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. 2025 Sep 26;104(39):e44593. doi: 10.1097/MD.0000000000044593

Gut bacterial metabolic pathway, immune cells, and peripheral artery disease: A Mendelian randomization study

Qingshuai Ren a,*, Yan Zhang b, Jianjun Pei a, Changquan Zhao a, Pengyu Wu a
PMCID: PMC13593180  PMID: 41029139

Abstract

The purpose of this investigation is to determine the causal association between gut bacterial metabolic pathway and peripheral artery disease (PAD), in addition to identifying and quantifying the function of immune cells as potential mediators. Gut microbiota, gut bacterial metabolic pathway, and immune cells, and PAD were discovered using extensive genome-wide association studies summary data. We employed Mendelian randomization to study the causal links between gut microbiota, gut bacterial metabolic pathway, immune cells, and PAD. Inverse variance weighting was utilized as the major statistical approach. Moreover, we utilized 2-step Mendelian randomization to evaluate the extent of the influence of immune cell-mediated gut bacterial metabolic pathway on PAD. Genetic prediction of 3 bacterial metabolism pathways (KETOGLUCONMET-PWY, PWY-5345, and PWY-6892) was related to a reduced risk of PAD. The KETOGLUCONMET-PWY (odd ratios [OR] = 0.899, 95% CI = 0.830−0.974, P = .009), PWY-5345 (OR = 0.844, 95% CI = 0.731–0.974, P = .020), and PWY-6892 (OR = 0.855, 95% CI = 0.744−0.982, P = .027) significantly decreased the risk of PAD. We found that the traits of the following 5 immune cells were negatively related to PAD development (OR < 1, P < .05): Treg panel: CD3 on CD39+ CD4+ (OR = 0.966, 95% CI = 0.938−0.994, P = .016), CD3 on CD28+ CD4+ (OR = 0.953, 95% CI = 0.912−0.996, P = .031); Maturation stages of T cell panel: CM CD4+ %CD4+ (OR = 0.986, 95% CI = 0.974−0.999, P = .039); B cell panel: CD19 on IgD− CD38dim (OR = 0.962, 95% CI = 0.927−0.998, P = .037), CD19 on IgD+ (OR = 0.976, 95% CI = 0.955−0.996, P = .022). Mediation analysis identified immune cells as partially mediating the impact of gut bacterial metabolic pathway on PAD. This study presents genetic causality correlations between specific bacterial metabolism pathways and PAD, emphasizing the instrumental function of immune cells as mediators.

Keywords: gut microbiota, immune cells, Mendelian randomization, peripheral artery disease

1. Introduction

Peripheral artery disease (PDA) is a kind of peripheral circulation dysfunction syndrome, usually referred to as lower extremity arterial disease, characterized by lower extremity atherosclerosis (AS) that causes arterial stenosis or even occlusion, leading to chronic or acute ischemic symptoms of lower extremity tissues.[1] Intermittent claudication (muscle spasms, fatigue, or pain in the lower extremities when moving) is a typical symptom of PAD.[2] With more than 236 million people living with PAD in 2015, an increase of 44% from 2000 and 17.1% from 2010, it is a growing global public health problem.[3–5] Because AS is a systemic process, people with PAD are not only at risk of limb-related adverse events but also at increased risk of developing myocardial infarction and stroke.[6] PAD was linked to nearly double the 10-year incidence of total mortality, cardiovascular mortality, and major coronary events.[7] Research shows that PAD increases the risk of death by 60% overall, 96% from cardiovascular disease, 45% from coronary artery disease, and 35% from cerebrovascular disorder.[8] Common major risk factors for PAD include age, diabetes, smoking, microvascular disease, and high triglyceride levels. Additionally, inflammation and thrombosis are involved in the occurrence and development of PAD.[9]

