Immunocyte phenotypes underlying the causal autoimmune features in narcolepsy type 1

Introduction

Narcolepsy is a neurological sleep disorder with a prevalence of 0.02–0.05% of the population worldwide (1). It is thought to be caused by selective loss or dysfunction of hypocretin neurons in the lateral hypothalamus, as hypocretin (also known as orexin) is essential for sleep regulation (2-4). It is characterized by excessive daytime sleepiness, cataplexy, hypnagogic hallucinations, sleep paralysis, and rapid eye movement (REM) sleep intrusion during wakefulness (5). Narcolepsy is divided into two distinct phenotypes based on hypocretin deficiency, narcolepsy type 1 (NT1) and narcolepsy type 2 (NT2). They are classified based on hypocretin-1 (hcrt-1) levels in the cerebrospinal fluid (CSF), presence of cataplexy, and human leukocyte antigen (HLA) gene susceptibility (6). Specifically, NT1 experiences cataplexy and shows a low or undetectable CSF hcrt-1, whereas NT2 presents with less severe clinical symptoms and has a normal CSF hcrt-1 (7). There is also a strong genetic association between narcolepsy type and HLA locus particularly human leukocyte antigen-DQ (HLA-DQ) and human leukocyte antigen-DR (HLA-DR) (8), with 89–98% of NT1 patients positive for HLA-DQB1*06:02 (9). Although these markers can be used to distinguish NT1 from NT2, approximately 10% of NT2 can progress to NT1 over the course of the disease, suggesting that hypocretin cells undergo a progressive destructive process during this transition (4,7,10,11).

HLA regions are closely associated with autoimmune diseases, signaling their potential role in narcolepsy pathophysiology (12,13). However, the lack of studies investigating hypocretin neuron-specific antibodies makes it difficult to define narcolepsy autoimmune disease (14,15). Despite this, data from genetic, immunologic, and clinical studies provide information on the immune-mediated pathophysiology of NT1 (9,16,17). Increasing evidence indicates that T cell is a key contributor to the attack of hypocretin neurons in narcolepsy, as high levels of certain T cell types have been found to target proteins normally expressed in neurons in the brain (18,19). However, the contribution of CD4⁺ T cells and CD8⁺ T cells to the autoimmune response against orexin-producing neurons remains unclear (20,21). Other immune cells such as B cells, natural killer T (NKT) cells, and monocytes are presumed to be involved in disease initiation and progression (10).

However, the mechanism through which hypocretin-producing cells activate immune cell destruction is underexplored. Epidemiologic studies show an increased incidence of narcolepsy after upper airway infections and H1N1 pandemic vaccination (22,23). Combined detection of HLA positivity and anti-streptococcal antibodies was identified close to the onset of narcolepsy (24). It is thus assumed that molecular mimicry or bystander activation towards infectious triggers and H1N1 vaccines are crucial immunological pathways for neuronal destruction (17,25,26). Furthermore, genome-wide association analysis demonstrated that genetic factors associated with autoimmunity, such as the T cell receptor alpha (TCRα) locus, tumor necrosis factor superfamily member 4 (TNFSF4/OX40L), Cathepsin H (CTSH) and P2RY11-DNMT1 loci, are present in narcolepsy (16,27,28). There is also evidence of immunoreactivity due to increased serum anti-Tribbles homolog 2 (TRIB2) antibodies in narcolepsy patients, correlated with the onset and severity of cataplexy and sleepiness (14,29,30). Thus, genetic predisposition and environmental triggers may contribute to the initiation of the autoimmune onset of narcolepsy with cataplexy. Increased levels of cytokines such as interleukin (IL)-4, IL-6, and tumor necrosis factor (TNF)-alpha tightly promote the ongoing disease process (31,32), but their mediation effect in the causal pathway towards disease risk is still controversial (33,34). In addition, narcolepsy is closely correlated with an elevated risk of other autoimmune diseases (e.g., multiple sclerosis and systemic lupus erythematosus), suggesting a shared onset (16).

