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Article

The Causal Relationship between Inflammatory Cytokines and Liver Cirrhosis in European Descent: A Bidirectional Two-Sample Mendelian Randomization Study and the First Conclusions

by
Shiya Shi
†,
Yanjie Zhou
†,
He Zhang
,
Yalan Zhu
,
Pengjun Jiang
,
Chengxia Xie
,
Tianyu Feng
,
Yuping Zeng
,
He He
,
Yao Luo
and
Jie Chen
*
Department of Laboratory Medicine, West China Hospital, Sichuan University, Chengdu 610041, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biomedicines 2024, 12(10), 2264; https://doi.org/10.3390/biomedicines12102264
Submission received: 15 August 2024 / Revised: 25 September 2024 / Accepted: 2 October 2024 / Published: 4 October 2024
(This article belongs to the Section Immunology and Immunotherapy)
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Abstract

:
Background: Observational studies have highlighted the pivotal role of inflammatory cytokines in cirrhosis progression. However, the existence of a causal link between inflammatory cytokines and cirrhosis remains uncertain. In this study, we conducted a bidirectional Mendelian randomization (MR) analysis at a summarized level to illuminate the potential causal relationship between the two variables. Methods: This study utilized genetic variance in cirrhosis and inflammatory cytokines from a genome-wide association study (GWAS) of European descent. The MR-PRESSO outlier test, Cochran’s Q test, and MR-Egger regression were applied to assess outliers, heterogeneity, and pleiotropy. The inverse variance weighted method and multiple sensitivity analyses were used to evaluate causalities. Furthermore, the validation set was used for simultaneous data validation. Results: The inflammatory cytokine monocyte chemoattractant protein 3 (MCP-3) was supposedly associated with a greater risk of cirrhosis. And cirrhosis was significantly correlated with increased levels of hepatocyte growth factor (HGF). Conclusions: This study suggests that MCP-3 might be associated with the etiology of cirrhosis, while several inflammatory cytokines could potentially play a role in its downstream development. Additionally, the progression of cirrhosis was associated with elevated levels of HGF, suggesting a possible role for liver repair functions.

1. Introduction

Cirrhosis is a common, chronic progressive liver disease characterized by diffuse liver injury resulting from the long-term or repeated action of one or more etiologies [1]. Common causes of cirrhosis include chronic viral hepatitis, long-term alcohol abuse, fatty liver disease, autoimmune liver disease, and genetic disorders, among others [2]. Once cirrhosis develops, the rate of progression can vary from weeks to decades, but it eventually leads to liver failure with multiple complications that impair quality of life and result in death [3,4,5]. Approximately 2 million deaths from liver disease occur globally each year, 50% of which are attributed to cirrhosis [6]. Consequently, several studies have focused on the diagnosis and treatment of cirrhosis [6,7,8].
Results from observational studies suggest that inflammation is a risk factor for cirrhosis, and further evidence indicates that serum inflammatory cytokine levels are strongly associated with the severity of cirrhosis, as well as with the duration of cirrhosis and short-term mortality [9,10,11]. Although the association of inflammatory cytokines with cirrhosis and the fact that lowering inflammatory cytokine levels can inhibit the extent of liver fibrosis have been widely discussed [12], observational studies linking specific inflammatory cytokine concentrations with cirrhosis are rare and are often susceptible to multiple confounding factors; furthermore, it is unclear whether there is a causal relationship between inflammatory cytokine and cirrhosis.
The mendelian randomization (MR) study leverages large-scale datasets from genome-wide association studies (GWASs), which provide valuable insights into the associations between various risk factors and diseases. As a powerful genetic instrumental variable (IV) approach, MR uses single nucleotide polymorphisms (SNPs) as IVs to explore potential causal relationships between specific exposures and disease outcomes [13,14]. Since SNPs are naturally occurring genetic variations, their associations with diseases are not influenced by reverse causality, enabling MR to effectively mitigate the impact of unmeasured confounding factors and produce more robust and accurate causal inferences. Furthermore, by ensuring that IVs must satisfy the three assumptions of relevance, independence, and exclusion restriction, MR reduces confounding, strengthens causal inferences, and excludes the possibility of causal inversions [15,16]. In this study, we used gene variants closely associated with specific inflammatory cytokines to assess their relationship with the risk of cirrhosis. A two-sample bidirectional MR analysis was performed using GWAS data to investigate the causal link between inflammatory cytokines and cirrhosis. The workflow is shown in Figure 1. Additionally, we used the same method with two additional GWAS databases to validate our findings.

2. Materials and Methods

2.1. Data Source

There are three core assumptions in MR analysis, namely, relevance, independence, and exclusion restriction [17]. We assumed that the selected IVs are related to the risk factor but not to any confounders in the risk factor–outcome association, and that they are not connected with the outcome via any pathway other than the risk factor for interest. Here, we performed a two-sample MR analysis using summary-level GWAS data on 41 circulating inflammatory cytokines and cirrhosis to explain the causal associations. When the characteristics were similar, we selected the GWAS summary statistics with the largest sample size. The demographic data for the cohorts used are provided in Table 1.
Publicly summarized GWAS data on 41 circulating inflammatory factors were obtained from a genome-wide meta-analysis of 8293 healthy Finnish subjects [18]. The meta-analysis was based on three Finnish cohorts, including the Cardiovascular Risk in Young Finns Study (YFS) and FINRISK 1997 and 2002. The average participant ages were 37 years for the YFS and 60 years for the FINRISK survey [18].
The genetic data of cirrhosis were extracted from two available GWAS datasets. Dataset 1 on cirrhosis, excluding cirrhotic outcomes secondary to primary biliary cholangitis and primary sclerosing cholangitis, were obtained from a GWAS of 218,792 Europeans in the International Oncology Unit (IEU) Open GWAS database [19] [finn-b-CIRRHOSIS_BROAD, n = 218,792]. Dataset 2 (Buch S. et al.) was obtained from the United Kingdom Biobank (UKB) queue (2701 patients and 16,206 controls) [20]. Cases of cirrhosis due to primary biliary cholangitis (ICD K74.3) and primary sclerosing cholangitis (ICD K83.0) remain excluded because these autoimmune diseases target biliary (rather than hepatic) parenchyma. There was no overlap in population selection between the exposure group and the outcome group. The GWAS data used in our study were obtained with informed consent from all study participants according to the protocol approved by the respective institutional review boards; therefore, no separate ethical approval was needed for this study. The reference genome build was HG19/GRCh37. Other GWAS details were shown in the original GWAS [20].

