
==== Front
BMC Pulm Med
BMC Pulm Med
BMC Pulmonary Medicine
1471-2466
BioMed Central London

39272013
3275
10.1186/s12890-024-03275-4
Research
Causal relationship between circulating glutamine levels and idiopathic pulmonary fibrosis: a two-sample mendelian randomization study
Xu Tao 12
Liu Chengyu 3
Ning Xuecong 1
Gao Zhiguo 1
Li Aimin 1
Wang Shengyun 1
Leng Lina 4
Kong Pinpin 3
Liu Pengshuai 5
Zhang Shusen xtzhangs@163.com

167
Zhang Ping victoryzping@163.com

1
1 https://ror.org/02s8x1148 grid.470181.b Department of Pulmonary and Critical Care Medicine, Affiliated Xing Tai People Hospital of Hebei Medical University, Xingtai, China
2 https://ror.org/03hqwnx39 grid.412026.3 0000 0004 1776 2036 Department of Internal Medicine, Graduate School of Hebei North University, Zhangjiakou, China
3 https://ror.org/04eymdx19 grid.256883.2 0000 0004 1760 8442 Graduate School of Hebei Medical University, Shijiazhuang, China
4 https://ror.org/02s8x1148 grid.470181.b Department of Rheumatology and Immunology, Affiliated Xing Tai People Hospital of Hebei Medical University, Xingtai, China
5 https://ror.org/02bzkv281 grid.413851.a 0000 0000 8977 8425 Graduate School of Chengde Medical University, Chengde, China
6 grid.478131.8 0000 0004 9334 6499 Hebei Province Xingtai People’s Hospital Postdoctoral Workstation, Xingtai, China
7 https://ror.org/04eymdx19 grid.256883.2 0000 0004 1760 8442 Postdoctoral Mobile Station, Hebei Medical University, Shijiazhuang, China
13 9 2024
13 9 2024
2024
24 4515 5 2024
6 9 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Idiopathic pulmonary fibrosis (IPF) is a progressive and debilitating respiratory disease with a median survival of less than 5 years. In recent years, glutamine has been reported to be involved in the regulation of collagen deposition and cell proliferation in fibroblasts, thereby influencing the progression of IPF. However, the relationships between glutamine and the incidence, progression, and treatment response of IPF remain unclear. Our study aimed to investigate the relationship between circulating glutamine levels and IPF, as well as its potential as a therapeutic target.

Methods

We performed a comprehensive Mendelian Randomization (MR) analysis using the most recent genome-wide association study summary-level data. A total of 32 single nucleotide polymorphisms significantly correlated to glutamine levels were identified as instrumental variables. Eight MR analysis methods, including inverse variance weighted, MR-Egger, weighted median, weighted mode, constrained maximum likelihood, contamination mixture, robust adjusted profile score, and debiased inverse-variance weighted method, were used to assess the relationship between glutamine levels with IPF.

Results

The inverse variance weighted analysis revealed a significant inverse correlation between glutamine levels and IPF risk (Odds Ratio = 0.750; 95% Confidence Interval : 0.592–0.951; P = 0.017). Sensitivity analyses, including MR-Egger regression and MR-PRESSO global test, confirmed the robustness of our findings, with no evidence of horizontal pleiotropy or heterogeneity.

Conclusion

Our study provides novel evidence for a causal relationship between lower circulating glutamine levels and increased risk of IPF. This finding may contribute to the early identification of high-risk individuals for IPF, disease monitoring, and development of targeted therapeutic strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-024-03275-4.

Keywords

Glutamine
Idiopathic pulmonary fibrosis
Mendelian randomization
GWAS
Causal relationship
This work was supported by the Natural Science Foundation of Hebei Province.No.H2021108003 issue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcBackground

Idiopathic pulmonary fibrosis is a chronic progressive lung disease characterized by the formation of scars in the lung tissue, ultimately leading to respiratory failure [1] .There are approximately 3 million people with IPF all over the globe. IPF mainly affects people over the age of 50, with more men than women, and the incidence increases with age [2]. The clinical manifestations of IPF usually include progressive dyspnea, dry cough, and pestle finger, and the median survival time after diagnosis is only 3 to 5 years [3]. Studies have reported that IPF may be associated with aging, smoking and genetic predisposition, but the detailed prevalence factors and pathogenesis are unclear, and there is no specific drug treatment with a very poor prognosis [4–6]. An in-depth study of the factors and pathogenesis of IPF is needed to screen high-risk groups for early intervention and to explore new therapeutic targets.

In recent years, the relationship between glutamine metabolism and IPF has attracted increasing attention from researchers. Glutamine is one of the most abundant amino acids in the human body and plays a crucial role in various physiological processes, such as protein synthesis, cellular energy metabolism, and regulation of immune function [7]. In the pathological process of IPF, abnormal activation and proliferation of fibroblasts are key factors leading to structural destruction and loss of function of lung tissue [2, 8, 9]. Several studies have suggested that glutamine metabolism may play a crucial regulatory role in IPF disease progression [10–19]. Glutamine is converted to glutamate by glutaminase (GLS1), which subsequently generates α-ketoglutarate (α-KG), a process that not only provides energy to cells but also participates in the regulation of collagen synthesis and cell signaling [10, 14]. In fibroblasts with IPF, increased glutamine catabolism is closely associated with excessive collagen deposition and increased cell proliferation [10, 19].

These findings provide new insights into the molecular mechanisms of IPF, but the lack of epidemiologic and clinical studies on the relationship between glutamine metabolism and IPF has hampered the development of therapeutic strategies targeting glutamine metabolism.

Mendelian randomization (MR), an innovative approach to genetic epidemiology, is unique in that it simulates experimental designs using genetic variants (typically single nucleotide polymorphisms), as powerful instrumental variables (IVs), providing a reliable way to assess associations between different factors [20]. This approach exploits the properties of independent segregation and random assignment of genes during meiosis to explore the causal relationship between exposure and outcome.

Genetic variations are randomly distributed during conception, typically independent of environmental risk factors, and occur before the onset of disease [21]. By using alleles as a proxy for actual exposure, MR not only helps to minimize the effects of potential confounders and reverse causation, but also provides more reliable causal inferences. Currently, there are no MR analyses investigating the causal relationship between glutamine levels and the risk of developing IPF.