Similar to other autoimmune diseases, narcolepsy has a detrimental impact on health outcomes, leading to a low life quality, and increased cardiovascular risk and all-cause mortality (35-37). Additionally, the efficacy of immunotherapy in the prevention or reduction of the loss of the hypocretin neurons remains controversial. Inconsistent results obtained from immunotherapy trials aimed at improving autoimmune conditions in patients with narcolepsy make it a challenge to confirm treatment benefits (38). This is because specific risk immune system genes and relevant signaling pathways in previous studies have given rise to different potential immunocyte phenotypes that regulate immune reactions and autoimmunity in narcolepsy. Therefore, the establishment of a causal relationship between hypocretin-targeting immune cells and narcolepsy is critical for determining suitable therapeutic targets and improving the efficacy of immunotherapy. However, the identification of autoreactive immune cell subtypes underlying immune-mediated responses against neurons in narcolepsy has not been well documented. Mendelian randomization (MR) is a robust epidemiological method that estimates the genetic variants linked to risk factors to determine the causal relationship between exposure and outcome (39). This is an effective method because it prevents confounding factors and reverse causation bias from influencing results. MR analysis has been employed to identify the associations between sleep traits and various diseases (40). Therefore, we performed two-sample MR and mediation analyses to evaluate the complex genetic pattern of the interactions among the immune system, inflammatory factors, and NT1. We present this article in accordance with the STROBE-MR reporting checklist (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2380/rc).

Methods

Study design

We assessed the potential causal relationship between immune cell traits and narcolepsy using a genome-wide association study (GWAS) dataset obtained from the FinnGen consortium (41). The application of an MR analysis that uses genetic variation as an instrumental variable (IV) to avoid unbiased causal effects is required to adhere to the assumptions of correlation, independence, and exclusion as follows: (I) the genetic variants are strongly correlated with exposure factors; (II) there was no significant association between the IVs and confounding factors; (III) the genetic variants only affect outcomes via exposure pathways. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The institutional review board of the FinnGen Consortium approved the GWAS protocol, and all participants provided signed informed consent. No additional ethical approval was required for this study, as it is a secondary analysis of the publicly available FinnGen GWAS dataset.

GWAS data sources

Immune cell traits as exposure data

The GWAS summary-level dataset of immune cells is publicly available from the FinnGen GWAS database directory (https://www.finngen.fi/fi). The initial dataset was sourced from the FinnGen Consortium for European ancestry. A total of 731 immune phenotypes (GCST90001391–GCST90002121) were incorporated into this study (42). Immune profiles included absolute cell (AC) counts (n=118), median fluorescence intensities (MFI) reflecting surface antigen levels (n=389), morphological parameters (MP) (n=32), and relative cell (RC) counts (n=192). The AC, MFI, and RC profiles encompassed classical dendritic cells (CDCs), T-cell maturation, bone marrow cells, monocytes, TBNK [T cells, B cells, and natural killer (NK) cells], and Treg (regulatory T cells) panels. The MR profiles were comprised of TBNK panels and CDC (cytotoxic lymphocytes). GWAS data of immune cells were studied using a sample of 313,00 individuals of European descent and approximately 21,303,723 genetic variants of single nucleotide polymorphisms (SNPs) affecting 731 immune cell traits.

Cytokines as exposure data

The GWAS summary-level dataset of cytokines was publicly accessed from GWAS Catalog (https://www.ebi.ac.uk/gwas/downloads). 91 cytokines (GCST90274758 to GCST90274848, N=14,824) were included in the study (43).

NT1 as outcome data

The summary results of the narcolepsy traits were publicly sourced from the directory of the FinnGen GWAS database. This dataset included 313,001 participants (198 patients with narcolepsy and 312,803 controls), comprising 86 males and 112 females. Genetic data for NT1 (GWAS ID, finn-bG6_NARCOCATA, narcolepsy, and cataplexy) encompassed 16,380,329 genetic variants of the SNPs. The mean age at disease onset of all participants with narcolepsy was 39.8 years with a mean 36.6-year-old males and mean 43.9-year-old females.