2.2. Selection of IVs

The selection of IVs for cirrhosis was based on the TwoSample MR package of R 4.0.3. SNPs with p-values < 5 × 10−8 and minor allele frequencies (MAFs) > 1% were selected. These SNPs were independently genetically clumped (linkage disequilibrium R2 < 0.01, distance window 10,000 kb) from the European samples [21] and were not palindromic (A/T or C/G) with intermediate effect allele frequencies (40–70%). Notably, for inflammatory cytokines such as IL-16, instrumental variables with p-values < 5 × 10−6 were selected due to the small number of SNPs [21].
When the p-value was <5 × 10−6, the assumption of relevance was satisfied. To screen IVs that met the independence assumption, potential confounders were included as covariates in individual-level GWAS analyses, and multiplicative validity tests were performed. Palindromic SNPs with effector alleles and other alleles as complements were discarded. Additionally, we evaluated the strength of the association between each IV and the exposure, excluding weak IVs. We estimate the proportion of phenotypic variance explained (R2) and calculate F-statistics in our statistical model. The formula R2 = 2 × MAF × (1-MAF) × β2 was used to determine R2, where MAF represents the minor allele frequency (EAF) of the SNP, and β represents the effect of the SNP on the trait. Finally, the F-statistic for each IV was calculated using the following formula: F = R2 (N − 2)/(1 − R2) [22]. IVs with an F-statistic of less than 10 were excluded due to potential genetic confounding or measurement error. Ultimately, these rigorously selected SNPs were used as the final IVs for subsequent MR analysis.

2.3. Statistical Analyses

In adherence to the specified criteria for instrumental variable selection, we conducted a meticulous screening of the dataset to identify IVs that met the necessary conditions. We used the PhenoScanner database (http://www.phenoscanner.medschl.cam.ac.uk/ accessed on 14 August 2024) to search for secondary phenotypes of the selected SNPs and excluded potentially confounding SNPs.
For the MR analysis, we first performed heterogeneity and pleiotropy testing. We used the MR-PRESSO outlier test to detect and remove abnormal instrumental variables. The strength of the IVs was evaluated using F statistics, and heterogeneity and horizontal pleiotropy between inflammatory cytokines and cirrhosis were investigated by way of Cochran’s Q test. Heterogeneity was defined as a p-value < 0.05 indicating statistical significance. MR-Egger regression (based on intercept terms) and Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) were used to assess horizontal pleiotropy [23,24]. When the MR-Egger outcome deviated from 0 or its p-value was <0.05, this indicated the presence of horizontal pleiotropy. In addition, we performed sensitivity analyses to detect and correct for pleiotropy in the estimates.
In this study, causal effect sizes were estimated mainly by way of the inverse-variance weighted method (MR-IVW method), the MR-Lasso method, the MR-Egger method, the weighted median method, the simple mode method, and the weighted mode method. When there was no heterogeneity or pleiotropy between inflammatory cytokines and cirrhosis, we performed MR analysis using the MR-IVW method, which provides accurate estimates [23]. When pleiotropy was present, analysis was performed preferentially using the MR-Egger method [25]. The weighted median method can provide valid estimates when heterogeneity and pleiotropy are present, as long as up to 50% of the instrumental variables are valid [26]. We also constructed scatter plots to visualize these univariate results.
Data analyses were performed using the MR-PRESSO (version 1.0) or TwoSample MR (version 0.5.8) packages in R software (version 4.3.2), with all tests being two-sided. Reporting follows the STROBE-MR statement.

3. Results

3.1. MR Analysis of Inflammatory Cytokines on Cirrhosis Risk

In this study, we first analyzed the training set (Dataset 1). Initially, only three cytokines had three or more genetic variants when SNPs were initially selected with p < 5 × 10−8. We then relaxed the threshold further, leading to another MR analysis (p < 5 × 10−7/−6), which identified 6 and 32 cytokines meeting our criteria, respectively. Eventually, all 41 cytokines were included with an adequate number of valid genetic variants. The variance explained by SNPs for each cytokine ranged from 1.4% to 9.6%, with all IVs statistics greater than 10, indicating robust strength. The complete data can be found in Supplementary Tables S1–S3.
A comprehensive assessment of the main analytical tools was conducted for all 41 risk factors. The IVW method was primarily used for all cytokines, except for Monocyte chemoattractant protein 3 (MCP3) and Platelet-derived growth factor BB (PDGF-BB). For MCP3, the weighted median method was selected due to significant heterogeneity in the IVW Q test, while IVW was used for PDGF-BB after excluding an outlier SNP identified using MR-PRESSO.
MR estimates of the effects of risk factors (inflammatory cytokines) on the outcome (cirrhosis) are provided in Supplementary Table S1 and Figure 2A. According to the IVW method, the inflammatory cytokine MCP3 was associated with a 33% greater risk of cirrhosis (IVW-OR = 1.332, 95% CI: 1.012–1.752; p = 0.041), a finding consistent with the weighted median estimation method (OR = 1.518, 95% CI: 1.225–1.881; p < 0.001). In contrast, the inflammatory cytokine interferon gamma-induced protein 10 (IP10) (IVW-OR = 0.707, 95% CI: 0.516–0.969; p = 0.031) and interleukin-1 receptor antagonist protein (IL-1RA) (IVW-OR = 0.725, 95% CI: 0.548–0.959; p = 0.024) were associated with a reduced risk of cirrhosis. A heatmap of the MR analysis methods for the association between 41 circulating cytokine levels and the risk of cirrhosis is displayed in Figure 3A. No evidence of pleiotropy in the associations of MCP3, IP10, or IL-1RA was observed, as measured using the MR-Egger intercept (p = 0.090, p = 0.609, p = 0.690), and no outlier SNPs were detected using the MR-PRESSO method. Furthermore, Cochran’s Q test revealed no evidence of directional pleiotropy effects for IP10 or IL-1RA (p = 0.700, p = 0.546) (Figure 2 and Figure 3; Supplementary Table S2). Details of the SNPs are presented in Supplementary Tables S1–S3.
In the validation set (Dataset 2), bidirectional two-sample MR analysis confirmed the causal relationships. Complete data on these SNPs can be found in Supplementary Tables S7–S9. As a result, the inflammatory cytokines MCP3, IL-5, and macrophage colony-stimulating factor (MCSF) were associated with a greater risk of cirrhosis (MCP3: IVW-OR = 1.021, 95% CI: 0.988–1.054; p = 0.021; IL-5: IVW-OR = 1.010, 95% CI: 1.002–1.018; p = 0.016; MCSF: IVW-OR = 1.003, 95% CI: 1.001–1.006; p = 0.011), while IP10 (IVW-OR = 0.741, 95% CI: 0.684–0.802; p < 0.001) was associated with a reduced risk of cirrhosis, according to the IVW method (Figure 2 and Figure 3). No evidence of pleiotropy in the associations of MCP3, IL-5, MCSF, and IP10 was observed, as measured by the MR-Egger intercept (p = 0.404, p = 0.453, p = 0.073, and p = 0.581), and no outlier SNPs were detected using the MR-PRESSO method. Furthermore, Cochran’s Q test revealed no evidence of directional heterogeneity effects for MCP3, IL-5, MCSF, or IP10 (p = 0.694, p = 0.446, p = 0.502, p = 0.981) (Supplementary Figures S1 and S2; Supplementary Table S8). Details of the SNPs are presented in Supplementary Tables S7–S9.

