Immune cells, circulating inflammatory factors and neurodevelopmental disorders: a bidirectional mendelian randomization and mediation analysis

Immune cells, circulating inflammatory factors and neurodevelopmental disorders: a bidirectional mendelian randomization and mediation analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Immune cells, circulating inflammatory factors and neurodevelopmental disorders: a bidirectional mendelian randomization and mediation analysis Zhiyue Liu, Lihong Wang, Lianhu Yu, Yongheng Zhao, Mengna Zhu, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4869464/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract The roles of various immune cells and circulating inflammatory factors in neurodevelopmental disorders (NDDs) remain controversial. Therefor we employed a two sample and bidirectional mendelian randomization and mediation method to explore the causal relationships between immune cells, circulating inflammatory factors, and NDDs. All data were originated from GWAS datasets. We found a significant positive causal relationship between 13 immune cells and ASD, including six CD8 + T cell, one CD3 + T cell, two CD20 + B cell, one CD38 + B cell, and two plasmacytoid DC. 9 inflammatory factors showed significant causal relationships with ASD: four interleukins (IL-7, IL-2, IL-18) were negatively associated, while five inflammatory factors were positively associated, such as TNF-α. 14 immune cells exhibited significant causal relationships with ADHD. CD3 on naive CD8br and CD4 on activated Treg were positively associated, while four CD27-expressing B cells were positively associated with ASD. Four CD40-expressing monocytes were negatively associated with ADHD. 7 inflammatory factors had significant causal relationships with ADHD: Fibroblast Growth Factor 23 levels (FGF-23), CD40L receptor levels, Glial Cell Line-Derived Neurotrophic Factor levels(GDNF), TNF-α were more important among these. Mediation analysis identified 12 mediating relationships, with three showing strong evidence: Natural killer cell receptor 2B4 levels (19.9%), Fibroblast Growth Factor 23 levels (11%) and Eotaxin levels (-5.95%). There were strongly causal relationships between immune cells, circulating inflammatory factors, and NDDs. Inflammatory factors mediated the pathways between immune cells and NDDs. Biological sciences/Immunology Health sciences/Diseases Health sciences/Health care Health sciences/Medical research Health sciences/Neurology Health sciences/Risk factors immune cells circulating inflammatory factors neurodevelopmental disorders bidirectional Mendelian randomization mediation analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1 Introduction For the past few years, the global incidence of neurodevelopmental disorders (NDDs) has increased 1 , causing significant economic, lifestyle, and psychological burdens on families and society. Childhood NDDs include autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), specific learning disorders, communication disorders, intellectual disability, and motor disorders 2 , which is a range of disabilities due to various disruptions in brain development 3 . A key feature common to these disorders is their onset during childhood. Children with these conditions often experience learning difficulties, impacting their future education, employment, and social interactions. For example, Children with ASD often exhibit difficulties in social interaction ,communication, and restrictive or repetitive behaviors and interests 4 . ADHD primarily manifests as an inability to maintain attention, hyperactivity, and impulsivity. These symptoms usually persist into adulthood. Currently, the pathogeneses of neurodevelopmental disorders were far from scientifically elucidated. Various views suggested that multiple factors contributed to these disorders, for example, maternal inflammation, epigenetic factors, and immune signal path 5 . Growing evidences showed that children with NDDs tend to exhibite abnormal immune and inflammatory responses, accompanied with immune-related diseases 6 . For instance, abnormal immune cells and dysregulated inflammatory cytokines have been identified in ASD and ADHD, nevertheless, the results of the different studies contradicted each other 7 , 8 . Compared to normal people, those with ASD showed various dysregulated immune cells and in their blood, cerebrospinal fluid, and brain tissue. These abnormalities included increased T cell, innate NK cells and monocytes 9 , 10 . Additionally, the levels of inflammatory factors in the plasma of ASD were significantly elevated, such as interleukin-6(IL-6), IL-1β, IL-12, tumor necrosis factor-α (TNF-α), IL-8, and so on 11 , 12 . However, other studies showed that IL-12 and IL-8 were no significant different in ASD and normal children. These inflammatory factors could cooperate with immune cells to jointly promote neuroinflammation in children with ASD 13 , 14 . This process could promote neuroinflammation, alter synapse formation, and affect neuronal function in children with ASD 15 . In children with ADHD, autoimmune and allergic diseases were more prevalent, such as asthma and atopic dermatitis 16 , 17 . Inflammation and immune regulation deficiencies were potential factors in ADHD 18 , 19 . Various immune cells had been shown to contribute to the development of ADHD, including CD8 + cytotoxic T cells, regulatory T cells (Treg cells) 20 , and CD4 + helper T cells 21 , 22 . CD8 + cytotoxic T cells invaded the nervous system through the blood-brain barrier, than disrupting the functions of microglia and neurons and participating in the pathogenesis of ADHD 21 . A recent large-sample analysis revealed a positive correlation between ADHD and circulating inflammatory factors, the most important of which were IL-6, IL-13, IL-16, and TNF-α 23 , 24 . However, another study reported that most protein levels in ADHD patients did not change, and TNF-α concentrations were lower compared to those in typically developing children 23 . Although numerous studies demonstrated that immune cells and circulating inflammatory factors played significant roles in the pathogenesis of NDDs, the current study did not draw accurate and non-heterogeneous conclusions. using genetic variation as an instrumental variable, mendelian randomization (MR) mitigate confounding bias and reverse causation, by leveraging the random assignment of genetic variants. MR can simulate a randomized controlled trial 25 . Therefore, we employed the MR approach to determine more causal relationships between immune cells(exposure), circulating inflammatory factors (exposure and mediator), and NDDs(outcome). Additionally, we explored the mediating role of circulating inflammatory factors between immune cells and NDDs. 2 Materials and methods Study design To scientifically evaluate the casual relationship between 731 immune cells, 91 circulating inflammatory factors, and NDDs (ASD and ADHD). At the same time, by using mediation analysis we evaluated the mediation effect of 91 circulating inflammatory factors between 731 immune cells and NDDs (Fig. 1 A). Firstly, as is shown in Fig. 1 B, MR must satisfy three important hypotheses 26 , 27 : (1) Correlation hypothesis: instrumental variables (IVs) are strongly related to exposure; (2) Exclusivity hypothesis: IVs can only affect outcomes by influencing exposure, and cannot affect outcomes by other ways; (3) Independence assumption: IVs are not related to confounder. Data source All data utilized in this study were sourced from Genome-Wide Association Study (GWAS) databases. All clinical participants in the original study used in this study legally signed written informed consent. (1) Exposure factors: the raw data of 731 immune cells were originated from a 2020 gene sequencing study of 3,757 Sardinians (Open GWAS: From ebi-a-GCST90001399 to ebi-a-GCST90002121) 28 . (2) The data for both exposure and mediator factors: the newest 91 circulating inflammatory factors were derived from a 2023 investigation of 14,824 Europeans, focusing on genetic loci associated with plasma levels of inflammation-related proteins (Study registration: GCST90274758 to GCST90274848) 29 . (3) Outcome factors: the raw data for ASD were sourced from a 2017 meta-analysis involving 18,381 ASD cases and 27,969 controls, diagnosing by ICD-9 or ICD-10 (Open GWAS: ieu-a-1185) 30 . Similarly, the raw data for ADHD came from the Psychiatric Genomics Consortium (PGC) study in 2017, encompassing 20,183 ADHD cases and 35,191 controls, diagnosing by ICD-9 or ICD-10 31 . All of the above GWAS data went through the following procedures: data quality control, genotype preprocessing, phenotypic preprocessing, association analysis and result integration. Table 1 provides a comprehensive overview of the specific GWAS datasets employed in the research, detailing their origins and study identifiers. Table 1 Information of GWAS datasets used in the MR study GWAS ID Trait Data Source Cases Population Year PMID From ebi-a-GCST90001399 to ebi-a-GCST90002121 731 Immune cells IEU Open GWAS 3,757 European 2020 PMC8517961 From GCST90274758 to GCST90274848 91 circulating inflammatory factors Open GWAS database 14,824 European 2023 PMC10457199 ieu-a-1185 Autism Spectrum Disorder iPSYCH-PGC 46,351 European 2017 PMC5441062 ieu-a-1183 Attention Deficit Hyperactivity Disorder iPSYCH-PGC 55,374 European 2017 PMC5992329 Instrumental variable selection SNPs meeting the following criteria were selected as IVs for MR, as shown in Fig. 1 C. Firstly, a significance threshold of P < 5e-6 was used to screen SNPs related to 731 immune cells and 91 circulating inflammatory factors, because SNPs that were selected by stricter threshold of P < 5e-8 were not suited to MR analysis. And this method (threshold of P < 5e-6) of appropriately relaxing the IVs selection threshold has been widely used in previous studies 32 , 33 . Additionally, to remove SNPs in linkage disequilibrium, criteria were set at R2 < 0.001 and kb = 10,000 34 . Finally, to further ensure the validity and relevance of MR, palindrome SNPs and weak instrumental SNPs (F < 10) were excluded 35 . The F statistic is calculated using the formula: F = R2 × (N − k−1)/k × (1 − R2). Through these three steps, we selected 1,538 SNPs that were closely related to the 731 immune cells (Table S1 ), and 1,784 SNPs closely related with the 91 circulating inflammatory factors were identified (Table S2 ). These selected SNPs laid a solid foundation for subsequent MR analysis, enhancing its scientific rigor. Statistical analyses The analysis in this study encompassed two main aspects, as illustrated in Fig. 1 C: bidirectional two-sample MR analysis and mediation analysis. It was widely known that Inverse Variance Weighted (IVW) was the primary MR method 36 , other three methods as supplementary. In the lack of pleiotropy, the IVW method provide unbiased and accurate estimates, and an IVW p-value < 0.05 is widely recognized for indicating a significant causal relationship 37 . At the same time, we used the False Discovery Rate (FDR) adjustment for the IVW p-value, adjusted p-value < 0.05 is considered to indicate a significant causal relationship between exposure and outcome 38 . Next, we conducted sensitivity analyses using three methods: leave-one-out analysis, heterogeneity testing (Cochran’s Q test), and pleiotropy testing (MR-Egger intercept test). three methods with a p-value < 0.05 indicating the presence of heterogeneity and horizontal pleiotropy, which can affect the stability of the IVW results 39 , 40 . Pleiotropy was tested using the MR-Egger intercept test, with a p-value < 0.05 indicating the presence of horizontal pleiotropy, which can affect the stability of the IVW results 39 . The leave-one-out analysis evaluated the influence of each single SNP on the MR results by removing one SNP at a time 41 . The main steps of this mediation MR analysis had two steps, shown in the Fig. 1 C. The mediation effect was calculated as β1 × β2, and the mediation ratio was calculated as (β1 × β2) /direct effect, mediation ratio represented the proportion of the causal relationship from exposure to outcome that was mediated by the intermediate factors. Based on mediation effect, we classified the discoverable mediators into different levels of evidence. When only a triangular relationship being (Fig. 1 A), three factors contain a potential mediation role. If there was a triangular relationship and the mediation effect was significantly different from 0, it indicated a significant mediating role. Triangular relationship meant there was a causal relationship between immune cells and NDDs, a relationship between circulating inflammatory factors and NDDs, and a causal relationship between immune cells and circulating inflammatory factors. All MR analysis progress were made using the R package in R.4.3.2, including “TwoSample MR” and “Mendelian Randomization”. 3 Result Identification of the causal effect of 731 immune cells on ASD We identified 1,538 SNPs associated with immune cells (Table S1 ). Initial analysis using the IVW method revealed that 13c types of immune cells demonstrated a significant causal relationship with ASD (P 1). The OR directions of these 12 immune cells were consistent across four methods, including IVW, MR-Egger, Weighted Median, and Weighted Mode, indicating their potential role in promoting incidence of ASD (Fig. 2 C). After FDR correction of IVW p-values (Table S3 ), CD4 on activated Treg had an adjusted p-value of 0.44002667 (P > 0.05), indicating no causal relationship. The remaining 12 immune cells reached statistical significance (P 0.05) for all results (Fig. 2 D). The MR-Egger intercept tests indicated no horizontal pleiotropy (P > 0.05), and Cochran's Q tests suggested no heterogeneity in the MR results (p > 0.05). Leave-one-out analysis demonstrated that removing a single SNP did not alter the MR results, with all leave-one-out results presented in Fig. 2 B. To validate the causal relationships between the 13 immune cells and ASD, we performed a reverse MR analysis. The IVW analysis of ASD on these 13 immune cells showed no significant reverse causal relationships (P > 0.05), shown in Table S4 . Overall, all of the above analysis results confirmed a robust and significant causal relationship between 12 immune cells and ASD, including CD62L- plasmacytoid DC %DC, TD CD8br AC, CD8br %leukocyte, CCR2 on CD62L + myeloid DC, CD8br and CD8dim %leukocyte, CD20 on IgD + B cell, CD28- CD8dim %T cell, IgD + CD38- %lymphocyte, CD127- CD8br AC, CD3 on HLA DR + CD8br, FSC-A on plasmacytoid DC, and CD20 on B cell. Identification of the causal effect of 91 circulating inflammatory factors on ASD In the second step, we identified 1,784 SNPs associated with inflammatory factors (Table S2 ), and verified the causal relationship between 91 circulating inflammatory factors and ASD. Initial analysis using the IVW method revealed that 9 circulating inflammatory factors demonstrated a significant causal relationship with ASD (P 1), and the OR directions of these 5 factors were consistent across all methods (IVW, MR-Egger, Weighted Median, and Weighted Mode), indicating their potential role in promoting incidence of ASD (Fig. 3 C). These factors included Natural killer cell receptor 2B4 levels, Fms-related tyrosine kinase 3 ligand levels, IL-18-R1, T-cell surface glycoprotein CD5 levels, and TNF-related apoptosis-inducing ligand levels. Three circulating inflammatory factors showed a negative correlation with ASD risk (OR < 1), and their OR directions were consistent across all four methods, indicating a protective effect against ASD. These factors included Interleukin-2 levels, Interleukin-2 receptor subunit beta levels, and Interleukin-7 levels. Sulfotransferase 1A1 levels showed a positive correlation with ASD, but the MR-Egger result was negative, indicating inconsistency