IV selection

IVs were selected from SNPs in the summary-level datasets of GWAS of immune cells and NT1. We used a significance level of 5×10⁻⁸ for each IV of immune cell traits to boost the pool of SNPs available for the MR analyses. Furthermore, we used a chain imbalance coefficient of r² and region width of 10,000 kb to eliminate the influence of pleiotropy on the results. The F-statistic was applied to diminish the potential impact of weak genetic instrument variables and to exclude IVs with F<10. SNPs with a minor allele frequency <0.01 were excluded to ensure data robustness, and SNPs associated with potential confounding factors were removed to prevent their impact on the data.

Statistical analysis

MR results were expressed as odds ratios (ORs) with the 95% confidence intervals (CIs). The causal relationships between 731 immunophenotypes and NT1 were estimated using two-sample MR. The inverse-variance weighted (IVW) method with Bonferroni correction was conducted as the primary analysis. In addition, we conducted four other MR methods, including the MR-Egger, weighted median, weighted mode, and simple mode, to verify the robustness of our results. For exploratory analysis, immune cells and cytokines with significant causal effects on NT1 were included in the mediation analysis (step 1 and step 2 by a two-step MR approach in Figure 1) (44). Multiple MR analyses were performed to explore the mediating effect of cytokines in the pathway from immune cells to narcolepsy, to determine if immune cells were causally associated with cytokines (step 3 in Figure 1). For sensitivity analysis, we performed Cochran’s Q test and MR Egger intercept test to address heterogeneity and pleiotropy within the summary estimates and selected IVs (45). To further verify the robustness of our results further, we used the MR pleiotropy residual sum and outlier (MR-PRESSO) test and leave-one-out analysis to identify and exclude outliers. All aforementioned MR methods and sensitivity analyses were applied to evaluate the causal associations of 91 cytokines with NT1 and immunophenotypes with cytokines. A p-value of <0.05, with a consistent direction of IVW and MR-Egger, was considered statistically significant. Bonferroni correction was applied to further validate the MR estimates of immunocyte phenotypes (P<0.008 for B cells, maturation stages of T cells, and monocyte panel; P<0.01 for cDC panel; P<0.01 for myeloid cell panel; and P<0.005 for TBNK and Treg panel). All analyses were conducted in R 4.2.1 with TwoSampleMR package and MRPRESSO package.

Figure 1 Flowchart of Mendelian randomization and mediation analysis on causal associations among immune cells, cytokines and narcolepsy type 1.

Results

Immunocyte phenotypes

Risk factors of immune phenotype for NT1

The summarized results of the IVW method used to describe immunophenotypic patterns significantly associated with narcolepsy were shown in Figures 2,3. After performing the Bonferroni correction, we identified 10 immune phenotypes as risk factors for the development of NT1 (Figure 2) including.

Figure 2 Forest plots shows causal associations between immune cell traits as risk factors and narcolepsy type 1. AC, absolute cell count; CI, confidence interval; HLA DR, human leukocyte antigen-DR; IVW, inverse variance weighting; MR, Mendelian randomization; NK, natural killer cell; NKT, natural killer T cell; nSNP, number of single-nucleotide polymorphisms; OR, odds ratio; SSC-A, side scatter area; TBNK, T cells; Treg, regulatory T cell.

Figure 3 Forest plots shows causal associations between immune cell traits as protective factors and narcolepsy type 1. BAFF-R, B-cell activating factor receptor; cDC, conventional dendritic cell; CI, confidence interval; DC, dendritic cell; IVW, inverse variance weighting; MR, Mendelian randomization; nSNP, number of single-nucleotide polymorphisms; OR, odds ratio; SSC-A, side scatter area; TBNK, T cells.