3.2. MR Analysis of the Influence of Cirrhosis on Inflammatory Cytokines

When 41 inflammatory cytokines were considered as outcome factors in the reverse MR, six significant SNPs were identified as IVs for cirrhosis. The IVW method was primarily employed to evaluate the associated effects on inflammatory cytokines, as there was no evidence of heterogeneity and horizontal pleiotropy. The MR analysis revealed that cirrhosis was significantly associated with elevated levels of hepatocyte growth factor (HGF), macrophage inflammatory protein-1 beta (MIP-1β), stromal cell-derived factor-1 alpha (SDF-1α), IL-12, MCP3, and stem cell factor (SCF). Conversely, cirrhosis was linked to decreased levels of IL-2 receptor alpha (IL-2RA), IL-5, IL-7, and tumor necrosis factor beta (TNF-β) (Figure 4 and Figure 5). These findings were consistent across both the training set (Dataset 1) and the validation set (Dataset 2), with the IVW method confirming similar associations in both analyses. Detailed results and additional information are provided in the supplementary materials, including Supplementary Tables S1–S12 and Supplementary Figures S1–S5.

4. Discussion

Inflammation, marked by abnormal cytokine levels and altered immune cell functions, is closely linked to the development of cirrhosis [9]. Cirrhosis is a common chronic progressive liver disease characterized by diffuse liver injury resulting from the long-term or repeated action of one or more etiologies [1]. Given the complex microenvironment associated with cirrhosis, understanding changes in the inflammatory landscape offers new insights into the intricate mechanisms involved. Abnormal cytokine levels indicate a systemic inflammatory state, potentially playing a key role in the progression of cirrhosis. In this study, we initially aimed to explore the casual relationships between 41 inflammatory cytokines and cirrhosis using bidirectional two-sample MR analysis. Notably, IP10 and IL-1RA were found to be associated with a reduced risk of cirrhosis. Conversely, MCP3 was linked to an increased risk, suggesting its potential role as an upstream cause of the condition. Moreover, genetically predicted cirrhosis was correlated with elevated levels of MCP3, MIP1β, SDF1α, IL-12, HGF, and SCF throughout the disease process. Interestingly, we observed a bidirectional causal relationship between higher MCP3 levels and cirrhosis. Hence, these findings suggest that persistently elevated MCP3 levels are linked to both the onset and progression of cirrhosis, highlighting the complex interplay of cytokines in liver disease pathogenesis. Several cytokines may act as initiators of liver cirrhosis, while other inflammatory cytokines are more likely to act downstream during disease progression.
Numerous studies have explored the correlation between cirrhosis and inflammatory cytokines. For instance, an observational study revealed elevated levels of proinflammatory cytokines, such as IL-6 and IL-8, in cirrhotic patients compared to healthy controls, with the levels increasing as cirrhosis progresses. Moreover, these cytokines were identified as potential predictors of liver injury and chronic liver fibrosis [1,11,27]. Salgüero et al. also noted that the inflammatory cytokines IL-6 and IL-1α correlated with the severity of cirrhosis based on the Child—Pugh score, suggesting that more severe cirrhosis is associated with higher levels of inflammatory cytokines [28]. Furthermore, key immune cells responsible for producing proinflammatory and anti-inflammatory cytokines, such as lymphocytes, monocytes, and neutrophils, exhibit abnormal functions and immune impairment in cirrhotic patients [11,29]. However, despite the wealth of observational studies, determining the causal relationship between specific inflammatory cytokines and cirrhosis remains challenging due to methodological limitations, including confounding factors and reverse causality. Consequently, elevated levels of inflammatory cytokines in cirrhotic patients may stem from the underlying cause of the disease, undetected infections, treatment side effects, or unidentified comorbidities.
The main conclusion of this MR study suggests that MCP3 may play a potential role in the progression of cirrhosis. MCP3 is a monocyte chemokine expressed by various cell types, existing as a monomer that binds to multiple receptors and attracts leukocytes, including monocytes and neutrophils [30,31]. Some studies have reported that MCP3 may be involved in liver fibrosis as well as the chronic progression of cirrhosis and hepatocellular carcinoma [32,33]. It has been observed that serum MCP3 levels in cirrhosis patients are higher than in control groups, suggesting a possible association between MCP3 levels and disease progression [33]. Additionally, there is evidence indicating that when MCP3 expression is inhibited by drugs, liver fibrosis may decrease as MCP3 levels drop [34]. Some have also proposed that the binding of MCP3 to its receptor CCR2 may promote the migration of hepatic stellate cells and Kupffer cells, potentially contributing to the progression of liver fibrosis [32]. In this study, results from both the test and validation sets indicate an association between MCP3 levels and an increased risk of cirrhosis, consistent with previous findings, though further research is still needed.
IP-10, a cytokine intricately associated with inflammation and the immune system, enhances the chemotactic activity of its receptors, thereby activating a spectrum of immune cells, including T cells, NK cells, and macrophages [35]. Anomalous expression levels of IP-10 are closely linked to a multitude of inflammatory disorders. Various studies have confirmed its significance in the etiology of liver fibrosis and cirrhosis [35,36,37]. These investigations consistently revealed heightened levels of IP-10 in patients with liver fibrosis, along with a positive correlation between IP-10 levels and the severity of fibrosis [36]. As a result, IP-10 has been proposed as a potential biomarker for assessing cirrhosis severity. However, our own research findings appear to challenge this prevailing hypothesis. According to our MR analysis, estimates from the MR-IVW method suggest that IP-10 is associated with a reduced risk of cirrhosis (OR = 0.707, 95% CI = 0.516–0.969; p = 0.031), and the same result was obtained in the validation set (IVW-OR = 0.741, 95% CI = 0.684–0.802; p < 0.001). These findings suggest that IP-10 may be associated with a reduced risk of cirrhosis. This inconsistency may be attributed to the limited dataset used in our MR analysis, which ultimately included only three SNPs associated with IP-10, potentially introducing stochastic errors. Furthermore, the specific mechanism through which IP-10 functions in the progression of fibrosis has not been determined, highlighting the imperative need for further research in this area.
When cirrhosis was defined as the exposure, the estimates from the IVW method showed that the progression of cirrhosis was significantly associated with increased levels of hepatocyte growth factor (HGF), MIP1β, SDF1α, IL-12, MCP3, and SCF and with decreased levels of IL-2RA, IL-5, IL-7, and TNF-β, which provided supportable evidence for our primary study. Among these, HGF is a protein that plays a crucial role in various biological processes, such as tissue regeneration, cell growth, and angiogenesis [38]. When the liver undergoes extensive damage and functional impairment in cirrhosis, HGF may be a critical factor in initiating the repair process. HGF has been demonstrated to promote liver regeneration by stimulating the proliferation and migration of hepatocytes [39,40,41]. Some studies suggest that increased concentrations of HGF may lead to a reduced activation of hepatic stellate cells, which are key players in the fibrosis process, thereby slowing the progression of cirrhosis [42,43,44]. Our study revealed that the progression of cirrhosis is associated with elevated levels of HGF (Dataset 1-OR = 1.052, 95% CI = 1.005–1.102; p = 0.031; Dataset 2-OR = 1.071, 95% CI = 1.009–1.136; p = 0.025), suggesting that disease progression may increase HGF expression, thereby activating liver repair functions. The mechanism of HGF in liver cirrhosis is complex, and as this is only a preliminary study, further research is needed to confirm these findings and elucidate the underlying mechanisms.
To our knowledge, this is the first MR study to investigate the causal relationship between cirrhosis and 41 inflammatory cytokines. Based on the results of our study, we suggest that MCP3 is likely correlated with the etiology of cirrhosis, while several inflammatory cytokines are more likely to be involved in the development of cirrhosis downstream. Furthermore, the progression of cirrhosis promotes an increase in HGF levels, enhancing liver repair functions. However, it is important to acknowledge several limitations. The lack of statistically significant associations for certain cytokines could be attributed to the limited sample size, which might have impacted the statistical power, or the potential influence of unaccounted-for confounding effects. Additionally, our study population primarily consists of individuals of European descent, which may introduce ancestral bias. Therefore, the findings may not be generalizable to non-European populations, and caution is needed when applying these results to other ethnic groups. We are currently recruiting cirrhosis patients to conduct genomic analysis in a Chinese cohort, although the sample size remains limited at this stage. In the future, we plan to use the recruited data to further investigate the relationship between cytokines and all-cause cirrhosis. Furthermore, our investigation utilized data from two large-scale GWASs, and the absence of specific demographic information and clinical records for the study subjects precluded subgroup analyses. It is worth noting that, although the cytokine cohort and both all-cause liver cirrhosis cohorts are of European descent, their unique backgrounds may introduce potential biases in the Mendelian randomization analysis that cannot be overlooked. Future research should carefully select datasets with more consistent population backgrounds to validate these findings and ensure the robustness of the results. We emphasize that, while we have conducted an in-depth analysis based on the existing data, future efforts should focus on collecting more comprehensive individual-level data to further validate and expand our research findings. And the results of the MR study only represent changes in risk factors across the lifespan, rather than effects at a specific time after intervention. Therefore, caution should be exercised when applying MR results to clinical interventions. Further research is needed to validate our findings and assess their relevance in clinical diagnostic processes and therapeutic decision-making.