In this study, we collected the most recent summary level data on genetic loci and glutamine levels in IPF subjects through a genome-wide association study (GWAS) using a two-sample MR study to analyze the causal relationship between glutamine levels and IPF prevalence.

Materials and methods

Study design

Figure 1 showed the design of this study. MR analysis was based on three main assumptions: [1] the correlation assumption: the IVs must be strongly associated with the exposure; [2] the independence assumption: the IVs are not related to confounders; and [3] the exclusivity assumption: the IVs affect the outcome only through the exposure and cannot be directly correlated with the outcome [22]. We used data from published studies or publicly available GWAS statistics, so no additional ethical statements or informed consent were required. Analyses were performed using the TwoSampleMR (version 0.5.10), Mendelian Randomization (version 0.8.0), and MRPRESSO packages in R language (version 4.3.2).

Fig. 1 The three corresponding main hypotheses in Mendelian randomization studies: instrumental variables are significantly associated with glutamine levels, they are independent of confounders, and they are associated with IPF only through glutamine levels. (IPF: Idiopathic pulmonary fibrosis)

Data sources

SNPs (Single Nucleotide Polymorphisms) were selected from the GWAS database as IVs for exposure (glutamine levels) and outcome (IPF).Summary statistics related to glutamine levels were obtained from the GWAS data [23], which included 114,751 subjects (https://gwas.mrcieu.ac.uk/). For IPF, we selected data from the FinnGen R10 (published on December 18, 2023) GWAS database (r10.finngen.fi), which included 2,189 IPF patients and 407,609 healthy controls. It is worth noting that the population we included was of European origin, which to some extent avoids the effects of population stratification. Since the GWAS data for exposure and outcome were obtained from different databases, it can be assumed that there is no population overlap. Regarding the assessment methods and diagnostic criteria for glutamine levels and IPF, we have maintained consistency with the original data of the included study databases.

Selection of instrumental variables

With the three assumptions based on MR analysis, we firstly selected SNPs associated with exposure, and the selected SNPs should be significantly associated with the target exposure at the genome-wide significance threshold (P < 5 × 10− 8) to fulfill the first hypothesis. Secondly, the sliding window size was set to 10,000 kb, with r2 < 0.01, to ensure the independence of the selected genetic variants. To fulfill the third hypothesis, we set P to less than 5 × 10− 5 to filter out SNPs significantly associated with outcome. At the same time, we introduced the Steiger test statistic to test whether the correlation between SNPs and outcome was greater than the correlation with exposure, and SNPs that did not pass the Steiger test were removed, somewhat strengthening the third hypothesis and avoiding the existence of reverse causality [24]. Next, F-statistics (BETA2/SE2) for glutamine levels were calculated for each SNP as a measure of instrumental strength [25], an F-statistic greater than 10 generally suggests that the selected instrumental variable is sufficiently strong, reducing the risk of bias associated with weak instruments, and thus substantiates the predictive relationship of the instrument with the exposure. SNPs with F-statistics below 10 were considered weak instrumental variables and were removed from subsequent analyses [26]. Finally, we also removed palindromic SNPs with mirror symmetry in the genome to improve data quality.

MR analysis

We used eight methods of analysis, including inverse variance weighted (IVW) [27], the Mendelian Randomization-Egger (MR-Egger) [28], the weighted median (WM) [29], the weighted mode methods [30], Constrained maximum likelihood (cML-MA) [31], the contamination mixture (ConMix) [32], the Robust adjusted profile score (MR-RAPS) [33], the Debiased inverse-variance weighted method(DIVW) [34]. Of these, IVW served as the primary method of analysis because it provided the greatest statistical power in the absence of pleiotropy. We first calculated the effect size of each instrumental variable and its standard error, and then combined these effect sizes using inverse variance weighting to estimate the overall causal effect. This approach assumes that all instrumental variables are valid, i.e. that there is no direct association between them and the outcome, except through exposure factors [35]. We then used the remaining seven methods as complementary methods to verify the robustness as well as the accuracy of the IVW results.

Sensitivity analysis

MR-Egger regression analysis was performed in order to assess the robustness of the results of our main analyses [28]. This method aims to detect and correct for possible horizontal multinomiality in instrumental variables. We report the value of the Egger intercept and the corresponding p-value for the presence of multinomiality. In addition, a global test of MR-PRESSO was also performed to assess the presence of horizontal multinomiality in all instrumental variables [36]. Next was an outlier detection to identify unusual instrumental variables that might influence the results. After detecting pleiotropy or outliers, we manually removed the outliers and revalidated the causal effects and compared the results with those of the original IVW analyses. To confirm the presence of heterogeneity, we assessed it using Cochran’s Q test, with P < 0.05 indicating the presence of heterogeneity [27]. Finally, a leave-one-out analysis was performed to determine whether the removal of individual SNPs significantly affected the results.

Moreover, several studies have suggested that smoking and gastroesophageal reflux disease (GERD) may act as risk factors for IPF [6, 37], and to prevent potential confounders from biasing our results, we similarly assessed the causal relationship between glutamine levels and smoking and GERD.

To ensure the rigor and validity of our study, we have verified this research against the checklist provided by the STROBE-MR guidelines (Additional file 1). For detailed information, please refer to the STROBE-MR website (https://www.strobe-mr.org/).

Results

In our study, each SNP considered has successfully undergone the Steiger Test (The last two columns in Table 1). This affirms that the association of each SNP with the exposure variable is more pronounced than with the outcome, thus precluding the possibility of reverse causality and reinforcing the exclusivity assumption of our Mendelian randomization methodology [24]. 32 SNPs significantly associated with glutamine levels (P < 5 × 10− 8) were finally included as IVs, and all of them had F-statistics greater than 10 (range: 29.207 to 2201.084), as detailed in Table 1.

