Home Examination of the causal role of immune cells in non-alcoholic fatty liver disease by a bidirectional Mendelian randomization study
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Examination of the causal role of immune cells in non-alcoholic fatty liver disease by a bidirectional Mendelian randomization study

  • Yu Li , Xiaodan Lv , Jianing Lin , Shiquan Li , Guangfu Lin , Zhixi Huang , Deyi Chen , Lichun Han , Lingling Zhan and Xiaoping Lv EMAIL logo
Published/Copyright: February 19, 2025

Abstract

Background

Non-alcoholic fatty liver disease (NAFLD) is a globally widespread disease. Recent investigations have highlighted a close association between immunity and NAFLD, but the causality between them has not been thoroughly examined.

Methods

A total of 731 immunological traits and NAFLD cohorts were derived from genome-wide association study summary data, and single nucleotide polymorphisms significantly associated with immune traits were identified as instrumental variables. Moreover, 731 phenotypes include absolute cell counts, median fluorescence intensity (MFI), morphological parameters, and relative cell counts. The bidirectional two-sample Mendelian randomization (MR) was performed primarily using the inverse-variance weighted methods, and sensitivity analysis was carried out simultaneously.

Results

Four immunophenotypes were identified to exert a protective effect against NAFLD, including HLA-DR+ CD4+ %lymphocytes, SSC-A on CD4+, CD24 MFI on IgDCD38, and CD8 MFI on CD28CD8br. Seven immunophenotypes were identified to be hazardous, including CD28+ CD45RA+ CD8dim%CD8dim, CD127 MFI on CD28+ DN (CD4CD8), CD20 MFI on IgD+ CD38br, CD20 MFI on transitional, IgD MFI on transitional, CD3 MFI on central memory CD8br, and CD45 MFI on CD33brHLA-DR+ CD14. However, reverse MR showed NAFLD had no causal effect on immunophenotypes.

Conclusion

The study demonstrated a potential causal link between several immunophenotypes and NAFLD, which contributes to advancing research and treatment of NAFLD based on immune-mediated mechanisms.

1 Introduction

In 1980, non-alcoholic fatty liver disease (NAFLD) was first proposed to define conditions with histological features similar to those of alcoholic liver diseases [1]. NAFLD progresses through non-alcoholic fatty liver and non-alcoholic steatohepatitis (NASH), potentially resulting in severe fibrosis and cirrhosis [2]. Although numerous studies have pinpointed effective interventions for specific cirrhosis-related complications [3], there remains a lack of viable therapeutic options for liver fibrosis, irrespective of the underlying etiology, including NAFLD [4]. With a global prevalence of 25%, NAFLD is acknowledged as the primary contributor to chronic liver diseases and cirrhosis, imposing a significant burden on the global economy, especially in the Middle East, Asia, and North Africa [5].

Unfortunately, the prevalence of metabolic risk factors for hepatocellular carcinoma (HCC), like NAFLD, is rising and could eventually become the predominant cause of HCC worldwide [6]. The precise etiology of NAFLD remains unclear. Research indicates that NAFLD development is attributed to metabolism, gut microbiota, immune responses, and environmental elements. A comprehensive understanding of the disease as a complex interplay of various etiological factors based on immune responses will help to refine the current clinical insight into NAFLD and unveil new therapeutic options [7,8]. A study has demonstrated that activation of silent information regulator 1 (SIRT1), a transcription factor associated with the pathogenesis of NAFLD, can significantly repress inflammatory responses during liver injury [9]. Importantly, activation of intestinal lymphocytes and immune responses in the liver is associated with chronic low-grade inflammation, a primary etiology of NAFLD [8].

The inflammatory environment in NASH is predominantly governed by immune cells from the innate and adaptive systems. Immune cells secrete inflammatory mediators to induce hepatocyte death, while stressed hepatocytes are more prone to cytokine-mediated cell death, thus releasing molecular substances known as damage-associated molecular patterns (DAMPs)[10]. Many infiltrated innate immune cells, encompassing neutrophils, monocytes, dendritic cells (DCs), and Kupffer cells, contain pattern recognition receptors (PRRs). DAMPs activate PRRs to induce sterile inflammation through immune responses [11]. Thus, innate immune responses are acknowledged as crucial contributors to NASH development. However, accumulating evidence suggests that adaptive immunity is equally important. It has been reported that liver injury and lobular inflammation are closely associated with the degree of recruitment of CD4+ and CD8+ T lymphocytes in the methionine choline-deficient model of NASH [12]. Evidence from high fructose-induced models of NAFLD supports that CD8+ T cell depletion can protect mice from developing steatosis [13]. However, contrary to previous beliefs that adaptive immunity predominantly facilitates NASH progression, recent research indicates that adaptive immune responses may be a double-edged sword [14,15]. While previous observational articles have unveiled the association between immune cells and NAFLD [16,17], this association may be disrupted by confounders and reverse causality. Additional evidence is warranted to uncover a more robust causal connection. Hence, it is urgent to adopt additional research methods to reveal the causality between immune inflammation and NAFLD, as well as to pinpoint potential treatments.