5. Conclusions

In conclusion, this study suggests that MCP-3 might be associated with the etiology of cirrhosis, while several inflammatory cytokines could potentially play a role in its downstream development. Additionally, the progression of cirrhosis was associated with elevated levels of HGF, suggesting a possible role for liver repair functions. However, as this is a preliminary study, further research is needed to confirm these findings and clarify the underlying mechanisms.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines12102264/s1, Figure S1: MR plots for relationship of MCP-3 with cirrhosis. Figure S2: Scatter plot of SNP effects on cirrhosis vs. IL-2ra, IL5, IL7, MIP1b, SCF, and HGF for Dataset 1. Figure S3: Scatter plot of SNP effects on cirrhosis vs. IL-2ra, IL5, IL7, MIP1b, SCF, and HGF for Dataset 2. Figure S4: Plots of leave-one-out analyses to assess the stability of inverse cirrhosis results for Dataset 1. Figure S5: Plots of leave-one-out analyses to assess the stability of inverse cirrhosis results for Dataset 2. Table S1: MR estimates for Dataset 1. Table S2: Pleiotropy and heterogeneity tests for Dataset 1. Table S3: SNPs information for Dataset 1. Table S4: The reverse MR estimates for Dataset 1. Table S5: Pleiotropy and heterogeneity tests of cirrhosis on 41 inflammatory cytokines for Dataset 1. Table S6: Details of cirrhosis predicting SNPs with inflammatory cytokines for Dataset 1. Table S7: MR estimates for Dataset 2. Table S8: Pleiotropy and heterogeneity tests for Dataset 2. Table S9: SNP information for Dataset 2. Table S10: The reverse MR estimates for Dataset 2. Table S11: Pleiotropy and heterogeneity tests of cirrhosis on forty-one inflammatory cytokines for Dataset 2. Table S12: Details of cirrhosis predicting SNPs with inflammatory cytokines for Dataset 2.

Author Contributions

Conceptualization, S.S. and Y.Z. (Yanjie Zhou); methodology, S.S. and Y.Z. (Yanjie Zhou); software, S.S. and Y.Z. (Yanjie Zhou); validation, S.S. and Y.Z. (Yanjie Zhou) and H.Z.; formal analysis, S.S. and Y.Z. (Yanjie Zhou); investigation, P.J. and Y.Z. (Yalan Zhu); resources, C.X. and Y.L.; data curation, T.F., Y.Z. (Yuping Zeng), and H.H.; writing—original draft preparation, S.S. and Y.Z. (Yanjie Zhou); writing—review and editing, S.S., Y.Z. (Yanjie Zhou), and H.Z; visualization, S.S.; supervision, J.C.; project administration, J.C.; funding acquisition, J.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Natural Science Foundation of China, grant numbers 82372319, 81873979 and 81401666, the Ministry of Science and Technology of the People’s Republic of China, grant number 2021YFC2009306, and the Natural Science Foundation of Sichuan Province, grant number 2022NSFSC0736.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The original data presented in the study are openly available in [the International Oncology Unit (IEU) Open GWAS database] at [doi:10.1371/journal.pgen.1008629], [the United Kingdom Biobank (UKB) database] at [doi:10.1038/ng.3417] and [the University of Bristol Research Data Repository] at [doi:10.1016/j.ajhg.2016.11.007], and no new data were generated in this research.

