly available summary statistics of GWAS. These serum metabolite data are accessible via the Metabolomics GWAS server: https://metabolomics.helmholtz-muenchen.de/gwas/. The IPF dataset is accessible at this website:https://www.thelancet.com/journals/lanres/article/PIIS2213-2600(20)30364-7/fulltext.

CRediT authorship contribution statement

Qiong-Chao Zou: Writing – original draft, Software. Jun-Pei Hu: Writing – review & editing, Writing – original draft, Software. Yan Cao: Formal analysis. Chang She: Data curation. Li-Hui Liang: Writing – review & editing, Formal analysis. Zheng-Yu Liu: Writing – review & editing, Investigation, Funding acquisition.

Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Abbreviation

MR Mendelian randomization

IPF Idiopathic pulmonary fibrosis

HDL High density lipoprotein

VLDL Very Low Density Lipoprotein

GWAS Genome-wide association study

SNP Nucleotide polymorphism

IVW Inverse-variance weighted method

LD Linkage disequilibrium

SUPERGNOVA SUPER GeNetic cOVariance Analyzer

LDSC Linkage Disequilibrium Score Regression

MR-Egger Mendelian Randomization-Egger regression

MR-PRESSO MR pleiotropy residual sum and outlier

OR Odds ratio

SMPDB Small Molecule Pathway Database

KEGG Kyoto Encyclopedia of Genes and Genomes

LyP Lysophosphatidylcholine

LPA Lysophosphatidic Acid

ETEPC Eicosatrienoylglycerophosphocholine

EETs epoxyeicosatrienoic acid

PGE2 Prostaglandin E2

Appendix A Supplementary data

The following are the Supplementary data to this article:Multimedia component 1

Multimedia component 1

Multimedia component 2

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Multimedia component 3

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Acknowledgements

The authors thank all the participants and researchers who contributed and collected data.

Appendix A Supplementary data to this article can be found online at https://doi.org/10.1016/j.heliyon.2024.e36125.
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References