Mendelian randomization (MR) is a methodological tool that leverages genetic variation as an instrumental variable (IV) to imitate the biological connection between a particular exposure and outcome. Genetic variants such as IVs are allocated from parents to offspring during gamete formation, constituting a form of natural randomization. This approach may minimize the influence of confounders, optimize resource allocation, and avoid reverse causality to a certain degree [13,18]. MR has been applied to infer causal relationships among various diseases [19]. This study employed bidirectional MR to uncover the causal connection of immune cells with NAFLD.

2 Materials and methods

2.1 Study design

A two-sample MR approach was utilized to determine the causal relationship between 731 immune cell signatures (spanning 7 panels) and NAFLD. The study flowchart is displayed in Figure 1. Genetic variations served as IVs, which required that valid IVs for causal inference satisfied three crucial assumptions: (1) IVs have a direct connection to the exposure; (2) IVs are independent of confounders, meaning that they are not associated with the outcome through confounding pathways; and (3) IVs influence the outcome exclusively via the exposure. All research referenced in the genome-wide association study (GWAS) was authorized by ethical review committees and obtained informed consent from each participant.

Figure 1 
                  The study flowchart. Assumption 1: IVs have direct connection to the exposure; Assumption 2: IVs are independent of confounders; Assumption 3: IVs influence the outcome exclusively via the exposure; NAFLD, non-alcoholic fatty liver disease; SNPs, Single-nucleotide polymorphisms; MR, Mendelian randomization.
Figure 1

The study flowchart. Assumption 1: IVs have direct connection to the exposure; Assumption 2: IVs are independent of confounders; Assumption 3: IVs influence the outcome exclusively via the exposure; NAFLD, non-alcoholic fatty liver disease; SNPs, Single-nucleotide polymorphisms; MR, Mendelian randomization.

2.2 GWAS data sources

Publicly available GWAS summary data for various immune traits are accessible from GCST0001391 to GCST0002121 [20]. A total of 731 immunophenotypes in the GWAS catalog were categorized into seven panels: B cells, conventional DCs, maturation stages of T cells, myeloid cells, monocytes, Treg, and T, B, natural killer (TBNK) cells. TBNK panel is a commonly employed immune-monitoring tool that allows for simultaneous detection of T, B, and NK cells. The 731 phenotypes include absolute cell counts (AC, n = 118), median fluorescence intensity (MFI, n = 389) for surface antigen levels, morphological parameters (MP, n = 32), and relative cell counts (RC, n = 192). GWAS initially focused on 731 immune traits, leveraging data from 3,757 European samples. Based on 3,757 Sardinian samples, GWAS discerned nearly 22 million single-nucleotide polymorphisms (SNPs) using high-density arrays after adjustment for age, sex, and age2 [20,21].

2.3 Data source for NAFLD

The largest genome-wide analysis for NAFLD was acquired from 4 cohorts of European participants with health records, encompassing 8,434 cases and 770,180 controls, as well as approximately 6.8 million SNPs [22].

2.4 Selection of IVs

Genetic variants were selected from GWAS for IV models according to recent studies [20,23]. Those with a P-value less than 1 × 10−5 were identified. Then, we eliminated SNPs with notable linkage disequilibrium, defined as r² > 0.001 and a distance <10,000 kilobases, to confirm the independence of the screened SNPs. The PhenoScanner database was utilized to examine whether SNPs meet both the independent and exclusion assumptions, while SNPs directly associated with confounders and outcomes were removed [24]. The F-value was calculated, and IVs with a value >10 were retained, indicating the absence of weak instrumental bias [25]. Finally, data from both databases were harmonized to ensure that the influence of exposure and outcomes aligned with the same effector allele. Additionally, palindromic SNPs were removed during the process.

2.5 Statistical analysis

2.5.1 MR analysis

Based on the summarized data from 731 immunological traits (n = 3,757) and NAFLD (n = 778,614) derived from GWAS, the analysis was done in R software 4.3.2 utilizing the “TwoSampleMR” package 0.5.8 (available at http://www.Rproject.org). Inverse-variance weighted (IVW), weighted mode, weighted median, MR-Egger, and simple mode were adopted to illustrate the causal association between 731 immune traits and NAFLD, with IVW as the primary method [26,27]. Findings were visualized utilizing scatter, forest, and funnel plots. Given the risk of type 1 errors in multiple testing, the false discovery rate (FDR) correction was implemented.

2.6 Sensitivity analysis

Horizontal pleiotropy was checked by the MR-Egger method and MR-PRESSO tests [28], with P > 0.05 indicating that the IVs of immune cells did not have significant horizontal pleiotropy for NAFLD. Cochran’s Q statistics was used to judge heterogeneity in both the IVW and MR-Egger methods [29], with P > 0.05 implying no significant heterogeneity. The robustness of results was testified via the leave-one-out method.