(I) TBNK panel: HLA DR⁺⁺ monocyte %leukocyte (OR =3.39, 95% CI: 2.15–5.35, P=1.44E-07), CD45 on CD4⁺ (OR =2.04, 95% CI: 1.3–3.19, P=0.002), SSC-A on NKT (OR =1.83, 95% CI: 1.22–2.75, P=0.003); HLA DR on HLA DR⁺ NK (OR =1.63, 95% CI: 1.25–2.11, P<0.001); (II) Treg panel: CD25 on CD45RA⁺ CD4 not Treg (OR =1.78, 95% CI: 1.21–2.62, P=0.003); (III) monocyte panel: HLA DR on monocyte (OR =1.74, 95% CI: 1.39–2.19, P=1.89E-06), HLA DR on CD14⁺ CD16⁻ monocyte (OR =1.64, 95% CI: 1.2–2.24, P=0.002), HLA DR on CD14⁺ monocyte (OR =1.64, 95% CI: 1.18–2.28, P=0.003); (IV) maturation stages of T cell panel : naïve CD4⁺ AC (OR =1.66, 95% CI: 1.15–2.39, P=0.007); (V) myeloid cell panel: HLA DR on CD33br HLA DR⁺ CD14dim (OR =1.63, 95% CI: 1.3–2.05, P=2.30E-05).

Protector factors of immune phenotype for NT1

We observed 5 immune phenotypes as protective factors for NT1 after implementing Bonferroni correction, as shown in Figure 3 including:

(I) TBNK panel: SSC-A on CD4⁺ (OR =0.6, 95% CI: 0.44–0.81, P<0.001); (II) cDC panel: CD80 on plasmacytoid DC (OR =0.59, 95% CI: 0.41–0.83, P=0.003), CD80 on CD62L⁺ plasmacytoid DC (OR =0.59, 95% CI: 0.41–0.83, P=0.003); (III) monocyte panel: HLA DR on CD14⁺ CD16⁺ monocyte (OR =0.54, 95% CI: 0.42–0.7, P=1.85E-06); (IV) B cell panel: B-cell activating factor receptor on CD20⁻ (OR =0.38, 95% CI: 0.24–0.58, P=1.39E-05).

Cytokine

The results of the IVW method for the association between cytokines and NT1 without Bonferroni correction were shown in Figure 3.

The 4 following inflammatory factors are correlated with an elevated incidence of NT1:

IL-6 levels (OR =2.53, 95% CI: 1.17–5.45, P=0.02), Eotaxin levels (OR =2.03, 95% CI: 1.19–3.46, P<0.001), Neurturin levels (OR =1.73, 95% CI: 1.01–2.95, P=0.046), TNF-beta levels (OR =1.42, 95% CI: 1.04–1.93, P=0.03).

Of these, 4 cytokines reduce the risk of developing NT1:

TNF ligand superfamily member 14 levels (OR =0.59, 95% CI: 0.38–0.92, P=0.02), eukaryotic translation initiation factor 4E-binding protein 1 levels (OR =0.49, 95% CI: 0.24–0.99, P=0.046), IL-15 receptor subunit alpha levels (OR =0.48, 95% CI: 0.26–0.86, P=0.02), and macrophage colony-stimulating factor 1 levels (OR =0.47, 95% CI: 0.26–0.84, P=0.01).

Mediation analysis of cytokines on immune cells and NT1

Two-step MR analysis was used to determine the impact of immune traits on NT1 via cytokines. (Figure 1, Figure S1, and Table S1), We found that IL-6 serves as a mediator in the immune-inflammatory pathway. Both HLA-DR on CD14⁺ monocytes and HLA-DR on monocytes exhibited weak correlations with IL-6 levels (OR =1.03, 95% CI: 1.00–1.06, P=0.049 and OR =1.02, 95% CI: 1.00–1.05, P=0.04, respectively). No evidence of pleiotropy or weak IVs were found (Table S2).

Sensitivity analyses

The sensitivity analyses shown in Tables 1,2 generally supported the robustness and reliability of the observed associations between the identified immunocyte phenotypes, cytokines, and NT1. Although Cochran’s Q test indicated heterogeneity among the IVs for certain immune cell traits, neither the MR-Egger intercept test nor the MR-PRESSO global test detected significant directional horizontal pleiotropy or outlier-driven bias (all pleiotropy P>0.05). These results indicate that the observed heterogeneity is attributable to differences in SNP-specific effect estimates rather than clear directional instrumental bias. Accordingly, the absence of directional pleiotropy confirms the validity and robustness of our primary IVW estimates.