1 Richeldi L. Collard H.R. Jones M.G. Idiopathic pulmonary fibrosis Lancet 389 10082 2017 1941 1952 28365056
2 Khateeb J. Fuchs E. Khamaisi M. Diabetes and lung disease: a neglected relationship Rev. Diabet. Stud.: Reg. Dev. Stud. 15 2019 1 15
3 Papiris S.A. Kannengiesser C. Borie R. Kolilekas L. Kallieri M. Apollonatou V. Genetics in idiopathic pulmonary fibrosis: a clinical perspective Diagnostics 12 12 2022
4 Somogyi V. Chaudhuri N. Torrisi S.E. Kahn N. Müller V. Kreuter M. The therapy of idiopathic pulmonary fibrosis: what is next? European respiratory review : an official journal of the European Respiratory Society 28 153 2019
5 Hunninghake G.M. Interstitial lung abnormalities: erecting fences in the path towards advanced pulmonary fibrosis Thorax 74 5 2019 506 511 30723182
6 Bajaj J.S. Garcia-Tsao G. Reddy K.R. O'Leary J.G. Vargas H.E. Lai J.C. Admission urinary and serum metabolites predict renal outcomes in hospitalized patients with cirrhosis Hepatology 74 5 2021 2699 2713 34002868
7 Zhang A. Sun H. Wang X. Serum metabolomics as a novel diagnostic approach for disease: a systematic review Anal. Bioanal. Chem. 404 4 2012 1239 1245 22648167
8 Geng Y. Li L. Yan J. Liu K. Yang A. Zhang L. PEAR1 regulates expansion of activated fibroblasts and deposition of extracellular matrix in pulmonary fibrosis Nat. Commun. 13 1 2022 7114 36402779
9 Sklepkiewicz P. Dymek B.A. Mlacki M. Koralewski R. Mazur M. Nejman-Gryz P. Inhibition of CHIT1 as a novel therapeutic approach in idiopathic pulmonary fibrosis Eur. J. Pharmacol. 919 2022 174792
10 Milara J. Hernandez G. Ballester B. Morell A. Roger I. Montero P. The JAK2 pathway is activated in idiopathic pulmonary fibrosis Respir. Res. 19 1 2018 24 29409529
11 Wang Q. Xie Z. Wan N. Yang L. Jin Z. Jin F. Potential biomarkers for diagnosis and disease evaluation of idiopathic pulmonary fibrosis Chin. Med. J. 136 11 2023 1278 1290 37130223
12 Hemani G. Zheng J. Elsworth B. Wade K.H. Haberland V. Baird D. The MR-Base platform supports systematic causal inference across the human phenome Elife 7 2018
13 Emdin C.A. Khera A.V. Kathiresan S. Mendelian randomization JAMA 318 19 2017 1925 1926 29164242
14 Shin S.Y. Fauman E.B. Petersen A.K. Krumsiek J. Santos R. Huang J. An atlas of genetic influences on human blood metabolites Nat. Genet. 46 6 2014 543 550 24816252
15 Kettunen J. Demirkan A. Würtz P. Draisma H.H. Haller T. Rawal R. Genome-wide study for circulating metabolites identifies 62 loci and reveals novel systemic effects of LPA Nat. Commun. 7 2016 11122
16 Duckworth A. Gibbons M.A. Allen R.J. Almond H. Beaumont R.N. Wood A.R. Telomere length and risk of idiopathic pulmonary fibrosis and chronic obstructive pulmonary disease: a mendelian randomisation study Lancet Respir. Med. 9 3 2021 285 294 33197388
17 Li P. Wang H. Guo L. Association between gut microbiota and preeclampsia-eclampsia: a two-sample Mendelian randomization study BMC Med. 20 1 2022 443 36380372
18 Xiao G. He Q. Liu L. Causality of genetically determined metabolites on anxiety disorders: a two-sample Mendelian randomization study J. Transl. Med. 20 1 2022 475 36266699
19 Skrivankova V.W. Richmond R.C. Woolf B.A.R. Yarmolinsky J. Davies N.M. Swanson S.A. Strengthening the reporting of observational studies in epidemiology using Mendelian randomization: the STROBE-MR statement JAMA 326 16 2021 1614 1621 34698778
20 Tam V. Patel N. Turcotte M. Bossé Y. Paré G. Meyre D. Benefits and limitations of genome-wide association studies Nat. Rev. Genet. 20 8 2019 467 484 31068683
21 Swanson S.A. Hernán M.A. The challenging interpretation of instrumental variable estimates under monotonicity Int. J. Epidemiol. 47 4 2018 1289 1297 28379526
22 Davies N.M. Holmes M.V. Davey Smith G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians BMJ 362 2018 k601 30002074
23 Burgess S. Labrecque J.A. Mendelian randomization with a binary exposure variable: interpretation and presentation of causal estimates Eur. J. Epidemiol. 33 10 2018 947 952 30039250
24 Lin Z. Pan I. Pan W. A practical problem with Egger regression in Mendelian randomization PLoS Genet. 18 5 2022 e1010166