across the four methods. After FDR correction of the IVW p-values (Table S5 ), the p-values for the 9 circulating inflammatory factors reached statistical significance (P < 0.05), suggesting a significant causal association with ASD. Sensitivity and heterogeneity analyses showed that TNF-related apoptosis-inducing ligand levels had heterogeneity (P < 0.05) but no horizontal pleiotropy. Leave-one-out analysis indicated no SNP affecting the robustness of the results (Fig. 3 B). For the remaining 8 circulating inflammatory factors, sensitivity and heterogeneity analyses showed no statistical significance (P > 0.05) (Fig. 3 D). Leave-one-out analysis also indicated that removing a single SNP did not alter the MR results (Fig. 3 B). To further validate the causal relationships between the 9 circulating inflammatory factors and ASD, we performed reverse MR analysis. The IVW analysis of ASD on these 9 circulating inflammatory factors showed no significant reverse causal relationships (P > 0.05) (Table S6 ). Overall, these analyses confirmed a robust and significant causal relationship between 7 circulating inflammatory factors and ASD, including IL-2β, IL-7, T-cell surface glycoprotein CD5 levels, Natural killer cell receptor 2B4 levels, IL-18-R1, IL-2, and Fms-related tyrosine kinase 3 ligand levels. Identification of the causal effect of 731 immune cells on ADHD The IVW results of immune cells and ADHD (Fig. 4 A) indicated an initial causal relationship between 14 immune cells and ASD (P < 0.05). The OR direction of HLA DR + T cell %T cell was inconsistent across the four analysis methods, and after FDR correction of the IVW P-value (Table S3 ), it did not have a statistically significant causal relationship (P > 0.05). The OR directions of the remaining 13 cells were consistent across the four analysis methods, and after FDR correction, the P-values for these 13 immune cells reached statistical significance (P 1) (Fig. 4 C), including CD27 on IgD- CD38dim, CD27 on sw mem, CD27 on memory B cell, CD4 on activated Treg, CD27 on unsw mem, and CD3 on naive CD8br. 7 immune cells were negatively correlated with ADHD risk (OR < 1), serving as protective factors against ASD, including CD40 on CD14 + CD16 + monocyte, SSC-A on monocyte, IgD on IgD + CD24+,, CD40 on CD14- CD16 + monocyte, CD40 on monocytes, CD40 on CD14 + CD16- monocyte, and SSC-A on CD14 + monocyte. Sensitivity and heterogeneity analyses of the study results (Fig. 4 D) showed that the MR-Egger intercept test and Cochran's test P-values for the 14 immune cells were all bigger than 0.05, indicating no heterogeneity or horizontal pleiotropy in the MR results. The leave-one-out analysis also confirmed the robustness of the MR results (Fig. 4 B). Subsequently, reverse MR analysis of ADHD and the 14 immune cells showed no reverse causal relationship (P > 0.05) (Table S5 ). In summary, all these analyses demonstrated a robust and significant causal relationship between 13 immune cells and ADHD, including CD40 on monocytes, CD27 on memory B cell, CD27 on sw mem, CD4 on activated Treg, CD3 on naive CD8br, SSC-A on monocyte, IgD on IgD + CD24+, CD40 on CD14 + CD16 + monocyte, CD27 on IgD- CD38dim, CD40 on CD14- CD16 + monocyte, CD40 on CD14 + CD16- monocyte, CD27 on unsw mem, and SSC-A on CD14 + monocyte. Identification of the causal effect of 91 circulating inflammatory factors on ADHD The IVW results of circulating inflammatory factors and ADHD (Fig. 5 A) indicated an initial causal relationship between 7 circulating inflammatory factors and ADHD (P < 0.05). After FDR correction, the P-values for these 7 circulating inflammatory factors reached statistical significance (P 1) (Fig. 5 C), including Eotaxin levels, GDNF, and TNF-related activation-induced cytokine levels. 4 circulating inflammatory factors were negatively correlated with ADHD risk (OR < 1), serving as protective factors against ADHD, including Adenosine Deaminase levels, FGF-23, CD40L receptor levels, and Urokinase-type plasminogen activator levels. Sensitivity and heterogeneity analyses of the study results (Fig. 5 D) showed that MR-Egger intercept test and Cochran's test P-values for the 7 circulating inflammatory factors were all greater than 0.05, indicating no heterogeneity or horizontal pleiotropy in the MR results. The leave-one-out analysis also confirmed the robustness of the MR results (Fig. 5 B). Subsequently, reverse MR analysis of ADHD and the 7 inflammation factors showed no reverse causal relationship (P > 0.05) (Table S6 ). In summary, all these analyses demonstrated a robust and significant causal relationship between 7 circulating inflammatory factors and ADHD, including Eotaxin levels, GDNF, FGF-23, TNF-related activation-induced cytokine levels, Adenosine Deaminase levels, CD40L receptor levels, and Urokinase-type plasminogen activator levels. Identification of the mediation effect of ASD and ADHD Based on the previous MR results with four different exposures and outcomes, we have confirmed various immune cells and circulating inflammatory factors with strong causal relationships with neurodevelopmental disorders. To determine the mediation effect of circulating inflammatory factors between immune cells and NDDs (ASD and ADHD), we employed the mediation MR method. For ASD, we identified 12 significantly related immune cells and 7 significantly related circulating inflammatory factors. We then selected these 12 immune cells (exposures) and the 7 circulating inflammatory factors (outcomes) for the MR analysis, obtaining the β1 results for immune cells on circulating inflammatory factors. The MR results (Fig. 6 A) showed that FSC-A on plasmacytoid DC and Natural killer cell receptor 2B4 levels had significant IVW results (P < 0.05), and the FDR-adjusted p-value < 0.05 (Table S7 ). There was no heterogeneity or horizontal pleiotropy (Fig. 6 D), and had high robustness of the MR results that confirmed by leave-one-out analysis (Fig. 6 C). Therefore, FSC-A on plasmacytoid DC as exposure and Natural killer cell receptor 2B4 levels as mediator with ASD as the outcome formed a triangular relationship. The mediation effect was significant with a mediation proportion of 19.9% (95%CI: 1.62%, 41.4%) and P = 0.04996888 (Fig. 6 B). Additionally, Sulfotransferase 1A1 levels as a mediator between CD28-CD8dim %T cell and ASD also formed a triangular relationship (Table S7 ) with a potential mediation effect (Table S8 ), mediation proportion − 14.4% (95%CI: -33%, 4.15%), P = 0.127929513. For ADHD, we identified 13 significantly related immune cells and 7 significantly related circulating inflammatory factors. We selected these 13 immune cells (exposures) and the 7 circulating inflammatory factors (outcomes) for the MR analysis, obtaining the β1 results for immune cells on circulating inflammatory factors. The MR results (Fig. 6 A) showed that CD27 on sw mem and Fibroblast growth factor 23 levels had significant IVW results (P < 0.05), and the FDR-adjusted P-value < 0.05 (Table S7 ). There was no heterogeneity or horizontal pleiotropy (Fig. 6 D), and the leave-one-out analysis confirmed the robustness of the MR results (Fig. 6 C). Therefore, CD27 on sw mem as exposure and Fibroblast growth factor 23 levels as mediator with ADHD as the outcome form a triangular relationship. The mediation effect was significant with a mediation proportion of 11% (95%CI: 0.156%, 21.8%) and P = 0.046777517, indicating that Fibroblast growth factor 23 levels significantly mediated the relationship between CD27 on sw mem and ADHD (Fig. 6 B). Similarly, Eotaxin levels significantly mediated the relationship between CD27 on memory B cell and ADHD (Fig. 6 B), forming a triangular relationship with significant IVW results (P < 0.05) and FDR-adjusted P-value < 0.05 (Table S7 ). There was no heterogeneity or horizontal pleiotropy (Fig. 6 D), and the leave-one-out analysis confirmed the robustness of the MR results (Fig. 6 C), mediation proportion − 5.95% (95%CI: -11.3%, -0.623%), P = 0.028573986. Furthermore, we found that CD40L receptor levels potentially mediated multiple immune cells and ADHD (Table S8 and Fig. S1 ), including, CD27 on memory B cell,, CD27 on sw mem, CD40 on CD14 + CD16- Monocyte, CD27 on unsw mem, CD40 on CD14 + CD16 + monocyte, CD27 on IgD- CD38dim, CD40 on CD14- CD16 + monocyte, and CD40 on monocytes. In conclusion, we identified 12 mediation relationships: 3 strong evidences and 9 potential evidences. Natural killer cell receptor 2B4 levels strongly mediated the relationship between FSC-A on plasmacytoid DC and ASD. Fibroblast growth factor 23 levels significantly mediated the relationship between CD27 on sw mem and ADHD. Eotaxin levels significantly mediated the relationship between CD27 on memory B cell and ADHD. Sulfotransferase 1A1 levels potentially mediated the relationship between CD28-CD8dim %T cell and ASD. CD40L receptor levels potentially mediated the relationships between multiple immune cells and ADHD. 4 Discussion This study, using MR analysis, demonstrated the strongly and potential causal relationships between immune cells, circulating inflammatory factors and NDDs (ASD and ADHD), and the mediation role of inflammatory factors between immune cells and NDDs. Immune cells and inflammatory factors played an important role in the pathogenesis of NDDs. Through MR Analysis, we can reveal which abnormal immune cells and inflammatory factors affect the onset and progression of NDDs, and provide future therapeutic targets for NDDs. ASD may be related to immune balance disorder, involving the imbalance of inflammatory factors and autoimmune disorders 6 . In the MR analysis of 731 immune cells and ASD, six T cells were found to be positively correlated with ASD, which were all marked by CD8br, including CD8br %leukocyte, TD CD8br AC, CD8br and CD8dim %leukocyte, CD28- CD8dim %T cell, CD127- CD8br AC, and CD3 on HLA DR + CD8br. This suggested that CD8 + T cells may greatly promote the onset of ASD. A clinical trial by Lopez-Cacho JM et al confirmed that the number of CD8 + T cells in ASD patients was higher than in healthy individuals, indicating a positive correlation between ASD and CD8 + T cells 42 . A mouse experiment demonstrated that CD8 + T cells could lead to ASD by affecting neural progenitor cells, resulting in brain NDDs and ASD behaviors in mice 43 . Another study on postmortem brain tissue of ASD patients provided more direct evidence that immune cells damaged the cerebrospinal fluid (CSF)–brain barrier. Among immune cells, CD3 + and CD8 + T cells were the most prevalent, with a few CD4 + T cells and CD20 + B cells 44 . In brain glial cells, CD8 + T cells produced cytotoxic effector molecules such as granzyme B, causing abnormal membrane vesicles in GFAP + astrocytes in the brains of ASD patients 44 . ASD children also had increased numbers of CD3 + TIM-3 + and CD8 + TIM-3 + cells compared to typically developing (TD) controls 45 . Therefore, T cells expressing CD8 and CD3 might be pathogenic immune cells in ASD. Next, this study found that two types of plasmacytoid DCs were positively correlated with ASD: FSC-A on plasmacytoid DC and CD62L- plasmacytoid DC %DC. A Spain study indicated a significant increase in the frequency of bone marrow dendritic cells in ASD children. Plasmacytoid dendritic cells (pDCs) were associated with the amygdala volume and developmental regression in ASD children 46 . pDCs performed antigen presentation and stimulated other immune cells, primarily through the secretion of inflammatory factors such as IFN-I. Our results also showed that 19.9% of the causal effect of FSC-A on plasmacytoid DC on ASD was mediated by Natural killer cell receptor 2B4 levels. Therefore, the effect of pDCs on ASD may be partially mediated by inflammatory factors. Then three B cells were positively correlated with ASD: IgD + CD38- %lymphocyte, CD20 on IgD + B cell, and CD20 on B cell. CD20 B cells damaged the CSF-brain barrier in ASD brain tissue, but there was no significant difference in B cell counts in peripheral blood 47 , indicating the need for larger clinical studies to fully confirm the relationship between B cells and ASD. In the MR analysis of 91 circulating inflammatory factors and ASD, three interleukins—IL-7, IL-2, and IL-2 Rb—were significantly negatively associated with ASD risk, while IL-18R1 and TNF-related apoptosis-inducing ligand (TRAIL) were positively associated with ASD development. Vojdani A et al. showed the peripheral blood levels and mRNA expression of IL-2 are lower in children with ASD 48 . Also IL-2 and IL-7 levels were negatively correlated with stereotypic behaviors 49 and intellectual scores in ASD 50 . The levels of IL-18 are elevated in the brains of children with ASD 51 , which activated astrocytes and lead to neuroinflammation and subsequent cognitive dysfunction 52 . Zhao H et al. reported elevated levels of TNF-α in the peripheral blood of ASD patients 12 and positively correlated with the severity of ASD symptoms 53 . A co-culture study of mice and human neurons revealed that TNF-α can upregulate glutamate levels, inducing neurotoxicity and promoting neuronal death and apoptosis. Using glutaminase inhibitors can alleviate this neurotoxicity 54 , suggesting potential therapeutic approaches for ASD children. In the MR analysis of 731 immune cells and ADHD, our results indicated that B cells, monocytes, and T cells had a causal relationship with the onset of ADHD, and these findings were consistent with the results for ASD, suggesting CD8 + and CD3 + T cells were potential common immune cells in NDDs. First, regarding T cells, CD3 on naive CD8br and CD4 on activated Treg were positively correlated with ADHD. Looman KIM et al. found peripheral blood immune cells in 756 children found that higher levels of helper T cell 1 (Th1) and CD8 + T cells were associated with higher attention problem scores 55 . A turkey case-control study showed a positive correlation between CD3 + CD4 + CD25 + Foxp3+ (Tregs) and ADHD 20 , consistent with our results. Secondly, our study found that four B cells expressing CD27 were positively correlated with ADHD: CD27 on unswitched memory, CD27 on switched memory, CD27 on IgD- CD38dim, and CD27 on memory B cells. A Stanford University cohort study indicated that children with certain atopic diseases had higher numbers of memory Treg cells, total B cells, and CD27 + IgA + memory B cells 56 . And these diseases had a strong correlation with ADHD, such as atopic dermatitis 57 and food allergies 58 . Therefore, ADHD may be closely related to CD27 + B cells. Moreover, subsequent mediation analysis showed that the causal effect of two CD27 + B cells on ADHD was realized through circulating inflammation faactors, but further studies were needed in the future. Thirdly, our results indicated that four monocyte types expressing CD40 were negatively correlated with ADHD, and the inflammatory cytokine CD40L receptor levels were also negatively correlated with ADHD. Avcil S et al. put forword that the monocyte/lymphocyte ratio (MLR) potentially served as a peripheral blood inflammatory marker for ADHD 59 . 