  1. Informed consent: Not applicable.

  2. Ethics approval: Not applicable.

3 Results

3.1 Causal effect of immunophenotypes on NAFLD

MR analysis, primarily based on the IVW method, following FDR correction (P FDR < 0.05), identified 11 immunophenotypes with causal associations with NAFLD. The comprehensive characteristics of the 11 immunophenotypes, including their parental populations, are shown in Table 1. HLA-DR+ CD4+ % lymphocytes (OR = 0.935, 95% CI = 0.879–0.995, P = 0.034, P FDR = 0.044), SSC-A on CD4+ (OR = 0.944, 95% CI = 0.895–0.997, P = 0.037, P FDR = 0.043), CD24 on IgDCD38 (OR = 0.963, 95% CI = 0.930–0.996, P = 0.029, P FDR = 0.046), and CD8 on CD28CD8br (OR = 0.935, 95% CI = 0.880–0.994, P = 0.031, P FDR = 0.045) showed a negative causal association with NAFLD (Figure 2). The scatter plots depicted a negative slope for these four immunophenotypes, indicating that the increase in the expression of these four immunophenotypes may decrease the likelihood of NAFLD (Figure 3). In contrast, CD28+ CD45RA+ CD8dim%CD8dim (OR = 1.032, 95% CI = 1.012–1.053, P = 0.001, P FDR = 0.003), CD127 on CD28+ DN (CD4CD8) (OR = 1.074, 95% CI = 1.004–1.150, P = 0.039, P FDR = 0.039), CD20 on IgD+ CD38br (OR = 1.046, 95% CI = 1.005–1.089, P = 0.027, P FDR = 0.046), CD20 on transitional (OR = 1.044, 95% CI = 1.003–1.087, P = 0.034, P FDR = 0.046), IgD on transitional (OR = 1.058, 95% CI = 1.007–1.113, P = 0.027, P FDR = 0.049), CD3 on central memory (CM) CD8br (OR = 1.053, 95% CI = 1.003–1.105, P = 0.039, P FDR = 0.041), and CD45 on CD33br HLA-DR+ CD14 (OR = 1.050, 95% CI = 1.003–1.100, P = 0.038, P FDR = 0.042) showed positive causal association with NAFLD (Figure 4). The scatter plots illustrated the positive slope of these seven immunophenotypes, indicating that as the expression of these seven immunophenotypes increased, the likelihood of NAFLD correspondingly enhanced (Figure 5).

Table 1

Comprehensive characteristics of the 11 immunophenotypes

Panel GWAS ID Trait Parental population Sample size Number of SNPs Trait type
TBNK ebi-a-GCST90001626 HLA DR+ CD4+ %lymphocyte CD45+ CD3+ CD4+ 3,595 15,160,296 Relative count
TBNK ebi-a-GCST90002081 SSC-A on CD4+ CD45+ CD3+ CD4+ 3,113 14,903,739 MP
B cell ebi-a-GCST90001769 CD24 on IgDCD38 CD19+IgDCD38 3,648 15,044,894 MFI
B cell ebi-a-GCST90001751 CD20 on IgD+CD38br CD19+ IgD+ CD38br 3,657 15,048,951 MFI
B cell ebi-a-GCST90001763 CD20 on transitional CD19+ CD38+ CD24+ 3,657 15,048,951 MFI
B cell ebi-a-GCST90001828 IgD on transitional CD19+ CD38+ CD24+ 3,657 15,048,951 MFI
Treg ebi-a-GCST90002120 CD8 on CD28CD8br CD8br CD28 2,920 14,849,646 MFI
Treg ebi-a-GCST90001665 CD28+ CD45RA+CD8dim%CD8dim CD4 CD8dim CD28+ CD45RA+ 3,440 15,147,619 Relative count
Treg ebi-a-GCST90001925 CD127 on CD28+ DN (CD4CD8) CD4 CD8 CD28+ 2,918 14,849,609 MFI
Maturation stages of T cell ebi-a-GCST90001846 CD3 on CM CD8br CD4 CD8br CD45RA CCR7+ 2,910 14,842,706 MFI
Myeloid cell ebi-a-GCST90002042 CD45 on CD33br HLA-DR+CD14 CD45+ 7ADD CD14 CD33br HLA DR+ 1,579 14,129,845 MFI
Figure 2 
                  Forest plots showed protective effects of immunophenotypes (study group n = 3,757) on NAFLD (study group n = 778,614). TBNK, T cells, B cells, Natural killer cells; br, bright; HLA, Human Leucocyte Antigen.
Figure 2

Forest plots showed protective effects of immunophenotypes (study group n = 3,757) on NAFLD (study group n = 778,614). TBNK, T cells, B cells, Natural killer cells; br, bright; HLA, Human Leucocyte Antigen.

Figure 3 
                  Causal effects of immune cells (study group n = 3,757) on NAFLD (study group n = 778,614). (a) Scatter plot between HLA-DR+ CD4+ % lymphocyte and NAFLD risk. (b) Scatter plot between SSC-A on CD4+ and NAFLD risk. (c) Scatter plot between CD24 on IgD−CD38− and NAFLD risk. (d) Scatter plot between CD8 on CD28−CD8br and NAFLD risk.
Figure 3

Causal effects of immune cells (study group n = 3,757) on NAFLD (study group n = 778,614). (a) Scatter plot between HLA-DR+ CD4+ % lymphocyte and NAFLD risk. (b) Scatter plot between SSC-A on CD4+ and NAFLD risk. (c) Scatter plot between CD24 on IgDCD38 and NAFLD risk. (d) Scatter plot between CD8 on CD28CD8br and NAFLD risk.

Figure 4 
                  Forest plots showed promotional effects of immunophenotypes (study group n = 3,757) on NAFLD (study group n = 778,614). TBNK, T cells, B cells, Natural killer cells; DN, double negative; br, bright; CM, central memory; HLA, Human Leucocyte Antigen.
Figure 4

Forest plots showed promotional effects of immunophenotypes (study group n = 3,757) on NAFLD (study group n = 778,614). TBNK, T cells, B cells, Natural killer cells; DN, double negative; br, bright; CM, central memory; HLA, Human Leucocyte Antigen.