Acknowledgments

The authors would like to gratefully acknowledge Binwu Ying for his instructive suggestions to refine the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Ginès, P.; Krag, A.; Abraldes, J.G.; Solà, E.; Fabrellas, N.; Kamath, P.S. Liver cirrhosis. Lancet 2021, 398, 1359–1376. [Google Scholar] [CrossRef] [PubMed]
  2. Huang, D.Q.; Terrault, N.A.; Tacke, F.; Gluud, L.L.; Arrese, M.; Bugianesi, E.; Loomba, R. Global epidemiology of cirrhosis—Aetiology, trends and predictions. Nat. Rev. Gastroenterol. Hepatol. 2023, 20, 388–398. [Google Scholar] [CrossRef] [PubMed]
  3. D’Amico, G.; Garcia-Tsao, G.; Pagliaro, L. Natural history and prognostic indicators of survival in cirrhosis: A systematic review of 118 studies. J. Hepatol. 2006, 44, 217–231. [Google Scholar] [CrossRef] [PubMed]
  4. Fabrellas, N.; Moreira, R.; Carol, M.; Cervera, M.; de Prada, G.; Perez, M.; Vazquez, E.; Sola, M.; Sancho, R.; Juanola, A.; et al. Psychological Burden of Hepatic Encephalopathy on Patients and Caregivers. Clin. Transl. Gastroenterol. 2020, 11, e00159. [Google Scholar] [CrossRef] [PubMed]
  5. Allen, A.M.; Kim, W.R.; Moriarty, J.P.; Shah, N.D.; Larson, J.J.; Kamath, P.S. Time trends in the health care burden and mortality of acute on chronic liver failure in the United States. Hepatology 2016, 64, 2165–2172. [Google Scholar] [CrossRef]
  6. Asrani, S.K.; Devarbhavi, H.; Eaton, J.; Kamath, P.S. Burden of liver diseases in the world. J. Hepatol. 2019, 70, 151–171. [Google Scholar] [CrossRef]
  7. Arroyo, V.; Angeli, P.; Moreau, R.; Jalan, R.; Clària, J.; Trebicka, J.; Fernández, J.; Gustot, T.; Caraceni, P.; Bernardi, M. The systemic inflammation hypothesis: Towards a new paradigm of acute decompensation and multiorgan failure in cirrhosis. J. Hepatol. 2021, 74, 670–685. [Google Scholar] [CrossRef]
  8. Friedman, S.L.; Pinzani, M. Hepatic fibrosis 2022: Unmet needs and a blueprint for the future. Hepatology 2022, 75, 473–488. [Google Scholar] [CrossRef]
  9. Engelmann, C.; Clària, J.; Szabo, G.; Bosch, J.; Bernardi, M. Pathophysiology of decompensated cirrhosis: Portal hypertension, circulatory dysfunction, inflammation, metabolism and mitochondrial dysfunction. J. Hepatol. 2021, 75 (Suppl. S1), S49–S66. [Google Scholar] [CrossRef]
  10. Rey, I.; Effendi-Ys, R. Association Between Serum IL-6, IL-10, IL-12, and IL-23 Levels and Severity of Liver Cirrhosis. Med. Arch. 2021, 75, 199–203. [Google Scholar] [CrossRef]
  11. Albillos, A.; Lario, M.; Álvarez-Mon, M. Cirrhosis-associated immune dysfunction: Distinctive features and clinical relevance. J. Hepatol. 2014, 61, 1385–1396. [Google Scholar] [CrossRef] [PubMed]
  12. Hammerich, L.; Tacke, F. Hepatic inflammatory responses in liver fibrosis. Nat. Rev. Gastroenterol. Hepatol. 2023, 20, 633–646. [Google Scholar] [CrossRef] [PubMed]
  13. Davey Smith, G.; Hemani, G. Mendelian randomization: Genetic anchors for causal inference in epidemiological studies. Hum. Mol. Genet. 2014, 23, R89–R98. [Google Scholar] [CrossRef] [PubMed]
  14. Skrivankova, V.W.; Richmond, R.C.; Woolf, B.A.R.; Davies, N.M.; Swanson, S.A.; VanderWeele, T.J.; Timpson, N.J.; Higgins, J.P.T.; Dimou, N.; Langenberg, C.; et al. Strengthening the reporting of observational studies in epidemiology using mendelian randomisation (STROBE-MR): Explanation and elaboration. BMJ 2021, 375, n2233. [Google Scholar] [CrossRef] [PubMed]
  15. Skrivankova, V.W.; Richmond, R.C.; Woolf, B.A.R.; Yarmolinsky, J.; Davies, N.M.; Swanson, S.A.; VanderWeele, T.J.; Higgins, J.P.T.; Timpson, N.J.; Dimou, N.; et al. Strengthening the Reporting of Observational Studies in Epidemiology Using Mendelian Randomization: The STROBE-MR Statement. JAMA 2021, 326, 1614–1621. [Google Scholar] [CrossRef] [PubMed]
  16. Sallis, H.M.; Palmer, T.; Tilling, K.; Davey Smith, G.; Munafò, M.R. Using allele scores to identify confounding by reverse causation: Studies of alcohol consumption as an exemplar. Int. J. Epidemiol. 2023, 52, 536–544. [Google Scholar] [CrossRef]
  17. Emdin, C.A.; Khera, A.V.; Kathiresan, S. Mendelian Randomization. JAMA 2017, 318, 1925–1926. [Google Scholar] [CrossRef] [PubMed]
  18. Ahola-Olli, A.V.; Würtz, P.; Havulinna, A.S.; Aalto, K.; Pitkänen, N.; Lehtimäki, T.; Kähönen, M.; Lyytikäinen, L.P.; Raitoharju, E.; Seppälä, I.; et al. Genome-wide Association Study Identifies 27 Loci Influencing Concentrations of Circulating Cytokines and Growth Factors. Am. J. Hum. Genet. 2017, 100, 40–50. [Google Scholar] [CrossRef]
  19. Emdin, C.A.; Haas, M.E.; Khera, A.V.; Aragam, K.; Chaffin, M.; Klarin, D.; Hindy, G.; Jiang, L.; Wei, W.Q.; Feng, Q.; et al. A missense variant in Mitochondrial Amidoxime Reducing Component 1 gene and protection against liver disease. PLoS Genet. 2020, 16, e1008629. [Google Scholar] [CrossRef]
  20. Buch, S.; Stickel, F.; Trépo, E.; Way, M.; Herrmann, A.; Nischalke, H.D.; Brosch, M.; Rosendahl, J.; Berg, T.; Ridinger, M.; et al. A genome-wide association study confirms PNPLA3 and identifies TM6SF2 and MBOAT7 as risk loci for alcohol-related cirrhosis. Nat. Genet. 2015, 47, 1443–1448. [Google Scholar] [CrossRef]