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3) interfered with the effects of CD40L on immunomodulatory and inflammatory responses, so drugs that reduced the amount of 1,25(OH)2D3 may help treat ADHD 60 . Therefor our research suggests that monocytes expressing CD40 and CD40L receptor levels are closely associated with ADHD, and may influenced ADHD risk by interacting with multiple inflammatory factors Numerous studies have shown that inflammatory factors play a significant role in the development of ADHD, and our results also suggested that these factors could be crucial in ADHD pathogenesis. Firstly, TNF-α was shown to increase the risk of both ASD and ADHD in MR analyses, with clinical data supporting higher peripheral blood TNF-α levels in ADHD patients, correlating positively with hyperactivity symptoms 61 . Thus, TNF-α could be a potential pathogenic inflammatory factor in NDDs. In our MR results, Fibroblast Growth Factor 23 (FGF-23) was protective against ADHD, whereas Glial cell line-derived neurotrophic factor levels (GDNF) promoted ADHD development. Both FGF-23 and GDNF were growth factors that regulated neurogenesis, differentiation, development, gliogenesis, and synaptogenesis, thereby influencing cognition 62 . Bilgic A and Yurteri N all proved elevated GDNF levels and decreased FGF levels in children with ADHD, consistent with our findings 63 , 64 . In mouse experiments, disruption of the FGFR gene led to increased spontaneous movement and a reduction in cortical inhibitory neurons. FGFR agonists significantly reduced hyperactivity 65 , and pathway analysis confirmed FGFR's role in ADHD etiology by activating FGFR1b and FGFR2b pathways 66 . Therefore, FGFR agonists and GDNF inhibitors may serve as new therapeutic targets for alleviating ADHD symptoms. Although the MR analysis in this study provided new insights into the causal relationships between immune cells, inflammatory factors, and NDDs, and offered advantages in reducing confounding and reverse causation, several limitations must be acknowledged. Firstly, all GWAS data used in this study were derived from European populations. Secondly, this study only established causal relationships between exposures and outcomes, leaving the underlying mechanisms unexplored and necessitating further research. Thirdly, while our sensitivity analyses did not show evidence of pleiotropy or heterogeneity, there may still be unknown confounding factors that could introduce bias into the results. Lastly, although related studies suggest potential interaction pathways for the mediation analysis results, the lack of conclusive literature support requires further validation through clinical trials. Therefore, we plan to make improvements in the future: firstly, we should continuously enrich the GWAS database; Secondly, clinical research and basic research should be continued to further explore causation and mechanism analysis. 5 Conclusion In summary, this study identified causal relationships between immune cells, circulating inflammatory proteins, and NDDs, establishing three mediators with strong evidence. These relationships could serve as valuable biomarkers and potential targets for understanding the biological mechanisms of NDDs and developing new therapies. Declarations Conflict of Interest The authors affirm that this research was conducted without any commercial or financial involvement that could be interpreted as a potential conflict of interest. Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Funding This work was supported by grants from the Qilu Hospital of Shandong University Pediatric development fund. Author Contribution ZYL: Writing – Conceptualization, study design, Supervision. LHW: Writing – original draft, MR analysis. KHY: Writing – introduction and discussion. YHZ: Writing – results and methods, mediation analysis. MNZ: Writing –introduction and discussion, Formal analysis. YW: Revising after reviewing the manuscript. AHC: Writing – review & editing, Supervision, Conceptualization. Acknowledgments The authors gratefully acknowledge the invaluable contributions of all the participants to the GWAS. Data Availability All original data supporting the findings of this study are included in the article and its Supplementary Material. For additional information or inquiries, please contact the corresponding author. References Salari, N. et al. The global prevalence of autism spectrum disorder: a comprehensive systematic review and meta-analysis. Ital. J. Pediatr. 48 , 112 (2022). Ismail, F. Y. & Shapiro, B. K. What are neurodevelopmental disorders? Curr. Opin. Neurol. 32 , 611–616 (2019). Thapar, A., Cooper, M. & Rutter, M. Neurodevelopmental disorders. Lancet Psychiatry . 4 , 339–346 (2017). Kodak, T. & Bergmann, S. Autism Spectrum Disorder: Characteristics, Associated Behaviors, and Early Intervention. Pediatr. Clin. North. Am. 67 , 525–535 (2020). Han, V. X., Patel, S., Jones, H. F. & Dale, R. C. Maternal immune activation and neuroinflammation in human neurodevelopmental disorders. Nat. Rev. Neurol. 17 , 564–579 (2021). Cohly, H. H. & Panja, A. Immunological findings in autism. Int. Rev. Neurobiol. 71 , 317–341 (2005). Masi, A., Glozier, N., Dale, R. & Guastella, A. J. The Immune System, Cytokines, and Biomarkers in Autism Spectrum Disorder. Neurosci. Bull. 33 , 194–204 (2017). Careaga, M. et al. Immune Endophenotypes in Children With Autism Spectrum Disorder. Biol. Psychiatry . 81 , 434–441 (2017). Bjorklund, G. et al. Immune dysfunction and neuroinflammation in autism spectrum disorder. Acta Neurobiol. Exp. (Wars) . 76 , 257–268 (2016). Ellul, P. et al. Regulatory T lymphocytes/Th17 lymphocytes imbalance in autism spectrum disorders: evidence from a meta-analysis. Mol. Autism . 12 , 68 (2021). Kordulewska, N. K. et al. Serum cytokine levels in children with spectrum autism disorder: Differences in pro- and anti-inflammatory balance. J. Neuroimmunol. 337 , 577066 (2019). Zhao, H. et al. Association of Peripheral Blood Levels of Cytokines With Autism Spectrum Disorder: A Meta-Analysis. Front. Psychiatry . 12 , 670200 (2021). Meltzer, A. & Van de Water, J. The Role of the Immune System in Autism Spectrum Disorder. Neuropsychopharmacology . 42 , 284–298 (2017). Bordeleau, M., Fernandez de Cossio, L., Chakravarty, M. M. & Tremblay, M. E. From Maternal Diet to Neurodevelopmental Disorders: A Story of Neuroinflammation. Front. Cell. Neurosci. 14 , 612705 (2020). Jones, K. L. et al. Autism with intellectual disability is associated with increased levels of maternal cytokines and chemokines during gestation. Mol. Psychiatry . 22 , 273–279 (2017). Cortese, S. et al. Association between attention deficit hyperactivity disorder and asthma: a systematic review and meta-analysis and a Swedish population-based study. Lancet Psychiatry . 5 , 717–726 (2018). Nielsen, T. C. et al. Association of Maternal Autoimmune Disease With Attention-Deficit/Hyperactivity Disorder in Children. JAMA Pediatr. 175 , e205487 (2021). Leffa, D. T., Torres, I. L. S. & Rohde, L. A. A Review on the Role of Inflammation in Attention-Deficit/Hyperactivity Disorder. Neuroimmunomodulation . 25 , 328–333 (2018). Quintero, J., Gutierrez-Casares, J. R. & Alamo, C. Molecular Characterisation of the Mechanism of Action of Stimulant Drugs Lisdexamfetamine and Methylphenidate on ADHD Neurobiology: A Review. Neurol. Ther. 11 , 1489–1517 (2022). Cetin, F. H. et al. Regulatory T cells in children with attention deficit hyperactivity disorder: A case-control study. J. Neuroimmunol. 367 , 577848 (2022). Kipnis, J. Multifaceted interactions between adaptive immunity and the central nervous system. Science . 353 , 766–771 (2016). Mohebiany, A. N. et al. Microglial A20 Protects the Brain from CD8 T-Cell-Mediated Immunopathology. Cell. Rep. 30 , 1585–1597e1586 (2020). Misiak, B. et al. Peripheral blood inflammatory markers in patients with attention deficit/hyperactivity disorder (ADHD): A systematic review and meta-analysis. Prog Neuropsychopharmacol. Biol. Psychiatry . 118 , 110581 (2022). Oades, R. D., Dauvermann, M. R., Schimmelmann, B. G., Schwarz, M. J. & Myint, A. M. Attention-deficit hyperactivity disorder (ADHD) and glial integrity: S100B, cytokines and kynurenine metabolism–effects of medication. Behav. Brain Funct. 6 , 29 (2010). Birney, E. Mendelian Randomization. Cold Spring Harb Perspect. Med. 12 (2022). Costello, R., McDonagh, J., Hyrich, K. L. & Humphreys, J. H. Incidence and prevalence of juvenile idiopathic arthritis in the United Kingdom, 2000–2018: results from the Clinical Practice Research Datalink. Rheumatol. (Oxford) . 61 , 2548–2554 (2022). Didelez, V. & Sheehan, N. Mendelian randomization as an instrumental variable approach to causal inference. Stat. Methods Med. Res. 16 , 309–330 (2007). Orru, V. et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. Nat. Genet. 52 , 1036–1045 (2020). Zhao, J. H. et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. Nat. Immunol. 24 , 1540–1551 (2023). Autism Spectrum Disorders Working Group of The Psychiatric Genomics. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. Mol. Autism . 8 , 21 (2017). Martin, J. et al. A Genetic Investigation of Sex Bias in the Prevalence of Attention-Deficit/Hyperactivity Disorder. Biol. Psychiatry . 83 , 1044–1053 (2018). Cao, R. R. et al. The immune factors have complex causal regulation effects on bone mineral density. Front. Immunol. 13 , 959417 (2022). Wang, Q. et al. Dissecting Causal Relationships Between Gut Microbiota, Blood Metabolites, and Stroke: A Mendelian Randomization Study. J. Stroke . 25 , 350–360 (2023). Yuan, J. et al. Genetically predicted C-reactive protein mediates the association between rheumatoid arthritis and atlantoaxial subluxation. Front. Endocrinol. (Lausanne) . 13 , 1054206 (2022). Ji, D., Chen, W. Z., Zhang, L., Zhang, Z. H. & Chen, L. J. Gut microbiota, circulating cytokines and dementia: a Mendelian randomization study. J. Neuroinflammation . 21 , 2 (2024). Bulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. Nat. Genet. 47 , 1236–1241 (2015). Davies, N. M., Holmes, M. V. & Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. BMJ . 362 , k601 (2018). Zhang, T., Cao, Y., Zhao, J., Yao, J. & Liu, G. Assessing the causal effect of genetically predicted metabolites and metabolic pathways on stroke. J. Transl Med. 21 , 822 (2023). Bowden, J., Davey Smith, G. & Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int. J. Epidemiol. 44 , 512–525 (2015). Burgess, S. & Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. Eur. J. Epidemiol. 32 , 377–389 (2017). Corbin, L. J. et al. BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization. Diabetes . 65 , 3002–3007 (2016). Lopez-Cacho, J. M. et al. Characterization of immune cell phenotypes in adults with autism spectrum disorders. J. Investig Med. 64 , 1179–1185 (2016). Jhun, M. et al. CD103 Deficiency Promotes Autism (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) Behavioral Spectra and Reduces Age-Related Cognitive Decline. Front. Neurol. 11 , 557269 (2020). DiStasio, M. M., Nagakura, I., Nadler, M. J. & Anderson, M. P. T lymphocytes and cytotoxic astrocyte blebs correlate across autism brains. Ann. Neurol. 86 , 885–898 (2019). Ahmad, S. F. et al. Dysregulation of T cell immunoglobulin and mucin domain 3 (TIM-3) signaling in peripheral immune cells is associated with immune dysfunction in autistic children. Mol. Immunol. 106 , 77–86 (2019). Breece, E. et al. Myeloid dendritic cells frequencies are increased in children with autism spectrum disorder and associated with amygdala volume and repetitive behaviors. Brain Behav. Immun. 31 , 69–75 (2013). Arteaga-Henriquez, G. et al. Activation of the Monocyte/Macrophage System and Abnormal Blood Levels of Lymphocyte Subpopulations in Individuals with Autism Spectrum Disorder: A Systematic Review and Meta-Analysis. Int. J. Mol. Sci. 23 (2022). Vojdani, A. et al. Low natural killer cell cytotoxic activity in autism: the role of glutathione, IL-2 and IL-15. J. Neuroimmunol. 205 , 148–154 (2008). Denney, D. R., Frei, B. W. & Gaffney, G. R. Lymphocyte subsets and interleukin-2 receptors in autistic children. J. Autism Dev. Disord . 26 , 87–97 (1996). Napolioni, V. et al. Plasma cytokine profiling in sibling pairs discordant for autism spectrum disorder. J. Neuroinflammation . 10 , 38 (2013). Businaro, R. et al. Interleukin-18 modulation in autism spectrum disorders. J. Neuroinflammation . 13 , 2 (2016). Alboni, S., Cervia, D., Sugama, S. & Conti, B. Interleukin 18 in the CNS. J. Neuroinflammation . 7 , 9 (2010). Xie, J. et al. Immunological cytokine profiling identifies TNF-alpha as a key molecule dysregulated in autistic children. Oncotarget . 8 , 82390–82398 (2017). Ye, L. et al. IL-1beta and TNF-alpha induce neurotoxicity through glutamate production: a potential role for neuronal glutaminase. J. Neurochem . 125 , 897–908 (2013). Looman, K. I. M. et al. Associations between T cells and attention problems in the general pediatric population: The Generation R study. JCPP Adv. 1 , e12038 (2021). Looman, K. I. M. et al. Associations of Th2, Th17, Treg cells, and IgA(+) memory B cells with atopic disease in children: The Generation R Study. Allergy . 75 , 178–187 (2020). Genuneit, J. et al. Infant atopic eczema and subsequent attention-deficit/hyperactivity disorder–a prospective birth cohort study. Pediatr. Allergy Immunol. 25 , 51–56 (2014). Yang, C. F., Yang, C. C. & Wang, I. J. Association between allergic diseases, allergic sensitization and attention-deficit/hyperactivity disorder in children: A large-scale, population-based study. J. Chin. Med. Assoc. 81 , 277–283 (2018). Avcil, S. Evaluation of the neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, and mean platelet volume as inflammatory markers in children with attention-deficit hyperactivity disorder. Psychiatry Clin. Neurosci. 72 , 522–530 (2018). Almerighi, C. et al. 1Alpha,25-dihydroxyvitamin D3 inhibits CD40L-induced pro-inflammatory and immunomodulatory activity in human monocytes. Cytokine . 45 , 190–197 (2009). Gustafsson, H. C. et al. The association between heightened ADHD symptoms and cytokine and fatty acid concentrations during pregnancy. Front. Psychiatry . 13 , 855265 (2022). Galvez-Contreras, A. Y., Campos-Ordonez, T. & Gonzalez-Castaneda, R. E. Gonzalez-Perez, O. Alterations of Growth Factors in Autism and Attention-Deficit/Hyperactivity Disorder. Front. Psychiatry . 8 , 126 (2017). Bilgic, A., Toker, A., Isik, U. & Kilinc, I. Serum brain-derived neurotrophic factor, glial-derived neurotrophic factor, nerve growth factor, and neurotrophin-3 levels in children with attention-deficit/hyperactivity disorder. Eur. Child. Adolesc. Psychiatry . 26 , 355–363 (2017). Yurteri, N., Sahin, I. E. & Tufan, A. E. Altered serum levels of vascular endothelial growth factor and glial-derived neurotrophic factor but not fibroblast growth factor-2 in treatment-naive children with attention deficit/hyperactivity disorder. Nord J. Psychiatry . 73 , 302–307 (2019). Muller Smith, K. et al. Deficiency in inhibitory cortical interneurons associates with hyperactivity in fibroblast growth factor receptor 1 mutant mice. Biol. Psychiatry . 63 , 953–962 (2008). Mooney, M. A. et al. Pathway analysis in attention deficit hyperactivity disorder: An ensemble approach. Am. J. Med. Genet. B Neuropsychiatr Genet. 