It is now widely recognized that the symbiotic relationship between people and gut microbiota plays a pivotal role in wellness and disease. The link between AS severity and the composition and structure of gut microbial populations has been more and more supported by recent studies. Various investigations link the gut microbiota and its byproducts to the formation and pathogenesis of AS. Cross-sectional studies indicate that the prevalence of Collinsella, Enterobacteriaceae, Streptococcaceae, and Klebsiella in the gut microbiota of symptomatic AS patients exceeds that of healthy controls. Conversely, the prevalence of bacteria that generate short-chain fatty acids (SCFA), including Faecalibacterium, Roseburia, and Ruminococcaceae, is diminished in individuals with symptomatic AS.[10] Previous studies have shown that patients with carotid AS have an excess of gut microbes (such as acidobacteria) associated with inflammation, while the abundance of beneficial bacteria (such as bacteria associated with butyrate) is higher in healthy individuals.[11] Gut microbiota affects AS. There are 3 main aspects. First, the local or distant infection may enhance the formation of AS. Second, individuals with AS had modified lipid metabolism, and specific bacterial taxa in the gut were found to correlate with plasma cholesterol levels.[12] Third, dietary components and specific substances processed by the gut flora can exert numerous effects on AS. Metabolites filtered or generated by the gut microbiota, such as trimethylamine N-oxide, SCFA, and secondary bile acids, have been shown to affect AS development.[13]

Immunometabolism is increasingly emerging as a prominent research focus in medicine, with extensive applications in the investigation of diabetes, cancer, and cardiovascular disorders. Infiltrating immune cells enhance the secretion of matrix metalloproteinases and adhesion molecules and generate reactive oxygen species, thereby facilitating the development of AS.[14,15] Consequently, it is imperative to adopt more effective strategies to modulate immune metabolism and preserve natural immune homeostasis.

While observational evidence connects gut microbiota and immunometabolism with PAD and its risk factors, causal proof for these correlations remains absent; observational research may be constrained by residual confounding and reverse causation. In order to overcome those limitations, we employed a genetic methodology termed Mendelian randomization (MR) to deduce causation. In order to determine the associations between gut bacterial metabolic pathway, immune cells, and PAD, this study performed a comprehensive MR analysis. A 2-sample, 2-step Mendelian randomization (TSMR) study was performed to examine the causal association between gut bacterial metabolic pathway and PAD and to evaluate the participation of immune cells in mediating the impacts of gut bacterial metabolic pathway on PAD.

2. Materials and methods

2.1. Study design

This study consisted of 3 stages to thoroughly examine the causal correlation between gut microbiota, gut bacterial metabolic pathway, immunocyte phenotype, and PAD (Fig. 1). Stage 1 involved our initial study, which was a 2-sample MR experiment aimed at assessing the causal influence of gut microbiota and gut bacterial metabolic pathway on PAD. Stage 2 examined the causative effects of 731 immunocyte phenotype on PAD. Stage 3 evaluated the mediation analysis of immunocyte phenotype in the pathway from gut bacterial metabolic pathway to PAD. We designated single-nucleotide polymorphisms (SNPs) as instrumental variables (IVs). MR depended on 3 fundamental assumptions: the IVs were strongly correlated with the exposure factors; the IVs were uncorrelated with confounding variables; and the IVs influenced the result only through exposure.

Figure 1.

Figure 1.

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An overview of the study design.

2.2. Data source

The current research acquired data from publicly accessible genome-wide association study (GWAS) datasets. We obtained summary data from 7738 individuals of European descent participating in the Dutch Microbiome Project, which includes 207 gut microbial taxa (5 phyla, 10 classes, 13 orders, 26 families, 48 genera, and 105 species) and 205 bacterial metabolic pathways.[16] The full GWAS summary statistical data for all 207 taxa and 205 pathways are instead available for direct download at NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/) under the study accession numbers GCST90027446-GCST90027857 (https://dutchmicrobiomeproject.molgeniscloud.org). The GWAS catalog (GCST90001391 to GCST90002121) provides an overview of GWAS statistics for every immunological characteristic.[17] It encompasses comprehensive data collected from 3757 Europeans and consisting of 731 immunophenotypes (https://www.ebi.ac.uk/gwas). Summary statistics from GWAS related to PAD were obtained from the FinnGen Consortium R6 release dataset, which includes 9021 PAD cases and 2,44,907 control individuals.[18] These datasets can be found at the following URLs: FinnGen (https://storage.googleapis.com/finngen-public-data-r6/summary_stats/finngen_R6_I9_PAD.gz).

