Attention deficit hyperactivity disorder and 19 autoimmune diseases: A Mendelian Randomization Study

doi:10.21203/rs.3.rs-4080252/v1

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Attention deficit hyperactivity disorder and 19 autoimmune diseases: A Mendelian Randomization Study

https://doi.org/10.21203/rs.3.rs-4080252/v1

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Background

Observational studies suggest a connection between autoimmune diseases (AD) and attention deficit hyperactivity disorder (ADHD). Nevertheless, the causality between AD and ADHD is yet to be determined. Therefore, we're employing a twin-sample analysis on Mendelian randomization, with a view to exploring the causality between ADHD and 19 AD.

Method

We evaluated the total statistics of the latest Genome-Wide Association Studies (GWAS) for ADHD2022 (38691 cases, 186843 controls) and 19 common ADs, including Autoimmune thyroiditis, Type 1 diabetes, Celiac disease, Vitamin B12 deficiency anemia, Idiopathic thrombocytopenic purpura, multiple sclerosis, Graves' disease, Sjogren's syndrome, Iridocyclitis, Ulcerative colitis, Crohn's disease, Psoriasis, Alopecia areata, Vitiligo, Rheumatoid arthritis (RA), Myasthenia gravis, Systemic sclerosis, Systemic lupus erythematosus, Ankylosing spondylitis. These summary statistics originated in publicly assessible, relatively large-scale GWAS meta-analyses conducted to date. The causality between ADHD and 19 ADs using inverse variance weighting (IVW), MR-Egger, and weighted median approaches. Cochran's Q statistic was adopted, for the purpose of measuring the heterogeneity of the instrumental variable (IV).

Result

The result indicate that ADHD potentially increases the risk of vitiligo (IVW OR = 2.385, 95%CI, 1.054–5.398, P = 3.69e-02) and suggests an association between ADHD and increased hazard of rheumatoid arthritis (IVW OR = 1.092, 95%CI, 1.009–1.182, P = 2.94e-02). However, no other significant causal relationships were observed in our analyses.

Conclusion

In this research, the causalities between ADHD and 19 ADs were comprehensively assessed. Besides, it was identified genetic evidence suggesting possible causalities between ADHD and vitiligo along with rheumatoid arthritis in European populations.

attention deficit hyperactivity disorder

autoimmune diseases

mendelian randomization

causality

vitiligo

rheumatoid arthritis

Autoimmune diseases (AD) cover a range of conditions where the immune system mistakenly targets and harms healthy tissues and organs in the body[1, 2]. Symptoms of AD vary based on the specific illness and affected organs, but common indicators encompass fatigue, joint pain, fever, skin issues, digestive problems, and neurological complications[3, 4]. Roughly 5% of the world's population suffer through autoimmune diseases. These conditions remain challenging to treat, burdening patients significantly and holding substantial economic implications. Nevertheless, the complete understanding of the disease's pathogenesis remains elusive[5]. Given the lack of a cure for AD, comprehending its causative factors might aid in decelerating its progression. Some studies[6–8] have found that risk factors for AD are multifaceted, containing genes, environments, and lifestyle. Genetic elements notably contribute, heightening the risk for individuals with a family history of the diseases. Additionally, environment elements, like smoking, disclosure to specific chemicals, infections, and dietary patterns significantly influence the development of those diseases. Lifestyle elements like pressure, exercise, and diet show associations with the onset and progression of AD.

Attention Deficit Hyperactivity Disorder (ADHD) is a chronic neurodevelopmental disorder which representatively emerges during childhood. Its fundamental characteristics involve issues with inattention, excessive activity, and impulsive emotional and behavioral displays[9, 10]. These symptoms persist into adolescence and may continue into adulthood. As per latest epidemiological surveys, the world`s ADHD prevalence among children is estimated at around 7.2%, with roughly half of the patients experiencing clinical symptoms that may persist into adulthood[11].