Discussion

To the best of our knowledge, this is the first MR analysis to investigate the causal relationship between multiple immunophenotypes and NT1 expression. We observed that 10 immunocyte traits in TBNK cell, Treg, monocyte cells, maturation stages of T cell, and myeloid cells were positively linked to the development of narcolepsy. Four inflammatory factors, including IL-6, eotaxin, neurturin, and TNF-beta, increase inflammatory responses to facilitate disease development. Additional mediation analysis showing the immune-inflammatory pathways suggested that IL-6 produced by HLA-DR on CD14⁺ monocytes and HLA-DR on monocytes is a pivotal mediator of NT1. These results extend the current findings on the basis of neurological autoimmunity in the pathophysiology of narcolepsy.

The genetic association of the HLA allele and immune pathway analysis in narcolepsy assumes an autoimmune origin for the disease (46). This is supported by many attempts to explore multiple immune cell subtypes involved in cell-specific autoimmune attack to hypocretin-producing neurons (47,48). Single-cell analysis of the immune signature of narcolepsy demonstrated that the activation of CD4⁺ and CD8⁺ T cells and cytokine production by B cells catalyze the susceptibility to narcolepsy (47). However, no differences were observed between the levels of CD4⁺ and CD8⁺ T cells, B cells, NK cells, NKT cells, and monocytes in peripheral blood mononuclear cells (PBMCs) samples (47). In contrast, our results not only confirmed the contribution of T cells, but also showed that NK cells, NKT cells, monocytes, and myeloid cells contribute to the immune reaction, which has not been fully investigated in narcolepsy (Figure 2).

The T cell-driven autoimmune response against hypocretin neurons causes the initiation and late-stage progression of narcolepsy. Importantly, questions regarding the contribution of CD4⁺ and CD8⁺ T cells as well as other immune cells or antibodies remain unanswered and require further investigation. CD4⁺ T cells indirectly target hypocretin-producing neurons because they bind to HLA class II, including HLA-DQ6 and HLA-DR, which are normally absent in neurons (8,49). CD8⁺ T cells can directly kill hypocretin-producing neurons via a pathway that recognizes antigens bound to HLA class I proteins that are expressed in neurons (49). Latorre et al. observed that high levels of T cells (a high frequency of hypocretin-specific CD8⁺T cells) in blood samples recognized and targeted hypocretin proteins present in the hypothalamus (50). Pedersen et al. observed that patients with NT1 had increased hypocretin-specific CD8⁺ T cell levels compared to HLA-DQB1*06:02-positive controls, possibly because of the activation of pre-existing CD8⁺ T cells facilitated by exogenous antigens (51). Bernard-Valnet et al. showed that cytotoxic CD8⁺ T cells potentially act as effector cells that cause hypocretin neuron destruction owing to T cell infiltration and cytolytic granule polarization in mouse models (52).

Some subsets of CD4⁺ T cells have been reported to be associated with the development of autoimmune diseases (e.g., type 1 diabetes, multiple sclerosis, and rheumatoid arthritis) (53-56). However, Latorre et al. showed that a large proportion of autoreactive CD4⁺ T cells failed to identify hypocretin peptides bound to HLA-DQB1*06:02-restricted HLA-DQ6 proteins but not HLA-DR proteins, suggesting a more nuanced mechanism (50). Despite efforts to identify T cells that target neuronal proteins, there is no strong recognition of narcolepsy-related T cell subtypes that initiate disease onset, as the blood and CSF samples used in previous studies were from a wide range of disease progressions (57). However, we found that three phenotypes of CD4⁺ T cells (CD45 on CD4⁺, naïve CD4⁺ AC, and CD25 on CD45RA⁺ CD4, not Tregs) were associated with an increased risk of narcolepsy. CD45 is a receptor-type protein tyrosine phosphatase that is essential for T cell activation via TCR signaling. CD45RA is a phenotypic marker of naïve T cells. CD25 expressed on activated T cells is the alpha chain of interleukin-2 receptor (IL-2R). CD25 expression on CD45RA⁺ CD4⁺ T cells, but not on Tregs, is likely to serve as an activated effector T cell. These findings support the hypothesis that genetic variants within TCR genes regulate autoimmunity, and that these immune responses are highly predisposed to narcolepsy susceptibility. Subsequent protein production is likely responsible for the observed symptoms. CD4⁺ and CD8⁺ T cells induce high levels of P2Y11 protein associated with increased sleep fragmentation as well as increased levels of IL-2, IL-4, IL-13, IL-21, and TNF, which affect B cell differentiation and are involved in age-dependent progression. Additionally, cytokine expression patterns are potentially associated with disease duration, which further elucidates the inflammatory process and autoimmune etiology. For example, CD69⁺ T cells are associated with short disease duration, whereas IL-4 producing T cell are associated with long disease duration (58).

