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The role of immune cell phenotypes and metabolites in the risk of ischemic stroke: a Mendelian randomization-based mediation analysis

BMC Neurology volume 25, Article number: 196 (2025) Cite this article

Abstract

Background

Ischemic stroke (IS) occurs when a blood clot obstructs a blood vessel supplying blood to the brain, leading to brain tissue damage due to insufficient oxygen and nutrients. The roles of immune cells and metabolites in IS are increasingly recognized, yet their specific mechanisms remain unclear.

Methods

This study conducted a comprehensive statistical analysis to explore the relationships between immune cell phenotypes, metabolite levels, and IS. We utilized methods such as inverse variance weighted (IVW), weighted median, and MR Egger to ensure robust results. Sensitivity analyses were performed to confirm the absence of significant heterogeneity or pleiotropy.

Results

We identified several immune cell phenotypes significantly associated with IS. Notably, IgD + CD24 + AC showed a positive association with IS (OR = 1.045601, p = 0.011562), while CD62L- HLA DR + + monocyte AC demonstrated a negative association (OR = 0.948673, p = 0.005415). Among metabolites, adenosine 5’-monophosphate (AMP) to cysteine ratio was positively associated with IS (OR = 1.083144, p = 0.000310), whereas xanthurenate levels were negatively associated (OR = 0.926100, p = 0.001614). Mediation analysis revealed a significant mediating effect of acetylcarnitine levels on the relationship between IgD + CD24 + AC and IS, with an estimated mediation effect of 0.00606 (p = 0.036834077).

Conclusion

Our study highlights the crucial roles of specific immune cell phenotypes and metabolites in IS, suggesting their potential as novel therapeutic targets or biomarkers. The mediation analysis underscores the complex interactions between immune cells and metabolites in IS, providing valuable insights for future research. These findings pave the way for further exploration of the pathophysiological mechanisms and therapeutic strategies for IS.

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Background

Ischemic stroke (IS) is the most prevalent form of acute cerebrovascular disease worldwide, primarily caused by a temporary or permanent reduction in cerebral blood flow due to thrombotic or embolic arterial occlusion [1]. It accounts for approximately 87% of all stroke cases, making it the dominant stroke subtype globally [2]. Despite progress in preventive strategies, the precise pathophysiological mechanisms and risk factors underlying IS remain incompletely understood.

Recent research has highlighted the pivotal role of immune cell–mediated inflammation in the development of IS [3]. Immune cells are essential not only for maintaining normal brain function but also contribute to neuroinflammation and secondary injury following ischemic events [4]. Elevated peripheral leukocyte counts and inflammatory biomarkers, such as C-reactive protein (CRP), have been associated with increased stroke risk [5]. Notably, patients with COVID-19 exhibit a markedly increased incidence of IS, further implicating innate immune activation and immune-mediated hypercoagulability in stroke pathogenesis [6, 7]. In parallel, metabolic disturbances have been increasingly recognized as contributing factors in IS. Metabolites, as end products or intermediates of biochemical processes, regulate diverse physiological functions, and their dysregulation can promote vascular dysfunction and inflammation [8]. Moreover, metabolomic profiling has revealed that metabolic signatures are closely linked to genetic variation, offering a window into the molecular mechanisms of disease [9]. Specific metabolites, such as trimethylamine N-oxide, have been identified as predictive markers of IS, emphasizing their potential in early diagnosis and risk stratification [10, 11].

Importantly, growing evidence suggests that immune function and metabolism are not independent processes but are intricately interconnected. Immune cell activity is tightly regulated by metabolic pathways, while metabolites can modulate immune responses, influencing inflammation, vascular integrity, and thrombotic potential. However, most studies exploring these relationships are observational and thus susceptible to confounding and reverse causality. To overcome these limitations, Mendelian randomization (MR) offers a powerful approach by leveraging genetic variants as instrumental variables to infer causality from large-scale genome-wide association studies (GWASs) [12]. Because these genetic variants are randomly allocated at conception, MR reduces bias and strengthens causal inference [13].

In this study, we aim to systematically investigate the causal relationships among immune cell phenotypes, blood metabolites, and the risk of ischemic stroke. By integrating MR and mediation analysis, we seek to elucidate how metabolites may influence IS through immune mechanisms, offering novel insights into the immunometabolic pathways underlying stroke pathogenesis.