171 , 815–826 (2016). Tables Table 1 is available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table1.InformationofGWASdatasetsusedintheMRstudy..xlsx TableS1.1538SNPsrelatedto731immunecells.xls TableS2.1784SNPsrelatedto91circulatinginflammatoryfactors.xls TableS3.ThecausaleffectsofASDandADHDon731immunecells.xls TableS4.ThereversecausaleffectsofASDandADHDon731immunecells.xls TableS5.ThecausaleffectsofASDandADHDon91circulatinginflammatoryfactors.xls TableS6.ThereversecausaleffectsofASDandon91circulatinginflammatoryfactors.xls TableS7.Thepostivecausaleffectsofimmunecellsoncirculatinginflammatoryfactors.xls TableS8ThemediationMRresultsofpotentialmediator.xlsx Cite Share Download PDF Status: Published Journal Publication published 14 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 20 Feb, 2025 Reviews received at journal 06 Feb, 2025 Reviewers agreed at journal 18 Jan, 2025 Reviewers agreed at journal 26 Nov, 2024 Reviews received at journal 18 Nov, 2024 Reviewers agreed at journal 14 Oct, 2024 Reviewers invited by journal 02 Sep, 2024 Editor assigned by journal 02 Sep, 2024 Editor invited by journal 22 Aug, 2024 Submission checks completed at journal 20 Aug, 2024 First submitted to journal 06 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4869464","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":354536957,"identity":"d17cb198-9053-4642-8416-1d6fd9cb6d77","order_by":0,"name":"Zhiyue Liu","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Zhiyue","middleName":"","lastName":"Liu","suffix":""},{"id":354536958,"identity":"b14b9c2a-7114-42ec-a27e-4286d283226a","order_by":1,"name":"Lihong Wang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Lihong","middleName":"","lastName":"Wang","suffix":""},{"id":354536959,"identity":"75191d2b-f182-4ca7-a62e-e6c15f07cb1f","order_by":2,"name":"Lianhu Yu","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Lianhu","middleName":"","lastName":"Yu","suffix":""},{"id":354536960,"identity":"8bfebe4a-9a08-4a1d-a963-dc1d098f8f55","order_by":3,"name":"Yongheng Zhao","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yongheng","middleName":"","lastName":"Zhao","suffix":""},{"id":354536961,"identity":"6b60d882-cd8f-4ba3-bf8c-1287c23ed09f","order_by":4,"name":"Mengna Zhu","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Mengna","middleName":"","lastName":"Zhu","suffix":""},{"id":354536962,"identity":"01576c95-18c0-41be-89dc-4b031b1cc972","order_by":5,"name":"Yu Wang","email":"","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Wang","suffix":""},{"id":354536963,"identity":"54e0ddf3-e029-4414-b250-7509839402e6","order_by":6,"name":"Aihua Cao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDACZgiVwM/MfPgBaVok29nSDEiyLMHgPI+CBFFKDY7zHnxcUWOXZ3yYh8GAocYmmqAWyWa+ZMMzx5KLzQ7zHnjAcCwtt4GQFn5mHjPJxgbmxG2H+RIMGBsOE9bCxsxj/rOxoT5xczOPgQRRWkC2MDY2HE7cwEysFslmHmPJhmPHE2ccBgZyAjF+MTh/xvBjQ011Yn//4cMPPtTYENaCChJIUz4KRsEoGAWjABcAANJEOg0fGN/vAAAAAElFTkSuQmCC","orcid":"","institution":"Qilu Hospital of Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Aihua","middleName":"","lastName":"Cao","suffix":""}],"badges":[],"createdAt":"2024-08-06 15:08:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4869464/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4869464/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-98020-0","type":"published","date":"2025-04-14T15:57:07+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":64783695,"identity":"d711fbd4-2e6c-4568-a97a-80ef51190745","added_by":"auto","created_at":"2024-09-18 18:59:10","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1609199,"visible":true,"origin":"","legend":"\u003cp\u003eResearch design flowchart. (A) Overall mediation MR study design, 731 immune cells as exposure, neurodevelopmental disorders (ASD, Autism Spectrum Disorder; ADHD, Attention deficit hyperactivity disorder) as outcome, and 91 circulating inflammatory factors as exposure and mediator. (B) Three core hypotheses of MR studies. (C) The data analysis process for MR studies.\u003c/p\u003e","description":"","filename":"FIGURE1Researchdesignflowchart.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/73255dffeffd2bcd1dcac965.jpg"},{"id":64783759,"identity":"8290113d-ea2b-4bf3-b134-0699508960c3","added_by":"auto","created_at":"2024-09-18 18:59:17","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1489271,"visible":true,"origin":"","legend":"\u003cp\u003eMR results of 731 immune cells with ASD. (A) Forest plot of positive MR analysis results between 731 immune cells with ASD. MR, Mendelian randomization; ASD, autism spectrum disorder. (B) The results of Leave-one-out analysis between 731 immune cells with ASD. (C) Scatter plot of MR analysis results between 731 immune cells with ASD. (D) The heterogeneity and horizontal pleiotropy results between 731 immune cells with ASD. Heterogeneity analysis included MR Egger and IVW. Horizontal pleiotropy analysis used MR Egger intercept method.\u003c/p\u003e","description":"","filename":"FIGURE2MRresultsOF731immunecellwithASD.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/38a51f5aebd71881be51ff82.jpg"},{"id":64783871,"identity":"b9824ee2-5501-4e8d-a787-7338b258f261","added_by":"auto","created_at":"2024-09-18 18:59:39","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1654508,"visible":true,"origin":"","legend":"\u003cp\u003eMR results of 91 circulating inflammatory factors with ASD. (A) Forest plot of positive MR analysis results between 91 circulating inflammatory factors with ASD. (B) The results of Leave-one-out analysis between 91 circulating inflammatory factors with ASD. (C) Scatter plot of MR analysis results between 91 circulating inflammatory factors with ASD. (D) The heterogeneity and horizontal pleiotropy results between 91 circulating inflammatory factors with ASD. Heterogeneity analysis included MR Egger and IVW. Horizontal pleiotropy analysis used MR Egger intercept method.\u003c/p\u003e","description":"","filename":"FIGURE3MRresultsof91ciculatinginflammationfactorswithASD.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/dd88321bbe614c236fcb59fd.jpg"},{"id":64783803,"identity":"d2fc0b67-d84b-4c52-b633-958bcf9fe19d","added_by":"auto","created_at":"2024-09-18 18:59:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1607002,"visible":true,"origin":"","legend":"\u003cp\u003eMR results of 731 immune cells with ADHD. (A) Forest plot of positive MR analysis results between 731 immune cells with ADHD. MR, Mendelian randomization; ADHD, Attention Deficit Hyperactivity Disorder. (B) The results of Leave-one-out analysis between 731 immune cells with ADHD. (C) Scatter plot of MR analysis results between 731 immune cells with ADHD. (D) The heterogeneity and horizontal pleiotropy results between 731 immune cells with ADHD. Heterogeneity analysis included MR Egger and IVW. Horizontal pleiotropy analysis used MR Egger intercept method.\u003c/p\u003e","description":"","filename":"FIGURE4MRresultsof731immunecellswithADHD.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/3f6aaca19582b1f0f6bacc13.jpg"},{"id":64783756,"identity":"a0f22e3b-1036-4548-84d3-db07e40da582","added_by":"auto","created_at":"2024-09-18 18:59:16","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1330537,"visible":true,"origin":"","legend":"\u003cp\u003eMR results of 91 circulating inflammatory factors with ADHD. (A) Forest plot of positive MR analysis results between 91 circulating inflammatory factors with ADHD. (B) The results of Leave-one-out analysis between 91 circulating inflammatory factors with ADHD. (C) Scatter plot of MR analysis results between 91 circulating inflammatory factors with ADHD. (D) The heterogeneity and horizontal pleiotropy results between 91 circulating inflammatory factors with ADHD. Heterogeneity analysis included MR Egger and IVW. Horizontal pleiotropy analysis used MR Egger intercept method.\u003c/p\u003e","description":"","filename":"FIGURE5MRresultsof91ciculatinginflammationfactorswithADHD.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/2b5fdd590822513703429cf9.jpg"},{"id":64783384,"identity":"879cdf51-f5be-4ea0-bb9b-915f85543f19","added_by":"auto","created_at":"2024-09-18 18:58:51","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":950490,"visible":true,"origin":"","legend":"\u003cp\u003eMediation results. (A) Forest plot of positive MR analysis results between 731 immune cells with 91 circulating inflammatory factors. (B) The results of mediation MR analysis. (C) The results of Leave-one-out analysis between 3 immune cells with 3 circulating inflammatory factors. (D) The heterogeneity and horizontal pleiotropy results between 3 immune cells with 3 circulating inflammatory factors.\u003c/p\u003e","description":"","filename":"FIGURE6Mediationresluts.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/96e65c6ffb81dd473385847a.jpg"},{"id":81051502,"identity":"a5c2ed08-6c3c-4839-ba92-0f1a4171df12","added_by":"auto","created_at":"2025-04-21 16:10:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9403387,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/788c115f-cb30-4ba2-b460-8b87c4931732.pdf"},{"id":64783559,"identity":"f5d3c9c8-3763-49c5-993f-52ec289280a3","added_by":"auto","created_at":"2024-09-18 18:59:01","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":10427,"visible":true,"origin":"","legend":"","description":"","filename":"Table1.InformationofGWASdatasetsusedintheMRstudy..xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/a5ff117ab0d06d7e5dcbf2b8.xlsx"},{"id":64783808,"identity":"9e50084d-df26-4edf-a0da-44d0e10b347b","added_by":"auto","created_at":"2024-09-18 18:59:25","extension":"xls","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":491008,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.1538SNPsrelatedto731immunecells.xls","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/0a254ca72b8317161c16e1c6.xls"},{"id":64783563,"identity":"5170f908-5be6-49c3-96bf-cf269f881097","added_by":"auto","created_at":"2024-09-18 18:59:02","extension":"xls","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":561664,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.1784SNPsrelatedto91circulatinginflammatoryfactors.xls","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/665ecac3f500d70f7b28ac06.xls"},{"id":64783754,"identity":"dfcc0f23-e863-4318-89a8-65eed7c7e846","added_by":"auto","created_at":"2024-09-18 18:59:16","extension":"xls","order_by":11,"title":"","display":"","copyAsset":false,"role":"supplement","size":47616,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.ThecausaleffectsofASDandADHDon731immunecells.xls","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/2b1a270e993d2f6d135b2ed3.xls"},{"id":64783658,"identity":"0019f499-cb64-4314-9705-d33c4bf6ee14","added_by":"auto","created_at":"2024-09-18 18:59:07","extension":"xls","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":50176,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.ThereversecausaleffectsofASDandADHDon731immunecells.xls","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/2785640202ebf790818ae104.xls"},{"id":64783558,"identity":"d55755b3-92d1-4e15-a232-c06715098063","added_by":"auto","created_at":"2024-09-18 18:59:01","extension":"xls","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":37888,"visible":true,"origin":"","legend":"","description":"","filename":"TableS5.ThecausaleffectsofASDandADHDon91circulatinginflammatoryfactors.xls","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/bd38cb855d29f46d5d7f7d0c.xls"},{"id":64783547,"identity":"d0d1908e-5057-4a2c-ab2c-d78fe11a1c2f","added_by":"auto","created_at":"2024-09-18 18:58:59","extension":"xls","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":35840,"visible":true,"origin":"","legend":"","description":"","filename":"TableS6.ThereversecausaleffectsofASDandon91circulatinginflammatoryfactors.xls","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/cfd5df7751647fa3e1c57078.xls"},{"id":64783796,"identity":"e21e71ed-2775-46e0-aede-b5c42de53103","added_by":"auto","created_at":"2024-09-18 18:59:21","extension":"xls","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":35328,"visible":true,"origin":"","legend":"","description":"","filename":"TableS7.Thepostivecausaleffectsofimmunecellsoncirculatinginflammatoryfactors.xls","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/29380ecda8e645f0d77212a8.xls"},{"id":64783924,"identity":"1cae7713-3266-4f2c-a916-b87989bde00d","added_by":"auto","created_at":"2024-09-18 18:59:48","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":10845,"visible":true,"origin":"","legend":"","description":"","filename":"TableS8ThemediationMRresultsofpotentialmediator.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4869464/v1/3fe08ebe22be189034ca5258.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Immune cells, circulating inflammatory factors and neurodevelopmental disorders: a bidirectional mendelian randomization and mediation analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eFor the past few years, the global incidence of neurodevelopmental disorders (NDDs) has increased\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, causing significant economic, lifestyle, and psychological burdens on families and society. Childhood NDDs include autism spectrum disorder (ASD), attention deficit hyperactivity disorder (ADHD), specific learning disorders, communication disorders, intellectual disability, and motor disorders\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e, which is a range of disabilities due to various disruptions in brain development\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A key feature common to these disorders is their onset during childhood. Children with these conditions often experience learning difficulties, impacting their future education, employment, and social interactions. For example, Children with ASD often exhibit difficulties in social interaction ,communication, and restrictive or repetitive behaviors and interests\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. ADHD primarily manifests as an inability to maintain attention, hyperactivity, and impulsivity. These symptoms usually persist into adulthood.\u003c/p\u003e \u003cp\u003eCurrently, the pathogeneses of neurodevelopmental disorders were far from scientifically elucidated. Various views suggested that multiple factors contributed to these disorders, for example, maternal inflammation, epigenetic factors, and immune signal path\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Growing evidences showed that children with NDDs tend to exhibite abnormal immune and inflammatory responses, accompanied with immune-related diseases \u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. For instance, abnormal immune cells and dysregulated inflammatory cytokines have been identified in ASD and ADHD, nevertheless, the results of the different studies contradicted each other\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Compared to normal people, those with ASD showed various dysregulated immune cells and in their blood, cerebrospinal fluid, and brain tissue. These abnormalities included increased T cell, innate NK cells and monocytes\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. Additionally, the levels of inflammatory factors in the plasma of ASD were significantly elevated, such as interleukin-6(IL-6), IL-1β, IL-12, tumor necrosis factor-α (TNF-α), IL-8, and so on\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. However, other studies showed that IL-12 and IL-8 were no significant different in ASD and normal children. These inflammatory factors could cooperate with immune cells to jointly promote neuroinflammation in children with ASD\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. This process could promote neuroinflammation, alter synapse formation, and affect neuronal function in children with ASD\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In children with ADHD, autoimmune and allergic diseases were more prevalent, such as asthma and atopic dermatitis\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e,\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Inflammation and immune regulation deficiencies were potential factors in ADHD\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Various immune cells had been shown to contribute to the development of ADHD, including CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells, regulatory T cells (Treg cells)\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, and CD4\u0026thinsp;+\u0026thinsp;helper T cells\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. CD8\u0026thinsp;+\u0026thinsp;cytotoxic T cells invaded the nervous system through the blood-brain barrier, than disrupting the functions of microglia and neurons and participating in the pathogenesis of ADHD\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. A recent large-sample analysis revealed a positive correlation between ADHD and circulating inflammatory factors, the most important of which were IL-6, IL-13, IL-16, and TNF-α\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e,\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. However, another study reported that most protein levels in ADHD patients did not change, and TNF-α concentrations were lower compared to those in typically developing children\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAlthough numerous studies demonstrated that immune cells and circulating inflammatory factors played significant roles in the pathogenesis of NDDs, the current study did not draw accurate and non-heterogeneous conclusions. using genetic variation as an instrumental variable, mendelian randomization (MR) mitigate confounding bias and reverse causation, by leveraging the random assignment of genetic variants. MR can simulate a randomized controlled trial\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Therefore, we employed the MR approach to determine more causal relationships between immune cells(exposure), circulating inflammatory factors (exposure and mediator), and NDDs(outcome). Additionally, we explored the mediating role of circulating inflammatory factors between immune cells and NDDs.