In addition to a T cell-mediated immune response, other autoimmune mediators also increase the risk of developing narcolepsy via potential immunological pathways responsible for hypocretin neuronal loss. Upregulation of HLA-DR expression in monocytes/macrophages tends to be activated toward infection or inflammation. In this study, we found that elevated expression of HLA-DR⁺⁺ on leukocytes (HLA-DR⁺⁺ monocyte % leukocytes) had the highest predisposition to narcolepsy (OR =3.39). As antigen-presenting cells (APCs), they interact with CD4⁺ T-cell for recognition and activation, by processing and presenting hypocretin-specific antigens. Moreover, the expression of HLA-DR in monocytes and their subsets (CD14⁺ CD16⁻ and CD14⁺) was significantly correlated with a high risk of narcolepsy. The four immune cell traits (HLA DR⁺⁺ monocyte %leukocyte, HLA DR on monocytes, HLA DR on CD14⁺ CD16⁻ monocytes, and HLA DR on CD14⁺ monocytes) observed in this study attained causal significance for antigen processing, antigen presentation, and downstream T cell stimulatory effects. These results are consistent with the APC mechanisms of narcolepsy.

Moreover, an immunochip study implicated an association between narcolepsy and polymorphisms in CTSH (27). CTSH is an endosomal lysosomal enzyme highly expressed in monocytes, dendritic cells, and B cells. Activated CTSH can modify the presentation of major histocompatibility complex class II peptide repertoire to T cells, inducing disease development. Moreover, monocytes in patients with NT1 secrete increased levels of IL-6, which is related to sleepiness, compared to those in controls that are positive for HLA-DQB1*06:02. Mediation analysis showed that IL-6 is associated with HLA DR on CD14⁺ monocytes and HLA DR on monocytes, which further indicated the process of antigen presentation to T cells by HLA class II-restricted APCs in the pathogenesis of narcolepsy. Although there are inconsistent results of etiological effect of circulating cytokines on narcolepsy (18,33,59), our results support the causal link between monocytes and IL-6 in immune-inflammatory processes. From a therapeutic perspective, our findings provide insights beyond orexin receptor agonist-based symptomatic therapies (46,60,61). Whereas orexin receptor agonists primarily compensate for downstream hypocretin deficiency, our results highlight upstream immune mechanisms, particularly monocyte-related antigen presentation and IL-6-mediated inflammation, that causally contribute to disease susceptibility. These observations indicate that the interplay between monocytes and IL-6 signaling is highly relevant to the immune pathophysiology of NT1 and highlights promising targets for future mechanistic and immunomodulatory studies (46,61). Our findings also help explain why broad immunosuppressive strategies such as systemic steroid administration cannot be universally recommended in NT1. In our analysis, not all cytokines were associated with increased NT1 risk, demonstrating that non-selective immunosuppression can inadvertently suppress both detrimental and potentially protective immune pathways. In addition, NT1 is often recognized after substantial hypocretin neuronal loss has already occurred, which may further limit the potential benefit of late-stage, non-specific immunosuppressive treatment (46,62).