2.3. Selection of IVs

We applied stringent criteria to choose IVs to guarantee the authenticity and precision of the inferred causation between gut microbiota, gut bacterial metabolic pathway, immune cells and PAD. Initially, we identified SNPs exhibiting significant correlations with gut microbiota, gut bacterial metabolic pathway, and immune cells, setting the significance threshold at P < 1 × 10–5. For PAD, SNPs with a P-value below the genome-wide statistical significance threshold (1 × 10–5) were selected as IVs. Subsequently, to mitigate potential bias resulting from strong linkage disequilibrium, we conducted a clumping process (R2 < 0.01 and clumping distance = 10,000 kb) to assess the linkage disequilibrium between the included SNPs.[19] A critical phase in the MR analysis was to verify that the impact of SNPs on the exposure variable was consistent with their effect on the outcome variable. Upon aligning the outcomes, we proceeded to discard palindromic SNPs, defined as SNPs harboring A/T or G/C alleles.

2.4. MR analysis

All analyses were conducted using R (version 4.4.1). The causal relationship between gut microbiota, gut bacterial metabolic pathway, and PAD was examined via the R package “TwoSampleMR.” Three regression models were chosen to ascertain causation: inverse variance weighting (IVW), MR-Egger, and weighted median. The IVW test was the principal method utilized owing to its dependability, with significance established at a P-value < .05. Subsequently, Bayesian weighted Mendelian randomization (BWMR) was carried out to validate the findings of the aforementioned 2-sample MR research. Pleiotropic effects were evaluated with MR-Egger regression analyses.[20] Suppose P > .05, the absence of pleiotropy is shown, regardless of whether the intercept term is not 0. Cochran’s Q test was employed to evaluate the variability between exposure and result. Using the leave-one-out method, a sensitivity analysis was conducted to evaluate the stability of the outcomes further.

2.5. Mediation analysis

We employed the TSMR mediation approach[21] to analyze the direct and indirect impacts of gut bacterial metabolic pathway and immunocyte phenotypes on PAD. The TSMR presumes an absence of interaction between exposure and mediator. Alongside the fundamental effect estimates of gut bacterial metabolic pathway on immunocyte phenotypes (β1) derived from the univariate MR analyses, 2 additional estimates were computed: the causal effect of the mediator (immunocyte phenotypes) on PAD (β2) and the causal effect of the exposure (significant gut bacterial metabolic pathway on PAD in the primary MR analysis) on PAD (β_all). Step 1: Assessing the whole effect of gut bacterial metabolic pathway on PAD MR (β_all). Step 2: Ensuring that PAD can be communicated to the gut bacterial metabolic pathway. Step 3: Analyzing immunocyte phenotypes in relation to PAD MR (get β2). Step 4: Correlating gut bacterial metabolic pathway with immunocyte phenotypes MR (get β1). Mediated effect (indirect effect): β1_2 = β1 × β2. The ratio of the overall effect mediated by mediators was determined by dividing the product of the mediating effect (β1 × β2) by the total effect (β_all).