Figure 5 
                  Causal effects of immune cells (study group n = 3,757) on NAFLD (study group n = 778,614). (a) Scatter plot between CD20 on IgD+ CD38br and NAFLD risk. (b) Scatter plot between CD20 on transitional and NAFLD risk. (c) Scatter plot between IgD on transitional and NAFLD risk. (d) Scatter plot between CD28+ CD45RA+ CD8dim%CD8dim and NAFLD risk. (e) Scatter plot between CD3 on CM CD8br and NAFLD risk. (f) Scatter plot between CD127 on CD28+ DN (CD4−CD8−) and NAFLD risk. (g) Scatter plot between CD45 on CD33br HLA-DR+ CD14− and NAFLD risk.
Figure 5

Causal effects of immune cells (study group n = 3,757) on NAFLD (study group n = 778,614). (a) Scatter plot between CD20 on IgD+ CD38br and NAFLD risk. (b) Scatter plot between CD20 on transitional and NAFLD risk. (c) Scatter plot between IgD on transitional and NAFLD risk. (d) Scatter plot between CD28+ CD45RA+ CD8dim%CD8dim and NAFLD risk. (e) Scatter plot between CD3 on CM CD8br and NAFLD risk. (f) Scatter plot between CD127 on CD28+ DN (CD4CD8) and NAFLD risk. (g) Scatter plot between CD45 on CD33br HLA-DR+ CD14 and NAFLD risk.

In addition to the IVW method, MR Egger (OR = 1.029, 95% CI = 1.006–1.052, P = 0.020), weighted median (OR = 1.030, 95% CI = 1.002–1.058, P = 0.036), simple mode (OR = 1.058, 95% CI = 1.004–1.114, P = 0.049), and weighted mode (OR = 1.029, 95% CI = 1.007–1.052, P = 0.016) yielded similar results on CD28+ CD45RA+ CD8dim%CD8dim. MR Egger (OR = 1.117, 95% CI = 1.016–1.226, P = 0.041), weighted median (OR = 1.082, 95% CI = 1.015–1.154, P = 0.015), and weighted mode (OR = 1.080, 95% CI = 1.011–1.155, P = 0.041) yielded similar results on CD3 on CM CD8br. MR Egger (OR = 0.948, 95% CI = 0.910–1.002, P = 0.044) yielded similar results on CD24 on IgDCD38. Weighted median (OR = 1.099, 95% CI = 1.008–1.198, P = 0.044) yielded similar results on CD127 on CD28+ DN (CD4CD8) (Table S1).

Forest plots showed SNP effects on the causal connections of immunophenotypes with NAFLD (Figure S1). In funnel plots, IVs were symmetrically distributed, which proved that the analysis followed the randomization principle (Figure S2).

3.2 Sensitivity analysis

Our findings indicated that all Q-pval values from the heterogeneity tests were >0.05, suggesting no significant heterogeneity. Additionally, MR-Egger and MR-PRESSO analyses demonstrated no significant horizontal pleiotropy for each immunophenotype, indicating that SNPs had no substantial impact on the outcome via exposure-unrelated factors (Table 2). The Leave-one-out plots had no significant biases, further proving the stability and reliability of the results (Figure S3).

Table 2

Tests for pleiotropy and heterogeneity between immune cells and NAFLD

Panel Exposure SNPs MR Presso global test MR_Egger regression Heterogeneity
Pval Intercept P_intercept Method Q Q-Pval
TBNK HLA DR+ CD4+ %lymphocyte 13 0.57 −0.01 0.27 MR Egger 10.40 0.49
IVW 11.78 0.46
SSC-A on CD4+ 21 0.74 0.00 0.70 MR Egger 15.76 0.67
IVW 15.92 0.72
Treg CD8 on CD28 CD8br 15 0.68 −0.01 0.46 MR Egger 10.67 0.64
IVW 11.26 0.67
CD28+CD45RA+ CD8dim%CD8dim 21 0.60 0.00 0.52 MR Egger 18.95 0.46
IVW 19.38 0.50
CD127 on CD28+ DN (CD4CD8) 17 0.16 0.01 0.52 MR Egger 21.53 0.12
IVW 22.15 0.14
B cells CD24 on IgDCD38 22 0.94 0.01 0.39 MR Egger 11.27 0.94
IVW 12.03 0.94
CD20 on IgD+ CD38br 20 0.80 0.00 0.72 MR Egger 15.13 0.65
IVW 15.26 0.71
CD20 on transitional 18 0.15 0.00 0.96 MR Egger 25.78 0.06
IVW 25.78 0.08
IgD on transitional 23 0.65 0.00 0.64 MR Egger 18.87 0.59
IVW 19.09 0.64
Maturation stages of T cells CD3 on CM CD8br 14 0.77 −0.02 0.18 MR Egger 7.41 0.83
IVW 9.44 0.74
Myeloid cells CD45 on CD33br HLA DR+ CD14 15 0.94 −0.02 0.31 MR Egger 6.14 0.94
IVW 7.26 0.92

3.3 Causal effect of NAFLD on immunophenotypes

The IVW method was the primary method of reverse MR analysis on the 11 immunophenotypes. No causal association was revealed between NAFLD and any immunophenotype (Table S2 and Figure S4).