  21. He, H.; Liao, S.; Zeng, Y.; Liang, L.; Chen, J.; Tao, C. Causal relationships between metabolic-associated fatty liver disease and iron status: Two-sample Mendelian randomization. Liver Int. Off. J. Int. Assoc. Study Liver 2022, 42, 2759–2768. [Google Scholar] [CrossRef] [PubMed]
  22. Palmer, T.M.; Lawlor, D.A.; Harbord, R.M.; Sheehan, N.A.; Tobias, J.H.; Timpson, N.J.; Davey Smith, G.; Sterne, J.A. Using multiple genetic variants as instrumental variables for modifiable risk factors. Stat. Methods Med. Res. 2012, 21, 223–242. [Google Scholar] [CrossRef] [PubMed]
  23. Burgess, S.; Butterworth, A.; Thompson, S.G. Mendelian randomization analysis with multiple genetic variants using summarized data. Genet. Epidemiol. 2013, 37, 658–665. [Google Scholar] [CrossRef]
  24. 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, 512–525. [Google Scholar] [CrossRef] [PubMed]
  25. Verbanck, M.; Chen, C.Y.; 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, 693–698. [Google Scholar] [CrossRef]
  26. 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. 2016, 40, 304–314. [Google Scholar] [CrossRef]
  27. Pratim Das, P.; Medhi, S. Role of inflammasomes and cytokines in immune dysfunction of liver cirrhosis. Cytokine 2023, 170, 156347. [Google Scholar] [CrossRef] [PubMed]
  28. Liu, L.; Chen, P.; Xiao, N.; Liu, Q.; Zhu, X. Interleukin-8 predicts short-term mortality in acute-on-chronic liver failure patients with hepatitis B-related-related cirrhosis background. Ann. Med. 2023, 55, 2287708. [Google Scholar] [CrossRef]
  29. Salgüero, S.; Medrano, L.M.; González-García, J.; Berenguer, J.; Montes, M.L.; Diéz, C.; Garcia-Broncano, P.; Llop-Herrera, E.; Pérez-Latorre, L.; Bellóno, J.M.; et al. Plasma IP-10 and IL-6 are linked to Child-Pugh B cirrhosis in patients with advanced HCV-related cirrhosis: A cross-sectional study. Sci. Rep. 2020, 10, 10384. [Google Scholar] [CrossRef]
  30. Casari, M.; Siegl, D.; Deppermann, C.; Schuppan, D. Macrophages and platelets in liver fibrosis and hepatocellular carcinoma. Front. Immunol. 2023, 14, 1277808. [Google Scholar] [CrossRef]
  31. Sinzel, M.; Tan, T.; Wendling, P.; Kalbacher, H.; Özbalci, C.; Chelius, X.; Westermann, B.; Brügger, B.; Rapaport, D.; Dimmer, K.S. Mcp3 is a novel mitochondrial outer membrane protein that follows a unique IMP-dependent biogenesis pathway. EMBO Rep. 2016, 17, 965–981. [Google Scholar] [CrossRef] [PubMed]
  32. Xu, L.L.; McVicar, D.W.; Ben-Baruch, A.; Kuhns, D.B.; Johnston, J.; Oppenheim, J.J.; Wang, J.M. Monocyte chemotactic protein-3 (MCP3) interacts with multiple leukocyte receptors: Binding and signaling of MCP3 through shared as well as unique receptors on monocytes and neutrophils. Eur. J. Immunol. 1995, 25, 2612–2617. [Google Scholar] [CrossRef]
  33. Seki, E.; de Minicis, S.; Inokuchi, S.; Taura, K.; Miyai, K.; van Rooijen, N.; Schwabe, R.F.; Brenner, D.A. CCR2 promotes hepatic fibrosis in mice. Hepatology 2009, 50, 185–197. [Google Scholar] [CrossRef]
  34. Owusu Sekyere, S.; Port, K.; Deterding, K.; Cornberg, M.; Wedemeyer, H. Inflammatory patterns in plasma associate with hepatocellular carcinoma development in cured hepatitis C cirrhotic patients. United Eur. Gastroenterol. J. 2021, 9, 486–496. [Google Scholar] [CrossRef]
  35. Rampa, D.R.; Feng, H.; Allur-Subramaniyan, S.; Shim, K.; Pekcec, A.; Lee, D.; Doods, H.; Wu, D. Kinin B1 receptor blockade attenuates hepatic fibrosis and portal hypertension in chronic liver diseases in mice. J. Transl. Med. 2022, 20, 590. [Google Scholar] [CrossRef] [PubMed]
  36. Shahera, U.; Munshi, S.; Jahan, M.; Nessa, A.; Alam, S.; Tabassum, S. IP-10, p53, and Foxp3 Expression in Hepatocytes of Chronic Hepatitis B Patients with Cirrhosis and Hepatocellular Carcinoma. Euroasian J. Hepato-Gastroenterol. 2016, 6, 149–153. [Google Scholar] [CrossRef]
  37. Talaat, R.M.; Elsharnoby, S.; Abdelkhalek, M.S.; El-Shenawy, S.Z.; Elmasry, S. The Impact of Interferon-γ (IFN-γ) and IFN-γ-Inducible Protein 10 (IP-10) Genes’ Polymorphism on Risk of Hepatitis C Virus-Related Liver Cirrhosis. Immunol. Investig. 2022, 51, 688–704. [Google Scholar] [CrossRef]
  38. Katoh, R.; Maekawa, S.; Osawa, L.; Komiyama, Y.; Nakakuki, N.; Takada, H.; Matsuda, S.; Muraoka, M.; Suzuki, Y.; Sato, M.; et al. Significance of serum IP-10/CXCL10 measurement in predicting post-direct acting antiviral treatment liver function in patients with HCV-decompensated liver cirrhosis. Hepatol. Res. Off. J. Jpn. Soc. Hepatol. 2023, 53, 280–288. [Google Scholar] [CrossRef]
  39. Zhao, Y.; Ye, W.; Wang, Y.D.; Chen, W.D. HGF/c-Met: A Key Promoter in Liver Regeneration. Front. Pharmacol. 2022, 13, 808855. [Google Scholar] [CrossRef]
  40. Dong, X.; Luo, Y.; Lu, S.; Ma, H.; Zhang, W.; Zhu, Y.; Sun, G.; Sun, X. Ursodesoxycholic acid alleviates liver fibrosis via proregeneration by activation of the ID1-WNT2/HGF signaling pathway. Clin. Transl. Med. 2021, 11, e296. [Google Scholar] [CrossRef]
  41. Liang, F.; Xu, X.; Tu, Y. Resveratrol inhibited hepatocyte apoptosis and alleviated liver fibrosis through miR-190a-5p /HGF axis. Bioorganic Med. Chem. 2022, 57, 116593. [Google Scholar] [CrossRef] [PubMed]
  42. Stöß, C.; Laschinger, M.; Wang, B.; Lu, M.; Altmayr, F.; Hartmann, D.; Hüser, N.; Holzmann, B. TLR3 promotes hepatocyte proliferation after partial hepatectomy by stimulating uPA expression and the release of tissue-bound HGF. FASEB J. Off. Publ. Fed. Am. Soc. Exp. Biol. 2020, 34, 10387–10397. [Google Scholar] [CrossRef]