3. Results

3.1. Causal effects of gut bacterial metabolic pathway with PAD

This investigation identified 207 gut microbial taxa and 205 bacterial metabolic pathways using gut microbiota GWAS data encompassing 7738 individuals as the exposure variables for the investigation. IVW, MR-Egger, BWMR, and weighted median regression were utilized to assess the causative connection between genetically predicted gut microbiota and PAD (Fig. 2). The study relied on the results of the IVW analysis as its principal reference indicator due to the negative findings of the heterogeneity and pleiotropy assessments (Table 1). The MR analysis outcomes suggested that PAD has no significant relationship with 207 gut microbial taxa, and only 3 bacterial metabolic pathways show potential correlations with PAD. Supplementary File 1, Supplemental Digital Content, https://links.lww.com/MD/Q24 shows detailed SNP information for 3 bacterial metabolism pathways and PAD. Figures 3 and 4 display the scatter plots and leave-one-out analyses of the bacterial metabolism pathways on PAD. Genetic prediction of 3 bacterial metabolism pathways (KETOGLUCONMET-PWY, PWY-5345, and PWY-6892) was related to a reduced risk of PAD.KETOGLUCONMET-PWY refers to ketogluconate metabolism, where two 2,5-didehydro-d-gluconate degradative pathways operate simultaneously, resulting in D-gluconate. PWY-5345 is a superpathway of L-methionine biosynthesis (by sulfhydrylation). PWY-6892 refers to thiazole component of thiamin diphosphate biosynthesis I. The KETOGLUCONMET-PWY (OR = 0.899, 95% CI = 0.830–0.974, P = .009), PWY-5345 (OR = 0.844, 95% CI = 0.731–0.974, P = .020), and PWY-6892 (OR = 0.855, 95% CI = 0.744–0.982, P = .027) significantly decreased the risk of PAD. The inverse MR analysis confirmed that the causal relationship between the 3 bacterial metabolism pathways and PAD was unidirectional (Table 2).

Figure 2.

Figure 2.

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Forest plots showed the causal associations between gut bacterial metabolic pathway and PAD. PAD = peripheral artery disease.

Table 1.

The heterogeneity and horizontal pleiotropy results of the gut microbiota and PAD in the forward MR analysis.

ID exposure Heterogeneity test Pleiotropy test
MR-Egger Inverse variance weighted MR-Egger
Q-value Q-df P-value Q-value Q-df P-value Intercept SE P-value
GCST90027487 5.004 7 .659 5.696 8 .681 0.022 0.027 .433
GCST90027548 5.867 7 .555 7.458 8 .488 −0.050 0.040 .248
GCST90027601 4.786 4 .310 5.059 5 .409 −0.019 0.041 .657
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MR = Mendelian randomization, PAD = peripheral artery disease.

Figure 3.

Figure 3.

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The scatter plots for association between 3 bacterial metabolism pathways and PAD. (A) KETOGLUCONMET-PWY; (B) PWY-5345; (C) PWY-6892. Note: SNP effects were plotted into lines for the inverse-variance weighted test (light blue line), MR-Egger (blue line), weighted median (green line), simple mode (light green line), and weighted mode (red line). The slope of the line corresponded to the causal estimation. MR = Mendelian randomization.

Figure 4.

Figure 4.

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The forest plots for the association between 3 bacterial metabolism pathways and PAD. (A) KETOGLUCONMET-PWY; (B) PWY-5345; (C) PWY-6892.

Table 2.

Reverse MR analysis results for the gut microbiota and PAD.

Exposure ID outcome Method nSNP b SE OR (95% CI) P-value
PAD GCST90027487 Inverse variance weighted 26 −0.016 0.075 0.984(0.850, 1.139) .831
PAD GCST90027548 Inverse variance weighted 25 −0.002 0.040 0.998(0.922, 1.080) .956
PAD GCST90027601 Inverse variance weighted 25 0.043 0.049 1.043(0.948, 1.149) .384
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MR = Mendelian randomization, PAD = peripheral artery disease.