Methods

Data sources

For metabolites, the GWAS data used in this preliminary evaluation were derived from a meta-analysis reported by Shin et al., which included 7,824 adults from the TwinsUK (mean age 53 years) and KORA (mean age 61 years) cohorts. This analysis revealed significant associations for 145 metabolic loci. After stringent quality control, a subset of 486 metabolites from the two cohorts was available for genetic analysis, including 309 known and 177 unknown metabolites. For immune cells, genetic data for 731 immune cell traits were obtained from the GWAS catalog (GCST0001391 to GCST0002121) [14]. This comprehensive resource provided recent reports on genetic loci for immune cell traits, including absolute cell (AC) counts (n = 118), median fluorescence intensity (MFI) reflecting surface antigen levels (n = 389), morphological parameters [MP] (n = 32), and relative cell (RC) counts (n = 192). Outcome data for IS were obtained from the IEU OpenGWAS database (ebi-a-GCST90018864), corresponding to a large-scale genome-wide association study (GWAS) conducted by Sakaue et al. in 2021 [15]. The study included 11,929 IS cases and 472,192 controls of European ancestry, totaling 484,121 individuals. The analysis was based on the GRCh37/hg19 genome build and included 24,174,314 SNPs [16].

Data extraction

For MR, it is crucial that the genetic variants used represent the microbiome traits; hence, we selected Single-nucleotide polymorphisms (SNPs) associated with IS with a p-value less than 1 × 10^-5, as used in previous MR studies. We selected SNPs associated with stroke and blood metabolites at the conventional GWAS threshold (P < 5 × 10^-8).

To ensure the independence of genetic instruments, we applied linkage disequilibrium (LD) clumping with a stringent threshold of r² < 0.001 using the 1000 Genomes European reference panel. This threshold is commonly used in Mendelian randomization studies to minimize bias from correlated variants and to satisfy the assumption of instrument independence. Proxy SNPs from the 1000 Genomes European reference panel (r2 ≥ 0.8) were added when no shared SNPs were available between exposure and outcome. SNPs with an effect allele frequency > 0.01 and F-statistic < 10 (a measure of IV strength) were excluded to avoid weak instrument bias [17].

Genetic analysis to clarify causal relationships

We first conducted bidirectional MR analyses to explore the causal relationships between immune cells and IS. The traditional MR method of inverse variance weighted (IVW) was used for effect estimation, reporting beta (β) values for continuous outcomes and odds ratios (OR) with 95% confidence intervals (CI) for binary outcomes [18]. P < 0.05 was considered nominally significant. IVW performs a meta-analysis of SNP-specific Wald estimates (SNP outcome estimate divided by SNP exposure estimate) to obtain the final estimate of the causal effect. To show the genetic correlation between immune cells and IS, we used bivariate LD score regression (LDSC) with GWAS summary statistics [19].

Mediation analysis linking “immune cells-metabolites-IS”

We used two mediation methods, two-step Mendelian randomization (TSMR) and multivariable Mendelian randomization (MVMR), to disentangle the direct and indirect effects of immune cells and blood metabolites on stroke. TSMR assumes no interaction between exposure and mediator [20]. Besides the baseline effect estimate for IS (β1) from univariate MR analysis, two additional estimates were calculated: (1) the causal effect of the mediator (blood metabolites) on IS (β2) and (2) the causal effect of exposure (immune cells) on the mediator (α). Multiple testing correction for all IVW results was done using the false discovery rate (FDR) method. Finally, we conducted MVMR as an alternative approach to validate the mediating role of metabolites found in TSMR. In MVMR, we estimated the controlled direct effect of exposure on the outcome (β2* for metabolites on IS) and the effect of immune cells on metabolites (β1* for immune cells). The indirect effect refers to the causal effect of GM on stroke via the mediator, estimated using the product of coefficients method (α × β2*). Thus, the mediated proportion can be calculated as “indirect effect/total effect” ([α × β2*]/β1) [21].

Sensitivity analysis

Up to four MR methods with different pleiotropy assumptions (MR-Egger, weighted median, simple mode, and weighted mode) were used to generate effect estimates as sensitivity analysis. We assessed horizontal pleiotropy using the MR-Egger method, which performs weighted linear regression with an unconstrained intercept. The intercept represents the average pleiotropy of the genetic variants (average direct effect on the outcome). If the intercept differs from zero (MR-Egger intercept P-value < 0.05), there is evidence of horizontal pleiotropy [22]. We also assessed heterogeneity using Cochran’s Q test (smaller P-values indicate higher heterogeneity and the potential for directional pleiotropy) and performed leave-one-out analysis to detect outlier SNPs. All statistical analyses and data visualizations were performed using R programming software (R4.2.3), including the “TwoSampleMR” R package (version 0.5.7) and “MRPRESSO” R package (version 1.0) [23]. The “forestploter” R package (version 1.1.1) generated the forest plots.