Table 1 Specific information on the 32 SNPs used for MR analysis

SNP	Chr	EA	NEA	EAF	Beta	Se	P-value	F	Dir	Steiger_pval	
rs10811663	9	A	G	0.086467	-0.048147	0.00804615	2.20E-09	42.039	TRUE	7.89E-07	
rs112081903	16	C	T	0.298884	-0.0253394	0.00451361	2.00E-08	30.887	TRUE	1.34E-05	
rs113674212	12	A	C	0.068167	0.134105	0.00819183	3.10E-60	262.77	TRUE	2.95E-46	
rs117001881	12	A	G	0.009852	-0.12046	0.021557	2.30E-08	32.495	TRUE	1.39E-05	
rs11993225	8	C	A	0.269902	0.0259672	0.00463689	2.10E-08	30.502	TRUE	1.08E-05	
rs1260326	2	C	T	0.604051	0.0789422	0.00421761	3.60E-78	343.09	TRUE	2.96E-59	
rs1274961	3	C	T	0.78286	-0.0296721	0.00500127	3.00E-09	34.358	TRUE	2.66E-07	
rs1323320	6	A	G	0.263336	-0.0258759	0.00468672	3.40E-08	29.817	TRUE	7.29E-06	
rs17096421	10	T	A	0.056414	-0.0513762	0.00907021	1.50E-08	32.255	TRUE	2.46E-05	
rs1750768	10	T	C	0.041941	0.0565226	0.0103452	4.70E-08	29.469	TRUE	0.000421366	
rs1998848	14	A	G	0.010532	0.166954	0.0209281	1.50E-15	66.702	TRUE	1.39E-10	
rs2039098	20	T	C	0.597154	-0.0241636	0.00419511	8.40E-09	32.244	TRUE	2.51E-06	
rs2168101	11	A	C	0.308286	-0.0658561	0.00460998	2.70E-46	212.65	TRUE	2.44E-33	
rs2657879	12	G	A	0.182526	-0.251133	0.0053259	1.00E-200	2201.1	TRUE	0	
rs28362590	5	T	G	0.754407	-0.0262054	0.00479807	4.70E-08	29.207	TRUE	0.000112916	
rs28929474	14	T	C	0.019978	0.0905463	0.0147746	8.90E-10	36.851	TRUE	5.12E-07	
rs35007880	14	T	G	0.513586	0.0331051	0.00416726	2.00E-15	62.867	TRUE	5.96E-12	
rs35261542	6	A	C	0.260932	0.0302132	0.00470514	1.40E-10	40.414	TRUE	1.93E-08	
rs3812316	7	G	C	0.129332	0.0708251	0.00614256	9.30E-31	129.78	TRUE	5.66E-23	
rs4365129	12	T	A	0.625244	-0.0265688	0.00431716	7.50E-10	37.972	TRUE	1.59E-06	
rs56335308	8	A	G	0.027368	0.117482	0.0126332	1.40E-20	84.379	TRUE	6.98E-14	
rs58673065	7	G	A	0.22926	0.0298844	0.00490242	1.10E-09	36.228	TRUE	1.30E-06	
rs62182473	2	T	C	0.265281	-0.0707191	0.00468972	2.20E-51	224.14	TRUE	6.27E-38	
rs7078003	10	T	C	0.178478	0.104082	0.00539996	8.80E-83	365.69	TRUE	3.72E-61	
rs7147721	14	G	A	0.462067	0.026091	0.00415066	3.30E-10	38.845	TRUE	1.40E-07	
rs738408	22	T	C	0.21654	0.0323695	0.00500826	1.00E-10	40.809	TRUE	9.75E-08	
rs78431863	11	T	C	0.041225	0.0794215	0.0104266	2.60E-14	57.247	TRUE	8.72E-09	
rs7925445	11	G	A	0.556154	0.0285458	0.00416485	7.20E-12	46.181	TRUE	3.36E-07	
rs79687284	1	C	G	0.034612	0.155571	0.0113041	4.30E-43	185.9	TRUE	6.75E-33	
rs838737	2	A	G	0.565472	-0.0234551	0.00416218	1.70E-08	31.031	TRUE	2.18E-06	
rs904538	17	A	C	0.462594	0.0270655	0.00413851	6.20E-11	41.809	TRUE	5.72E-08	
rs9482770	6	C	T	0.448711	-0.0323379	0.00416649	8.40E-15	59.398	TRUE	1.17E-11	
SNP: single-nucleotide polymorphism; Chr: chromosome; EA: effect allele; NEA: other allele; EAF: effect allele frequency; Se: standard error; Dir: Steiger_dir (A value of TRUE indicates that the directional test for an individual SNP is valid)

Relationship between glutamine levels and IPF.

In the preliminary analysis, we screened 33 SNPs for analysis based on the conditions described above, and P was less than 0.05 for IVW and the remaining seven methods; however, when sensitivity analysis was performed, the Cochran’s Q test to validate the IVW method showed P = 0.008, indicating the presence of heterogeneity. Outliers were detected after performing MR-PRESSO analysis (rs142525555). After removing the outliers and re-analyzing, we found that glutamine levels were negatively correlated with the risk of developing IPF, i.e., lower glutamine levels may contribute to the development of IPF. Table 2 presents the analytical outcomes derived from the eight methodologies employed in our study. Figure 2 shows the scatterplots generated by the eight methods, and the Cochran’s Q test result of P = 0.082 indicates that there is no heterogeneity. The analysis of pleiotropy using the MR-Egger intercept test indicated the absence of horizontal pleiotropy (intercept = 0.010, P = 0.373), and the results of MR-PRESSO also supported the absence of horizontal pleiotropy (OR = 0.750; 95% CI: 0.592 to 0.951; P = 0.024) (Table 3). The symmetric and unbiased funnel plot based on the results increased the credibility of the findings (Fig. 3). Leave-one-out analysis showed that only one genetic variant, rs2657879, influenced the association between glutamine levels and IPF (Fig. 4).