3.2. Causal effects of gut bacterial metabolic pathway on immune cells

Figure 5 presents the outcomes of the genetically predicted IVW approach for 3 bacterial metabolic pathways in relation to 5 immune cell groupings. It is shown that there was a positive association of 3 bacterial metabolism pathways on 5 immune cells by IVW test (OR > 1, P < .05). Cochran’s Q test indicated the absence of heterogeneity (P > .05), and no evidence of pleiotropy was observed with the MR‐Egger regression test (P > .05). Supplementary File 2, Supplemental Digital Content, https://links.lww.com/MD/Q24 shows detailed SNP information for 3 bacterial metabolism pathways and 5 immunocyte phenotype. Figures 6 and 7 demonstrate the scatter plots and leave-one-out analyses of the bacterial metabolism pathways on immunocyte phenotype.

Figure 5.

Figure 5.

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Causal effects of 3 bacterial metabolism pathways on 5 immunocyte phenotype.

Figure 6.

Figure 6.

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The scatter plots for association between 3 bacterial metabolism pathways on 5 immunocyte phenotype. (A) CD3 on CD39+ CD4+; (B) CD3 on CD28+ CD4+; (C) CM CD4+ %CD4+; (D) CD19 on IgD− CD38dim; (E) CD19 on IgD+. Note: SNP effects were plotted into lines for the inverse-variance weighted test (light blue line), MR-Egger (blue line), weighted median (green line), simple mode (light green line), and weighted mode (red line). The slope of the line corresponded to the causal estimation. MR = Mendelian randomization.

Figure 7.

Figure 7.

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The forest plots for the association between 3 bacterial metabolism pathways on 5 immunocyte phenotype. (A) CD3 on CD39+ CD4+; (B) CD3 on CD28+ CD4+; (C) CM CD4+ %CD4+; (D) CD19 on IgD− CD38dim; (E) CD19 on IgD+.

3.3. Causal effects of immune cells on PAD

Figure 8 illustrates the results of the genetically predicted IVW method for 5 immune cell types in relation to PAD. The findings indicate that these 5 immune cell characteristics are negatively correlated with the development of PAD, suggesting that these cells are linked to a reduced risk of PAD. (OR < 1, P < .05). Treg panel: CD3 on CD39+ CD4+ (OR = 0.966, 95% CI = 0.938–0.994, P = .016), CD3 on CD28+ CD4+ (OR = 0.953, 95% CI = 0.912–0.996, P = .031); maturation stages of T cell panel: CM CD4+ %CD4+ (OR = 0.986, 95% CI = 0.974–0.999, P = .039); B cell panel: CD19 on IgD− CD38dim (OR = 0.962, 95% CI = 0.927–0.998, P = .037), CD19 on IgD+ (OR = 0.976, 95% CI = 0.955–0.996, P = .022). Supplementary File 3, Supplemental Digital Content, https://links.lww.com/MD/Q24 shows detailed SNP information for 5 immune cells and PAD. Figures 9 and 10 exhibit the scatter = plots and leave-one-out analyses of the immunocyte phenotype on PAD.

Figure 8.

Figure 8.

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MR analysis result: between immunocyte phenotype (5 types) and PAD. PAD = peripheral artery disease.

Figure 9.

Figure 9.

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The scatter plots for association between immunocyte phenotype and PAD. (A) CD3 on CD39+ CD4+; (B) CD3 on CD28+ CD4+; (C) CM CD4+ %CD4+; (D) CD19 on IgD− CD38dim; (E) CD19 on IgD+. Note: SNP effects were plotted into lines for the inverse-variance weighted test (light blue line), MR-Egger (blue line), weighted median (green line), simple mode (light green line), and weighted mode (red line). The slope of the line corresponded to the causal estimation. MR = Mendelian randomization, PAD = peripheral artery disease.

Figure 10.

Figure 10.

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The forest plots for the association between immunocyte phenotype and PAD. (A) CD3 on CD39+ CD4+; (B) CD3 on CD28+ CD4+; (C) CM CD4+ %CD4+; (D) CD19 on IgD− CD38dim; (E) CD19 on IgD+. PAD = peripheral artery disease.