Ethical approval and consent to participate

This study is based on publicly available data. Individual studies within each GWAS received approval from relevant institutional review boards and obtained informed consent from participants or caregivers, legal guardians, or other proxies.

Results

Effect of immune cell phenotypes on IS

We conducted comprehensive statistical analyses to investigate the potential influence of immune cell phenotypes on the risk of ischemic stroke (IS). A total of 30 immune cell phenotypes showed significant associations with IS at a threshold of p < 0.05 using the inverse variance weighted (IVW) method. To enhance the robustness of our findings, we applied the weighted median and MR Egger methods to exclude exposures with inconsistent effect directions. Sensitivity analyses revealed no evidence of significant heterogeneity or horizontal pleiotropy (see Supplementary Table 1 for heterogeneity and Supplementary Table 2 for pleiotropy).

Notably, IVW results indicated that IgD⁻ CD27⁻ B cells (% of lymphocytes) cells were positively associated with IS (OR = 1.033, p = 0.037), as were IgD + CD24 + AC cells (OR = 1.046, p = 0.012), CD62L − plasmacytoid DC AC (OR = 1.028, p = 0.034), CD25^hi CD45RA − CD4 not Treg cells (% of CD4⁺ T cells)s (OR = 1.022, p = 0.028), CD8^dim AC (OR = 1.024, p = 0.037), BAFF-R on IgD − CD38^dim (OR = 1.057, p = 0.031), and CD3 on naive CD8^br (OR = 1.024, p = 0.036). Conversely, the following phenotypes showed a negative association with IS: CD62L − HLA-DR + + monocyte AC (OR = 0.949, p = 0.005), CD33^br HLA-DR + CD14 − AC (OR = 0.983, p = 0.004), CD4 + CD8^dim %lymphocyte (OR = 0.968, p = 0.020), CD4 + CD8^dim %leukocyte (OR = 0.955, p = 0.009), CD19 on IgD − CD27− (OR = 0.957, p = 0.010), CD20 on IgD + CD24− (OR = 0.979, p = 0.011), CD20 on naive-mature B cell (OR = 0.979, p = 0.014), and CD20 on IgD+ (OR = 0.977, p = 0.021).

Effect of IS on Immune Cell Phenotypes. To assess potential reverse causality, we performed reverse Mendelian randomization analyses on the immune cell phenotypes mentioned above. Results (Supplementary Table 3) indicated that 26 immune cell types did not show a reverse causal relationship with IS, supporting the directionality of our primary findings.

Effect of metabolites on IS

All instrumental variables (SNPs) associated with metabolites had F-statistics > 10, confirming the absence of weak instrument bias. IVW analysis revealed several metabolites with statistically significant associations with IS. The AMP to cysteine ratio was positively associated with IS (OR = 1.083, p = 0.000), as was the glucose to fructose ratio (OR = 1.316, p = 0.001). In contrast, xanthurenate (OR = 0.926, p = 0.002), N2,N2-dimethylguanosine (OR = 0.896, p = 0.002), caffeine to paraxanthine ratio (OR = 0.931, p = 0.002), 1-stearoyl-2-linoleoyl-GPI (18:0/18:2) (OR = 0.934, p = 0.002), and bilirubin degradation product C16H18N2O5 (4) (OR = 0.949, p = 0.002) were negatively associated with IS, suggesting potential protective roles. Additionally, 5-acetylamino-6-formylamino-3-methyluracil (OR = 1.038, p = 0.002), glutamate (OR = 1.131, p = 0.003), and 1-linoleoyl-GPE (18:2) (OR = 1.051, p = 0.003) showed positive associations, indicating elevated IS risk. No significant heterogeneity or pleiotropy was observed (see Supplementary Tables 4 and 5).

Association between immune cells and metabolites

Further analysis revealed that IgD + CD24 + AC was positively associated with metabolite GCST90199669 across several MR methods. Inverse variance weighted analysis showed a significant association (OR = 1.065, p = 0.012; 95% CI: 1.014–1.119). the result of MR Egger and the weighted mode showed in Supplementary Tables 6 and 7 for heterogeneity and pleiotropy.