Table 2 Causal effect of glutamine levels on IPF

Exposure	Outcome	Method	Nsnp	OR(95%CI)	P	
Glutamine levels	IPF	IVW	32	0.750(0.592–0.951)	0.017	
Glutamine levels	IPF	MR Egger	32	0.667(0.471–0.945)	0.03	
Glutamine levels	IPF	WM	32	0.703(0.533–0.926)	0.012	
Glutamine levels	IPF	Weighted mode	32	0.704(0.548–0.904)	0.01	
Glutamine levels	IPF	ConMix	32	0.721(0.584–0.889)	0.004	
Glutamine levels	IPF	RAPS	32	0.744(0.599–0.925)	0.008	
Glutamine levels	IPF	DIVW	32	0.749(0.593–0.946)	0.015	
Glutamine levels	IPF	cML-MA	32	0.745(0.606–0.916)	0.005	
IPF: idiopathic pulmonary fibrosis; IVW: inverse variance weighted; WM: weighted median; ConMix: contamination mixture; RAPS: robust adjusted profile score; DIVW: debiased inverse-variance weighted method; cML-MA: constrained maximum likelihood; OR: odds ratio; CI: confidence interval

Fig. 2 Scatter plots assessing the causal relationship between glutamine levels and IPF using eight methods. (MR: Mendelian randomization; SNP: single-nucleotide polymorphism)

Table 3 Pleiotropy and heterogeneity tests of MR

Test	Method	Effect size	P	
Heterogeneity	Cochran’s Q test	41.332(QMR Egger)	0.082	
	Cochran’s Q test	42.457(QIVW)	0.082	
Pleiotropy	MR-Egger regression	0.011(egger intercept)	0.373	
	MR-PRESSO global test	43.609(RSSobs)	0.116	
MR: Mendelian randomization; IVW: inverse variance weighted; RSS: residual sum of squares

Fig. 3 Funnel plot based on the results of the analysis. The horizontal coordinate mainly refers to the degree of variability and the vertical coordinate mainly refers to the total effect size. The positions of the values of the main effect sizes are marked with a line, and the points on the left and right sides can be distributed symmetrically, indicating the absence of bias

Fig. 4 Results of “leave-one-out” sensitivity analysis of IPF glutamine levels in MR analysis. MR results were calculated for the remaining SNPs after removing them one by one

Confounding factors

Glutamine level was selected as an exposure factor and smoking and GERD as outcomes separately for MR analysis to exclude the effects caused by potential confounders. And as shown in Table 4, there was no causality between glutamine level and these two, which fulfilled the second hypothesis of MR analysis.

Table 4 The IVW estimates of the primary and replicated instrument variables on the IPF potential risk factors

Exposure	Outcome	OR	95% CI	P	
Glutamine levels	Smoking	0.984	0.832–1.164	0.854	
	GERD	1.015	0.939–1.098	0.706	
GERD: gastroesophageal reflux disease

Discussion

Glutamine plays multiple roles in cell biology, including serving as an energy source to support intestinal, immune cell, and renal function; participation in nucleic acid synthesis to promote cell proliferation and repair; and maintenance of intracellular nitrogen balance [38]. It also plays a key role in regulating inflammatory responses and oxidative stress, which are essential for maintaining cellular homeostasis and tissue health [39, 40]. In the context of fibrotic diseases, regulation of glutamine metabolism may be associated with abnormal fibroblast activation and tissue scarring [41]. Glutamine not only plays a part in lung tissue, but is also associated with fibrotic processes in other organs. For example, in liver and kidney disease, glutamine metabolism disorders are associated with the progression of tissue fibrosis [42, 43]. This amino acid may play a key role in multi-organ fibrosis by influencing extracellular matrix synthesis and cell signaling, which may regulate fibrosis-related cellular activities on a systemic level [43] (Fig. 5).

Fig. 5 Mechanisms related to the involvement of glutamine in pathologic fibrosis

Several studies on IPF have confirmed that glutamine metabolism plays an important part in the development of the disease [10–19]. Olsen et al. found that transglutaminase 2 (TG2) expression was increased in lung tissue from patients with IPF and correlated with collagen deposition, which is a key component of lung tissue structure (19). One study showed that glutamine metabolizing enzymes (e.g., LOXLs and TG2) regulate fibroblast proliferation and ECM deposition. Lian et al. reported that the glutamine transporter protein ASCT2 inhibited reduced fibroblast activation and collagen deposition, thereby attenuating lung fibrosis [15]. Xiang et al. demonstrated that glutamine metabolism and its derived metabolite α-ketoglutarate (α-KG) were involved in mediating the proliferation, migration, and collagen formation of IPF fibroblasts [10]. Another report found that SLC1A5 was highly expressed in fibrotic lung fibroblasts and lung fibroblasts from IPF patients. TGF-β-induced pro-fibrotic target expression, cell migration, and anchorage-independent growth require SLC1A5 activity. Glutamine plays a key role in TGF-β-induced activation and differentiation of myofibroblasts and is taken up by cells primarily through the cell surface transporter protein SLC1A5. Researchers have targeted SLC1A5 inhibition with the small molecule inhibitor V-9302 and observed improved lung fibrosis in a bleomycin-treated mouse model of lung fibrosis [18].

In this study, a two-sample MR method was used for the first time to investigate the causal relationship between circulating glutamine levels and the risk of IPF prevalence. This methodology differs from traditional observational studies in that it uses genetic variation as an instrumental variable and is not subject to the traditional confounders typically encountered in observational studies. Results suggesting that glutamine levels increase the risk of IPF prevalence were consistent and statistically significant, while sensitivity analyses revealed the absence of horizontal pleiotropy and heterogeneity. As the first study to analyze the causal relationship between glutamine levels and the risk of IPF using MR, three hypotheses were validated to minimize the drawbacks of traditional observational studies, including potential confounding and reverse causality. Meanwhile, the newly published FinnGen R10 GWAS data was used, which includes 2,408 different disease endpoints covering a wide range of disease areas, including cardiovascular, endocrine and metabolic disorders and tumors, providing researchers with rich data to explore the genetic basis of different diseases. For IPF, a total of 19,345,607 SNP information from 2,189 patients and 407,609 controls were included, and the robust data ensured the validity of the results. In addition to the four common MR methods (IVW, MR-Egger, WM, weighted mode), MR methods (cML-MA, ConMix, MR-RAPS, DIVW) that were rarely used in previous MR analyses were innovatively added. And each MR method has its own characteristics that can complement each other to a certain extent in data interpretation. The conclusions drawn from all MR methods were consistent and statistically significant (OR < 1 P < 0.05), which increased the credibility of the conclusions. In addition, the population included in the study was of European ancestry and relatively genetically homogeneous, which reduces the impact of potential genetic heterogeneity on MR analyses and thus improves the accuracy of causal inferences.