3.4. Indirect effect of immune cells between gut bacterial metabolic pathway and PAD

Immune cells were analyzed as a mediator of the causality from gut bacterial metabolic pathway to PAD. Three bacterial metabolism pathways were found to be linked to increased immunocyte phenotype, which in turn were linked to a reduced likelihood of developing PAD. In total, the indirect effect of immunocyte phenotype between gut bacterial metabolic pathway and PAD was negative (Fig. 11).

Figure 11.

Figure 11.

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Schematic diagram of the immunocyte phenotype mediation effect. (A) Estimated proportion of the association between PAD and KETOGLUCONMET-PWY mediated by CD3 on CD39+ CD4+. (B) Estimated proportion of the association between PAD and KETOGLUCONMET-PWY mediated by CD3 on CD28+ CD4+. (C) Estimated proportion of the association between PAD and PWY-5345 mediated by CM CD4+ %CD4+. (D) Estimated proportion of the association between PAD and PWY-6892 mediated by CD19 on IgD- CD38dim. (E) Estimated proportion of the association between PAD and PWY-6892 mediated by CD19 on IgD+.

4. Discussion

Prior research has demonstrated that worldwide population aging, in conjunction with risk factors including hypertension, hyperlipidemia, diabetes, and smoking, constitutes the principal risk factors for PAD.[22] Conversely, limited research has investigated the possible protective function of the gut microbiota concerning PAD. To investigate the potential immune cell-mediated causal relationship between gut microbiota and PAD, we performed the first multi-sample bidirectional MR investigations and mediation analysis. We discovered that 3 bacterial metabolic pathways were genetically associated with PAD via 5 immunocyte phenotypes. Nonetheless, a reverse MR study revealed that PAD was not genetically associated with the 3 bacterial metabolic pathways. All sensitivity studies consistently corroborated the findings of our original study, illustrating the dependability and stability of this MR analysis. This represents the initial extensive genetic correlation analysis of the gut microbiota, immune cells, and PAD. The incorporation of GWAS genetic data renders the conclusions immune to environmental confounders.

Although epidemiological insights and related studies have provided evidence that the gut microbiota may change in the pathogenesis of AS,[23] the causal relationship between PAD patients and gut microbiota is poorly studied. Tian et al[22] conducted a study in which 3 bacterial taxa, specifically the genus Coprococcus2, RuminococcaceaeUCG004, and RuminococcaceaeUCG010, were identified as protective factors against PAD. Conversely, the family XI and the genera Lachnoclostridium and LachnospiraceaeUCG001 were recognized as risk factors. Shi et al[24] conducted research that identified Family XI and the genus Lachnoclostridium, as well as the genus Lachnospiraceae UCG001, as potential risk factors for PAD. Conversely, the class Actinobacteria, family Acidaminococcaceae, genus Coprococcus2, genus Ruminococcaceae UCG004, genus Ruminococcaceae UCG010, and the order NB1n were pinpointed as potential protective factors against PAD. Previous studies have shown that gut microbiota can affect the occurrence of AS by producing metabolites, including trimethylamine-N-oxide, SCFA, and secondary bile acids.[13] Our research has identified these 3 bacterial metabolism pathways as potential protective factors for PAD, named KETOGLUCONMET-PWY, PWY-5345, and PWY-6892. KETOGLUCONMET-PWY is ketogluconate metabolism, that in Corynebacterium sp. SHS 0007, two 2,5-didehydro-d-gluconate degradative pathways resulting in D-gluconate operate simultaneously.[25] Escherichia coli possesses genes for ketogluconate reductase, indicating the probable existence of metabolic pathways for ketogluconate.[26] The metabolism of ketogluconate may confer a metabolic advantage to E coli, enabling it to compete with other bacteria by sustaining redox balance. While typically seen as limited to certain bacteria, ketogluconate metabolism may be prevalent across several species. This is unsurprising, as ketogluconates can function as the exclusive source of carbon and energy for numerous bacteria.[27] PWY-5345 is a superpathway of L-methionine biosynthesis (by sulfhydrylation).[28] L-methionine (met) is one of the proteinogenic amino acids. Protein synthesis, methylation of DNA, rRNA, and xenobiotics, and the production of cysteine, phospholipids, and polyamines are just a few of the many crucial cellular processes that rely on L-methionine. Bacillus subtilis, Corynebacterium glutamicum, Corynebacterium glutamicum ATCC 13032, Leptospira meyeri, Pseudomonas aeruginosa, Pseudomonas putida, and Saccharomyces cerevisiae are known to possess the PWY-5345.[28–31] PWY-6892 describes only the synthesis of the thiazole moiety of thiamin. This particular pathway describes a pathway that was studied in E coli K-12 and Salmonella enterica enterica serovar Typhimurium.[32] Thiamin diphosphate, called vitamin B1, is essential for energy metabolism. It serves as a crucial cofactor for numerous enzymes, including transketolase, pyruvate dehydrogenase, pyruvate decarboxylase, and α-ketoglutarate dehydrogenase.[33] Our research has identified 3 bacterial metabolism pathways, primarily concerning the metabolic pathways of sugars, proteins, and vitamins in the gut microbiota, as potential protective factors for PAD. Nonetheless, the specific underlying mechanisms remain unidentified and necessitate additional inquiry.