Mediation analysis

Mediation analysis demonstrated a significant indirect effect of acetylcarnitine on the association between IgD + CD24 + AC and IS. The estimated mediation effect was 0.006 (95% CI: 0.001–0.026, p = 0.037), with 13.6% of the total effect mediated through acetylcarnitine (CI: 0.8–26.3%) (Fig. 1).

Fig. 1
figure 1

Mediation analysis illustrating the indirect effect of acetylcarnitine on the relationship between IgD⁺ CD24⁺ atypical cells (AC) and ischemic stroke (IS). The path coefficients indicate a significant mediation effect, with 13.6% of the total effect mediated through acetylcarnitine (95% CI: 0.8–26.3%). Statistical significance was observed in the mediation path (p = 0.037)

Full size image

Discussion

IS occurs when blood flow to a part of the brain is obstructed, leading to significant neurological damage. Understanding the roles of immune cells and metabolites in this process is crucial for developing new therapeutic strategies. Our study conducted comprehensive statistical analyses to explore the impact of immune cell phenotypes and metabolite levels on IS. We utilized multiple methods, including IVW, weighted median, and MR Egger analyses, to ensure the robustness of our results. Additionally, sensitivity analyses were performed to rule out heterogeneity and pleiotropy.

In the analysis of immune cell phenotypes, we found several immune cell types significantly associated with IS. These results reveal the potential roles of specific immune cells in IS. For instance, the positive association between IgD + CD24 + AC and IS suggests that this cell type may play a promotive role in the injury process. Conversely, the negative association of CD62L- HLA DR + + monocyte AC indicates a potential protective role. These findings provide clues for further research on the functions of immune cells in IS. In the metabolite analysis, we identified several metabolites significantly associated with IS risk. For example, the positive associations of adenosine 5’-monophosphate to cysteine ratio and glucose to fructose ratio with IS suggest that these metabolites may increase the risk of IS. Conversely, the negative association of xanthurenate indicates its potential protective role. These results suggest that specific metabolites may play crucial roles in the pathological process of IS and could serve as potential biomarkers and therapeutic targets.

In recent years, significant progress has been made in understanding the interaction mechanisms between immune cell subsets and metabolites in cerebrovascular diseases. These studies have revealed the complex role of the immune system in cerebrovascular events and how metabolites influence immune responses, thereby affecting disease progression and prognosis. Immune cell subsets play crucial roles in cerebrovascular diseases such as ischemic stroke, which are often accompanied by marked inflammatory responses. In the acute phase, pro-inflammatory cytokines (such as IL-1, IL-8, and TNF) are upregulated, exacerbating neuronal damage. However, research has shown that these pro-inflammatory mediators also activate the sympathetic nervous system and the hypothalamic-pituitary-adrenal axis, leading to the release of stress hormones and glucocorticoids. These hormones promote immune cell apoptosis and increase the production of anti-inflammatory cytokines (such as TGF-β), thereby inhibiting the inflammatory response [24]. Moreover, the immune status of the gut and lungs is closely related to the inflammatory response following cerebral ischemia. Changes in the gut microbiota and pulmonary microenvironment may influence systemic immune responses, thereby impacting the outcome of stroke [25]. Metabolites play a critical role in modulating immune responses and inflammation. For instance, studies have found that mice with cardiomyocyte-specific deletion of the Pten gene exhibit significantly enhanced cardiac contractility after myocardial infarction. These mice show increased expression of genes related to energy metabolism and decreased expression of genes associated with inflammation and extracellular matrix remodeling, suggesting that metabolic regulation may improve cardiovascular outcomes by modulating inflammation [26]. With the advancement of multi-omics technologies, researchers can comprehensively analyze molecular changes following stroke and identify thousands of potential disease biomarkers. The application of these technologies facilitates a deeper understanding of the interactions between immune cell subsets and metabolites, offering new insights for the diagnosis and treatment of cerebrovascular diseases [27]. In summary, the interplay between immune cell subsets and metabolites in cerebrovascular diseases is a current research hotspot. A deeper understanding of these mechanisms will aid in the development of new diagnostic and therapeutic strategies, ultimately improving patient outcomes.