Many studies[12–14] have claimed the relationships between neurological and psychiatric disorders and abnormalities in the immune system; nevertheless, the relationships keep unsure. Similarly, some immune-associated conditions, like asthma, atopic dermatitis, ulcerative colitis, ankylosing spondylitis, juvenile arthritis, celiac disease, and autoimmune thyroid (AT) diseases have also been linked to ADHD[15–18]. A meta-analysis and systemic review by Chen et al[19]. in 2016, based on 10 studies, revealed an overall increased risk of autism among children whose mothers had autoimmune illnesses. Two population-based researches have been implemented: a discovery was an increased risk of autoimmune diseases among ADHD patients[16], while another study found a correlation between type 1 diabetes and increased risk of phrenoblabias, like ADHD[20]. However, some research[21, 22] did not clearly establish a prominent relationship between AD and ADHD. Therefore, it is imperative to elucidate the causality between ADHD and various types of autoimmune illnesses.

Mendelian randomization (MR) is just one statistical technique applied to epidemiology and genetics, with the objective to determining causalities between risk elements and results[23]. MR is on the grounds of the principles of Mendelian inheritance laws, which describe how genetic variants are assigned at random in the course of meiosis[24]. This approach uses instrumental variables (IVs), particularly single nucleotide polymorphisms (SNPs) that are linked to the risk factor of interest (GERD) genetic variants to detect if the selected risk factors have an obvious causal effect upon the results of interest or not[25]. Without randomized controlled trials (RCTs), MR researches present one alternative tactic for causal intervention[26]. Thus, MR has the superiority over conventional observational researches in that MR lessens the risk of baffling and reverse causality, getting it an advantageous tool for detecting the causality in epidemiological researches[27]. Currently, there hasn't been a Mendelian analysis exploring the causality between ADHD and AD. Therefore, this research aimed at establishing a causality between ADHD and 19 ADs (Autoimmune thyroiditis, Type 1 diabetes, Celiac disease, Vitamin B12 deficiency anemia, Idiopathic thrombocytopenic purpura, multiple sclerosis, Graves' disease, Sjogren's syndrome, Iridocyclitis, Ulcerative colitis, Crohn's disease, Psoriasis, Alopecia areata, Vitiligo, Rheumatoid arthritis (RA), Myasthenia gravis, Systemic sclerosis, Systemic lupus erythematosus, Ankylosing spondylitis) with one two-sample MR approach.

Exposure data

SNPs concerning ADHD were chosen as instrumental variables (IVs) supplied by GWAS dataset of the Psychiatric Genomics Consortium (https://pgc.unc.edu/), and a GWAS was implemented on ADHD (F = 22) in 2022[28]. The GWAS indicated the ADHD result (included 38 691 cases and 186843 controls). The total participators were Europeans. We extracted SNPs based upon their genome-wide levels of significance (p < 5×10^− 5). Details of ADHD GWAS study was provided in Table 1.

Table 1

Details of ADHD GWAS
Trials	Sample sizes (Case/Control)	Number of IVs	R²	F-statistic	PMID
ADHD	38691/275986	52	0.005	22	36702997
ADHD: Attention Deficit Hyperactivity Disorder

Table 2

Details of outcome GWAS
Trials	Sample sizes (Case/Control)	PMID	GWAS ID	Year	Consortium	Number of SNPs	Population
Autoimmune thyroiditis	244/ 187684	NA	finn-b-E4_THYROIDITAUTOIM	2021	NA	16380358	European
Type 1 diabetes	9266/15574	32005708	ebi-a-GCST010681	2020	NA	12783129	European
Celiac disease	4533/10750	20190752	ebi-a-GCST000612	2010	NA	518292	European
Vitamin B12 deficiency anemia	1707/211115	NA	finn-b-D3_ANAEMIA_B12_DEF	2021	NA	16380452	European
Idiopathic thrombocytopenic purpura	675/488749	34594039	ebi-a-GCST90018865	2021	NA	24199770	European
multiple sclerosis	47429/68374	31604244	ieu-b-18	2019	International Multiple Sclerosis Genetics Consortium	6304359	European
Graves' disease	1678/456942	34594039	ebi-a-GCST90018847	2021	NA	24189816	European
Ulcerative colitis	5371/412561	34594039	ebi-a-GCST90018933	2021	NA	24187301	European
Crohn's disease	12194/28072	28067908	ebi-a-GCST004132	2017	NA	9457998	European
Alopecia areata	289/211139	NA	finn-b-L12_ALOPECAREATA	2021	NA	16380450	European
Vitiligo	131/207482	NA	finn-b-L12_VITILIGO	2021	NA	16380442	European
Rheumatoid arthritis	8255/409001	34594039	ebi-a-GCST90018910	2021	NA	24175266	European
Myasthenia gravis	1873/36370	35074870	ebi-a-GCST90093061	2022	NA	23679120	European
Systemic sclerosis	302/213145	NA	finn-b-M13_SYSTSLCE	2021	NA	16380454	European
Systemic lupus erythematosus	5201/9066	26502338	ebi-a-GCST003156	2015	NA	7071163	European
Ankylosing spondylitis	1462/164682	NA	finn-b-M13_ANKYLOSPON	2021	NA	16380022	European
Sjogren. Syndrome	2495/365533	NA	NA	2022	NA	16383308	European
Iridocyclitis	7306/357814	NA	NA	2022	NA	1048576	European
Psoriasis	9267/364071	NA	NA	2022	NA	1048575	European