This study demonstrates a potential causal relationship between circulating glutamine levels and the risk of developing IPF, a finding with important implications for clinical practice. First, the measurement of circulating glutamine levels as a potential biomarker may help in the early identification of individuals at high risk for IPF, especially those with a family history of IPF or other known risk factors. Second, monitoring of circulating glutamine levels may provide new perspectives in the management of patients with IPF. Regular testing of glutamine levels may help to better monitor disease progression and assess treatment efficacy. Moreover, our findings provide new theoretical support for the development of glutamine-related drugs for the prevention and treatment of IPF.

Nevertheless, there are still some limitations. First, it may not be possible to directly generalize our conclusions to other races and populations because of genetic differences and environmental factors among races and populations; To address this limitation and enhance the external validity of our findings, we advocate for future research to include diverse populations from varying ethnic backgrounds. Such studies will be instrumental in assessing the consistency of the glutamine-IPF association across different genetic contexts and environments. By doing so, the broader applicability of our results can be established, and a more comprehensive understanding of the global health implications related to glutamine metabolism and IPF can be achieved. Furthermore, we recommend that future studies consider stratification by ethnicity, as well as the integration of genetic ancestry data, to better understand the interaction between genetic variants and environmental exposures. This approach will not only validate our findings but also potentially uncover novel insights into the role of glutamine in IPF pathogenesis across diverse populations. Furthermore, the constraints inherent in the aggregated GWAS data preclude stratified MR analyses based on age, gender, and height, and certain confounding factors, including immune function regulation and cellular energy metabolism, may elude comprehensive validation. Third, despite the absence of detected pleiotropy in our analyses, including assessments via MR-Egger regression and MR-PRESSO, we cannot conclusively rule out its potential presence. Finally, our study was linear, so it could not accurately capture nonlinear relationships, and larger prospective cohort studies are still needed in the future to validate the complex relationship between the two.

It cannot be overlooked that, although our study establishes a significant inverse correlation between circulating glutamine levels and the risk of idiopathic pulmonary fibrosis (IPF) using Mendelian randomization, further exploration into the mechanistic link between glutamine metabolism and IPF pathogenesis is warranted.

Glutamine, recognized for its multifaceted roles in cellular processes, likely influences IPF through its impact on fibroblast activity and extracellular matrix synthesis. To elucidate these mechanisms, We plan to initiate the following experimental studies at the opportune moment: [1] Investigate the direct effects of glutamine on lung fibroblast proliferation and collagen production in vitro [2]. Examine the alterations in key enzymes of glutamine metabolism and their association with the progression of pulmonary fibrosis using animal models [3]. Explore the therapeutic potential of modulating glutamine metabolism in IPF, providing insights into novel intervention strategies.

These endeavors will enrich our understanding of glutamine’s role in IPF and may pave the way for targeted therapies and prognostic biomarkers.

Conclusions

Overall, our study suggests that lower circulating glutamine levels may increase the risk of developing IPF. This finding provides new insights into understanding the pathogenesis of IPF. At the same time, it may provide theoretical support for early identification of high-risk populations, monitoring of disease progression, and development of new therapeutic strategies. However, there are still some limitations for the current study and larger samples are needed to confirm our findings.

Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary Material 1

Acknowledgements

We acknowledge the GWAS for free use.

Author contributions

Conceptualization: SZ and PZ; Writing original draft preparation: TX and CL; Data processing and analysis: TX, CL, ZG, XN, AL, SW, LL, PK and PL; Supervision and project administration: SZ and PZ; Review and editing: SZ and PZ.

Funding

This work was supported by the Natural Science Foundation of Hebei Province (No.H2021108003).

Data availability

The analysis in this research relied on publicly available datasets, which can be accessed through the IEU Open GWAS Project (https:// gwas.mrcieu.ac.uk/) and FinnGen R10 GWAS database (https://r10.finngen.fi/).