The regulatory functional mechanisms of HLA-DR on CD33br HLA-DR⁺ CD14dim from a subset of myeloid cells, dendritic cells, or other myeloid-derived suppressor cells in narcolepsy have not yet been reported. However, we found that the presence of NK cells, such as HLA-DR on HLA-DR⁺ NK cells, plays a role in the immunogenetic aspects of narcolepsy. HLA-DR expression in cytotoxic lymphocytes suggests their activation or immunoregulatory function. HLA-DR⁺ NK cells share phenotypic features of both NK cells and dendritic cells, which produce cytokines such as interferon-gamma (IFN-γ) and present certain antigens to CD4⁺ and CD8⁺ T cells, leading to activation and proliferation. CD4⁺ T cells produce IFN-γ, which stimulates CD8⁺ T cells and drives a T helper 1 (Th1) cell-mediated cellular response. Corroborating this mechanism, high plasma levels of IFN-γ have been observed in patients with narcolepsy. Vuorela et al. demonstrated the upregulation of IFN-γ stimulated by T-cell reactivity to single peptides from hemagglutinin, neuraminidase, or nucleoprotein, either in vivo or NT1 patients (26). Similarly, SSC-A in NKT cells is also associated with IFN-γ production. Finally, this study found that IL-6, eotaxin, neurturin, and TNF-α have a tendency to increase the risk of narcolepsy. In this context, it still remains debated that changes in levels of serum, plasma and CSF cytokine can be indicative of an ongoing disease process or are consequences of hypocretin neuronal loss in narcolepsy (18,34,63). Further studies on the role of cytokines as biomarkers for hypocretin-deficient narcolepsy are warranted. At the same time, the cytokines identified in this study are pleiotropic rather than exclusive to NT1. Inflammatory mediators such as IL-6 and TNF-related factors have also been implicated in other autoimmune diseases associated with narcolepsy, including systemic lupus erythematosus and multiple sclerosis (62,64). These findings reflect shared autoimmune-inflammatory pathways across immune-mediated disorders rather than an NT1-specific cytokine signature. In NT1, their biological relevance is better interpreted in conjunction with the accompanying immune-cell context rather than as disease-specific biomarkers in isolation. Nevertheless, in NT1 these cytokines operate within a specific context of monocyte activation and HLA-DR-related antigen presentation, as supported by our mediation findings.

Conclusions

Our study demonstrated a causal relationship between ten risk immunophenotypes and NT1 via bidirectional MR analysis, highlighting the involvement of various immune cells in immune modulation and autoimmunity in narcolepsy. IL-6 expression mediated by monocyte subsets plays a role in the pathogenesis of narcolepsy. These findings reveal the underlying mechanism of autoimmune and inflammatory dysregulation in narcolepsy, providing evidence for immunological intervention and the prevention of narcolepsy.

Figure 4 Mendelian randomization results of the relationship between cytokines and narcolepsy type 1. Beta, effect estimate; CI, confidence interval; EIF4EBP1, eukaryotic translation initiation factor 4E-binding protein 1 levels; IL-15RA, interleukin-15 receptor subunit alpha levels; IVW, inverse variance weighting; M-CSF1, macrophage colony-stimulating factor 1 levels; MR, Mendelian randomization; nSNP, number of single-nucleotide polymorphisms; OR, odds ratio; TNF, tumor necrosis factor; TNFSF14, tumor necrosis factor ligand superfamily member 14 levels.

Acknowledgments

We thank the participants and investigators of the FinnGen study and GWAS Resource for the public data.

Footnote

Reporting Checklist: The authors have completed the STROBE-MR reporting checklist. Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2380/rc

Peer Review File: Available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2380/prf

Funding: This work was supported by the National Youth Foundation of China (No. 82301290, to H.Q., No. 82300040, to R.Q.) and State Key Laboratory of Respiratory Disease (No. SKLRD-Z-202509, to H.Q.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jtd.amegroups.com/article/view/10.21037/jtd-2025-aw-2380/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The institutional review board of the FinnGen Consortium approved the GWAS protocol, and all participants provided signed informed consent. No additional ethical approval was required for this study, as it is a secondary analysis of the publicly available FinnGen GWAS dataset.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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