  43. Shivaramu, S.; Maiti, S.K.; Banu, S.A.; Kalaiselvan, E.; Sharun, K.; Mishra, M.; Mohan, D.; Palakkara, S.; Kumar, S.; Sahoo, M.; et al. Synergistic Hepatoprotective Effects of Mesenchymal Stem Cells and Platelet-Rich Plasma in a Rat Model of Bile Duct Ligation-Induced Liver Cirrhosis. Cells 2024, 13, 404. [Google Scholar] [CrossRef] [PubMed]
  44. Gharbia, S.; Nazarie, S.R.; Dinescu, S.; Balta, C.; Herman, H.; Peteu, V.E.; Gherghiceanu, M.; Hermenean, A.; Costache, M. Adipose-Derived Stem Cells (ADSCs) Supplemented with Hepatocyte Growth Factor (HGF) Attenuate Hepatic Stellate Cell Activation and Liver Fibrosis by Inhibiting the TGF-β/Smad Signaling Pathway in Chemical-Induced Liver Fibrosis Associated with Diabetes. Cells 2022, 11, 3338. [Google Scholar] [CrossRef] [PubMed]
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Figure 1. The workflow diagram of this study.
Figure 1. The workflow diagram of this study.
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Figure 2. Forest plots of MR analysis of the relationship between 41 circulating cytokine levels and the risk of cirrhosis for (A) Dataset 1 and (B) Dataset 2. Bold in figure indicates p ≤ 0.05, which is statistically significant. CI, confidence interval; OR, odds ratio; MR, mendelian randomization.
Figure 2. Forest plots of MR analysis of the relationship between 41 circulating cytokine levels and the risk of cirrhosis for (A) Dataset 1 and (B) Dataset 2. Bold in figure indicates p ≤ 0.05, which is statistically significant. CI, confidence interval; OR, odds ratio; MR, mendelian randomization.
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Figure 3. Heatmaps of the five MR analysis methods for the association between 41 circulating cytokine levels and the risk of cirrhosis for (A) Dataset 1 and (B) Dataset 2. MR, Mendelian randomization. p values for variance inverse variance weighting, IVW-lasso, weighted median, MR-Egger, weighted mode and simple mode are indicated from the outside in. * p < 0.05.
Figure 3. Heatmaps of the five MR analysis methods for the association between 41 circulating cytokine levels and the risk of cirrhosis for (A) Dataset 1 and (B) Dataset 2. MR, Mendelian randomization. p values for variance inverse variance weighting, IVW-lasso, weighted median, MR-Egger, weighted mode and simple mode are indicated from the outside in. * p < 0.05.
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Figure 4. Forest plots of MR analysis of the relationship between cirrhosis and the risk of 41 circulating cytokine levels for (A) Dataset 1 and (B) Dataset 2. Bold in figure indicates p ≤ 0.05, which is statistically significant. CI, confidence interval; OR, odds ratio; MR, mendelian randomization.
Figure 4. Forest plots of MR analysis of the relationship between cirrhosis and the risk of 41 circulating cytokine levels for (A) Dataset 1 and (B) Dataset 2. Bold in figure indicates p ≤ 0.05, which is statistically significant. CI, confidence interval; OR, odds ratio; MR, mendelian randomization.
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Figure 5. Heatmaps of the five MR analysis methods for the association between cirrhosis and the risk of 41 circulating cytokine levels for (A) Dataset 1 and (B) Dataset 2. MR, Mendelian randomization. p values for variance inverse variance weighting, IVW-lasso, weighted median, MR-Egger, weighted mode, and simple mode are indicated from the outside in. * p < 0.05.
Figure 5. Heatmaps of the five MR analysis methods for the association between cirrhosis and the risk of 41 circulating cytokine levels for (A) Dataset 1 and (B) Dataset 2. MR, Mendelian randomization. p values for variance inverse variance weighting, IVW-lasso, weighted median, MR-Egger, weighted mode, and simple mode are indicated from the outside in. * p < 0.05.
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Table 1. The demographic data for the GWAS databases used.
Table 1. The demographic data for the GWAS databases used.
CharacteristicAbbreviationGWAS IDSample SizeNumber of SNPsCases DefinitionExclusion CriteriaQuality Control
InterleukinsInterleukin-1-betaIL-1βebi-a-GCST0044483309983,642The GWAS meta-analysis included up to 8293 Finnish individuals from three independent population cohorts: the Cardiovascular Risk in Young Finns Study (YFS), FINRISK1997, and FINRISK2002. The study cohort characteristics are reported in Table S1 in the original study. On average, the YFS participants are younger than the C23FINRISK1997 and FINRISK2002 participants (37 versus 60 years).-Linear regression with a probe as a dependent variable was used to test associations between cytokine-associated variants and transcripts. Age and sex were used as covariates. Genotype dosage was calculated for each included variant with Qctool (version 2) software. More details referred to the original study.
Interleukin-1-receptor antagonistIL-1RAebi-a-GCST00444736389,564,741
Interleukin-2IL-2ebi-a-GCST00445534759,512,914
Interleukin-2 receptor antagonistIL-2RAebi-a-GCST00445436779,583,519
Interleukin-4IL-4ebi-a-GCST00445381249,786,064
Interleukin-5IL-5ebi-a-GCST00445233649,450,731