Declarations

Ethics approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

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References

1. Mei Q Liu Z Zuo H Yang Z Qu J Idiopathic pulmonary fibrosis: an update on Pathogenesis Front Pharmacol 2022 12 797292 10.3389/fphar.2021.797292 35126134
Mei Q, Liu Z, Zuo H, Yang Z, Qu J. Idiopathic pulmonary fibrosis: an update on Pathogenesis. Front Pharmacol. 2022;12:797292.35126134 10.3389/fphar.2021.797292
2. Martinez FJ Collard HR Pardo A Raghu G Richeldi L Selman M Idiopathic pulmonary fibrosis Nat Rev Dis Primers 2017 3 1 1 19 10.1038/nrdp.2017.74
Martinez FJ, Collard HR, Pardo A, Raghu G, Richeldi L, Selman M, et al. Idiopathic pulmonary fibrosis. Nat Rev Dis Primers. 2017;3(1):1–19.10.1038/nrdp.2017.74
3. Liu YM Nepali K Liou JP Idiopathic pulmonary fibrosis: current status, recent progress, and emerging targets J Med Chem 2017 60 2 527 53 10.1021/acs.jmedchem.6b00935 28122457
Liu YM, Nepali K, Liou JP. Idiopathic pulmonary fibrosis: current status, recent progress, and emerging targets. J Med Chem. 2017;60(2):527–53.28122457 10.1021/acs.jmedchem.6b00935
4. Zhang CY Zhong WJ Liu YB Duan JX Jiang N Yang HH EETs alleviate alveolar epithelial cell senescence by inhibiting endoplasmic reticulum stress through the Trim25/Keap1/Nrf2 axis Redox Biol 2023 63 102765 10.1016/j.redox.2023.102765 37269686
Zhang CY, Zhong WJ, Liu YB, Duan JX, Jiang N, Yang HH, et al. EETs alleviate alveolar epithelial cell senescence by inhibiting endoplasmic reticulum stress through the Trim25/Keap1/Nrf2 axis. Redox Biol. 2023;63:102765.37269686 10.1016/j.redox.2023.102765
5. Zhang D Povysil G Kobeissy PH Li Q Wang B Amelotte M Rare and common variants in KIF15 contribute to genetic risk of idiopathic pulmonary fibrosis Am J Respir Crit Care Med 2022 206 1 56 69 10.1164/rccm.202110-2439OC 35417304
Zhang D, Povysil G, Kobeissy PH, Li Q, Wang B, Amelotte M, et al. Rare and common variants in KIF15 contribute to genetic risk of idiopathic pulmonary fibrosis. Am J Respir Crit Care Med. 2022;206(1):56–69.35417304 10.1164/rccm.202110-2439OC
6. Zhu J Zhou D Yu M Li Y Appraising the causal role of smoking in idiopathic pulmonary fibrosis: a mendelian randomization study Thorax 2024 79 2 179 81 10.1136/thorax-2023-220012 37217291
Zhu J, Zhou D, Yu M, Li Y. Appraising the causal role of smoking in idiopathic pulmonary fibrosis: a mendelian randomization study. Thorax. 2024;79(2):179–81.37217291 10.1136/thorax-2023-220012
7. Newsholme P Procopio J Lima MMR Pithon-Curi TC Curi R Glutamine and glutamate–their central role in cell metabolism and function Cell Biochem Funct 2003 21 1 1 9 10.1002/cbf.1003 12579515
Newsholme P, Procopio J, Lima MMR, Pithon-Curi TC, Curi R. Glutamine and glutamate–their central role in cell metabolism and function. Cell Biochem Funct. 2003;21(1):1–9.12579515 10.1002/cbf.1003
8. Richeldi L Collard HR Jones MG Idiopathic pulmonary fibrosis Lancet 2017 389 10082 1941 52 10.1016/S0140-6736(17)30866-8 28365056
Richeldi L, Collard HR, Jones MG. Idiopathic pulmonary fibrosis. Lancet. 2017;389(10082):1941–52.28365056 10.1016/S0140-6736(17)30866-8
9. Heukels P Moor CC von der Thüsen JH Wijsenbeek MS Kool M Inflammation and immunity in IPF pathogenesis and treatment Respir Med 2019 147 79 91 10.1016/j.rmed.2018.12.015 30704705
Heukels P, Moor CC, von der Thüsen JH, Wijsenbeek MS, Kool M. Inflammation and immunity in IPF pathogenesis and treatment. Respir Med. 2019;147:79–91.30704705 10.1016/j.rmed.2018.12.015
10. Xiang Z Bai L Zhou JQ Cevallos RR Sanders JR Liu G Epigenetic regulation of IPF fibroblast phenotype by glutaminolysis Mol Metabolism 2023 67 101655 10.1016/j.molmet.2022.101655
Xiang Z, Bai L, Zhou JQ, Cevallos RR, Sanders JR, Liu G, et al. Epigenetic regulation of IPF fibroblast phenotype by glutaminolysis. Mol Metabolism. 2023;67:101655.10.1016/j.molmet.2022.101655
11. Philp CJ Siebeke I Clements D Miller S Habgood A John AE Extracellular matrix cross-linking enhances fibroblast growth and protects against Matrix Proteolysis in Lung Fibrosis Am J Respir Cell Mol Biol 2018 58 5 594 603 10.1165/rcmb.2016-0379OC 29053339
Philp CJ, Siebeke I, Clements D, Miller S, Habgood A, John AE, et al. Extracellular matrix cross-linking enhances fibroblast growth and protects against Matrix Proteolysis in Lung Fibrosis. Am J Respir Cell Mol Biol. 2018;58(5):594–603.29053339 10.1165/rcmb.2016-0379OC
12. Wang S Li X Ma Q Wang Q Wu J Yu H Glutamine metabolism is required for alveolar regeneration during Lung Injury Biomolecules 2022 12 5 728 10.3390/biom12050728 35625656
Wang S, Li X, Ma Q, Wang Q, Wu J, Yu H, et al. Glutamine metabolism is required for alveolar regeneration during Lung Injury. Biomolecules. 2022;12(5):728.35625656 10.3390/biom12050728
13. Hamanaka RB O’Leary EM Witt LJ Tian Y Gökalp GA Meliton AY Glutamine metabolism is required for Collagen Protein Synthesis in lung fibroblasts Am J Respir Cell Mol Biol 2019 61 5 597 606 10.1165/rcmb.2019-0008OC 30973753
Hamanaka RB, O’Leary EM, Witt LJ, Tian Y, Gökalp GA, Meliton AY, et al. Glutamine metabolism is required for Collagen Protein Synthesis in lung fibroblasts. Am J Respir Cell Mol Biol. 2019;61(5):597–606.30973753 10.1165/rcmb.2019-0008OC
14. Bai L Bernard K Tang X Hu M Horowitz JC Thannickal VJ Glutaminolysis Epigenetically regulates antiapoptotic gene expression in idiopathic pulmonary fibrosis fibroblasts Am J Respir Cell Mol Biol 2019 60 1 49 57 10.1165/rcmb.2018-0180OC 30130138
Bai L, Bernard K, Tang X, Hu M, Horowitz JC, Thannickal VJ, et al. Glutaminolysis Epigenetically regulates antiapoptotic gene expression in idiopathic pulmonary fibrosis fibroblasts. Am J Respir Cell Mol Biol. 2019;60(1):49–57.30130138 10.1165/rcmb.2018-0180OC