Immune cells are becoming recognized for their crucial function in facilitating vascular regeneration and repair following injuries, including ischemia. Our findings indicated that 5 immune cells are negatively correlated with the development of PAD, suggesting that these cells are linked to a reduced risk of PAD. The association between these 5 immune cells and PAD has not been directly investigated in previous studies. Treg cell CD3 on CD39+ CD4+, Treg cell CD3 on CD28+ CD4+, maturation stages of T cell CM CD4+ %CD4+, and B cell CD19 on IgD− CD38dim and CD19 on IgD+. Regulatory T cells (Tregs) are critically involved in neovascularization, an important compensatory mechanism in PAD.[34] Tregs appear to be atheroprotective through the induction of tolerance, the inhibition of atherogenic T cell subsets and suppression of inflammation.[35] The relationship between CD4+ Tcell and PAD in previous studies is contradictory.[36] Stabile et al[37] suggested that CD4+ T cells, which enhance inflammation through cytokine secretion, facilitate arteriogenesis in animal models of PAD. In some observational studies, a decline in CD4 count was associated with an elevated risk of endothelial dysfunction and cardiovascular disease (CVD).[38] However, the absence of CD4 was shown to substantially decrease the development of atherosclerosis in apolipoprotein E knockout mice.[39] Zhou et al[40] found that compared with healthy subjects, CD3+ T cells were significantly reduced in patients with venous thromboembolism (VTE) or coronary artery atherosclerosis, promoting the occurrence and development of venous and arterial thrombosis. Enjyoji et al[41] suggested that CD39, as an adenosine triphosphate (ATP) diphosphohydrolase in vascular and endothelial cells, plays a dual role in regulating hemostatic and thrombotic responses. Hou et al[42] conducted studies indicating that increased CD28 expression on CD4 regulatory T cells may serve as a preventive factor against ischemic heart disease. In contrast to T cells, only few B cells can be detected locally within the atheroma, and there are few studies on B cells.[35] Zhang et al[43] observed an increase in CD19-positive B lymphocytes following ischemic stroke, which correlated with carotid AS. Naturally occurring immunoglobulin (Ig)M appears to exert a protective effect by inhibiting the uptake of oxidized low-density lipoprotein (oxLDL) by macrophages and by promoting the clearance of apoptotic bodies, a process facilitated by the local deposition of complement.[35,44] In contrast, IgG antibodies directed against oxLDL may increase the uptake of cholesterol, thereby promoting atherosclerosis. However, a protective role has been demonstrated for IgG specific to peptide 210 of ApoB100. IgG that cross-reacts with bacterial antigens has been shown to be produced within lesions, suggesting a potential role for infectious agents in the disease process. IgG recognizing endothelial antigens, such as heat shock protein 60 (HSP60), may directly cause endothelial dysfunction.[35] There is limited research on the correlation between immune cells and PAD. To further establish this connection, more comprehensive clinical trials will be necessary in the future.