25 Bowden J. Davey Smith G. Haycock P.C. Burgess S. Consistent estimation in Mendelian randomization with some invalid instruments using a weighted median estimator Genet. Epidemiol. 40 4 2016 304 314 27061298
26 Ong J.S. MacGregor S. Implementing MR-PRESSO and GCTA-GSMR for pleiotropy assessment in Mendelian randomization studies from a practitioner's perspective Genet. Epidemiol. 43 6 2019 609 616 31045282
27 Bulik-Sullivan B.K. Loh P.R. Finucane H.K. Ripke S. Yang J. Patterson N. LD Score regression distinguishes confounding from polygenicity in genome-wide association studies Nat. Genet. 47 3 2015 291 295 25642630
28 Zhang Y. Lu Q. Ye Y. Huang K. Liu W. Wu Y. SUPERGNOVA: local genetic correlation analysis reveals heterogeneous etiologic sharing of complex traits Genome Biol. 22 1 2021 262 34493297
29 Frolkis A. Knox C. Lim E. Jewison T. Law V. Hau D.D. SMPDB: the small molecule pathway database Nucleic Acids Res. 38 Database issue 2010 D480 D487 19948758
30 Kanehisa M. Goto S. Sato Y. Furumichi M. Tanabe M. KEGG for integration and interpretation of large-scale molecular data sets Nucleic Acids Res. 40 Database issue 2012 D109 D114 22080510
31 Zambrano S. Blanca A.J. Ruiz-Armenta M.V. Miguel-Carrasco J.L. Arévalo M. Mate A. L-carnitine attenuates the development of kidney fibrosis in hypertensive rats by upregulating PPAR-γ Am. J. Hypertens. 27 3 2014 460 470 24413708
32 Zambrano S. Blanca A.J. Ruiz-Armenta M.V. Miguel-Carrasco J.L. Arévalo M. Vázquez M.J. L-Carnitine protects against arterial hypertension-related cardiac fibrosis through modulation of PPAR-γ expression Biochem. Pharmacol. 85 7 2013 937 944 23295156
33 Houten S.M. Wanders R.J.A. Ranea-Robles P. Metabolic interactions between peroxisomes and mitochondria with a special focus on acylcarnitine metabolism Biochim. Biophys. Acta, Mol. Basis Dis. 1866 5 2020 165720
34 Yang S.K. Xiao L. Song P.A. Xu X. Liu F.Y. Sun L. Effect of L-carnitine therapy on patients in maintenance hemodialysis: a systematic review and meta-analysis J. Nephrol. 27 3 2014 317 329 24535997
35 Li M. Li H. Yang S. Liao X. Zhao C. Wang F. L-carnitine attenuates TGF-β1-induced EMT in retinal pigment epithelial cells via a PPARγ-dependent mechanism Int. J. Mol. Med. 47 6 2021
36 Salama A. Fayed H.M. Elgohary R. L-carnitine alleviated acute lung injuries induced by potassium dichromate in rats: involvement of Nrf2/HO-1 signaling pathway Heliyon 7 6 2021 e07207
37 Fondevila M.F. Fernandez U. Heras V. Parracho T. Gonzalez-Rellan M.J. Novoa E. Inhibition of carnitine palmitoyltransferase 1A in hepatic stellate cells protects against fibrosis J. Hepatol. 77 1 2022 15 28 35167910
38 Baker D.L. Umstot E.S. Desiderio D.M. Tigyi G.J. Quantitative analysis of lysophosphatidic acid in human blood fractions Ann. N. Y. Acad. Sci. 905 2000 267 269 10818461
39 Alsafadi H.N. Staab-Weijnitz C.A. Lehmann M. Lindner M. Peschel B. Königshoff M. An ex vivo model to induce early fibrosis-like changes in human precision-cut lung slices Am. J. Physiol. Lung Cell Mol. Physiol. 312 6 2017 L896 l902 28314802
40 Mendoza F.A. Mansoor M. Jimenez S.A. Treatment of rapidly progressive systemic sclerosis: current and futures perspectives Expert opinion on orphan drugs 4 1 2016 31 47 27812432
41 Murph M. Tanaka T. Pang J. Felix E. Liu S. Trost R. Liquid chromatography mass spectrometry for quantifying plasma lysophospholipids: potential biomarkers for cancer diagnosis Methods Enzymol. 433 2007 1 25 17954226
42 Knowlden S. Georas S.N. The autotaxin-LPA axis emerges as a novel regulator of lymphocyte homing and inflammation J. Immunol. 192 3 2014 851 857 24443508
43 Navab M. Chattopadhyay A. Hough G. Meriwether D. Fogelman S.I. Wagner A.C. Source and role of intestinally derived lysophosphatidic acid in dyslipidemia and atherosclerosis J. Lipid Res. 56 4 2015 871 887 25646365
44 Del Bas J.M. Caimari A. Rodriguez-Naranjo M.I. Childs C.E. Paras Chavez C. West A.L. Impairment of lysophospholipid metabolism in obesity: altered plasma profile and desensitization to the modulatory properties of n-3 polyunsaturated fatty acids in a randomized controlled trial Am. J. Clin. Nutr. 104 2 2016 266 279 27305954