15. Lian N Jin H Zhu W Zhang C Qi Y Jiang M Inhibition of glutamine transporter ASCT2 mitigates bleomycin-induced pulmonary fibrosis in mice Acta Histochem 2022 124 8 151961 10.1016/j.acthis.2022.151961 36265204
Lian N, Jin H, Zhu W, Zhang C, Qi Y, Jiang M, et al. Inhibition of glutamine transporter ASCT2 mitigates bleomycin-induced pulmonary fibrosis in mice. Acta Histochem. 2022;124(8):151961.36265204 10.1016/j.acthis.2022.151961
16. Liu G Summer R Reclaiming the balance: blocking glutamine uptake to restrain pulmonary fibrosis Am J Respir Cell Mol Biol 2023 69 4 378 9 10.1165/rcmb.2023-0189ED 37463521
Liu G, Summer R. Reclaiming the balance: blocking glutamine uptake to restrain pulmonary fibrosis. Am J Respir Cell Mol Biol. 2023;69(4):378–9.37463521 10.1165/rcmb.2023-0189ED
17. Takeuchi T Tatsukawa H Shinoda Y Kuwata K Nishiga M Takahashi H Spatially resolved identification of Transglutaminase substrates by Proteomics in Pulmonary Fibrosis Am J Respir Cell Mol Biol 2021 65 3 319 30 10.1165/rcmb.2021-0012OC 34264172
Takeuchi T, Tatsukawa H, Shinoda Y, Kuwata K, Nishiga M, Takahashi H, et al. Spatially resolved identification of Transglutaminase substrates by Proteomics in Pulmonary Fibrosis. Am J Respir Cell Mol Biol. 2021;65(3):319–30.34264172 10.1165/rcmb.2021-0012OC
18. Choudhury M Schaefbauer KJ Kottom TJ Yi ES Tschumperlin DJ Limper AH Targeting pulmonary fibrosis by SLC1A5-Dependent glutamine transport blockade Am J Respir Cell Mol Biol 2023 69 4 441 55 10.1165/rcmb.2022-0339OC 37459644
Choudhury M, Schaefbauer KJ, Kottom TJ, Yi ES, Tschumperlin DJ, Limper AH. Targeting pulmonary fibrosis by SLC1A5-Dependent glutamine transport blockade. Am J Respir Cell Mol Biol. 2023;69(4):441–55.37459644 10.1165/rcmb.2022-0339OC
19. Olsen KC Sapinoro RE Kottmann RM Kulkarni AA Iismaa SE Johnson GVW Transglutaminase 2 and its role in pulmonary fibrosis Am J Respir Crit Care Med 2011 184 6 699 707 10.1164/rccm.201101-0013OC 21700912
Olsen KC, Sapinoro RE, Kottmann RM, Kulkarni AA, Iismaa SE, Johnson GVW, et al. Transglutaminase 2 and its role in pulmonary fibrosis. Am J Respir Crit Care Med. 2011;184(6):699–707.21700912 10.1164/rccm.201101-0013OC
20. Davey Smith G Hemani G Mendelian randomization: genetic anchors for causal inference in epidemiological studies Hum Mol Genet 2014 23 R1 R89 98 10.1093/hmg/ddu328 25064373
Davey Smith G, Hemani G. Mendelian randomization: genetic anchors for causal inference in epidemiological studies. Hum Mol Genet. 2014;23(R1):R89–98.25064373 10.1093/hmg/ddu328
21. Davies NM Holmes MV Davey Smith G Reading mendelian randomisation studies: a guide, glossary, and checklist for clinicians BMJ 2018 362 k601 10.1136/bmj.k601 30002074
Davies NM, Holmes MV, Davey Smith G. Reading mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ. 2018;362:k601.30002074 10.1136/bmj.k601
22. Lawlor DA Harbord RM Sterne JAC Timpson N Davey Smith G Mendelian randomization: using genes as instruments for making causal inferences in epidemiology Stat Med 2008 27 8 1133 63 10.1002/sim.3034 17886233
Lawlor DA, Harbord RM, Sterne JAC, Timpson N, Davey Smith G. Mendelian randomization: using genes as instruments for making causal inferences in epidemiology. Stat Med. 2008;27(8):1133–63.17886233 10.1002/sim.3034
23. Richardson TG Leyden GM Wang Q Bell JA Elsworth B Davey Smith G Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation PLoS Biol 2022 20 2 e3001547 10.1371/journal.pbio.3001547 35213538
Richardson TG, Leyden GM, Wang Q, Bell JA, Elsworth B, Davey Smith G, et al. Characterising metabolomic signatures of lipid-modifying therapies through drug target mendelian randomisation. PLoS Biol. 2022;20(2):e3001547.35213538 10.1371/journal.pbio.3001547
24. Hemani G Tilling K Smith GD Orienting the causal relationship between imprecisely measured traits using GWAS summary data PLoS Genet 2017 13 11 e1007081 10.1371/journal.pgen.1007081 29149188
Hemani G, Tilling K, Smith GD. Orienting the causal relationship between imprecisely measured traits using GWAS summary data. PLoS Genet. 2017;13(11):e1007081.29149188 10.1371/journal.pgen.1007081
25. Pierce BL Ahsan H Vanderweele TJ Power and instrument strength requirements for mendelian randomization studies using multiple genetic variants Int J Epidemiol 2011 40 3 740 52 10.1093/ije/dyq151 20813862
Pierce BL, Ahsan H, Vanderweele TJ. Power and instrument strength requirements for mendelian randomization studies using multiple genetic variants. Int J Epidemiol. 2011;40(3):740–52.20813862 10.1093/ije/dyq151
26. Staiger D, Stock JH. Instrumental Variables Regression with Weak Instruments [Internet]. National Bureau of Economic Research; 1994 Jan [cited 2024 Mar 9]. Report No.: t0151. https://www.nber.org/papers/t0151
27. Burgess S Butterworth A Thompson SG Mendelian randomization analysis with multiple genetic variants using Summarized Data Genet Epidemiol 2013 37 7 658 65 10.1002/gepi.21758 24114802
Burgess S, Butterworth A, Thompson SG. Mendelian randomization analysis with multiple genetic variants using Summarized Data. Genet Epidemiol. 2013;37(7):658–65.24114802 10.1002/gepi.21758
28. 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 10.1093/ije/dyv080 26050253
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):512–25.26050253 10.1093/ije/dyv080
29. Bowden J Davey Smith G Haycock PC Burgess S Consistent estimation in mendelian randomization with some Invalid instruments using a weighted median estimator Genet Epidemiol 2016 40 4 304 14 10.1002/gepi.21965 27061298