Interleukin-6IL-6ebi-a-GCST00444681899,790,590
Interleukin-7IL-7ebi-a-GCST00445134099,692,306
Interleukin-8IL-8ebi-a-GCST00444535269,517,348
Interleukin-9IL-9ebi-a-GCST00445036349,567,876
Interleukin-10IL-10ebi-a-GCST00444476819,793,415
Interleukin-12p70IL-12p70ebi-a-GCST00443982709,799,886
Interleukin-13IL-13ebi-a-GCST00444335579,539,073
Interleukin-16IL-16ebi-a-GCST00443034839,551,485
Interleukin-17IL-17ebi-a-GCST00444277609,786,653
Interleukin-18IL-18ebi-a-GCST00444136369,785,222
ChemokinesCutaneous T cell attractingCTACKebi-a-GCST00442036319,568,408
EotaxinEotaxinebi-a-GCST00446081539,793,404
Growth-regulated protein alphaGRPaebi-a-GCST00445735059,528,505
Interferon gamma-induced protein 10IP-10ebi-a-GCST00444036859,576,881
Monocyte chemoattractant protein-1MCP1ebi-a-GCST00443882939,801,908
Monocyte chemoattractant protein-3MCP3ebi-a-GCST0044378437,630,881
Monokine induced by gamma interferonMIGebi-a-GCST00443536859,579,894
Macrophage inflammatory protein 1aMIP1αebi-a-GCST00443435229,519,267
Macrophage inflammatory protein 1bMIP1βebi-a-GCST00443382439,802,973
Regulated on activation, normal T cell expressed and secretedRANTESebi-a-GCST00443134219,523,827
Stromal-cell-derived factor 1 alphaSDF-1αebi-a-GCST00442759989,736,366
Growth factorsBeta-nerve growth factorβNGFebi-a-GCST00442135319,537,863
Granulocyte-colony stimulating factorGCSFebi-a-GCST00445879049,788,961
Fibroblast growth factor basicFGFBasicebi-a-GCST00445975659,790,946
Hepatocyte growth factorHGFebi-a-GCST00444982929,802,538
Macrophage colony stimulating factorMCSFebi-a-GCST0044368409,184,521
Platelet-derived growth factor BBPDGFbbebi-a-GCST00443282939,800,009
Stem cell factorSCFebi-a-GCST00442982909,796,683
Stem cell growth factor betaSCGFβebi-a-GCST00442836829,574,890
Vascular endothelial growth factorVEGFebi-a-GCST00442271189,784,803
OthersInterferon gammaIFN -γebi-a-GCST00445677019,785,363
Macrophage Migration Inhibitory FactorMIFebi-a-GCST00442334949,537,573
Tumor necrosis factor alphaTGFαebi-a-GCST00442634549,500,449
Tumor necrosis factor betaTGFβebi-a-GCST00442515596,304,298
TNF-related apoptosis inducing ligandTRAILebi-a-GCST00442481869,698,525
CIRRHOSISCirrhosis-finn-b-CIRRHOSIS_BROAD1931 cases and 216,861 controls16,380,466Cirrhosis ascertained through ICD-10 code K74, ICD-9 code 571 (hepatic fibrosis and cirrhosis)The study excluded cases of cirrhosis secondary to primary biliary cholangitis and primary sclerosis cholangitis, as these autoimmune disorders are directed against the biliary (and not hepatic) parenchyma < 30 g/day for men, chronic viral hepatitis (hepatitis B and hepatitis C), autoimmune liver diseases, hereditary hemochromatosis, α1-antitrypsin deficiency, Wilson’s disease, and drug-induced liver injuryA genome-wide association study in each cohort was performed using logistic regression with adjustment for age, sex, and ten principal components of ancestry. We tested the association of fourteen million variants with a minor allele frequency of greater than 0.1% with cirrhosis in each cohort. PLINK (2015-1-25) was used for all analyses. To combine estimates across cohorts, inverse variance fixed-effects meta-analysis, as implemented by METAL (2010-9-1), was used. Quantile–quantile analysis was used to examine the presence of population stratification. No evidence of inflation was observed (lambda 1.02; Supplementary Figure S2 in the original study). Both additive and recessive analyses were performed.
-UK Biobank2701 cases and 16,206 controls-Hospitalization or death due to physician-diagnosed cirrhosis: K70.2 (alcoholic fibrosis and sclerosis of the liver), K70.3 (alcoholic cirrhosis of the liver), K70.4 (alcoholic hepatic failure), K74.0 (hepatic fibrosis), K74.1 (hepatic sclerosis), K74.2 (hepatic fibrosis with hepatic sclerosis), K74.6 (other and unspecified cirrhosis of liver), K76.6 (portal hypertension), or I85 (esophageal varices). Controls were free of liver disease.--
-: The information is not present in the original data.
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Shi, S.; Zhou, Y.; Zhang, H.; Zhu, Y.; Jiang, P.; Xie, C.; Feng, T.; Zeng, Y.; He, H.; Luo, Y.; et al. The Causal Relationship between Inflammatory Cytokines and Liver Cirrhosis in European Descent: A Bidirectional Two-Sample Mendelian Randomization Study and the First Conclusions. Biomedicines 2024, 12, 2264. https://doi.org/10.3390/biomedicines12102264

AMA Style

Shi S, Zhou Y, Zhang H, Zhu Y, Jiang P, Xie C, Feng T, Zeng Y, He H, Luo Y, et al. The Causal Relationship between Inflammatory Cytokines and Liver Cirrhosis in European Descent: A Bidirectional Two-Sample Mendelian Randomization Study and the First Conclusions. Biomedicines. 2024; 12(10):2264. https://doi.org/10.3390/biomedicines12102264

Chicago/Turabian Style

Shi, Shiya, Yanjie Zhou, He Zhang, Yalan Zhu, Pengjun Jiang, Chengxia Xie, Tianyu Feng, Yuping Zeng, He He, Yao Luo, and et al. 2024. "The Causal Relationship between Inflammatory Cytokines and Liver Cirrhosis in European Descent: A Bidirectional Two-Sample Mendelian Randomization Study and the First Conclusions" Biomedicines 12, no. 10: 2264. https://doi.org/10.3390/biomedicines12102264

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