NA: Not available

Outcome data

We acquired data for 16 types of autoimmune diseases from IEU OpenGWAS project (https://gwas.mrcieu.ac.uk). The sample sizes for each are as follows: Autoimmune Thyroiditis (244cases/187684controls), Type 1 Diabetes (9266cases/ 15574 controls), Celiac Disease (4533cases/ 10750 controls), Vitamin B12 Deficiency Anemia (4533cases/10750controls), Idiopathic Thrombocytopenic Purpura (675 cases /488749controls), Multiple Sclerosis (47429cases/68374controls), Graves' Disease (1678cases/456942controls), Ulcerative Colitis (5371cases/412561controls), Crohn's Disease (12194cases/28072controls), Alopecia Areata (289cases/211139controls), Vitiligo (131cases/207482controls), Rheumatoid Arthritis (8255cases/409001controls), Myasthenia Gravis (1873cases/36370controls), Systemic Sclerosis (302cases/213145controls), Systemic Lupus Erythematosus (5201cases/9066controls), Ankylosing Spondylitis (1462cases/164682controls).

Furthermore, data for Sjogren's Syndrome(2495cases/365533controls) with the phenocode "M13_SJOGREN", Iridocyclitis(7306cases/357814controls) phenocode with the phenocode "H7_IRIDOCYCLITIS" and Psoriasis (9267cases/364071controls) the phenocode "L12_PSORIASIS" was supplied by FinnGen (https://www.finngen.fi/en). Details of the outcome data was provided in Table 2.

Selection of genetic instrumental variables

The flow chart of two-sample Mendelian analysis is depicted in Fig. 1.To ensure the genetic variant validity as IVs and attain unbiased causality, single nucleotide polymorphisms (SNPs) as exposure IVs must meet three basic conditions as (Fig. 1): (1) IVs applied to analyses need to be strongly correlated to the exposure; (2) the IVs need to be independent of confounders possibly related to the chosen exposure and the result; and (3) IVs should influence the result merely by exposure but not other biological pathways affecting the outcome i.e. with out any horizontal pleiotropic effect[29]. If all three of these assumptions are met, causality between exposure and result can be figured out, besides, immeasurable confounding and reverse causality are unlikely.