Bowden J, Davey Smith G, Haycock PC, Burgess S. Consistent estimation in mendelian randomization with some Invalid instruments using a weighted median estimator. Genet Epidemiol. 2016;40(4):304–14.27061298 10.1002/gepi.21965
30. Hartwig FP Davey Smith G Bowden J Robust inference in summary data mendelian randomization via the zero modal pleiotropy assumption Int J Epidemiol 2017 46 6 1985 98 10.1093/ije/dyx102 29040600
Hartwig FP, Davey Smith G, Bowden J. Robust inference in summary data mendelian randomization via the zero modal pleiotropy assumption. Int J Epidemiol. 2017;46(6):1985–98.29040600 10.1093/ije/dyx102
31. Yin Q Zhu L Does co-localization analysis reinforce the results of mendelian Randomization? Brain 2024 147 1 e7 8 10.1093/brain/awad295 37658826
Yin Q, Zhu L. Does co-localization analysis reinforce the results of mendelian. Randomization? Brain. 2024;147(1):e7–8.37658826 10.1093/brain/awad295
32. Burgess S Foley CN Allara E Staley JR Howson JMM A robust and efficient method for mendelian randomization with hundreds of genetic variants Nat Commun 2020 11 1 376 10.1038/s41467-019-14156-4 31953392
Burgess S, Foley CN, Allara E, Staley JR, Howson JMM. A robust and efficient method for mendelian randomization with hundreds of genetic variants. Nat Commun. 2020;11(1):376.31953392 10.1038/s41467-019-14156-4
33. Yu K Chen XF Guo J Wang S Huang XT Guo Y Assessment of bidirectional relationships between brain imaging-derived phenotypes and stroke: a mendelian randomization study BMC Med 2023 21 1 271 10.1186/s12916-023-02982-9 37491271
Yu K, Chen XF, Guo J, Wang S, Huang XT, Guo Y, et al. Assessment of bidirectional relationships between brain imaging-derived phenotypes and stroke: a mendelian randomization study. BMC Med. 2023;21(1):271.37491271 10.1186/s12916-023-02982-9
34. Ye T Shao J Kang H Debiased inverse-variance weighted estimator in two-sample summary-data mendelian randomization Annals Stat 2021 49 4 2079 100 10.1214/20-AOS2027
Ye T, Shao J, Kang H. Debiased inverse-variance weighted estimator in two-sample summary-data mendelian randomization. Annals Stat. 2021;49(4):2079–100.10.1214/20-AOS2027
35. Didelez V Sheehan N Mendelian randomization as an instrumental variable approach to causal inference Stat Methods Med Res 2007 16 4 309 30 10.1177/0962280206077743 17715159
Didelez V, Sheehan N. Mendelian randomization as an instrumental variable approach to causal inference. Stat Methods Med Res. 2007;16(4):309–30.17715159 10.1177/0962280206077743
36. Verbanck M Chen CY Neale B Do R Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases Nat Genet 2018 50 5 693 8 10.1038/s41588-018-0099-7 29686387
Verbanck M, Chen CY, Neale B, Do R. Detection of widespread horizontal pleiotropy in causal relationships inferred from mendelian randomization between complex traits and diseases. Nat Genet. 2018;50(5):693–8.29686387 10.1038/s41588-018-0099-7
37. Reynolds CJ, Fabiola Del Greco M, Allen RJ, Flores C, Jenkins RG, Maher TM et al. The causal relationship between gastro-oesophageal reflux disease and idiopathic pulmonary fibrosis: a bidirectional two-sample Mendelian randomisation study. European Respiratory Journal [Internet]. 2023 [cited 2024 Mar 7];61(5). https://erj.ersjournals.com/content/61/5/2201585.short
38. Cruzat V Macedo Rogero M Noel Keane K Curi R Newsholme P Glutamine: metabolism and Immune function, supplementation and clinical translation Nutrients 2018 10 11 1564 10.3390/nu10111564 30360490
Cruzat V, Macedo Rogero M, Noel Keane K, Curi R, Newsholme P. Glutamine: metabolism and Immune function, supplementation and clinical translation. Nutrients. 2018;10(11):1564.30360490 10.3390/nu10111564
39. Wang Y Guo YR Liu K Yin Z Liu R Xia Y KAT2A coupled with the α-KGDH complex acts as a histone H3 succinyltransferase Nature 2017 552 7684 273 7 10.1038/nature25003 29211711
Wang Y, Guo YR, Liu K, Yin Z, Liu R, Xia Y, et al. KAT2A coupled with the α-KGDH complex acts as a histone H3 succinyltransferase. Nature. 2017;552(7684):273–7.29211711 10.1038/nature25003
40. Zhang S Li X Yuan T Guo X Jin C Jin Z Glutamine inhibits inflammation, oxidative stress, and apoptosis and ameliorates hyperoxic lung injury J Physiol Biochem 2023 79 3 613 23 10.1007/s13105-023-00961-5 37145351
Zhang S, Li X, Yuan T, Guo X, Jin C, Jin Z, et al. Glutamine inhibits inflammation, oxidative stress, and apoptosis and ameliorates hyperoxic lung injury. J Physiol Biochem. 2023;79(3):613–23.37145351 10.1007/s13105-023-00961-5
41. Wu H Yu Y Huang H Hu Y Fu S Wang Z Progressive Pulmonary fibrosis is caused by elevated mechanical tension on alveolar stem cells Cell 2020 180 1 107 e12117 10.1016/j.cell.2019.11.027 31866069
Wu H, Yu Y, Huang H, Hu Y, Fu S, Wang Z, et al. Progressive Pulmonary fibrosis is caused by elevated mechanical tension on alveolar stem cells. Cell. 2020;180(1):107–e12117.31866069 10.1016/j.cell.2019.11.027
42. Du K Hyun J Premont RT Choi SS Michelotti GA Swiderska-Syn M Hedgehog-YAP Signaling Pathway regulates glutaminolysis to control activation of hepatic stellate cells Gastroenterology 2018 154 5 1465 e147913 10.1053/j.gastro.2017.12.022 29305935
Du K, Hyun J, Premont RT, Choi SS, Michelotti GA, Swiderska-Syn M, et al. Hedgehog-YAP Signaling Pathway regulates glutaminolysis to control activation of hepatic stellate cells. Gastroenterology. 2018;154(5):1465–e147913.29305935 10.1053/j.gastro.2017.12.022
43. Henderson NC Rieder F Wynn TA Fibrosis: from mechanisms to medicines Nature 2020 587 7835 555 66 10.1038/s41586-020-2938-9 33239795
Henderson NC, Rieder F, Wynn TA. Fibrosis: from mechanisms to medicines. Nature. 2020;587(7835):555–66.33239795 10.1038/s41586-020-2938-9