The intestinal microbiota contributes to numerous physiological processes, such as sustaining metabolic stability, modulating immunological responses, and providing resistance to infections. Various studies suggest that gut microbiota are intimately associated with the onset of PAD. This study revealed that the metabolic pathways of gut microbiota can reduce the incidence of PAD via modulating immune cells. This study’s conclusions derive from an investigation of the causal link at the genetic level and the application of various MR methods for causal inference and result validation. Thus, the study’s results are strong and unaffected by horizontal pleiotropy or confounding variables. Nonetheless, our investigation possesses multiple limitations. Our research was initially limited to individuals of European descent, thereby constraining the application of our findings to other people. Additional study in diverse communities is essential. Second, despite the extensive coverage of gut microbiota and immune cells in this study, many unidentified gut bacteria were excluded, and the functions and mechanisms of numerous immune cells in disease are still not fully elucidated. Third, while we have considered multiple risk factors associated with immunocyte phenotype as mediators, the mediating pathways encompassed in this study remain quite limited. Consequently, we necessitate a more extensive GWAS database, along with sophisticated analytical techniques and experimental validation, to elucidate the correlation between immune cell-mediated gut microbiota and PAD, as well as to comprehend the mechanisms of its impact.

5. Conclusion

This investigation exemplifies a novel use of mediated MR analysis, employing genome-wide data to clarify the causal relationships among gut bacterial metabolic pathway, immune cells, and PAD. It outlines genetic causal relationships between several bacterial metabolic pathways and PAD, highlighting the instrumental function of immunocyte phenotype as mediators. Additional research is necessary to validate our outcomes, and the validity of the causal links should be assessed in other non-European people.

Author contributions

Funding acquisition: Qingshuai Ren, Jianjun Pei.

Methodology: Yan Zhang, Jianjun Pei.

Software: Yan Zhang, Jianjun Pei.

Writing – original draft: Qingshuai Ren.

Writing – review & editing: Changquan Zhao, Pengyu Wu.

Supplementary Material

medi-104-e44593-s001.xlsx (50.6KB, xlsx)
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Abbreviations:

AS
extremity atherosclerosis
GWAS
genome-wide association studies
MR
Mendelian randomization
PAD
peripheral artery disease
SCFA
short-chain fatty acids
TSMR
2-step Mendelian randomization.

This work was supported by Medical Science Research Project of Hebei (20250932).

This study utilized public data, with each GWAS approved by the Institutional Review Board and informed consent obtained from all participants or their legal reps. The study was performed according to the guidelines of the Declaration of Helsinki and was approved by the Ethics Committee of the North China University of Science and Technology Affiliated Hospital (Approval Number: SQ2024055).

The authors have no conflicts of interests to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

How to cite this article: Ren Q, Zhang Y, Pei J, Zhao C, Wu P. Gut bacterial metabolic pathway, immune cells, and peripheral artery disease: A Mendelian randomization study. Medicine 2025;104:39(e44593).

The full GWAS summary statistical data for all 207 taxa and 205 pathways are instead available for direct download at NHGRI-EBI GWAS Catalog (https://www.ebi.ac.uk/gwas/) under the study accession numbers GCST90027446-GCST90027857 (https://dutchmicrobiomeproject.molgeniscloud.org). Clinical trial number: not applicable.

Contributor Information

Yan Zhang, Email: zhangyan19820901@126.com.

Jianjun Pei, Email: 19931569735@163.com.

Changquan Zhao, Email: zhcquan15383056227@163.com.

Pengyu Wu, Email: 2323251464@qq.com.

References

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