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cp\u003eStudy design\u003c/p\u003e \u003cp\u003eTo scientifically evaluate the casual relationship between 731 immune cells, 91 circulating inflammatory factors, and NDDs (ASD and ADHD). At the same time, by using mediation analysis we evaluated the mediation effect of 91 circulating inflammatory factors between 731 immune cells and NDDs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Firstly, as is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, MR must satisfy three important hypotheses\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e,\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e: (1) Correlation hypothesis: instrumental variables (IVs) are strongly related to exposure; (2) Exclusivity hypothesis: IVs can only affect outcomes by influencing exposure, and cannot affect outcomes by other ways; (3) Independence assumption: IVs are not related to confounder.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eData source\u003c/p\u003e \u003cp\u003eAll data utilized in this study were sourced from Genome-Wide Association Study (GWAS) databases. All clinical participants in the original study used in this study legally signed written informed consent. (1) Exposure factors: the raw data of 731 immune cells were originated from a 2020 gene sequencing study of 3,757 Sardinians (Open GWAS: From ebi-a-GCST90001399 to ebi-a-GCST90002121)\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. (2) The data for both exposure and mediator factors: the newest 91 circulating inflammatory factors were derived from a 2023 investigation of 14,824 Europeans, focusing on genetic loci associated with plasma levels of inflammation-related proteins (Study registration: GCST90274758 to GCST90274848)\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. (3) Outcome factors: the raw data for ASD were sourced from a 2017 meta-analysis involving 18,381 ASD cases and 27,969 controls, diagnosing by ICD-9 or ICD-10 (Open GWAS: ieu-a-1185)\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Similarly, the raw data for ADHD came from the Psychiatric Genomics Consortium (PGC) study in 2017, encompassing 20,183 ADHD cases and 35,191 controls, diagnosing by ICD-9 or ICD-10\u003csup\u003e31\u003c/sup\u003e. All of the above GWAS data went through the following procedures: data quality control, genotype preprocessing, phenotypic preprocessing, association analysis and result integration. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e provides a comprehensive overview of the specific GWAS datasets employed in the research, detailing their origins and study identifiers.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eInformation of GWAS datasets used in the MR study\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGWAS ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrait\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eData Source\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCases\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePopulation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePMID\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrom ebi-a-GCST90001399 to ebi-a-GCST90002121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e731 Immune cells\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIEU Open GWAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3,757\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePMC8517961\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrom GCST90274758 to GCST90274848\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e91 circulating inflammatory factors\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpen GWAS database\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePMC10457199\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eieu-a-1185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAutism Spectrum Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eiPSYCH-PGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e46,351\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePMC5441062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eieu-a-1183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAttention Deficit Hyperactivity Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eiPSYCH-PGC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e55,374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEuropean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePMC5992329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eInstrumental variable selection\u003c/p\u003e \u003cp\u003eSNPs meeting the following criteria were selected as IVs for MR, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. Firstly, a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5e-6 was used to screen SNPs related to 731 immune cells and 91 circulating inflammatory factors, because SNPs that were selected by stricter threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5e-8 were not suited to MR analysis. And this method (threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;5e-6) of appropriately relaxing the IVs selection threshold has been widely used in previous studies\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e,\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Additionally, to remove SNPs in linkage disequilibrium, criteria were set at R2\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and kb\u0026thinsp;=\u0026thinsp;10,000\u003csup\u003e34\u003c/sup\u003e. Finally, to further ensure the validity and relevance of MR, palindrome SNPs and weak instrumental SNPs (F\u0026thinsp;\u0026lt;\u0026thinsp;10) were excluded\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. The F statistic is calculated using the formula: F\u0026thinsp;=\u0026thinsp;R2 \u0026times; (N\u0026thinsp;\u0026minus;\u0026thinsp;k\u0026minus;1)/k \u0026times; (1\u0026thinsp;\u0026minus;\u0026thinsp;R2). Through these three steps, we selected 1,538 SNPs that were closely related to the 731 immune cells (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), and 1,784 SNPs closely related with the 91 circulating inflammatory factors were identified (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). These selected SNPs laid a solid foundation for subsequent MR analysis, enhancing its scientific rigor.\u003c/p\u003e \u003cp\u003eStatistical analyses\u003c/p\u003e \u003cp\u003eThe analysis in this study encompassed two main aspects, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC: bidirectional two-sample MR analysis and mediation analysis. It was widely known that Inverse Variance Weighted (IVW) was the primary MR method\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e, other three methods as supplementary. In the lack of pleiotropy, the IVW method provide unbiased and accurate estimates, and an IVW p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is widely recognized for indicating a significant causal relationship\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. At the same time, we used the False Discovery Rate (FDR) adjustment for the IVW p-value, adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 is considered to indicate a significant causal relationship between exposure and outcome\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Next, we conducted sensitivity analyses using three methods: leave-one-out analysis, heterogeneity testing (Cochran\u0026rsquo;s Q test), and pleiotropy testing (MR-Egger intercept test). three methods with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating the presence of heterogeneity and horizontal pleiotropy, which can affect the stability of the IVW results\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e,\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Pleiotropy was tested using the MR-Egger intercept test, with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicating the presence of horizontal pleiotropy, which can affect the stability of the IVW results\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. The leave-one-out analysis evaluated the influence of each single SNP on the MR results by removing one SNP at a time\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe main steps of this mediation MR analysis had two steps, shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. The mediation effect was calculated as β1\u0026thinsp;\u0026times;\u0026thinsp;β2, and the mediation ratio was calculated as (β1\u0026thinsp;\u0026times;\u0026thinsp;β2) /direct effect, mediation ratio represented the proportion of the causal relationship from exposure to outcome that was mediated by the intermediate factors. Based on mediation effect, we classified the discoverable mediators into different levels of evidence. When only a triangular relationship being (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), three factors contain a potential mediation role. If there was a triangular relationship and the mediation effect was significantly different from 0, it indicated a significant mediating role. Triangular relationship meant there was a causal relationship between immune cells and NDDs, a relationship between circulating inflammatory factors and NDDs, and a causal relationship between immune cells and circulating inflammatory factors.\u003c/p\u003e \u003cp\u003eAll MR analysis progress were made using the R package in R.4.3.2, including \u0026ldquo;TwoSample MR\u0026rdquo; and \u0026ldquo;Mendelian Randomization\u0026rdquo;.\u003c/p\u003e"},{"header":"3 Result","content":"\u003cp\u003eIdentification of the causal effect of 731 immune cells on ASD\u003c/p\u003e \u003cp\u003eWe identified 1,538 SNPs associated with immune cells (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Initial analysis using the IVW method revealed that 13c types of immune cells demonstrated a significant causal relationship with ASD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). According to the IVW odds ratio (OR) analysis, except for CCR2 on CD62L\u0026thinsp;+\u0026thinsp;myeloid DC, 12 immune cells showed a positive correlation with ASD risk (OR\u0026thinsp;\u0026gt;\u0026thinsp;1). The OR directions of these 12 immune cells were consistent across four methods, including IVW, MR-Egger, Weighted Median, and Weighted Mode, indicating their potential role in promoting incidence of ASD (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). After FDR correction of IVW p-values (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), CD4 on activated Treg had an adjusted p-value of 0.44002667 (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), indicating no causal relationship. The remaining 12 immune cells reached statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting a significant pathogenic association with ASD. Sensitivity and heterogeneity analyses showed no statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) for all results (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). The MR-Egger intercept tests indicated no horizontal pleiotropy (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and Cochran's Q tests suggested no heterogeneity in the MR results (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Leave-one-out analysis demonstrated that removing a single SNP did not alter the MR results, with all leave-one-out results presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. To validate the causal relationships between the 13 immune cells and ASD, we performed a reverse MR analysis. The IVW analysis of ASD on these 13 immune cells showed no significant reverse causal relationships (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), shown in Table \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, all of the above analysis results confirmed a robust and significant causal relationship between 12 immune cells and ASD, including CD62L- plasmacytoid DC %DC, TD CD8br AC, CD8br %leukocyte, CCR2 on CD62L\u0026thinsp;+\u0026thinsp;myeloid DC, CD8br and CD8dim %leukocyte, CD20 on IgD\u0026thinsp;+\u0026thinsp;B cell, CD28- CD8dim %T cell, IgD\u0026thinsp;+\u0026thinsp;CD38- %lymphocyte, CD127- CD8br AC, CD3 on HLA DR\u0026thinsp;+\u0026thinsp;CD8br, FSC-A on plasmacytoid DC, and CD20 on B cell.\u003c/p\u003e \u003cp\u003eIdentification of the causal effect of 91 circulating inflammatory factors on ASD\u003c/p\u003e \u003cp\u003eIn the second step, we identified 1,784 SNPs associated with inflammatory factors (Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), and verified the causal relationship between 91 circulating inflammatory factors and ASD. Initial analysis using the IVW method revealed that 9 circulating inflammatory factors demonstrated a significant causal relationship with ASD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). According to the IVW odds ratio (OR) analysis, 5 circulating inflammatory factors showed a positive correlation with ASD risk (OR\u0026thinsp;\u0026gt;\u0026thinsp;1), and the OR directions of these 5 factors were consistent across all methods (IVW, MR-Egger, Weighted Median, and Weighted Mode), indicating their potential role in promoting incidence of ASD (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). These factors included Natural killer cell receptor 2B4 levels, Fms-related tyrosine kinase 3 ligand levels, IL-18-R1, T-cell surface glycoprotein CD5 levels, and TNF-related apoptosis-inducing ligand levels. Three circulating inflammatory factors showed a negative correlation with ASD risk (OR\u0026thinsp;\u0026lt;\u0026thinsp;1), and their OR directions were consistent across all four methods, indicating a protective effect against ASD. These factors included Interleukin-2 levels, Interleukin-2 receptor subunit beta levels, and Interleukin-7 levels. Sulfotransferase 1A1 levels showed a positive correlation with ASD, but the MR-Egger result was negative, indicating inconsistency across the four methods. After FDR correction of the IVW p-values (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e), the p-values for the 9 circulating inflammatory factors reached statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting a significant causal association with ASD. Sensitivity and heterogeneity analyses showed that TNF-related apoptosis-inducing ligand levels had heterogeneity (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) but no horizontal pleiotropy. Leave-one-out analysis indicated no SNP affecting the robustness of the results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). For the remaining 8 circulating inflammatory factors, sensitivity and heterogeneity analyses showed no statistical significance (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Leave-one-out analysis also indicated that removing a single SNP did not alter the MR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). To further validate the causal relationships between the 9 circulating inflammatory factors and ASD, we performed reverse MR analysis. The IVW analysis of ASD on these 9 circulating inflammatory factors showed no significant reverse causal relationships (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOverall, these analyses confirmed a robust and significant causal relationship between 7 circulating inflammatory factors and ASD, including IL-2β, IL-7, T-cell surface glycoprotein CD5 levels, Natural killer cell receptor 2B4 levels, IL-18-R1, IL-2, and Fms-related tyrosine kinase 3 ligand levels.