45 Sakai N. Chun J. Duffield J.S. Lagares D. Wada T. Luster A.D. Lysophosphatidic acid signaling through its receptor initiates profibrotic epithelial cell fibroblast communication mediated by epithelial cell derived connective tissue growth factor Kidney Int. 91 3 2017 628 641 27927603
46 Kaffe E. Katsifa A. Xylourgidis N. Ninou I. Zannikou M. Harokopos V. Hepatocyte autotaxin expression promotes liver fibrosis and cancer Hepatology 65 4 2017 1369 1383 27981605
47 Kang Y.P. Lee S.B. Lee J.M. Kim H.M. Hong J.Y. Lee W.J. Metabolic profiling regarding pathogenesis of idiopathic pulmonary fibrosis J. Proteome Res. 15 5 2016 1717 1724 27052453
48 Luo F. Le N.B. Mills T. Chen N.Y. Karmouty-Quintana H. Molina J.G. Extracellular adenosine levels are associated with the progression and exacerbation of pulmonary fibrosis Faseb. J. 30 2 2016 874 883 26527068
49 Herman-de-Sousa C. Pinheiro A.R. Paramos-de-Carvalho D. Costa M.A. Ferreirinha F. Magalhães-Cardoso T. Opposing effects of adenosine and inosine in human subcutaneous fibroblasts may Be regulated by third party ADA cell providers Cells 9 3 2020
50 Zhou Y. Yang J. Sun G.Y. Liu T. Duan J.X. Zhou H.F. Soluble epoxide hydrolase inhibitor 1-trifluoromethoxyphenyl-3- (1-propionylpiperidin-4-yl) urea attenuates bleomycin-induced pulmonary fibrosis in mice Cell Tissue Res. 363 2 2016 399 409 26310139
51 Tao J.H. Liu T. Zhang C.Y. Zu C. Yang H.H. Liu Y.B. Epoxyeicosatrienoic acids inhibit the activation of murine fibroblasts by blocking the TGF-β1-Smad2/3 signaling in a PPARγ-dependent manner Oxid. Med. Cell. Longev. 2022 2022 7265486
52 Innes J.K. Calder P.C. Omega-6 fatty acids and inflammation Prostaglandins Leukot. Essent. Fatty Acids 132 2018 41 48 29610056
53 Tortosa-Caparrós E. Navas-Carrillo D. Marín F. Orenes-Piñero E. Anti-inflammatory effects of omega 3 and omega 6 polyunsaturated fatty acids in cardiovascular disease and metabolic syndrome Crit. Rev. Food Sci. Nutr. 57 16 2017 3421 3429 26745681
54 Alvarez M.L. Lorenzetti F. Role of eicosanoids in liver repair, regeneration and cancer Biochem. Pharmacol. 192 2021 114732
55 Cagnina R.E. Duvall M.G. Nijmeh J. Levy B.D. Specialized pro-resolving mediators in respiratory diseases Curr. Opin. Clin. Nutr. Metab. Care 25 2 2022 67 74 34954726
56 Lu Y. Fang J. Zou L. Cui L. Liang X. Lim S.G. Omega-6-derived oxylipin changes in serum of patients with hepatitis B virus-related liver diseases Metabolomics : Official journal of the Metabolomic Society 14 3 2018 26 30830341
57 Shoieb S.M. El-Ghiaty M.A. Alqahtani M.A. El-Kadi A.O.S. Cytochrome P450-derived eicosanoids and inflammation in liver diseases Prostag. Other Lipid Mediat. 147 2020 106400
58 Scorletti E. Byrne C.D. Omega-3 fatty acids and non-alcoholic fatty liver disease: evidence of efficacy and mechanism of action Mol. Aspect. Med. 64 2018 135 146
59 Fraser D.A. Wang X. Lund J. Nikolić N. Iruarrizaga-Lejarreta M. Skjaeret T. A structurally engineered fatty acid, icosabutate, suppresses liver inflammation and fibrosis in NASH J. Hepatol. 76 4 2022 800 811 34915054
60 Wu B Pan F Wang Q Association between blood metabolites and basal cell carcinoma risk: a two-sample Mendelian randomization study Front. Endocrinol. (Lausanne) 15 2024 Front Endocrinol (Lausanne)
61 Matveyenka M. Rizevsky S. Kurouski D. The degree of unsaturation of fatty acids in phosphatidylserine alters the rate of insulin aggregation and the structure and toxicity of amyloid aggregates FEBS Lett. 596 11 2022 1424 1433 35510803
62 Jia H. Liu J. Fang T. Zhou Z. Li R. Yin W. The role of altered lipid composition and distribution in liver fibrosis revealed by multimodal nonlinear optical microscopy Sci. Adv. 9 2 2023 eabq2937
63 Wymann M.P. Schneiter R. Lipid signalling in disease Nat. Rev. Mol. Cell Biol. 9 2 2008 162 176 18216772
64 Yang J. Pan X. Xu M. Downregulation of HMGCS2 mediated AECIIs lipid metabolic alteration promotes pulmonary fibrosis by activating fibroblasts Respir. Res. 25 1 2024 176 38658970