4 Discussion

The links between immune cells and NAFLD and the impact of genetics on NAFLD progression are not well understood [30]. Hence, the MR techniques were used to determine the possible causal association between 731 immune traits and NAFLD utilizing public genetic information. After effective screening, 199 SNPs associated with 11 immunophenotypes and NAFLD were screened as IVs. Our findings indicated that four immunophenotypes decreased the risk of NAFLD, including HLA-DR+ CD4+ % lymphocytes and SSC-A on CD4+ in the TBNK panel, CD24 on IgDCD38 in the B-cell lineage, and CD8 on CD28 CD8br in the Treg panel. In contrast, CD127 on CD28+ DN and CD28+ CD45RA+ CD8dim%CD8dim in the Treg lineage, CD20 on IgD+ CD38br, CD20 on transitional, IgD on transitional in the B-cell panel, CD3 on CM CD8br in the matured T-cell panels, and CD45 on CD33brHLA-DR+ CD14 in the myeloid cell panel promoted NAFLD development. With IVW as the key method, analyses utilizing weighted median, MR Egger, and simple mode also yielded consistent results with those obtained from IVW in certain immune cell characteristics. This further strengthens our conclusions and enhances the reliability of the results.

T and B lymphocytes, representatives of adaptive immunity, demonstrate crucial roles in regulating immune responses and inflammation. T cells are grouped into CD4+, CD8+, and Treg cells. Our findings discovered that CD3 on CM CD8br promoted NAFLD development. Consistent with the recent literature reports, the frequency of CM CD8+ T cells in human peripheral blood is positively associated with hepatic steatosis and lobular inflammation [31]. In addition, the frequency of CM CD8+ T cells is also significantly increased in the liver of NAFLD mouse models [32]. However, their precise mechanisms on NAFLD progression require further investigations.

Various preclinical and clinical studies have demonstrated that CD4+ T cells also contribute to NASH progression [33,34]. CD4+ T cell depletion using therapeutic antibodies could decrease the production of inflammatory cytokines and fibrosis, underscoring their significance in the clinical progression of NASH [35]. In contrast, we identified two CD4+ T cell subsets that exhibited protective effects against NAFLD:HLA-DR+ CD4+ lymphocytes and SSC-A+ CD4+ T cells. Recent articles have revealed the heterogeneity of CD4+ T cells [36]. Experimental evidence suggests that antigen-presenting cells (APCs) expressing Notch ligands can induce developing CD4+ T cells to express the anti-inflammatory cytokine interleukin (IL)-10, thereby exerting an opposite effect to typical CD4+ T cells [37]. IL-10 is vital in negatively regulating inflammation, mainly by selectively blocking inflammatory cytokines, cell-surface molecules, chemokines, and other molecules involved in inflammation [38]. Recent findings uncover that a newly discovered CD4+ T cell subset attenuates palmitate-induced lipotoxicity in the absence of IL-17 in a PI3K/AKT-dependent fashion [39,40]. This further highlights the heterogeneity of CD4+ T cells and the distinct functions of various T cell subsets. CD4+ T cell subsets identified in our research should be validated through further investigations.

Our research revealed that CD8 on CD28CD8br Treg cells exerted protective effects against NAFLD. They belong to CD8+ suppressor T cells [41] and are involved in the development of autoimmune diseases and immune tolerance in organ transplantation [42,43]. On the one hand, CD8+ CD28 Treg cells upregulate ILT3 and ILT4 on DCs and monocytes, making these APCs tolerogenic cells, exhibiting low levels of costimulatory molecules and antigen-specific non-responsiveness in CD4+ T helper cells [44]. CD8+ CD28 Treg cells are activated by the TLR2 pathway in macrophages predominantly via the production of IL-4 and IL-10, which are critical in preventing inflammatory responses [45]. These mechanisms all support the potential protection of CD8+ CD28 Treg cells on NAFLD. CD127, also known as the IL-7R α chain, regulates the expression of recombination activating genes in double-negative T cells (DNTs) and initiates the VDJ rearrangement of the TCRβ chain, thus promoting the survival and proliferation of DNTs [46]. DNTs can activate the NLRP3 and TNFR2-STAT5-NF-κB signaling pathway by secreting TNF-α, thereby facilitating the differentiation of Th9 cells and contributing to liver fibrosis [47]. Earlier research has indicated that a subset of cytotoxic/inhibitory lymphocytes, characterized by CD3+ CD4CD8dim, exhibits high expression of CD45RA in the peripheral blood lymphocytes of healthy individuals [48]. In comparison to CD8dim T cells expressing CD45RO, these cells expressing CD45RA are in a naive state. Another study indicates that CD8dim T cells with migratory capacity express high levels of CD28 [49]. CD28 is an important co-stimulatory molecule for T cells that plays a critical role in inflammatory diseases by upregulating inflammatory cytokines [50]. Therefore, we speculate that CD28+ CD45RA+ CD8dim T cells may be a subset of naive CD8+ T cells with strong proliferative, activation, and migratory capacities. However, the relationship between CD28+ CD45RA+ CD8dim T cells and NAFLD still requires further research for confirmation.