\u003c/p\u003e \u003cp\u003eIdentification of the causal effect of 731 immune cells on ADHD\u003c/p\u003e \u003cp\u003eThe IVW results of immune cells and ADHD (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA) indicated an initial causal relationship between 14 immune cells and ASD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The OR direction of HLA DR\u0026thinsp;+\u0026thinsp;T cell %T cell was inconsistent across the four analysis methods, and after FDR correction of the IVW P-value (Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e), it did not have a statistically significant causal relationship (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The OR directions of the remaining 13 cells were consistent across the four analysis methods, and after FDR correction, the P-values for these 13 immune cells reached statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a significant causal relationship with ADHD. Among these 13 immune cells, 6 immune cells were positively correlated with ADHD risk (OR\u0026thinsp;\u0026gt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), including CD27 on IgD- CD38dim, CD27 on sw mem, CD27 on memory B cell, CD4 on activated Treg, CD27 on unsw mem, and CD3 on naive CD8br. 7 immune cells were negatively correlated with ADHD risk (OR\u0026thinsp;\u0026lt;\u0026thinsp;1), serving as protective factors against ASD, including CD40 on CD14\u0026thinsp;+\u0026thinsp;CD16\u0026thinsp;+\u0026thinsp;monocyte, SSC-A on monocyte, IgD on IgD\u0026thinsp;+\u0026thinsp;CD24+,, CD40 on CD14- CD16\u0026thinsp;+\u0026thinsp;monocyte, CD40 on monocytes, CD40 on CD14\u0026thinsp;+\u0026thinsp;CD16- monocyte, and SSC-A on CD14\u0026thinsp;+\u0026thinsp;monocyte. Sensitivity and heterogeneity analyses of the study results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD) showed that the MR-Egger intercept test and Cochran's test P-values for the 14 immune cells were all bigger than 0.05, indicating no heterogeneity or horizontal pleiotropy in the MR results. The leave-one-out analysis also confirmed the robustness of the MR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Subsequently, reverse MR analysis of ADHD and the 14 immune cells showed no reverse causal relationship (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan refid=\"MOESM5\" class=\"InternalRef\"\u003eS5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, all these analyses demonstrated a robust and significant causal relationship between 13 immune cells and ADHD, including CD40 on monocytes, CD27 on memory B cell, CD27 on sw mem, CD4 on activated Treg, CD3 on naive CD8br, SSC-A on monocyte, IgD on IgD\u0026thinsp;+\u0026thinsp;CD24+, CD40 on CD14\u0026thinsp;+\u0026thinsp;CD16\u0026thinsp;+\u0026thinsp;monocyte, CD27 on IgD- CD38dim, CD40 on CD14- CD16\u0026thinsp;+\u0026thinsp;monocyte, CD40 on CD14\u0026thinsp;+\u0026thinsp;CD16- monocyte, CD27 on unsw mem, and SSC-A on CD14\u0026thinsp;+\u0026thinsp;monocyte.\u003c/p\u003e \u003cp\u003eIdentification of the causal effect of 91 circulating inflammatory factors on ADHD\u003c/p\u003e \u003cp\u003eThe IVW results of circulating inflammatory factors and ADHD (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA) indicated an initial causal relationship between 7 circulating inflammatory factors and ADHD (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After FDR correction, the P-values for these 7 circulating inflammatory factors reached statistical significance (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating a significant causal relationship with ADHD (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e). Among these, 3 circulating inflammatory factors were positively correlated with ADHD risk (OR\u0026thinsp;\u0026gt;\u0026thinsp;1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), including Eotaxin levels, GDNF, and TNF-related activation-induced cytokine levels. 4 circulating inflammatory factors were negatively correlated with ADHD risk (OR\u0026thinsp;\u0026lt;\u0026thinsp;1), serving as protective factors against ADHD, including Adenosine Deaminase levels, FGF-23, CD40L receptor levels, and Urokinase-type plasminogen activator levels. Sensitivity and heterogeneity analyses of the study results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD) showed that MR-Egger intercept test and Cochran's test P-values for the 7 circulating inflammatory factors were all greater than 0.05, indicating no heterogeneity or horizontal pleiotropy in the MR results. The leave-one-out analysis also confirmed the robustness of the MR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). Subsequently, reverse MR analysis of ADHD and the 7 inflammation factors showed no reverse causal relationship (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (Table \u003cspan refid=\"MOESM6\" class=\"InternalRef\"\u003eS6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, all these analyses demonstrated a robust and significant causal relationship between 7 circulating inflammatory factors and ADHD, including Eotaxin levels, GDNF, FGF-23, TNF-related activation-induced cytokine levels, Adenosine Deaminase levels, CD40L receptor levels, and Urokinase-type plasminogen activator levels.\u003c/p\u003e \u003cp\u003eIdentification of the mediation effect of ASD and ADHD\u003c/p\u003e \u003cp\u003eBased on the previous MR results with four different exposures and outcomes, we have confirmed various immune cells and circulating inflammatory factors with strong causal relationships with neurodevelopmental disorders. To determine the mediation effect of circulating inflammatory factors between immune cells and NDDs (ASD and ADHD), we employed the mediation MR method.\u003c/p\u003e \u003cp\u003eFor ASD, we identified 12 significantly related immune cells and 7 significantly related circulating inflammatory factors. We then selected these 12 immune cells (exposures) and the 7 circulating inflammatory factors (outcomes) for the MR analysis, obtaining the β1 results for immune cells on circulating inflammatory factors. The MR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) showed that FSC-A on plasmacytoid DC and Natural killer cell receptor 2B4 levels had significant IVW results (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the FDR-adjusted p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). There was no heterogeneity or horizontal pleiotropy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), and had high robustness of the MR results that confirmed by leave-one-out analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Therefore, FSC-A on plasmacytoid DC as exposure and Natural killer cell receptor 2B4 levels as mediator with ASD as the outcome formed a triangular relationship. The mediation effect was significant with a mediation proportion of 19.9% (95%CI: 1.62%, 41.4%) and P\u0026thinsp;=\u0026thinsp;0.04996888 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Additionally, Sulfotransferase 1A1 levels as a mediator between CD28-CD8dim %T cell and ASD also formed a triangular relationship (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e) with a potential mediation effect (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e), mediation proportion \u0026minus;\u0026thinsp;14.4% (95%CI: -33%, 4.15%), P\u0026thinsp;=\u0026thinsp;0.127929513.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor ADHD, we identified 13 significantly related immune cells and 7 significantly related circulating inflammatory factors. We selected these 13 immune cells (exposures) and the 7 circulating inflammatory factors (outcomes) for the MR analysis, obtaining the β1 results for immune cells on circulating inflammatory factors. The MR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) showed that CD27 on sw mem and Fibroblast growth factor 23 levels had significant IVW results (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and the FDR-adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). There was no heterogeneity or horizontal pleiotropy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), and the leave-one-out analysis confirmed the robustness of the MR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Therefore, CD27 on sw mem as exposure and Fibroblast growth factor 23 levels as mediator with ADHD as the outcome form a triangular relationship. The mediation effect was significant with a mediation proportion of 11% (95%CI: 0.156%, 21.8%) and P\u0026thinsp;=\u0026thinsp;0.046777517, indicating that Fibroblast growth factor 23 levels significantly mediated the relationship between CD27 on sw mem and ADHD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Similarly, Eotaxin levels significantly mediated the relationship between CD27 on memory B cell and ADHD (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB), forming a triangular relationship with significant IVW results (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and FDR-adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 (Table \u003cspan refid=\"MOESM7\" class=\"InternalRef\"\u003eS7\u003c/span\u003e). There was no heterogeneity or horizontal pleiotropy (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD), and the leave-one-out analysis confirmed the robustness of the MR results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), mediation proportion \u0026minus;\u0026thinsp;5.95% (95%CI: -11.3%, -0.623%), P\u0026thinsp;=\u0026thinsp;0.028573986. Furthermore, we found that CD40L receptor levels potentially mediated multiple immune cells and ADHD (Table \u003cspan refid=\"MOESM8\" class=\"InternalRef\"\u003eS8\u003c/span\u003e and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), including, CD27 on memory B cell,, CD27 on sw mem, CD40 on CD14\u0026thinsp;+\u0026thinsp;CD16- Monocyte, CD27 on unsw mem, CD40 on CD14\u0026thinsp;+\u0026thinsp;CD16\u0026thinsp;+\u0026thinsp;monocyte, CD27 on IgD- CD38dim, CD40 on CD14- CD16\u0026thinsp;+\u0026thinsp;monocyte, and CD40 on monocytes.\u003c/p\u003e \u003cp\u003eIn conclusion, we identified 12 mediation relationships: 3 strong evidences and 9 potential evidences. Natural killer cell receptor 2B4 levels strongly mediated the relationship between FSC-A on plasmacytoid DC and ASD. Fibroblast growth factor 23 levels significantly mediated the relationship between CD27 on sw mem and ADHD. Eotaxin levels significantly mediated the relationship between CD27 on memory B cell and ADHD. Sulfotransferase 1A1 levels potentially mediated the relationship between CD28-CD8dim %T cell and ASD. CD40L receptor levels potentially mediated the relationships between multiple immune cells and ADHD.\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study, using MR analysis, demonstrated the strongly and potential causal relationships between immune cells, circulating inflammatory factors and NDDs (ASD and ADHD), and the mediation role of inflammatory factors between immune cells and NDDs. Immune cells and inflammatory factors played an important role in the pathogenesis of NDDs. Through MR Analysis, we can reveal which abnormal immune cells and inflammatory factors affect the onset and progression of NDDs, and provide future therapeutic targets for NDDs.\u003c/p\u003e \u003cp\u003eASD may be related to immune balance disorder, involving the imbalance of inflammatory factors and autoimmune disorders\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. In the MR analysis of 731 immune cells and ASD, six T cells were found to be positively correlated with ASD, which were all marked by CD8br, including CD8br %leukocyte, TD CD8br AC, CD8br and CD8dim %leukocyte, CD28- CD8dim %T cell, CD127- CD8br AC, and CD3 on HLA DR\u0026thinsp;+\u0026thinsp;CD8br. This suggested that CD8\u0026thinsp;+\u0026thinsp;T cells may greatly promote the onset of ASD. A clinical trial by Lopez-Cacho JM et al confirmed that the number of CD8\u0026thinsp;+\u0026thinsp;T cells in ASD patients was higher than in healthy individuals, indicating a positive correlation between ASD and CD8\u0026thinsp;+\u0026thinsp;T cells\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. A mouse experiment demonstrated that CD8\u0026thinsp;+\u0026thinsp;T cells could lead to ASD by affecting neural progenitor cells, resulting in brain NDDs and ASD behaviors in mice\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Another study on postmortem brain tissue of ASD patients provided more direct evidence that immune cells damaged the cerebrospinal fluid (CSF)\u0026ndash;brain barrier. Among immune cells, CD3\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;T cells were the most prevalent, with a few CD4\u0026thinsp;+\u0026thinsp;T cells and CD20\u0026thinsp;+\u0026thinsp;B cells \u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. In brain glial cells, CD8\u0026thinsp;+\u0026thinsp;T cells produced cytotoxic effector molecules such as granzyme B, causing abnormal membrane vesicles in GFAP\u0026thinsp;+\u0026thinsp;astrocytes in the brains of ASD patients\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. ASD children also had increased numbers of CD3\u0026thinsp;+\u0026thinsp;TIM-3\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;TIM-3\u0026thinsp;+\u0026thinsp;cells compared to typically developing (TD) controls\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Therefore, T cells expressing CD8 and CD3 might be pathogenic immune cells in ASD.\u003c/p\u003e \u003cp\u003eNext, this study found that two types of plasmacytoid DCs were positively correlated with ASD: FSC-A on plasmacytoid DC and CD62L- plasmacytoid DC %DC. A Spain study indicated a significant increase in the frequency of bone marrow dendritic cells in ASD children. Plasmacytoid dendritic cells (pDCs) were associated with the amygdala volume and developmental regression in ASD children\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. pDCs performed antigen presentation and stimulated other immune cells, primarily through the secretion of inflammatory factors such as IFN-I. Our results also showed that 19.9% of the causal effect of FSC-A on plasmacytoid DC on ASD was mediated by Natural killer cell receptor 2B4 levels. Therefore, the effect of pDCs on ASD may be partially mediated by inflammatory factors. Then three B cells were positively correlated with ASD: IgD\u0026thinsp;+\u0026thinsp;CD38- %lymphocyte, CD20 on IgD\u0026thinsp;+\u0026thinsp;B cell, and CD20 on B cell. CD20 B cells damaged the CSF-brain barrier in ASD brain tissue, but there was no significant difference in B cell counts in peripheral blood\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e, indicating the need for larger clinical studies to fully confirm the relationship between B cells and ASD.