According to pooled data of GWAS exposures, a series of quality control steps were performed, with the aim of selecting qualified SNPs. To begin with, Select SNPs strongly linked to ADHD as IVs. Use two different thresholds to choose IVs. First threshold selects SNPs below the threshold of genome-wide statistical significance (5×10^− 8) as IVs. Unluckily, this initial selection only covers a small number of ADHD cases as IVs. For the purpose of detecting the correlation between ADHD and other factors like AD more comprehensively, we employed a second threshold below the level of genome-wide significance (5×10^− 5) to expand the IV set, aiming to discover more potential causal relationships, and assured that the total instrumental SNPs used for exposures were not within linkage disequilibrium (LD) (R2 < 0.001 in 10,000 kb) as SNPs among powerful LDs can cause bias Results. Second, the exposure SNPs were extracted from the outcome of GWAS pooled data. Besides, SNPs strongly linked to the outcome (p < 5×10^− 8) is discarded. Third, exposure and outcome SNPs were reconciled for the purpose of maintaining consistency of effect alleles and palindromes and incompatible SNPs were removed. Fourth, MR-PRESSO (Multiple Residuals and Outliers of Efficacy) global experiments were conducted, with the aim of identifying the inconsistencies between genetic relationships across genetic variants and removing peripheral genetic variants. Ultimately, R2 and F statistics was worked out for each SNP. R2 was figured out, with the objective to representing the percentage of variance in the exposure factor interpreted by IV. The F-statistic was figured out, with the aim of representing the strength of relation between instrument and the target exposure[30], with an F-statistic of ≥ 10 for a SNP indicating that the selected SNP was valid with sufficient strength for reporting. Therefore, with F < 10, SNPs was eliminated from MR analyses[31].

Statistical Analysis

Three MR methods (IVW, MR-Egger regression, alongside weighted median) were adopted with a view to calculating the causality. IVW results were employed as the primary outcome[32]. In our research, a stochastic effects model was applied to IVW because it maintained conservative estimations even while the heterogeneity was explored. The MR-Egger regression model offered the correspondingly robust estimations independent of IV validity. Nevertheless, MR-Egger methods are susceptible to outliers, leading to relative imprecision and low efficacy[33]. While at least half of IVs were effective, the approach of weighted median examined the median effect across the total instrumental SNPs, with a view to producing an unbiased estimation of the effect[34]. Af last, it is shown the significance value p < 0.05, indicating the statistics is nominally significant.

Analyses on sensitivity are key to detecting potential heterogeneity and pleiotropy of MR estimations in MR studies. MR-Egger regression was implemented, for the sake of assessing the potential pleiotropic SNP effects. As for MR-Egger regression, the intercept term is capable of being a useful illustration of whether directional horizontal pleiotropy (HP) is propelling the outcomes of MR analyses. The experiment of MR-Egger intercept (P ≥ 0.05) illustrated that there was no HP. As to heterogeneity, the Cochran Q test was figured out, with the aim of checking for heterogeneity between disparate genetic variants and a Cochran Q (P ≥ 0.05) would be considered less heterogeneous. In addition, for the purpose of testing the consistency of each SNP, a LOO test was implemented, eliminating each genetic variant one after the other. On condition that causality remains statistically significant after excluding non-specific SNPs, this provides more plausible evidence for the association. The total statistical tests were implemented via "TwoSampleMR" package[35] (version 0.5.7) and the "MR-PRESSO" package[36] in the R software (version 4.2.3).

Associations of ADHD with AD

Final IV analysis investigating the association between ADHD and 19 AD traits is documented in (Supplementary Material 2 Table S1 to Table S19). Notably, each SNP (greater than 10 in number) serves as a robust instrumental variable, highlighted in Table 1. Figure 2 delineates that ADHD potentially elevates vitiligo risk (IVW OR = 2.385, 95%CI, 1.054–5.398, P = 3.69e-02). Intriguingly, the MR-Egger presented comparison results (OR = 7.029, 95%CI, 0.391-126.341, P = 1.92e-01). Concurrently, the weighted median (OR = 2.420, 95%CI, 0.812–7.213, P = 1.13e-01) showed a comparable risk estimate, albeit without statistical significance (Supplementary Material 1, Figures S1-S3). Figure 2 did not reveal horizontal pleiotropy in the experiments of MR-Egger intercept (P = 0.964), Cochran Q test (P = 0.227), or evidence of heterogeneity in the leave-one-out (LOO) analysis (Supplementary Material 1, Figure S2). Furthermore, our analysis identified a potential increase in rheumatoid arthritis risk associated with ADHD (IVW OR = 1.092, 95%CI, 1.009–1.182, P = 2.94e-02). Nevertheless, the MR-Egger displayed contradictory outcomes (OR = 1.186, 95%CI, 0.958–1.468, p = 1.24e-01). Similarly, the weighted median estimate (OR = 1.134, 95%CI, 1.004–1.280, p = 4.28e-02) suggested a similar risk estimate without statistical significance (Supplementary Material 1, Figures S4-S6). In Fig. 2, the MR-Egger test (P = 0.42), Cochran Q test (P = 0.328), alongside LOO analysis (Supplementary Material 1, Figure S5) did not reveal horizontal pleiotropy or evidence of heterogeneity.