Furthermore, our mediation analysis revealed that IgD + CD24 + AC cells mediate the effect of acetylcarnitine levels on IS. This finding provides a new mechanism explaining the complex interactions between immune cells and metabolites in IS. Despite the important insights provided by our study, there are some limitations. First, although we used multiple methods to ensure the robustness of our results, the inherent limitations of observational studies remain. Second, our data were derived from specific populations, which may not be generalizable to other populations. Additionally, our analysis relied on existing genetic data and metabolite measurements, which may involve measurement errors and biases. This study has several notable strengths. First, we utilized a two-sample Mendelian randomization (MR) design based on large-scale GWAS datasets, which reduces confounding and reverse causation, providing stronger evidence for causal inference. Second, the integration of immune cell phenotypes, metabolite profiles, and ischemic stroke (IS) outcomes allowed for a comprehensive investigation of the immuno-metabolic mechanisms underlying IS risk. Third, we employed multiple complementary MR methods and sensitivity analyses to ensure the robustness and reliability of our findings. The mediation analysis further contributed to elucidating potential biological pathways linking immune responses and metabolic alterations to IS development. However, certain limitations should also be acknowledged. First, the genetic instruments used were derived from European populations, which may limit the generalizability of our findings to other ethnic groups. Second, although MR minimizes confounding, it cannot fully eliminate bias from horizontal pleiotropy, despite our efforts to assess and adjust for it. Third, the interpretation of mediation analysis in MR remains complex and should be viewed with caution, as it relies on additional assumptions regarding linearity and no interaction between exposure and mediator. Finally, while we included a wide range of immune and metabolic traits, other potentially relevant factors may have been overlooked due to data availability. Despite these limitations, our study provides novel insights into the causal pathways linking immune and metabolic factors to ischemic stroke and highlights potential biomarkers and therapeutic targets for future investigation.

Conclusion

Overall, our study highlights the significant roles of immune cell phenotypes and metabolites in IS. Specific immune cells and metabolites show significant associations with IS, suggesting their potential as novel therapeutic targets or biomarkers. The mediation analysis further emphasizes the complex interactions between immune cells and metabolites in IS. These findings provide directions for future research, particularly in exploring immune and metabolic regulatory mechanisms and developing new therapeutic strategies.

Data availability

No datasets were generated or analysed during the current study.

Abbreviations

IS:

Ischemic stroke

IVW:

Inverse variance weighted

CRP:

C-reactive protein

MR:

Mendelian randomization

GWASs:

Genome-wide association studies

IV:

Instrumental variable

AC:

Absolute cell

MFI:

Median fluorescence intensity

MP:

Morphological parameters

RC:

Relative cell

OR:

Odds ratio

CI:

Confidence interval

SNP:

Single-nucleotide polymorphisms

LDSC:

LD score regression

TSMR:

Two-sample MR

MVMR:

Multivariable Mendelian randomization

FDR:

False discovery rate

AMP:

Adenosine 5’-monophosphate

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Acknowledgements

We would like to thank all participants in this study.

Funding

The study was funded by the Hebei Provincial Science and Technology Programme Funding (Key R&D Programme) Projects (20377710D); Hebei Provincial Administration of Traditional Chinese Medicine Scientific Research Programme Key Projects (Z2022008); Graduate Innovation Funding Project of Hebei University of Traditional Chinese Medicine in 2025 (XCXZZBS2025020).

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Authors and Affiliations

  1. Graduate School, Hebei University of Chinese Medicine, Shijiazhuang, 050091, China

    Qiming Liu, Rui Shi, Yiting Gu, Jiayun Zhang, Shiduo Wang & Tiantian Xu

  2. Department of Neurology, The First Affiliated Hospital of Hebei University of Chinese Medicine, Shijiazhuang, 050011, China

    Zhe Zhang & Junbiao Tian

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Contributions

QL, RS, YG, and JT designed the research. QL, RS, YG, JZ, SW, TX, and ZZ collected, analyzed the data, and drafted the manuscript. QL, RS and JT revised the manuscript. All authors contributed to the article and approved the submitted version.

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Correspondence to Junbiao Tian.

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Ethics approval and consent to participate

This study utilized data from the IEU online database to conduct a Mendelian randomization analysis. As the data were obtained from a public database and did not involve direct interaction with human participants, ethical approval was not required. The data used are anonymized and publicly available, ensuring the privacy and confidentiality of individuals. Therefore, no informed consent was necessary for this research.

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Liu, Q., Shi, R., Gu, Y. et al. The role of immune cell phenotypes and metabolites in the risk of ischemic stroke: a Mendelian randomization-based mediation analysis. BMC Neurol 25, 196 (2025). https://doiorg.publicaciones.saludcastillayleon.es/10.1186/s12883-025-04205-5

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BMC Neurology

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