\u003c/p\u003e \u003cp\u003eIn the MR analysis of 91 circulating inflammatory factors and ASD, three interleukins\u0026mdash;IL-7, IL-2, and IL-2 Rb\u0026mdash;were significantly negatively associated with ASD risk, while IL-18R1 and TNF-related apoptosis-inducing ligand (TRAIL) were positively associated with ASD development. Vojdani A et al. showed the peripheral blood levels and mRNA expression of IL-2 are lower in children with ASD\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Also IL-2 and IL-7 levels were negatively correlated with stereotypic behaviors\u003csup\u003e\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e and intellectual scores in ASD\u003csup\u003e\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e. The levels of IL-18 are elevated in the brains of children with ASD\u003csup\u003e\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, which activated astrocytes and lead to neuroinflammation and subsequent cognitive dysfunction\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Zhao H et al. reported elevated levels of TNF-α in the peripheral blood of ASD patients \u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e and positively correlated with the severity of ASD symptoms\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. A co-culture study of mice and human neurons revealed that TNF-α can upregulate glutamate levels, inducing neurotoxicity and promoting neuronal death and apoptosis. Using glutaminase inhibitors can alleviate this neurotoxicity\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e, suggesting potential therapeutic approaches for ASD children.\u003c/p\u003e \u003cp\u003eIn the MR analysis of 731 immune cells and ADHD, our results indicated that B cells, monocytes, and T cells had a causal relationship with the onset of ADHD, and these findings were consistent with the results for ASD, suggesting CD8\u0026thinsp;+\u0026thinsp;and CD3\u0026thinsp;+\u0026thinsp;T cells were potential common immune cells in NDDs. First, regarding T cells, CD3 on naive CD8br and CD4 on activated Treg were positively correlated with ADHD. Looman KIM et al. found peripheral blood immune cells in 756 children found that higher levels of helper T cell 1 (Th1) and CD8\u0026thinsp;+\u0026thinsp;T cells were associated with higher attention problem scores\u003csup\u003e\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e. A turkey case-control study showed a positive correlation between CD3\u0026thinsp;+\u0026thinsp;CD4\u0026thinsp;+\u0026thinsp;CD25\u0026thinsp;+\u0026thinsp;Foxp3+ (Tregs) and ADHD\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, consistent with our results.\u003c/p\u003e \u003cp\u003eSecondly, our study found that four B cells expressing CD27 were positively correlated with ADHD: CD27 on unswitched memory, CD27 on switched memory, CD27 on IgD- CD38dim, and CD27 on memory B cells. A Stanford University cohort study indicated that children with certain atopic diseases had higher numbers of memory Treg cells, total B cells, and CD27\u0026thinsp;+\u0026thinsp;IgA\u0026thinsp;+\u0026thinsp;memory B cells\u003csup\u003e\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e. And these diseases had a strong correlation with ADHD, such as atopic dermatitis\u003csup\u003e\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e and food allergies\u003csup\u003e\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e. Therefore, ADHD may be closely related to CD27\u0026thinsp;+\u0026thinsp;B cells. Moreover, subsequent mediation analysis showed that the causal effect of two CD27\u0026thinsp;+\u0026thinsp;B cells on ADHD was realized through circulating inflammation faactors, but further studies were needed in the future.\u003c/p\u003e \u003cp\u003eThirdly, our results indicated that four monocyte types expressing CD40 were negatively correlated with ADHD, and the inflammatory cytokine CD40L receptor levels were also negatively correlated with ADHD. Avcil S et al. put forword that the monocyte/lymphocyte ratio (MLR) potentially served as a peripheral blood inflammatory marker for ADHD\u003csup\u003e\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. 1α,25-dihydroxyvitamin D3 (1,25(OH)2D3) interfered with the effects of CD40L on immunomodulatory and inflammatory responses, so drugs that reduced the amount of 1,25(OH)2D3 may help treat ADHD\u003csup\u003e\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e\u003c/sup\u003e. Therefor our research suggests that monocytes expressing CD40 and CD40L receptor levels are closely associated with ADHD, and may influenced ADHD risk by interacting with multiple inflammatory factors\u003c/p\u003e \u003cp\u003eNumerous studies have shown that inflammatory factors play a significant role in the development of ADHD, and our results also suggested that these factors could be crucial in ADHD pathogenesis. Firstly, TNF-α was shown to increase the risk of both ASD and ADHD in MR analyses, with clinical data supporting higher peripheral blood TNF-α levels in ADHD patients, correlating positively with hyperactivity symptoms\u003csup\u003e\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u003c/sup\u003e. Thus, TNF-α could be a potential pathogenic inflammatory factor in NDDs.\u003c/p\u003e \u003cp\u003eIn our MR results, Fibroblast Growth Factor 23 (FGF-23) was protective against ADHD, whereas Glial cell line-derived neurotrophic factor levels (GDNF) promoted ADHD development. Both FGF-23 and GDNF were growth factors that regulated neurogenesis, differentiation, development, gliogenesis, and synaptogenesis, thereby influencing cognition\u003csup\u003e\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e\u003c/sup\u003e. Bilgic A and Yurteri N all proved elevated GDNF levels and decreased FGF levels in children with ADHD, consistent with our findings\u003csup\u003e\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e,\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u003c/sup\u003e. In mouse experiments, disruption of the FGFR gene led to increased spontaneous movement and a reduction in cortical inhibitory neurons. FGFR agonists significantly reduced hyperactivity\u003csup\u003e\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u003c/sup\u003e, and pathway analysis confirmed FGFR's role in ADHD etiology by activating FGFR1b and FGFR2b pathways\u003csup\u003e\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u003c/sup\u003e. Therefore, FGFR agonists and GDNF inhibitors may serve as new therapeutic targets for alleviating ADHD symptoms.\u003c/p\u003e \u003cp\u003eAlthough the MR analysis in this study provided new insights into the causal relationships between immune cells, inflammatory factors, and NDDs, and offered advantages in reducing confounding and reverse causation, several limitations must be acknowledged. Firstly, all GWAS data used in this study were derived from European populations. Secondly, this study only established causal relationships between exposures and outcomes, leaving the underlying mechanisms unexplored and necessitating further research. Thirdly, while our sensitivity analyses did not show evidence of pleiotropy or heterogeneity, there may still be unknown confounding factors that could introduce bias into the results. Lastly, although related studies suggest potential interaction pathways for the mediation analysis results, the lack of conclusive literature support requires further validation through clinical trials. Therefore, we plan to make improvements in the future: firstly, we should continuously enrich the GWAS database; Secondly, clinical research and basic research should be continued to further explore causation and mechanism analysis.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eIn summary, this study identified causal relationships between immune cells, circulating inflammatory proteins, and NDDs, establishing three mediators with strong evidence. These relationships could serve as valuable biomarkers and potential targets for understanding the biological mechanisms of NDDs and developing new therapies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of Interest\u003c/h2\u003e \u003cp\u003eThe authors affirm that this research was conducted without any commercial or financial involvement that could be interpreted as a potential conflict of interest.\u003c/p\u003e \u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflict of interest\u003c/h2\u003e \u003cp\u003e \u003cb\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/b\u003e \u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the Qilu Hospital of Shandong University Pediatric development fund.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZYL: Writing \u0026ndash; Conceptualization, study design, Supervision. LHW: Writing \u0026ndash; original draft, MR analysis. KHY: Writing \u0026ndash; introduction and discussion. YHZ: Writing \u0026ndash; results and methods, mediation analysis. MNZ: Writing \u0026ndash;introduction and discussion, Formal analysis. YW: Revising after reviewing the manuscript. AHC: Writing \u0026ndash; review \u0026amp; editing, Supervision, Conceptualization.\u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors gratefully acknowledge the invaluable contributions of all the participants to the GWAS.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll original data supporting the findings of this study are included in the article and its Supplementary Material. For additional information or inquiries, please contact the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSalari, N. et al. The global prevalence of autism spectrum disorder: a comprehensive systematic review and meta-analysis. \u003cem\u003eItal. J. Pediatr.\u003c/em\u003e \u003cb\u003e48\u003c/b\u003e, 112 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsmail, F. Y. \u0026amp; Shapiro, B. K. What are neurodevelopmental disorders? \u003cem\u003eCurr. Opin. Neurol.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 611\u0026ndash;616 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThapar, A., Cooper, M. \u0026amp; Rutter, M. Neurodevelopmental disorders. \u003cem\u003eLancet Psychiatry\u003c/em\u003e. \u003cb\u003e4\u003c/b\u003e, 339\u0026ndash;346 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKodak, T. \u0026amp; Bergmann, S. Autism Spectrum Disorder: Characteristics, Associated Behaviors, and Early Intervention. \u003cem\u003ePediatr. Clin. North. Am.\u003c/em\u003e \u003cb\u003e67\u003c/b\u003e, 525\u0026ndash;535 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan, V. X., Patel, S., Jones, H. F. \u0026amp; Dale, R. C. Maternal immune activation and neuroinflammation in human neurodevelopmental disorders. \u003cem\u003eNat. Rev. Neurol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 564\u0026ndash;579 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCohly, H. H. \u0026amp; Panja, A. Immunological findings in autism. \u003cem\u003eInt. Rev. Neurobiol.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 317\u0026ndash;341 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasi, A., Glozier, N., Dale, R. \u0026amp; Guastella, A. J. The Immune System, Cytokines, and Biomarkers in Autism Spectrum Disorder. \u003cem\u003eNeurosci. Bull.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 194\u0026ndash;204 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCareaga, M. et al. Immune Endophenotypes in Children With Autism Spectrum Disorder. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e. \u003cb\u003e81\u003c/b\u003e, 434\u0026ndash;441 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBjorklund, G. et al. Immune dysfunction and neuroinflammation in autism spectrum disorder. \u003cem\u003eActa Neurobiol. Exp. (Wars)\u003c/em\u003e. \u003cb\u003e76\u003c/b\u003e, 257\u0026ndash;268 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEllul, P. et al. Regulatory T lymphocytes/Th17 lymphocytes imbalance in autism spectrum disorders: evidence from a meta-analysis. \u003cem\u003eMol. Autism\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 68 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKordulewska, N. K. et al. Serum cytokine levels in children with spectrum autism disorder: Differences in pro- and anti-inflammatory balance. \u003cem\u003eJ. Neuroimmunol.\u003c/em\u003e \u003cb\u003e337\u003c/b\u003e, 577066 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, H. et al. Association of Peripheral Blood Levels of Cytokines With Autism Spectrum Disorder: A Meta-Analysis. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e12\u003c/b\u003e, 670200 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeltzer, A. \u0026amp; Van de Water, J. The Role of the Immune System in Autism Spectrum Disorder. \u003cem\u003eNeuropsychopharmacology\u003c/em\u003e. \u003cb\u003e42\u003c/b\u003e, 284\u0026ndash;298 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBordeleau, M., Fernandez de Cossio, L., Chakravarty, M. M. \u0026amp; Tremblay, M. E. From Maternal Diet to Neurodevelopmental Disorders: A Story of Neuroinflammation. \u003cem\u003eFront. Cell. Neurosci.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 612705 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones, K. L. et al. Autism with intellectual disability is associated with increased levels of maternal cytokines and chemokines during gestation. \u003cem\u003eMol. Psychiatry\u003c/em\u003e. \u003cb\u003e22\u003c/b\u003e, 273\u0026ndash;279 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCortese, S. et al. Association between attention deficit hyperactivity disorder and asthma: a systematic review and meta-analysis and a Swedish population-based study. \u003cem\u003eLancet Psychiatry\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e, 717\u0026ndash;726 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNielsen, T. C. et al. Association of Maternal Autoimmune Disease With Attention-Deficit/Hyperactivity Disorder in Children. \u003cem\u003eJAMA Pediatr.\u003c/em\u003e \u003cb\u003e175\u003c/b\u003e, e205487 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLeffa, D. T., Torres, I. L. S. \u0026amp; Rohde, L. A. A Review on the Role of Inflammation in Attention-Deficit/Hyperactivity Disorder. \u003cem\u003eNeuroimmunomodulation\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 328\u0026ndash;333 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuintero, J., Gutierrez-Casares, J. R. \u0026amp; Alamo, C. Molecular Characterisation of the Mechanism of Action of Stimulant Drugs Lisdexamfetamine and Methylphenidate on ADHD Neurobiology: A Review. \u003cem\u003eNeurol. Ther.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1489\u0026ndash;1517 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCetin, F. H. et al. Regulatory T cells in children with attention deficit hyperactivity disorder: A case-control study. \u003cem\u003eJ. Neuroimmunol.\u003c/em\u003e \u003cb\u003e367\u003c/b\u003e, 577848 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKipnis, J. Multifaceted interactions between adaptive immunity and the central nervous system. \u003cem\u003eScience\u003c/em\u003e. \u003cb\u003e353\u003c/b\u003e, 766\u0026ndash;771 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMohebiany, A. N. et al. Microglial A20 Protects the Brain from CD8 T-Cell-Mediated Immunopathology. \u003cem\u003eCell. Rep.\u003c/em\u003e \u003cb\u003e30\u003c/b\u003e, 1585\u0026ndash;1597e1586 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMisiak, B. et al. Peripheral blood inflammatory markers in patients with attention deficit/hyperactivity disorder (ADHD): A systematic review and meta-analysis. \u003cem\u003eProg Neuropsychopharmacol. Biol. Psychiatry\u003c/em\u003e. \u003cb\u003e118\u003c/b\u003e, 110581 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOades, R. D., Dauvermann, M. R., Schimmelmann, B. G., Schwarz, M. J. \u0026amp; Myint, A. M. Attention-deficit hyperactivity disorder (ADHD) and glial integrity: S100B, cytokines and kynurenine metabolism\u0026ndash;effects of medication. \u003cem\u003eBehav. Brain Funct.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 29 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBirney, E. Mendelian Randomization. \u003cem\u003eCold Spring Harb Perspect. Med.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCostello, R., McDonagh, J., Hyrich, K. L. \u0026amp; Humphreys, J. H. Incidence and prevalence of juvenile idiopathic arthritis in the United Kingdom, 2000\u0026ndash;2018: results from the Clinical Practice Research Datalink. \u003cem\u003eRheumatol. (Oxford)\u003c/em\u003e. \u003cb\u003e61\u003c/b\u003e, 2548\u0026ndash;2554 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDidelez, V. \u0026amp; Sheehan, N. Mendelian randomization as an instrumental variable approach to causal inference. \u003cem\u003eStat. Methods Med. Res.