IVW analysis suggests no causal relationship between ADHD and Autoimmune thyroiditis (OR = 1.022, P = 9.53e-01), Type 1 diabetes (OR = 0.922, P = 9.3e-01), Celiac disease (OR = 1.154, P = 3.21e-01), Vitamin B12 deficiency anemia (OR = 1.134, P = 2.55e-01), Idiopathic thrombocytopenic purpura (OR = 0.933, P = 6.48e-01), multiple sclerosis (OR = 1.136, P = 5.57e-02), Graves' disease (OR = 1.019, P = 7.84e-01), Sjogren syndrome (OR = 0.893, P = 2.51e-01), Iridocyclitis (OR = 0.933, P = 2.04e-01), Ulcerative colitis (OR = 1.130, P = 6.51e-02), Crohn's disease (OR = 0.979, P = 7.25e-01), Psoriasis (OR = 0.947, P = 3.24e-01), Alopecia areata (OR = 1.466, P = 1.68e-01), Myasthenia gravis (OR = 1.178, P = 1.67e-01), Systemic sclerosis (OR = 1.259, P = 3.70e-01), Systemic lupus erythematosus (OR = 0.913, P = 5.12e-01), Ankylosing spondylitis (OR = 0.965, P = 7.58e-01) (Supplementary Material S1, Figures S7-S57). Notably, Autoimmune thyroiditis (P = 0.002), Type 1 diabetes (P = 0.003), Celiac disease (P = 0.045) exhibited significant heterogeneity, whereas other indicators did not demonstrate heterogeneity (Fig. 2).

As far as we know, this presents the first large-scale two-sample MR study aimed at investigating potential causal relationships between ADHD and 19 autoimmune diseases (ADs). In contrast to conventional observational researches, MR analysis can circumvent some confounding factors, thereby allowing for a convenient elucidation of causal relationships between diseases from a genetic perspective. This research indicates a possible causality between ADHD and increased risk of vitiligo and rheumatoid arthritis.

Skin conditions and psychiatric disorders may be closely interrelated, stress can precipitate skin conditions, exacerbating their symptoms, while skin disorders can impact mental well-being and lead to certain psychiatric symptoms, commonly believed to have mutual links with the immune system and psychiatric illnesses[37]. Depression, for instance, has been reported to alter the quantity and function of immune system cells, thereby varying the release of pro-inflammatory cytokines. It is believed that physiological elements implicated in emotion and pressure regulation trigger onset or exacerbation of diverse skin conditions[38]. A previous study[39] found that 36.6% of vitiligo patients had ADHD along with other psychiatric illnesses, autoimmunity is among the mechanisms involved in the etiology of ADHD; some studies[40, 41] suggest certain immune-related factors may be associated with ADHD. For instance, inflammatory responses and certain aspects of the immune system may be linked to the pathogenesis of ADHD. Research indicates abnormal levels of certain inflammatory mediators in pediatric and adult ADHD patients. Our discoveries are favor of a potential causality between vitiligo and ADHD, suggesting exploration of vitiligo in ADHD patients is warranted. Patel et[42] al.'s analysis of 87,053,155 children and adults found a relationship between vitiligo and ADHD (OR = 2.59, 95%CI, 1.92–3.49). One Taiwanese research[43] in 2013 discovered a connection between AT diseases and ankylosing spondylitis with ADHD. It has been reported that family history of type 1 diabetes[44], rheumatoid arthritis[45], multiple sclerosis[45], systemic lupus erythematosus[46] increase the ADHD risk. This contrasts with the conclusions drawn from our current study, possibly due to inherent limitations in observational studies (sample size, environmental factors, genetic factors).