Our research indicated that four distinct types of B cells were associated with NAFLD progression. Previous investigations have shown the complex involvement of B cells in NAFLD progression due to the diverse B cell subtypes and their activities [51]. On the one hand, CD24 is heavily glycosylated and localized to lipid rafts on the B cell surface [52]. It is an initial protein expressed during the maturation of B cells in the late pre-B cell stage, like marginal B cells [53]. It can modulate immune functions by secreting IL-10 [54], thereby hindering NAFLD progression. On the other hand, the function of CD24 varies among B-cell subtypes and is linked to energy metabolism during B-cell differentiation [55]. Investigations have revealed that intrahepatic B cells are activated in mouse models of NASH, and NASH progression in mice can be markedly ameliorated through B-cell deficiency [56]. CD20, a surface protein specific to B cells, is the target of anti-CD20 antibodies in therapies for depleting B cells [57]. By targeting B cells, anti-CD20 monoclonal antibody therapy reduces inflammatory activity. Although the precise mechanism remains uncertain, this therapy can clinically relieve multiple diseases, such as multiple sclerosis and asthma [58,59]. This is in line with our findings and could offer a target for NAFLD treatment.

DCs originating from the myeloid lineage are also known as conventional dendritic cells (cDCs), which are integral components of the innate immune system and play a crucial role in both innate and adaptive immune responses [60]. Our research identified an immunophenotype characterized by CD45 on CD33brHLA-DR+ CD14, which is derived from myeloid cells and may be a phenotype of cDCs that promotes NAFLD development. In patients with NAFLD/NASH, cDC1s are more abundant and activated, critically driving liver pathology by promoting the reprogramming of inflammatory T cells [61]. However, in mouse models, CD103 cDC1s have been identified as a protective subset of DCs that modulate the balance of proinflammatory and anti-inflammatory and protect the liver from metabolic injury [62]. cDC2s act as potent stimulators of CD4+ T cells, leading to the differentiation of helper T cells and guiding the immune system toward different pathways [63]. However, research on its relationship associated with NAFLD is currently limited. Further study is required to uncover the role of DCs in the development of NAFLD.

Nevertheless, the reverse MR analysis revealed that NAFLD did not appear to have a causal effect on immunophenotypes. However, as normal-NAFL-NASH progresses, immune-activated cell infiltration is significantly increased, indicating the remodeling of the immune microenvironment alongside disease progression [64]. The accumulation of liver metabolites due to NAFLD may lead to immune dysregulation. For instance, the depletion of fatty acid-induced cytotoxic CD4+ and self-reactive CXCR6+ CD8+ T cells, both essential for immune surveillance, could potentially initiate NAFLD and HCC progression [65]. Additionally, a recent study detected the distinct immunophenotypes and functions at different stages of NAFLD through cytometry by time-of-flight and bioinformatic analysis and revealed that the disease stages were associated with an inactive phenotype compared to controls [66]. Therefore, further foundational and clinical studies are warranted to establish the causal relationship between NAFLD and immunophenotypes.

This study, by MR analysis, illustrated the association between immune cells and NAFLD using data from a well-powered GWAS cohort. The merits of this investigation are highlighted as follows. First, the results were not disrupted by horizontal pleiotropy and confounders, preventing the likelihood of reverse causality. Second, the causal association between certain immunophenotypes and NAFLD was elucidated, paving the way for new immune targets in NAFLD treatment and providing a crucial theoretical basis for developing immunotherapeutic targets. Furthermore, an FDR was utilized to address statistical biases from multiple comparisons and to control false positives in multiple hypothesis testing.

5 Limitation

Nevertheless, our research also has constraints. First, our research relied on a European database, which might introduce demographic bias into the MR findings. Subsequent studies should incorporate various ethnic backgrounds while also segmenting data by gender and other demographic factors. Second, the results were analyzed using a relaxed threshold, which could result in some false positives, although it facilitated a more detailed exploration of the pronounced link between immune cells. Moreover, confounders could not be ruled out completely, although sensitivity analysis was performed to exclude SNPs associated with potential confounders. Further investigation is necessary to uncover the complex connection between diverse innate and adaptive immune cells and NAFLD and to delineate their precise mechanisms.

6 Conclusion

The MR analysis reveals a potential genetic link between immunophenotypes and NAFLD. Furthermore, our results refine the theoretical understanding of NAFLD-immune crosstalk, providing a fresh framework for immunoregulation in NAFLD therapy.

Abbreviations

AC

absolute cell counts

APCs

antigen presenting cells

CM

central memory

DAMPs

damage-associated molecular patterns

DCs

dendritic cells

FDR

false discovery rate

GWAS

genome-wide association study

HCC

hepatocellular carcinoma

IV

instrumental variable

IVW

inverse-variance weighted

MFI

median fluorescence intensity

MP

morphological parameters

MR

Mendelian randomization

NAFLD

non-alcoholic fatty liver disease

NASH

non-alcoholic steatohepatitis

PRRs

pattern recognition receptors

RC

relative cell counts

SNPs

single-nucleotide polymorphisms

TBNK

T cells, B cells, and natural killer cells


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Acknowledgements

The authors thank all involved participants and investigators.