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e, 309\u0026ndash;330 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrru, V. et al. Complex genetic signatures in immune cells underlie autoimmunity and inform therapy. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, 1036\u0026ndash;1045 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, J. H. et al. Genetics of circulating inflammatory proteins identifies drivers of immune-mediated disease risk and therapeutic targets. \u003cem\u003eNat. Immunol.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 1540\u0026ndash;1551 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAutism Spectrum Disorders Working Group of The Psychiatric Genomics. Meta-analysis of GWAS of over 16,000 individuals with autism spectrum disorder highlights a novel locus at 10q24.32 and a significant overlap with schizophrenia. \u003cem\u003eMol. Autism\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 21 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMartin, J. et al. A Genetic Investigation of Sex Bias in the Prevalence of Attention-Deficit/Hyperactivity Disorder. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e. \u003cb\u003e83\u003c/b\u003e, 1044\u0026ndash;1053 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCao, R. R. et al. The immune factors have complex causal regulation effects on bone mineral density. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e, 959417 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, Q. et al. Dissecting Causal Relationships Between Gut Microbiota, Blood Metabolites, and Stroke: A Mendelian Randomization Study. \u003cem\u003eJ. Stroke\u003c/em\u003e. \u003cb\u003e25\u003c/b\u003e, 350\u0026ndash;360 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan, J. et al. Genetically predicted C-reactive protein mediates the association between rheumatoid arthritis and atlantoaxial subluxation. \u003cem\u003eFront. Endocrinol. (Lausanne)\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 1054206 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi, D., Chen, W. Z., Zhang, L., Zhang, Z. H. \u0026amp; Chen, L. J. Gut microbiota, circulating cytokines and dementia: a Mendelian randomization study. \u003cem\u003eJ. Neuroinflammation\u003c/em\u003e. \u003cb\u003e21\u003c/b\u003e, 2 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBulik-Sullivan, B. et al. An atlas of genetic correlations across human diseases and traits. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e47\u003c/b\u003e, 1236\u0026ndash;1241 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies, N. M., Holmes, M. V. \u0026amp; Davey Smith, G. Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians. \u003cem\u003eBMJ\u003c/em\u003e. \u003cb\u003e362\u003c/b\u003e, k601 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, T., Cao, Y., Zhao, J., Yao, J. \u0026amp; Liu, G. Assessing the causal effect of genetically predicted metabolites and metabolic pathways on stroke. \u003cem\u003eJ. Transl Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 822 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J., Davey Smith, G. \u0026amp; Burgess, S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. \u003cem\u003eInt. J. Epidemiol.\u003c/em\u003e \u003cb\u003e44\u003c/b\u003e, 512\u0026ndash;525 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBurgess, S. \u0026amp; Thompson, S. G. Interpreting findings from Mendelian randomization using the MR-Egger method. \u003cem\u003eEur. J. Epidemiol.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e, 377\u0026ndash;389 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorbin, L. J. et al. BMI as a Modifiable Risk Factor for Type 2 Diabetes: Refining and Understanding Causal Estimates Using Mendelian Randomization. \u003cem\u003eDiabetes\u003c/em\u003e. \u003cb\u003e65\u003c/b\u003e, 3002\u0026ndash;3007 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLopez-Cacho, J. M. et al. Characterization of immune cell phenotypes in adults with autism spectrum disorders. \u003cem\u003eJ. Investig Med.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e, 1179\u0026ndash;1185 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJhun, M. et al. CD103 Deficiency Promotes Autism (ASD) and Attention-Deficit Hyperactivity Disorder (ADHD) Behavioral Spectra and Reduces Age-Related Cognitive Decline. \u003cem\u003eFront. Neurol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 557269 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDiStasio, M. M., Nagakura, I., Nadler, M. J. \u0026amp; Anderson, M. P. T lymphocytes and cytotoxic astrocyte blebs correlate across autism brains. \u003cem\u003eAnn. Neurol.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e, 885\u0026ndash;898 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmad, S. F. et al. Dysregulation of T cell immunoglobulin and mucin domain 3 (TIM-3) signaling in peripheral immune cells is associated with immune dysfunction in autistic children. \u003cem\u003eMol. Immunol.\u003c/em\u003e \u003cb\u003e106\u003c/b\u003e, 77\u0026ndash;86 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBreece, E. et al. Myeloid dendritic cells frequencies are increased in children with autism spectrum disorder and associated with amygdala volume and repetitive behaviors. \u003cem\u003eBrain Behav. Immun.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 69\u0026ndash;75 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArteaga-Henriquez, G. et al. Activation of the Monocyte/Macrophage System and Abnormal Blood Levels of Lymphocyte Subpopulations in Individuals with Autism Spectrum Disorder: A Systematic Review and Meta-Analysis. \u003cem\u003eInt. J. Mol. Sci.\u003c/em\u003e \u003cb\u003e23\u003c/b\u003e (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVojdani, A. et al. Low natural killer cell cytotoxic activity in autism: the role of glutathione, IL-2 and IL-15. \u003cem\u003eJ. Neuroimmunol.\u003c/em\u003e \u003cb\u003e205\u003c/b\u003e, 148\u0026ndash;154 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDenney, D. R., Frei, B. W. \u0026amp; Gaffney, G. R. Lymphocyte subsets and interleukin-2 receptors in autistic children. \u003cem\u003eJ. Autism Dev. Disord\u003c/em\u003e. \u003cb\u003e26\u003c/b\u003e, 87\u0026ndash;97 (1996).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNapolioni, V. et al. Plasma cytokine profiling in sibling pairs discordant for autism spectrum disorder. \u003cem\u003eJ. Neuroinflammation\u003c/em\u003e. \u003cb\u003e10\u003c/b\u003e, 38 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBusinaro, R. et al. Interleukin-18 modulation in autism spectrum disorders. \u003cem\u003eJ. Neuroinflammation\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 2 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlboni, S., Cervia, D., Sugama, S. \u0026amp; Conti, B. Interleukin 18 in the CNS. \u003cem\u003eJ. Neuroinflammation\u003c/em\u003e. \u003cb\u003e7\u003c/b\u003e, 9 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXie, J. et al. Immunological cytokine profiling identifies TNF-alpha as a key molecule dysregulated in autistic children. \u003cem\u003eOncotarget\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 82390\u0026ndash;82398 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYe, L. et al. IL-1beta and TNF-alpha induce neurotoxicity through glutamate production: a potential role for neuronal glutaminase. \u003cem\u003eJ. Neurochem\u003c/em\u003e. \u003cb\u003e125\u003c/b\u003e, 897\u0026ndash;908 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLooman, K. I. M. et al. Associations between T cells and attention problems in the general pediatric population: The Generation R study. \u003cem\u003eJCPP Adv.\u003c/em\u003e \u003cb\u003e1\u003c/b\u003e, e12038 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLooman, K. I. M. et al. Associations of Th2, Th17, Treg cells, and IgA(+) memory B cells with atopic disease in children: The Generation R Study. \u003cem\u003eAllergy\u003c/em\u003e. \u003cb\u003e75\u003c/b\u003e, 178\u0026ndash;187 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenuneit, J. et al. Infant atopic eczema and subsequent attention-deficit/hyperactivity disorder\u0026ndash;a prospective birth cohort study. \u003cem\u003ePediatr. Allergy Immunol.\u003c/em\u003e \u003cb\u003e25\u003c/b\u003e, 51\u0026ndash;56 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, C. F., Yang, C. C. \u0026amp; Wang, I. J. Association between allergic diseases, allergic sensitization and attention-deficit/hyperactivity disorder in children: A large-scale, population-based study. \u003cem\u003eJ. Chin. Med. Assoc.\u003c/em\u003e \u003cb\u003e81\u003c/b\u003e, 277\u0026ndash;283 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvcil, S. Evaluation of the neutrophil/lymphocyte ratio, platelet/lymphocyte ratio, and mean platelet volume as inflammatory markers in children with attention-deficit hyperactivity disorder. \u003cem\u003ePsychiatry Clin. Neurosci.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 522\u0026ndash;530 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlmerighi, C. et al. 1Alpha,25-dihydroxyvitamin D3 inhibits CD40L-induced pro-inflammatory and immunomodulatory activity in human monocytes. \u003cem\u003eCytokine\u003c/em\u003e. \u003cb\u003e45\u003c/b\u003e, 190\u0026ndash;197 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGustafsson, H. C. et al. The association between heightened ADHD symptoms and cytokine and fatty acid concentrations during pregnancy. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e13\u003c/b\u003e, 855265 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalvez-Contreras, A. Y., Campos-Ordonez, T. \u0026amp; Gonzalez-Castaneda, R. E. Gonzalez-Perez, O. Alterations of Growth Factors in Autism and Attention-Deficit/Hyperactivity Disorder. \u003cem\u003eFront. Psychiatry\u003c/em\u003e. \u003cb\u003e8\u003c/b\u003e, 126 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBilgic, A., Toker, A., Isik, U. \u0026amp; Kilinc, I. Serum brain-derived neurotrophic factor, glial-derived neurotrophic factor, nerve growth factor, and neurotrophin-3 levels in children with attention-deficit/hyperactivity disorder. \u003cem\u003eEur. Child. Adolesc. Psychiatry\u003c/em\u003e. \u003cb\u003e26\u003c/b\u003e, 355\u0026ndash;363 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYurteri, N., Sahin, I. E. \u0026amp; Tufan, A. E. Altered serum levels of vascular endothelial growth factor and glial-derived neurotrophic factor but not fibroblast growth factor-2 in treatment-naive children with attention deficit/hyperactivity disorder. \u003cem\u003eNord J. Psychiatry\u003c/em\u003e. \u003cb\u003e73\u003c/b\u003e, 302\u0026ndash;307 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuller Smith, K. et al. Deficiency in inhibitory cortical interneurons associates with hyperactivity in fibroblast growth factor receptor 1 mutant mice. \u003cem\u003eBiol. Psychiatry\u003c/em\u003e. \u003cb\u003e63\u003c/b\u003e, 953\u0026ndash;962 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMooney, M. A. et al. Pathway analysis in attention deficit hyperactivity disorder: An ensemble approach. \u003cem\u003eAm. J. Med. Genet. B Neuropsychiatr Genet.\u003c/em\u003e \u003cb\u003e171\u003c/b\u003e, 815\u0026ndash;826 (2016).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"immune cells, circulating inflammatory factors, neurodevelopmental disorders, bidirectional Mendelian randomization, mediation analysis","lastPublishedDoi":"10.21203/rs.3.rs-4869464/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4869464/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe roles of various immune cells and circulating inflammatory factors in neurodevelopmental disorders (NDDs) remain controversial. Therefor we employed a two sample and bidirectional mendelian randomization and mediation method to explore the causal relationships between immune cells, circulating inflammatory factors, and NDDs. All data were originated from GWAS datasets. We found a significant positive causal relationship between 13 immune cells and ASD, including six CD8\u0026thinsp;+\u0026thinsp;T cell, one CD3\u0026thinsp;+\u0026thinsp;T cell, two CD20\u0026thinsp;+\u0026thinsp;B cell, one CD38\u0026thinsp;+\u0026thinsp;B cell, and two plasmacytoid DC. 9 inflammatory factors showed significant causal relationships with ASD: four interleukins (IL-7, IL-2, IL-18) were negatively associated, while five inflammatory factors were positively associated, such as TNF-α. 14 immune cells exhibited significant causal relationships with ADHD. CD3 on naive CD8br and CD4 on activated Treg were positively associated, while four CD27-expressing B cells were positively associated with ASD. Four CD40-expressing monocytes were negatively associated with ADHD. 7 inflammatory factors had significant causal relationships with ADHD: Fibroblast Growth Factor 23 levels (FGF-23), CD40L receptor levels, Glial Cell Line-Derived Neurotrophic Factor levels(GDNF), TNF-α were more important among these. Mediation analysis identified 12 mediating relationships, with three showing strong evidence: Natural killer cell receptor 2B4 levels (19.9%), Fibroblast Growth Factor 23 levels (11%) and Eotaxin levels (-5.95%). There were strongly causal relationships between immune cells, circulating inflammatory factors, and NDDs. Inflammatory factors mediated the pathways between immune cells and NDDs.\u003c/p\u003e","manuscriptTitle":"Immune cells, circulating inflammatory factors and neurodevelopmental disorders: a bidirectional mendelian randomization and mediation analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-18 18:14:45","doi":"10.21203/rs.3.rs-4869464/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-02-20T06:46:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-02-07T00:02:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"122084555760319650121803408767629706000","date":"2025-01-19T00:28:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"195734552213905746327870339699264798556","date":"2024-11-26T20:37:04+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-18T20:30:00+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"188507122400831737775158532515031388709","date":"2024-10-14T11:28:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-02T15:21:07+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-02T15:18:46+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-22T15:03:15+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-20T14:35:04+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-06T15:07:30+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fb49b843-b462-4e63-84c3-0c26505bfe18","owner":[],"postedDate":"September 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":37676402,"name":"Biological sciences/Immunology"},{"id":37676403,"name":"Health sciences/Diseases"},{"id":37676404,"name":"Health sciences/Health care"},{"id":37676405,"name":"Health sciences/Medical research"},{"id":37676406,"name":"Health sciences/Neurology"},{"id":37676407,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-04-21T16:09:32+00:00","versionOfRecord":{"articleIdentity":"rs-4869464","link":"https://doi.org/10.1038/s41598-025-98020-0","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-14 15:57:07","publishedOnDateReadable":"April 14th, 2025"},"versionCreatedAt":"2024-09-18 18:14:45","video":"","vorDoi":"10.1038/s41598-025-98020-0","vorDoiUrl":"https://doi.org/10.1038/s41598-025-98020-0","workflowStages":[]},"version":"v1","identity":"rs-4869464","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4869464","identity":"rs-4869464","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}