RA has been a chronic inflammatory autoimmune disease affecting 0.5–1% of the world`s population, characterized by high heritability[47]. Another important research[48] indicated an active correlation between genetic susceptibility to rheumatoid arthritis and perceptual and psychiatric symptoms between adolescents and children, contending shared genetic risks between rheumatoid arthritis and psychiatric disorders. Some studies[49, 50] have also demonstrated correlations between rheumatoid arthritis and ADHD, possibly due to shared genetic susceptibility among RA, psychiatric disorders, and certain conditions in the course of maternal pregnancy that might intensify the risk of psychiatric disorders among offspring. A previous study[41] claimed an increased risk of bipolar affective disorder (BAD) among the patients suffering from RA (OR 2.06 [1.34–3.17]), possibly indicating an association between bipolar affective disorder and rheumatoid arthritis. Thus, prior research revealing neuroinflammation involvement in the etiology of BAD highlighted a potential link between these two conditions and their association with poorer rheumatoid arthritis prognosis. Combining observational studies, MR analysis, and clinical trial evidence, the causal relationship between ADHD and AD warrants further exploration at a more specialized level.

The study has several strengths, firstly: the data for this study came from GWAS summary statistics originate in the biggest and most recent researches. Besides, there were no overlapping samples in this research. That will significantly intensify the statistical power of causal inference. Second, main advantage is the MR design, which avoids confounding by confounders and reverse causality. Third, all the included SNPs F-statistics were above 10, thus the included genetic tools were comparably strong. Fourth: Three complementary MR analyses were used by multiple sensitivity analyses to reduce the false-positive ratio, thus assuring the precision of our conclusions.

However, this study still has several limitations, firstly: though it is difficult to determine the degree of specimen overlap, there may be a certain overlap between disclosure and outcome study participators. Luckily, the great approach applied to this study, with an F-statistic significantly was more than 10, need to minimize any potential bias introduced through overlapping samples, secondly: problems, like RNA editing, DNA methylation, and transposon inactivation are inevitable drawbacks of MR analyses, and thirdly: the summary data from GWAS originated in European descent only, and our outcomes might not be completely representative of the overall population, Fourth: we don't have consider gender factors on the analysis of the data, because the data is not available, whether gender differences affect the results need to be further research. However, considering biological plausibility and employing multi-stage statistical methods, strict multiple testing correction might be overly cautious, potentially leading to the oversight of ADs causally linked to ADHD. Furthermore, ensuring coherence in interpreting Mendelian randomization analysis results with other measures (like analyses on instrumental strength and sensitivity conducted in our study) is crucial. Hence, we remain optimistic about the academic significance of our research. Future investigations ought to aim at validating our current discoveries through larger specimen sizes and various populations to set up the robustness in our obtained outcomes. Additionally, it's worth to employ different MR methods for replication analysis to affirm the identified genetic relationships.

The causalities between ADHD and 19 ADs were comprehensively evaluated in this research. Additionally, it was identified genetic evidence suggesting possible causalities between ADHD and vitiligo, along with rheumatoid arthritis among European populations. This study might provide a new insight into the mechanisms underlying ADHD and ADs occurrence.

Ethics statement

Ethical approval was not provided for this study on human participants because we used the publicly available GWAS catalog to conduct a two-sample MR study. No additional ethical approval was required due to the re-analysis of previously summary-level data.

FUNDING:

none

Author Contribution

YL and LM: present idea, perform MR analysis and manuscript writing. ZCY: evaluate the quality of MR and manuscript writing. YL: Search of the database and quality assessment. YL and ZCY: figure and table drawing. YL and LM: study supervision and final approvement. All authors contributed to the article and approved the submitted version.

ACKNOWLEDGMENTS

We are grateful to the participants and researchers of the IEU OpenGWAS project, Psychiatric Genomics Consortium, and the FinnGen study for their valuable contributions to this study.

DATA AVAILABILITY STATEMENT

The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material.

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No competing interests reported.

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Attention deficit hyperactivity disorder and 19 autoimmune diseases: A Mendelian Randomization Study

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Abstract

Background

Method

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Introduction

Materials and methods

Exposure data

Outcome data

Selection of genetic instrumental variables

Statistical Analysis

Result

Associations of ADHD with AD

Discussion

Conclusion

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ACKNOWLEDGMENTS

DATA AVAILABILITY STATEMENT

References

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