  1. Funding information: This study was funded by grants from the National Natural Science Foundation of China (81860104), the Natural Science Foundation of Guangxi Zhuang Autonomous Region (2023GXNSFDA026024), the Development and Application of Medical and Health Appropriate Technology Project in Guangxi Zhuang Autonomous Region (S2018049), the Self-financing Project of Health Commission of Guangxi Zhuang Autonomous Region (Z20200398), the Innovation Project of Guangxi Graduate Education (YCBZ2022079), the Self-financing Project of Health Commission of Guangxi Zhuang Autonomous Region (Z-A20230474), and the Youth Science Foundation of Guangxi Medical University (GXMUYSF202316).

  2. Author contributions: Xiaoping Lv and Yu Li designed the study. Yu Li, Xiaodan Lv, and Jianing Lin wrote the manuscript. Yu Li, Shiquan Li, Guangfu Lin, Zhixi Huang, Deyi Chen, Lingling Zhan, and Lichun Han performed the statistical analysis. All authors confirmed the published version of the manuscript.

  3. Conflict of interest: The authors state that there are no conflicts of interest to disclose.

  4. Data availability statement: GWAS summary data for immune traits are accessible from GCST0001391 (https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90001391/) to GCST0002121 (https://gwas.mrcieu.ac.uk/datasets/ebi-a-GCST90002121/). NAFLD was acquired from GWAS meta-analysis.

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Received: 2024-04-20
Revised: 2025-02-05
Accepted: 2025-02-06
Published Online: 2025-02-19

© 2025 the author(s), published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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  93. Dexmedetomidine suppresses microglial activation in postoperative cognitive dysfunction via the mmu-miRNA-125/TRAF6 signaling axis
  94. Analysis of serum metabolomics in patients with different types of chronic heart failure
  95. Diagnostic value of hematological parameters in the early diagnosis of acute cholecystitis
  96. Pachymaran alleviates fat accumulation, hepatocyte degeneration, and injury in mice with nonalcoholic fatty liver disease
  97. Decrease in CD4 and CD8 lymphocytes are predictors of severe clinical picture and unfavorable outcome of the disease in patients with COVID-19
  98. METTL3 blocked the progression of diabetic retinopathy through m6A-modified SOX2
  99. The predictive significance of anti-RO-52 antibody in patients with interstitial pneumonia after treatment of malignant tumors
  100. Exploring cerebrospinal fluid metabolites, cognitive function, and brain atrophy: Insights from Mendelian randomization
  101. Development and validation of potential molecular subtypes and signatures of ocular sarcoidosis based on autophagy-related gene analysis
  102. Widespread venous thrombosis: Unveiling a complex case of Behçet’s disease with a literature perspective
  103. Uterine fibroid embolization: An analysis of clinical outcomes and impact on patients’ quality of life
  104. Discovery of lipid metabolism-related diagnostic biomarkers and construction of diagnostic model in steroid-induced osteonecrosis of femoral head
  105. Serum-derived exomiR-188-3p is a promising novel biomarker for early-stage ovarian cancer
  106. Enhancing chronic back pain management: A comparative study of ultrasound–MRI fusion guidance for paravertebral nerve block
  107. Peptide CCAT1-70aa promotes hepatocellular carcinoma proliferation and invasion via the MAPK/ERK pathway
  108. Electroacupuncture-induced reduction of myocardial ischemia–reperfusion injury via FTO-dependent m6A methylation modulation
  109. Hemorrhoids and cardiovascular disease: A bidirectional Mendelian randomization study
  110. Cell-free adipose extract inhibits hypertrophic scar formation through collagen remodeling and antiangiogenesis
  111. HALP score in Demodex blepharitis: A case–control study
  112. Assessment of SOX2 performance as a marker for circulating cancer stem-like cells (CCSCs) identification in advanced breast cancer patients using CytoTrack system
  113. Risk and prognosis for brain metastasis in primary metastatic cervical cancer patients: A population-based study
  114. Comparison of the two intestinal anastomosis methods in pediatric patients
  115. Factors influencing hematological toxicity and adverse effects of perioperative hyperthermic intraperitoneal vs intraperitoneal chemotherapy in gastrointestinal cancer
  116. Endotoxin tolerance inhibits NLRP3 inflammasome activation in macrophages of septic mice by restoring autophagic flux through TRIM26
  117. Lateral transperitoneal laparoscopic adrenalectomy: A single-centre experience of 21 procedures
  118. Petunidin attenuates lipopolysaccharide-induced retinal microglia inflammatory response in diabetic retinopathy by targeting OGT/NF-κB/LCN2 axis
  119. Procalcitonin and C-reactive protein as biomarkers for diagnosing and assessing the severity of acute cholecystitis
  120. Factors determining the number of sessions in successful extracorporeal shock wave lithotripsy patients
  121. Development of a nomogram for predicting cancer-specific survival in patients with renal pelvic cancer following surgery
  122. Inhibition of ATG7 promotes orthodontic tooth movement by regulating the RANKL/OPG ratio under compression force
  123. A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer
  124. 10.1515/med-2025-1282
  125. Review Articles
  126. The effects of enhanced external counter-pulsation on post-acute sequelae of COVID-19: A narrative review
  127. Diabetes-related cognitive impairment: Mechanisms, symptoms, and treatments
  128. Microscopic changes and gross morphology of placenta in women affected by gestational diabetes mellitus in dietary treatment: A systematic review
  129. Review of mechanisms and frontier applications in IL-17A-induced hypertension
  130. Research progress on the correlation between islet amyloid peptides and type 2 diabetes mellitus
  131. The safety and efficacy of BCG combined with mitomycin C compared with BCG monotherapy in patients with non-muscle-invasive bladder cancer: A systematic review and meta-analysis
  132. The application of augmented reality in robotic general surgery: A mini-review
  133. The effect of Greek mountain tea extract and wheat germ extract on peripheral blood flow and eicosanoid metabolism in mammals
  134. Neurogasobiology of migraine: Carbon monoxide, hydrogen sulfide, and nitric oxide as emerging pathophysiological trinacrium relevant to nociception regulation
  135. Plant polyphenols, terpenes, and terpenoids in oral health
  136. Laboratory medicine between technological innovation, rights safeguarding, and patient safety: A bioethical perspective
  137. End-of-life in cancer patients: Medicolegal implications and ethical challenges in Europe
  138. The maternal factors during pregnancy for intrauterine growth retardation: An umbrella review
  139. Intra-abdominal hypertension/abdominal compartment syndrome of pediatric patients in critical care settings
  140. PI3K/Akt pathway and neuroinflammation in sepsis-associated encephalopathy
  141. Screening of Group B Streptococcus in pregnancy: A systematic review for the laboratory detection
  142. Giant borderline ovarian tumours – review of the literature
  143. Leveraging artificial intelligence for collaborative care planning: Innovations and impacts in shared decision-making – A systematic review
  144. Cholera epidemiology analysis through the experience of the 1973 Naples epidemic
  145. Risk factors of frailty/sarcopenia in community older adults: Meta-analysis
  146. Case Reports
  147. Delayed graft function after renal transplantation
  148. Semaglutide treatment for type 2 diabetes in a patient with chronic myeloid leukemia: A case report and review of the literature
  149. Diverse electrophysiological demyelinating features in a late-onset glycogen storage disease type IIIa case
  150. Giant right atrial hemangioma presenting with ascites: A case report
  151. Laser excision of a large granular cell tumor of the vocal cord with subglottic extension: A case report
  152. EsoFLIP-assisted dilation for dysphagia in systemic sclerosis: Highlighting the role of multimodal esophageal evaluation
  153. Rapid Communication
  154. Biological properties of valve materials using RGD and EC
  155. Letter to the Editor
  156. Role of enhanced external counterpulsation in long COVID
  157. Expression of Concern
  158. Expression of concern “A ceRNA network mediated by LINC00475 in papillary thyroid carcinoma”
  159. Expression of concern “Notoginsenoside R1 alleviates spinal cord injury through the miR-301a/KLF7 axis to activate Wnt/β-catenin pathway”
  160. Expression of concern “circ_0020123 promotes cell proliferation and migration in lung adenocarcinoma via PDZD8”
  161. Corrigendum
  162. Corrigendum to “Empagliflozin improves aortic injury in obese mice by regulating fatty acid metabolism”
  163. Corrigendum to “Comparing the therapeutic efficacy of endoscopic minimally invasive surgery and traditional surgery for early-stage breast cancer: A meta-analysis”
  164. Corrigendum to “The progress of autoimmune hepatitis research and future challenges”
  165. Retraction
  166. Retraction of “miR-654-5p promotes gastric cancer progression via the GPRIN1/NF-κB pathway”
  167. Special Issue Advancements in oncology: bridging clinical and experimental research - Part II
  168. Unveiling novel biomarkers for platinum chemoresistance in ovarian cancer
  169. Lathyrol affects the expression of AR and PSA and inhibits the malignant behavior of RCC cells
  170. The era of increasing cancer survivorship: Trends in fertility preservation, medico-legal implications, and ethical challenges
  171. Bone scintigraphy and positron emission tomography in the early diagnosis of MRONJ
  172. Meta-analysis of clinical efficacy and safety of immunotherapy combined with chemotherapy in non-small cell lung cancer
  173. Special Issue Computational Intelligence Methodologies Meets Recurrent Cancers - Part IV
  174. Exploration of mRNA-modifying METTL3 oncogene as momentous prognostic biomarker responsible for colorectal cancer development
  175. Special Issue The evolving saga of RNAs from bench to bedside - Part III
  176. Interaction and verification of ferroptosis-related RNAs Rela and Stat3 in promoting sepsis-associated acute kidney injury
  177. The mRNA MOXD1: Link to oxidative stress and prognostic significance in gastric cancer
  178. Special Issue Exploring the biological mechanism of human diseases based on MultiOmics Technology - Part II
  179. Dynamic changes in lactate-related genes in microglia and their role in immune cell interactions after ischemic stroke
  180. A prognostic model correlated with fatty acid metabolism in Ewing’s sarcoma based on bioinformatics analysis
  181. Special Issue Diabetes
  182. Nutritional risk assessment and nutritional support in children with congenital diabetes during surgery
  183. Correlation of the differential expressions of RANK, RANKL, and OPG with obesity in the elderly population in Xinjiang
  184. A discussion on the application of fluorescence micro-optical sectioning tomography in the research of cognitive dysfunction in diabetes
  185. A review of brain research on T2DM-related cognitive dysfunction
  186. Special Issue Biomarker Discovery and Precision Medicine
  187. CircASH1L-mediated tumor progression in triple-negative breast cancer: PI3K/AKT pathway mechanisms
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